Asymmetric Consumption Smoothing

Baugh, Brian; Ben‐David, Itzhak; Park, Hoonsuk; Parker, Jonathan A.

doi:10.3386/w25086

Cited by 23 publications

(28 citation statements)

References 45 publications

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“…Visual inspection of Figure 5b shows that high-asset households with positive income shocks increase consumption, but do not appear to decrease consumption when income falls. This finding is consistent with evidence in Baugh et al (2018).…”

Section: Consumption Smoothing By Assetssupporting

confidence: 93%

Wealth, Race, and Consumption Smoothing of Typical Income Shocks

et al. 2020

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We study the consumption response to typical labor income shocks and investigate how these vary by wealth and race. First, we estimate the elasticity of consumption with respect to income using an instrument based on firm-wide changes in monthly pay. While much of the consumption-smoothing literature uses variation in unusual windfall income, this instrument captures the income variation that households typically experience. In addition, because it can be constructed for every worker in every month, it allows for more precision than most previous estimates. We implement this approach in administrative bank account data and find an average elasticity of 0.23, with a standard error of 0.01. The increased precision also allows us to address an open question about the extent of heterogeneity by wealth in the elasticity. We find a much lower consumption response for high liquidity households, which may help discipline structural consumption models.

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Section: Consumption Smoothing By Assetssupporting

confidence: 93%

Wealth, Race, and Consumption Smoothing of Typical Income Shocks

et al. 2020

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“… Fairlie (2020) documents that African-American business owners experiencing a drop of 26% in business activity from pre-COVID-19 levels compared to only 11% drop for White business owners by May, 2020. Farrell et al (2020a) similarly finds that cash balances of White-owned restaurants doubled in May compared to only 38% increase for Black-owned restaurants.5 These studies estimate the extent to which households can smooth transitory variation in income generated by, for example, randomized timing of disbursement of economic stimulus(Parker et al, 2013;Broda and Parker, 2014), arrival of tax refunds(Baugh et al, 2020), household liquidity shock(Gross and Souleles, 2002), and unemployment insurance (Ganong and Noel, 2019).…”

mentioning

confidence: 99%

Revenue Collapses and the Consumption of Small Business Owners in the Early Stages of the COVID-19 Pandemic

Kim¹,

Parker²,

Schoar³

2020

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For useful discussions, we thank Diana Farrell, Fiona Greig, and Chris Wheat. This research was made possible by a data-use agreement between the authors and the JPMorgan Chase Institute (JPMCI), which has created de-identified data assets that are selectively available to be used for academic research. All statistics from JPMCI data, including medians, reflect cells with multiple observations. The opinions expressed are those of the authors alone and do not represent the views of JPMorgan Chase & Co.

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“…Our data is not a random sample of the population, but it appears to be widely representative, with some exceptions. In Baugh et al (2018) and Baugh et al (2020), the authors illustrate the income distribution of users in this database relative to the U.S. Census. While the raw sample differs from the true income distribution in the United States, the sample covers users with a wide range of incomes rather than solely identifying users of a particular income group.…”

Section: Transaction-level Linked-account Datamentioning

confidence: 99%