Trade Structure and Economic Growth

Raja Kalia, Fabio Méndez a, and Javier Reyesa,b

a Department of Economics, Business Building Room 402, Sam M. Walton College of Business, University of Arkansas, Fayetteville, AR, 72701, USA. b Corresponding author. Tel: +(479) 575-6079. E-mail address: jreyes@walton.uark.edu

October 30th, 2006.

Acknowledgements: The authors greatly benefited from comments and suggestions provided by participants of the Midwest International Economics Group Fall 2005 Meetings and the Fifth Workshop of the Regional Integration Network (sponsored by LACEA), as well as Rossitza B. Wooster, Christopher Laincz, Christopher P. Ball, and Ari Van Assche. Additionally the authors would like to acknowledge that partial research funding was provided by the Summer Research Grant program of the Sam M. Walton College of Business at the University of Arkansas.

Trade Structure and Economic Growth

Abstract

How do the number of trade partners and the concentration of trade among partners affect

the economic growth of a country? We refer to these characteristics as the structure of

trade, and explore this question empirically in this study. We find that the structure of

trade, independently from of the level of trade itself, has an important effect on the rate of

economic growth. The results of the study suggest that the number of trading partners is

positively correlated with growth across all countries, and this effect is more pronounced

for rich countries. Trade concentration is positively correlated with growth for all

countries, and the effect is concentrated in poor countries. Previous work has overlooked

these characteristics of trade, though we find them to be quite relevant and that they could

lead to new ways of understanding the trade-growth relationship.

JEL classifications: O1; F43; O47; F0; O4 Keywords: Trade; Growth; Trade Structure; Trade Concentration.

1

1 Introduction

The effects of international trade on economic growth have been the subject of a rich

debate. Still, the main question of whether (and how) trade enhances growth remains

unanswered, as the conclusions of both theoretical and empirical studies are highly

sensitive to changes in the assumptions made, the variables used to measure trade

openness, the sample data used, and the econometric technique employed (see, for

example, Grossman and Helpman (1991), Matsuyama (1992), Walde and Wood (2005),

Rodriguez and Rodrik (2000), Yannikkaya (2003)).

Noticeably, most of the analysis within this debate involves trade measures regarding

export and import volumes or shares, trade policies regarding tariffs or custom barriers,

and related measures of trade openness1. Little or no attention has been given to the trade

strategies followed and the types of trade relations established; even though they too have

been subject to noticeable changes. Just in the last ten years, over 130 regional trade

agreements have been reported to the World Trade Organization (compared to 124

reported during 1948-1994)2 and key players, like the United States and the European

Union, have adopted a more aggressive pace in their bilateral trade negotiations.

In fact, the changes experienced over the last decades of globalization are more

pronounced in terms of the structure of trade than in the volume of trade itself (or in the

average trade policies). As shown in Table 1, while the share of total trade in GDP for

the average economy went from 58.3% in 1970 to 88.5% in 2003, the average number of

trading partners more than doubled as it went from 46.4 to 93.9 in that same period.

Similarly, as shown in Table 2, when measuring the trade concentration with the standard

Herfindahl index, trade concentration decreases from 2201 to 1580 for the average

2

economy between 1970 and 2003. Today, most countries are less reliant on one trading

partner (or group of partners) than they were 30 years ago.

There are reasons to believe that such characteristics regarding the structure of trade

have significant implications for economic growth. In this paper, we study these

implications. Specifically, in this paper, we assemble a dataset that tracks the number of

trading partners for a given country and the volume of exports and imports. We then

construct a measure of the concentration of trade and use these variables to examine the

relationship between the international trade structure and economic growth in a panel of

over 155 countries for the period 1980-2000.

Our results provide new insights into the mechanisms that make international trade a

positive influence for growth. We find that the structure of trade, independently from

trade openness, has an important effect on the rate of economic growth. The number of

trading partners is found to be positively correlated with growth across all countries; this

effect is found to be greater for rich countries. Trade concentration is also found to be

positively correlated with growth for all countries in general; but this correlation is

mostly attributed to poor countries only. These results are robust to changes in the

econometric specification used, the number of explanatory variables included, and

corrections for heteroskedasticity and endogeneity.

In terms of policy, understanding the effects of trade structure on growth is important.

As our empirical results suggest, the effects of trade structure on growth vary

significantly between poor and rich economies. Additionally, the results present

evidence supporting the argument that trade relations established with rich countries are

3

more beneficial than those established with poor countries. Understanding these

differences should yield better policy prescriptions.

Even though the structure of trade is a complex concept that might be relevant in more

than one sense, we regard our efforts as a first step in this new direction. Empirical

measures of trade characteristics or trade patterns and configuration are scarce.

Theoretical models that incorporate those elements and guide our intuition are rare as

well. As this paper demonstrates, however, the study of trade structure characteristics

could lead to new ways of understanding the trade-growth relationship.

Several other studies have gone beyond the simpler measures of trade. Dollar

(1992), Sachs and Warner (1995) and Warcziarg (1998), for example, create their own

indicators of openness. However, as pointed out by Rodriguez and Rodrik (2000), these

measures might not achieve the purpose they were conceived for, since they are likely

proxies for a wide range of policy and institutional differences. The measures of the

number of trading partners and the concentration of trade used in this paper, in contrast,

are clearly related to trade and are simpler to interpret.

Additionally, there is a related branch of the literature that has looked for evidence

regarding the effects of trade on total factor productivity. Representative papers of this

literature include Coe and Helpman (1995), Keller (1998) and Edwards (1998). While

these papers look for changes on productivity that arise as the result of increased trade

openness (ratio of total trade to GDP), we look for changes that arise as the result of the

structure of trade, independently from trade openness.

The remainder of the paper is organized as follows: The next section describes the

conceptual arguments that characterize the expected empirical relationship between our

4

measures of trade structure and the rate of output growth. Section 3 presents the

econometric specifications employed to test these arguments. Section 4 introduces the

dataset and discusses the trade structure variables employed. Section 5 presents the

empirical results obtained and, finally, the last section summarizes our conclusions.

2 Theoretical Background

The likely effects of international trade on economic growth can be formally derived

from endogenous growth models that study the creation, diffusion and adoption of better

production technologies. Some of these models are now well known in the literature, as

is the case for Romer (1990), Grossman and Helpmann (1991), and Rivera-Batiz and

Romer (1991), and the idea that trade promotes growth through the transmission and

creation of knowledge is widely accepted among economists3.

In these models, the trade of goods and services can generate positive effects on

economic growth from at least two sources: First, international trade may generate

“knowledge spillovers” that expand the number of intermediate goods known by

domestic producers, where these intermediate goods can be understood as new ideas,

designs, or managerial processes that lead to increased productivity. That is,

international trade may act as a diffusion mechanism for foreign technologies.

