My geographical economics guest lecture at the Beijing Foreign Studies University

1. ECONOMICS AND GEOGRAPHY
NATURE, EXTERNALITIES AND POLICIES
Guest lecture at the Beijing Foreign Studies University
April 2012
by
Waldo Krugell
School of Economics

2. Outline
1) What, How, For Whom and WHERE?
2) Before ‘geographical economics’.
3) The core model of geographical economics.
4) Beyond the core model.
5) Evidence from South Africa.
6) The way forward.

3. 1) What, how, for whom and WHERE?
• First-year students are typically taught that Economics is
the study of how scarce resources are used to satisfy
unlimited wants and needs – how society answers the
questions of WHAT, HOW and FOR WHOM to produce.
• The question of WHERE production and consumption
takes place receives little attention.
• But can a South African come to BFSU and tell anyone
anything interesting about economics and geography?
• We need a quick comparison between South Africa and
China.

4. 1) WHERE: Population
South Africa = Zhejiang
with 50 million people

5. 1) WHERE: Cities
• 93 Chinese cities have a
population >5 million
• Johannesburg + East
Rand + Pretoria = 6.8m
• Durban = 2.8 million
• Cape Town = 2.6 million

6. 1) WHERE: GDP per capita
South Africa = Inner Mongolia
With GDP pc of $13 000

7. 1) WHERE: Why South Africa?
Development started at the coast through
trade between West and East
But then minerals fueled industrialization
inland
REFERENCE
Industrial Development Points
Deconcentration Points
Grand Apartheid left many people in
peripheral homelands and the economy was
closed to trade

8. 1) WHERE: Why South Africa
• So why study geographical economics in South Africa?
• It could be part of a bigger development debate of geography vs.
institutions.
• SA has a unique history and spatial distribution of economic
activity.
• Since the opening up of the economy spatial inequality has
increased, benefitting open places with better human capital.
• The transformation of government has resulted in local authorities
that are Constitutionally responsible for development of their
areas.
• The academic literature is made up of divergent
contributions from urban and regional planners,
geographers and economists, but few mention ‘economic
geography’.

9. 1) What, how, for whom and WHERE?
• Development has dimensions of density and distance.
• The stylized facts show:
• Economic production is concentrated.
• Living standards diverge before converging.
• Agglomeration forces shape the spatial economy.
• People migrate to profit from proximity to density.
• As transport costs fall, specialisation and trade increases.
• Thus the question arises, how can we explain the
unequal distribution of economic activity?

10. 2) Before ‘geographical economics’
• Before Krugman (1991) and the development of
‘geographical economics’, economists tried to explain
the location of economic activity in:
• Urban economics.
• Regional economics.
• Growth theory.
• Development economics.
• Trade theory.
• A detailed discussion of each is not necessary, but it
is possible to give each theory’s view of the forces
that draw economic activity together and those that
drive it apart.

11. 2) Before ‘geographical economics’
Agglomeration forces Dispersion forces
Urban economics
External economies due to Transport costs
spillovers associated with: Land rents
 Information sharing
 Pooled labour market
 Existence of specialised suppliers
Regional economics
Internal economies of scale Transport costs
Large demand Distance

12. 2) Before ‘geographical economics’
Agglomeration forces Dispersion forces
Development economics
 Large market offers economies of
scale (Rosenstein-Rodan favours a
“big push”)
 External economies of scale due
to spillovers (Myrdal emphasises
cumulative causation)
 Backward and forward linkages
between firms (Hirschman)

13. 2) Before ‘geographical economics’
Agglomeration forces Dispersion forces
Neo-classical growth theory
Differences in the determinants of Differences in the determinants of
growth can be location-specific: growth can be location-specific:
First-nature geography gives a cost First-nature geography gives a cost
advantage e.g. proximity to a large disadvantage e.g. being land-locked
market or access to the ocean that
lower transport costs
New growth theory
External economies due to localised
spillovers associated with
endogenous determinants of
growth: Human capital, R&D,
Infrastructure

14. 2) Before ‘geographical economics’
Agglomeration forces Dispersion forces
Neo-classical trade theory
First-nature geography: uneven
distribution of endowments
determines comparative advantage
New trade theory
Market size and consumers’ love Transport costs
for variety allow manufacturers to
achieve internal economies of scale

15. 2) Before ‘geographical economics’
• Initially the term ‘new economic geography’ was
popular, but this was replaced by ‘geographical
economics’.
• As the overview showed, Krugman’s explanation of
the location of economic activity was not that 'new'.
• But the contribution was to incorporate economies
of scale and imperfect competition that interact with
some form of local advantages and to then
endogenously determine the size of economic
activity in different locations in a general equilibrium
framework.

