WorldCat Identities

Mill, Roy

Overview
Works: 2 works in 6 publications in 1 language and 50 library holdings
Genres: Academic theses 
Roles: Author
Classifications: H11,
Publication Timeline
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Most widely held works by Roy Mill
Linking Individuals Across Historical Sources: a Fully Automated Approach by Ran Abramitzky( )

5 editions published in 2018 in English and held by 49 WorldCat member libraries worldwide

Linking individuals across historical datasets relies on information such as name and age that is both non-unique and prone to enumeration and transcription errors. These errors make it impossible to find the correct match with certainty. In the first part of the paper, we suggest a fully automated probabilistic method for linking historical datasets that enables researchers to create samples at the frontier of minimizing type I (false positives) and type II (false negatives) errors. The first step guides researchers in the choice of which variables to use for linking. The second step uses the Expectation-Maximization (EM) algorithm, a standard tool in statistics, to compute the probability that each two records correspond to the same individual. The third step suggests how to use these estimated probabilities to choose which records to use in the analysis. In the second part of the paper, we apply the method to link historical population censuses in the US and Norway, and use these samples to estimate measures of intergenerational occupational mobility. The estimates using our method are remarkably similar to the ones using IPUMS', which relies on hand linking to create a training sample. We created an R code and a Stata command that implement this method
Inequality and discrimination in historical and modern labor markets by Roy Mill( )

1 edition published in 2013 in English and held by 1 WorldCat member library worldwide

This dissertation uses two disparate settings to investigate sources of racial and ethnic inequality in labor markets. In the first setting 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. The second setting is an online employer-freelancer matching platform freelancer.com. I study the effect of a freelancer's country-of-origin on the employer's decision of whether to hire them. 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 the good match's country. I discuss how these findings contributes to our understanding of matching, learning, and discrimination in online settings
 
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Audience level: 0.59 (from 0.59 for Linking In ... to 0.77 for Linking In ...)

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