GEOGRAPHY AND DEMAND-DRIVEN INDUSTRIALISATION

Preliminary Version!

Holger Breinlich

LSE and CEP

2

1 Introduction Industrialisation, that is the decline in agriculture’s share in GDP, and the corresponding rise of manufacturing (and later services)1, is generally considered to be an essential part of a successful development strategy2. It is accompanied by strong rises in income levels and accumulation of factors of production. Although the direction of causality is open to dispute, it is generally considered to run both ways (Chenery, Robinson, and Syrquin, 1989). Accordingly, the determinants of industrialisation have been of major interest in policy circles, in the public discussion and academic debates.

In explaining why some countries industrialise earlier than others and some do not seem to industrialise at all, development economics has since long considered local demand as one decisive factor. Rosenstein-Rodan (1943) argued that lacking demand might prevent profitable implementation of advanced production techniques if these are based on increasing returns to scale. Only events which lead to rises in local demand, like agricultural productivity increases or co-ordinated large-scale industrialisation in several sectors (the so-called “Big Push”), can lead a country out of this underdevelopment trap. In two papers, Murphy et al. (1989a, 1989b) formalised and extended the ideas of Rosenstein-Rodan and other earlier authors (e.g. Fleming, 1955). They showed under which conditions the original “Big Push” argument holds and considered the effects of agricultural productivity shocks and export booms under different distributions of income.

Murphy et al.’s seminal contributions generated a sizeable literature, almost exclusively theoretical in focus. Most importantly, Murphy et al.’s (1989a) model has been modified to include intermediate goods (Temple and Voth, 1998; Ciccone, 2002) and versions with more open economies have been proposed (Fafchamps and Helms, 1996; Skott and Ros, 1997)3.

However, the existing literature has always restricted attention to single economies, mostly closed ones, and has consequently neglected potential interactions between locations. Notice that this point goes beyond the standard criticism that in an ever more integrated world economy, the focus on local demand seems to be outdated. Though this argument certainly has its merits4, the basic problem is not the fact that most studies ignore foreign demand (indeed some try to give it a limited role). Rather, the importance of geographical position relative to other locations in a world with positive transport costs is being overlooked or abstracted from. A country in central Africa will profit much less from high levels of world demand simply because it is further away from its main sources in Europe, Japan, and the United States.

A second strand of the literature has critically discussed the role of agricultural productivity that is central to Murphy et al. (1989b) and many related studies. For example, Matsuyama (1992) and Duranton (1998) take issue with the standard view in the development literature that increased agricultural productivity is a prerequisite for industrialisation. Using a

1 The manifold changes usually associated with industrialisation, like the reallocation of labour and capital from rural to urban areas and the arrival of mass production, are caused by this basic change in production structure; see Chenery, Robinson, and Syrquin (1986) and Chenery and Syrquin (1989). 2 See, for example, Chenery, Robinson, and Syrquin (1986) 3 Obviously, the latter papers introduce some component which preserves an important role for local demand, like non-tradable intermediates or upper bounds on exports due to rapidly increasing marginal costs as production expands beyond local demand. 4 On this point, compare the justification advanced by Murphy et al. (1989a) themselves who cite work by Chenery, Robinson and Taylor (1986). Those authors show that expansion of domestic demand accounts for 50-70 percent of increases in industrial output. However, one might argue that these figures still leave important room for foreign demand, particularly since they are for the period 1950-1970 and cannot take into account subsequent decreases in transportation cost and trade liberalisations. Indeed, Ades and Glaeser (1999) find that local demand becomes less and less important for growth as openness increases.

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Ricardian comparative advantage argument, they point out that in an open economy, increased productivity can actually lead to deindustrialisation as countries specialise in agriculture5.

While not denying the importance of above arguments, I think that here again, a closer look at potential interactions between locations might yield more subtle predictions than the simple juxtaposition of open and closed economies. Whether agricultural productivity shocks lead to industrialisation or deindustrialisation depends on the degree of remoteness of a location and the structure of economies in its nearer vicinity. That is, the same trade liberalisation policy might lead to very different results in different geographical contexts.

Empirically, there exists indeed some tentative evidence that interactions between locations might be important. For example, Fafchamps and Helms (1996), in a study of industry structure in the Guatemalan highlands, find that central locations show large shares in scale intensive industries even with low local demand. Davis and Weinstein (1998) find significant effects of “local” demand on production structure in the sense of a home-market effect only if they add a distance weighted sum of demand derived from geographical neighbours. Finally, in section 2 of this paper, I will present some preliminary but suggestive empirical results that support the notion that the degree of industrialisation is influenced by proximity to sources of demand.

Accordingly, the aim of this paper is to try to fill the gaps in the existing literature mentioned above by constructing a multi-location model with varying levels of transportation costs. In the context of industrialisation, it should be seen as an attempt to arrive at a more general concept of “local” demand that takes relative location more seriously and moves beyond the open/closed economy paradigm. The benefits from doing so may be a better understanding of certain empirical regularities that defy explanation in the single-economy framework of the existing literature. First, the standard prediction of market size models is qualified. Now it is not national market size per se but also the level of integration between locations that matters for industrialisation. This may explain why there is no obvious correlation between population size and the level of industrialisation (China and India are cases in point)6. More generally, the model is compatible with the empirical evidence on the importance of geographical position for production structures cited above. Finally, it also sheds some new light on the debate about the link between agricultural productivity and industrialisation.

In terms of modelling approaches, this paper borrows heavily from the techniques developed in the New Economic Geography (NEG) (for an overview, see Fujita, Krugman and Venables, 1999). Those techniques provide a fairly tractable way of incorporating transport costs in a multi-location model, though this usually comes at the price of the absence of closed-form solutions which in turn makes the use of simulation methods unavoidable.

