University of Southern California Dissertations and Theses

Essays in environmental economics

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Essays in environmental economics

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Content ESSAYS IN ENVIRONMENTAL ECONOMICS
by
Ruozi Song
A Dissertation Presented to the
FACULTY OF THE USC GRADUATE SCHOOL
UNIVERSITY OF SOUTHERN CALIFORNIA
In Partial Fulfillment of the
Requirements for the Degree
DOCTOR OF PHILOSOPHY
(ECONOMICS)
May 2023
Copyright 2023 Ruozi Song
I dedicate this thesis to myself, my family, and the people who have made me
who I am.
ii
Acknowledgements
I am incredibly grateful to my supportive advisors. Paulina Oliva has been a role model
and taught me how to see the bigger picture and dig deeper into rigorous research simul-
taneously. Antonio Bento has been generous with his time, guided me, and opened the
door of environmental economics for me. Robert Metcalfe’s sharp vision of the direction of
the field has greatly improved my work. Tom Chang’s broad interest and research agenda
have inspired me to continue research with curiosity.
I would like to thank other faculties at the University of Southern California who have
been always encouraging and helpful, including Matthew Kahn, Jonathan Libgober, Pablo
Kurlat, Monica Morlacco, Jeff Weaver, Vittorio Bassi, and Geert Ridder. Together you
helped me to overcome difficulties and enjoy research. I would also like to thank Bingling
Gong and Qinfeng Xu at Fudan University and Irasema Alonso at New York University,
who introduced me to the joys of economics research.
This dissertation would not have been possible without my caring and supportive
friends and peers, particularly Rajat Kochhar, Liying Yang, and Xiongfei Li. I also thank
my sister for always being there for me during the ups and downs and my parents for their
unlimited love and support.
iii
Contents
. . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . .
Dedication . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . ii
Acknowledgements . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . iii
Abstract . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . xi
Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 1
Chapter 1: Pollution Taxes as a Second-Best: Accounting for Multidimensional
Firm Heterogeneity in Environmental Regulations . . . . . . . . . . . . . . . . . 3
1.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 3
1.2 Background and Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 11
1.2.1 Environmental Regulations in China . . . . . . . . . . . . . . . . . . . 11
1.2.2 Cement Industry in China . . . . . . . . . . . . . . . . . . . . . . . . . 13
1.2.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 15
1.3 Model . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 18
1.3.1 Setup . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 19
1.3.2 Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 22
1.3.3 Welfare-Seeking Regulators . . . . . . . . . . . . . . . . . . . . . . . . 23
1.3.4 Statistics of Welfare Implications and Policy Counterfactuals . . . . . 24
1.4 Empirical Evidence . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
1.4.1 Firm Heterogeneity . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 26
iv
1.4.2 Differential Impacts of Emissions Taxation . . . . . . . . . . . . . . . 29
1.4.2.1 Empirical Strategy . . . . . . . . . . . . . . . . . . . . . . . . 29
1.4.2.2 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 30
1.4.2.3 Robustness . . . . . . . . . . . . . . . . . . . . . . . . . . . . 31
1.5 Model Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
1.5.1 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 33
1.5.2 Firm-Level Cost Functions . . . . . . . . . . . . . . . . . . . . . . . . . 34
1.5.3 Goodness of Fit . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.5.4 Environmental Damage . . . . . . . . . . . . . . . . . . . . . . . . . . 38
1.6 Simulation Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 40
1.7 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 43
Chapter 2: Electric Vehicle Sharing: Crowding Adoption Out or In? . . . . . . . . 45
2.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 45
2.2 Background . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.2.1 The BlueLA Program . . . . . . . . . . . . . . . . . . . . . . . . . . . . 49
2.2.2 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 53
2.3 Identification Strategy . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 54
2.4 Results . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 58
2.4.1 Estimating the BlueLA Impact . . . . . . . . . . . . . . . . . . . . . . 58
2.4.1.1 Testing the Interpretation ofβ . . . . . . . . . . . . . . . . . 61
2.4.2 Interpreting the Results Using Institutional Details . . . . . . . . . . 62
2.5 Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 63
2.5.1 Drivers of the Treatment Effect . . . . . . . . . . . . . . . . . . . . . . 63
2.5.2 Spillovers Across zipcodes . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.5.3 Car Model Influence . . . . . . . . . . . . . . . . . . . . . . . . . . . . 64
2.6 Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 65
v
Chapter 3: Does Market Power in India’s Agricultural Markets Hinder Farmer
Climate Change Adaptation? . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.1 Introduction . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 67
3.2 Background on Agricultural Markets in India . . . . . . . . . . . . . . . . . . 79
3.2.1 History . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 79
3.2.2 Agricultural Produce Market Committee: Regulations . . . . . . . . 80
3.2.3 Monopsony Power . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 82
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
3.3.1 Yields . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 83
3.3.2 Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 85
3.3.3 Intermediary Markets . . . . . . . . . . . . . . . . . . . . . . . . . . . 87
3.3.4 Quantity Arrivals and Prices . . . . . . . . . . . . . . . . . . . . . . . 90
3.4 Empirical Methods and Results . . . . . . . . . . . . . . . . . . . . . . . . . . 92
3.4.1 Effect of Climate Shocks on Yields . . . . . . . . . . . . . . . . . . . . 92
3.4.1.1 Panel Approach . . . . . . . . . . . . . . . . . . . . . . . . . 93
3.4.1.2 Long Differences Approach . . . . . . . . . . . . . . . . . . 96
3.4.1.3 Adaptation . . . . . . . . . . . . . . . . . . . . . . . . . . . . 97
3.4.2 Effect of Competition on Mitigation of Climate Shocks . . . . . . . . 99
3.4.2.1 Measuring Market Power . . . . . . . . . . . . . . . . . . . . 100
3.4.2.2 Panel Approach . . . . . . . . . . . . . . . . . . . . . . . . . 101
3.4.2.3 Panel Approach: Arrivals Data . . . . . . . . . . . . . . . . 104
3.4.2.4 Hybrid Border Discontinuity Design . . . . . . . . . . . . . 105
3.4.3 Mechanisms . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 114
3.4.3.1 Analytical Framework . . . . . . . . . . . . . . . . . . . . . . 114
3.4.3.2 Effect of Competition on Prices: Heterogeneous Impact by
Weather . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 117
3.4.3.3 Changes in Input Use . . . . . . . . . . . . . . . . . . . . . . 120
vi
3.5 Theory . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
3.5.1 Basic Environment . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 123
3.5.2 Competitive Equilibrium . . . . . . . . . . . . . . . . . . . . . . . . . 128
3.6 Estimation . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
3.6.1 Demand . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 136
3.6.2 Supply . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 140
3.7 Counterfactual Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 144
3.8 Discussion and Conclusion . . . . . . . . . . . . . . . . . . . . . . . . . . . . 147
References . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 149
Appendices . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 166
I Appendix Figures . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
J Appendix Tables . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 175
K Appendix Derivations . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 182
K.1 Comparative Statistics from the Theoretical Framework . . . . . . . . 182
K.2 Coal Price Pass-Through . . . . . . . . . . . . . . . . . . . . . . . . . . 182
K.3 Joint Distribution of TFP and Labor Intensity . . . . . . . . . . . . . . 183
K.4 Probability of Choosing Market . . . . . . . . . . . . . . . . . . . . . . 184
K.5 Profit Function of Farmer . . . . . . . . . . . . . . . . . . . . . . . . . 185
K.6 Land Allocation Problem . . . . . . . . . . . . . . . . . . . . . . . . . 186
K.7 Quantity Supplied to Market . . . . . . . . . . . . . . . . . . . . . . . 188
K.8 Average Conditional Productivity . . . . . . . . . . . . . . . . . . . . 189
K.9 Consumers Utility Maximisation . . . . . . . . . . . . . . . . . . . . . 190
vii
List of Tables
1.1 Summary Statistics for Cement Markets . . . . . . . . . . . . . . . . . . . . . 18
1.2 Differential Effects of Emission Taxation on Firm-Level Outcomes . . . . . . 32
1.3 Demand Elasticity Estimation Using Instrument Variable Approach . . . . . 35
1.4 Estimated Structural Parameters . . . . . . . . . . . . . . . . . . . . . . . . . 36
2.1 Summary Statistics by Groups of zipcodes in Jan 2021 . . . . . . . . . . . . . 54
2.2 Main Results on the Influence of BlueLA Charging Stations on Adoption of
EVs . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 60
3.1 Effect of Temperature on Yields: Panel and Long Difference Estimates . . . . 95
3.2 Competition and Mitigation of Climate Shocks: Panel Approach with Yields 103
3.3 Competition and Mitigation of Climate Shocks: Panel Approach with Arrivals106
3.4 Competition and Mitigation of Climate Shocks: Border Discontinuity . . . . 112
3.5 Effect of Competition on Prices Post Climate Shocks . . . . . . . . . . . . . . 119
3.6 Heterogeneous Impact of Climate Shocks on Input Usage and Crop Mix . . 124
3.7 Emission Standards in China’s Cement Industry . . . . . . . . . . . . . . . . 175
3.8 Effect of Temperature on Yields (Panel Approach): Robustness Tests . . . . 176
3.9 Effect of Temperature on Yields (Long-Differences): Robustness Tests . . . . 177
3.10 Effect of Out-of-State Competition on Mitigation of Climate Shocks . . . . . 178
3.11 Competition and Mitigation of Climate Shocks: Robustness Tests . . . . . . 179
3.12 Competition and Mitigation of Climate Shocks—Arrivals: Robustness Tests 180
3.13 Effect of Out-of-State Competition on Mitigation—Arrivals . . . . . . . . . . 181
viii
List of Figures
1.1 Average Emission Intensity in China’s cement industry . . . . . . . . . . . . 13
1.2 Map of Cement Plants in the Main Data . . . . . . . . . . . . . . . . . . . . . 17
1.3 Firm Heterogeneity in the Same Market and the Same Year . . . . . . . . . . 27
1.4 Coal Price Pass-Through by Province . . . . . . . . . . . . . . . . . . . . . . . 28
1.5 Event Study: The Effect of High Tax Rates on Firm-Level Emission Intensity 33
1.6 Estimated Firm-Level Costs . . . . . . . . . . . . . . . . . . . . . . . . . . . . 37
1.7 Comparison of Actual and Outside Moments . . . . . . . . . . . . . . . . . . 38
1.8 Estimated City-Level Marginal Costs and Benefits (RMB) . . . . . . . . . . . 40
1.9 Baseline Welfare (billion RMB) . . . . . . . . . . . . . . . . . . . . . . . . . . 41
1.10 Simulated Welfare Change by Province . . . . . . . . . . . . . . . . . . . . . 42
2.1 The Locations of BlueLA Stations and Chargers and EV Adoptions . . . . . 52
2.2 Event study: The Effect of BlueLA on New Vehicle Adoptions . . . . . . . . 56
2.3 Treatment Effects of BlueLA on EV Adoptions . . . . . . . . . . . . . . . . . 57
2.4 Top 10 Makes of Replacement Vehicles in CC4A . . . . . . . . . . . . . . . . 65
3.1 Climatological Changes Over India Between 1960-70 and 2010-20 . . . . . . 71
3.2 APMC Market Yards or Mandis in India . . . . . . . . . . . . . . . . . . . . . 81
3.3 Geographic Distribution of APMC Markets . . . . . . . . . . . . . . . . . . . 90
3.4 Percentage of Short-Run Impacts Offset by Adaptation . . . . . . . . . . . . . 98
3.5 Geographic Distribution of Competition Aggregated to District Level . . . . 101
3.6 Interpreting the Border Discontinuity Design . . . . . . . . . . . . . . . . . . 109
3.7 Geographical Distribution of Markets Selected Using 50 kms Bandwidth . . 109
3.8 Impact of Extreme Heat Offset by Competition: Border Discontinuity . . . . 113
ix
3.9 Cement Production Process . . . . . . . . . . . . . . . . . . . . . . . . . . . . 167
3.10 Key Regions for Emission Standards in China . . . . . . . . . . . . . . . . . . 168
3.11 Emissions Tax Rates in China . . . . . . . . . . . . . . . . . . . . . . . . . . . 169
3.12 Correlation Between Two Market Power Measures . . . . . . . . . . . . . . . 170
3.13 A Representative BlueLA Charging Station . . . . . . . . . . . . . . . . . . . 171
3.14 Fraction of Cropland in each ECMWF (Weather) Gridcell . . . . . . . . . . . 172
3.15 ICRISAT Districts and APMC States in Sample . . . . . . . . . . . . . . . . . 173
3.16 Coefficient Plot, GDD Distribution, and Extreme Heat Exposure by Season . 174
x
Abstract
This dissertation comprises three chapters in environmental economics. The first chap-
ter studies how to design emissions taxation given substantial firm heterogeneity in cost
structure and market power. Using both a theoretical framework and empirical analysis
in the context of the Chinese cement industry, I show that (1) emissions taxation strength-
ens market concentration of polluting firms; (2) firm heterogeneity matters for the overall
welfare impact of emissions taxation through the firm’s market power and the correlation
between local abatement costs and benefits; (3) an emission tax that accounts for local
firm heterogeneity coupled with output-based rebates can generate a 4.72 billion RMB
(0.67 billion dollars) welfare increase.
The second chapter investigates whether a lack of information can create barriers for
low-to-middle-income households to adopt green technology, specifically, electric vehicles
(EVs). Using the BlueLA program in Los Angeles as a quasi-experiment for information
treatment, we find that this non-price intervention is associated with a 33 percent increase
in new EV adoptions among low-to-middle-income households, which justifies a substan-
tial portion of public investment.
The third chapter documents that the pre-existing market power distortion in India’s
intermediary market can hinder farmer climate change adaptation. Using the variation in
the market power of local crop intermediaries due to historical agricultural laws, we show
that (1) farmers selling in the intermediary markets with less market power suffer sub-
stantially less from extreme heat; (2) the farmer’s economic loss due to extreme weather
could be mitigated by 13.8 percent if the restrictions on cross-border trading are removed.
xi
Introduction
This dissertation is composed of three essays in environmental economics. It seeks to un-
derstand how to facilitate and induce an efficient and less costly transition to sustainable
growth in both developed and developing countries, particularly through the lens of ad-
dressing pre-existing distortions and environmental injustice.
Building on empirical findings that firms are substantially different in productivity
and market power, Chapter 1, ”Pollution Taxes as a Second-Best: Accounting for Multidi-
mensional Firm Heterogeneity in Environmental Regulations”, studies how the local firm
heterogeneity can have first-order welfare effects in a second-best setting of spatially uni-
form emissions taxation. A theoretical framework that incorporates firm heterogeneity in
cost structure and market power shows that the first-order welfare effects can be gener-
ated through the firm’s market power and the correlation between local abatement costs
and benefits. Using time variation in emissions tax levels within provinces and compre-
hensive firm-level data in the context of the Chinese cement industry, differential impacts
of emissions taxation are documented. We find emissions taxation strengthens market
concentration. Then, by matching the observed firms’ product price, output volume, and
emission intensity with the model prediction, we structurally estimate firm-level marginal
abatement costs to assess the importance of incorporating firm heterogeneity in policy de-
sign. A counterfactual analysis shows a homogeneous emission tax that accounts for local
firm heterogeneity coupled with output-based rebates can generate a 4.72 billion RMB
(0.67 billion dollars) welfare increase.
1
In addition to firms, certain consumers can benefit or suffer more during the transition
to sustainable growth. This creates concerns related to environmental injustice. For ex-
ample, low-to-middle-income households are less likely to adopt electric vehicles (EVs),
and thus, suffer more from pollution generated by gasoline vehicles. Chapter 2, ”Electric
Vehicle Sharing: Crowding Adoption Out or In?” co-authored with Jonathan A. Libgober
explores how to facilitate low-to-middle-income households to adopt EVs through inter-
ventions other than commonly-studied subsidies. We evaluate the effect of the BlueLA
program that puts EVs for public use in many heavily trafficked areas on EV adoption
using a difference-in-differences strategy. We find that this non-price intervention is as-
sociated with a 33 percent increase in new EV adoptions among low-to-middle-income
households, which justifies a substantial portion of public investment. While the program
provides a substitute for car ownership, our findings are consistent with the hypothesis
that increasing familiarity with EVs could facilitate adoption.
Pre-existing distortions can slow down the transition to sustainable growth. In Chap-
ter 3, ”Does Market Power in India’s Agricultural Markets Hinder Farmer Climate Change
Adaptation?” co-authored with Rajat Kochhar, we document that the pre-existing market
power distortion in India’s intermediary market can hinder farmer climate change adap-
tation. The state-level agricultural laws that restricted farmers from cross-border trading
created spatial discontinuities in intermediary market power. Using a border discontinu-
ity design, we find that farmers selling in the intermediary markets with less market power
suffer substantially less from extreme heat. This can be explained by an increase in input
usage from farmers who anticipate higher prices after climate shocks in high-competition
markets. The counterfactual analysis suggests that the farmer’s economic loss due to ex-
treme weather could be mitigated by 13.8 percent if the restrictions on cross-border trading
are removed.
2
Chapter 1
Pollution Taxes as a Second-Best: Accounting for
Multidimensional Firm Heterogeneity in Environmental
Regulations
1.1 Introduction
Large and persistent productivity differences across firms in producing both the intended
products and the by-product, i.e. emissions
1
, have been documented in many studies
(Syverson 2004, Hsieh and Klenow 2009, Lyubich, J. Shapiro, and Walker 2018, J. S. Shapiro
and Walker 2020). Though firm heterogeneity can be sizeable, it is less problematic when
market-based approaches to pollution regulation are employed. For example, Pigouvian
taxation enables producers to internalize pollution costs such that firm-specific marginal
pollution abatement costs are equalized to the Pigouvian tax. Then, regulators can set
the Pigouvian tax to be equal to marginal damage of emissions
2
to achieve the social op-
timum, without requiring firm-specific information. Though the differential impacts that
environmental regulations create across different locations and demographic groups have
been covered extensively in recent literature, the focus has been on the ex-post assessment
1
Emission productivity is defined as output per ton emission.
2
The marginal damage of emissions is defined as the monetary costs that are imposed on residents from
one ton of emissions.
3
of the distributional impacts of these policies rather than on the optimal design of pol-
icy(Fowlie, Holland, and Mansur 2012, Grainger and Ruangmas 2018, Hernandez-Cortes
and Meng 2020, J. S. Shapiro and Walker 2021). Importantly, local and firm heterogene-
ity can have first-order welfare effects in the outcome of policy instruments as (1) typical
emission taxes are spatially uniform
3
, while pollution damage varies at the local level, and
(2) polluting firms can enjoy various degrees of market power and distinct cost structures
at the same spatial unit. Both factors can alter the efficiency of a policy, and the spatial
correlation between them may affect the efficiency of a spatially uniform policy.
This paper studies how and when the differential impacts of environmental regula-
tions can have a first-order effect on designing an efficient policy. More specifically, what
are the channels that enable local firm heterogeneity to impact efficiency at an aggregate
level, besides affecting the distribution of costs and benefits? When do welfare-seeking
regulators need to care about the differences between polluting firms? What is the magni-
tude of welfare loss, if any, when firm heterogeneity is ignored by regulators? We answer
these questions under the framework of a spatially uniform emission tax
4
, which has been
shown to be feasible in practice.
A growing literature has documented how environmental regulations reshape man-
ufacturing sectors through production adjustment, changes in output composition, de-
creasing pollution per unit of output, and entry and exit of firms (Bovenberg and Goulder
2002, Levinson 2009, J. S. Shapiro and Walker 2018). These environmental regulations
also impose unequal abatement costs on firms that possess substantially different market
power and cost structures (J. S. Shapiro and Walker 2020). However, less attention has
been paid to incorporating these findings into the design of environmental regulations.
To build a theoretical foundation for how local firm heterogeneity can affect efficiency
at an aggregate level, in the first part of the paper, I develop a micro-founded quantitative
model with heterogeneous polluting firms competing in Cournot fashion, consumers, and
3
For example, Fuel taxes in the United States vary at the state level.
4
Spatial uniform emission taxes can still vary across spatial units, e.g. states or provinces, and industries.
4
a welfare-maximizing regulator. Firms differ in market power and cost functions, both in
production and in abating emissions. They can adjust output level and emission intensity
which is defined as emission per output when facing the emission tax. This model incor-
porates four key features from empirical literature that allow emission taxation to impact
firms differently: (1) varying degrees of market power across firms within the same mar-
ket
5
; (2) firm-specific cost structures in both production costs and emissions abatement
costs; (3) cross-firm reallocation; and (4) heterogeneous pollution damages across space
6
.
Under the restriction that emission tax only varies at an aggregate spatial unit level, local
heterogeneity in market power, costs, and pollution damage can create two wedges in the
optimal tax rate under this second-best setting. The first wedge stems from the differen-
tial market power of firms, and the second stems from the spatial correlation between the
marginal cost of abatement of local firms and local marginal damage of pollution. The
sum of these two terms provides a statistic that is useful for testing empirically whether
firm heterogeneity matters for designing a welfare-maximizing regulation.
Bringing the model with firm heterogeneity to an empirical setting poses several chal-
lenges. First, firm-level emission abatement costs, even with detailed firm-level data, can
be difficult to measure. Commonly-adopted accounting and engineering estimates are
obtained by calculating the installment, operating, and maintenance costs of abatement
technologies, such as end-of-pipe filters. But these estimates can neglect firms’ strategic
behaviors under emission taxation, which may alter emission abatement costs. For ex-
ample, firms may choose to reduce output to comply. The corresponding loss in revenue
due to emission taxation should be counted as a part of emission abatement costs. Firms
who find it optimal to reduce emission intensity can also upgrade production or emission
5
Firms in the context of spatial competition can possess different levels of market power within the same
market, depending on their proximity to their consumers. This is especially true for polluting industries, e.g.
steel, power, and cement, when transportation costs are substantial. Also, firms’ market power can come
from non-market factors such as connections with local governments and banks to access cheaper credit.
6
Spatial variation in pollution damages can come from both pollution dispersion and location-specific
characteristics, such as population density, age distribution, etc.
5
processes
7
. This can alter the operation and maintenance costs of abatement technologies
and bias engineer estimates which are based on existing technologies. Second, it is dif-
ficult to validate whether firm-level market power is measured properly. Most literature
defines market power using market concentration, e.g. Herfindahl–Hirschman index, or
firm-level measures based on the number of nearby firms weighted by distance to other
firms and their firm sizes (N. H. Miller and Osborne 2014, Macchiavello and Morjaria
2021, Allen and Atkin 2022). However, these measures may fail to capture other impor-
tant contributors to market power, which can stem from non-market factors like access to
cheaper credit, especially in a context with weak institutions.
We tackle these challenges in the context of China’s cement industry. The distinctive
features of the Chinese cement industry, variation from the multiple adjustments in emis-
sion tax rates across time and locations in China, and the comprehensive and novel firm-
and market-level data collected make it possible to test the validity of the model, estimate
it empirically, and run policy counterfactuals.
The cement industry in China is one of the major emission sources of both carbon and
air pollutants and accounts for 58% of global cement production in 2015. The cement
market is concentrated in China with the top 25% largest cement firms taking up 50% of
total production (Liu, Tong, Zheng, Cheng, Qin, Shi, et al. 2021) — a common feature of
polluting capital-intensive industries. Also, cement firms compete in local markets that
vary in their input markets and local demand. This creates substantial spatial differentia-
tion in cement firms. Due to high transportation and storage costs, spatial location is one
critical source of market power. Furthermore, cement products are highly homogeneous
and firm-level heterogeneity in market power should not come from different quality of
cement products, which is often difficult to observe in practice. These features make it
credible to differentiate cement firms only based on market power and cost structures,
7
There are multiple methods and combinations for firms to reduce emission intensity. For example,
during the production process, firms can opt for higher-quality coal with less SO
2
content, re-optimize how
to deliver air into the kiln to make combustion more efficient, and reduce overall cement output. Converters
and filters installed at the end of the pipe can also reduce most pollutants such as SO
2
, NO
x
, and dust.
6
without taking into account other common sources of firm heterogeneity documented in
the literature (Syverson 2004, Hottman, Redding, and Weinstein 2016).
China launched an environmental taxation regime in 2003, which allows each province
to decide its emission tax rates within a range determined by the central government.
During our study period, from 2011 to 2018, adjustments in tax rates occurred three times
on average across provinces, at different points in time. Tax rates range from 0 RMB per
ton emission to 12,000 RMB, or equivalently 1,740 dollars, per ton of emissions
8
.
This paper combines several novel data sets, including firm-specific product-level price,
firm-level input, output, emission, characteristics such as age and capacity, as well as
market-level sales data to study heterogeneous firm decisions when emissions taxation
are levied in the same market. Product-level prices allow us to estimate firm-specific
market power through two data-driven approaches: deviation of firm-specific price from
market-level price and coal cost pass-through. These data-driven approaches require no
strong assumptions about the sources of market power. Furthermore, underlying firm-
level abatement cost curves can be structurally recovered from observing firms’ decisions
on output and emission intensity under different emission tax rates.
In the second part of the paper, I document substantial firm heterogeneity in emission
intensity and market power, through both deviation of firm-specific price from market-
level price and coal cost pass-through, in the same market with similar environmental
regulations. The extent of firm heterogeneity is even larger than the US counterparts, as
documented in Lyubich, J. Shapiro, and Walker 2018. Next we use time variation in emis-
sions tax levels within provinces to identify differential impacts of emissions tax based
on observables. We find that all firms increase the price by around 0.52% significantly
if emissions tax increases by 10%. But state-owned enterprises (SOEs), larger firms, and
cleaner firms tend to increase prices even more. This confirms the hypothesis that there
8
Detailed information on tax rate adjustments were collected from announcements by each provincial
government. Currency is converted based on the exchange rate in 2021.
7
exists differential market power of firms even within the same market and industry. Fur-
thermore, we estimate how firms react to emissions tax through two common methods,
adjusting output and emission intensity. The emissions tax reduces the emission intensity
of all firms by 0.29%. But SOEs, larger firms, and more-polluting firms reduce their emis-
sion intensity by an additional 0.83%, 0.29%, and 0.28%, respectively. The same firms also
increase their output relative to their counterparts. This suggests that firms face a trade-
off between decreasing output and decreasing emission intensity under all emission taxes.
In summary, all emission taxes further increase market power in the cement industry, as
larger firms increase their market share and revenue. These heterogeneous responses can
be explained by the underlying market power and cost structure of production and emis-
sion abatement, as predicted by the theoretical model.
The third part of the paper estimates the quantitative model with heterogeneous pol-
luting firms. The model is estimated by matching the observed firms’ product price, out-
put volume, and emission intensity with the model prediction; assuming the underlying
abatement cost function — a one-to-one mapping between emission abatement costs and
emission intensity — stays the same for each firm between 2011 to 2018. Two sets of pa-
rameters can be estimated. The parameters that govern the curvature and scale of firm-
specific abatement cost functions are estimated from adjustments in emission intensity in
response to changes in emissions tax rates. The parameters in production cost functions
are estimated using the output adjustments. The demand elasticity is estimated using a
standard instrument variable approach, where supply-side cost shifters — coal prices and
wages — are used as the instruments for market-level prices. The demand elasticity of
cement in China is -2.95, which is slightly more elastic than the estimates documented in
Fowlie, Reguant, and Ryan 2016 (-2.26). We find that on average the marginal cost of pro-
ducing one-ton of cement, including the abatement cost, is 289 RMB (42 dollars). Lastly, I
conduct the empirical test derived from the model and find that firm heterogeneity does
matter for designing an efficient emission tax in the context of China’s cement industry.
8
In the counterfactual exercise, I simulate the welfare changes across provinces under
the emissions tax incorporating firms’ market power, and spatial correlation between the
cost of abatement and pollution damage. Total welfare increases by 3.4% when emissions
tax rates are changed from the ones under the status quo to the ones we derive from the
model. If emissions tax revenue is recycled through output subsidies, total welfare in-
creases further by 1.2%, as output subsidies can address market power distortion without
distorting firms’ emission behavior. Compared to the status quo, a combination of emis-
sions taxation, which accounts for local firm heterogeneity, and output-based rebates from
tax revenue recycling can generate a 4.72 billion RMB (0.67 billion dollars) welfare increase
in the Chinese cement industry.
The methodology in this paper can be further applied to study the economic costs of
various environmental regulations, such as carbon tax and emission standards, in other
concentrated polluting industries. This is especially crucial for regulations that tackle car-
bon emissions as the damage of carbon emission is global while economic costs are local.
Related Literature. This paper contributes to three distinct pieces of literature in envi-
ronmental and development economics. First, this paper provides a micro-founded model
and new empirical evidence for the existing literature in the choice of price instruments
and quantity instruments under uncertainty in both costs and benefits (Weitzman 1974,
Pizer 2002, Burtraw, Holt, Palmer, and Shobe 2022). When the compliance cost of regula-
tions and benefits are certain, price instruments (e.g. emission tax) should be equivalent
to quantity instruments (e.g. emission trading). However, when there is more uncertainty
on the cost side, price instruments are preferred. Firm heterogeneity is one form of cost
uncertainty as it is difficult for regulators to observe the private information of an individ-
ual firm. Most existing studies in this area are either theoretical, or estimate costs/benefits
through simulations. This paper estimates the extent of uncertainty using empirical data
and provides new empirical evidence on the choice between price and quantity instru-
ments.
9
Second, this paper contributes to policy design in second-best settings (Buchanan 1969,
Goulder 1998, Bento, Kaffine, K. Roth, and Zaragoza-Watkins 2014, Fowlie and Muller
2019). Although the literature on optimal emissions tax has extended to incorporating
practical issues such as market power in polluting industries (Ryan 2012, Fowlie, Reguant,
and Ryan 2016, Cardoso 2020), less is known about how to design environmental regula-
tions when there is substantial local firm heterogeneity on market power and cost struc-
ture. The availability of novel and comprehensive firm-level data, has made it possible
for us to extend this analysis from the market level to the local firm level and estimate
the local correlation between firms’ abatement costs and emissions damage at a granular
level. This paper provides a theoretical framework and shows that the local correlation be-
tween costs and benefits should be incorporated even when designing spatially uniform
emissions taxation.
In addition, our paper further extends the recent growing literature on applying em-
pirical industrial organization techniques (Ericson and Pakes 1995, Bajari, Benkard, and
Levin 2007) to study the cost of environmental regulations (Ryan 2012, Fowlie, Reguant,
and Ryan 2016 and Cardoso 2020). However, due to data limitations, existing literature
on estimating the cost of environmental regulations often assumes that firms only adjust
output in reaction to environmental regulations, without changing their production and
abatement technologies. This can be a fair assumption in the context of developed coun-
tries. However, firms in our study demonstrate a tremendous decline in emission inten-
sity. Our paper builds on this empirical finding and proposes a flexible partial equilibrium
model, where firms can not only adjust output but can also alter emission intensity. Thus,
the cost of environmental regulations includes both economic losses due to output ad-
justment and the higher marginal cost of abatement to maintain abatement technologies.
Data limitations have also led to existing literature examining market power at the market
level through market concentration or market-level cost pass-through (N. H. Miller and
Osborne 2014, N. H. Miller, Osborne, and Sheu 2017, Ganapati, J. S. Shapiro, and Walker
10
2020). The detailed firm-level data we use allows us to examine firm-level market power
using two different data-driven approaches.
The remainder of the paper is structured as follows. Section 3.2 describes the cement
industry in China, the background of relevant environmental regulations, and data con-
struction. Section 1.3 presents a model with heterogeneous firms and provides a theo-
retical foundation for how firm heterogeneity affects the efficiency of emissions tax. Sec-
tion 1.4 documents substantial firm heterogeneity in emission intensity and market power
within the same market, and identifies differential impacts of emissions tax on various
firms classified by observables. Section 3.6 outlines the estimation strategy of the struc-
tural model. Section 1.6 presents simulations under alternative policy designs. Section 3.8
concludes the paper and discusses future research.
1.2 Background and Data
1.2.1 Environmental Regulations in China
China’s GDP grew by 588 percent during the two decades after the Reform and Opening
in 1978. One major driving force of the growth was industrial manufacturing, which led
to tremendous air and water pollution. Air pollution in China mostly is sourced from
industrial coal combustion and transportation. The World Health Organization (WHO)
estimates that outdoor air pollution led to 300,000 premature deaths per year in China
(Cohen, Ross Anderson, Ostro, K. D. Pandey, Krzyzanowski, K¨ unzli, et al. 2005).
To ease the growing concerns about pollution, the Chinese Ministry of Environmental
Protection (MEP) started to collect more detailed monitor-level pollution data and firms’
emission data, and release various environmental regulations. By the end of 2019, 1,634
pollution monitors have been placed in all provinces and cities, with 6 monitors in each
city on average. The annual Environmental Statistical Database, which contains both eco-
logical data and firms’ self-reported production and emission data, has been collected
11
since 1999. In addition, starting in 2007, the MEP required plants in high-emitting indus-
tries to install and operate Continuous Emissions Monitoring Systems (CEMS). By the
end of 2013, 14,410 firms had joined the system and kept uploading hourly, automatically
recorded pollutant-specific emission data to an online platform for each province. This
CEMS data makes it possible to cross-check self-reported emission data.
Emission standards and emission taxation are the two major instruments of environ-
mental regulations in China. The MEP in the central government issues emission stan-
dards for each sector and updates them occasionally. Starting in 2013, the MEP issues two
separate sets of emission standards each time, general standards and special standards.
The special standards are more stringent and apply only to key regions, which are more
politically important
9
. Emission taxation was established in 2003 in a form of charges for
emission permits before 2018, and emissions tax after 2018
10
. Compared to emission stan-
dards, emission taxation leaves local governments more flexibility to adjust the tax rates.
Normally, the MEP in the central government set up a price floor (RMB/ton) for each
pollutant, SO
2
, NO
2
, and CO. Provincial governments can adjust the tax rates across time.
There was only one major change in emission standards for the cement industry in the
period of interest, from 2011 to 2018, which took effect in July 2015. More detailed infor-
mation on changes in emission standards for the cement industry in the last two decades is
documented in Table 3.7. Emission tax rates, however, have been adjusted three times, on
average, in each province at different times over the study period. I collected each change
in tax rates across provincial governments and time by searching for the announcement
on each official website of the provincial government. Figure 3.11 documents the detailed
changes in tax rates across provinces over time. In Figure 1.1, I plot the average SO
2
emis-
sion intensity of cement plants in China, defined as the amount SO
2
generated to produce
9
Most cities in the key region are either the capital of the province or nearly Beijing, which is the capital
of China. A map of cities in the key region is in Figure 3.10
10
The MEP in local governments managed emission permits, while after 2018, the Tax Bureau in local
governments collect emissions tax.
12
one ton of cement, between 2011 and 2018. The change in emission standards in 2015 al-
tered the trend of emission intensities. But there was still a constant decrease in emission
intensity over time, which suggests that changes in emission tax rates can be responsible
for the decline as well.
(a) SO
2
(b) NO
x
Figure 1.1: Average Emission Intensity in China’s cement industry from 2011 to 2018
Note: This figure demonstrates the trend of average SO
2
and NOx emission intensity, defined as the amount of emission generated
(ton) to produce one ton of cement, in China’s cement industry. Data is collected from the Annual Environmental Statistic Database.
1.2.2 Cement Industry in China
Producing cement involves several key steps. First, the key material, limestone, is trans-
ported into a kiln, where it is heated to around 1,400 degrees Celsius. The heating process
uses the most energy among all the other key steps. 94.3% of Chinese cement plants re-
lied on only coal in 2018, which makes the heating process generates the most emission.
The heated ground limestone becomes cement clinker. Next, after cooling down, clinker
is ground into fine powder in a cement mill. This step consumes relatively less energy,
and thus, generates fewer emissions. Most cement plants integrate the heating and grind-
ing steps. Finally, the fine powder, which is the final product of cement, is then packed
into bags or trucks and delivered to construction sites. There, Cement is mixed with water,
sand, and gravel to generate fresh concrete and used for constructing buildings and roads.
Figure 3.9 documents these steps in detail and their associated energy consumption.
13
Cement product is very standard. Thus, the extent of quality differentiation is very
limited. Also, moisture in the air can be quickly absorbed by cement and makes it unus-
able. Thus, it is hard to store a large amount of cement and hedge future risk. As a result,
cement production can be very responsive to demand.
The concentration in China’s cement market has been increasing during the last decade.
From 2014 to 2019, the share of the top ten cement firms, based on capacity, increased
from less than 40% to more than 50%. Several factors contribute to the increasing concen-
tration. First, as transportation costs rise, cement plants that are close to raw materials or
construction sites gain more advantages. Transportation costs can take up around 30%
of cement price. Also, anecdotal evidence shows that larger firms can manage to comply
with increasingly stringent environmental regulations, while many smaller firms suffer
more. This can further expand the market share of larger firms. Figure 1.2 displays the
spatial distribution of cement plants in China.
Demand. Cement firms in China compete in a local market. Due to high transportation
costs and a low value-to-weight ratio, more than 80% of cement firms ship cement within
300 kilometers by trucks. In the empirical analysis, we treat each province as a local mar-
ket for the cement industry. Cement is mostly used in constructing buildings and roads.
Thus, demand for cement is highly correlated to local economic conditions and popula-
tion. In addition, there is a limited capacity for consumers to substitute cement with other
materials due to high costs.
International Trade. Imports and exports of cement in China take up a very limited
share of overall cement production. In 2018, the total domestic cement production was
1.96 billion tons, while imports and exports of cement only took up 0.05 percent and 0.38
percent respectively. Thus, in the theoretical framework, we do not take into account the
effect of environmental regulations on international trade.
Abatement Technologies. Clinker production is the most polluting process in the ce-
ment industry. Thus, all the available pollution abatement technologies try to reduce the
14
emission of this heating process. There are five major categories of technologies to abate
emissions associated with clinker production: (1) upgrading cement kiln types, (2) in-
creasing energy efficiency, (3) reducing clinker to cement ratio, (4) reducing sulfur con-
tent in coal, and (5) employing end-of-pipe abatement technologies(Liu, Tong, Zheng,
Cheng, Qin, Shi, et al. 2021). Nearly 100 percent of clinker production employs precal-
ciner kilns, which are the most efficient type of kilns available. Thus, there is not much
room to further reduce emissions through changes in kiln types. But under the other four
categories, there are multiple technologies or a combination of different technologies to
reduce emissions. For example, air can be delivered into a kiln in a way such as coal com-
bustion is more efficient, which can increase energy efficiency. Also, a proportion of SO
2
can be absorbed in a kiln through the reaction with calcium oxide. However, it is hard to
observe the exact technologies each plant uses to reduce overall emissions.
1.2.3 Data
In this section, I discuss how several novel data sets are combined to provide comprehen-
sive and detailed information on the regional cement markets in China, plant-level pro-
duction, emission, and sales (both prices and quantities), and other economic conditions
like energy prices and housing prices.
Chinese Environmental Statistical Database. To study firm-level heterogeneity in
emission and production, I obtain firm-level information from the Chinese Environmen-
tal Statistical Database (CESD) from 2011 to 2018, which is collected and managed by
the MEP. The CESD provides the most detailed annual data on plant-level characteristics,
such as location and 4-digit industry code, output (value), energy consumption, abate-
ment investment, and emissions across various pollutants. Plants in the CESD are sampled
based on the annual accumulated emissions. Since cement plants are capital-intensive and
highly polluting, most of the cement plants should exceed the threshold and show up in
the CESD data. I verify this by comparing the plants in the CESD and a complete list of
15
cement plants provided by the China Cement Association. I find that 96 percent of cement
plants are included in the CESD.
There are two caveats of the CESD. First, all information is collected by the MEP through
self-reported surveys. It is reasonable to doubt the accuracy of the data, especially the
one on emission information. Thus, emissions information in the CESD is cross-checked
with information from Continuous Emissions Monitoring Systems (CEMS), which are
placed in the chimneys of plants and automatically record hourly pollutant-specific emis-
sion data. I match firms in the CESD with the ones in CEMS based on firm ID and location
and cross-check the emissions data after 2014
11
. Emissions in the two data sets are highly
correlated. The second caveat is that cement plants can only be filtered through the 4-digit
industry code
12
. However, not all these cement plants are involved in highly-polluting
clinker production. Some plants may only provide grinding services or transportation,
which generate relatively neglectable emissions. I collect information on whether a plant
owns kilns from China Cement Association and further filter out irrelevant plants.
China Cement Association. To complement firm-level data in the CESD, I obtain more
detailed information from the China Cement Association (CCA), which is critical to study
firm-level market power. The CCA, established in 1987, is a non-profit organization that
collaborates with cement firms, universities, and research institutions to assist growth and
collaboration in the cement industry in China. It surveys a full list of existing cement
plants and collects detailed information. First, I obtain a complete survey of all cement
plants with functioning kilns and their production capacity. This allows me to filter out
other firms in the cement industry that do not engage in highly-polluting clinker produc-
tion. Also, monthly product-level sales prices
13
of each firm is provided by the CCA. This
makes it possible to measure firm-level market power using two data-driven approaches,
11
CEMS cover a comprehensive list of plants only after 2014.
12
The 4-digit industry code for cement plants is 3011.
13
Product-level sales prices are the so-called factory door prices, which are prices that do not account for
transportation costs. Final prices paid by consumers are measured at the regional market level.
16
deviation of firm-specific price from market-level price and coal cost pass-through, with-
out strong assumptions on the sources of market power. Lastly, monthly regional market-
level data like sales and final prices, that account for transportation costs, is obtained to
estimate market-level demand function. Imports and exports data is used to verify the
validity of ignoring international trade in the theoretical framework.
Figure 1.2: Map of Cement Plants in the Main Data
Note: This figure displays the cement firms and their capacities (in 10,000 tons) in the main data.
Firm Registration Database. Using available data sets, it is hard to confirm the number
of firm exits in the Chinese cement industry. When certain firms stop showing up in the
