Alex Bell

Post-Doctoral Scholar

Working Papers

(with Neviana Petkova)

How do mentors shape kids’ identities and later-life outcomes? To evaluate this question, we leverage program administrative records and microdata from a 1991 RCT that randomized disadvantaged children’s eligibility for a popular mentoring program. Our re-analysis of the multitude of outcomes collected by the original short-run survey suggests that kids’ behaviors improved during the time they were with mentors. A linkage to later-life administrative records shows that treated youth were 10 percentage points more likely to attend college and also showed positive (though less significant) effects on teen birth and marriage. RCT estimates of earnings effects are imprecise. However, using a larger dataset of program administrative records, we develop a supplementary research design comparing matched versus unmatched applicants that replicates key findings from the RCT, and also reveals significant long-term positive earnings gains from program participation on the order of 20%. Through the lens of a model in which adults of differing socioeconomic status influence kids’ decision-making, we estimate that mentors may have the potential to mitigate on the order of 2/3 of the disadvantage that ordinarily hampers low-income childrens’ socioeconomic trajectories in adulthood. Although our estimates suggest that mentoring programs will not fully equalize economic opportunities for disadvantaged youth, the program’s relatively low costs and substantial benefits may place it among the most cost-effective interventions of its type to be evaluated. 

(with Sophie Calder-Wang and Shusheng Zhong)

Understanding how housing markets price neighborhood amenities is key to unpacking socioeco- nomic disparities. Yet prior approaches to amenity pricing have suffered from the key confounder of unmeasured neighborhood quality, often leading to wrong-signed estimates. In this paper, we develop a novel proxy-based method that allows us to better estimate the price of housing amenities in the midst of unobserved neighborhood quality. Using detailed migration data, we construct an innovative measure of locational desirability–Geographic PageRank–to use as the proxy variable for quality. We show that this new approach can successfully correct the “wrong-signed” problem in the amenities valuation literature when applied to a standard measure of environmental air quality. The estimated amenity prices are key inputs to evaluating the returns to investment in local public goods or environmental policies, including their roles in reducing housing disparities.

Previous research on prices of job amenities has suffered from simultaneity bias due to workers’ unobserved offer sets, resulting in “wrong-signed” compensating wage differentials. I propose a simple amenity pricing framework that uses an imprecise proxy for workers’ offer sets to identify amenity prices holding offer sets fixed. Using price estimates for a set of observed job characteristics across various public survey datasets, I find a large role for costly amenity substitution in explaining the gender
ay gap (on the order of two-thirds) and little role for amenities to explain inequalities by race or by parent background.

(with TJ Hedin, Geoffrey Schnorr, and Till von Wachter). Conditionally accepted at Journal of Labor Economics.

This paper provides estimates of the effect of unemployment insurance benefits on labor supply outcomes over the business cycle using 20 years of administrative claims, earnings, and employer data from California. A regression kink design exploiting nonlinear benefit schedules provides experimental estimates of behavioral labor supply responses throughout the unemployment spell that are comparable over time. For a given unemployment duration, the behavioral effect of UI benefit levels on labor supply is unchanged over the business cycle from 2002 to 2019. However, due to increased coverage from extensions in benefit durations , the duration elasticity of UI benefits rises during recessions. The behavioral effect during the start of the COVID-19 pandemic is substantially lower at all weeks of the unemployment spell.

Journal Articles

(with Thomas J. Hedin, Peter Mannino, Geoffrey Schnorr, and Till von Wachter), RSF: The Russell Sage Foundation Journal of the Social Sciences, 2023.

To what extent did jobless Americans benefit from unemployment insurance (UI) during the COVID-19 pandemic? This article documents geographic disparities in access to UI during 2020. We leverage aggregated and individual-level claims data to perform an integrated analysis across four measures of access to UI. In addition to the traditional UI recipiency rate, we construct rates of application among the unemployed, rates of first payment among applicants, and exhaustion rates among paid claimants. Through correlations across California counties and across states, we show that areas with more disadvantaged residents had less access to UI during the pandemic. Although these disparities are large in magnitude, cross-state analysis suggests that policy can play a salient role in mitigating them.

