While r _eal_ wages may be a natural indicator of city wage premia, the main challenge is the availability of price data at a local level. Even if prices are available, it is not clear whether the estimated agglomeration economies with respect to real wages represent sorting on human capital or compensation for adverse urban amenities rather than true productivity premia of cities (Chauvin et al., 2017). Wages are usually only proportional to and not equal to labor productivity by a factor that depends on the local monopsony power of the firm. Thus, the use of total factor productivity (TFP) is preferred since urban costs do not play a role and it avoids making any assumption about the relationship between the local monopsony power and agglomeration economies (Combes and Gobillon, 2015).

Melo et al. (2009) show that elasticities of TFP with respect to density are on average estimated to be larger than those obtained for wages, typically around 50 percent larger. In France, for example, the elasticity of TFP with respect to density of 0.035-0.040 whereas with the same data, the elasticity is 0.027 for wages (Combes et al., 2012). It is difficult to interpret the difference between the two types of estimates. In wage equations, all the effects are re-scaled by the share of labor in the production function. Moreover, agglomeration economies percolating through the cost of inputs other than labor, such as land and intermediate inputs affect wages, but not TFP. A further possible reason for the difference in estimates obtained from wage and TFP regressions is that most researchers have not managed to successfully control for worker skills in wage regressions.

# _Underlying data_

Elasticity estimates vary by the underlying data used for analysis. Spatially aggregated industry- or worker-level data might yield different elasticity estimates relative to micro firm- or worker-level data because with spatial aggregation the information on industry, firm and worker attributes is lost. Even within micro data, productivity measures derived from firm level data versus those collected through household or labor force surveys vary. This may be due to the type of firms in the data set. For instance, some countries have a cut-off on employee size in their firm census data, while comparable wage elasticity using household surveys may have a different threshold or criterion. In particular, elasticity estimates using firm level data might be lower than worker level data. For instance, Chun et al. (2019) find the wage elasticity for China to be negative 0.113 using firm level data compared to other studies that find huge estimates of ranging from 0.07 to 0.29 when using worker-level data (Combes et al. 2019).

# _Spatial scale_

Studies evaluate the spatial extent of local spillovers ranging from the broadest administrative level (regions) to the finest administrative level (e.g. villages and neighborhood). Most studies end up somewhere in the middle – administrative level 2 or 3 (e.g. municipalities or districts). The spatial scope of agglomeration effects depends on the nature of activity. For example, while knowledge and technology intensive activities will need to be co-located, other interactions such as input–output linkages can take place at a larger scale. A common approach is to consider an individual or location defined at a fine scale and to draw rings with increasing radius around it. Agglomeration benefits dissipate with distance and are rarely significant beyond a threshold distance and hence broader spatial scales yield lower benefits (See Rosenthal and Strange, 2003; Desmet and Fafchamps, 2005 for evidence on the US and Rice et al., 2006 for the UK ; Di Addario and Patacchini, 2008 for Italy).

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