All Papers

This page lists all papers released by the Opportunity Insights team. These studies were supported by the Opportunity Insights staff and sponsors listed on our Team page. For questions related to any of these studies or the data associated with them, please email [email protected].

Most Recent

The Intergenerational Transmission of Employers and the Earnings of Young Workers

Social Capital II: Determinants of Economic Connectedness

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                    [post_content] => We build a publicly available database that tracks economic activity at a granular level in real time using anonymized data from private companies. We report daily statistics on consumer spending, business revenues, employment rates, and other key indicators disaggregated by ZIP code, industry, income group, and business size. Using these data, we study how COVID-19 affected the economy by analyzing heterogeneity in its impacts. We first show that high-income individuals reduced spending sharply in mid-March 2020, particularly in areas with high rates of COVID-19 infection and in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent ZIP codes. These businesses laid off many of their employees, leading to widespread job losses especially among low-wage workers in affluent areas. High-wage workers experienced a “V-shaped” recession that lasted a few weeks, whereas low-wage workers experienced much larger job losses that persisted for several months. Building on this diagnostic analysis, we estimate the causal effects of policies aimed at mitigating the adverse impacts of COVID-19. State-ordered reopenings of economies had small impacts on spending and employment. Stimulus payments to low-income households increased consumer spending sharply, but little of this increased spending flowed to businesses most affected by the COVID-19 shock, dampening its impacts on employment. Paycheck Protection Program loans increased employment at small businesses by only 2%, implying a cost of $377,000 per job saved. These results suggest that traditional macroeconomic tools – stimulating aggregate demand or providing liquidity to businesses – have diminished capacity to restore employment when consumer spending is constrained by health concerns. During a pandemic, it may be more fruitful to mitigate economic hardship through social insurance. More broadly, this analysis shows how public statistics constructed from private sector data can support many research and policy analyses without compromising privacy, providing a new tool for empirical macroeconomics.
                    [post_title] => The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data
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                    [post_content] => Low levels of social interaction across class lines have generated widespread concern and are associated with worse outcomes, such as lower rates of upward income mobility. Here, we analyze the determinants of cross-class interaction using data from Facebook, building upon the analysis in the first paper in this series.

We show that about half of the social disconnection across socioeconomic lines—measured as the difference in the share of high-socioeconomic status (SES) friends between low- and high-SES people—is explained by differences in exposure to high-SES people in groups such as schools and religious organizations. The other half is explained by friending bias—the tendency for low-SES people to befriend high-SES people at lower rates even conditional on exposure.

Friending bias is shaped by the structure of the groups in which people interact. For example, friending bias is higher in larger and more diverse groups and lower in religious organizations than in schools and workplaces. Distinguishing exposure from friending bias is helpful for identifying interventions to increase cross-SES friendships (economic connectedness).

Using fluctuations in the share of high-SES students across high school cohorts, we show that increases in high-SES exposure lead low-SES people to form more friendships with high-SES people in schools that exhibit low levels of friending bias. Hence, socioeconomic integration can increase economic connectedness in communities where friending bias is low. In contrast, when friending bias is high, increasing cross-SES interaction among existing members may be necessary to increase economic connectedness.

To support such efforts, we release privacy-protected statistics on economic connectedness, exposure, and friending bias for each ZIP code, high school, and college in the U.S. at <a href="https://www.socialcapital.org">https://www.socialcapital.org</a>.
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                    [post_content] => In this paper—the first in a series of two papers that use data on 21 billion friendships from Facebook to study social capital—we measure and analyze three types of social capital by ZIP code in the United States: (i) connectedness between different types of people, such as those with low vs. high socioeconomic status (SES); (ii) social cohesion, such as the extent of cliques in friendship networks; and (iii) civic engagement, such as rates of volunteering.

These measures vary substantially across areas, but are not highly correlated with each other. We demonstrate the importance of distinguishing these forms of social capital by analyzing their associations with economic mobility across areas. The fraction of high-SES friends among low-SES individuals—which we term economic connectedness—is among the strongest predictors of upward income mobility identified to date, whereas other social capital measures are not strongly associated with economic mobility.

If children with low- SES parents were to grow up in counties with economic connectedness comparable to that of the average child with high- SES parents, their incomes in adulthood would increase by 20% on average. Differences in economic connectedness can explain well-known relationships between upward income mobility and racial segregation, poverty rates, and inequality.

To support further research and policy interventions, we publicly release privacy-protected statistics on social capital by ZIP code at <a href="https://www.socialcapital.org">https://www.socialcapital.org</a>.
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                    [post_content] => We examine the geographic incidence of local labor market growth across locations of childhood residence. We ask: when wages grow in a given US labor market, do the benefits flow to individuals growing up in nearby or distant locations?

We begin by constructing new statistics on migration rates across labor markets between childhood and young adulthood. This migration matrix shows 80% of young adults migrate less than 100 miles from where they grew up. 90% migrate less than 500 miles. Migration distances are shorter for Black and Hispanic individuals and for those from low-income families. These migration patterns provide information on the first order geographic incidence of local wage growth.

Next, we explore the responsiveness of location choices to economic shocks. Using geographic variation induced by the recovery from the Great Recession, we estimate the elasticity of migration with respect to increases in local labor market wage growth. We develop and implement a novel test for validating whether our identifying wage variation is driven by changes in labor market opportunities rather than changes in worker composition due to sorting. We find that higher wages lead to increased in-migration, decreased out-migration and a partial capitalization of wage increases into local prices.

Our results imply that for a two-rank-point point increase in annual wages (approximately $1600) in a given commuting zone (CZ), approximately 99% of wage gains flow to those who would have resided in the CZ in the absence of the wage change. The geographically concentrated nature of most migration and the small magnitude of these migration elasticities suggest that the incidence of labor market conditions across childhood residences is highly local. For many individuals, the “radius of economic opportunity” is quite narrow.
                    [post_title] => The Radius of Economic Opportunity: Evidence from Migration and Local Labor Markets
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                    [post_date] => 2020-04-07 01:35:58
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                    [post_content] => There is widespread concern that economics does not attract as broad or diverse a pool of talent as it could. For example, less than one-third of undergraduates who receive degrees in economics are women, significantly lower than in math or statistics. This article presents a case study of a new introductory undergraduate course at Harvard, “Using Big Data to Solve Economic and Social Problems,” that enrolled 400 students, achieved nearly a 50-50 gender balance, and was among the highest-rated courses in the college.

