metadata: authors: 0: author: Queiroz, Cesar
1: author: Gautam, Surhid
docna: 0: docna: Road infrastructure and economic development
            : some diagnostic indicators
docty: Policy Research Working Paper
subtopic: City to City Alliances,National Urban Development Policies & Strategies,Regional Urban Development,Urban Economics,Educational Sciences,Food Security,Multi Modal Transport,Urban Economic Development,Transport Services,Urban Communities
teratopic: Transport
abstracts: cdata!: The authors investigate the association
            between per capita income and the magnitude and quality of
            road infrastructure. They adopt an empirical approach,
            directly comparing or correlating a countries income with
            selected variables associated with existing road networks.
            Cross-section analysis of data from 98 countries, and time
            series analysis of U.S. data since 1950, show consistent and
            significant associations between economic development (per
            capita GNP) and road infrastructure (per capita length of
            paved network). The data shows that the per capita stock of
            road infrastructure in high income economies is dramatically
            greater than in middle- and low-income economies. For
            instance, the average density of paved roads (km/million
            inhabitants) varies from 170 in low-income economies to
            1,660 in middle-income and 10,110 in high-income economies.
            That is, the average density of paved roads in high-income
            economies is 59 times that in the low-income group. Road
            conditions also seem to be associated with economic
            development: the density of paved roads in good condition
            varies from 40 km/million inhabitants in low-income
            economies to 470 middle-income and 8,550 in high-income
            economies. The empirical information presented can be used
            as indicators of areas of weakness or strength in a
            country's stock of road infrastructure.
repnme: repnme: Road infrastructure and economic
            development : some diagnostic indicators
display_title: Road infrastructure and economic
            development : some diagnostic indicators
listing_relative_url: /research/1992/06/442302/road-infrastructure-economic-development-some-diagnostic-indicators
topicv3: Agriculture and Food Security m1326205 435,Transport m1326661 135,Education m1327048 470,Urban Development m1327371 699,Road Transport m1327629 1801
publishtoextweb_dt: 2010-07-01T00:00:00Z
historic_topic: Transport
origu: Transportation, Water and Urban Development Department
no_of_pages: 40
model_extractions: <!-- Start of picture text --> Policy Research<br><!-- End of picture text --> WPS 921

- This paper a joint product of the Infrastructure Operations Division, Western Africa Department, and - the Transport Division, Infrastructure and Urban Developmcnt Department is part of a larger effort to define the macroeconomic linkages and impact ofinfrastructure. Copies of the paperare available free from the World Bank, 1818 H Street NW, Washington, DC 20433. Please contact Marie Laygo, room H3-151, extension 31261 (June 1992, 35 pages).

Queiroz and Gautam investigate the association between per capita income and the magnitude and quality of road infrastructure. They adopt an empirical approach, directly comparing or correlating a country's income with selected variables associated with existing road networks.

density of paved roads (km/million inhabitants) varies from 170 in low-income economies to 1,660 in middle-income and 10,110 in highincome economies. That is, the average density of paved roads in high-income economies is 59 times that in the low-income group.

Cross-section analysis of data fromi 98 countries, and time-series analysis of U.S. data since 1950, show consistent and significant associations between economic development (per capita GNP) and road infrastructure (per capita length of paved road network).

Road conditions also seem to be associated with economic development: The average density of paved roads in good condition varies from 40 km/million inhabitants in low-income economies to 470 in middle-income and 8,550 in high-income economies.

The data show that the per capita stock of road infrastructure in high-income economies is dramatically greater than in middle- and lowincome economies. For instance, the average The empirical information presented can be used as indicators of areas of weakness or strength in a country's stock of road infrastructure.

The Policy ResearchWorking Paper Series disseminates the findings of work under way in theBank. Anobjectiveof the series **_;s_** to get these findings out quickly, even if presentations are less than fully polished. The findings, interpretations, and conclusions in these papers do not necessarily represent official Bank policy.

Produced by the Policy Research Dissemination Center
# **ROAD INFRASTRUCTURE AND ECONOMIC DEVELOPMENT: SOME DIAGNOSTIC INDICATORS**

**By**

Cesar Queiroz Senior Highway Engineer Western Africa Department World Bank 1818 H Street, NW Washington, DC 20433 USA Tel: (202) 473-5525 Fax: (202) 473-8249 and Surhid Gautam Research Assistant Infrastructure and Urban Development Department World Bank 1818 H Street, NW Washington, DC 20433 USA Tel: (202) 473-3743 Washington, D.C.
Road Infrastructure and Economic Development Some Diagnostic Idicators <!-- Start of picture text --> by<br>Cesar ueiroz<br>and<br>Surhid Gautam<br><!-- End of picture text -->

