ECON 705 PROJECT SPRING 2012

INFRASTRUCTURE AND ECONOMIC GROWTH

By

Omor Sharif

Jaime Ketten

Qun Wang

Introduction

This study examines the relationship between infrastructure and economic growth across the

world in 139 countries. Our sources are from the World Bank and the Penn World Table.

Research from 2006 and 2009 was used to illustrate whether infrastructure plays a positive role

in economic performance. It links cross-sectional data for every country and panel data. The

goal of this study is to develop a model that explains variations factors of infrastructure effects

on economic development.

What is infrastructure?

Infrastructure is basic physical and organizational structures needed for the operation of a

society or enterprise, or the services and facilities necessary for an economy to function. Many

development economists also refer it to as social overhead capital. Hirshman (1958) provided

appropriate definition of infrastructure. Social overhead capital shares technical features from

users to non-users. And the term typically refers to the technical structures that support a

society, such as roads, water supply, electrical grids, and telecommunications.

What is economic development?

Economic development generally refers to the sustained, concerted actions of policymakers and

communities that promote the standard of living and economic health of a specific area.

Economic development can also be referred to as the quantitative and qualitative changes in the

economy. Such actions can involve multiple areas including development of human capital,

critical infrastructure, regional competitiveness, environmental sustainability, social inclusion,

health, safety, literacy, and other initiatives. Economic development differs from economic

growth. Whereas economic development is a policy intervention endeavor with aims of

economic and social well being of people, economic growth is a phenomenon of market

productivity and rise in GDP. Consequently, as economist Amartya Sen points out: “economic

growth is one aspect of the process of economic development.”

Purpose of research

Fox and Smith (1990) mentioned that the impact of infrastructure on metropolitan areas cannot

be extended automatically to smaller cities but must be tested empirically. Similarly, results of

national level studies cannot be generalized to specific states or regions. So in our test, we agree

with their conclusion and focus on countries fields. We are interested in what are the different

effects between infrastructure and economic growth across the world. We examine the total

statistics among 139 countries first. Then, we examine the specific regions, such as South Africa,

Europe, Middle East, and Latin America. Infrastructure has a direct impact on not only personal

health but also economic growth, and the infrastructure crisis threatened to nation’s future

prosperity. So we want to examine the relationship between infrastructure and economic

growth.

Empirical studies on infrastructure and economic growth

Easterly and Rebelo (1993) examined whether the level of policy variables permanently

increased the economic growth rate and whether the investments of information and

telecommunications could raise the economic growth rate. They found that public infrastructure

investment played an essential role of total and public investment. Infrastructure in

transportation and communication is consistently correlated with economic growth.

World Bank (1994) assumed that infrastructure has a close relationship with economic growth.

And in the case of China, the lack of infrastructure hindered the economic growth.

Because of open door policy in 1979 in China, economic growth averaging 9% per year resulted

to an expansion in intercity traffic. However, the traffic growth imposed strains on the

transportation infrastructure. And the poor transportation infrastructure led to 1% of China’s

GNP on annual economic costs.

Yoshino and Nakahigashi (2000) assumed that the productivity effect of social capital stock by

industry, sector and region. By industry, the productivity effect of social capital stock is large in

the tertiary industry, by sector, the productivity effect of social capital stock is large in

information and telecommunication, and environment sectors, and by region, the effect is large

in regions with large urban areas.

Yoshida (2000) showed a positive analysis of the correlations between economic growth and the

infrastructure in Japan. It included energy, electricity, and transportation sectors during the last

century. He found that in the early development stage, the growth rate in infrastructure was

higher than the growth rate of GNP per capital.

There are also negative relationship results between infrastructure and economic growth.

Deverajan, Swaroop and Zou (1996) drew analytical conclusions about developing countries

based on the endogenous growth theory in order to verify which type of government

expenditures promote economic growth. The major conclusion is that infrastructure has a

negative effect on the economic growth rate because infrastructure in developing countries is

oversupplied compared to the economic scale.

Mosley (1985) insisted that it is obviously important that aid go not only to the poorest

countries but also to the poorest people within recipient countries. So he believed that aid

should devote to rural development and social infrastructure instead of industrial development.

Kocherlakota and Yi (1996) presented evidence supporting endogenous growth models using

time series data for the US, together with various policy variables including the infrastructure to

show that there is no policy variable that permanently raises the economic growth rate.

The Data and Variables

We performed various regressions to study the association between growth and infrastructure.

In our study the response variables are the growth rate and per capita output of a country. For

per capita output we have used RGDPL measure since it is comparable across both years and

countries. This data is obtained from the Penn World Table Version 7 (See Heston et al. 2007). In

addition, we have considered several economic variables as control variables to see how they

impact the association. Among them we have population (actual population of country in

thousands), urban population (the percentage of people living in urban area) and life expectancy

(in years) data from World Bank. And we listed the variables in the table.