Second, international trade may produce incentives for local research and

development. Trade expands the number of potential buyers and thus, the potential for

economic profits associated with innovation, brand recognition, patent registrations, and

any improvements over competing firm’s products. Trading goods with foreign markets

also forces the producer to react to a new environment and innovate in the process.

5

These innovations might take the form of necessary adjustments to packaging and

transportation methods due to local climates and infrastructure, product adjustments

made to entice consumers with different tastes, innovations in the production process that

comply with foreign sanitary regulations, etc.

What is still to be explained are the actual mechanisms that diffuse technology across

countries and the process through which countries adopt these newer technologies. In

this paper, we argue that both the number of trading partners and the concentration of

trade among partners are important elements of those mechanisms.

2.1 Economic growth and the number of trading partners

Every country generates knowledge through research or experience; some countries

generate more knowledge than others. But when countries trade the goods and services

that embody those original ideas, the knowledge is indirectly transferred to the trading

partner who learns by induction. In this manner, international trade facilitates the

transmission of technologies across countries; where the amount of technological

diffusion depends on how much a country trades and with how many partners.

In our econometric specification, besides including a measure of trade openness, we

also include the number of trading partners. For any given amount of goods traded, the

exposure to new ideas is greater when the number of countries involved in the trade

grows larger, as each partner contributes an original amount of knowledge. Thus, ceteris

paribus, countries with more trading partners would be expected to face a greater number

of foreign technologies and to experience greater economic growth as the adoption of

those technologies increases productivity.

6

In addition, as the number of trading partners increases, the potential market in which

domestic producers can sell their products expands both in size and diversity. Both

effects are expected to support growth. The expanded size of the potential market attracts

local and foreign investments. These investments, in turn, have been shown to play an

important role for technological diffusion and innovation (Grossman and Helpman

(1991)). Noticeably, the size of the potential market is related to the number of trading

partners but is not necessarily related to traditional measures of trade openness that

capture actual volumes of imports and exports.

In turn, the expanded diversity of the market puts all producers in contact with a wider

rage of consumer tastes, government regulations, climates, etc. As local producers seek

to compete in more foreign markets, they accommodate to the particular characteristics of

these markets and innovate in the process. The greater the number of trading partners,

the greater the innovations that become necessary. As mentioned before, these

innovations might take the form of necessary adjustments to packaging and transportation

methods, product adjustments made to entice alien consumers, innovations in the

production process that comply with foreign sanitary regulations, and others.

Finally, as the number and diversity of partner countries increases, the number of

potential competitors for the local market increases as well. Competition, in turn, leads

to higher productivity and greater economic growth. Evidence on the effects of

competition on productive efficiency can be found in Vickers and Yarrow (1991) and

Bourbakri and Cosset (1998).

The marginal benefit of an additional trading partner, however, could be different in

poor economies than in rich economies. On the one hand, if newer technologies increase

7

the productivity of older technologies, then the effect of an additional trading partner on

growth should be smaller for a poor economy since they have a smaller stock of

knowledge to begin with. On the other hand, because the stock of knowledge is smaller

in poor countries, the contribution of an additional trading partner in terms of new

knowledge (and thus, its impact on growth) is likely to be greater for undeveloped

economies. While these effects operate in opposite directions, they both suggest

asymmetry between rich and poor countries.

Similarly, the effects of having a greater number of rich (technologically superior)

trading partners could be different from those of having a greater number of poor

(technologically inferior) trading partners. If the benefits of trade come mainly from

technological diffusion, then it could be expected that a country learns more by trading

with a rich partner than by trading with a poor partner; as the imports from richer

countries are likely to be composed of relatively more technology intensive goods (see

Schneider (2005) for an empirical analysis of a similar argument).

In our econometric specifications we explore both the possibility that an increase in

the number of trading partners generates an effect on economic growth that differs

between poor and rich countries, and the possibility that the effect of increasing the

number of rich trading partners is different from the effect of increasing the number of

poor trading partners.

2.2 Economic growth and the concentration of trade

There are also reasons to expect that the concentration of trade among trading partners

may have a positive impact on output growth. First, whenever technologies from

8

different countries act as substitutes and compete with each other, then concentrating

trade on a partner(s) with a single technological configuration could facilitate technology

adoption. The implementation and dissemination of foreign innovations is made easier

when more people become familiar with the language and the conventions of these

innovations (see Yanikkaya (2003), for related ideas).

Consider the case of an economy faced with two alternative technologies. Both

technologies are equally efficient, but they were conceived by different sources and do

not produce the desired results when combined. In this case, the economy might be

better off by adopting only one of them (instead of both); since this might speed up the

learning process and the collaboration across industries that use the same technology.

Examples of such technologies can be found in American and European video formats

and home heating technologies, alternative medical procedures, alternative drug

prescriptions and related drug interactions, or in economic papers that model time in

either discrete or continuous form.

Second, given the total number of partners and the total volume of trade, it may be

cost-efficient for countries to concentrate trade on one or a few partners. Concentrating

trade might help minimize trade costs associated with the congestion of insufficient

infrastructure like ports, airports, diplomatic posts or costumes personnel. Thus, where

the trade related infrastructure is not well developed, concentrating trade might lower

transportation costs. These costs have been shown to affect trade significantly. Frankel

et al. (1995), for example, point to transportation costs as one main reason why trade

blocs may form. Similarly, it is possible that trade-related public policies that promote

local investments become easier and cheaper to implement when trade is concentrated

9

among few partners. Such policies might include, for example, the dissemination of

foreign regulation manuals, public information regarding national and foreign trade

barriers, the coordination of national and international sanitary regulations, etc.

Finally, small economies that depend on certain industries for a significant share of

their GDP might benefit from greater trade concentration as far as these specific

industries benefit from greater concentration. As suggested by Pickering and Sheldon

(1984), concentration at the industry level might lead to economies of scale in production

and marketing, as well as stronger competitive positions.

As with the number of trading partners, the asymmetry between rich and poor

countries can also be expected from the effects of trade concentration on economic

growth. Because rich countries hold and create more knowledge to begin with, have a

more diversified economy, have better infrastructure and more efficient governments,

they would be expected to benefit less from trade concentration than poor countries do.

3 Econometric Specifications

In what follows, we marshal the conceptual arguments presented above to explore the

following hypothesis:

− There is an empirical relationship between trade structure and economic growth.

Past empirical studies that fail to control for trade structure may suffer from

omitted variable biases.

− An increase in the number of partners is associated with an increase in the rate of

economic growth. This effect differs between rich and poor countries.

10

− Given the volume of trade and the number of trading partners, an increase on the

concentration of trade is associated with an increase in the rate of economic

growth. This effect is smaller for rich countries.

Empirically, the relationship between trade and growth has been tested in a variety of

econometric specifications. The earlier literature, represented by Feder (1982), focused

on the relationship between exports and growth. More recent empirical studies, however,

have used an empirical formulation closer to that of Barro (1991) or Levine and Renelt

(1992). We will use the latter, more recent specification for most of the analysis and use

the former only as a robustness test of our results.