# Table of Contents

|Introduction|2|
|---|---|
|How Roads Influence Development|3|
|Sources of Data|3|
|Cross-Section Analysis|4|
|Time-Series Analysis of U.S. Data|6|
|Comparison of Cross-Section and llme-Series Analyses|7|
|Comparison of Road Supply in the World Economies|7|
|A Discussion of Causality|9|
|Conclusions|10|
|References|12|
|Figures|16|
|Table|25|
|Appendix|27|
# **ROAD INERASTRUCTURE AND** ECONOMIC **DEVELOPMENT:** SOME **DIAGNOSTIC INDICATORS**

# by Cesar Queiroz and Surhid Gautam

_An investigation of the association between per capita income and the magnitude and quality of road infrastructure is carried out. The approach adopted is empirical in that selected variables on existing road networks are directly compared or correlated with a country's income. Cross-section analysis of data from 98 countries, and timeseries analysis of U.S. data since 1950 showed consistent and significant associations between economic development, in terms of per capita gross national product (GNP), and road infrastructure, in terms of per capita length of paved road network. The data show that the per capita stock of road infrastructure in high-income economies is dramatically greater than in middle and low-income economies. For instance, the average density of paved roads (km/million inhabitants) varies from 170 in lowincome economies to 1,660 in middle and 10,110 in high-income economies, the latter being 5,800 percent higher than the low-income group. Road condition also seems to be associated with economic development: the average density of paved roads in good condition (knmmillion inhabitants) varies from 40 in low-income economies to 470 in middle and 8,5.50 in high-income economies. The empirical information presented can be used as indicators of areas of weaknesses or strengths in a country's road infrastructure stock._

# Introduction

Road transport is an important sector of economic activity, especially in developing countries, where it plays an essential role in marketing agricultural products and providing access to health, education and agricultural inputs and extension services. The impact of road transportation in developed regions is also significant. As an example, in the United States it accounts for 15 per cent of the Gross National Product (GNP) and 84 percent of all spending on transportation (1). An efficient road system gives a country a competitive edge in moving goods economically. Conversely, lack of accessibility or poor road conditions are barriers to agriculture, industry and trade, and may hinder the entire development effort. Nevertheless, the contributions of transport to national development may be difficult to quantify in economic terms.

This paper presents information which can be used as indicators of areas of weaknesses or strengths in a country's road infrastructure stock. The approach adopted is empirical in that selected variables on existing road networks are directly compared or correlated with a country's income. As pointed out by Owen (2), comparisons of income and road infrastructure are not meant to imply that a road by itself is capable of developing a country or region, but that it is a necessary element in the development process.
# How Roads Influence Development

Transportation plays a multifaceted role in the pursuit of development objectives. Restriction of accessibility limits efficient factor mobility, and defers the transfer of human and material resources to places where they can be employed most productively. Conversely, transportation development helps to attain an efficient distribution of population, industry and income.

Rural areas with low standards of living are characteristically those with inadequate methods of moving people and goods, probably because of deficient access between villages.and markets, schools, medical, economic, administrative and social services which affect the day to day lives of rural people (3). Transportation is an essential ingredient of almost everything man does to supply himself with the necessities of life. Road transport is particularly important for developing countries, where it provides about 80 to 90 percent of the total inland and/or border crossing transport of people and goods. An effective road network can hasten progress in agricultural and rural development, industry and trade, the viability of urban areas, and the expansion of jobs, education and personal opportunity (4). The World Bank's Long-Term Perspective Study _(5)_ emphasizes that although better market incentives (especially related to prices and inputs) to farmers remain important factors in agriculture, the effects of these would be blunted if the physical barriers and economic costs of transporting goods to and from markets remain high.

# Sources of Data

Data used in this analysis were gathered from different sources. GNP and population data come from the statistical annexes of "Sub-Saharan Africa - From Crisis to Sustainable Growth" (5) and the World Development Report 1990 (6). The data on road length, classification and condition were compiled from different World Bank reports (7, 8), "World Road Statistics 1985-1989" (9), Highway Statistics (10), Statistical Abstracts of the United States (11), Annual Bulletin of Transport Statistics for Europe (12), and World Transport Data (13).

The main variables included in this study are defined as follows. Gross National Product (GNP) is the measurement of a nation's total market value of the final goods and services that are produced in the economy during a given time period, normally one year. GNP per capita is a country's gross national product divided by its population, henceforth denoted by PGNP. Spatial road density is a country's road length per land area, and road density is per capita length of the road network.

Road conditions are defined as in the World Bank policy paper on road deterioration (5): (a) Good: paved roads substantially free of defects and requiring only routine maintenance, or unpaved roads needing only routine grading and spot repairs; (b) Fair: paved roads having significant defects and requiring resurfacing or strengthening, or unpaved roads needing reshaping or resurfacing (regraveling) and spot repair of drainage; and (c) Poor: paved roads with extensive defects and 3
requiring immediate rehabilitation or reconstruction, or unpaved roads neading reconstruction and major drainage works.

The sample countries for which the required data was available are listed in ^he Appendix. Naturally, the information on the total length of paved and unpaved road networks is deemed more reliable than the percentages in good, fair or poor condition. Information on road condition from different countries is often collected by different methods and on the basis of different definitions.

