Roy Mill

Menlo Park, California, United States Contact Info
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Publications

  • On the Optimality of Line Call Challenges in Professional Tennis

    International Economic Review

    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…

    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.

    Other authors
    • Ran Abramitzky
    • Liran Einav
    • Shimon Kolkowitz
    See publication

  • Linking Individuals Across Historical Sources: a Fully Automated Approach

    Historical Methods: A Journal of Quantitative and Interdisciplinary History

    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…

    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 a Stata command that implements this method.

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Languages

  • Hebrew

    Native or bilingual proficiency

  • English

    Native or bilingual proficiency

  • Russian

    Elementary proficiency

  • Arabic

    Elementary proficiency

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