1. University of Siegen
Faculty III
Department of economics
UNIV.-PROF. DR. KARL-JOSEF KOCH
Seminar ”Economic growth”
SUMMER SEMESTER 2015
TOPIC: TRADE AND GROWTH:
Trade Policy and Economic Growth in Sub-Saharan Africa:
A Panel Data Approach
Name: Zhang Yi
Deadline: June 22nd

2. Contents
1 Introduction 3
2 Objective 4
3 Methodology 4
3.1 Hypothesis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.2 Method of Analysis . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 4
3.3 Data . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4 Model 5
4.1 Model Overview . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 5
4.2 Parameters and Equations . . . . . . . . . . . . . . . . . . . . . . . . . . . 6
4.3 Regression Estimate . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.3.1 Growth Regression . . . . . . . . . . . . . . . . . . . . . . . . . . . . 7
4.3.2 Investment Regression . . . . . . . . . . . . . . . . . . . . . . . . . 9
4.3.3 Openness Regression . . . . . . . . . . . . . . . . . . . . . . . . . . 10
5 Conclusion 11
References 13
A Appendices 14
A.1 Growth regression estimates code . . . . . . . . . . . . . . . . . . . . . . . 14
A.2 Investment regression estimates code . . . . . . . . . . . . . . . . . . . . . 15
A.3 Openness regression estimates code . . . . . . . . . . . . . . . . . . . . . 16
1

3. List of Symbols
Y real GDP per capita
LB active labour force
I
Y investment GDP ratio
TAR average tariff rate
REER real effective exchange rate
OPEN openness(ratio of trade to GDP)
PI
P ratio of price of investment goods to price of GDP
QoP quality of port infrastructure
CPIA country policy and institutional assessment
POP population
POPDNST population density
List of Tables
Table 1: Growth Regression Estimates
Table 2: Investment Regression Estimates
Table 3: Openness Regression Estimates
2

4. 1 Introduction
A huge number of studies have already proved a crucial rule that there is a positive
relationship between international trade policy with economic growth. This relation is
not only in direct way but also in indirect way through investment. As we all known, the
world economy has been experiencing an important change since the economic crisis in
2008. Will the relationship between trade policy and economic growth changes during
this period?
Here we are going to set an empirical analysis to discuss the relationship between
trade policy and economic growth in Sub-Saharan Africa countries over the period 2008
to 2012. especially, we use average tariff rate and real effective exchange rate as two
trade policies which are going to influence the economics growth when trade occurs.
Besides that, there are also many data to measure the economic growth, such as: the
ratio of trade to GDP, real GDP per capita and the ratio of investment output to GDP.
In addition, several data also need to be considered here which would have essential
influences on economic growth as well, for example: labour force, institutional quality,
geographic factor and population as well as population density
From several previous multi-country case studies about trade and growth, we can
get the conclusion that more open economies grow quickly. That because trade could
increase the efficiency of production which will increase the economic growth in direct
way. At the same time, when open for trade, there will be many foreign investment, such
as capital, technology and knowledge, into the country, so that will presume the economic
growth in indirect way. So, in this paper, we are going to discuss how international trade
presume economic growth both in direct and in indirect ways, and also how trade policies
influence international trade.
In the first part, we are going to clear and definite the objective of this paper. The sec-
ond part is about the hypothesis and method as well as data we will use in the following
model. The third part is the main part, we are going to set the model and use regression
to estimate the relations between trade policies with data which could reflect economic
growth.
3