We first summarize the course’s content and pedagogical approach. We then illustrate how this approach differs from that taken in traditional courses by showing how canonical topics – income inequality, tax incidence, and adverse selection – are taught differently. Then, drawing upon students’ comments and prior research on effective teaching practices, we identify elements of the course’s approach that appear to underlie its success: connecting the material to students’ own experiences; teaching skills that have social and career value; and engaging students in scientific investigation of real-world problems. We conclude by discussing how these ideas for improving instruction in economics could be applied in other courses and tested empirically in future research.
                    [post_title] => Expanding and Diversifying the Pool of Undergraduates Who Study Economics: Insights from a New Introductory Course at Harvard
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 [post_date] => 2020-02-12 10:46:00
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 [post_content] => In our paper released in 2017 (<a href="https://opportunityinsights.org/wp-content/uploads/2018/03/coll_mrc_summary.pdf">“Mobility Report Cards: The Role of Colleges in Intergenerational Mobility”</a>), we used anonymized data from the federal government to publish statistics for each college in the U.S.  on the distribution of students’ earnings in their thirties and their parents’ incomes.  We showed that students from low-income families have excellent long-term outcomes after attending selective schools, but that there are very few low-income students at these schools.

Building on those <a href="https://opportunityinsights.org/paper/mobilityreportcards/">earlier findings</a>, we analyze how changes in the allocation of students to colleges would affect segregation by parental income across colleges and intergenerational mobility in the United States. We study these questions by linking data from tax records on parents’ incomes and students’ earnings outcomes for each college to data on students’ ACT and SAT test scores. We generate four findings:

First, we find that low- and middle-income students attend selective schools at much lower rates than their peers from richer families with the same test scores.  As a result, simply equalizing attendance rates across parental income groups conditional on test scores would reduce the under-representation of low-income students at selective schools by 38%.

Second, at the most selective schools (Ivy-plus), the middle class is heavily under-represented relative to others with the same test scores; equalizing attendance rates conditional on test scores would increase the fraction of such students from 28% to 38%, about 30% of the way to the “all college goer” benchmark.  However, doing the same for low-income students would only increase their share from 3.8% to 4.4% of the class, closing just 6% of the gap.

Third, in order to fully close the gap to the “all college goer” benchmark, we show that low-income students would need to attend all schools at rates similar to those with 160 points higher SAT scores.  To give a sense of the practice magnitude of this difference, 7.3% of low-income students currently attend Ivy-plus schools; attendance rates for these students would need to rise to 25.8%.  This is roughly the advantage that legacy students, as well as various other preferred groups, receive in the admissions process.

Finally, we show that these changes in college attendance patterns would have substantial effects on upward mobility in among college-goers in the U.S.  Prior work shows large gaps in the expected outcomes of college students from high- and low-income families; equalizing attendance rates for students with the same test scores would reduce the outcome gap by 15%.  Going fully to the “all college goer” benchmark would reduce the outcome gap by 25%.

We conclude that changing how students are allocated to colleges could substantially reduce segregation and increase intergenerational mobility, even without changing colleges’ educational programs or total educational spending.

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service, the U.S. Treasury Department, the Federal Reserve Board of Governors, ACT, or the College Board. The results in this paper build on those reported in an earlier NBER working paper entitled “Mobility Report Cards: The Role of Colleges in Intergenerational Mobility.” This work was conducted under IRS contract TIRNO-16-E-00013 and reviewed by the Office of Tax Analysis at the U.S. Treasury. Chetty, Friedman, Saez, and Yagan acknowledge funding from the Russell Sage Foundation, the Bill & Melinda Gates Foundation, the Robert Wood Johnson Foundation, the Schmidt Futures Foundation, the Center for Equitable Growth at UC-Berkeley, the Washington Center for Equitable Growth, the UC Davis Center for Poverty Research, the Alfred P. Sloan Foundation, the Laura and John Arnold Foundation, the Chan Zuckerberg Initiative, the Overdeck Foundation, and Bloomberg Philanthropies.

[post_title] => Income Segregation and Intergenerational Mobility Across Colleges in the United States
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                    [post_date] => 2019-11-08 11:38:51
                    [post_date_gmt] => 2019-11-08 17:38:51
                    [post_content] => The impacts of many policies, such as efforts to increase upward income mobility or improve health outcomes, are only observed with long delays. For example, it can take decades to see the effects of early childhood interventions on lifetime earnings. This problem has greatly limited researchers’ and policymakers’ ability to test and improve policies and arises frequently in our own work at Opportunity Insights on the determinants of economic opportunity.

In this study, we develop a new method of estimating the long-term impacts of policies more rapidly and precisely using short-term proxies. We predict long-term outcomes (e.g., lifetime earnings) using short-term outcomes (e.g., earnings in early adulthood or test scores). We then show that the causal effects of policies on this predictive index (which we term a “surrogate index”, following terminology in the statistics literature) can help us learn about their long-term impacts more quickly under certain assumptions that are described in the full paper.

We apply our method to analyze the long-term impacts of a job training experiment in California. Using short-term employment rates as surrogates, we show that one could have estimated the program's impact on mean employment rates over a 9 year horizon within 1.5 years, with a 35% reduction in standard errors.

The success of the surrogate index in this job training application suggests that our method could be applied to predict the long-term impacts of other programs as well. Going forward, we hope to build a public library of early indicators (surrogate indices) for social science by harnessing historical experiments along with the large-scale datasets we have built. If you would like to contribute to this effort by reporting a surrogate index that predicts long-term impacts estimated in an experiment, as in the GAIN program, please <a href="mailto:info@opportunityinsights.org?subject=Surrogate%20Index">contact us</a>.
 [post_title] => The Surrogate Index: Combining Short-Term Proxies to Estimate Long-Term Treatment Effects More Rapidly and Precisely
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                    [post_date] => 2019-08-03 13:33:38
                    [post_date_gmt] => 2019-08-03 18:33:38
                    [post_content] => Low-income families in the United States tend to live in neighborhoods that offer limited opportunities for upward income mobility. One potential explanation for this pattern is that families prefer such neighborhoods for other reasons, such as affordability or proximity to family and jobs. An alternative explanation is that they do not move to high-opportunity areas because of a lack of information or barriers that prevent them from making such moves.

