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Essays in Economics and Education
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Turley, Patrick Ansel. 2016. Essays in Economics and Education. Doctoral dissertation, Harvard
University, Graduate School of Arts & Sciences.
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Essays in Economics and Education

A dissertation presented
by
Patrick Ansel Turley
to
The Department of Economics

in partial fulfillment of the requirements
for the degree of
Doctor of Philosophy
in the subject of
Economics

Harvard University
Cambridge, Massachusetts

May 2016
© 2016 Patrick Ansel Turley
All rights reserved.
Dissertation Advisor: Professor David Laibson Patrick Ansel Turley

Essays in Economics and Education

Abstract

Education is a fundamental input of human capital formation. In this dissertation we
explore topics related to how much and what time of human capital individuals invest in, and the
long term-consequences of these investments. We begin with by measuring the degree to which
financial incentives can affect a college student’s field of study. Next, we attempt to identify
genetic variants associated with increased educational attainment and examine the biological
systems implicated by this analysis. Last, we test for heterogeneous treatment effects of
education on health across the distribution of observed health and across a genetic predictor of
health.
In chapter 1, we examine whether students respond to immediate financial incentives
when choosing their college major. From 2006-07 to 2010-11, low-income students in technical
or foreign language majors could receive up to $8,000 in SMART Grants. Since income-
eligibility was determined using a strict threshold, we determine the causal impact of this grant
on student major with a regression discontinuity design. Using administrative data from public
universities in Texas, we determine that income-eligible students were 3.2 percentage points
more likely than their ineligible peers to major in targeted fields. We measure a larger impact of
10.2 percentage points at Brigham Young University.
iii
In chapter 2 we find that, educational attainment (EA) is strongly influenced by social
and other environmental factors, but genetic factors are also estimated to account for at least 20%
of the variation across individuals. We report the results of a genome-wide association study
(GWAS) for EA that extends our earlier discovery sample of 101,069 individuals to 293,723
individuals, and a replication in an independent sample of 111,349 individuals from the UK
Biobank. We now identify 74 genome-wide significant loci associated with number of years of
schooling completed. Single-nucleotide polymorphisms (SNPs) associated with educational
attainment are disproportionately found in genomic regions regulating gene expression in the
fetal brain. Candidate genes are preferentially expressed in neural tissue, especially during the
prenatal period, and enriched for biological pathways involved in neural development. Our
findings demonstrate that, even for a behavioral phenotype that is mostly environmentally
determined, a well-powered GWAS identifies replicable associated genetic variants that suggest
biologically relevant pathways. Because EA is measured in large numbers of individuals, it will
continue to be useful as a proxy phenotype in efforts to characterize the genetic influences of
related phenotypes, including cognition and neuropsychiatric disease.
In 1972, the mandatory minimum age at which a student could drop out of school in
England and Wales was raised from 15 to 16, constraining roughly 15 percent of the student
population. In chapter 3, we exploit this discontinuous increase in educational attainment to
estimate the impact of education on body mass index (BMI) and diabetes approximately 40 years
later. While previous literature found no significant effect of education on health, they were not
able to investigate whether these effects vary along the distribution of health outcomes. We are
able to detect large effects on BMI in the upper quantiles of observed BMI, as large as 2 BMI
points at the 90th percentile of BMI, from a baseline of 35.6. Using a genetic predictor of BMI,
iv
we also find that those with higher genetic risk of obesity see smaller reductions in BMI as a
result of the increase in compulsory schooling while large reductions are seen in those with low
genetic risk. Taken together our results point to the importance of considering heterogeneity
when estimating the impacts of education on health.