5. 2 Objective
As what we have mentioned before, open for international trade stimulates both economic
growth and investment. Meanwhile, average weighted tariff and real effective exchange
rate as the trade policies could influence the level of openness that would further influence
on economic growth through openness.
In this paper, we use Sub-Saharan countries as examples to estimate the relations
between trade and growth in order to find out what are the effects of these trade policies
work on economics growth.
To achieve this goal of the paper, there are three aspects we have to discuss. At
first, we have to estimate the direct impact of international trade on economic growth.
Secondly, we are going to estimate how international trade affects investment, which
would further affect economic growth as the indirect way. Last but not least, we have to
pay much attention on how trade policies influence on international trade, and then that
will work on economic growth through the ways what we mentioned above.
3 Methodology
3.1 Hypothesis
So as to achieve the objective of this paper, we could make a hypothesis firstly that trade
policies have negative impacts on economic growth. Specifically, this relation between
trade policy and economic growth includes the following contents.
On the one hand, open for trade could lead to an exploitation of comparative advan-
tage and a reduction of trade barriers, which would further presume the welfare gains in
this country. Besides that, the reallocation of resources and capital accumulation from
trade would raise the GDP and also cause higher productivity at the same time. On the
other hand, policies which foster trade could lead to an expansion of the market for input
as well as output, furthermore, the rate of technological progress raises and then cause
the productivity growth as well.
3.2 Method of Analysis
We will focus on the empirical analysis through econometric techniques so as to discuss
the relations between trade with growth, trade with investment as well as trade policies
4

6. with openness. Here, we are going to use generalized least square (GLS)[N.Gujarati,
C.Porter (2010)]1 estimation technique on Matlab regress with the data from 2008 to
2012 in the Sub-Saharan countries to see which trade policy has positive or negative
influence on economic growth.
3.3 Data
As we all known, there are several policies to influence trade in different ways, and it is
also too difficult to measure all these effects. Typically, in this paper, we are going to use
weighted mean average tariff rate and real effective exchange rate as two kinds of trade
policies which affect economic growth.
In addition, there also are several aspects to measure economic growth, such as: real
GDP per capita, openness (which means the ratio of trade to GDP).
Besides that, in order to discuss the indirect impact of trade policy through investment,
we also need several data to measure investment, for example: investment GDP ratio,
ratio of price of investment goods to price of GDP.
Last but not least, there are several data also be needed to measure other influence
part of production, trade and investment process, namely: labour force, geographic factor,
institutional quality and population as well as population density.
All the data above are gathered from trustworthy websites such as UNCTAD (2015)2,
WDI (2015)3, IMF (2015)4, PWT (2015)5, ect.
4 Model
4.1 Model Overview
In order to achieve dynamic analysis, we have to use panel data. That allows this study
is of time series cross country as well as cross-sectional aspects of the problem. As
what we have mentioned in last part, we focus on the trade policies, such as average
tariff and real effective exchange rate, as the major independent variables to openness.
1
Damodar N.Gujarati, Dawn C.Porter (2010), Essentials of Econometrics, 4th edition, Copyright by The
McGraw-Hill companies, Inc, China Machine Press.
2
UNCTAD, United Nations Conference on Trade and Development, http://unctadstat.unctad.org/wds/
ReportFolders/reportFolders.aspx
3
WDI, The World Bank, http://data.worldbank.org/
4
IMF, International Monetary Fund, http://www.imf.org/external/index.htm
5
PWT, Penn World Table, https://pwt.sas.upenn.edu/php_site/pwt_index.php
5

7. Meanwhile, we have to discuss not only the direct effect of openness on economic growth
but also the indirect effect on economic growth through investment.
4.2 Parameters and Equations
As what we have hypothesized in last part, we are going to estimate whether our hypoth-
esis is right or not?
There are three equations in this model, a growth equation, an investment equation
and an openness equation. We use grow equation to estimate the direct impact of open-
ness. The investment equation help us to estimate the indirect impact of openness. And
then, the openness equation is about how trade policies influence on openness.
We use the subscript i to denote country, and subscript t to denote the period.
Growth real GDP per capita
lnYit = η0 + η1lnOPENit + η2lnLBit + η3
I
Y it
+ µit............eq (1)
Investment ratio
I
Y it
= δ0 + δ1lnYit + δ2lnOPENit + δ3INSTit + δ4
PI
P it
+ νit............eq (2)
Trade ratio
lnOPENit = θ0+θ1QoPit+θ2lnTARit+θ3lnREERit+θ4lnPOPit+θ5POPDNSTit+εit..........eq
(3)
Where:
Y is real GDP per capita
LB is active labour force
I
Y is investment GDP ratio
6