We test between these explanations using a two phase randomized controlled trial with housing voucher recipients in Seattle and King County. We first provided a bundle of resources to facilitate moves to high-upward-mobility neighborhoods: information about high-opportunity areas, short-term financial assistance, customized assistance during the housing search process, and connections to landlords. This bundled intervention increased the fraction of families who moved to high-upward-mobility areas from 15% in the control group to 53% in the treatment group.

To understand the mechanisms underlying this effect, we ran a second phase with three arms: (1) information about high-opportunity areas and financial assistance only; (2) reduced support services in addition to information and financial assistance; and (3) full support services, as in the original bundled intervention. The full services had five times as large a treatment effect as the information and financial incentives treatment and three times as large an effect as the reduced support intervention, showing that high-intensity, customized support enables moves to opportunity.

Interviews with randomly selected families reveal that the program succeeded by relaxing families’ bandwidth constraints and addressing their specific needs, from identifying suitable units to providing emotional support to brokering with landlords. Families induced to move to higher opportunity areas tend to stay in their new neighborhoods in subsequent years and report higher levels of neighborhood satisfaction after moving. Our findings imply that many low-income families do not have a strong preference to stay in low-opportunity areas and that barriers in the housing search process are a central driver of residential segregation by income.
                    [post_title] => Creating Moves to Opportunity: Experimental Evidence on Barriers to Neighborhood Choice
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[8] => WP_Post Object
                (
                    [ID] => 2646
                    [post_author] => 8
                    [post_date] => 2019-07-29 16:20:13
                    [post_date_gmt] => 2019-07-29 21:20:13
                    [post_content] => We conduct a comparative welfare analysis of 133 historical policy changes over the past half-century in the United States, focusing on policies in social insurance, education and job training, taxes and cash transfers, and in-kind transfers. For each policy, we use existing causal estimates to calculate both the benefit that each policy provides its recipients (measured as their willingness to pay) and the policy’s net cost, inclusive of long-term impacts on the government’s budget. We divide the willingness to pay by the net cost to the government to form each policy’s Marginal Value of Public Funds, or its “MVPF”. Comparing MVPFs across policies provides a unified method of assessing their impact on social welfare. Our results suggest that direct investments in low-income children’s health and education have historically had the highest MVPFs, on average exceeding 5. Many such policies have paid for themselves as governments recouped the cost of their initial expenditures through additional taxes collected and reduced transfers. We find large MVPFs for education and health policies amongst children of all ages, rather than observing diminishing marginal returns throughout childhood. We find smaller MVPFs for policies targeting adults, generally between 0.5 and 2. Expenditures on adults have exceeded this MVPF range in particular if they induced large spillovers on children. We relate our estimates to existing theories of optimal government policy and we discuss how the MVPF provides lessons for the design of future research.

This research was funded by the National Science Foundation #CAREER1653686 (Hendren) and #DGE1745303 (Sprung-Keyser)), the Sloan Foundation (Hendren), the Bill and Melinda Gates Foundation (Hendren), and the Chan Zuckerberg Initiative (Hendren). Any opinions, findings, and conclusions or recommendations expressed in this material are those of the author(s) and do not necessarily reflect the views of the National Science Foundation.
 [post_title] => A Unified Welfare Analysis of Government Policies
 [post_excerpt] => 
 [post_status] => publish
 [comment_status] => closed
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 [post_name] => welfare
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 [pinged] => 
 [post_modified] => 2023-05-16 09:55:03
 [post_modified_gmt] => 2023-05-16 14:55:03
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[9] => WP_Post Object
 (
 [ID] => 2254
 [post_author] => 8
 [post_date] => 2019-03-11 06:08:30
 [post_date_gmt] => 2019-03-11 11:08:30
 [post_content] => Releasing statistics based on small samples – such as estimates of social mobility by Census tract, as in the <a href="http://www.opportunityatlas.org">Opportunity Atlas</a> – is very valuable for policy but can potentially create privacy risks by unintentionally disclosing information about specific individuals. To mitigate such risks, we worked with researchers at the <a href="https://privacytools.seas.harvard.edu/">Harvard Privacy Tools Project</a> and Census Bureau staff to develop practical methods of reducing the risks of privacy loss when releasing such data. This paper describes the methods that we developed, which can be applied to disclose any statistic of interest that is estimated using a sample with a small number of observations.

We focus on the case where the dataset can be broken into many groups (“cells”) and one is interested in releasing statistics for one or more of these cells. Building on ideas from the differential privacy literature, we add noise to the statistic of interest in proportion to the statistic’s maximum observed sensitivity, defined as the maximum change in the statistic from adding or removing a single observation across all the cells in the data. Intuitively, our approach permits the release of statistics in arbitrarily small samples by adding sufficient noise to the estimates to protect privacy. Although our method does not offer a formal privacy guarantee, it generally outperforms widely used methods of disclosure limitation such as count-based cell suppression both in terms of privacy loss and statistical bias.

We illustrate how the method can be implemented by discussing how it was used to release estimates of social mobility by Census tract in the <a href="http://www.opportunityatlas.org/">Opportunity Atlas</a>. We also provide a step-by-step guide and illustrative Stata code to implement our approach.
 [post_title] => A Practical Method to Reduce Privacy Loss when Disclosing Statistics Based on Small Samples
 [post_excerpt] => 
 [post_status] => publish
 [comment_status] => closed
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 [post_name] => differential-privacy
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 [pinged] => 
 [post_modified] => 2023-05-16 09:55:07
 [post_modified_gmt] => 2023-05-16 14:55:07
 [post_content_filtered] => 
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 [guid] => http://opportunityinsights.org/?post_type=paper&p=2254
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[10] => WP_Post Object
                (
                    [ID] => 2225
                    [post_author] => 8
                    [post_date] => 2019-02-05 16:07:40
                    [post_date_gmt] => 2019-02-05 22:07:40
                    [post_content] => Many countries provide financial incentives to spur innovation, ranging from tax incentives to research and development grants. In this paper, we study how such financial incentives affect individuals' decisions to pursue careers in innovation. We first present empirical evidence on inventors' career trajectories and income distributions using de-identified data on 1.2 million inventors from patent records linked to tax records in the U.S. We find that the private returns to innovation are extremely skewed – with the top 1% of inventors collecting more than 22% of total inventors' income – and are highly correlated with their social impact, as measured by citations. Inventors tend to have their most impactful innovations around age 40 and their incomes rise rapidly just before they have high-impact patents. We then build a stylized model of inventor career choice that matches these facts as well as recent evidence that childhood exposure to innovation plays a critical role in determining whether individuals become inventors. The model predicts that financial incentives, such as top income tax reductions, have limited potential to increase aggregate innovation because they only affect individuals who are exposed to innovation and have no impact on the decisions of star inventors, who matter most for aggregate innovation. Importantly, these results hold regardless of whether the private returns to innovation are known at the time of career choice. In contrast, increasing exposure to innovation (e.g., through mentorship programs) could have substantial impacts on innovation by drawing individuals who produce high-impact inventions into the innovation pipeline. Although we do not present direct evidence supporting these model-based predictions, our results call for a more careful assessment of the impacts of financial incentives and a greater focus on alternative policies to increase the supply of inventors.

