Africa’s Trade with China: Good for Growth?

June 2007

Lisa Feng Yung Chen1

Stanford University Economics and International Relations, Class of 2006

International Policy Studies, Class of 2007

Advisor: Aprajit Mahajan

Abstract: Trade between China and African countries has dramatically increased in recent years, at an unprecedented rate. At the same time, robust economic growth in Sub-Saharan African countries has accompanied this trade boom. The question remains however, whether or not trade with China has actually induced this growth. Furthermore, recent media reports have suggested that Africa’s trade with China may instead be detrimental to Africa’s development. This study examines the impact of Chinese trade alone on African growth. Controlling for endogeneity issues and institutional effects, it finds that evidence to suggest that Chinese trade has had a positive impact on African growth.

1 I would like to sincerely thank the following individuals, without whom this thesis would not be possible. To my thesis advisor, Aprajit Mahajan, who allowed me to take risks and learn from my own mistakes: Thank you for your patient guidance, from my first attempt to my final work. To Chonira Aturapane, who inspired me to work on this topic: Thank you for being there to help me address every challenge, every step of the way to a new thesis. To my advisor Timothy Bresnahan, who first encouraged me to try research: Thank you for everything. I would not have achieved so much at Stanford without your guidance and support. To Geoffrey Rothwell, who is always unafraid of incorporating humor into teaching: Thank you for having those conversations with me that wander everywhere and nowhere, but always mean something. To Joanne Yoong, the best TA there ever was in the Economics department: You have been a great mentor and role model. I am also grateful to Mark Tendall for an enjoyable summer’s honors college, and to Sean Chu for his taking the time to help me work out criticisms. Finally, I would like to say thanks to Rushabh Doshi and Ben Backes, my Econ-homies, who successfully peer-pressured me into doing a thesis without realizing it; to my drawmates and fellow South African travelers, who always encouraged when I felt discouraged, and with whom I share many memorable study breaks; to Nick Fram, for his help with the gravity model; and last but not least, to Matthew McLean, for his unconditional love and constant support, for taking the time to carefully read this paper over and over and offer comments and suggestions, so that I could be proud of my work.

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Introduction

Trade between China and African countries has dramatically increased in recent years, at

an unprecedented rate. Overall trade with China surged by 39% between 2004 and 2005, and in

2006, it reached a value of US$55.5 billion from just US$4 billion a decade earlier. Robust

economic growth in Sub-Saharan African countries has accompanied this trade boom; growth

rates have essentially doubled in the past five years. Progress this great has not been seen in the

region since the early 1970’s (UNComtrade). The question remains however, whether or not

trade with China has actually helped to induce this growth.

Most recent studies of trade between Africa and China have concentrated on

disaggregating trade flows and understanding its determinants. In addition, while theory and

empirics have clearly demonstrated that trade openness and liberalization cause growth in

general2, no study to this date has examined the impact of Chinese trade alone on African growth.

This has become an important question in the face of numerous media reports that assert that

trade with China is not good for Africa. These claims have ranged from its destruction of

African businesses to its support of bad governance. Such concerns may directly affect income

and growth, or could be rather detrimental to development and thus long-term growth prospects.3

If this is the case, then as the poorest region in the world, Africa may need to change the way it

trades with China.

This study seeks to examine this question and fill the gap in the literature by analyzing

available panel data for 46 Sub-Saharan African countries from 1961 to 2005. To examine the

impact of Chinese trade on African growth and not the reverse, the gravity model is employed as

2 See Krueger 1998, Frankel and Rose 2002, and Wacziarg and Welch 2003. 3 Some examples include “China in Africa: All Trade, with no Political Baggage” in the New York Times in August 2004, “In Africa, China Trade Brings Growth, Unease” in the Washington Post in June 2006, and “A Cautious Welcome” in The Economist in February 2007.

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an instrument for Chinese trade. Controlling for this endogeneity concern and institutional

effects, it finds evidence that suggests that trade with China has had a positive impact on African

growth.

The paper proceeds as follows. Section 1 first gives a brief overview of Africa-China

trade flows, and the potential benefits and costs to African countries as a result. It also

underscores the importance of this topic, with regard in particular to development. Section 2

examines existing literature related to this topic, in order to provide a theoretical and empirical

foundation on which to analyze the link between trade and growth. Section 3 presents an

overview of the methodology and model specifications. Section 4 provides a description of the

data and its sources. Section 5 analyzes the results and provides sensitivity and robustness

checks. Section 6 discusses the policy implications of the findings, and section 7 concludes with

prospects for further work on the topic.

Section 1. Africa-China Trade Flows: Costs and Benefits

The past decade has been an era of relatively robust growth for Sub-Saharan Africa. The

rising price of oil has certainly played a role in determining the high growth rates in many of

these countries, but even excluding oil rich countries, the fastest growing group has had an

average growth rate of over 4.5% since the mid-1990s. For a region that has been riddled with

poverty and low levels of growth for decades, this is a welcome change.4

4 Those that are experiencing slower growth, or even zero and negative growth, tend to be those which are experiencing or have been engaged in internal conflict of some sort. Low growth countries (under 2%) include Zimbabwe, Democratic Republic of the Congo, Burundi, Guinea-Bissau, Central African Republic, and Cote d’Ivoire (Word Bank World Development Indicators). For a complete breakdown of growth by country, see in Appendix, Graph A.1.

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Over the same period, trade with China has increased dramatically. Exports in the past

five years to China have been growing at an average rate of 48% per year, and now account for a

tenth of all exports. The rapid growth of the Chinese economy has created a large demand for

many of Africa’s major export commodities, especially for oil and raw materials such as

minerals, metals, and timber. For the African countries that have diversified and even moved up

the technology ladder, China serves as a huge export market for goods such as light

manufactures or semi-processed agricultural goods.

Graph 1. Sharp increase in GDP mirrors sharp increase in trade with China

05

1015

GD

P

0.2

.4.6

.8Tr

ade

with

Chi

na

1960 1970 1980 1990 2000 2010Year

ChinaTrade GDP

Annual Averages for Africa (in billions)Rise in GDP and Trade with China

Source: WDI and UN COMTRADE.

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In the other direction, Sub-Saharan Africa has seen a surge in imports of China’s cheap

manufactured goods. Also imported are capital goods and intermediate inputs for product

assembly in Africa, which are then shipped to third markets. Imports totaled over US$15 billion

in 2005 alone, and climbed by 42.9% the following year (Broadman 2006, The Economist

2007).5

Such trade flows are sure to command attention not only from economists and

international organizations, but from news media and governments as well. Some discuss the

potential for increased African growth that trade with China can bring. Others demonstrate

concern for the nature of China-African trade flows, and whether or not they are actually good

for the development and long-term growth of Africa. This is a valid concern since Sub-Saharan

Africa remains the poorest region in the world. As we will see, while the potential benefits are

likely to have a direct impact on growth, the majority of criticisms tend to be toward cost factors

that indirectly affect growth, through channels such as governance or income distribution. It is

beyond the scope of this paper to address the direct effects of Chinese-African trade on such

channels; however, one can consider this paper as focusing on the overall, net impact on growth.

5 At the time of binding this thesis, there have been no bilateral trade agreements signed between China and any Sub-Saharan African country. For a list of all Sub-Saharan African countries and their WTO status, see Appendix Table A.1.

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Graph 2. Growth in GDP has coincided with growth in trade with China

22.6

22.8

2323

.223

.4G

DP

1516

1718

1920

Trad

e w

ith C

hina

1990 1995 2000 2005Year

ln_Imports ln_Exportsln_GDP

Log of Annual Averages for Africa (1990-2005)Growth in GDP and Trade with China

Source: WDI and UN COMTRADE. See Appendix Graph 2.1 through Graph 2.8 for the same graph by country.

There are many potential benefits from increased trade with China, both in relation to

growth and development in general. First, China is almost a natural trading partner with most

African countries, as significant complementarities in their natural endowment of resources

occur. As China’s demand for Africa’s resources continues to soar, increased world prices of

primary commodities may improve the terms of trade for the African countries.

Second, as China becomes a major player in the world economy, its industries are rapidly

modernizing. It has a growing middle class with increased purchasing power, hungry for imports.

Thus, while African exports to China are currently dominated by oil and other resources,

growing demand will created the need for more goods and services – goods and services Africa

can provide. This not only includes traditional agricultural exports, but will also tend towards

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nontraditional exports ranging from light manufactured products and household consumer goods

to processed commodities and tourism (Broadman 2006).

There are also other export opportunities awaiting Sub-Saharan Africa. Successful

Chinese industries are growing larger, and as they do, they will count on primary and

intermediate supplies from Africa for their products. China’s growing economic prosperity has

also meant shifts in its comparative advantage within and across certain industries. Many

imports from Africa are and will be needed to support these shifts. One example is the increased

imports of cotton in recent years from countries such as Cameroon and Tanzania. Cotton

farmers in China have switched to more profitable crops, and thus cotton imports were needed to

meet the demands of China’s booming textile industry (Jenkins and Edwards 2006).

In addition, as with trade in general, technology and skill transfers will be especially

beneficial, particularly in a region that lacks both. Trade is inextricably linked with FDI, which

can foster exchange of outside know-how to African workers. Trade openness with China will

bring in capital goods that are necessary to promote productivity and growth, especially as

Chinese firms can more cheaply produce technological products with reverse engineering.6

Statistics from UN COMTRADE reveal that 33% of Chinese exports to Africa are machinery

and transportation equipment.

6 Whether or not one agrees with reverse engineering because of conflicts with intellectual property rights and patent infringement, it still benefits Africa.

