Households or Locations?

Abstract

Policy makers in developing countries, including India, are increasingly sensitive to the links between spatial transformation and economic development. However, the empirical knowledge available on those links is most often insufficient to guide policy decisions. There is no shortage of case studies on urban agglomerations of different sorts, or of benchmarking exercises for states and districts, but more systematic evidence is scarce. To help address this gap, this paper combines insights from poverty analysis and urban economics, and develops a methodology to assess spatial performance with a high degree of granularity. This methodology is applied to India, where individual household survey records are mapped to “places” (both rural and urban) below the district level. The analysis disentangles the contributions household characteristics and locations make to labor earnings, proxied by nominal household expenditure per capita. The paper shows that one-third of the variation in predicted labor earnings is explained by the locations where households reside and by the interaction between these locations and household characteristics such as education. In parallel, this methodology provides a workable metric to describe spatial productivity patterns across India. The paper shows that there is a gradation of spatial performance across places, rather than a clear rural-urban divide. It also finds that distance matters: places with higher productivity are close to each other, but some spread their prosperity over much broader areas than others. Using the spatial distribution of this metric across India, the paper further classifies places at below-district level into four tiers: top locations, their catchment areas, average locations, and bottom locations. The analysis finds that some small cities are among the top locations, while some large cities are not. It also finds that top locations and their catchment areas include many high-performing rural places, and are not necessarily more unequal than average locations. Preliminary analysis reveals that these top locations and their catchment areas display characteristics that are generally believed to drive agglomeration economies and contribute to faster productivity growth.