## 5.3 Endogeneity Concerns

The initial papers using the growth regressions undertaken above (Glaeser et al, 1992, Henderson et al 1995 and Combes, 2000) did not address possible endogeneity issues. It is likely that in a cross section there are unobserved factors correlated with 2003 characteristics and employment or that there is a problem with reverse causality (Combes and Gobillion, 2015). For example local amenities, such as transport infrastructure, that we have not controlled for might attract firms and increase employment, but also lead to increased density. Using employment growth rather than its level, as the dependent variable, which has been undertaken in this paper, partly mitigates these concerns as it is more plausible to think that amenities are not correlated both with density and _growth_ in employment than that they are not correlated with both density and the level of employment.

But we do acknowledge that there are endogeneity concerns. The concern that has been addressed in the literature is instrumenting current population density with historical values (Combes and Gobillion 2015, Collier et al 2018). We have obtained district population totals from the 1970 census (Møller‐Jensen and Knudsen, 2008). Table 7 shows the IV regressions when we instrument population density for 2000 using the 1970 values. The instrument is always very strong, but the estimated coefficients are, like in OLS, fairly small and never statistically significant. It would be better to use population values from further back in time, but we currently do not have access to such data by district in Ghana.

Other endogeneity concerns have been addressed in other papers using system GMM or firm‐level panel data. System GMM requires at least 3 time periods. We could use the 1987 firm census data, but we have concerns about the coverage of firms in the more rural districts because it is clear from the data that the coverage of firms in rural areas was limited (Kerr and McDougall, 2019).

# 6 Conclusion

In most African economies manufacturing output and employment is growing, but the rate of growth is slower than that of other sectors, resulting in declining shares of GDP, and what Rodrik (2016) has called premature industrialization. A number of possible explanations for this situation have been put forward including a lack of a business and investment climate suited to firm growth and a lack of access to credit, as well as more recent explanations such as first mover advantages, the increasing sophistication of manufacturing and the extent of global value chains in which buyers have substantial market power.

Agglomeration has been shown to be a source of growth in many developed and developing countries but there is some evidence that agglomeration economies are weaker in African economies than they are in other parts of the world (Collier et al, 2018).

In this paper we have investigated the impact of various agglomerative forces that have been identified in the literature, using data from two Ghanaian firm censuses. The study is, to our knowledge, the first to use nationally representative firm data including both the formal and informal sectors to investigate the impact of agglomerative forces on employment growth in an African economy. We followed Combes (2000) in examining the importance of both urbanization and localization economies, which was possible with the Ghanaian firm census data we have made use of.

We have shown that Accra, the largest district and the most densely populated in Ghana, has increased its share of manufacturing employment. This would suggest that agglomeration economies are present and contributing to employment and productivity growth. But our main 15