Anticipation and Consumption
Open accessAbstract
This paper proposes that the marginal propensity to consume out of windfalls depends on a new state variable, the time horizon over which households anticipate receiving payments. We test this using a natural experiment from the randomized disbursement dates of a U.S. fiscal stim- ulus payment and assess external validity using randomized controlled trials on unconditional cash transfers in Kenya and Malawi. The data show evidence of excess anticipation-dependence: Consumption responds more to receiving additional income after a shorter anticipation duration, beyond what standard models predict. While households receiving stimulus payments do not increase spending in advance, additional consumption expenditure in the month after receiving payment drops over 40 percent for each additional week a household awaits payment. We estimate a mental-accounting model that incorporates this novel form of history dependence and discuss policy implications. Our approach reconciles conflicting results that consumption responds to anticipated payments in some settings but not others.
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Revisiting Event-Study Designs: Robust and Efficient Estimation
Borusyak, K., Jaravel, X., & Spiess, J. (2024) · Review of Economic Studies · doi:10.1093/restud/rdae007 · Cited by 2,280 · see citing works
Beyond baseline and follow-up: The case for more T in experiments
McKenzie, D. (2012) · Journal of Development Economics · doi:10.1016/j.jdeveco.2012.01.002 · Cited by 871 · see citing works
Havranek, T., & Sokolova, A. (2020) · Review of Economic Dynamics · doi:10.1016/j.red.2019.05.004 · Cited by 141 · see citing works
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Revisiting event study designs: robust and efficient estimation
(2022) · arXiv (preprint) · doi:10.47004/wp.cem.2022.1122 · Cited by 607 · see citing works
Two-stage differences in differences
Gardner, J. (2022) · arXiv · doi:10.48550/arxiv.2207.05943 · Cited by 333 · see citing works
“In the event-study context, this same approach has also been suggested by Thakral and Tô (2020) in their study of the how the anticipation of future consumption influences spending decisions.”
- Authors
- Thakral, N., & Tô, L.
- Affiliations
- Brown University (US)
- Corresponding author
- Thakral, Neil
- Venue
- SSRN Electronic Journal
- Publisher
- Elsevier BV
- Published
- 2026
- ISSN
- 1556-5068 (ISSN-L)
- OpenAlex
- W3112520232
- Cited by
- 17
- References
- 3
- Citations by year
- 2026: 1 · 2025: 1 · 2024: 4 · 2023: 4 · 2022: 4 · 2021: 1 · 2020: 1 · 2019: 1
- Type
- posted content
- Language
- en
- Open access
- green
- doi.org
- Locations
- SSRN Electronic Journal (acceptedVersion)
- Topics
- Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
- Social Sciences › Business, Management and Accounting › Accounting › Financial Literacy, Pension, Retirement Analysis
- Social Sciences › Economics, Econometrics and Finance › General Economics, Econometrics and Finance › Monetary Policy and Economic Impact
- Keywords
- windfall gains, marginal propensity to consume, cash transfers, mental accounting, history dependence, consumer spending
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