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A Structural Analysis of the Impact of Policy on Economic Opportunity
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| 자료유형 | 학위논문 |
|---|---|
| 서명/저자사항 | A Structural Analysis of the Impact of Policy on Economic Opportunity. |
| 개인저자 | Kleineberg, Tatjana. |
| 단체저자명 | Yale University. Economics. |
| 발행사항 | [S.l.]: Yale University., 2019. |
| 발행사항 | Ann Arbor: ProQuest Dissertations & Theses, 2019. |
| 형태사항 | 165 p. |
| 기본자료 저록 | Dissertations Abstracts International 81-04A. Dissertation Abstract International |
| ISBN | 9781088315507 |
| 학위논문주기 | Thesis (Ph.D.)--Yale University, 2019. |
| 일반주기 |
Source: Dissertations Abstracts International, Volume: 81-04, Section: A.
Advisor: Moscarini, Giuseppe. |
| 이용제한사항 | This item must not be sold to any third party vendors.This item must not be added to any third party search indexes. |
| 요약 | This dissertation uses structural tools to analyze how policies affect economic opportunities of children from different socio-economic backgrounds who grow up in different neighborhoods of the United States.Chapter 1: "Can We Save the American Dream? An Analysis of the Effects of School Funding and Rent Subsidies on Local Opportunities"Neighborhoods in the United States differ substantially in the educational and economic opportunities that they offer to children who grow up in them. In the first chapter, joint work with Fabian Eckert, we develop and estimate a structural spatial equilibrium model of residential and education choice to study the effects of school financing and rent subsidy policies on education outcomes, intergenerational mobility, and welfare-at the local and aggregate level. Our model generates persistent effects of children's neighborhoods on adult outcomes through local labor market access and local human capital formation. Local school funding is an important component of the latter. We estimate the model using a range of US Census datasets by fitting model predictions to regional data of the actual US geography. We use the estimated model to study the effects of counterfactual policy interventions, in particular, the equalization of school funding across all students. We find that general equilibrium responses in local prices and local skill compositions significantly dampen the partial equilibrium effects of the policy, so that effects on education outcomes and intergenerational mobility are positive but only moderate in general equilibrium. In addition, we discuss additional policy experiments, such as guaranteeing a minimum level of school funding to all students and providing rent subsidies to low-income parents who live in selected neighborhoods.Chapter 2: "Benchmarking Global Optimizers"A key challenge of structural analyses is the estimation of model parameters. Estimation strategies oftentimes aim at minimizing the distance between moments that are observed in the data and corresponding moments that are constructed from structural economic models. To implement such estimation strategies, economists oftentimes have to minimize difficult objective functions. Finding the parameter values that correspond to the global minimum of the objective function is crucial to derive model implications and to implement policy counterfactuals. The second chapter, joint work with Antoine Arnoud and Fatih Guvenen, benchmarks the performance of seven global and three local optimizers in optimizing several difficult multidimensional objective functions. First, we apply the algorithms to optimize a small suite of multidimensional test functions that are commonly used to benchmark algorithms in applied mathematics. To understand optimizers' performance in applications that are common in economics, we further apply the same algorithms to a generalized method of moments (GMM) estimation problem, which estimates seven parameters. The benchmarking exercise can help applied economists to select the best algorithm to estimate structural models. Overall, we find that the Tiki-Taka algorithm (TIKTAK) performs best on the economic application and test functions. Our results furthermore show that the reliability of different optimizers can vary depending on the characteristics of the problem that is optimized and the computational budget that is available. Experimenting with different algorithms when estimating a model can therefore be very helpful. We find that the next-best performing optimizer for the economic application is the Multi-Level Single-Linkage (MLSL) algorithm. For the test functions, the next-best optimizers are the Stochastic Global Optimization (StoGo) and the Controlled Random Search (CRS) algorithm. |
| 일반주제명 | Economics. Public policy. |
| 언어 | 영어 |
| 바로가기 | 
: 이 자료의 원문은 한국교육학술정보원에서 제공합니다. |