database, I can not distinguish firm exits from missing data entries. Thus, in the main
data, I only include firms that survived over the whole study period, from 2011 to 2018.
To verify a cement plant was established before 2011, I match the CESD with the Firm
Registration Database from Qichacha, which provides firm information upon registration
such as time of establishment and registered capital, based on firm name and location.
17
National Bureau of Statistics of China. To estimate regional market demand, I need to
collect information both on supply-side factors and demand-side controls. Monthly hous-
ing prices and annual GDP, as well as monthly coal prices, natural gas prices, electricity
prices, and manufacturing wages, are obtained from the National Bureau of Statistics of
China.
Province Number of Firms Average Output Average Price Average Emission Intensity Tax Rate
10,000 tons RMB/ton of cement kg/ton of cement RMB/ton of emissions
Yunnan 136 137.38 311.29 8.00 1,263.16
Sichuan 117 243.71 287.16 8.38 4,105.26
Shandong 106 171.03 303.83 4.28 6,315.79
Anhui 105 471.48 285.72 4.72 1,263.16
Hebei 105 147.46 293.30 9.14 5,684.21
Xinjiang 96 56.57 347.65 16.67 1,263.16
Guizhou 89 261.50 307.33 9.12 2,526.32
Henan 88 191.41 292.06 5.57 5,052.63
Zhejiang 84 249.56 330.94 4.09 1,263.16
Hunan 83 152.87 284.39 7.43 2,526.32
Guangdong 80 328.38 327.50 6.89 1,894.74
Shanxi 79 106.86 271.59 7.33 1,894.74
Inner Mongolia 77 139.60 296.78 8.20 1,263.16
Guangxi 74 355.38 293.12 3.49 1,894.74
Jiangxi 72 221.15 313.78 6.35 1,263.16
Hubei 70 202.55 329.68 4.91 2,526.32
Shaanxi 64 175.60 270.04 9.10 1,263.16
Liaoning 60 122.49 284.10 4.83 1,263.16
Gansu 59 153.19 301.04 9.44 1,263.16
Jiangsu 54 415.61 296.87 4.76 5,684.21
Chongqing 54 326.81 298.37 15.94 2,526.32
Fujian 49 163.78 311.04 5.41 1,263.16
Ningxia 35 129.26 242.97 6.83 1,263.16
Heilongjiang 33 70.02 361.99 2.50 1,263.16
Jilin 28 173.93 371.69 2.97 1,263.16
Qinghai 18 99.74 302.14 5.92 1,263.16
Hainan 10 248.79 353.79 7.35 2,526.32
Beijing 8 77.13 328.29 2.88 12,631.58
Tianjin 3 80.89 315.79 2.58 10,526.32
Table 1.1: Summary Statistics for Cement Markets
Note: This table displays the summary statistics for cement markets, which are defined at the provincial level. Emission intensity is
measured in kilograms of SO
2
emissions when producing one ton of cement.
1.3 Model
To build a theoretical foundation for how local firm heterogeneity can affect the efficiency
of environmental regulations at an aggregate level, I develop a micro-founded quantitative
model with heterogeneous polluting firms in this section. Regulators seek to maximize
18
aggregate welfare in each regional market and address the pollution externality by set-
ting up spatially uniform emissions tax, while pollution damage differs at the local level.
Regulators do not have complete information on individual firms’ market power and cost
structures. Instead, they observe the distribution of market power and cost structures
across all firms in the same market.
Under this second-best setting, a key insight from the model is that optimal emission
taxation design may not only depend on the industry average of firms’ costs and market
power, but also the variance and covariance between firms’ costs and pollution damage.
This theoretical framework provides a statistic so that it can be tested empirically whether
local firm heterogeneity and differences in pollution damage matter for efficiency at an
aggregate level.
1.3.1 Setup
I set up a quantitative partial equilibrium model in each market. Firms are assumed to op-
erate independently across different markets. Regulators seek to maximize total welfare
within the same market. Thus, the model setup is the same across markets at different
times. Throughout the following analysis, I omit the market and time subscript for conve-
nience.
A. Firms
LetN be the number of firms in each market. Firms, that produce homogeneous products,
compete in Cournot fashion and generate aggregate output,Q. The resulting market-level
price, P (Q), is the price consumers pay after accounting for transportation costs. Since
the cement is a homogeneous product, in an equilibrium, consumers pay the same price,
P (Q), for cement from different firms. Firm i charges a factory door price,p
i
, which is the
product price before shipment.
19
Firms differ in market power, costs, which include production costs and emission abat-
ing costs separately, and emission intensity (defined as the amount of emission to produce
per unit of output). ϵ
i
is a firm-specific and time-invariant parameter that captures the
market power of firm i. It is defined as the ratio between firm i’s factory door price, p
i
,
and the market price after accounting for transportation costs, P (Q). The higher the ϵ
i
,
the higher the market power is. This measure can capture the market power from spa-
tial advantage and other non-market factors. For example, if a firm i is located closer to
its customers, transportation costs are lower so that firm i can charge a high factor door
price. In addition, if the same firm is preferred by consumers due to personal connections,
ϵ
i
is higher as well. Firmi’s production cost is a function of output,c
i
(q
i
). To abate emis-
sion, it encounters an abatement cost,c
i
a
(e
i
)q
i
, wherec
i
a
(e
i
) is a firm-specific marginal cost
of emission abatement. c
i
a
(·) represents a one-to-one mapping between firm i’s emission
intensity and the marginal abatement cost to maintain this level of emission intensity. It
decreases in emission intensity. e
i
represents firm i’s emission intensity.
Under an emission tax rateτ
e
, firm i can adjust either its output level,q
i
, or its emis-
sion intensitye
i
, which is defined as emission per output. Since firms can choose from a
composite of abatement technologies and it is hard to observe the exact technologies firms
employ, both of the decisions are treated as continuous variables. The marginal emission
abatement cost,c
i
a
(e
i
)q
i
, changes as the firm changes emission intensity. But I assume that
the underlying marginal abatement cost function,c
i
a
(·), stays the same for each firm. Thus,
firm i’s marginal abatement costs just move along the functionc
i
a
(·) when the emission tax
rate changes.
Firm i’s profit maximization problem is:
max
q
i
,e
i
(P (q
i
+
X
j̸=i
q
∗
j
))ϵ
i
q
i
−c
i
(q
i
)−τ
e
e
i
q
i
−c
i
a
(e
i
)q
i
, (1.1)
whereq
∗
j
is the optimal output chosen by other firms in the same market.
20
The optimal output and emission intensity level under the emission tax rateτ
e
from
the first-order conditions is:
[q
i
] : (P (Q) +
∂P (Q)
∂Q
q
i
)ϵ
i
| {z }
marginal revenue
= (
dc
i
(q
i
)
dq
i
+τ
e
e
i
+c
i
a
(e
i
)
| {z }
marginal cost
) (1.2)
[e
i
] : −
dc
a
(e
i
)
de
i
| {z }
marginal abatement cost
=τ
e
(1.3)
These two conditions generate conventional firms’ optimization rules. When a firm chooses
an optimal level of output, its marginal revenue from one more output should be equal
to the marginal cost. This can also be interpreted as the firm is indifferent between losing
the profit from producing one output and saving the cost related to emissions, emissions
tax, and the marginal abatement cost, for producing the same output. Also, the marginal
abatement cost should be the same as the emission tax rate. It indicates that the higher
the emission tax rate, the lower the emission intensity. But the marginal abatement cost
increases simultaneously.
This model incorporates several key features from empirical literature that allow emis-
sion taxation to shape firms differently: (1) market power, (2) distinct cost structures in
both production and emission abatement, and (3) cross-firm reallocation through differ-
ent output changes across firms.
B. Demand
Since cement is fairly homogeneous, I assume that customers have a constant price elas-
ticity of demand across markets. The aggregate demand function in each marketm is:
lnQ
m
=α
0m
+α
1
lnP
m
, (1.4)
21
whereQ
m
is the aggregate output in marketm, andP
m
is the market price that accounts
for transportation costs. α
1
is the price elasticity of demand. The interceptα
0m
is market-
specific and captures market-specific differences in aggregate demand arising from eco-
nomic conditions.
1.3.2 Equilibrium
The following conditions have to be satisfied in an equilibrium: (1) each firm i chooses
the level of output and emission intensity such that the profit, defined in Equation 1.1 is
maximized; (2) consumers are indifferent to cement from different cement plants after
transportation costs are accounted for; and (3) market is clear. More specifically, the fol-
lowing equations define an equilibrium in each regional market in each period. I omit the
subscript for market and time for convenience.
q
i
=
dc
i
(q
i
)
dq
i
+c
i
a
(e
i
) +τ
e
e
i
−ϵ
i
P
ϵ
i
P/α
1
Q (1.5)
−
dc
a
(e
i
)
de
i
=τ
e
(1.6)
P =
P
i
[(
dc
i
(q
i
)
dq
i
+c
i
a
(e
i
) +τ
e
e
i
)/ϵ
i
]
N + 1/α
1
(1.7)
Q =exp(α
0
)P
α
1
(1.8)
There are several insights from market equilibrium. First, firms with a lower marginal
production cost or a lower marginal abatement cost enjoy a larger market share. This is
consistent with the finding that more efficient and cleaner firms tend to have a larger mar-
ket share, as documented in Weber 2021. Second, firms with higher market power tend to
have a larger market share. Third, increasing emissions tax rates decrease firms’ emission
intensity and increases both market-level prices and firm-level factory door prices. Finally,
firms with a lower marginal cost of abatement reduce emission intensity more than those
22
with a higher marginal cost of abatement. These insights will be tested empirically in
Section 1.4.
1.3.3 Welfare-Seeking Regulators
In each region/market, welfare-seeking environmental regulators address the pollution
externality by setting up a spatially uniform emissions tax. The welfare measure at each
period in each market is composed of total consumer surplus (CS), producer surplus (Π),
emissions tax revenue (T), and total pollution damage (Φ). Here, I assume a conventional
utilitarian social welfare function, as in Fowlie, Reguant, and Ryan 2016 and Ida, Ishihara,
Ito, Kido, Kitagawa, Sakaguchi, et al. 2022, with equal weights on different components of
the welfare measure:
W (τ
e
) =CS(τ
e
) +PS(τ
e
) +T (τ
e
)− Φ(τ
e
)
=
Z
Q
∗
0
P (z)dz−P (Q
∗
)Q
∗
+
X
i
π
i
(τ
e
) +
X
i
τ
e
e
∗
i
q
∗
i
−
X
i
ϕ
i
e
∗
i
q
∗
i
whereQ
∗
is the aggregate output in the equilibrium;q
∗
i
ande
∗
i
is firm i’s optimal level of
output and emission intensity.
Environmental regulators seek to maximize the total welfare in the region:
max
τe
W (τ
e
)
Letµ i
be firm i’s operating markup,µ i
=p
i
−c
i
−c
i
a
(e
i
). The marginal benefit of pollu-
tion abatement of firm i,ϕ
i
, can be decomposed to regional average pollution damage and
a firm-specific deviation from the average, ϕ
i
=ϕ+η
i
. Under the first-best setting where
regulators can design firm-specific emissions tax rates, the optimal taxation design is:
τ
∗
e
=ϕ
i
−
P
i
µ i
dq
i
dτe
P
i
de
i
q
i
dτe
| {z }
market power
(1.9)
23
When it is only feasible to design a spatially uniform emissions tax rate in each region,
the optimal taxation design under this second-best setting is:
˜ τ
e
=ϕ−
P
i
µ i
dq
i
dτe
P
i
de
i
q
i
dτe
| {z }
market power
+
P
i
η
i
de
i
q
i
dτe
P
i
de
i
q
i
dτe
| {z }
correlation between cost and benefit
. (1.10)
Under the restriction that emission tax only varies at an aggregate spatial unit level, local
heterogeneity in market power, costs, and pollution damage can create two wedges in the
optimal tax rate under this second-best setting. The first wedge stems from the differential
market power of firms, and the second one is from the spatial correlation between the
marginal cost of abatement of local firms and local marginal damage of pollution. When
firms compete in a form of oligopoly, they are already producing at a level that is below
social optimal. Thus, firms’ market power creates a negative tax wedge in the optimal
design of emissions tax rates. In addition, where there is a spatial correlation between
the cost of pollution abatement and marginal pollution damage, the optimal emissions
taxation design can depend on the covariance between cost and benefit. Simultaneous
high pollution damage and low abatement cost create a positive wedge in the optimal
emission tax rate. This provides a micro-founded perspective to explain the importance
of understanding the correlation between costs and benefits in policy designs, as proposed
in Weitzman 1974.
1.3.4 Statistics of Welfare Implications and Policy Counterfactuals
When should welfare-seeking environmental regulators care about local firm heterogene-
ity? The theoretical framework above provides two natural statistics that can be tested em-
pirically to answer this question. First, if the second term in Equation 1.10,t
2
=
P
i
η
i
de
i
q
i
dτe
P
i
de
i
q
i
dτe
,
is close to zero, then environmental regulators can ignore the heterogeneity in pollution
damage and only focus on addressing the distortion from market power. This statistic
24
represents the correlation between the local marginal benefits of pollution abatement and
the marginal costs of emissions abatement of the local firms. It can be estimated using the
marginal benefits of pollution abatement measures, which is discussed in detail in Section
1.5.4, and the marginal change in firms’ emissions if the emissions tax rate increases by 1
unit, which can be estimated in a reduced-form way. Second, if the sum of the two wedges
in Equation 1.10, defined as t
1
=
P
i
µ i
dq
i
dτe
P
i
de
i
q
i
dτe
+
P
i
η
i
de
i
q
i
dτe
P
i
de
i
q
i
dτe
, is close to zero, then environmental
regulators can just set the emissions tax rate as the average marginal pollution damage in
the region. The first term can be estimated using data on firms’ markup and the marginal
output adjustments of firms if the emissions tax rate increases by 1 unit, which can be
obtained using reduced-form estimates.
To assess the welfare gain from the emissions taxation derived from the theoretical
framework, I run the following policy counterfactuals.
Alternative emissions tax rates. The first policy counterfactual we investigate uses
the optimal emission tax rates derived from the theoretical framework. I will compare the
welfare gain in each region by switching the emissions tax rates from the ones under the
status quo to the optimal ones.
Output-based rebates. To address the distortion from market concentration, tax rev-
enues from emissions taxation can be recycled to firms in a form of output-based rebates.
More specifically, the emissions tax firm i needs to pay becomes:
t(s;τ
e
,e
i
,q
i
) =τ
e
(e
i
−s)q
i
(1.11)
25
The output-based rebates are equivalent to production subsidies in theory. The new mar-
ket equilibrium under the output-based rebates is:
q
i
=
dc
i
(q
i
)
dq
i
+c
i
a
(e
i
) +τ
e
(e
i
−s)−ϵ
i
P
ϵ
i
P/α
1
Q (1.12)
−
dc
a
(e
i
)
de
i
=τ
e
(1.13)
P =
P
i
[(
dc
i
(q
i
)
dq
i
+c
i
a
(e
i
) +τ
e
(e
i
−s))/ϵ
i
]
N + 1/α
1
(1.14)
Q =exp(α
0
)P
α
1
(1.15)
Under this new equilibrium, the aggregate output increases, and the market-level price
decreases. The emission intensity decision of firms is not affected by the output-based
rebating. Thus, the deadweight loss from market concentration can be mitigated through
output-based rebates.
1.4 Empirical Evidence
In this section, I first document substantial firm heterogeneity in output and emission
intensity within the same regional market in the Chinese cement industry. This validates
the relevance to study how firm heterogeneity matters for efficient policy designs in the
context. Then, the differential impacts of emissions taxation on output, emission intensity,
and prices are estimated using two-way fixed effects estimation. The differential impacts
can be explained by two key dimensions of firm heterogeneity, market power, and cost
structures. The empirical results are consistent with model predictions in Section 1.3.2.
1.4.1 Firm Heterogeneity
To measure the local heterogeneity of cement firms in the same market, I regress annual
firm-level output, as well as SO
2
and NO
2
emission intensity with province-year fixed
26
effects and firms’ ages as the control. Then residuals from these regressions are plotted in
Figure 1.3. The age of a firm can be considered a proxy for technology. But even within this
narrowly defined cement industry and regional market and after controlling for ages, there
still exists substantial firm heterogeneity. For example, province-year characteristics, such
as market conditions and environmental regulations, and firms’ ages can only explain 5.2
percent of the variation in firms’ output and 13.8 percent of the variation in firms’ emission
intensity. Thus, similar to cement plants in the U.S, as documented in Lyubich, J. Shapiro,
and Walker 2018, cement plants in China show a substantial and even larger extent of firm
heterogeneity.
(a) Output (R
2
: 0.052) (b) Age (R
2
: 0.070)
(c) SO
2
(R
2
: 0.138) (d) NO
2
(R
2
:0.051)
Figure 1.3: Firm Heterogeneity in the Same Market and the Same Year
Note: This figure illustrates firm-level heterogeneity in the same market and the same year. Histograms report the residual output,
age, SO
2
, and NO
2
emission intensity, after adding province-year fixed effects and controlling for firm ages (except for the age
regression). The Red line indicates the median and the dotted black line indicates one standard deviation. Small R
2
s suggests that
even after controlling for age, there still exists a substantial extent of firm heterogeneity in the same region market in the same year.
In addition to firm size and emission intensity, I employ a data-driven approach to
measure firm-level market power. Coal is a major input for clinker production. When
27
the coal price increases, a firm with higher market power can pass through some of the
increasing costs to customers, by raising the product price. Thus, coal price pass-through
can be used to measure firm-level market power. I estimate coal price pass-through by
capturing how much change in monthly product prices is associated with a change in
monthly coal prices from 2011 to 2018. More detailed steps are documented in K.2.
Figure 1.4 illustrates the distribution of coal price pass-through by province. The mar-
ket power also shows large heterogeneity across locations. On average, if the coal price
increases by 1 RMB per ton, the cement price will increase by 0.45 RMB per ton. The ce-
ment price of a firm, whose market power is at the 75th quantile, will increase by 0.36 RMB
per ton more compared to the one at the 25th quantile.
Figure 1.4: Coal Price Pass-Through by Province
Note: This map displays the average coal price pass-through across firms in each market. Coal price pass-through is defined as the
change in product price when the coal price increases by one unit. It suggests that market concentration is different across locations.
More detailed information on estimating the coal price pass-through is documented in K.2.
28
1.4.2 Differential Impacts of Emissions Taxation
1.4.2.1 Empirical Strategy
I estimate differential impacts of emission taxation on firms’ output, emission intensity,
and prices, by exploiting the variation from both the intensity of tax exposure and the
timing of tax rate changes. Firms are classified based on four observables in 2011, which
is before any changes in emissions tax rates in the study period, to study the differential
impacts: (1) ownership (privately-owned or state-owned), (2) size (output is below the
median or above the median in the same market), (3) emission intensity (SO
2
emission
intensity is below the median or above the median in the same market), and (4) age (ages
are below median age or above in the same market).
A conventional two-way fixed effect design is employed to estimate the differential
impacts of emission taxation:
y
ist
=α
0
τ
st
+α
1
τ
st
×Group
i
+X
it
β +γ
i
+η
t
+ϵ
ist
(1.16)
where y
ist
is firm i’s output/emission intensity/factory door price in market/province s
and year t; τ
st
is emissions tax rates in province s in year t; Group
i
indicates which cat-
egories firm i falls into; X
it
is a set of controls which include the stringency of emission
standards and provincial characteristics like GDP and housing price; γ
i
and η
t
are firm
fixed effects and year fixed effects respectively.
One major identification assumption is that changes in emissions tax rates are not corre-
lated with unobserved time-varying factors that can affect firms’ outcomes. One possible
way to violate the identification assumption is through co-existing environmental regu-
lations that can be corrected with emissions taxation. I document detailed information
on environmental regulations that involve the cement industry in Section 3.2. Another
major environmental regulation is emission standards, which only changed once during
the study period. In the regressions, I control for various emission standards that apply
29
to cement firms in different markets. The second identification assumption is that firms’
outcomes should not change if there were no changes in emissions tax rates, after adding
various fixed effects and controls in Equation 1.16. We test this assumption using an event
study framework in Section 1.4.2.3.
1.4.2.2 Results
Table 1.2 shows the estimates on differential impacts of emissions taxation. First, these
estimates can be interpreted by examining each category separately. When facing higher
emissions tax rates, privately-owned firms decrease output and emission intensity signifi-
cantly. But they can mitigate some of the loss from increasing cement prices. For privately-
owned firms, a one percent increase in emission tax rate is associated with a 0.025 percent
decrease in output, a 0.027 percent decrease in emission intensity, and a 0.067 percent in-
crease in cement price. From 2011 to 2018, emissions tax rates increase by 412 percent
on average, then privately-owned firms decrease their output by 10.3 percent, decrease
emission intensity by 11.1 percent, and increase their cement price by 27.6 percent over
this period. Compared to privately-owned firms, when facing a one percent increase in
tax rates, state-owned enterprises (SOE) increase their output by 0.034 percent relatively,
further reduce the emission intensity by 0.083 percent, and further increase the price by
0.018 percent. This suggests that compared to privately-owned firms, state-owned firms
can benefit from emissions taxation relatively by expanding it market share.
Similar to privately-owned firms, smaller firms and dirtier firms decrease output and
emission intensity, and increase the price, when facing emissions taxation. Compared to
smaller firms, larger firms reduce emission intensity more, but also increase price more.
Notice that the sign of the effects on output is always opposite to the sign of the effects on
emission intensity, for firms falling into group 2. This suggests that firms face a trade-off
between reducing output and reducing emission intensity, which is consistent with the
30
theoretical framework in Section 1.3.2. In Panel B, dirtier plants reduce their emission in-
tensity even more, which shows that it is relatively cheaper for them to reduce emission
intensity instead of reducing output. This is also consistent with the practical observa-
tion that it normally costs less for dirtier plants to abate emissions, as there are still many
available technologies for them to choose from.
1.4.2.3 Robustness
The second identification assumption we haven’t addressed yet requires that firms’ out-
comes should not change systematically over time in the absence of changes in emissions
tax rates, after adding various fixed effects and controls in Equation 1.16. To address this,
I employ an event study framework. An event occurs if the new emissions tax rate exceeds
2,000 RMB per ton of emission, which is the median tax rate and still relatively small com-
pared to the maximum tax rate, 12,000 RMB per ton of emission.
Since different provinces changed their emissions tax rates at a different time, this bi-
nary treatment, the event I define, follows a staggered roll-out design. One fast-growing
literature in treatment effect raises the issue of potential bias of two-way fixed effect mod-
els, especially with heterogeneous treatment effect (Goodman-Bacon 2018, J. Roth and
Sant’Anna 2021). Thus, I incorporate one of the latest event study estimators, proposed in
Callaway and Sant’Anna 2020, that can address this issue. Provinces where emissions tax
rates never exceeded 2,000 RMB per ton of emission (16 out of 33 provinces) are set as the
control group.
Figure 1.5 displays the average impact of entering a high tax rate category on the av-
erage percentage change in firms’ emission intensity. There is no significant pre-trend
before events occur. This suggests that the second identification assumption is likely to be
satisfied.
31
Panel A: log(Output)
Ownership Size SO
2
Intensity Age
Private, SOE Small, Large Low, High New, Old
G1, G2 G1, G2 G1, G2 G1, G2
(1) (2) (3) (4)
log(Tax) −0.025
∗∗∗
−0.031
∗∗∗
−0.048
∗∗∗
0.024
(0.010) (0.009) (0.014) (0.023)
log(Tax)×G2 0.034
∗∗∗
0.015 0.016 −0.071
∗∗
(0.012) (0.019) (0.014) (0.030)
Observations 12,942 12,942 12,942 12,942
R
2
0.923 0.927 0.926 0.960
Panel B: log(SO
2
Emission Intensity)
Ownership Size SO
2
Intensity Age
Private, SOE Small, Large Low, High New, Old
G1, G2 G1, G2 G1, G2 G1, G2
(1) (2) (3) (4)
log(Tax) −0.027
∗
−0.026
∗
−0.032
∗∗
−0.062
∗∗
(0.015) (0.014) (0.013) (0.026)
log(Tax)×G2 −0.083
∗∗∗
−0.029
∗∗∗
−0.028
∗∗
0.039
∗
(0.028) (0.036) (0.013) (0.022)
Observations 12,942 12,942 12,942 12,942
R
2
0.789 0.790 0.808 0.854
Panel C: log(Price)
Ownership Size SO
2
Intensity Age
Private, SOE Small, Large Low, High New, Old
G1, G2 G1, G2 G1, G2 G1, G2
(1) (2) (3) (4)
log(Tax) 0.067
∗∗∗
0.062
∗∗∗
0.080
∗∗∗
0.037
∗∗∗
(0.007) (0.008) (0.008) (0.012)
log(Tax)×G2 0.018
∗
0.015
∗
−0.015
∗
−0.017
(0.010) (0.008) (0.009) (0.014)
Observations 12,942 12,942 12,942 12,942
R
2
0.734 0.734 0.741 0.754
Table 1.2: Differential Effects of Emission Taxation on Firm-Level Outcomes
Note: This table estimates the elasticity of output, SO
2
emission intensity, and firms’ factory door prices concerning emissions tax
rates from different groups of firms. Firms are classified based on four pre-treatment observables, ownership (privately-owned or
state-owned), size, emission intensity, and age. Emissions tax rates are continuous. On average, three changes in tax rates occurred in
each province between 2011 to 2018. Firm and year fixed effects are included and emission standards are controlled for. Standard
errors are clustered at the firm level.
∗
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
32
(a) SO
2
(b) NO
2
Figure 1.5: Event Study: The Effect of Entering a High Tax Rate Category on Percentage
Changes in Firm-Level Emission Intensity
Note: This figure shows the effect of entering a high tax rate category, when tax rates are higher than 2,000 RMB/ton, on the average
percentage change of a firm’s emission intensity. I follow Callaway and Sant’Anna 2020 to estimate the dynamic treatment effect that
addresses potential bias in a staggered roll-out design under two-way fixed effects due to heterogeneous treatment effects. This figure
suggests that there is no significant pre-trend on firms’ emission intensity before emissions tax rates are significantly high. Standard
errors are clustered at firm level.
1.5 Model Estimation
The empirical evidence in the above section suggests that the theoretical model can reflect
those empirical findings. In this section, I bring the model to the empirics by structurally
estimating key parameters. To do so, I need to make some assumptions about the func-
tional form of firms’ underlying cost functions.
1.5.1 Demand
I estimate the demand function using the following specification:
lnQ
mt
=α
m
+α
1
lnP
mt
+α
2
X
mt
+ϵ
mt
lnQ
mt
is the natural log of total market demand in market m and time t. A market
is defined at the provincial level. α
1
is the constant elasticity of demand. To cope with
the endogenous price due to simultaneous equation issues, supply-side cost shifters, coal
33
prices, and wages are applied as instruments for market-level prices. X
mt
includes de-
mand shifters such as population and housing price, which is a proxy for construction
demand, in market m and time t.
Table 1.3 summarizes the estimation of price elasticity of demand using various spec-
ifications. All specifications include market/province fixed effects. No other controls that
potentially shift cement demand are included in the first specification. In the subsequent
specifications, demand shifters such as housing price, clinker price, and storage-capacity
ratio are included. Specification 4 is selected as the preferred one, as it takes into consid-
eration the most comprehensive demand shifters that can bias our estimate for the price
elasticity of demand. Thus, the price elasticity of demand is -2.952
14
throughout the rest
of the analysis.
1.5.2 Firm-Level Cost Functions
Firms encounter both production costs and emission abatement costs. I assume firms have
constant marginal production costs, and the underlying marginal abatement cost function,
a one-to-one mapping between the marginal abatement cost and emission intensity, stays
the same over the study period. This is a reasonable assumption as abatement technologies
have not had breakthroughs during this period.
More specifically, assume each firm has a linear production cost function, c
i
(q
i
) =c
i
q
i
,
and linear abatement cost function, c
i
a
(e
i
)q
i
. The marginal cost of abatement function is
c
i
a
(e
i
) =A
i
e
−δ
i
−A
0
. δ governs the curvature of the marginal abatement cost function,
whileA
i
accounts for firm-specific characteristics that can decide the level of the marginal
abatement cost function. As emission intensity goes down, it is more expensive to operate
and maintain abatement technologies. Also, when there is no environmental regulations,
14
The estimate is higher in absolute value than the price elasticities of demand estimated in the context
of developed countries in the literature. For example, Ryan 2012 estimates a demand elasticity of -2.26 using
the U.S. Geological Survey (USGS) for all the Portland cement producers in the United States from 1980 to
1999. Using similar data from USGS over the period 1981–2009, Fowlie, Reguant, and Ryan 2016 estimate a
demand elasticity of -2.03.
34
Dependent variable: Log output
(1) (2) (3) (4)
Log price −0.523
∗∗∗
−0.600
∗∗∗
−1.244
∗∗∗
−2.952
∗∗∗
(0.145) (0.190) (0.418) (0.859)
Log housing price 0.164 0.215 0.206
(0.149) (0.199) (0.266)
Log clinker price 1.445
∗∗∗
(0.473)
Log capacity ratio −1.104
∗∗
−1.263
∗∗
(0.409) (0.469)
Observations 1,499 1,461 1,461 1,392
R
2
0.752 0.769 0.766 0.729
First-stage F-test 307.05 251.37 120.17 40.10
Table 1.3: Demand Elasticity Estimation Using Instrument Variable Approach
Note: The unit of observation is a province-year-month from 2011 to 2018. Province fixed effects are included in all specifications.
Standard errors are clustered at the province level. First-stage F-test reports the Kleibergen-Paap statistics.
∗
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
firm i’s emission intensity is e
0
i
= (
A
i
A
0
)
1
δ
, which is firm-specific. This reflects substantial
firm heterogeneity in emission intensity documented in Section 1.4.
Then, the quantitative model is structurally estimated by matching the observed firms’
product price, output volume, and emission intensity with the model. Two sets of param-
eters are supposed to be estimated, the marginal production cost, c
i
, and parameters in
the marginal abatement cost function,{A
i
,δ,A
0
}.
Abatement costs. The equilibrium condition for optimal emission intensity in Equation
1.6 can be re-written as:
log(e
it
) =
log(A
i
δ)
1 +δ
−
log(τ
eit
)
1 +δ
+u
it
, (1.17)
whereu
it
represents mean-zero measurement errors. The parameter that governs the cur-
vature of the marginal abatement cost function,δ can be estimated through the variation
35
in multiple changes in emissions tax rates faced by the same firm. The firm-specific pa-
rameterA
i
can be estimated through firm fixed effects.
Production costs. The equilibrium condition for optimal output in Equation 1.5, com-
bined with the market clear condition in Equation 1.7 and Equation 1.8, can be re-written
as:
q
it
Q
mt
P
mt
α
1
+p
it
−τ
eit
e
it
(1 + 1/δ) =c
i
−A
0
+v
it
(1.18)
whereq
it
andp
it
is firm i’s output and factory door price respectively; Q
mt
is aggregate
output in the market;v
it
is the mean-zero measurement errors. c
i
−A
0
can be identified
through fixed effects, but c
i
andA
0
can not be identified separately.
Table 1.4 shows the estimates of market-level parameters. The parameter that governs
the convexity of a marginal abatement cost function,δ, is estimated to be 5.150 with a very
small standard error. This suggests that the marginal abatement cost function is fairly
convex. Since the intercept of the marginal abatement cost function,A
0
, and the marginal
Estimate Standard Error
Demand elasticity: α
1
-2.952 0.859
Convexity of marginal abatement cost function: δ 5.150 0.964
Table 1.4: Estimated Structural Parameters
production costc
i
can not be identified separately, I plot the total marginal cost, which is
a sum of the marginal production cost and the marginal abatement cost, in Panel (b) in
Figure 1.6. On average, the marginal cost of producing one ton of cement, including both
the marginal production cost and the marginal abatement cost, is 289 RMB (42 dollars).
The distribution of estimated marginal costs falls in a reasonable range compared to the
distribution of product prices in the data. The distribution of the firm-specific parameters
that decide firms’ emission intensity under no environmental regulations, A
i
, is plotted
in Panel (a) in Figure 1.6. This distribution reflects the empirical finding that there exists
substantial firm heterogeneity in emission intensity under the status quo.
36
(a) LogA
i
(b) Marginal Cost: c
i
+c
a
(e
i
)
Figure 1.6: Estimated Firm-Level Costs
1.5.3 Goodness of Fit
I make use of a moment that is not matched when estimating parameters to cross-check
the goodness of fit of the model. Recall that the marginal cost of abatement function is as-
sumed to bec
i
a
(e
i
) =A
i
e
−δ
i
−A
0
. Then, under no environmental regulations, firms’ emis-
sion intensity ise
0
i
= (
A
i
A
0
)
1
δ
orlog(e
0
i
) =
log(A
i
)
δ
−
log(A
0
)
δ
. The standard error oflog(e
0
i
) should
be very similar to that of
log(A
i
)
δ
. In Figure 1.7, I plot the distribution of
log(A
i
)
δ
, which is
structurally estimated, and the distribution of log(e
0
i
) from actual data. e
0
i
is measured
using firms’ emission intensity in 2011 when stringent environmental regulations had not
been implemented. These two distributions have very similar dispersion. The standard
error for the distribution from model prediction is 1.279 and is 1.359 for the distribution
from actual data.
In addition, in the model, firm-specific market power is measured using the ratio be-
tween firms’ factory door prices (before shipment) and the market price (after shipment).
The higher the ratio, the larger the market power a firm has. To cross-check whether this
is a credible measure of market power, we compare it with coal price pass-through, an-
other data-driven measure for market power estimated in Section 1.4. Figure 3.12 shows
that these two measures, estimated using different variations, are highly correlated. This
suggests that these two market power measures seem to be credible.
37
Figure 1.7: Comparison of Actual and Outside Moments
Note: One moment that has not been used in structural estimation is
log(A
i
)
δ
=log(e
0
i
) +
log(A
0
)
δ
. ’Model prediction’ plots the
distribution of
log(A
i
)
δ
, and ’data’ plots the distribution of actuallog(e
0
i
).e
0
i
is measured using firms’ emission intensity in 2011 when
stringent environmental regulations had not been implemented. SinceA
0
can not be identified, ’model prediction’ should generate a
parallel shift of the distribution of the log of emission intensity without regulations. Dispersion of the two distributions is almost
identical, with the standard error being 1.279 from model prediction and 1.359 from actual data.
1.5.4 Environmental Damage
After estimating firm-related costs, the marginal damage of pollution needs to be mea-
sured to understand the welfare implications of firm heterogeneity. Emissions create dam-
age not directly from firms’ emissions, but through pollution concentration. The chal-
lenge here is to measure marginal pollution damage per unit of emission, rather than the
commonly-known marginal pollution damage when air quality, e.g. PM
10
concentration,
decreases by one unit. Then, the key step is to link the firm’s emission to pollution con-
centration.
There is a rich literature in environmental science that builds mathematical or statisti-
cal procedures for identifying and quantifying the sources of air pollutants at a receptor
location
15
. More studies in economics literature start to apply those models to measure
15
These procedures are called Air Pollutant Receptor Modeling: https://www.epa.gov/scram/air-
pollutant-receptor-modeling
38
pollution damage per unit of emissions and emissions dispersion (Hernandez-Cortes and
Meng 2020, J. S. Shapiro and Walker 2020).
For simplicity, I build a simplified version of the conversion model to measure the mon-
etary value of the decline of one ton of emission. First, Annual average PM
10
is regressed
on annual total emissions from industrial sectors, collected from Chinese Annual Statisti-
cal Yearbooks, controlling for weather conditions, elevation, and the number of passengers
and the amount of freight transported. Then, I apply the finding in Ito and Zhang 2020
that the willingness to pay for clean air is around$1.34 annually per household to remove
1 ug/m
3
of PM
10
.
Panel (a) in Figure 1.8 shows the average marginal costs per ton of cement, aggregated
at the city level, using the structural estimation above. Compared to the variation in the
marginal production cost, there is a larger variation in the marginal abatement cost in
the Chinese cement industry. This is consistent with what is documented in Lyubich,
J. Shapiro, and Walker 2018 about US manufacturing firms. Marginal benefits per ton
SO
2
abatement are documented in Panel (b) in Figure 1.8. The average marginal benefit
per ton SO
2
abatement is 1,496 RMB (214 dollars). There is a significant local correlation
between costs and benefits in the context of the Chinese cement industry. It can be welfare-
enhancing if firm heterogeneity is incorporated into designing spatial uniform taxation at
the provincial level.
39
(a) Marginal Costs per Ton of Cement
(b) Marginal Benefits per Ton of Abate-
ment
Figure 1.8: Estimated City-Level Marginal Costs and Benefits (RMB)
Note: This figure shows the estimated marginal costs of cement production and the marginal benefits of pollution abatement by the
city. The left figure in Panel (a) shows the marginal production cost and the right figure shows the marginal abatement cost per ton
of cement to maintain the emission intensity level. Notice that the marginal cost of production,c
i
, and the constant in the marginal
abatement cost function,A
0
, can not be separately identified, as documented in Section 1.5.2. Thus, the left figure shows the
distribution ofc
i
−A
0
and the right figure shows the distribution of the marginal abatement cost function without the constant term.
These two figures represent the variation, rather than the levels, of marginal cost functions. Panel (b) shows the estimated marginal
benefit per ton of SO
2
abatement.
1.6 Simulation Results
In this section, we discuss results from counterfactual simulations that are based on two
policy counterfactuals in Section 1.3.4. In both of the policy counterfactuals, I stick to the
existing tax structure, where emissions tax rates vary at the provincial level.
40
The baseline welfare is measured under the status quo, cement production, and emis-
sions in 2018 in the Chinese cement industry. Total welfare is composed of economic surplus,
defined as the summation of consumer surplus, producer surplus, and emissions taxa-
tion, and environmental damage, which is measured using the marginal benefits of pollution
abatement estimated in Section 1.5.4. Figure 1.9 displays the spatial distribution of eco-
nomic surplus and environmental damage across provinces. In total, the Chinese cement
industry generated welfare that worthies 101.76 billion RMB in 2018, which is equivalent
to 14.54 billion dollars.
(a) Economic Surplus (b) Environmental Damage
Figure 1.9: Baseline Welfare (billion RMB)
Note: This figure shows the baseline welfare under the status quo in 2018. Economics surplus is defined as the summation of
consumer surplus, producer surplus, and emissions taxation. Environmental damage is the monetary value of pollution damage
from cement production.
In the first simulation, the emissions tax rates under the status quo are adjusted to the
optimal ones that incorporate firm heterogeneity and spatial differentiation in marginal
pollution damage. I find that on average, total welfare increases by 3.4 percent across
provinces, which worthies 3.46 billion RMB (0.49 billion dollars). Panel (a) in Figure 1.10
shows the spatial variation in the welfare gain. Provinces with higher welfare gains are
the ones that demonstrate spatial correlation in costs and benefits, as shown in Figure 1.8.
41
To further address the distortion due to market concentration, tax revenues from emis-
sions taxation are recycled back to firms in a form of output-based rebates in the second
counterfactual. On average, output-based rebates further increase welfare by another 1.2
percent, which is 1.26 billion RMB (0.18 billion dollars). Panel (b) in Figure 1.10 shows
that provinces where the cement industry is more concentrated and thus has a higher coal
price pass-through, as shown in Figure 1.4, tend to gain more from the output-based re-
bates. Compared to the status quo, a combination of emissions taxation, which accounts
for local firm heterogeneity, and output-based rebates from tax revenue recycling can gen-
erate a 4.72 billion RMB (0.67 billion dollars) welfare increase in the Chinese cement in-
dustry.
(a) New Tax Regime (b) New Tax Regime + Output-Based Rebates
Figure 1.10: Simulated Welfare Change by Province
Note: This figure shows the simulated percentage changes in welfare under two policy counterfactuals. The left figure displays the
percentage change of welfare by the province under the optimal emissions taxation that accounts for firm heterogeneity, compared to
that under the status quo. The right figure shows the additional welfare change if the tax revenue from the optimal emissions
taxation is recycled through output-based rebates.
42
1.7 Conclusion
This paper studies the importance to account for firm heterogeneity and spatial variation
in marginal pollution damage when designing a spatial uniform environmental regula-
tion. I provide a theoretical framework to demonstrate how differential impacts of en-
vironmental regulations, commonly treated as a second-order factor, can matter for effi-
ciency under the second-best setting. Two factors enter the optimal emissions taxation
design, the extent of market power and covariance between the costs of abatement and
the benefits of emission abatement. This theoretical framework provides a natural statis-
tic that can be tested empirically to check whether local heterogeneity matters for efficient
policy designs.
Then, I apply the model to the Chinese cement industry, document the extent of hetero-
geneity, and, test whether heterogeneity matters for efficiency in this context. By switching
to the emissions tax rates that incorporate local heterogeneity, total welfare increases by
3.4 percent. If tax revenues are recycled in a form of output-based rebates to address the
distortion from market concentration, total welfare further increases by 1.2 percent.
It is important to acknowledge several caveats of the paper. First, I study the short-term
effect of environmental regulations, where I assume the underlying abatement technolo-
gies remain the same. Second, I build a static partial equilibrium model with heteroge-
neous firms that only incorporates a single industry. In reality, there can be cross-industry
reallocation through consumers’ substitution in the short-run and firms’ entries and exits
in the long run.
This framework can be extended in future research. First, how to design efficient car-
bon taxation, which takes into account the co-benefits of pollution abatement, can be stud-
ied. Carbon taxation involves global collaboration. Firm heterogeneity and spatial varia-
tion in pollution damage are more salient when designing carbon taxation. Then, at what
level of government should the tax be, and how to deal with the limited information of
regulators? Second, future research can be conducted in a more interdisciplinary way. A
43
more complicated pollution dispersion model can be embedded to account for the spatial
spillover of emission damage. This is especially critical to design environmental regula-
tions to mitigate carbon.
44
Chapter 2
Electric Vehicle Sharing: Crowding Adoption Out or In?
1
2.1 Introduction
Whenever a new and more socially efficient technology emerges, society has a natural in-
terest in spurring its adoption. The traditional approach to achieving this end has been to
directly subsidize its purchase. If an individual does not internalize their positive exter-
nality, subsidies can align private incentives with the social optimum.
However, if consumers are insufficiently familiar with the new technology—likely the
most common case—this logic can run into problems. Computing the optimal subsidy
requires determining the difference between an individual’s privately optimal adoption
incentive and the social optimum. But when adoption requires individuals to abandon a
known alternative, uncertainty over the former may imply the optimal “no-uncertainty”
subsidy is insufficient to achieve efficiency. Subsidies may need to be increased relative to
this benchmark in order to compensate, potentially making them prohibitively costly. The
greater familiarity with older technologies provides a powerful default in their favor.
1
Co-authored with Jonathan Libgober
45
This paper documents that a non-price intervention related to Electric Vehicles (EVs)—
and in fact, one that provided a substitute for ownership—spurred new adoptions. We ar-
gue that the mechanism behind this finding is via increasing familiarity
2
of the uncertain
technology, counteracting the aforementioned limitation of subsidies. Increasing adop-
tions of EVs is a major public policy goal in the United States at many levels of govern-
ment, as well as internationally. The larger aim is to reduce dependence on gas vehicles
and increasing energy efficiency toward creating a more sustainable economy. Toward
this end, the Infrastructure Investment and Jobs Act passed in late 2021 included billions
of dollars for EV Charging stations and other investments in electric vehicles. California
in particular has devoted considerable attention to increasing EV usage; in 2020, Governor
Newsom signed an executive order requiring that in 2035, all cars and passenger trucks
sold in California to be zero-emission vehicles. Despite this urgency, the takeup of EVs has
been relatively slow. While the rate of takeup has been increasing, with a 12.9% increase in
EV sales in Q2 2022 from Q2 2021, this still only amounted to 5.6 % of the total market Cox
Automotive 2022. There appears to be substantial work to do to take EV adoption rates to
a point where achieving the 100% target be practically feasible and non-disruptive.
We analyze the impact of the BlueLA car sharing program on EV adoption. This pro-
gram placed Electric Vehicles throughout low-to-middle income neighborhoods of Los
Angeles in busy areas, with the idea of increasing the accessibility of green technology to
communities that traditionally benefit less from them. For our purposes, the most impor-
tant features of the BlueLA program were the following: First, anyone had the ability to use
an EV through the program, with added convenience for those in the immediate vicinity
of its locations. Second, the EVs from the program were placed in highly central locations,
obtainable due to its status as a public program. Third, as a (partially) public program,
usage costs were quite low, typically cheaper than competitive car sharing programs.
2
We use the term “familiarity” in an informal sense, as a catch-all to refer to the gap between a con-
sumer’s true (objective) surplus from consumption and the believed (subjective) surplus from consump-
tion. We do not distinguish, for instance, risk aversion on the benefits of EV adoption versus awareness per
se.
46
Despite being a direct substitute to car ownership, we find that the BlueLA program
has so far had a substantial positive impact on EV adoption. We interpret this finding as
suggesting increasing familiarity with EVs may be an important component of the push to
spur adoption.
3
Despite the program substituting for car ownership conditional on famil-
iarity, complementarity emerges when this channel is present. To benchmark our results,
we compare these findings to other interventions. Even our most conservative estimates
find that the impact of BlueLA was comparable to the impact of a direct 10% subsidy, and
some rough quantification of the value of this increase in takeup in itself would justify a
large part of the State’s investment.
4
We note that our data used comes from those individ-
uals who used this particular subsidy for the purpose of EV adoption; thus, our preferred
interpretation is that the subsidy was twice as effective in areas which were exposed to the
BlueLA treatment. It is also worth bearing in mind that the increased adoption is only a
secondary benefit of the program, and one that is (arguably) more significant only at the
state level.
5
We document a number of other patterns which are suggestive of our mechanism
that an informational story is responsible for the finding. First, we consider the impact
