2019
DOI: 10.1111/jmcb.12617
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Employment in the Great Recession: How Important Were Household Credit Supply Shocks?

Abstract: I pool data from all large multimarket lenders in the United States to estimate how many of the over 7 million jobs lost in the Great Recession can be explained by reductions in the supply of mortgage credit. I construct a mortgage credit supply instrument at the county level, the weighted average (by prerecession mortgage market shares) of liquidity‐driven lender shocks during the recession. The reduction in mortgage supply explains about 15% of the employment decline. The job losses are concentrated in const… Show more

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Cited by 15 publications
(14 citation statements)
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“…For example, high default rates for second-home buyers likely contributed to the poor health of the financial system during the recession, and so likely affected overall credit supply. In turn, lower credit supply during the recession likely contributed to the job losses (Duygan-Bump et al 2015;Chodorow-Reich 2014;Garcia 2018). Nonetheless, the lack of significance in the nontradable and other employment models does ameliorate concerns about instrument validity, since the instrument is not correlated with local shocks affecting overall employment, that is, it is unlikely that vacation localities had higher shares of second-home originations because those localities experienced a positive shock during the boom that increased overall employment.…”
Section: Resultsmentioning
confidence: 99%
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“…For example, high default rates for second-home buyers likely contributed to the poor health of the financial system during the recession, and so likely affected overall credit supply. In turn, lower credit supply during the recession likely contributed to the job losses (Duygan-Bump et al 2015;Chodorow-Reich 2014;Garcia 2018). Nonetheless, the lack of significance in the nontradable and other employment models does ameliorate concerns about instrument validity, since the instrument is not correlated with local shocks affecting overall employment, that is, it is unlikely that vacation localities had higher shares of second-home originations because those localities experienced a positive shock during the boom that increased overall employment.…”
Section: Resultsmentioning
confidence: 99%
“…One of the main contributions of this paper is isolating the effect of second-home buying (as instrumented via the vacation share of housing) on changes in construction employment and house prices during the 2000s. I do so by showing that the vacation share of housing is uncorrelated with major determinants of the housing boom identified in the literature, including: the interaction of changes in housing demand with supply constraints (Saiz 2010;Aladangady 2017); the use of alternative mortgages such as interest-only or balloon mortgages (Barlevy and Fisher 2012;Foote et al 2008); the expansion in subprime credit (Mian and Sufi 2009;Demyanyk and Hemert 2011;Gerardi et al 2008); and the boom-bust in private-label securitization (Keys et al 2010;Nadauld and Sherlund 2009;Mian and Sufi 2018;Garcia 2018).…”
Section: Introductionmentioning
confidence: 95%
“…We, thus, estimate the fraction of noninstitutional buyers with mortgages that are for second or investment homes at the county level as follows. 12 Using first liens for single family homes from the Black Knight McDash data, we construct the share of mortgages originated each year between 2000 and 2014 to individuals (that is, not to institutions) who will not be occupying the property as their primary residence. We multiply that fraction with the share of individual buyers in the CoreLogic Solutions Deeds data who take out a mortgage to purchase their property.…”
Section: Individual Investors With Mortgagesmentioning
confidence: 99%
“…Our paper is also related to the literature that studies the effects of credit booms and busts on economic outcomes such as employment, building permits, wages, and rents. This literature Garcia (2019). We share with this literature a common feature, in that we rely on local economic variation to infer the importance of credit supply.…”
Section: Introductionmentioning
confidence: 99%
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