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service, U.S. Department of the Treasury. We would particularly like to thank Philippe Aghion for inspiration and many insightful comments. This research was funded by the National Science Foundation, the National Institute on Aging Grant T32AG000186, the European Research Council, the Economic and Social Research Council at CEP, the Washington Center for Equitable Growth, and the Kauffman Foundation. The views expressed herein are those of the authors and do not necessarily reflect the views of the National Bureau of Economic Research.
 [post_title] => Do Tax Cuts Produce More Einsteins? The Impacts of Financial Incentives vs. Exposure to Innovation on the Supply of Inventors
 [post_excerpt] => 
 [post_status] => publish
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 [post_name] => do-tax-cuts-produce-more-einsteins-the-impacts-of-financial-incentives-vs-exposure-to-innovation-on-the-supply-of-inventors
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 [pinged] => 
 [post_modified] => 2023-05-16 09:55:12
 [post_modified_gmt] => 2023-05-16 14:55:12
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[11] => WP_Post Object
                (
                    [ID] => 1652
                    [post_author] => 8
                    [post_date] => 2018-09-30 23:54:28
                    [post_date_gmt] => 2018-10-01 04:54:28
                    [post_content] => We construct a publicly available atlas of children’s outcomes in adulthood by Census tract using anonymized longitudinal data covering nearly the entire U.S. population.  For each tract,  we  estimate children’s earnings distributions, incarceration rates, and other outcomes in adulthood by parental income, race, and gender.  These estimates allow us to trace the roots of outcomes such as poverty and incarceration back to the neighborhoods in which children grew up.  We find that children’s outcomes vary sharply across nearby tracts:  for children of parents at the 25th percentile of the income distribution,  the standard deviation of mean household income at age 35 is $5,000 across tracts within counties.  We illustrate how these tract-level data can provide insight into how neighborhoods shape the development of human capital and support local economic policy using two applications.

First, we show that the estimates permit precise targeting  of  policies  to  improve  economic  opportunity  by  uncovering  specific  neighborhoods where  certain  subgroups  of  children  grow  up  to  have  poor  outcomes.   Neighborhoods  matter at a very granular level:  conditional on characteristics such as poverty rates in a child’s own Census tract,  characteristics of tracts that are one mile away have little predictive power for a  child’s  outcomes.   Our  historical  estimates  are  informative  predictors  of  outcomes  even  for children  growing  up  today  because  neighborhood  conditions  are  relatively  stable  over  time.

Second, we show that the observational estimates are highly predictive of neighborhoods’ causal effects, based on a comparison to data from the Moving to Opportunity experiment and a quasi-experimental research design analyzing movers’ outcomes.  We then identify high-opportunity neighborhoods that are affordable to low-income families,  providing an input into the design of affordable housing policies.  Our measures of children’s long-term outcomes are only weakly correlated with traditional proxies for local economic success such as rates of job growth, showing that the conditions that create greater upward mobility are not necessarily the same as those that lead to productive labor markets.

<a href="http://www.opportunityatlas.org">Click here to view the Opportunity Atlas</a>

Any opinions and conclusions expressed herein are those of the authors and do not necessarily reflect the views of the U.S. Census Bureau. All results have been reviewed to ensure that no confidential information is disclosed. The statistical summaries reported in this paper have been cleared by the Census Bureau’s Disclosure Review Board release authorization number CBDRB-FY18-319. 
 [post_title] => The Opportunity Atlas: Mapping the Childhood Roots of Social Mobility
 [post_excerpt] => 
 [post_status] => publish
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 [post_name] => the-opportunity-atlas
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 [post_modified] => 2023-05-16 09:55:16
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[12] => WP_Post Object
                (
                    [ID] => 992
                    [post_author] => 2
                    [post_date] => 2018-03-01 21:54:27
                    [post_date_gmt] => 2018-03-02 03:54:27
                    [post_content] => We study the sources of racial and ethnic disparities in income using de-identified longitudinal data covering nearly the entire U.S. population from 1989-2015. We document three sets of results.

First, the intergenerational persistence of disparities varies substantially across racial groups. For example, Hispanic Americans are moving up significantly in the income distribution across generations because they have relatively high rates of intergenerational income mobility. In contrast, black Americans have substantially lower rates of upward mobility and higher rates of downward mobility than whites, leading to large income disparities that persist across generations. Conditional on parent income, the black-white income gap is driven entirely by large differences in wages and employment rates between black and white men; there are no such differences between black and white women.

Second, differences in family characteristics such as parental marital status, education, and wealth explain very little of the black-white income gap conditional on parent income. Differences in ability also do not explain the patterns of intergenerational mobility we document.

Third, the black-white gap persists even among boys who grow up in the same neighborhood. Controlling for parental income, black boys have lower incomes in adulthood than white boys in 99% of Census tracts. Both black and white boys have better outcomes in low-poverty areas, but black-white gaps are larger on average for boys who grow up in such neighborhoods. The few areas in which black-white gaps are relatively small tend to be low-poverty neighborhoods with low levels of racial bias among whites and high rates of father presence among blacks. Black males who move to such neighborhoods earlier in childhood earn more and are less likely to be incarcerated. However, fewer than 5% of black children grow up in such environments.

These findings suggest that reducing the black-white income gap will require efforts whose impacts cross neighborhood and class lines and increase upward mobility specifically for black men.

The views expressed are those of the authors and not necessarily those of the U.S. Census Bureau. The statistical summaries reported in this paper have been cleared by the Census Bureau's Disclosure Review Board release authorization number CBDRB-FY18-195.
 
