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Regional Economic Impacts of Highway Projects*

Mark Roberts1, Uwe Deichmann2, Bernard Fingleton3,

Tuo Shi1, and Andreas Kopp4

VERY PRELIMINARY DRAFT – not for citation

September 2009

1 Department of Land Economy, University of Cambridge 2 Development Research Group, World Bank

3 Department of Economics, University of Strathclyde 4 Energy, Transport and Water Department, World Bank

* This paper presents preliminary results from a research project that is supported by the Research Support Budget of the World Bank. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent.

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Regional Economic Impacts of Highway Projects

Abstract

Interregional transport infrastructure represents some of the largest public investment programs in developing countries. The objectives are typically to increase economic efficiency—overall economic growth—as well as spatial equity—helping lagging areas of a country to catch up with leading ones. But recent theoretical and empirical work on the ‘new economic geography’ suggests that, at least initially, lowering transport costs may reinforce concentration of economic activity. This paper describes ongoing research, building on the work by Krugman and others, to develop a geographically explicit framework for assessing the impacts of large scale transport investments on regional economies. The goal is to develop tools for ex-ante impact evaluation that complement macro-economic approaches as well as engineering-oriented operational practice. We illustrate our approach with an application to China’s massive expansion of highways, the National Expressway Network (NEN).

1. Introduction

Each year, governments around the world devote considerable expenditure to transport

infrastructure projects designed to improve connectivity between leading and lagging

regions—partly in the belief that this will bring large aggregate benefits and partly in the

belief that it will promote inter-regional economic convergence. In the European Union

(EU), for instance, considerable investment is being directed towards the development of

a Trans-European Transport Network (TEN-T) which is designed to, inter alia, bolster

competitiveness and promote economic and social cohesion (Bröcker, 2002).

Likewise, several large developing countries have recently committed significant funds to

the expansion of national transportation networks with similar objectives in mind. The

most visible has been China, where massive expenditure on the construction of a 41,000

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km National Expressway Network (NEN) has constituted a central plank of the

government's "Go West" initiative, which is intended to help narrow regional inequalities

which have emerged over the last three decades as a result of the spectacular growth of

the country's coastal provinces.1 Between 1990 and 2005, China spent approximately

US$ 40 billion annually on the building of roads—about a third of which has gone

towards the NEN. Meanwhile, India is nearing the completion of its US$ 13 billion

"Golden Quadrilateral"2, a 5,846 km network of 4-6 lane expressways which constitutes

the first phase of the country's National Highways Development Project. The Brazilian

government intends to spend, by 2010, US$ 17 billion on upgrading its expressways as

part of a US$ 250 billion infrastructure program.3 Finally, international donors recently

committed US$ 1 billion for the "North-South Corridor", an initiative in east and

southern Africa which will result in the eventual upgrade of 8,000 km of roads.4

Major inter-regional transportation projects such as those outlined above are frequently

pursued with the twin objectives of achieving both increased efficiency and spatial

equity: that is to say, of both increasing productivity and well-being at the national level,

as well as of increasing the equity of economic outcomes across space. However, new

theories of the determinants of the spatial distribution of economic activity and well-

being (the so-called "new economic geography", or NEG, literature) which have emerged

1 In 2005, the leading provincial level region of Shanghai had a level of GDP per capita that was 3.34 times the national average, while the poorest performing region of Guizhou had a level that was only 0.34 times the national average (The World Bank, 2008, chp. 2). 2 So-called because it connects Delhi, Mumbai, Kolkata and Chennai, thereby forming a quadrilateral of sorts. 3 “Global Perspectives: Brazil's Highway Headaches”, Blueprint America (www.pbs.org), October 26th, 2008. 4 “New dawn for trade in Africa as UK Government commits to North South Corridor”, UK Department for International Development (DFID), 06 April 2009.

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over the last two decades5 imply that the benefits of large scale infrastructure investments

may not always manifest themselves in ways anticipated by policymakers.

Far from promoting convergence through stimulating the relocation of firms to lagging

regions, these theories suggest that the resulting transport cost reductions may well

encourage increased agglomeration in the already leading regions at the expense of the

lagging ones, thereby engendering increased regional divergence. The main driver of

these dynamics is the existence of internal economies of scale, which interact with both

transport costs and factor mobility to generate potentially powerful agglomeration

economies (at least in some sectors of the economy). Reducing transport costs affects the

relative strengths of both agglomeration and dispersion forces, and, therefore, the spatial

distribution of economic activity. As such, there exists the possibility of a trade-off

between efficiency (towards which agglomeration may make a contribution) and spatial

equity.

This gives rise to two main policy questions relating to large scale transportation

investments. The first is the standard CBA question regarding efficiency: Do these

investments generate economic and welfare benefits that outweigh their (significant)

costs? If there are winners and losers, are the gains sufficient to compensate the losers—

either through some form of redistributive mechanism or, for instance, by encouraging

labor mobility and remittances? The second question addresses the issue of spatial

equity: do improvements in inter-regional transportation infrastructure tend to spread-out 5 See Brakman et al. (2009) and Fujita et al (1999) for comprehensive reviews.

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economic activity and thus predominantly benefit lagging areas as is frequently the

intention of policymakers? Or, do they lead to further concentration as firms in leading

areas that benefit from greater agglomeration economies take advantage of lower

transport costs to expand exports into lagging areas and as workers gravitate in ever-

increasing numbers to areas of already existing prosperity?

This paper reports on ongoing research to improve our ability to answer these policy

questions, specifically in a developing country context. By using an NEG framework

that captures the spatial general equilibrium effects of changes in transport costs, we aim

to expand the impact evaluation toolbox beyond macro-analysis of infrastructure benefits

(such as those based on Aschauer 1989), as well as beyond operational practice that tends

to focus on benchmarks such as travel time reductions, vehicle operating costs and

avoided accidents.6 In particular, we consider the benefits of adopting an NEG approach

to evaluating infrastructure benefits vis-à-vis other approaches; outline a methodology for

the operationalization of NEG theory for this purpose; and present some early results

based on the construction of China's NEN. Given that this research is still in its formative

stages, our application to China is, at this point, primarily intended to illustrate

methodology. We intend to fully develop this application over the next three months and,

in the concluding section of this paper, discuss the issues which need to be confronted to

do so.

6 Such as those supported by the Highway Development and Management Model HDM4 (www.worldbank. org/transport/roads/rd_tools/hdm4.htm).

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Our project is not the first to draw on NEG theory, or elements of it, in an attempt to

build operational models for the purposes of evaluating the benefits of major

transportation projects. But it is the first to do so in a developing country context. A

number of previous studies have estimated the so-called NEG wage equation for China

(Au and Henderson, 2006a; Bosker et al., 2009; Hering and Poncet, 2008, amongst

them), but no previous study has made use of a full structural NEG model to look at the

impacts of transportation infrastructure improvements, drawing upon a detailed

Geographic Information Systems (GIS) data set of the country's road network.

Furthermore, the application to developing countries brings with it unique challenges.

While it is safe to ignore the rural sector when thinking about reductions in transport

costs in the context of developed countries, this sector cannot be ignored in a developing

country context. As such, our application to China makes use of an NEG model

consisting of both an urban and a rural sector, with transport costs in both sectors.

Finally, our methodological approach is unique insofar as the (ultimate) intention is to

combine estimation of key model parameters with simulation of transport infrastructure

improvements using the complete structure of an NEG model, making, in the process, a

distinction between impacts associated with the model's short- and long-run equilibrium

solutions.

