location premiums as an alternative performance indicator. The location premium is computed as the fraction of local expenditure per capita that cannot be accounted for by observable household characteristics, following Li and Rama (2015b). The growth rate of the location premium provides a good proxy of local productivity growth.

We consider three types of locations: states, districts and places. The latter result from a breakdown of the district into up to four types of agglomerations: small rural (fewer than 5,000 inhabitants), large rural, small urban (fewer than one million inhabitants) and large urban. The methodology to process this disaggregation, developed by Chatterjee et al. (2015), exploits the fact that NSS 2004–05 and NSS 2011– 12 follow a stratified multistage sampling design. Each district of a state or union territory is stratified into rural and urban areas. Within each stratum, first‐stage units are ordered by their population and then further stratified into small rural, large rural, small urban and large urban places.

As for the potential drivers of local growth, we construct 30 indicators to measure the potential drivers of growth identified above, making sure that there are several of them for each of the nine conceptual buckets. To construct these indicators, we draw primarily from the Spatial Database for South Asia compiled by the World Bank (Li et al. 2015). The detailed metadata is available on request.

Of these 30 indicators considered, 27 are measured at the district level and three at the state level:

1. _Geography_ (3). We capture climate through temperature and precipitation.  To reduce the impact of short‐term fluctuations we use averages in the preceding decade. We capture topography through the average altitude of each location.

2. _Infrastructure_ (4). Indicators cover electricity, connectivity, irrigation and housing. The connectivity measure is the density of railway and metro stations. Data on road density, based on Open Street Maps (a crowdsourcing platform) is also available, but it is too recent to be reliable.

3. _Market access_ (2). We compute the weighted average of economic activity across all neighboring districts, discounted by distance, for up to 400 kilometers. We do so using GDP and nighttime light intensity as alternative measures of district‐level economic activity.

4. _Economic structure_ (7). We include traditional measures on the sectoral composition of the economy (except for agriculture, to avoid collinearity), a measure of output diversification, an indicator on mineral production capacity, and two measures of the distribution of firms by size.

5. _Employment structure_ (2). Because the employment or unemployment rate is not very relevant in India’s case, we use the shares of self‐employment and wage employment relative to the working‐ age population to provide a snapshot of the local employment structure.

6. _Urbanization_ (2). To mitigate biases that may arise from the administrative definition of urban areas, we use population density instead of population size to measure city size. We use the population share of the state capital in a district to proxy for city governance.

7. _Human capital_ (3). We use three measures of educational attainment – primary, secondary and tertiary – in line with the city growth literature. By differentiating between the three levels of education we allow nonlinearity in the impact of schooling on local growth.

8. _Social inclusion_ (4). We consider the fraction of households with a bank account, the gap in secondary educational attainment between men and women, the shares of scheduled castes, and the share of scheduled tribes in the total population.

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