Several NEG papers have also argued for the importance of geography in different contexts. For example, Redding and Venables (2000) find evidence that geographical location is important for standards of living and Redding and Schott (2003) provide an additional explanation for this by demonstrating geography’s impact on levels of educational attainment. If one accepts the claim that industrialisation is a prerequisite for growth, the present paper can be seen as providing another mechanism through which geography affects income levels (namely through its impact on levels of industrialisation).

5 Indeed, the comparative study of different industrialisation episodes seems to provide evidence for the importance of such considerations (e.g. Belgium and Switzerland vs. the Netherlands in Europe; New England vs. the South in the USA; and different Asian economies since 1945, see Matsuyama, 1992, and Duranton, 1998, and the sources cited in these papers). 6 Of course, some authors notice this problem (e.g. Fafchamps and Voth, 1998) and mention that the level of integration is important. Their formal models, however, leave no room for such an explanation.

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A theoretical NEG contribution directly related to the phenomenon of industrialisation is the paper by Puga and Venables (1996). However, their focus is on the implications of intermediate goods usage for the forming of agglomerations and the sequential spread of industries across countries. The present paper abstracts from linkages created by intermediate goods and has no role for cumulative causation. Also, the issue of the different roles of agricultural productivity shocks is not analysed by Puga and Venables7. Insofar, the present contribution is probably more related to the above literature on demand-driven industrialisation, both thematically and in terms of the underlying economic mechanisms.

The remainder of the paper is organised as follows. Section 2 provides some suggestive evidence for the importance of relative geographical position for levels of industrialisation. Section 3 describes the formal model and section 4 discusses simulation results. Finally, section 5 concludes.

7 The latter authors use increases in labour endowment as the force that drives changes in their model. Though this would also be possible in the model presented later on, this direction has not been pursued so far.

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2 Some Evidence on Geography and Industrialisation In the following, I will present some rough empirical evidence that relative location may matter for industrialisation. While the preliminary results obtained below cannot substitute for a more careful analysis, they should at least demonstrate that the claim about the importance of relative location is not in direct conflict with basic empirical facts.

In order to check whether demand derived from surrounding locations matters for the level of industrialisation, I regress GDP shares of value added in manufacturing on a measure of proximity to foreign markets8. The latter is taken to be the sum of all other countries’ GNP9, weighted by the inverse of bilateral distances which is taken to proxy for transportation costs between locations10. Formally:

∑≠

−×=ij

ijj distGNP 1(i)Centrality

As several studies point out, the level of industrialisation is strongly positively correlated with national per capita income11. Also, the share of manufacturing in GDP changes most at lower levels of income whereas it remains fairly stable and even declines in rich, industrialised countries12. These observations require two main adjustments to my regression. First, as the focus of this paper is on the process of industrialisation in the developing world, I exclude high-income countries from my regression sample, though of course, all available countries are used to calculate the centrality measure13. Second, I include per capita GNP as an additional regressor to control for the possibility that the correlation between neighbouring GNP and local industrialisation levels is simply driven by spatially correlated GNPs and not by demand linkages. That is, high manufacturing shares might be due to a positive influence of surrounding GNP levels on local GNP (as suggested by the literature on technological externalities, for example). This in turn will increase the manufacturing share via the correlation of income and industrialisation levels mentioned above. The inclusion of per capita GNP should also control in part for the accumulation of production factors in the course of the industrialisation process: countries with higher income tend to have higher levels of human and physical capital which in turn favours a high manufacturing share14.

Data on manufacturing shares and GNP in 1995 USD are taken from the World Development Indicators 2001, data on bilateral distances from the NBER World Trade Database (Feenstra et al., 1997; Feenstra, 2000). The largest available sample is for 1996 and contains 88 countries. In that year, shares of manufacturing in GDP range from 3.4% (Angola) to 31% (Romania), with a mean of 14.4%.

8 “Manufacturing” as defined by the World Bank Development Indicators 2001 contains ISIC sections 15-37. In the present context, this indicator is preferable to the broader group “Industry” (ISIC 10-45), as the latter also includes mining, water and gas. The size of those sectors is probably more dependent on the availability of natural resources and not levels of demand. 9 I use GNP rather than GDP as the former is a better measure of purchasing power in that it focuses on locally available income rather than production. 10 The exponent -1 is chosen in accordance with standard results from trade-flow gravity equations. 11 See in particular Abegaz (2002), Chenery, Robinson, and Syrquin (1986), and Chenery and Syrquin (1989). 12 Abegaz (2002), Chenery, Robinson, and Syrquin (1986), and Chenery and Syrquin (1989). 13 I use the World Bank’s income classification and exclude all countries with gross national income per capita in excess of 9,265 USD in 1999 (“high income countries”). Robustness checks including or excluding this and other income groups are planned. 14 See, for example, Chenery, Robinson, and Syrquin (1986). The causation between levels of human and physical capital and the level of industrialisation probably runs both ways: high levels of the former make the adoption of modern production technologies possible which in turn increase the need for human and physical capital. For my regression, however, I only need that per capita GNP is a reasonable proxy for the two types of capital.

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As manufacturing shares are limited to a range between 0 and 1, I first perform a logistic transformation, yielding the dependent variable ltshareM15:

−

=i

ii shareM

shareMltshareM1

log

The econometric specification then estimated is:

iiii pcGNPproximityltshareM εββα +++= )log()log( 21

Results are presented below in table 1. As expected from other results in the literature, per capita GNP is highly significant, both statistically and economically. The same is true for the proximity measure. According to the results, moving Angola to the geographical position of Poland, for example (and thus much closer to the rich EU-countries), would almost triple its proximity measure and increase its manufacturing share from 3.4% to 12.5%, ceteris paribus16.

Table 1: Manufacturing share and proximity to locations of foreign demand ltshareM log(proximity) 0.347 (2.13)* log(pcGNP) 0.205 (4.02)** Constant -10.933 (3.15)** Observations 88 R-squared 0.26 Robust t statistics in parentheses * significant at 5%; ** significant at 1%

To conclude, a more careful analysis would try to control for a number of additional variables. Besides physical and human capital levels, land abundance and levels of inequality seem to be potentially important determinants17. Also, a derivation of measures of proximity more solidly grounded in theory and estimated from bilateral trade flows along the lines of Redding and Venables (2000) seems promising.