# Cross-Section Analysis

We employ an empirical approach to explore the association between road infrastructure and economic development. Different regression analyses were carried out using GNP/capita as dependent variable and selected indicators of magnitude and condition of road networks as independent variables. Independent variables used in the analyses included: a) spatial road density (i.e., road length per land area) of paved and unpaved roads classified in good, fair or poor condition; and (b) road density or per capita length (km/million population) of paved and unpaved roads in good, fair or poor condition.

A sample of the relationships resulting from analyses of the data described above, with per capita GNP as the dependent variable, is given in Table 1. The most significant relationship was between per capita GNP and density of paved road network. Figure 1 shows this relationship for 98 developed and developing countries in 1988, along with the scatter diagram. The resulting correlation equation is:

# PGNP = 1.39 x LPR

where PGNP is per capita GNP ($/inhabitant) and LPR is the per capita length of paved roads (km/million inhabitants). The R squared value is 0.76, the number of degrees of freedom is 97, and the t-statistic of the coefficient is 20.7. By changing units in the above equation, one can show that there exists on the average $1.39 of per capita GNP for each millimeter of paved road in a country. A less significant regression equation (with an R squared value of 0.50) was obtained between per capita GNP and the spatial density of paved roads (Figure 2): LGNP = 2.25 + 0.49 x LD, where LGNP is the logarithm of per capita GNP ($Iinhabitant), and LD is the logarithm of spatial density of paved roads (km/l,000 square kn of land area); the number of degrees of freedom is 96 and the t-statistic of the coefficient is 9.6.

The methodological, conceptual, and statistical problems over cross-country studies of growth are well described by Levine and Renelt (14). Notwithstanding these problems, the relationship described above seems quite reliable because of the relatively large sample size (98 countries) and its statistical significance. It is also relatively consistent with the results of a time-series analysis of U.S. data, as shown later in the paper.

4
The coefficient in the above equation (i.e. 1.39) can be used as a rough indicator of the adequacy of paved roads stock in a country. Countries where the ratio between per capita GNP and paved roads density is well above 1.39 are relatively underendowed in terms of their stock of paved roads. Such is the case for example of South Korea (ratio=15.5) and Bolivia (ratio=5.0). Conversely, countries with low ratios can be viewed as relatively overendowed in terms of paved roads stock. This is ttie case, for example, of Venezuela: with a ratio of 0.3, this country should probably concentrate more in maintaining the existing network instead of paving new roads.

Road costs vary widely across countries and over time and depend on a number of circumstances. Costs of new road construction can vary from less than $50,000 a kilometer for a gravel road to more than $1 million a kilometer for a fourlane access-controlled divided highway (7). If we assume that paved roads cost $300,000/km (which is reasonable for developing countries), the equation PGNP= 1.39LPR can be used to show that on the average there exists $4.60 of per capita GNP for each dollar invested on paved roads:

# PGNP = 4.6 x I

where I is the investment in paved roads (in dollars), i.e., the product of the lengtk of paved roads and $300,000/km.

A rationale for the equation above is that "infrastructure investments contribute to economic growth by increasing the productivity of other economic inputs" (15). Although correlation does not imply causality, it is significant that economic development and road infrastructure are closely associated.

A fundamental problem with cross-country estimation of supply functions is the establishment of the direction of causality. Supply functions implicitly assume that such direction runs from road stock to output or productivity. Therefore, causality is an interesting issue to be highlighted for future research: Does an increase in road stock cause growth or is it the other way around? On a technical point an argument could be made that increasing GNP results in less restricted maintenance and development budgets and hence improved road infrastructure. To prove that better roads lead to better GNP growth is not within the scope of this paper. If further research proves this beyond any doubt, the above equation could be interpreted as follows: an investment of $1 to expand the paved road network of a country corresponds, on the average, to an increase of $4.6 (or about five times) of the country's GNP. Lest the unwary reader interprets this as a rate of return of 460 percent, one should bear in mind that roads stock is just one of a large number of inputs required to produce a certain level of output. Without causality, the relationship PGNP = 4.6xI merely implies that for every $1 of a country's paved road capital stock, we observe $4.6 of GNP (a brief discussion of causality is presented later).

5
Moreover, the importance of unpaved roads should ,rot be underestimated. For example, in many agricultural areas, unpaved roads are the feeder system critical to the marketing of the agricultural surplus which indirectly supports the higher productive urb. l economy. The density of unpaved roads was not included in the above equations because of its high correlation with the density of paved roads. It seems therefore prudent to interpret LPR as a proxy for a countty's road stock, both paved and unpaved.