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Chart 1. Composition of Exports to and from China

Source: Adapted directly from Broadman (2006), World Bank

Lastly, there are benefits which accrue from more competition. Competition itself tends

to increase efficiency and productivity of firms, making them better at what they do.

Competition also means cheaper imports of goods from China, resulting in a gain in consumer

surplus. Because Africans are now paying less for the goods and services they want and need,

they are effectively richer with a higher real income.

Unfortunately, there may be downsides to this seemingly perfect relationship, as each

benefit may entail its own costs as well. One aspect is the rise of internationally competitive

Chinese exporters, who have already displaced many domestic businesses in the textile and

apparel industries. This creates unemployment in countries already burdened by high

unemployment rates, and transitions in comparative advantage will surely yield other social costs.

According to the Afrobarometer, many Africans see the influx of Chinese goods, but do not feel

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as if they have improved their economic situation (Timberg 2006). The increase in consumer

surplus from cheap imports may in the long run outweigh the loss of jobs. However, negative

impacts may persist if the structural rigidities in African markets do not allow for the efficient

reallocation of resources, and then the “short run” will matter.

A second potential negative aspect of Africa-China trade revolves around the issue of oil

and other natural resources being the primary exports China is interested in. Economically, even

if they do drive growth, there are worries that trade dependence on exhaustible resources is an

insecure path to development. In political economy, people are concerned about the potential

resource curse. Promoters of democracy and human rights have pointed out China’s increasingly

close ties to troubled governments like that of Sudan, which also happens to be the chief exporter

of petroleum. This concern is particularly of China and not other countries in general for two

reasons – China’s foreign policy and the sheer increase in recent trade volume. They argue that

continued trade with China will only keep unfavorable regimes in power and stifle change, as

China follows “a policy of noninterference in other countries’ internal affairs.” Some even see

the Chinese as the next colonizers of Africa (Whi 2006).

These benefits and costs are important to consider, because either they have affected

growth or will affect growth in the long-run. The most extensive and detailed examination of the

recent pattern of trade flows between Africa and China by the World Bank has provided

significant evidence that they will continue to grow and develop. The bad news is, it would be

almost impossible to precisely measure and estimate each benefit and cost in terms of its impact

on African growth. Despite this constraint, the good news remains that newly available

literature and constructed data may provide the mechanism to analyze the net impact of Chinese

trade flows on African growth, and whether or not it has a significant causal effect. If trade with

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China is a positive and significant contributor to African growth, we can then assess particular

policies that would aid in maximizing benefits and minimizing the costs of such activities.

Robust growth and sound policies to support it can only add the goals of poverty alleviation and

development.

Section Two. Literature Review

Existing literature related to the topic revolves around assessing the effects of

liberalization and trade openness on growth, for the world and Africa in particular, and the

determinants of Africa-China trade flows. They provide a theoretical and empirical foundation

on which to base the methodology and results of this current study.

Over the past decade, there has been convergence in the general trade literature toward

the consensus that trade and liberalization does indeed cause growth. The greatest critique of

this literature was brought by Rodriguez and Rodrik (1999), who argued that the evidence

linking trade openness and growth overstates their positive relationship. They pointed out that

existing literature faced endogeneity issues, omitted variable bias, and had difficulties accurately

measuring trade restrictiveness/openness.

Frankel and Rose (2002) addressed the first pair of issues. They used the gravity model

to instrument for trade openness, and found that not only were their results significant, but also

greater in magnitude than OLS had estimated. In addition, because Rodriguez and Rodrik (1999)

were concerned with the direct effects of geography and institutions on growth, they included

measures of both for their sensitivity analysis. They found that their results were robust to these

additions – that trade openness has a significant causal impact on growth.

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Wacziarg and Welch (2003) avoid difficulties in measuring trade openness by running a

fixed effects regression on an index of liberalization, which is based on the identification of trade

liberalization episodes. They find an increase in GDP growth post-trade liberalization, as well as

positive effects on investment rates. Furthermore, they use country case studies to address the

issue of liberalization coinciding with other government polices that may affect growth. This

demonstrated the importance of having complementary supporting policies, and avoiding

counter-productive ones.

Authors have asked whether liberalization and trade openness have a differential impact

on growth in Africa in comparison to the rest of the world, since the increase in African income

levels have not kept up with that of the world. The evidence thus far for Africa leans toward the

suggestion that it is not difference, though the debate is still ongoing. Early in this line of

literature were studies such as Ukpolo (1994), using time series for eight African countries. He

does not find a significant impact of manufactured exports on growth, though there appear to be

some positive linkages. More recent Africa-specific studies are finding the effect of

liberalization and trade to be significant and positive on growth. Sukar and Ramakrishna (2001)

conduct an empirical analysis based on the neo-classical growth model, and find that trade

plays an important role in enhancing the economic growth of Ethiopia, underscoring the

importance of outward looking strategies.

According to Greenaway, Morgan, and Wright (2002), problems with mis-specification

and usage of different liberalization indices are responsible for early inconclusiveness. Their

evidence points to J curve type response for developing countries, robust through specification

changes. Similarly, Tsangarides (2005) demonstrates that what is good for growth for the world

is also generally good for Africa, though the marginal impacts may be different. In addition, he

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finds that variables controlling for the impact of institutions are actually only robust when

estimating with an Africa-only data sample. This highlights the importance of such factors in

determining the growth of this region.

Literature that focuses on the economic impacts of China-Africa trade in particular is a

more recent phenomenon. Broadman (2006) uses the gravity model to assess the determinants of

bilateral trade flow between China and African countries. Based on cross-sectional estimates, he

finds that infrastructure quality and factors between borders (such as port quality) are just as

important as trade policy itself in facilitating trade.

Jenkins and Edwards (2006) find that the likely overall impact of this trade on the poor

will depend on the types of goods involved and the conditions under which they are produced.

Import competition concerns are real, but have also been exaggerated. Preliminary evidence

suggests Chinese exports to Africa have mainly been at the expense of exporters from other

regions, which reduces the likelihood of displacing local producers. Lastly, Jenkins and Edwards

(2006) evaluate the direct and indirect impacts of trade with and FDI from China and India,

distinguishing between competitive and complementary effects. The overall impact will vary by

country and is conditional on a number of factors. More specifically, they determine that

countries like Lesotho will stand to lose the most. Their current labor to land ratio favors the

prior – in direct competition with China’s advantage.

Section 3. Methodology

Studies using cross-sectional data to analyze liberalization and growth have been heavily

criticized because they 1) essentially fail to model the dynamics of the relationship, 2) do not

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consider general trends over time, and 3) are less reliable due to problems with

heteroskedasticity. Greenway et al. (2002) suggest that there is much to be gained by working

in a panel context instead. Therefore, this study begins with all available trade data on the 47

Sub-Saharan countries, from 1961 to 2005. In order to control for possible convergence, the data

is then separated into nine 5-year time periods for each country, beginning with 1961-1965.7 By

limiting the data to Sub-Saharan Africa, we get less noise and more precise estimates; however,

the major downside to this is the significant reduction in sample size compared to when using

every country in the world, and thus an important loss of variation. As will be seen, this loss of

variation will present some challenges in the current analysis.

I follow the core model specification from Mankiw et al (1992), widely agreed upon in

the literature to be the most appropriate empirical specification for modeling growth. This

augmented specification has its roots in the Solow model, and includes a measure for initial per

capita GDP, investment/capital, population growth, and human capital. Initial per capita GDP is

used to control for convergence, as countries that start out from a lower GDP base tend to grow

faster.8 Capital or investment is another necessary control as greater levels of investment and

capital accumulation directly contribute to growth. Capital in the classic sense increases the

productivity of workers, and more investment allows for the greater accumulation of capital.

Population growth rates also directly affect growth. Population size is predicted to have a

positive impact on income levels because larger countries can better take advantage of

economies of scale or the diversification of resource use. Population growth however, has the

opposite effect in standard neoclassical theory. The faster the population growth, the poorer a

7 Tsangarides (2005) also breaks up data into 5-year time periods when examining growth empirics under model uncertainty, particularly for the case of Africa. 8 The convergence term is a standard inclusion for growth literature; however, convergence itself is not an uncontroversial theory/assumption. See Pritchett (1997).

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country is expected to be (Mankiw et al 1992). Human capital is the last explanatory variable in

the augmented standard growth model. As individuals in a country become more educated

and/or have more experience, their productivity increases, and therefore so does economic

growth.

An index for liberalization is added next to control for overall trade openness, as greater

trade openness has been shown to induce growth. Krueger (1998) points to many reasons – the

benefits of new technology and production techniques, cheaper and better quality capital inputs,

domestic innovation spurred by access to foreign markets, increased efficiency in industries due

to competition, and a stronger feedback mechanism that allows for the effective management of

exchange rates. Such channels through which trade affects growth are exactly why the relative

costs and benefits of trade with China examined earlier are critical to understand.

The liberalization index is used in place of data on trade openness (total imports plus

exports over GDP) because it is more useful in a panel analysis. Wacziarg and Welch (2003)

have demonstrated the positive and significant impact of liberalization on trade openness, and

thus the validity of the proxy. Finally, the variable of interest, trade openness with China, is

added to the model. The core model for this analysis is thus:

(1) lnyi,t = α + γ1ln(CHINA) i,t + γ2LIB i,t + β0lny i,t_base + β1n i,t + β2ln(Capital) i,t +

β3HC i,t + ε i,t

, where for each country i and time period t, the dependent variable lnyi,t is the log of average per

capita GDP, ln(CHINA) i,t is the log of Chinese trade over GDP, LIB i,t is dummy representing

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liberalization, lny i,t_base is the per capita GDP in the first year of time period t9, In(Capital) i,t is

log of gross fixed capital formation, n i,t is population growth, and HC i,t is a measure of human

capital.

