[PDF] Essays In Public Economics And Advanced Applied Econometrics eBook
Essays In Public Economics And Advanced Applied Econometrics Book in PDF, ePub and Kindle version is available to download in english. Read online anytime anywhere directly from your device. Click on the download button below to get a free pdf file of Essays In Public Economics And Advanced Applied Econometrics book. This book definitely worth reading, it is an incredibly well-written.
This dissertation explores various research questions in public economics and advanced applied econometrics. The three chapters of the dissertation analyze how to ensure access to high quality and well-coordinated health and elder care for the vulnerable populations in the U.S., and evaluate hospital care quality as measured by impact of health shocks on labor market outcomes in Denmark. In this process, I develop advanced applied econometric methods by integrating cutting-edge big data and machine learning tools with rigorous empirical economic analysis.
This dissertation consists of two chapters of in-depth empirical analyses in the domain of public economics, and one chapter of causal inference methodology regarding distributional robustness. The first chapter is titled ``The Effect of Air Pollution on Academic Performances: Evidence from South Korea, " and investigates the causal effect of air pollution on academic performances. While the effect of air pollution on health has been extensively studied, little is known about its effect on education, especially in a causal context. Here, I exploit the unique geography of Korea and a meteorological phenomenon called Asian Dust Storm (ADS) to get exogenous shocks of air pollution carried by the wind from China. Using two stage least squares regression, I find that an increase in particulate matter (PM10) leads to an increase in the share of students who underperform, while its effect on the share of students who overperform is not different from zero. I find similar results for elementary and middle school test outcomes, and find that air pollution disproportionately affects the types of schools associated with low socioeconomic status. Looking at both short term and long term effect of air pollutants, I find that air pollution has both acute and cumulative effect on the academic performances of the students. I explore health as a mechanism through which air pollution affects academic outcomes, and find that the most detrimental effect comes from the most harmful to health pollutants--PM10 and ozone--and do not find any evidence of preemptive absenteeism or mobilization due to air pollution on the school level. The second chapter, co-authored with Mark Duggan, Irena Dushi, and Gina Li, is titled ``The Effects of Changes in Social Security's Delayed Retirement Credit: Evidence from Administrative Data." The delayed retirement credit (DRC) increases monthly OASI (Old Age and Survivors Insurance) benefits for primary beneficiaries who claim after their full retirement age (FRA). For many years, the DRC was set at 3.0 percent per year (0.25 percent monthly). The 1983 amendments to Social Security more than doubled this actuarial adjustment to 8.0 percent per year. These changes were phased in gradually, so that those born in 1924 or earlier retained a 3.0 percent DRC while those born in 1943 or later had an 8.0 percent DRC. In this paper, we use administrative data from the Social Security Administration (SSA) to estimate the effect of this policy change on individual claiming behavior. We focus on the first half of the DRC increase (from 3.0 to 5.5 percent) given changes in other SSA policies that coincided with the later increases. Our findings demonstrate that the increase in the DRC led to a significant increase in delayed claiming of social security benefits and strongly suggest that the effects were larger for those with higher lifetime incomes, who would have a greater financial incentive to delay given their longer life expectancies. The third chapter, co-authored with Hongseok Namkoong, is titled `Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects." In this chapter, we propose the worst-case treatment effect (WTE) across all subpopulations of a given size, a conservative notion of topline treatment effect. Compared to the average treatment effect (ATE), whose validity relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and positive findings guarantee uniformly valid treatment effects over subpopulations. We develop a semiparametrically efficient estimator for the WTE, leveraging machine learning-based estimates of the heterogeneous treatment effect and propensity score. By virtue of satisfying a key (Neyman) orthogonality property, our estimator enjoys central limit behavior--oracle rates with true nuisance parameters--even when estimates of nuisance parameters converge at slower rates. For both randomized trials and observational studies, we establish a semiparametric efficiency bound, proving that our estimator achieves the optimal asymptotic variance. On real datasets where robustness to covariate shift is of core concern, we illustrate the non-robustness of ATE under even mild distributional shift, and demonstrate that the WTE guards against brittle findings that are invalidated by unanticipated covariate shifts.