16. 3) The core model
Consumers in 1 g Consumers in 2
Mobility (λi)
Farm Manufacturing Manufacturing Farm
workers in 1 workers in 1 workers in 2 workers in 2
Income
Income
(labor)
(labor)
c
Spending (goods)
Spending (goods)
Income
Income
N1 manufacturing firms N2 manufacturing firms
N1 varieties (elasticity ε) N2 varieties (elasticity ε)
internal returns to scale internal returns to scale
monopolistic competition monopolistic competition
a
e d
δ Spending on T
(farm labor)
(farm labor)
manufactures Spending on δ
1-δ 1-δ
manufactures
Spending
Spending
on food
on food
b f
Farms in 1 Farms in 2
Direction of Direction of
(goods and services flows) money flows
Source: Brakman, Garretsen & Van Marrewijk, 2009

17. 3) The core model
• Solving the model means finding an equilibrium where the
world demand for food and each variety of manufactures is
equal to the world supply and no producer is earning
excess profits.
• The key features are:
• δ the fraction of income spent on manufactures.
• ρ the love-of-variety effect – an increase in the number of
varieties more than proportionally increases utility.
• Production in manufacturing is characterised by internal
economies of scale.
• There is constant mark-up of price over marginal cost.
• The core model uses iceberg transport costs where T≥1
indicates the number of goods that need to be shipped to
ensure that one unit of a variety of manufactures arrives per
unit of distance.

18. 3) The core model
• The spatial distribution of economic activity is determined by
the initial distribution of manufacturing workers and the
mobility of these workers and firms.
• The result is that the attractiveness of a region is related to
the purchasing power in all regions and relative to the
distance from the market.
• Analytically there are three short-run equilibria.
c. agglomerate in region 2 a. spreading b. agglomerate in region 1
1 1 1
0 0 0
region 1 region 2 region 1 region 2 region 1 region 2
Source: Brakman, Garretsen & Van Marrewijk, 2009

19. 3) The core model
• The mobile manufacturing work force implies that
the short-run equilibrium can change.
• If the real wages in the manufacturing sector is higher in
region 1 than in region 2, manufacturing workers will leave
region 2 and settle in region 1.
• Modeling the dynamic forces requires numerical
simulation, varying the possible distributions of the
mobile manufacturing workers.
• The following figure shows how the relative real
wage in region 1 varies as the share of the mobile
workforce in region 1 varies.

20. 3) The core model
1,03
relative real wage (w1/w2)
E
F
C D
1
B
A
0,97
0 0,5 1
share of manufacturing workers in region 1 (lambda1)
Source: Brakman, Garretsen & Van Marrewijk, 2009

21. 3) The core model
• One of the key parameters of the core model, that
identifies the regions, is the transport cost.
• Varying the level of transport costs gives a number
of interesting solutions:
• If transport costs are large, the spreading equilibrium is the
only stable equilibrium.
• If transport costs are small, the two agglomerating
equilibria are stable.
• For a range of intermediate values of transport costs, there
are five possible equilibria.

22. 3) The core model
• For transport costs below the Panel a
sustain point there is complete 1
S1
agglomeration. λ1
• For transport costs above the
breakpoint spreading across the 0.5
B
two regions is stable.
• There is always an intermediate
level of transport costs at which 0
1 S0 Transport costs T
agglomeration is sustainable, Sustain points Stable equilibria
while simultaneously spreading of Break point Unstable equilibria
manufacturing activity is a stable Basin of attraction for spreading equilibrium
equilibrium. Basin of attraction for agglomeration in region 1
Basin of attraction for agglomeration in region 2
Source: Brakman, Garretsen & Van Marrewijk, 2009

23. 3) The core model
• The way the core model is set up creates a propensity for
agglomeration.
• Internal economies of scale means that increasing production at a
plant would lower costs and manufacturers would be inclined to
produce more at a single location.
• But this has to be weighed up against transport costs.
• The mechanism through which agglomeration takes place
is labour mobility.

