bfsu geograph econ lecture

ECONOMICS AND GEOGRAPHY NATURE, EXTERNALITIES AND POLICIES

Guest lecture at the Beijing Foreign Studies UniversityApril 2012

by

Waldo Krugell

School of Economics

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.

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.

1) WHERE: Population

South Africa = Zhejiang with 50 million people

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

1) WHERE: GDP per capita

South Africa = Inner MongoliaWith GDP pc of $13 000

1) WHERE: Why South Africa?

REFERENCE

Industrial Development Points

Deconcentration Points

Development started at the coast through trade between West and East

But then minerals fueled industrialization inland

Grand Apartheid left many people in peripheral homelands and the economy was closed to trade

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’.

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?

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.


Agglomeration forces Dispersion forces

Urban economicsExternal economies due to spillovers associated with: Information sharing Pooled labour market Existence of specialised suppliers

Transport costs

Land rents

Regional economicsInternal economies of scale

Large demand

Transport costs

Distance



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)



Neo-classical growth theoryDifferences in the determinants of growth can be location-specific:

First-nature geography gives a cost advantage e.g. proximity to a large market or access to the ocean that lower transport costs

Differences in the determinants of growth can be location-specific:

First-nature geography gives a cost disadvantage e.g. being land-locked

New growth theoryExternal economies due to localised spillovers associated with endogenous determinants of growth: Human capital, R&D, Infrastructure



Neo-classical trade theoryFirst-nature geography: uneven distribution of endowments determines comparative advantage

New trade theoryMarket size and consumers’ love for variety allow manufacturers to achieve internal economies of scale

Transport costs


• 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.

3) The core model

N1 manufacturing firmsN1 varieties (elasticity )internal returns to scalemonopolistic competition

Farms in 1 Farms in 2

Spen

ding

1-

N2 manufacturing firmsN2 varieties (elasticity )internal returns to scalemonopolistic competition

Manufacturingworkers in 2

Farmworkers in 2

Consumers in 2

Manufacturingworkers in 1

Farmworkers in 1

Consumers in 1

Inco

me

Spen

ding

(goo

ds)

(far

m la

bor)

(lab

or)

Inco

me

(lab

or)

Inco

me

Spending onmanufactures

Spen

ding

on f

ood

Inco

me

(far

m la

bor)

Spen

ding

on f

ood

1-Spending on

manufactures

(goo

ds)

T

a

c

b

de

f

Direction of (goods and services flows)

Direction of money flows

Mobility (i)

g

Source: Brakman, Garretsen & Van Marrewijk, 2009

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.

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

0

1

region 1 region 2

a. spreading

0

1

region 1 region 2

b. agglomerate in region 1

0

1

region 1 region 2


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.

3) The core model

0,97

1

1,03

0 0,5 1

share of manufacturing workers in region 1 (lambda1)

rela

tive

real

wag

e (w

1/w

2)

A

DC

B

E

F


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.

3) The core model

• For transport costs below the sustain point there is complete agglomeration.

• For transport costs above the breakpoint spreading across the two regions is stable.

• There is always an intermediate level of transport costs at which agglomeration is sustainable, while simultaneously spreading of manufacturing activity is a stable equilibrium.

Sustain points

Break point

Transport costs T10

1

1

0.5

Stable equilibria

Unstable equilibria

B

S0

S1

Basin of attraction for spreading equilibrium

Basin of attraction for agglomeration in region 1


Panel a

Sustain points

Break point

Transport costs T10

1

1

0.5

Stable equilibria

Unstable equilibria

B

S0

S1




Sustain points

Break point

Transport costs T10

1

1

0.5

Stable equilibria

Unstable equilibria

B

S0

S1




Panel a


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.

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.

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.

4) Beyond the core modelSource: WDR, 2009


• 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.


• 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.

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’.


• 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.


• 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.


• The most concentrated industries, apart from Iron & Steel and Motor, are smaller industries.


• 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.


• 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.


• 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.

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.


• -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

Deviation of Log of Real per capita Income: All districts

Standard Deviation of Log of Real per capita Income: Richest 20% districts

Standard Deviation of Log of Real per capita Income: Poorest 20% of districts

Standard Deviation of Log of Real GGP for All districts

199019962000

0.61470.52580.5466

0.32290.29440.3153

0.23790.10630.1082

1.521.511.55


• Distribution dynamics:• Krugell, Koekemoer and

Allison (2005) analysed kernels of incomes per magisterial districts.

• The results confirm a highly unequal distribution.

• Over the period 1996-2004 more places grew poor and a few places grew richer.

0

0.01

0.02

0.03

0.04

0.05

0.06

0.07

0.08

0 5 10 15 20 25 30 35

1996 2000 2004

0

0.0002

0.0004

0.0006

0.0008

0.001

0.0012

0.0014

0.0016

50 70 90 110 130 150 170 190 210 230

1996 2000 2004


• 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.


• 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.


• Distribution dynamics:• Bosker & Krugell (2008) were also the first to use spatial

econometrics in the SA context.

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.


Does openness matter for local growth?

Openness, education and population growth are positively associated with growth.

The more specialized regions compared to the national export portfolio, experienced the fastest GDP growth rates.

dependent variable: GDP growth 1996-2001

Manufacturing

share in exports

index1 index2 index3

export indicator -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] ln gdp 1996 -0.03 -0.05 0.10 0.05 [0.84] [0.76] [0.54] [0.79] human capital 1996 1.30*** 1.13*** 1.22** 1.21*** [0.00] [0.00] [0.00] [0.00] ln distance -0.01 0.07 -0.11 -0.001 [0.94] [0.69] [0.57] [0.99] avg. Annual 0.72** 0.79** 0.70* 0.69* population growth [0.05] [0.04] [0.06] [0.06] Rho (parameter on spatial lag) -1.83*** [0.00]

-1.95*** [0.00]

-1.85*** [0.00]

-1.93*** [0.00]

No of observations 234 235 235 235 log likelihood -522.1 -526.6 -525.9 -527.7


• 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.


• Over the period 1996 to 2004, exporters seem to have located further away 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.

Naudé & Matthee (2007)

Pre

dict

ed m

anu

fact

ure

d e

xpor

ts in

20

04

Pre

dict

ed

man

ufa

ctu

red

exp

orts

in 1

996

0 200 400 600 800Distance from port

Manufactured exports in 1996 Manufactured Exports in 2004

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.

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.

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.

Intermediate inputs

• On average, firms in GautengoUse more domestic inputs,oUse less imported inputs,oAre sellers of intermediate inputs,oAre more likely to subcontract production, andoHold 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.

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.

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.

Knowledge

• In the 2003 and 2007 surveys:oA greater proportion of firms in Gauteng and P.E. used

foreign licensed technology.oMost firms have e-mail, but fewer use an own web site

to communicate with clients.

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.

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