The Impact of 2021 Advance Child Tax Credit Payments on Low-Income Households’ Labor Supply
by Zixin “Ellen” Zhang
Abstract
Studies have established that the Advance 2021 Child Tax Credit (CTC) payments substantially reduced poverty and food insecurity, but some claim that the CTC payments may create negative labor supply effects that could offset its hardship-reduction benefits. Researchers have used a variety of methods to measure how the monthly CTC payments affect the labor supply of households, but the results vary from significant decreases to no significant change to even increases in household labor supply. Using a method novel to this literature, I estimate the labor supply impacts of Advance 2021 CTC by analyzing labor supply changes in response to real amounts of CTC received, which varies by household depending on regional cost-of-livings. Through fixed effects linear regressions across many different combinations of household type and income level, I find that, on average, receiving Advance CTC caused a statistically significant decrease in household labor supply. However, for different household subgroups, I find both statistically significant and insignificant labor supply impacts as well as both increases, decreases, and no change in households’ labor supply due to monthly CTC payments. This suggests that the impacts of 2021 Advance CTC on household labor depend heavily on a household’s situation, specifically income level and household composition. These household-specific patterns align with prior research on the Advance 2021 CTC and how welfare payments are used by families.
Professor Thomas Nechyba, Faculty Advisor
JEL Codes: C31, H24, I38, J22
Technological Impacts on Return to Education in Brazil
by Yirui Zhao
Abstract
The wage return to education has been studied for a long time. Acemoglu and Autor (2010) connect the decrease of medium-level job opportunities in the U.S. with technological advances. Their theoretical model predicts that if technology replaces routine jobs, workers with medium-level skills will experience decreases in wages relative to both high-skill workers (who become more productive with the improved technology) and low-skill workers (who can less easily be replaced since their work is not routine). Moreover, their theoretical model predicts that if medium-skill workers are closer substitutes for low-skill workers than they are for high-skill workers, the relative return of high-skill workers to low-skill workers should increase. Using education as proxy of skill (Acemoglu & Autor, 2012), this paper checks if these three predictions about relative wage returns to education also hold in Brazil. This paper finds that the impact of technological change on the Brazilian formal labor market between 1986 and 2010 is consistent with predicted changes in the return to education for medium-skill workers relative to both low and high skill workers. The impact is consistent with predicted changes in the return to education for high-skill workers relative to low-skill workers when Lula’s presidency is considered in the model.
Michelle Connolly, Faculty Advisor
Rafael Dix-Carneiro, Faculty Advisor
Daniel Xu, Faculty Advisor
JEL Codes: J24; J31; O33
Short and Long-Term Impacts of a Large-Scale Natural Disaster on Individual Labor Outcomes: Evidence from the 2004 Indian Ocean Tsunami
by Tony Sun
Abstract
Natural disasters are often highly disruptive to the livelihoods of impacted populations. This paper investigates the effects of the 2004 Indian Ocean tsunami on male wages and labor supply from its immediate aftermath into the long run. Using fixed effects models that account for individual-specific heterogeneity, I find evidence of significant real wage declines for workers from heavily damaged areas that persist beyond the short-term. This long-term wage effect contrasts with previous literature, particularly in the context of relatively less severe disasters. Male workers also increased their hours-of-work following the tsunami, which suggests reliance on labor markets to smooth income losses and shifted their labor towards less disrupted industries. Additionally, I document the heterogeneity of tsunami impact on wages and hours-of-work by birth cohort and education, as well as by industry and sector of employment.
Professor Duncan Thomas, Faculty Advisor
Professor Michelle P. Connolly, Faculty Advisor
JEL Codes: J2; J21; J30; O10; Q54
Revisiting California Proposition 209: Changes in Science Persistence Rates and Overall Graduation Rates
by Anh-Huy Nguyen
Abstract
California Proposition 209 outlawed race-based affirmative action in the University
of California (UC) system in 1998. However, the UC system subsequently shifted towards
race-blind affirmative action by also reweighing factors other than race in the
admissions process. To evaluate the hypothetical changes in the science persistence rate
and graduation rate of all applicants if racial preferences had been removed entirely, I
estimate baseline and counterfactual admissions models using data from between 1995-
1997. Using a general equilibrium framework to fix the total number of admits and
enrollees, I find that the removal of racial preferences leads to a cascade of minority
enrollees into less selective campuses and a surge of non-minority enrollees into more
selective campuses. The improved matching between students and campuses results in
higher science persistence rates and graduation rates across the pool of all applicants.