In addition to the core model, other control variables are included to prevent omitted

variable bias. First, it is not unreasonable to believe that there have been general trends over

time not captured in the other variables. TREND is added to control for such an effect, and takes

on a value of 1 for the first period (1961-65), 2 for the second (1966-1970), and so forth.

(2) lnyi,t = α + γ1ln(CHINA) i,t + γ2LIB i,t + β0lny i,t_base + β1n i,t + β2ln(capital) i,t +

β3HC i,t + δ1TREND + ε i,t

Second, because there is a significant number of oil producing countries in Africa, I

include a dummy variable OIL to capture the effect of being an oil country on growth. This is

especially important given that China trades more with oil-producing countries. To separate the

effect of having oil alone from that of openness with China, I also estimate:

(3) lnyi,t = α + γ1ln(CHINA) i,t + γ2LIB i,t + β0lny i,t_base + β1n i,t + β2ln(capital) i,t +

β3HC i,t + δ1TREND + δ2OIL + ε i,t

Third, institutional quality may have an effect on growth. The role of property rights and

the rule of law are especially important, as they help determine how conducive a country is to

development. For example, strong property rights and low risk of internal conflict may promote

9 Including a measure for initial GDP is necessary due to the convergence hypothesis, which dictates that income at the end of a period will depend on income at the beginning of a period, and that countries beginning from a lower level of growth will grow faster than those at higher levels. Frankel and Rose (2002)

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infrastructure developments, as people are more secure that they can benefit from their

investments. Countries with better institutions then, are likely to see their incomes rise more

than those with worse institutions. World governance indicators (WGI) will be used to proxy for

institutional quality and governance.

(4) lnyi,t = α + γ1ln(CHINA) i,t + γ2LIB i,t + β0lny i,t_base + β1n i,t + β2ln(capital) i,t +

β3HC i,t + δ1TREND + δ2OIL + μWGI + ε i,t

Finally, there remains a concern about the direction of causality between trade and

growth. Greater trade openness with China may cause greater growth, but it may also be that

greater growth has induced greater trade openness for China. To test whether the variable

CHINA faces issues of endogeneity, I conduct the Hausman Specification Test for simultaneous

equations. As expected, CHINA is not an exogenous variable.10

I correct for simultaneity by using instrumental variables. Similar to Frankel and Rose

(2002), I use the gravity model to predict a geography and population induced trade share for

each country. The main difference is that while these authors create one prediction per country

for a cross-sectional analysis, this study requires a prediction for every country-time period pair

for panel analysis. In addition, common gravity models include variables such as a dummy for

common language or common religion; as such commonalities may induce trade but not growth

itself. However, because we are only examining trade relations between China and African

countries, we are left with model (i) below. Equations (1) through (3) will be estimated then

with 2SLS, with the first stage being:

10 See Appendix for the complete test procedures and results. Both the Durbin-Wu-Hausman Test and the Hausman Specification Test were used. Results Log Part I shows the procedures for the Durbin-Wu-Hausman test. Results Log Part II shows the procedures for the Hausman Specification test.

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(i) ln(CHINA)i,t = σ1nChina,t + σ2Areai + σ3Distancei,China+ σ4Landlockedi +

σ4Islandi

where n is the population growth for China varying each time period t, Area is the total surface

area, Distance is the distance between China and country i in miles, and Landlocked and Island

are dummies indicating whether country i is landlocked or an island respectively.

Section 4. Data Description and Sources

As Tsangarides (2005) has pointed out, adding additional variables to the basic Solow

Model often has changed the significance/insignificance and sometimes the sign of various

coefficients. Even using a basic augmented Solow model with human capital yields different

results across studies. Differences in results can also be attributed to different datasets.

The main issue with this study is the availability of data. Its focus on Sub-Saharan Africa

– a region for which data is scarce and sometimes unreliable – will make standard sensitivity and

additional robustness checks a challenge. Even within the core model, including more reliable

measures of human capital cuts the number of observations by more than half. Including an

index for institutional quality reduces those observations by another third. Therefore, whenever

it is reasonable, I will attempt to mitigate the effects of missing data. (i.e. by using different

measures or augmenting indices)

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Core Models

The dependent variable is growth (lnyi,t), calculated as the log of GDP in current US

dollars divided by the population per year.11 Base GDP (lnyi,t_base) is calculated the same way,

but only for the initial year of each five-year period. If GDP per capita is missing for this initial

year, I take the following year to be the base year, and so forth. Data for these variables comes

from the World Development Indicators database, as well as that for population growth (n i,t) and

gross fixed capital formation (ln(Capital) i,t). Gross fixed capital formation is used instead of

FDI because it is a better measure for the type of investment – investment in capital goods –

thought to contribute to growth.

Trade openness with China (ln(CHINA) i,t) is our key variable of interest, and is defined

as the sum of exports to and from China divided by GDP for each year.12 Import and export data

for each trading relationship is available separately by each country through US COMTRADE.

Technically, Chinese exports to one African country should be the same as the country’s imports

of Chinese goods for that year. This is actually not always the case, due to reporting error or

lack of reporting all together. To mitigate this problem, I averaged the reported figures for each

trading partnership each year, and imputed the data from one partner if it was missing from the

other.

11 We take the log of GDP per capita to be an approximation for growth, though strictly it should be considered as a relative change in income. 12 We define trade openness with China in accordance with standard trade literature, with openness equal to total imports plus total exports all divided by GDP. Some may argue that a better way to look at the differential impact of Chinese trade on African growth (as opposed to trade in general) would be to divide by total trade instead of GDP. Essentially, the key measure would instead be a trade ratio defined as (Chinese imports + exports) divided by (Total imports + exports). However, while one can instrument for the numerator or denominator separately, there is no IV for the ratio itself, and thus this definition of openness is not used. The measure that is used, ln(China), serves its purpose for this paper just as well, especially since liberalization is used to capture the effects of overall trade. The way to interpret this measure (ln(CHINA)) is thus: ceterus paribus, how much does a one percent change in trade share with China affect African growth?

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The liberalization variable (LIB) is a dummy, based on a revised version of the

Wacziarg-Welch’s (2003) identification of trade liberalization episodes. LIB takes on a value of

1 for every period during and after liberalization, and 0 otherwise. Their index ends in 2001, but

there were several African WTO members determined to be “closed.” It is highly likely that

some of these countries were in periods of transition during the authors’ classifications. There

were also countries missing liberalization data completely. I update this index in two ways: 1)

according to liberalization episodes classified by Salinas and Aksoy (2006) using World Bank’s

Trade Assistance Evaluations, and 2) by examination of whether they fit the Sachs and Warner

criteria for open economies using data from Economic Freedom of the World.13

For the purposes of maximizing the number of observations for each specification, I

utilize the schooling data that is available also through the World Development Indicators. I

consider two separate measures of human capital (HC i,t) – the secondary school enrollment rate

and the primary school completion rate. The secondary school enrollment rate is the number of

students enrolled per year divided by the population of children in the proper age group for 1990-

2005. The primary school completion rate is the number of students per year who finish their

primary education as a percentage of those enrolled for 1990 – 2005.14

13 Countries that switch from being closed to open (or changed time of liberalization) were Central African Republic, Madagascar, Senegal, Togo, and Gabon. The newly coded country is Namibia. The Sachs and Warner criteria for openness are 1) average tariff rates under 40%, 2) nontariff barriers covering less than 40% of trade, 3) a black market exchange rate that is not depreciated by 20% or more relative to the official exchange, 4) No state monopoly on major exports, and 5) a non-socialist economic system. Criteria 1 and 2 were examined using the breakdowns of the Economic Freedom Index. Criteria number 5 was based upon the description of the country from the CIA World Factbook 2007. 14 As of the time of analysis, secondary enrollment rates or primary completion rates prior to 1990 were not available in either UN or World Bank online databases. UNESCO has a separate database for secondary enrollment levels prior to 1990; however, they do not have the rates defined as the Gross Enrollment Ratios. An attempt to estimate these rates by dividing by the number of school-age children was advised against by Professor Joel Samoff from the African Studies department. Not only does the “start age” for secondary schooling vary widely across African countries due to structural differences, it varies widely within countries as well. This is a consequence of the poverty-stricken region, where for example, many children begin school late or repeat grades due to labor responsibilities at home. This is supported by the available data, where enrollment rates for primary education many times exceed 100%, but primary completion is often less than half of that.

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OIL is a dummy created to separate the effect of being an oil country on growth.

Countries were classified as oil producers if petroleum (code SITC Rev 2. 27) was one of their

top five exports according to data from UN COMTRADE. 15

In order to support institutions and proper policy-making, the World Bank recently

created a set of indices known as the World Governance Indicators to measure institutional

quality. Each index runs from a score of -2.5 to 2.5, with higher scores representing better

institutional quality. In line with previous works, I select four indices – Government

Effectiveness, Regulatory Quality, Rule of Law, and Control of Corruption – to form a

composite index for each country-year from 1995-2005 (WGI).16 Missing values for years in

between which data was available was imputed as an average between the two.

