Around 1 in 10 people live in slums. What are the economic determinants and what is the role of public policy?
"On the determinants of slum formation" by Calvacanti ( @TiagoCa01150760), da Mata ( @damatadan), and Santos
https://bit.ly/3gTLkyf  ( @EJ_RES)

Highlights

In 2014 around 881 million people lived in slums, around 1/3 of the urban population ( https://unhabitat.org/world-cities-report).
Slum settlements usually have limited access to clean water, proper sewage treatment, or adequate heating, and therefore are associated with worse quality of life.
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Despite the large number of slum dwellers around the world, not much is known about the economic determinants of slum formation -- what are the respective roles of average incomes, inequality, migration, and public planning policies, for instance?
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The paper addresses this gap through a combination of reduced-form and structural modeling, drawing on data for Brazil's urbanization process.
Brazil exemplifies a country that has undergone rapid urbanization, accompanied by a rise in the number of slums and slum dwellers.
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What might drive this development? Regression analysis of panel data for 123 urban agglomerations in Brazil for 1980-2000 suggest that higher levels of income inequality, stricter regulation and larger urban populations are associated with the presence of more slums.
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To investigate the exact mechanisms explaining slum growth and evaluate the role of policy, the paper develops a general equilibrium model of a city in which households can choose to live in formal or informal housing settlements.
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Living in an informal settlement is cheaper, but informal housing is also insecure: households suffer both direct welfare costs and pay a 'time cost' to protect informal plots and recover from health problems related to an inadequate urban structure.
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2 income thresholds separate formal from informal housing: (i) the opportunity cost of protecting the informal plot is for richer households; and (ii) poorer households unable to meet building constraints (e.g minimal lot sizes) are bound to live in informal settlements.
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To investigate the quantitative importance of alternative determinants of slum formation, the model is calibrated to match moments for the city of Sao Paulo.
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Quantitative analysis suggests that income inequality and rural-to-urban migration had the strongest impacts on slum formation over the period 1980-2000; changes in per capita income explain a smaller fraction.
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Model-based policy evaluation(i)decreasing barriers to formalization may largely slow down slum formation; (ii)incomplete slum upgrading interventions can have unintended impacts, as a sole improvement in slum infrastructure can motivate more people to live in such areas.
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This study is supported by the Keynes Fund @Cambridge_Uni Faculty of Economics ( http://www.keynesfund.econ.cam.ac.uk/research-output/cavalcanti-gray-zones-causes-and-consequences-slums).

To read more about the supported research, please visit http://www.keynesfund.econ.cam.ac.uk .