8. TAR is average tariff rate
REER is real effective exchange rate
OPEN is openness(ratio of trade to GDP)
PI
P is ratio of price of investment goods to price of GDP
QoP is quality of port infrastructure
CPIA is country policy and institutional assessment
POP is population
POPDNST is population density
4.3 Regression Estimate
The system of equation is recursive, allowing us to use single equation estimation if there
is no correlation between the error terms —– a condition in which we test.[Asfaw,Henok
Arege (2015)]6 In order to observe data which are relevant to trade policies, investment
or economic growth. We will pick different groups of data as the observations for each
regression estimate. For example, we have observed the labour force, openness, invest-
ment ratio and real GDP per capita of 30 counties to estimate the growth regression for
five years from 2008 to 2012, because the lack of more available data in any others to
support our estimation. The following discussion is based on the result we get from Mat-
lab, which help us to estimate the regression equation for all the observations. All the
matlab codes and regression figures are in the appendix A.
4.3.1 Growth Regression
The table 1 shows the result of growth regression following the growth equation. All the
independent variables: labour force, openness and investment output ratio are significant
at 1% level of significance. The sign of the three variables is not as same as what we
have learnt from the regression estimate in Asfaw,Henok Arege (2015)7, data in that
paper is at period from 2000 to 2008.
The growth regression reflects the direct impacts of openness contribute to economic
growth. We can see there is a positive relation between openness and economic growth.
6
Henok Arege Asfaw(2015), Trade Policy and Economic Growth in Sub-Saharan Africa: A Panel Data Ap-
proach, American Journal of Trade and Policy, Number 1/2015.
7
Henok Arege Asfaw(2015), Trade Policy and Economic Growth in Sub-Saharan Africa: A Panel Data Ap-
proach, American Journal of Trade and Policy, Number 1/2015.
7

9. TABLE 1 GROWTH REGRESSION ESTIMATES
Dependent Variable: Log of real GDP per capita
Independent V ariables Estimate SE tStat pValue
(intercept) 6.5701 1.062 6.1867 5.8656e−09
ln of (openness) 0.73052 0.17372 4.2051 4.5315e−05
investment output ratio 0.092979 0.012733 7.3021 1.691e−11
ln of (labour force) −0.35761 0.058113 −6.1538 6.9198e−09
Number of observations:150, Error degrees of freedom: 146
R-squared: 0.514, Adjusted R-Squared 0.504
F-statistic vs. constant model:51.5, p-value = 9.12e−23
That means the more open economy, the higher economic growth will be.
Furthermore, the influence of openness could be divided by two parts, one is human
capital input, and the other is physical capital input. Here, we use two variables labour
force and investment output ratio to reflect the two aspects.
From the regression result in the table, we found out that labour force has a negative
effect on the economic growth during the period from 2008 to 2012, which is totally re-
verse to what we have get in the analysis before. However, there is still a positive effect
of investment output ratio on the economic growth, more physical capital input contribute
to more economic growth.
We could make a brave guess here the reason why labour force contributes negatively
to economic growth? On the one hand, it is because of large unemployment existing
currently, more labour input causes larger employment pressure in the society. On the
other hand, because technology instead of labour become the essential part of production
in some industry, the demand of labour become less than before. Industries used to be
labour-intensive but turn into technology-intensive recently.
In addition, openness may also have indirect influence on economic growth through
investment, which we are going to discuss in the following investment equation.
8

