||Panel Fixed Effects|-0.019***<br>(0.007)|-0.018***<br>(0.002)|_[1.00]_|-0.020***<br>(0.007)|-0.018**<br>(0.008)|
|---|---|---|---|---|---|---|
|Endoeneit|IV estimation -|-0.002|-0.000||||
|gy|contemporaneous|(0.005)|(0.001)|_[0.05]_|||
||IV estimation - historic|-0.001|-0.000||||
|||(0.007)|(0.000)|_[0.03]_|||
||Industrycontrol|0.012**|0.012***||0.011***|0.010***|
|Firm||(0.005)|(0.002)|_[1.00]_|(0.004)|(0.004)|
|Heterogeneity|Firm size control|-0.004*|-0.004***||-0.004*|-0.004*|
|||(0.002)|(0.001)|_[0.99]_|(0.002)|(0.002)|
||Local area human capital|-0.012***|-0.010***||-0.012***|-0.010**|
|Worker Sortin||(0.004)|(0.002)|_[1.00]_|(0.004)|(0.004)|
|g|Individual-level human capital|-0.014**|-0.013***||-0.014**|-0.011*|
|||(0.006)|(0.003)|_[1.00]_|(0.006)|(0.006)|
||Constant|-0.002|-0.005||0.000|-0.004|
|||(0.010)|(0.005)|_[1.00]_|(0.010)|(0.009)|
||Observations|1,242|1,242||1,242|1,242|

Notes: Outcome is the reported agglomeration elasticity. All models are estimated under PET-PEESE, “precision-effect estimates with standard errors” following Stanley and Doucouliagos (2019), which to correct for publication bias includes a squared standard error term, and its interaction with a developing country dummy, and with a post-1990 dummy. The standard error terms are not reported for parsimony. PET-PEESE estimates are precision-weighted, using the inverse of the reported estimate’s variance as the weight. Robust standard errors are presented in parentheses, these are clustered at the study level for all model selection methods except Bayesian Model Averaging (BMA). The square brackets reflect the probability of each variable’s model inclusion under BMA. The General model includes all control variables. BIC and AIC are the specifications (from all 17million combinations of controls) that minimize the Bayesian and Akaike Information Criteria respectively. ***, ** and * reflect significance at the 1, 5 and 10 percent levels respectively.

# 6. Estimating urban costs

Our meta-analysis in section 5 suggests that urban costs play a major role in explaining differences in agglomeration benefits across studies. Most studies measure “gross” agglomeration benefits of density rather than advantages of locating in crowded, congested and crime prone places that “net” out these benefits. In sum, the evidence base for urban costs is underdeveloped. Using novel data on crime, congestion and pollution from hundreds of cities around the world, including those in developing countries, we present estimates of density elasticity of urban costs in Table 7. The table makes the following points: First, urban costs are significantly higher in _levels_ in developing countries, and this is true across our measures of congestion and crime, and depending on the specification, pollution, suggested by the positive developing country dummy (and mirroring Figure 4). In levels, for the average city density in our data, pollution is on average 16-28 percent higher (although not significantly different from zero in column 1), 19-30 percent more hours are lost hours to congestion and homicides are 4 times as likely.

Second, although developing countries have higher urban cost levels, their cost _elasticities_ may not be different than that observed in developed countries. Relative to developed countries, the elasticity of 23