The broader economic consequences of transport infrastructure investments

Journal of Transport Geography 19 (2011) 1–12

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Journal of Transport Geography journal homepage: www.elsevier.com/locate/jtrangeo

The broader economic consequences of transport infrastructure investments q T.R. Lakshmanan * Boston University, Department of Geography and Environment, 675, Commonwealth Ave., Boston, MA 02215, United States

a r t i c l e

i n f o

Keywords: Transport infrastructure Broader economic effects General equilibrium effect Transport

a b s t r a c t A major question in Economic Geography relates to the scale and nature of transport infrastructure’s contribution to the broader economy. While Cost-Benefit Analysis (CBA) is the most widely used of the three potential approaches, the recent interest in the wider economic benefits of transport infrastructure has spawned a variety of macroeconomic models. However, the estimates of magnitudes and direction of economic impacts of infrastructure by various macroeconomic models are sharply different, and these models shed little light on causal mechanisms linking transport and the economy. This paper has two aims: first, to highlight the wider economic benefits of transport infrastructure from the observed role of railroads and waterways in economic development, and two by reviewing recent theoretical developments to identify the multiple causal mechanisms which link transport and economic growth such as : market expansion, gains from trade, technological shifts, processes of spatial agglomeration and processes of innovation and commercialization of new knowledge in urban clusters (made possible by transport improvements). Hence the need for developing general equilibrium analyses of transport-economy linkages. Ó 2010 Elsevier Ltd. All rights reserved.

1. Introduction and overview One of the persistent analytical issues in the Economic Geography of Transport relates to the assessment of the nature and magnitude of the contribution that transport infrastructure makes to the economy. Three analytical approaches have developed over several decades in response to this issue (Lakshmanan and Anderson, 2002). The first analytical approach is microeconomic—transparent and causal—describing (a) the direct time and cost savings from transport improvements, (b) the indirect impacts of these cost and time savings in the form of lower assembly costs in production and gains from logistical reorganization, and (c) the associated costs including external costs. However, this approach, typified by Cost-Benefit Analysis (CBA), is deficient in not treating the further ‘network’ or the general equilibrium effects of transport improvements on transport-using sectors in the broader economy. Hence the current concern in the field to go beyond CBA analysis towards developing methods which capture the broader economic benefits of transport infrastructure investments. It is in this context that in the last two or three decades that a second approach, namely the macroeconomic modeling stream has appeared. These macroeconomic models argue that there are q The Fleming lecture Presented at the Association of American Geographers Boston, April 2008. * Tel.: +1 617 353 7551; fax: +1 617 358 0205. E-mail address: [email protected]

0966-6923/$ - see front matter Ó 2010 Elsevier Ltd. All rights reserved. doi:10.1016/j.jtrangeo.2010.01.001

externalities to investments in infrastructure which are not captured in microeconomic CBA studies. These economy-wide cost reductions and output expansions deriving from transport infrastructure are identified in these macroeconomic models (e.g. Mera, 1973; Aschauer, 1989; Nadiri and Mamuneas, 1996). While the overall inference from these (over 100 macro) models offer a positive and modest economic contribution of transport infrastructure, the utility of such a result is open to question in view of two serious drawbacks of this macroeconomic modeling stream: first, the sharp differences and conflicts among these models on the magnitudes and direction of economic impacts of infrastructure, and second, these macroeconomic models offer little clue to the mechanisms linking transport improvements and the broader economy (Lakshmanan and Anderson, 2007; Lakshmanan, 2008). There is an extensive literature, however, on the broader economic consequences of transport capital as well as on the economic processes involved in the generation of these wider economic benefits, as gleaned from the Economic History studies of economic transformation attendant on large past investments in railroads and waterways around the world. Further, recent theoretical developments have enhanced our contemporary understanding of how transport infrastructure improvements open up markets, achieve gains from trade, promote inter-regional integration, and enhance the performance of factor markets. This has improved our understanding of how transport improvements initiate interactive economic effects which ripple through the broader economy—identifying the many market mechanisms and technical and structural processes, which interact with one another and

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generate what may be termed as general equilibrium effects of transport improvements. The upshot of these full effects of transport infrastructure is the growth of total factor productivity (TFP) in the economy. Further, there are two other economic mechanisms, in large urban activity clusters (made possible by transport improvements), one dealing with spatial agglomeration benefits, and the other with innovation and commercialization of new knowledge. These two mechanisms have been elaborated in recent theoretical research, respectively, in the ‘New Economic Geography’ and ‘Innovation through networks’ associated with the ‘Economies of Variety’. The aim of this paper is to offer an overview of such broader economic consequences ensuing from transport infrastructure investments, and of analytical frameworks capable of capturing these effects. Section 2 offers a brief survey of the microeconomic approach to transport assessment, specifically the Cost-Benefit Analysis (CBA)—from its basic version to more sophisticated recent extensions. Further, this section explores the linkages between the transport-providing sectors and the transport-using sectors, which raises the possibility of wider economic benefits and the current inability of CBA to capture them. Section 3 highlights the attributes and capabilities of macroeconomic models which argue that they are able to incorporate externalities to investments in infrastructure which are not captured in microeconomic CBA studies. However, as noted earlier, these macroeconomic models suffer from their highly variable results and their ‘black box’ character. Section 4 attempts to identify the wider economic benefits of transport capital (Canals, Railroads, the Interstate Highway System, etc.) in the form of past economic and spatial transformation (recorded in Economic History) and the underlying economic processes involved in the generation of such broader economic benefits. Section 5 describes the many ways in which we have gained from recent theoretical developments an increased understanding how transport infrastructure improvements influences productivity on a broad geographical scale. These include gains from inter-regional and international trade, and from solving coordination problems in development. Further, this section makes clear the linkages between transport infrastructure and agglomeration economies. Such agglomeration economies can be of the Marshall–Arrow–Romer variety or the ‘‘Jacobsian” variety as elaborated in the literature on ‘Economies of Variety’. Section 6 concludes the paper.

