Fields:
Economic History, Applied Microeconomics

Expected Graduation Date:
June, 2013

DISSERTATION COMMITTEE:

Ran Abramitzky (Committee Chair):
ranabr@stanford.edu

Liran Einav:
leinav@stanford.edu

Avner Greif:
avner@stanford.edu

Gavin Wright:
write@stanford.edu

Job Market Paper

Race, Skin Color, and Economic Outcomes in Early 20th Century America (with Luke Stein)
(This version updated 12/30/2012)

We study the effect of race on economic outcomes using unique data from the first half of the twentieth century, a period in which skin color was explicitly coded in population censuses as “White,” “Black,” or “Mulatto.” We construct a panel of siblings by digitizing and matching records across the 1910 and 1940 censuses and identifying all 12,000 African-American families in which enumerators classified some children as light-skinned (“Mulatto”) and others as dark-skinned (“Black”). Siblings coded “Mulatto” when they were children (in 1910) earned similar wages as adults (in 1940) relative to their Black siblings. This within-family earnings difference is substantially lower than the Black-Mulatto earnings difference in the general population, suggesting that skin color in itself played only a small role in the racial earnings gap. To explore the role of the more social aspect that might be associated with being Black, we then focus on individuals who “passed for White,” an important social phenomenon at the time. To do so, we identify individuals coded “Mulatto” as children but “White” as adults. Passing for White meant that individuals changed their racial affiliation by changing their social ties, while skin color remained unchanged. We compare passers to their siblings who did not pass. Passing was associated with substantially higher earnings, suggesting that race in its social form could have significant consequences for economic outcomes.

A short description of the census-matching technique

A short description of the digitization system

We study professional tennis players’ decisions of whether to challenge umpires’ calls using data on over 2,000 challenges in 35 tennis tournaments. The decision to challenge, which is simple to characterize, trades off reversing the umpire’s call against losing subsequent challenge opportunities. Qualitatively, players are more likely to challenge when the stakes are greater and when the option value of challenging is lower, as theory predicts. Quantitatively, players’ actual behavior is close to an optimal challenging strategy prescribed by a simple dynamic model. Our findings illustrate that professional decision makers develop decision rules that can approximate optimal behavior quite well

Download data and code used to create the tables and figures (Stata and MATLAB)

This paper uses data from the online employer-freelancer matching platform freelancer.com to study the determinants of a match between an employer and a freelancer. Having to rely on a relatively small number of characteristics, employers use the freelancer's country of origin and reputation scores to infer the expected service quality. I find that freelancers from developing countries are less likely to be hired when they have no individual reputation, and as individual reputation becomes better this country effect disappears. This setting also allows me to study how employers' experience in past hires affects their behavior in current hires. I show that following a good match with a freelancer, employers are more likely to hire freelancers from that good match's country. I discuss how these findings contributes to our understanding of matching, learning, and discrimination in online settings.

Click here for a visualization of the city-level trade on the website.

We use data on all US federally awarded contracts since 2002 to measure how the relative power of Congressmen affects the amount of money sent to their districts, controlling for time-invariant individual characteristics and national trends. We find that political power and opportunity, strategic behavior, ideology, and committee membership all play important roles in the allocation of federal contract dollars across congressional districts. We score representatives' campaign contributions according to how local their donors base is. We break down the effect of power changes by the measure of how local Representatives are, and find that Representatives who are more locally focused in terms of their campaign contributions leverage increases in political power or political circumstance in order to increase allocations of federal contracts to their districts

Work in Progress

The Long Term Effects of Schools and Good Teachers: Northern Teachers and Southern Freedmen, 1860-1920

Sex Ratio and the Marriage Market in India (with Ran Abramitzky, Latika Chaudhary, Luojia Hu, Analia Schlosser)

How Economic Conditions and Jewish Immigration to Weimar Germany Affected Support for the NSDAP (with Ran Abramitzky, Attila Ambrus, Hanna Halaburda, and Joachim Voth)




Other

Links to my Stata tutorial and graduate computing camp slides (MATLAB slides written and taught by Marc Hafstead)