Agglomeration, Trading Relationships and Enterprise Performance in Sub-Saharan Africa Inaugural...
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Agglomeration, Trading Relationships and Enterprise Performance in Sub-Saharan Africa Inaugural Lecture March 2012 Måns Söderbom Department of Economics
Agglomeration, Trading Relationships and Enterprise Performance
in Sub-Saharan Africa Inaugural Lecture March 2012 Mns Sderbom
Department of Economics University of Gothenburg
Slide 2
Part I Recap: Africas Economic Performance
Slide 3
May 13th, 2000 A bleak outlook at the turn of the century
Slide 4
May 13th, 2000July 15th, 2000 A bleak outlook at the turn of
the century
Slide 5
GDP per capita for Sub-Saharan Africa, 1980-1999 Note: The
graph shows real GDP per capita, expressed in constant 2000 US$.
Source: World Development Indicators.
Slide 6
January 17th, 2004July 2nd, 2005 maybe not quite hopeless?
Slide 7
December 2011 at a dark time for the world economy, Africas
progress is a reminder of the transformative promise of growth.
--The Economist, December 2011.
Slide 8
GDP per capita for Sub-Saharan Africa, 1995-2009 Average growth
rate: +1.8% p.a. Note: The graph shows real GDP per capita,
expressed in constant 2000 US$. Source: World Development
Indicators.
Slide 9
GDP per capita for Sub-Saharan Africa, 1960-2009
Slide 10
Part II Industry and Development
Slide 11
"Industry rather than agriculture is the means by which rapid
improvement in Africa's living standards is possible... --Kwame
Nkrumah (1965)
Slide 12
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.
Slide 13
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'?
Slide 14
Africas de-industrialization The share of manufacturing output
in GDP (%) in Sub-Saharan Africa
Slide 15
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
Slide 16
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?
Slide 17
Part III Answers to smaller questions
Slide 18
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
Slide 19
Question 1: Do personal trading relationships and physical
proximity affect the decisions of managers and economic
outcomes?
Slide 20
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).
Slide 21
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
Slide 22
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.
Slide 23
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
Slide 24
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
Slide 25
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.
Slide 26
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.
Slide 27
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)
Slide 28
Survey locations Ethiopia
Slide 29
Survey locations Sudan
Slide 30
Summary of findings
Slide 31
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.
Slide 32
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?
Slide 33
Question 2: How do productivity and profitability of incumbent
firms respond to increased agglomeration?
Slide 34
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
Slide 35
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
Slide 36
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
Slide 37
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!
Slide 38
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)
Slide 39
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).
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.
Slide 43
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.
Slide 44
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)
Slide 45
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.
Slide 46
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.