2. Microeconomic analysis of transport effects The microeconomic analysis of transport infrastructure investments focuses on the improvements in the productivity of individual firms due to such investments.1 Cost-Benefit Analysis (CBA) is the favored tool for assessing such microeconomic benefits. CBA techniques go back 7 decades to President Roosevelt’s Flood Control Act (1936), which set the familiar requirement of ‘the benefits to whomsoever they accrue to be in excess of the estimated costs’. The CBA methodology has been extended by subsequent Presidents progressively to projects of physical environment, safety, environment and health. While the analytical process has improved over the years, both the modeling technique (partial equilibrium) and criterion of assessment (economic efficiency) remain the dominant paradigm for the contemporary cost-benefit evaluation. Highway (or other modal) infrastructure improvements (e.g. new roads, expansion or improvement of existing roads, and effective capacity expansion though Intelligent transportation Systems 1 These benefits may accrue to firms themselves as higher profit, or can be passed along to consumers as lower prices, or some combination of the two. It depends on the structure of the market.

(ITS) reduce costs for two reasons: (a) distance reduction through less circuity due to network expansion and (b) transport network capacity expansion leading to congestion reduction, leading to further cost savings. Fig. 1 portrays these transport time and cost savings in the basic CBA analytical framework. Here the demand function DD represents the number of trips that would be made at different costs—ranging from the fewer trips at higher costs and the many more at lower generalized trip costs. The intersection of the demand function and the horizontal supply function determines the number of trips made (T0), before the infrastructure improvement. The area CS is defined by the difference between DD and SS summed up from zero to T0 is the initial level of user benefit provided by the transportation system. This gap between what people are willing to pay and the market price is known as the consumer surplus. The effect of an infrastructure improvement is a downward shift in the supply function to S1, implying a greater number of trips (T1). User benefits increase in two ways: the area A which represents the reduction in cost enjoyed on all trips that were made prior to the infrastructure improvement and B which is the user benefit on the incremental trips (T1  T0). This theoretical argument provides the rationale for a very straightforward calculation of benefits. The summed areas A + B are determined by calculating a pertrip reduction in cost—including tolls (where relevant) and factors such as labor, capital depreciation and fuel costs which can be estimated as functions of the change in travel time and distance—and multiplying by the number of trips. Since the number of trips are different before and after the infrastructure improvement, the user benefit is approximated by the ‘‘rule of half”: ½(S1  S0)(T1  T0). This general procedure is repeated for personal transportation users as well as freight transportation users and the sum of freight and personal benefits are weighed against the project cost (Mackie and Nellthorp, 2001). A number of refinements on this method are possible. In particular, provision of transportation services may have a number of negative or positive external effects. Air pollution, for example, is a negative external effect that increases with the number of trips made T. Fig. 2 shows a case where the private cost of travel S is augmented by an external cost e. Because of the upward sloping nature of the S + e functions, the estimated benefit (in this case a social benefit) will be lower than the user benefit in Fig. 1. Naturally, if the result of the project is to reduce pollution, a positive adjustment is needed. In order to implement this adjustment, it must be possible to calculate the net change in external cost in monetary terms and subtract it from the calculated user benefit. Where congestion is an issue, the horizontal supply function may be replaced by one with an upward sloping segment to account for the fact that beyond some design capacity travel time is an increasing function of the number of users. The effect of the

Fig. 1. User benefits.

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So+e

Generalized Cost/Trip

S1+e

D

Co

So

C1

S1 D To

T1

Number of Trips

Fig. 2. CBA with externalities.

infrastructure improvement is then to increase the design capacity, thus shifting the upward sloping segment to the right as in Fig. 3. The user benefit is defined as in this case as ½(C1  C0)(T1  T0). In this case, the scope of the analysis must be fairly broad because congestion on various elements of an infrastructure network tends to be interrelated. For example, if the analysis addresses the addition of a lane to a particular road, it is generally necessary to assess impacts on travel time not only on that road but on all roads that are sufficiently connected to experience positive or negative congestion impacts from the road widening (Only in the unlikely case that the cost of travel is equal to its marginal cost can the analysis be limited to a single road, Mohring, 1993.) It is also possible that induced trips for personal transportation may negate much of the benefit that might otherwise have accrued to freight users. CBA can be extended to address these and a number of other market distortions that result in divergence between private benefits and social benefits (Venables and Gasiorek, 1999, Part 1). However recent research indicates that there are a number of benefits from transportation infrastructure whose analysis lies beyond the conventional supply and demand for transportation services. These include logistics costs effects, facilities consolidations, and other location effects. These effects and the challenges of capturing them are described in a CBA framework elsewhere (Lakshmanan and Anderson, 2002). Most CBA studies are thus both limited in scope and partial in nature. This need to go beyond CBA and capture the broader economic benefits of transport has been addressed in two different streams of economic modeling and analysis. First is a macroeconomic model which attempts to capture the overall output effects and cost effects of transport investments in the national or regional economies. We address the scope and achievements of this research in Section 3. A second approach is to analyze the links between the transport sector and the large number of transport using economic sectors— which raises the possibility of wider economic benefits and costs. The magnitude and direction of such benefits is critically depen-

dent on the nature of competition in the transport-using markets. Under the usual assumption of perfect competition, market pressures will require firms to respond fully to lowered transport costs (Sactra, 1999; Venables and Gasiorek, 1999). The response of firms, however, under imperfect competition will be strategic. If lower transport costs allow some firms to expand their market share, economies of scale and agglomeration come into play and lead to wider economic benefits of transport infrastructure improvements. In that case, the scope of analysis of economic consequences of transport infrastructure should be framed in a context where all direct and indirect responses (to transport investments) by economic agents should be taken into account—in other words, a general equilibrium approach in the assessment of the broader consequences of transport infrastructure. This approach is presented in Sections 4 and 5. 3. Macroeconomic modeling of transport-economy linkages Investments in transport infrastructure will increase the efficiency and reduce the prices of production inputs. Not only do costs such as those of skilled labor and material assembly become lower, but increases in the capacity of transport infrastructure lead to an increased quality of service. A six lane limited access highway not only has greater capacity than a two lane road, it is also faster and safer-thereby generating new demands. Fig. 4 shows how these effects could be conceptualized for a market economy with perfect competition. Where infrastructure is inadequate, the firms are confronted by high marginal costs (MC1) at every level of production, and given the market price of their output, produce Q1 units of output. As infrastructure services are improved, the marginal cost curve shifts to a lower level (MC2). The result is twofold. There is a total cost savings of abcd for the earlier level of output Q1, and an output expansion effect bce as Q2  Q1 additional units are produced. These cost reduction and output expansion effects of transport infrastructure are captured in the macroeconomic approach empirically by the formulation and estimation of production functions and cost functions. 3.1. Production and cost functions: a definitional digression The production function approach aims at estimating the contribution that transport and other forms of public capital make to private production. Since such infrastructure capital is available to all firms in an area, it is viewed as entering the production function of all the area’s firms as a factor additional to private factors. The aggregate production function is of the form: MC1

MC2

Cost ($) Generalized Costs per Trip

So

D

S1

e

b Price a

Co

c d

C1 D

Q1 To

T1

Fig. 3. Congestion effects.