15 For details on the standard logistic model, see Abegaz (2002). 16 Note that the regression coefficient are not immediately interpretable as they relate to the log-transformed manufacturing share. 17 See Leamer (1987) on the role of endowments of labour, capital and land in the process of structural transformation. Murphy et al. (1989b) show how income inequality may affect industrialisation.

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3 The Model

In this section, I develop a multi-location model with transport costs. The model is set in a world with R locations. In each location, there is an agricultural sector and a large number of manufacturing sectors. Both agricultural and manufacturing goods production takes place under product differentiation. The former goods are differentiated according to the location of production, the latter additionally across sectors (more on this below). I start with a description of preferences and production structures and then briefly describe the equilibrium which will be analysed in detail using simulations in section 4. 3.1 Preference Structure

For notational clarity, let me first point out the following convention: l will index producer/exporter location, j importer location, and i product type. The order within indices is producer/exporter, importer, product (l, j, i).

The representative consumer in location j maximises a Cobb-Douglas utility function over consumption of an agricultural and a manufactured composite good. The latter is represented by a double CES-subutility function, the former by a simple CES-subutility function.

)ln()1()ln( AAMU jjj −−+= αα )1/(/)1()1/(

1

/)1(

−−−

=

−

= ∫ ∑θθθθσσ

σσ dimMi

R

lljij

)1/(

1

/)1(−

=

−

= ∑σσ

σσR

iljj aA

Mj is location j’s consumption of the composite manufacturing good which consists of a large number of goods produced by different sectors i, where i є [0,1] 18. Not all sectors have to be active at any given time (see below). I denote mlji the amount of product i produced in location l and demanded in j. Above specification thus assumes that the same good produced in different countries is effectively seen as a different variety of that good (an Armington assumption). In principle, it should be expected that σ>θ, i.e. that a variety of the same good produced in different countries is more easily substituted than different goods among each other. However, to simplify the subsequent analysis, I assume that σ=θ. This simplifies the expression for Mj to:

)1/(

1

/)1(−

=

−

= ∑∫

σσσσ

R

j iljij dimM

Aj is consumption of the agricultural composite and alj is the amount of the variety produced in l that is consumed in j (every location produces one differentiated variety, see below for details). A denotes the minimal consumption of agricultural goods, i.e. the subsistence level. This preference structure guarantees that below a certain threshold income, only agricultural goods are consumed. Above this level, the relative share of agricultural goods in consumption declines – this is the so-called Engel’s law which has strong empirical foundations. 18 As usual with CES-utility functions, the distinction between varieties within the same sector and goods produced in different sectors is not clear-cut. I prefer to talk of sectors as goods are also differentiated by production techniques (see below).

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The budget constraint is given by: PAjAj + PMjMj = Yj. PMj and PAj are price indices for the manufacturing and agricultural composite goods (defined below).

This maximisation problem yields demand for agricultural and manufacturing goods in location j as follows19:

αα AYP

A jAj

j +−= 1

−=

Mj

Ajjj P

APYM α

For a given Mj, consumers seek to minimize cost, i.e. they solve

∫ ∑

=i

R

llji

Mlji dimp

1min s.t. M

)1/(

1

/)1(

−

=

−

= ∫ ∑

σσσσ

i

R

jljij dim

Solving the resulting Lagrangian for varieties mlji and ml’ji’ and dividing the two expressions by each other yields:

=

lji

jiljillji pp

mm ''''

Inserting this expression into the budget constraint yields consumer demand in location j for product i produced in location l as:

( ) MjMj

Mlji

AjjMj

MljijMj

Mljilji EPpAPYPpMPpm 11 )()()()()()( −−−−− =−== σσσσσσ α

where ( APYE A

jjMj −= α ) (1)

denotes consumer spending on manufactured goods in location j (which is α times the income not spent on subsistence consumption of agricultural goods) and the price index Pj

M is defined as:

)1/(1

1

1)(

σσ

−

−

=

= ∫ ∑i

R

l

Mlji

Mj dipP (2)

By analogy, we get demand for agricultural goods produced in l from location j as:

[ ] AjAjAljAjjAj

AljjAj

Aljlj EPpAPYPpAPpa 11 )()()1()()()()( −−−−− =+−== σσσσσσ αα

where αα APYE AjjAj +−= )1( (3)

19 The expenditure share of agriculture is:

j

AjjA Y

APYES

αα +−=

)1( . Taking the derivative with respect to income

Yj yields: 22j

Aj

j

jj

Aj

j

A

Y

AP

Y

YAYP

YES α

α−

∂∂

=∂

∂ . So the expenditure share declines unless agricultural prices rise too fast

with increasing nominal income.

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is expenditure on agricultural goods in location j and the price index PAj is defined as: )1/(1

1

1)(

σσ

−−

=

= ∑R

l

AljAj pP (4)

The prices pMlji and pA

lj consist of the mill price charged in location j plus iceberg-type transportation costs TljM and TljA, respectively. Inserting this and summing over all locations, we get total demand for manufactured good variety i produced in l and for agricultural goods produced in l:

∑∑=

−−−

=

− ==R

jMjMjliljM

R

jjMjljili EPpTMPpm

1

1

1)()( σσσσσ

∑∑=

−−−

=

− ==R

jAjAj

AlljA

R

jjAj

Aljl EPpTAPpa

1

1

1)()()( σσσσσ

Aggregate income in location l, Yl, consists of income from labour and profits in the manufacturing sector (see below). I assume perfect labour mobility between sectors (though not between locations) and thus equalisation of wages:

ilwww lAlMli , ∀==

So aggregate income can be written as:

∫∫ Π+=Π++=i

lili

liMlAlll diwdiLLwY )( (5)

where Π denotes profits and the integral runs over all sectors that are already producing (see below). Also, I made use of a full employment condition and normalized total labour supply to 1 in each location, i.e.