v
Table of Contents

Front Matter ..................................................................................................................................... i
Title page ................................................................................................................................... i
Copyright page .......................................................................................................................... ii
Abstract .................................................................................................................................... iii
Table of Contents ..................................................................................................................... vi
List of Figures ......................................................................................................................... vii
List of Tables ......................................................................................................................... viii
Acknowledgements .................................................................................................................. ix
Chapter 1 ..........................................................................................................................................1
I. Introduction ............................................................................................................................2
II. The SMART Grant Program .................................................................................................7
III. Data ....................................................................................................................................10
IV. Identification ......................................................................................................................15
V. Results .................................................................................................................................23
VI. Conclusion .........................................................................................................................50
Chapter 2 ........................................................................................................................................53
Chapter 3 ........................................................................................................................................78
I. Introduction ..........................................................................................................................79
II. Institutional Background .....................................................................................................83
III. Data ....................................................................................................................................84
IV. Mean Effect of Education on Health .................................................................................91
V. Heterogeneous Effects ......................................................................................................105
VI. Genetic Heterogeneity .....................................................................................................117
VII. Robustness ......................................................................................................................137
VIII. Discussion .....................................................................................................................142
IX. Conclusion .......................................................................................................................146
References ....................................................................................................................................148

vi
List of Figures
Chapter 1
Figure 1.1: Covariate Checks ........................................................................................................ 21
Figure 1.2: Total SMART Grant, SMART Majors ....................................................................... 25
Figure 1.3: Ever Receive SMART Grant....................................................................................... 26
Figure 1.4: SMART Major in Junior Year .................................................................................... 32
Figure 1.5: SMART Major in Senior Year .................................................................................... 33
Figure 1.6: SMART Degrees ......................................................................................................... 34
Figure 1.7: Fraction SMART Classes—BYU Only ...................................................................... 35
Figure 1.8: Estimates by Year ....................................................................................................... 42
Figure 1.9: Various Bandwidths .................................................................................................... 48
Chapter 2
Figure 2.1: Manhattan Plot ............................................................................................................ 57
Figure 2.2: Quantile-quantile Plot ................................................................................................. 58
Figure 2.3: TheDdistribution of Effect Sizes ................................................................................. 59
Figure 2.4: Assessing Population Stratification ............................................................................ 61
Figure 2.5: Replication .................................................................................................................. 62
Figure 2.6: Genetic Correlations .................................................................................................... 64
Figure 2.7: Q-Q plots of Other Phenotypes ................................................................................... 65
Figure 2.8: Regional Association Plots ......................................................................................... 66
Figure 2.9: Tissue-level Biological Annotation ............................................................................. 68
Figure 2.10: Overview of Biological Annotation .......................................................................... 70
Figure 2.11: Gene-level Biological Annotation ............................................................................. 72
Figure 2.12: PredictivePpower of PGS in Sweden by Birth Cohort .............................................. 76
Chapter 3
Figure 3.1: Geographic Distribution of the BMI Genetic Score .................................................... 92
Figure 3.2: McCrary Test Scatter Plot ........................................................................................... 96
Figure 3.3: Balance Tests .............................................................................................................. 97
Figure 3.4: Fraction in School till Various Ages by Date of Birth .............................................. 100
Figure 3.5: RD Estimates for Fraction in School till Various Ages ............................................ 101
Figure 3.6: Mean BMI by Date of Birth ...................................................................................... 104
Figure 3.7: Fraction with Self-reported Diabetes by Date of Birth ............................................. 106
Figure 3.8: Various Quantiles of BMI by Date of Birth .............................................................. 111
Figure 3.9: The Effect of the 1972 ROSLA on the Distribution of BMI .................................... 112
Figure 3.10: Effect of School Till Age 16 on the Distribution of BMI (Compliers) ................... 114
Figure 3.11: RD Estimates for Fraction with a BMI above Thresholds ...................................... 115
Figure 3.12: 2SLS Estimates for Fraction with a BMI above Thresholds ................................... 116
Figure 3.13: RD Estimates for Fraction with a BMI above Thresholds (Rescaled) .................... 118
Figure 3.14: First-Stage Estimates by Genetic BMI Score Tercile ............................................. 122
Figure 3.15: Reduced-Form Estimates for BMI by Genetic BMI Score Tercile ......................... 124
Figure 3.16: Reduced-Form for the 90th P-tile of BMI by Genetic BMI Score Tercile ............. 126
Figure 3.17: Reduced-Form Estimates for Diabetes by Genetic BMI Score Tercile .................. 129
Figure 3.18: The Effect of Staying in School till Age 16 on BMI by BMI Score Tercile........... 131
Figure 3.19: The Effect of Staying in School till Age 16 on Diabetes by BMI Score Tercile .... 132
Figure 3.20: Bandwidth Analysis of First Stage .......................................................................... 138
Figure 3.21: Bandwidth Analysis of Reduced-Form (BMI) ........................................................ 139
Figure 3.22: Bandwidth Analysis of Reduced-Form (90th Percentile of BMI) .......................... 140
Figure 3.23: Bandwidth Analysis of Reduced-Form (Diabetes) ................................................. 141
vii
List of Tables
Chapter 1
Table 1.1: Summary Statistics ................................................................................................12
Table 1.2: SMART Grant Receipt ...........................................................................................27
Table 1.3: Effects on Major .....................................................................................................30
Table 1.4: Yearly Discontinuities ............................................................................................39
Table 1.5: STEM and Language Outcomes .............................................................................45
Table 1.6: Heterogeneity by Sophomore Major ......................................................................49
Chapter 2
Table 2.1: Selected candidate genes implicated by bioinformatics analyses ...........................74
Chapter 3
Table 3.1: Summary Statistics .................................................................................................87
Table 3.2: McCrary Test and Balance Tests ............................................................................98
Table 3.3: First-Stage Estimates ............................................................................................102
Table 3.4: Mean Effect of Education on BMI .......................................................................103
Table 3.5: Mean Effect of Education on Diabetes .................................................................107
Table 3.6: Genetic Heterogeneity in the First-Stage ..............................................................121
Table 3.7: Genetic Heterogeneity in the Reduced-Form for BMI .........................................123
Table 3.8: Genetic Heterogeneity in the Reduced-Form for the 90th Percentile of BMI ......125
Table 3.9: Genetic Heterogeneity in the Reduced-Form for Diabetes...................................128
Table 3:10: Genetic Heterogeneity in the 2SLS Estimates for BMI and Diabetes ................130
Table 3.11: Selected Genetic Correlations with BMI ............................................................134
Table 3.12: The Common Contributions to Heterogeneity of the BMI and EA Scores ........136
Table 3.13: Local Linear versus Global Polynomial .............................................................143