Number of Trips

Q2

Output

MC1= marginal cost with infrastructure deficiencies MC2= marginal cost with improved infrastructure Fig. 4. Infrastructure and the efficiency of production.

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Y ¼ YðX; PKÞ

ð1Þ

where Y represents the aggregate output of the economy, X represents a vector of private factors of production [usually labor (L), capital (K), and sometimes expanded to include Energy (E), and Materials (M)], and PK is a vector of public capital (e.g. transport infrastructure) services. Production functions are familiar to most analysts and are easy to interpret. If the relationship between increases in infrastructure capital and the economy’s output is positive and significant, one can argue that infrastructure investment is an important determinant of economic output. A typical measure estimated from the production function (1) to shed light on the role of public capital on economic output and productivity improvements is the output elasticity of transport infrastructure. The output elasticity is the percentage change in output for a 1% change in public (transport) capital stock.

ePK ¼

PK @Y  Y @PK

C ¼ CðY; Px PKÞ

ð3Þ

where C is the total cost of producing output Y, the vector Px are the prices for the various private factor inputs such as labor and capital, and PK is publicly financed capital services. The conditional input demands {applying Shepard’s (1970) lemma to (3)} are:

@C @Pi

8i

ð4Þ

The relevant cost elasticity computed from (3) for our purposes is the percent reduction in output costs for a percent increase in infrastructure stocks. While the results will depend on the functional forms chosen for Eqs. (1) and (3), the cost function approach has advantages and offers potentially a richer framework to address a variety of additional questions pertaining to transporteconomy linkages raised in this report and elsewhere (Lakshmanan et al., 1984).3 2

3.2. Selective review of macroeconomic studies of transport-economy linkages

ð2Þ

where ePK is the output elasticity of infrastructure capital. Duality theory suggests that we can derive the underlying production function parameters from the cost functions.2 The same rationale can be used to insert transport capital stock as an unpaid input into a production function or cost function. The costs of output in a firm are determined by: the cost of different input factors such as labor, capital, etc., the level of the firm’s output, and the stock of infrastructure capital. In a cost function, firms choose the quantities of private inputs (e.g. labor, capital, etc.) so as to minimize the private costs of producing output Y.

X i ðY; Px ; PKÞ ¼

In practice, the quality of estimates of such elasticities and other parameters estimated in these studies depends on the functional form chosen for representing the production or cost functions. Most production function studies of transport infrastructure assume a Cobb-Douglas specification (e.g. Aschauer, 1989; Munnell, 1990). The Cobb-Douglas formulation imposes a priori restrictions on the substitutability of factor inputs. More recent studies adopt flexible functional forms for specifying the models to be estimated. Such flexible functional forms do not impose restrictions such as constant returns to scale on the parameters of the cost functions, and indeed allow testing of hypotheses of relationships among the production factors. Examples are the translog and the Generalized Quadratic (Elhance and Lakshmanan, 1988; Keeler and Ying, 1988; Lynde and Richmond, 1992, 1993; Nadiri and Mamuneas, 1996).

Under certain regularity conditions, there is unique correspondence between production and cost functions, and both functions contain all the information about the underlying technology (Shepard, 1970; Diewert, 1974). 3 In a production function, factor inputs are exogenous variables which determine the endogenous variable of output. From Eq. (4) above, in a cost equation the factor inputs are endogenous and depend on private factor prices and the stock of publiclyfunded infrastructure. Thus, the estimation of a production function like (1) Y = Y(X, PK) suffers from simultaneous equation bias and the OLS estimation will be biased. Other advantages for the cost functions include: (a) the ability to get direct estimates of Allen elasticities of substitution, which indicate the level and pattern of complementarity and substitution between factors of production, (b) the direct estimation of the effect of public infrastructure capital on demand for inputs (the second partial derivative of the cost function), (c) since the joint estimation of the cost function and the derived demand for inputs increases the degrees of freedom, the statistical precision of the estimates is improved, and (d) From the ability to compute easily the marginal benefit of infrastructure capital (the first derivative of cost with respect to public capital), as well as the optimal level of infrastructure, a richer analytical framework can be developed to address questions such as the willingness of the private sector to pay for additional increases in infrastructure and the optimality of provided public capital for the private industries.

