Raising the Bar for Productive Cities in Latin America and the Caribbean 2018
DOI: 10.1596/978-1-4648-1258-3_ch2
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The Many Dimensions of Urbanization and the Productivity of Cities in Latin America and the Caribbean

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Cited by 7 publications
(3 citation statements)
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“…36 First of all, Table 2 and Figure 4 show that we always find a positive agglomeration wage premium, regardless of the approach used to define metro areas, or whether we restrict the sample to urban workers and/or metro areas only. Compared to recent estimates of this premium for India (7.6%) and China (19.2%), Colombia (about 5%), and several other Latin American countries (1.2%)see Chauvin et al, 2017;Duranton (2016) and Roberts (2018b), respectively -, our estimates are on the larger side, especially when including only metro-districts.…”
Section: Table 2 Andcontrasting
confidence: 84%
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“…36 First of all, Table 2 and Figure 4 show that we always find a positive agglomeration wage premium, regardless of the approach used to define metro areas, or whether we restrict the sample to urban workers and/or metro areas only. Compared to recent estimates of this premium for India (7.6%) and China (19.2%), Colombia (about 5%), and several other Latin American countries (1.2%)see Chauvin et al, 2017;Duranton (2016) and Roberts (2018b), respectively -, our estimates are on the larger side, especially when including only metro-districts.…”
Section: Table 2 Andcontrasting
confidence: 84%
“…5 While the preferred approach of economists to defining cities and metropolitan areas tends to be rooted in a labor market perspective based on the use of commuting flow data, as with the definition of MSAs in the US, such data is hard to come by for many countries in the world, especially for many developing countries. As such, attempts to define globally consistent data sets of cities based on the "true" extents of these cities have instead relied on either the use of estimated travel times to approximate commuting sheds (World Bank, 2008;Uchida and Nelson, 2009;Ellis and Roberts, 2016); approaches that associate cities with dense clusters of population (Dijkstra and Poelman, 2014;Roberts, 2018b); or approaches that rely on global satellite imagery and which identify cities based on their built-up area or the amount of light they emit at night (Ellis and Roberts, 2016;CAF, 2017).…”
Section: Introductionmentioning
confidence: 99%
“…The exceptions includeRoberts (2018),Beyer et al (2020), andGibson et al (2021). 9 In the DMSP-OLS data, nighttime light intensity is measured on a digital number (DN) scale that has a range of 0-63.…”
mentioning
confidence: 99%