Pub Date : 2024-02-01Epub Date: 2023-07-06DOI: 10.1093/qje/qjad031
Monica P Bhatt, Sara B Heller, Max Kapustin, Marianne Bertrand, Christopher Blattman
Gun violence is the most pressing public safety problem in American cities. We report results from a randomized controlled trial () of a community-researcher partnership called the Rapid Employment and Development Initiative (READI) Chicago. The program offered an 18-month job alongside cognitive behavioral therapy and other social support. Both algorithmic and human referral methods identified men with strikingly high scope for gun violence reduction: for every 100 people in the control group, there were 11 shooting and homicide victimizations during the 20-month outcome period. Fifty-five percent of the treatment group started programming, comparable to take-up rates in programs for people facing far lower mortality risk. After 20 months, there is no statistically significant change in an index combining three measures of serious violence, the study's primary outcome. Yet there are signs that this program model has promise. One of the three measures, shooting and homicide arrests, declines 65 percent ( after multiple testing adjustment). Because shootings are so costly, READI generates estimated social savings between $182,000 and $916,000 per participant (), implying a benefit-cost ratio between 4:1 and 18:1. Moreover, participants referred by outreach workers-a pre-specified subgroup-show enormous declines in both arrests and victimizations for shootings and homicides (79 and 43 percent, respectively) that remain statistically significant even after multiple testing adjustments. These declines are concentrated among outreach referrals with higher predicted risk, suggesting that human and algorithmic targeting may work better together.
{"title":"Predicting and Preventing Gun Violence: An Experimental Evaluation of READI Chicago.","authors":"Monica P Bhatt, Sara B Heller, Max Kapustin, Marianne Bertrand, Christopher Blattman","doi":"10.1093/qje/qjad031","DOIUrl":"10.1093/qje/qjad031","url":null,"abstract":"<p><p>Gun violence is the most pressing public safety problem in American cities. We report results from a randomized controlled trial (<math><mi>N</mi><mspace></mspace><mo>=</mo><mspace></mspace><mn>2</mn><mo>,</mo><mspace></mspace><mn>456</mn></math>) of a community-researcher partnership called the Rapid Employment and Development Initiative (READI) Chicago. The program offered an 18-month job alongside cognitive behavioral therapy and other social support. Both algorithmic and human referral methods identified men with strikingly high scope for gun violence reduction: for every 100 people in the control group, there were 11 shooting and homicide victimizations during the 20-month outcome period. Fifty-five percent of the treatment group started programming, comparable to take-up rates in programs for people facing far lower mortality risk. After 20 months, there is no statistically significant change in an index combining three measures of serious violence, the study's primary outcome. Yet there are signs that this program model has promise. One of the three measures, shooting and homicide arrests, declines 65 percent (<math><mi>p</mi><mspace></mspace><mo>=</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>13</mn></math> after multiple testing adjustment). Because shootings are so costly, READI generates estimated social savings between $182,000 and $916,000 per participant (<math><mi>p</mi><mspace></mspace><mo>=</mo><mspace></mspace><mn>0</mn><mo>.</mo><mn>03</mn></math>), implying a benefit-cost ratio between 4:1 and 18:1. Moreover, participants referred by outreach workers-a pre-specified subgroup-show enormous declines in both arrests and victimizations for shootings and homicides (79 and 43 percent, respectively) that remain statistically significant even after multiple testing adjustments. These declines are concentrated among outreach referrals with higher predicted risk, suggesting that human and algorithmic targeting may work better together.</p>","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":11.1,"publicationDate":"2024-02-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://www.ncbi.nlm.nih.gov/pmc/articles/PMC10898100/pdf/","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"139984158","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"OA","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Does a school district that expands school choice provide better outcomes for students than a neighborhood-based assignment system? This paper studies the Zones of Choice (ZOC) program, a school choice initiative of the Los Angeles Unified School District (LAUSD) that created small high school markets in some neighborhoods but left attendance zone boundaries in place throughout the rest of the district. We study market-level impacts of choice on student achievement and college enrollment using a differences-in-differences design. Student outcomes in ZOC markets increased markedly, narrowing achievement and college enrollment gaps between ZOC neighborhoods and the rest of the district. The effects of ZOC are larger for schools exposed to more competition, supporting the notion that competition is a key channel. Demand estimates suggest families place substantial weight on schools’ academic quality, providing schools with competition-induced incentives to improve their effectiveness. The evidence demonstrates that public school choice programs have the potential to improve school quality and reduce neighborhood-based disparities in educational opportunity.
