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Dissertation on Estimating School Choice Effects Using Centralized School Assignments.

紀錄類型:	書目-電子資源 : Monograph/item
正題名/作者:	Dissertation on Estimating School Choice Effects Using Centralized School Assignments./
作者:	Liu, Siyan.
出版者:	Ann Arbor : ProQuest Dissertations & Theses, : 2021,
面頁冊數:	106 p.
附註:	Source: Dissertations Abstracts International, Volume: 83-01, Section: A.
Contained By:	Dissertations Abstracts International83-01A.
標題:	Enrollments. -
電子資源:	https://pqdd.sinica.edu.tw/twdaoapp/servlet/advanced?query=28552516
ISBN:	9798522941284

Dissertation on Estimating School Choice Effects Using Centralized School Assignments.
Liu, Siyan.

Dissertation on Estimating School Choice Effects Using Centralized School Assignments. - Ann Arbor : ProQuest Dissertations & Theses, 2021 - 106 p.

Source: Dissertations Abstracts International, Volume: 83-01, Section: A.

Thesis (Ph.D.)--North Carolina State University, 2021.

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

This dissertation provides empirical evidence on estimating the treatment effects of school choice programs using quasi-experimental data. Chapter 1 implements the new econometric method proposed by Abdulkadiroglu et al. (2017a) in the context of the magnet school program in Wake County, North Carolina. Chapter 2 gives an in-depth analysis of the assignment propensity scores used in Chapter 1. Using simulation data, this chapter proposes two measures for the "ideal" propensity score distribution for the identification of treatment effects.This dissertation contributes to the recent line of literature on estimating school choice effects using data from centralized assignments. The magnet program in Wake County, North Carolina, is leveraged to address school overcrowding issues by attracting students from overcrowded traditional schools to magnet schools. Chapter 1 evaluates how the magnet program helps students from overcrowded base schools and accounts for differential treatment effects for two student subgroups that vary by their base school overcrowding status. In doing so, this chapter shows an additional benefit of the magnet program of alleviating overcrowding without harming student outcomes.School overcrowding is a pressing issue faced by many large urban school districts. Chapter 1 evaluates the role of an existing school choice program in alleviating overcrowding in Wake County, NC. The Wake County magnet school program uses centralized assignment to reallocate students with policies to attract students from affluent areas to undersubscribed schools in areas with a larger proportion of economically disadvantaged students. Wake County leverages this system to address school overcrowding at traditional schools through prioritizing applicants of students from overcrowded schools. This paper fully exploits the random variation in the offer of magnet seats at all magnet schools through conditioning on assignment propensity scores and causally identifies the effect of magnet attendance by overcrowding status of origin schools following the method in Abdulkadiroglu et al. (2017a). Results suggest no evidence of differential gains in maths and literacy skills from magnet attendance by students from overcrowded base schools. However, students from a high-performance overcrowded base school experience higher magnet effects on reduced absenteeism compared to students from a high-performance non-overcrowded base school. Chapter 2 closely examines the method used in Chapter 1 by Abdulkadiroglu et al. (2017a). Their approach fully exploits the random variation created by lotteries through conditioning on assignment propensity scores, or the probability of receiving an offer from schools.Chapter 2 studies how characteristics of an assignment process contribute to differences in values and distributions of applicants' propensity scores. I develop measures for the "ideal" propensity score distributions for practitioners using the propensity score conditioning method. I then examine the relationship between these measures and causal identification through simulated data on student outcomes. This chapter gives intuition behind the random variation captured by propensity scores and evaluates the precision of treatment effect estimates given a propensity score distribution.Chapter 3 showcases my other line of research on retirement. This chapter studies the income replacement rates of North Carolina public employees as they transition to retirement using administrative records merged with survey data.

ISBN: 9798522941284Subjects--Topical Terms:

3562827
Enrollments.

Dissertation on Estimating School Choice Effects Using Centralized School Assignments.
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520 $a This dissertation provides empirical evidence on estimating the treatment effects of school choice programs using quasi-experimental data. Chapter 1 implements the new econometric method proposed by Abdulkadiroglu et al. (2017a) in the context of the magnet school program in Wake County, North Carolina. Chapter 2 gives an in-depth analysis of the assignment propensity scores used in Chapter 1. Using simulation data, this chapter proposes two measures for the "ideal" propensity score distribution for the identification of treatment effects.This dissertation contributes to the recent line of literature on estimating school choice effects using data from centralized assignments. The magnet program in Wake County, North Carolina, is leveraged to address school overcrowding issues by attracting students from overcrowded traditional schools to magnet schools. Chapter 1 evaluates how the magnet program helps students from overcrowded base schools and accounts for differential treatment effects for two student subgroups that vary by their base school overcrowding status. In doing so, this chapter shows an additional benefit of the magnet program of alleviating overcrowding without harming student outcomes.School overcrowding is a pressing issue faced by many large urban school districts. Chapter 1 evaluates the role of an existing school choice program in alleviating overcrowding in Wake County, NC. The Wake County magnet school program uses centralized assignment to reallocate students with policies to attract students from affluent areas to undersubscribed schools in areas with a larger proportion of economically disadvantaged students. Wake County leverages this system to address school overcrowding at traditional schools through prioritizing applicants of students from overcrowded schools. This paper fully exploits the random variation in the offer of magnet seats at all magnet schools through conditioning on assignment propensity scores and causally identifies the effect of magnet attendance by overcrowding status of origin schools following the method in Abdulkadiroglu et al. (2017a). Results suggest no evidence of differential gains in maths and literacy skills from magnet attendance by students from overcrowded base schools. However, students from a high-performance overcrowded base school experience higher magnet effects on reduced absenteeism compared to students from a high-performance non-overcrowded base school. Chapter 2 closely examines the method used in Chapter 1 by Abdulkadiroglu et al. (2017a). Their approach fully exploits the random variation created by lotteries through conditioning on assignment propensity scores, or the probability of receiving an offer from schools.Chapter 2 studies how characteristics of an assignment process contribute to differences in values and distributions of applicants' propensity scores. I develop measures for the "ideal" propensity score distributions for practitioners using the propensity score conditioning method. I then examine the relationship between these measures and causal identification through simulated data on student outcomes. This chapter gives intuition behind the random variation captured by propensity scores and evaluates the precision of treatment effect estimates given a propensity score distribution.Chapter 3 showcases my other line of research on retirement. This chapter studies the income replacement rates of North Carolina public employees as they transition to retirement using administrative records merged with survey data.
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