(with Thomas J. Hedin, Peter Mannino, Roozbeh Moghadam, Carl Romer, Geoffrey Schnorr, and Till von Wachter), AEA Papers and Proceedings, 2022.

To better measure the full extent of the impact of the COVID-19 crisis on workers and the labor market, this paper estimates three measures of the cumulative impact of the pandemic on workers across intensive and extensive margins using longitudinal administrative unemployment insurance (UI) data from California. During the first year of the crisis, 30 percent of the labor force filed a UI claim, over 50 percent of recipients spent more than 6 months on the program, and the mean work time lost was 13 weeks. Less advantaged workers and counties saw much higher rates of claiming and long-term unemployment.

(with Raj Chetty, Xavier Jaravel, Neviana Petkova, and John van Reenen), The Quarterly Journal of Economics, 2019.

We characterize the factors that determine who becomes an inventor in the United States, focusing on the role of inventive ability (“nature”) versus environment (“nurture”). Using deidentified data on 1.2 million inventors from patent records linked to tax records, we first show that children’s chances of becoming inventors vary sharply with characteristics at birth, such as their race, gender, and parents’ socioeconomic class. For example, children from high-income (top 1%) families are 10 times as likely to become inventors as those from below-median income families. These gaps persist even among children with similar math test scores in early childhood—which are highly predictive of innovation rates—suggesting that the gaps may be driven by differences in environment rather than abilities to innovate. We directly establish the importance of environment by showing that exposure to innovation during childhood has significant causal effects on children’s propensities to invent. Children whose families move to a high-innovation area when they are young are more likely to become inventors. These exposure effects are technology class and gender specific. Children who grow up in a neighborhood or family with a high innovation rate in a specific technology class are more likely to patent in exactly the same class. Girls are more likely to invent in a particular class if they grow up in an area with more women (but not men) who invent in that class. These gender- and technology class–specific exposure effects are more likely to be driven by narrow mechanisms, such as role-model or network effects, than factors that only affect general human capital accumulation, such as the quality of schools. Consistent with the importance of exposure effects in career selection, women and disadvantaged youth are as underrepresented among high-impact inventors as they are among inventors as a whole. These findings suggest that there are many “lost Einsteins”—individuals who would have had highly impactful inventions had they been exposed to innovation in childhood—especially among women, minorities, and children from low-income families.

(with Raj Chetty, Xavier Jaravel, Neviana Petkova, and John van Reenen), Journal of the European Economic Association, 2019.

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 deidentified data on 1.2 million inventors from patent records linked to tax records in the United States. 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

(with Xavier Jaravel and Neviana Petkova), American Economic Review, 2018

Teamwork has become an essential feature of modern economies and knowledge production (Wuchty, Jones, and Uzzi 2007; Jones 2010; Crescenzi, Nathan, and Rodríguez-Pose 2016; Jaffe and Jones 2015; Seaborn 1979). We investigate empirically the importance of team-specific capital for the compensation and patent production of inventors, using administrative tax and patent data for the population of US patent inventors from 1996 to 2012. Conceptually, while general human capital
augments productivity at all firms (Becker 1975), and while firm-specific capital augments productivity with any existing or future collaborators within the firm (Topel 1991), the idea of team-specific capital is that an inventor may be more productive
with their existing co-inventors. Team-specific capital encompasses skills, experiences, and knowledge that are useful only in the context of a specific collaborative relationship: high team-specific capital means that the collaborative dynamics in the

Other Policy Writing (non-peer-reviewed)

The Recovery Reports: Socioeconomic Impacts of COVID Relief. Washington Center for Equitable Growth, December 2022.

