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Summary: John M. Abowd is the Chief Scientist and Associate Director for Research and Methodology, US Census Bureau. He completed his AB in Economics at Notre Dame in 1973 and his PhD in Economics at University of Chicago in 1977 under Arnold Zellner. During his academic career, John has held faculty positions at Princeton, the University of Chicago, and, since 1987 at Cornell University where he is the Edmund Ezra Day Professor Emeritus of Economics, Statistics and Data Science. John was trained as a statistician and labor economist, and his economic research has focused on the rigorous empirical evaluation of labor market institutions. In the late 1990s, he began working with the Census Bureau on projects that would end up leveraging administrative and survey records into official statistical products. Through that work, he has developed a research agenda focused on issues necessary to generate those products, including data privacy, synthetic data, total error analysis, data linkage, and missing data problems, among others. [ABSTRACT FROM AUTHOR]

We use Texas's constitutional amendment in 1997 that expanded the scope of home equity loans as a source of exogenous variation to estimate the effects of relaxing credit constraints on small businesses. We find, using standard panel data methods and restricted‐use microdata from the US Census Bureau, that the Texas amendment increased the use of home equity finance by small businesses, increased new business and job creation and reduced firm exit and job loss. The effects are larger and significant for businesses with fewer than 10 employees. [ABSTRACT FROM AUTHOR]

Brazil, like many countries, is reluctant to publish business-level data, because of legitimate concerns about the establishments’ confidentiality. A trusted data curator can increase the utility of data, while managing the risk to establishments, either by releasing synthetic data, or by infusing noise into published statistics. This paper evaluates the application of a differentially private mechanism to publish statistics on wages and job mobility computed from Brazilian employer-employee matched data. The publication mechanism can result in both the publication of specific statistics as well as the generation of synthetic data. I find that the tradeoff between the privacy guaranteed to individuals in the data, and the accuracy of published statistics, is potentially much better that the worst-case theoretical accuracy guarantee. However, the synthetic data fare quite poorly in analyses that are outside the set of queries to which it was trained. Note that this article only explores and characterizes the feasibility of these publication strategies, and will not directly result in the publication of any data. [ABSTRACT FROM AUTHOR]

The use of formal privacy to protect the confidentiality of responses in the 2020 Decennial Census of Population and Housing has triggered renewed interest and debate over how to measure the disclosure risks and societal benefits of the published data products. We argue that any proposal for quantifying disclosure risk should be based on prespecified, objective criteria. We illustrate this approach to evaluate the absolute disclosure risk framework, the counterfactual framework underlying differential privacy, and prior-to-posterior comparisons. We conclude that satisfying all the desiderata is impossible, but counterfactual comparisons satisfy the most while absolute disclosure risk satisfies the fewest. Furthermore, we explain that many of the criticisms levied against differential privacy would be levied against any technology that is not equivalent to direct, unrestricted access to confidential data. More research is needed, but in the near term, the counterfactual approach appears best-suited for privacy versus utility analysis. [ABSTRACT FROM AUTHOR]

This document is a reply to three letters from Muralidhar et al., Kenny et al., and Hotz et al. regarding a recent paper by Jarmin et al. The authors express gratitude for the feedback and engagement from these scholars, which has led to improvements in the Census Bureau's disclosure avoidance system. They emphasize the importance of continued discussion and debate to further benefit the Census Bureau and its statistical products. The authors provide their affiliations and state that they are employed by the U.S. Census Bureau, except for one author who is retired. The document is published under a Creative Commons Attribution License. [Extracted from the article]

Et spørgeskema udsendt til danske virksomheder under nedlukningen under den første bølge af coronavirus viser at hjælpepakkerne gik til de mest udsatte virksomheder og var med til at redde 81.000 jobs. [ABSTRACT FROM AUTHOR]