of charger availability, treating this as a proxy for the true average utility for EV adop-
tion in an area (e.g., Li 2019). We find that this does not interact with any of our re-
sults, and also document that our treatment effects are negatively correlated with charger
availability—suggesting less of an impact in areas where familiarity is higher. Second,
when analyze the trend over time, finding that the effect takes approximately one year to
show up. This is consistent with lags in information diffusion (whereas pure taste-based
explanations would be concentrated more immediately after introduction). Third, effects
3
Graziano and Gillingham 2015 documents a similar phenomenon in the context of solar panels, namely
that increased visibility of solar panels in a neighborhood leads to more adoption.
4
From our conversations with The State of California’s agency in charge of environmental programs,
CARB, this possibility was not a direct consideration. CARB invested $4.6 Million into the program; the
overall cost was $31 million.
5
E.g., the company administering the program, in principle, has a natural incentive to discourage car
ownership to increase profitability; local entities might benefit more from the program as an amenity, etc.
47
are localized—we document no effect in zip codes adjacent to those with BlueLA adoption,
rather than those directly effected. This suggests immediate interaction with the program
is important, consistent with information diffusion being facilitated by immediate visibil-
ity (whereas other stories, such as greater latent utility through peer effects, would be less
concentrated). Fourth, we find no brand-specific effect; this is notable since all BlueLA cars
use the same make and model, which a priori might enable a brand specific, “taste-based”
channel (similar to persuasive advertising). The lack of a differential increase for the car
type the program used rules out this channel. Taken together, the patterns we document
as well as our understanding of the mechanisms of the program suggest an informational
mechanism as responsible.
We therefore advance evidence that non-price instruments may be important for in-
creasing takeup. The EV market features both high costs to implement adoption as well
as apparent susceptibility of purchasing behavior to information prevision. There is also
substantial precedent for the aforementioned idea that efficient subsidies are prohibitive to
be effective when it comes to EVs. Muehlegger and David S Rapson n.d. survey a number
of findings and document that even conservatively, the elasticity estimates on EV adop-
tion suggest that the cost of inducing a new EV adoption to be larger than the face value of
the subsidy, if not larger than a direct purchase of EVs themselves. On consumer aware-
ness, several studies find many consumers do not know available subsidies or are mistaken
about basic EV characteristics Slowik and Jin 2017, or appear to misoptimize when evalu-
ating the costs and benefits of EV ownership Bushnell, Muehlegger, and David S. Rapson
2022. Using data from Sweden, Tebbe 2023 documents substantial peer effects in EV adop-
tion, advancing a similar (informational) mechanism to the one we propose. Roberson
and Helveston 2020 provide experimental evidence suggestive of our mechanism, show-
ing that usage of EVs (in the form of test drives, arguably similar to what BlueLA provides
to communities where present) increases consideration of them. Other work shows that
market pressures are not closing the informational gap; Cahill, Davies-Shawhyde, and
48
Turrentine 2014, Rubens, Noel, and Sovacool 2018, Lynes 2018 show that dealers tend to
discourage EV adoption, often to the point of being actively deceptive (with Tesla being
a major exception). Dealer bias toward gas cars implies only consumers already inclined
to purchase EVs would do so at the dealership. Our findings underscore that initiatives
which enable greater consumer familiarity with EVs may increase subsidy effectiveness,
correcting this apparent market failure.
2.2 Background
2.2.1 The BlueLA Program
We start with background information about the BlueLA program, elaborating on certain
institutional details which play motivate and clarify our analysis.
BlueLA is a car sharing program which is particularly aimed at increasing accessibility
of green technology to income groups which are traditionally excluded from it. These cars
can be found at charging stations located in various neighborhoods in LA, but in keeping
with the goal of the program, typically in areas where they would be used by low-income
residents. Figure 3.13 shows a typical BlueLA charging station, which also shows the stan-
dard design of a BlueLA car. For our purposes, the notable feature is that these cars are
highly visible and in areas in which they are easily accessible. The program itself is a
public-private partnership, operated by a private company (Blink Mobility, as of Septem-
ber 2020, and the French company Bollor´ e prior to that) and funded by the California Air
Resources Board (CARB). The LA Department of Transportation (LADOT), in addition to
providing some funding, also provides infrastructure for the program; for instance, pro-
viding permits to locate car chargers in heavily trafficked areas which would otherwise be
used for street parking. However, the cars themselves and the chargers that are used are
both purchased and maintained by Blink. All cars are highly standardized, following the
49
same designs and same type, specifically Chevy Bolts.
6
CARB’s initial grant to BlueLA
was for $4.6 million dollars, while the program had around 18 million dollars of private
investment and additional grants for a grand total cost of $31 Million.
Each BlueLA charging station usually involves enough chargers for a small number of
cars, typically around five. Importantly, the chargers are themselves only usable exclu-
sively by cars that are part of the BlueLA program, and the spots are designated exclu-
sively for these cars as well. There are a number of implications of this fact; perhaps most
obviously, the chargers which are installed for the program cannot possibly be used by
other EV cars that are available. But it also is often a key source of the difficulty in intro-
ducing BlueLA more widely, in that finding a location where a charger can be placed is
typically one main challenge administratively. BlueLA has formal criteria which they use
to help determine and evaluate spot locations; these include density, proximity to tran-
sit (with priority to locations near hubs), employment density (with priority to employ-
ment/retail centers), income levels (with priority to high density areas with affordable
housing/multi-family housing), transit modal shares, and suitability of EVs. However,
in our conversations with BlueLA, it was conveyed to us that the main stumbling block
was finding parking spaces for the cars and chargers. Figure 2.1a shows the location of
zipcodes with BlueLA Chargers at the end of 2021 and the map of the locations of EV
Chargers as of March 2022.
BlueLA started to be operable in April 2018, and has had a steady rate of growth since
its start. In April 2019, the program had provided 80 electric vehicles, 130 charge points,
and 26 charging stations, having served nearly 2000 members and over 12,000 trips. Since
then the program has continued to grow; as of with currently over 300 cars and 40 loca-
tions and expansion plans continue. There have been three different class of users since
inception; the standard membership costs 5 dollars per month and 20 cents per minute
of use; community members (who qualify based on income) pay 1 dollar per month and
6
Chevy Bolts have been the exclusive car model used since Blink has operated the program; Bollor´ e used
their own vehicle, the Bollor´ e Bluecar.
50
15 cents per minute of use; and one-month trial members, who pay 40 cents per minute
but do not pay any up front fee.
7
Packages are also offered for extended rentals of either 3
hours or 5 hours at lower rates. Community members have taken about 54 % of all trips.
Figure ?? shows the number of members broken down by membership type.
Initially the agenda of the program was completely unrelated to EV Adoption, simply
to provide carsharing in underserved areas using green technology. However, around
2020, BlueLA started explicitly seeking to educate communities about EVs. Advertising
around BlueLA often highlights the benefits of driving Electric Cars. For instance, on the
company’s Blink Mobility blog, significant attention is paid to the convenience of owning
Electric Vehicles. One of blogpost is entitled Thinking about buying an EV? Rent One by
the Hour!, and advances our proposed mechanism explicitly, suggesting users view the
program as a low-cost step toward deciding whether to purchase an EV—despite also
(naturally) suggesting users also consider the program as a primary option instead car
ownership. This latter force underscores the program as a substitute, suggesting that the
effect of increased familiarity itself should be even larger than what our estimates identify.
7
For reference, a current Zipcar membership costs 7 dollars per month and 11 dollars per hour, although
often in private lots, in contrast to BlueLA chargers which take street parking spots.
51
(a) Public EV Chargers in LA, Dec. 2021, with BlueLA Stations
(b) Relationship between public EV chargers and takeup in Los
Angeles by zip code as of Jan. 2021
Figure 2.1: The Locations of BlueLA Stations and Chargers and EV Adoptions
Note: Panel 2.1a shows the locations of BlueLA stations and chargers. BlueLA data used in the map is from BlueLA’s Report to City of
Los Angeles; Charger information is from the Department of Energy’s Alternative Fuels Data Center; EV takeups is from the State of
California.
52
2.2.2 Data
To estimate the effect of the BlueLA rollout, we combine two data sources; first, we ob-
tained rebate data from the Clean Cars 4 All Program (CC4A). This rebate data is pub-
licly available data at the transaction level on the usage of a particular subsidy for EV usage
targeted at individuals from lower income brackets. This data includes the subsidy, the
vehicle purchased, and the zipcode in which the recipient of the subsidy levels. Second,
we obtained from LADOT the documented opening date for each location. We use this to
determine when the program enters each particular zip code.
We note that for all relevant observations, whenever BlueLA has “entered” a zipcode,
it has remained in that zipcode throughout the rest of our sample. Since the location
of the subsidy usage is at the zipcode level, we aggregate each specific location to be at
the zipcode level as well. This allows us to merge the two data sets so that the unit of
observations is the number of purchases of EVs in a given zipcode.
For some additional robustness checks, we supplement this with data on the number
of charging stations in each zipcode. This data is from the Alternative Fuels Data Center,
which has also been used in other papers (e.g., Li 2019). This provides us the number of
public chargers present in any zipcode for each quarter.
Table 2.1 presents some summary statistics regarding the zip codes into which BlueLA
has been introduced. Anticipating some of our methodology to come, we group zip codes
into “cohorts” depending on when the first BlueLA charging station was introduced. As
we explain more below, part of our analysis will estimate treatment effects for each cohort.
8
We can therefore use this data to see how our estimates vary with zip code characteristics,
which will allow us to assess mechanisms which may be suggestive of our findings.
8
In this table, data on income comes from 2016-2020 American Community Survey, and are 5-Year esti-
mates, and data on EV adoptions comes from the State of California.
53
Cohort # Zip Codes Avg. Income Avg. # EVs Avg. # Chargers
($)
2018 Q1 6 48,322 1,970 6
2018 Q2 4 40,481 1,104 27
2018 Q3 4 44,083 2,038 11
2018 Q4 2 49,068 1,726 4
2019 Q1 4 37,216 2,202 54
2019 Q4 2 56,389 3,023 12
2020 Q2 4 58,195 2,120 7
2020 Q3 2 45,499 1,539 52
Table 2.1: Summary Statistics by Groups of zipcodes in Jan 2021
Note: A cohort is defined as a group of zipcodes that share the same starting date as the first BlueLA stations. The cohort name
indicates the quarter when the first BlueLA stations were set up. Income data is from the 2016-2020 American Community Survey
5-Year estimates. Public charger information is from the Department of Energy’s Alternative Fuels Data Center and we calculate the
number of total public chargers available for each zip code by Jan 2021. The total number of EVs is from the State of California, which
provides the universal EV counts by zipcodes by Jan 2021.
2.3 Identification Strategy
We follow two strategies toward determining the impact of BlueLA on adoption. First,
and perhaps most straightforwardly, we estimate the following equation:
NewEVs
i,t
=α
i
BlueLA
i
+βBlueLA
i
×Entry
i,t
+δX
i,t
+γ
i
+η
t
+ε
it
, (2.1)
whereNewEVs
i,t
denotes the number of new EV adoptions in zipcodei at timet,BlueLA
i
is an indicator variable that denotes the event that a BlueLA charging station ever exists in
zipcodei, whereasEntry
i,t
is an indicator variable that denotes the event that a BlueLA
charging station exists in zipcodei at timet. Lettingβ
i,t
denotes the the number of adop-
tions at timet in zipcodei due to the introduction of BlueLA, then in the above equation,
β =
X
i,t
w
i,t
β
i,t
. (2.2)
Under the assumption that thew
i,t
are all positive and normalized to sum to 1,β represents
a weighted average of eachβ
i,t
coefficient. However, a substantial amount of recent work
has shown that the positivity assumption need not hold; see J. Roth, Sant’Anna, Bilinski,
54
and Poe 2022 as well as De Chaisemartin and d’Haultfoeuille 2022 for recent surveys of
this literature. We address this issue after presenting our results.
Our identification assumption for β is that the trend of EV adoption in neighborhoods
with BlueLA car sharing stations would have been the same as other neighborhoods had
BlueLA never entered. We test this assumption via an event study approach using the
equation:
N
it
=
k=6
X
k≥−6,k̸=−1
δ
k
D
k
it
+γ
i
+η
t
+ϵ
it
, (2.3)
where the dummy variablesD
k
it
indicate time compared to the event. We define s
i
as the
date when zipcodei had the first BlueLA station; D
−6
it
= 1 ift−s
i
≤−6 (quarters) and
0 otherwise;D
k
it
= 1 ift−s
i
=k and 0 otherwise for k=-4,-3,...,5; andD
6
it
= 1 ift−s
i
≥ 6
and 0 otherwise. Note that the dummy fork =−1 is omitted in equation (2.3) so that the
post-treatment effects are relative to the period 5 quarters prior to the establishment of
BlueLA stations. We also consider an event study where we includeX
it
as a right hand
side variable; theδ
k
coefficients from Equation (2.3) are plotted in Figure 2.2a, whereas
the coefficients where chargers are included as a control are plotted in Figure 2.2b. Over-
all, these look quite similar to one another, and do not feature any statistically significant
coefficients prior to time 0.
9
In fact, as the coefficient ξ tends to be close to 0, we do not
comment on this further.
9
We include both of these plots since, in general, having a statistically significant coefficient without
controls does not necessarily imply that these coefficients will remain significant when controls are added.
55
(a) Without Charger Control (b) With Charger Control
Figure 2.2: Event study: The Effect of BlueLA on New Vehicle Adoptions
Note: The figure reports the coefficient plot of linear panel regressions with two-way fixed effects. Standard errors are two-way cluster
standard errors at year-quarter and zipcode level at 5% significance level. These plots are identical to Figure 2.3b, but with the trend
lines removed.
One concern with conducting pre-trend test using event study framework with Two-
Way Fixed Effect is that the test can be under-powered, passing a pre-trend test can at best
be uninformative and at worst introduce additional bias into a design. The issues involved
are discussed in J. Roth 2022, which also provides methodology for testing whether the
pre-trend tests have sufficient power. To verify the robustness of pre-trend test that uses
a Two-Way Fixed Effect framework, we first check the maximum linear pre-trend our test
can detect, given a power of 0.8
10
. Then we calculate what the expected value of the
coefficients would have been conditional on passing the pre-trend test had this in fact
been the true trend.
Figure 2.3b represents the results from our pre-trend tests and subsequent power anal-
yses with and without controlling number of public chargers. First, plotting each of the
point estimates as well as the confidence intervals for each one, we note that none of the
coefficients on δ
k
are statistically significant from 0 for k< 0, normalizingδ
−1
= 0. It also
shows that the calculation as described in the previous paragraph suggests our tests have
sufficient power, since the maximum detectable linear trend can match the trend of treat-
ment effect well. Thus, it is unlikely that the estimated effect of BlueLA stations is driven by
10
A power of 0.8 means that the pre-trend test can significantly identify pre-trend correctly 80% of times.
56
some underlying pre-trend that we fail to detect, as the pre-trend test should have enough
power to pick up such pre-trend. Also, the tests with and without charger control are very
similar, which suggests that the number of public chargers shouldn’t bias our estimates.
(a) Histogram of Two-Way Fixed Effect Weights (b) Parallel Trend Test
(c) Average Effects by Cohort (d) Dynamic Effects by year
Figure 2.3: Treatment Effects of BlueLA on EV Adoptions
Note: Panel 2.3a shows the histogram of two-way fixed effect weights. Panel 2.3b conducts the parallel trend test proposed in J. Roth
2022. The Black dots are estimated coefficients of conventional TWFE; Red squares are maximum linear trend detectable with power
0.8; Blue triangles are coefficient expectation conditional on passing. Panel 2.3c and 2.3d shows the average effects of BlueLA on EV
adoptions by cohort and by year respectively. Standard errors are two-way cluster standard errors at year-quarter and zipcode level at
5% significance level.
Of independent interest is that the statistical significance emerges roughly one year
after the introduction of the BlueLA chargers. This is consistent with our proposed mech-
anism, since information diffusion (in contrast to immediate “taste-shocks”) takes time.
As we would not expect this to be immediate under our favored mechanism, it seems
sensible to observe a delay in the impact of the program on EV adoption.
57
Our second empirical strategy, which complements the direct estimation of Equation
(2.1) is to implement the Difference-in-Difference (DiD) estimator of Callaway and Sant’Anna
2021. This estimator avoids the issues of conventional two-way fixed effects regressions
with heterogeneous treatment effect, in particular issues related to negative weights. In-
stead of pooling time-varying treatments and calculating the average treatment effect di-
rectly, treatment cohorts are defined by the groups of zipcodes that share the same starting
date of first BlueLA station. Control units are those zipcodes where no BlueLA station ever
set up. Then, the average treatment effect can be estimated for each treatment cohort by
comparing each treatment cohort and all the control units, which reduces to the canonical
DiD setup with two groups and two periods. We also use these cohort-specific estimates
in our discussion of mechanisms, analyze the extent to which variation in the cohorts (not
used in estimation) correlates with the treatment effect.
2.4 Results
2.4.1 Estimating the BlueLA Impact
We now present our main regression results on the purchases of new EVs via the intro-
duction of the BlueLA program. In Table 2.2, we presented results from a linear panel
model; since our dataset involves a large number of 0 purchases, we also estimate a Pois-
son model. We find statistically significant increases in adoption once BlueLA enters a zip
code. It is important to keep in mind that the coefficients on each variable correspond to
a number of new adoptions, at the zipcode/quarter level. Overall, EV adoption rates are
somewhat low over the sample, and so to interpret these magnitudes it is perhaps helpful
to consider the total number of EV adoptions implied by these estimates, as shown in Panel
A in Table 2.2. Our sample involves 112 zipcodes, which are the set of zipcodes which have
had at least one adoption in our sample; keeping in mind that these are at the quarterly
level, we therefore have multiplying by 448 gives a total estimate for the number of EV
58
adoptions attributable to the variable of interest. On the other hand, the total number of
EV Adoptions in LA in 2020 in these zipcodes is 304. Therefore, we should not expect any
of these coefficients to be particularly large. However, translating the estimates by adjust-
ing the scale accordingly, Table 2.2 suggests BlueLA lead to, most conservatively, 99 new
adoptions over a year period, and the estimate from Poisson regressions suggests 136 new
adoptions.
To help interpret the magnitude of these coefficients, we can compare to the impacts
of other interventions, most notably direct subsidy interventions. A number of studies es-
timating the impact of a subsidy have been conducted; Muehlegger and David S Rapson
2018 estimate that a 10% subsidy on EVs for low- and middle-income individuals cor-
responds to 32%-34% increase in new EVs. Using the most conservative estimate from
Table 2.2 gives a similar effect—specifically, 32.6%—of the BlueLA program; noting that
the negative binomial model is interpreted as a percentage, the same calculation suggests
an even larger increase of 44.7%.
11
We note that the subsidy studied in Muehlegger and
David S Rapson 2018 amounts to roughly 5000 per car; using this number as a conversion
between how to have developed a similar effect via subsidy, this would correspond to a
benefit of about $495,000 per year using most conservative Table 2.2 estimate and about
$680,000 per year using the most conservative estimate from Poisson regressions. While
relatively small, this appears sizable for a side effect of the program (whereas the sole
benefit of a subsidy is through the channel of car ownership). Of course, it is also worth
commenting that these individuals have access to the subsidies as well, so admittedly this
might be “double counting” the benefits to an EV purchase. Using the usual caution with
such extrapolations, these rough calculations correspond to at least 10% of CARB’s initial
investment in BlueLA being recovered in one year; in fact, the benefit we identify is larger
since this amounts to the average effect taken over the entire six year sample, and could
11
The negative binomial regression identifies the percent change in EV purchases; we then convert it to
average increase in annual EV adoption by taking the product of the percentage increase and the average
annual EV adoption. We then divide by the total number of adoptions in 2020 to obtain this percentage.
59
Panel A: Effect on Number of New EV Adoption
Linear Poisson CSDID
(1) (2) (3) (4) (5)
BlueLA 0.076 0.033
(0.074) (0.228)
BlueLA× Entry 0.231
∗∗
0.221
∗∗
0.572
∗∗∗
0.577
∗∗∗
0.391
∗∗
(0.110) (0.102) (0.133) (0.128) (0.181)
Observations 3,136 3,136 3,136 3,136 3,136
R
2
0.106 0.282
Log-Likelihood -2891.864 -2891.875
Induced annual EV adoption 99 136 175
Percentage increase in 2020 32.6% 44.7% 57.6%
Panel B: Effect on Number of New EV Adoption in Adjacent Zipcodes
Linear Poisson CSDID
(1) (2) (3) (4) (5)
Adjacent BlueLA −0.025 -0.155
(0.074) (0.225)
Adjacent BlueLA× Entry 0.064 0.087 0.389
∗∗∗
0.368
∗∗∗
0.238
∗∗
(0.103) (0.096) (0.126) (0.122) (0.112)
Observations 2,716 2,716 2,716 2,716 2,716
R
2
0.094 0.287
Log-Likelihood -2327.345 -2327.578
Year-Quarter FEs Yes Yes Yes Yes
Zipcode FEs Yes Yes
Table 2.2: Main Results on the Influence of BlueLA Charging Stations on Adoption of EVs
Note: Panel A reports the average effect of BlueLA Stations on the number of incremental EV purchases in each zipcode each quarter,
using linear, Poisson panel regressions, and Callaway and Sant’Anna’s estimator in Figure 2.3c respectively. The unit of observation is
at year-quarter and zip code levels. BlueLA indicates whether there are or there will be BlueLA stations in a zip code. Entry indicates
whether there are already BlueLA stations in a zip code. Panel B reports the effect of BlueLA stations in an adjacent zipcode on the
number of incremental EV purchases in zip codes without any BlueLA stations. Zipcodes (28 out of 112) with at least one BlueLA
station during our study period are excluded from the sample. Of the remaining zip codes, 24 of them are adjacent to zip codes with
at least one BlueLA station. The unit of observation is at year-quarter and zip code levels. Adjacent BlueLA indicates whether there are
or there will be BlueLA stations in an adjacent zip code. Entry indicates whether there are already BlueLA stations in an adjacent zip
code. All regressions include the number of public chargers available as a control. Standard errors are two-way cluster standard
errors at year-quarter and zip code levels.
∗
p<0.1;
∗∗
p<0.05;
∗∗∗
p<0.01
60
indeed be durable in the future. Thus, our results suggest that a non-trivial portion of the
initial investment of CARB
12
was paid back for through the channel we identify.
2.4.1.1 Testing the Interpretation ofβ
As mentioned in Section 2.3, recent research has noted that staggered rollout designs can
suffer from interpretability issues and bias, when the treatment effect is heterogeneous
across groups and periods so that the weights in equation (2.2) are not all positive. While
the regressions highlighted in the previous section are the most straightforward, a concern
with interpretability and bias would emerge if in fact our estimates were subject to the
same issues. We now follow a number of strategies to address these issues.
The first is to follow De Chaisemartin and d’Haultfoeuille 2020 to test whether the neg-
ative weight issue occurs. Figure 2.3a shows the actual weights of each group-period pair
in Two-Way Fixed Effect regression (2.2). Only 4 out of 252 pairs suffer from negative
weights, and even then these are very small. So, while perhaps possible, it seems implau-
sible that such estimates would invalidate our results, at worst tempering the previous
conclusions somewhat.
The second is to implement the Callaway and Sant’Anna 2021 estimator, described
above in Section 2.3, noting that this estimator is not subject to these particular concerns.
This produces treatment effect estimates by cohort, where (i) cohorts are defined by the
groups of zipcodes that share the same starting date of first BlueLA station, and (ii) con-
trol units are those zipcodes where no BlueLA station ever set up. Figure 2.3c shows the
average treatment effect of the BlueLA program by cohort. Our study period covers 2015
to 2021 at quarter and zipcode level. The first BlueLA stations were up and running in
April 2018, 13 quarters after. Aside from being less susceptible to bias related to treatment
staggering, this figure also illustrates more precisely which specific cohorts have had a
12
We focus on CARB since it was the entity whose primary interest was in reducing dependence of EVs,
as per the mandate from state government; the other organizations involved, like LADOT or Blink Mobility,
arguably have other more pressing motivates like assisting local transport or profit from the program.
61
more significant effect on takeup. Interestingly, we find positive average treatment effects
for all but three cohorts (2018Q1, 2018Q2, and 2019Q1), which we cannot distinguish from
0. Other than these three cohorts, the effect seems to be fairly stable. Table 2.1 shows the
summary statistics by cohorts in terms of average household income, number of public
chargers, and number of universal EV takeup. We study correlations between these sum-
mary statistics and the treatment effects derived in Section 2.5.1.
To translate these cohort-specific estimates into an aggregated treatment effect compa-
rable to those from our other panel models, we take an weighted average over the treat-
ment effects of different cohorts estimated via this approach, putting more weight with
larger group sizes, as shown in Table 2.2. The average treatment effect we obtain corre-
spond to a coefficient of β equal to 0.391; an even more significant impact of the program
an adoption, suggesting that the potential sources of bias related to the staggering of treat-
ments actually leads to an understatement of the program’s effectiveness. This estimate
corresponds to a total yearly increase of 175 new EV adoptions, which using the same
reasoning as before, would correspond to 57.6% increase.
2.4.2 Interpreting the Results Using Institutional Details
We summarize some key points regarding the institutional details that are important for
understanding these findings.
First, as textitasized, the BlueLA program is a direct substitute to car ownership. It is en-
tirely possible that some individuals would have needed to purchase a car but no longer
need to due to the program; in fact, the program advocates this, as one would expect.
Therefore, insofar as we are estimating the impact of an increase in familiarity due to adop-
tion, this particular program could in principle by itself decrease adoption. In other words,
while our proposed channel is through the increase in awareness and availability of EVs,
our intervention should have the direct effect of lowering EV takeup.
62
Second, the introduction of a BlueLA chargers do not appear endogenous to any deci-
sions that would influence purchasing. The main variable of concern to us is charger avail-
ability; the reason being, it could be that BlueLA chooses locations based on EV charger
availability, and that EV charger availability further lowers the cost of owning an EV. Con-
trolling for this does not influence our conclusion.
One could imagine that changing neighborhood characteristics from gentrification both
predict the introduction of BlueLA as well as EV purchasing behavior; however, our data
comes from entirely low income members, who are also disproportionately the targets of
the BlueLA program (as increased community engagement with green technology is an
explicit mandate). In other words, since our sample is already restricted based on income,
it seems less plausible for changing demographics of a particular zipcode to be correlated
with both the introduction of BlueLA as well as new EV adoption.
2.5 Mechanisms
We now present direct evidence that the informational channel is most significant. We
seek to disentangle informational drivers (e.g., awareness or understanding) from those
which influence the latent utility from consumption. Above, we showed that chargers
did not influence the relative impact of the program. We now present a number of other
findings which underscore this.
2.5.1 Drivers of the Treatment Effect
An advantage of having access to cohort-specific estimates is to use heterogeneity in these
cohorts to obtain a rough understanding of which properties drives the effects we docu-
ment. Toward this end, we compute simple correlations between the summary statistics
reported in Table 2.1 and the estimated treatment effects at the cohort levels. To be clear,
we do not seek to be overly formal in this analysis. Perhaps surprisingly, we find a negative
63
correlation between the number of chargers and our treatment effect, -.357; though one
might expect this to be positive as well (since living in an area with more chargers sug-
gests more convenience for EV usage), this is consistent with our story since areas with
lots of chargers likely have a higher degree of “baseline awareness.” This is consistent with
number of chargers itself also spurring an increase in adoption, something we document
in Figure 2.1b. Still, insofar as chargers represent a measure of “average consumption util-
ity from EV usage,” it suggests our results are not driven by a direct change to this variable
caused by the introduction of BlueLA.
2.5.2 Spillovers Across zipcodes
One question of interest is whether the effect we identify is isolated to the areas where
the EVs are located. Specifically, there are various reasons why the informational channel
we identify might have an effect; one channel is usage, but another channel is akin to
advertising, specifically seeing more EVs in the local area. While the former channel is
likely only to be relevant in close proximity to the charging stations, the latter need not be.
Therefore, we look at whether there is any impact on zipcodes adjacent to those with EV
chargers. Our results are negative on this point; We also follow the same two-way fixed
effect design as in section 2.3, Equation (2.3) to estimate the effect of BlueLA stations in
adjacent zipcodes on EV adoption. Table 2.2 shows that there is a significant but smaller
spillover effect. This suggests that the channel is more local, either requiring a greater
degree of contact or actual easy usage in order for a significant effect to show up.
2.5.3 Car Model Influence
An alternative explanation of EV purchase increase can be that consumers like the driving
experience of the specific car make used in BlueLA program, which has nothing to do
with learning about EVs in general. BlueLA only provides Chevy Bolt EVs. If this is
the case, then we should observe that consumers tend to purchase Chevy Bolt EV as the
64
replacement vehicle. However, Figure 2.4 shows that the most popular car make is Toyota
among participants in the CC4A rebate program, rather than Chevy Bolt.
Figure 2.4: Top 10 Makes of Replacement Vehicles in CC4A
2.6 Conclusion
This paper documents that zipcodes for which BlueLA charging stations were introduced
experienced a greater increase in EV adoption relative to other zipcodes which did not
experience any such introduction. Given that the program functionally makes EVs easily
accessible for all individuals in an a given area, our interpretation of this finding is that
greater familiarity and access to EVs can increase their adoption, perhaps overcoming a
signifcant hurdle toward the goal of universal adoption advanced by several policymakers.
65
On the one hand, the obvious caveats to this conclusion of course apply. We view our
analysis as underscoring the message that it would likely be worthwhile for policymakers
and economists to think about non-price instruments that could help facilitate an increase
in adoption, particularly as it relates to information, familiarity or accessibility. On the
other hand, based on existing work on the car market as well as green technology as a
whole, this gap appears to remain a significant once facing consumers. Hence another
area for future work may be in analyzing why markets fail to correct such gaps in the first
place, as well as what kinds of mechanisms may be introduced to combat them.
66
Chapter 3
Does Market Power in India’s Agricultural Markets Hinder
Farmer Climate Change Adaptation?
1
3.1 Introduction
This paper analyzes whether market distortions induced by a country’s institutions in-
hibit its adaptation to climate change. Though the negative impacts of a departure from
perfect competition are well documented Arrow 1962; Ashenfelter, Hosken, and M. Wein-
berg 2014, these detrimental effects risk being exacerbated by the climate crisis. One major
source of these distortions is government regulations which can concentrate market power
in the hands of a few economic agents. In light of this, do institutional policies, that dic-
tate an agents’ market power, impede climate change adaptation? And would eliminating
these distortions by establishing free markets enhance welfare by aiding adaptation? In
this article, we address these questions in the context of competition in India’s agricultural
markets, and the role of market power, in facilitating farmer adaptation to climate change.
Our analysis is motivated by a simple observation: in a world where climate change
will result in crop production losses and fall in agricultural productivity IPCC 2022, farmer
adaptation is crucial, and critically depends on institutional and policy constraints. A
1
Co-authored with Rajat Kochhar
67
country’s agricultural policy can play a dominant role in building resilience and reduc-
ing exposure to the impacts of climate change—with the potential to either advance or
distort (through the imposition of soft limits
2
) adaptation behavior Mees 2017; Valdivieso,
Andersson, and Villena-Rold´ an 2017; Oo, Van Huylenbroeck, and Speelman 2017.
3
For
instance, Annan and Schlenker 2015 show that federal crop insurance policy in the United
States creates a moral hazard problem, disincentivizing adaptation and consequently ex-
acerbating losses. Similarly, agricultural laws in India, which create market power for
agricultural intermediaries Chatterjee 2019, may also disincentivize adaptation. Consider
the case where post climate shock adaptation may be dependent on higher input usage,
which in turn is contingent on higher expected prices. The market power of intermedi-
aries may, however, constrain farmer prices from rising beyond a level that impels farmers
to adapt to climate shocks. Thus, the impact of climate change on agriculture is inherently
dependent on the capacity to effectively adapt. But how and to what extent this capacity is
constrained by government-induced distortions to market competition remains an open
question.
Addressing these questions empirically poses three challenges: first, competition is
not directly observable, making it difficult to credibly measure its intensity OECD 2021;
second, causal identification of competition on adaptation suffers from both potential en-
dogeneity in competition and in adaptation response; and lastly, limited simultaneous
variation in climate shocks and market competition makes it challenging to detect any sig-
nificant causal effects, should they exist. We tackle these challenges by studying the impact
of spatial competition between intermediaries on farmer adaptation in India, focusing on a
law that restricts farmers to selling their produce to intermediaries within their own state.
The Indian context affords us progress on all three challenges.
2
Soft adaptation limit is defined as the existence of adaptive options to avoid intolerable risks, but which
are currently unavailable.
3
See Kuruppu and Willie 2015 and S.-a. Robinson 2018 for a discussion on how the governance archi-
tecture can act as a bottleneck to adaptation in small island developing states.
68
Central to our approach are the state-specific Agriculture Produce and Marketing Com-
mittee (APMC) Acts which regulate the first sale and purchase of agricultural commodi-
ties within each state in India. Two provisions in these laws are noteworthy for our pur-
pose: first, farmers in a state are restricted to sell their produce at government designated
physical markets (known as mandis) within their own state; second, output can only be
sold to government-licensed intermediaries, each of whom requires a market-specific li-
cense to operate in the respective APMC mandi.
4
Importantly, other wholesalers, retail
traders, or food processing companies cannot buy directly from the farmer. Therefore, the
spatial arbitrage constraints imposed on farmers by the law — restricting access to licensed
intermediaries within a state border — reduce the competition faced by intermediaries. In
essence, state-specific institutional setup governing the sale of agricultural output gener-
ates spatially varying monopsony power for licensed intermediaries, a source of variation
which we can exploit to address the empirical challenges.
The context allows us, first, to accurately measure competition at the mandi level, of-
fering spatially granular variation in competition intensity between intermediaries. We
collect novel microdata from India on the geolocations of mandis, and combine it with
the daily quantity arrivals and prices of agricultural produce there-within. Subsequently,
drawing on the standard measure of market access in trade literature Donaldson and
Hornbeck 2016; Allen and Atkin 2016, we measure the competition intensity faced by each
intermediary as the inverse-distance weighted sum of the value of trade at all other mar-
kets near a origin market site, but in the same state.
5
4
Intermediaries, or middlemen, tend to be the principal buyers of farmers’ output in developing coun-
tries Reardon 2015. The license to operate in a mandi is provided to them by the APMC board under whose
jurisdiction the mandi falls. Unlike farmers, there are no sale restrictions on the intermediary, who is free to
transport the purchased produce and sell it to retailers all over the country.
5
Chatterjee 2019 defines spatial competition as the number of markets in the neighborhood of each mar-
ket weighted by the inverse of their distance. Thus, there is no variable controlling for the size of the markets
in his measure. This is similar to the market potential measure in C. D. Harris 1954, who defines it as the sum-
mation of markets accessible to a point divided by their distances from that point. Similarly, Macchiavello
and Morjaria 2021 use the number of proximate competitors as a measure of competition. We employ these
different measures for testing the robustness of our results.
69
Second, the interstate trade restrictions on farmers help us overcome the potential en-
dogeneity in the location of intermediaries. Potential bias in estimating the impact of mar-
ket power on adaptation can arise if, for instance, markets were placed in areas with higher
predisposition for farmer innovation. The APMC Acts establish a discontinuity at the bor-
der in the competition faced by intermediaries. This allows us to employ a hybrid border-
discontinuity design with market pairs, akin to Chatterjee 2019. We form market pairs by
matching mandis that are in close proximity with each other but lie on different sides of a
state border, thereby allowing us to difference out unobserved factors, other than compe-
tition, that affect adaptation.
Third, India, with an estimated 263 million agricultural workers Census of India 2011
spread across 15 agro-climatic regions Ahmad, Habib Kanth, Parvaze, and Sheraz Mahdi
2017—each with substantial spatial heterogeneity in competition intensity—offers signifi-
cant variation to study the effect of market power on farmer adaptation to climate change.
6
Agricultural households, which account for 48 percent of total households in India NABARD
2018, have been incentivized to invest in an adaptation portfolio owing to an unprece-
dented increase over the past several decades in both maximum temperature, and fre-
quency and intensity of extreme heat days (R. Krishnan, Sanjay, Gnanaseelan, Mujumdar,
Kulkarni, and Chakraborty 2020; also see 3.1).
7
Notably, this effect is expected to worsen,
with India projected to have the highest climate-change induced increase in heat expo-
sure and vulnerability to crop production losses relative to other nations IPCC 2022; Jones,
6
India has the highest number of agricultural workers in the world. 263 million people (54.6 percent
of India’s total workforce) are employed in agriculture. The figure of 263 million comprises of 119 million
cultivators/farmers and 144 million agricultural laborers. There is, however, some debate about the total
farmer population in India, with official figures ranging between 100 and 150 million. The main source of
contention is the absence of a standard definition of who constitutes a farmer. See Damodaran 2021 and
Narayanan and Saha 2021 for a detailed discussion.
7
An agricultural household is defined as a household that received some value of produce more than 5000
(equivalent to US$63 using the average USD-INR exchange rate in calendar year 2021) from agricultural
activities (e.g., cultivation of field crops, horticultural crops, fodder crops, plantation, animal husbandry,
vermiculture, sericulture, etc.) and had at least one self-employed member in agriculture, either in principal
status or in subsidiary status during last 365 days.
70
Tebaldi, O’Neill, Oleson, and Gao 2018.
8
This is relevant because it motivates our use of
extreme heat (defined as temperatures ≥ 35
◦
C or 95
◦
F) as a proxy for climate shocks.
(a) Change in Maximum Temperature (b) Change in Extreme Heat Days
Figure 3.1: Climatological Changes Over India Between 1960-70 and 2010-20
Note: The weather data for Panel (a) comes from Terraclimate 2018, which has a monthly temporal resolution and a 4-km (1/24th
degree) spatial resolution. Change in maximum temperature is calculated by taking the average maximum temperature for each grid
point within Indian boundaries for two time periods: 1960-70 and 2010-2020; and then differencing the two. The weather data for
Panel (b) is sourced from India Meteorological Department 2009 which uses 395 weather stations to provide a 1× 1 degree gridded
daily temperature dataset starting from 1951 up until 2020. Extreme heat days were defined for each grid cell as days with maximum
temperature greater than the 95th percentile of the temperature distribution in the respective grid cell between 1950-2020. Change in
number of extreme heat days is calculated by taking the total number of extreme heat days between 1960-70 and comparing the same
to the total number of extreme heat days between 2010-20.
The empirical analysis proceeds in three steps. 3.4.1 motivates our core question —
the role of market power in adaptation — by asking if Indian farmers have adapted in
the long-run. Evidence to the contrary would indicate that constraints imposed by distor-
tionary institutional policies on adaptation may be persistent and binding; 3.4.2 explores
8
India’s average temperature has risen by approximately 0.7
◦
C between 1901–2018. By the end of this
century, the average temperature across India is projected to rise by 4.4
◦
C relative to the 1976–2005 average,
under the RCP8.5 scenario R. Krishnan, Sanjay, Gnanaseelan, Mujumdar, Kulkarni, and Chakraborty 2020.
Furthermore, the frequency of summer heat waves over India is projected to be 3 to 4 times higher by the end
of the 21
st
century under the RCP8.5 scenario, as compared to the 1976–2005 baseline period. The average
duration of heat wave events is also projected to approximately double Rohini, Rajeevan, and Mukhopadhay
2019. Finally, Mishra, Smoliak, Lettenmaier, and Wallace 2012 and A. G. Turner and Annamalai 2012 project
a steady decline in the total precipitation during monsoon months.
71
whether intermediary market power mitigates the deleterious impact of climate shocks;
3.4.3 investigates the mechanisms.
We begin by documenting evidence of limited long-run yield-stabilizing adaptation in
India. Following Burke and Emerick 2016, we measure long-run adaptation as the differ-
ence between panel and long-differences estimates of the effect of extreme heat on yields.
The panel estimates capture short-run within-year adjustments by farmers, while the long-
differences estimates encapsulate long-run transformational adaptations. Their difference,
thus, reflects the share of the short-run impacts that are offset in the longer run. Using fine
geospatial crop yields and weather data from 1968 to 2017, we find that both methods yield
significant but similar estimates — each additional day of extreme heat reduces yields by
1.0 to 2.7 percent — indicating that long-run adaptations have likely offset none of the
short-run impacts of adverse climate. Therefore, the bottlenecks farmers face in adopting
short-run strategies have a direct and cumulative impact on their ability to adapt in the
long-term, making it imperative to recognise and address these constraints.
Our core result is that market power of intermediaries arising out of institutional poli-
cies acts as a major constraint on the farmers’ post-climate shock adaptation efforts. Using
a hybrid border-discontinuity design, we find that a farmer selling in the 75
th
percentile of
competition compared to one that faces the 25
th
percentile of competition achieves a 4.5
to 5.2 percent higher output on average for each additional degree-day of extreme heat.
This result is robust to different distance thresholds, ranging from 25 to 50 kilometers, be-
tween market pairs. We corroborate these findings using (i) a panel approach, and (ii)
a panel approach but changing the spatial unit of analysis from a mandi to a district. As
before, the results unequivocally indicate that monopsony power thwarts adaptation — a
one standard deviation increase in competition helps a farmers alleviate between 15 and
37 percent of the negative impact of extreme heat.
72
Next, in order to investigate the mechanisms underlying the relationship between mar-
ket power and adaptation, we build a simple agricultural household model with incom-
plete input markets Arag´ on, Oteiza, and Rud 2021. This allows us to derive predictions on
how and when farmers would invest in their adaptation portfolio in the event of an exoge-
nous negative shock. Subsequently, we provide evidence consistent with the predictions
highlighted in the model.
The model yields the following prediction — in the event of a negative weather shock,
farmers could increase their input usage if the output prices are expected to rise beyond a
certain threshold. This could happen, for instance, if extreme heat reduces yields and,
hence, aggregate supply. However, the magnitude of increase in prices is likely to be
greater in high competition areas as a large set of intermediaries now compete for lower
output. Lower spatial competition between intermediaries translates into lower farmer
prices Chatterjee 2019. We hypothesize that climate shocks interact with market power to
further exacerbate these pre-existing distortions, thereby incentivizing only the farmers in
high competition areas to adjust their input usage as a response to extreme heat. This, in
turn, helps alleviate the crop production losses associated with heat stress.
We find evidence consistent with the mechanisms highlighted in the model: (i) the
