geograph econ lecture

Putting the economy in its place Geographical Economics in South Africa

by

Waldo Krugell

School of EconomicsNorth-West University

Potchefstroom Campus

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.

All this while one of the most remarkable aspects of economic activity is its unequal distribution across the earth.


Source: WDR, 2009


In The Wealth of Nations Adam Smith (1776) wrote: “It is upon the sea coast, and along the banks of navigable rivers, that industry of every kind naturally begins to sub-divide and improve itself, and it is frequently not until a long time after that those improvements extend themselves to the inland parts of the country”.

The 2009 World Development Report states that place is the most important correlate of a person’s welfare.


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.

Demand: Consumers decide how much to spend on food and

manufactures using a Cobb-Douglas utility function.

Then they decide how much to spend on particular varieties of manufactures

IF m 1 IPM m

IPc mjj 1

3) The core model

Supply: Food production is by constant returns to scale under

perfect competition. Production of the food sector equals food employment

Production is manufacturing is characterised by internal economies of scale.

The amount of labour required to produce xi units of variety i is given bywhere coefficients f and m and the fixed and marginal labour input requirements.

There is constant mark-up of price over marginal cost. Production size per variety determines the number of

varieties produced in a region.

mF 1

ii mxfl

3) The core model

Transport 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.

To determine the equilibrium, you must be clear on what you are solving for.

The short-run equilibrium determine the endogenous variables: income Ir, the price index Pr and the wage rate Wr in region r.

That is, given the distribution of the manufacturing workforce λr and the parameters of the model:

The share of income spent on manufactures, δm. Transport cost, T. The elasticity of substitution, ε.

3) The core model

Laborers in thefood sector inregion 1; 1(1-)L

Laborers in thefood sector inregion 2; 2(1-)L

Laborers in themanufacturing sectorin region 1; 1L

Laborers in themanufacturing sectorin region 2; 2L

Laborers in thefood sector (1-)L

Laborers in themanufacturing sector; L

Total number of laborers; L

(1-)

1 2 1 2

Note: 1 + 2 = 1 Note: 1 + 2 = 1 Mobility (section 3.9)


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.

Section 14.4 explains the derivation of the price index, income and wage rate. The price index in region 1 is the weighted average of

the price of locally produced goods and imported goods from region 2.

The income in a region is the sum of the farm and manufacturing wages.

Wages depend on the income in both regions, transport costs and the price charged relative to the price index.

3) The core model

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 costs.

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.


Source: 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.


Scale economies amplify with density

And attenuate with distance

Doubling economic density increases productivity by 6% (Ciccone & Hall, 1996)

Doubling employment density increases productivity by 4.5-5% (Ciccone, 2002)

Increasing distance from the city centre by 1% leads to a 0.13% decline in productivity (Hansen, 1990)Doubling the distance to a regional market lowers profits by 6% (Henderson, 1994).


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.

5.2) The role of cities

Naudé & Krugell (2003b) examined the role of cities in economic development in SA. In 2000, 19 large urban areas produced 70% of

SA’s GDP. The total annual average income in rural areas in

2000 was R18’506 compared to R51’107 in urban areas.

Overall South Africa’s cities tend to be small, with six large cities and no mega city.

To determine whether there is scope for growth of cities that rank-size distribution of cities was estimated.


q was estimated as 0.75, which means that Zipf’s law does not hold for South Africa.

0

50000

100000

150000

200000

2500001 9 17 25 33 41 49 57 65 73 81 89 97 105

113

121

Rank

No o

f H

ou

seh

old

s


Naudé & Krugell (2003b) also calculated the Relative Specialization Index (RZI) and Hirschman-Herfindahl Index (HHI) for six cities. The results show that: Johannesburg and Cape Town tend to be offering primarily

localisation economies (by having the lowest HHI and highest RZI values).

Ethekwini (Durban) and the others offer urbanisation economies.

In particular, Durban is the most diversified of the various cities in South Africa, and Johannesburg the least diversified.

Apart from Durban, South Africa’s large cities such as Johannesburg and Cape Town are more specialized in services (finance) than manufacturing, a trend consistent with international patterns.

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.

This is in collaboration with Neil Rankin at Wits.

5.4) Firm-level evidence

The analysis examines four sources of economic geography external economies: Intermediate inputs, the labour market, infrastructure and

access to knowledge.

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 scope of agglomeration

We estimate production functionso With firm-level data from the World Bank Enterprise

surveys (2003, 2007),o And place-specific determinants of outputs with data

from IHS Global Insight’s Regional Economic Explorer database,

o For firms in Johannesburg/ East Rand/ Pretoria, Cape Town, Port Elizabeth and Durban.

o Thus it is cross-section analysis and the model is estimated by OLS. The issue of endogeneity of location.


