Some of the gaps between the two variables of interest can be attributed to the household themselves, while others are to a larger extent due to the location where the households live. Controlling for household characteristics such as size and age composition allows to account for different denominators when reporting labor earnings on a per capita basis. Controlling for household assets arguably takes care of nonlabor incomes. And controlling for social background and religion goes some way towards introducing household preferences and norms. On the other hand having migrant members, commuting for work, or receiving social assistance transfers is arguably influenced by location characteristics, such as the reach of social protection systems and the availability of job opportunities at the local level.

Our benchmark specification is inspired by the first step of the empirical strategy considered by both poverty maps and urban economics: where �� are location effects. Ideally, this equation should also include household effects to control for unobservable household characteristics, such as work ethic or entrepreneurial spirit. But doing so would require panel data, which is not available in India’s case. While being aware that these unobservable household characteristics could bias the estimates, we believe that the risk is mitigated by the use of a large number of observable characteristics among the explanatory variables of the regression. If this is correct, the estimated location effects should provide a reasonably good approximation to the spatial variation in productivity, hence to the magnitude of agglomeration economies across the country.

Our approach allows us to disentangle the contribution household characteristics and location effects make to labor earnings. This understanding is highly relevant from a policy perspective. Implicit in the approach is the assumption that households make the most of both their assets and the opportunities provided by the places where they live. Educational investments, occupational choices and migration decisions (either permanent or seasonal) are shaped by the interaction between household characteristics and location characteristics. But this interaction is somewhat overlooked by traditional poverty analysis as it emphasizes households over locations, and focuses its recommendations on upgrading skills and other household assets, or on better targeting resource transfers to the poor. These interventions are certainly important, but there may be a need to rebalance development priorities and bring more attention to local externalities—both positive and negative – affecting household choices.

Further, the approach allows us to get a reasonable assessment of the spatial variations of productivity without being too constraining on the underlying mechanisms. Many channels have been highlighted as potential sources of agglomeration economies, including the pooling of labor, the sharing of resources and productive amenities, reductions in transportation costs, and knowledge spillovers (Marshall 1890, Jacobs 1969, Krugman 1991). We see exploring these channels carefully together with other local factors as the next step in our research agenda. But as a first step, location effects provide us a workable metric to describe spatial productivity patterns across India.

A key element of our methodology is to conduct the analysis across all locations, regardless of whether they are administratively urban or rural. Many poverty analyses focus on rural areas, because that is where a majority of the poor live. Urban economics, on the other hand, focuses on cities and towns. But in a 6