10. TABLE 2 INVESTMENT REGRESSION ESTIMATES
Dependent Variable: Investment output ratio
Independent V ariables Estimate SE tStat pValue
(intercept) −14.583 2.9324 −4.9731 2.5528e−06
ln of (real GDP per capia) 1.3002 0.33252 3.9103 0.00016291
price of investment ratio −0.24054 0.32628 −0.73722 0.46262
CPIA 0.22994 0.39077 0.58843 0.55749
ln of (openness) 1.9474 0.55764 3.4923 0.00069962
Number of observations: 111, Error degrees of freedom: 106
R-squared: 0.281, Adjusted R-Squared 0.254
F-statistic vs. constant model: 10.3, p-value = 4.11e−07
4.3.2 Investment Regression
The above table 2 reflects the result of investment regression according to investment
equation. All the independent variables: real GDP per capita, ratio of price of invest-
ment goods to price of GDP, CPIA index and Openness are significant at 10% level of
significance.
We have to pay much attention here; the CPIA is the abbreviation of Country Policy
and Institutional Assessment. The World Bank uses the CPIA to rate the policy and insti-
tutional performance of its approximately 135 IBRD and IDA recipient countries. [Nancy
Alexander (2010)] 8 CPIA reflects more aspects of a country, such as: economic man-
agement, structural policies and social policy. We are going to use the average rate score
of some Sub-Saharan countries to represent their institutional quality. All data we used in
this regression estimation is at period 2008 to 2010.
The investment regression reflects the indirect impacts of openness contribute to eco-
nomic growth. In this regression estimate, we can find out that openness have a signif-
icant positive impact on investment as well. As what we have already discussed in last
regression, investment positively influence economic growth directly. So, there is an pos-
itive influence of openness on economic growth indirectly. All in all, the overall effect of
8
Nancy Alexander(2010), The Country Policy and Institutional Assessment (CPIA) and Allocation of IDA Re-
sources: Suggestions for Improvements to Benefit African Countries, Presented to A Meeting of the African
Caucus of Finance Ministers, Central Bank Governors, And Executive Directors of the IMF and World Bank,
Freetown, Sierra Leone August 16, 2010.
9

11. openness on economic growth is the sum of the indirect and direct effects. The direct
impact level of openness is η1 = 0.73, while the term δ2 ∗ η3 = 0.18 shows the indirect
impact level of openness. Obviously, both the two terms are positive.
At the same time, there is also a positive effect of real GDP per capita on investment
output ratio. Together with the result of last regression estimate. We can find out that there
is mutually positive relationship between investment with economic growth. That means
more investment presume higher growth; in turn higher growth attract more investment.
However, the relative price of investment goods with GDP has a significant negative
effect on investment output ratio. Because, if the price of investment good become higher
than before, people will have less willingness to invest in these goods. Besides that, the
CPIA is not significant enough in this estimation to show its effects on investment.
4.3.3 Openness Regression
Table 3 shows us the final regression estimates of openness which is influenced by sev-
eral variables, such as: geographic factor (here we use the quality of port infrastructure
to represent it), population as well as population density, and the two main trade policies,
namely average tariff rate and real effective exchange rate. All variables are significant at
1% level of significance.
Except of the quality of port infrastructure, all variables have significant negative rela-
tions with openness. That means a well developed transportation condition is better for
countries to open for trade.
We also find that both population and population density negatively influence on open-
ness. That means higher population density offer more opportunity for internal trade
instead of international trade.
As what we have hypothesis before, average tariff rate and real effective exchange
rate, both of them negatively affect openness. On the one hand, higher tariff rate would
be harmful not only to import side but also to export side. On the other hand, even higher
exchange rate could benefit to import but is harmful to export too much. So, the overall
influence of trade policies on openness is negative. Furthermore, they would negatively
affect economics growth because of the restrict openness.
As all the analysis we did above, the direct impact level of openness is η1 = 0.73, while
the indirect impact level is δ2∗η3 = 0.18. In this regression estimate, we can get the impact
level of trade policy variables on openness is (θ2 + θ3) = −0.45. So, the overall impact of
10