The remainder of this paper is organized as follows. In section 2 we provide a brief

introduction to NEG theory, consider the potential "value-added" of adopting an NEG-

based approach to benefit evaluation over and above more traditional approaches, and

address some common reservations to the use of an NEG framework. Section 3 provides

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a broad overview of the methodological approach we are pursuing in our project. In

section 4, we illustrate key aspects of this approach using our (early) application to

China's NEN. The concluding section, section 5, discusses the next steps in making this

illustrative application more concrete.

2. The value added of an NEG approach to benefit estimation

2.1. A quick introduction to NEG theory

Originating with the work of Krugman (1991a, 1991b), the new economic geography

(NEG) represents a body of theory which aims to explicitly model the interplay between

the agglomeration and dispersion forces which shape an economy's internal spatial

distribution of economic outcomes. Although, as pointed out by critics such as Martin

(1999), there is nothing new about many of the ingredients of NEG models, NEG has

nevertheless succeeded in integrating these within a general equilibrium framework

(Ottaviano and Thisse, 2004, p 2576). One of the key ingredients which is common to all

NEG models is the existence of transport costs.7 Thus, for example, in the seminal model

of Krugman, transport costs in the manufacturing sector interact with the fixed costs of

setting-up a plant within the sector and labour mobility to create agglomeration

economies, which take the form of a pecuniary externality. Counteracting the resultant

centripetal force is a centrifugal force associated with the existence of a dispersed, and

immobile, agricultural population. By strengthening the centripetal force relative to the

centrifugal force, falling transport costs can have symmetry breaking results, whereby a

7 More generally, these costs can be interpreted as the costs of conducting transactions at a distance. As such, they may include not only transport costs, but more general trade costs, including costs of acquiring information about products.

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spatially uniform distribution of manufacturing activity no longer represents a stable

equilibrium.

More generally, within NEG theory, declining transport costs change the relative strength

of centripetal and centrifugal forces. In many models, the resultant predicted relationship

between transport costs and the spatial distribution of economic activity is not necessarily

the predictable catastrophic agglomeration of the original Krugman model. Rather, a bell-

shaped relationship between declining transport costs and the spatial concentration of

activity is a characteristic prediction of, for example, the models of Fujita and Krugman

(1995) and Krugman and Venables (1995). According to this relationship, a reduction in

transport costs promotes, at first, widening regional disparities and an increased

agglomeration of activity before eventually inducing convergence and a dispersion of

activity. For a given level of agricultural transport costs, this is also the predicted

relationship between manufacturing transport costs and the spatial distribution of activity

in the two region version of the model (Fujita et al, 1999, chp 7) that we modify and

generalize for application to China in section 4 of this paper.

2.2. Alternative approaches to benefit estimation and what an NEG approach has to add

An NEG-based approach to evaluating the benefits of inter-regional transportation

infrastructure projects needs to be compared against two alternative approaches. The first

is the standard CBA approach which focuses exclusively on the transport market. Thus,

the projected benefits of a project are calculated by measuring the reductions in travel

time, vehicle operating costs and transport related accidents that are the anticipated result

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of a project. In the absence of externalities or market imperfections, transport economics

shows that such benefits accurately capture total project benefits (Wheaton, 1977; see

also Laird et al., 2005, p. 542 and Vickerman, 2000, pp. 15-16). This is because other

benefits, such as favorable land price changes, are apparent rather than real—they are not

distinct benefits, but simply reflections of the transport benefits in other markets (World

Bank, 2006, pp 2-3). Such an approach has the important virtue of being extremely well

tried and tested and, relatively, easy to apply. Consequently, at a policy level, it

constitutes the standard approach towards the ex ante assessment of benefits, especially at

the level of individual projects. However, on the downside, this approach is incapable of

capturing the potential additional benefits that are associated with the knock-on effects of

the travel time reductions induced by a project on the relative strengths of agglomeration

and dispersion forces within an economy, and the subsequent repercussions for the spatial

distribution of economic activity. As we have seen, these forces represent the raison

d'être of NEG theory.

The second approach to evaluating benefits is provided by the macro level empirical

literature. This seeks to establish social rates of return to infrastructure investment

through the estimation of, for example, aggregate production functions which have been

augmented with various measures of infrastructure. In contrast to the standard CBA

approach, this is capable of capturing the additional positive externalities, including the

potential benefits associated with NEG-style forces, which may be associated with major

transportation projects. However, this literature has been plagued by empirical

controversy due to, inter alia, problems of endogeneity and measurement error ever since

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the extremely large estimated elasticities of output with respect to public capital obtained

by Aschauer (1989). The recent review and meta-analysis of this literature by Straub

(2008) highlights the existence of considerable empirical uncertainty of the productivity

and growth effects of infrastructure. Indeed, estimates of benefits have been found to

vary significantly according to the details of the particular sample and estimation

methods used.

Relative to the above approaches, an NEG-based approach to evaluating benefits has the

potential to add value in several respects. Firstly, as already stated, such an approach is

capable of capturing the spatial general equilibrium effects, associated with the changing

strength of agglomeration and dispersion forces, which are assumed away by the standard

CBA approach. Second, because an NEG-based approach is founded on an explicit

spatial general equilibrium framework, it links the potential impacts of projects to key

structural characteristics of an economy, such as the existing level of transport costs in

different sectors, the relative importance of sectors characterized by increasing returns to

scale relative to those characterized by constant returns to scale and the degree of factor

mobility (and, therefore, the size and extent of policy and institutional barriers on labor

mobility, such as those associated with the permanent household registration, i.e. Hukou,

system in China). In this way, an NEG-based approach suggests that the impacts of

projects are likely to be heterogeneous across both countries and time, varying according

to structural conditions. Consequently, an NEG-based approach has the potential to

explain the seemingly contradictory results which exist in the macro level empirical

literature. These contradictions find no explanation within that literature itself because

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estimates from macro level studies represent something of a black box as regards the

causal mechanisms which drive estimated productivity and welfare gains from

transportation infrastructure improvement.

Finally, and related to the previous point, an NEG-based approach has the potential to

capture both the short- and long-run aggregate and spatial economic impacts connected

with a project, where we may associate short-run impacts with a fixed spatial and sectoral

distribution of employment and long-run impacts with additional effects emanating from

labor mobility and migration. It is potentially capable of answering the question of

whether there exists a variable time-profile of impacts on, for example, inequalities

between leading and lagging regions. For example, where a project initially gives rise to

increased inequality in real wages between regions, will migration and labor mobility

eventually arbitrage away such differences or will it exacerbate them?

3.3. Objections to an NEG-based approach

In our consultations to date, we have heard expressed several reservations to the adoption

of an NEG-based approach to benefit evaluation, some of which we share, but believe

can be overcome, and others we regard as misplaced. The concerns which we share relate

primarily to the modelling of transport costs, in particular, their functional form, within

NEG theory. As will become clear, the functional forms for the relationship between

transport costs and travel time which we specify in our application to China are

somewhat ad hoc, so far as empirical justification is concerned, and we also make equally

ad hoc assumptions regarding parameter values for these functional forms. This is one

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reason why this application remains, at this early stage, primarily intended to be

illustrative of some of the key aspects of the methodological approach that we are

pursuing. We shall return to this issue in section 5, as well as covering other challenges

which must be overcome to render our application to China more concrete.