 [post_title] => Race and Economic Opportunity in the United States: An Intergenerational Perspective
 [post_excerpt] => 
 [post_status] => publish
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 [ping_status] => closed
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 [post_name] => race
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 [pinged] => 
 [post_modified] => 2023-05-16 09:55:21
 [post_modified_gmt] => 2023-05-16 14:55:21
 [post_content_filtered] => 
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 [guid] => http://opportunityinsights.org/?post_type=data&p=992
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[13] => WP_Post Object
                (
                    [ID] => 520
                    [post_author] => 2
                    [post_date] => 2017-12-04 19:07:40
                    [post_date_gmt] => 2017-12-05 01:07:40
                    [post_content] => Innovation is widely viewed as the engine of economic growth. As a result, many policies have been proposed to spur innovation, ranging from tax cuts to investments in STEM (science, technology, engineering, and math) education. Unfortunately, the effectiveness of such policies is unclear because we know relatively little about the factors that induce people to become inventors. Who are America’s most successful inventors and what can we learn from their experiences in designing policies to stimulate innovation?

We study the lives of more than one million inventors in the United States using a new de-identified database linking patent records to tax and school district records. Tracking these individuals from birth onward, we identify the key factors that determine who becomes an inventor, as measured by filing a patent.1 Our results shed light on what policies can be most effective in increasing innovation, showing in particular that increasing exposure to innovation among women, minorities, and children from low-income families may have greater potential to spark innovation and growth than traditional approaches such as reducing tax rates.

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service, U.S. Department of the Treasury, or the National Institutes of Health.
 [post_title] => Who Becomes an Inventor in America? The Importance of Exposure to Innovation
 [post_excerpt] => 
 [post_status] => publish
 [comment_status] => closed
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 [post_password] => 
 [post_name] => losteinsteins
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 [pinged] => 
 [post_modified] => 2023-05-16 09:55:30
 [post_modified_gmt] => 2023-05-16 14:55:30
 [post_content_filtered] => 
 [post_parent] => 0
 [guid] => http://opportunityinsights.org/?post_type=data&p=520
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[14] => WP_Post Object
                (
                    [ID] => 536
                    [post_author] => 2
                    [post_date] => 2017-12-04 19:23:00
                    [post_date_gmt] => 2017-12-05 01:23:00
                    [post_content] => We  characterize  intergenerational  income  mobility  at  each  college  in  the  United  States  using data  for  over  30  million  college  students  from  1999-2013.

We  document  four  results.   First, access  to  colleges  varies  greatly  by  parent  income.   For  example,  children  whose  parents  are in  the  top  1%  of  the  income  distribution  are  77  times  more  likely  to  attend  an  Ivy  League college than those whose parents are in the bottom income quintile.

Second, children from low- and high-income families have similar earnings outcomes conditional on the college they attend, indicating that low-income students are not mismatched at selective colleges.

Third, rates of upward mobility – the fraction of students who come from families in the bottom income quintile and reach the top quintile – differ substantially across colleges because low-income access varies significantly  across  colleges  with  similar  earnings  outcomes.   Rates  of  bottom-to-top  quintile mobility are highest at certain mid-tier public universities, such as the City University of New York and California State colleges.  Rates of upper-tail (bottom quintile to top 1%) mobility are highest at elite colleges, such as Ivy League universities.

Fourth, the fraction of students from low-income families did not change substantially between 2000-2011 at elite private colleges, but fell sharply at colleges with the highest rates of bottom-to-top-quintile mobility.  Although our descriptive analysis does not identify colleges’ causal effects on students’ outcomes, the publicly available statistics constructed here highlight colleges that deserve further study as potential engines of upward mobility.

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the
Internal Revenue Service or the U.S. Treasury Department.  This work was conducted under IRS contract TIRNO-16-E-00013 and reviewed by the Office of Tax Analysis at the U.S. Treasury.
 [post_title] => Mobility Report Cards: The Role of Colleges in Intergenerational Mobility
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 [post_status] => publish
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 [post_name] => mobilityreportcards
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 [pinged] => 
 [post_modified] => 2023-05-16 09:55:26
 [post_modified_gmt] => 2023-05-16 14:55:26
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[15] => WP_Post Object
                (
                    [ID] => 599
                    [post_author] => 2
                    [post_date] => 2017-12-02 21:21:44
                    [post_date_gmt] => 2017-12-03 03:21:44
                    [post_content] => To what extent are children’s opportunities for upward economic mobility shaped by the neighborhoods in which they grow up? We study this question using data from de-identified tax records on more than five million children whose families moved across counties between 1996 and 2012.

The study consists of two parts. In part one, we show that the area in which a child grows up has significant causal effects on her prospects for upward mobility. In part two, we present estimates of the causal effect of each county in the United States on a child’s chances of success. Using these results, we identify the properties of high- vs. low-opportunity areas to obtain insights into policies that can increase economic opportunity.

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service or the U.S. Treasury Department.  This work is a component of a larger project examining the effects of  tax expenditures on  the  budget  deficit  and  economic  activity.   All  results  based  on  tax  data  in  this  paper  are  constructed  using statistics originally reported in the SOI Working Paper “The Economic Impacts of Tax Expenditures:  Evidence from Spatial Variation across the U.S.,” approved under IRS contract TIRNO-12-P-00374.
 [post_title] => The Impacts of Neighborhoods on Intergenerational Mobility II: County-Level Estimates
 [post_excerpt] => 
 [post_status] => publish
 [comment_status] => closed
 [ping_status] => closed
 [post_password] => 
 [post_name] => neighborhoodsii
 [to_ping] => 
 [pinged] => 
 [post_modified] => 2023-05-16 09:57:36
 [post_modified_gmt] => 2023-05-16 14:57:36
 [post_content_filtered] => 
 [post_parent] => 0
 [guid] => http://opportunityinsights.org/?post_type=data&p=599
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[16] => WP_Post Object
                (
                    [ID] => 606
                    [post_author] => 2
                    [post_date] => 2017-12-03 21:37:52
                    [post_date_gmt] => 2017-12-04 03:37:52
                    [post_content] => To what extent are children’s opportunities for upward economic mobility shaped by the neighborhoods in which they grow up? We study this question using data from de-identified tax records on more than five million children whose families moved across counties between 1996 and 2012.

The study consists of two parts. In part one, we show that the area in which a child grows up has significant causal effects on her prospects for upward mobility.