Following Barro (1991) and Levine and Renelt (1992), the rate of growth of GDP per-

capita ( yy& ) is regressed against a vector Cr

of standard explanatory variables and an

additional set of explanatory variables that capture the issues of interest. The vector Cr

is

composed of measures of the initial per-capita level of income (y0), the rate of population

growth ( LL& ), the secondary school enrollment ratio (SED), and the ratio of investment

to GDP (I/Y)4.

With respect to the variables measuring trade, a wide variety of measures have been

used in the empirical growth literature. The most commonly used measure is the ratio of

total trade (exports plus imports) to GDP (see, for example, Easterly, Loayza and Montiel

(1997), or Frankel and Romer (1999)). Other, less common measures include composite

indices of openness (Dollar (1992), Sachs and Warner (1995), Wacziarg (1998)) and

measures of trade restrictions and policies such as tariffs and export taxes (as in Harrison

(1996) and Edwards (1988)). Since our focus of attention is not on the volume of trade or

11

the degree of openness in economic policies, but on the structure of trade, we choose to

utilize the simpler and more popular variable of Total Trade as a percentage of GDP to

control for trade openness5.

Finally, we include our measures of trade structure in a vector →

TS of additional

explanatory variables. The resulting econometric specification takes the following form:

εψφβα +⋅+⋅+⋅⋅+=→

TSTradeCyy r&

(2).

Throughout the empirical results section we either conduct the standard OLS regressions

(using the averages for the whole sample consider) to estimate equation (2), or use five-

year averages for all variables and conduct fixed effect regressions6. When conducting

fixed effects regressions, all regressions include a country-specific term as well as a

period-specific term.

As pointed out by authors like Easterly et al. (1997) and Temple (1999), there are

several reasons why it is preferable to take advantage of the time-series aspects of our

data in addition to the cross-sectional variation. First, given that the variables of interest

vary significantly over time, the use of cross-sectional averages misses out on much of

the information emerging from changes that occur over time in each country. Second, the

use of panel data allows us to control for omitted time-specific effects and country-

specific effects and, thus, to diminish the endogeneity biases associated with institutional

or sociopolitical factors, and world wide shocks. In this regard, the use of five year

averages allows one to take advantage of the time series aspects of the data while still

capturing long-run aspects of economic growth.

12

As mentioned earlier, we use the alternative specification proposed by Feder (1982)

to test the robustness of our results. Feder (1982) tested a two-sector model of growth in

which export goods generate positive externalities for domestic production. In his basic

econometric formulation, the rate of growth of output ( YY& ) is determined by the

investment to output ratio (I/Y), the rate of population growth ( LL& ) and the product of

the exports to output ratio (X/Y) and the rate of growth of exports ( XX& ).

We modify Feder’s formulation in two ways. First, when studying the effects of

exports on growth we also control for the structure of trade as described by our data.

Second, we reformulate the specification so that per-capita income growth (y) is used as

the dependent variable. The resulting specification is summarized in equation (3):

εχγβα +⋅+⎟⎟⎠

⎞⎜⎜⎝

⎛⋅⋅+⎟⎟

⎠

⎞⎜⎜⎝

⎛⋅+⎟

⎠⎞

⎜⎝⎛⋅=

→

TSYX

XX

LL

YI

yy &&&

)( (3)

Other authors contemporaneous to Feder (1982), like Balassa (1978) or Tyler (1981)

used simpler one-sector models for which the relevant exports-related regressor in the

econometric specification was ( )XX& instead of ( ) ( )YXXX ⋅& as in equation (3). In

our robustness tests we allow for these possibilities as well.

4 Data Description and Trade Structure Variables

The empirical analysis uses data from a large sample of countries during the period

1980-2000. Values of annual population growth (POP), real income per capita (GDP),

annual GDP growth, secondary school enrollment rates (SED), the investment share of

GDP (Investment), the share of total trade to GDP (Trade Openness), and the share of

13

government expenditures in GDP (Government) were extracted from World Bank's

World Development indicators (2004).

With respect to the variables capturing the structure of trade, we incorporate two

variables into the analysis: the total number of trade partners a country had at each point

in time and the concentration of trade volumes among these partners. The number of

trading partners for each country was extracted from the Direction of Trade Statistics

database (DOT) from the International Monetary Fund.

Also from the DOT database, we use the monetary values of total imports and total

exports for each country/trading partner pair and construct a Herfindahl-Hirschman

concentration index of trade (HHI) for all countries. This index measures the

concentration of trade among all trading partners; where a low number indicates low

concentration. The index was computed as follows:

2

∑∑ ⎟⎟

⎟

⎠

⎞

⎜⎜⎜

⎝

⎛=

→

→N

jN

j ji

ji

T

THHI

Where N and jiT → denote the total number of trading partners and the total value of trade

(exports plus imports) between countries i and j, respectively7. Table 2 presents

summary statistics for the HHI across regions of the world and confirms our initial claim

that the changes on the trade structure have been more pronounced than changes on the

levels of trade.

It should be noted that even though the HHI index described above is a function

of the number of trading partners, these two variables are not necessarily related and, a

priori, there should not be a multicolinearity problem for the regression analysis. The

addition of a new trading partner could result in a higher, lower, or constant degree of

14

trade concentration. The actual result depends on the level of trade emerging from the

addition of the new trading partner, with respect to the already existing trade volumes.

Having said this, there is a possibility that the relationship between the number of

trading partners and trade concentration is such that, by chance, there is multicolinearity

between the variables. To explore this possibility, we computed the correlation between

the changes in the number of trading partners with the changes in trade concentration

(HHI), the results show that the correlation coefficient between these two variables is

0.14, suggesting that multicolinearity between these variables is not an issue for the

econometric analysis.

Appendix 1 shows the source and the definition of all data variables used in this

study. Additionally, Table 3 offers a summary of descriptive statistics for these variables

for each of the samples explored; all countries, poor countries and rich countries.

5 Empirical Estimation Results

We start by using five year averages of all relevant variables and conducting fixed

effects estimations of equation (2). The specific time periods for the averages are 1980 –

1984, 1985 – 1989, 1990 – 1994, and 1995 – 1999. All estimations included a country

specific term as well as a period specific term. The presence of both country and period

specific effects could not be rejected for any of the estimated regressions presented here

based on a Likelihood Ratio test. Additionally, the results presented are corrected for

heteroskedasticity using White’s correction method.

Tables 4, 5 and 6 present the results of estimating equation (2) for the whole sample

of countries, the sample of poor countries, and the sample of rich countries, respectively.

15

In all these tables, column (1) presents the estimation results for the base specification

where the trade structure variables are not included, while columns (2) - (6) present those

obtained for specifications that include the trade structure variables. Columns (2) – (4)

present the main results for the trade structure variables and columns (5) - (6) explore the

possibility that the effect of increasing the number of rich trading partners is different

from the effect of increasing the number of poor trading partners. To this effect we

compute the number of rich and poor trading partners and include them separately in the

regression analysis. A country was categorized as poor if its income in 1980 was smaller

than the income of the median economy in 1980.