Instruments for Trade Openness with China

All data for the explanatory variables in the gravity model (nChina,t, Areai, Distancei,China,

Landlockedi, and Islandi ) are from Fram (2005). China’s population growth (nChina,t) will affect

its trade with African countries both from a supply and demand perspective. As the Chinese

population increases, so does its labor force, which can drive down the cost of production and

make cheaper goods available for exports to Africa. A greater population also means that China

will demand more imports. Thus, the expected sign for σ1 is positive. 15 The 13 countries that are classified as oil producing countries are Equatorial Guinea, Sudan, Nigeria, Cameroon, Senegal, Mozambique, South Africa, Kenya, Cape Verde, Angola, The Republic of Congo, Gabon, and Seychelles. 16 The definitions for each WGI index is as follows: Government Effectiveness combines responses on the quality of public service provision, the quality of the bureaucracy, the competence of civil servants, the independence of the civil service from political pressures, and the credibility of the government’s commitment to policies. Regulatory Quality focuses on the policies themselves, including measures of the incidence of market-unfriendly policies such as price controls or inadequate bank supervision, as well as perceptions of the burdens imposed by excessive regulation in areas such as foreign trade and business development. Rule of Law includes several indicators which measure the extent to which agents have confidence in and abide by the rules of society, and include perceptions of the incidence of crime, the effectiveness and predictability of the judiciary, and the enforceability of contracts. Control of Corruption is an inverse measure of the extent of corruption, conventionally defined as the exercise of public power for private gain, and is based on scores of variables from polls of experts and surveys. World Bank, Governance and Anti-Corruption (2005)

Lisa Chen 21

As mentioned earlier, there is a great potential for agricultural exports to and from Africa.

The total surface area affects the potential supply of farmable land, which affects the amount of

production a country can sustain. On one hand, more production leads to more potential exports,

and so in this case the coefficient on Area should be positive. On the other hand, more

production equals more self-sufficiency and less trade as well. Thus, the expected sign for Area

is ambiguous.

Distance from China, measured in miles, affects trade in a negative way – the further

away a country is from its trading partner, presumably the higher the transportation and

transaction costs which can impede trade. An African country that is landlocked may also have

less trade with China, because of the lack of direct access to ports for shipping as a method of

transporting goods.

Finally, islands have the exact opposite problem facing landlocked countries. They are

not linked by roads to facilitate the movement of goods across borders, and thus cannot 1) easily

take advantage of other countries’ trade infrastructure or 2) engage straightforwardly in value-

added production arising from goods undergoing multiple production processes in Africa. Both

landlocked and island countries, because of similar constraints with exporting, may find

importing goods from China to be more costly. Therefore, both imports and exports should be

less for landlocked or island countries, and the coefficients of these variables negative.

The results of the first stage regression are shown below, with p-values reported in

parenthesis under the estimated coefficient. Robust standard errors were used, and the intercept

is not reported.

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(i) ln(CHINA)i,t = 4.33nChina,t – 1.11e-14 Areai + .0000994Distancei,China – .854Landlockedi (.000) (.751) (.535) (.009)

– .459Islandi (.282)

The first stage p-value for the Wald chi-squared statistic is zero. Most of the expected signs are

present, except for distance. The coefficient on Area is negative, which is also the result found

by Frankel and Rose (2002). The overall R2 for this estimation is .26. The correlation between

ln(CHINA) and the generated instrument is .51.

A valid critique of these instruments for panel data is that the explanatory variables in the

Gravity Model are time invariant, with the exception of population growth. Therefore, all the

variation in the predictions for a certain country across time will be driven by only population

growth. The lack of better instruments for trade is also problematic in cross-sectional analyses.

The R2 in Frankel and Rose’s first-stage estimation is only .28, though their correlation between

trade openness and the generated IV is better at .72. Unfortunately, this is the best IV model for

trade thus far, and must be used since simultaneity is a more serious issue.

Section 5. Empirical Findings and Robustness Checks

Once again, due to data limitations, sample sizes vary significantly from specification to

specification. Results are therefore presented step by step to demonstrate the sensitivity analyses

and point out decreases in observations in the process. It should be noted that if missing data is

non-random, results may be driven entirely by sample selection, and not only by the specification

itself. Table 1 presents the estimates for equations (1) through (4), with “a” and “b”

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corresponding to the sample using the secondary enrollment rate and primary completion rate

respectively as a measure of human capital.

Table 1. Panel IV Estimates of Equations (1) - (4)

(1) § (2a) (3a) (2b) (3b)

ln(CHINA) 0.212*** 0.244*** 0.234** 0.247* 0.154 0.210* 0.212* 0.308 0.154[3.04] [2.58] [2.50] [1.68] [1.23] [1.79] [1.83] [1.40] [0.95]

LIB -0.169 -0.047 -0.043 -0.176 -0.179* -0.13 -0.135 -0.18 -0.196[1.29] [0.51] [0.47] [1.60] [1.72] [1.53] [1.60] [1.23] [1.46]

ln(Base Y) 0.582*** 0.057 0.053 0.033 0.035 0.096** 0.090* 0.068 0.075*[10.90] [1.59] [1.47] [1.02] [1.13] [2.08] [1.95] [1.57] [1.84]

Population Growth -0.085 0.013 0.012 0.022 0.021 0.013 0.013 -0.013 -0.017[1.51] [0.63] [0.58] [0.64] [0.63] [0.50] [0.52] [0.31] [0.43]

ln(Capital) 0.173*** 0.127** 0.113* 0.055 0.052 0.124* 0.083 0.02 0.009[4.81] [2.02] [1.76] [0.73] [0.77] [1.85] [1.22] [0.21] [0.11]

Secondary Enrollment Rate 0.026*** 0.025*** 0.030*** 0.029***[6.97] [6.69] [7.48] [7.57]

Primary Completion Rate 0.013*** 0.012*** 0.021*** 0.020***[4.31] [4.16] [4.32] [4.55]

WGI 0.416*** 0.392*** 0.448* 0.439**[2.64] [2.74] [1.90] [2.21]

OIL 0.225 0.337* 0.558** 0.531**[0.99] [1.71] [2.20] [2.20]

Trend -0.268*** -0.255*** -0.18 -0.078 -0.159** -0.154** -0.275 -0.105[3.59] [3.44] [1.27] [0.63] [2.02] [1.97] [1.32] [0.67]

Constant 0.633 5.811*** 5.938*** 6.856** 5.581** 4.788** 5.485*** 8.365** 6.373**[0.84] [3.11] [3.20] [2.28] [2.16] [2.47] [2.83] [1.99] [2.01]

Observations 249 106 106 72 72 121 121 67 67Number of Countries 46 43 43 43 43 42 42 39 39

§ Newey-West Corrected Standard Errors§§ Absolute value of z statistics in brackets§§§ * significant at 10%; ** significant at 5%; *** significant at 1%

(4a) (4b)

All estimations are conducted using IV and Panel Random Effects.17 Column (1)

displays the results for the core specification prior to augmentation with human capital.18 The

17 Random Effects were chosen over Fixed Effects because the scope of this research is within the African region, and not across all countries of the world. It is also reasonable to assume that the effects of trade with China and liberalization have an overall general effect, and then have random idiosyncrasies from country to country. Nevertheless, regressions for model (2) (augmented Solow model with trend) were also attempted with fixed effects, using China’s population growth as the instrument for ln(CHINA). The results are shown in Table A.3. of the

Lisa Chen 24

estimate for ln(CHINA) is positive and significant at the 1% level, providing evidence that trade

openness with China has had a positive impact on growth. When the secondary enrollment rate

is added as a proxy for human capital, trade openness with China remains significant, even after

controlling for trends through time and effect of oil producers. Using primary completion rate as

a proxy instead yields similar results, though the level of significance drops to 10%. Overall, it

appears that a 1% increase in trade with China causes growth to increase by at least .2%.

Next, the table exhibits the results for (4), which includes the WGI index under each

measure of human capital. When the WGI index is included, all but one of the specifications fail

to remain significant. Nonetheless, the coefficients on ln(CHINA) are positive under every

model specification and additions of controls. Given that including measures of institutional

quality reduces the sample to only about 70 observations, the finding that trade openness with

China induces positive growth is at the very least weakly robust.

Moving on to the other potential determinants of growth, I find that population is

insignificant across all model specifications, an unsurprising result found by previous empirics in

the development growth literature.19 The coefficient on ln(Capital) is continuously positive, and

significant until the institutional quality measure is included. This could be due to the high level

of correlation between the two.

For both measures of human capital– secondary enrollment rate and primary completion

rate – the coefficients are positive and significant at the 1% level for all specifications, rendering

Appendix. The coefficients for ln(CHINA) remain positive and significant, using both measures of human capital. Interestingly, the coefficient on liberalization is positive and significant. 18 This estimate has Newey-West corrected standard errors. Unfortunately, this procedure is unavailable for panel IV estimates when human capital measures are included. 19 Tsangarides (2005) note’s that the evidence of robustness is weak for population growth as a determinant of growth. He finds this to be true for both the World and Africa-only data under multiple growth model specifications.

Lisa Chen 25

it the most robust explanatory variable included. This argues for the basic Solow model to be

augmented with measures for human capital in order to avoid serious omitted variable bias.

The results for OIL and TREND also indicate that they should be included in

specifications. The coefficient on TREND is negative and highly significant for specifications

without the index for institutional quality, and does not change signs for all specifications. The

estimates for OIL are also robust, especially when using the primary completion rate as the

human capital measure. The magnitude of its effect on growth is relatively large as well.

Growth for oil countries is on average .5% higher than for non-oil countries.