Variable Symbol

Country COUNTRY

Infrastructure 2006 INF06

Infrastructure 2009 INF09

Real GDP Per Capita 1980 GDP80

Real GDP Per Capita 2006 GDP06

Real GDP Per Capita 2009 GDP09

Growth Rate of GDP 2006-2009 GROW

Growth Rate of GDP 1980-2009 GROW2

Log Population 2006 POP06

Log Population 2009 POP09

Change in Population CHAPOP

Log Urban Population 2006 URB06

Log Urban Population 2009 URB06

Log Life Expectancy 2006 LIFE06

Log Life Expectancy 2009 LIFE09

South Asia SASIA

Europe EUR

Middle East and North Africa MID

Sub-Saharan Africa AFR

Central and Latin America LAT

East Asia and Pacific PAC

Low Income LOW

Lower Middle Income LOWMID

Upper Middle Income UPMID

High Income HIGH

OECD OECD

To answer the question whether countries should invest in infrastructure and to examine the

relationship between infrastructure and economic growth or level of output, we need a

measure of a country’s quality of infrastructure. Infrastructure of a country is hard to observe

directly, so we examine variables that reflect development indirectly. We have found that World

Bank has data on countries’ Logistics Performance Index (LPI) in 2006 and 2009 (See Arvin et al.

2007 and 2010). They use six different indexes to construct the LPI and one of them is ‘Quality of

Infrastructure related to trade and transport’. The LPI index and quality of infrastructure is

available for 150 countries in year 2006 and 155 countries 2009. Each country is assigned a value

between 1 to 5, where 1 indicates poorest performance and 5 indicates best performance.

The infrastructure index is comprised of two major components- 1) quality of the physical

transport infrastructure (e.g. ports, roads, warehouses) 2) quality of telecommunications and IT

infrastructure essential for timely information exchange in modern trade. Concerns about

infrastructure exist even for the high performing countries because of the challenges of

maintaining physical infrastructure at a level able to satisfy fast increasing demands.

Other than quality of infrastructure there are several key factors that are aggregated to

construct LPI such as, competence of private and public logistics service providers, performance

of customs and other borders agency, corruption and transparency, reliability of trading and

supply chain. Word bank publishes country's index on each of the six categories (again on a scale

of 1 to 5), they are- infrastructure, customs, international shipments, tracking and tracing,

domestic logistics cost and timeliness. The individual index for each categories are then

combined together to compute LPI of a country.

LPI and Infrastructure index are based on a world-wide survey of global freight forwarders and

express carriers. The participants in the survey are the professionals involved with multinational

logistics service providers and main express carriers within a country. The respondents were

asked to evaluate the logistics performance and the environment and institutions in support of

logistics operations in the country in which they are based. The view of the professionals are

critical in that they play a direct role in the choice of shipping routes and gateways and influence

the firms’ decisions about the ‘location of production facility, choice of suppliers and selection of

target markets’.

Econometric Evaluation

Initially we ran regressions to see the effect of infrastructure on GDP per capita. We wanted to

see how a change in initial infrastructure would have on future GDP per capita. Since we only

had two observations for infrastructure, we used 2006 as our initial year for infrastructure and

other variables. We had complete information for 139 countries at this point. To see how initial

infrastructure affected Log GDP per capita in 2009 we used the equation:

Yi,2009= β0 + β1Xi,2006 + β2Wi,2006 + εi

We anticipated that initial infrastructure would have a positive effect on future GDP per capita,

that is, a country with a higher quality infrastructure will tend to have a higher GDP per capita.

Our first results show that initial infrastructure is highly significant and positively affects GDP per

capita with an R-square of .6227. The estimated effect of an increase of 1 in infrastructure in

the index would raise GDP per capita by e.65584 or 92.676%. That is to say if a country could go

from a quality of infrastructure score of 2.3 to 3.3, on the 5-point scale, they could practically

double their GDP per capita, leaving population, urban population, and life expectancy

unchanged. That is a very significant increase! Countries with very low initial scores, such as

Afghanistan with a 1.1, could more easily than most increase their infrastructure to a 2.1 (more

so than America could improve from a 4.07 to a 5.07 as that would be beyond the 5-point scale)

and potentially double their GDP per capita! There is of course one question that can be raised

from this observation. How would you measure a 1-point increase on the infrastructure scale

and how much investment would that cost? That is an important question, and one that is

beyond the scope of this study unfortunately.

We then added initial GDP per capita to help increase our explanatory power and R-square.

Adding GDP per capita for 2006 (our initial year for the study) we got counter-intuitive results.

Our R-square shot up to .9949, but shows that with GDP per capita in 2006 constant, a higher

initial infrastructure would actually make you poorer by e-.0818 or 7.8%. That did not seem to

follow from theory or our assumptions, so we gathered GDP per capita in 1980 as a better

representation of initial income. Doing this we lost 25 countries that either did not exist yet or

had no data collection in 1980, so our data set fell to 114 countries. Using 1980 as our initial

year for GDP per capita, it showed once again that a higher initial infrastructure would positively

affect future GDP per capita. This time the magnitude of a 1-point increase in initial

infrastructure was smaller with an increase in GDP per capita of 52.89%, which is still large.