The results of the basic specification regarding trade openness as measured by total

trade to GDP ratio resemble those of other studies that use similar methodologies and

data (see, for example, Harrison (1996), Yannikaya (2003)). Trade openness has a

positive and significant coefficient for the poor countries sample, a mostly positive but

statistically insignificant coefficient for the total sample, and a negative but mostly

insignificant coefficient for the rich countries sub-sample. It is only for poor countries

that the level of trade openness appears to impact growth positively and significantly.

With respect to the number of trading partners, the estimated coefficient is positive and

statistically significant for almost all of the econometric specifications across the three

samples explored in the analysis8. Additionally, the value of the estimated coefficients

for the number of trading partners imply that the effects of this variable on GPD growth

rates is of considerable economic magnitude. Focusing on Table 5, where the results of

the estimation for the poor countries sample are presented, we see that the estimated

coefficient for the number of trading partners, in column (4), is approximately 0.00052.

16

This coefficient implies that, everything else constant, increasing the number of trading

partners by 10 (just a fourth of the standard deviation) is associated with an increase in

the rate of growth of 0.52 percentage points9. Given that the average rate of annual

growth in most undeveloped countries lies beneath one percent, the potential benefits of

increasing the number of trading partners appears to be substantial.

When the sample is divided into rich and poor countries, the estimated effect of an

additional trading partner is much smaller for the poor countries sub sample than for the

rich countries sub sample. The average value for the estimated coefficient is

approximately 0.0004 for poor countries and 0.0008 for rich countries (see Tables 5 and

6). As explained before, poor countries are likely to benefit less from additional trading

partners when different technologies act as complementary inputs and when the

acquisition of knowledge is mostly channeled through one relatively advanced trading

partner.

We now turn to the discussion of the effects that trade concentration has on economic

growth. The estimated coefficients for the trade concentration variable were, for most of

the cases considered, positive and statistically significant for both the total sample and the

poor countries sub sample. In contrast, for the rich countries sub sample, the estimated

coefficient was often insignificant and in some cases negative. Since the level of

concentration increases as the HHI index increases, the results imply that poor countries

benefit from more concentrated trade while the evidence for the rich countries is mixed at

best.

The estimated coefficients for the index of concentration show that the economic

magnitude of the effects from trade concentration could also be considerable. For

17

example, the coefficients for the poor and rich countries sub samples, reported in column

4 of Tables 5 and 6, imply that for poor countries a one standard deviation increase in the

level of trade concentration (HHI index) leads to an increase in the GDP growth rate of

0.32 of a standard deviation (1.42 percentage points), while for rich countries the

corresponding increase in the GDP growth rate is 0.19 of a standard deviation (0.97

percentage points)10. Again, the economic impact is significant.

As noted before, columns (5) - (6) in Tables 4 through 6 present the results obtained

for the analysis of the hypothesis regarding technological diffusion arising mainly from

rich trading partners. The evidence provided from this expanded analysis generally favors

this argument. The estimated coefficient for the number of rich trading partners is mostly

positive and statistically significant for all samples studied, while the coefficient for the

number of poor trading partners is always negative, but only significant for the whole

sample and the rich countries sub sample.

The conclusion that emerges from the arguments stated in section 2 and the empirical

evidence presented above is that the elements that characterize the structure of trade do

influence the rate of economic growth in a significant manner beyond the effects of trade

openness. The evidence also supports our hypothesis regarding the differences between

rich and poor countries. While both groups of countries seem to benefit from a greater

number of trading partners, rich countries appear to benefit more and these benefits (for

all countries) come mainly from trading with rich countries. Finally, while more trade

concentration is beneficial in general, it seems to be more significant for poor countries

than for developed economies.

18

5.1 Robustness of the Empirical Findings: Alternative Specification

As a first robustness test for our results, we use the alternative econometric

specification introduced by Feder (1982). Once again, the main modifications are the

inclusion of the number of countries to which a given country exports to and the HHI

index as explanatory variables of the economic growth rate. The resulting base

econometric specification is that presented in equation (3). We should emphasize here

that the specification presented in equation (3) is based on the argument that the effects of

trade on economic growth arise from exporting activities, therefore the HHI index used in

these regressions is computed only using export data for each country.

Tables 7 presents the results for the whole sample of countries, the poor countries

sample, and the rich countries sample, respectively. The estimated coefficients for the

export growth term in Table 7 (all countries) are not statistically significant. At first

glance, this result seems to be at odds with previous studies that reported a positive and

significant coefficient for this variable. Noticeably, however, past studies often based

their main results on samples of poor or “less developed” countries (including Feder

(1982), Balasa (1978) and Tyler (1981)). In columns (3) through (6), where we consider

the two sub samples, poor and rich countries respectively, the results are more similar to

prior studies. The coefficient for exports growth is positive and significant poor countries,

while it is negative for rich countries.

With respect to the effect of the number of trading partners on growth, our previous

conclusions remained unaltered. The estimated coefficient for the number of rich trading

partners is mostly positive and significant across the three samples considered, while the

one for the number of poor trading partners is negative and often insignificant. Thus, the

19

results from this alternative specification also suggest that the benefits of established

trading relationships on economic growth arise from trade links with rich countries.

Similarly, the results obtained for the trade concentration match those obtained with

the original econometric specification to the extent that trade concentration affects

growth positively. The estimated coefficients for the trade concentration, in this case

measured by export concentration, on the GDP growth rate are always positive and

significant.

5.2 Robustness of the Empirical Findings: Addressing endogeneity

A separate concern regarding the robustness of our results is that the number of

trading partners could be contemporaneously correlated with the error term, i.e., presence

of endogeneity. The logic behind this concern is that as a country’s GDP expands, this

country will search for other markets in which to sell its products and/or other countries

will look to establish trading relationships with this country. If this is so, then the results

presented so far might be biased.

With respect to the trade structure variables, there are a couple of reasons why we do

not think endogeneity biases are too severe in our results. First, although one could be

concerned that the rate of growth of output might influence the rate of growth in the

number of partners, it does not seem apparent why the rate of growth of output should

affect the total number of trading partners. We are considering level effects, while the

endogeneity concern seems more plausible for rate of growth effects. Second, trading

relationships (and in some cases bilateral trade volumes) are at least partially influenced

by governments’ international trade policies; which can be viewed as exogenous.

20

Finally, regarding trade concentration levels, there is no reason to believe that higher or

lower GDP growth rates could lead to higher or lower trade concentration among trading

partners. Therefore we do not believe that endogeneity could be an issue when using

trade concentration variable.

