There is a strong and growing interest in helping families move to areas with higher economic opportunity. We exploit variation in the Earned Income Tax Credit (EITC) to examine how increasing household income affects migration, with a focus on women from rural and economically distressed areas. We find that higher income increases migration out of rural and distressed areas—primarily among unmarried mothers—to areas with higher employment and earnings, and lower unemployment rates. Many of these moves occur across counties or commuting zones, but we find no effect on moving across states. We also find decreases in living “doubled up” with another family, and reductions in commute length. We are the first to show that the EITC helps women move to economic opportunity, with the most likely mechanism being relaxing household financial constraints.

Domestic abuse is a pervasive global problem. Here we analyze two approaches to reducing violent DA recidivism. One involves charging the perpetrator with a crime; the other provides protective services to the victim on the basis of a formal risk assessment carried out by the police. We use detailed administrative data to estimate the average effect of treatment on the treated using inverse propensity-score weighting (IPW). We then make use of causal forests to study heterogeneity in the estimated treatment effects. We find that pressing charges substantially reduces the likelihood of violent recidivism. The analysis also reveals substantial heterogeneity in the effect of pressing charges. In contrast, the risk-assessment process has no discernible effect.

Workers acquire skills through formal schooling, through training provided by governments, and through training provided by firms. This chapter reviews, synthesizes, and augments the literature on the last of these, which has languished in recent years despite the sizable contribution of firm training to the overall stock of worker human capital. We engage with research on the determinants of receipt of firm training, the effects of firm training on workers outcomes, and various policy debates related to firm training, including training taxes, training subsidies, non-compete agreements, and the minimum wage. Our discussion emphasizes the complex measurement issues associated with firm training and the interplay of applied theory and applied econometrics in the related empirical literature.

We provide simple tests for selection on unobserved variables in the Vytlacil-Imbens-Angrist framework for Local Average Treatment Effects (LATEs). Our setup allows researchers not only to test for selection on either or both of the treated and untreated outcomes, but also to assess the magnitude of the selection effect. We show that it applies to the standard binary instrument case, as well as to experiments with imperfect compliance and fuzzy regression discontinuity designs, and we link it to broader discussions regarding instrumental variables. We illustrate the substantive value added by our framework with three empirical applications drawn from the literature.

By integrating a “big” dataset of Internet Speedtest® measurements from Ookla® with data on household incomes from the American Community Survey (ACS), we attempt to measure Internet speeds across income tiers. In the Ookla data, each measurement is technically rigorous but the sample frame is unknown. The ACS provides necessary information on income and Internet access from a known sample frame. Our likelihood combines these data and endogenizes selection effects to identify Internet speed distributions by income tier. We credibly identify the speed distribution for middle and high-income households. However, because the participation rate of low-income households in the Speedtest data is so limited, the speed estimates for these households are not identified.

John Snow in 1849 proposed the intestinal fecal-oral theory for cholera and provided substantial evidence supporting the theory and particularly the prediction that cholera was water-borne. Snow’s analysis fits naturally within a Neyman-Rubin causal framework, and Snow is credited with two tools (randomization as an instrumental variable and difference-in-differences design) widely used in causal analysis today. Nonetheless, although water was widely accepted in the 1850s as a causal factor, Snow’s theory was not. This seeming puzzle cannot be resolved within the Neyman-Rubin framework and requires a broader conception of abductive scientific inquiry, as advocated by Peirce (and recently Heckman and Singer 2017). The methodology of scientific research programmes of Lakatos (1980) provides a concrete framework for such a broader conception of scientific inquiry, and for understanding the competition among theories in the 1850s. A “rational reconstruction” of the case of cholera illustrates the logic of inquiry: alternative theories (rationally) protected against refutation by incorporating water as a causal factor, but Snow’s theory was superior (progressive in Lakatos’s terms) – producing new predictions corroborated by new facts. Besides illustrating how the abductive process of scientific inquiry works with an important history of science example, we hope to encourage quantitative social scientists, especially those who rely on potential outcome frameworks, to see their research in this larger context. Finally, this is part of an effort to support teaching statistical data analysis as part of the broader process of scientific inquiry. [237 words] University of Chicago, Harris Public Policy 1307 E 60th St. Suite 3037 Chicago IL 60637 203-252-4897

