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Agglomeration, Trading Relationships and Enterprise Performance in Sub-Saharan Africa Inaugural Lecture March 2012 Mns Sderbom Department of Economics University of Gothenburg

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Part I Recap: Africas Economic Performance

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May 13th, 2000 A bleak outlook at the turn of the century

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May 13th, 2000July 15th, 2000 A bleak outlook at the turn of the century

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GDP per capita for Sub-Saharan Africa, 1980-1999 Note: The graph shows real GDP per capita, expressed in constant 2000 US$. Source: World Development Indicators.

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January 17th, 2004July 2nd, 2005 maybe not quite hopeless?

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December 2011 at a dark time for the world economy, Africas progress is a reminder of the transformative promise of growth. --The Economist, December 2011.

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GDP per capita for Sub-Saharan Africa, 1995-2009 Average growth rate: +1.8% p.a. Note: The graph shows real GDP per capita, expressed in constant 2000 US$. Source: World Development Indicators.

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GDP per capita for Sub-Saharan Africa, 1960-2009

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Part II Industry and Development

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"Industry rather than agriculture is the means by which rapid improvement in Africa's living standards is possible... --Kwame Nkrumah (1965)

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Manufacturing: The darling of policy makers The manufacturing sector is often considered an engine of growth: Source of modernization through structural change: workers move from rural to urban sector, i.e. from agriculture to industry (cf. Lewis model). Creates skilled jobs: management, accounting, engineering etc. Generates spillover effects: e.g. innovations or ideas developed by one firm benefit the whole sector.

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Manufacturing much less constrained by land than agriculture. With high population growth & pressure on land, diversification beyond agriculture is necessary. Manufacturing exports was a key factor in the rapid development of the Asian 'tigers' - can manufacturing in countries that are poor today serve as a similar 'engine of growth'?

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Africas de-industrialization The share of manufacturing output in GDP (%) in Sub-Saharan Africa

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Is Africas deindustrialization a problem? 1.What you make matters! Structural change currently absent. 2.Natural resources have proven an uncertain source of growth (cf. the 'natural resource curse'). 3.Increasing pressure on high quality land for agriculture. 4.Africa might miss the boat for industrial production. Lack of diversity and sophistication may pose a threat to the region's long-run growth

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A big research agenda Why is there so little industry in Africa? Why do most firms record modest levels of performance while some perform very well? How do market failures impact on industry? Information imperfections Rigid labour markets Poor access to credit What are the links between firm performance and the lives of ordinary Africans?

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Part III Answers to smaller questions

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Today Ill discuss some findings that shed some light on the following - much narrower - issues: 1)Do personal trading relationships and physical proximity affect the decisions of managers and economic outcomes in the industrial sector? (Fafchamps & Sderbom, 2011). 2)How do productivity and profitability of incumbent firms respond to increased agglomeration? (Bigsten, Gebreeyesus, Siba & Sderbom, 2011). 18

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Question 1: Do personal trading relationships and physical proximity affect the decisions of managers and economic outcomes?

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Two simple observations: 1.Many MFG firms in SSA rely on informal, personal relationships when doing business - in response to information problems and other market failures. 2.Many MFG firms in SSA have rudimentary technology and use business practices far from best practice. Low productivity. The diffusion of technology & ideas important determinant of productivity growth (e.g. Parente & Prescott -94, JPE).

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Very little is known about the diffusion of technology and ideas for firms in SSA. Do informal relationships speed up diffusion and the adoption of new technology & business practices? We focus on networks of trading partner relationships: Suppliers Clients Other MFG firms

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Adoption Decisions The decision to adopt a new technology or business practice may depend on the decisions made by other firms. Adoption decisions may be strategic complements, so that the incentive to adopt strengthens as other members of the network adopt (e.g. e-mail). Firms within networks will tend to be similar. Adoption decisions may be strategic substitutes, so that the incentive to adopt weakens as other members of the network adopt (e.g. to reduce competition). Firms within networks tend to be different.

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We study these mechanisms using data for MFG firms in Ethiopia and Sudan These countries cover large areas and have a small manufacturing sector. High transport costs and information problems generate natural protection from competition from firms located elsewhere The average technological level of the surveyed firms is low, hence even simple innovations may boost productivity. Context

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We know whether any two firms in our sample are connected in a network sense: They may do business with each other They may have a common supplier They may have a common client We ask whether decisions, perceptions and outcomes are more similar across firms belonging to the same network. Network Data

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Testing Strategy Empirical methods for network analysis Each firm is a node and we observe whether firm i has adopted practice y i Network links: Vector g ij contains dummy variables for whether firms i and j are connected Geographical distance between i and j: d ij. The absolute difference of y across firms i and j depends on network variables, geographical distance and control variables.

