Table 5: Results of regressions on the determinants of location premiums across sub-national areas<sup>a</sup>

|Dependent variable:|1|2|3|4|5|
|---|---|---|---|---|---|
|Location premium (ln)||||||
|Population|0.049***|0.013*|0.005|0.023*|0.002|
|density (ln)||||||
|Average years of<br>schooling (ln)||0.576***|0.574***|||
|% of WAP with||||0.021***|0.020***|
|higher education||||||
|Market access (ln)|||0.015***||0.027***|
|Mean air temperature (ln)|0.03|0.044|0.051|0.036|0.045|
|Terrain ruggedness (ln)|-0.031**|-0.024***|-0.017|-0.026*|-0.024|
|Total precipitation (ln)|-0.028|-0.008|-0.01|-0.003|-0.001|
|Constant|-0.99***|-2.37***|-2.70***|-1.28***|-1.82***|
|n|5,750|5,750|5,050|5,750|5,050|
|_R_<sup>2</sup>|0.757|0.814|0.831|0.785|0.804|
|Adj. _R_<sup>2</sup>|0.756|0.813|0.830|0.785|0.803|

> a _∗∗∗p <_ 0 _._ 01 _, ∗∗ p <_ 0 _._ 05 _, ∗p <_ 0 _._ 1. In all columns, country effects have been controlled for and standard errors have been clustered by country. In all columns, the dependent variable is the estimated location premium (measured in natural logs) from a series of country-level first-stage regressions after controlling for observable worker characteristics within the broad sample (all wage/salary employees aged from 14 to 65 years old) and survey-year fixed effects. Worker characteristics included age, age-squared, marital status, gender, and years of schooling. WAP stands for the working-age population. The source of the population data is the Gridded Population of the World (GPW), v4.

# around 6 percent<sup>30</sup>

From the above, it seems that the theories of human capital externalities and, to a lesser extent, market access provide more important explanations of urban success in LAC than does the theory of agglomeration economies. Traditional agglomeration economies that are associated with population density, but with neither human capital nor market access, appear to be virtually non-existent. This could be linked to the high levels of population density that are characteristic of cities in LAC. One plausible hypothesis is then that the high levels of population density are leading to excessive congestion, which is in turn negating the positive externalities that are normally associated with urban density.<sup>31</sup>

> 30These are comparable to other measures of human capital externalities in the literature. For example, Rauch (1993) find that one year of average education increases wages by between 3 and 5 percent. Acemoglu and Angrist (2000) find a comparable growth of 7 percent. However, IV estimates are much smaller and not significant. The latter results exclude lower education levels. The aforementioned estimates come from approaches that generally do not control for market access and agglomeration effects like ours, but that incorporate methods that control for the endogeneity of average education.

> 31We must acknowledge some caveats about drawing this conclusion. It may be that, relative to our measures of human capital and market access, population density is a relatively poor measure of agglomeration. Indeed, boundaries of sub-national administrative units often conform only poorly to the _true_ boundaries of cities (i.e. to true labor or housing market boundaries). To the extent that agglomeration can be poorly measured, this could

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