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radius of each school. For a given pollutant and monitor, we calculate the total number
of days that exceed the standards for that pollutant and then divide by the total number of
days that are tested. Since students usually take the California Standards Tests in April or
May, we use pollution data from September through May as an approximation of the
pollution experienced during the school year. Then for a given pollutant at a given
school in a given year, we take the weighted average of the percent of days exceeding the
standard at each monitor, where the weighting is based on the inverse distance to the
school. Thus we give monitors that are closer to the school more weight relative to ones
that are further away. We have placed the summary statistics for our pollution variables
in Panel B of Table 3.1, while in Table 3.2 we show the correlation matrix for the
pollution variables. For all schools and years, an average of 0.0004%, 0.003%, 1.94%,
11.78%, and 28.32% days of the school year are above the standards for CO, NO2, O3,
PM10, and PM2.5, respectively. The correlation matrix for the pollutants in Table 3.2
suggests that simultaneously using the different pollution measures is likely to cause a
severe multicollinearity problem, and thus we follow the literature and enter them one at
a time. PM10 and PM2.5 are particularly highly correlated; O3 is uncorrelated with the
other measures.
The summary statistics for the control variables are in Panel C of Table 3.1. In
terms of the ethnic composition of the students, on average, 35% of the students are
White, 11.1% Black, 42.6% Hispanic, 7.6% Asian, and 3.8% Other. The average class
size is 26 and nearly all teachers are fully certified at an average of 95%. Further, on
average, 50% of students receive a free or reduced-price lunch. The average percent of
116
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 125 |
| Contributing entity | University of Southern California |
| Repository email | cisadmin@lib.usc.edu |
| Full text | 116 radius of each school. For a given pollutant and monitor, we calculate the total number of days that exceed the standards for that pollutant and then divide by the total number of days that are tested. Since students usually take the California Standards Tests in April or May, we use pollution data from September through May as an approximation of the pollution experienced during the school year. Then for a given pollutant at a given school in a given year, we take the weighted average of the percent of days exceeding the standard at each monitor, where the weighting is based on the inverse distance to the school. Thus we give monitors that are closer to the school more weight relative to ones that are further away. We have placed the summary statistics for our pollution variables in Panel B of Table 3.1, while in Table 3.2 we show the correlation matrix for the pollution variables. For all schools and years, an average of 0.0004%, 0.003%, 1.94%, 11.78%, and 28.32% days of the school year are above the standards for CO, NO2, O3, PM10, and PM2.5, respectively. The correlation matrix for the pollutants in Table 3.2 suggests that simultaneously using the different pollution measures is likely to cause a severe multicollinearity problem, and thus we follow the literature and enter them one at a time. PM10 and PM2.5 are particularly highly correlated; O3 is uncorrelated with the other measures. The summary statistics for the control variables are in Panel C of Table 3.1. In terms of the ethnic composition of the students, on average, 35% of the students are White, 11.1% Black, 42.6% Hispanic, 7.6% Asian, and 3.8% Other. The average class size is 26 and nearly all teachers are fully certified at an average of 95%. Further, on average, 50% of students receive a free or reduced-price lunch. The average percent of 116 |