1=+= MlAll LLL . (6)

3.2 Production Structure a) Agricultural Sector

Each location produces a differentiated variety of the agricultural good. This can be justified either simply by an Armington-type assumption (the same type of product from different locations is in fact seen as different goods) or by appeal to a comparative advantage argument (i.e. a country has a Ricardian comparative advantage in the production of a certain agricultural goods).

The agricultural sector in each location is perfectly competitive, operates under constant returns to scale and uses labour as its only input. As noted above, the amount of labour employed in agriculture in location l is LAl and agricultural productivity is denoted by θAl :

AlAlSl Lz θ=

where zlS denotes supply of the local variety.

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Recall from above discussion that demand for agricultural goods produced in l is given by:

∑=

−−−=R

jAjAjAlljAl EPpTa

1

1)( σσσ

In order to satisfy this demand, TljA units must be shipped for every unit demanded, yielding an effective agricultural demand for goods from l of:

AlAl

R

jAjAj

AlljA

Dl MApEPpTz σσσσ −

=

−−− ==∑1

11 )(

In the above, I introduced a summary measure for access to foreign markets. It is what Redding and Venables (2000) name market access and define as a transport cost weighted sum of local expenditures (times a competition effect PAj

σ-1). Formally, agricultural market access is defined as:

∑=

−−=R

jAjAjljAAl EPTMA

1

11 )( σσ (7)

Profit maximisation under perfect competition in the agricultural sector implies that prices are set equal to the cost of producing one unit of output:

Al

lAl

wpθ

=

Inserting this result in the demand equation, setting supply equal to demand (zlS

= zlD) and

solving for LAl, we get:

AllAlAl MAwL σσθ −−= 1 (8)

Finally, equilibrium production will thus be . AllAll MAwz σσθ −=

b) Manufacturing Sector

Production in manufacturing takes place under increasing returns to scale with a fixed cost requirement. As in agricultural production, labour is the only factor used. Formally, the production technology takes the form:

( ) MliMliiSli LFx θ=+

where xli is production of good i in location l, Fi is the fixed cost requirement in industry i, and θMli is a productivity parameter (indicating productivity of manufacturing production of good i in location l). LMli denotes labour employed in sector i in location l. The fixed cost Fi

vary by sector and I assume that 0>∂∂iFi . This roughly reflects increasing scale intensity in

more “advanced” sectors (Chenery and Syrquin, 1989). I choose the term “advanced” as the higher the fixed cost requirement, the later a sector will start producing (see below).

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Again recall from above that demand for manufacturing goods produced by sector i in location l is given by:

∑=

−−−=R

jMjMjliljMli EPpTm

1

1)( σσσ

In order to satisfy this demand, TljM units must be shipped for every unit demanded, yielding an effective demand for goods from sector i in l of:

Mlli

R

jMjMjliljM

Dl MApEPpTx σσσσ −

=

−−− ==∑1

11 )(

where the term MAMl stands for manufacturing market access and is again defined as the transport cost weighted sum of local expenditures (times a competition effect PMj

σ-1):

∑=

−−=R

jMjMjljMMl EPTMA

1

11 )( σσ (9)

If a manufacturing firm decides to take up production in location l, it has to choose which variety to produce. It does so by choosing the sector with the highest profits. I assume that a firm entering a sector with existing production enters into Bertrand-competition with the incumbent and will thus make zero profits. Accordingly, it will be indifferent between entering into the same sector and not entering at all. Because of this, it is reasonable to assume that a firm will wait until it can make non-negative profits in another sector. The profits of a firm in location l and sector i are then given by:

)( iliMli

lMlilili Fx

wpx +−=Π

θ.

If a firm enters, it sets prices to maximise profits, which results in a fixed mark-up over marginal costs. In the following, I assume that productivity is equal across industries and

locations and I choose it to be 1−

==σ

σθθ MMli . This implies that the resulting profit

maximising price is the same across industries in a location l and equals the wage rate:

lMli

lMli wwp =

−=

θσσ

1 for all i

Inserting the profit maximising price and substituting for xli in the equation for profits yields:

( iMlll

li FMAww )1( −−=Π − σσ

σ )

Intuitively, for every unit sold a firm makes σθσ

σ l

M

l ww=×

−

−1

1 in profits (the mark-up

over marginal cost times the marginal cost). However, it has to pay for the fixed input

requirement which requires M

ii

FFθσ

σ =−1 units of labour and thus costs il F)1( −σ

σw

.

A firm will enter a sector if it makes non-negative profits. Thus, sector i will be active if

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( ) .0)1( 0)1( ≥−−⇔≥−− −−iMlliMll

l FMAwFMAww σσσ

σσ (10)

It will be shown below that there is a fixed order of entry, namely firms with lower fixed costs enter first. Thus, I can derive the “marginal” sector il in location l, i.e. the sector that makes zero profits and will just be active:

−=⇔=−−

−−−

1 0)1( 1

σσ

σσ Mll

liMllMAw

FiFMAw (11)

The final equation that is needed to close the model concerns the labour market. By substituting for xli and using our choice for θM, we can derive labour demand in manufacturing sector i in l from the production function as:

( )iMllMli FMAwL −−= −σ

σσ 1

.

Thus, total labour demand from manufacturing will be:

( ) )(111

0l

i

ilMlliMllMl iiMAwdiFMAwL

l

Γ×−+−=−−= ∫=

−−

σσ

σσ

σσ σσ

, (12)

where Г(i) denotes the primitive function of Fi20. Note that as total labour supply is unity, this

is also the share of manufacturing in total employment. Using the labour market clearing condition (6) and the labour demand in agriculture (8), we can use above result to derive the equilibrium wage as:

σσ

σσ

σσθ

11

)(11

1

Γ−−

−+=

−

l

lMlAlAl

l

i

iMAMAw (13)

3.3 Equilibrium

The nine equations (1-5, 7, 9, 11, 13) yielding wages, market access measures, price indices, expenditure levels, income, and the marginal firm can be used to characterise the equilibrium of the model.