There are two types of reservation that we have encountered, but which we consider to be

misplaced: (1) the reservation that an NEG-based approach must necessarily be biased

towards producing inflated estimates of benefits because of the theory's "assumption" of

agglomeration economies; and (2) reservations regarding the use of a framework reliant

on assumptions of Dixit-Stiglitz monopolistic competition. The first reservation is

relatively easily dealt with. NEG theory does not assume the existence of agglomeration

economies. Rather, such economies are the endogenous outcome of several forces within

NEG models and, depending on the relative strength of such forces, an economy may

find itself, for example, in a scenario characterized by multiple equilibria, some of which

are characterized by agglomeration, or in a scenario in which there is a unique stable

equilibrium with no agglomeration whatsoever. As such, it is perfectly possible, in an

NEG world, for a reduction in transport costs, caused by a major programme of inter-

regional transport infrastructure expenditure, to have no predicted beneficial impacts

above and beyond those considered by a standard CBA exercise. Again, one of the key

lessons of the NEG is that it all depends on key structural parameters within an economy,

which makes the whole issue of agglomeration impacts ultimately an empirical question

which should be guided by appropriate theory.

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The second set of reservations regarding assumptions of Dixit-Stiglitz competition are

ones which have also found a voice in the literature. In particular, Neary (2001) has been

very critical of the NEG for its overwhelming tendency to follow Krugman (1991a,

1991b) in making this assumption. This is because "the Dixit-Stiglitz model has almost

nothing to say about individual firms."8 As a consequence, "except for the fact that it

incorporates increasing returns, the new economic geography has industrial

organization underpinnings which are very rudimentary." (Neary, 2001, p 18). However,

while this might be true, is there a viable alternative to the assumption of Dixit-Stiglitz

competition whose benefits outweigh its costs?

Our answer to the above question is almost certainly not. This is based on a consideration

of the history of economic thought of the Cournot competition literature—Cournot

competition being the alternative market structure assumption to Dixit-Stiglitz

competition which we have heard most frequently suggested. In particular, the Cournot

competition literature emerged as an attempt to respond to the discovery that the

agglomeration equilibrium in the Hotelling model does not exist (d'Aspremont,

Gabszewicz and Thisse, 1979). The reason for this is that even an infinitesimal price cut

by one of the competitors will allow him to capture the entire market. Consequently,

there is a strong incentive for firms to seek protection by distance, which creates a

tendency for the spatial dispersion of industry. Cournot competition overcomes this

problem through the assumptions that there is no market entry in response to positive

8 In particular, the assumption of Dixit-Stiglitz competition sterilizes considerations of direct strategic interaction. However, contrary to common perception, it does not imply the complete absence of interactions between firms- "Indeed, each firm must figure out what will be the total output (or, alternatively, the average price index) in equilibrium when choosing its own quantity or price, or when deciding whether to enter the market." (Ottaviano and Thisse, 2004, p 2578).

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abnormal profits, and that firms renounce to compete in prices. These assumptions

provide firms with sufficient protection that they might want to locate in the same place.

This raises several questions: namely, (i) do firms indeed collude in not competing in

prices? And (ii), do we observe that there is no entry to imperfectly competitive markets

empirically? Not surprisingly, recent reviews of the agglomeration literature (Duranton

and Puga, 2004) either make no mention of the Cournot competition agglomeration

literature or present it purely as part of pre-1990s history of economic thought (Ottaviano

and Thisse, 2004). This is primarily because, in industrial organization research, the

exogenous restriction of firms' strategy space which the Cournot competition model (i.e.

the exogenous restriction of not competing on prices) implies has been rejected. In order

to be able to adopt an alternative approach based on Cournot competition, therefore, it

would be necessary to show that the assumption of Cournot behavior is a game-theoretic

equilibrium.

Furthermore, models based on Cournot competition, are partial equilibrium models; there

are no repercussions on factor markets. There is also an absence of factor mobility and,

therefore, demand. This is in contrast to NEG models based on Dixit-Stiglitz

monopolistic competition. Despite the starkness of the Dixit-Stiglitz assumptions, an

NEG approach based on these appear to be more general than the partial-equilibrium

spatial oligopoly models.9

9 Ottaviano and Thisse (2004, p 2577) note that "Unfortunately, models of spatial competition are plagued by the frequent nonexistence of an equilibrium in pure strategies... Thus, research has faced a modeling trade-off: to appeal to mixed strategies, or to use monopolistic competition in which interactions between firms are weak. For the sake of simplicity, Krugman and most of the economics profession have retained the second option, which is not unreasonable once we address spatial issues at a macro-level. In addition,

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3. Methodology

Previous attempts to operationalize NEG theory, or at least elements of it, for the

purposes of evaluating the benefits of major inter-regional transportation projects have

largely taken the form of the development of Spatial Computable General Equilibrium

(SCGE) models. Most notable amongst these is the EU's development of the CGEurope

model, which it has applied in several projects and reports10 to assess the impacts of

proposed TEN-T projects which aim to better connect lagging European regions with

leading ones. Further examples include the RAEM model, which has been developed for

the Netherlands and applied to the ex ante assessment of a number of proposed magnetic

levitation rail projects (Oosterhaven and Elhorst, 2008). While our approach bears some

resemblance to a SCGE approach, particularly in its use of detailed transportation

network data, it should not be mistaken for such an approach. Rather, our approach builds

more directly on the academic NEG empirical literature. In particular, it builds on

literature which seeks to estimate the so-called NEG wage equation, which is a predicted

relationship, generic to most NEG models, between nominal wages and a measure of a

region's market potential (Au and Henderson, 2006a; Bosker et al., 2009; Brakman et al,

2006; Hering and Poncet, 2008). It also builds on literature which uses a full structural

NEG model to simulate the short-run spatial economic impacts of a generalized reduction

in transport costs, where this reduction is modelled as a change in a scalar parameter

models of monopolistic competition have shown a rare ability to deal with a large variety of issues related to economic geography, which are otherwise unsatisfactorily treated by the competitive paradigm..." 10 These include its IASON (Integrated Appraisal of Spatial Effects of Transport Investments and Policies) project, its EPSON (European Spatial Planning Observation Network) Territorial Impact of EU Transport and TEN Policies project, and its ASSESS report. For an overview of the theory underlying this model see Bröcker (2002).

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which enters into an assumed transport cost function based on straight-line distance

(Brakman et al., 2006; Fingleton, 2005, 2007).

More precisely, the methodology which we are developing consists of two broad stages.

In stage (1), estimates of key NEG model parameters are obtained through estimation of

the NEG-wage equation for different sectors of the economy (e.g. the urban and rural

sectors). Estimation makes use of GIS derived data on minimum travel times for the

transportation network, past or future changes to which we wish to evaluate the impact

of. Meanwhile, in stage (2), a numerical solution is obtained for a full structural NEG

model under a baseline scenario and the fit of this numerical solution to actual regional

economic data is examined. This helps both to provide an assessment of the relevance of

the model, and, therefore, NEG-style forces, to the country under study and to indicate

areas where additional model development might be required. Following this, the

changes to the transportation network which are the subject of evaluation are introduced

and a new numerical solution to the model is obtained. This numerical solution represents

the counterfactual solution. The regional economic impacts of the network changes can

then be assessed by comparing the counterfactual with the baseline solution.11

It is possible to apply the above methodology such that the baseline scenario corresponds

to either the situation prior to or following the period over which the infrastructure

investment has taken place. Furthermore, it is possible to simulate the impacts on both the

11 Numerical solution of the model is necessitated by the fact that NEG models are only analytically tractable under artificially abstract assumptions concerning the nature of geographic space such as, for example, the "racetrack" economy assumption (Fujita et al., 1999). Such assumptions are incompatible with the operationalization of NEG for applied policy purposes.

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model's short- and long-run equilibrium solutions. Using the short-run solution will

capture the impacts on key economic variables taking the distribution of employment

both across regions and between sectors to be fixed to that under the baseline scenario.

Meanwhile, using the long-run solution will capture the additional impacts associated

with labor mobility and migration. Simulating the long-run equilibrium does, however,

introduce an extra layer of complexity, not least because of the crudeness of the

modelling of migration decisions in NEG theory to date.