In part two, we present estimates of the causal effect of each county in the United States on a child’s chances of success. Using these results, we identify the properties of high- vs. low-opportunity areas to obtain insights into policies that can increase economic opportunity.

[post_title] => The Impacts of Neighborhoods on Intergenerational Mobility I: Childhood Exposure Effects
                    [post_excerpt] => 
                    [post_status] => publish
                    [comment_status] => closed
                    [ping_status] => closed
                    [post_password] => 
                    [post_name] => neighborhoodsi
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                    [pinged] => 
                    [post_modified] => 2023-05-16 09:55:35
                    [post_modified_gmt] => 2023-05-16 14:55:35
                    [post_content_filtered] => 
                    [post_parent] => 0
                    [guid] => http://opportunityinsights.org/?post_type=data&p=606
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[17] => WP_Post Object
                (
                    [ID] => 546
                    [post_author] => 2
                    [post_date] => 2016-12-01 19:58:47
                    [post_date_gmt] => 2016-12-02 01:58:47
                    [post_content] => One of the defining features of the “American Dream” is the ideal that children have a higher standard of living than their parents. We assess whether the U.S. is living up to this ideal by estimating rates of “absolute income mobility” – the fraction of children who earn more than their parents – since 1940.

We measure absolute mobility by comparing children’s household incomes at age 30 (adjusted for inflation using the Consumer Price Index) with their parents’ household incomes at age 30. We find that rates of absolute mobility have fallen from approximately 90% for children born in 1940 to 50% for children born in the 1980s. Absolute income mobility has fallen across the entire income distribution, with the largest declines for families in the middle class. These findings are unaffected by using alternative price indices to adjust for inflation, accounting for taxes and transfers, measuring income at later ages, and adjusting for changes in household size.

Absolute mobility fell in all 50 states, although the rate of decline varied, with the largest declines concentrated in states in the industrial Midwest, such as Michigan and Illinois. The decline in absolute mobility is especially steep – from 95% for children born in 1940 to 41% for children born in 1984 – when we compare the sons’ earnings to their fathers’ earnings.

Why have rates of upward income mobility fallen so sharply over the past half-century? There have been two important trends that have affected the incomes of children born in the 1980s relative to those born in the 1940s and 1950s: lower Gross Domestic Product (GDP) growth rates and greater inequality in the distribution of growth. We find that most of the decline in absolute mobility is driven by the more unequal distribution of economic growth rather than the slowdown in aggregate growth rates. When we simulate an economy that restores GDP growth to the levels experienced in the 1940s and 1950s but distributes that growth across income groups as it is distributed today, absolute mobility only increases to 62%. In contrast, maintaining GDP at its current level but distributing it more broadly across income groups – at it was distributed for children born in the 1940s – would increase absolute mobility to 80%, thereby reversing more than two-thirds of the decline in absolute mobility.

These findings show that higher growth rates alone are insufficient to restore absolute mobility to the levels experienced in mid-century America. Under the current distribution of GDP, we would need real GDP growth rates above 6% per year to return to rates of absolute mobility in the 1940s. Intuitively, because a large fraction of GDP goes to a small fraction of high-income households today, higher GDP growth does not substantially increase the number of children who earn more than their parents. Of course, this does not mean that GDP growth does not matter: changing the distribution of growth naturally has smaller effects on absolute mobility when there is very little growth to be distributed. The key point is that increasing absolute mobility substantially would require more broad-based economic growth.

We conclude that absolute mobility has declined sharply in America over the past half-century primarily because of the growth in inequality. If one wants to revive the “American Dream” of high rates of absolute mobility, one must have an interest in growth that is shared more broadly across the income distribution.
                    [post_title] => The Fading American Dream: Trends in Absolute Income Mobility Since 1940
                    [post_excerpt] => 
                    [post_status] => publish
                    [comment_status] => closed
                    [ping_status] => closed
                    [post_password] => 
                    [post_name] => the-fading-american-dream
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                    [pinged] => 
                    [post_modified] => 2023-05-16 09:56:08
                    [post_modified_gmt] => 2023-05-16 14:56:08
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                    [post_parent] => 0
                    [guid] => http://opportunityinsights.org/?post_type=data&p=546
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[18] => WP_Post Object
                (
                    [ID] => 573
                    [post_author] => 2
                    [post_date] => 2016-04-01 20:43:06
                    [post_date_gmt] => 2016-04-02 01:43:06
                    [post_content] => How can we reduce socioeconomic disparities in health outcomes? Although it is well known that there are significant differences in health and longevity between income groups, debate remains about the magnitudes and determinants of these differences. We use new data from 1.4 billion anonymous earnings and mortality records to construct more precise estimates of the relationship between income and life expectancy at the national level than was feasible in prior work. We then construct new local area (county and metro area) estimates of life expectancy by income group and identify factors that are associated with higher levels of life expectancy for low-income individuals.

Our findings show that disparities in life expectancy are not inevitable. There are cities throughout America — from New York to San Francisco to Birmingham, AL — where gaps in life expectancy are relatively small or are narrowing over time. Replicating these successes more broadly will require targeted local efforts, focusing on improving health behaviors among the poor in cities such as Las Vegas and Detroit. Our findings also imply that federal programs such as Social Security and Medicare are less redistributive than they might appear because low-income individuals obtain these benefits for significantly fewer years than high-income individuals, especially in cities like Detroit.

Going forward, the challenge is to understand the mechanisms that lead to better health and longevity for low-income individuals in some parts of the U.S. To facilitate future research and monitor local progress, we have posted annual statistics on life expectancy by income group and geographic area (state, CZ, and county) at The Health Inequality Project website. Using these data, researchers will be able to study why certain places have high or improving levels of life expectancy and ultimately apply these lessons to reduce health disparities in other parts of the country.
                    [post_title] => The Association Between Income and Life Expectancy in the United States, 2001-2014
                    [post_excerpt] => 
                    [post_status] => publish
                    [comment_status] => closed
                    [ping_status] => closed
                    [post_password] => 
                    [post_name] => lifeexpectancy
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                    [pinged] => 
                    [post_modified] => 2023-05-16 09:56:24
                    [post_modified_gmt] => 2023-05-16 14:56:24
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                    [post_parent] => 0
                    [guid] => http://opportunityinsights.org/?post_type=data&p=573
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[19] => WP_Post Object
                (
                    [ID] => 557
                    [post_author] => 2
                    [post_date] => 2016-01-01 20:15:33
                    [post_date_gmt] => 2016-01-02 02:15:33
                    [post_content] => We show that differences in childhood environments play an important role in shaping gender gaps in adulthood by documenting three facts using population tax records for children born in the 1980s.

