流动性约束对劳动力供给的影响：来自利率上限的证据

Federal Reserve Board, Washington, D.C.ISSN 1936-2854 (Print) The Effect of Liquidity Constraints on Labor Supply: Evidencefrom Interest Rate Ceilings Kabir Dasgupta, Brenden J. Mason 2025-110 Dasgupta, Kabir, and Brenden J. Mason (2025).“The Effect of Liquidity Constraints onLabor Supply:Evidence from Interest Rate Ceilings,” Finance and Economics Discus-sion Series 2025-110.Washington:Board of Governors of the Federal Reserve System,https://doi.org/10.17016/FEDS.2025.110. NOTE: Staff working papers in the Finance and Economics Discussion Series (FEDS) are preliminarymaterials circulated to stimulate discussion and critical comment.The analysis and conclusions set forthare those of the authors and do not indicate concurrence by other members of the research staff or the The Effect of Liquidity Constraints on Labor Supply: Evidencefrom Interest Rate Ceilings Kabir Dasgupta∗and Brenden J. MasonDecember 9, 2025 Abstract We exploit the spatiotemporal variation in US states’ interest rate ceilings on small-dollarloans to identify the effect of liquidity constraints on labor supply.Exogenously-cappedinterest rates lead to consumers being shut out of the market for cash loans.In response,labor supply increases by approximately 0.4 hours per week. We also find that the propensity JEL Classification: D15, G5, G23, J22 Keywords:Liquidity Constraints; Labor Supply; Usury; Payday Lending; Credit Section 1: Introduction and Overview How do consumers cope with negative shocks to their income or expenditures?Neoclassicaleconomic theory makes a clear prediction: consumers will draw down a buffer stock of savings.If the shock is novel or if, for whatever reason, there are no savings, then consumers will borrow, In this paper we empirically test whether workers increase their labor supply in the face of aliquidity constraint. Credibly establishing a causal relationship between labor supply and liquid-ity constraints is difficult on account of the endogeneity. An adverse macroeconomic shock could We identify the causal effect of liquidity constraints on labor supply by exploiting the spa-tiotemporal variation in US states’ interest rate ceilings, which typically apply to small-dollar(cash) loans. In particular, our research design is a quasi-experiment that compares two groups As a stylized example, residents of Massachusetts who live close to Rhode Island, wheresmall-dollar cash credit, e.g., payday lending, is legal, can be assumed to have easier access topayday loans.On the other hand, Massachusetts residents who live close to New Hampshireexperienced a break in their access to payday loans. This is because in New Hampshire, small-dollar cash credit was legally available up to and including 2008.But in 2009 high-interest strategy was first used by Melzer (2011) to estimate the real costs, e.g., difficulty paying bills,of access to payday loans. Interest rate ceilings always apply to payday loans, but they often Using self-reported monthly measures of labor supply from the county-level Current Pop-ulation Survey (CPS) across the years 2002 to 2019, we find that ‘hours worked’ statisticallyincreases by approximately 0.4 hours per week for workers aged between 25 and 64—0.55 hoursper week for those with less than a bachelor’s degree. The latter group are the workers who are We corroborate the findings above by performing several robustness checks.We also runour analyses on annual ‘hours worked’ data from the American Community Survey (ACS). Ourresults are qualitatively, but not statistically, confirmed. Moreover, to get a continuous measure We run a few placebo tests using our CPS data as well. Our findings are notably smaller aswe increase the distance: our findings do not hold as strongly when we change the definition of As a final set of analyses, we bring in two additional datasets and employ a slightly dif-ferent identification strategy.Our CPS analysis uses county-level distance (and instrumentedsellers), but all of our variation is generated by a few states on the East Coast—Pennsylvania, for individual-level fixed effects. To broaden the scope, therefore, we run a standard difference-in-differences (DID) regression on self-reported measures of labor supply using the National Longitudinal Survey of Youth (1997 cohort; NLSY97, henceforth). The NLSY97 has geographicindicators as well as self-reported measures labor supply.1With this NLSY97 data, we use thespatiotemporal variation in payday loan usage across all states.Hence, the variation comes Finally, it has been well noted within the labor economics literature that self-reported mea-sures of ‘hours worked’ can suffer from measurement error. To accommodate this critique, werun a DID analysis using monthly hours worked as reported at the state level by firms. The data These findings build upon the previous literature within the nexus of borrowing constraintsand labor supply, of which there are not many papers. Pijoan-Mas (2006) and Athreya (200