4. An illustrative application to China

We illustrate implementation of the model and its use for evaluating possible changes in

regional economies due to transport sector investments with an application to China. We

use a multi-region NEG model to simulate the aggregate and regional economic impacts

of China's National Expressway Network (NEN). This application combines regional

economic accounts data with estimates of minimum road network travel times which

have been derived using GIS techniques for both the baseline and counterfactual

scenarios. Because of data limitations12, in this particular application our “baseline”

corresponds to the situation after implementation of the infrastructure investment

program—i.e., using data for 2007. Our counterfactual is the situation without upgraded

or newly constructed expressways, which was the situation in the early to mid 1990s.

This application is still under development. The description here is primarily intended for

the purposes of methodological illustration, including the demonstration of the types of

12 We have complete data available for a large set of geographic units for 2007, but due to re-aggregation of administrative units over time, these do not match data available for a much smaller set of spatial units for the 1990s.

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results that our approach can be expected to generate. At this point, our application only

makes use of the second part of our methodology. Thus, it makes assumptions about key

parameter values that we intend to estimate in the next phase. Furthermore, at this stage,

we focus only on simulating the short-run impacts of the NEN. Although the results we

report cannot yet be considered reliable estimates of impacts, our application does

demonstrate that a relatively simple two-sector NEG model is able to adequately describe

the spatial distribution of key economic variables across Chinese regions in 2007. This is

despite the complete neglect, at this stage, of alternative forces shaping China's economic

geography. The application also showcases the regional economic accounts and GIS road

network dataset that we have been developing.

4.1. The model

Our model for China extends Krugman's (1991a, 1991b) original model in several

important directions. We retain the two-sector structure of this model with increasing

returns in one of the sectors and constant returns in the other, but generalize it from two

to more than 300 regions. Following Fujita et al. (1999, chapter 7), we furthermore

extend the presence of both transport costs and "love of variety" preferences (and,

therefore, product differentiation) from just the increasing returns sector to both sectors.

This has the benefit of introducing an extra dispersion force into the model, thereby

dampening the well-known agglomeration bias of the original Krugman model (Fujita et

al, 1999, chp. 7). Finally, rather than following the NEG convention of interpreting the

increasing returns sector as "manufacturing" and the constant returns sector as

"agriculture", in line with the discussion in section 1, we instead think of these sectors as

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corresponding to the urban and rural sectors of the Chinese economy. This allows us to

simulate not only the aggregate and regional economic impacts of the NEN, but also its

impacts on urban-rural disparities within individual prefectural level regions.13

4.1.1. Micro-assumptions

More concretely, on the consumption side of the model, the utility function is taken to be

Cobb-Douglas, θθ −= 1RCU , where C denotes the level of consumption of a composite

commodity produced by the urban sector and R the level of consumption of a composite

commodity produced by the rural sector. Both C and R are functions of the separate

varieties of commodities produced within the respective sectors. This is captured by the

use of constant elasticity of substitution (CES) sub-utility functions for C and R:

)1/(

1

/)1()(−

=

−⎥⎦

⎤⎢⎣

⎡= ∑

σσσσ

x

iicC [1]

)1/(

1

/)1()(−

=

−⎥⎦

⎤⎢⎣

⎡= ∑

ηη

ηηy

j

jrR [2]

The quantity of each variety produced by the urban sector is c(i), where i∈{1,…, x} and x

represents the number of varieties. Analogously, the quantity produced of each rural

variety is r(j), where j∈{1,…, y} and y is the number of varieties. σ and η denote the

elasticity of substitution between varieties produced in the urban and rural sectors

respectively, where both σ and η > 1 and, in general, we expect η ≠ σ.

13 So far as we are aware, NEG models have not previously been applied in such a manner.

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The objective of households is to maximize utility subject to their budget constraint.

Given the static nature of the model, income in the budget constraint is equal to the sum

of expenditure on C and R.

Turning to the production side, there is free entry and exit of firms in both sectors, but

only urban sector production is characterized by internal economies of scale. These arise

from the existence of a fixed labor requirement, s, for the production of c(i), where labor

is assumed to constitute the only input and each urban firm produces only one variety.

Thus, )(iacsL += , where a denotes the marginal labor requirement. However, firms in

both sectors incur transport costs when shipping commodities to a region other than that

in which they are located. In particular, the cost of transporting a unit of output from

region k to region r is denoted by CkrT for the urban sector and R

krT for the rural sector.

The profit maximising free on board (f.o.b.) price of the rural sector commodity is equal

to the marginal cost of production. Given the normalization that the marginal labor

requirement in the sector is unity, this implies pR = wR , i.e. that the price is equal to the

nominal rural wage rate. By contrast, the profit maximising f.o.b. price for urban sector

varieties is pC = wCaμ, where wC is the nominal urban wage and μ = σ/(σ - 1) represents

a fixed mark-up on marginal costs.14 Using the additional normalization a = 1/μ then

implies pC = wC. Finally, the price of region k’s output in region r, taking account of the

14 μ is also equal to the ratio of average to marginal costs for urban sector firms. It therefore also provides a measure of the degree of returns to scale in equilibrium.

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cost of transport from k to r (c.i.f.), is given by CCkr wT and RR

kr wT for the urban and rural

sectors respectively.

4.1.2. Reduced Form of the Model in Short-Run Equilibrium

The assumption of free entry and exit of firms in both sectors in response to non-zero

profits combined with the absence of worker mobility both between regions and sectors,

allows for the derivation, for each region k∈{1,…,N}, of five simultaneous non-linear

equations. Taken together, these equations represent the reduced form of the model's

short-run equilibrium:

σ

σσ/1

1

11 )()( ⎥⎦⎤

⎢⎣⎡= ∑

=

−−N

r

Ckr

Crr

Ck TGYw [3]

ηηη

/1

1

11 )()( ⎥⎦

⎤⎢⎣

⎡= ∑

=

−−N

r

Rkr

Rrr

Rk TGYw [4]

)1/(1

1

1)(σ

σλ−

=

−⎥⎦

⎤⎢⎣

⎡= ∑

R

r

Ckr

Crr

Ck TwG [5]

)1/(1

1

1)(η

ηφ−

=

−⎥⎦

⎤⎢⎣

⎡= ∑

N

r

Rkr

Rrr

Rk TwG [6]

Rkk

Ckkk wwY φθθλ )1( −+= [7]

where Ckw and R

kw denote nominal urban and rural wage rates respectively in region k;

CkG and R

kG the respective price indices; and kY the level of nominal income in k. The

terms λr and φr are equal to the respective shares of a region in the national supply of

urban and rural workers. Meanwhile, θ is the preference parameter for the composite

urban sector commodity, which, under utility maximization, is also equal to the share of

22

expenditure on urban output. Following Fujita et al. (1999), we assume that θ is equal to

the national share of urban workers in total employment.

Equation [3] represents the well-known NEG wage equation, according to which wages

in the urban sector are a transport cost discounted function of incomes in all other regions

with which trade takes place. The local price index for urban sector varieties also enters

into this equation because we are concerned here with nominal wages. In most NEG set-

ups, this equation is taken as only applying to the increasing returns sector. However,

equation [4] shows that, in our model, a similar relationship also holds for the rural

sector, which is characterised by constant returns to scale. This is because of the presence

of transport costs in this sector. It is the estimation of equations [3] and [4] that will

correspond to the first stage of our methodology, and which will allow us to obtain

estimates of, in particular, the key parameters σ and η when our application to China is

fully developed. Equations [5] – [7] make clear that both prices and incomes in all

regions within the full structural model are endogenously determined, which reflects the

general equilibrium nature of the model.