# Time-Series Analysis of U.S. Data

A vast amount of historic data is available on the road network and economy of the United States (10, 11). By carrying out a time-series analysis of U.S. data from 1950 to 1988, we found a significant positive relationship between per capita GNP (PGNP, in $1,000/inhabitant, using 1982 constant dollars) and density of paved roads (LPR, in km/i,000 inhabitants):

# PGNP = -3.39 + 1.24LPR

with an R squared value of 0.93; the number of degrees of freedom is 37, and the<sup>t-</sup> statistic of the coefficient is 21.4 (Figure 3). The intercept (i.e., -3.4) in the above equation is difficult to interpret. However, a null GNP is well beyond the inference space. Moreover, if we force the equation through the origin, the resulting regression equation is still signiiicant: PGNP = 0.97LPR, with an R squared of 0.88.

An interesting exercise consists in running regressions between PGNP and LPR using different time lags: we found the highest correlation existed when PGNP for a given year was associated with LPR four years earlier (Figure 4). This seems to indicate that paved roads had an effect on GNP, but there was a time lag of about four years between construction and ultimate impact. This four-year time lag<sup>is</sup> in broad agreement with the "half a decade" lag period observed by Aschauer (16). Aschauer has shown that productivity (i.e., output per unit of private capital and labor) is positively related to government spending on infrastructure, including roads. Analyzing data from the United States for the period 1949 to 1985, he observed<sup>that</sup> underinvestment in infrastructure started in about 1968, and the effects of deterioration became evident half a decade later, when a productivity slump<sup>began in</sup> the U.S.

It should be noted, however, that the above result was obtained for only one country, and using only one independent variable in the equation. This<sup>is an area</sup> specifically recommended for further research, in that similar exercises could be carried out for other countries with inclusion of additional explanatory variables<sup>in the</sup> equations.

6
# Comparison of Cross-Section and Time-Series Analyses

It is interesting to compare the equations resulting from the crosssection analysis of data from 98 countries (circa 1988) and from the time-series analysis of the U.S. data (1950 to 1988). The time-series equation PGNP = -3.4 + 1.24LPR was derived with constant 1982 dollars. To make it comparable with the cross-sectional equation, it should be expressed in 1988 constant dollars taking into account the change in the GNP implicit price deflator between 1982 and 1988. i.e., a factor of 1.213 (11). The resulting equation is: PGNP88 = -4.1 + 1.50 x LPR where PGNP88 is real per capita GNP (1988 $1,000/inhabitant) and LPR is the per capita length (or density) of paved roads (km/thousand population).

The inference spaces for both equations can be approximately defined by: (a) cross-sectional analysis: paved road density between 60 and 20,000 km/million population; and (b) time-series analysis: paved road density between 8,000 and 20,000 km/million population. Figure 5 depicts the two equations according to their inference space. As can be seen in the figure, there is relatively good consistence between both equations.

# Comparson of Road Supply in the World Economies

A comparison between the supply and condition of paved road networks in 98 developing and developed countries is shown in Figure 6. The country groups in the figure are defined as (6): (a) Low-incom **e** conomies are those with a GNP per capita of $545 or less in 1988; (b) Middle-incom **e** conomies are those with a GNP per capita of more than $545 but less than $6,000 in 1988; and (c) High-incom **e** conomies are those with a GNP per capita of $6,000 or more in 1988.

For the analyses described in this paper, data was available for 42 lowincome economies (average per capita GNP of $320); 43 middle-income economies (average PGNP=$1720/capita); and 13 high-incom **e** conomies (average PGNP =$ 17,420/capita).

As shown in Figure 6, the supply of road infrastructure in high-income economies is dramatically higher than in middle and low-incom **e** conomies. For instance, the average density of paved roads (km/million inhabitants) varies from 170 in low-incom **e** conomies to 1,660 (plus 876 percent) in middle and 10,110 in high7
income economies, the latter being 5,%q0 percent higher than the low-income group. Road condition is also associated with economic development: the average density of paved roads in good condition (km/million inhabitants) varies from 40 in low-income economies to 470 in middle and 8,550 in high-incom **e** conomies (an increase of 21,000 percent over the low-income group).

In the particular case of Africa, there is a similar trend between low and middle-income economies, as shown in Figure 7. While the increase in average per capita GNP between the two country groups is 220 percent, the per capita length of paved roads in good condition increases by about 370 percent with the increase in income.

The results above seem to indicate that economic development has a link with paved roads density, and also to the maintenance standards of these roads. A similar trend exists for unpaved roads, since there is high correlation between the extent of a country's paved and unpaved road networks.

The limited resources devoted to the upkeep of road networks in developing countries in the last decade, together with the growth of heavy freight traffic, have created a large backlog of road maintenance and rehabilitation needs. In several countries many kilometers of roads have de.eriorated from good to fair and from fair to poor condition. It is not exceptional for sections of main trunk roads to have lost most or all of their black top, thus causing a shrinkage of a country's passable road network. Although many other factors are involved, several countries where GNP per capita has decreased in recent years have also faced significant deterioration in their road networks. This trend is illustrated in Figure 8, which shows a decline in real per capita GNP and road condition between 1984 and 1989 for several African countries.

