Download Agglomeration, Trading Relationships and

Agglomeration, Trading Relationships and
Enterprise Performance in Sub-Saharan Africa
Inaugural Lecture
March 2012
Måns Söderbom
Department of Economics
University of Gothenburg
Part I
Recap: Africa’s Economic Performance
A bleak outlook at the turn of the century
May 13th, 2000
A bleak outlook at the turn of the century
May 13th, 2000
July 15th, 2000
480
500
520
540
560
580
GDP per capita for Sub-Saharan Africa, 1980-1999
1980
1985
1990
Year
1995
Note: The graph shows real GDP per capita, expressed in constant 2000 US$.
Source: World Development Indicators.
2000
…maybe not quite hopeless?
January 17th, 2004
July 2nd, 2005
December 2011
“…at a dark time for the world economy, Africa’s progress is a reminder of the
transformative promise of growth.” --The Economist, December 2011.
600
650
GDP per capita for Sub-Saharan Africa, 1995-2009
450
500
550
Average growth rate: +1.8% p.a.
1995
2000
2005
Year
Note: The graph shows real GDP per capita, expressed in constant 2000 US$.
Source: World Development Indicators.
2010
400
450
500
550
600
650
GDP per capita for Sub-Saharan Africa, 1960-2009
1960
1970
1980
1990
Year
2000
2010
Part II
Industry and Development
"Industry rather than agriculture is the means by
which rapid improvement in Africa's living
standards is possible...“
--Kwame Nkrumah (1965)
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.
• 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'?
Africa’s de-industrialization
13
14
15
16
17
18
The share of manufacturing output in GDP (%) in Sub-Saharan Africa
1960
1970
1980
1990
Year
2000
2010
Is Africa’s 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
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’?
Part III
Answers to smaller questions
Today I’ll 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 & Söderbom, 2011).
2) How do productivity and profitability of incumbent
firms respond to increased agglomeration? (Bigsten,
Gebreeyesus, Siba & Söderbom, 2011).
18
Question 1:
Do personal trading relationships and
physical proximity affect the decisions of
managers and economic outcomes?
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).
• 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
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.
Context
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.
Network Data
• 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.
Testing Strategy
• Empirical methods for network analysis
• Each firm is a ‘node’ and we observe whether firm i
has adopted practice yi
• Network links: Vector gij contains dummy variables
for whether firms i and j are connected
• Geographical distance between i and j: dij.
• The absolute difference of y across firms i and j
depends on network variables, geographical distance
and control variables.
Dyadic regression model:
N(N-1) observations.
• H0: 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: xij = xji = |zi – zj|.
• If γ>0, firms that share similar z tend to have similar y.
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)
Survey locations Ethiopia
Survey locations Sudan
Summary of findings
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.
Insights (cont’d)
• 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?
Question 2:
How do productivity and profitability of
incumbent firms respond to increased
agglomeration?
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
• 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
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
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!
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)
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).
Regression Results:
Output Prices & Agglomeration
Dependent variable: log output price. Fixed effects regressions.
40
Regression results:
Productivity & Agglomeration
Dependent variable: log TFP. Fixed effects regressions.
41
Effects across towns?
• 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.
42
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 doesn’t
seem to matter much
• Counting all firms w/in 100km – small & insig
• Main results shown above robust.
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)
Final thoughts
• If clustering is as beneficial as some
commentator argue - why don’t 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.
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.
Thank you