The Impact of Consumer Credit Access on Employment, Earnings and Entrepreneurship

Herkenhoff, Kyle; Phillips, Gordon; Cohen-Cole, Ethan

doi:10.3386/w22846

Cited by 44 publications

(32 citation statements)

References 30 publications

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“…The model retains the focus on real, as opposed to nominal, factors. 71 The aggregate production function continues to be given by 70 In related work Harmenberg and Oberg (2016) analyze the dynamics of consumption expenditures on durables in the presence of time-varying income risk. 71 In this chapter we abstract completely from nominal frictions that make output partially demanddetermined.…”

Section: Inequality and Aggregate Economic Activitymentioning

confidence: 99%

“…71 The aggregate production function continues to be given by 70 In related work Harmenberg and Oberg (2016) analyze the dynamics of consumption expenditures on durables in the presence of time-varying income risk. 71 In this chapter we abstract completely from nominal frictions that make output partially demanddetermined. Representative papers that contain a lucid discussion of the demand-and supply-side determinants of aggregate output fluctuations in heterogeneous agent New Keynesian models are Gornemann, Kuester and Nakajima (2012), Challe, Matheron, Ragot and Rubio-Ramirez (2015) and Kaplan, Moll and Violante (2016).…”

Section: Inequality and Aggregate Economic Activitymentioning

confidence: 99%

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Macroeconomics and Household Heterogeneity

Krueger

Mitman

Perri

2016

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The goal of this chapter is to study how, and by how much, household income, wealth, and preference heterogeneity amplify and propagate a macroeconomic shock. We focus on the U.S. Great Recession of 2007-2009 and proceed in two steps. First, using data from the Panel Study of Income Dynamics, we document the patterns of household income, consumption and wealth inequality before and during the Great Recession. We then investigate how households in different segments of the wealth distribution were affected by income declines, and how they changed their expenditures differentially during the aggregate downturn. Motivated by this evidence, we study several variants of a standard heterogeneous household model with aggregate shocks and an endogenous cross-sectional wealth distribution. Our key finding is that wealth inequality can significantly amplify the impact of an aggregate shock, and it does so if the distribution features a sufficiently large fraction of households with very little net worth that sharply increase their saving (i.e. they are not hand-to mouth) as the recession hits. We document that both these features are observed in the PSID. We also investigate the role that social insurance policies, such as unemployment insurance, play in shaping the cross-sectional income and wealth distribution, and through it, the dynamics of business cycles.

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Section: Inequality and Aggregate Economic Activitymentioning

confidence: 99%

Section: Inequality and Aggregate Economic Activitymentioning

confidence: 99%

Macroeconomics and Household Heterogeneity

Krueger

Mitman

Perri

2016

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“…24 We choose m such that m y 0 = .21 to match the median back-end debt-to-income ratio in the 2009 Panel Study of Income Dynamics (PSID). We evaluate the model at various values of remaining principal, x 0 , and we set next year's principal balance to reflect the mortgage payment x 1 = x 0 − m. 25 We assume the savings rate is 4 percent, and we set the borrowing rate to 12 percent to match the real historic credit card borrowing rate (Herkenhoff (2013)). From the 2007 Survey of Consumer Finances (SCF) we set the credit limit-to-annual income ratio to 40 percent, which implies ϕ = .4 · y 0 .…”

Section: Parameters Of Portfolio Constraints Modelmentioning

confidence: 99%

Can't Pay or Won't Pay? Unemployment, Negative Equity, and Strategic Default

Gerardi

Herkenhoff

Ohanian

et al. 2015

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Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Prior research has found that job loss, as proxied for by regional unemployment rates, is a weak predictor of mortgage default. In contrast, using micro data from the PSID, this paper finds that job loss and adverse financial shocks are important determinants of mortgage default. Households with an unemployed head are approximately three times as likely to default as households with an employed head. Similarly, households that experience divorce, report large outstanding medical expenses, or have had any other severe income loss are much more likely to default. While household-level employment and financial shocks are important drivers of mortgage default, our analysis shows that the vast majority of financially distressed households do not default. More than 80 percent of unemployed households with less than one month of mortgage payments in savings are current on their payments. We argue that this has important implications for theoretical models of mortgage default as well as for loss mitigation policies. Finally, this paper provides some of the first direct evidence on the extent of strategic default. Wealth data suggest a limited scope for strategic default, with only one-third of underwater defaulters having enough liquid assets to cover one month's mortgage payment. Terms of use: Documents in

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“…Our paper is also related to several recent papers that study the interactions between the labor market and the credit markets, especially via the use of credit market information, e.g., Bos, Breza and Liberman (2015); Herkenhoff (2015); Herkenhoff, Phillips and Cohen-Cole (2016);and Dobbie, Goldsmith-Pinkham, Mahoney and Song (2016). The most relevant comparison is Bos, Breza and Liberman (2015), who study a regulatory change in Sweden that removed negative information (bankruptcy, defaults) from some borrowers' credit reports.…”

Section: ) Introductionmentioning

confidence: 99%