First, gender gaps in employment rates, earnings, and college attendance vary substantially across the parental income distribution. Notably, the traditional gender gap in employment rates is reversed for children growing up in poor families: boys in families in the bottom quintile of the income distribution are less likely to work than girls.

Second, these gender gaps vary substantially across counties and commuting zones in which children grow up. The degree of variation in outcomes across places is largest for boys growing up in poor, single-parent families.

Third, the spatial variation in gender gaps is highly correlated with proxies for neighborhood disadvantage. Low-income boys who grow up in high-poverty, high-minority areas work significantly less than girls. These areas also have higher rates of crime, suggesting that boys growing up in concentrated poverty substitute from formal employment to crime.

Together, these findings demonstrate that gender gaps in adulthood have roots in childhood, perhaps because childhood disadvantage is especially harmful for boys.
                    [post_title] => Childhood Environment and Gender Gaps in Adulthood
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                    [post_date] => 2016-05-01 21:43:27
                    [post_date_gmt] => 2016-05-02 02:43:27
                    [post_content] => There are large differences in individuals’ economic, health, and educational outcomes across neighborhoods in the United States. Motivated by these disparities, the U.S. Department of Housing and Urban Development designed the Moving to Opportunity (MTO) experiment to determine whether providing low-income families assistance in moving to better neighborhoods could improve their economic and health outcomes.

The MTO experiment was conducted between 1994 and 1998 in five large U.S. cities. Approximately 4,600 families living in high-poverty public housing projects were randomly assigned to one of three groups: an experimental voucher group that was offered a subsidized housing voucher that came with a requirement to move to a census tract with a poverty rate below 10%, a Section 8 voucher group that was offered a standard housing voucher with no additional contingencies, and a control group that was not offered a voucher (but retained access to public housing).

Previous research on the MTO experiment has found that moving to lower-poverty areas greatly improved the mental and physical health of adults. However, prior work found no impacts of the MTO treatments on the earnings of adults and older youth, leading some to conclude that neighborhood environments are not an important component of economic success.

In this study, we present a new analysis of the effect of the MTO experiment on children’s long-term outcomes. Our re-analysis is motivated by new research showing that a neighborhood’s effect on children’s outcomes may depend critically on the duration of exposure to that environment. In particular, Chetty and Hendren (2015) use quasi-experimental methods to show that every year spent in a better area during childhood increases a child’s earnings in adulthood, implying that the gains from moving to a better area are larger for children who are younger at the time of the move.

In light of this new evidence on childhood exposure effects, we study the long-term impacts of MTO on children who were young when their families moved to better neighborhoods. Prior work has not been able to examine these issues because the younger children in the MTO experiment are only now old enough to be entering the adult labor market.

For older children (those between ages 13-18), we find that moving to a lower-poverty neighborhood has a statistically insignificant or slightly negative effect. More generally, the gains from moving to lower-poverty areas decline steadily with the age of the child at the time of the move. We do not find any clear evidence of a “critical age” below which children must move to benefit from a better neighborhood. Rather, every extra year of childhood spent in a low-poverty environment appears to be beneficial, consistent with the findings of Chetty and Hendren (2015).

The MTO treatments also had little or no impact on adults' economic outcomes, consistent with previous results. Together, these studies show that childhood exposure plays a critical role in neighborhoods’ effects on economic outcomes.

The experimental voucher increased the earnings of children who moved at young ages in all five experimental sites, for Whites, Blacks, and Hispanics, and for boys and girls. Perhaps most notably, we find robust evidence that the experimental voucher improved long-term outcomes for young boys, a subgroup where prior studies have found little evidence of gains.

Our estimates imply that moving a child out of public housing to a low-poverty area when young (at age 8 on average) using a subsidized voucher like the MTO experimental voucher will increase the child's total lifetime earnings by about $302,000. This is equivalent to a gain of $99,000 per child moved in present value at age 8, discounting future earnings at a 3% interest rate. The additional tax revenue generated from these earnings increases would itself offset the incremental cost of the subsidized voucher relative to providing public housing.

We conclude that offering low-income families housing vouchers and assistance in moving to lowerpoverty neighborhoods has substantial benefits for the families themselves and for taxpayers. It appears important to target such housing vouchers to families with young children – perhaps even at birth – to maximize the benefits. Our results provide less support for policies that seek to improve the economic outcomes of adults through residential relocation. More broadly, our findings suggest that efforts to integrate disadvantaged families into mixed-income communities are likely to reduce the persistence of poverty across generations.

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal Revenue Service or the U.S. Treasury Department.  This work is a component of a larger project examining the  effects  of  tax  expenditures  on  the  budget  deficit  and  economic  activity.   All  results  based  on  tax  data  in  this paper are constructed using statistics originally reported in the SOI Working Paper “The Economic Impacts of Tax Expenditures:  Evidence from Spatial Variation across the U.S.,” approved under IRS contract TIRNO-12-P-00374 and presented at the Office of Tax Analysis on November 3,  2014.  MTO participant data are highly confidential. HUD allowed the authors special access to the experimental data under Data License DL14MA001, approved March 28, 2014.
 [post_title] => The Effects of Exposure to Better Neighborhoods on Children: New Evidence from the Moving to Opportunity Experiment
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                    [post_date] => 2013-09-01 22:02:59
                    [post_date_gmt] => 2013-09-02 03:02:59
                    [post_content] => Are teachers’ impacts on students’ test scores (“value-added”) a good measure of their quality? One reason this question has sparked debate is disagreement about whether value-added (VA) measures provide unbiased estimates of teachers’ causal impacts on student achievement. We test for bias in VA using previously unobserved parent characteristics and a quasi-experimental design based on changes in teaching staff. Using school district and tax records for more than one million children, we find that VA models which control for a student’s prior test scores exhibit little bias in forecasting teachers’ impacts on student achievement.
                    [post_title] => Measuring the Impacts of Teachers I: Evaluating Bias in Teacher Value-Added Estimates
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                    [post_content] => Are teachers’ impacts on students’ test scores (“value-added”) a good measure of their quality? This question has sparked debate partly because of a lack of evidence on whether high value-added (VA) teachers improve students’ long-term outcomes. Using school district and tax records for more than one million children, we find that students assigned to high-VA teachers are more likely to attend college, earn higher salaries, and are less likely to have children as teenagers. Replacing a teacher whose VA is in the bottom 5% with an average teacher would increase the present value of students’ lifetime income by approximately $250,000 per classroom.
                    [post_title] => Measuring the Impacts of Teachers II: Teacher Value-Added and Student Outcomes in Adulthood
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                    [post_content] => We use administrative records on the incomes of more than 40 million children and their parents to describe three features of intergenerational mobility in the United States.

