Three Essays in the Economics of Education: Dissertation Summary

This dissertation consists of three self-contained chapters investigating current issues in the economics of education. The first chapter investigates the effects of school closing policies on student achievement by examining over 200 school closings in Michigan. The second chapter examines the effects of a shortened school year policy on student achievement. Changing the length of the school year has dramatic potential effects for student achievement, but the magnitude of these effects will depend on the extent to which parents and teachers respond to the policy change. This study examines student achievement in public schools in Hawaii, which furloughed teachers on 17 Fridays during the 2009–2010 school year. The final chapter, coauthored with Seth Gershenson and Michael Hayes, looks at teacher grade reassignments in elementary schools. We use teacher-level micro data from Michigan to document the prevalence and distribution of grade-level reassignments across different types of schools and teachers

The Effect of Gender Inequality on Growth: A Cross-Country Empirical Study

Many papers investigating this topic have been subject to problems of multicollinearity. This paper seeks to avoid this problem by reformulating gender inequality as a ratio instead of simply examining female and male education as separate factors. The results indicate that, controlling for multicollinearity, high levels of gender inequality have a damping effect on growth. Furthermore, these results are robust to not only changes in included variables but changes in the specification of gender inequality

Revisiting the effects of unemployment insurance extensions on unemployment:A measurement-error-corrected regression discontinuity approach

This study documents two potential biases in recent analyses of UI benefit extensions using boundary-based identification: bias from using county-level aggregates and bias from across-border policy spillovers. To examine the first bias, the analysis uses a regression discontinuity approach that accounts for measurement error in county-level aggregates. These results suggest much smaller effects than previous studies, casting doubt on the applicability of border-based designs. The analysis then shows substantial spillover effects of UI benefit duration on across-border work patterns, consistent with increased tightness in high-benefit states and providing evidence against a dominant vacancy reduction response to UI extensions

A correction for regression discontinuity designs with group-specific mismeasurement of the running variable

When the running variable in a regression discontinuity (RD) design is measured with error, identification of the local average treatment effect of interest will typically fail. While the form of this measurement error varies across applications, in many cases the measurement error structure is heterogeneous across different groups of observations. We develop a novel measurement error correction procedure capable of addressing heterogeneous mismeasurement structures by leveraging auxiliary information. We also provide adjusted asymptotic variance and standard errors that take into consideration the variability introduced by the estimation of nuisance parameters, and honest confidence intervals that account for potential misspecification. Simulations provide evidence that the proposed procedure corrects the bias introduced by heterogeneous measurement error and achieves empirical coverage closer to nominal test size than “naive” alternatives. Two empirical illustrations demonstrate that correcting for measurement error can either reinforce the results of a study or provide a new empirical perspective on the data

The Differential Privacy Corner: What has the US Backed Itself Into?

An expanding body of data privacy research reveals that computational advances and ever-growing amounts of publicly retrievable data increase re-identification risks. Because of this, data publishers are realizing that traditional statistical disclosure limitation methods may not protect privacy. This paper discusses the use of differential privacy at the US Census Bureau to protect the published results of the 2020 census. We first discuss the legal framework under which the Census Bureau intends to use differential privacy. The Census Act in the US states that the agency must keep information confidential, avoiding “any publication whereby the data furnished by any particular establishment or individual under this title can be identified.” The fact that Census may release fewer statistics in 2020 than in 2010 is leading scholars to parse the meaning of identification and reevaluate the agency’s responsibility to balance data utility with privacy protection. We then describe technical aspects of the application of differential privacy in the U.S. Census. This data collection is enormously complex and serves a wide variety of users and uses -- 7.8 billion statistics were released using the 2010 US Census. This complexity strains the application of differential privacy to ensure appropriate geographic relationships, respect legal requirements for certain statistics to be free of noise infusion, and provide information for detailed demographic groups. We end by discussing the prospects of applying formal mathematical privacy to other information products at the Census Bureau. At present, techniques exist for applying differential privacy to descriptive statistics, histograms, and counts, but are less developed for more complex data releases including panel data, linked data, and vast person-level datasets. We expect the continued development of formally private methods to occur alongside discussions of what privacy means and the policy issues involved in trading off protection for accuracy