Mera (1973) carried out the first study which found that public infrastructure—including transport and communications infrastructure—contributes to aggregate private production in ways similar to that of privately supplied inputs and that its impact on productivity could be assessed through the use of the production function framework. He divided Japan into eight regions, and concluded that from 1954 to 1963 (a period of intense reconstruction of the Japanese economy), investments in transport and communication substantially contributed to private production in the manufacturing and service sectors. The output elasticities of 0.35 for the manufacturing sector and about 0.40 for the service sector implied that a 1% increase in infrastructure stocks led respectively to 0.35% and 0.40% increases in the outputs of Japanese manufacturing and service sectors. For over a decade and a half, there were limited additions to this line of research with which to compare these results on the economic contributions of infrastructure. The few studies that emerged in this period (Ratner, 1983; Wigran, 1984; Elhance and Lakshmanan, 1988), while noted in the fields of Regional Science and Development Economics, failed to attract the attention of mainstream macroeconomic analysts. The latter group did not come on board till one of them entered the field in a dramatic fashion (Aschauer, 1989). In the 20 years since, however, there has been a virtual explosion of analytical studies on the economic contributions of public infrastructure within the framework of production and cost functions. 3.3. The Aschauer story: claims and counter claims Aschauer (1989) used an aggregate Cobb-Douglas production function with time series data, and obtained an output elasticity for all nonmilitary public capital of 0.39 and for ‘‘core” public capital (highways, airports, utilities, mass transit, and water and sewer systems) of 0.24. Since the output elasticities sum to 1 in this Cobb-Douglas, the relative contributions of privately supplied labor and capital were correspondingly smaller than those estimated by previous studies. The annual percentage changes in total factor productivity due to public capital estimated from the coefficients of his production function turn about to be large. Alicia Munnell (1990) used a similar procedure, but different data, on aggregate private capital stock for a longer time period (1948–1987). Her output elasticities were comparable (0.31–0.39 for core-public capital) to Aschauer’s. The Aschaeur–Munnel studies were carried out at a time when many observers were concerned about factors that were behind the slowdown in US productivity since 1973. Aschauer and Munnel suggested that their production function findings suggested that

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much of the productivity decline since the early 1970s was due to under-investment in public infrastructure. This inference has acted as a lightening rod, drawing extensive critiques from other US economists, who appeared to have largely neglected previous studies of infrastructure productivity in the United States as well as in Europe and Asia. Critics noted two types of statistical problems associated with the use of aggregate time series data in the Aschauer study. The data may create a spurious relationship between production inputs and output because they both tend to grow over time. Further, there might be time lags between the construction of public infrastructure and producers’ use of it, which could make estimates about productivity obtained from time series data unreliable. A smaller group of critics suggest that public infrastructure makes little, if any, contribution to the overall economy (Hulten and Schwab, 1991). Responses to both of these criticisms have led to sophisticated analytical reforms and reinterpretations of earlier findings (Lynde and Richmond, 1992). More sophisticated analysis to such time series data—cointegration analysis or error-correction models led to more moderate results than Aschaeur.

of annual highway investments is intended for maintenance. As noted above, this study provides a variety of additional information pertaining to optimality of highway capital, and the contribution of highway capital to total factor productivity growth, so that questions relating to crowding out effects of transport infrastructure can be posed. Table 1 provides a summary of the output and cost elasticities of highway and other public capital in the various country and regional studies. However, this inference of a modest positive economic contribution of infrastructure investments masks some sharp differences and conflicts in the results of recent studies. If one compares the different measures of economic contribution of infrastructure (e.g. output elasticities, cost elasticities or rates of return of transport infrastructure), there appear to be sharply different results among the recent studies:

3.4. Nadiri and Mamuneas contribution

This large variety of conflicting results can not be attributed to methodological deficiencies as many of them are associated with recent studies employing sophisticated functional forms and statistical methods.

The Nadiri and Mamuneas, 1996 macroeconomic model is sophisticated and incorporates explicitly demand and supply factors, including the contribution of highway capital, which affect the productivity performance of 35 industries. A cost function specified in a flexible functional form explores the interaction among highway capital, private sector inputs and outputs in the US Economy for the period 1947–1989. For each industry, cost and demand functions are estimated separately and the parameter estimates of the model utilized to decompose Total Factor Productivity (TFP) growth. While the cost elasticities vary by sector, the overall aggregate cost elasticity is –0.044, and the overall output elasticity is 0.051. The rate of return of highway capital (the ratio of the sum of industry marginal benefits to cost minus the depreciation rate of highway capital) varies over the period. It is high initially at around 37% until 1968—well above the rate of return to private capital— during a period of introduction of the new technology of high speed, safe, divided highways of the Interstate System and a period of rapid network expansions with its nonlinear effects (Fig. 5). In the latter years, the rates of return to highway capital drops to levels closer to that of private capital, as the interstate highway system gets completed and a significant and increasing proportion

   

for the same country overall, and at different periods of time, for different countries at comparable stages of development, for countries at different stages of development and, where threshold effects and accelerated growth are evident.

3.5. Differential results for the same country or countries at similar stages of development Table 2 illustrates one aspect of this dissonance among the studies about the impact of public capital. Pereira (2001) and Demetriades and Mamuneas (D–M, 2000) apply sophisticated production functions to analyze the relationships between public capital and output in 12 OECD countries for approximately the same period, using respectively a Vector Auto Regressive/Error

Table 1 Summary of output and cost elasticities of highway and other public capital in various countries. Country

Sample

Infrastructure measure

Elasticity range

United States

Aggregate (ts) States (xs) States (ts/xs) Regions, trucking industry (ts/xs) Regions (ts/xs)

Public capital

Output: 0.05–0.39

Public capital Highway capital Highway capital

Output: 0.19–0.26 Output: 0.04–0.15 Cost: 0.044 to 0.07

Transportation and communication infrastructure Public capital

Output: 0.35–0.42

Japan

United Kingdom

Aggregate (ts)

France

Regions (xs)

Public capital

Germany

Industry (ts/xs)

Public capital, highway capital

India

Aggregate (ts), states (xs) National, 26 industries

Economic infrastructure: roads, rail, electric capacity Transportation, communication and electricity, public capital

Mexico

Fig. 5. Net rates of return of highway capital, private capital, and private interest rate, 1951–1989, computed by Nadiri and Mamuneas (1996).

Note: ts = time series; xs = cross section.

Cost: negative, statistically significant Output: positive, statistically significant Cost: negative, statistically significant Cost: 0.01 to 0.47 Returns to public capital: 5.4–7.3%

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Table 2 Productivity effects of public capital: sharply dissonant results.

Pereira (2001) Vector auto regressive/ error correction. mechanism—data early 1960s to later 1980s Demetriades and Mamuneas (2000). Flexible functional form for profit function (data for 1972–1991)

US

Japan

UK

Sweden

Germany

L.R. (10 year) 0.2573

0.2525

0.1430

0.2270

0.1905

Table 3 Transport infrastructure productivity in countries at different stages of development. Source: Canning and Bennathan, 2001.