24. 3) The core model
• The core model has some distinctive characteristics:
• There is a home-market effect similar to trade models.
• Endogenous asymmetry.
• Multiple equilibria.
• The possibility of cumulative causation.
• Self-fulfilling expectations from the cumulative causation.
• It is the last two of these characteristics that take
explanations of the location of economic activity beyond
the core model to external economies of scale.

25. 4) Beyond the core model
• Just as firms and farms deliver final and intermediate
goods and services, towns and cities deliver
agglomeration economies to producers and workers.
• Agglomeration economies include the benefits of:
• Localisation – being near other producers of the same commodity
or service. There is input-sharing and competition within the
industry.
• Urbanisation – being close to producers of a wide range of
commodities or services. There is industrial diversity that fosters
innovation.

26. 4) Beyond the core model
Source: WDR, 2009

27. 4) Beyond the core model
• Cities facilitate scale economies of all types:
• Sharing:
• Broadening the market of input suppliers allows them to exploit internal
economies of scale.
• Sharing inputs permits suppliers to provide highly specialised goods and
services.
• Matching:
• If there is a greater range of skills available, employers can better
match to their needs.
• And workers face less risk in locations with many possible employers.
• Learning:
• Concentration accelerates spillovers of knowledge.

28. 4) Beyond the core model
• Today, research emphasises the tension between benefits
from the concentration of economic activity and costs
arising from that spatial concentration.
• The result is not only agglomeration or spreading
equilibria, but the view that there exists a portfolio of
places.
• Large cities tend to be more diversified and service oriented.
• Smaller cities tend to be industrially specialised.
• In this context, policymakers are concerned about
institutions, infrastructure and interventions.

29. 5) Evidence from South Africa
• Why study geographical economics in South Africa?
• It could be part of a bigger development debate of geography vs.
institutions.
• SA has a unique history and spatial distribution of economic
activity.
• The transformation of government has resulted in local authorities
that are Constitutionally responsible for development of their
areas.
• The academic literature is made up of divergent
contributions from urban and regional planners,
geographers and economists, but few mention ‘economic
geography’.

30. 5) Evidence from South Africa
• The literature:
• Topics studied at sub-national level.
• Agriculture, manufacturing, tourism, infrastructure, employment,
poverty and inequality.
• Recently: spatial aspects of the labour market.
• Studies of demographics.
• Rural questions and the rural-urban divide.
• Cities and urban management and planning.
• Fiscal decentralisation and LED issues.
• Spatial development initiatives.

31. 5) Evidence from South Africa
• A specific look at economic geography comes from
Fedderke & Wollnick (2008):
• They examined the spatial distribution of manufacturing.
• Using data from the Manufacturing Census 1970-1996.
• Looking at regional specialisation and industry concentration at the
provincial level.
• The descriptions show that:
• Manufacturing value added is dominated by Gauteng.
• There is no consistent trend towards regional specialisation of
despecialisation – but there was specialisation between 1993 and
1996 when the economy was opened up.

32. 5) Evidence from South Africa
• The most concentrated industries, apart from Iron & Steel and Motor,
are smaller industries.

33. 5) Evidence from South Africa
• The determinants of geographical concentration:
• They examined measures of scale, linkages and technology.
• Internal scale economies encourage concentration.
• Industries with low labour intensity and extractive industries with
high capital intensity are dispersed.
• Industries with strong inter-firm-linkages are also less
concentrated, possibly due to high transport costs.
• Concentration of human capital intensive industries reflects SA
skills shortages.
• High industry-specific productivity gradients are associated with
concentration.

34. 5) Evidence from South Africa
• And then there is the research on the SA evidence of
geographical economics that I have been involved with:
• This has been part of a WorkWell research programme with
colleagues at NWU-Pukke and collaborators abroad.
• The work has been funded by the NRF and VW Stiftung.
• Together, we have examined a range of topics:
• Growth and convergence.
• The role of cities.
• The location of exporters.
• Firm-level evidence of whether geography matters.