4.2. Data, model parameterisation and implementation issues

To apply the model to simulate the impacts of the construction of China's NEN we make

use of a data set for 2007 which covers a sample of 331 prefectural level regions.15 This

data set has been constructed from several different sources published by the National

Bureau of Statistics for China (namely, the Regional Economic, Urban Statistical and

15 Both this data set and the construction of our GIS road networks data are discussed in more detail in the appendix.

23

China Statistical Yearbooks for 2008), with additional data coming from various

provincial statistical yearbooks.16 Included in the data set are measures of both urban

and rural employment.17 Using these measures, we are able to directly calculate the

values of the parameters λr, φr and θ for 2007. The 331 prefectural level regions include

280 prefectural cities (out of a total of 283).18 However, these "cities" are not to be

understood in the traditional sense of the word. Indeed, the definition of a prefectural city

typically covers not only a central urban area (the city proper), but also the, frequently

extensive, rural periphery which surrounds this city.19 Included in this periphery are

smaller urban centres (county-level cities). In addition to the 280 prefectural cities, we

also have Beijing, Tianjin and Shanghai, which, although they are municipalities, we treat

as prefectural level regions. The remainder of the sample consists largely of a mixture of

"Prefectures", "Ethnically Minority Autonomous Prefectures" and "Leagues", which are

even more rural than prefectural cities.20 Overall, our sample accounted for about 93 % of

overall Chinese employment and about 99 % of overall GDP in 2007.21

16 These yearbooks are published by individual provincial statistical bureaus. 17 In our main sources, data is only available on urban employment and total employment. Consequently, we calculate rural employment as total employment minus urban employment. 18 We exclude Tianshui City in Gansu Province and Lhasa City in Tibet on account of a lack of data. Also excluded is Karamay City, which is an oil-producing and refining center located in Xinjiang autonomous region. The performance of this region cannot be explained by NEG-style forces. 19 Reflecting this, on average, 65.4 % of a prefectural city's population was classified as agricultural in 2007. This average is based on the full set of 283 prefectural cities. 20 In addition, the sample includes Shihezi City, a county-level city under the direct jurisdiction of Xinjiang Province, and Chongqing Municipality, which, on account of its size, we have divided into three separate areas which correspond to the so-called "one hour economic circle" and the two "wings." These areas are as defined in the Chongqing Municipal Government's development strategy (“Guidelines on pushing forward the coordinated urban-rural reform and development in Chongqing Municipality”, State Council, Jan 26th, 2009). 21 Previous studies in the empirical NEG literature which have made use of prefecture level region data for China are Au and Henderson (2006a), Bosker et al. (2009) and Hering and Poncet (2008). These studies, however, include only the prefectural cities in their samples. Consequently, their geographical coverage of China is less extensive.

24

In addition to values for λr, φr and θ, implementation requires us to make assumptions

about the functional form for transport costs in both the urban and rural sectors. Whereas

these are ordinarily modelled as functions of straight line distance, we model them as

functions of the minimum estimated road network travel time between regional

population centres. For the rural sector, we maintain the traditional exponential form for

transport costs so that krRtRkr eT τ= , where tkr denotes the estimated travel time between

regions k and r, and assume that τR = 1. This implies that costs increase very sharply

with tkr. By contrast, for the urban sector, we assume a power functional form for

transport costs, Ckr

Ckr AtT τ+= 1 . We assume that A = 1 and τC = 0.82. This implies that the

costs of transporting urban sector output are concave with respect to travel time. Au and

Henderson (2006a) assume a similar functional form in estimating the NEG wage

equation using data on the urban areas of prefectural cities for 1997, except using straight

line distances instead of travel time, and the assumed value of τC is based on this study.

At present, we do not yet model the costs of transporting output within regions. Rather, as

reflected in the assumed transport cost functions, we normalize these costs to unity across

all regions.22

We also require values for σ and η. Eventually, we intend to estimate both of these

parameters by explicitly estimating equations [3] and [4] using an extended version of

our current dataset. For the time being, however, we assume that σ = 3. This value is

based on Au and Henderson (2006a) and Bosker et al. (2009). In particular, Au and

22 As discussed further in section 5, our modelling of transport costs, in particular, their functional form, remains, at this stage, the least developed of our analysis and is one of the major reasons why our application to China currently remains tentative.

25

Henderson (2006a) obtain values of σ ≈ 2 in estimating a version of equation [3], while

Bosker et al. (2009) estimate σ = 3.83 using panel data for 264 prefectural cities covering

the period 1995-2005. With these parameters, the value of η is that which maximizes the

linear correlation between the simulated nominal output series from our baseline scenario

and the actual nominal GDP for 2007.23 The maximum correlation occurs for η = 9.24

This implies substantially more substitutability between rural varieties than assumed for

varieties of urban goods.25 On the whole, this seems plausible.

One final issue is the assumption, implicit so far in our model, that the Chinese economy

is closed to international trade. This assumption means that wages depend only on

domestic market potential in equations [3] and [4]. In light of China's rapid export led

growth over the last three decades, we consider this to be unrealistic, especially for the

urban sector. We follow Au and Henderson (2006a) by adding an international

component to market potential for the urban sector in equation [3]. We model the

strength of this international component for a region k as varying inversely with the costs

of transporting output to and from the nearest city with a major international port. In

particular, we assume 82.01 kportC

kport tT += where kportt is the minimum estimated road

network travel time between k and either Shanghai or Qinhuangdao City (whichever is

less).

23 As measured by the Pearson product moment correlation coefficient for data in our 331 regions. 24 We arrived at η = 9 by conducting a numerical search over values of η in the range 1 < η ≤ 20. The search was restricted to integer values of η. 25 Each region produces its own distinctive exogenously determined fixed measure of rural product varieties, and the value of η designates the constant cross-region elasticity of substitution, with a value of infinity corresponding to completely undifferentiated or homogeneous varieties, and increasingly smaller values indicating increasing differentiation between regions.

26

4.3. Results

4.3.1. Model fit

Before simulating the impacts of the construction of the National Expressway Network,

we can assess how well the baseline (short-run equilibrium) solution of our model, fits

the actual data. From Table 1, there is a very strong positive correlation between the

simulated output levels (SIM Y) for the 331 prefectural level regions and their actual

2007 GDP levels (GDP). A strong correlation also exists between simulated levels of

output per worker (SIM Y PW) and actual GDP per worker (GDP PW), as well as

between simulated levels of output per capita (SIM Y PC) and actual GDP per capita

(GDP PC). Unfortunately, our data set does not include measures of urban and rural

wages, but it does have a variable labeled "Average Wages of Staff and Workers"

(ACTUAL W) and also includes measures of urban disposable income per capita

(URBAN Y PC) and rural household per capita net income (RURAL Y PC). Comparing

ACTUAL W with a weighted average of simulated urban and rural wages (SIM W)26

gives a correlation coefficient of around 0.56, which is similar to the correlations for

simulated urban wages (SIM W-UR) and simulated rural wages (SIM W-RUR) with

URBAN Y PC and RURAL Y PC respectively.

26 The weights we use in constructing SIM W are the respective shares of the urban and rural sectors in a region's employment in 2007.

