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Bogota

Resilient housing joins the machine learning revolution

Sarah Elizabeth Antos's picture
Also available in: Español | Français  | 中文 

 World Bank

Machine learning algorithms are excellent at answering “yes” or “no” questions. For example, they can scan huge datasets and correctly tell us: Does this credit card transaction look fraudulent? Is there a cat in this photo?

But it’s not only the simple questions – they can also tackle nuanced and complex questions.

Today, machine learning algorithms can detect over 100 types of cancerous tumors more reliably than a trained human eye. Given this impressive accuracy, we started to wonder: what could machine learning tell us about where people live? In cities that are expanding at breathtaking rates and are at risk from natural disasters, could it warn us that a family’s wall might collapse during an earthquake or rooftop blow away during a hurricane?

An Evaluation of Bogota’s Pro-Poor Transport Subsidies— How effective are they?

Camila Rodriguez's picture

Public transport is an important mode of transport, especially for low-income populations. Cities, however, struggle to provide public transport services for fares that are both affordable and financially sustainable. Since meeting both goals is quite difficult, transport systems either end up relying on high levels of subsidies or charging transit fares that are too expensive for the city’s poor.

To tackle this challenge, the World Bank in 2013 supported the city authorities of Bogotá, Colombia, in designing a pro-poor transport subsidy scheme that would help low-income populations have access to more affordable public transport. In Bogotá fares for its new public transit system are set higher -closer to cost-recovery levels-, than in other cities that provide greater public subsidies to their operators. Despite having more sustainable fares, Bogotá risks excluding people from its transport services—in fact, households in the poorest areas of the city spend a greater percentage of their income on transport, between 16% to 27%, compared to a maximum of 4% in areas that are relatively richer.

Investment Promotion with Impact: The Case of Invest in Bogota

Over the last two decades the number of investment promotion agencies (IPAs) has mushroomed from only a few dozen in the early 1980s to roughly 250 agencies worldwide today.  Despite this growth, relatively little attention has been paid towards whether or not investment promotion agencies actually have an impact on the growth in FDI to a location. 

Figure 1: Bogota, Colombia # of inbound FDI projects (by quarter) between 2003-2011

 Source: fDi Markets Database, Authors Calculations