Still, we make an effort to correct for any potential endogeneity by using Instrumental

Variables in our previous estimations. As a first step, we perform an OLS regression for

the whole sample using long run averages (1980-2000) for all variables included in the

estimation of equation (2). We then compute the IV estimation and compare the results.

As for our choice of instrumental variables, we use physical access to international waters

and tropical climate, both obtained from the Sachs and Warner dataset11, and a variable

for the type of political regime, obtained from Easterly and Lu’s Global Development

Network Growth Database12. Geographic and socio-political variables should serve as

good instruments for the number of trading partners since trade relationships are partly

determined by geographical characteristics of countries, as well as political relations.

The results of these estimations for the total sample are presented in columns (1) and

(2) of Table 8. As mentioned above, the fixed effects approach is preferable to OLS,

therefore we just use this set of results as a first step in our efforts to address the

possibility of our results being affected by endogeneity. The results presented in Table 8

show that the effects of the total number of trading partners are strongly significant for

both the OLS and the OLS - IV estimation13.

Unfortunately, the instruments used for the OLS regression cannot be used for the

fixed effects regressions because they do not change over time. Therefore, following

Bloom et. al. (2004) and Ranis et. al. (2000), we use predetermined but not strictly

21

exogenous variables as instruments for the IV estimation. The predetermined variables

that are used in the auxiliary regression are the values for the previous period for GDP

per capita, trade openness, and the number of trading partners. The argument for using

these variables in the auxiliary regression is that endogneity, if present, emerges because

an increase in economic growth could result in a higher level of trade and higher number

of trading partners. Therefore by using the lag values of the number of trading partners,

GDP per capita, and trade openness as instrumental variables, we break the possible

contemporaneous effect between economic growth and the number of trading partners.

This methodology is justified since the correlation between the residuals at time t are not

correlated with the lagged variables. This approach can also be used for the cases where

we split the number of trading partners into rich and poor. The only variation is that in

that case the auxiliary regression uses the lagged value of the number of rich and poor

trading partners.

Columns (3)-(8) of Table 8 present the results obtained for the three samples

considered in the analysis. We present the results for the regressions for which the

number of trading partners was divided into rich and poor partners, but the same

conclusions are reached for the case where the total number of trading partners is

considered. For comparison purposes, columns (3), (5), and (7) report the results for the

original panel regression for the adjusted sample (i.e. removing the first cross section in

order to match the time period considered for the IV estimation). Columns (4), (6), and

(8) report the results for the IV estimation.

It is clear from the results presented that the conclusions regarding the relevancy of

the number of rich and poor trading partners for rich countries and the whole sample still

22

hold. A higher number of rich trading partners has a positive effect on the economic

growth rate for these two samples, while a higher number of poor trading partners has a

negative effect. Regarding trade concentration, the results show that it affects growth

positively for the overall sample and the poor countries sub sample, but has a negative

effect on economic growth for the rich countries. Once again consistent with the original

results presented in Tables 4 through 6.

Finally, the results presented in columns (3), (5), and (7) of Table 8 can also be

used as a robustness check for our original results when these results are compared to

those presented in column (6) of Tables 4, 5 and 6. In this case the robustness check is

with respect to the sample period. The results of Table 8 are based on a sample period

that would correspond to the cross sections of 1985 – 1989, 1990 – 1994, and 1995 –

1999, while those presented in Tables 4, 5, and 6 also include the cross section of 1980 –

1984. When the results are compared across the two sample periods, the signs are the

same, and the magnitudes and statistical significance are relatively similar. This suggests

that our results are robust to sample period selection as well.

6 Conclusions and Future Research

There are reasons to believe that the structure of trade has significant implications for

the rate of economic growth. Most empirical studies overlook the conditions under

which trade takes place and concentrate mostly on trade volumes and measures of

openness. In this paper, instead of concentrating on the level of trade volumes or the

degree of openness of an economy, we give attention to the structure of trade as

23

characterized by the number of trading partners a country has, and the concentration of

trade among those partners.

We find that the number of trading partners is positively correlated with growth across

all countries; this effect is greater for rich countries. Trade concentration is positively

correlated with growth for all countries in general; this effect is mostly attributed to poor

countries. These results are robust to changes in the econometric specification used, the

number of explanatory variables included, and corrections for heteroskedasticity.

In terms of policy, our findings may inform policy makers’ as well as the international

community that ponders the benefits of international trade agreements. In particular, our

findings suggest that high trade concentration, such as is the case for developing

countries whose trade is concentrated with a major economic power14, may not be a

matter of serious concern.

24

Appendix 1: Variable Definitions

Real income per capita (GDP): Yearly Gross Domestic Product per-capita, constant

1995 USA dollars. World Development Indicators (WDI), 2004

GDP Growth: Annual growth rate of GDP per-capita. WDI, 2004

Annual population growth (POP): Average annual population growth. WDI, 2004

Secondary education (SED): Gross Enrollment Rate in secondary schooling. WDI,

2004

Investment share of GDP (Investment): Yearly Gross Fixed Capital formation (% of

GDP). WDI, 2004

Trade Openness: Total Exports plus Total Imports as percentage of GDP. WDI, 2004

Government: General government final consumption expenditures (% of GDP). WDI,

2004

Number of Trading Partners: Total number of countries with whom a given country

trades (exporting or importing). Direction of Trade Statistics, IMF 2004

25

References Balassa, Bela (1978), “Exports and Economic Growth: Further Evidence”, Journal of

Development Economics, vol. 5, no. 2, pp. 181-189.

Barro, Robert J. (1991), “Economic Growth in a Cross-Section of Countries”, Quarterly

Journal of Economics, vol. 106, no. 2, pp. 407-443.

Bloom DE, Canning D, Sevilla J. (2004) “The effect of health on economic growth: a

production function approach”, World Development, vol. 32, pp 1-13.

Boubakri, Narjess and Jean-Claude Cosset (1998). “The Financial and Operating

Performance of Newly Privatized Firms: Evidence from Developing Countries”, The

Journal of Finance, vol. 53, no. 3, pp 1081-1110.

Coe, David T. and Elhanan Helpman (1995), “International R&D Spillovers”, European

Economic Review, vol. 39, no. 5, pp 859-887.

Dollar, David (1992), “Outward-Oriented Developing Economies Really Do Grow More

Rapidly: Evidence from 95 LDCs, 1976-1985”, Economic Development and Cultural

Change, vol. 40, no. 3, pp. 523-544.

Easterly, William, Norman Loayza and Peter Montiel (1997), “Has Latin America’s post-

reform Growth Been Disappointing?”, Journal of International Economics, vol. 43,

no. 2, pp. 287-311.

Edwards, Sebastian (1993), “Openness, Trade Liberalizations, and Growth in Developing

Countries”, Journal of Economic Literature, vol. 31, no. 3, pp. 1358-1393.

Edwards, Sebastian (1998), “Openness, Productivity and Growth: What Do We Really

Know?”, The Economic Journal, vol. 108, no. 447, pp. 383-398.