27

Table 1: Correlation between simulated variables and actual data for 2007

SIM W SIM W-UR

SIM W-RUR SIM Y SIM

Y PW SIM Y PC

ACTUAL W 0.5605 0.2503 0.4208 0.5517 0.5449 0.5893

URBAN Y PC 0.6339 0.5669 0.4369 0.6329 0.6292 0.7250

RURAL Y PC 0.7567 0.5952 0.5803 0.6300 0.7578 0.8082

GDP 0.5834 0.5975 0.4271 0.9268 0.5712 0.5906

GDP PW 0.7224 0.3054 0.5438 0.4625 0.6997 0.6434

GDP PC 0.7212 0.3950 0.5426 0.5160 0.6975 0.7293

Notes: The reported correlation co-efficients are Pearson product moment correlation coefficients. Correlations involving ACTUAL W, GDP, GDP PW and GDP PC are based on the full sample of 331 regions; correlations involving URBAN Y PC and RURAL Y PC are based on 317 regions due to data unavailability for 14 regions

The correlation between SIM W-RUR and RURAL Y PC is also shown in figure 1. From

this, the model appears to substantially over-predict rural wages for 8 prefectural level

regions, which are labelled. At the current stage of model development, we refrain from

attempts to identify the reasons for these outliers.

Figure 1: Scatterplot of actual rural household net income per capita –v– simulated rural wage level

28

Given that the model is able to simulate both urban and rural wages, it can also simulate

urban-rural disparities within regions. Thus, we are able to compare simulated urban-

rural wage ratios with a proxy from our dataset, which is the ratio of urban to rural

income per capita. The mean simulated ratio (3.043) is remarkably similar to the mean

for our proxy from the actual data (2.942). But our model over-predicts the dispersion of

urban-rural wage ratios across regions, as is clear from Figure 2a. Figure 2b also shows

that our model simulates a relatively large number of regions (47) as having 2007 urban-

rural wage ratios which are less than unity. This is when, in reality, the minimum ratio of

actual urban to rural income per capita was 1.66 (for Mudanjiang City, Heilongjiang

Province).

Figure 2(a): Histogram of simulated and actual urban-rural disparities across prefectural level regions; (b) scatterplot of simulated versus actual urban-rural disparities

4.3.2. Changes in estimated travel times from the National Expressway Network

These preliminary results show that our model does a reasonable job in reproducing the

actual geography of some key economic variables in China in 2007, notwithstanding

remaining problems, particularly in predicting rural wages for some regions and in

29

accurately mimicking the distribution of urban-rural disparities. These issues as well as

some other loose ends to the model remain to be resolved before the current application

can be considered concrete.

With this in mind, we now turn to simulating the aggregate and spatial economic impacts

of the building of the NEN. We first compare estimated minimum travel times by road

before and after the introduction of the network. In particular, Figure 3 shows the

distribution of estimated time savings. The distribution is truncated at 0 and,

consequently, is positively skewed. Reflecting this, the mean reduction in travel time is

8.7 hours and the median reduction is 7.7. The standard deviation is 5.9 hours. Plotting

travel time against straight line distance for the 55,615 unique combinations of regions in

our prefectural sample for both before and after gives Figure 4. As this figure illustrates,

the construction of the expressway network has essentially resulted in a parallel shift

downwards in the relationship between travel time and distance. Thus, the new network

has led to a fairly uniform reduction in travel times across the whole of China.27

27 This is confirmed by regressions that show that the estimated slope coefficient of the relationship between travel time and distance is identical to 2 decimal places when comparing before and after. This constant co-efficient is accompanied by a large fall in the estimated intercept.

30

Figure 3: Travel time savings Figure 4: Travel times –v– distance

4.3.3. Estimated aggregate and spatial economic impacts

Efficiency

We can obtain an aggregate estimate of the benefits of building the NEN by calculating

the level of aggregate simulated real income for the Chinese economy associated with

both the baseline and counterfactual solutions of the model. This suggests benefits equal

to 4.5 % of aggregate real simulated income in 2007. These estimated benefits represent a

permanent level effect on overall Chinese real income. It is necessary to calculate the

benefits in real terms because the model implies large impacts of the highway network on

both nominal income levels and prices across regions.28

Equity

Figure 5 shows the distribution of simulated gains/losses in real income, real urban wages

and real rural wages across the 331 prefectural regions, here represented by points at the

28 Aggregate simulated real income is calculated as ]1)()/[(

1θθ −∑

=

RrGC

rGN

r rY .

31

location of their main city. In figure 5(a) we see that, for real income, all regions share in

the benefits. However, these benefits are far from being uniformly distributed. They

range from a minimum of 0.18 % of 2007 real income for Hotan Prefecture in Xinjiang

province to a maximum of 23.5 % for Zhuhai City, which is located in Guangdong

province.

Figure 5: Changes (%) in simulated levels of: (a) real income, (b) real urban wages and (c) real rural wages in the scenario with expressways relative to the counterfactual scenario (without expressways) a.

b. c.

32

At the sectoral level, the picture is more mixed, particularly in the urban sector (figure

5b). Thus, although 86.1 % of regions have higher real urban wages in 2007 than would

have otherwise been the case in the absence of the expressways, for the remainder, real

wages are simulated as being lower than under the counterfactual scenario. In a number

of cases, the simulated losses relative to the counterfactual are substantial. For 14 regions,

the model suggests that real urban wages in 2007 would have been higher by 10 % or

more had the National Expressway Network not been built. For five regions (Taizhou

City, Jiangsu province; Guangan City, Sichuan province; Anshun City, Guizhou

province; Qujing City, Yunna province; and Changji Hui A.P, Xinjiang province) the

gain in real urban wages would have been in excess of 20 %.

Likewise, in the rural sector, 15 regions are simulated as having lower real wages in 2007

in the baseline scenario with expressways than in the counterfactual scenario without

expressways (Figure 5c). In four cases (Ningbo, Zhoushan and Zhuhai cities, Zhejiang

province; and Urumqi City, Xinjiang province), according to the simulation results, real

wages would have been higher by 5 % or more in the counterfactual scenario, and in two

cases (Ningbo City and Urumqi City) they would have been higher by 10 % or more. In

general, the benefits in the urban sector are strongly negatively correlated with those in

the rural sector. Specifically, the Pearson correlation coefficient between the differences

in real urban wages and real rural wages in the two scenarios is equal to –0.796. This

explains why simulated losses in real urban or real rural wages do not translate into

simulated losses in overall real income.

33

Turning to the impacts on urban-rural disparities, 115 regions (37.7 % of the sample) are

simulated as having lower urban-rural wage ratios as a result of the travel time reductions

associated with the NEN, while the remainder have higher ratios than would otherwise be

the case (Figure 6). Despite this, averaging across all regions, the simulated urban-rural

wage ratio is lower under the 2007 scenario, where it is equal to 3.07, than under the

counterfactual (before the investments), where it is equal to 3.15. Consistent with this,

the largest fall in the urban-rural wage ratio is for Guangan City (Sichuan province)

where it is simulated that it would have been 6.11 in the absence of the expressways

rather than 3.35. By contrast, the largest increase in the simulated urban-rural wage ratio

is Luohe City (Hanan province) where it is 4.78 in the baseline compared to 4.10 in the

counterfactual.

Figure 6: Simulated urban-rural disparities in the baseline scenario (with expressways) versus in the counterfactual scenario (without expressways)

5. Summary and the way forward In this paper, we have reported on progress on a research project which is intended to

improve our ability to answer two key policy questions related to large-scale inter-

regional transportation infrastructure projects. First is the efficiency question – what are

34

the aggregate benefits associated with such projects in developing countries? Second is

the spatial equity question – what is the spatial distribution of benefits? In particular, do

such projects primarily benefit lagging regions, thereby promoting their catch-up with

leading regions, as is frequently the intention of policymakers? Or, by heightening the

relative strength of agglomeration forces, do they benefit mainly the leading regions,

possibly even at the expense of the lagging ones?