viii
Acknowledgments
For their help in completing this dissertation and support during my graduate years,
thanks to my committee David Laibson, Larry Katz, and Dan Benjamin. And a very special
thanks to Jonathan Jala, who help proof-read this document and who has supported me through
the ups and downs of the years.
For chapter 1, we would like to thank the Texas Higher Education Coordinating Board
and Brigham Young University for providing the data. We also would like to thank two
anonymous referees, Sandra Black, Lawrence Katz, Dayanand Manoli, Amanda Pallais, Carole
Voulgaris, Robert Turley, participants in the University of Texas at Austin Labor Lunch, the
Education and Transition to Adulthood Group of the Population Research Center at the
University of Texas at Austin, and participants at the STATA Texas Empirical Micro Conference
for helpful comments on the draft. We would also like to thank Kelli Bird for data on the timing
of FAFSA filing.
The research for chapter 2 was carried out under the auspices of the Social Science
Genetic Association Consortium (SSGAC). The SSGAC seeks to facilitate studies that
investigate the influence of genes on human behavior, well-being, and social-scientific outcomes
using large genome-wide association study meta-analyses. The SSGAC also provides
opportunities for replication and promotes the collection of accurately measured, harmonized
phenotypes across cohorts. The SSGAC operates as a working group within the CHARGE
consortium. This research has also been conducted using the UK Biobank Resource. This study
was supported by funding from the Ragnar Söderberg Foundation (E9/11), the Swedish Research
Council (421-2013-1061), The Jan Wallander and Tom Hedelius Foundation, an ERC
Consolidator Grant (647648 EdGe), the Pershing Square Fund of the Foundations of Human
ix