positive effect of higher competition on intermediary prices is compounded after a cli-
mate shock, and (ii) farmers in high competition areas increase their input usage, indi-
cating post climate shock adaptation. Specifically, a one standard deviation increase in
competition causes the pre-existing difference in crop prices to increase by 0.5 to 0.6 per-
centage points, conditional on both areas being exposed to a week of extreme heat. Next,
we use household level survey data to show that this rise in prices incentivizes farmers
to increase their input use within the growing season. Our estimates suggest that a one
standard deviation increase in competition leads to a 1.2 and 1.7 percent increase in land
and labor inputs, respectively, for each additional day of extreme heat. Furthermore, in-
put costs associated with labor, irrigation, fertilizers, and farm equipment also experience
73
a significant increase. Consistent with the adaptation portfolio, we also find evidence
of crop diversification at a macro-scale (i.e., district-level) in high competition areas, in-
dicating crop-mix as a potential avenue for increased resilience. In summary, productive
adjustment, incentivized by increasing prices, attenuates undesirable drops in output, but
is limited to high competition areas.
One way to counter the market power distortions generated by archaic institutional
policies is to remove the inter-state trade restrictions. However, the welfare impacts of
such a policy change cannot be deduced directly from the data, and require a structural
model. Specifically, our empirical strategy is inadequate in encapsulating three pivotal
general equilibrium effects. First, removing trade restrictions will not only affect prices
in mandis near the state borders, but also have a knock-on effect on prices in markets that
are not in close proximity to state borders. Second, change in intermediary prices will
incentivize farmers to re-optimize their choice of crops, intermediate inputs, and mar-
ket for sale. This will alter supply, thereby impacting retail prices, which will eventually
feed back into mandi prices. Finally, and more importantly, climate change will influence
productivity differences across crops and fields, altering comparative advantage between
different regions of India. This evolution of comparative advantage will interact with a
change in market power of intermediaries, shaping the adaptation portfolio of farmers.
A model, therefore, helps us understand how the policy change aids in mitigating the
consequences of climate change.
To estimate adaptation gains from removing interstate trade restriction, we develop
a spatial general equilibrium model of trade in the agricultural markets, drawing on the
work of Costinot, Donaldson, and C. Smith 2016 and Chatterjee 2019. In our framework,
every state consists of a large number of fields with heterogeneous productivity across
multiple crops. Each field is represented by a farmer who makes two decisions: (i) crop
and input choice, and (ii) intermediary market for sale post-harvest. The former decision
74
is influenced by the relative productivity differences across crops and fields, i.e. com-
parative advantage, which determines the pattern of specialization within and between
states. The latter decision relies on the farmers’ transportation costs between competing
markets — each of which is represented by an intermediary — and determines the level of
market power. In order to ensure the model resembles reality, we add three key features.
First, farmers cannot cross state border. To incorporate these trade restrictions, we assume
transportation costs are infinite if the farm and market lie on different sides of the border.
Second, intermediaries are price makers. This is modeled through a Bertrand competition
which ensures intermediaries act strategically when purchasing crops, internalizing their
market power. Third, intermediaries are allowed to sell across state borders. Therefore,
geography and trade restrictions create spatial heterogeneity influencing farmers’ arbi-
trage opportunities, and consequently, creating spatial variation in the monopsony power
that intermediaries can exert.
The competitive equilibrium of our model and any subsequent counterfactual analy-
sis will depend on five key parameters: (i) the elasticity of substitution between different
varieties of the same crop; (ii) the elasticity of substitution between different crops; (iii)
within-field heterogeneity in productivity; (iv) trade costs and; (v) dispersion of idiosyn-
cratic shocks to the trade cost. All these parameters are estimated using a rich micro-level
data set on field level crop productivity, inland trade data on agricultural commodities,
geolocation of markets, as well as prices and quantity arrivals of different crops in each
market. Finally we use the estimated parameters and information on the pattern of com-
parative advantage across fields and crops to simulate our model under the no–climate
change scenario, and compare it to two counterfactual scenarios. In the first, we study the
welfare consequences of a decline in crop and field productivity due to climate change,
but in the presence of trade restrictions on farmers. In the second counterfactual scenario,
we study the welfare consequences of climate change but without trade restrictions. The
75
difference between the two counterfactual helps us ascertain the magnitude of mitigation
that transpires once trade restrictions are removed.
Our model suggests that the welfare impact of climate change is substantially miti-
gated once inter-state trade restrictions are lifted. Specifically, we find that climate change
reduces welfare in India by 2.1 percent of total GDP, assuming no policy change. How-
ever, increase in competition arising out of abolishing trade barriers enables farmers to
receive a higher price, which changes the source and magnitude of adjustment, allowing
a 13.8 percent alleviation in the welfare losses. This illustrates how market distortions cre-
ated by government policies could hinder adaptation, and how removing the same could
expand the adaptation portfolio of farmers, thus helping countries mitigate the negative
consequences of climate change.
Related Literature: This paper contributes to several strands of literature. First, it
contributes to the broader literature on the impact of market concentration on economic
outcomes. A large body of research shows that market power has negative consequences
for consumer surplus Dafny, Duggan, and Ramanarayanan 2012; N. H. Miller and M. C.
Weinberg 2017, economic inequality Comanor and Smiley 1975, employee welfare Prager
and Schmitt 2021, as well as productivity and innovation Aghion, C. Harris, Howitt, and
Vickers 2001; Aghion, Bloom, Blundell, Griffith, and Howitt 2005; Holmes, Levine, and
Schmitz 2012. Interestingly, impeding competition is also linked with anti-democratic out-
comes like concentrated economic and political power, political instability, and corruption
Becker 1958; J. A. Robinson and Acemoglu 2012. We instead focus on the role of market
competition in incentivizing adaptation to climate change. In line with previous studies,
we find that (intermediary) market power has harmful implications, and can put soft lim-
its on adaptation in agriculture.
Second, this work relates to the literature on inefficiencies generated by government
policies and institutional features. An extensive literature has documented the adverse ef-
fects of government regulations: labor regulations hurt output and productivity Holmes
76
1998; Besley and Burgess 2004; licensing regulations which restrict firm entry lead to mar-
ket concentration, decelerate employment growth, and increase corruption Djankov, La
Porta, Lopez-de-Silanes, and Shleifer 2002; Bertrand and Kramarz 2002; product market
regulations (e.g. trade tariffs) adversely impact competition, average firm size and profits
Blanchard and Giavazzi 2003, and; cost-of-service regulations in the utilities sector reduce
efficiency Fabrizio, Rose, and Wolfram 2007; Cicala 2022. We complement this literature by
finding evidence that regulations governing the sale and purchase of agricultural products
can distort competition and disincentivize adaptation. In this regard, our study is closest
to Annan and Schlenker 2015 who find that a highly subsidized crop insurance program in
the United States, providing coverage to farmers against crop losses, inhibits adaptation.
However, the disincentive to adapt in their setup is a result of moral hazard, while the
disincentive in our setting is driven by government induced distortions in market power
of intermediaries. Thus, our paper documents how government regulations, intended
to protect farmers from exploitation by middlemen, have inadvertently distorted compe-
tition and hindered adaptation, thereby exacerbating the dead-weight loss arising from
climate change.
Third, we contribute to the literature on adaptation to climate change, and the mecha-
nisms that underpin it. There is mounting evidence on the deleterious impact of climate
change on several economic indicators like productivity, education, health, etc. Burgess,
Deschenes, Donaldson, and Greenstone 2017; Park, Goodman, Hurwitz, and J. Smith 2020;
E. Somanathan, R. Somanathan, Sudarshan, and Tewari 2021.
9
As a natural progression,
subsequent studies have focused on adaptation efforts, i.e. how these damaging effects can
be mitigated. Researchers have documented the positive role of air conditioners Barreca,
Clay, Deschenes, Greenstone, and J. S. Shapiro 2016; Zivin and Matthew E Kahn 2016,
9
There are numerous studies on the potential impact of climate change on agriculture in India Guit-
eras 2009; Mall, R. Singh, Gupta, Srinivasan, and Rathore 2006; Economic Survey of India 2018 and the
United States Mendelsohn, Nordhaus, and Shaw 1994; Schlenker, Hanemann, and Fisher 2005; Deschˆ enes
and Greenstone 2007; Fisher, Hanemann, Roberts, and Schlenker 2012; Schlenker and Roberts 2009. A re-
view of the impact of global warming on agriculture in developing countries is provided by Mendelsohn
2009.
77
expansion of bank branches Burgess, Deschenes, Donaldson, and Greenstone 2017, and
relocation Deschenes and Moretti 2009 in combating mortality and productivity losses
caused by climate change. Agricultural adaptation has been linked with changing crop-
mix Auffhammer and Carleton 2018; Taraz 2018, using drought-tolerant seeds Boucher,
Carter, Flatnes, Lybbert, Malacarne, Marenya, et al. 2021, labor input adjustments Arag´ on,
Oteiza, and Rud 2021, and migration Feng, Oppenheimer, and Schlenker 2015; Hermans
and McLeman 2021. In a similar vein, this article finds that farmers rely on input adjust-
ments and changing the crop mix to attenuate losses arising from climate shocks. Impor-
tantly, however, this adaptation portfolio is only accessible to farmers in high competition
areas — on account of higher expected prices — and not where government policies have
distorted market power.
Finally, our paper is related to the growing body of literature focusing on trade and
adaptation to climate change Costinot, Donaldson, and C. Smith 2016; Reilly and Hohmann
1993; Randhir and Hertel 2000. While the literature has focused on how international trade
can help alleviate climate change losses, we show that removing domestic trade barriers
would also go a long way in accelerating adaptation efforts. In this regard, we build on the
quantitative spatial general equilibrium model of Costinot, Donaldson, and C. Smith 2016
by moving away from the assumption of perfectly competitive environment, and adding
spatial variation in market power of intermediaries. This allows us to quantify the wel-
fare gains from adaptation to climate change through a reduction in intermediary market
power, an outcome of dismantling domestic trade barriers.
Roadmap: The organization of the paper is as follows. 3.2 provides an overview of the
institutional background of agricultural trade in India, particularly the APMC markets.
In 3.3, we describe our data sources and the construction of variables. 3.4 presents the
empirical strategy and the results from our econometric analysis. A theoretical model of
climate change and trade is laid out in 3.5, its estimation in 3.6, and the counterfactual
analysis in 3.7. Conclusions and areas for future research are discussed in 3.8.
78
3.2 Background on Agricultural Markets in India
To help understand how government regulations concerning agriculture marketing cre-
ated spatial competition distortions amongst intermediaries, i.e. the paper’s specific con-
text, we provide a detailed overview of the origin of these laws. 3.2.1 delves into the his-
tory, 3.2.2 details the provisions, while 3.2.3 provides insight into the unintended conse-
quences of these provisions.
3.2.1 History
The regulation of agricultural marketing in India has its roots in pre-independence poli-
cies introduced during the British Raj. The British government wanted to ensure sustained
supplies of cotton at reasonable prices for textile mills in the United Kingdom. In order
to facilitate this, the first regulated cotton market was set up in Karanja (Maharashtra)
in 1886. Subsequently, the Berar Cotton and Grain Market Act, 1887 was introduced which
empowered the British to establish a trading supervisory committee and, thereafter, des-
ignate any place as a market for sale and purchase of agricultural produce within a district.
In 1928, under the chairmanship of Lord Linlithgow,
10
the British government’s Royal
Commission on Agriculture in India expanded the scope of regulated markets. Simply, the
commission recommended: (i) extending regulation of marketing practices to all crops,
and (ii) establishment of regulated markets. To quote from the report:
“It is only in Berar that the constitution of markets is regulated by special leg-
islation and that the management is in the hands of elected committees. ...
The most hopeful solution of the cultivator’s marketing difficulties seems to lie
in the improvement of communications and the establishment of regulated
markets, and we recommend for the consideration of other provinces, the es-
tablishment of regulated markets on the Berar system. ... The Bombay Act is,
10
Lord Linlithgow was Governor General and Viceroy of India from 1936 to 1943.
79
however, definitely limited to cotton markets and the bulk of the transactions
in Berar markets is also in that crop. We consider that the system can conve-
niently be extended to other crops. ... We consider that the management of
these markets should be vested in a market committee.”
In pursuance of these ideals, the British Government in India circulated a Model Bill in
1931 to regulate trade practices and establish market yards in the countryside. However,
only a few provinces adopted these laws (Central Provinces, Madras, Baroda, Bombay,
Punjab, and Mysore). At its core, however, the establishment of regulated markets under
the British was intended to control the price, quantity, buyer, and type of goods sold, with
the direct aim of ensuring cheap supplies for England.
Post independence, the focus of the government shifted towards incentivizing farmers
to heighten agricultural production. Moreover, the government sought to protect cultiva-
tors from exploitative middlemen who often forced farmers to sell at low prices. In pursuit
of this objective, government regulation was seen as an effective instrument to facilitate
fair and competitive compensation for farmers. Consequently, a large number of states
enacted and enforced the Agriculture Produce Marketing Regulation (APMR) Acts from
the late 1960s to the early 1980s.
11
The provisions within these Acts, and how they create
monopsony power for intermediaries, is explained in the following subsection.
3.2.2 Agricultural Produce Market Committee: Regulations
Agriculture is a state subject under the Indian constitution, i.e. states have the power and
responsibility to legislate on agricultural marketing. In accordance with these legislative
powers, each state has enacted laws under the APMR Act to regulate agricultural trade
within its boundaries. These laws permit state governments to designate certain areas
within the geographical confines of the state as market areas ( mandis). Each market area
is governed by an Agricultural Produce Marketing Committee (APMC) — constituted of
11
All states, except Kerala, Jammu and Kashmir and Manipur, enacted such laws.
80
elected traders, farmers, and government representatives from the area — which is tasked
with framing and enforcing the rules governing agricultural marketing. The committee is
also responsible for setting up market yards where agricultural trade takes place.
These state-specific Acts mandate that the sale or purchase of agricultural commodi-
ties can only be executed in specified market areas, yards, or sub-yards located within the
state (see images in 3.2). In particular, it requires that all food produce should be brought
by the farmers to a market yard in their region and then sold through an auction. Fur-
thermore, intermediaries (buyers) who wish to trade in a certain market area are required
to obtain a license from the market committee. Additionally, the Act also mandates that
sellers and traders pay a market fee on all trade that takes place within the market area.
This institutional setup was designed to ensure that farmers had access to organized mar-
kets operating under the supervision of the government; such oversight was intended to
minimize the risks of exploitation by traders and middlemen. However, the provisions
distorted market competition, which we discuss in detail below.
(a) APMC Market in Bhatinda, Punjab (b) APMC Market in Yavatmal, Maharashtra
Figure 3.2: APMC Market Yards or Mandis in India
Note: Panel (a) and Panel (b) show two designated APMC market yards, also known as mandis, which were established under the
state-specific Agricultural Produce Marketing Committee (APMC) Acts. These yards are the first point of contact between the
farmers and intermediaries. All agricultural produce must be brought to these mandis by farmers in that region, and sales are made
through auction. Intermediaries require a license to operate within a mandi, but are free to transport the purchased produce and sell
it across the country.
81
3.2.3 Monopsony Power
Though noble in their intentions, the APMC laws introduced an unintended consequence:
monopoly power for market committees in their respective area. APMC legislation crim-
inalizes setting up competing markets and buying agricultural produce from outside the
designated market yards. Importantly, as the APMC laws are state-specific, their jurisdic-
tion does not stretch beyond state boundaries. This, coupled with the requirement that
farmers can only sell their produce in the APMC of their region, implies that farmers can-
not cross state boundaries to sell their produce. In essence, jurisdictional boundaries and
strict market regulations distort competition. This negatively impacts farmers’ bargaining
power and, consequently, lowers the probability of receiving fair prices for their produce.
Along with between-market competition, within-market competition is also impacted
by collusion amongst traders. Market committees, responsible for granting licenses, are
usually dominated by the trader lobby. This creates a conflict of interest as existing traders
prevent market entry to preserve their profits. The licensing regime, therefore, artifi-
cially reduces the number of buyers in the market. Furthermore, since wholesalers, retail
traders, and large processors cannot buy directly from the farmers, they rely on licensed
traders to act as intermediaries. This behavior also impacts prices: various studies Abhi-
jit Banerji and Meenakshi 2004; Meenakshi and Banerji 2005 document non-transparent
price discovery processes resulting from trader collusion. This ultimately renders farm-
ers subject to exploitation by intermediaries who act as financiers, information brokers,
and traders. Notably, farmer exploitation creates further opportunities for rent-seeking as
intermediaries can buy low and sell high, capturing the difference as profits.
In summary, while the APMC laws were intended to protect farmer exploitation by reg-
ulating agricultural marketing, many exploitative conditions have gradually resurfaced,
mostly as unintended consequences of these laws. They limit between-market competi-
tion by creating legal barriers to entry, prohibiting farmers to sell outside APMC markets,
and restricting the set of buyers to licensed intermediaries within the state. One way to
82
counter this would be through within-market competition among intermediaries. How-
ever, collusion among traders is rampant, with evidence of price manipulation and re-
stricted buyer entry, effectively creating a monopsony. The net result is an exploitative
system of interlocked transactions that robs farmers of discretion across important selling
decisions.
3.3 Data
Our goal is to study the extent of long-run adaptation, and the role of institution-led dis-
tortions in market competition on adaptation to climate change. To this end, we need four
main types of data, which we draw from varied sources: (i) estimates of yields for our
sample of crops; (ii) weather data to construct estimates of climate shocks; (iii) location
of intermediary markets (mandis) to construct the competition measure, and; (iv) daily
prices and arrivals (quantity of crop brought to a mandi) data for the sample of crops.
Below, we provide detailed information on all the data sources.
3.3.1 Yields
Our agricultural data on yields comes from the International Crop Research Institute for
the Semi-Arid Tropics ICRISAT 2018. In collaboration with the Tata Cornell Institute of
Agriculture and Nutrition (TCI), ICRISAT 2018 provides district level data on area (’000
ha), production (’000 tons), and yields (kg/ha) for 19 major crops in 313 districts of 20
states of India at an annual level from the year 1966 to 2017.
12
Our unit of analysis is,
thus, the crop-district-year. There were 313 districts in 16 states in 1966.
13
Over the next
50 years, four new states were created from the 16 states to make it 20 states, with the
number of districts in the 20 states increasing to 571. The database is, therefore, divided
12
As of 2022, India is divided into 28 states and 8 union territories, with the states being further subdi-
vided into 776 districts. Year refers to the agricultural year, i.e. June 1
st
to May 31
st
Sanghi, K. K. Kumar, and
McKinsey Jr 1998.
13
This excludes northeastern states (except Assam) and Jammu and Kashmir.
83
into 2 datasets: apportioned and unapportioned. Apportioned includes only the 1966 base
districts, with data on districts formed after 1966 given back to their parent district. This
has resulted in a consistent and comparable time series data for all the districts since 1966.
Unapportioned, on the other hand, includes all the districts formed until 2015 in 20 states
of India, but it only spans the years 1990 to 2015. We, thus, use the apportioned dataset for
our analysis given its longer time horizon.
14
We divide the crops based on the growing season, of which there are two main ones
in India: Kharif and Rabi. The Kharif cropping season is from July–October during the
south-west monsoon, with crops harvested from the third week of September to October.
The Rabi cropping season is from October–March during winter, with harvesting in the
spring months between April and May. The ICRISAT 2018 database does not separate
the agricultural data by growing season (except for sorghum or millets). Therefore, we
do not have estimates of what proportion of the crop was grown in each season. This is,
however, necessary to ascertain as otherwise there is a risk of yielding spurious estimates
of the relationship between climate variables and agricultural production. For instance,
modeling yearly yields of a crop as a function of annual number of extreme heat days will
be invalid if the production was, predominantly, limited to one growing season. Favor-
ably for us, the production of most crops is concentrated to one of the two seasons, with
negligible cultivation in the other. Agricultural Statistics at a Glance 2020, released by the
Directorate of Economics and Statistics, Government of India, provides all India estimates
of agricultural production of crops by season, averaged between the years 2014 to 2019.
We use the share of production in each season to classify commodities into either of the
two cropping seasons. For e.g., if a majority of the total production of a crop was con-
densed to the Kharif season, we classify the crop as Kharif. Of course, given India’s varied
topography and climate, there could be variation in the cropping season for the same crop
across different regions. We address this issue in 3.3.4.
14
For a description of the methodology for apportioning newly formed districts to their parent district,
and a list of districts formed after 1966, see Appendices 1 and 2 of ICRISAT 2018.
84
3.3.2 Weather
Our climate data are drawn from European Centre for Medium-Range Weather Forecasts
(ECMWF), an independent intergovernmental organisation and research institute head-
quartered in the United Kingdom. We use the fifth generation of ECMWF atmospheric
reanalyses of the global climate ERA5-Land 2021 dataset that provides gridded temper-
ature (Kelvin) and precipitation (depth in metres) data at a 0.1
◦
× 0.1
◦
(9km) horizontal
resolution.
15
The data is made available at an hourly temporal resolution with coverage
from January 1950 to present.
There was a mismatch in spatial resolution between weather and agricultural data: the
former was available at a very high spatial resolution (9km× 9km grid cells), while the
resolution of the latter was coarser and aggregated to a bigger administrative unit (district
level). This implies that several weather grid cells fell within the boundaries of each dis-
trict. To address this, we take a weighted mean of the temperature (weighted sum in case
of precipitation) across all cells within the district. In order to calculate weights, note first
that districts in India can be fairly large with heterogeneous geographical features, and
contain areas with little to no agricultural activity (e.g. Himalayas in North and East In-
dia, or deserts in Gujarat and Rajasthan). Consequently, weather conditions in such parts
of the district may be irrelevant for agricultural production within that unit. Therefore, we
rely on fine scale land cover data to use as an aggregation weight. Specifically, we use the
Global Food Security-support Analysis Data at 30m resolution GFSAD30 2017 which pro-
vides satellite-derived cropland extent maps in collaboration with National Aeronautics
and Space Administration (NASA) and the United States Geological Survey (USGS) for
South Asia for the year 2015. The database divides land into three categories: water (ocean
15
Temperature of air measured at 2m above the surface of land, sea or in-land waters. Temperature
measured in Kelvin was converted to degrees Celsius (
◦
C) by subtracting 273.15.
85
and water bodies), non-cropland, and cropland. For our purpose, all weather variables
were aggregated based on weights proportional to the cropland extent (see 3.14).
16
Next, we provide details on the construction of the weather variables used in the em-
pirical analysis. Schlenker and Roberts 2009 have documented strong non-linearities in the
relationship between exposure to weather conditions and agricultural outcomes. To cap-
ture this, we use the concept of Growing Degree Days (GDD), which measures cumulative
temperature exposure between two temperature thresholds during a period of time. The
process of creating exposure bins for all district-month-year combinations involved the
following steps. First, we use the hourly cropland-weighted weather data, aggregated to a
district level, to calculate the daily minimum and maximum temperature for each district
in India. Next, we derive how much time is spent at each temperature bin for all districts.
These bins were 1
◦
C wide, ranging from−10
◦
C to 50
◦
C. Finding the number of hours a
district is exposed to each 1
◦
C interval requires intra-daily distribution of temperature,
which required making assumptions about the temperature-time path. Specifically, the
distribution of temperatures within each day was approximated using a sinusoidal curve
Ortiz-Bobea 2021, which generates a series of points at 15-minute intervals, between mini-
mum and maximum temperatures of each day. Following this, we computed the exposure
bins (measured in hours) by determining the frequency of these 15-minute interval points
throughout the month.
17
As a final step, we compute growing degree days from these ex-
posure bins by converting the number of hours in each exposure bin to days (divide by
24), and subsequently aggregating them between a low thresholdh and a high threshold
h using the expression:
GDD
htoh
=
h−1
X
k=h
z
k
(3.1)
16
Cropland extent was defined as lands cultivated with plants harvested for food, feed, and fiber, includ-
ing both seasonal crops (e.g., wheat, rice, corn, soybeans, cotton) and continuous plantations (e.g., coffee,
tea, rubber, cocoa, oil palms). Cropland fallows are lands uncultivated during a season or a year but are
farmlands and are equipped for cultivation, including plantations (e.g., orchards, vineyards, coffee, tea,
rubber). Further details are available at globalcroplands.org.
17
By construction, summing over all bins across a month for a district equals the number of hours in that
month.
86
wherez
k
is the exposure in days to thek
th
temperature bin. Essentially,GDD
htoh
mea-
sures the amount of time a crop was exposed to temperatures between a given lower and
upper bound.
3.3.3 Intermediary Markets
An empirical analysis of the impact of competition on mitigation of climate shocks requires
information on market power, which is a function of the number, size, and location of
intermediary markets. Our primary measure of competition is defined at a wholesale
market level, and is calculated as an inverse distance weighted sum of total trade across
all neighboring markets in the same state (see 3.4.2 for details). Given the spatial nature
of this statistic, it was important to determine the exact geospatial location of each market.
The steps employed to create this dataset are detailed below.
First, we needed a comprehensive list of all wholesale intermediary markets in the
country. For this purpose, we used the Directory of Wholesale Agricultural Produce Assem-
bling Markets in India published in 2004 by the Directorate of Marketing and Inspection
(DMI), Ministry of Agriculture, Government of India Chimalwar, Tabhane, Verma, H.
Singh, and Bhatia 2004.
18
The directory lists 5,983 markets in the country, and provides
information on the name of the market, name of and distance to the nearest railway sta-
tion, district and state of each market, and the commodities traded therewith. These 5,983
markets form our universe of wholesale intermediaries (mandis) in India.
However, not all of these markets observed active trade and/or reported the daily quan-
tities and prices of commodities arriving in the marketplace. Therefore, as a second step,
we remove from our initial sample the subset of markets for which there did not exist any
price or quantity data since 2001. The assumption here is that data does not exist because
these markets did not see any trade during this time period.
19
For this exercise, we use the
18
We used the latest version published in 2004. There are also three older directories published in the
years 1963, 1992 and 2000.
19
Mandis can be of three types: primary, secondary, and non-regulated Chimalwar, Tabhane, Verma,
H. Singh, and Bhatia 2004. The missing trade data pertains to the latter two. Our analysis is focused on
87
Agmarknet dataset provided by the Ministry of Agriculture and Farmers Welfare in India,
which collates data on daily arrivals and producer prices for all government-regulated
agricultural markets in India since 2001.
20
We match the DMI list of 5,983 markets with
the list of markets in the Agmarknet data, and include a market in our sample if there was
even a single day of trading at the market for any of the 19 major commodities (selected
from ICRISAT 2018) from 2001 onwards. Next, we remove all markets in the state of Bi-
har, which dismantled the APMC markets in 2006, and markets in Kerala, Jammu and
Kashmir, and Manipur, which never enacted the APMC Act. We also remove markets in
the north-eastern states, certain Union Territories, and islands, where agriculture is not
practiced on any substantial scale.
21
This gives us a final sample of 2,938 markets in 20
states.
The third step involved a significant undertaking of finding the exact geolocation of
these 2,938 markets. The problems with using a Google API to identify the coordinates of
a market in India are manifold. First, India’s linguistic diversity means APMC markets are
denoted on Google Maps by local names in different states.
22
This implies that there does
not exist a uniform text string which could be used to search the latitude and longitude
coordinates of the markets. Second, though using the coordinates of the village centroid is
primary markets, which are large yards where the first trade between farmers and intermediaries takes
place. In essence, these yards are the first point of contact with the farmers. Secondary and non-regulated
markets are smaller with rarely, if any, farmers participating. They are mostly used for further trading of
the agricultural produce purchased by the intermediaries from the primary markets. Given that our chief
focus is on farmers, and we have data on quantities and prices for all primary markets, the missing data for
secondary markets is not a major concern.
20
The Agmarknet data can be accessed at https://agmarknet.gov.in/.
21
This includes Andaman and Nicobar Islands, Arunachal Pradesh, Chandigarh, Dadra and Nagar
Haveli, Daman and Diu, Lakshadweep, Meghalaya, Mizoram, Nagaland, Puducherry, Sikkim, and Tripura.
The states included in the final analysis are shown in 3.15.
22
Examples of the most common names in each state include: Agricultural Market Committee, Agriculture
Market Yard, Rythubazar, or Farmer Grain Market in Andhra Pradesh; Regulated Market Committee or Noti-
fied Mandi in Assam and Orissa; Krishi Upaj Mandi or Galla Mandi in Chhattisgarh, Rajasthan and Madhya
Pradesh; Khetiwadi Utpadan Samiti Market in Gujarat; Anaaj Mandi or Grain Market in Haryana and Punjab;
RMC Yard in Karnataka; Krushi Utpanna Bazar Samiti in Maharashtra; Regulated Market or Weekly Shandi in
Tamilnadu; Galla Mandi Samiti or Naveen Mandi Sthal in Uttar Pradesh; Krishak Bajar, Anaj Hat Tala or Kisan
Mandi in West Bengal.
88
a potential proxy for the geolocation of markets, Indian towns and villages can be expan-
sive, and sometimes have multiple markets in the vicinity. Ignoring these distances and
markets could lead to an erroneous competition measure. Furthermore, various village
names are repeated, sometimes even within the same state, which could lead to inaccu-
racies in the collation of spatial location data. Given these complications, we, therefore,
conducted a search on Google Maps using unique keywords for each market. For each
market in a state, our keywords included the market name, postal address, and district
followed by the commonly used designation for wholesale markets in that state. We re-
placed the designation with different monikers of APMC markets if our search did not
turn up a valid result. In case of uncertainty, we further refined our search by calculating
the distance between the market identified by our search results and the nearest railway
station mentioned in the directory by DMI. We then compared our figure with the distance
to the same railway station given in the directory, and only if the difference was minuscule
(less than 10 percent) was the market selected.
As a final step, we corroborated our findings, wherever possible, with a dataset by the
Pradhan Mantri Gram Sadak Yojana (PMGSY) which provides information on approxi-
mately 770,000 geo-tagged rural facilities, 20 percent of which are agricultural.
23
We did
not use this as our primary source for geolocation of markets because the dataset is only
available for rural India, and does not cover facilities in urban centers. Moreover, in most
states, it classifies smaller retail markets as also mandis, making it difficult to differentiate
wholesale markets from retail markets. However, it proved useful in validating — and
confirming in case of uncertainty — our Google Maps search results in rural areas.
Notwithstanding different searches involving various strings and the use of PMGSY
dataset, 13 percent (386 markets) of the markets could not be precisely geocoded. In such
23
The dataset is provided by the Online Management, Monitoring and Accounting System (OMMAS)
arm of PMGSY and is available at http://omms.nic.in/. The agricultural facilities include cold storages,
collection centres, mandis, warehouse, etc.
89
cases, we used the centroid coordinates of the village or town. The geographic distribution
of all wholesale markets in the country is plotted in 3.3.
Figure 3.3: Geographic Distribution of APMC Markets
Note: The map shows the geographic distribution of 2,938 APMC markets by district and state. Each dot represents an APMC market.
Geographic coordinates were found through Google Maps, using data from the Directory of Wholesale Agricultural Produce Assembling
Markets in India published by the Directorate of Marketing and Inspection, Ministry of Agriculture, Government of India Chimalwar,
Tabhane, Verma, H. Singh, and Bhatia 2004, and further corroborated with a dataset on geo-tagged rural facilities provided by the
Pradhan Mantri Gram Sadak Yojana (PMGSY).
3.3.4 Quantity Arrivals and Prices
The Ministry of Agriculture and Farmers Welfare aggregates commodity level, daily quan-
tity arrivals and producer prices received by farmers across government-regulated agri-
cultural markets in India. Information is available starting 2001 for 344 agricultural and
livestock commodities from approximately 4,000 markets spread across more than 650
90
districts of India. Though this data is available on the government’s Agmarknet portal, we
downloaded the same from the portal maintained by the Centre for Economic Data and
Analysis (CEDA) of Ashoka University, as they have collated the data in a format that is
easily downloadable and also corrected for certain inconsistencies.
24
Our sample is comprised of 52 major commodities which mirror the 19 crops in the
ICRISAT 2018 dataset.
25
One potential concern with the latter dataset is that it does not
classify regional crop production based on season. However, growing season for the same
crop may differ across regions.
26
For instance, if we classify an agricultural commodity as
a Kharif crop in a region where it is, in fact, grown in the winter months, the weather
conditions ascribed to the crop yields will be erroneous, leading to spurious results. In
this regard, high frequency arrivals data helps us attribute the right growing season for
crops traded in a market as we can deduce the time of harvest based on its arrival date in
an APMC market. Therefore, to correctly classify the growing season for each crop in each
market, we use the following algorithm: for every crop-market pair, we first aggregate all
arrivals up to a monthly level, and compute the monthly average across all years (2001
onwards). This gives us the average quantity traded in a market for every month across
all years. Second, we use this monthly average to find the proportion of quantity traded
in each month. Finally, we determine the growing season based on the month with the
maximum proportion of arrivals. Accordingly, if the peak arrivals was between October
to February, we classify the crop as Kharif, if it was between March to June, we classify it
as Rabi, and Zaid (summer season) otherwise.
24
The data from CEDA can be accessed at https://agmarknet.ceda.ashoka.edu.in/
25
ICRISAT 2018 tends to aggregate several crops under a single head. For instance, it contains minor
pulses as a crop, but this classification includes numerous pulses for which we have disaggregated data at
the market level in the Agmarknet database. This is the source of discrepancy between the number of crops
in ICRISAT 2018 and Agmarknet (19 versus 52).
26
To give an example, rice growing season in India varies depending upon climatic conditions, soil types,
and water availability. Eastern and southern regions of the country have favorable temperature for rice
cultivation throughout the year, leading to two or three crops of rice every year. Northern and western
regions, on the other hand, grow only one crop of rice from May to November M. P. Singh 2009.
91
The market-wise growing season classification is then used to construct the price and
quantity traded variables at a market-crop-year level. For quantity in each year, we sum
the daily arrivals in a market across the agricultural season, while for prices, we use the
modal price of the crop in the market across the growing season.
3.4 Empirical Methods and Results
The empirical section is divided into three parts. We start with 3.4.1 which motivates our
question on the distortionary impact of market power on adaptation by examining if there
is any evidence of long-run yield-stabilizing adaptation to extreme heat in India. If farmers
were able to neutralize the negative impact of institutional challenges over the long-term,
then studying their distortionary impact in the short-run would just be a cursory exercise.
This is followed by 3.4.2, which estimates the effect of market competition in mitigating the
damaging impact of extreme heat. We approach this question using panel data methods
and then proceed to strengthen our identification strategy through a border regression
discontinuity design. Finally, in 3.4.3, we identify the potential mechanisms driving the
impact of market competition on adaptation.
3.4.1 Effect of Climate Shocks on Yields
This subsection estimates the share of the negative short-run impacts of extreme heat that
are offset in the longer run. We run two separate regressions. First, 3.4.1.1 uses a panel
approach, akin to Deschˆ enes and Greenstone 2007, to estimate the effect of random year-
to-year variation in district weather conditions on agricultural yields for the time period
from 1968 to 2017. Second, 3.4.1.2 uses a long-differences approach proposed by Burke
and Emerick 2016 to model long-run district-level changes in yields between two different
points in time as a function of long-run changes in temperature and precipitation. Finally,
in 3.4.1.3, we compare panel and long differences coefficients which offers a test of whether
92
the shorter run damages of climatic variation on agricultural outcomes are mitigated in
the longer run.
3.4.1.1 Panel Approach
The panel approach uses short-run variation in climate, which is plausibly random, within
a given area to estimate the effect of extreme heat on agricultural productivity. Our econo-
metric model takes the following form:
log(Yields)
cdsy
=α +
6
X
j=1
β
j
GDD
{j}dsy
+ θPrecip
dsy
+ δ (Precip
dsy
)
2
+ π
cd
+
γ
y
+ f
s
(y) + ξ
cdsy
(3.2)
wherelog(Yields)
cdsy
refers to the log of yields (in kg/ha) for cropc in districtd of state
s in agricultural year y (July-June). The key explanatory variable is GDD
{j}dsy
, which
captures the daily distribution of daily temperatures in district d of state s in year y. It
denotes the number of days in district d of state s in agricultural year y on which the
daily mean temperature fell in thej
th
of the six temperature bins (in
◦
C), namely< 15
◦
C,
> 35
◦
C, and four 5
◦
C wide bins in between. Precip
dsy
and (Precip
dsy
)
2
denote the linear
and quadratic polynomial function of total rainfall (in m of water equivalent per day)
for district d in state s and year y. For our main specification, we include crop-district,
π
cd
, and agricultural year, γ
y
, fixed effects, while f
s
(y) refers to state-specific linear and
quadratic time-trend. The fixed effects imply that identification comes only from weather
variation across years within a particular district for each crop after differencing out any
state-specific time trends and macro variations across all states in a year. ξ
cdsy
denotes
the error term. Note that the model above is run separately for Kharif and Rabi crops, so
agricultural year refers to the particular cropping season in that agricultural year.
27
27
Specifically, the weather variables for Kharif crops are defined as the sum of the growing degree days
or precipitation in the months of June, July, August, and September for a particular agricultural year, while
93
We estimate separate coefficients β
j
for each of the temperature bin regressors. Since
the number of days in a particular standardized cropping season always sums to the same
amount, we have to use one bin as a reference category.
28
We use 20−25
◦
C as the reference
category for Kharif crops, and 15− 20
◦
C as the reference category for Rabi crops, with
the coefficients for the reference categories consequently normalized to zero.
29
We use
two-way clustered robust standard errors, with clustering at the crop-state and year level.
Results are presented in columns (1) and (2) of 3.1.
The results from the panel regression indicate that extreme heat has a significant nega-
tive impact on productivity, with each additional degree-day of heat above 35
◦
C reducing
yields by 1 percent and 1.8 percent for Kharif and Rabi crops, respectively. For Rabi crops,
an additional degree-day between 30−35
◦
C is also detrimental, with yields experiencing
a sharp decline by 1 percent in comparison to the yields during the optimal temperature of
15−20
◦
C degrees. The larger impact on Rabi crops is expected, as they are sown in winter
and harvested in early spring and, therefore, will be more sensitive to extreme heat.
Given that panel estimates capture within-year adjustments by farmers Guiteras 2009
— such as modification of inputs or cultivation techniques — the negative results indicate
that short-run adjustment are unable to mitigate the harmful effects of extreme heat.
30
Next, we estimate the effect of climate shocks over the long-run which allows for the pos-
sibility of transformational adaptations, for e.g. crop switching or exit from farming.
the weather variables for Rabi crops pertain to the months of October, November, December, January, and
February of the agricultural year.
28
For Kharif season, the number is 122, calculated as the total number of days between June-September.
For Rabi season, the number is 151, calculated as the total number of days between October-February.
29
Reference category was selected based on the optimal temperature during the ripening (grain filling)
stage for the key crop in the season — rice for Kharif, and wheat for Rabi. Grain filling is one of the most
sensitive temperature stages for rice and wheat, with a strong bearing on final yields P. Krishnan, Ramakr-
ishnan, K. R. Reddy, and V. Reddy 2011. The mean optimum temperature during this stage is between 21.2
to 24.2 for rice S´ anchez, Rasmussen, and Porter 2014, and 15-20 for wheat Jenner 1991; Wardlaw 1974
30
The ability of panel models to capture long-run climatic adaptation remains a subject of active research.
See McIntosh and Schlenker 2006 and M´ erel and Gammans 2021 for a discussion.
94
Panel Long Differences
Kharif Rabi Kharif Rabi
(1) (2) (3) (4)
Bin<15
dsy
0.005 0.001 0.016 −0.015
(0.005) (0.002) (0.039) (0.014)
Bin 15-20
dsy
0.007 0.002
(0.004) (0.018)
Bin 20-25
dsy
−0.004 −0.012
(0.002) (0.012)
Bin 25-30
dsy
−0.001 −0.003 0.005 −0.016
(0.002) (0.002) (0.005) (0.012)
Bin 30-35
dsy
−0.005
∗
−0.010
∗∗∗
−0.004 −0.029
∗∗∗
(0.002) (0.002) (0.008) (0.011)
Bin>35
dsy
−0.010
∗∗
−0.018
∗∗
−0.023
∗∗∗
−0.027
∗
(0.004) (0.004) (0.008) (0.015)
Fixed Effects
Crop× District ! !
Crop× State ! !
Year ! !
State Time-Trend ! !
Num. obs. 125,279 56,436 2,189 1,082
Adj. R
2
0.743 0.809 0.563 0.580
Table 3.1: Effect of Temperature on Yields: Panel and Long Difference Estimates
Notes: Clustered robust standard errors are in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
Columns (1) and (2) provide estimates of the effect of climate shocks on yields using a panel approach, as specified in eq:panel. The
dependent variable is the natural logarithm of yields (in kg/ha) for cropc in districtd of states in agricultural yeary. Columns (3)
and (4) provide estimates of the effect of climate change on agricultural yields from a long-differences approach Burke and Emerick
2016, as specified in 3.3. The dependent variable is the change in logged value of yields for crop c in districtd of states between two
periods, wherein the two periods are 1970 and 2015, with endpoints calculated as five-year average. Data, sourced from ICRISAT 2018,
are for 313 Indian districts of 20 states at an annual level from the year 1966 to 2017. The independent variables,Bin
htoh
, measure
the amount of time, in days, a crop was exposed to temperatures between a given lower and upper bound. The coefficient of interest is
the estimate on Bin>35
dsy
, which represents extreme heat. Columns (1) and (3) provide estimates for Kharif crops (July–October),
while columns (2) and (4) provide estimates for Rabi crops (October–March). Standard errors for panel estimates are clustered at the
crop-state and year level, while standard errors for long-difference estimates are clustered at the district-level.
95
3.4.1.2 Long Differences Approach
The Long Differences model uses the approach developed by Burke and Emerick 2016 to
identify the effect of climate change (as opposed to shocks) on agricultural productiv-
ity. Long differencing uses variation in longer run climate change and, therefore, helps
to account for long run adjustments to temperature. Specifically, we construct longer run
yield and weather averages at two different points in time for each location, and calculate
changes in average yields as a function of changes in average temperature and precipita-
tion. The model is as follows:
∆ log