35. 5) Evidence from South Africa
• But first a word about the data:
• This research mainly made use of Global Insight’s Regional
Economic Explorer database.
• With magisterial districts as the spatial unit of analysis.
• A number of the studies also included export data from Customs
and Excise.
• I have also used firm-level data from the 2000 National Enterprise
Survey and 2003 and 2007 World Bank ICA surveys.

36. 5.1) Growth and convergence
• β-convergence and the determinants of sub-national
growth:
• Naudé & Krugell (2003a, 2006) used panel data regression models
and found evidence of β-convergence but it is slow.
• Convergence is conditional on:
• Initial capital stock
• Education levels.
• The share of exports in gross value added.
• Distance from Johannesburg.

37. 5.1) Growth and convergence
• σ-convergence:
• Naudé and Krugell (2006) calculated the coefficient of
variation of income per capita across the magisterial
districts and found some evidence of σ-convergence.
Year Standard Standard Standard Standard
Deviation of Deviation of Deviation of Log Deviation of
Log of Real Log of Real of Real per Log of Real
per capita per capita capita Income: GGP for All
Income: All Income: Poorest 20% of districts
districts Richest 20% districts
districts
1990 0.6147 0.3229 0.2379 1.52
1996 0.5258 0.2944 0.1063 1.51
2000 0.5466 0.3153 0.1082 1.55

38. 5.1) Growth and convergence
0.08
• Distribution dynamics:
0.07
0.06 1996 2000 2004
• Krugell, Koekemoer and 0.05
Allison (2005) analysed
0.04
0.03
kernels of incomes per 0.02
magisterial districts. 0.01
0
• The results confirm a highly
0 5 10 15 20 25 30 35
unequal distribution. 0.0016
• Over the period 1996-2004
0.0014
0.0012
more places grew poor and a 0.001
1996 2000 2004
few places grew richer. 0.0008
0.0006
0.0004
0.0002
0
50 70 90 110 130 150 170 190 210 230

39. 5.1) Growth and convergence
• Distribution dynamics:
• Bosker & Krugell (2008) used Markov chain analysis to
quantify the intra-distributional movements.
• The results showed:
• Regions below the national GDP per capita level became
poorer in relative terms.
• The transition probabilities indicate that the chance of one of
the poorest regions to move up in the income distribution is
not significantly different from zero.
• The probability of moving a group down is for all groups
higher than the probability of moving up.
• But regions with a GDP per capita higher than the national
average are the least likely to move a group down.

40. 5.1) Growth and convergence
• Distribution dynamics:
• If the distribution continues to evolve as it did between
1996 and 2004, it will result in a distribution where 98% of
the regions earn less than 0.36 times the national level of
GDP per capita (which is only R7965 per capita).
• However, calculation of mobility indices show that the
number of years it will take for the distribution to be
halfway towards this steady state is 58 years.

41. 5.1) Growth and convergence
• Distribution dynamics:
• Bosker & Krugell (2008) were also the first to use spatial
econometrics in the SA context.

42. 5.3) The location of exporters
• Does openness matter for local growth?
• Naudé, Bosker & Matthee (2009) estimate growth regressions
where openness is measured by the share of exports in a
magisterial district’s GDP.
• To determine whether export specialisation or diversification is
better they calculate three indices:
• (index 1) a Herfindahl-index which examines trends in export revenue or
specialisation of the regions relative to overall South African export
specialisation,
• (index 2) a relative specialization index which measures the degree of
specialization by the sum of each industry’s absolute deviation of that
industry’s share in a district’s total exports from that industry’s share in
total South African exports at the national level,
• (index 3) a normalised Herfindahl index, measuring a district’s own
export concentration.
• About 22 magisterial districts in South Africa are responsible for 85
per cent of the country’s manufacturing exports.

