# 1 Introduction

Recent GDP growth in many African countries has been impressive. But this growth has partially been driven by high commodity prices. To avoid a reliance on high global commodity prices policy makers thus wish to diversify away from commodities and into manufacturing, following the path of East Asian countries. But Rodrik (2016) has argued that African economies have experienced premature deindustrialization, a peaking of manufacturing shares at levels of GDP much below other countries experienced as they transformed from agriculture to manufacturing to services‐based economies. The phenomenon of premature deindustrialization is not a foregone conclusion, and thus it is still important to examine potential opportunities for manufacturing growth in Africa.

A number of challenges to manufacturing growth in Africa exist. The traditional explanations include bad business and investment climates, lack of access to credit and resulting low levels of investment. Explanations for the more recently discussed phenomenon of premature deindustrialization include that is has become difficult to industrialize for those countries late to the process, for example because of first mover advantages, the increasing sophistication of manufacturing and the extent of global value chains in which buyers have substantial market power (World Bank, 2018). High relative labor costs are also an important contributor to a lack of competitiveness of manufacturing in sub‐ Saharan African (Ceglowski et al 2015).

One further potential explanation for low manufacturing growth and premature deindustrialization that has not been much explored is that African firms have been unable to take advantage of the benefits of agglomeration that come with urbanization (Jedwab, 2013), or that these benefits are much lower in African economies than elsewhere in the developing world (Collier et al 2018).

Agglomeration effects are the benefits that accrue from firms being located close to other firms and workers, and these represent a potentiality important externality that policy makers may be interested in encouraging (Combes and Gobillion, 2015). Agglomeration effects include physical spillovers, where transport costs for a firm are lowered by the presence of another firm, intellectual spillovers, flows of ideas across firms or industries, or labor market pooling, where workers choose to locate in areas with high concentrations of firms. In this paper we look for the effects of various forms of agglomeration economies for Ghanaian manufacturing, since the lack of such economies is one possible explanation for premature industrialization (Collier et al, 2018, Jedwab, 2013).

To undertake this analysis this we use data from two firm censuses collected by the Ghana Statistical Service (GSS). These are the National Industrial Census (NIC) of 2003 and the Integrated Business Establishment Survey (IBES) of 2014. We create a district‐industry panel using 110 districts and 13 industries and examine how district, industry and district‐industry characteristics affect employment growth. We also merge into the industry‐district panel data from the population census, spatial information about each district and administrative data on imports and exports for each industry.

The rest of the paper proceeds as follows. Section 2 describes the state of manufacturing in Africa and Ghana, and the literature on agglomeration economies and their measurement. Section 3 discusses the firm census data, as well as the other sources of data, that we use. Section 4 provides a description of the changes in Ghanaian manufacturing, whilst in section 5 we examine the effects of agglomeration and other factors on employment growth. Section 6 concludes.

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