Next we wanted to see how initial infrastructure affected growth over the period we had data

for infrastructure. We took the growth rate of GDP per capita from 2006 to 2009 for our initial

139 countries and looked at what effects initial infrastructure and our other independent

variables had on it. We ended up with the pattern that infrastructure was highly significant and

negative with an R-square of .1892. That would seem to say that as you gained 1-point in the

infrastructure index that you would grow almost 2% slower had you not changed from your

initial infrastructure score. This could seem to come from the Solow idea that as you get

wealthier you seem to grow slower and slower until you get to a steady-state. However, it

seems to also be counter-intuitive. As you have a better quality infrastructure it gets easier to

transport and trade products, which gives a boost not only to the sector of the economy

involved in shipping, but also it allows other industry sectors to ramp up production since it is

easier and more cost effective to ship goods. Therefore we went back to the idea of a longer-

term initial income.

This time we used a growth rate going from 1980-2009 which gives us a better basis for long-

term growth, since the growth rate in the span from 2006-2009 includes the global depressions

brought about by the 2008 crashes in many economies it isn’t a good estimator. With this

change we get the result we expect that initial infrastructure has a significant and positive effect

on the long-term growth rate of a country with an R-square of .4629. The magnitude is given as

a 1.5% increase to a country’s growth rate by increasing initial infrastructure by 1-point,

everything else equal. This seems to say that countries that have a better infrastructure would

have a higher growth rate.

We then looked at how a country’s change in infrastructure affects their growth rate. We just

looked at the period from 2006-2009 since this is the period we can see the change in

infrastructure occurs. Using initial income from 2006 we get that a positive change in

infrastructure has a positive effect on the growth rate of GDP per capita with an R-square

of .1158. This shows that doubling a country’s infrastructure would increase their growth rate

by 13%. This is significant, but essentially impossible to do, however it does show a positive

correlation between the two. However, when we look at a regression using 1980 as our initial

GDP per capita the change in infrastructure is insignificant, with an R-square of .2003. So we see

that an investment in infrastructure has a near zero effect on the growth rate, but it does follow

from the latter that it has a small positive effect.

We then compare developing countries in different regions to developed countries to see the

effects of infrastructure on different continents. We used only the 114 countries that had initial

incomes in 1980, omitting many countries that were brought about by the splitting of the

U.S.S.R. and others in Africa and the Middle East that came into existence after 1980. The

results show that infrastructure is significant and positive with a 40% increase to GDP per capita

with a 1-point increase in the infrastructure scale, with an R-square of .9334. In this regression

it shows that East Asia and Pacific countries, Central and Latin American countries, Sub-Saharan

countries, and Middle-East and North African countries are significantly and negatively different

from developed countries. That means that if developing countries in these regions have the

same infrastructure, population, urban population, life expectancy, and initial income as

developed countries, then their GDP per capita in 2009 is lower by a range from 26.68% for

Central and Latin America to 40.46% for Sub-Saharan African countries. The second set of

results show that initial infrastructure is highly significant and positive for growth rate of GDP

per capita, with an R-square of .5399. The same four regions were once again significant and

negative, while developing countries in South Asia and Europe and Central Asia seem to be no

different from developed countries. So developing countries in the Middle-East, Africa, Central

and Latin America, and South Asia and the Pacific seem to grow at a slower rate than developed

countries, with all else equal. The third set of regressions looking at change in infrastructure as

it relates to growth of GDP per capita shows no significance of any variable, but the model is

highly significant and has an R-square of .2891. So there seems to be no difference for any

country on the effect of increasing infrastructure on the increase in GDP per capita.

Lastly, we looked at the effects of infrastructure on GDP per capita for income groups. We

once again only compared the 114 countries with initial GDP in 1980. For this regression we are

comparing low income, lower-middle income, upper-middle income, and high income countries

to the OECD countries. The results show that initial infrastructure is highly significant and

positively affects GDP per capita in 2009 with an R-square of .9321. This regression also shows

that upper-middle and lower-middle income countries are significantly different from OECD

countries, meaning they would be wealthier than their OECD counterparts if they were to have

all other measures equal. Low income countries and High income countries would be about the

same. When looking at how initial infrastructure affects the long-term growth rate, the

regressions show that infrastructure is highly significant and positive, having about a 1.86%

increase to growth with a 1-point change in infrastructure, with an R-square of .5308. This

regression also shows lower-middle and upper-middle income countries are significantly

different from OECD countries having a higher growth rate, all other variables equal. Low and

high income countries are not significantly different than OECD countries when it comes to their

growth rates. This result is an interesting observation in that lower-middle and upper-middle

income countries are converging toward OECD countries. There are barriers however for low

income countries, as they grow at the same rate as OECD countries, so they will not be

converging toward the OECD and high income countries. Poor countries must do something to

break into the lower-middle income groups before they can hope to catch up to the rest of the

world, this is a troubling thought. When we look at how the changes in infrastructure affect

growth, we see that an investment in infrastructure of 1-point will bring about an 8.57%

increase to growth in GDP per capita, with an R-square of .2545. Once again we have the result

that lower-middle income and upper-middle income countries will have a higher growth rate, all

other variables equal.

Conclusions

Based on our regressions it does appear that infrastructure is a significant and positive

determinant of growth. The magnitude of return on investment to infrastructure is unclear due

to our measure of infrastructure as an index and based on surveys of logistics professionals.