These regressions also highlight the importance of institutions in contributing to growth

for African nations. Results under (4a) and (4b) reveal that institutional quality (WGI) is a

positive, significant, and robust determinant of growth.20

Not all variables behave as expected. The results for some explanatory variables are

contradictory to theory and some past empirics as well. As mentioned earlier, Wacziarg and

Welch (2003) find that liberalization was a significant determinant of growth, and thus should

have a positive coefficient. However, γ2 appears to be negative and persistently negative in every

specification. It is even significant at the 10% under (4a) with all controls. One explanation for

this may be that after controlling for other determinants of growth, liberalization itself is not a

significant determinant of growth. If this is the case, then the findings here would not

necessarily contradict Wacziarg and Welch for two reasons. One, they do not use control

variables with liberalization episodes, and they lump other effects with country and time fixed

effects. Two, they specify that many governments may actually enact counter-productive polices

20 One caveat is the low number of observations available when using WGI indicators and 5-year time periods. Nonetheless, it is the best measure of institutional quality available for Sub-Saharan Africa. The International Country Risk Guide offers more yearly data, but does not include as many African countries, and therefore was eliminated as the choice index to represent institutional quality.

Lisa Chen 26

that prevent the benefits of liberalization to materialize, and have shown cases where this is

precisely the reason that liberalization fails to contribute positively to growth.

The coefficient on the variable to control for base GDP, β0, also displays the “wrong”

sign. The convergence theory would predict β0 to be negative, as countries that start out from

lower levels of income should grow faster. Instead, β0 is positive throughout all specifications,

and actually significant in (1) and when using the primary completion rate as the measure for

human capital. This provides evidence suggesting that the convergence theory does not hold in

Africa, and/or that the J-curve specified by Greenaway et al (2001) is in effect.

Robustness Check: Adding Geographic Determinants of Growth

A major criticism of using geographically constructed instruments for trade in growth

models is that geography itself may have a direct impact on growth. This was the view held by

Rodriguez and Rodrick (1999), and refuted by Frankel and Rose (2002). Following the Frankel

and Rose approach, two geographic controls are added as sensitivity checks to ensure that the

results are still valid. The first geographic control variable is “Tropics”, determined by Sachs

and Warner (1997) as the approximate fraction of a country’s land area that is subject to a

tropical climate. Tropical climate can affect a country’s growth prospects through two channels

– labor productivity and prospects for sustainable agriculture. Since parasitic diseases such as

malaria are highly prevalent in tropical climates, constant exposure and infection without

treatment is a key source of low labor productivity. In addition, such areas are associated with

fragile soil, unreliable rain, frequent natural disasters, and pest infestations – all of which act as

impediments to successful agriculture, and thus a key growth prospect (Sachs and Warner 1997).

Lisa Chen 27

Equations (1) through (4) are re-estimated with the “tropics” control variable, and the results are

presented in Table 2.

Table 2. Panel IV Estimates of Equations (1) - (4), with Tropics

(1) § (2a) (3a) (2b) (3b)

ln(CHINA) 0.250*** 0.322*** 0.312** 0.354* 0.257 0.251* 0.275* 0.484 0.315[3.05] [2.60] [2.54] [1.86] [1.64] [1.81] [1.94] [1.62] [1.51]

ln(Base Y) 0.571*** 0.074* 0.069* 0.03 0.033 0.121** 0.120** 0.058 0.068[9.96] [1.83] [1.70] [0.84] [0.99] [2.32] [2.26] [1.17] [1.52]

(4a) (4b)

LIB -0.172 -0.072 -0.065 -0.158 -0.159 -0.15 -0.151 -0.128 -0.147[1.37] [0.68] [0.62] [1.29] [1.42] [1.57] [1.55] [0.75] [0.99]

Population Growth -0.028 0.022 0.021 0.039 0.035 0.02 0.023 0.029 0.019[0.54] [0.90] [0.87] [0.98] [0.97] [0.67] [0.77] [0.53] [0.39]

ln(Capital) 0.130*** 0.05 0.034 -0.054 -0.046 0.058 0.001 -0.144 -0.131[3.14] [0.62] [0.42] [0.49] [0.50] [0.70] [0.02] [0.93] [1.12]

Secondary Enrollment Rate 0.027*** 0.026*** 0.032*** 0.030*** 0.013*** 0.013*** 0.025*** 0.023***[6.63] [6.33] [6.96] [7.25] [4.15] [4.01] [4.33] [4.71]

Primary Completion Rate

WGI 0.446** 0.410** 0.507* 0.480**[2.33] [2.47] [1.70] [2.05]

OIL 0.239 0.365* 0.577** 0.608**[1.01] [1.74] [2.26] [2.38]

Trend -0.313*** -0.300*** -0.236 -0.135 -0.177* -0.185** -0.38 -0.206[3.33] [3.21] [1.41] [0.96] [1.92] [1.96] [1.49] [1.12]

Tropics -1.036*** -0.838** -0.874** -0.386 -0.409 -1.047** -1.118*** -0.836* -0.785**[5.17] [2.20] [2.31] [0.98] [1.20] [2.50] [2.72] [1.65] [1.98]

Constant 2.564** 8.694*** 8.888*** 10.287** 8.808** 7.212*** 8.442*** 13.840** 11.224**[2.50] [3.29] [3.39] [2.41] [2.49] [2.75] [3.15] [2.19] [2.50]

Observations 233 99 99 67 67 111 111 62 62Number of code 40 40 40 40 38 38 36 36

§ Newey-West Corrected Standard Errors§§ Absolute value of z statistics in brackets§§§ * significant at 10%; ** significant at 5%; *** significant at 1%

As expected, the tropical variable has a significant and negative coefficient through most

specifications. The key thing to note is that even when controlling for tropical climate, the

coefficient on ln(CHINA) retains the same level of significance as it had before. In fact, the

magnitudes of the coefficients are actually greater. For example, comparing columns (2a) in

Lisa Chen 28

Table 2 to that in Table 1, we see that a 1% increase in trade openness with China leads to

a .32% versus a .24% increase in growth.

As an additional robustness check, a different geographic control variable is used. Table

3 shows the results for when Equations (1) – (4) are again re-estimated, this time with “distance

to the equator” instead of “tropics”. Distance to the equator is calculated by dividing the

absolute value of the country’s latitude coordinate by 90. The data comes from Hall and Jones

(1999). Because these two geographic controls are highly correlated (.74), they are not

simultaneously included to avoid multicollinearity. The results for estimations with “distance to

the equator” are similar to when “tropics” is used as a geographic control. The coefficient on

ln(CHINA) remains significant, and the coefficient on distance to the equator is positive and

significant as well.

Lisa Chen 29

Table 3. Panel IV Estimates of Equations (1) - (4), with Distance to Equator

(1) § (2a) (3a) (2b) (3b)

ln(CHINA) 0.270*** 0.309** 0.298** 0.350* 0.243 0.212* 0.237* 0.549 0.324[3.21] [2.55] [2.49] [1.81] [1.54] [1.67] [1.82] [1.62] [1.50]

ln(Base y) 0.576*** 0.068* 0.062 0.027 0.032 0.102** 0.104** 0.051 0.068[9.55] [1.71] [1.59] [0.79] [0.99] [2.11] [2.07] [0.98] [1.47]

LIB -0.234* -0.096 -0.091 -0.161 -0.172 -0.160* -0.165* -0.133 -0.18[1.77] [0.93] [0.89] [1.35] [1.58] [1.78] [1.78] [0.74] [1.20]

Population Growth -0.039 0.021 0.02 0.038 0.033 0.015 0.018 0.033 0.017[0.71] [0.89] [0.85] [0.99] [0.91] [0.54] [0.66] [0.56] [0.34]

ln(Capital) 0.153*** 0.076 0.061 -0.045 -0.029 0.095 0.039 -0.155 -0.114[3.43] [0.96] [0.77] [0.40] [0.31] [1.21] [0.48] [0.88] [0.97]

Secondary Enrollment Rate 0.027*** 0.026*** 0.033*** 0.031***[6.35] [6.05] [6.82] [7.32]

Primary Completion Rate 0.013*** 0.013*** 0.026*** 0.023***[4.05] [3.96] [3.96] [4.61]

WGI 0.464** 0.417** 0.578* 0.520**[2.28] [2.40] [1.68] [2.09]

OIL 0.247 0.343 0.598** 0.611**[1.00] [1.61] [2.31] [2.31]

Trend -0.304*** -0.291*** -0.235 -0.128 -0.154* -0.162* -0.438 -0.217[3.30] [3.17] [1.39] [0.90] [1.82] [1.86] [1.53] [1.14]

Distance to Equator 1.757*** 2.032* 2.202* 0.15 0.441 2.577** 2.874** 1.539 1.662[2.65] [1.65] [1.79] [0.11] [0.40] [1.96] [2.25] [0.84] [1.25]

Constant 1.087 7.112*** 7.222*** 9.736** 7.914** 5.012** 6.122*** 13.945** 10.143**[1.06] [2.90] [2.98] [2.27] [2.30] [2.23] [2.65] [1.99] [2.28]

Observations 233 99 99 67 67 111 111 62 62Number of code 40 40 40 40 38 38 36 36

§ Newey-West Corrected Standard Errors§§ Absolute value of z statistics in brackets§§§ * significant at 10%; ** significant at 5%; *** significant at 1%

(4a) (4b)

Separating the Effects of Oil and Non-Oil Countries

Investigations of the impact of trade on growth usually exclude oil producing countries.

Because of the nature of the resource at hand, analyses with these countries tend to display

considerable volatility (Salinas and Aksoy 2006). However, it would be incorrect to omit these

countries from our sample, as over a quarter of Sub-Saharan African countries are oil producers,

and thus major trading partners with China. Instead, as I examine the relationship of trade

openness with China, I also examine whether or not it has had a differential effect on growth of

Lisa Chen 30

oil countries versus non-oil countries. The effect of liberalization is also separated between oil

and non-oil producers.