43. 5.3) The location of exporters

44. 5.3) The location of exporters
dependent variable: GDP growth 1996-2001
Does openness matter for Manufacturing
share in index1 index2 index3
local growth? export indicator
exports
-0.013*** 1.82* 2.80** 0.88
[0.01] [0.09] [0.04] [0.35]
Openness 0.014** 0.012* 0.015** 0.014**
[0.02] [0.06] [0.02] [0.03]
Openness, education and ln gdp 1996 -0.03 -0.05 0.10 0.05
[0.84] [0.76] [0.54] [0.79]
population growth are human capital 1996 1.30*** 1.13*** 1.22** 1.21***
positively associated with
[0.00] [0.00] [0.00] [0.00]
ln distance -0.01 0.07 -0.11 -0.001
growth. avg. Annual
[0.94]
0.72**
[0.69]
0.79**
[0.57]
0.70*
[0.99]
0.69*
population growth [0.05] [0.04] [0.06] [0.06]
Rho
The more specialized regions (parameter on spatial -1.95*** -1.85*** -1.93***
compared to the national lag) -1.83*** [0.00] [0.00] [0.00] [0.00]
export portfolio, experienced No of observations
log likelihood
234
-522.1
235
-526.6
235
-525.9
235
-527.7
the fastest GDP growth rates.

45. 5.3) The location of exporters
• What determines local exports?
• Matthee & Naudé (2008), estimated the determinants of magisterial
district exports in South Africa as a function of a geographical
component, the home-market effect of each district and specific
district features.
• They find that the home-market effect and distance are significant
determinants of local exports.
• Internal distance and thus by implication domestic transport cost, may
influence the extent to which different localities in the country can be
expected to be successful in exporting.

46. 5.3) The location of exporters
• Over the period 1996 to
2004, exporters seem to
have located further away
Predicted manufactured exports in 2004
Predicted manufactured exports in 1996
from the hub within the first
100km.
• The level of manufactured
exports in the second
‘band’ (originating around
400km from the hub) has
increased significantly.
0 200 400 600 800
Naudé & Matthee (2007) Distance from port
Manufactured exports in 1996 Manufactured Exports in 2004

47. 5. Evidence beyond growth and exports
• The World Development Reports states that as
countries develop, economic activities become more
concentrated.
• The concentration of people in cities and towns occurs
quickly.
• The concentration of economic activity in leading areas
continues for longer.
• Divergence in living standards happened quickly, but
convergence is slower.

48. 5.4) Firm-level evidence
• The current line of work is to find firm-level evidence
that location matters for South African manufacturers.
• Available data come from the 2000 National
Enterprise Survey and 2003 and 2007 World Bank
Investment Climate Assessment survey.
• The analysis examines four sources of economic
geography external economies:
• Intermediate inputs, the labour market, infrastructure and
access to knowledge.

49. 5.4) Firm-level evidence
• The data allow one to distinguish between firms in the
large South African cities, coastal vs. land-locked.
• Analysis indicates that location does matter for
manufacturers.
• The World Bank surveys were not designed to examine
agglomeration economies.
• But it is possible to gather information from a number of
items that can be related back to the sources of
agglomeration economies per place.
• Section 4 of the paper presents a number of tables with
information about agglomeration gleaned from the firms’
responses to the questions in the surveys.

50. Intermediate inputs
• On average, firms in Gauteng
o Use more domestic inputs,
o Use less imported inputs,
o Are sellers of intermediate inputs,
o Are more likely to subcontract production, and
o Hold fewer days of inventory, when compare to firms in
Cape Town, Durban and P.E.
• Firms in the coastal cities tend to use more
foreign inputs and imported machinery and
equipment.

51. The labour market
 On average, firms in Gauteng
o Employ greater proportions of managers and
professionals,
o Employ more workers with higher levels of education,
o Pay higher wages to production workers and
professionals,
o Reported that it is only moderately difficult to recruit
skilled technical staff.
 Firms in PE employ a larger proportion of semi-
skilled production staff.
 And those in Durban a larger share of unskilled
staff.

52. Infrastructure
• Access to land, electricity supply and
transportation were seen as major obstacles to
doing business in 2003.
• In 2007 more firms in Gauteng and Cape Town
experienced electricity supply as a major obstacle
and owned or shared a generator.

53. Knowledge
• In the 2003 and 2007 surveys:
o A greater proportion of firms in Gauteng and P.E. used
foreign licensed technology.
o Most firms have e-mail, but fewer use an own web site
to communicate with clients.

54. 6) The way forward
• There is a new push for industrial policy:
• Ideas of a developmental state.
• Infrastructure-driven development, with IDZs and EPZ.
• Policy recommendations from the World Bank.
• Policies should be spatially neutral.
• But support links to agglomeration.
• Support through social spending.
• Recommendations for further research.
• Historical analysis.
• Better local data – specifically firms.
• RCT for new policy initiatives.