There is no clear definition based on our index on how to improve infrastructure in such a way

as to improve by 1-point, nor how much investment it would take to do so. The purpose of the

study was to determine if investing in infrastructure brought about a higher rate of growth and

wealth to a country. This would determine whether a country should invest its resources into

increasing the quality of trade and transport-related infrastructure so that they could get a

return on that investment in the form of GDP per capita. The results of this econometric study

do seem to indicate that the effects of increasing infrastructure significant and positive toward

future GDP per capita.

By studying regions we wanted to see if it a specific region would benefit more directly from

investing in infrastructure. The results here seem to say that undeveloped countries do not

seem to benefit as much from investment as developed countries. It may be more in a

developing countries interest to invest their money in other ways such as bringing about

industries, which may later increase the benefit to an increase in infrastructure. The results may

suggest that having high quality roads with no industry would not necessarily improve GDP per

capita, but would rather decrease it. This especially seems to be the case in Sub-Saharan Africa,

not as much so in South Asia and Europe and Central Asia though.

We also looked at how important investing in infrastructure may be by income levels. The

results seem to be that lower-middle income and upper-middle income countries have the most

to gain by investing in infrastructure, as they are significantly different and positive from OECD

countries. These are developing countries that have some industry, but are not quite up to the

rich countries, by investing in infrastructure they can compete better globally with their industry

and start to play catch up to the high income countries. It seems that low income countries will

not benefit anymore than high income countries when it comes to infrastructure, so if they are

looking to play catch up and improve their growth rate over rich countries, it may be best to find

a different form of investment.

Further study with these variables may be of more interest. Since the Logistics Performance

Index has only been done for 2006 and 2009, this is more of a short-term study. It would be

nice to revisit this after many years with a better long-term trend of growth available. The short

period of this study falls around the global crisis that has been enduring since 2008, so growth

rates are not at the usual long-term trends for most countries, which may skew many of the

results and possibly give wrong conclusions. Other failings of this study are the monetary

impacts for policy-makers that don’t seem to come to play with the way the Logistics

Performance Index is calculated. It may be of interest to continue a similar study with specific

measure of infrastructure taken into account such as roads, rail, airports, and ports that can be

fiscally measured, so that results can be put better into monetary terms. This would be a much

more potent study if it could say a $100 billion investment in roads would bring about 1.5%

growth in GDP per capita in the next ten years. This study has nothing to say on that matter, but

it may be of encouragement to those skeptical of spending public funds on highways and

improving ports.

Reference

Infrastructure Online Compact Oxford English Dictionary

http://www.askoxford.com/concise_oed/infrastructure (accessed January 17, 2009)

Sullivan, arthur; Steven M. Sheffrin (2003). Economics: Principles in action. Upper Saddle River, New Jersey 07458: Pearson Prentice Hall. pp. 474. ISBN 0-13-063085-3.

Sen, A. (1983). Development: Which Way Now? Economic Journal, Vol. 93 Issue 372. Pp.745-762.

Heston A., R. Summers and B. Aten (2011). Penn World Table Version 7.0. Center for International Comparisons of Production, Income and Prices at the University of Pennsylvania.

Arvis, J. F., M. A. Mustra, et al. (2007). "Connecting to Compete: Trade logistics in the global economy." World Bank. Washington, DC. http://www. worldbank. org/lpi.

Arvis, J. F., M. A. Mustra, et al. (2010). "Connecting to Compete 2010: Trade Logistics in the Global Economy World Bank." Washington, DC.

Yoshino, N. and M. Nakahigashi (2000). "The Role of Infrastructure in Economic Development (Preliminary Version)." Unpublished manuscript.

Devarajan, S., V. Swaroop, et al. (1996). "The composition of public expenditure and economic growth." Journal of monetary economics 37(2): 313-344.

Mosley, P. (1985). "The political economy of foreign aid: A model of the market for a public good." Economic Development and Cultural Change 33(2): 373-393.

Kocherlakota, N. R. and K. M. Yi (1996). "A simple time series test of endogenous vs. exogenous growth models: An application to the United States." The Review of Economics and Statistics: 126-134.

APPENDIX

The MEANS Procedure

Variable N Mean Std Dev Minimum Maximum ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ country 139 70.0000000 40.2699226 1.0000000 139.000000 inf06 139 2.6058993 0.7260722 1.1000000 4.2900000 inf09 139 2.6749640 0.7423468 1.3500000 4.3400000 chainf 139 0.0086350 0.0443926 -0.1310142 0.1768761 gdp80 114 8.2163573 1.2541026 5.9416235 10.3151498 grow2 114 0.0151422 0.0175992 -0.0513748 0.0845331 gdp06 139 8.6800754 1.3572059 5.8803383 11.5823161 gdp09 139 8.7490557 1.3322209 5.9842548 11.9775677 grow 139 0.0229934 0.0351930 -0.0419788 0.1774051 pop06 139 16.2669068 1.5121605 13.0660829 20.9940713 urb06 139 15.5887563 1.5596101 11.3344637 20.1097636 life06 139 4.2151681 0.1596531 3.8082839 4.4112894 sasia 139 0.1151079 0.3203064 0 1.0000000 eur 139 0.0719424 0.2593271 0 1.0000000 mid 139 0.1654676 0.3729460 0 1.0000000 afr 139 0.1582734 0.3663172 0 1.0000000 latam 139 0.1582734 0.3663172 0 1.0000000 pac 139 0.1223022 0.3288194 0 1.0000000 low 139 0.2446043 0.4314070 0 1.0000000 lowmid 139 0.2446043 0.4314070 0 1.0000000 upmid 139 0.1654676 0.3729460 0 1.0000000 high 139 0.1294964 0.3369628 0 1.0000000 oecd 139 0.2158273 0.4128829 0 1.0000000ƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒƒ

Independent Vars Log RGDPL 2009Intercept 4.97607**

(.26042)7.1818**(.74731)

8.28684**(.64222)

-6.35710**(1.72652)

-.06856(.33062)

-6.50346**(1.27763)

Infrastructure 2006 1.44786**(.09629)

1.48609**(.09412)

1.00985**(.10032)

0.65584**(.08929)

-0.0818**(.02012)

0.42452**(.08259)

Log Population 2006 -0.14172**(.04519)

-1.20021**(.14302)

-0.79747**(.1226)

0.04758γ(.02614)

0.03396(.12209)

Log Urban Pop 2006 1.11326**(.14506)

0.72888**(.12336)

-0.03993(.02565)

-0.02579(.11777)

Log Life Expectancy 2006

3.56027**(.40091)

-.00707(.09281)

2.20056**(.33802)

Log RGDPL 2006(139 obs)

1.02638**(.01645)

Log RGDPL 1980(114 obs)

0.56345**(.07182)

R-Square .6227 .6481 .755 .8458 .9949 .9223Model P-Value <.0001** <.0001** <.0001** <.0001** <.0001** <.0001**

** Significant at 1% alpha-level, * Significant at 5% alpha-level, γ Significant at 10% alpha-level

Independent Vars

Growth Rate 2006-2009

Intercept(139 obs)

0.07303**(.01028)

0.04294(.03043)

0.03930(.03131)

-0.07674(.10558)

-.02285(.11021)

Infrastructure 2006

-0.0192**(.0038)

-0.01972**(.00383)

-0.01815**(.00489)

-0.02095**(.00546)

-0.0273**(.00671)

Log Population 2006

0.00193(.00184)

0.00543(.00697)

0.00862(.0075)

0.01586γ(.00871)

Log Urban Pop 2006

-0.00368(.00707)

-0.00672(.00754)

-.01331(.00855)

Log Life Expectancy 2006

0.02821(.02452)

-.00236(.03094)

Log RGDPL 2006 0.00879(.00548)

R-Square .1569 .1637 .1654 .1735 .1892Model P-Value <.0001** <.0001** <.0001** <.0001** <.0001**** Significant at 1% alpha-level, * Significant at 5% alpha-level, γ Significant at 10% alpha-level

Independent Vars

Growth Rate 1980-2009

Intercept(114 obs)

-0.00226(.00575)

-0.04098*(.01688)

-0.05282**(.01685)

-0.023077**(.05079)

-.22426**(.04406)

Infrastructure 2006

0.00657**(.00209)

0.00585**(.00206)

0.01067**(.00261)

0.0057*(.00281)

0.01464**(.00285)

Log Population 2006

0.00248*(.00102)

0.01291**(.00375)

0.0174**(.00375)

0.00117(.00421)

Log Urban Pop 2006

-0.01095**(.0038)

-0.01538**(.00379)

-.0008891(.00406)

Log Life Expectancy 2006

0.04447**(.01205)

0.07588**(.01166)

Log RGDPL 1980 -0.0151**(.00248)

R-Square .0813 .1279 .1891 .2792 .4629Model P-Value .0021** .0005** <.0001** <.0001** <.0001**** Significant at 1% alpha-level, * Significant at 5% alpha-level, γ Significant at 10% alpha-level

Independent Vars

Growth Rate 2006-2009

Intercept 0.022**(.00302)

0.01682(.03241)

0.01131(.03108)

0.07817(.10278)

0.03809(.11507)

0.05406(0.09319)

Change in Infrastructure

0.11495γ(.06701)

0.11369γ(.06771)

0.13518*(.06512)

0.13681*(.06529)

0.13312*(.06556)

0.07908(.06485)

Log Population 2006

0.00031913

(.00199)

0.02077**(.00593)

0.01776*(.00739)

0.01367(.00908)

0.00187(.00936)

Log Urban Pop 2006

-0.02099**(.00577)

-0.01795*(.0073)

-0.01392(.00896)

-0.00232(.00907)

Log Life Expectancy 06

-0.01554(.02276)

0.0023(.03231)

0.01749(.02581)

Log RGDPL 2006(139 obs)

-0.00362(.00465)

Log RGDPL 1980(114 obs)

-0.01224*(.00474)

R-Square .021 .0212 .1087 .1118 .1158 .2003Model P-Value .0886γ .2328 .0014** .003** .0054** .0002**

** Significant at 1% alpha-level, * Significant at 5% alpha-level, γ Significant at 10% alpha-level

Developing Countries GDP09 = INF06 Grow = INF06 Grow = ΔINFIntercept(114 obs)

-2.98799γ(1.61328)

-0.10303γ(.05563)

0.18544(.12677)

