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Heterogeneous effects of poverty on attention

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  • Farbmacher, Helmut
  • Kögel, Heinrich
  • Spindler, Martin

Abstract

We examine heterogeneity in the effect of poor financial circumstances on attention. Our analysis uses data from an experiment, which randomly assigned low-income individuals to perform a cognitive test before or after payday. On average, and based on traditional subgroup analysis, the experiment did not suggest that the poorer financial circumstances before payday impeded cognitive function. Using the causal forest method, however, our heterogeneity analysis suggests that there are indeed detrimental effects among young and elderly individuals with very low incomes. We can confirm this finding in an independent experiment, using only traditional subgroup analysis.

Suggested Citation

  • Farbmacher, Helmut & Kögel, Heinrich & Spindler, Martin, 2021. "Heterogeneous effects of poverty on attention," Labour Economics, Elsevier, vol. 71(C).
    • Handle: RePEc:eee:labeco:v:71:y:2021:i:c:s0927537121000634
    DOI: 10.1016/j.labeco.2021.102028
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    References listed on IDEAS

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    Cited by:

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    3. Timothée Demont & Daniela Horta Sáenz & Eva Raiber, 2023. "Turning worries into cognitive performance: Results from an online experiment during Covid," Working Papers hal-03953178, HAL.
    4. Bernard, René, 2023. "Mental accounting and the marginal propensity to consume," Discussion Papers 13/2023, Deutsche Bundesbank.
    5. Quiroga Gutierrez, Ana Cecilia, 2024. "Picture this: Making health insurance choices easier for those who need it," Journal of Behavioral and Experimental Economics (formerly The Journal of Socio-Economics), Elsevier, vol. 111(C).
    6. Sharafi, Zahra, 2023. "Poverty and perseverance: The detrimental effect of poverty on effort provision," Journal of Development Economics, Elsevier, vol. 162(C).
    7. Eva Raiber & Daniela Horta Saenz & Timothée Demont, 2023. "Turning worries into performance: Results from an online experiment during COVID," French Stata Users' Group Meetings 2023 08, Stata Users Group.
    8. Strittmatter, Anthony, 2023. "What is the value added by using causal machine learning methods in a welfare experiment evaluation?," Labour Economics, Elsevier, vol. 84(C).

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    More about this item

    Keywords

    Poverty; Cognition; Attention; Heterogeneous effects; Causal forest;
    All these keywords.

    JEL classification:

    • D14 - Microeconomics - - Household Behavior - - - Household Saving; Personal Finance
    • D91 - Microeconomics - - Micro-Based Behavioral Economics - - - Role and Effects of Psychological, Emotional, Social, and Cognitive Factors on Decision Making
    • I32 - Health, Education, and Welfare - - Welfare, Well-Being, and Poverty - - - Measurement and Analysis of Poverty

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