(5) lnyi,t = α + [λ1ln(CHINA)*OIL i,t + λ2 ln(CHINA)*NONOIL i,t] +

[λ3LIB*OIL i,t + λ4LIB*NONOIL i,t] +

β0lny i,t_base + β1n i,t + β2ln(capital) i,t + β3HC i,t + δTREND + μWGI + ε i,t

Estimates for this regression (5) are displayed in Table 4, with the first column omitting

human capital measures to maximize available observations. Equations under (5a) and (5b) use

secondary school enrollment and primary completion rates respectively.

The results continue to demonstrate that trade openness may indeed induce growth. The

coefficients on ln(CHINA*OIL) and ln(CHINA*NONOIL) are positive for every specification.

The first column (5) indicates that there is a significant differential effect between oil producers

and non-oil producers. For oil countries, a 1% increase in trade openness with China results in

a .36% increase in growth, as opposed to a .23% increase for non-oil countries. This general

trend continues to be true when we add secondary enrollment rates. But interestingly, using

primary completion rates instead reverses the trend, and actually displays higher coefficients for

non-oil countries than oil countries, though here, only λ2 is significant. This may be due to the

differences in which countries report secondary enrollment vs. primary completion rates.

Lisa Chen 31

Table 4. Estimation of Equation (5): Separation of Oil and Non-Oil Countries

(5) (5a) (5a) (5b) (5b)

ln(CHINA*OIL) 0.360*** 0.292* 0.422 0.151 0.177[3.46] [1.84] [1.02] [0.59] [0.44]

ln(CHINA*NONOIL) 0.234*** 0.225** 0.32 0.208* 0.253[3.12] [2.40] [1.50] [1.69] [1.01]

LIB*OIL 0.093 0.285 0.252 -0.001 0.085[0.41] [0.94] [0.57] [0.01] [0.19]

LIB*NONOIL -0.393** -0.081 -0.237 -0.183* -0.184[2.31] [0.88] [1.60] [1.87] [1.14]

ln(Base Y) 0.655*** 0.039 -0.003 0.111 0.02[9.60] [1.02] [0.09] [1.39] [0.51]

Population Growth -0.084 0.016 0.031 0.012 -0.001[1.33] [0.82] [0.88] [0.40] [0.02]

ln(Capital) 0.223*** 0.150** 0.03 0.073 0.041[4.37] [2.07] [0.27] [1.03] [0.36]

Secondary Enrollment Rate 0.024*** 0.030***[4.88] [4.24]

Primary Enrollment Rate 0.015*** 0.017***[3.39] [2.95]

WGI 0.335* 0.302[1.89] [1.41]

Trend -0.263*** -0.289 -0.148 -0.203[3.37] [1.06] [1.30] [0.73]

Constant -0.443 5.442*** 8.815* 5.437*** 7.358[0.41] [2.75] [1.73] [2.96] [1.37]

Observations 249 106 72 121 67# of Countries 46 43 43 42 39

§ Newey-West Corrected Standard Errors for first column§§ Absolute value of z statistics in brackets§§§ * significant at 10% ; ** significant at 5% ; *** significant at 1%

Once again, including the WGI index renders both λ1 and λ2 insignificant; however, it

should be noted that 1) the coefficients remain positive, and 2) the number of observations has

decreased to less than a third of the original count. The final step in this series of robustness

checks is to add in the geographic control variables to the estimation of Equation (5). The results

of these estimations are shown in Table 5 and Table 6, for the inclusion of “tropics” and

“distance to equator” respectively.

Lisa Chen 32

Table 5. Estimation of Equation (5), with Tropics

(5) § (5a) (5a) (5b) (5b)

ln(CHINA*OIL) 0.296*** 0.309* 0.396 0.21 0.105[2.85] [1.74] [0.87] [0.74] [0.24]

ln(CHINA*NONOIL) 0.249*** 0.304** 0.378 0.311** 0.323[3.04] [2.50] [1.44] [2.00] [1.13]

LIB*OIL -0.158 0.172 0.164 -0.061 -0.148[0.71] [0.49] [0.36] [0.19] [0.29]

LIB*NONOIL -0.229 -0.133 -0.216 -0.178 -0.118[1.42] [1.26] [1.45] [1.51] [0.61]

ln(Base Y) 0.600*** 0.048 -0.002 0.153* 0.035[9.05] [1.14] [0.06] [1.69] [0.76]

Population Growth -0.034 0.023 0.051 0.026 0.03[0.64] [1.01] [1.34] [0.73] [0.67]

ln(Capital) 0.156*** 0.07 -0.075 -0.042 -0.111[2.97] [0.76] [0.48] [0.50] [0.70]

Secondary Enrollment Rate 0.025*** 0.032***[4.48] [3.94]

Primary Completion Rate 0.016*** 0.020***[3.37] [2.87]

WGI 0.387** 0.435*[1.97] [1.75]

Trend -0.290*** -0.261 -0.195 -0.16[3.12] [0.86] [1.47] [0.54]

Tropics -0.991*** -0.812* -0.431 -1.078*** -0.858*[4.86] [1.73] [0.84] [3.01] [1.73]

Constant 1.939 8.237*** 11.228* 9.208*** 10.693[1.57] [2.97] [1.68] [3.55] [1.62]

Observations 233 99 67 111 62# of Countries 40 40 38 36

§ Newey-West Corrected Standard Errors§§ Absolute value of z statistics in brackets§§§ * significant at 10%; ** significant at 5%; *** significant at 1%

As when adding geographic controls to estimating Equations (1) through (4), the results

still show that trade with China has had a positive impact on growth. All significance levels

reached when estimating (5) without “tropics” or “distance to equator” are retained. Recall from

Tables 2 and 3 that the magnitude of the coefficient on ln(CHINA) are greater with the

introduction of the controls. Tables 5 and 6 give a clue as to whether this increase in magnitude

is affecting oil or non-oil producing countries. It appears that most of the effect is driven by non-

Lisa Chen 33

oil countries, as the coefficients for ln(CHINA*NONOIL) have increased while those for

ln(CHINA*OIL) have decreased in multiple specifications. Overall, these results offer some

evidence that trade openness with China has differential positive effects on growth between oil

and non-oil countries.

Table 6. Estimation of Equation (5), with Distance to Equator

(5) § (5a) (5a) (5b) (5b)

ln(CHINA*OIL) 0.359*** 0.27 0.533 0.123 0.237[3.25] [1.48] [0.91] [0.40] [0.51]

ln(CHINA*NONOIL) 0.272*** 0.292** 0.442 0.257* 0.374[3.11] [2.46] [1.31] [1.69] [1.24]

LIB*OIL -0.037 0.137 0.287 -0.116 0.014[0.18] [0.41] [0.57] [0.35] [0.02]

LIB*NONOIL -0.379** -0.155 -0.222 -0.208* -0.186[2.15] [1.50] [1.44] [1.82] [0.92]

ln(Base Y) 0.629*** 0.038 -0.011 0.127 0.027[8.24] [0.91] [0.28] [1.34] [0.57]

Population Growth -0.049 0.021 0.052 0.019 0.029[0.82] [0.98] [1.28] [0.55] [0.64]

ln(Capital) 0.189*** 0.092 -0.108 -0.017 -0.11[3.34] [1.03] [0.48] [0.21] [0.68]

Secondary Enrollment Rate 0.023*** 0.033***[4.12] [3.17]

Primary Completion Rate 0.016*** 0.022***[3.19] [2.99]

WGI 0.393* 0.432*[1.66] [1.69]

Trend -0.273*** -0.343 -0.154 -0.243[2.96] [0.91] [1.11] [0.76]

Distance to Equator 1.412** 2.182 0.418 2.668** 1.874[2.06] [1.38] [0.18] [2.28] [1.10]

Constant 0.292 6.682*** 12.517 6.986*** 10.645[0.23] [2.65] [1.38] [3.09] [1.54]

Observations 233 99 67 111 62# of Countries 40 40 38 36

§ Newey-West Corrected Standard Errors§§ Absolute value of z statistics in brackets§§§ * significant at 10%; ** significant at 5%; *** significant at 1%

Lisa Chen 34

Section 6. Policy Implications

The empirical evidence indicates that trade with China has had a positive impact on both

oil and non-oil countries. Therefore, the first set of policy recommendations focuses on policies

directed at increasing the amount of trade between China and Sub-Saharan Africa.21

First, the critical point is that policies should be enacted to encourage African exports to

China. One component of this is that African countries must begin to think about product

diversification to meet the demands of rising incomes in China. This can mean more movement

towards light-manufacturing and food products, or the exploration of opportunities for tourism.

A second component is to increase the value of exports to China by exploiting opportunities for

value-added processing. For example, the processing work on aluminum or on parts along the

cotton-textile-apparel chain can be done locally before exporting to China (Broadman 2006).

For these things to occur, help from the WTO and other international organizations is

necessary. They have the resources to help build capacity (i.e. Aid-for-Trade programs), provide

crucial information, and offer technical assistance and sound trade policy advice.22 But

international organizations cannot do everything. Governments should be solid partners in

creating trade-facilitating infrastructures, and must revamp regulations which hinder progress

toward trade, such as overbearing customs regimes. For instance, improved coordination among

African countries on border relations will help facilitate the movement of goods for processing

21 Broadman (2006) concludes that while formal trade and investment policy are important, the priority for policy reforms for African nations should be on “behind-the-border” and “between-the-border” conditions, as they are the major elements which will influence the extent and effects of commerce with China. The combination of underdeveloped market institutions and weak governance with constraints on business competition is what hinders additional trade with China. While I agree to an extent, there is considerable interdependence of policies and factors that affect trade. Thus, I take the more holistic approach and consider all aspects together and broadly. 22 This would include help towards establishing a better process by which quality data can be collected, in order to facilitate better research and thus policy making.