Infrastructure 0.33771**(.09583)

0.01165**(.0033)

0.04775(.06425)

South Asia -0.15657(.19625)

-0.01811(0.0677)

0.02607γ(.01479)

Europe and Central Asia

-0.21659(.20416)

-0.00747(.00704)

0.02401(.01476)

Middle East and North Africa

-0.35254*(.15435)

-0.01216*(.00532)

-0.00588(.01097)

Sub-Saharan Africa -0.51952**(.16108)

-0.01791**(.00555)

-0.00913(.01171)

Central and Latin America

-0.31039*(.13848)

-0.0107*(.00478)

0.00857(.00947)

East Asia and Pacific -0.41675**(.14613)

-0.01437**(0.00504)

-0.0072(.01147)

R-Square .9334 .5399 .2891Model P-Value <.0001** <.0001** .0002**

** Significant at 1% alpha-level, * Significant at 5% alpha-level, γ Significant at 10% alpha-level

Countries By Income GDP09 = INF06 Grow = INF06 Grow = ΔINFIntercept(114 obs)

-5.56045**(1.24874)

-0.19174**(.04306)

0.05859(.0939)

Infrastructure 0.53804**(.08948)

0.01855**(.00309)

0.08567(.06389)

Low Income 0.04157(.10997)

0.00143(0.00379)

0.00984(.00793)

Lower-Middle Income 0.30224**(.10584)

0.01042**(.00435)

0.01843*(.00793)

Upper-Middle Income 0.3969**(.12628)

0.01369**(.00435)

0.02169*(.00943)

High Income 0.19137(.13178)

0.0066(.00454)

0.00992(.0104)

R-Square .9321 .5308 .2545Model P-Value <.0001** <.0001** .0002**

** Significant at 1% alpha-level, * Significant at 5% alpha-level, γ Significant at 10% alpha-level

East Asia and Pacific developing only: 24

American Samoa Malaysia SamoaCambodia Marshall Islands Solomon IslandsChina Micronesia, Fed. Sts ThailandFiji Mongolia Timor-LesteIndonesia Myanmar TuvaluKiribati Palau TongaKorea, Dem. Rep. Papua New Guinea VanuatuLao PDR Philippines VietnamEurope and Central Asia developing only: 23

Albania Kosovo Russian FederationArmenia Kyrgyz Republic SerbiaAzerbaijan Latvia TajikistanBelarus Lithuania TurkeyBosnia and Herzegovina Macedonia, FYR TurkmenistanBulgaria Moldova UkraineGeorgia Montenegro Uzbekistan

Kazakhstan Romania

Latin America and the Caribbean developing only: 30

Antigua and Barbuda Dominican Republic NicaraguaArgentina Ecuador PanamaBelize El Salvador ParaguayBolivia Grenada PeruBrazil Guatemala St. Kitts and NevisChile Guyana St. LuciaColombia Haiti St. Vincent and the GrenadinesCosta Rica Honduras SurinameCuba Jamaica UruguayDominica Mexico Venezuela, RBMiddle East and North Africa developing only: 13

Algeria Jordan TunisiaDjibouti Lebanon West Bank and GazaEgypt, Arab Rep. Libya Yemen, Rep.

Iran, Islamic Rep. Morocco

Iraq Syrian Arab Republic

South Asia all developing: 8

Afghanistan India PakistanBangladesh Maldives Sri Lanka

Bhutan Nepal

Sub-Saharan Africa developing only: 48

Angola Gambia, The NigeriaBenin Ghana RwandaBotswana Guinea São Tomé and PrincipeBurkina Faso Guinea-Bissau SenegalBurundi Kenya SeychellesCameroon Lesotho Sierra LeoneCape Verde Liberia SomaliaCentral African Republic Madagascar South AfricaChad Malawi South SudanComoros Mali SudanCongo, Dem. Rep. Mauritania SwazilandCongo, Rep Mauritius TanzaniaCôte d'Ivoire Mayotte TogoEritrea Mozambique UgandaEthiopia Namibia ZambiaGabon Niger Zimbabwe

Low-income economies ($1,005 or less) 35

Afghanistan Gambia, The MyanmarBangladesh Guinea NepalBenin Guinea-Bissau NigerBurkina Faso Haiti RwandaBurundi Kenya Sierra LeoneCambodia Korea, Dem Rep. SomaliaCentral African Republic Kyrgyz Republic TajikistanChad Liberia TanzaniaComoros Madagascar TogoCongo, Dem. Rep Malawi UgandaEritrea Mali ZimbabweEthiopia MozambiqueLower-middle-income economies ($1,006 to $3,975) 56

Angola India São Tomé and PrincipeArmenia Iraq SenegalBelize Kiribati Solomon IslandsBhutan Kosovo Sri LankaBolivia Lao PDR SudanCameroon Lesotho SwazilandCape Verde Marshall Islands Syrian Arab RepublicCongo, Rep. Mauritania Timor-LesteCôte d'Ivoire Micronesia, Fed. Sts. TongaDjibouti Moldova TurkmenistanEgypt, Arab Rep. Mongolia TuvaluEl Salvador Morocco UkraineFiji Nicaragua UzbekistanGeorgia Nigeria VanuatuGhana Pakistan VietnamGuatemala Papua New Guinea West Bank and GazaGuyana Paraguay Yemen, Rep.Honduras Philippines ZambiaIndonesia SamoaUpper-middle-income economies ($3,976 to $12,275) 54