1 2 3 4 Constant 2.537

(4.44) 2.546 (4.36)

2.390 (4.18)

2.234 (3.98)

Capital 0.177 (3.71)**

0.177 (3.66)**

0.163 (3.39)**

0.175 (3.61)**

Labour -0.020 (-0.55)

-0.021 (-0.54)

-0.016 (-0.42)

0.002 (0.06)

Materials inputs 0.598 (12.54)**

0.598 (12.41)**

0.596 (12.55)**

0.599 (12.64)**

Indirect inputs 0.087 (1.72)*

0.087 (1.72)*

0.103 (2.04)**

0.104 (2.06)**

Exporter dummy 0.007 (0.07)

Location dummy 0.212 (2.17)**

Cape Town dummy -0.108 (-0.91)

Durban dummy -0.298 (-0.90)*

PE dummy -0.715 (-2.31)**

Adjusted R2 0.57 0.56 0.57 0.57

Firm-level specification, 2003 WB dataset

The scope of agglomeration 5 6 7 8 Constant -2.660

(-1.34) 0.369 (0.32)

9.612 (1.20)

2.17 (3.53)

Capital 0.175 (3.61)**

0.188 (3.93)**

0.174 (3.61)**

0.164 (3.38)**

Labour 0.002 (0.06)

-0.003 (-0.08)

0.002 (0.06)

-0.021 (-0.56)


0.598 (12.61)**

0.600 (12.64)**

0.600 (12.53)**

Indirect inputs 0.104 (2.06)**

0.091 (1.81)*

0.104 (2.06)**

0.098 (1.94)*

GDP per capita 0.597 (1.34)

Economically active population

0.158 (1.67)*

Population density 0.069 (0.42)

Education 0.952 (2.17)**

Manufacturing employment -0.602 (-1.69)*

Tress index -1.239 (-0.60)

Location quotient -0.606 (-1.21)

Exports as % of GDP 0.125 (1.58)

Adjusted R2 0.57 0.57 0.57 0.56

Firm-level specification + location-specific covariates, 2003 WB dataset


9 10 11 12 Constant 2.342

(17.65) 2.350

(17.06) 2.315

(17.81) 2.438

(18.37) Capital 0.034

(4.84)** 0.033

(4.81)** 0.029

(4.11)** 0.028

(4.06)** Labour 0.025

(2.48)** 0.024

(2.23)** 0.027

(2.75** 0.027

(2.73)** Materials inputs 0.721

(52.1)** 0.721

(51.8)** 0.715

(52.65)** 0.714

(52.31)** Indirect inputs 0.134

(8.61)** 0.134

(8.58)** 0.137

(5.44)** .014

(9.11)** Exporter dummy 0.007

(0.22)

Location dummy 0.137 (5.44)**

Cape Town dummy -0.152 (-4.49)**

Durban dummy -0.115 (-3.05)**

PE dummy -0.143 (-3.13)**

Adjusted R2 0.91 0.91 0.92 0.92

Firm-level specification, 2007 WB dataset


13 14 15 16 Constant 1.129

(3.63) 2.018 (9.65)

-0.159 (-0.08)

1.975 (13.41)

Capital 0.028 (4.06)**

0.034 (4.78)**

0.028 (4.06)**

0.028 (4.09)**

Labour 0.027 (2.73)**

0.025 (2.50)**

0.072 (2.73)**

0.027 (2.73)**


0.722 (52.26)**

0.714 (52.31)**

0.714 (52.35)**

Indirect inputs 0.142 (9.11)**

0.130 (8.28)**

0.142 (9.11)**

0.146 (9.44)**

GDP per capita -0.129 (-1.15)

Economically active population

0.135 (5.00)**

Population density -0.050 (-1.59)

Education 0.180 (9.65)**

Manufacturing employment -0.468 (-4.82)**

Tress index 0.991 (1.84)*

Location quotient 0.022 (0.27)

Exports as % of GDP 0.118 (5.30)**

Adjusted R2 0.92 0.92 0.92 0.92

Firm-level specification + location-specific covariates, 2007 WB dataset

The sources of agglomeration

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 Gautengo Use more domestic inputs,o Use less imported inputs,o Are sellers of intermediate inputs,o Are more likely to subcontract production, ando 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.

The labour market

On average, firms in Gautengo 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:

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.

6) The way forward

Industrial policy and Zumanomics. Policy recommendations from the World

Bank. Recommendations for further research.

geograph econ lecture

Education

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economic production

trade theory

growth theory differences

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