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Essays in Labor Economics.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Essays in Labor Economics./
作者:	Patnaik, Arpita.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	211 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
Contained By:	Dissertations Abstracts International83-02B.
標題:	Gender differences. -
電子資源:	http://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28645091
ISBN:	9798534658170

Essays in Labor Economics.
Patnaik, Arpita.

Essays in Labor Economics. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 211 p.

Source: Dissertations Abstracts International, Volume: 83-02, Section: B.

Thesis (Ph.D.)--The University of Wisconsin - Madison, 2021.

This item must not be sold to any third party vendors.

In the first chapter, I study the implications of short-term costs imposed by pricing structures on college major choice and the role of financial constraints. I examine the effect of major-specific pricing policies on major choice and on the distribution of low-income students across majors. Using the introduction of a surcharge policy in the Engineering and Business programs, I find that raising the program specific tuition by \\$1000 (11\\%) decreases the probability of graduating in Business by 33\\% and in Engineering by 12\\% and this is driven by the response of low-income students. I then exploit this price variation to identify the labor market returns to these majors. Using these estimates, I find that students are highly responsive to prices despite large earnings losses from switching majors. Motivated by the empirical evidence, I develop and estimate a structural model of college major choice that quantifies the importance of direct price effects and credit constraints. The model estimates suggest that credit constraints rationalize the sensitivity of students to changes in pricing structures. Complementing price differentials with expansions of borrowing limits and means-tested subsidies can minimize the distortion created by pricing. The second chapter characterizes remote work or work-from-home (WFH) jobs and quantifies the welfare gain from these work arrangements. Using data from the ATUS and CPS, I develop a measure of locational flexibility at work. Motivated by the patterns of sorting in the data, I then develop and estimate a generalized model of sectoral choice with amenities. The structural estimates point to differences by gender in the returns to education and experience, compositional differences as well as preferences. I also find that the gender wage gap persists in remote work at 3.9 dollars, most of which is determined by differences in the returns to observable characteristics. With the help of this framework, I find that women have higher valuation (WTP) of these jobs than men. On average, women are willing to pay 3.8 percent of the average hourly wage for locationally flexible jobs whereas men have a low willingness to pay (0.6 percent of hourly wage) for these jobs. Further, college graduates value remote work more than workers without college education with college educated women in particular valuing remote work the most at 4.3 percent of the hourly wage. In chapter three, we estimate a rich model of college major choice using a panel of experimentally derived data. Our estimation strategy combines two types of data: data on self-reported beliefs about future earnings from potential human capital decisions, and survey-based measures of risk and time preferences. We show how to use this data to identify a general life-cycle model, allowing for rich patterns of heterogeneous beliefs and preferences. Our data allow us to separate perceptions about the degree of risk or perceptions about the current versus future payoffs for a choice from the individual's preference for risk and patience. Comparing our estimates of the general model to estimates of models which ignore heterogeneity in risk and time preferences, we find that these restricted models are likely to overstate the importance of earnings to major choice. Additionally, we show that while men are less risk averse and patient than women, gender differences in non-pecuniary tastes, rather than gender differences in risk aversion and patience over earnings, are the primary driver of gender gaps in major choice.

ISBN: 9798534658170Subjects--Topical Terms:

3548331
Gender differences.
Subjects--Index Terms:

Labor economics

Essays in Labor Economics.
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500 $a Source: Dissertations Abstracts International, Volume: 83-02, Section: B.
500 $a Advisor: Wiswall, Matthew James.
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506 $a This item must not be sold to any third party vendors.
520 $a In the first chapter, I study the implications of short-term costs imposed by pricing structures on college major choice and the role of financial constraints. I examine the effect of major-specific pricing policies on major choice and on the distribution of low-income students across majors. Using the introduction of a surcharge policy in the Engineering and Business programs, I find that raising the program specific tuition by \\$1000 (11\\%) decreases the probability of graduating in Business by 33\\% and in Engineering by 12\\% and this is driven by the response of low-income students. I then exploit this price variation to identify the labor market returns to these majors. Using these estimates, I find that students are highly responsive to prices despite large earnings losses from switching majors. Motivated by the empirical evidence, I develop and estimate a structural model of college major choice that quantifies the importance of direct price effects and credit constraints. The model estimates suggest that credit constraints rationalize the sensitivity of students to changes in pricing structures. Complementing price differentials with expansions of borrowing limits and means-tested subsidies can minimize the distortion created by pricing. The second chapter characterizes remote work or work-from-home (WFH) jobs and quantifies the welfare gain from these work arrangements. Using data from the ATUS and CPS, I develop a measure of locational flexibility at work. Motivated by the patterns of sorting in the data, I then develop and estimate a generalized model of sectoral choice with amenities. The structural estimates point to differences by gender in the returns to education and experience, compositional differences as well as preferences. I also find that the gender wage gap persists in remote work at 3.9 dollars, most of which is determined by differences in the returns to observable characteristics. With the help of this framework, I find that women have higher valuation (WTP) of these jobs than men. On average, women are willing to pay 3.8 percent of the average hourly wage for locationally flexible jobs whereas men have a low willingness to pay (0.6 percent of hourly wage) for these jobs. Further, college graduates value remote work more than workers without college education with college educated women in particular valuing remote work the most at 4.3 percent of the hourly wage. In chapter three, we estimate a rich model of college major choice using a panel of experimentally derived data. Our estimation strategy combines two types of data: data on self-reported beliefs about future earnings from potential human capital decisions, and survey-based measures of risk and time preferences. We show how to use this data to identify a general life-cycle model, allowing for rich patterns of heterogeneous beliefs and preferences. Our data allow us to separate perceptions about the degree of risk or perceptions about the current versus future payoffs for a choice from the individual's preference for risk and patience. Comparing our estimates of the general model to estimates of models which ignore heterogeneity in risk and time preferences, we find that these restricted models are likely to overstate the importance of earnings to major choice. Additionally, we show that while men are less risk averse and patient than women, gender differences in non-pecuniary tastes, rather than gender differences in risk aversion and patience over earnings, are the primary driver of gender gaps in major choice.
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