{"title":"The Impact of Public School Choice: Evidence from Los Angeles’ Zones of Choice","authors":"Christopher Campos, Caitlin Kearns","doi":"10.1093/qje/qjad052","DOIUrl":"https://doi.org/10.1093/qje/qjad052","url":null,"abstract":"Does a school district that expands school choice provide better outcomes for students than a neighborhood-based assignment system? This paper studies the Zones of Choice (ZOC) program, a school choice initiative of the Los Angeles Unified School District (LAUSD) that created small high school markets in some neighborhoods but left attendance zone boundaries in place throughout the rest of the district. We study market-level impacts of choice on student achievement and college enrollment using a differences-in-differences design. Student outcomes in ZOC markets increased markedly, narrowing achievement and college enrollment gaps between ZOC neighborhoods and the rest of the district. The effects of ZOC are larger for schools exposed to more competition, supporting the notion that competition is a key channel. Demand estimates suggest families place substantial weight on schools’ academic quality, providing schools with competition-induced incentives to improve their effectiveness. The evidence demonstrates that public school choice programs have the potential to improve school quality and reduce neighborhood-based disparities in educational opportunity.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":13.7,"publicationDate":"2023-10-11","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50166716","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Raj Chetty, John Friedman, Nathaniel Hendren, Michael Stepner
Abstract We build a publicly available database that tracks economic activity in the U.S. at a granular level in real time using anonymized data from private companies. We report weekly statistics on consumer spending, business revenues, job postings, and employment rates disaggregated by county, sector, and income group. Using the publicly available data, we show how the COVID-19 pandemic affected the economy by analyzing heterogeneity in its impacts across subgroups. High-income individuals reduced spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses laid off many of their employees, leading to widespread job losses, especially among low-wage workers in such areas. High-wage workers experienced a “V-shaped” recession that lasted a few weeks, whereas low-wage workers experienced much larger, more persistent job losses. Even though consumer spending and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the impacts of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus payments led to sharp increases in spending early in the pandemic, but much smaller responses later in the pandemic, especially for high-income households. Real-time estimates of marginal propensities to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in consumer spending and job losses, but cannot restore full employment when the initial shock to consumer spending arises from health concerns. More broadly, our analysis demonstrates how public statistics constructed from private sector data can support many research and real-time policy analyses, providing a new tool for empirical macroeconomics.
{"title":"THe Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data","authors":"Raj Chetty, John Friedman, Nathaniel Hendren, Michael Stepner","doi":"10.1093/qje/qjad048","DOIUrl":"https://doi.org/10.1093/qje/qjad048","url":null,"abstract":"Abstract We build a publicly available database that tracks economic activity in the U.S. at a granular level in real time using anonymized data from private companies. We report weekly statistics on consumer spending, business revenues, job postings, and employment rates disaggregated by county, sector, and income group. Using the publicly available data, we show how the COVID-19 pandemic affected the economy by analyzing heterogeneity in its impacts across subgroups. High-income individuals reduced spending sharply in March 2020, particularly in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent, dense areas. Those businesses laid off many of their employees, leading to widespread job losses, especially among low-wage workers in such areas. High-wage workers experienced a “V-shaped” recession that lasted a few weeks, whereas low-wage workers experienced much larger, more persistent job losses. Even though consumer spending and job postings had recovered fully by December 2021, employment rates in low-wage jobs remained depressed in areas that were initially hard hit, indicating that the temporary fall in labor demand led to a persistent reduction in labor supply. Building on this diagnostic analysis, we evaluate the impacts of fiscal stimulus policies designed to stem the downward spiral in economic activity. Cash stimulus payments led to sharp increases in spending early in the pandemic, but much smaller responses later in the pandemic, especially for high-income households. Real-time estimates of marginal propensities to consume provided better forecasts of the impacts of subsequent rounds of stimulus payments than historical estimates. Overall, our findings suggest that fiscal policies can stem secondary declines in consumer spending and job losses, but cannot restore full employment when the initial shock to consumer spending arises from health concerns. More broadly, our analysis demonstrates how public statistics constructed from private sector data can support many research and real-time policy analyses, providing a new tool for empirical macroeconomics.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-10-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135548610","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Studies of human memory indicate that features of an event evoke memories of prior associated contextual states, which in turn become associated with the current event’s features. This retrieved-context mechanism allows the remote past to influence the present, even as agents gradually update their beliefs about their environment. We apply a version of retrieved-context theory, drawn from the literature on human memory, to explain three types of evidence in the financial economics literature: the role of early-life experience in shaping investment choices, occurrence of financial crises, and the impact of fear on asset allocation. These applications suggest a recasting of neoclassical rational expectations in terms of beliefs as governed by principles of human memory.