First, we characterize the joint distribution of parent and child income at the national level.  The conditional expectation  of  child  income  given  parent  income  is  linear  in  percentile  ranks.   On  average,  a 10 percentile increase in parent income is associated with a 3.4 percentile increase in a child’s income.

Second, intergenerational mobility varies substantially across areas within the U.S. For example, the probability that a child reaches the top quintile of the national income distribution starting from a family in the bottom quintile is 4.4% in Charlotte but 12.9% in San Jose.

Third,  we explore the factors correlated with upward mobility.  High mobility areas have (1) less residential segregation,  (2) less income inequality,  (3) better primary schools,  (4) greater social  capital,  and  (5)  greater  family  stability.   While  our  descriptive  analysis does  not  identify the causal mechanisms that determine upward mobility, the publicly available statistics on intergenerational mobility developed here can facilitate research on such mechanisms.

The opinions expressed in this paper are those of the authors alone and do not necessarily reflect the views of the Internal  Revenue  Service  or  the  U.S.  Treasury  Department.  This  work  is  a  component  of  a  larger  project  examining the  effects  of  tax  expenditures  on  the  budget  deficit  and  economic  activity.   All  results  based  on  tax  data  in  this paper  are  constructed  using  statistics  originally  reported  in  the  SOI  Working  Paper  “The  Economic  Impacts  of  Tax Expenditures:   Evidence  from  Spatial  Variation  across  the  U.S.,”  approved  under  IRS  contract  TIRNO-12-P-00374 and presented at the National Tax Association meeting on November 22, 2013. 
 [post_title] => Where is the Land of Opportunity? The Geography of Intergenerational Mobility in the United States
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                    [post_content] => We present new evidence on trends in intergenerational mobility in the U.S. using administrative earnings records.

We find that percentile rank-based measures of intergenerational mobility have remained extremely stable for the 1971-1993 birth cohorts. For children born between 1971 and 1986, we measure intergenerational mobility based on the correlation between parent and child income percentile ranks. For more recent cohorts, we measure mobility as the correlation between a child’s probability of attending college and her parents’ income rank. We also calculate transition probabilities, such as a child’s chances of reaching the top quintile of the income distribution starting from the bottom quintile. Based on all of these measures, we find that children entering the labor market today have the same chances of moving up in the income distribution (relative to their parents) as children born in the 1970s. However, because inequality has risen, the consequences of the “birth lottery” – the parents to whom a child is born – are larger today than in the past.

The views expressed in this paper are those of the authors and do not necessarily represent the views or policies of the US  Treasury Department or the Internal Revenue Service or the National Bureau of Economic Research.
 [post_title] => Is the United States Still a Land of Opportunity? Recent Trends in Intergenerational Mobility
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                    [post_content] => In Project STAR, 11,571 students in Tennessee and their teachers were randomly assigned to classrooms within their schools from kindergarten to third grade. This paper evaluates the long-term impacts of STAR by linking the experimental data to administrative records.

We first demonstrate that kindergarten test scores are highly correlated with outcomes such as earnings at age 27, college attendance, home ownership, and retirement savings. We then document four sets of experimental impacts.

First, students in small classes are significantly more likely to attend college and exhibit improvements on other outcomes. Class size does not have a significant effect on earnings at age 27, but this effect is imprecisely estimated.

Second, students who had a more experienced teacher in kindergarten have higher earnings.

Third, an analysis of variance reveals significant classroom effects on earnings. Students who were randomly assigned to higher quality classrooms in grades K-3 -- as measured by classmates' end-of-class test scores -- have higher earnings, college attendance rates, and other outcomes. Finally, the effects of class quality fade out on test scores in later grades but gains in non-cognitive measures persist.
                    [post_title] => How Does Your Kindergarten Classroom Affect Your Earnings? Evidence from Project STAR
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            [post_content] => We build a publicly available database that tracks economic activity at a granular level in real time using anonymized data from private companies. We report daily statistics on consumer spending, business revenues, employment rates, and other key indicators disaggregated by ZIP code, industry, income group, and business size. Using these data, we study how COVID-19 affected the economy by analyzing heterogeneity in its impacts. We first show that high-income individuals reduced spending sharply in mid-March 2020, particularly in areas with high rates of COVID-19 infection and in sectors that require in-person interaction. This reduction in spending greatly reduced the revenues of small businesses in affluent ZIP codes. These businesses laid off many of their employees, leading to widespread job losses especially among low-wage workers in affluent areas. High-wage workers experienced a “V-shaped” recession that lasted a few weeks, whereas low-wage workers experienced much larger job losses that persisted for several months. Building on this diagnostic analysis, we estimate the causal effects of policies aimed at mitigating the adverse impacts of COVID-19. State-ordered reopenings of economies had small impacts on spending and employment. Stimulus payments to low-income households increased consumer spending sharply, but little of this increased spending flowed to businesses most affected by the COVID-19 shock, dampening its impacts on employment. Paycheck Protection Program loans increased employment at small businesses by only 2%, implying a cost of $377,000 per job saved. These results suggest that traditional macroeconomic tools – stimulating aggregate demand or providing liquidity to businesses – have diminished capacity to restore employment when consumer spending is constrained by health concerns. During a pandemic, it may be more fruitful to mitigate economic hardship through social insurance. More broadly, this analysis shows how public statistics constructed from private sector data can support many research and policy analyses without compromising privacy, providing a new tool for empirical macroeconomics.
            [post_title] => The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data
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Measurement

The Economic Impacts of COVID-19: Evidence from a New Public Database Built Using Private Sector Data

Raj Chetty, John Friedman, Michael Stepner, The Opportunity Insights Team

Forthcoming, Quarterly Journal of Economics | April 2023

All Papers

This page lists all papers released by the Opportunity Insights team. These studies were supported by the Opportunity Insights staff and sponsors listed on our Team page. For questions related to any of these studies or the data associated with them, please email [email protected].

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