This report focuses on preliminary research surrounding the three largest income support programs totaling $1.6 trillion that were paid directly to U.S. individuals, workers, and families amid the first two years of the COVID-19 pandemic: economic impact payments, Unemployment Insurance, and the expanded Child Tax Credit. 

(with Thomas J. Hedin, Peter Mannino, Roozbeh Moghadam, Carl Romer, Geoffrey Schnorr, and Till von Wachter)

The California Policy Lab partnered with the Employment Development Department (EDD) early in the crisis to analyze unemployment claims data. On April 29th, 2020, CPL released its first analysis, and it was clear, even then, that the crisis would disproportionately harm the state’s most vulnerable workers. The report is a snapshot of ten key trends from the unemployment crisis, based on 16 reports the research institute has released during the crisis, and suggests steps for how to translate this evidence into policy to reform the unemployment system.

 

(with Thomas J. Hedin, Peter Mannino, Roozbeh Moghadam, Geoffrey Schnorr, and Till von Wachter) (Submitted to US DOL in November 2021)

Unemployment Insurance (UI) benefits provided a lifeline to workers who lost their jobs during the pandemic. However, access to these benefits has been uneven across communities and states (Edwards, 2020). Identifying and documenting these disparities is an important step to addressing them and to rendering the UI system more equitable. Utilizing a  conceptual framework of unemployment claims, we developed three metrics to measure access to UI benefits across the claim lifecycle. We then analyzed these measures to provide insight into differential access to UI benefits across U.S. states and across counties within California. 

(with Thomas J. Hedin, Geoffrey Schnorr, and Till von Wachter)

The Extended Benefits (EB) program automatically extends how long a person can claim Unemployment Insurance (UI) benefits when the share of workers claiming regular UI benefits reaches a certain level. However, a key measure of unemployment through which EB triggers on and off does not count individuals receiving benefits through extension programs, such as Pandemic Emergency Unemployment Compensation (PEUC) or EB. As a result, when a large share of unemployed workers transition from regular UI to extension programs, EB can mechanically trigger off, even if the total number of UI claimants remains unchanged or is increasing.

(with Thomas J. Hedin, Geoffrey Schnorr, and Till von Wachter)

Historically, the share of unemployed workers receiving regular UI benefits (recipiency rate) in California has been relatively low (as has also been the case in other states). This Data Point combines administrative data from California’s Employment Development Department (EDD) with monthly Current Population Survey (CPS) data to construct an improved recipiency rate to measure the extent to which unemployed and underemployed Californians are receiving regular UI benefits.

(with Thomas J. Hedin, Geoffrey Schnorr, and Till von Wachter)

Under the CARES Act, Congress created two federal unemployment programs, Pandemic Unemployment Assistance (PUA) and Pandemic Emergency Unemployment Compensation (PEUC). However, these two programs are slated to expire on December 26th, 2020 unless Congress passes new legislation to extend them. If Congress is unable to pass such legislation, our latest projections find that over one million Californians will stop receiving unemployment insurance benefits by the end of 2020, despite remaining unemployed. This Data Point analyzes a bi-partisan proposal, the Emergency COVID Relief Act of 2020 that would extend the PUA and PEUC programs by 16 weeks (through April 17th) while also extending the maximum length a person could receive benefits under these programs. We show this proposal would be extremely effective in reducing the incidence of benefit exhaustion, reducing the number of claimants who stop receiving benefits before April 10th by 95%. This new proposal would provide nearly $10 billion in direct payments to unemployed Californians by mid-April, with indirect benefits spilling over to business owners and the wider labor market.

(with Thomas J. Hedin, Geoffrey Schnorr, and Till von Wachter)

The “Lost Wages Assistance” (LWA) program provides grants to participating states in order to fund a $300 per week supplementary payment to individuals receiving unemployment insurance (UI) benefits, so long as they meet certain eligibility requirements. This Data Point provides an overview of the impact of the LWA program in California as well as policy considerations for any additional federal unemployment supports.