From the solutions of the non-linear equation system formed by above equations, we can furthermore derive solutions to two variables featuring centrally in the simulations in the next section. First, manufacturing share in GDP. Together with the fraction of the total labour force employed in manufacturing (equation 12), it serves as the indicator for the level of industrialisation in this model. The second variable measures the real income available in a location, which here is the sum of nominal expenditures on agricultural and manufacturing goods (which equals nominal income), divided by the corresponding price indices. Formally:

20 Here and in the following we restrict attention to functional forms of Fi which yield Г(0)=0.

13

lAlllMlMl

llMlMl

lAl

i

ilMlMl

i

ilMlMl

lAllMl

lMl

zPiwMAPiwMAP

zPdiwMAP

diwMAP

zPxPxP

l

l

+=

+=

+= −

−

=

−

=

−

∫

∫σ

σ

σ

σ

0

0GDPMShare

where zl and xl are total agricultural and manufacturing production in location l. The latter is obtained as the integral over the output of all active firms, the former is as defined above. The second variable, Yreal, is given as:

Y realAl

Al

Ml

Ml

PE

PE

+= -

14

4 Simulations The model described above has no closed-form solution due to the non-linearity of its equations. However, simulation techniques can be used to reveal its key properties21. The simplest possible case involves three locations. This is the minimum to make the notion of centrality meaningful. In particular, imagine three locations situated on a line, where I call the location in the middle the “centre”. The two other locations are at equal distance from the centre and I call them “periphery”.

Transport costs (TC) between locations are given by a 3x3-matrix, where the element (i,j) contains transport costs between i and j. As I use iceberg-type transportation costs, diagonal elements are equal to one, off-diagonal elements bigger than or equal to one. I also assume that TCij = TCji so that the matrix is symmetric. One also has to make an assumption about the functional form the fixed cost Fi take. In accordance with the assumption from section 3.2, I assume in the following that F(i)=a+b*i, a ≥ 0, b>0.

To give a brief overview of the following, the next subsection will further explain, and graphically illustrate, the entry-condition (10) which determines the equilibrium number of firms. In the following sections, I will return to the topics presented in the introduction and show how the results of the model relate to the questions and problems posed there. I will both present simulation results and try to give an economic intuition for why the solutions obtain. Obviously, decomposing the results of a simultaneous equilibrium into a sequence of economic events - with the corresponding implications for causality - will never be fully satisfying. Still, presenting some economic intuition should improve the understanding of the model. Finally, the results presented below do not reflect any attempt at calibration but are only designed to illustrate the qualitative predictions of the model.

4.1 Entry Condition: Equilibrium Number of Firms

Let us start with a visualisation of one of the central equations of the model, the entry-condition (10), from which il, the last entrant (=the equilibrium number of firms) is derived:

( ) 0)1( 0)1(1

≥−−⇔≥−−−

−il

MlliMll

l FwMAwFMAwwσ

σσ

σσ

σσ

Location 3: Periphery

Location 1: Periphery

Location 2: Centre

net revenues fixed cost

As explained above, firms enter as long as they can make positive profits in a sector not yet occupied. It is instructive to see what happens as the number of firms in a given location increases. Obviously, what happens in one location has an impact on the equilibrium in other locations through market access and price index effects. To simplify the analysis, I assume zero transport costs between locations and see what happens as I increase il uniformly in all three locations. The assumption of zero transport costs means that the distinction centre/periphery does not matter and I can focus on any of the three locations. The qualitative

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21 All simulation results in this paper are obtained on Mathlab V6.5, using a modified Newton-Raphson algorithm.

picture with positive transport costs is the same but differences between locations appear (more on this in the following sections).

Now, as more and more firms enter in a given location, the main change that takes place is an increase in competition. Correspondingly, output per firm and prices for manufacturing goods fall, reducing net revenues. At the same time, new entrants face higher fixed costs. Thus, their profits must be smaller than those of the last firm previously in the market, due to both the general fall in profits and the increase in fixed costs. Consequently, as more and more firms enter the market, profits of the last entrant converge towards zero and then become negative (see Figure 1).

Figure 1: Entry Condition. Simulation parameters: see appendix.

4.2 Market Integration, Income Levels, and Industrialisation

I will now show how the model relates to the questions and issues raised in the introduction. I start the analysis by juxtaposing the two polar cases of free trade vs. prohibitively high trade costs in both manufacturing and agricultural goods. In the former case, the location of demand is expected to be unimportant whereas in the latter case only local demand matters (the focus of most of the existing literature). In both scenarios, I consider the effects of a series of uniform increases in agricultural productivity in all three locations. In order to abstract from issues of comparative advantage for the moment, I assume identical agricultural productivity rates22. Except for relative position, locations are identical in all other aspects, too. This ensures that with both free trade and very high trade costs, all locations display identical responses to agricultural productivity shocks. In the former case, price indices and market access levels are equal across locations and in the latter case, impacts from other locations are virtually nil.

Results for three key variables are displayed in figure 2. As seen, the share of manufacturing in GDP and labour usage, as well as real income increase as agricultural productivity goes up,

22 As manufacturing productivity is set fixed to equal the mark-up over marginal cost, “comparative advantage” in this model comes down to absolute productivity differences in agriculture. More on this in section 4.4.