{"title":"A Retrieved-Context Theory of Financial Decisions","authors":"Jessica A Wachter, Michael Jacob Kahana","doi":"10.1093/qje/qjad050","DOIUrl":"https://doi.org/10.1093/qje/qjad050","url":null,"abstract":"Studies of human memory indicate that features of an event evoke memories of prior associated contextual states, which in turn become associated with the current event’s features. This retrieved-context mechanism allows the remote past to influence the present, even as agents gradually update their beliefs about their environment. We apply a version of retrieved-context theory, drawn from the literature on human memory, to explain three types of evidence in the financial economics literature: the role of early-life experience in shaping investment choices, occurrence of financial crises, and the impact of fear on asset allocation. These applications suggest a recasting of neoclassical rational expectations in terms of beliefs as governed by principles of human memory.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":13.7,"publicationDate":"2023-10-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50166722","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ali Hortaçsu, Olivia R Natan, Hayden Parsley, Timothy Schwieg, Kevin R Williams
Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct subunits. Using comprehensive data and internal models from a large U.S. airline, we establish that airline pricing is not well approximated by a model of the firm as a unitary decision-maker. We show that observed prices, however, can be rationalized by accounting for organizational structure and for the decisions by departments that are tasked with supplying inputs to the observed pricing heuristic. Simulating the prices the firm would charge if it were a rational, unitary decision-maker results in lower welfare than we estimate under observed practices. Finally, we discuss why counterfactual estimates of welfare and market power may be biased if prices are set through decomposition, but we instead assume that they are set by unitary decision-makers.
{"title":"Organizational Structure and Pricing: Evidence from a Large U.S. Airline","authors":"Ali Hortaçsu, Olivia R Natan, Hayden Parsley, Timothy Schwieg, Kevin R Williams","doi":"10.1093/qje/qjad051","DOIUrl":"https://doi.org/10.1093/qje/qjad051","url":null,"abstract":"Firms facing complex objectives often decompose the problems they face, delegating different parts of the decision to distinct subunits. Using comprehensive data and internal models from a large U.S. airline, we establish that airline pricing is not well approximated by a model of the firm as a unitary decision-maker. We show that observed prices, however, can be rationalized by accounting for organizational structure and for the decisions by departments that are tasked with supplying inputs to the observed pricing heuristic. Simulating the prices the firm would charge if it were a rational, unitary decision-maker results in lower welfare than we estimate under observed practices. Finally, we discuss why counterfactual estimates of welfare and market power may be biased if prices are set through decomposition, but we instead assume that they are set by unitary decision-makers.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":13.7,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167160","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract This paper examines the tradeoffs of monitoring for wasteful public spending. By penalizing unnecessary spending, monitoring improves the quality of public expenditure and incentivizes firms to invest in compliance technology. I study a large Medicare program that monitored for unnecessary health care spending and consider its effect on government savings, provider behavior, and patient health. Every dollar Medicare spent on monitoring generated ${$}$24–29 in government savings. The majority of savings stem from the deterrence of future care, rather than reclaimed payments from prior care. I do not find evidence that the health of the marginal patient is harmed, indicating that monitoring primarily deters low-value care. Monitoring does increase provider administrative costs, but these costs are mostly incurred up-front and include investments in technology to assess the medical necessity of care.