Yields

cds
=α +
6
X
j=1
β
j

∆ GDD
{j}ds

+ θ

∆ Precip
ds

+ δ

∆ Precip
ds

2
+
π
cs
+ ξ
cds
(3.3)
where ∆ log

Yields

cds
is the change in logged value of yields for cropc in districtd of state
s between two periods. In our main specification, the two periods are 1970 and 2015, with
endpoints calculated as five-year averages for each variable to smooth out any idiosyn-
cratic noise. ∆ GDD
{j}ds
is the average difference in the number of growing degree days
in thej
th
temperature bin in districtd between the two periods, while ∆ Precip
ds
refers to
the change in average rainfall between the two periods in a given district. We also include
crop-state fixed effects, π
cs
, to account for any crop- and state-specific trends. The identi-
fying variation, therefore, comes from temperature changes within different districts in a
state after differencing out crop-specific trends. The key coefficient of interest is β
6
, which
measures how yields are affected by exposure to extreme heat, i.e. > 35
◦
C. Like before,
the analysis is run separately for Kharif and Rabi crops, and the coefficients for the refer-
ence categories are normalized to zero. Error terms are assumed to be correlated within
districts, and consequently, the standard errors are clustered at the district-level. Results
are presented in columns (3) and (4) of 3.1.
96
The long differences estimates are higher in magnitude than estimates from the panel
approach, and suggest that one unit increase in exposure to heat above 35
◦
C results in a
significant 2.3 and 2.7 percent decline in yields for Kharif and Rabi crops, respectively. As
before, exposure to degree days between 30−35
◦
C are also damaging for crops in the Rabi
season, with yields dropping by 2.9 percent relative to one additional day in the reference
bin of 15− 20
◦
C. Note that the magnitude of these effects is net of any transformational
adaptations made by farmers over the 45-year estimation period, for e.g. crop switching
or exit from farming.
3.4.1.3 Adaptation
We can compare panel and long differences coefficients, in the style of Burke and Emerick
2016, to estimate adaptation to extreme heat in the long-run. The logic is as follows: panel
models identify the short-run responses to weather, while long differences models iden-
tify the impact of long-run changes in climate, embodying any adaptation that farmers
have undertaken over the estimation period. Comparing the two estimates can, therefore,
allow us to test whether the shorter run detrimental effects of extreme heat on agricul-
tural outcomes are in fact mitigated over a longer horizon. We quantify the magnitude of
adaptation as 1−β
LD
j
/β
FE
j
, withj = 6, i.e. > 35
◦
C temperature bin, and it gives us the
percentage of the negative short-run impact of extreme heat on yields that is offset in the
long-run. β
LD
j
here refers to the estimate ofβ
{j=6}ds
in the long differences model in 3.3,
andβ
FE
j
refers to the estimate ofβ
{j=6}dsy
in the panel model in 3.2. A positive estimate
would signify adaptation, with farmers demonstrating better adjustability to rising tem-
peratures over the long-run, compared to shorter run heat shocks. Contrarily, a null or
negative result provides evidence of a failure to alleviate short-term yield losses from ex-
posure to extreme heat through adaptation in the long-run; worse still, this could indicate
mitigation measures available in the short-run prove untenable in the long term.
97
Given thatβ
FE
j
andβ
LD
j
are estimated using separate regressions, we need to quantify
the uncertainty in the adaptation estimate. We bootstrap our data 5,000 times, sampling
districts with replacement to preserve the within-cluster features of the error Cameron,
Gelbach, and D. L. Miller 2008. Therefore, if thed
th
cluster (district) is selected, then all
data (dependent and regressor variables) in that cluster appear in the resample.
31
This
procedure is run separately for Kharif and Rabi crops for two time periods: 1970-2015 and
1990-2015. We then use the distribution of bootstrapped adaptation estimates to test, for
each season and time period of interest, the null hypothesis of ”no adaptation” to extreme
heat—i.e., that 1−β
LD
j
/β
FE
j
= 0. Results are presented in 3.4.
Figure 3.4: Percentage of Short-Run Impacts Offset by Adaptation
Note: 3.4 shows the percentage of the short-run impacts of extreme heat on agricultural productivity for Kharif and Rabi cropping
seasons that are mitigated in the longer run. Each box plot corresponds to a particular season and time period as labeled on the left,
and represents 5,000 bootstrap estimates of 1−β
LD
j
/β
FE
j
for that time period. The dark line in each distribution is the median, the
blue dot the mean, the grey box the interquartile range, and the whiskers represent the fifth to ninety-fifth percentile. The red dashed
lines in each box plot represents the 2-sided confidence intervals for the test that 1−β
LD
j
/β
FE
j
= 0.
31
That is, we take a draw of districts with replacement, estimate both long differences and panel model for
those districts, compute the extreme heat coefficients for the two models, calculate the adaptation measure,
and repeat 5,000 times for a given time period.
98
Results suggest that long-run adaptation to extreme heat has been absent, and in fact,
the deleterious impact of weather shocks over the long-run is higher relative to the short-
run when adaptation avenues could be more limited. Median estimates (dark black lines)
from the bootstrap distribution are negative for all the cases. Long-run point estimates are
higher than short-run estimates by 48 to 132 percent for Kharif crops, and by 45 to 85 per-
cent higher for Rabi crops. However, even though the estimates are negative, the 2-sided
confidence intervals (red dashed lines) for all cases span zero. Thus, longer run adap-
tations appear to have mitigated none of the large negative short-run impacts of extreme
heat on productivity. More likely, short-term adaptation measures mitigate a portion of
the damaging effects, but the same measures prove to be unsustainable over the long-run.
In summary, our results on adaptation in the long-run indicate that the bottlenecks
farmers face in adopting short-run strategies have a direct and cumulative impact on their
ability to adapt in the long-term. It should be noted that various studies document that
Indian farmers correctly perceive climatic changes, which makes the lack of adaptation
we find in the long-run puzzling Datta, Behera, et al. 2022. A clearer understanding of the
effect of distortionary policies on year-to-year adaptation is, therefore, crucial to assess
their persistent impact and also shed light on heterogeneity in adaptation across regions
with high and low competition.
3.4.2 Effect of Competition on Mitigation of Climate Shocks
We start by constructing a local spatial competition measure in 3.4.2.1. This measure is
then used in 3.4.2.2 to estimate, using a panel regression, the effect of competition on adap-
tation. In our analysis thus far, adaptation has been measured by the magnitude of the
fall in district-wise yields mitigated by competition. We modify this definition in 3.4.2.3,
where we now use spatially disaggregated market arrivals data to measure adaptation.
Finally, in 3.4.2.4, to address endogeneity concerns, we implement a border discontinuity
design with market pairs to causally identify these effects.
99
3.4.2.1 Measuring Market Power
We construct a measure of local competition at the market level, Comp
1m
, by taking a
weighted sum of the total value of trade at all other markets near the origin market site,
provided they are all in the same state. The weights are the inverse of distances of the
neighboring markets (n) to the origin market (m), while the total value of trade (Y
n
)
refers to the sum of the value of agricultural produce traded in the neighboring marketn
between the years 2000 to 2021. For any marketm,
Comp
1m
=
X
n∈M\{m}

