Can the Unemployed Borrow? Implications for Public Insurance

Braxton, John Carter; Herkenhoff, Kyle; Phillips, Gordon

doi:10.3386/w27026

Cited by 45 publications

(38 citation statements)

References 45 publications

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“…Our findings on the use of consumer credit warrant further discussion. According to the predictions of standard consumption theory and existing empirical evidence (Braxton, Herkenhoff, and Phillips, 2020), borrowers use unsecured consumer credit for consumption smoothing in the face of a negative transitory income shock, which implies countercyclical credit demand (Hundtofte, Olafsson, and Pagel, 2019). By contrast, we find a strong reduction in consumer credit use in the wake of the COVID-19 shock.…”

Section: Introductionsupporting

confidence: 48%

The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data

2020

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We use credit card data from the Federal Reserve Board's FR Y-14M reports to study the impact of the COVID-19 shock on the use and availability of consumer credit across borrower types from March through August 2020. We document an initial sharp decrease in credit card transactions and outstanding balances in March and April. While spending starts to recover by May, especially for risky borrowers, balances remain depressed overall. We find a strong negative impact of local pandemic severity on credit use, which becomes smaller over time, consistent with pandemic fatigue. Restrictive public health interventions also negatively affect credit use, but the pandemic itself is the main driver. We further document a large reduction in credit card originations, especially to risky borrowers. Consistent with a tightening of credit supply and a flight-to-safety response of banks, we find an increase in interest rates of newly issued credit cards to less creditworthy borrowers.

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Section: Introductionsupporting

confidence: 48%

The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data

2020

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“…We use the unemployment benefits replacement rate of 45 percent. This is in line with the 40 percent used by Shimer (2005) , the 42 percent in Braxton, Herkenhoff, and Phillips (2018), and the 50 -60 percent range in Krueger and Mueller (2010). We also normalize the mean of the ability for low-educated workers to be one.…”

Section: Parameterizing the Modelmentioning

confidence: 64%

Unemployment and Development

Feng¹,

Lagakos²,

Rauch³

2018

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This paper draws on household survey data from countries of all income levels to measure how average unemployment rates vary with income per capita. We document that unemployment is increasing with GDP per capita. Furthermore, we show that this fact is accounted for almost entirely by low-educated workers, whose unemployment rates are strongly increasing in GDP per capita, rather than by high-educated workers, whose unemployment rates are not correlated with income. To interpret these facts, we build a model with workers of heterogeneous ability and two sectors: a traditional sector, in which self-employed workers produce output without reward for ability; and a modern sector, in which firms hire in frictional labor markets, and output increases with ability. Countries differ exogenously in the productivity level of the modern sector. The model predicts that as productivity rises, the traditional sector shrinks, as progressively less-able workers enter the modern sector, leading to a rise in overall unemployment and in the ratio of low-educated to high-educated unemployment rates. Quantitatively, the model accounts for around one third of the crosscountry patterns we document.

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“…Our paper relates to theoretic and quantitative models of credit lines (Drozd and Nosal (2008), Mateos-Planas and Seccia (2006), Mateos-Planas and Seccia (2013), Drozd and Serrano-Padial (2013), Drozd and Serrano-Padial (2017), Raveendranathan (2019), and Braxton, Herkenhoff, and Phillips (2018)). Drozd and Nosal (2008), Raveendranathan (2019), and Braxton et al (2018) have incorporated long-term credit lines into models with imperfect competition (via search and bargaining) in the credit market.…”

Section: Introductionmentioning

confidence: 99%

Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry

Herkenhoff¹,

Raveendranathan²

2020

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How are the welfare costs from monopoly distributed across U.S. households? We answer this question for the U.S. credit card industry, which is highly concentrated, charges interest rates that are 3.4 to 8.8 percentage points above perfectly competitive pricing, and has repeatedly lost antitrust lawsuits. We depart from existing competitive models by integrating oligopolistic lenders into a heterogeneous agent, defaultable debt framework. Our model accounts for 20 to 50 percent of the spreads observed in the data. Welfare gains from competitive reforms in the 1970s are equivalent to a one-time transfer worth between 0.24 and 1.66 percent of GDP. Along the transition path, 93 percent of individuals are better off. Poor households benefit from increased consumption smoothing, while rich households benefit from higher general equilibrium interest rates on savings. Transitioning from 1970 to 2016 levels of competition yields welfare gains equivalent to a one-time transfer worth between 1.87 and 3.20 percent of GDP. Lastly, homogeneous interest rate caps in 2016 deliver limited welfare gains.JEL codes: D14, D43, D60, E21, E44, G21

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Can the Unemployed Borrow? Implications for Public Insurance

Cited by 45 publications

References 45 publications

The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data

The COVID-19 Shock and Consumer Credit: Evidence from Credit Card Data

Unemployment and Development

Who Bears the Welfare Costs of Monopoly? The Case of the Credit Card Industry

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