Essays in Labor Economics and Econometrics Público
Cahn, Yisroel (Spring 2022)
Abstract
This dissertation explores methods of estimating and evaluating policy interventions that have heterogeneous effects on the outcome of interest.
Policy evaluation generally compares an observed outcome of interest with the estimated counterfactual outcome had the policy not been implemented. Chiefly , this is done by comparing means or means conditional on a subgroup of the population. However, if the policy has heterogeneous effects on the distribution of outcomes — for example, if a policy affects low-wage workers differently than high-wage workers, then simply comparing means masks the diversity of outcomes a policy maker might be interested in. This is particularly relevant if the policy maker is interested in inequality or poverty. The first chapter surveys the literature on interpreting heterogeneous outcomes of a policy intervention.
The second chapter looks at an example — minimum wage policy. Recent proposals to raise the U.S. Federal minimum wage to $15 an hour are designed to improve the welfare of low-wage workers, but may involve important economic trade-offs. Although the effects of minimum wage on employment and wages have been well studied, little is known about its effects on hours worked which may be responsive to minimum wage changes. One reason for the lack of research is that hours are only observed for those who are employed, but workers could be exiting or entering the market and biasing the results. I fill this gap in the literature by employing a Heckman-type selection model to estimate the effects of minimum wage on hours worked, accounting for possible employment effects. Using U.S. Current Population Survey data, I found that increases to the minimum wage increased the hours worked of low-wage workers. However, I also found that the effects varied by industry—fast food and accommodation service workers saw their hours decrease while most other industries’ minimum wage workers saw their hours increase, suggesting that market structure could be causing these findings. Additionally, I propose a new method to estimate jointly determined outcomes, showing that hours and wages both increased for low-wage workers.
The final chapter uses machine learning methods to predict intergenerational income mobility in the United States. The machine learning methods are (1) non-parametric and are not sensitive to functional form misspecification, (2) give an out-of-sample performance indicator, and (3) allow for predictors to be ranked by how importance they are to the overall prediction. I find that family wealth, and not parent income, is the most important predictor of child income for large increases in the income distribution.
Table of Contents
Inequality and Policy Evaluation 1
Introduction 1
Facts about Inequality and Poverty in the US 3
ComparingDistributions 5
Measuring Inequality and the Social Welfare Function 5
Conclusion 12
Estimating Jointly Determined Outcomes: How Minimum Wage Affects Wages and Hours Worked 13
Introduction 13
The Minimum Wage Puzzle 13
Wages and Hours as a Joint Outcome 20
The Remainder of the Chapter 25
Literature Review 26
Predictions of Minimum Wage Effects on Employment 26
Empirical Results on Low-wage Workers 27
Distribution Effects of Minimum Wage 28
Employment Selection Model 30
Joint Distribution Model 31
Counterfactual Analysis Setting 31
Identification 33
Counterfactual Distribution 34
Estimation of the Counterfactual Distribution 35
Inference 37
Data 38
Data Cleaning 38
DataVisualization 39
Results 47
Effects on Minimum Wage Workers 47
Effects on Minimum Wage Workers by industry 50
Joint Distribution Results 50
Distribution Effects with Conditional Mean Methods 62
Conclusion 64
Who Needs Help: Off-the-shelf Machine Learning Methods to Predict Income Mobility 65
Introduction 65
Terminology 67
Data 67
Methods 71
Measuring Mobility 71
Random Forest 73
Gradient Boosting 74
Variable Importance 75
Results 76
Conclusion 83
Appendix A Simulation 84
Appendix B Proofs 88
Appendix C Jointly Dependent Outcomes 90
Bibliography 93
About this Dissertation
| School | |
|---|---|
| Department | |
| Degree | |
| Submission | |
| Language |
|
| Research Field | |
| Palavra-chave | |
| Committee Chair / Thesis Advisor | |
| Committee Members |
Primary PDF
| Thumbnail | Title | Date Uploaded | Actions |
|---|---|---|---|
|
|
Essays in Labor Economics and Econometrics () | 2022-03-30 13:16:20 -0400 |
|
Supplemental Files
| Thumbnail | Title | Date Uploaded | Actions |
|---|