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Slums and Pandemics


  • Luiz Brotherhood
  • Tiago Cavalcanti
  • Daniel da Mata
  • Cezar Santos


This paper studies the role of slums in shaping the economic and health dynamics of pandemics. Using data from millions of mobile phones in Brazil, an event-study analysis shows that residents of overcrowded slums engaged in less social distancing after the outbreak of Covid-19. We develop a choice-theoretic equilibrium model in which individuals are heterogeneous in income and some people live in high-density slums. The model is calibrated to Rio de Janeiro. Slum dwellers account for a disproportionately high number of infections and deaths. In a counterfactual scenario without slums, deaths increase in non-slum neighborhoods. Policy simulations indicate that: reallocating medical resources cuts deaths and raises output and the welfare of both groups; mild lockdowns favor slum individuals by mitigating the demand for hospital beds, whereas strict confinements mostly delay the evolution of the pandemic; and cash transfers benefit slum residents to the detriment of others, highlighting important distributional effects.

Suggested Citation

  • Luiz Brotherhood & Tiago Cavalcanti & Daniel da Mata & Cezar Santos, 2020. "Slums and Pandemics," Working Papers w202015, Banco de Portugal, Economics and Research Department.
  • Handle: RePEc:ptu:wpaper:w202015

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    References listed on IDEAS

    1. Jan K. Brueckner & Harris Selod, 2009. "A Theory of Urban Squatting and Land-Tenure Formalization in Developing Countries," American Economic Journal: Economic Policy, American Economic Association, vol. 1(1), pages 28-51, February.
    2. J. Vernon Henderson & Matthew A. Turner, 2020. "Urbanization in the Developing World: Too Early or Too Slow?," Journal of Economic Perspectives, American Economic Association, vol. 34(3), pages 150-173, Summer.
    3. Tiago Cavalcanti & Daniel Da Mata & Marcelo Santos, 2019. "On the Determinants of Slum Formation," Economic Journal, Royal Economic Society, vol. 129(621), pages 1971-1991.
    4. Luiz Brotherhood & Philipp Kircher & Cezar Santos & Michèle Tertilt, 2020. "An Economic Model of the Covid-19 Epidemic: The Importance of Testing and Age-Specific Policies," CESifo Working Paper Series 8316, CESifo.
    5. Michael Kremer, 1996. "Integrating Behavioral Choice into Epidemiological Models of AIDS," The Quarterly Journal of Economics, Oxford University Press, vol. 111(2), pages 549-573.
    6. Michael Kremer, 1996. "Integrating Behavioral Choice into Epidemiological Models of the AIDS Epidemic," NBER Working Papers 5428, National Bureau of Economic Research, Inc.
    7. Tat Y. Chan & Barton H. Hamilton & Nicholas W. Papageorge, 2016. "Health, Risky Behaviour and the Value of Medical Innovation for Infectious Disease," Review of Economic Studies, Oxford University Press, vol. 83(4), pages 1465-1510.
    8. Rafael Henrique Moraes Pereira & Tim Schwanen, 2013. "Tempo de Deslocamento Casa - Trabalho no Brasil (1992-2009): Diferenças Entre Regiões Metropolitanas, Níveis de Renda e Sexo," Discussion Papers 1813, Instituto de Pesquisa Econômica Aplicada - IPEA.
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    More about this item

    JEL classification:

    • C63 - Mathematical and Quantitative Methods - - Mathematical Methods; Programming Models; Mathematical and Simulation Modeling - - - Computational Techniques
    • D62 - Microeconomics - - Welfare Economics - - - Externalities
    • E17 - Macroeconomics and Monetary Economics - - General Aggregative Models - - - Forecasting and Simulation: Models and Applications
    • I10 - Health, Education, and Welfare - - Health - - - General
    • I18 - Health, Education, and Welfare - - Health - - - Government Policy; Regulation; Public Health
    • O18 - Economic Development, Innovation, Technological Change, and Growth - - Economic Development - - - Urban, Rural, Regional, and Transportation Analysis; Housing; Infrastructure

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