{"title":"Monitoring for Waste: Evidence from Medicare Audits","authors":"Maggie Shi","doi":"10.1093/qje/qjad049","DOIUrl":"https://doi.org/10.1093/qje/qjad049","url":null,"abstract":"Abstract This paper examines the tradeoffs of monitoring for wasteful public spending. By penalizing unnecessary spending, monitoring improves the quality of public expenditure and incentivizes firms to invest in compliance technology. I study a large Medicare program that monitored for unnecessary health care spending and consider its effect on government savings, provider behavior, and patient health. Every dollar Medicare spent on monitoring generated ${$}$24–29 in government savings. The majority of savings stem from the deterrence of future care, rather than reclaimed payments from prior care. I do not find evidence that the health of the marginal patient is harmed, indicating that monitoring primarily deters low-value care. Monitoring does increase provider administrative costs, but these costs are mostly incurred up-front and include investments in technology to assess the medical necessity of care.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-28","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135425849","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract The paper develops a model of nonmarket allocation of resources such as the awarding of grants to meritorious projects, honors to outstanding students, or journal slots to quality publications. On the supply side, the available budget of grants is awarded to applicants who are evaluated most favorably according to the noisy information available to reviewers. On the demand side, stronger candidates are more likely to obtain grants and thus self-select into applying, given that applications are costly. We establish that if evaluation is perfect, grading on a curve inefficiently discourages even the very best candidates from applying. More generally, when the budget is insufficient to award grants to all applicants, the equilibrium unravels if information is symmetric enough—the paradox of relative evaluation. Leveraging a technique based on the quantile function pioneered by Lehmann, we characterize a broad set of nonmarket allocation rules under which an increase in evaluation noise in a field (or course) raises equilibrium applications in that field, and reduces applications in all other fields. We empirically confirm these comparative statics by exploiting a change in the rule for apportioning the total budget to applications in different fields at the European Research Council, showing that a one standard deviation increase in own evaluation noise leads to a 0.4 standard deviation increase in the number of applications and budget share. Moreover, we derive insights for the design of evaluation institutions, particularly regarding the endogenous choice of noise by fields or courses and the optimal aggregation of fields into panels.
{"title":"Grantmaking, Grading on a Curve, and the Paradox of Relative Evaluation in Nonmarkets","authors":"Jérôme Adda, Marco Ottaviani","doi":"10.1093/qje/qjad046","DOIUrl":"https://doi.org/10.1093/qje/qjad046","url":null,"abstract":"Abstract The paper develops a model of nonmarket allocation of resources such as the awarding of grants to meritorious projects, honors to outstanding students, or journal slots to quality publications. On the supply side, the available budget of grants is awarded to applicants who are evaluated most favorably according to the noisy information available to reviewers. On the demand side, stronger candidates are more likely to obtain grants and thus self-select into applying, given that applications are costly. We establish that if evaluation is perfect, grading on a curve inefficiently discourages even the very best candidates from applying. More generally, when the budget is insufficient to award grants to all applicants, the equilibrium unravels if information is symmetric enough—the paradox of relative evaluation. Leveraging a technique based on the quantile function pioneered by Lehmann, we characterize a broad set of nonmarket allocation rules under which an increase in evaluation noise in a field (or course) raises equilibrium applications in that field, and reduces applications in all other fields. We empirically confirm these comparative statics by exploiting a change in the rule for apportioning the total budget to applications in different fields at the European Research Council, showing that a one standard deviation increase in own evaluation noise leads to a 0.4 standard deviation increase in the number of applications and budget share. Moreover, we derive insights for the design of evaluation institutions, particularly regarding the endogenous choice of noise by fields or courses and the optimal aggregation of fields into panels.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152916","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abstract Policymakers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner’s curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner’s curse biases our estimates for selected options upwards and can invalidate conventional confidence intervals. This paper develops estimators and confidence intervals that eliminate this winner’s curse. We illustrate our results by studying selection of job-training programs based on estimated earnings effects and selection of neighborhoods based on estimated economic opportunity. We find that our winner’s curse corrections can make an economically significant difference to conclusions, but still allow informative inference.
{"title":"Inference on Winners","authors":"Isaiah Andrews, Toru Kitagawa, Adam McCloskey","doi":"10.1093/qje/qjad043","DOIUrl":"https://doi.org/10.1093/qje/qjad043","url":null,"abstract":"Abstract Policymakers, firms, and researchers often choose among multiple options based on estimates. Sampling error in the estimates used to guide choice leads to a winner’s curse, since we are more likely to select a given option precisely when we overestimate its effectiveness. This winner’s curse biases our estimates for selected options upwards and can invalidate conventional confidence intervals. This paper develops estimators and confidence intervals that eliminate this winner’s curse. We illustrate our results by studying selection of job-training programs based on estimated earnings effects and selection of neighborhoods based on estimated economic opportunity. We find that our winner’s curse corrections can make an economically significant difference to conclusions, but still allow informative inference.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152734","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Abi Adams-Prassl, Kristiina Huttunen, Emily Nix, Ning Zhang
In this paper, we link every police report in Finland to administrative data to identify violence between colleagues, and the economic consequences for victims, perpetrators, and firms. This new approach to observe when one colleague attacks another overcomes previous data constraints limiting evidence on this phenomenon to self-reported surveys that do not identify perpetrators. We document large, persistent labor market impacts of between-colleague violence on victims and perpetrators. Male perpetrators experience substantially weaker consequences after attacking female colleagues. Perpetrators’ relative economic power in male-female violence partly explains this asymmetry. Turning to broader implications for firm recruitment and retention, we find that male-female violence causes a decline in the proportion of women at the firm, both because fewer new women are hired and current female employees leave. Management plays a key role in mediating the impacts on the wider workforce. Only male-managed firms lose women. Female-managed firms exhibit a key difference relative to male-managed firms: male perpetrators are less likely to remain employed after attacking their female colleagues.