Output elasticity of paved roads 1.03

0.499

0.358

1.217

Countries in lower quartile of incomes

Countries in middle quartile of incomes

Countries in upper quartile of incomes

0.05

0.09

0.04

0.768

Correction Mechanism (VAR/ECM) framework and a flexible functional form for the profit function. First, the D–M (2000) study estimates output elasticities of public capital for the US (and for Sweden and Germany) four times as large as the Pereira (2001) study does. For UK and Japan, the estimates are twice as large. Further, the five OECD countries in Table 2 are affluent industrialized countries with comparable levels of technological evolution, industrial composition and income and consumption. As the various transport-using firms respond to transport infrastructure and service improvements in an economy, the many market mechanisms and structural processes interact and generate the economic effects rippling through the economy and culminating in the growth in GDP. Such effects in these five economies can be expected to be of comparable magnitude. Yet, D–M (2000) study’s estimates of the output elasticities, however, range from 1.03 (US) to O.358 (UK); Pereira’s estimates range from 0.2573 (US) to 0.143 (UK). Such sharp differences in parameters for the same country and for countries in comparable levels of development need an explanation. Fig. 5 traces the variation of infrastructure productivity over time in the US (Nadiri and Mamuneas, 1996). That study identifies net rates of return of Highway capital (which makes up a major part of public capital):  between 30% to 45% for years 1951–1967,  from 15% to 30% for years 1968–1978 and,  below 15% for 1979–1987. The net rate of return of public capital was higher than that of private capital from 1951 to 1978. In subsequent years, private capital had higher rates of return than highway capital. Fernald’s (1999) analysis of public capital’s contribution to US industry productivity between 1953 and 1989 suggests a similar time pattern of effects. He suggests that the massive road-building of the 1950s and 1960s (the interstate system) offered ‘a one-time’ increase4 in the level of productivity (in the pre-1973 period). Demetriades and Mamuneas (2000), on the other hand, arrive at a time pattern of productivity effects in the US, different from that espoused by Nadiri–Mamuneas and Fernald. They identify net rates of return of public capital, which exceed consistently private net rates of return of private capital in the US all the way from 1972 to 1991. Indeed, the estimated long-run net rates of return to public capital in the US (and Canada, Japan, Germany, France, Italy and UK) remained above those of private capital. In other

4 Fernald (1999) argues that the aggregate correlation between productivity and public capital primarily reflects causation from public capital to productivity, and the slowdown in productivity growth after 1973 may reflect the public investment patterns in that period.

Fig. 6. Elasticity of output with respect to paved roads.

words, an extra dollar of investment in the early 1990s (according to Demetriades and Mamuneas) would have been socially more productive in the long-run if it were invested as public capital. Thus, for the period of the mid 1970s to early 1990s, two different patterns of productivity performance of public capital are offered by the Nadiri–Mamuneas and Fernald studies on the one hand and by Demetriades and Mamuneas on the other. 3.6. Countries at different stages of development and threshold effects Table 3 presents the estimates of the elasticities of output with respect to public capital for a panel of countries in different stages of development (Canning and Bennathan, 2001). There is an inverted U shape, with higher elasticities in middle income countries and somewhat lower in the low and the high ends of the income distribution. The rates of return to paved roads displayed in Fig. 6 and categorized in Table 3 are obtained from a translog production function (Canning and Bennathan, 2001) in a set of countries which span the world income distribution. High rates of return to paved roads are evident in some middle income developing countries (Chile, Columbia, South Korea, and the Philippines). By contrast, low rates of return accrue to paved roads in affluent developed countries and in some developing countries.5 3.7. Mechanisms linking transport improvements and the economy While the macroeconomic models help determine whether and to what degree transport infrastructure lowers production costs, 5 It is generally observed that private returns to capital are quite low in the poorer developing countries, and that diminishing returns to capital set in slowly in affluent industrialized countries—because they can keep their marginal productivity up by accumulating large amounts of human capital (Canning and Bennathan, 2001). The higher returns to private capital are also understandable in the middle income newly industrializing countries (NICs), which have received in recent years considerable flows of foreign direct investment (and associated technologies) from developed countries and participate in the global production system. If one assumes that NICs have invested in transport infrastructure to facilitate participation in global production and trade, a legitimate question arises: whether the high rates of return to paved roads observed in such countries reflect an expansion of transport networks to a critical density at which inter-regional economic integration occurs, thereby promoting regional specialization and accelerated growth in those economies.

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increases the level of economic output, and enhances the productivity of private capital, its analytical apparatus is a ‘black box’ variety. We have little inkling about the causal mechanisms and processes which translate infrastructure improvements into output and productivity enhancements. Such mechanisms are activated by the monetary and time savings induced by transport infrastructure improvements, and experienced at the regional and inter-regional levels by economic agents in different types of markets. The lowered costs and greater accessibility for transport-using production sectors and firms shipping goods from firms to retail outlets, and for households engaged in shopping and in commuting are likely to lead to the types and sequences of consequences such as: expansion of markets, higher efficiencies through scale economies, economic restructuring through entry and exit firms exposed to new competition, spatial agglomeration economies and innovation benefits in spatial clusters made possible by transport infrastructure, etc. Indeed, these and other economic mechanisms and consequences of transport infrastructure have been analyzed and reported in the case of railroads and waterways in many countries in the Economic History literature. We turn briefly to this literature to highlight the broad range of wider consequences of transport infrastructure.