There is a strong and growing interest in helping families move to areas with higher economic opportunity. We exploit variation in the Earned Income Tax Credit (EITC) to examine how relaxing budget constraints a ects migration, with a focus on women from rural and economically distressed areas. We nd that relaxing budget constraints increases migration out of rural and distressed areas, to areas with higher labor force participation and lower unemployment rates. Many of these moves occur across counties or commuting zones, but we nd no e ect on moving across states. We also nd decreases in living doubled up with another family, and reductions in commute length. We are the rst to show that the EITC relaxes budget and credit constraints and helps women move to economic opportunity. *Direct correspondence to Jacob Bastian, Rutgers University, Department of Economics. Address: New Jersey Hall, 75 Hamilton Street, New Brunswick, NJ 08901-1248. Email: jacob.bastian@rutgers.edu. Dan Black, University of Chicago, Harris School of Public Policy. We are grateful for helpful comments from Leah Boustan, Ciprian Domnisoru, Hank Farber, Jarkko Harju, Andrew Haughwout, Jennifer Hunt, Ben Hyman, Tuomas Kosonen, Ilyana Kuziemko, Je Smith, Bryan Stuart, Evan Taylor, Riley Wilson, Jim Ziliak, and seminar participants at the International Institute of Public Finance, National Tax Association, New York Federal Reserve, Princeton University, Rutgers University, University of Hong Kong, and the VATT Institute for Economic Research. Hyunji Ahn, Xiaoyu Fu, and April Wang provided excellent research assistance. Jacob Bastian was funded by the Smith Richardson Foundation.

The paper assesses gender differences in pre-labor market specialization among the college-educated and highlights how those differences have evolved over time. Women choose majors with lower potential earnings (based on male wages associated with those majors) and subsequently sort into occupations with lower potential earnings given their major choice. These differences have narrowed over time, but recent cohorts of women still choose majors and occupations with lower potential earnings. Differences in undergraduate major choice explain a substantive portion of gender wage gaps for the college-educated above and beyond simply controlling for occupation. Collectively, our results highlight the importance of understanding gender differences in the mapping between college major and occupational sorting when studying the evolution of gender differences in labor market outcomes over time.

resource shocks can help studying how low-skilled men respond to changes in labor market conditions

In this paper, we exploit new data to assess gender differences in pre-labor market specialization among the college educated and highlight how those differences have evolved over time.We highlight new results pertaining to gender differences in the mapping between undergraduate major and subsequent occupational sorting.To perform our analysis, we introduce new indices in potential wage space that measure gender differences in major choice and separately the subsequent occupational sorting conditional on major choice.We highlight that women both choose majors with lower potential earnings (based on male wages associated with those majors) and that they then subsequently sort into occupations with lower potential earnings given their major choice.We highlight that these differences have narrowed over time but recent cohorts of women still choose majors and occupations with lower potential earnings.Differences in undergraduate major choice explains a substantive portion of gender wage gaps for the college educated above and beyond simply controlling for occupation.Collectively, our results highlight the importance of understanding gender differences in pre-labor market human capital specialization and the mapping between college major and occupational sorting when studying the evolution of gender differences in labor market outcomes over time.

This chapter shows how China's welfare state is partially responsible for its imbalanced economic growth model, characterised by an overreliance on export-led manufacturing industries and the laggard development of the service economy. It argues that the expansive Chinese pension system facilitates the expansion of the manufacturing sector by subsidising the training of workers with industrial specific skills, as this system is designed to do, but it has an unintended consequence. The political-economic logic has shown that China's pension system is dictated by the need of the Chinese economy. Rebalancing the Chinese economy, the overriding priority in Chinese government's post-crisis macroeconomic policy, therefore requires adjustments to the Chinese pension system. The chapter examines the structure of the Chinese economy through the lens of this study's political-economic framework. China's …

We examine inferences about old-age mortality that arise when researchers use survey data matched to death records. We show that even small rates of failure to match respondents can lead to substantial bias in the measurement of mortality rates at older ages. This type of measurement error is consequential for three strands in the demographic literature: (1) the deceleration in mortality rates at old ages; (2) the black-white mortality crossover; and (3) the relatively low rate of old-age mortality among Hispanics, often called the "Hispanic paradox." Using the National Longitudinal Survey of Older Men matched to death records in both the U.S. Vital Statistics system and the Social Security Death Index, we demonstrate that even small rates of missing mortality matching plausibly lead to an appearance of mortality deceleration when none exists and can generate a spurious black-white mortality crossover. We confirm these findings using data from the National Health Interview Survey matched to the U.S. Vital Statistics system, a data set known as the "gold standard" (Cowper et al. 2002) for estimating age-specific mortality. Moreover, with these data, we show that the Hispanic paradox is also plausibly explained by a similar undercount.