12. TABLE 3 OPENNESS REGRESSION ESTIMATES
Dependent Variable: Log of openness
Independent V ariables Estimate SE tStat pValue
(intercept) 6.7709 0.43002 15.745 2.8966e−34
Quality of Port 0.044193 0.022893 1.9304 0.055334
ln of (population) −0.10741 0.017307 −6.2065 4.5184e−09
Population density −0.0010032 0.0002166 −4.6315 7.5079e−06
ln of (tariff) −0.20721 0.059166 −3.5022 0.0005991
ln of (exchange rate) −0.24084 0.066038 −3.647 0.00035908
Number of observations: 165, Error degrees of freedom: 159
R-squared: 0.285, Adjusted R-Squared 0.262
F-statistic vs. constant model: 12.7, p-value = 2.33e−10
trade policies on economic growth is calculated by formula (θ2 + θ3)(η1 + δ2 ∗ η3) = −0.41
5 Conclusion
Several studies have already proved that there is a positive relationship between interna-
tional trade with economic growth. China, as a typical example, have been developing
so fast just after open for international trade since the last decade in 20th century. Addi-
tionally, different trade policies could lead to different development result of international
trade, that will further affect much on economic growth. Empirical studies could be able to
provide confident evidences to support the discussion about the effects of trade policies
on economic growth.
Specially, we used weighted mean average tariff rate and real effective exchange rate
as the two kinds of trade policy to discuss how they influence on economic growth in this
paper. In order to do so, we separated this task by steps. Because trade policies could
not affect economic growth directly. On the one hand, only the change of openness (the
ratio of trade to GDP) could reflect of the effects different trade policies. On the other
hand, we also discussed the effect of openness on economic growth both in direct way
and also in in direct way through investment.
We used regression estimates to discuss the relations between trade policies with
11

13. openness as well as openness with economic growth in two ways. Finally, we found out
that there are negative relations between trade policies and economic growth. Based on
the trade policies we discussed in this paper. The reasons as follows. on the one hand,
the increase in weighted mean average tariff rate is harmful to both import and export
aspects of international trade, that would further lower economic growth. on the other
hand, even the increase in real effective exchange rate could be benefit to import, but is
much more harmful to export, which is the essential part of trade. So higher real effective
exchange rate could lower economic growth heavily. All in all, policies like increasing tariff
and increasing exchange rate is bad for growth.
In this paper, we have used data in Sub-Saharan countries from 2008 to 2012. how-
ever, data here is not large enough due to the unavailable data in some countries or
years. In order to achieve more appropriate analysis. We could search for much more
data because larger data lead to more correct regression estimates.
12

14. References
Henok Arege Asfaw(2015), Trade Policy and Economic Growth in Sub-Saharan Africa: A
Panel Data Approach, American Journal of Trade and Policy, Number 1/2015.
Nancy Alexander(2010), The Country Policy and Institutional Assessment (CPIA) and
Allocation of IDA Resources: Suggestions for Improvements to Benefit African Coun-
tries, Presented to A Meeting of the African Caucus of Finance Ministers, Central Bank
Governors, And Executive Directors of the IMF and World Bank, Freetown, Sierra
Leone August 16, 2010.
IMF(2015), Regional Economic Outlook:Sub-Saharan Africa Navigating Headwinds,
International Monetary Fund, Publication Services.
Damodar N.Gujarati, Dawn C.Porter (2010), Essentials of Econometrics, 4th edition,
Copyright by The McGraw-Hill companies, Inc, China Machine Press.
UNCTAD, United Nations Conference on Trade and Development, http:
//unctadstat.unctad.org/wds/ReportFolders/reportFolders.aspx.
WDI, The World Bank, http://data.worldbank.org/.
IMF, International Monetary Fund, http://www.imf.org/external/index.htm.
PWT, Penn World Table, https://pwt.sas.upenn.edu/php_site/pwt_index.php.
13

15. A Appendices
A.1 Growth regression estimates code
14

16. A.2 Investment regression estimates code
15

17. A.3 Openness regression estimates code
16