Combes (2000) investigated a number of possible localization and urbanization economies in French “ _zones d’emploi_ ”, including diversity and specialization discussed in the previous paragraph, in an attempt to examine the relative importance of urbanization and localization economies. The other variables he used to test for urbanization and localization agglomeration economies were competition, average firm size and employment density. Competition is the extent of within‐sector competition, measured by the Herfindahl index of the shares of employment of firms in the same industry and locality, which is a localization economy. Combes (2000) also used average firm size as a measure of within‐firm scale economies. He argues that if scale economies are external to firms then only city size should matter and not the average size of plants in the industry‐district. The final agglomeration economy examined in this paper is the most obvious and most widely used test of urbanization economies ‐ the effect of employment density in the locality. We include all these measures in our empirical analysis of agglomeration economies in Ghana.

# Testing for Agglomeration Economies

The possibility of agglomeration economies is most often tested for by examining the effect of agglomeration on productivity, either by using firm‐level data, such as value added per worker or average earnings in the firm, or individual level data, usually earnings. But the effects of agglomeration on employment growth are often studied, and this is what we undertake below, following a substantial literature reviewed in Combes and Gobillion (2015).

# The Modifiable Area Unit Problem

Whilst the literature testing for agglomeration economies initially used data from cities, with Glaeser et al (1992) being the path‐breaking contribution, there is also a substantial literature examining possible agglomeration economies for entire countries or several countries together (Combes and Gobillion, 2015), although the trend has generally been towards smaller spatial units or using individual firm or worker data. In this paper we use 110 districts, corresponding to administrative boundaries, covering the entirety of Ghana. Using data aggregated from firms for areas rather than firms themselves implies the possibility of what is called the Modifiable Area Unit Problem (MAUP). This refers to the negative influences that the use of discrete spatial boundaries can have on the conclusions drawn from empirical analysis. Auvray & Agha (2018) discuss the MAUP and consider two effects: the zoning effect and the scale effect. The zoning effect is that the particularities of a border are arbitrary and can spuriously influence growth measures in specific locations. Auvray & Agha (2018) used simulations to show that the division of one total area into x number of subareas of equal size can lead to substantially different results for measuring spatial concentration depending on the shapes of the areas.

The second MAUP effect is the scale effect. Auvray & Agha (2018:7) note that for a given area, empirical results are affected by the level of geographic aggregation. Briant et al (2010) notes that MAUP is more of a concern in testing for agglomeration economies when the size of the geographical areas is large. Briant et al (2010: 291) describe the Zones de Emploi regions in France “fairly small”, with an average size of around 1500 square kilometers and find that the MAUP is less of a concern when dealing with smaller areas. The average size of the Ghanaian districts we use in this paper is around 50% larger than the French regions and thus the MAUP may not be too much of a concern. Briant et al (2010) also note that the MAUP is less of concern when the independent and dependent variables are aggregated in the same way, as is done in our empirical analysis below. The districts we use are also of a similar size to the areas used in much of the research in this literature, so the general conclusion is the MAUP may not be a serious concern. We discuss the size of the districts used further in our description of the data we use below.

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