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though at declining rates23. Intuitively, the boost to agricultural productivity raises supply and, for a given level of demand, lowers prices of agricultural goods. Accordingly, real income rises which increases spending on both manufacturing and agricultural goods (though the rise is relatively higher for manufacturing due to Engel’s law as described in section 3.1). Manufacturing prices and profits rise, causing existing firms to expands production and new firms to enter. Increased production puts upward pressure on the manufacturing wage, triggering a shift of labour out of agriculture. Though the increase in productivity also initially increased labour demand in agricultural sector, the fall due to the price decline compensates for this. In the new equilibrium, wages have risen and manufactures’ labour share has increased, though production in agriculture is still higher than before due to the increased productivity. Higher wages and total profits mean higher nominal income, while the strong decline in agricultural prices yields even stronger increases in real income levels. Relative output of agricultural goods to manufacturing goods actually goes up, but the relative price movements assure that manufactures’ share in GDP grows. Note that these effects coincide with the standard view in the development literature: productivity increase in agriculture allow for cheap supply of agricultural goods produced by a decreasing share of the population which in turn liberates workers for manufacturing24.

Figure 2: Manufacturing share in GDP and labour usage, and real income levels for integration; simulation parameters: see appendix.

23 See section 3 for definitions of these shares in terms of the model. 24 See, for example, Matsuyama (1992). If one takes the concept of industrialisation employed in Murphy et al. (1989a,b) and most of the corresponding literature literally, the share of labour working in manufacturing actually declines as more productive techniques are employed there. One might argue that the constant returns to scale production taking place prior to industrialisation is not to be seen as manufacturing production in the sense of economic statistics. However, this begs the question why the same product should be placed in different output categories according to the technique used to produce it.

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As to the difference between free trade and autarky, it is clear from the graphs that consumers in an integrated market enjoy higher real income and the GDP share of manufacturing is higher. Intuitively, prohibitively high transportation costs mean that each location faces higher prices for both agricultural and manufacturing goods. This in turn reduces real income relative to free trade and leaves relatively less to be spent on manufacturing goods (again, due to Engel’s law). However, as agricultural productivity picks up and real income rises, the fraction of income spent on subsistence consumption declines and GDP share and labour employed in manufacturing converge towards the free trade level. The contrary is true for real income levels as consumers cannot profit from increased production and reduced agricultural price levels in other locations.

In conclusion, economic integration boosts manufacturing shares most for low agricultural productivity levels and correspondingly low local income. Though real income differences persist and even grow as productivity picks up, this matters less for industrialisation as the share of income spent on subsistence consumption converges towards zero in both scenarios.

Above results suggest both policy recommendations and an interesting testable prediction. First, if industrialisation is the primary goal of economic policy, economic integration has its largest effects for countries with poor agricultural productivity. Second, real income levels show the opposite behaviour: benefits are higher if productivity levels are already high.

4.3 Home Market Effects and Agricultural Trade Costs

The next issue raised in the introduction and section two is the apparent importance of demand derived from surrounding locations. That is, with positive transport costs central locations seem to show higher shares of manufacturing in GDP.

I first consider a situation with positive but decreasing transport costs in manufacturing. For the moment, transportation of agricultural goods is costless though this will change subsequently. Figure 3 again displays the absolute changes in the same three key as in the previous section. Also shown is the percentage difference between the central location (C) and the two peripheral location (P). Due to the assumed symmetry, values for the latter are always identical.

Analysing the results reveals two distinct phases. First, as transport costs in manufacturing come down from their relatively high initial values, a home-market effect emerges more clearly: at intermediate levels of transport costs, firms can combine the advantages of proximity to consumers with scale intensive production by choosing central locations. In terms of the model, market access for manufactured goods increases relatively more rapidly in the central location. The expansion of output and the associated wage rises also increase expenditure on manufactured goods, further augmenting the advantage of the centre. However, increased profits mean that new firms enter the market, driving prices and profits down again until a new equilibrium is reached. In the periphery, the availability of cheap imports of manufacturing goods causes a contraction in the manufacturing output share and shifts labour to the agricultural sector.

However, as trade costs decrease further and converge towards zero, centrality becomes less important again25. In fact, we approach the case of free trade described in the previous section. What is more troubling is the corresponding decline in both GDP and labour share of manufacturing in the centre. What seems to happen is that as trade costs converge towards zero, there is increased competition from the periphery while wage increases accelerate. Both 25 Note that this trend sets in at measurably positive transport costs. At least in this model, the home market effect does not to seem strongest at marginally positive transport costs.

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factors tend to reduce profits, cause firms to exit the market and shift labour into the agricultural sector. The decrease in the manufacturing share is even faster than the decline in total manufacturing output as prices fall as well. In the periphery, the picture is even more complex. On the one hand, increased demand due to better access to other markets over-compensates the effect of increased competition (just as it did in the central location earlier on). Accordingly, profits and labour demand rise, firms enter the market and manufacturing output expands. However, the decline in prices causes the GDP share to actually continue to fall.

Figure 3: Manufacturing share in GDP and labour usage, real income levels at different levels of TC(manufacturing) and TC(agriculture)=0 (simulation parameters: see appendix).

In how far the last results reflect real economic phenomena or whether they are an artefact of this model certainly requires further investigation. Note that this might also partly modify the conclusions of the last section: if an increase in the manufacturing output share is the primary

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goal of economic policy, rather than short term increases in real income (e.g. because of the assumed long-term benefits of industrialisation), intermediate levels of integration might be the optimal solution26.

Next, let me reintroduce transport cost in agriculture and perform the same experiment as above. Results for the same variables are shown in Figure 4. The first apparent difference is that levels of real income are generally lower. Obviously, this is due to the fact that transport costs in agriculture mean that imports of agricultural goods have become more expensive relative to free trade.

Figure 4: Manufacturing share in GDP and labour usage, real income levels at different levels of TC(manufacturing) and TC(agriculture)>0 (simulation parameters: see appendix).

26 In this context, it would be interesting to see which level of integration yields the maximum level of industrialisation. As seen in section 4.2, prohibitively high transport costs yield lower levels of industrialisation than free trade which suggest that the optimum will lie somewhere in between.