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H 0 : Network links and distance irrelevant: = = 0 May be because diffusion is very fast or very slow; because complements and substitutes cancel each other out; or because networks are simply irrelevant for diffusion. Strategic complements: negative, positive Strategic substitutes: positive, negative Control vector: x ij = x ji = |z i z j |. If >0, firms that share similar z tend to have similar y. Dyadic regression model: N(N-1) observations.

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Data Firm-level data collected by the World Bank in Ethiopia (2006; N=304) and Sudan (2007; N=401). Virtually the same questionnaire and sampling strategies in the two countries. Manufacturing firms with >5 employees. Furniture, wood/metal, food, textiles/garments. Module on trading partners: Respondents were asked to name up to three clients and three suppliers. Basis for network variables. Wide geographical coverage (see maps)

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Survey locations Ethiopia

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Survey locations Sudan

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Summary of findings

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Insights A common argument in the current policy discussion is that agglomeration can be important a source of productivity gains, e.g. because of spillovers Do trading partner networks speed up the diffusion of new business practices & technology? No strong evidence that this is the case.

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Insights (contd) Geographical proximity seems to imply greater differences in technology & business practices Maybe because of strategic substitution, driven by a desire to find a niche and not have to compete. Suggests firms may not have strong incentives to agglomerate How do prices & productivity respond to increased agglomeration?

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Question 2: How do productivity and profitability of incumbent firms respond to increased agglomeration?

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Agglomeration, Competition & Firm Performance A common argument: Geographical agglomeration (clustering) of enterprises can cause improved firm performance. Mechanisms: Externalities: information spillovers, technological diffusion, better access to skilled labor, lower transaction costs.. Competition: Local markets + entry of new firms => existing firms are forced to reduce slack, cut costs and organize production more efficiently. 34

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Several studies documenting positive agglomeration effects for the US and Europe But for Sub-Saharan Africa evidence on the role of agglomeration for industrial development is particularly scarce. 35

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An unusual data set Firm level census data 1996-2006. All firms in the formal mfg sector Slightly less than 700 firms in -96; nearly twice as many in 2006. 82 towns identified. Detailed data on production volumes and output prices. Separate analyses: Prices and agglomeration Physical productivity and agglomeration

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Main hypothesis Increased agglomeration of firms in a particular geographical area results in more externalities and more local competition Productivity will increase and output prices will decrease Revenues mask the two effects so net effect on profits ambiguous!

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Testing framework: Agglomeration and Output Prices Predictions: Agglomeration raises local competitive pressure and reduces output prices. Bad for firms, good for consumers. If agglomeration raises physical productivity, this will reduce prices further as the cost cuts are being passed onto customers. Good for firms & consumers. (A lot of controls here, e.g. firm fixed effect, product FE, time)

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Testing framework: Agglomeration and Physical Productivity Prediction: Agglomeration raises physical productivity, through externalities and/or higher competitive pressure Construct total factor productivity (A) as the difference between physical output (e.g. tonnes, litres, cans) and an input index (Cobb-Douglas).

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Regression Results: Output Prices & Agglomeration 40 Dependent variable: log output price. Fixed effects regressions.

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Regression results: Productivity & Agglomeration 41 Dependent variable: log TFP. Fixed effects regressions.

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Effects across towns? 42 The results discussed above are all based on the assumption that clusters coincide with towns. Some towns are located close to each other. Equipped with the geographic coordinates of each town, we test for agglomeration effects across towns.

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Summary of results The number of firms in nearest neighboring town has a small negative (positive) effect on prices (productivity), significant at 10% level. The distance to the nearest neighbor doesnt seem to matter much Counting all firms w/in 100km small & insig Main results shown above robust.

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Conclusions Agglomeration raises productivity somewhat Agglomeration lowers output prices Net effect on revenues close to zero! So no strong incentive for firms in this economy to agglomerate higher competition disincentive. Results consistent with findings in my paper with Fafchamps (suggesting strategic substitution dominates complementarities)

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Final thoughts If clustering is as beneficial as some commentator argue - why dont we see more of it in Africa? Popular response: There are coordination problems and policy can help overcome these. Our findings suggest firms weigh externality gains against the adverse effects of stronger competition on prices & revenues.

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More research needed Causal interpretation may not be warranted. We should look more closely at the incentives of firms to form clusters endogeneously. Market structure & integration may matter here. If markets are localized (due to high transport costs, lack of information etc.), local rents will be available so solving the coordination problem may not be enough firms have weak incentives to agglomorate. This might be very different in a more integrated market, where local rents are less important, cf. Silicon Valley.

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Thank you