individual fixed effects and density is 0.21 (Mion and Naticchioni, 2009). There is also evidence of spatial sorting in Spain and in the United Kingdom (de la Roca and Puga, 2012; D’Costa and Overman, 2014).<sup>2</sup>

## _Urban costs_

Systematic evidence about urban costs is nearly absent - most agglomeration studies use nominal wages, rather than real wages that account for local urban costs . Urban costs take a variety of forms. In larger cities, housing is more expensive, commutes are longer, and the bundle of amenities that these cities offer may differ. Residential mobility implies that urban (dis)amenities and commuting costs are reflected into land prices. The elasticity of urban costs with respect to city population is the product of three quantities: the elasticity of unit land prices at the city center with respect to population, the share of land in housing, and the share of housing in consumption expenditure. It is critical to estimate the elasticity with respect to urban cost because, _first_ , city size is an outcome of a tradeoff between agglomeration economies and urban costs (Henderson, 1974; Fujita and Ogawa, 1982). The costs of agglomeration can partly explain why firms and workers do not move to larger cities. _Second_ , urban policies such as barriers to labor mobility and zoning limits (e.g. Duranton, 2008) are imposed to curb population growth and costs such as housing and congestion in cities. An estimation of the association of urban cost with city size helps understand the efficacy of these policies in achieving their stated objectives.<sup>3</sup> The topic remains underexplored because of a lack of an integrated framework to guide empirical work and appropriate data.

Although agglomeration benefits are higher, urban costs may explain why cities do not expand further. Urban costs have been estimated for France and Colombia using land and housing price data (Combes et al., 2013; Combes et al., 2019; Duranton, 2016). In France, the elasticity of urban costs with respect to population ranges from 0.016 to 0.05, while the estimates for agglomeration effects in France range from 0.015 to 0.03 (Combes _et al._ , 2010). Similar results on the elasticity of urban costs are obtained for Colombia as well (Duranton, 2016). In both these countries, cities operate at near aggregate constant returns to scale, suggesting that the benefits of agglomeration are balanced by their costs.

The costs of density on outcomes such as pollution, crime, and congestion is significantly high, and often higher than productivity elasticities. Ahlfeldt and Pietrostefani (2019) suggest that a log- point increase in density is associated with higher rents (0.15), pollution concentration (0.13), mortality risk (0.09) and crime (0.085). Thus, to understand the true advantages of larger cities, it is critical to estimate agglomeration costs alongside benefits. Cities in developing countries face the same downsides of density, that is, congestion, crime and diseases, however, they lack the financial resources to invest in infrastructure such as transport network, clean water and air, and sewerage to mitigate these risks. They also lack the public capacity to enforce urban regulations (Glaeser and Porteba, 2020).

# 3. Data

We rely on two separate datasets: one for the meta-analysis of agglomeration elasticities, and another for the analysis of urban costs. First, we discuss the construction of the sample of papers used for the meta-analysis, and second, the urban cost data.

> 2 For more evidence see Bacolod et al. 2009; Abel et al., 2012; Lindley and Machin, 2014 for the United States; Di Addario and Patacchini, 2008 for Italy; Groot and de Groot, 2014 for the Netherlands.

> 3 In France Combes et al. (2013) find that urban costs are much lower when the physical growth of cities is not restricted.

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