Page 44 |
Save page Remove page | Previous | 44 of 171 | Next |
|
small (250x250 max)
medium (500x500 max)
Large (1000x1000 max)
Extra Large
large ( > 500x500)
Full Resolution
All (PDF)
|
This page
All
|
35
Kennedy, 2002). Instead of observing the true level of health,
, we observe
where is the measurement noise. Equation (1.1) becomes:
(1.2) Δ Δ Δ Δ Δ Δ
where Δ Δ Δ. This will lead to an inconsistent estimate of because
the error term Δ is correlated with Δ through the measurement error Δ. An
instrumental variable model is an appropriate way to deal with classical measurement
error.8
The first stage equation is:
(1.3) Δ Δ Δ Δ Δ
where represents the instrumental variables for individual i, in state r for year t. In
the second stage equation, I use the predicted values of self-reported health, Δto
estimate the effect of health on health satisfaction and life satisfaction.
(1.4)
Δ Δ Δ Δ Δ Δ
8 By using an IV approach I am implicitly assuming that the measurement error can be modeled as classical
measurement error, which is uncorrelated with the true value of the explanatory variable. I think that this is
a reasonable approximation in my context. I realize that it is not strictly true in my application since if
health satisfaction takes on the lowest possible value the measurement error can only be positive, while if it
takes on its highest possible value the measurement error must be negative.
35
Object Description
| Title | Essays on health and well-being |
| Author | Zweig, Jacqueline Smith |
| Author email | smith2@usc.edu; jackiesmith04@yahoo.com |
| Degree | Doctor of Philosophy |
| Document type | Dissertation |
| Degree program | Economics |
| School | College of Letters, Arts and Sciences |
| Date defended/completed | 2011-03-23 |
| Date submitted | 2011 |
| Restricted until | Restricted until 26 Apr. 2012. |
| Date published | 2012-04-26 |
| Advisor (committee chair) |
Easterlin, Richard A. Ham, John C. |
| Advisor (committee member) | Melguizo, Tatiana |
| Abstract | This dissertation is comprised of three chapters that use microeconometric techniques to investigate the factors that affect people’s well-being. In the first two chapters, well-being is defined as life satisfaction or health satisfaction. The first chapter explores how the movement from socialism to capitalism affected the life satisfaction and health satisfaction of East Germans relative to West Germans after reunification. The second chapter examines whether women are happier, less happy, or equally happy as men in countries at various stages of development. The third chapter examines whether pollution affects the academic performance of school children; their academic performance and achievements will have important implications for their future well-being. |
| Keyword | happiness; well-being |
| Geographic subject | Germany |
| Geographic subject (state) | California |
| Geographic subject (country) | USA |
| Coverage date | 1990/2010; 2002/2008 |
| Language | English |
| Part of collection | University of Southern California dissertations and theses |
| Publisher (of the original version) | University of Southern California |
| Place of publication (of the original version) | Los Angeles, California |
| Publisher (of the digital version) | University of Southern California. Libraries |
| Provenance | Electronically uploaded by the author |
| Type | texts |
| Legacy record ID | usctheses-m3782 |
| Contributing entity | University of Southern California |
| Rights | Zweig, Jacqueline Smith |
| Repository name | Libraries, University of Southern California |
| Repository address | Los Angeles, California |
| Repository email | cisadmin@lib.usc.edu |
| Filename | etd-Zweig-4500 |
| Archival file | uscthesesreloadpub_Volume23/etd-Zweig-4500.pdf |
Description
| Title | Page 44 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 35 Kennedy, 2002). Instead of observing the true level of health, , we observe where is the measurement noise. Equation (1.1) becomes: (1.2) Δ Δ Δ Δ Δ Δ where Δ Δ Δ. This will lead to an inconsistent estimate of because the error term Δ is correlated with Δ through the measurement error Δ. An instrumental variable model is an appropriate way to deal with classical measurement error.8 The first stage equation is: (1.3) Δ Δ Δ Δ Δ where represents the instrumental variables for individual i, in state r for year t. In the second stage equation, I use the predicted values of self-reported health, Δto estimate the effect of health on health satisfaction and life satisfaction. (1.4) Δ Δ Δ Δ Δ Δ 8 By using an IV approach I am implicitly assuming that the measurement error can be modeled as classical measurement error, which is uncorrelated with the true value of the explanatory variable. I think that this is a reasonable approximation in my context. I realize that it is not strictly true in my application since if health satisfaction takes on the lowest possible value the measurement error can only be positive, while if it takes on its highest possible value the measurement error must be negative. 35 |