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Secondly, the home market effect is much less pronounced as is most clearly seen in the right-hand panels indicating percentage differences. Clearly, a relative specialisation in manufacturing - which still can be observed – comes at the cost of having to import more agricultural goods which decreases the available income for expenditure on manufactures more than with zero transport costs. This is directly reminiscent of the argument advanced by Davis (1998) that home-market effects are likely to break down in the presence of agricultural trade costs. Note, however, that in contrast to Davis, home-market effects are still possible here even with trade costs in agriculture exceeding those in manufacturing. This seems to be due to the assumption of differentiated agricultural goods (absent in Davis’ paper who assumes one homogenous good). This means that the required rise in agricultural trade following the specialisation of one location is less pronounced than with one homogenous agricultural good27.

Third, the convergence process that sets in for low enough manufacturing transport costs is slightly different. Due to the continuing presence of agricultural trade costs, agricultural goods are relatively cheaper in the central location which hence maintains its higher real income levels, though the difference decreases. Accordingly, the quantity demanded of both agricultural goods and manufacturing goods is higher in the centre. In agriculture, the usual home-market effect applies and production increases more than proportionately. In manufacturing, zero transport costs mean that the additional demand is satisfied in equal shares from production in all locations. Thus, in the central location a shift of labour into the agricultural sector takes place. However, lower agricultural prices keep the share of manufacturing in GDP above those of the periphery28.

Finally, it seems that with positive agricultural transport costs, the share of manufacturing in GDP is generally lower. Besides the effect of lower income combined with Engel’s law, the reason for this is probably also the higher price level for agricultural goods which push up their share in GDP.

In summary, the model proposed here can account for the apparent correlation between measures of proximity to sources of demand outside the own location. However, the magnitude of the relationship is relatively complex, depending on the level of transport costs in both manufacturing and agricultural goods.

4.4 Agricultural Productivity and Remoteness

Finally, let me pick up on the comments on the varying impacts of agricultural productivity shocks and its dependence on geographical position.

I start by replicating the results central to the existing literature: in a closed economy, positive agricultural productivity shocks promote industrialisation, while in an open economy, specialisation in agriculture may lead to de-industrialisation.

Figures 5 and 6 display results for those two polar cases. For the same reasons as in section 4.2, it does not matter where the productivity shock occurs. Results for the location concerned do not vary with its geographical position: both with autarky and free trade, relative position does not matter.

27 Compare what Davis calls his key insight (Davis, 1998, p.1265): “The key insight is that differentiated-goods trade falls approximately in proportion to the difference in world income, so less than one for one, while trade in the homogeneous good rises essentially one for one with the production shift. Unless trade costs are relatively high for the differentiated goods … the home market effect will not arise.”. 28 With the chosen parameters, the differences are minimal and hard to spot in the graphs. At zero TC in manufacturing, the percentage differences (Centre-Periphery)/Periphery are: 0.8% for real income, 0.2% for the GDP share of manufacturing, and -0.5% for the labour share.

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Figure 5: Effects of increased agricultural productivity in one location (here: centre) in the absence of transport costs. Parameters: see appendix.

As we see, results are as expected. With free trade, a positive agricultural productivity shock raises real income in the location concerned but leads to a dramatic de-industrialisation. In economic terms, higher productivity leads to increases in the supply of agricultural goods and the demand for labour in this sector. Accordingly, agricultural wages are pushed up and labour flows into agriculture, though falling prices partly offset this trend. As there are no transport costs, the remaining locations also profit from the decline in agricultural prices in the form of rising real income levels. However, as here there are no productivity gains in agriculture, the rising demand for manufacturing leads to price increases, output expansion, higher manufacturing wages and labour flows into the manufacturing sector. The key insight is that although higher agricultural productivity raises real income and manufacturing expenditure in the concerned location, wage increases lead to a crowding out of the manufacturing sector. The location specialises in agriculture and imports manufactured goods from abroad.

With prohibitively high transport costs, the picture is reversed. While the other locations are unaffected due to the absence of trade, the location experiencing the agricultural shock profits in form of increases in real income and the GDP share of manufacturing. The underlying mechanism is the same as described in section 4.2 for the closed economy case. The key difference to the free-trade situation is that agricultural prices fall by much more in the closed

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economy as there are no export markets which could pick up the additional production. Consequently, agricultural wage increases are much lower and the expenditure effect on manufacturing dominates. Also note that the increase in real income relative to other locations is much higher here, though in absolute terms, it stays well below the free-trade case.

Figure 6: Effects of increased agricultural productivity in one location (here: centre) with prohibitively high transport costs. Parameters: see appendix.

Let me now illustrate the less well known (though probably more relevant case) with intermediate levels of transportation costs. There are two different kinds of consequences. First, as might have been expected, the reaction to agricultural productivity shocks lies somewhere in between the two polar cases just analysed. Secondly, the geographical position of the location experiencing the shock now matters. In Figure 7, the effects of a shock in the central location are displayed, while Figure 8 shows the consequences of increased agricultural productivity in the left location.

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Figure 7: Effects of increased agricultural productivity in central location with intermediate transport costs. Right figures show relative differences centre-periphery. Parameters: see appendix.

The first thing to notice is that the effects of a shock on the left location (location 1) are less pronounced than those on the central location. Intuitively, the left location is effectively nearer to the case of a closed economy, as transport costs to other locations are on average higher than in the centre. Insofar, countries with similar levels of tariff and non-tariff barriers may still experience very different consequences of agricultural reforms aimed to increase productivity, depending on their geographic position. Second, the impact of an agricultural shock in one location on the remaining ones also depends on the levels of bilateral transport costs. As seen in Figure 8, the right location (location 3) profits much less from the shock than the centre, both in terms of real income increases and industrialisation. Again, this insight seems relatively intuitive. A parallel appears here to the literature on geography and wage levels: in both frameworks the impact of a shock declines with distance and thus stays geographically limited29.

29 See, for example, Hanson (1998).

24

Figure 8: Effects of increased agricultural productivity in left location with intermediate transport costs. Right figures show relative differences left - centre/right location. Parameters: see appendix.

