This paper outlines the case for using the Marginal Value of Public Funds (MVPF) in empirical welfare analysis. It compares the MVPF approach with more traditional welfare metrics such as the Cost-Benefit Ratio and the Net Social Benefits criterion. It outlines the advantages of the MVPF approach relative to these metrics. In building the case for the MVPF, this paper also addresses several misconceptions about the MVPF that appear in recent literature.
PaperAn earlier draft circulated under the title "The Advantages of the MVPF: A Comment on Garcia and Heckman (2022)", and is availabe
here.
Investing in college carries high returns but comes with considerable risk. Financial products like equity contracts can mitigate this risk, yet college is typically financed through non-dischargeable, government-backed student loans. This paper argues that adverse selection has unraveled private markets for college-financing contracts that mitigate risk. We use survey data on students' expected post-college outcomes to estimate their knowledge about future outcomes and quantify the threat of adverse selection in markets for equity contracts and several state-contingent debt contracts. We find students hold significant private knowledge of their future earnings, academic persistence, employment, and loan repayment likelihood, beyond what is captured by observable characteristics. Our empirical results imply that a typical college-goer must expect to pay back $1.64 in present value for every $1 of equity financing to cover the financier's costs of covering those who would adversely select their contract. We estimate that college-goers are not willing to accept these terms so that private markets unravel. Nonetheless, our framework quantifies significant welfare gains from government subsidies that would open up these missing markets and partially insure college-going risks.
Paper
Executive Summary
Slides
Unraveling Example Slides
Should choice be offered in social insurance programs? The paper presents a conceptual framework that identifies the key forces determining the value of offering choice, reviews some existing evidence on these forces, and aims to guide further empirical research in different in- surance domains. The value of offering choice is higher the larger the variation in individual valuations, but gets reduced by both selection on risk and selection on moral hazard. The imple- mentation of choice-based policies is further challenged by the presence of adverse selection and choice frictions or the obligation to offer basic uncompensated care. These inefficiencies can be seen as externalities, which do not rationalize the absence of providing choice per se, but point to the need for regulatory policies and the potential value of corrective pricing à la Pigou.
PaperDraft prepared for the 2022 edition of the Annual Review of Economics.
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 spend- ing 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 Pro- tection 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 – stimulat- ing 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.
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 low-income families prefer such neighborhoods for other reasons, such as affordability or proximity to family and jobs. An alternative explanation is that families do not move to high-opportunity areas because of barriers that prevent them from making such moves. We test between these two explanations using a randomized controlled trial with housing voucher recipients in Seattle and King County. We provided services to reduce barriers to moving to high-upward-mobility neighborhoods: customized search assistance, landlord engagement, and short-term financial assistance. The intervention increased the fraction of families who moved to high-upward-mobility areas from 14% in the control group to 54% in the treatment group. Families induced to move to higher opportunity areas by the treatment do not make sacrifices on other dimensions of neighborhood quality and report much higher levels of neighborhood satisfaction. These findings imply that most low-income families do not have a strong preference to stay in low-opportunity areas; instead, barriers in the housing search process are a central driver of residential segregation by income. Interviews with families reveal that the capacity to address each family's needs in a specific manner – from emotional support to brokering with landlords to financial assistance – was critical to the program's success. Using quasi-experimental analyses and comparisons to other studies, we show that more standardized policies – increasing voucher payment standards in high-opportunity areas or informational interventions – have much smaller impacts. We conclude that redesigning affordable housing policies to provide customized assistance in housing search could reduce residential segregation and increase upward mobility substantially.
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.