Lisa Chen 35

and export. These improvements fall under the category of strengthening institutions in general,

which, as we have seen impacts growth and undoubtedly supports trade itself.

In addition, policy-makers should reevaluate and lower the trade barriers of their

respective countries. Some tariffs prevent the necessary materials to enter for intermediate

processing, which means that the country in question will lose out on export opportunities. Non-

tariff barriers will have a similar impeding effect, but worse in the sense that the country may not

even benefit from tariff revenues.

Furthermore, general education and skills training should be encouraged, made

accessable and improved in quality. Education allows a workforce to be more flexible and

adaptable to the demands of the global market. All these policies will help African producers

take advantage of future demands from the Chinese economic powerhouse.

Lastly, Africa is not solely responsible for increasing trade with China. China currently

has a system of escalating tariffs on Africa’s leading exports, making it more difficult for the

value-added processing to occur before items are exported. China must reduce these tariffs as

they not only distort producer incentives, but also prevent Africa from reaching and benefiting

from their own comparative advantage.

Additional Considerations

Continued trade expansion with China will yield increased growth rates for Africa, but

only under relatively stable conditions can this trade occur. As stated earlier, there have been

concerns about the perceptions of “ordinary” Africans on trade with China that they are not

directly benefiting from the increase in growth. The first criticisms mentioned were very much

centered on income distribution, and the relationship between inequality and growth is a

Lisa Chen 36

pertinent matter. While the literature on this is inconclusive, theory suggests an inverted-U

relationship: At low income levels, growth first increases inequality, and then as income levels

rise, high growth levels actually reduce inequality. To the extent that this is true for Africa, the

finding of a positive effect of Chinese trade on African growth can validate the concern for

greater inequality. Fortunately, the empirical literature on this direction of causality, including

Dollar and Kraay (2002), find no impact of growth on inequality. This is not to say that growth

however, cannot help to address these concerns. The World Bank has focused on strategies to

aid in pro-poor growth, such as assisting difficult short-term labor transitions to take advantage

of the long-term opportunities trade can offer (World Bank 2005).

The relationship between growth and income distribution is more likely the other way

around, with inequality having a direct impact on growth. However, the empirical evidence on

this is inconclusive. Perotti (1996) focusing on the effects of inequality through the channels of

sociopolitical stability and credit constraints finds a negative relationship. To the extent that the

relationship is the case for Africa, policies should also be enacted to alleviate job loss and the

unequal distribution of wealth from increased trade with China. On the other hand, Forbes

(2000) finds a positive relationship in the short and medium run. In either scenario, the

government in the long run should still enact policies to ensure that the benefits of growth be

directed to the alleviation of poverty. 23

The governments of African countries will first need to reevaluate their domestic tax

system and redistribution policies, and balance the tradeoffs between alleviating poverty and

encouraging trade activity. Social safety networks and job-training programs will also

undoubtedly be necessary to support Africans who have lost out from Chinese import-

competition. If changes to, or the creation of such policies is further delayed or not enacted, 23 See Appendix Table A.3 for a list of the inequality and growth literature and their findings.

Lisa Chen 37

building frustration and dissatisfaction may create political turmoil that will undermine trade,

and undermine growth. Protests in South Africa and Zimbabwe against cheap clothing imported

from China during President Hu’s last visit are two examples of such dissatisfaction (The

Economist 2007).

The second major category of criticisms revolved around the idea of the resource curse,

and concerns that trade with China would bolster and uphold bad governance. My results have

two implications that attempt to ease these criticisms. First, since such concerns were geared

primarily toward the oil exporting countries, we can focus on the results for oil-producing

countries alone. There was a greater positive impact of trade openness with China on growth for

these countries than for non-oil producing ones. If we assume that the resource curse has

manifested in low growth rates for oil countries, then trade with China is helping to reverse the

trend, not contribute to it. Second, the results demonstrate that trade with China has had a

positive impact on growth. To the extent that higher income levels dictate better governance,

trade with China may indirectly improve the institutional quality of African countries or mitigate

the effects of bad governance.24

Finally, investment and trade are undoubtedly interconnected, and Chinese investors are

taking advantage of relatively untouched opportunities in Africa. Currently, the accumulated

stock of foreign direct investment from China alone stands at approximately $1.2 billion. This is

concentrated primarily in extractive sectors for natural resources, though some signs of

diversification are apparent. African countries must help facilitate investment in other areas, in

order to promote further technology and skills transfers, and thus growth. One option may be to

better cooperate with China, a country which understands the need for growth, in linking

investment with skills development and infrastructure projects. 24 See Treisman (2000) and Al-Marhubi (2004).

Lisa Chen 38

Section 7. Conclusion

There is still further work to be done. Additional sensitivity checks should be added, for

example, by altering the length of the time period used to determine whether or not results differ

with different numbers of observations. Another improvement may be to attempt the dynamic

version of the models specified in this paper, and see whether or not the J curve effect is strongly

present with the current sample.

In addition, a valid criticism of this paper would be that other explanatory variables may

be endogenous to growth. Variables that are used to proxy human capital and quality of

governance are prime examples. Bils and Klenow (2000) demonstrate the reverse causality of

schooling and growth by providing evidence to suggest that faster growth can induce more

schooling by increasing its expected return. Variables that proxy for institutional quality are also

at high risk for simultaneity. The literature on how corruption and governance affect growth is

almost always accompanied by the instrumenting of corruption with British colonial status,

Protestantism, and/or ethnolinguistic fractionalization.25 Therefore, it is acknowledged that

such endogeneity issues may reduce the robustness of the findings. Future work is

recommended to develop a more comprehensive examination of these issues to precisely and

robustly determine causation.26

Nevertheless, this study has provided some initial evidence that trade openness with

China positively contributes to African growth, and should certainly continue to expand. African

governments must work alongside international organizations to enact sound domestic and

25 For more on the links between corruption and growth, see Mauro (1995), Meon and Sekkat (2005), and Treisman (2007). 26 In accordance with trade and growth literature, instrumental variables are only used for the key variable of interest. Here, it is a purposeful attempt to keep the focus of the paper.

Lisa Chen 39

international policies to facilitate future trade flows with China. As long as trade with China

continues to play a hand in driving growth in Africa, the prospects of development and poverty

reduction are bright. Trade with China is good for African growth, and that is good for everyone.

Lisa Chen 40

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Lisa Chen 46

APPENDIX

Source: Broadman (2006), page 7

Graph A.1. Average GDP growth rate by country, 1996 – 2005

Lisa Chen 47

Table A.1. Sub-Saharan Africa Countries and WTO Status

Country WTO Member WTO ObserverAngola November 23, 1996Benin February 22, 1996Botswana May 31, 1995Burkina Faso June 3, 1995Burundi April 23, 1995Cameroon December 13, 1995Cape Verde As of May 21, 2007Central African Republic May 31, 1995Chad October 19, 1996ComorosDemocratic Republic of Congo January 1, 1997Republic of CongoCote d'Ivoire January 1, 1995Equitorial Guinea As of May 21, 2007EritreaEthiopia As of May 21, 2007Gabon January 1, 1995The GambiaGhana January 1, 1995Guinea October 25, 1995Guinea-Bissau May 31, 1995Kenya January 1, 1995Lesotho May 31, 1995LiberiaMadagascar November 17, 1995Malawi May 31, 1995Mali May 31, 1995Mauritania May 31, 1995Mauritius January 1, 1995Mozambique August 26, 1995Namibia January 1, 1995Niger December 13, 1996Nigeria January 1, 1995Rwanda May 22, 1996Sao Tome & Principe As of May 21, 2007Senegal January 1, 1995Seychelles As of May 21, 2007Sierra Leone July 23, 1995SomaliaSouth Africa January 1, 1995Sudan As of May 21, 2007SwazilandTanzania January 1, 1995Togo May 31, 1995Uganda January 1, 1995Zambia January 1, 1995Zimbabwe March 5, 1995

Lisa Chen 48

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Lisa Chen 49

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Lisa Chen 50

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Lisa Chen 51

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Lisa Chen 52

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Lisa Chen 53

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Lisa Chen 54

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Lisa Chen 56

Testing for Endogeneity of ln(CHINA) Results Log Part I: Durbin-Wu-Hausman Test for Endogeneity Step 1: Regress ln(CHINA) on all variables considered exogenous in both simultaneous equations. xtreg lnchina chinapop area distance landlock1 island1 lib basegdp popgrowth lncapital primcomplete secondary oil WGI; Random-effects GLS regression Number of obs = 506 Group variable (i): code Number of groups = 39 R-sq: within = 0.8426 Obs per group: min = 1 between = 0.1405 avg = 13.0 overall = 0.2376 max = 15 Random effects u_i ~ Gaussian Wald chi2(13) = 2284.24 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ lnchina | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- chinapop | 1.57e-08 8.64e-10 18.13 0.000 1.40e-08 1.74e-08 area | -5.72e-14 3.74e-14 -1.53 0.127 -1.31e-13 1.62e-14 distance | .0004958 .0002282 2.17 0.030 .0000486 .000943 landlock1 | -.9580849 .4375937 -2.19 0.029 -1.815753 -.1004169 island1 | 1.441125 .6042337 2.39 0.017 .2568486 2.625401 lib | -.0552279 .079545 -0.69 0.487 -.2111333 .1006774 basegdp | -.0412613 .020581 -2.00 0.045 -.0815993 -.0009233 popgrowth | -.0600511 .0221332 -2.71 0.007 -.1034314 -.0166709 lncapital | .783855 .0718486 10.91 0.000 .6430344 .9246757 primcomplete | .024192 .0072977 3.32 0.001 .0098889 .0384952 secondary | -.0577781 .0090778 -6.36 0.000 -.0755702 -.039986 oil | -.6443171 .4388973 -1.47 0.142 -1.50454 .2159057 WGI | -.9193766 .0945966 -9.72 0.000 -1.104783 -.7339706 _cons | -41.95344 2.073156 -20.24 0.000 -46.01676 -37.89013 -------------+---------------------------------------------------------------- sigma_u | .89989716 sigma_e | .10601497 rho | .98631129 (fraction of variance due to u_i) ------------------------------------------------------------------------------ Step 2: Get the residuals, and then perform an augmented regression by regressing ln(y) on its exogenous determinants and these residuals. predict lnchina_res, u; xtreg lnpcgdp lnchina_res lib basegdp popgrowth lncapital primcomplete secondaryoil WGI;