Conversely, several countries that were able to improve their road infrastructure in the same period had also an increase in real income per capita (Figure 9). Ghana is a good example: From 1984 to 1989, its per capita GNP increased by 11 percent--from $350 annually to $390; in the same period, the density of paved roads in good condition expanded by 102 percent, from 56 to 113 km per million inhabitants (Figure 9). On the basis of the findings described above, improved road infrastructure in Ghana is likely to contribute to further economic growth.

Regarding tt., nacroeconomic linkages of infrastructure, authors such as Ingram (15) assert that the conceptua **l** ink between infrastructure (including roads) and the supply side of the economy is as follows: (a) reductions in the stock of infrastructure capital can shift the production possibility frontier inward and reduce the economy's possible output; and (b) increases in infrastructure capital can shift the production possibility frontier outward and provide a source of growth for the economy. This linkage is in line with the data shown in Figures 8 (reduction in both 8
per capita GNP and density of paved roads in good condition) and 9 (increase in both per capita GNP and density of paved roads in good condition).

# A Discussion of Causality

Assessing the impact of road infrastructure on economic performance is not straightforward because many other factors are involved. As we mentioned earlier, direction of causation between changes in income and changes in road infrastructure are not clear cut. One could argue that causation in the equations described in this paper could run in either direction. However, there are .*jme indications that roads should precede development, of which a few examples are: (a) In estimating the aggregate supply response of agriculture, Chhibber (17) showed that both price and non-price variables have significant effect. Although he did not explicitly consider a proxy for road stock in his analyses, it was implied that non-price variables included transport and communication facilities.

(b) Binswangex (18) found that the lack of roads is a significant constraint on the supply response of agriculture.

(c) In India, a Central Road Research Institute study by Dhir, Lal and Mfital (19) has shown that literacy, agricultural yield and health care increase with road density.

(d) Shah (20) used a restricted equilibrium framework to estimate the contribution of public investment in infrastructure to private sector profitability in Mexico. He concluded that a policy emphasis should be to upgrade the public infrastructure (including roads) so that scale economies could be exploited in the future.

(e) The linkage between roads and development has also been acknowledged by country leaders. As an example, President Bush has asserted that the interstate highway system fueled development in the U.S. for a generation, uniting the states as never before - economically, politicaily, socially (21).

(f) Hirschman (22) pointed out that highway construction can be conceived as the laying down of a "prerequisite" for further development. As such, it permits and invites, rather than compel, other activities to follow suit. This is in line with Owen's (2) assertion that comparisons of income and road infrastructure are not meant to imply that a road by itself is capable of developing a country or region, but that it is a necessary element in the development process.

(g) Using US data in the period 1949 to 1985, Aschauer (16), has shown that productivity (i.e., output per unit of private capital and labor) is positively related to government spending on infrastructure, including roads. He also observed that underinvestment in the U.S. infrastructure started in about 1968, and the effects 9
of deterioration became evident half a decade later. In a different paper, Aschauer (23) offered several checks on the direction of causation, concluding that increases in public capital stock lead to higher total factor productivity, which is a proxy for per capita income.

(h) Using 1965 data from 47 less developed countries and 19 developed countries, Antle (24) demonstrated the importance of transportation and communication facilities for raising aggregate agricultural productivity.

(i) An analysis of the economic rates of return of World Bank financed projects in the period 1968 to 1984, carried out by Israel (25), indicates that transportation investments, particularly roads, are among the most productive.

Therefore, the notion that road infrastructure is a necessary element in the development process is supported by several pieces of research. However, many factors can influence the impact of roads on income. In particular, an exploration of the linkages between policy distortions and the actual outcome of infrastructure investments, carried out by kauffman (26), concluded that a distorted policy environment reduced significantly the ex-post return of the investments. A good example of policies that would probably increase the impact of road investments on productivity was given by Small, Winston and Evans (27). Their policy recommendations include a set of pavement-wear taxes for heavy trucks, a set of congestion taxes for all vehicles, and a program of optimal investments in road durability. Such policies are based on two economic principles: efficient pricing to regulate demand for highway services and efficient investment to minimize the total public and private cost of providing them (27).

# Conclusions

The analyses in this paper show that there is a statistically significant relationship between road infrastructure and economic development on a worldwide basis: cross-section analysis of data from 98 countries (circa 1988), and time-series analysis of U.S. data between 1950 and 1988 showed significant relationships between per capita gross national product (PGNP) and density (i.e., per capita length, LPR) of paved road network. Moreover, there is relatively good consistency between the regression equations from cross-section and time-series analyses, when compared according to their respective inference space. Because of the high correlation between the densities of paved and unpaved roads, LPR should be interpreted as a proxy for a country's road stock, both paved and unpaved.