4. Lessons from economic history Economic historians have attempted to measure in many countries the impact of the diffusion of railroad networks on economic growth and development.6 In the process, they have shown how the time and cost savings induced by railroad expansion course through the countries’ economies linking product and factor markets, promoting inter-regional trade, specialization and, increasing returns to scale, and reallocating economic activities. A frequently used measure of the importance of railroads to a country’s economy is Social Savings, computed as the costs of coping without railroads for 1 year. A counterfactual situation is envisaged where the producers, in the context of closing down of the railroad network for a year, transport the same volume of freight to the same destinations using alternative modes.7 Table 4 provides estimates of social savings for railroads (which have been in full operation) in 10 countries. The closure of a fully operational rail network has a considerable penalty in terms of GNP loss, especially for Spain, Mexico, and Argentina. In continental economies such as US and Russia railroads did not provide a much cheaper service than waterways per ton-mile of freight over long and similar routes, with the result that social savings are lower. However, Fogel’s (1964) social savings measure is viewed currently as static and ignoring the ‘forward linkages’ from railways to the economy (Williamson, 1974), and a variety of indirect and induced effects of railways as gleaned from many studies of long-run impacts of railways and case studies (Foreman-Peck, 1991). Table 5 lists some of these wider effects of railroad infrastructure from seven case studies. In 19th century India, railroads lowered transport costs 80% per mile, thereby initiating grain bulk shipments, creating an India-wide market for foodgrains, and pro6 There was a rapid expansion of rail networks across Europe—growing from 3000 km of track in 1840–362,000 km by 1913 (O’Brien, 1982). US and many countries in Latin America and India witnessed rapid growth in their railroads in a comparable period. 7 Extra costs are incurred since freight will now move along longer and circuitous routes, at lower speeds, and at higher tariffs. First formulated by Fogel (1964) for the US, social savings have been computed for many countries. There can be some problems with the data quality and assumptions on prices in these estimates.

Table 4 Estimates of social savings on freight transported by railways, 1865–1913. Source: Patrick O’Brien (1983). Country

Date

SS Expressed as a share of GNP (%)

England and Wales England and Wales USA USA Russia France Germany Spain Spain Belgium Belgium Mexico Argentina

1865 1890 1859 1890 1907 1872 1890 1878 1912 1865 1912 1910 1913

4.1 11.0 3.7 8.9 4.6 5.8 5.0 11.8 18.5 2.5 4.5 25–39 21–26

moting a convergence of prices across India (Hurd, 1975).8 In a separate study of factor markets in India, Collins (1999) showed that falling transport costs in Indian railroads facilitated regional wage convergence by facilitating both labor mobility and inter-regional commodity trade, especially in the areas surrounding the premier cities of Calcutta and Bombay. In late 19th and early 20th centuries European Russia, rail networks promoted market integration, based on the realization of gains from trade (Metzer, 1974). The narrowing of commodity price differentials increased regional specialization of production thereby improving resource allocation. In this regard, Metzer (1984) and O’Brien (1991) argue that the benefits from market integration are additional to those embodied in Fogel’s Social savings, and these integration benefits lead to internal and external economies that promote efficiency and enhance production (as compared to the pre-railroad situation). Railroad investments in Brazil represented a purchase of specialization and enhanced productivity (Summerhill, 2005a). This impact was large for overland movements given the absence of an affordable alternative to railroads, which further attracted large inflows of labor and capital which were used in other activities that raised national income. In the case of Argentina, the benefits from railroads built with British capital went largely to Argentine producers and consumers, enhanced aggregate productivity gains, and the transformation of the Argentine pampas (Summerhill, 2005b). The TFP gains deriving from the Spanish railroads were substantial, both through the shift from alternative modes of transport and through productivity improvements within the railroad networks.

4.1. US Railroad expansion and regional economic transformation (2nd Half of 19th Century) As the US Railroads expanded across the length and breadth of the country in the second half of the 19th Century, the consequent economic benefits were further augmented by the feedback effects from new institutions and markets (such as large capital markets, postal service expansion, rise of wholesaling, logistical improvements, etc.) which were made possible by major railroad expansion. Fig. 7 highlights some of these related and interacting technical, market, and institutional developments which yielded additional economic effects. The upshot of these developments over the half century in the railroads and other sectors stimulated by railroads was a major transformation of the regional economies in the US (Fishlow, 1965; Chandler, 1965). 8 The prices of grain in some districts in 1860s were 8–10 times higher than prices in others (Hurd, 1975).

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Table 5 The wider effects of railroad investments (1850–1914). Author

Country

Broader effects of transportation infrastructure

Hurd (EEH, 1975) Collins (EEH, 1999) Metzer (JEH, 1974)

India (1861–1921) India (1873–106) Czarist Russia (1870– 1910) Brazil (1898–1913)

Prices across India began to converge and India-wide market in grains developed Wage dispersion narrowed, real wages in initially low wage areas grew faster Evolution of a national grain market. Improved inter-regional terms of trade. Narrower prices P regional specialization P Better resource allocation A purchase of specialization that boosted productivity

Argentina (1857– 1913) Spain (1850–1913)

Social savings 12–26% of GDP, Most gains went to Argentina producers and consumers

Summerhill (JEH, 2005a) Summerhill (Mimeo, 2005b) Herronz-Loncan (JEH, 2006) Fishlow (1965)

US Midwest (1848– 1890)

Growth accounting studies. TFP gains of Spanish RR. By 1914 11% of income per capita growth (cf. 14% in UK) Case against Fogel Agricultural and industrial expansion of Great Lakes States and integration into US and world economies

Only a selective listing of the cascade of these successive economic effects that ensued from the cost and time savings due to railroad expansion in 19th century from the Northeast US to Midwest first and later to the rest of the country is possible here. As the lower costs and increased accessibility due to railroads coursed through markets and were experienced by different market actors (producers, consumers, laborers) a successive series of economic impacts ensued. This cascade of economic consequences include: expansion of settlements and agriculture, which in turn led to market expansion and regional integration. There was regional specialization in agriculture and industry and the gains from trade, promotion of volume production and the realization of scale economies. The railroads enabled lower inventories and facilitated a logistical revolution and the rise of a wholesaling sector. Such were the general equilibrium effects of railroad investments. At the same time, massive railroad expansion promoted new markets and services. The massive amounts of capital required for

railroad growth encouraged the need to tap idle savings and channel them into railroad investment, which in turn induced the development of financial institutions and of raising the national savings rates. Further in this era—with increasing capacity to move industrial inputs and outputs over long distances—the mass production techniques (e.g. volume production of goods with interchangeable parts, developed earlier in New England in industries such as rifles, clocks, etc.) were extended to mass produce a broad range of new goods, thereby expanding markets, income, and consumption. Railroads also facilitated the rise of the complementary communication service, namely the postal service, which promoted transport of consumer goods. These converging developments promoted in turn mass production, distribution and consumption of goods. The long term outcome of these developments induced by major railroad expansion was the integration of the Northeast to the Midwest to form the ‘‘Manufacturing Belt” of the US (Chandler, 1965; Lakshmanan and Anderson, 2007; Kim and Margo, 2003).