Our analytical approach is based on the so-called new economic geography. The value-

added of an NEG approach to evaluating benefits relative to standard CBA practices and

the approach adopted in the macro level empirical literature is that the NEG provides for

an explicitly spatial general equilibrium framework, which incorporates both

agglomeration and dispersion forces. It allows moving beyond a consideration of benefits

measured simply as travel time reductions, changes in vehicle operating costs or changes

in traffic related accidents to the potentially wider impacts associated with induced

changes in the spatial distribution of economic activity. Furthermore, within an NEG

framework, these impacts can be linked to a relatively small set of key structural

parameters, which might help to explain the apparently contradictory results for different

types of samples which have been a characteristic feature of the macro level empirical

literature. An NEG-based approach is also ideally suited to addressing the spatial equity

question, including the possibility of trade-offs between efficiency and spatial equity.

More concretely, we have outlined a two-stage methodology for the operationalization of

NEG for the purposes of evaluating the benefits of major inter-regional transportation

35

projects and illustrated key aspects of this methodology with an application to the

construction of China's National Expressway Network. From an operational point of

view, this may be an atypical application. In a project context it is more likely that the

task is to evaluate the impacts of an investment in a specific transport network link rather

than an upgrade of an entire system. The modelling approach proposed in this paper can

obviously be applied in such contexts as well, while still incorporating economy wide

impacts of even very partial improvements.

As emphasized throughout, this application remains in its preliminary stages. We intend

to expand and improve the model in three specific areas. The first question which we

must confront concerns the appropriate modelling of transport costs. The use of estimated

travel times that are explicitly derived using GIS techniques from detailed digital

representations of a country's road network—both prior and following major

investments—is already a major step forward in modelling these costs. But there remains

the question of what is the appropriate functional form which maps these travel times to

transport costs? As emphasized in section 4.2, our current choices of functional form

are somewhat ad hoc, as are the values we choose to parameterize these functional

forms.29

The second task is to explicitly implement the first stage of our methodology. Instead of

assuming values for, in particular, the elasticities of substitution between varieties in the

urban (σ) and rural sectors (η), the goal is to estimate these parameters. This will require 29 A related issue is the estimation of travel times within regions. We can again make use of an extensive GIS database for China to compute summary statistics for intra-prefectural travel times that should better reflect urban-rural linkages in each geographic unit.

36

the estimation of the NEG wage equations (i.e., equations [3] and [4]) associated with

both sectors. Although previous studies have estimated the NEG wage equation using

Chinese regional data and we referred to the most relevant of these studies to select our

assumed value for σ in section 4.2, no study to date has estimated the wage equation for

China's rural sector. Furthermore, estimating the wage equations will also provide us with

an opportunity to empirically investigate non-NEG influences on regional economic

outcomes in China. These include, for example, the importance of first-nature geography

(i.e. natural endowments) and the role of human capital. What we learn about these forces

will then be fed back into the development of our model.30

Finally, thus far, we have only simulated the impacts of the construction of China's NEN

associated with the short-run equilibrium of the NEG model we have been developing.

However, to complete the application, we also intend to simulate the outcomes associated

with the model's long-run equilibrium. This will allow us to capture the additional effects

associated with the migration of labor both between regions and between sectors.

30 As should be clear from examining the structural equations [3] – [7] in our model, estimating these equations will involve tackling difficult issues of endogeneity, for instance with respect to project placement. This is because, with an NEG approach, the key determinants of a region's market potential are endogenously determined.

37

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40

Appendix

Data sources and data integration

A1. Economic data

Original sources

The main original sources for our prefectural level dataset are the Regional Economic

Statistical Yearbook 2008 (RESY 2008), the Urban Statistical Yearbook 2008 (USY 2008)

and the China Statistical Yearbook 2008 (CSY 2008) published by the National Bureau of

Statistics of China, as well as relevant provincial statistical yearbooks published by the

respective provincial statistics bureaus. With data from these sources, we construct a

dataset of 331 entities for analysis at the prefectural level of China.

Prefectural regions in China

Under the central government, the hierarchy of the administrative divisions of China is

made up of four tiers of regional units: the provincial level, the prefectural level, the

county level and the township level. The province is the basic unit of the country’s

territory, similar to the state in the United States, although with much less autonomy.

Also at the provincial level are the municipality and the autonomous region. Below the

provinces are the prefectural level units. By the end of 2007, there were 333 prefectural

level regions in China, of which 283 were ‘Prefectural Cities’ (CSY 2008).

Because it usually has a significant rural component, a prefectural city is quite different

from a traditionally defined city. A typical prefectural city consists of three kinds of units

that are considered counties. The first is called 'District under Cities', and all the districts

41

together form the 'City Proper' (Shiqu in Chinese) of the prefecture, which mostly

consists of urban areas, and the political and economic centre of the whole prefecture.

The second is called 'County', which, with its own urban centre, covers most of the rural

areas of the prefectural city. The third is called 'County-level City', which was once a

county and then renamed as a 'city' due to its increased urbanization level. For the 283

prefectural cities, on average by 2007 65.4% of a prefectural city’s population is

registered as agricultural population, while around 80% of the population of counties and

county-level cities under a prefectural city is registered as agricultural population; 15.5%

of the GDP of a prefectural city is from the primary sector, while for counties and

county-level cities under it the percentage is 23.2% (USY 2008).31

The other prefecture-level regions include ‘Prefectures’, ‘Ethnic Minority Autonomous

Prefectures (A.P)’ and ‘Leagues’, of which the last two kinds are dominated by ethnic

minority populations. These regions are even less urbanized than prefectural cities. A

prefecture can become a prefectural-level city after meeting certain criteria.32 Although

not included in the 333 prefectural regions, ‘Districts’ under municipalities are also at the

prefectural level administratively.33

31 Of the 283 prefectural cities, 13 do not have any counties or county-level cities and are thus excluded in the respective calculation. 32 Non-agricultural population of the county-level cities under the prefecture must be over 150, 000 (120, 000 if the population density is under 50 persons/ kilometre2); non-agricultural population of the prefectural council’s location must be over 120,000 (100, 000 if the population density is under 50 persons/ kilometre2); the total GDP must be over 2.5 billion RMB with the tertiary industry’s share over 30% ; the total fiscal revenue must be over 150 million RMB. Source: Ministry of Civil Affairs. 33 Notice that the ‘District’ under a prefectural city and the ‘District’ under a municipality are at different administrative levels

42

To get the widest coverage possible, our dataset includes all 283 prefectural cities, with

the exceptions of Tianshui City in Gansu Province and Lhasa City in Tibet due to missing

data. For the same reason, the 6 prefectures in Tibet are also excluded, while all the other

prefectures, autonomous prefectures and leagues are included. We also follow Hering and

Poncet (2008) and Bosker et al. (2009) by including three municipalities, Beijing, Tianjin

and Shanghai, as 3 single entities alongside the prefectural units. Although these

municipalities are administratively at the provincial level, they resemble some large

prefectural cities, both in terms of population and territory size, except that they are much

more urbanized. Unlike Hering and Poncet (2008), we divide Chongqing Municipality

into three regions as its population and territory size, not to mention its urbanization rate,

are closer to a small province.34 Shihezi City, a county-level city under the direct

jurisdiction of Xinjiang Province, also enters the dataset. Finally, we drop Karamay City

in China’s remote western region, whose economic structure is heavily biased by

dominance of the oil production and refining industry.

As a result, there are 331 entities in our sample. Overall, they account for 86.2% of

China’s land area, 96.2% of its population, 93 % of its total employed population and

99% of its GDP. Relevant previous studies down to the prefecture level of China (Au

and Henderson, 2006a, 2006b; Hering and Poncet, 2008; Bosker et al., 2009) have only

focused on the prefectural cities. Comparatively, our sample thus has larger coverage.