The per capita stock of road infrastructure in high-income economies is dramatically greater than in middle and low-income economies. For instance, the average density of paved roads (kmn/million inhabitants) varies from 170 in lowincome economies to 1,660 (plus 876 percent) in middle and 10,110 in high-income economies, the latter being 5,800 percent higher than the low-income group. Road condition also seems to be associated with economic development: the average 10
density of paved roads in good condition (km/million inhabitants) varies from 40 in low-income economies to 470 in middle and 8,550 in high-income economies. There is also a clear contrast between road infrastructure and income in low and middleincome economies in Africa: while the difference in average per capita GNP between the two country groups is 220 percent, the density of paved roads in good condition varies by about 370 percent from one group to the other, using 1989 data.

Causality is an issue highlighted for future research: Does an increase in road stock cause growth or is it the other way around? Assessing the impact of the supply and quality of road infrastructure on economic performance is a complex area of research with potentially important implications on the intemational infrastructure lending strategy to developing countries.

# Acknowledgements

The authors benefitted from comments of James Wright, Jean Doyen, Peter Morris, Charles L. Wright, Manuel Santos, Harold Young, Hiroshi Ueno, John Riverson, Antoine G. Hobeika, Merron L. Latta, Jeremy Warford, Ajay Chhibber, Arturo Israel, Peter Cook, John Roome and Mohsen Fardi.

11
# Rekences

1. Roads to Serve the Nation-The Story of Road Development in the United States. U.S. Federal Highway Administration, Publication No. FHWA-PL-89024, Washington, D.C., 1989.

2. Wilfred Owen. Transportation and World Development. The Johns Hopkins University Press, Baltimore, 1987.

3. Riverson, J. D. N., and S. Carapetis. The Potential of Intermediate Means of Transport in Improving Rural Travel and Transport in Sub-Saharan Africa. Sub-Saharan Africa Transport Program, World Bank, Washington, D.C., 1991.

4. World Highways. International Road Federation, Vol. XLI, No. 8, Washington, D.C., November/December 1990.

_5._ Sub-Saharan Africa - From Crisis to Sustainable Grownth. A Long-Term Perspective Study, World Bank, Washington, D.C., 1990.

6. World Development Report 1990, World Bank, Washington, D.C., June 1990.

7. Road Deterioration in Developing Countries: Causes and Remedies, The World Bank, Washington, D.C., 1988.

8. UNTACDA U, Roads Sub-sector Working Group, "Strategy Paper", Africa Technical Department, infrastructure Division, The World Bank, Washington, D.C., Dec. 1990.

9. World Road Statistics 1985-1989, International Road Federation, Washington, D.C., 1990.

10. Highway Statistics, U.S. Department of Transportation, Federal Highway Administration, Washington, D.C. (Different Issues).

11. Statistical Abstracts of the United States 1991: The National Data Book. Bureau of the Census, U.S. Department of Commerce, Washington, D.C. (Different Issues).

12. Annual Bulletin of Transport Statistics for Europe, United Nations, New York, 1990.

13. World Transport Data, International Road Transport Union, Geneva, 1990.

12
14. Levine, Ross, and David Renelt. 1991. Cross-Country Studies of Growth and Policy: Methodological, Conceptual, and Statistical Problems. Working Paper WPS 608, World Bank, Washington, D.C.

_15._ Ingram, Gregory K. Note on the Macroeconomic Linkages of Infrastructure. World Bank, Washington, D.C., October 1989.

16. Aschauer, David A. Infrastructure Expenditures and Macro Trends. In Proceedings of the Africa Infrastructure Symposium, World Bank, Washington, D.C., 1989.

17. Chhibber, Ajay. The Aggregate Supply Response: A Survey. In Simon Commander, ed., Structural Adjustment and Agriculture: Theory and Practice in Africa and Latin America. Overseas Development Institute, London, 1989.

18. Binswanger, Hans. The Policy Response of Agriculture. In Proceedings of the World Bank Annual Conference on Development Economics 1989. World Bank, Washington, D.C., 1990.

19. Dhir, M., N. Lal and K. Mital. The Development of Low-Volume Roads in India. Fourth International Conference on Low-Volume Roads, Transportation Research Board, TRR 1106, Vol. 2, Washington, D.C., 1987.

20. Shah, Anwar. Dynamics of Public Infrastructure, Industrial Productivity and Profitability. World Bank, Washington, D.C., 1990 (Forthcoming, The Review of Econiomics and Statistics, Harvard University).

21. Washington Post, The. What Path Lies Ahead for U.S. Highways? No. 183, 114th Year, June 6, Washington, D.C., 1991.

22. Hirschman, Albert 0. The Strategy for Economic Development. Yale University Press, Inc., New Haven, 1958.

23. Aschauer, David A. Is Public Expenditure Productive? Journal of Monetary Economics 23, Elsevier Science Publishers B.V. (North-Holland), Amsterdam, 1989, p. 177-200.

24. Antle, John M. Infrastructure and Aggregate Agricultural Productivity: International Evidence. Economic Development and Cultural Change, Vol. 31, No. 3. The University of Chicago Press, April 1983.