Feder, Gershon (1982), “On Exports and Economic Growth”, Journal of Development

Economics, vol. 12, no. 1, pp. 59-73.

26

Frankel, Jeffrey A. and David Romer (1999), “Does Trade Cause Growth?” The

American Economic Review, vol. 89, no. 3, pp. 379-399.

Frankel, Jeffrey A. Ernesto Stein and Shang-jin Wei (1995), “Trading Blocs and the

Americas: The Natural, the Unnatural, and the Super-natural”. Journal of

Development Economics, vol. 47, no. 1, pp. 61-95.

Grossman, G. and E. Helpman (1991), Innovation and Growth in the Global Economy,

MIT Press, Cambridge, MA.

Harrison, Ann (1996), “Openness and Growth: A Time-Series, Cross-Country Analysis

for Developing Countries”, Journal of Development Economics, vol. 48, no.2, pp.

419-447.

Keller, Wolfgang (1998), “Are International R&D Spillovers Trade-Related? Analyzing

Spillovers Among Randomly Matched Trade Partners”, European Economic Review,

vol. 42, no. 8, pp. 1469-1481.

Levine, Ross and David Renelt (1992). A Sensitivity Analysis of Cross-Country Growth

Regressions. The American Economic Review, vol. 82, no. 4, pp. 942-963

Matsuyama, Kiminori (1992), “Agricultural Productivity, Comparative Advantage, and

Economic Growth”, Journal of Economic Theory, vol. 58, no. 2, pp. 317-334.

Pickering, J.F. and I.M. Sheldon (1984), “International Trade performance and

concentration in British industry”, Applied Economics, Vol. 16, pp. 421-442.

Ranis G, Stewart F, Ramirez A. (2000), “Economic growth and human development.

World Development, vol. 28, pp 197-219.

Rivera-Batiz, Luis and Paul M. Romer (1991), “Economic Integration and Endogenous

Growth”, The Quarterly Journal of Economics, vol. 106, no. 2, pp. 531-555.

27

Rodriguez, Francisco and Dani Rodrik (2001), “Trade Policy and Economic Growth: A

Sceptic’s Guide to the Cross-National Evidence”, NBER Macroeconomics Annual

2000. Cambridge, MA: MIT Press, pp. 261-324.

Romer, Paul (1990),“Endogenous Technological Change”, Journal of Political Economy,

vol. 98, no. 5, pp. S71-S102.

Sachs, Jeffrey and Andrew Warner (1995), “Economic reform and the process of global

integration”, Brookings Papers on Economic Activity, vol. 1, pp. 1-118.

Schneider, Patricia Higinio (2005), International trade, economic growth and intellectual

property rights: A panel data study of developed and developing countries”, Journal

of Development Economics, vol 78, pp. 529-547.

Temple, Jonathan (1999), “The New Growth Evidence”, Journal of Economic Literature,

vol. 37, no. 1, pp. 112-156.

Tyler, William G. (1981), “Growth and Export Expansion in Developing Countries:

Some Empirical Evidence”, Journal of Development Economics, vol. 9, no. 3, pp.

121-130.

Vickers, John and George Yarrow (1991). “Economic Perspectives on Privatization”,

The Journal of Economic Perspectives, vol. 5, issue 2, pp. 111-132

Wacziarg, Romain (1998), “Measuring the Dynamic Gains from Trade”, The World

Bank Economic Review, vol. 15, no. 3, pp. 393-429

Walde, Klaus and Christina Wood (2005), “The Empirics of Trade and Growth: Where

are the Policy Recommendations?”, International Economics and Economic Policy,

vol. 1, no. 2-3, pp. 275-292.

Yanikkaya, Halit (2003), “Trade Openness and Economic Growth: A Cross-Country

Empirical Investigation”, Journal of Development Economics, vol. 72, no. 1, pp. 57-

89.

28

Table 1. Trade Trends 1970-2000

All Countries Europe and USA Latin America Africa Asia

Trade (%GDP)

Number of partners*

Trade (%GDP)

Number of partners*

Trade (%GDP)

Number of partners*

Trade (%GDP)

Number of partners*

Trade (%GDP)

Number of partners*

1970-1974 58.30 46.43 65.22 105.54 54.34 38.27 58.96 31.79 54.82 47.45 1975-1979 68.33 51.38 74.05 109.81 67.88 45.53 67.15 35.08 65.65 52.70 1980-1984 75.37 52.91 80.36 95.11 76.24 52.83 69.60 38.38 79.54 62.23 1984-1989 72.06 56.89 78.98 95.58 74.39 57.61 64.78 42.63 75.02 69.78 1990-1994 78.25 67.71 84.95 112.52 78.69 66.91 68.09 51.54 80.91 80.01 1995-1999 83.45 85.94 96.08 146.89 79.10 80.36 74.49 64.56 86.35 96.33 2000-2003 88.52 93.98 108.61 161.49 77.09 87.05 76.47 71.36 94.04 104.33

* Number of partners = number of countries to whom goods where exported to (IZ)

Table 2. HHI Trends 1970-2000

All Countries Europe and USA Latin America Africa Asia

1970-1974 2201 1131 2273 2216 2291 1975-1979 1977 986 2419 1927 1873 1980-1984 1730 979 2289 1626 1507 1984-1989 1706 999 2316 1446 1519 1990-1994 1647 1058 2215 1373 1531 1995-1999 1564 981 2137 1383 1346 2000-2003 1580 912 2005 1485 1332

29

Table 3. General descriptive statistics (Country-Averages from 1980-2000).

GDP Growth Rate Initial GDP Population Sedondary

Education Investment Trade (%GDP)Number of

Trading Partners

Number of Rich Trading

Partners

Number of Poor Trading

Partners

Concentration (HHI)

Weighted Exports Growth

Exports Growth

Concentration (HHI)

Number of Trading Partners

Number of Rich Trading

Partners

Number of Poor Trading

Partners

Mean 0.0096 5,766.9260 1.7912 55.7700 22.1647 77.8120 89.7679 52.0720 37.7468 0.1614 227.9661 6.5375 0.2036 72.2016 42.8307 29.4105 Maximum 0.3201 45,951.9500 8.7285 146.3200 86.7935 268.8018 198.0000 90.2000 108.4000 0.8190 19,401.8000 691.3065 0.8193 188.0000 88.4000 100.0000 Minimum -0.4365 49.3231 -5.0608 2.7000 2.5255 2.3515 0.4000 0.2000 0.2000 0.0326 -1,005.1040 -41.1255 0.0393 0.4000 0.2000 0.2000 Std. Dev. 0.0493 8,762.2910 1.3685 33.2156 8.4763 43.4732 48.0790 21.0372 28.2960 0.1115 891.8052 30.2144 0.1483 48.6401 23.0584 26.7492

