Urban economics, on the other hand, squarely focuses on cities. This other strand of literature aims to quantify agglomeration economies, as reflected in spatial disparities in nominal wages. Underpinned by theories of local externalities, its basic premise is that firms and workers are more productive in large and dense urban environments (Rosen 1979, and Roback 1982). The analyses emphasize location characteristics perceived as being directly relevant to the strength of such local externalities, including population size, population density, and employment density (Combes, Duranton, and Gobillon 2008, Combes et al. 2010, Glaeser and Maré 2001). Location characteristics are also highlighted in connection to the potential channels underpinning agglomeration economies. For example, locations may be more productive because of knowledge spillovers, in which case the level of local skills is a variable of interest (Rauch 1993, Moretti 2004a and 2004b, Rosenthal and Strange 2008). Other location characteristics usually considered are natural resource endowments and climate (see Duranton 2014, Gill and Goh 2009, Glaeser and Gottlieb 2009, Puga 2010, and Rosenthal and Strange 2004 for reviews).

In the context of advanced economies, urban economics has made important progress in identifying the implications of location characteristics for employment and pay, and for economic development more generally. However, the urban economics approach on its own may also be insufficient to fully understand the implications of the rural-urban transformation in developing countries. Its unit of observation is typically the city, which leaves out not only the rural areas where a large fraction of the population still lives, but also the increasingly blurred areas at the urban fringe. There are also important data limitations, as only a minority of workers in developing countries are wage earners, and data on their nominal earnings are often partial and unreliable (World Bank, 2012).

These two strands of literature have so far developed largely disconnected from each other, to the point that studies belonging to one of them are rarely cited in studies from the other. Both poverty analysis and urban economics use disaggregated household or individual data to predict an indicator of expenditure or income, but they do so very differently. And yet, in their different ways these two analytical bodies are dealing with same issue, namely taking into account the role of location in explaining prosperity.

In this paper, we draw insights from the two strands of literature and develop a hybrid methodology to assess spatial performance with a high level of granularity. As in urban economics, we are interested in the spatial distribution of labor productivity. Earnings from labor are indeed the most important component of household income in developing countries. But given the limitations of wage data when a majority of workers are farmers or self-employed, we approximate labor earnings through nominal household expenditures per capita, as in poverty analysis. A key element of our methodology is to conduct the analysis across all locations, regardless of whether they are administratively urban or rural.

We illustrate this methodology in the case of India. This is the country with the largest number of poor people, worldwide (World Bank 2015). It is also a country at an early stage in the urbanization process, where regular wage workers only account for 18 percent of the labor force and information on wages or labor earnings is available for only 45 percent of it (NSSO 2012). These characteristics make India ideally suited to combine insights from urban economics and poverty analysis. Moreover, the nature of the available household survey data allows us to generate estimates with fairly high spatial disaggregation. Building on an approach developed by Chatterjee et al. (2015) we can indeed distinguish between small rural, large rural, small urban and large urban areas within each district. While not all developing countries have household survey data supporting such level of granularity, we believe that the methodology proposed in this paper can be applied to other country settings and yield insights about their own rural-urban transformations.

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