34 The division is based on Chongqing’s ‘One Circle, Two Wings’ regional development strategy that divides the municipality into the ‘One Hour Economic Circle’, ‘Northeast Wing’ and ‘Southeast Wing’. Actually it is most of the ‘two wings’ that had been added to the jurisdiction of Chongqing in 1997 as it was separated from Sichuan Province and upgraded to a municipality. The corresponding data is thus collected from the Chongqing Statistical Yearbook 2008 instead of RESY 2008.

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Construction of the main variables

Based on the simplest version of Fujita et al. (1999), our model has a two-sector structure

with an increasing returns sector and a constant returns sector. According to the typical

urban-rural dual economic structure of prefectural regions discussed above, we identify

the increasing returns sector and constant returns sector with a prefectural region’s urban

areas and rural areas respectively.

Following this urban-rural dichotomy, we construct the main variables that are used to

solve the 5 simultaneous non-linear equations in our model. To get the value of shares of

a region in the total supply of C- and M-sector workers (φ and λ respectively) that are

assumed to be exogenous in the short-run, we use the statistics of Urban Employed

Persons (UEP) and Total Employed Persons (TEP), which are available from RESY

2008.35 We then calculate the Rural Employed Persons (REP) as the residual of TEP

minus UEP.36 With the data of UEP and REP, we calculate the value of φ and λ for each

prefectural unit for the year 2007. We also get the share of a prefecture's total

employment which is in the constant returns sector (SHARE C) and the share of which is

in the increasing returns sector (SHARE M), which can be use to calculate the simulated

combined wage.

35 Employed Persons refer to persons aged 16 and over who are engaged in gainful employment and thus receive remuneration payment or earn business income. This indicator reflects the actual utilization of the labour force during a certain period of time (CSY 2008). 36 Without direct data on Rural Employed Persons at the prefecture level, our treatment can be justified by the fact that, according to various provincial statistical yearbooks, the numbers of urban employed persons and rural employmed persons exactly sum to the number of total employed persons at the provincial level.

44

To calibrate the model, we maximize the correlation between simulated income and

actual GDP to select the value of the elasticity of substitution parameter for the rural

sector before comparing simulated values of the model’s key endogenous variables with

their actual observable counterparts to assess model “fit”. With respect to the actual wage

rate, we construct three variables. The only directly present statistics reflecting actual

wages is the Average Wages of Staff and Workers (ACTUAL W) from RESY 2008,

although the concept of Staff and Workers37 does not completely correspond to that of

Employed Persons that is used to get the simulated wage. Therefore, we also use the data

of TEP and population as well as GDP for each prefecture to calculate the GDP per

worker (GDP PW) and GDP Per Capita (GDP PC) for each prefectural unit.

There are two categories of directly available data that reflect the income level of urban

areas and rural areas for each prefectural unit from RESY 2008, which are Urban

Household Disposable Income (URBAN Y PC)38 and Rural Household Per Capita Income

(RURAL Y PC)39 respectively.

37 Average Wage refers to the average wage in money terms per person during a certain period of time for staff and workers in enterprises, institutions, and government agencies, which reflects the general level of wage income during a certain period of time and is calculated as follows:

Time Referenceat Workers and Staff ofNumber Average

Time Referenceat Workers and Staff of Bill WageTotal

WageAverage =

Staff and Workers refer to persons who work in, and receive payment from, units of state ownership, collective ownership, joint ownership, share holding ownership, foreign ownership, and ownership by entrepreneurs from Hong Kong, Macao, and Taiwan, and other types of ownership and their affiliated units. They do not include 1) persons employed in township enterprises, 2) persons employed in private enterprises, 3) urban self-employed persons, 4) retirees, 5) re-employed retirees, 6) teachers in the schools run by the local people, 7) foreigners and persons from Hong Kong, Macao and Taiwan who work in urban units, and 8) other persons not to be included by relevant regulations. (CSY 2008) 38 Disposable Income of Urban Households refers to the actual income at the disposal of members of the households which can be used for final consumption, other non-compulsory expenditure and savings. This is equal to total income minus income tax, personal contributions to social security and subsidies for keeping diaries in being a sample household. The following formula is used:

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Table A 1: Summary statistics for the key variables40

N Minimum Maximum Mean Std. Deviation

Φ 331 .00 .01 .0030 .0022

λ 331 .00 .03 .0030 .0037

SHARE C 331 .01 .93 .6889 .1778

SHARE M 331 .07 .99 .3111 .1778

ACTUAL W 331 9658.0 49310.0 21072.8 5444.2

GDP 331 10.1 12188.9 837.7 1241.9

GDP PW 331 6133.0 226829.0 36857.6 27599.7

GDP PC 331 3405.0 98398.0 20214.4 15476.1

URBAN Y PC 317 5873.0 33593.0 12231.2 3581.5

RURAL Y PC 331 1232.0 11606.0 4410.6 1833.2

Valid N (listwise) 316

Disposable income = total household income - income tax - personal contribution to social security - subsidy for keeping diaries for a sampled household 39 Net Income of Rural Households refers to the total income of rural households from all sources minus all corresponding expenses. The formula for calculation is as follows: Net income = total income - household operation expenses - taxes and fees - depreciation of fixed assets for production - subsidy for participating in household survey - gifts to non-rural relatives (CSY 2008) 40 Karamay City is the prefectural city with both the highest levels of GDP per capita and per worker, despite being located in Xinjiang autonomous region (i.e. about as far in China's interior as possible). This performance is attributable to its status as an oil-producing & refining region (http://en.wikipedia.org/wiki/Karamay).

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A2. Transport cost estimates We employ a spatially explicit model of transport infrastructure by using geographic

information systems (GIS) data of the Chinese road network. Georeferenced road

information for China is from the Australian Consortium for the Asian Spatial

Information and Analysis Network (ACASIAN; www.asian.gu.edu.au). The base

network consists of 20,899 line segments with attribute information indicating the type of

road represented by each link. After including the expressway network, the complete

database has 31,538 segments. Figures A1 and A2 show the network without and with

expressways.41

Standard spatial analysis techniques allow us to determine the most likely route through

the network that will connect each prefecture with each of the remaining 330 prefectural

units. Our measure of transport cost is network travel time. While road upgrading might

not significantly change distances between two urban centers, it typically reduces travel

times. For each road type we determined a suitable travel speed (design speed) ranging

from 10 km/h for unpaved city streets to 75 km/h for expressways (Table 1).42 Given the

GIS calculated real world length of each segment, we compute the time required to

traverse each road link which is the basis for computing the fastest inter-prefecture

network routes.

41 See also World Bank (2007). 42 These speed estimates were determined by transport sector specialists working in the East Asia region.

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Table A2: Road types and assumed feasible travel speed

Road type Pavement type

Assumed speed (km/h)

City street paved 10 Local road unpaved 10 paved 15 Motorway unpaved 20 paved 30 National highway unpaved 35 paved 50 Provincial highway unpaved 35 paved 50 Expressway paved 75

We compute the fastest routes through the network between each prefecture pair using a

standard shortest-path (Dijkstra) algorithm, where in this application, rather than

distance, travel time is minimized. This involves identification of 54,615 travel times for

the base network and for the complete network. A simple initial measure of the

importance of each road network link is the number of times each link is used. Figures

A3 and A4 show the resulting maps for the network without and with expressways. The

major network arteries in the eastern regions of China appear prominently in these maps.

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Figure A 1: Road network before the expansion of the National Expressway Network

Figure A 2: Road network with expressways

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Figure A 3: Importance of network links (before NEN)

Figure A 4: Importance of network links (full network with highways)