Observations 663 641 780 635 630 651 742 742 742 742 562 563 742 742 742 742

Mean 0.0067 812.2830 2.2442 33.0677 21.3017 69.4226 80.7333 48.7260 32.0073 0.1475 150.0608 4.8841 0.2022 62.3993 38.8066 23.5927 Maximum 0.2982 3,404.7060 7.0285 101.7300 70.6529 231.9663 191.6000 89.6000 102.2000 0.5034 2,459.3360 38.7992 0.7748 183.4000 87.2000 96.8000 Minimum -0.3250 49.3231 -5.0608 2.7000 2.5255 12.8760 2.0000 1.4000 0.6000 0.0467 -877.5405 -37.0092 0.0480 2.0000 1.4000 0.6000 Std. Dev. 0.0448 667.4884 1.1238 22.6379 9.3171 36.8659 37.7185 17.8924 20.9598 0.0921 277.5435 8.2488 0.1417 36.9367 18.8727 19.1981

Observations 276 276 276 249 262 266 273 273 273 273 250 251 273 273 273 273

Mean 0.0118 9,513.4510 1.5431 70.4148 22.7790 83.6083 95.0269 54.0196 41.0949 0.1695 290.3903 7.8677 0.2044 77.9075 45.1731 32.8043 Maximum 0.3201 45,951.9500 8.7285 146.3200 86.7935 268.8018 198.0000 90.2000 108.4000 0.8190 19,401.8000 691.3065 0.8193 188.0000 88.4000 100.0000 Minimum -0.4365 97.6258 -3.9789 3.2700 6.1789 2.3515 0.4000 0.2000 0.2000 0.0326 -1,005.1040 -41.1255 0.0393 0.4000 0.2000 0.2000 Std. Dev. 0.0523 10,097.9500 1.4267 30.6325 7.7777 46.6768 52.5151 22.4573 31.3425 0.1208 1,167.9797 39.8876 0.1522 53.5194 24.8976 29.7960

Observations 387 365 504 386 368 385 469 469 469 469 312 312 469 469 469 469

POOR COUNTRIES

RICH COUNTRIES

EXPORTS BASEDTOTAL TRADE

ALL COUNTRIES

POOR COUNTRIES SAMPLE

RICH COUNTRIES SAMPLE

ALL COUNTRIES

30

Table 4. Dependent variable: per capita GDP growth (All Countries)

(1) (2) (3) (4) (5) (6)Initial GDP -0.000004 ** -0.000004 * -0.000005 * -0.000005 * -0.000003 ** -0.000003 **

-2.395678 -2.804989 -2.666757 -3.119962 -2.229205 -2.396127Population 0.003804 0.005185 *** 0.004564 0.005933 ** 0.005604 *** 0.005984 **

1.310745 1.736725 1.571616 2.044834 1.846349 2.075951Secondary Education -0.000434 -0.000489 *** -0.000341 -0.000396 -0.000052 -0.000035

-1.371912 -1.653094 -1.229374 -1.518711 -0.357029 -0.238816Investment 0.001912 * 0.001847 * 0.001929 * 0.001846 * 0.001810 * 0.001810 *

4.002104 4.120541 3.990859 4.129383 3.946391 3.965089Trade Openness (Total Trade to GDP ratio) 0.000041 -0.000018 -0.000024 -0.000076 0.000109 0.000076

0.471501 -0.173721 -0.234321 -0.717285 1.625355 1.077607Number of Trading Partners 0.000784 * 0.000762 *

3.686365 4.797275Number of Rich Trading Partners 0.002537 * 0.002436 *

3.796425 3.901482Number of Poor Trading Partners -0.000976 ** -0.000905 ***

-1.998974 -1.895677Total Trade HHI 0.122581 ** 0.139734 * 0.090824 *

2.377725 4.369265 3.881132

No. Observation 512 506 506 506 506 506Adj. R squared 0.4417 0.4744 0.4572 0.4862 0.5182 0.5227(*) 1%, (**) 5%, and (***) 10%, t-statistics in italics.

ALL COUNTRIES

31

Table 5. Dependent variable: per capita GDP growth (Poor Countries)

(1) (2) (3) (4) (5) (6)Initial GDP -0.000051 * -0.000053 * -0.000050 * -0.000052 * -0.000049 * -0.000048 *

-4.517157 -4.384735 -4.618537 -4.664286 -4.371293 -4.498285Population 0.006432 ** 0.006781 ** 0.007270 * 0.007924 * 0.007176 ** 0.008262 *

2.240495 2.356131 2.777914 3.082697 2.555777 3.108087Secondary Education 0.000029 -0.000009 0.000075 0.000026 -0.000010 0.000027

0.114332 -0.036045 0.331289 0.115835 -0.041575 0.120222Investment 0.002462 * 0.002493 * 0.002320 * 0.002342 * 0.002462 * 0.002297 *

4.027496 4.352627 3.643316 4.010316 3.981291 3.598189Trade Openness (Total Trade to GDP ratio) 0.000361 ** 0.000327 ** 0.000329 ** 0.000272 *** 0.000370 * 0.000329 **

2.464323 2.055764 2.322198 1.651022 2.600000 2.344537Number of Trading Partners 0.000354 0.000524 **

1.504774 2.349184Number of Rich Trading Partners 0.001163 0.001219 ***

1.510314 1.617521Number of Poor Trading Partners -0.000197 -0.000039

-0.528160 -0.120612Total Trade HHI 0.132831 * 0.154613 * 0.157979 *

5.246587 5.145123 5.864371

No. Observation 236 236 236 236 236 236Adj. R squared 0.4833 0.4848 0.4974 0.5041 0.4921 0.5126(*) 1%, (**) 5%, and (***) 10%, t-statistics in italics.

POOR COUNTRIES

32

Table 6. Dependent variable: per capita GDP growth (Rich Countries)

(1) (2) (3) (4) (5) (6)Initial GDP -0.000004 ** -0.000004 ** -0.000005 ** -0.000004 ** -0.000003 -0.000003

-2.026270 -2.216887 -2.162535 -2.308894 -1.604933 -1.591552Population -0.002602 -0.002357 -0.002294 -0.002418 -0.002759 -0.002702

-1.115898 -0.566990 -0.879154 -0.629746 -1.445532 -1.323933Secondary Education -0.000670 *** -0.000656 *** -0.000558 *** -0.000553 *** 0.000060 0.000060

-1.722326 -1.845457 -1.654267 -1.710508 0.380469 0.385018Investment 0.001255 ** 0.001028 0.001412 ** 0.001175 *** 0.001266 ** 0.001222 **

2.027497 1.616657 2.255940 1.907210 2.152944 2.059598Trade Openness (Total Trade to GDP ratio) -0.000225 -0.000261 *** -0.000308 *** -0.000322 ** -0.000064 -0.000048

-1.399505 -1.773236 -1.785954 -2.030919 -0.424180 -0.289760Number of Trading Partners 0.000828 * 0.000759 *

7.434122 9.390180Number of Rich Trading Partners 0.003418 * 0.003476 *

5.682781 5.301107Number of PoorTrading Partners -0.001767 * -0.001793 *

-3.179349 -3.093731Total Trade HHI 0.078877 0.080765 ** -0.033357

1.198480 2.341196 -1.000213

No. Observation 276 270 270 270 270 270Adj. R squared 0.5069 0.5454 0.5187 0.5473 0.6349 0.6333(*) 1%, (**) 5%, and (***) 10%, t-statistics in italics.