In particular, the gains are driven by minority students who were admitted under racial
preferences, because the gains from better matching across UC campuses outweigh the
losses from potentially being pushed outside the UC system. Non-minority students
who are originally rejected under racial preferences also benefit, as some are induced
into the system in the counterfactual, where they are more likely to graduate. I also
investigate claims that applicants may have strategically gamed during the admissions
process by misrepresenting their interest in the sciences in order to maximize their
admissions probability. While there exist incentives to apply in different majors across
the campuses, I find evidence that applicants often fail to game optimally, suggesting
that they may not be fully informed of their relative admissions probabilities in the
sciences and non-sciences.
Professor Peter Arcidiacono, Faculty Advisor
JEL Codes: I23, I28, J24, H75
Predicting the Work Task Replacement Effects of the Adoption of Machine Learning Technology
by Shreya Hurli
Abstract
This paper develops a methodology to attempt to predict which tasks in the workforce will be resistant to the replacement of labor by machine learning technology in the near future given current technology and technology adoption trends. Tasks are individual activities completed as parts of a job. Prior research in the field suggests that characteristics of tasks (non-roteness, creativity, analysis/cognitive work) that make them harder for machine learning technology to complete are good predictors of whether those tasks will be resistant to replacement in the workforce. This study utilizes O*NET (Occupational Information Network) task description and education data from October 2015 to August 2020 and Bureau of Labor Statistics salary data to use task characteristics to predict tasks’ resistance to replacement. Normalized scores, average salaries, and average worker education levels are calculated to quantify the relative presence or absence of non-roteness, creativity, and cognitive work in a task. This paper then uses the calculated scores, salary, and education data, as well as a number of interaction terms as inputs to a support vector machine (SVM) model to predict which tasks will be resistant to decline in their shares of workplace tasks weighted by the jobs under which the tasks fall. Using task characteristics, the SVM predicts that just approximately 39% of tasks are likely to be resistant to replacement. These tasks tend to be highly non-deterministic (very non-rote, analytical/cognitive, and/or creative) in nature.
Professor David Berger, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: J23, J24, O33
Maternal Grandparent Living Arrangements and the Motherhood Wage Penalty for Mothers in China
by Mary Wang
Abstract
Living arrangements of mothers in China significantly impact their annual wages and motherhood wage penalties. I study how the presence of mothers’ parents, or the maternal grandparents, affect mothers’ wages for each child living in the mothers’ households. Existing literature finds that mothers in China not only experience a motherhood wage penalty, but also observe wage impacts from the living arrangements of their family members, such as the paternal and maternal grandparents. Although existing research on motherhood wage penalties references the China Health and Nutrition Survey, I use data from the China Family Panel Studies, the most recent and comprehensive panel survey that reflects the social and economic transformations of contemporary China. To extend and update the analysis of living arrangements on the motherhood wage penalty, I present evidence of the impact of living arrangements on the motherhood wage penalty, distinguishing between the presence of the maternal grandmother, maternal grandfather, and both maternal grandparents. While I find clear evidence that the presence of the maternal grandmother in the household counters the motherhood wage penalty, due to the lack of data on single mothers, I am not able to find conclusive evidence of a difference in the impact of grandparents on the motherhood wage penalty for single mothers compared with married mothers.