Albania Ecuador NamibiaAlgeria Gabon PalauAmerican Samoa Grenada PanamaAntigua and Barbuda Iran, Islamic Rep. PeruArgentina Jamaica RomaniaAzerbaijan Jordan Russian FederationBelarus Kazakhstan SerbiaBosnia and Herzegovina Latvia SeychellesBotswana Lebanon South AfricaBrazil Libya St. Kitts and NevisBulgaria Lithuania St. LuciaChile Macedonia, FYR St. Vincent and the Grenadines

China Malaysia SurinameColombia Maldives ThailandCosta Rica Mauritius TunisiaCuba Mayotte TurkeyDominica Mexico UruguayDominican Republic Montenegro Venezuela, RBHigh-income economies ($12,276 or more) 70

Andorra Germany NorwayAruba Gibraltar OmanAustralia Greece PolandAustria Greenland PortugalBahamas, The Guam Puerto RicoBahrain Hong Kong SAR, China QatarBarbados Hungary San MarinoBelgium Iceland Saudi ArabiaBermuda Ireland SingaporeBrunei Darussalam Isle of Man Sint MaartenCanada Israel Slovak RepublicCayman Islands Italy SloveniaChannel Islands Japan SpainCroatia Korea, Rep. St. MartinCuraçao Kuwait SwedenCyprus Liechtenstein SwitzerlandCzech Republic Luxembourg Trinidad and TobagoDenmark Macao SAR, China Turks and Caicos IslandsEstonia Malta United Arab EmiratesEquatorial Guinea Monaco United KingdomFaeroe Islands Netherlands United StatesFinland New Caledonia Virgin Islands (U.S.)France New Zealand

French Polynesia Northern Mariana IslandsHigh-income OECD members 31

Australia Hungary PolandAustria Iceland PortugalBelgium Ireland Slovak RepublicCanada Italy SloveniaCzech Republic Israel SpainDenmark Japan SwedenEstonia Korea, Rep. SwitzerlandFinland Luxembourg United KingdomFrance Netherlands United StatesGermany New Zealand

Greece Norway

LPI AND INFRASTRUCTURE INDEX OF 2006 AND 2009 (SOURCE: WORLD BANK)

Country Name LPI 2006 LPI 2009Quality of trade and

transport-related infrastructure 2006

Quality of trade and transport-related

infrastructure 2009

World 2.7401333 2.866129 2.5804 2.6372258

Afghanistan** 1.21 2.24 1.1 1.87

Albania** 2.08 2.46 2.33 2.14Algeria** 2.06 2.36 1.83 2.06Angola** 2.48 2.25 2.25 1.69Argentina** 2.98 3.1 2.81 2.75Armenia 2.14 2.52 1.78 2.32Australia** 3.79 3.84 3.65 3.78Austria** 4.06 3.76 4.06 3.68Azerbaijan* 2.29 2.64 2 2.23Bahrain** 3.15 3.37 3.4 3.36Bangladesh** 2.47 2.74 2.29 2.49Belgium** 3.89 3.94 4 4.01Benin** 2.45 2.79 1.89 2.48Bhutan** 2.16 2.38 1.95 1.83Bolivia** 2.31 2.51 2.08 2.24

Bosnia and Herzegovina 2.46 2.66 2.26 2.22

Brazil** 2.75 3.2 2.75 3.1Bulgaria** 2.87 2.83 2.47 2.3

Burkina Faso** 2.24 2.23 1.89 1.89

Cambodia** 2.5 2.37 2.3 2.12Cameroon** 2.49 2.55 2 2.1Canada** 3.92 3.87 3.95 4.03Chad** 1.98 2.49 1.8 2Chile** 3.25 3.09 3.06 2.86China** 3.32 3.49 3.2 3.54Colombia** 2.5 2.77 2.28 2.59Comoros** 2.48 2.45 2.46 1.76Costa Rica** 2.55 2.91 2.43 2.56

Cote d'Ivoire** 2.36 2.53 2.22 2.37

Croatia 2.71 2.77 2.5 2.36

Cyprus** 2.92 3.13 2.91 2.94

Czech Republic 3.13 3.51 3 3.25

Denmark** 3.86 3.85 3.82 3.99Djibouti** 1.94 2.39 1.92 2.33Dominican Republic** 2.38 2.82 2.18 2.34

Ecuador** 2.6 2.77 2.36 2.38

Egypt, Arab Rep.** 2.37 2.61 2 2.22

El Salvador** 2.66 2.67 2.42 2.44Eritrea 2.19 1.7 2 1.35Estonia 2.95 3.16 2.91 2.75Ethiopia** 2.33 2.41 1.88 1.77Finland** 3.82 3.89 3.81 4.08France** 3.76 3.84 3.82 4Gabon** 2.1 2.41 2.4 2.09

