The first set of independent variables are the two urbanization economies we can examine, employment density and diversity. The next set are the localization economies specialization, firm size and competition. We also include the proportion of skilled workers in an industry district to examine the possibility of human capital externalities. Combes and Gobillion (2015) note that the human capital externalities literature does not often also control for local economic structure but with the data we have we are able to control for both.

Other controls include the distance to Accra and Kumasi, as measures of transport costs for exporting and to large markets. We also include the proportion of firms in rural areas within the district, as 2014 is likely to have covered rural areas better. To take account of the overall state of each industry we control for the log of exports and imports in each industry.

A general conclusion from Table 5 is that there is substantial variation in most of the independent variables. All the independent variables measuring agglomeration have standard deviations larger than the means. The average number of persons employed per square kilometer is 100 the standard deviation is 3 times the mean and there are also large variations in the literacy rates and proportions of the adult population that are literate. In the next section we examine the effects of these independent variables in 2003 on employment growth between 2003 and 2014.

# 5.2 Determinants of Employment growth

Our regression has as a dependent variable the _change_ in employment between 2003 and 2014 in an industry‐district, whilst the independent variables are the 2003 characteristics of an industry‐district. We seek to explain the growth in employment by the characteristics of industry‐districts in 2003: Skilled is the proportion of skilled workers in the firms in each industry‐district, ie this is a test of human capital externalities. Dist is a vector of the two distance variables‐ distance to Accra and distance to Kumasi. We used the inverse hyperbolic sin function since distance is then zero for industries in Accra and Kumasi and so a log transformation is not possible. W is a vector which contains the level of exports and imports in each industry.

Table 6 shows the OLS regression results. In column 1 we control only for population density, one of the standard ways of testing for agglomeration economies. The effect is small, negative but not statistically significant. In the other columns we gradually add in further regressors. Looking across the other regressions as we add in further independent variables the coefficients on population density are positive, small (between 0.02 and 0.04) but never statistically significant. As a comparison Collier et al (2018) used average wages as their dependent variable and firm‐level data for developing countries, finding an elasticity with respect to density of between 0.06 and 0.08 across 51 countries. When we experimented with employment density from either the population or firm census we obtain very similar results‐ coefficients are small and not statistically significant.

One response to these results is that the regressions should mechanically imply and find that density leads to employment growth. The argument is that with large scale migration to Accra and other more urban districts with higher density there is mechanically going to be more employment growth in those areas, and thus it is surprising that our results do not find this. But in limiting our analysis to firms with 5 or more persons engaged in the firm censuses we believe we mitigate this concern. In using the firm census data we are asking whether increased density resulted in employment growth 13