1
distance
mn

Y
n
× 1{state of m = state of n} (3.4)
whereM is the set of all markets in India. Competition in any marketm is driven by three
factors: the number of neighboring markets (n), a farmer’s ease of access to alternative
markets, which we incorporate throughdistance
mn
, and the size of the alternative mar-
kets, which we proxy using the value of trade over the last two decades (Y
n
). Therefore,
theComp
1m
measure will assign a greater weight to a proximal market. Furthermore, a
neighboring market with large trade volumes will lead to a higher competition measure,
as opposed to a market with limited trade.
We also create an analogous local competition measureComp
2m
, similar to Chatterjee
2019, by taking an inverse distance weighted sum of other markets near a particular market
site but in the same state. The only difference between the two measures is that we do
not include the value of trade (Y
n
) in the latter, and competition is only defined by the
proximity of markets. Finally, since our unit of analysis is crop-district-year, we aggregate
competition to a district level by averaging the competition measure for all markets in a
districtd of states. This also gives us an opportunity to define a third, more crude measure
of competition,Comp
3ds
, which equals the density of markets, i.e. the number of markets
per square km in a districtd of states. The geographic distribution of competition using
theComp
1m
measure, aggregated to a district level, is illustrated in 3.5.
100
Figure 3.5: Geographic Distribution of Competition Aggregated to District Level
Note: The map shows the geographic distribution of competition at a district level. Competition is measured for each of the 2,938
APMC markets (represented by black dots) as the weighted sum of the total value of trade at all other markets near the origin market
site, provided they are all in the same state (see 3.4). The weights are the inverse of distances of the neighboring markets (n) to the
origin market (m), while the total value of trade refers to the sum of the value of agricultural produce traded in the neighboring
marketn between the years 2000 to 2021. Since our unit of analysis is crop-district-year, we aggregate competition to a district level by
averaging the competition measure for all markets in a districtd of states.
3.4.2.2 Panel Approach
We run a panel model to estimate how market competition, measured at a district level,
mitigates the adverse effects of extreme heat on crop yields. Our main specification takes
the following form:
log(Yields)
cdsy
=α +
6
X
j=1
η
j
GDD
{j}dsy
+
6
X
j=1
Ω j
(GDD
{j}dsy
×Comp
ds
) +
ϕPrecip
ds
+δ (Precip
ds
)
2
+λ
cy
+λ
dct
+λ
sy
+ξ
cdsy
(3.5)
where Comp
ds
is the aggregate measure of competition at the district level, and equals
either the mean value of the market level competition measure, Comp
im
∀i∈{1,2}, for
all marketsm in districtd, or the number of markets per square km in districtd of states
(Comp
3ds
). Since our baseline competition measure (Comp
1m
) is calculated as an inverse
101
distance weighted sum of total value of trade across all years in neighboring markets, and
we do not have data on the date of construction of markets,Comp
ds
is time invariant across
the length of our sample.
To control for confounds, we include multiple fixed effects, including the following in
our most rigorous specification: a crop-year fixed effect, λ
cy
, that controls for changes in
national or world prices of the commodity; a district-crop-decade fixed effect, λ
dct
, that
controls for slow-moving changes in crop-specific costs, in the area allocated to the crop,
in preferences, or in technologies; and a state-year fixed effect, λ
sy
, that controls for state-
specific cost or demand shocks common to all crops. Certain specifications also include
f
s
(y), which is a state-specific linear and quadratic time-trend. Note that the inclusion
of any form of district fixed effects implies that the level effect of time-invariant district
specific competition ( Comp
ds
) is swept out and cannot, therefore, be estimated. Finally,
we compute robust standard errors clustered at the state-year and crop level to account for
cropping decisions and other shocks which are likely to be spatially and serially correlated.
Results are presented in 3.2.
Our results suggest that there is significant mitigation of the effect of extreme heat ow-
ing to increased competition. Depending on the specification, each additional degree-day
of heat above 35
◦
C reduces yields by 1.4 to 3.2 percent. Importantly, in areas with lower
intermediary market power, this effect is attenuated, with the coefficient on the interac-
tion term between extreme heat and competition significantly positive and ranging from
0.001 to 0.003. To interpret the scale of this number, we can compute the impact of a one
standard deviation increase in competition on the effect of heat shocks on yields. Divid-
ing this byη
j
gives us the percentage of impact mitigated. We find that a one standard
deviation increase in market competition can help farmers mitigate the impact of extreme
heat by 13.2 percent in our most rigorous specification in column (6) with crop-year, state-
year and district-crop-decade fixed effects. The effect is substantially larger in column (3),
where we control for crop-district and year fixed effects and add in state time trends, with
102
Dependent Variable: log(Yields)
cdsy
(1) (2) (3) (4) (5) (6)
Bin 30-35
dsy
−0.004
∗
−0.004
∗
−0.011
∗∗∗
−0.002 −0.013
∗∗∗
−0.010
∗∗∗
(0.002) (0.002) (0.003) (0.002) (0.003) (0.002)
Bin>35
dsy
−0.026
∗∗∗
−0.025
∗∗∗
−0.014
∗∗
−0.033
∗∗∗
−0.018
∗∗∗
−0.015
∗∗∗
(0.005) (0.006) (0.006) (0.007) (0.006) (0.004)
Bin<15
dsy
× Comp
ds
−0.002 −0.002 −0.000 −0.000 0.000 0.001
(0.001) (0.002) (0.001) (0.002) (0.001) (0.001)
Bin 15-20
dsy
× Comp
ds
0.003 0.003 0.002 0.002 0.000 −0.000
(0.002) (0.002) (0.001) (0.002) (0.001) (0.001)
Bin 25-30
dsy
× Comp
ds
−0.000 0.000 0.002 −0.000 0.001 0.001
(0.001) (0.001) (0.001) (0.001) (0.001) (0.001)
Bin 30-35
dsy
× Comp
ds
−0.001 −0.001 0.001 −0.001 0.001 0.001
(0.001) (0.002) (0.001) (0.002) (0.001) (0.001)
Bin>35
dsy
× Comp
ds
0.004
∗∗∗
0.004
∗∗∗
0.003
∗∗
0.005
∗∗
0.003
∗∗
0.002
∗
(0.001) (0.001) (0.002) (0.002) (0.001) (0.001)
Fixed Effects
Crop !
District ! !
Year ! !
Crop× District !
Crop× Year ! ! ! !
District× Year !
State× Year !
District× Crop× Decade ! !
Effect Mitigated (in %) 18.5 20.9 31.7 18.8 20.5 15.1
Num. obs. 59,593 59,593 59,593 59,593 59,593 59,593
Adj. R
2
0.624 0.615 0.805 0.635 0.829 0.844
Table 3.2: Competition and Mitigation of Climate Shocks: Panel Approach with Yields
Notes: Clustered robust standard errors are in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
Columns (1) to (6) provide estimates of how market competition, measured at a district level, mitigates the adverse effects of extreme
heat on crop yields (eq:panelcompadaptation).Thedependentvariable,log(Yields)
cdsy
, refers to the log of yields (in kg/ha) for crop
c in districtd of states in agricultural yeary (July-June). Data, sourced from ICRISAT 2018, are for 313 Indian districts of 20 states at
an annual level from the year 1966 to 2017.Comp
ds
is the aggregate measure of competition at the district level. For this purpose, we
first calculate competition for each of the 2,938 APMC markets as the weighted sum of the total value of trade at all other markets near
the origin market site, provided they are all in the same state. The weights are the inverse of distances of the neighboring markets (n)
to the origin market (m), while the total value of trade refers to the sum of the value of agricultural produce traded in the neighboring
marketn between the years 2000 to 2021. Second, we aggregate competition to a district level by averaging the competition measure
for all markets in a districtd of states. The independent variables related to temperature,Bin
htoh
, measure the amount of time, in
days, a crop was exposed to temperatures between a given lower and upper bound. The coefficient of interest is the estimate on the
interaction term betweenBin> 35
dsy
(extreme heat) andComp
ds
. It can be interpreted as the supplementary impact of an additional
degree day of extreme heat for a given level of competition. The antepenultimate row, titled Effect Mitigated (in %), provides estimates
of the impact of extreme heat mitigated by a one standard deviation increase in district competition. Coefficients related to the effect
of temperatures less than 30
◦
C on yields have been omitted for brevity. Standard errors are clustered at the state-year and crop level.
103
one standard deviation increase in competition leading to an attenuation of 31.3 percent.
The rest of the specifications give us a number between these two extreme values.
3.4.2.3 Panel Approach: Arrivals Data
To this point in our paper, we have used the attenuation in district-wise crop-specific yields
as a measure of adaptation. However, potential concerns could arise regarding mismea-
surement of the district level competition variable as the same was constructed by av-
eraging the market competition across all mandis in the district. Particularly, if farmers
regularly cross district borders within the state boundaries to sell their produce, then the
average competition across district mandis may not be a true indicator of the monopsony
power faced by farmers. Therefore, we address this by using microdata on the daily quan-
tity arrivals of each crop at a market. Arrivals reflect the daily quantity traded of a crop in
a particular mandi, and the sum across the growing season acts as a proxy for the total
production of the crop during the agricultural year.
Our econometric specification closely follows 3.5, except that our unit of analysis is
now market-crop-year, and we replace yields at the district level with quantity arrivals at
each market. Specifically,
log(Arrivals)
cmdsy
=α +
6
X
j=1
η
j
GDD
{j}dsy
+
6
X
j=1
Ω j
(GDD
{j}dsy
×Comp
mds
) +
ϕPrecip
ds
+δ (Precip
ds
)
2
+λ
m
+λ
cy
+λ
dt
+λ
sy
+ξ
cmdsy
(3.6)
wherelog(Arrivals)
cmdsy
refers to the natural logarithm of the quantity of cropc arriving
in marketm situated in districtd of states in agricultural yeary. Comp
mds
is the market
level measure of competition, calculated as either the weighted sum of the total value
of trade at all other markets in the same state near the origin market site (Comp
1m
), or
the inverse distance weighted sum of other markets in the same state near a particular
market site (Comp
2m
). We include crop-year fixed effects ( λ
cy
) to account for changes in
104
national or world prices of commodities, and district-decade fixed effects ( λ
dt
) to factor
out slow moving district-specific technological changes. We also control for state-specific
cost or demand shocks common to all crops by including state-year fixed effects ( λ
sy
), and
individual market time-invariant idiosyncrasies by adding individual market fixed effects
(λ
m
). The inclusion of market fixed effects implies that the level effect of time-invariant
market specific competition ( Comp
mds
) is swept out and cannot, therefore, be estimated.
η
6
can now be interpreted as the effect of an additional degree-day of extreme heat in
the district on quantity arrivals, while the coefficient of interest, Ω 6
, indicates the magni-
tude of impact mitigated by competition. We cluster our standard errors two-way, both at
the state-decade level and the crop level. Results are presented in 3.3.
The results mirror the estimates from the previous subsection — in fact, the mitiga-
tion effects are larger. Depending on the specification, each additional degree-day of heat
above 35
◦
C reduces quantity arrivals by 2.3 to 3.0 percent. However, as before, this effect
is significantly allayed in high-competition areas. A one standard deviation increase in
market competition can help farmers mitigate the impact of extreme heat by 36.2 percent
in our most rigorous specification in column (4) with market, crop-year, state-year, and
district-decade fixed effects. In the remaining columns, the effect sizes range from 29.6
to 36.9 percent. This suggests that our results using district-level yields as a measure of
adaptation were biased downwards, and using arrivals data as a proxy helps correct this
bias.
3.4.2.4 Hybrid Border Discontinuity Design
Although our results are consistent across different empirical specifications, one can still
be concerned about other forms of unobserved heterogeneity. For example, if a large num-
ber of markets were set up in regions where farmers had a higher potential for innovation,
105
Dependent Variable: log(Arrivals)
cmdsy
(1) (2) (3) (4)
Bin 30-35
dsy
0.001 0.001 0.002 0.001
(0.006) (0.006) (0.006) (0.006)
Bin>35
dsy
−0.023
∗
−0.023
∗
−0.030
∗
−0.023
∗
(0.013) (0.013) (0.016) (0.014)
Bin<15
dsy
× Comp
mds
−0.000 −0.000 −0.001 −0.000
(0.002) (0.002) (0.002) (0.002)
Bin 15-20
dsy
× Comp
mds
0.002 0.002 0.002 0.002
(0.001) (0.001) (0.002) (0.001)
Bin 25-30
dsy
× Comp
mds
0.000 −0.000 0.000 −0.000
(0.001) (0.001) (0.001) (0.001)
Bin 30-35
dsy
× Comp
mds
−0.000 −0.000 −0.001 0.000
(0.001) (0.001) (0.002) (0.001)
Bin>35
dsy
× Comp
mds
0.002
∗∗∗
0.002
∗∗∗
0.003
∗∗
0.002
∗∗∗
(0.001) (0.001) (0.001) (0.001)
Fixed Effects
Market ! !
Crop× Year ! ! ! !
District× Decade !
Market× Decade !
Market× Year !
State× Year ! ! ! !
Effect Mitigated (in %) 36.9 35.4 29.6 36.2
Num. obs. 148,814 148,814 148,814 148,814
Adj. R
2
0.433 0.450 0.450 0.437
Table 3.3: Competition and Mitigation of Climate Shocks: Panel Approach with Arrivals
Notes: Clustered robust standard errors are in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
Columns (1) to (4) provide estimates of how market competition mitigates the adverse effects of extreme heat on quantity arrivals
at each market (eq:arrivalspanelcompadaptation).Thedependentvariable,log(Arrivals)
cmdsy
, refers to the natural logarithm of the
quantity of cropc arriving in marketm situated in districtd of states in agricultural yeary. Data, sourced from Centre for Economic
Data and Analysis (CEDA) of Ashoka University, comprises of quantity arrivals of 52 major commodities in 2,938 APMC markets from
2001 to 2021. Comp
mds
is the measure of competition at the market level, and equals the weighted sum of the total value of trade at
all other markets near the origin market site, provided they are all in the same state. The weights are the inverse of distances of the
neighboring markets (n) to the origin market (m), while the total value of trade refers to the sum of the value of agricultural produce
traded in the neighboring marketn between the years 2000 to 2021. The independent variables related to temperature, Bin
htoh
,
measure the amount of time, in days, a crop was exposed to temperatures between a given lower and upper bound. The coefficient of
interest is the estimate on the interaction term betweenBin> 35
dsy
(extreme heat) andComp
mds
. It can be interpreted as the effect
of an additional degree-day of extreme heat in the district on quantity arrivals. The antepenultimate row, titled Effect Mitigated (in %),
provides estimates of the impact of extreme heat mitigated by a one standard deviation increase in market competition. Coefficients
related to the effect of temperatures less than 30
◦
C on quantity arrivals have been omitted for brevity. Standard errors are clustered at
the state-decade and crop level.
106
then the coefficient on the interaction between competition and weather could just be cap-
turing the effect of farmer ingenuity. To overcome this issue, we implement a hybrid bor-
der discontinuity design with market pairs. We match all markets which are less thanx
kilometers apart (bandwidth) but lie on different sides of a state boundary. We try dif-
ferent values of the bandwidth ranging from 25 kms to 50 kms, and all multiples of five
therein. For each bandwidth, we obtain a sample of market pairs, with markets belonging
to a pair lying in close proximity spatially but divided by a state border. The empirical
strategy involves regressing — for each market pair — the difference in arrivals on: (i) the
difference in competition; (ii) the average weather conditions across the two markets, and
(iii) the interaction between the two. We call it hybrid because even though there is a dis-
continuity in competition at the border, the regression involves weather variables which
are continuous.
The basic rationale behind employing the border discontinuity design is that other de-
terminants of arrivals like demand, weather, productivity via soil quality, farmer ingenu-
ity, and transportation costs will vary continuously across a state boundary. This should,
therefore, help to assuage concerns about unobserved heterogeneity. One could be con-
cerned that geographical conditions change discontinuously at the border. However, post
independence in 1947, Indian states were redrawn along linguistic principles, rather than
administrative, economic, or geographic factors Chari 2016; Samaddar 2020.
32
Neverthe-
less, an important determinant of farmer adaptation which could change discontinuously
at the state border is each state’s policy on weather shocks. To address this confound-
ing effect, we add market pair-year fixed effects. Thus, the only remaining discontinuity
32
The Government of India appointed the States Reorganisation Commission in December 1953 which
advocated the following: To consider linguistic homogeneity as an important factor but not to consider it as an
exclusive and binding principle Parameswaran and Chattopadhyay 2014. In August 1956, the Indian Parliament
enacted the States Reorganisation Act, which remains India’s largest collective administrative reorganisation.
While due consideration was given to administrative and economic factors, it recognized for the most part
the linguistic principle and redrew state boundaries on that basis A. Kumar 2019.
107
across state borders which could potentially aid in attenuating the impact of extreme heat
is local competition, as farmers are not allowed to sell their output across state borders.
33
In essence, the advantage of this design is that we can difference out unobserved fac-
tors other than competition that affect adaptation by choosing market pairs in close geo-
graphical proximity to each other. To better illustrate the design, 3.6 presents a graphical
representation of our hybrid border discontinuity design. Along the same state border,
we have two market pairs (Pair 1 and Pair 2), with markets in each pair within 25 kms of
each other but lying on different sides of the border. For Pair 1, there is no difference in
competition, while for Pair 2, market C has a higher competition than market D. Now, in
the event of extreme heat (bad weather), the difference in arrivals should not change for
Pair 1, as both markets have the same competition and will be equally affected. However,
for Pair 2, the difference in arrivals should increase because farmers in Market C have been
able to attenuate some of the impact through higher competition. The spatial feature of the
design is illustrated in 3.7, which presents the geographical distribution of all 652 markets
selected using the bandwidth of 50 kms.
34
Note that only markets less than 50 kms apart
and situated in different states will be considered as a pair. This results in 1,210 market
pairs for the said bandwidth.
33
As Chatterjee 2019 mentions, Indian languages change gradually over distance. Therefore, farmers and
intermediaries in close geographical proximity but settled on different sides of a state border should be able
to communicate with each other.
34
Our preferred bandwidth is 25 kms. We illustrate the geographical distribution of markets selected
with the 50 kms bandwidth as that leads to more market pairs, offering a vivid visualization.
108
Market A
(low comp)
Market B
(low comp)
Market C
(high comp)
Market D
(low comp)
Pair 1 Pair 2
State Border
Distance≤ 25km
∆ Comp = 0 ∆ Comp> 0
Good Weather : ∆ Arrivals =x
Good Weather : ∆ Arrivals =y
Bad Weather : ∆ Arrivals =x
Bad Weather : ∆ Arrivals>y
Figure 3.6: Interpreting the Border Discontinuity Design
Note: 3.6 presents a graphical representation of the hybrid border regression discontinuity design in 3.7; see text for details.
Figure 3.7: Geographical Distribution of Markets Selected Using 50 kms Bandwidth
Note: The map shows the geographic distribution of markets used in the hybrid border discontinuity design with a bandwidth of 50
kms. The dots represent the sample of market pairs which lie in close spatial proximity but are divided by a state border. There are
652 markets for the distance threshold of 50 kms. The empirical strategy involves regressing — for each market pair — the difference
in arrivals on: (i) the difference in competition; (ii) the average weather conditions across the two markets, and (iii) the interaction
between the two. Competition refers to the weighted sum of the total value of trade at all other markets near the origin market site,
provided they are all in the same state (see Equation (4)). The weights are the inverse of distances of the neighboring markets (n) to
the origin market (m), while the total value of trade refers to the sum of the value of agricultural produce traded in the neighboring
market n between the years 2000 to 2021.
109
The hybrid border discontinuity model linking difference in arrivals to the interaction
between differences in competition and weather variables is as follows:
∆ log(Arrivals)
cmm
′
y
=α +
6
X
j=1
η
j
GDD
{j}cmm
′
y
+
6
X
j=1
Ω j
(GDD
{j}cmm
′
y
× ∆ Comp
mm
′) +
ϕPrecip
mm
′
y
+δ (Precip)
2
mm
′
y
+λ
cy
+λ
mm
′
y
+λ
bct
+ξ
cmm
′
y
(3.7)
where ∆ log(Arrivals)
cmm
′
y
is the difference in the natural log of arrivals of crop c in agri-
cultural yeary between marketsm andm
′
which lie on different sides of the state boundary
b. ∆ Comp
mm
′ is the time-invariant difference in competition measure Comp
1m
between
marketsm andm
′
. GDD
{j}cmm
′
y
denotes the number of days in the cropping season, for
cropc in yeary, on which the daily mean temperature fell in thej
th
of the six temperature
bins (in
◦
C) at the district border betweenm andm
′
. To arrive at this variable, we average
the GDD’s in the districts containing the markets which, by design, lie on either sides of
the state border. The precipitation variables, too, are constructed in a similar manner.
We control for confounding factors by adding three fixed effects in our most stringent
specification: a crop-year fixed effect ( λ
cy
) that controls for changes in global or national
prices of the crop c; a market pair-year fixed effect ( λ
mm
′
y
) that controls for differences
in market specific infrastructure, policies and cost or demand shocks that are common to
all crops; and a state border-crop-decade fixed effect ( λ
bct
) that accounts for differences in
slow moving changes in crop-specific costs, in the area allocated to the crop, in preferences,
or in technology. As an aside, we can include market pair-year fixed effects as there are
multiple crops within that dimension. Importantly, these different crops within the same
market pair-year level are not subjected to the same weather. For example, kharif and rabi
arrivals in a market will be exposed to different weather conditions in the same agricultural
year.
35
Also note that the data for each market pair only includes crops which had the same
cropping season across both markets. Therefore, if rice in market A is a Kharif crop and
35
The identification of cropping season for each market-crop is made possible using the time series on
arrivals data, and is explained in detail in 3.3.4.
110
rice in Market B is a Rabi crop, we dropped rice as a commodity for market pair A-B. Hence,
the identifying variation comes from the differing weather conditions that different crops
within a market pair and year were exposed to, after differencing out any crop specific
fixed effect. Finally, the inclusion of any form of market pair fixed effects implies that the
level effect of time-invariant difference in market competition ( ∆ Comp
mm
′) is swept out
and cannot, therefore, be estimated.
The interpretation of the coefficients changes slightly as compared to previous speci-
fications. Though previously η
j
measured the effect of spending an additional day in the
j
th
temperature bin on arrivals, it now measures the effect on the difference in arrivals.
If our discontinuity in competition assumption is correct, then the only thing impacting
the difference in arrivals during extreme heat should be competition, which is captured
by the coefficient Ω j
. In other words, η
6
should not be significantly different from 0. If
that is not the case, it would indicate the presence of extraneous factors affecting arrivals
during extreme heat which, if correlated with competition, could bias our results. Thus,
the coefficient on η
6
acts as a placebo check. However, this implies that we cannot calcu-
late the percentage of impact mitigated by competition, as we do not obtain an estimate of
the marginal effect of climate shocks on arrivals. We cluster our standard errors two-way,
both at the border-year level and crop level. Results are presented in 3.4 for our preferred
bandwidth of 25 kms.
Our results with the border discontinuity design are in harmony with our previous
specifications and deliver the same message: competition helps in fostering adaptation
to climate shocks. All estimates of the interaction term between extreme heat and differ-
ence in competition are positive and significant, irrespective of the fixed effects used. A
farmer selling in the 75
th
percentile of competition compared to one that faces the 25
th
percentile of competition achieves a 4.5 to 5.2 percent higher yield on average for an ad-
ditional degree-day of extreme heat. Reassuringly, the estimates on the temperature bins
themselves are insignificant, as was expected if the regression was correctly specified. To
111
Dependent Variable: ∆ log(Arrivals)
c{mm
′
}by
Markets≤ 25km Apart
(1) (2) (3) (4)
Bin 30-35
cmm
′
y
−0.029 −0.016 0.016 0.011
(0.048) (0.016) (0.030) (0.031)
Bin>35
cmm
′
y
0.029 0.037 0.025 0.036
(0.034) (0.026) (0.031) (0.027)
Bin<15
cmm
′
y
× ∆ Comp
mm
′ 0.005 0.012 0.012 0.014
∗
(0.008) (0.008) (0.008) (0.008)
Bin 15-20
cmm
′
y
× ∆ Comp
mm
′ 0.012 0.010 0.010 0.007
(0.014) (0.009) (0.012) (0.012)
Bin 25-30
cmm
′
y
× ∆ Comp
mm
′ 0.011 0.012 0.013 0.012
(0.008) (0.008) (0.009) (0.009)
Bin 30-35
cmm
′
y
× ∆ Comp
mm
′ 0.003 0.007 0.006 0.006
(0.006) (0.006) (0.006) (0.007)
Bin>35
cmm
′
y
× ∆ Comp
mm
′ 0.015
∗∗
0.018
∗∗
0.018
∗∗
0.017
∗∗
(0.007) (0.006) (0.008) (0.008)
Fixed Effects
Market-Pair× Year ! ! ! !
Border× Crop ! !
Crop× Year ! !
Border× Crop× Decade !
75th — 25th Percentile (in %) 4.5 5.2 5.2 5.1
Num. obs. 2,899 2,899 2,899 2,899
Adj. R
2
0.454 0.545 0.536 0.534
Table 3.4: Competition and Mitigation of Climate Shocks: Border Discontinuity
Notes: Clustered robust standard errors are in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
Columns (1) to (4) provide estimates, using a 25 kms hybrid border discontinuity approach, of how
market competition mitigates the adverse effects of extreme heat on quantity arrivals at each market
(eq:arrivals
b
order
d
isccompadaptation).Thedependentvariable,∆ log(Arrivals)
c{mm
′
}by
, refers to the difference in the natu-
ral log of arrivals of crop c in agricultural year y between markets m and m’ which lie on different sides of the state boundary b.
Data, sourced from Centre for Economic Data and Analysis (CEDA) of Ashoka University, comprises of quantity arrivals of 52 major
commodities in 2,938 APMC markets from 2001 to 2021. ∆ Compmm’ is the time-invariant difference in competition measure between
marketsm andm’. GDD
{j}cmm
′
y
denotes the number of days in the cropping season, for cropc in yeary, on which the daily mean
temperature fell in thej
th
of the six temperature bins (in
◦
C) at the district border betweenm andm
′
. The coefficient of interest is
the estimate on the interaction term betweenBin>35
cmm
′
y
(extreme heat) and ∆ Comp
mm
′. The antepenultimate row, titled 75th
— 25th Percentile (in %), provides estimates of the higher yield experienced by a farmer selling in the 75
th
percentile of competition
compared to one that faces the 25
th
percentile of competition for an additional degree-day of extreme heat. Coefficients related to the
effect of temperatures less than 30
◦
C on yields have been omitted for brevity. Standard errors are clustered two-way at the border-year
and crop level.
112
test the robustness of the adaptation results, we also present the coefficient on Ω 6
from
regressions using different bandwidths in 3.8. Like before, the effect sizes are positive
and significant, although smaller in magnitude. One additional degree day in the highest
temperature bin leads to a difference in the range of 1.9 to 3.6 percent in yields between
farmers in the 75
th
percentile of competition relative to farmers in the 25
th
percentile. Note
that the confidence intervals for the 25 kms bandwidth are larger, which is expected given
the low number of market pairs due to the shorter distance.
Figure 3.8: Impact of Extreme Heat Offset by Competition: Border Discontinuity
Note: 3.8 provides estimates, using a hybrid border-discontinuity design with different distance bandwidths, of how market
competition mitigates the adverse effects of extreme heat on quantity arrivals at each market. The distance thresholds used for each
estimate are labeled on the X-axis. The point estimates (red dots) on the Y-axis correspond to the coefficient of interest — the
estimate on the interaction term betweenBin>35
cmm
′
y
(extreme heat) and ∆ Comp
mm
′ — in 3.7. ∆ Comp
mm
′ is the
time-invariant difference in competition measure between markets m andm’.GDD
{j}cmm
′
y
denotes the number of days in the
cropping season, for cropc in yeary, on which the daily mean temperature fell in thej
th
of the six temperature bins (in
◦
C) at the
district border betweenm andm
′
. To interpret the point estimates, we calculate the difference in yields experienced by a farmer
selling in the 75
th
percentile of competition compared to one that faces the 25
th
percentile of competition, for each additional
degree-day of extreme heat. This is indicated by the labels next to the red dots. The whiskers represent the 90
th
percentile confidence
intervals, with standard errors clustered two-way at the border-year and crop level.
113
3.4.3 Mechanisms
Given the result that market competition leads to higher adaptation, we now turn our
attention towards ascertaining the mechanisms behind our findings. 3.4.3.1 presents an
analytical framework that uses a simple agricultural model to derive predictions on input
usage post a climate shock. We then test empirically whether these predictions hold in the
data, the results of which are presented in 3.4.3.2 and 3.4.3.3.
3.4.3.1 Analytical Framework
In this subsection, we present a simple agricultural household model to examine how
subsistence farmers would adjust their input decisions in the event of an exogenous heat
shock. Closely following the work of Taylor and Adelman 2003 and Arag´ on, Oteiza, and
Rud 2021, we present a framework where production and consumption decisions are
linked. This transpires because the farmer is both a producer, choosing the allocation
of inputs to crop-production, and a consumer, choosing the allocation of income to con-
sumption.
We start with an agricultural production function with two inputs, land (T) and labor
(L). The household has an endowment of landT
e
, which can be used for production or
non-productive activities like leisure.
36
Household utilityU(c,t) is a function of consump-
tion of a market good (c) and land used for leisure (t). Households are price takers and
obtain income by renting their land, and selling their produce in the market at pricep. The
production function is defined by F(A,L,T ), whereA is farmer’s total factor productivity.
Specifically, we use A to capture the productivity shock due to exposure to extreme heat.
Consistent with our results on the relationship between crop yields and temperature, we
36
We follow Arag´ on, Oteiza, and Rud 2021 in this regard. The inclusion of land directly in the utility
function is a modeling device to create a positive shadow price (i.e., an opportunity cost of using land) and
should not be taken literally. Since land cannot be sold or rented out, without this device, the model would
predict that farmers will always use all available land. This prediction is inconsistent with the empirical
observation that as a proportion of cultivable area, 13.4 percent of the land was left fallow in 2010–2011, an
increase from 10.6 per cent in 1970–1971 Ranganathan and G. Pandey 2018.
114
assume that extreme heat has a detrimental effect on productivity. Each growing season,
the household maximizes utility by choosing simultaneously the amount of land allocated
to productive and nonproductive uses, and the labor to be employed. Finally, we assume
that both the utility and the production functions are increasing and strictly concave.
Under the extreme assumption that all input markets exist and are well functioning,
the household’s production and consumption decisions can be decoupled Benjamin 1992.
This separation result is driven by the possibility of trade in complete markets. In this
scenario, the farmer’s optimal input usage can simply be inferred by solving the profit
maximization problem, max
{T,L}
π =pF (A,T,L)−rK−wL, where r and w refer to input
prices. Under such a setting, a negative productivity shock, such as extreme heat, would
always reduce input usage.
37
The prediction above changes in the case of incomplete markets, which is a more re-
alistic setting in the context of India Rosenzweig and Wolpin 1993. To illustrate this, we
consider a mixed market scenario. Specifically, we assume that there is no input market
for land, but there is a well-functioning input market for labor. In this simplified setting,
the farmer’s problem becomes:
max
{T,L}
U(c,t)
subject to
c≤pF (A,T,L)−wL
T +t≤T
e
(3.8)
37
Assuming a Cobb-Douglas technologyf =AT
α
L
β
, the optimalT equals

pA

α
r

1−β

β
w

β

1/γ
, while
the optimalL equals

pA

α
r

α

β
w

1−α

1/γ
, whereγ = (1−α−β). Differentiating these two terms with
respect toA, we get
dT
dA
=

pA
α+β

α
r

1−β

β
w

β

1/γ
/γ, and
dL
dA
=

pA
α+β

α
r

α

β
w

1−α

1/γ
/γ. Both of
these are positive as long asα +β< 1.
115
The two first order condition are U
t
=pU
c
F
t
andpF
L
=w. Taking total derivatives of
the first order conditions with respect to A, followed by some algebra, we obtain:
dT
dA
=
(F
LA
F
TL
U
c
/F
LL
) +F
A
U
tc
−pF
A
F
T
U
cc
−F
TA
U
c
pF
2
t
U
cc
− 2F
T
U
ct
+F
TT
U
c
+ (U
tt
/p)− (F
2
TL
U
c
/F
LL
)
(3.9)
Assuming a Cobb-Douglas technologyF (A,T,L) =AT
α
L
β
, we can show that the nec-
essary and sufficient condition for dT/dA< 0, i.e. land usage increases with a negative
productivity shock, is:
p>
1
αF

T
U
tc
U
cc
−
U
c
U
cc

(3.10)
Intuitively, the inequality suggests that, in the presence of incomplete markets, farm-
ers could increase their input usage post a negative weather shock if the output price is
expected to rise. The increase in output prices could occur because of two reasons: first, a
negative effect on aggregate supply coupled with inelastic demand could increase prices;
and second, local competition in the markets could interact with a fall in supply to drive
the prices even higher. The former effect will be common to all areas, but the latter would
be restricted to high competition areas.
Another alternative, but not mutually exclusive, mechanism that could cause this phe-
nomenon is high risk aversion amongst farmers. This can be seen in 3.10 where an in-
crease in the coefficient of absolute risk aversion, −U
cc
/U
c
, increases the probability of
satisfying the inequality. In this context, high risk aversion would imply that farmers are
more likely to use supplementary inputs to attenuate the fall in agricultural output and
minimize the drop in consumption. This response is analogous to coping mechanisms
to smooth consumption, such as selling disposable assets. The key distinction is that it
involves adjustments in productive decisions.
116
The model predicts that an increase in land usage post a negative productivity shock
also increases the likelihood of an increase in the use of labor inputs. To see this, note that
the necessary and sufficient condition for labor inputs to increase post a weather shock is:
dT
dA
<−
T
αA
(3.11)
Therefore, if the increase in land usage following a negative productivity shock is large
enough, labor inputs on the farm will also rise.
With this framework in mind, our empirical analysis focuses on examining the effect
of competition on prices, and how the same varies across different weather conditions.
We subsequently test whether input usage increases in areas which experience price rise
during heat stress, as predicted by the model.
3.4.3.2 Effect of Competition on Prices: Heterogeneous Impact by Weather
This subsection aims to causally identify the effect of competition on prices during in-
clement weather. Besides its intrinsic interest, the heterogeneous effect of weather on the
correlation between competition and prices could also help inform the mechanism behind
the adaptive behavior documented in previous sections. The econometric specification
takes the following form:
log(Prices)
cmdsy
=α +
6
X
j=1
η
j
GDD
{j}dsy
+
6
X
j=1
Ω j
(GDD
{j}dsy
×Comp
mds
) +
ϕPrecip
dsy
+δ (Precip
dsy
)
2
+λ
m
+λ
cy
+λ
dt
+λ
sy
+ξ
cmdsy
(3.12)
wherelog(Prices)
cmdsy
refers to the natural logarithm of the price of cropc in marketm
situated in district d of state s in agricultural year y. This is the price during the main
agricultural season pertaining to the crop-market pair, and is calculated as the mean of
the daily modal price. The regressors and fixed effects have the exact same definition as
117
in 3.6. η
6
can now be interpreted as the effect of an additional degree-day of extreme heat
in the district on prices in markets with low competition, while the coefficient of inter-
est, Ω 6
, indicates the supplementary impact of high competition on prices during heat
stress. As before, the inclusion of market fixed effects ( λ
m
) implies that the level effect of
time-invariant market specific competition ( Comp
mds
) is swept out and cannot, therefore,
be estimated. However, Chatterjee 2019 shows, in a similar setting, that increasing spa-
tial competition by one standard deviation causes prices received by farmers to increase
between 2.7 and 6.4 percent. Though not shown here, we also calculate the difference
in prices between high and low competition areas and our results are similar, with a one
standard deviation increase in competition leading to a 5.8 to 6.6 percent increase in prices.
Thus, there is a positive effect of competition on farmer prices on average, holding con-
stant the weather. Our aim is to establish how this relationship changes during inclement
weather. Results on heterogeneous impact by weather are presented in 3.5.
Our results indicate that the positive effect of local competition on prices is exacerbated
during periods of extreme heat. The estimate of Ω 6
is positive and significant: markets
with higher level of competition experience a larger gain in price with each additional
degree day of extreme heat. If the effect was being driven solely by a fall in supply, we
would expect prices in low competition markets during heat stress to also increase relative
to prices in the same markets during good weather. Nevertheless, as indicated by the
coefficient on Bin> 35
dsy
, the effect sizes are positive but not significant, irrespective of
the specification. Notably, the effect sizes are large, but the lack of significance most likely
stems from low power caused by very few markets with competition tending to zero.
To interpret the coefficients, we calculate the difference in prices between high and
low competition areas when exposed to the mean number of extreme heat days during
the growing season (7.3 days). Note that this is in addition to the positive difference in
prices that exists during good weather. We find that a one standard deviation increase in
competition causes the difference in prices to increase by 0.53 to 0.57 percentage points,
118
Dependent Variable: log(Price)
cmdsy
(1) (2) (3) (4)
Bin 30-35
dsy
0.297 0.248 0.295 0.251
(0.827) (0.828) (0.923) (0.835)
Bin>35
dsy
1.180 1.323 1.563 1.297
(0.758) (0.796) (1.071) (0.774)
Bin<15
dsy
× Comp
mds
−0.131 −0.134 −0.150 −0.124
(0.205) (0.209) (0.214) (0.206)
Bin 15-20
dsy
× Comp
mds
0.188 0.186 0.176 0.190
(0.117) (0.114) (0.122) (0.118)
Bin 25-30
dsy
× Comp
mds
0.124 0.125 0.120 0.128
(0.079) (0.079) (0.085) (0.082)
Bin 30-35
dsy
× Comp
mds
0.026 0.022 −0.009 0.020
(0.120) (0.121) (0.125) (0.121)
Bin>35
dsy
× Comp
mds
0.227
∗∗
0.220
∗∗
0.230
∗
0.236
∗∗
(0.111) (0.106) (0.115) (0.107)
Fixed Effects
Market ! ! !
Crop× Year ! ! ! !
District× Decade !
District× Year !
Market× Decade !
State× Year ! ! !
Increase in Prices (in pp) 0.555 0.538 0.563 0.577
Num. obs. 147,005 147,005 147,005 147,005
Adj. R
2
0.877 0.879 0.879 0.878
Table 3.5: Effect of Competition on Prices Post Climate Shocks
Notes: Clustered robust standard errors are in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
Columns (1) to (4) provide causal estimates of the effect of competition on prices following a period of extreme heat
(eq:pricespanelcompweather).Thedependentvariable,log(Price)
cmdsy
, refers to the natural logarithm of the price of cropc in mar-
ketm situated in districtd of states in agricultural yeary. Data, sourced from Centre for Economic Data and Analysis (CEDA) of
Ashoka University, comprises of prices of 52 major commodities in 2,938 APMC markets from 2001 to 2021.Comp
mds
is the measure
of competition at the market level, and equals the weighted sum of the total value of trade at all other markets near the origin market
site, provided they are all in the same state. The weights are the inverse of distances of the neighboring markets (n) to the origin
market (m), while the total value of trade refers to the sum of the value of agricultural produce traded in the neighboring marketn
between the years 2000 to 2021. The independent variables related to temperature,Bin
htoh
, measure the amount of time, in days, a
crop was exposed to temperatures between a given lower and upper bound. The coefficient of interest is the estimate on the interaction
term betweenBin> 35
dsy
(extreme heat) andComp
mds
. It can be interpreted as the supplementary impact of high competition on
prices during heat stress. The antepenultimate row, titled Increase in Prices (in pp), provides estimates of the effect of a one standard
deviation increase in competition on the difference in prices, given that both areas were exposed to a week of extreme heat. Coefficients
related to the effect of temperatures less than 30
◦
C on prices have been omitted for brevity. All coefficients have been multiplied by
1000 for illustrative purposes. Standard errors are clustered at the state-decade and crop level.
119
given that both areas were exposed to a week of extreme heat. Therefore, monopsony
power tends to aggravate the already existing price distortions. A simple back of the en-
velope calculation suggests that this translates to an extra yearly income in the range of 172
($3) and 31,642 ($608), depending on the crop being grown by the average farmer.
38
This
is equivalent to an increase of 0.4-69 percent in yearly net receipts from crop production
for an average agricultural household in India.
We have shown that during extreme heat, the prices received by farmers in high com-
petition areas increase, while prices in low competition areas do not. The analytical frame-
work presented in 3.4.3.1 predicts that this increase in prices should lead to higher input
usage in high competition areas, which in turn could help alleviate the crop production
losses associated with heat stress. The next section tests this hypothesis.
3.4.3.3 Changes in Input Use
We examine changes in input use as a potential margin of adjustment to high temperatures,
and how this differs between low and high competition areas. We combine household
survey with spatial weather and competition data to construct a comprehensive dataset
containing agricultural, socioeconomic, competition, and weather variables. The unit of
observation is the household-year. The household data is a repeated cross section from
the India Human Development Survey (IHDS), a nationally representative multi-topic
survey conducted in 2005 and 2011-12 Desai, Vanneman, and National Council of Ap-
plied Economic Research 2005, 2012. Our primary focus is on the income, social capital
and agricultural part of the survey, which asks questions on input usage and expendi-
ture in the last one year. Using the date of interview, we can construct household specific
weather variables, i.e. the number of growing degree days in each temperature bin and
precipitation over the last 12 months is specific to each household.
39
38
The to $ conversion was based on the historical average USD-INR exchange rate of 52.004 from 1
st
January, 2000 to 31
st
December, 2020, as published by Investing.com.
39
Households in the same district and interviewed in the same month-year will have identical values for
the weather variables.
120
The generic estimating equation is as follows:
Y
hdsy
=α +
6
X
j=1
η
j
GDD
{j}hdsy
+
6
X
j=1
Ω j
(GDD
{j}hdsy
×Comp
ds
) +
ϕPrecip
dsy
+δ (Precip
dsy
)
2
+ψZ
hdsy
+λ
d
+λ
sy
+ξ
hdsy
(3.13)
whereY
hdsy
refers to either input usage or input costs of householdh situated in district
d of state s in agricultural year y, GDD
{j}hdsy
refers to the number of growing degree
days in thej
th
temperature bin which the household was exposed to over the course of
the 12 months prior to the interview,Precip
dsy
is the analogous rainfall counterpart, and
Comp
ds
denotes the mean value of the market level competition measure across all mandis
in the district. Z
hdsy
is a vector of household characteristics, and includes religion, caste,
main income source, total land endowment, and permanent fallow land of the household,
in addition to the occupation and education of the household head. Finally, we control
for district and state-year fixed effects to account for, first, district specific determinants of
TFP as well as other drivers of input, and second, changes in agricultural prices at the state
level. Standard errors are clustered at the state-year level to allow for spatial dependence.
If the model prediction is accurate, then we expect the interaction term between high tem-
perature and competition (Ω 6
) to be positive and significant, indicating increased input
usage in high competition areas during heat stress. Results are presented in 3.6.
As predicted by the model, input usage and expenditure increases in high competition
areas during periods of high temperature. Columns (1) and (2) focus on changes in land
and labor inputs. For each additional degree day of extreme heat, a one standard deviation
increase in competition increases the land cultivated and labor employed by 1.2 and 1.7
percent, respectively. This estimate already controls for household endowments and per-
manent fallow land, and thus is not simply picking up changes in the size composition of
farmers. Columns (3) to (6) relate to input costs, specifically expenditure on labor, irriga-
tion, equipment, and fertilizers over the past 12 months. For each of these categories, the
121
effect sizes on Ω 6
are positive and significant, indicating farmers in high competition areas
expend more when faced with inclement weather. A one standard deviation increase in
competition would cause a farmer, experiencing an additional day in the extreme temper-
ature bin, to increase their labor expenditure by 122 ($2.3), irrigation expenditure by 31
($0.6), expenditure on farm equipment by 98 ($1.9), and expenditure on fertilizers by 157
($3.0).
In addition to adjustments in input usage and costs, we also find evidence of crop
diversification at a macro-scale ( i.e., district-level) in high competition areas, indicating
crop-mix as a potential avenue for increased resilience. To measure crop diversity, we
follow Auffhammer and Carleton 2018 and construct an indicator of concentration, the
Herfindahl–Hirschman Index (HHI), based on area planted to different crops in a given
year and district. The HHI for districtd and yeary is defined as follows:
HHI
dy
=
C
X
c=1
s
2
dcy
(3.14)
wheres
dcy
=a
dcy
/
C
P
c=1
a
dcy
is the share of total planted area in districtd dedicated to crop
c in yeary. C is the total number of crops, which in our data set comprises the 19 major
and minor crops available in ICRISAT 2018. The regression specification in 3.13 changes
slightly, with the unit of observation now districtd and agricultural yeary. Furthermore,
the regressandY
dsy
now denotes the crop-mix, while we dispense with the household con-
trol variables. Results presented in column (7) imply that each additional day of extreme
heat reduces the HHI significantly in areas with higher competition, indicating higher
crop diversity. The point estimates suggest that for each additional day of extreme heat, a
one standard deviation increase in competition leads to a 0.13 percent fall in HHI. Coupled
with the evidence that farmers adjust their land during the growing season, we interpret
these findings as suggestive evidence that the additional land is planted with distinct crops
in order to diversify the weather risk.
122
Our main results suggest that farmers adjust input use within the growing season as
a mechanism to cope with the negative effects of extreme temperatures, but only in high
competition areas. Farmers in these areas adjust their use of land, both in terms of area
planted and crop composition, as a response to extreme heat. Furthermore, they increase
labor usage, reflected both in the number of workers hired and total wages paid. Addi-
tionally, the expenditure on irrigation, equipment hired to work on the farm, and fertilizer
and manure also rises. These margins of adjustment attenuate undesirable drops in out-
put and consumption caused by heat. Importantly, the mechanism for these productive
adjustments are prices, which rise in high competition areas during heat stress, further in-
flating the pre-existing monopsony distortions. In this sense, our findings are consistent
with models of subsistence farmers in a context of incomplete markets Taylor and Adel-
man 2003; De Janvry, Fafchamps, and Sadoulet 1991, which predict a rise in input usage if
prices increase following a negative productivity shock.
3.5 Theory
3.5.1 Basic Environment
Our setup closely follows the environment assumed by ?. We consider a national econ-
omy comprising multiple states, indexed byi∈I≡{1,...,I}. Within each state there are
two factors of production, labor and land, which can be used to produce multiple crops,
indexed byk∈K≡{1, ..., K}, and an outside good. The outside good can be thought
of as a composite of manufactured goods and services. Labor is homogeneous, perfectly
mobile within a state, and immobile across states. The termN
i
denotes the total endow-
ment of labor, and w
it
denotes the wage in state i at time t. Land comes in the form of
heterogeneous fields, indexed by f∈F
i
≡{1, ..., F
i
}, each comprising a continuum of
heterogeneous parcels, indexed byω∈ [0,1]. We lets
f
i
denote the area in hectares of field
f in statei.
123
Inputs Input Costs () Crop Mix
log(Land)
hdsy
log(Labor)
hdsy
Labor
hdsy
Irrigation
hdsy
Equipment
hdsy
Fertilizers
hdsy
HHI
dsy
(1) (2) (3) (4) (5) (6) (7)
Bin 30-35
dsy
−0.007 −0.026 −186.367 −10.872 67.362 −110.610
∗
0.511
∗
(0.012) (0.021) (134.622) (19.305) (124.858) (63.290) (0.294)
Bin>35
dsy
−0.020 −0.014 −137.790 −16.740 −60.710 −176.421
∗
0.480
(0.012) (0.015) (101.053) (16.826) (89.583) (92.065) (0.481)
Bin<15
dsy
× Comp
ds
0.012 0.021 100.256
∗∗
9.924 −46.148 −76.222 0.000
(0.009) (0.013) (44.907) (29.171) (89.536) (127.411) (0.000)
Bin 15-20
dsy
× Comp
ds
0.014
∗∗
0.018 −16.132 21.854 8.843 46.186 −0.197
(0.005) (0.012) (48.905) (26.599) (90.759) (105.920) (0.155)
Bin 25-30
dsy
× Comp
ds
0.005 0.026
∗∗
109.807
∗∗
44.160 56.101 −13.769 −0.194
(0.006) (0.011) (55.445) (29.828) (67.600) (100.552) (0.133)
Bin 30-35
dsy
× Comp
ds
0.008 0.025
∗∗
125.444
∗
23.881
∗
−28.514 −98.107 −0.039
(0.006) (0.011) (66.089) (13.473) (79.673) (56.889) (0.087)
Bin>35
dsy
× Comp
ds
0.009
∗∗
0.014
∗
93.363
∗
24.120
∗
73.561
∗
122.220
∗∗
−0.267
∗
(0.004) (0.008) (51.790) (13.887) (41.445) (46.165) (0.142)
Fixed Effects
District ! ! ! ! ! ! !
State× Year ! ! ! ! ! ! !
Num. obs. 25,592 20,517 24,652 27,654 28,256 21,179 4,624
Adj. R
2
0.580 0.352 0.243 0.187 0.187 0.380 0.944
Table 3.6: Heterogeneous Impact of Climate Shocks on Input Usage and Crop Mix
Notes: Clustered robust standard errors are in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
All columns represent estimates from different versions of the estimating 3.13, which examines changes in input use or crop-mix as a
potential margin of adjustment to high temperatures, and how this differs between low and high competition areas. The dependent
variable in Columns (1) and (2) represents land and labor inputs used by householdh situated in districtd of states in agricultural year
y, respectively. The dependent variable in columns (3) to (6) relates to input costs, specifically expenditure (in ) on labor, irrigation,
equipment, and fertilizers over the past 12 months. The dependent variable in column (7) represents an indicator of crop concentration,
the Herfindahl–Hirschman Index (HHI), based on area planted to different crops in a given year and district (3.14). The independent
variable related to competition intensity,Comp
ds
, is the measure of competition at the district level. For this purpose, we first calculate
competition for each of the 2,938 APMC markets as the weighted sum of the total value of trade at all other markets near the origin
market site, provided they are all in the same state. The weights are the inverse of distances of the neighboring markets (n) to the origin
market (m), while the total value of trade refers to the sum of the value of agricultural produce traded in the neighboring marketn
between the years 2000 to 2021. Second, we aggregate competition to a district level by averaging the competition measure for all
markets in a districtd of states. The independent variables related to temperature,Bin
htoh
, measure the amount of time, in days, a
crop was exposed to temperatures between a given lower and upper bound. The coefficient of interest is the estimate on the interaction
term betweenBin> 35
dsy
(extreme heat) andComp
ds
. It can be interpreted as the supplementary impact of high competition on
household input usage (for columns (1)-(6)), or crop mix (for column (7)), during heat stress. The household data is a repeated
cross section from the India Human Development Survey (IHDS), a nationally representative multi-topic survey conducted in 2005
and 2011-12 Desai, Vanneman, and National Council of Applied Economic Research 2005, 2012. Data for crop mix is sourced from
ICRISAT 2018, which has data on area under cultivation for 19 crops in 313 Indian districts of 20 states at an annual level from the year
1966 to 2017. Coefficients related to the effect of temperatures less than 30
◦
C on the dependent variable have been omitted for brevity.
Standard errors are clustered at the state-year level to allow for spatial dependence.
124
Preferences—Each statei at timet has a representative agent who derives utility from
consuming the outside good,C
0
it
, and a composite of all crops,C
it
:
U
it
=C
0
it
+β
i
ln(C
it
) (3.15)
The quasi-linear form of the utility function in 3.15 implies that there are no income effects.
Moreover, the total demand for crops depends only on a state-specific and time-invariant
demand shifter,β
i
≥ 0. Assuming that the crops in our analysis account for a small fraction
of consumers’ expenditure across states, the absence of income effects acts as a minor
limitation of our study.
Aggregate crop consumption at timet,C
it
, depends on the consumption of each crop,
C
k
it
, which itself depends on the consumption of varieties from different origins, C
k
jit
:
C
it
=