This dissertation consists of two chapters of in-depth empirical analyses in the domain of public economics, and one chapter of causal inference methodology regarding distributional robustness. The first chapter is titled ``The Effect of Air Pollution on Academic Performances: Evidence from South Korea, " and investigates the causal effect of air pollution on academic performances. While the effect of air pollution on health has been extensively studied, little is known about its effect on education, especially in a causal context. Here, I exploit the unique geography of Korea and a meteorological phenomenon called Asian Dust Storm (ADS) to get exogenous shocks of air pollution carried by the wind from China. Using two stage least squares regression, I find that an increase in particulate matter (PM10) leads to an increase in the share of students who underperform, while its effect on the share of students who overperform is not different from zero. I find similar results for elementary and middle school test outcomes, and find that air pollution disproportionately affects the types of schools associated with low socioeconomic status. Looking at both short term and long term effect of air pollutants, I find that air pollution has both acute and cumulative effect on the academic performances of the students. I explore health as a mechanism through which air pollution affects academic outcomes, and find that the most detrimental effect comes from the most harmful to health pollutants--PM10 and ozone--and do not find any evidence of preemptive absenteeism or mobilization due to air pollution on the school level. The second chapter, co-authored with Mark Duggan, Irena Dushi, and Gina Li, is titled ``The Effects of Changes in Social Security's Delayed Retirement Credit: Evidence from Administrative Data." The delayed retirement credit (DRC) increases monthly OASI (Old Age and Survivors Insurance) benefits for primary beneficiaries who claim after their full retirement age (FRA). For many years, the DRC was set at 3.0 percent per year (0.25 percent monthly). The 1983 amendments to Social Security more than doubled this actuarial adjustment to 8.0 percent per year. These changes were phased in gradually, so that those born in 1924 or earlier retained a 3.0 percent DRC while those born in 1943 or later had an 8.0 percent DRC. In this paper, we use administrative data from the Social Security Administration (SSA) to estimate the effect of this policy change on individual claiming behavior. We focus on the first half of the DRC increase (from 3.0 to 5.5 percent) given changes in other SSA policies that coincided with the later increases. Our findings demonstrate that the increase in the DRC led to a significant increase in delayed claiming of social security benefits and strongly suggest that the effects were larger for those with higher lifetime incomes, who would have a greater financial incentive to delay given their longer life expectancies. The third chapter, co-authored with Hongseok Namkoong, is titled `Robust Causal Inference Under Covariate Shift via Worst-Case Subpopulation Treatment Effects." In this chapter, we propose the worst-case treatment effect (WTE) across all subpopulations of a given size, a conservative notion of topline treatment effect. Compared to the average treatment effect (ATE), whose validity relies on the covariate distribution of collected data, WTE is robust to unanticipated covariate shifts, and positive findings guarantee uniformly valid treatment effects over subpopulations. We develop a semiparametrically efficient estimator for the WTE, leveraging machine learning-based estimates of the heterogeneous treatment effect and propensity score. By virtue of satisfying a key (Neyman) orthogonality property, our estimator enjoys central limit behavior--oracle rates with true nuisance parameters--even when estimates of nuisance parameters converge at slower rates. For both randomized trials and observational studies, we establish a semiparametric efficiency bound, proving that our estimator achieves the optimal asymptotic variance. On real datasets where robustness to covariate shift is of core concern, we illustrate the non-robustness of ATE under even mild distributional shift, and demonstrate that the WTE guards against brittle findings that are invalidated by unanticipated covariate shifts.