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5 Conclusion

The implications of geography for demand-driven industrialisation have been largely neglected in the existing literature. This is despite the fact that there seems to be at least tentative empirical evidence that the position of locations relative to each other or the level of economic integration of a country do indeed matter. This paper is a first attempt to close this gap in the literature. A multi-location model of industrialisation with transport costs is presented and its properties are analysed via simulation methods. Qualitative predictions seem to be largely in accordance with the observations made in the first two sections. Also, at least three additional predictions arise which should in principle be empirically testable. First, the effects of economic integration on real income and industrialisation levels vary in specific ways with agricultural productivity and maybe also with the exact extent of integration (sections 4.2 and 4.3). Second, the extent of home-market effects in industrialisation depends on the level of transport costs in both agricultural and manufacturing trade (section 4.3). Finally, the effects of agricultural productivity shocks on the location experiencing the shock, and the impact on its surroundings depend to a large extent on geography (section 4.4): first, peripherality acts as a protection against de-industrialisation, though at the cost of lower income levels; and second, the impact on other locations declines with distance so that the effect of shocks stay localised.

Besides being able to formally analyse different empirical phenomena at least partly inaccessible to existing models, the model presented here also has a unifying effect. Different issues surrounding demand-driven industrialisation can be analysed in a single framework. Of course, these advantages come at a cost. Necessarily, some issues had to be left out. For example, the effect of income inequality on industrialisation – a central theme in Murphy et al. (1989b) – plays no role in the present model. More importantly, the absence of analytical results makes a heavy reliance on simulation results necessary. This naturally raises questions about the generality of the results obtained. Nevertheless, I think that the complex impact of geography on demand-driven industrialisation requires analysis in a formal framework. The present model is a first small step in that direction.

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REFERENCES

- Abegaz (2002):”Structural Convergence in Manufacturing Industries between Leaders and Latecomers”, Journal of Development Studies.

- Ades, A.F. and E.L. Glaeser (1999):”Evidence on Growth, Increasing Returns, and the Extent of the Market”, Quarterly Journal of Economics, 1025-1045.

- Ciccone, Antonio (2002):”Input chains and Industrialization”, Review of Economic Studies, 69, 565-587.

- Chenery, Robinson, and Syrquin (1986):”Industrialisation and Growth”, World Bank.

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- Davis, Donald R. and David E. Weinstein (1998):”Market Access, Economic Geography and Comparative Advantage: An Empirical Assessment”, NBER Working Paper 6787.

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- Fujita, M., P. Krugman and A.J. Venables (1999), ”The Spatial Economy: Cities, Regions and International Trade”, MIT Press, Cambridge MA.

- Hanson, G. (1998), ”Market Potential, Increasing Returns, and Geographic Concentration”, NBER Working Paper 6429.

- Leamer, Edward E. (1987): “Paths of Development in the Three-Factor, n-Good General Equilibrium Model”, Journal of Political Economy, 95(5), 961-999.

- Matsuyama, K. (1992):”Agricultural productivity, comparative advantage, and economic growth”, Journal of Economic Theory, 58, 317-334.

- Murphy, Kevin M., A. Shleifer and R.V. Vishny (1989a):”Industrialization and the Big Push”, Journal of Political Economy, 97(5), 1003-1026.

- Murphy, Kevin M., A. Shleifer and R.V. Vishny (1989b):”Income Distribution, Market Size, and Industrialisation”, Quarterly Journal of Economics, 104(3), 537-564.

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- Redding, S. and A.J. Venables (2000), ”Economic Geography and International Inequality”, CEPR Discussion Paper, 2568.

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- Rosenstein-Rodan, Paul N. (1943), “Problems of Industrialization of Eastern and South-Eastern Europe”, Economic Journal, 53, 202-11.

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APPENDIX: SIMULATION PARAMETERS Figure 1 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [1 1 1], F(i)=0.1+0.5i, no transportation cost.

Figure 2 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [.3 .3 .3] increasing to [1 1 1], F(i)=1.5+0.05i.

Transportation cost matrices:

Agriculture/Manufacturing (free trade):

1 1 1

1 1 1

1 1 1

Agriculture/Manufacturing (“autarky”):

1 6 11

6 1 6

11 6 1

Figure 3 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [.8 .8 .8], F(i)=1.5+0.05i.

Transportation cost matrices:

Manufacturing trade, starts at

1.0000 1.8000 2.6000

1.8000 1.0000 1.8000

2.6000 1.8000 1.0000

Agriculture trade costs (free trade):

1 1 1

1 1 1

1 1 1

and decreases in several steps to:

1.0000 1.1000 1.2000

1.1000 1.0000 1.1000

1.2000 1.1000 1.0000

and then to free trade.

Figure 4 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [.8 .8 .8], F(i)=1.5+0.05i.

Transportation cost matrices:

Manufacturing trade: as in fig. 3

Agriculture trade costs:

1 2 3

2 1 2

3 2 1

Figure 5 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [.8 .8 .8] increasing to [.8 1.6 .8], F(i)=1.5+0.05i, no transportation costs.

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Figure 6 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [.8 .8 .8] increasing to [.8 1.6 .8], F(i)=1.5+0.05i.

Transportation cost matrices:

Manufacturing:

1 11 21

11 1 11

21 11 1

Agriculture:

1 11 21

11 1 11

21 11 1

Figure 7 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [.8 .8 .8] increasing to [.8 1.6 .8], F(i)=1.5+0.05i.

Transportation cost matrices:

Manufacturing: Agriculture:

1 2 3 1 2 3

2 1 2 2 1 2

3 2 1 3 2 1

Figure 8 σ = 5, R = 3, A = [0.2 0.2 0.2], α = [0.7 0.7 0.7], θ = [.8 .8 .8] increasing to [1.6 .8 .8], F(i)=1.5+0.05i.

Transportation cost matrices:

Manufacturing:

1 2 3

2 1 2

3 2 1

Agriculture:

1 2 3

2 1 2

3 2 1

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