X
k∈K
(β
k
i
)
1/φ
(C
k
it
)
(φ−1)/φ


φ/(φ−1)
(3.16)
C
k
it
=


X
j∈I
(β
k
ji
)
1/σ
(C
k
jit
)
(σ−1)/σ


σ/(σ−1)
(3.17)
where φ> 0 denotes the elasticity of substitution between different crops (e.g., rice vs
wheat), and σ > 0 denotes the elasticity of substitution between different varieties of a
given crop (e.g., West Bengal vs Punjab rice). Finally,β
k
i
≥ 0 denotes crop and state spe-
cific demand shocks, whereas β
k
ji
≥ 0 denotes crop and origin-destination specific demand
shocks. The functional form implies that all states export each crop that they produce to
all other states (as long asβ
k
ji
> 0).
Technology—The outside good is produced under constant returns to scale using labor
only. The termA
0
it
> 0 denotes labor productivity in statei’s outside sector at timet. In the
agriculture sector, we assume that labor and parcels of land are perfect complements in
the production of each crop. CombiningL
fk
it
(ω) hectares of parcelω withN
fk
it
(ω) workers
enables a representative farmer to produce:
125
Q
fk
it
(ω) =A
fk
it
(ω) min{L
fk
it
(ω),N
fk
it
(ω)/ν
f
i
(ω)} (3.18)
whereA
fk
it
(ω)≥ 0 denotes the total factor productivity (TFP) of parcelω in field f if allo-
cated to cropk in statei at timet, andν
f
i
(ω)> 0 measures the time-invariant labor intensity
of the production process. Inspired by ? gravity model of trade, we assume that TFP and
labor intensity are independently drawn for each (i,f,ω,t) from a Fr´ echet distribution:
Pr{A
f1
it
(ω)≤a
1
, ..., A
fK
it
(ω)≤a
K
,ν
f
i
(ω)≤ν}
= exp



−γ


X
k∈K
(a
k
/A
fk
it
)
−θ
+ (ν/ν
i
)
−θ





(3.19)
where the constantγ is set such thatA
fk
it
=E[A
fk
it
(ω)] andν
i
=E[ν
fk
i
(ω)].
40
The termA
fk
it
≥
0 captures the average productivity of field f for growing cropk in statei at timet and is,
thus, shared by all plotsω∈f. A highA
fk
it
implies that on average all plots in farmf have
high productivity for growing cropk. In other words, it measures the comparative and
absolute advantage of a field in producing particular crops. The parameter θ> 1 measures
the extent of technological heterogeneity within each field. A higher value of θ will imply
higher specialization across different farms. Since we do not have access to disaggregated
data on labor intensity, we require average labor intensityν
i
> 0 to be identical across crops,
fields, and time. However, agriculture is allowed to be more labor intensive in some states
than in others.
Market Choice—This part of the model takes inspiration from the market setup of ?.
Upon harvest, farmers optimally choose the market where they want to sell, indexed by
40
Formally, we setγ≡ Γ

θ− 1
θ
!
−θ
, where Γ(·) denotes the gamma function; i.e.,
Γ(t) =
R
+∞
0
u
t−1
exp(−u)du for anyt> 0.
126
m∈M≡{1,...,M}. We assume that farmers are subject to iceberg trade costs, such that
the quantity of cropk actually reaching any marketm from farmf at timet is:
Q
fk
mit
(ω) =
Q
fk
it
(ω)
τ
f
mt
(3.20)
Trade costs between farmf and marketm at timet are constant for all parcelsω∈f,
and are defined as:
τ
f
mt
= (1 +ζd
f
m
)·ξ
f
mt
(3.21)
whered
f
m
is the geodesic distance between farmf and marketm, andζ is a scale param-
eter. The shock term,ξ
f
mt
, represents origin farm-market specific costs like broken roads,
availability of a truck, or a strike among intermediaries, which are not observable to the
econometrician but are known to the farmers. We follow ?, and assume for tractability
thatξ
f
mt
is drawn from a Weibull distribution such that:
Pr
h
ξ
f
mt
≤ξ
i
= 1− exp

−Υξ
λ

(3.22)
λ> 0 is the shape parameter and can be interpreted as an inverse measure of the dispersion
of shocks. Υ> 0 is the scale parameter and controls the efficiency of transporting goods
to a market. The distribution of shocks is i.i.d. across crops and over time, and shocks are
independent across markets. To incorporate trade restrictions,τ
f
mt
is set to∞ if farmf and
marketm lie in different states.
Intermediary—Each marketm can be thought of as an intermediary, a chain linking the
farmer to the consumer. Though each market can have multiple intermediaries, only a few
are active and cartelization among intermediaries is common. Incumbent intermediaries
also prevent new entrants Chand 2012. This fact makes our simplifying assumption that
each market is served by a single intermediary not too unrealistic.
127
An intermediarym in statei can purchase multiple cropsk∈K at timet, and sells the
same to retailers/consumers at priceP
rk
it
. Unlike the farmer, the intermediary is allowed
to cross state borders. However, interstate trade in crops may be subject to iceberg trade
costs. In order to sell one unit of a cropk in statej, intermediaries from statei must ship
Ψ
k
ij
units. Non-arbitrage therefore requires the price of a cropk produced in statei and
sold in statej to be equal to
P
rk
ijt
= Ψ
k
ij
P
rk
it
(3.23)
whereP
rk
it
denotes the local price of the domestic variety of cropk in statei.
3.5.2 Competitive Equilibrium
In a competitive equilibrium, all consumers maximize their utility, all farmers and inter-
mediaries maximize their profits, and all markets clear.
Farmer Profit Maximisation —In the outside sector, profit maximization requires that
w
it
=A
0
it
whenever the outside good is produced. Throughout this model, we assume that
labor endowments,N
it
, are large enough for the outside good to be produced in all states.
Thus, we can useA
0
it
in place of the wagew
it
and treat it as an exogenous parameter.
In the agricultural sector, profit maximization requires that the farmer first choose a
cropk, and subsequent to harvest, choose a marketm to sell. The price that farmers get in
marketm for cropk at timet is denoted byP
k
mit
. We can use backward induction to solve
for the farmer’s choice. Let
Ω fk
mit
≡ Pr{P
k
mit
Q
fk
mit
(ω) = max{P
k
1it
Q
fk
1it
(ω),...,P
k
Mit
Q
fk
Mit
(ω)}} (3.24)
128
denote the probability that a farmer, tilling parcel ω of a field f located in state i and
growing cropk at timet, chooses marketm. Given distributional assumptions:
41
Ω fk
mit
=




P
k
mit
1 +ζd
f
m




λ
P
m
′
∈M




P
k
m
′
it
1 +ζd
f
m
′




λ
(3.25)
This expression has an intuitive explanation. The probability of choosing a marketm
for cropk depends on how large the distance adjusted price of the crop inm is relative
to the distance adjusted price index of the crop. A higher price of crop k in market m
increases the probability of farmers selling their output inm, whereas an increase in the
price in other marketsm
′
relative tom reduces this probability. Similarly, if the distance to
m is large, that will depress the probability of choosingm. Currently, the farmers in state
i only take into account the prices in markets situated ini. An opening of trade borders
would lead the farmer to also factor in the prices in all other statesj∈I.
Conditional on choosing marketm, the farmer decides the cropk to grow at timet. Let
π
fk
it
(ω) denote the profits from parcel ω∈f in statei when farmer decides to growk at
timet. It can be expressed as:
42
π
fk
it
(ω) =A
fk
it
(ω)L
fk
it
(ω)P
fk
it
−w
it
N
fk
it
(ω) (3.26)
41
See K.4 for derivation.
42
See K.5 for derivation.
129
where
P
fk
it
=
X
m
′
∈M
Ω fk
m
′
it
P
k
m
′
it
=
P
m
′
∈M
(P
k
m
′
it
)
λ+1

1 +ζd
f
m
′

λ
P
m
′
∈M




P
k
m
′
it
1 +ζd
f
m
′




λ
(3.27)
denotes a probability weighted price of cropk for farmerf at timet, aggregated across
all markets. Profit maximisation requires that all parcels of land are (i) allocated to the
crop that maximizes the value of their marginal product if such value is greater than the
wage bill associated with operating that parcel, or (ii) left unused if the maximum value
of their marginal product is less than the wage bill. Given the production function in 3.18,
the land allocation can be solved as a simple discrete choice problem.
43
Let
∆ fk
it
≡ Pr{A
fk
it
(ω)P
fk
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
f1
it
, ...,A
fK
it
(ω)P
fK
it
}} (3.28)
denote the probability that a parcelω of a field f located in statei is allocated to cropk
at timet. Since there is a continuum of parcels within each field, ∆ fk
it
also corresponds to
the share of parcels allocated to that crop.
Given our distributional assumptions, standard algebra implies:
44
∆ fk
it
=
(A
fk
it
P
fk
it
)
θ
(α
it
)
θ
+
P
k
′
(A
fk
′
it
P
fk
′
it
)
θ
(3.29)
where α
it
≡A
0
it
ν
i
parameterizes cross-state differences in labor costs, because of differ-
ences in either wages or labor intensity. The higher α
it
is, the more costly it is to hire
workers to produce crops, and the smaller the share of a field f allocated to any given
43
See K.6 for derivation.
44
We use the property that given n draws{z
1
,...,z
n
}, where z
i
is distributed Fr´ echet with F
i
(z) =
exp{−(T
i
z
−θ
)}, the probability thatz
i
= max{z
1
,...,z
n
} is ∆ i
=T
i
/
n
P
j=1
T
j
M. Turner 2019.
130
cropk. Likewise, the higher the average value of the marginal product of land,A
fk
it
P
fk
it
,
the higher the share of field f allocated to cropk. In our model, the extent of technological
heterogeneity,θ, determines the elasticity of the relative supply of land to various crops.
Whenθ is higher, parcels are more homogeneous within a field, which makes the supply
of land more sensitive to changes in prices,P
fk
it
, or productivity,A
fk
it
.
LetQ
k
mit
=
P
f∈F
i
Ω fk
mit
R
1
0
Q
fk
it
(ω)dω denote the total output of cropk supplied to market
m in statei at timet. Intuitively, it is the expected output of cropk across all parcels of
land inf, weighted by the probability of choosing marketm, and this expression is then
summed across all the fields f in statei. Using the production function in 3.18 and the
law of iterated expectations, we must have:
45
Q
k
mit
=
X
f∈F
i
s
f
i
∆ fk
it
Ω fk
mit
E[A
fk
it
(ω)|A
fk
it
(ω)P
fk
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
f1
it
, ...,A
fK
it
(ω)P
fK
it
}] (3.30)
Given our distributional assumptions, one can also check that:
46
E[A
fk
it
(ω)|A
fk
it
(ω)P
fk
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
f1
it
, ...,A
fK
it
(ω)P
fK
it
}]
=A
fk
it
× (∆
fk
it
)
−1/θ
(3.31)
Note that because of the endogenous selection of fields into crops, the average productivity
conditional on a crop being produced is strictly greater than the unconditional average,
i.e. A
fk
it
× (∆
fk
it
)
−1/θ
>A
fk
it
45
See K.7 for derivation.
46
See K.8 for derivation.
131
Combining the above two equations, we obtain the following expression for the supply
of cropk in marketm in statei at timet:
Q
k
mit
=
X
f∈F
i
s
f
i
A
fk
it
Ω fk
mit
(∆
fk
it
)
(θ−1)/θ
=
X
f∈F
i
s
f
i
A
fk
it


















P
k
mit
1 +ζd
f
m




λ
P
m
′
∈M




P
k
m
′
it
1 +ζd
f
m
′




λ


















(A
fk
it
P
fk
it
)
θ
(α
it
)
θ
+
P
k
′
(A
fk
′
it
P
fk
′
it
)
θ




(θ−1)/θ
(3.32)
The quantity supplied of cropk at marketm is, thus, a function of the average TFP of
the crop, the price of the crop in other markets within the state, and also the productivity
and price of crops other thank.
Intermediary Price Setting—Each intermediarym can purchase multiple cropsk
′
∈K,
offering price P
k
′
mit
. They purchaseQ
k
′
mit
units of cropk
′
from the farmer, and sell the same
to retailers/consumers in different parts of the country at a price P
rk
′
ijt
. We assume that
there is no restriction on where the intermediary can sell, but transportation costs are
incurred only if the produce is sold outside the state.
Every intermediary exerts market power over farmers, which we model as Bertrand
competition for crops.
47
When deciding what price to offer for a crop, intermediaries form
expectations about how farmers respond. In other words, they internalize the upward
sloping crop supply curve in 3.32: each additional unit they purchase increases the price
of every other unit. ? has a similar setting but models exporters instead of local interme-
diaries. Additionally, he assumes that each exporter only buys a single crop, whereas in
our case, we assume that an intermediary can purchase all crops supplied in the market.
47
Market power can also be modeled as Cournot competition, but 3.32 does not lend itself to a closed
form inverse supply curve.
132
An intermediarym maximises the following profit function
max
n
P
k
′
mit
∀k
′
o
X
k
′
∈K

P
rk
′
it
−P
k
′
mit

Q
k
′
mit
(3.33)
subject to the supply curve in 3.32, whereP
rk
′
it
represents the retail price of commodityk
′
in statei at timet. The first order condition for price P
k
mit
can be expressed as:

P
rk
mit
−P
k
mit





X
f∈F
i
Q
fk
mit




λ




1− Ω fk
mit
P
k
mit




+ (θ− 1)Ω
fk
mit




λ + 1
P
fk
′
it
−
λ
P
k
mit












| {z }
tialQ
k
mit
/tialP
k
mit
−
(θ− 1)
X
k
′
∈K
X
f∈F
i

P
rk
′
mit
−P
k
′
mit





Q
fk
′
mit
Ω fk
mit
∆ fk
it




λ + 1
P
fk
′
it
−
λ
P
k
mit








| {z }
−tialQ
k
′
mit
/tialP
k
mit
=Q
k
mit
(3.34)
? states that the extent of oligopsony power of an intermediarym over an inputk can
be parametrized through an inverse input supply elasticityη
k
mit
, defined as:
η
k
mit
≡
tialP
k
mit
tialQ
k
mit
×
Q
k
mit
P
k
mit
If an intermediary has oligopsony power over input k, the input priceP
k
mit
increases if
more inputs are purchased. This, thus, has the interpretation of an input price ‘markdown
ratio’. Also, we can define inverse cross input supply elasticity as:
η
kk
′
mit
≡
tialP
k
mit
tialQ
k
′
mit
×
Q
k
′
mit
P
k
mit
which reflects how the price of commodity k in marketm changes if there is a change in
the supply of commodityk
′
to the said market.
133
Additionally, we define markup µ as the ratio of retail prices over marginal costs:
µ k
mit
≡
P
rk
mit
P
k
mit
Using these three definitions, 3.34 can be rewritten as:
P
k
mit
=




X
k
′
̸=k




µ k
′
mit
− 1
η
kk
′
mit




P
k
′
mit
Q
k
′
mit








η
k
mit
1 +η
k
mit
−µ k
mit




1
Q
k
mit
(3.35)
Thus, the price for cropk paid by an intermediarym is a function of the markdown
for not onlyk, but also the markdown fork caused by quantity supplied of other crops. It
also depends on the markup the intermediary may expect to receive in the retail market.
Finally, the intermediaries sell the produce to the consumer/retailer, with the retail
price of cropk in statei,P
rk
it
, set such that all the intermediaries selling in statei (includ-
ing from statej̸=i) sell at the same price, i.e.P
rk
jit
=P
rk
it
∀j∈I. Utility Maximisation—
Given equations (3.15), (3.16), (3.17) and (3.23), utility maximization by the representa-
tive agent in each state requires that:
48
C
k
jit
=β
i
β
k
i
(
ˆ
P
rk
it
)
1−φ
P
l∈K
β
l
i
(
ˆ
P
rl
it
)
1−φ
β
k
ji
(Ψ
k
ji
P
rk
jt
)
−σ
P
n∈I
β
k
ni
(Ψ
k
ni
P
rk
nt
)
1−σ
∀ i,j∈I,k∈K (3.36)
where
ˆ
P
rk
it
≡


X
n∈I
β
k
ni

Ψ
k
ni
P
rk
nt

1−σ


1/1−σ
(3.37)
denotes the CES retail price index associated with cropk in statei at timet.
Market Clearing—Define Q
k
it
as the total output of cropk produced in statei at time
t. Since farmers are only allowed to sell their produce in statei,Q
k
it
=
P
m∈M
Q
k
mit
. Trade in
48
See K.9 for derivation.
134
crops is subject to iceberg trade costs, which implies market clearing for all varieties of all
crops requires
Q
k
it
=
X
j∈I
Ψ
k
ij
C
k
ijt
∀{i,j}∈I andk∈K (3.38)
Parcels of land may remain idle if the value of their marginal product is below the labor
cost required to produce on these parcels. Thus, by construction, land demand is weakly
less than land supply at all locations. Finally, under the assumption that the outside good
is produced in all states, the amount of labor demanded by the outside sector adjusts to
guarantee labor market clearing at the wage equal toA
0
it
.
DEFINITION 1. Given parameters β
i
,β
k
i
,β
k
ji
(demand shifters), φ,σ (elasticities of
substitution), λ
t
,ζ,Ψ
k
ij
(trade cost for farmers and intermediaries), θ (technological het-
erogeneity), andµ (intermediary markup), a competitive equilibrium consists of, for each
statei∈I≡{1, ..., I} and each time periodt:
1. inputs for crops{L
fk
it
(ω),N
fk
it
(ω)}
k∈K,f∈F
i
, and outside good{N
0
it
},
2. output of crops{Q
fk
it
(ω)}
k∈K,f∈F
i
, and outside good{Q
0
it
},
3. optimal market choice at each farm{m(f)},
4. domestic trade flows {X
k
ijt
}
k∈K,j∈I
, which is the total value of exports of cropk∈K
from statei to statej, expressed in ,
5. consumer prices{P
rk
it
}
k∈K
, intermediary prices,{P
k
mit
}
k∈K,m∈M
, and outside good
price{P
0
it
},
6. final crop consumption {C
k
it
}
k∈K
and outside good consumption{C
0
it
},
such that:
1. farmers maximise their profits by choosing the optimal crop (3.29) and market (3.25);
2. intermediaries maximise their profits according to 3.34
135
3. consumers maximise their utility to solve 3.36
4. market for all crops clears, which requires:
X
f∈F
i
Q
fk
it
=
X
j∈I
Ψ
k
ij
C
k
ijt
∀i∈I andk∈K : (3.39)
In the remainder of this paper we will use the model outlined in this section to study
the consequences of climate change. We will compute competitive equilibria for states
with contemporary agricultural productivities and trade restrictions, compute competi-
tive equilibria for counterfactual economies with post–climate change productivities and
open trade borders, and then compare welfare levels across equilibria. However, we first
need to estimate the unknown structural parameters of our model, and we describe below
the methodology and data used.
3.6 Estimation
To simulate the model described in 3.5 and run counterfactual, we require estimates of
demand- and supply-side parameters. 3.6.1 details the estimation methodology for de-
mand side parameters, while 3.6.2 focuses on the supply side parameters.
3.6.1 Demand
We follow Costinot, Donaldson, and C. Smith 2016 closely for our demand side estimation.
Similar to their methodology, it involves three steps, each pertaining to a different level of
the nested demand system. The first step uses data on bilateral shipment flows (total
quantity and not total value) of crops between states (N
k
ijt
), retail prices (P
rk
it
), and crop
yields at the district level (A
k
dit
) to estimate the elasticity of substitution between different
state varieties of a given crop,σ. In addition, it allows us to estimate a composite of the
lower-level demand shifters (β
k
ijt
) and trade costs for intermediaries (ψ
k
ijt
). Second, we
136
use the estimates from the previous step to construct crop-specific retail price indices,
ˆ
P
rk
it
. This, combined with data on crop quantity, N
k
jt
=
P
i∈I
N
k
ijt
, allows us to estimate φ
— the elasticity of substitution between different crops — and mid-level demand shifters,
β
k
jt
. Finally, we construct data on total crop expenditures, X
jt
=
P
X
k
jt
, to estimate the
upper-level demand shifters,β
jt
.
Step 1—Define the value of exports of crop k at time t from state i to state j, X
k
ijt
=
P
rk
ijt
C
k
ijt
. Using the non-arbitrage condition in 3.23, we can rewrite the value of exports as:
X
k
ijt
=

Ψ
k
ijt
P
rk
it

C
k
ijt
=β
jt
β
k
jt

ˆ
P
rk
jt

1−φ
P
l∈K
β
l
jt

ˆ
P
rl
jt

1−φ
β
k
ijt

Ψ
k
ijt
P
rk
jt

1−σ
P
n∈I
β
k
njt

Ψ
k
njt
P
rk
nt

1−σ
∀{i,j}∈I andk∈K (3.40)
When estimating the lower level of our demand system, we consider the cases of zero and
nonzero inter-state trade flows separately. If X
k
ijt
= 0, we simply setβ
k
ijt

Ψ
k
ijt

1−σ
= 0. If
X
k
ijt
> 0, we take logs and rearrange equation 3.40 as:
ln

X
k
ijt
/X
k
jt

=M
k
jt
+ (1−σ)ln

P
rk
it

+ε
k
ijt
(3.41)
where the first term on the right-hand side,
M
k
jt
≡−ln



X
n∈I;X
k
njt
>0
β
k
njt

P
rk
nt
Ψ
k
njt

1−σ



can be treated as an importer-crop-year fixed effect while the final term ε
k
ijt
≡ ln

β
k
ijt

Ψ
k
ijt

1−σ

reflects idiosyncratic year-specific demand shocks across varieties of different crops as well
as trade costs. Without loss of generality, we normalize these shocks such that
X
i∈I;X
k
ijt
>0
ε
k
ijt
= 0 (3.42)
137
Equilibrium retail prices of crop (P
rk
it
) depend on demand shocks, ε
k
ijt
. To address this
endogeneity in 3.41, we need exogenous supply shocks that are correlated withP
rk
it
but
uncorrelated withε
k
ijt
. We construct the following instrument based on the ICRISAT 2018
data,
Z
rk
it
= ln


1
D
i
X
d∈D
i
A
k
dit


(3.43)
which corresponds to the log of the arithmetic average of cropk’s yields across all districts
in statei at timet. Our exclusion restriction is thatE
h
Z
rk
it
ε
k
ijt
i
= 0.
Note that our data contains information on quantity of crops traded between states,
and not their value. However, our use of the Eaton and Kortum 2002 model allows us to
overcome this missing data problem. This is because their model predicts that the fraction
of quantity imported byj originating fromi,
N
k
ijt
P
i∈I
N
k
ijt
, should equal the fraction ofj’s value
of imports fromi,
X
k
ijt
P
i∈I
X
k
ijt
, in expectation. This implies that we can replace the left-hand
side of 3.41 with ln

N
k
ijt
,
P
i∈I
N
k
ijt
!
to estimate the model.
The results from the instrumental variable regression are reported in xxx. Our estimate
of the elasticity of substitution between different varieties of the same crop, σ, is 25.66, with
a standard error of 14.6 when clustered at the crop-importer and crop-exporter levels. Fur-
thermore, our instrument has a strong first stage (F-stat of 34.28) and has the expected
negative sign with a coefficient of -0.052 — implying a one percent increase in yields leads
to a 5.2 percent fall in retail prices. Though our elasticity estimate is higher than Costinot,
Donaldson, and C. Smith 2016, this is expected given we are looking at substitution be-
tween different varieties of a crop but produced in the same country. Therefore, the quality
differentiation across varieties will be lower, making it easier to substitute between them.
Having estimatedσ, we subsequently solve forβ
k
ijt

Ψ
k
ijt

1−σ
as residuals. Specifically,
we find β
k
ijt

Ψ
k
ijt

1−σ
for alli,j∈I andk∈K for whichX
k
ijt
> 0 so that equations (3.41)
and (3.42) simultaneously hold for all crops, states and years. This estimation procedure
does not allow us to identify separately lower-level demand shifters,β
k
ijt
, from trade costs,
138
Ψ
k
ijt
. However, the composite shock,β
k
ijt

Ψ
k
ijt

1−σ
, is sufficient to construct equilibria in
3.7.
Step 2—The second step of our demand estimation is similar to the first one: the retail
price index,
ˆ
P
rk
jt
, plays the role of the individual crop price,P
rk
it
, whereas crop expenditure,
X
k
jt
, plays the role of bilateral trade flows, X
k
ijt
. Note that unlikeP
rk
it
, we do not observe
ˆ
P
rk
jt
in the data and construct it as
ˆ
P
rk
jt
=


X
i∈I
β
k
ijt

Ψ
k
ijt
P
rk
it

1−σ


1/1−σ
using data on crop prices,P
rk
it
, as well as our estimates ofσ andβ
k
ijt

Ψ
k
ijt

1−σ
from Step
1.
For all crops and states with positive quantity traded in yeart,X
k
jt
> 0, we can again
use 3.40 and take logs to get
ln

X
k
jt
/X
jt

=M
jt
+ (1−φ)ln

ˆ
P
rk
jt

+ε
k
jt
(3.44)
where the first term on the right-hand side,
M
jt
≡−ln



X
l∈K;X
l
jt
>0
β
l
jt

ˆ
P
rl
jt

1−φ



can now be treated as an importer-time fixed effect, and the final term, ε
k
jt
≡ ln

β
k
jt

, re-
flects idiosyncratic year-specific demand shocks across crops. Without loss of generality,
we again normalize these shocks such that
X
k∈K;X
k
jt
>0
ε
k
jt
= 0 (3.45)
139
There still exists endogeneity issues between demand shocks (ε
k
jt
) and prices (
ˆ
P
rk
jt
) at
this higher level of aggregation, which could potentially bias our estimates ofφ. To address
this, we now instrument
ˆ
P
rk
jt
withZ
rk
jt
. The exclusion restriction now equalsE
h
Z
rk
jt
ε
k
jt
i
= 0.
As before, since we do not have data on either the value of specific crops imported from all
exporters or the total value of crops imported, we replaceln

X
k
jt
/X
jt

withln

N
k
jt
/N
jt

.
Results, reported in xxx, indicate that the IV estimate for the elasticity of substitution
between crops,φ, equals 9.39 with standard errors of 2.3 when clustered at the importer
level. Also, the first stage estimate equals -0.055, which can be interpreted as a one percent
increase in yields leading to a 5.5 percent fall in prices. As in Step 1, once the elasticity of
substitution,φ, is known, we can solve forβ
k
jt
for allj∈I andk∈K such thatX
k
jt
> 0, as
residuals using equations (3.44) and (3.45).
Step 3—The final step of our procedure estimates the upper-level demand shifters, β
jt
.
The assumption of log preferences at the upper level implies thatβ
jt
’s can be read directly
from data on total expenditure across crops. Specifically, using 3.40, we can show that
β
jt
=X
jt
for allj∈I at timet.
Since we only have data on the quantity of crops imported, and not on the value of
imports, we need a proxy for the price of imports to constructX
jt
. To this end, we assume
that the value of exports of cropk fromi toj, X
k
ijt
, equals the average price ofk across
all the markets m within state i, multiplied by the quantity exported from i to j, N
k
ijt
.
Summing this value across alli∈I andk∈K for statej provides us withX
jt
for yeart.
3.6.2 Supply
There are four supply side parameters we need to estimate: the inverse measure of the
dispersion of shocks (λ), the scale parameter for the trade costs (ζ), the extent of tech-
nological heterogeneity (θ), and the state-specific labor cost shifters ( α
i
). We proceed in
two steps. First, we use data on crop prices in different markets ( P
k
mit
), distance between
farms and markets (d
f
m
), and crop quantity produced in each state (Q
k
it
) to estimateλ and
140
ζ using a generalized method of moments (GMM) estimation procedure. Then, we use
the previous estimates along with data on farm productivity (A
fk
i
) in a nonlinear least
squares (NLS) framework to estimateθ andα
i
.
Step 1—We know that Ω fk
mit
from 3.25 represents the probability that a farmerf located
in statei and growing cropk at timet, chooses marketm. This probability can be used to
calculate the share of cropk produced in statei that reaches a marketm at timet. Denoting
the same byS
k
mit
, we can calculate it as the share of cropk that each farmer takes to market
m at timet (Q
fk
it
), summed across all farmers, and divided by the total quantity of cropk
produced in statei at timet (Q
k
it
). The expression takes the following form:
S
k
mit
=
P
f∈F
i
Ω fk
mit
Q
fk
it
Q
k
it
=
X
f∈F
i


















P
k
mit
1 +ζd
f
m




λ
Q
fk
it
P
m
′
∈M




P
k
m
′
it
1 +ζd
f
m
′




λ














,
Q
k
it
(3.46)
Now, we use 3.46 to carry out a GMM procedure to estimateλ andζ. In particular, we
choose Θ ={λ,ζ}, with true parameter value Θ
0
, to minimize the distance between mo-
ments of the data and their estimated counterparts. Letg(Y
m
,θ) be a continuous and con-
tinuously differentiable function of θ andY
m
, where the latter is a market-specific vector
of parameters like prices and distance to farms. Then the population moment conditions
are such that:
E[g(Y
m
,Θ
0
)] =E






S
k
mit
−
P
f∈F
i
Ω fk
mit
Q
fk
it
Q
k
it






= 0
141
The corresponding sample moments are given by:
g
m
(Θ) =
1
M
X
m∈M
g(Y
m
,Θ) = 0
Our GMM estimator can, therefore, be written as:
ˆ
Θ = arg min
(Θ)


1
M
X
m∈M
g(Y
m
,Θ)


T
ˆ
W


1
M
X
m∈M
g(Y
m
,Θ)


(3.47)
where
ˆ
W is the optimal weighting matrix. We use numerical methods to find the required
gradients. Note that we do not have data on total crop produced per farm in any year,
so we proxy that using ICRISAT 2018 data on district level yearly output of crops (Q
k
dit
).
Specifically, we assume that all the farms f that fell within the district had the same output,
which we can calculate as follows:
Q
fk
it
=
Q
k
dit
F
i
Finally,Q
k
it
is the sum of output of cropk across all districtsd in statei at timet.
Our estimates ofλ andζ equal 1.84 and 0.07, respectively. Sinceλ is inversely related
to the dispersion of transportation cost shocks, a low value ofλ implies that farmers face
substantial heterogeneity in trade costs across different states. To interpret the scale pa-
rameterζ, we use 3.21 to calculate the elasticity of trade costs with respect to distance:
tialτ
tiald
×
d
τ
=
ζd
1 +ζd
.
The equation above implies that the change in trade costs with respect to change in distance
is not uniform, and depends on the original distance being traversed. For instance, if the
distance between the farm and the market is 10 kms, a 10 percent increase in distance — or
1 km — leads to a 4.1 percent increase in trade costs for the farmer. On the other hand, if the
distance was 100 kms, increasing the same by 10 percent will increase the transportation
costs by approximately 8.7 percent.
142
Step 2—The remaining supply-side parameters that need to be estimated are the extent
of technological heterogeneity,θ, as well as the state-specific labor cost shifters in different
years,α
it
. We do not need to estimate the productivity of fields for different crops, A
fk
i
—
the main variable that changes in our model under a climate change scenario — as it is di-
rectly observable in the GAEZ data. However, GAEZ data is not available at a yearly level;
rather there is only one observation per field for the time period 1980-2010. Therefore,
we only use data pertaining to the year 2010 for estimating the supply parameters in this
step.
49
Naturally, the labor cost shifters too will only pertain to the year 2010. Henceforth,
we remove the time subscript (t) in this subsection.
Using 3.32, we can denote the predicted supply of cropk in statei as a function of the
unknown parametersθ andα
i
:
Q
k
i
(θ,α
i
) =
X
m∈M
i
X
f∈F
i
s
f
i
A
fk
i


















P
k
mi
1 +ζd
f
m




λ
P
m
′
∈M




P
k
m
′
i
1 +ζd
f
m
′




λ



















(A
fk
i
P
fk
i
)
θ
(α
i
)
θ
+
P
k
′
∈K
(A
fk
′
i
P
fk
′
i
)
θ





(θ−1)/θ
(3.48)
Next, letL
i
(θ,α
i
) denote the predicted land allocated to all crops in statei as a function
ofθ andα
i
. This is calculated as the share of field f which is allocated to cropk (∆ fk
i
from
3.28), multiplied by the field size, s
f
i
, and sum across all crops and fields. Specifically,
L
i
(θ,α
i
)≡
X
k
X
f
s
f
i