{"title":"Violence Against Women at Work","authors":"Abi Adams-Prassl, Kristiina Huttunen, Emily Nix, Ning Zhang","doi":"10.1093/qje/qjad045","DOIUrl":"https://doi.org/10.1093/qje/qjad045","url":null,"abstract":"In this paper, we link every police report in Finland to administrative data to identify violence between colleagues, and the economic consequences for victims, perpetrators, and firms. This new approach to observe when one colleague attacks another overcomes previous data constraints limiting evidence on this phenomenon to self-reported surveys that do not identify perpetrators. We document large, persistent labor market impacts of between-colleague violence on victims and perpetrators. Male perpetrators experience substantially weaker consequences after attacking female colleagues. Perpetrators’ relative economic power in male-female violence partly explains this asymmetry. Turning to broader implications for firm recruitment and retention, we find that male-female violence causes a decline in the proportion of women at the firm, both because fewer new women are hired and current female employees leave. Management plays a key role in mediating the impacts on the wider workforce. Only male-managed firms lose women. Female-managed firms exhibit a key difference relative to male-managed firms: male perpetrators are less likely to remain employed after attacking their female colleagues.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":13.7,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"50167175","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Ellora Derenoncourt, Chi Hyun Kim, Moritz Kuhn, Moritz Schularick
Abstract The racial wealth gap is the largest of the economic disparities between Black and white Americans, with a white-to-Black per capita wealth ratio of 6 to 1. It is also among the most persistent. In this paper, we construct the first continuous series on white-to-Black per capita wealth ratios from 1860 to 2020, drawing on historical census data, early state tax records, and historical waves of the Survey of Consumer Finances, among other sources. Incorporating these data into a parsimonious model of wealth accumulation for each racial group, we document the role played by initial conditions, income growth, savings behavior, and capital returns in the evolution of the gap. Given vastly different starting conditions under slavery, racial wealth convergence would remain a distant scenario, even if wealth-accumulating conditions had been equal across the two groups since Emancipation. Relative to this equal-conditions benchmark, we find that observed convergence has followed an even slower path over the last 150 years, with convergence stalling after 1950. Since the 1980s, the wealth gap has widened again as capital gains have predominantly benefited white households, and convergence via income growth and savings has come to a halt.
{"title":"Wealth of Two Nations: The U.S. Racial Wealth Gap, 1860–2020","authors":"Ellora Derenoncourt, Chi Hyun Kim, Moritz Kuhn, Moritz Schularick","doi":"10.1093/qje/qjad044","DOIUrl":"https://doi.org/10.1093/qje/qjad044","url":null,"abstract":"Abstract The racial wealth gap is the largest of the economic disparities between Black and white Americans, with a white-to-Black per capita wealth ratio of 6 to 1. It is also among the most persistent. In this paper, we construct the first continuous series on white-to-Black per capita wealth ratios from 1860 to 2020, drawing on historical census data, early state tax records, and historical waves of the Survey of Consumer Finances, among other sources. Incorporating these data into a parsimonious model of wealth accumulation for each racial group, we document the role played by initial conditions, income growth, savings behavior, and capital returns in the evolution of the gap. Given vastly different starting conditions under slavery, racial wealth convergence would remain a distant scenario, even if wealth-accumulating conditions had been equal across the two groups since Emancipation. Relative to this equal-conditions benchmark, we find that observed convergence has followed an even slower path over the last 150 years, with convergence stalling after 1950. Since the 1980s, the wealth gap has widened again as capital gains have predominantly benefited white households, and convergence via income growth and savings has come to a halt.","PeriodicalId":48470,"journal":{"name":"Quarterly Journal of Economics","volume":null,"pages":null},"PeriodicalIF":0.0,"publicationDate":"2023-09-18","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"135152728","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":1,"RegionCategory":"经济学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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