RICH COUNTRIES

33

Table 7. Dependent variable: per capita GDP growth (Feder’s Specification)

(1) (2) (3) (4) (5) (6)Investment 0.00101 * 0.00056 * 0.00201 * 0.00201 * 0.00101 * 0.00056 *

4.92839 3.78343 8.66811 27.14964 4.92839 3.78343Population 0.00051 0.00029 0.00817 * 0.00507 ** 0.00051 0.00029

0.14279 0.08281 3.40702 2.19853 0.14279 0.08281Weighted Exports Growth -0.000011 * 0.000046 * -0.000011 *

-8.06184 7.35430 -8.06184Exports Growth -0.00036 * 0.00159 * -0.00036 *

-34.46021 5.31426 -34.46021Number of Rich Trading Partners 0.00222 * 0.00226 * 0.00099 0.00105 0.00222 * 0.00226 *

2.76417 2.93876 1.37622 1.41803 2.76417 2.93876Number of Poor Trading Partners -0.00080 *** -0.00083 *** -0.00010 -0.00010 -0.00080 ** -0.00083 ***

-1.69576 -1.86866 -0.28792 -0.34025 -1.69576 -1.86866HHI Index (Exports) 0.04146 ** 0.03918 ** 0.04159 * 0.05265 * 0.04146 ** 0.03918 **

2.00007 2.58948 4.66913 4.60104 2.00007 2.58948

Adj. R Squared 0.616174 0.637905 0.547639 0.548540 0.616174 0.637905Number of observations 529 524 247 247 282 277(*) 1%, (**) 5%, and (***) 10%, t-statistics in italics.

All Countries Poor Countries Rich countries

34

Table 8. Dependent variable: per capita GDP growth (Robustness Regressions)

(1) OLS (2) OLS IV (3) Panel (4) Panel IV (5) Panel (6) Panel IV (7) Panel (8) Panel IVInitial GDP -0.0000002 -0.0000011 ** -0.0000050 * -0.0000050 * -0.0000626 * -0.0000609 * -0.0000059 ** -0.0000065 **

-0.793490 -2.631488 -2.947152 -3.335380 -7.231790 -7.562165 -2.758716 -2.349752Population -0.005041 ** -0.002078 0.008261 * 0.008507 * 0.009778 * 0.009640 * -0.001020 -0.000069

-2.552059 -0.799102 3.704539 3.833978 4.953186 4.996621 -0.550121 -0.017446Secondary Education -0.000180 ** -0.000051 -0.000163 -0.000234 0.000032 0.000011 0.000013 -0.000246

-2.037032 -0.569925 -0.789295 -1.372010 0.084817 0.028280 0.090371 -1.576464Investment 0.001536 * 0.001509 * 0.001513 ** 0.001582 *** 0.001581 ** 0.001579 ** 0.001128 ** 0.001563 ***

4.271307 3.154588 2.139334 1.723113 1.632060 1.658527 2.567255 1.793756Trade Openness (Total Trade to GDP ratio) 0.000107 ** 0.000192 ** 0.000134 *** 0.000228 0.000273 0.000261 0.000003 0.000175

2.125885 2.167317 1.706251 1.213165 1.373829 1.333385 0.015130 1.217736Number of Trading Partners 0.000206 * 0.000369 *

4.441109 2.930752Number of Rich Trading Partners 0.002960 * 0.001096 * 0.001269 ** 0.000949 0.003740 * 0.001260 *

4.381577 4.243621 1.913360 1.578527 5.030071 4.544451Number of Poor Trading Partners -0.0014020 * -0.0001860 -0.0000936 0.0002470 -0.0021240 * -0.0005000 *

-2.670692 -0.351714 -0.495250 0.482186 -3.401437 -2.710275Total Trade HHI -0.0132780 -0.0211140 0.0588890 * 0.0878930 * 0.1763360 * 0.1640970 * -0.0796660 * -0.0541400 *

-1.079674 -1.375304 3.806073 3.516244 4.732703 4.087528 -3.330870 -6.329680

No. Observation 120 91 400 361 180 177 220 184Adj. R squared 0.4410 0.4016 0.5692 0.4168 0.6177 0.5568 0.6268 0.3681(*) 1%, (**) 5%, and (***) 10%, t-statistics in italics.

All Countries Poor Countries Rich Countries

35

1 For a more details on this see Edwards (1993), Temple (1999) and Walde and Wood

(2005) for literature reviews. 2 Many more are in the works. See the WTO secretariat web link

http://www.wto.org/english/tratop_e/region_e/regfac_e.htm).

3 Other authors like Feder (1982) or Matsuyama (1992) have modeled growth in two

sector models where the exporting sector generates positive externalities for the rest of

the economy. While these models are different from the one by Romer (1990), the

intuition behind the effects of trade through the exchange of ideas is similar.

4 In order to check for the robustness of the results, additional explanatory variables like

the ratio of government expenditures to GDP or the primary school enrollment were also

included inCr

. Including these other variables produced no changes in our results.

5 Authors like Harrison (1996) and Edwards (1998), however, have argued that most

measures of trade openness and trade policy used in the literature capture roughly the

same aspects and could be used interchangeably.

6 When conducting fixed effects regressions, the variable for the initial income level per-

capita is the value at the beginning of each five-year period.

7 When equation (3) is estimated, the data used to calculate this index was limited to

Export volumes instead of Trade volumes.

8 The exception is the estimated coefficient for column (2) of Table 5.

9 Throughout the study, gross growth rates were used and the size of the coefficients

reflects this choice. For example, a one percent growth rate was recorded in the data as

0.01.

36

10 The descriptive statistics, Table 3, report that the standard deviation of the GDP growth

rate is equal to 0.044, while that for the HHI index is 0.092. Therefore a one standard

deviation of the HHI index implies a change of (0.154*0.092)=0.0142, or 1.42

percentage points, in the GDP growth rate, which is about 0.32 of a standard deviation of

the dependent variable (0.0142/0.044).

11 Sachs and Warner data set is published on the Center for International Development

Web site accessible from http://www.cid.harvard.edu/

12 http://www.worldbank.org/research/growth/GDNdata.htm

13 The results for the poor and rich country samples are not presented here for matters of

space, but the conclusions follow those discussed for the overall sample, in other words

they statistical significance and signs of the relevant variables remain.

14 Mexico’s trade with the U.S., for instance.