25. Israel, Arturo. Infrastructure Framework Paper. Infrastructure and Urban Development Department, World Bank, Washington, D.C., 1991.

13
26. Kauffman, Daniel. Determinants of the Productivity of Projects in Developing Countries: Evidence from 1,200 Projects. Background Paper to World Developing Report 1991, World Bank, Washington, D.C., 1991.

27. Small, Kenneth A., Winston, Clifford and Evans, Carol A. Road Work: A New Highway Pricing and Investment Policy. The Brookings Institution, Washington, D.C., 1989.

14
# List of Pigures

1. Relationship between per capita GNP and paved road infrastructure

2. Relationship between per capita GNP and spatial density of paved<sup>roads</sup> 3. Relationship between per capita GNP and paved road density in the<sup>U.S.</sup> 4. Correlation between per capita GNP and paved roads in the U.S. using different time lags 5. Comparison between cross-section and time-series analyses 6. Comparison between the extent and condition of paved road networks<sup>in 98</sup> developing and developed countries 7. Comparison between the extent and condition of paved road networks in<sup>low</sup> and middle-income economies in Africa 8. Sample of African countries with deteriorating per capita GNP and road condition between 1984 and 1989 9. Sample of African countries with improved per capita GNP and road condition between 1984 and 1989

# List of Tablea

1. Sample of relationships between per capita GNP (dependent variable) and road infrastructure variables

# Appndix

List of countries with data available on population, per capita gross national<sup>product,</sup> length of paved and unpaved road networks, and road condition. The vast<sup>majority of</sup> the data was obtained for 1988. However, for a few countries 1988 data<sup>was not</sup> available; then 1987 or 1989 data was used. Countries are sorted by per<sup>capita GNP.</sup> The appendix includes three annexes: (1) data on population, per capita<sup>GNP and</sup> paved and unpaved road density; (2) data on land area, per capita GNP and<sup>paved and</sup> unpaved road spatial density; and (3) U.S. data used in the time-series<sup>analysis.</sup> 15
# Relationship between per capita GNP and Paved Road Density (circa 1988)

## GNP/Capita, $

<!-- Start of picture text --> 50000<br>5000<br>500 98 countries<br>R squared: 0.76<br>PGNP 1.39 x LPR<br>50 1<br>50 500 5000 50000<br>Paved Roads, km/million inhabitants<br><!-- End of picture text --> FIGURE 1
# Relationship between per capita GNP and Paved Road Spatial Density (circa 1988)

## GNP/Capita, $

<!-- Start of picture text --> 100000-<br>10000 a ' a<br>1000 -<br>100- 98 countries<br>R squared = 0.50<br>PGNP * 178 Density<br>10 - 1 I 111111 I Il 11 1l I II11111<br>0.1 1 10 100 1000 10000<br>Paved roads, km/1,000 sq km<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 2<br><!-- End of picture text -->
# Relationship Between Real per Capita GNP and Paved Road Density in the U.S.

I-' <!-- Start of picture text --> GNP/Capita in '82 Constant Dollars (000)<br>18 -<br>16 - PGNP--3.4 1.24xLPR<br>14- R squared: 0.93<br>1950 to 1988<br>~12-<br>10<br>8-<br>6<br>4- I l l<br>8 9 10 11 12 13 14 15<br>Paved Road Density (km/thousand pop)<br><!-- End of picture text --> FIGURE 3
# Correlation Between Real per capita GNP and Lagged Paved Road Density in the U.S. for the Equation PGNP=aLPR

<!-- Start of picture text --> R squared<br>0.92<br>0.90 . .. ....... .<br>0.88 - ---- ------- .. .....<br>0.84 1<br>-2 -1 0 1 2 3 4 5<br>Number of Years Lagged<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 4<br><!-- End of picture text -->
# Comparison Between Cross Section and Time Series Analyses

## Real GNP/Capita, 1988 $1,000

<!-- Start of picture text --> 30 -<br>25 98 Countries<br>20 R squared: 0.76 U.S.A.<br>-<br>o 15- // 1950- 1988<br>10 R squared: 0.93<br>0- I I<br>0 5 10 15 20 25 30<br>Paved Road Density, km/thousand pop<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 5<br><!-- End of picture text -->
# Average Road Density in Low, Middle, and High Income Economies

<!-- Start of picture text --> GNP/capita, $ Density, km/mil inhab<br>100000-<br>-100000<br>17420 10110 8660 10000<br>10000<br>1660<br>1000<br>1720 470<br>1000 170<br>320 4010<br>100<br>10<br>PGNP < $545 $545-$6,000 PGNP > $6,000<br>GNP/Capita Paved Roads<br><!-- End of picture text --> GNP/Capita Emi Good Paved Roads FIGURE 6
# GNP and Road Density in Low and Middle Income Economies in Africa