(A
fk
i
P
fk
i
)
θ
(α
i
)
θ
+
P
k
′
∈K
(A
fk
′
i
P
fk
′
i
)
θ





(3.49)
To estimate θ and α
i
, we follow the same procedure as Costinot, Donaldson, and C.
Smith 2016, i.e. choose a value of θ, and conditional on it, find the vector of labor cost
shifters,α
i
, such that the total amount of land allocated to crops as predicted by the model,
49
We could have used yields data from ICRISAT 2018 but GAEZ is more accurate and spatially disaggre-
gated (district versus farm).
143
L
i
(θ,α
i
), exactly matches the total amount of land allocated to crops in the data, L
i
, for
all states. Next, given the vector of labor cost shifters (α
i
) for all states, we search forθ
such that the difference between the output predicted by the model, Q
k
i
(θ,α
i
), and output
observed in the data,Q
k
i
, is minimised. This algorithm can be formally expressed as the
following non-linear least squares problem:
min
θ,α
i
X
i∈I
X
k∈K
h
lnQ
k
i
(θ,α
i
)− lnQ
k
i
i
2
subject to
L
i
(θ,α
i
) =L
i
for all i∈I.
Our estimate ofθ equals 1.82, which suggests that within-field, within-crop productiv-
ity dispersion in Indian agriculture is large. This is reassuringly close to the estimate of
Sotelo 2020, who finds a value of 1.658 for θ using data from Peru.
3.7 Counterfactual Analysis
We now use the estimated parameters to simulate our model and run policy counterfactu-
als that involve climate change, with and without inter-state trade barriers. Specifically, we
can use the model to run the following two counterfactuals: (i) welfare impact of climate
change under a policy with inter-state trade restrictions, and; (ii) welfare impact of climate
change under a policy without inter-state trade restrictions. Under both counterfactuals,
we allow for production and trade patterns to fully adjust.
To run either of these counterfactuals, we first need to solve for the competitive equi-
librium before climate change. This equilibrium is characterized by the market supply
curve in 3.32, and the following three conditions: (i) intermediary profit maximization in
3.34; (ii) consumer utility maximisation in 3.36, and; (iii) market clearing in 3.38. Crop
productivity for different farms, A
fk
i
, is the structural parameter which will change un-
der the climate change scenario. Therefore, for the first equilibrium, we use pre-climate
144
change GAEZ data. Subsequently, for the counterfactual equilibrium with climate change,
we use the exact same equations and structural parameters except for crop yields, which
is replaced with post-climate change productivity, (A
fk
i
)
′
, as measured by GAEZ.
The key mechanism driving any differences between the equilibrium with and without
climate change is the change in productivity across different farms and crops caused by
global warming. Climate change will affect comparative advantage in crop yields across
different regions of the country. This, in turn, alters supply as farmers change the use of
intermediate inputs and substitute between crops, which then impacts mandi and retail
prices — an effect that feeds back into the prices farmers can get.
It is worth noting that the welfare consequences of changes in comparative advantage
will also depend crucially on the spatial competition faced by farmers, as it directly in-
centivizes farmer adaptation. To see this, consider the following example: a farmer near
a state border grows rice, but climate change shifts their comparative advantage towards
wheat. The price offered to wheat farmers in the nearby mandi, however, is not competitive
due to intermediary market power. Thus, despite rice yields falling, the farmer does not
substitute. Lifting border restrictions would necessarily improve the welfare outcome by
increasing the farmer’s choice set, both in terms of accessible markets and crop choices.
The channel for this welfare improvement is intuitive. First, opening state borders di-
rectly impacts the probability of the farmer choosing a market, as seen in 3.25. This occurs
because reducing distance — and, thus, transportation costs — between farms and po-
tential markets increases farmers’ arbitrage opportunities. Second, the change in market
choice probability subsequently affects farmers’ probability of allocating land to a crop
through changes in the average value of the marginal product of the field (3.29). These
changes in farmer input decisions, in turn, change quantities supplied to each market
(3.32). Now, the prices received by farmers will be affected through two sources: the
change in quantity, and the change in bargaining power, as the intermediary faces in-
creased competition now from across the border. This increase in competition affects each
145
intermediaries share in the market, which will affect the markdowns. Importantly, the
changes in intermediary market power near the borders has ripple effects across inte-
rior markets through this change in quantity 3.34. Finally, changes in production, market
choice of farmers and intermediary market power could also adjust retail prices which in
turn will feed back into the prices farmers’ receive. This change would eventually incen-
tivize the farmer at the border to substitute from rice to wheat, as predicted by comparative
advantage. Therefore, change in production and incomes brought about by opening trade
borders could aid in mitigating the climate change impact.
Our assumption of quasi-linear preferences allows us to compute welfare changes as
changes in social surplus, expressed as a fraction of GDP in the initial equilibrium:
∆ W
i
=
(Y
i
)
′
−Y
i
+ (β lnC
i
−P
i
C
i
)
′
− (β lnC
i
−P
i
C
i
) + (π)
′
−π
Y
i
(3.50)
whereY
i
andπ
i
are the GDP and intermediary profits in the initial equilibrium, respec-
tively, while primes denote the analogous variable in the counterfactual equilibrium.
We find that climate change reduces welfare in India by 2.1 percent of total GDP, as-
suming border restrictions for farmers remain in place. Note that up until now, we have
set the distance between farms in state i and markets in state j, for i̸=j, as∞. In the
subsequent counterfactual where we remove the trade barriers, the distance is set to the
actual geodesic distance, similar to if farms and markets were in the same state. Under
this counterfactual, where farmers can access markets across state borders, the country
still experiences a 1.81 percent fall in GDP. However, this is 13.8 percent lower, implying a
mitigation of the negative impacts. This illustrates how market distortions created by gov-
ernment policies could hinder adaptation, and how removing the same could expand the
adaptation portfolio of farmers, thus helping countries mitigate the negative consequences
of climate change.
146
3.8 Discussion and Conclusion
Extreme and frequent heat events, induced by climate change, are predicted to acceler-
ate crop failures, leading to increased food prices and greater food insecurity IPCC 2022.
Given this portentous scenario, a higher magnitude and rate of farmer adaptation are cru-
cial to flatten the slope of the climate damage function. However, to what extent does the
effectiveness of adaptation responses depend on a country’s institutional framework? We
offer an insight into this question by studying the impact of institution-led distortions in
market competition on farmer climate-change adaptation in India. Using spatial variation
in intermediary market power — an unintended consequence of regulations governing
agricultural marketing — we show that higher competition among buyers of agricultural
produce helps farmers alleviate the detrimental impact of extreme heat. This effect is pri-
marily driven by an increase in input usage in more competitive areas, a response to higher
expected prices post climate shocks. Subsequently, we structurally estimate a spatial trade
equilibrium model to test the implications of eliminating these market distortions under a
climate change scenario. Our results indicate that there is potential for substantial welfare
gains if government policies distorting market competition are removed, highlighting the
positive role of free markets in facilitating adaptation.
Though our setting — distortions in intermediary competition emanating from Indian
agricultural laws — is a specific one, we believe that many of its characteristics, and the
lessons derived from it, apply more broadly. First, we show that well-intended govern-
ment policies can distort adaptation behavior. A similar result is outlined by Annan and
Schlenker 2015, who show that federal crop insurance can lead to moral hazard, and thus,
discourage private adaptation efforts. Similarly, Matthew E. Kahn and Lall 2021 hypothe-
size that government investment in resilience infrastructure can encourage migration into
risky areas, increasing the population’s overall risk exposure. Second, intermediary power
in agricultural value chains is ubiquitous in developing economies, for e.g. Ecuador Zavala
2020, Kenya Dhingra and Tenreyro 2020, and Rwanda Macchiavello and Morjaria 2021,
147
among others. Our results indicate that adaptation to climate change in such countries
will therefore, be rendered more challenging as farmers would also need to overcome the
distortions to adaptation incentives caused by market power.
While State intervention in case of market failures is valuable, there needs to be recog-
nition that government and private individuals respond to each other Kousky, Luttmer,
and Zeckhauser 2006. Therefore, these strategic interactions need to be internalized when
designing policies, else the resulting distortions arising from unintended consequences
could have negative implications in a world afflicted by climate change.
148
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166
Appendices
I Appendix Figures
Figure 3.9: Cement Production Process
Note: From Czigler, Reiter, Schulze, and Somers 2020
167
Figure 3.10: Key Regions for Emission Standards in China
168
(a) Emissions Tax Rates by Province in 2018
(b) Average Emissions Tax Rates by Year
Figure 3.11: Emissions Tax Rates in China
Note: This figure reports emissions tax rate changes by province across years. Panel (a) shows the spatial variation of the emissions
tax rates. Panel (b) shows average emissions tax rate changes over time.
169
Figure 3.12: Correlation Between Two Market Power Measures
Note: This figure reports the correlation between two market power measures. This first one is used in the theoretical model and is
defined as the ratio between firms’ factor door prices and the market price. The second is firm-level coal price pass-through. The high
correlation between these two measures suggests that the market power measures are consistent across different specifications.
170
Figure 3.13: A Representative BlueLA Charging Station
171
Figure 3.14: Fraction of Cropland in each ECMWF (Weather) Gridcell
Note: The cropland shares are computed based on 30m land cover data from the Global Food Security-support Analysis Data 2015
GFSAD30 2017.
172
(a) Districts in Sample for Analysing Effect of
Extreme Heat
(b) States in Sample for Analysing Effect of
Competition
Figure 3.15: ICRISAT Districts and APMC States in Sample
Note: Panel (a) shows the 313 districts (filled in goldenrod color) covered in ICRISAT 2018’s Apportioned database, which provides
yields for 25 major crops district-wise from 1966-2017. The district boundaries pertain to the year 1966. There were a total of 349
districts in 1966. Therefore, the 36 districts not included in the ICRISAT 2018 database are filled in grey. Panel (b) shows the 19 states
(filled in goldenrod color) which constitute the sample used to analyse the effect of competition on various economic outcomes. These
states include 2,938 wholesale intermediary Mandis geolocated within their boundaries, which forms our final sample of markets.
The state boundaries pertain to the year 2020. 9 States and 8 Union Territories not included in the sample are filled in grey.
173
(a) Effect of Extreme Heat on Yields: Kharif (b) Effect of Extreme Heat on Yields: Rabi
(c) Growing Season Distribution: Kharif (d) Growing Season Distribution: Rabi
(e) Map of Extreme Heat Exposure: Kharif (f) Map of Extreme Heat Exposure: Rabi
Figure 3.16: Coefficient Plot, GDD Distribution, and Extreme Heat Exposure by Season
174
J Appendix Tables
Standards ID Effective Date Pollutant General Standards Special Standards
mg/m
3
mg/m
3
GB 4915-2004
07-01-2006
Particulate Matter 100 100
SO
2
400 400
NO
2
800 800
Fluoride 10 10
01-01-2010
Particulate Matter 50 50
SO
2
200 200
NO
2
800 800
Fluoride 5 5
GB 4915-2013 07-01-2015
Particulate Matter 30 20
SO
2
200 100
NO
2
400 320
Fluoride 5 3
Table 3.7: Emission Standards in China’s Cement Industry
Note: Starting in 2013, the emission standards specify special standards in the key region and general standards everywhere else. The
effective dates in the table apply to existing plants. The emission standards for new plants normally occurred one year before the
dates for existing plants. Since only cement plants that survived the whole study period are included in the main data, we only focus
on existing plants.
175
Dependent Variable: log(Yields)
cdsy
Kharif Rabi
(1) (2) (3) (4) (5) (6) (7) (8) (9) (10)
Bin<15
dsy
0.010
∗∗∗
0.002 0.011 0.004 0.004 0.002 0.004
∗
0.002 0.004
∗∗∗
0.003
∗
(0.003) (0.006) (0.007) (0.003) (0.004) (0.001) (0.002) (0.003) (0.001) (0.001)
Bin 15-20
dsy
0.007
∗∗
0.000 0.008 0.004 0.003
(0.003) (0.004) (0.005) (0.003) (0.003)
Bin 20-25
dsy
−0.002 −0.001 −0.004 0.000 0.001
(0.002) (0.001) (0.003) (0.001) (0.002)
Bin 25-30
dsy
−0.001 −0.003
∗∗
−0.002 −0.004
∗∗∗
−0.004
∗∗
−0.001 −0.002 −0.002 −0.002
∗
−0.001
(0.002) (0.001) (0.003) (0.001) (0.001) (0.001) (0.001) (0.002) (0.001) (0.001)
Bin 30-35
dsy
−0.005
∗∗
−0.005
∗∗∗
−0.005 −0.006
∗∗∗
−0.006
∗∗∗
−0.009
∗∗∗
−0.005
∗∗
−0.009
∗∗∗
−0.006
∗∗∗
−0.006
∗∗∗
(0.003) (0.002) (0.003) (0.001) (0.002) (0.002) (0.002) (0.002) (0.001) (0.001)
Bin>35
dsy
−0.011
∗∗∗
−0.011
∗∗∗
−0.011
∗∗
−0.011
∗∗∗
−0.011
∗∗∗
−0.017
∗∗∗
−0.014
∗∗
−0.018
∗∗∗
−0.014
∗∗∗
−0.014
∗∗∗
(0.004) (0.002) (0.004) (0.002) (0.002) (0.004) (0.004) (0.004) (0.004) (0.004)
Fixed Effects
District ! ! ! ! ! !
Crop× Year ! ! ! ! ! ! ! ! ! !
Crop× State ! !
State× Year ! ! ! ! ! !
District× Decade ! !
District× Crop× Decade ! !
State Time-Trend ! !
Num. obs. 125,279 125,279 125,279 125,279 125,279 60,429 60,429 60,429 60,429 60,429
Adj. R
2
0.599 0.622 0.701 0.628 0.836 0.724 0.742 0.754 0.754 0.871
Table 3.8: Effect of Temperature on Yields (Panel Approach): Robustness Tests
Notes: two-way clustered robust standard errors in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
176
Dependent Variable: log(Yields)
cds
Cross Section 2-Period Panel 3-Period Panel
Kharif Rabi Kharif Rabi Kharif Rabi
Bin<15
dst
−0.038
∗
−0.006 −0.007 −0.019
∗
0.036 0.006
(0.021) (0.018) (0.018) (0.010) (0.027) (0.015)
Bin 15-20
dst
−0.003 0.016 0.027
(0.019) (0.012) (0.022)
Bin 20-25
dst
−0.005 −0.039
∗∗∗
−0.015
(0.012) (0.009) (0.009)
Bin 25-30
dst
−0.001 −0.008 −0.006 −0.035
∗∗∗
−0.018
∗∗∗
0.001
(0.008) (0.016) (0.005) (0.010) (0.005) (0.012)
Bin 30-35
dst
−0.016
∗∗
−0.011 −0.017
∗∗∗
−0.031
∗∗∗
−0.021
∗∗∗
0.000
(0.008) (0.014) (0.006) (0.007) (0.005) (0.008)
Bin>35
dst
−0.017
∗
−0.034
∗
−0.019
∗∗∗
−0.037
∗∗∗
−0.044
∗∗∗
−0.050
∗∗∗
(0.009) (0.019) (0.006) (0.013) (0.006) (0.017)
Time Period
Period 1 1990-2015 1990-2015 1970-1990 1970-1990 1970-1980 1970-1980
Period 2 1995-2015 1995-2015 1985-1995 1985-1995
Period 3 2000-2015 2000-2015
Fixed Effects
Crop× State ! ! ! ! ! !
Time Period ! ! ! !
Num. obs. 2,636 1,267 4,877 2,397 7,283 3,547
Adj. R
2
0.510 0.382 0.219 0.227 0.185 0.121
Table 3.9: Effect of Temperature on Yields (Long-Differences): Robustness Tests
Notes: clustered robust standard errors in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
177
Dependent Variable: log(Yields)
cdsy
(1) (2) (3) (4) (5) (6)
Bin 30-35
dsy
−0.004
∗∗
−0.004 −0.011
∗∗∗
−0.002 −0.014
∗∗∗
−0.010
∗∗∗
(0.001) (0.003) (0.003) (0.002) (0.003) (0.003)
Bin>35
dsy
−0.022
∗∗∗
−0.020
∗∗∗
−0.011
∗
−0.029
∗∗∗
−0.015
∗∗
−0.014
∗∗∗
(0.004) (0.005) (0.006) (0.006) (0.006) (0.004)
Bin<15
dsy
× Comp’
ds
−0.000 −0.000 −0.000 −0.000 −0.000 −0.000
(0.000) (0.001) (0.000) (0.001) (0.000) (0.000)
Bin 15-20
dsy
× Comp’
ds
0.000 0.001 0.000 0.000 0.000 −0.000
(0.000) (0.001) (0.000) (0.000) (0.000) (0.000)
Bin 25-30
dsy
× Comp’
ds
−0.000 −0.000 0.001 −0.000 0.000 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Bin 30-35
dsy
× Comp’
ds
−0.000 −0.000 0.000 −0.000 0.000 0.000
(0.000) (0.000) (0.000) (0.000) (0.000) (0.000)
Bin>35
dsy
× Comp’
ds
0.000 0.000 0.001 0.001 0.000 0.000
(0.001) (0.000) (0.000) (0.001) (0.000) (0.000)
Fixed Effects
Crop !
District ! !
Year ! !
Crop× District !
Crop× Year ! ! ! !
District× Year !
State× Year !
District× Crop× Decade ! !
Num. obs. 59,593 59,593 59,593 59,593 59,593 59,593
Adj. R
2
0.623 0.614 0.805 0.635 0.829 0.844
Table 3.10: Effect of Out-of-State Competition on Mitigation of Climate Shocks
Notes: clustered robust standard errors in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
178
Dependent Variable: log(Yields)
cdsy
Comp
2m
Comp
3ds
(1) (2) (3) (4) (5) (6) (7) (8)
Bin 30-35
dsy
−0.002 −0.002 −0.001 −0.006
∗
−0.002 −0.003 −0.001 −0.011
∗∗∗
(0.003) (0.004) (0.003) (0.003) (0.003) (0.003) (0.003) (0.003)
Bin>35
dsy
−0.038
∗∗∗
−0.037
∗∗∗
−0.043
∗∗∗
−0.023
∗∗
−0.034
∗∗∗
−0.032
∗∗∗
−0.040
∗∗∗
−0.021
∗∗
(0.010) (0.006) (0.009) (0.009) (0.007) (0.007) (0.006) (0.007)
Bin<15
dsy
× Comp
ds
−0.004 −0.003 −0.001 −0.005 −2.752 −2.399 −0.874 −0.723
(0.003) (0.003) (0.003) (0.004) (2.416) (2.394) (2.847) (2.055)
Bin 15-20
dsy
× Comp
ds
0.005 0.005 0.004 0.000 3.383 2.851 3.571 1.456
(0.004) (0.003) (0.004) (0.002) (2.918) (2.954) (3.692) (2.545)
Bin 25-30
dsy
× Comp
ds
−0.002 −0.001 −0.001 −0.003 −1.209 −1.327 0.683 −3.176
(0.003) (0.003) (0.003) (0.003) (2.781) (2.817) (3.638) (2.189)
Bin 30-35
dsy
× Comp
ds
−0.003 −0.004 −0.003 −0.004 −2.321 −2.544 −2.490 −0.957
(0.004) (0.004) (0.003) (0.003) (1.957) (1.971) (2.401) (1.829)
Bin>35
dsy
× Comp
ds
0.014
∗∗
0.015
∗∗∗
0.015
∗∗∗
0.007
∗
9.973
∗∗∗
9.926
∗∗∗
11.719
∗∗∗
5.048
∗
(0.005) (0.004) (0.005) (0.004) (2.733) (2.752) (3.185) (2.843)
Fixed Effects
Crop ! !
District ! ! ! !
Year ! !
Crop× Year ! ! ! ! ! !
District× Year ! !
District× Crop× Decade ! !
State Time-Trend ! ! ! ! ! !
Effect Mitigated (in %) 24.5 26.2 23.2 19.6 22.3 23.6 21.9 18.2
Num. obs. 59,783 59,783 59,783 59,783 59,783 59,783 59,783 59,783
Adj. R
2
0.627 0.618 0.637 0.831 0.626 0.617 0.636 0.831
Table 3.11: Competition and Mitigation of Climate Shocks: Robustness Tests
Notes: clustered robust standard errors in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
179
Dependent Variable: log(Arrivals)
cmdsy
(1) (2) (3) (4)
Bin 30-35
dsy
−0.009 −0.008 −0.013 −0.007
(0.008) (0.010) (0.012) (0.010)
Bin>35
dsy
−0.035
∗
−0.036
∗∗
−0.056
∗∗
−0.030
∗
(0.019) (0.016) (0.023) (0.015)
Bin<15
dsy
× Comp
mds
0.003 0.003 0.008 0.002
(0.004) (0.005) (0.006) (0.004)
Bin 15-20
dsy
× Comp
mds
0.012
∗
0.012 0.019
∗∗
0.009
(0.006) (0.007) (0.008) (0.007)
Bin 25-30
dsy
× Comp
mds
0.008
∗∗
0.008
∗∗
0.013
∗∗
0.006
(0.004) (0.004) (0.006) (0.004)
Bin 30-35
dsy
× Comp
mds
0.006
∗
0.006 0.010 0.004
(0.004) (0.004) (0.007) (0.004)
Bin>35
dsy
× Comp
mds
0.013
∗
0.013
∗∗
0.023
∗∗
0.010
∗
(0.007) (0.006) (0.010) (0.006)
Fixed Effects
Market ! !
Crop× Year ! ! ! !
District× Decade !
Market× Decade !
Market× Year !
State× Year ! ! !
Effect Mitigated (in %) 65.2 65.4 74.8 60.2
Num. obs. 156,724 156,724 156,724 156,724
Adj. R
2
0.434 0.451 0.449 0.429
Table 3.12: Competition and Mitigation of Climate Shocks—Arrivals:
Robustness Tests
Notes: clustered robust standard errors in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
180
Dependent Variable: log(Arrivals)
cmdsy
(1) (2) (3) (4)
Bin 30-35
dsy
0.001 0.001 0.001 0.001
(0.006) (0.006) (0.006) (0.006)
Bin>35
dsy
−0.019 −0.019 −0.025
∗
−0.019
(0.012) (0.012) (0.014) (0.012)
Bin<15
dsy
× Comp’
mds
0.000 0.000 −0.000 0.000
(0.001) (0.001) (0.001) (0.001)
Bin 15-20
dsy
× Comp’
mds
0.000 0.000 0.000 0.000
(0.000) (0.000) (0.000) (0.000)
Bin 25-30
dsy
× Comp’
mds
−0.000 −0.000 −0.000 −0.000
(0.000) (0.000) (0.000) (0.000)
Bin 30-35
dsy
× Comp’
mds
−0.000 −0.000 −0.000 0.000
(0.000) (0.000) (0.001) (0.000)
Bin>35
dsy
× Comp’
mds
0.001 0.000 0.000 0.000
(0.001) (0.000) (0.001) (0.001)
Fixed Effects
Market ! !
Crop× Year ! ! ! !
District× Decade !
Market× Decade !
Market× Year !
State× Year ! ! !
Num. obs. 148,814 148,814 148,814 148,814
Adj. R
2
0.433 0.450 0.449 0.437
Table 3.13: Effect of Out-of-State Competition on Mitigation—Arrivals
Notes: clustered robust standard errors in parenthesis.
∗∗∗
p< 0.01;
∗∗
p< 0.05;
∗
p< 0.1.
181
K Appendix Derivations
K.1 Comparative Statistics from the Theoretical Framework
dP
m
dτ
e
=
(1 +
1
δ
)
P
i
e
i
P
i
ϵ
i
+
1
α
1
> 0 (K.1)
dp
i
dτ
e
=
ϵ
i
(1 +
1
δ
)
P
i
e
i
P
i
ϵ
i
+
1
α
1
> 0 (K.2)
dQ
m
dτ
e
=
α
1
(1 +
1
δ
)(
P
i
e
i
)Q
m
((
P
i
ϵ
i
+
1
α
1
)P
m
=
(1 +
1
δ
)
P
i
e
i
P
i
ϵ
i
+
1
α
1
dQ
m
dP
m
< 0 (K.3)
dc
a
(e
i
)
dτ
e
=
e
i
δ
(K.4)
de
i
dτ
e
=−
(A
i
δ)
1
1+δ
1 +δ
τ
−
1
1+δ
−1
e
(K.5)
whereµ i
=p
i
−
dc
i
(q
i
)
dq
i
−c
a
(e
i
)−τ
e
e
i
.
K.2 Coal Price Pass-Through
dP
dMC
=
dP/P
dMC/MC
| {z }
pass-through elasticity
×
P
MC
|{z}
markup
Pass-through elasticity:
p
ist
=ρmc
it
+X
it
+η
i
+π
t
+ϵ
ist
• p
ist
: log of output price of plant i in province s at time t (monthly)
• mc
it
log of the marginal cost of plant i at time t
• Bartik instrument for marginal cost: product of (lagged) industry fuel input shares
and national time-series variation in the prices of these fuels
182
K.3 Joint Distribution of TFP and Labor Intensity
Assume that the total factor productivity (TFP) of parcelω in field f if allocated to cropk
in statei at timet,A
fk
it
(ω)≥ 0, is Fr´ echet distributed with
Pr[A
fk
it
(ω)≤a
k
] = exp

−

a
k
/s
k

−θ

∀k∈K (K.6)
whereθ> 0 is a shape parameter, ands> 0 is the scale parameter. DenoteE
h
A
fk
it
(ω)
i
=
A
fk
it
, which is given by
A
fk
it
=s
k
Γ((θ− 1)/θ) forθ> 1,∀k∈K
where Γ(·) denotes the Gamma function, i.e. Γ(t) =
+∞
R
0
u
t−1
exp(−u)du for anyt> 0.
Using the above definition, and setting γ≡ Γ((θ− 1)/θ)
−θ
, K.6 becomes
Pr[A
fk
it
(ω)≤a
k
] = exp

−γ

a
k
/A
fk
it

−θ

∀k∈K (K.7)
Also, assume labor intensity, ν
f
i
(ω), which is constant across crops and time, is dis-
tributed Fr´ echet such that
Pr[ν
f
i
(ω)≤ν] = exp
n
−γ (ν/ν
i
)
−θ
o
(K.8)
whereν
i
denotesE
h
ν
f
i
(ω)
i
. Given that TFP and labor intensity are independently drawn
for each (i,f,ω,t), and using K.7 and (K.8), the joint CDF can, therefore, be written as
Pr{A
f1
it
(ω)≤a
1
, ..., A
fK
it
(ω)≤a
K
,ν
f
i
(ω)≤ν}
=
Y
k∈K
exp

−γ

a
k
/A
fk
it

−θ

· exp
n
−γ (ν/ν
i
)
−θ
o
= exp



−γ


X
k∈K
(a
k
/A
fk
it
)
−θ
+ (ν/ν
i
)
−θ





183
K.4 Probability of Choosing Market
Derivation of probability of choosing marketm for cropk, 3.24 in text.
A farmer in statei chooses marketm∈M at timet if:
P
k
mit
Q
fk
mit
(ω)≥P
k
m
′
it
Q
fk
m
′
it
(ω)∀ m
′
∈M\{m} (K.9)
Our assumption of iceberg trade costs for farmers (3.20 in text) implies that Q
fk
mit
(ω) =
Q
fk
it
(ω)/τ
f
mt
. Using this, we can rewrite the condition above. Therefore, a farmer chooses
marketm∈M at timet if:
τ
f
mt
P
k
mit
= min







τ
f
1t
P
k
1it
, ..,
τ
f
mt
P
k
mit
, ...,
τ
f
Mt
P
k
Mit







(K.10)
Our assumption of Weibull distributed trade cost shocks (3.22), and the distribution’s
property of being closed under scale transformations implies:
τ
f
mt
P
k
mit
∼ Weibull




λ,
Υ
−1/λ
(1 +ζd
f
m
)
P
k
mit




LetG
f
mt
denote the c.d.f. of
τ
f
mt
P
k
mit
. Then:
G
f
mt
(ϵ) = Pr




τ
f
mt
P
k
mit
≤ϵ




= 1− exp


−Υϵ
λ

P
k
mit
1 +ζd
f
m
!
λ


184
The probability of choosing marketm for cropk can now be written as:
Ω fmk
it
= Pr




τ
f
mt
P
k
mit
≤ min
m
′







τ
f
m
′
t
P
k
m
′
it











= Pr




τ
f
mt
P
k
mit
≤ min
m
′
̸=m







τ
f
m
′
t
P
k
m
′
it











=
Z
∞
0
Y
m
′
̸=m

1−G
f
m
′
t
(ϵ)

dG
f
mt
(ϵ)
We can use the c.d.f.G
f
m
′
t
(ϵ) = 1−exp


−Υϵ
λ

P
k
m
′
it
1+ζd
f
m
′
!
λ


, and the corresponding p.d.f.
dG
f
mt
(ϵ) =λϵ
λ−1
Υ

P
k
mit
1+ζd
f
m

λ
exp

−Υϵ
λ

P
k
mit
1+ζd
f
m

λ
!
dϵ to get,
Ω fk
mit
=λΥ

P
k
mit
1 +ζd
f
m
!
λ
Z
∞
0
Y
m
′
exp


−Υϵ
λ


P
k
m
′
it
1 +ζd
f
m
′


λ


ϵ
λ−1
dϵ
=λΥ

P
k
mit
1 +ζd
f
m
!
λ
Z
∞
0
exp


−



X
m
′


P
k
m
′
it
1 +ζd
f
m
′


λ


Υϵ
λ


ϵ
λ−1
dϵ
=λΥ

P
k
mit
1 +ζd
f
m
!
λ








−exp


−


P
m
′

P
k
m
′
it
1+ζd
f
m
′
!
λ


Υϵ
λ


λΥ
P
m
′

P
k
m
′
it
1+ζd
f
m
′
!
λ








∞
0
=




P
k
mit
1 +ζ·d
f
m




λ
P
m
′
∈M




P
k
m
′
it
1 +ζ·d
f
m
′




λ
K.5 Profit Function of Farmer
Derivation of profits for a farmer growing crop k in farmf at timet, 3.26 in text.
185
Given the production function in 3.18, the profit for a farmer from parcel ω∈f in state
i who grows cropk at timet is given by:
π
fk
it
(ω) = (P
k
1it
A
fk
it
(ω)L
fk
it
(ω)−w
it
N
fk
it
(ω))· Ω fk
1it
+ ...
+ (P
k
Mit
A
fk
it
(ω)L
fk
it
(ω)−w
it
N
fk
it
(ω))· Ω fk
Mit
=


X
m
′
∈M
Ω fk
m
′
it
P
k
m
′
it


(A
fk
it
(ω)L
fk
it
(ω))−


X
m
′
∈M
Ω fk
m
′
it


| {z }
=1
(w
it
N
fk
it
(ω))
Using the expression for the probability of choosing a market (Ω fk
mit
) in 3.25, we can write
the above as:
π
fk
it
(ω) =A
fk
it
(ω)L
fk
it
(ω)














P
m
′
∈M
(P
k
m
′
it
)
λ+1

1 +ζd
f
m
′

λ
P
m
′
∈M




P
k
m
′
it
1 +ζd
f
m
′




λ














| {z }
=P
k
it
−w
it
N
fk
it
(ω) (K.11)
K.6 Land Allocation Problem
Derivation of probability that a parcelω of a field f located in statei is allocated to cropk at time
t, 3.28 in text.
Conditional on choosing to grow a crop, farmer in farmf and statei chooses cropk at
timet if:
π
fk
it
(ω)>π
fk
′
it
(ω) ∀ (k
′
̸=k)∈K
We can use the profit function in K.11 to write the above condition as:
A
fk
it
(ω)L
fk
it
(ω)P
k
it
− w
it
N
fk
it
(ω)>A
fk
′
it
(ω)L
fk
′
it
(ω)P
k
′
it
−w
it
N
fk
′
it
(ω) (K.12)
∀ (k
′
̸=k)∈K
186
The Leontief production function in 3.18 impliesL
fk
it
(ω) =
N
fk
it
(ω)
ν
f
i
(ω)
∀k∈K. Also, once a
farmer decides to grow a crop, they will use the entire land area available since profits are
an increasing function of production inputs. Thus, L
fk
it
(ω) =L
fk
′
it
(ω)∀k
′
∈K. K.12 can
then be written as:
A
fk
it
(ω)P
k
it
>A
fk
′
it
(ω)P
k
′
it
∀ (k
′
̸=k)∈K (K.13)
The farmer in statei also has an outside option which entails working in statei’s outside
sector and producing the outside good. With labor productivity denoted byA
0
it
, and pro-
duction under constant returns to scale using only labor, the profit maximisation problem
of statei’s outside sector can be written as:
max
{N
0
it
}
π
0
it
=A
0
it
N
0
it
−w
it
N
0
it
Differentiating the above w.r.t. {N
0
it
}, we find that profit maximisation in state i’s outside
sector requiresw
it
=A
0
it
. Therefore, a farmer chooses to grow cropk over working in state
i’s outside sector if:
A
fk
it
(ω)P
k
it
>A
0
it
ν
f
i
(ω) (K.14)
Combining K.13 and K.14, we can deduce that a farmer in statei will grow cropk in parcel
ω∈f at timet if:
A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
} (K.15)
187
K.7 Quantity Supplied to Market
Derivation of quantity of cropk supplied to marketm in statei at timet, 3.30 in text. LetQ
k
mit
denote the quantity of cropk supplied to marketm in statei at timet. Then
Q
k
mit
=
X
f∈F
i
Ω fk
mit
Z
1
0
Q
fk
it
(ω)dω (K.16)
Assume thatω∼U
[0,1]
. Thus, the probability density function ofω is:
f(ω) =









1 for 0≤ω≤ 1
0 for ω< 0 or ω> 1
Also, by law of iterated expectations,
E[Q
fk
it
(ω)] =E
k
[E[Q
fk
it
(ω)|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}]]
K.16 can, therefore, be written as
Q
k
mit
=
X
f∈F
i
Ω fk
mit
∆ fk
it
E[Q
fk
it
(ω)|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}] (K.17)
Furthermore, note that
E[L
fk
it
(ω)|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}]
=E[L
fk
it
(ω)]
=
Z
1
0
L
fk
it
(ω)f(ω)dω
=s
f
i
188
Using (i) the production function in 3.18; (ii) the fact that conditional on choosing cropk,
A
fk
it
(ω)⊥L
fk
it
(ω), and; (iii) the previous expression, K.17 can be expressed as:
Q
k
mit
=
X
f∈F
i
s
f
i
Ω fk
mit
∆ fk
it
E[A
fk
it
(ω)|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}] (K.18)
K.8 Average Conditional Productivity
Derivation of average productivity conditional on a crop being produced, 3.31 in text.
Using the definition of a c.d.f. and formula for conditional probability, we can write:
Pr{A
fk
it
(ω)≤a|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}}
=
1
∆ fk
it
Pr{A
fk
it
(ω)≤a,A
0
it
ν
f
i
(ω)≤P
k
it
A
fk
it
(ω),P
l
it
A
fl
it
(ω)≤P
k
it
A
fk
it
(ω)∀l̸=k}
=
1
∆ fk
it
Pr







A
0
it
ν
f
i
(ω)
P
k
it
≤A
fk
it
(ω)≤a,
P
l
it
P
k
it
A
fl
it
(ω)≤A
fk
it
(ω)≤a∀l̸=k







Define A
fk
it
(ω) =χ as a Fr´ echet distributed random variable. Then,
Pr{A
fk
it
(ω)≤a|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}}
=
1
∆ fk
it
Z
a
0
Pr







A
0
it
ν
f
i
(ω)
P
k
it
≤χ,
P
l
it
P
k
it
A
fl
it
(ω)≤χ∀l̸=k







f(χ)dχ
=
1
∆ fk
it
Z
a
0
Y
l̸=k
Pr







P
l
it
P
k
it
A
fl
it
(ω)≤χ







Pr







A
0
it
ν
f
i
(ω)
P
k
it
≤χ







f(χ)dχ (K.19)
189
Given the p.d.f. for Fr´ echet distributed TFP and labor intensity in 3.19, we can derive the
following c.d.f’s:
Pr

A
fk
it
(ω)P
k
it
≤x

= exp
(
−γ

x/A
fk
it
P
k
it

−θ
)
Pr

A
0
it
(ω)ν
f
i
(ω)≤ν

= exp

−γ
h
ν/A
0
it
ν
i
i
−θ

Using the above, K.19 can be written as
Pr{A
fk
it
(ω)≤a|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}}
=
Z
a
0
exp







−γχ
−θ
(A
fk
it
)
θ
∆ fk
it







θγ
∆ fk
it
(χ)
−1−θ
(A
fk
it
)
θ
dχ
= exp









−




a
A
fk
it
(∆
fk
it
)
−1/θ
γ
1/θ




−θ









Thus, the c.d.f. is Fr´ echet distributed with shape parameterθ and scale parameter equiv-
alent toA
fk
it
(∆
fk
it
)
−1/θ
γ
1/θ
. Then,
E[A
fk
it
(ω)|A
fk
it
(ω)P
k
it
= max{A
0
it
ν
f
i
(ω),A
f1
it
(ω)P
1
it
, ...,A
fK
it
(ω)P
K
it
}
=A
fk
it
(∆
fk
it
)
−1/θ
γ
1/θ
Γ




1−
1
θ




=A
fk
it
× (∆
fk
it
)
−1/θ
where Γ(·) denotes the gamma function
K.9 Consumers Utility Maximisation
Derivation of representative consumers’ consumption of cropk, imported fromj toi, at timet; 3.36
in text.
190
Consumer solves the following maximisation problem:
max
n
C
it
,C
k
it
,C
k
jit
o
U
it
=C
0
it
+β
i
lnC
it
subject to
C
it
=


X
k∈K
(β
k
i
)
1/φ
(C
k
it
)
(φ−1)/φ


φ/(φ−1)
C
k
it
=


X
j∈I
(β
k
ji
)
1/σ
(C
k
jit
)
(σ−1)/σ


σ/(σ−1)
E
it
≥
X
k∈K
X
j∈I
h
P
rk
jit
C
k
jit
i
+C
0
it
P
rk
jit
= Ψ
k
ji
P
rk
jt
whereE
it
is household income for the representative consumer in statei at timet.
Setting up the Lagrangian and solving, we get
C
k
jit
= (β
i
)
σ
(C
it
)
(1−φ)σ/φ

C
k
it

(σ−φ)/φ

β
k
i

σ/φ
β
k
j
i

Ψ
k
ji
P
rk
jt

σ
(K.20)
Defining the CES price index associated with crop k in staten at timet as:
ˆ
P
rk
it
≡


X
n∈I
β
k
ni

Ψ
k
ni
P
rk
nt

1−σ


1/1−σ
(K.21)
Using K.20 and (K.21) in 3.17 gives us:
C
k
it
= (β
i
)
φ
β
k
i
(C
it
)
1−φ

ˆ
P
rk
it

φ
(K.22)
191
Substituting K.22 in 3.16 implies:
C
it
=β
i


X
k∈K
β
k
i

ˆ
P
rk
it

1−φ


1/(φ−1)
(K.23)
Finally, use K.21, (K.22) and (K.23) in K.20 to get:
C
k
jit
=β
i
β
k
i
(
ˆ
P
rk
it
)
1−φ
P
l∈K
β
l
i
(
ˆ
P
rl
it
)
1−φ
β
k
ji
(Ψ
k
ji
P
rk
jt
)
−σ
P
n∈I
β
k
ni
(Ψ
k
ni
P
rk
nt
)
1−σ
∀ i,j∈I,k∈K
192

Abstract (if available)

Abstract This dissertation comprises three chapters in environmental economics. The first chapter studies how to design emissions taxation given substantial firm heterogeneity in cost structure and market power. Using both a theoretical framework and empirical analysis in the context of the Chinese cement industry, I show that (1) emissions taxation strengthens market concentration of polluting firms; (2) firm heterogeneity matters for the overall welfare impact of emissions taxation through the firm’s market power and the correlation between local abatement costs and benefits; (3) an emission tax that accounts for local firm heterogeneity coupled with output-based rebates can generate a 4.72 billion RMB (0.67 billion dollars) welfare increase.

The second chapter investigates whether a lack of information can create barriers for low-to-middle-income households to adopt green technology, specifically, electric vehicles (EVs). Using the BlueLA program in Los Angeles as a quasi-experiment for information treatment, we find that this non-price intervention is associated with a 33 percent increase in new EV adoptions among low-to-middle-income households, which justifies a substantial portion of public investment.

The third chapter documents that the pre-existing market power distortion in India’s intermediary market can hinder farmer climate change adaptation. Using the variation in the market power of local crop intermediaries due to historical agricultural laws, we show that (1) farmers selling in the intermediary markets with less market power suffer substantially less from extreme heat; (2) the farmer’s economic loss due to extreme weather could be mitigated by 13.8 percent if the restrictions on cross-border trading are removed.

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Asset Metadata

Creator Song, Ruozi (author)

Core Title Essays in environmental economics

School College of Letters, Arts and Sciences

Degree Doctor of Philosophy

Degree Program Economics

Degree Conferral Date 2023-05

Publication Date 04/26/2023

Defense Date 03/28/2023

Publisher University of Southern California (original), University of Southern California. Libraries (digital)

Tag climate change,energy transition,environmental economics,market power,OAI-PMH Harvest

Format theses (aat)

Language English

Contributor Electronically uploaded by the author (provenance)

Advisor Bento, Antonio (committee chair), Oliva, Paulina (committee chair), Chang, Tom (committee member), Metcalfe, Robert (committee member)

Creator Email ruozison@usc.edu,songruozi2013@gmail.com

Permanent Link (DOI) https://doi.org/10.25549/usctheses-oUC113078054

Unique identifier UC113078054

Identifier etd-SongRuozi-11719.pdf (filename)

Legacy Identifier etd-SongRuozi-11719.pdf

Document Type Dissertation

Format theses (aat)

Rights Song, Ruozi

Internet Media Type application/pdf

Type texts

Source 20230426-usctheses-batch-1031 (batch), University of Southern California (contributing entity), University of Southern California Dissertations and Theses (collection)

Access Conditions The author retains rights to his/her dissertation, thesis or other graduate work according to U.S. copyright law. Electronic access is being provided by the USC Libraries in agreement with the author, as the original true and official version of the work, but does not grant the reader permission to use the work if the desired use is covered by copyright. It is the author, as rights holder, who must provide use permission if such use is covered by copyright.

Repository Name University of Southern California Digital Library

Repository Location USC Digital Library, University of Southern California, University Park Campus MC 2810, 3434 South Grand Avenue, 2nd Floor, Los Angeles, California 90089-2810, USA

Repository Email cisadmin@lib.usc.edu