Professor Peter Arcidiacono, Faculty Advisor
Professor Michelle Connolly, Faculty Advisor
JEL Codes: J12, J16, J21
The Effect of Marriage on the Wages of Americans: Gender and Generational Differences
By William Song and Theresa Tong
A substantial body of literature on the wage effects of marriage finds that married American men earn anywhere from 10% to 40% higher wages than unmarried men on average, while married American women earn up to 7% less than unmarried women, even after controlling for traits such as background, education, and number of children. Because this literature focuses heavily on men born in a single time period, we study both men and women in two different generational cohorts of Americans (Baby Boomers and Millennials) from the National Longitudinal Surveys of Youth to examine how the wage effects of marriage differ between genders and across time. Using a fixed effects approach, we find that Millennial women—but not Baby Boomer women—experience an increase in wages after marriage, and we replicate the finding from the literature that men experience an increase in wages after marriage as well. However, after controlling for wage trajectory-based selection into marriage by using a modified fixed effects approach that allows wage trajectories to vary by individual, we find that the wage effects of marriage are no longer statistically significant for any group in our data, suggesting that the wage differences between married and unmarried individuals found in previous studies are primarily a result of selection.
Advisors: Professor Marjorie McElroy, Professor Michelle Connolly | JEL Codes: C33; D13; J12; J13; J22; J30
Immigrant Workers in a Changing Labor Environment: A study on how technology is reshaping immigrant earnings
By Grace Peterson
This research determines how automation affects immigrant wages in the US and how closely this impact follows the skills-biased technical change (SBTC) hypothesis. The present study addresses this question using American Community Survey (ACS) data from 2012 to 2016 and a job automation probability index to explain technological change. This research leverages OLS regressions to evaluate real wage drivers, grouping data by year, immigration status, and education level. According to the SBTC hypothesis, high skill immigrant wages should be less negatively affected by technological change than low skill immigrant wages. Univariate analysis suggests that the SBTC hypothesis is even stronger for US = immigrants than native-borns, as high skill immigrants have a lower average probability than low skill immigrants of having their jobs automated, and the difference in effect on high versus low skilled workers is larger for immigrant than native-borns. However, multivariate analysis asserts that technological change affects low skill immigrants’ wages less than high skilled individuals’ wages, which counters the SBTC hypothesis.
Advisors: Professor Grace Kim | JEL Codes: J15, J24, J31, J61, E24
The Impact of Collegiate Athletic Success and Scandals on Admissions Applications
By William J. Battle-McDonald
This paper examines how the quantity and quality of admissions applications to Division 1 colleges and universities were affected by two non-academic factors: (1) performance of a school’s men’s basketball and football teams; and (2) scandals associated with these athletic programs. Admissions data from 2001 – 2017 were compared to team performance during their football and basketball seasons in order to understand how these non-academic factors contribute to an individual’s decisions to apply for admission. A multivariate linear regression model with school and year fixed effects supported the hypothesis that athletic success positively affects the quantity of applications, increasing them by up to 3% in basketball and 11% in football in the following application period. Seasonal football success was also shown to have negative impacts on the distribution of standardized testing scores of future applicant classes, however these scores were shown to increase when a team played their best season in five or more years. Additional analysis of the effects of athletic program scandals reveals a significant negative effect on the number of applications received, although a deep dive into a few of the most prominent scandals suggests that the benefits associated with violating NCAA rules may, under the right circumstances, be well worth the risk.
Advisor: Professor James Roberts | JEL Codes: I23, J24, L82, L83, Z2
The Impact of Violence in Mexico on Education and Labor Outcomes: Do Conditional Cash Transfers Have a Mitigating Effect?
By Hayley Jordan Barton
This research explores the potential mitigating effect of Mexico’s conditional cash transfer program, Oportunidades, on the education and labor impacts of increased homicide rates. Panel data models are combined with a difference-in-differences approach to compare children and young adults who receive cash transfers with those who do not. Results are very sensitive to specification, but Oportunidades participation is shown to be positively associated with educational attainment regardless of homicide increases. Homicides are associated with decreases in likelihood of school enrollment and compulsory education completion; however, they also correspond with increases in educational attainment, with a larger effect for Oportunidades non-recipients.
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Advisors: Professor Charles Becker, and Professor Michelle Connolly | JEL Codes: C23; D15; I20; I38; J24
