_Marshallian economies_ ) or interactions with diverse sectors ( _urbanization or Jacobian economies)._ Workers may gain human capital by interacting with a larger number and quality of workers, thus contributing to the dynamic learning effects (see dotted diagonal line in figure 2). US cities that experience higher growth in the number of college graduates also see a rapid increase in average wages, beyond the obvious compositional effects, than those with stagnating stock of human capital, suggesting important knowledge spillovers (Moretti 2004). More recently, researchers have recognized that it is not only about bringing more and better “clones” to the city but also a greater variety of them, which is arguably part of agglomeration externalities (see Duranton, 2016; Diamond, 2016; Atkins, Faber, Navarro, 2018; Handbury and Weinstein 2015).

_Agglomeration/Congestion Costs:_ However, cities also present agglomeration costs due to higher crime, disease and congestion in transport, land and housing. Such costs—and the necessary compensation for them-- are often not considered in interpreting estimates of the wage elasticity but they should be as they may create a positive elasticity in the absence of any productivity gains.<sup>5</sup> The same issue affects other measures of city-level productivity, namely, revenue based firm level productivity measure, TFPR, which is the residual of the _value_ of output controlling for capital, labor, and sometimes human capital. Firms charging higher output prices may appear as more productive, even if these gains are actually representing higher costs or market power (Foster et al. 2008). The next section attempts to isolate these distinct effects empirically, focusing particularly on wages, total factor productivity and marginal costs, each of which captures one or more elements discussed above, and has limitations relating to interpretation, measurement and endogeneity.

# **III. Measurement Challenges and Estimation Methodology**

# Data

Table 1 details the sources of data for the four countries used. Each is an annual manufacturing survey containing the standard input and performance variables for plants of over 10 workers or, in the case of Indonesia, 20. In addition, the included countries meet four criteria which sharply reduce the universe of possible candidates. _First_ , they need to have data on geographical location of plants, and to be linkable to other data sets that tabulate population density, as well as variables such as precipitation, temperature, elevation, terrain ruggedness and slope needed for instruments _Second_ , though we treat the data in a panel context, identification is largely driven by cross sectional variation in population density and hence we need large countries with many heterogeneous subnational units. Three of the four have between 200 and 300

> 5 For example, the cost elasticities with respect to city size in France and Colombia are comparable in magnitude to that of agglomeration (Combes, Duranton, and Gobillon 2019; Duranton 2016). Burger, Ianchovichina and Akbar (2022) also estimate the “pure” firm productivity gains of density, net of negative externalities associated with limited mobility, crime, and pollution and suggest that the benefits of agglomeration are lost through both limited uncongested mobility and congestion.

5