Demographers often form estimates by combining information from two data sources-a challenging problem when one or both data sources are incomplete. A classic example entails the construction of death probabilities, which requires death counts for the subpopulations under study and corresponding base population estimates. Approaches typically entail 'back projection', as in Wrigley and Schofield's seminal analysis of historical English data, or 'inverse' or 'forward projection' as used by Lee in his important reanalysis of that work, both published in the 1980s. Our paper shows how forward and backward approaches can be optimally combined, using a generalized method of moments (GMM) framework. We apply the method to the estimation of death probabilities for relatively small subpopulations within the United States (men born 1930-39 by state of birth by birth cohort by race), combining data from vital statistics records and census samples.

“Matching” is a statistical technique used to evaluate the effect of a treatment by comparing the treated and non-treated units in an observational study. Matching provides an alternative to older estimation methods, such as ordinary least squares (OLS), which involves strong assumptions that are usually without much justification from economic theory. While the use of simple OLS models may have been appropriate in the early days of computing during the 1970s and 1980s, the remarkable increase in computing power since then has made other methods, in particular matching, very easy to implement.

Many of the key issues confronting modern societies are closely tied to labor market outcomes: What factors contribute to the persistence of poverty and deprivation? Why does long-term unemployment damage re-entry prospects into labor markets? Along which dimensions is economic inequality increasin g, and to what extent should we be concerned about these trends? To what degree is inequality transmitted within families across generations? Why does race play such an important role in economic success in the U.S.? How are male-female differences in economic outcomes shifting over time? In this essay we suggest that a well-designed survey that follows individuals within households over a long horizon is crucial for sorting some facets of these questions. We provide some thoughts about how a future household survey should be designed for the purpose facilitating high-value research in empirical labor economics.

Many studies link cross-state variation in compulsory schooling laws to early-life educational attainment, thereby providing a plausible way to investigate the causal impact of education on various lifetime outcomes. We use this strategy to estimate the effect of education on older-age mortality of individuals born in the early twentieth century U.S. Our key innovation is to combine U.S. Census data and the complete Vital Statistics records to form precise mortality estimates by sex, birth cohort, and birth state. In turn we find that virtually all of the variation in these mortality rates is captured by cohort effects and state effects alone, making it impossible to reliably tease out any additional impact due to changing educational attainment induced by state-level changes in compulsory schooling.

Criminology & Public PolicyVolume 14, Issue 4 p. 639-646 COMMENTARY Comments on Domínguez and Raphael Dan A. Black, Corresponding Author Dan A. Black University of ChicagoDirect correspondence to Dan A. Black, University of Chicago, 1155 E. 60th Street, Suite 193, Chicago, IL 60637 (e-mail: danblack@uchicago.edu).Search for more papers by this authorRobert M. Solow, Robert M. Solow Massachusetts Institute of TechnologySearch for more papers by this authorLowell J. Taylor, Lowell J. Taylor Carnegie Mellon UniversitySearch for more papers by this author Dan A. Black, Corresponding Author Dan A. Black University of ChicagoDirect correspondence to Dan A. Black, University of Chicago, 1155 E. 60th Street, Suite 193, Chicago, IL 60637 (e-mail: danblack@uchicago.edu).Search for more papers by this authorRobert M. Solow, Robert M. Solow Massachusetts Institute of TechnologySearch for more papers by this authorLowell J. Taylor, Lowell J. Taylor Carnegie Mellon UniversitySearch for more papers by this author First published: 21 October 2015 https://doi.org/10.1111/1745-9133.12169Citations: 8Read the full textAboutPDF ToolsExport citationAdd to favoritesTrack citation ShareShare Give accessShare full text accessShare full-text accessPlease review our Terms and Conditions of Use and check box below to share full-text version of article.I have read and accept the Wiley Online Library Terms and Conditions of UseShareable LinkUse the link below to share a full-text version of this article with your friends and colleagues. Learn more.Copy URL Share a linkShare onFacebookTwitterLinkedInRedditWechat Citing Literature Volume14, Issue4November 2015Pages 639-646 RelatedInformation

“Matching” is a statistical technique used to evaluate the effect of a treatment by comparing the treated and non-treated units in an observational study. Matching provides an alternative to older estimation methods, such as ordinary least squares (OLS), which involves strong assumptions that are usually without much justification from economic theory. While the use of simple OLS models may have been appropriate in the early days of computing during the 1970s and 1980s, the remarkable increase in computing power since then has made other methods, in particular matching, very easy to implement.