Agriculture, transportation and the timing of urbanization: Global analysis at the grid cell level

Abstract

Standard-Nutzungsbedingungen:Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden.Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen.Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Abstract This paper addresses the timing of a location's historical transition from rural to urban activity. We test whether urbanization occurs sooner in places with higher agricultural potential and comparatively lower transport costs, using worldwide data that divide the earth's surface at half-degree intervals into 62,290 cells. From an independent estimate of each cell's rural and urban population history over the last 2,000 years, we identify the date at which each cell achieves various thresholds of urbanization. Controlling for unobserved heterogeneity across countries through fixed effects and using a variety of spatial econometric techniques, we find a robust association between earlier urbanization and agro-climatic suitability for cultivation, having seasonal frosts, better access to the ocean or navigable rivers, and lower elevation. These geographic correlations become smaller in magnitude as urbanization proceeds, and there is some variance in effect sizes across continents. Aggregating cells into countries, we show that an earlier urbanization date is associated with higher per capita income today. Terms of use: Documents in

Malaria suitability, urbanization and persistence: Evidence from China over more than 2000 years

“…With respect to statistical inference, we cluster the standard errors at 2×2 degree 'super grids' (cf., Motamed et al (2014)). In Appendix C.2, we show that we obtain very similar standard errors when we employ the approach of to account for spatial correlation or cluster the standard errors at the province level.…”

“…Our results further contribute to the discussion regarding the determinants of the spatial distribution of economic activity. There are several papers documenting that natural advantages play a key role in explaining this distribution (e.g., Davis and Weinstein (2002); Rappaport and Sachs (2003); Bosker et al (2007); Miguel and Roland (2011); Motamed et al (2014)). Our work also relates to the studies that show that now obsolete natural or man-made factors that represented a (dis-)advantage in past times gave rise to a persistent spatial equilibrium (e.g., Redding et al (2010); Bleakley and Lin (2012); Michaels and Rauch (forthcoming); Jedwab et al (forthcoming); Jedwab and Moradi (2016); Nunn and Puga (2012)).…”

Urbanization, market development and malnutrition in farm households: evidence from the Demographic and Health Surveys, 1986–2011

“…The data are individual observations of women and children from 83 Demographic and Health Surveys (DHS) across 43, 850 survey clusters in 46 countries conducted between 1986 and 2011 (ICF International 2014), merged with historical data on each survey cluster's nearby historical urbanization rates (Motamed et al 2014) and the country's per-capita national income at the time of the DHS survey (Heston et al 2012). Merging the individual-level survey data with clusterlevel urbanization data permits us to address variation within each country's rural population's access to urban markets, while controlling for the overall level of socioeconomic development.…”

“…In order to merge the DHS data with urbanization and national income data, we spatially joined the DHS survey clusters with grid-cell measurement of geographic and urbanization variables using ArcGIS 10.2 (ESRI 2011). The underlying geographic and urbanization data are a 0.5 degree by 0.5 degree global grid from Motamed et al (2014), a study of how each location's agroecological conditions and access to transportation influenced their timing of urbanization, as measured using historical data on rural and urban population densities from Klein-Goldewijk et al (2010). Here, we use those same data to provide a historical measure of market development, defined as the duration in years before 2000 that each grid-cell containing each DHS survey cluster reached 10 % of its population living in towns and cities.…”

“…As a result, the number of years since a region became 10 % urbanized provides a simple, internationally comparable measure of a region's market development. Each measurement of the urbanization variable may correspond to multiple cities within a grid-cell, and are not weighted by overall population within the grid-cell (Motamed et al 2014). Despite this potential complication, we expect that the indicator will provide a useful proxy of whether farmers in each survey cluster have had access to urban goods and services earlier or later, especially given the relatively fine spatial resolution of the cluster-level observations.…”