This dissertation is comprised of two essays in political economy and one in micro-econometrics. Each of them proposes an alternative methodology to improve on the estimation of a specific economic phenomenon. The first essay studies the political allocation of US federal resources to localities taking into consideration that the states are also actively involved in allocating resources to localities. I found that federal funds are biased towards localities within states that are not represented by the same party as the one that represents the federal government. This finding implies that a strategic federal government takes into account that non-aligned states have different spending priorities. These results suggest that past research on the allocation of federal resources to localities has shown biased estimates when the political allocation of resources is not studied in the context of a multi-layered government environment. The second essay exploits the existence of extended interlude periods (i.e., time between elections and government change date) from Latin American countries to identify a causal effect of a change in the probability of electoral defeat on a change in the budget deficit. Theoretical studies on the strategic use of debt argue that governments issue more debt when facing a higher probability of electoral defeat. Testing this hypothesis has proven challenging since measures of that probability are potentially endogenous. Since my identification strategy is focused on identifying the effects of electoral surprises, I provide a plausible source of exogenous variation. I find that the higher the increase in the probability of electoral defeat (victory), the larger the increase (decrease) in the deficit. The third essay studies the properties of a maximum likelihood estimator (MLE) of dynamic panel data models with fixed effect when difference GMM methods suffer from weak identification. While previous studies propose moments to solve the weak identification under difference GMM for stationary processes only, this study shows that MLE solves the weak identification issue not only when the process is stationary, but also when it is not.
Robert Engle received the Nobel Prize for Economics in 2003 for his work in time series econometrics. This book contains 16 original research contributions by some the leading academic researchers in the fields of time series econometrics, forecasting, volatility modelling, financial econometrics and urban economics, along with historical perspectives related to field of time series econometrics more generally. Engle's Nobel Prize citation focuses on his path-breaking work on autoregressive conditional heteroskedasticity (ARCH) and the profound effect that this work has had on the field of financial econometrics. Several of the chapters focus on conditional heteroskedasticity, and develop the ideas of Engle's Nobel Prize winning work. Engle's work has had its most profound effect on the modelling of financial variables and several of the chapters use newly developed time series methods to study the behavior of financial variables. Each of the 16 chapters may be read in isolation, but they all importantly build on and relate to the seminal work by Nobel Laureate Robert F. Engle.
The study of public economics has undergone dramatic changes in the past two decades. Major developments in economic theory have revolutionized the subject and have changed the way we view the role of government. The constraints of information and institutions have called into question the ability of the government to carry out some of its traditional tasks, but have also led to new instruments and approaches for dealing with the problem of economic policy such as the design of the redistribution and tax system. Understanding the importance of the economic, behavioral and institutional constraints facing government is critical for evaluating policy options. This is ultimately an empirical issue. This book of a symposium on empiricial public finance indicates the richness and diversity of empirical approaches that have been used to shed light on the problems of applied public finance and its application.
In Chapter 1, I propose a two-step instrumental variable approach to identify group pattern with modal regression (modal IV) with endogeneity. The proposed modal IV estimator and its confidence interval are bias-corrected. Simulation studies shown that when the clusters in the data are clear, modal IV regression can successfully identify the heterogeneous group coefficients and the confidence interval has a decent coverage rate. Modal IV regression can possibly apply to many existing literature which allows heterogeneous parameters. Chapter 2 evaluating how policy impacts vary by different context. A national policy can involve various contexts where participant reactions to the same treatment are distributed differently. We propose a modal regression approach to find grouped patterns and how policy effects depend on them. Then, we apply it to evaluating the New Rural Cooperative Medical Scheme, a health insurance program targeting all rural residents in China. Results suggest that: there were three representative rural contexts across China; the health program reduced the out-of-pocket medical spending for the majority of rural residents in only one of the contexts; the contexts where the majority of participants did not financially benefit from the program involves households with higher medical expenditure. Chapter 3 explores a well studied economic question under a new context. We studies the effect of competition on product variety in a novel market: a live streaming platform. We propose a multi-task graph convolutional neural network approach to measure market competition intensity. We leverage a natural experiment, an unexpected account suspension of the most popular streamer, to identify the effect. Our results shows that, market competition increases product variety, and retail prices are sticky and do not adjust to the exogenous competition shock.