• Wednesday 19 Oct 2016
  • Evidence-Based Policy Proposals for a Sound Urban Development in Latin America

    Side Events
    Venue: R2
    Lead Organization:
    • Development Bank Of Latin America (CAF).
    Partner Organizations:
    • UN-Habitat,
    • Fundacion Avina.

    The economic forces of globalization and technological change accelerate urbanization and increase population density. In turn, the effect of urban density on the wellbeing of people depends on the degree to which its negative externalities (such as congestion and pollution) can be offset by its agglomeration opportunities (such as specialization and exchange). Most Latin America cities have not fully benefited from increasing urban density and thus have a lower per capita income than similarly urbanized cities in other parts of the globe. In this context, a most pressing challenge of Latin American governments is how to tilt the balance between the negative and the positive externalities of urban density in favor of the latter. CAF-Development Bank of Latin America will contribute to the understanding of the challenges of urban development in Latin America through its 2017 development flagship report (RED 2017). To this end, CAF is currently funding a dozen academic papers that study, with rigorous methodologies and with original data, important issues such as urban segregation, slums formation, congestion, housing and job opportunities. The funded papers were selected through a public contest. The over 250 submissions received were reviewed by an international prestigious academic committee. This panel features three of the most interesting policy relevant contributions, which illustrate both the benefits and the costs of increasing urban density. For instance, one contribution will show that urbanization increases the diversity of skills and thus fosters more complex industries. This results in an increase of formal employment and productivity. Yet, another contribution will show how the urban sprawl increases segregation and inequality. The third paper will show how newly available satellite data, combined with machine learning techniques, can help us identify the incidence and growth of informal settlements or slums.