Massive demand for capital

Major Expansion of Rail Roads

Time Savings Reduced Costs Faster Land Travel

Speedier Travel Lower Inventories Agriculture Expansion

Rise of Wholesaling

Rise of new capital markets & financial tools

New Settlements

Market Expansion & Interregional Trade Higher Savings Rate

Regional specialization, economies of scale, further market growth Rise of Mass. Production Technologies (VPGIP)

Mass production of consumer goods & higher consumption

Rise of Postal Service

Integration of Midwest, MidAtlantic and New England into ‘The Manufacturing Belt’

Fig. 7. US railroad expansion and regional economic transformation (second half of 19th century).

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Transport Infrastructure Investments

Improved Freight/Service Attributes: (lower costs, time-savings, more reliability, new services)

Increased Accessibility, Specialization and Market Expansion (Gains from Trade)

Improved Labor Supply

Increasing Returns to Scale & Spatial Agglomeration Effects

Export & Import Expansion & Competitive Pressures Innovation & Technical Diffusion

Economic Restructuring Exit/entry of firms

Expanded Production

TFP (Total Factor Productivity) & GDP Growth

Fig. 8. Transport infrastructure and economy-wide benefits.

5. The broader economic consequences of transport: an overview Fig. 8 offers one view of the mechanisms and processes underlying the wider economic benefits of transport infrastructure investments. It is a contemporary version of what the Economic Historians, (e.g. Williamson, 1974; O’Brien, 1983) call ‘‘forward linkages” of transport infrastructure. The lower costs and increased accessibility due to transport improvements modify the marginal costs of transport producers, the households’ mobility and demand for goods and services. Such changes ripple through the market mechanisms endogenizing employment, output, and income in the short run. Over time dynamic development effects derive from the mechanisms set in motion when transport service improvements activate a variety of interconnected economy-wide processes and yield a range of sectoral, spatial, and regional effects, that augment overall productivity. The lower costs and enhanced accessibility due to transport infrastructure and service improvements expand markets for individual transport-using firms. As such market expansion links the economies of different localities and regions, there is a major consequence in terms of shifting from local and regional autarky to increasing specialization and trade and the resultant upsurge in productivity. Thus, the US Interstate Highway System, the TransEuropean Network (TEN) Program and super-efficient ocean ports all contribute to ‘‘Smithian” growth—growth arising from specialization and trade. As transport infrastructure and service improvements lower costs and increase accessibility to various market actors—input

suppliers, labor, and customers—market expansion, increased integration and mutually sustaining growth will ensue. It is useful to organize the underlying mechanisms into (Anderson and Lakshmanan, 2007): (a) (b) (c) (d)

Gains from Trade. Technology Diffusion. Coordination Device and the ‘Big Push’, and Gains from Agglomerations, which are made possible by Transport.

Further, as sustained improvements in transport infrastructure make possible agglomerations or large spatial clusters of firms and individuals (e.g. cities), a variety of agglomeration economies and endogenous growth effects follow, thereby augmenting the economic effects of physical transport infrastructure (Lakshmanan and Anderson, 2007). 5.1. Gains from trade The lowering of travel time and costs, and the service improvements induced by transport infrastructure expands the markets for individual transport-using firms. As such market expansion links the economies of different localities and regions, there is a major consequence in terms of a shift from local and regional autarky to increasing specialization and trade and the consequent upsurge in productivity. This is as true for inter-regional trade between highly differentiated regional economies in continental regions such as the United States or the European Union as for cross-border

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international trade in Free Trade Areas (FTAs). The US Interstate Highway System, the Trans-European Network program and the emergence of super-efficient ocean ports all contribute to ‘‘Smithian” growth—growth arising due to specialization and trade. Such efficiency gains from specialization and trade derive from two mechanisms:  the framework of David Ricardo’s theory of comparative advantage, whose theoretical extensions are explored in the Heckscher–Ohlin–Samuelson framework, and  the explanation of gains from trade provided as a benefit to inter-regional trade via scale economies that are realized as producers target broader markets—as detailed in the ‘New Economic Geography’ (Fujita et al., 1999). As opportunities for exporting and importing goods are enhanced, several channels of economic effects open both in product markets and in factor markets. First, export expansion will lead, as noted above, to higher levels of output, which allow higher sales to cover fixed costs of operation, yielding efficiencies. Second, increasing imports put competitive pressures on local prices. Such pressures lead not only to the removal of monopoly rents but also to improved efficiency. Schumpeterian dynamics come into play— firm entry, exit, expansion, and contraction. As firms promote leaner production processes, which lower costs of production and raise productivity, further restructuring of the economy occurs. Third, lower transport costs and increased accessibility enlarge the markets for labor and other factor inputs. Firms will likely draw labor from a broader area and with a greater range of attributes improving labor supply and with lower costs. Similar effects in land and other factor markets are likely as transport improvements open up new land for economic activities, as the US Interstate highway system accelerated suburbanization and when air transport improvements helped fill ski, beach, and golf resorts. 5.2. Coordination failure and the big push Rosenstein-Rodan (1943) and Hirschman (1958) described a problem of development in traditional economies whereby investment in increasing returns to scale technologies (i.e. industrialization) in a single production sector is not profitable, but simultaneous investment in such technologies by several sectors is profitable. This can occur for several reasons. Most notably, when there are linkages between sectors by means of intermediate goods, expanding one sector expands the market of other sectors. Thus if all sectors industrialize at once there are mutually supportive intermediate demands that allow them all to achieve scale economies. This is the justification for a ‘‘big push” industrialization policy as a means to address the coordination failure. Another reason for coordination failure is the fact that no single sector can support the transportation or other infrastructure necessary for its industrialization. If all sectors industrialize at once, they can jointly support this infrastructure. In light of this, investment in public infrastructure may be viewed as a policy to overcome coordination failure for two reasons. First, it may be sufficient to make it profitable for all sectors to industrialize independently. Second, even if it is not sufficient to create independent profitability, it may provide a signal to firms in all sectors to anticipate widespread industrialization.

is potential for knowledge and technology growth in the process of learning by doing and by using. The newly industrialized countries of East Asia provide successful examples of such technology adoption, and learning that facilitates technological shifts and productivity upsurge. There have been times in history when expanded freight services have made radical changes in the structure of production possible. e.g. the development first of canals and later of railroads made it possible for huge areas of the central lowlands of the US to be developed for specialized agriculture serving a national market.