<!-- Start of picture text --> GNP/Capita, $ Roads, km/mil inhabitants<br>1200 1200<br>1000 938 951 1000<br>800 8 00<br>--<br>600 600<br>400 386400<br>200 -200<br>0 0<br><!-- End of picture text --> PGNP($545 PGNP4$545 GNP/Capita . Paved Roads EZ Good Paved Roads FIGURE 7
# Comparison Between Real GNP and Good Paved Road per Million Inhabitants (1984 and 1989)

<!-- Start of picture text --> $/capita km/mil. inhab<br>Congo 1984 1140 422<br>1989 940 287<br>Zimbabwe 1984 760 488<br>1989 650 360<br>Zambia 1984 470 344<br>1989 390 280<br>Gambia, The 1984 260 172<br>1989 220 136<br>Tanzania 1984 210 57<br>1989 130 36<br>11111 I I (fI~I I I I I I l111111 I I I liltI<br>10000 1000 100 100 1000<br>GNP/Capita ME Good Paved Roads<br><!-- End of picture text --> FIGURE 8
# Comparison Between Real GNP and Good Paved Roads per Million Inhabitants (1984 and 1989)

<!-- Start of picture text --> $/capita km/mil. inhab<br>Botswana 1984 960 - 1770<br>1989 1600 - 1802<br>Mauritania 1984 450 289<br>1989 500 446<br>Ghana 1984 350 56<br>1989 390 113<br>Zaire 1984 140 E 14<br>1989 260 31<br>10000 1000 100 500 5000<br>GNP/Capita Good Paved Roads<br><!-- End of picture text --> <!-- Start of picture text --> FIGURE 9<br><!-- End of picture text -->
# Table 1. Summary of Statistical Models Relating Income and Road Infrastructure

|Equation|Estimation<br>method|No. of<br>observ.|<br>Constant|Xi|X2|X3|X4|R<br>X5<br>squared|Remarks|
|---|---|---|---|---|---|---|---|---|---|
|1|OLS|98|732.8|0.01<br>(0.03)|1.67<br>(5.38)|||0.82|World|
|2|OLS|98|0|0.32<br>(1.33)|1.39<br>(4.63)|||0.81|World|
|3|OLS|98|2.6|1.39<br>(20.74)||||0.76|World|
|4|OLS|98|0|1.39<br>(21.06)||||0.76|World|
|5|OLS|83|564||1.18<br>(7.37)|||0.39|World|
|6|OLS|83|0||1.63<br>(10.1-8)|||0.18|World|
|7|OLS|39|-3.39|1.24<br>(21.4)||||0.93|USA|
|8|OLS|39|0|0.97<br>(88.18)||||0.88|USA|
|9|OLS|38|-2.9|1.22<br>(21.4)||||0.93|USA lag 1 yr.|
|10|OLS|38|0|0.99<br>(92.5)||||0.89|USA lag 1 yr.|
|1.1|<br>OLS|37|-2.5|1.2<br>(21.1)||||0.92|USA lag 2yrs.|
|12|OLS|37|0|1.0<br>(100)||||0.90|USA lag 2 yrs.|
|13<br>1 4|OLS<br>OLS|41<br>41|258<br>0|0.44<br>(2.6)<br>0.74||||0.14<br>0.03|Africa<br>Africa|
# Table 1. Summary of Statistical Models Relating Income and Road Infrastructure

|Equation|Estimatfon<br> <br>method|<br>No. of<br>observ.|<br>Constant|Xi|X2|X3|X4|X5|R<br>squared|Remarks|
|---|---|---|---|---|---|---|---|---|---|---|
|15<br>16<br>17<br>18|OLS<br>OLS<br>OLS<br>OLS|41<br>41<br>41<br>21|335<br>195<br>0<br>300|(6.2)|0.59<br>(2.8)|0.21<br>(3.5)<br>0.29<br>(7.25)|||0.16<br>0.23<br>0.17<br>0.32|Africa<br>Africa<br>Africa<br>Africa|
|19<br>20<br>|OLS<br>OLS<br>|21<br>21<br>|300<br>0<br>||0.60<br>(1.5)<br>66<br>(.99)||||0.38<br>0.34|Africa<br>Africa|
|21<br>|OLS<br>|98<br>|1.3<br>||||6.7||0.59|World|
|22<br>|OLS<br>_|98<br>_ _ _ _ _ _ _|3.7<br>**_**_ _ _ _ _ _ _ _<br>_<br>_ _ _ _ _ _<br>_ _ _|_ _ _ _ _ _<br>_ _ _|_ _ _ _ _ _|_ _|_ _ _ _ _ _ _(1|8.7<br>3 .0 )1_|0.53<br> <br>_<br>_<br>_|World|

Notes: Xi = Paved roads (km/million inhabitants) X2 = Good paved roads (km/million inhabitants) X3 = Unpaved roads (km/million inhabitants) X4 = Paved Roads (kmf000 sq. km) X5 = Good Paved Roads (kmOOO sq. km) OLS = Ordinary Least Square t statistics in parenthesis