Lisa Chen 57

Random-effects GLS regression Number of obs = 506 Group variable (i): code Number of groups = 39 R-sq: within = 0.6750 Obs per group: min = 1 between = 0.3833 avg = 13.0 overall = 0.4177 max = 15 Random effects u_i ~ Gaussian Wald chi2(9) = 879.37 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ lnpcgdp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnchina_res | .3124072 .0687032 4.55 0.000 .1777514 .447063 lib | -.0285392 .0206234 -1.38 0.166 -.0689602 .0118819 basegdp | -.021745 .0053959 -4.03 0.000 -.0323207 -.0111693 popgrowth | .0271423 .0050281 5.40 0.000 .0172874 .0369973 lncapital | .3023712 .0181781 16.63 0.000 .2667427 .3379997 primcomplete | .0059453 .001831 3.25 0.001 .0023566 .009534 secondary | .0015999 .0027214 0.59 0.557 -.003734 .0069337 oil | .4517304 .1848868 2.44 0.015 .0893588 .8141019 WGI | .0064871 .0238255 0.27 0.785 -.04021 .0531841 _cons | -.2717794 .3309333 -0.82 0.412 -.9203968 .3768379 -------------+---------------------------------------------------------------- sigma_u | .46012932 sigma_e | .02698222 rho | .99657307 (fraction of variance due to u_i) ------------------------------------------------------------------------------ Step 3: Test to see if the coefficient on the residual is significantly different from zero. Small p-value indicates that OLS is not a consistent estimator. The variable of interest ln(CHINA) is endogenous. test lnchina_res; ( 1) lnchina_res = 0 chi2( 1) = 20.68 Prob > chi2 = 0.0000

Lisa Chen 58

Results Log Part II: Hausman Specification Test for Endogeneity Step 1: First assume ln(CHINA) is endogenous. Regress ln(y) on all growth determinants, using the gravity model to instrument for ln(CHINA). xtivreg lnpcgdp (lnchina = chinapop area distance landlock1 island1) lib basegdppopgrowth lncapital primcomplete secondary oil WGI; G2SLS random-effects IV regression Number of obs = 506 Group variable: code Number of groups = 39 R-sq: within = 0.6313 Obs per group: min = 1 between = 0.6293 avg = 13.0 overall = 0.6538 max = 15 Wald chi2(9) = 900.02 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ lnpcgdp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnchina | .1591039 .0153921 10.34 0.000 .128936 .1892718 lib | -.09293 .0221106 -4.20 0.000 -.136266 -.0495939 basegdp | -.006398 .0057534 -1.11 0.266 -.0176745 .0048784 popgrowth | .0648693 .0063654 10.19 0.000 .0523934 .0773452 lncapital | .0921325 .0273796 3.37 0.001 .0384694 .1457957 primcomplete | -.0095456 .0023866 -4.00 0.000 -.0142233 -.0048679 secondary | .0245794 .0034623 7.10 0.000 .0177934 .0313654 oil | .5490536 .2069496 2.65 0.008 .1434399 .9546673 WGI | .2592386 .0346087 7.49 0.000 .1914067 .3270704 _cons | 4.719091 .5875563 8.03 0.000 3.567502 5.87068 -------------+---------------------------------------------------------------- sigma_u | .5052302 sigma_e | .02738744 rho | .99707012 (fraction of variance due to u_i) ------------------------------------------------------------------------------ Instrumented: lnchina Instruments: lib basegdp popgrowth lncapital primcomplete secondary oil WGI chinapop area distance landlock1 island1 Step 2: Get predicted values and store them as “xtivreg”. predict p1; estimates store xtivreg;

Lisa Chen 59

Step 3. Now assume ln(CHINA) is not endogenous. Regress ln(y) on all growth determinants,without using instrumental variables. xtreg lnpcgdp lnchina lib basegdp popgrowth lncapital primcomplete secondary oilWGI; Random-effects GLS regression Number of obs = 506 Group variable (i): code Number of groups = 39 R-sq: within = 0.6943 Obs per group: min = 1 between = 0.4931 avg = 13.0 overall = 0.5138 max = 15 Random effects u_i ~ Gaussian Wald chi2(9) = 1002.70 corr(u_i, X) = 0 (assumed) Prob > chi2 = 0.0000 ------------------------------------------------------------------------------ lnpcgdp | Coef. Std. Err. z P>|z| [95% Conf. Interval] -------------+---------------------------------------------------------------- lnchina | .0716173 .0089898 7.97 0.000 .0539976 .089237 lib | -.0627349 .0198849 -3.15 0.002 -.1017085 -.0237613 basegdp | -.0135721 .0051928 -2.61 0.009 -.0237499 -.0033943 popgrowth | .0439318 .0052535 8.36 0.000 .0336351 .0542285 lncapital | .2022793 .020741 9.75 0.000 .1616276 .242931 primcomplete | -.0014377 .0019355 -0.74 0.458 -.0052311 .0023558 secondary | .0135169 .0027984 4.83 0.000 .0080321 .0190018 oil | .4801275 .182083 2.64 0.008 .1232514 .8370036 WGI | .1236518 .026756 4.62 0.000 .0712111 .1760925 _cons | 2.058139 .4192054 4.91 0.000 1.236511 2.879767 -------------+---------------------------------------------------------------- sigma_u | .45838938 sigma_e | .02602605 rho | .99678671 (fraction of variance due to u_i) ------------------------------------------------------------------------------ Step 4: Get predicted values, and then run the Hausman Specification Test. Results below demonstrate there is a significant difference between the coefficients from the two regressionsindicating that OLS is an inconsistent estimator. ln(CHINA) is endogenous. predict p2; hausman xtivreg ., sigmamore;

Lisa Chen 60

---- Coefficients ---- | (b) (B) (b-B) sqrt(diag(V_b-V_B)) | xtivreg . Difference S.E. -------------+---------------------------------------------------------------- lnchina | .1591039 .0716173 .0874866 .0106893 lib | -.09293 -.0627349 -.0301951 .0026719 basegdp | -.006398 -.0135721 .0071741 .0005389 popgrowth | .0648693 .0439318 .0209375 .0024007 lncapital | .0921325 .2022793 -.1101468 .0136774 primcomplete | -.0095456 -.0014377 -.008108 .0009716 secondary | .0245794 .0135169 .0110625 .001428 oil | .5490536 .4801275 .0689261 .0459425 WGI | .2592386 .1236518 .1355868 .0164428 ------------------------------------------------------------------------------ b = consistent under Ho and Ha; obtained from xtivreg B = inconsistent under Ha, efficient under Ho; obtained from xtreg Test: Ho: difference in coefficients not systematic chi2(9) = (b-B)'[(V_b-V_B)^(-1)](b-B) = 35.22 Prob>chi2 = 0.0001 (V_b-V_B is not positive definite)

Lisa Chen 61

Table A.2. Panel IV Estimates of Equations (2), Fixed Effects

ln(CHINA) 0.201*** 0.095***[4.52] [2.72]

LIB -0.108*** -0.207***[4.30] [8.29]

ln(Base Y) 0.002 -0.001[0.20] [0.06]

Population Growth 0.007 -0.024***[0.78] [2.76]

ln(Capital) 0.199*** 0.229***[5.48] [7.99]

Secondary Enrollment Rate 0.011***[5.56]

Primary Completion Rate 0.002***[2.58]

Trend -0.189*** -0.070***[5.52] [3.03]

Constant 4.385*** 2.709***[4.16] [3.50]

Observations 587 576Number of Countries 43 42

§ Absolute value of z statistics in brackets§§ * significant at 10%; ** significant at 5%; *** significant at 1%

Lisa Chen 62

Table A.3. Inequality and Growth Literature

Impact of growth on income distribution .

Dollar and Kraay (2002) no Easterly (1999) no Chen and Ravallion (1997) no Deininger and Squire (1996) no Impact of income inequality on growth .

Forbes (2000) positiveLi and Zhou (1998) positiveBarro (2000) no Lopez (2004) no Alesina and Rodrik (1994) negativePerotti (1996) negativeImpact of asset inequality on growth .

Deininger and Squire (1998) negativeBirdsall and Londono (1997) negativeImpact of redistribution on growth .

Easterly and Rebelo (1993) positivePerotti (1996) positive

Source: The World Bank, Growth and Inequality Web Page. <http://go.worldbank.org/AKKLH75ES0>