5.3.1. Gains from agglomeration supported by transport Finally, Fig. 8 suggests that the two mechanisms in the oval boxes, one dealing with innovation and the other with spatial arrangements in the economy. These two mechanisms create, in the context of transport infrastructure improvements, conditions (in activity clusters) which enhance economic performance, and promote total factor productivity and endogenous growth. Our understanding of these two mechanisms of innovation and spatial arrangements derive from recent research on ‘‘Innovation-Friendly Locales” and the ‘New Economic Geography’. Transport improvements can have an endogenous growth effect to the degree they impact the rate of growth of the economy through the creation and commercialization of new knowledge— thereby promoting Total Factor Productivity (TFP) growth, and the rate of growth of the economy. In the contemporary knowledge economy, firms are concerned with the reduction of a new class of costs—adaptive costs—incurred by the firm as it monitors the environment for changes in technology and products, identifies competitive strategies, and implements such strategies quickly enough to retain or improve market share (Hage and Alter, 1997; Lakshmanan and Button, 2009) The key notion in this case of spatial proximity is that innovation derives from the Jacobsian Economies (Jacobs, 1969) or the Economies of Variety (Quigley, 1998) and the firms minimize their adaptive costs by participating in economic networks in the activity cluster or agglomeration—made possible by transport infrastructure improvements. Research on imperfect competition and the increasing returns to scale extends to locational analysis and emphasizes the importance of the interactions between transport costs on the one hand and market size and economies of scale on the other.9 With dropping transport costs and economies of scale, a firm in a location gains a larger market area and dominance, which in turn promotes the concentration of other firms in the same location. This idea of a location with good access to markets and suppliers for one firm improves market and supply access for other producers there, and the process of cumulative causation (where a location becomes more attractive to successive firms as more firms locate) derives from earlier ideas in Economic Geography. The central feature of this theory of agglomeration (as has been noted for a long time in economic geography and regional science) is the presence of external economies of scale in the Marshallian sense. Different firms clustered in a location experience positive externalities in the form of agglomeration economies, industrial complexes and social networks engaged in untraded interdependencies. In short order, regional specialization develops. Indeed, without increasing returns to scale in the context of transport improvements, it is impossible to account for the observed spatial concentration of

5.3. Technological shifts Associated with the growth of inter-regional and cross-country trade there has been an upsurge in intra-industry trade and the exchange of intermediate goods. There are opportunities here for adopting new technical knowledge associated with imports. There

9 The core idea of the ‘new economic geography’ is the notion of increasing returns, an idea that has earlier transformed both trade theory and growth theory (Fujita et al., 1999). Taking advantage of Dixit and Stiglitz’s (1977) formalization of monopolistic competition, tractable models of competition in the presence of increasing returns have been developed in the fields of industrial organization, international trade, economic growth and location theory.

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firms and regional specialization in regional and national economies. In contemporary spatial agglomerations of economic activity— where there are frequent transactions between suppliers and customers and where high-end business services often accompany goods delivery—the cost of transactions is likely to be lower inside such centers than outside them. Further, some inter-regional links gain advantages from the existence of increasing returns to transportation and transactions, which may help form transportation and transaction hubs as noted by Krugman (1999). The notion of density (of economic activities, social opportunities and transaction options) and economic milieu in such locations as leading to selfreinforcing and cumulative causation effects have been used by Johansson (1998) and Ciccone and Hall (1996). 6. Concluding comments This paper offers an overview of analytical approaches that have been developed over several decades in order to estimate the nature and magnitude of economic consequences of transport infrastructure investments. Much of this effort was directed earlier to appraisal of transport projects via a CBA framework. The increasing disaffection with CBA and the interest in the broader system wide consequences of transport infrastructure investments led to the use of macroeconomic modeling in recent decades. That approach has been limited by: (a) the conflicts and differences in the estimates of economic impacts of transport and (b) the lack of information on the mechanisms underlying economic change. The consequent major thrust of this paper is to throw some light on the broader—indeed in some cases on ‘transformational— ‘consequences of transport infrastructure. Transport improvements open up markets and create conditions, in the context of spatial agglomerations and technical change and diffusion, which influence economic structure and performance. A broad variety of interactions takes place within firms and between firms, within sectors and between sectors and more broadly within and between households and organizations. Hence the first inference we draw is the importance of general equilibrium analysis of transport-economy linkages. The implication is that the impacts of transport improvements must be examined in a general equilibrium fashion, dealing with linkages between sectors and within sectors, where sectors exhibit different transport requirements, varying competitive strengths, and diverse spatial markets. These effects are realized through the operation of product markets and factor (labor, land, etc.) markets and technological and structural changes. Since these interactions are not only numerous, multiple, and complex but may also operate to enhance or dampen the initial economic impacts of transport improvements, a more disaggregate analysis than is currently the case is called for in future analyses of transport-economy linkages. Indeed there has been a recent formulation of such a computable general equilibrium model which aims to capture these broad dynamic transport-economy linkages (Sue Wing et al., 2008). Future research in this area may fruitfully develop along the lines of this or other models of similar scope. References Anderson, William P., Lakshmanan, T.R., 2007. Infrastructure and productivity: what are the underlying mechanisms? In: Karlsson, Charlie, Anderson, William P., Johansson, Borje, Kobayashi, Kiyoshi (Eds.), The Management and Measurement of Infrastructure: Performance, Efficiency and Innovation. Edward Elgar, UK, pp. 147–164. Aschauer, D.A., 1989. Is public expenditure productive? Journal of Monetary Economics 23, 177–200. Canning, David, Esra, Bennathan, 2001. The Social Rate of Return on Infrastructure Investments, World Bank Research Project on ‘‘Infrastructure and Growth: A Multicountry Panel Study”, 48 p.

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