municipalities, with only Ethiopia having 91 towns. _Third_ , they need to tabulate product price data that is essential for calculating technical efficiency. _Fourth_ , they need to be accessible. The only advanced country with price data we were able to get access to was Belgium which did not meet the second criterion. In the end, however, we have substantial representativeness across regions (Africa-Ethiopia; Asia-Indonesia; Latin America- Colombia and Chile) as well as level of development ranging from Ethiopia with per capita income $US (2020) 2,422, Indonesia 12,068, Colombia 14,570, and Chile 25,068 (World Bank). After matching to geographical covariates, plant count varies from 3,465 over 9 years in Ethiopia, to 34,366 in over 15 years in Indonesia. The surveys contain the geographical location of each plant which is then matched to local population and geospatial controls. Annexes A.1 and A.2 provide detailed data description for each of the countries, including spatial variables.

# Measurement and Estimation Challenges

The common use of the nominal wage elasticity arises partly because wages capture all agglomeration-related sources of productivity growth – ease of capital accumulation, better job matching, knowledge spillovers and so on – but also because the absence of location specific production data and local prices often dictates a reliance on labor force data.<sup>6</sup> In the present work, we use plant-level average wages (total wages/number of workers) directly observed from the production data which can be directly compared to other plant-level productivity measures. Since we do not have geographically disaggregated data on local consumption baskets, we do not calculate a measure of real purchasing power.

Our second principal measure, revenue-based TFP, is the Hicks-neutral measure of plant’s efficiency, commonly used in the literature to capture the residual output variation after controlling for tangible inputs of production. This strips out agglomeration effects arising from availability of capital or other inputs and thereby focuses on externalities arising from matching and spillovers.<sup>7</sup> Because it is based on nominal revenue (sales), this measure incorporates prices and thereby potentially confounds higher costs and plant’s market power, with efficiency. Our data permit constructing plant-level price indices which, using the method in Foster et al. (2008), then allows to work in physical units of output and building a measure of technical efficiency:

> 6 Even when price data (e.g. local housing costs) are available, it is not clear whether real wages capture agglomeration effects (Chauvin et al., 2017) because the elasticity of wages with respect to housing costs, for example in a sample of US cities examined in Beaudry et al. (2014) is nearly 1 such that “people follow jobs” rather than wages (nominal or real).

> 7 Further, as Combes and Gobillon (2015) note, wages may differ from true gains to the degree that local firms have monopsony power and TFPR avoids making any assumption about the relationship between the local monopsony power and agglomeration.  By construction, however, a limitation of this measure is the assumption that all employees within a firm earn the same average wage. Thus, it masks any underlying agglomeration mechanisms arising from heterogeneity in skills and occupations. For a more granular analysis with administrative worker-level data, see De la Roca and Puga (2017) and Quintero and Roberts (2018).

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