Cited by 4Decision-Making and Behavioral EconomicsAgricultural risk and resilienceIncome, Poverty, and Inequality

Cognitive droughts

Open access
Lichand, Guilherme, Mani, Anandi·2020·University of Zurich

Abstract

Poverty involves both low income levels and high income uncertainty. Do both these dimensions of being poor capture attention in ways that distort decision-making and trap people in poverty? We examine these issues using real-life shocks faced by farmers in Brazil: random payday variation affecting income levels, and rainfall shocks that affect income uncertainty. We find that it is income uncertainty that systematically has adverse cognitive effects; low income levels affect only the poorest households. The net adverse impacts on cognitive function prevail even though both dimensions of poverty reallocate attention to scarce-resource tasks. These results broaden our understanding of the impacts of uncertainty by exploring a psychological channel distinct from risk aversion, and help reconcile apparently contradictory evidence on the cognitive impact of poverty in previous studies.

Integrity signals

Registry

DataCite · 10.5167/uzh-185364

Cites

Statistics

Recomputing p-values needs the paper’s full text.

Claims the paper makes

We couldn’t retrieve this paper’s full text, so its claims and self-check weren’t run. The registry and citation checks still apply.

Claims other papers make about this one

Each is checked against this paper’s own text: does it say what the citing paper says it does?

  • Induced scarcity-related worries led to tunneling.

    “Similar to the field study, they found that induced scarcity-related worries led to tunneling.”

    From Poverty and economic decision making: a review of scarcity theory (2022) · cites this paper as Lichand and Mani 2020

  • The validity of the tunneling measures in Lichand and Mani (2020) is questionable because tunneling was not directly observed and effects on individual measures differed and some pointed in the opposite direction.

    “This questions the validity of the tunneling measures.”

    From Poverty and economic decision making: a review of scarcity theory (2022) · cites this paper as Lichand and Mani 2020

  • Participants were more likely to tunnel in the period before payday than after payday, with larger effect sizes closer to payday.

    “They found that participants were more likely to tunnel in the period before payday than after payday. More specifically, the effect sizes were larger closer to payday.”

    From Poverty and economic decision making: a review of scarcity theory (2022) · cites this paper as Lichand and Mani 2020

  • Both a low level of income and greater uncertainty in income induce tunneling.

    “Overall, the authors conclude that both a low level of income and greater uncertainty in income induces tunneling.”

    From Poverty and economic decision making: a review of scarcity theory (2022) · cites this paper as Lichand and Mani 2020

  • In a field study among Brazilian farmers, participants exposed to less rainfall were more likely to tunnel than participants facing more rainfall.

    “They found that participants exposed to less rainfall were more likely to tunnel (i.e., scarcity-related demands captured their attentional resources) than participants facing more rainfall.”

    From Poverty and economic decision making: a review of scarcity theory (2022) · cites this paper as Lichand and Mani 2020

Cited by

Sorted by citations

Authors
Lichand, G., & Mani, A.
Venue
University of Zurich
Publisher
University of Zurich
Published
2020-02
OpenAlex
W6948450082
Cited by
4
References
0
Citations by year
2024: 1 · 2022: 1 · 2021: 2
Type
report
Language
en
Open access
Licence
other-oa
Locations
  • Zurich Open Repository and Archive (University of Zurich) (acceptedVersion, other-oa)
  • Econstor (Econstor) (submittedVersion)
  • Zurich Open Repository and Archive (University of Zurich)
Funders
  • Bill and Melinda Gates Foundation
  • Yale University
  • University of Warwick
Topics
  • Social Sciences › Decision Sciences › General Decision Sciences › Decision-Making and Behavioral Economics
  • Life Sciences › Agricultural and Biological Sciences › Soil Science › Agricultural risk and resilience
  • Social Sciences › Sociology and Political Science › Income, Poverty, and Inequality
Keywords
poverty, income risk, cognitive function, attention, decision-making, rainfall shocks, farmers, poverty trap, Brazil

About these checks

Registry, retraction, Cites and statistics results are computed, not judged, with one exception: a printed reference that no index lists is read once by a model before it counts as a citation error. Claims are extracted by a model and kept only when their quote appears verbatim in the text; each cited source is then checked, with a model’s help, for whether it says what the claim relies on. Those verdicts can be wrong — read the claim’s quote and the cited source before relying on one.

Statistics and arithmetic are recomputed, not judged.

Confidence bands are shown only for fields calibrated on our annotated test set. None is calibrated yet, so no band is shown.

What counts as a claim ›How we measure confidence ›How we check this ›

Are you an author, or do you see a problem with this page? Request a review