Gambia, The** 2.52 2.49 2.33 2.17

Germany** 4.1 4.11 4.19 4.34Ghana** 2.16 2.47 2.25 2.52Greece** 3.36 2.96 3.05 2.94Guatemala** 2.53 2.63 2.13 2.37Guinea** 2.71 2.6 2.33 2.1

Guinea-Bissau** 2.28 2.1 2.25 1.56

Guyana** 2.05 2.27 1.78 1.99Haiti** 2.21 2.59 2.14 2.17Honduras** 2.5 2.78 2.32 2.31Hong Kong SAR, China** 4 3.88 4.06 4

Hungary** 3.15 2.99 3.12 3.08India** 3.07 3.12 2.9 2.91Indonesia** 3.01 2.76 2.83 2.54

Iran, Islamic Rep.** 2.51 2.57 2.44 2.36

Ireland** 3.91 3.89 3.72 3.76Israel** 3.21 3.41 3 3.6Italy** 3.58 3.64 3.52 3.72Jamaica** 2.25 2.53 2.03 2.07Japan** 4.02 3.97 4.11 4.19Jordan** 2.89 2.74 2.62 2.69Kazakhstan 2.12 2.83 1.86 2.66Kenya** 2.52 2.59 2.15 2.14

Korea, Rep.** 3.52 3.64 3.44 3.62

Kuwait 2.99 3.28 2.83 3.33

Kyrgyz Republic 2.35 2.62 2.06 2.09

Lao PDR** 2.25 2.46 2 1.95Latvia 3.02 3.25 2.56 2.88Lebanon** 2.37 3.34 2.14 3.05Liberia** 2.31 2.38 2.14 2Lithuania 2.78 3.13 2.3 2.72

Luxembourg** 3.54 3.98 3.86 4.06

Macedonia, FYR 2.43 2.77 2.29 2.55

Madagascar** 2.24 2.66 2.13 2.63Malaysia** 3.48 3.44 3.33 3.5Mali** 2.29 2.27 1.9 2Mauritius** 2.13 2.72 2.29 2.29Mexico** 2.87 3.05 2.68 2.95Moldova 2.31 2.57 1.94 2.05Mongolia** 2.08 2.25 1.92 1.94

Mozambique** 2.29 2.29 2.08 2.04

Namibia** 2.16 2.02 2 1.71Nepal** 2.14 2.2 1.77 1.8

Netherlands** 4.18 4.07 4.29 4.25

New Zealand** 3.75 3.65 3.61 3.54

Nicaragua** 2.21 2.54 1.86 2.23Niger** 1.97 2.54 1.4 2.28Nigeria** 2.4 2.59 2.23 2.43Norway** 3.81 3.93 3.82 4.22Oman** 2.92 2.84 2.86 3.06Pakistan** 2.62 2.53 2.37 2.08Panama** 2.89 3.02 2.79 2.63Papua New Guinea** 2.38 2.41 2 1.91

Paraguay** 2.57 2.75 2.47 2.44Peru** 2.77 2.8 2.57 2.66Philippines** 2.69 3.14 2.26 2.57Poland** 3.04 3.44 2.69 2.98Portugal** 3.38 3.34 3.16 3.17Qatar 2.98 2.95 2.63 2.75Romania** 2.91 2.84 2.73 2.25

Russian Federation 2.37 2.61 2.23 2.38

Rwanda** 1.77 2.04 1.53 1.63

Saudi Arabia* 3.02 3.22 2.95 3.27

Senegal** 2.37 2.86 2.09 2.64

Serbia 2.28 2.69 2.18 2.3

Sierra Leone** 1.95 1.97 1.83 1.61

Singapore** 4.19 4.09 4.27 4.22

Slovak Republic 2.92 3.24 2.68 3

Slovenia 3.14 2.87 3.22 2.65

Solomon Islands** 2.08 2.31 2 2.23

Somalia** 2.16 1.34 1.63 1.5

South Africa** 3.53 3.46 3.42 3.42

Spain** 3.52 3.63 3.51 3.58Sri Lanka** 2.4 2.29 2.13 1.88Sudan** 2.71 2.21 2.36 1.78Sweden** 4.08 4.08 4.11 4.03Switzerland** 4.02 3.97 4.13 4.17

Syrian Arab Republic** 2.09 2.74 1.91 2.45

Tajikistan 1.93 2.35 2 2Tanzania** 2.08 2.6 2 2Thailand** 3.31 3.29 3.16 3.16Togo** 2.25 2.6 2.25 1.82Tunisia** 2.76 2.84 2.83 2.56Turkey** 3.15 3.22 2.94 3.08Uganda** 2.49 2.82 2.17 2.35Ukraine 2.55 2.57 2.35 2.44

United Arab Emirates 3.73 3.63 3.8 3.81

United Kingdom** 3.99 3.95 4.05 3.95

United States** 3.84 3.86 4.07 4.15

Uruguay** 2.51 2.75 2.38 2.58Uzbekistan 2.16 2.79 2 2.54

Venezuela, RB** 2.62 2.68 2.51 2.44

Vietnam** 2.89 2.96 2.5 2.56

Yemen, Rep. 2.29 2.58 2.08 2.35

Zambia** 2.37 2.28 2 1.83

** Country is part of 114 observations with initial income in 1980

Figure: International Logistics Performance Index (LPI) ranking in 2009