income convergence in african countries: evidence from a stationary test with multiple structural...

INCOME CONVERGENCE IN AFRICAN COUNTRIES:

EVIDENCE FROM A STATIONARY TEST WITH MULTIPLE

STRUCTURAL BREAKS

OMID RANJBAR†, CHIEN-CHIANG LEE

‡, TSANGYAO CHANG* AND MEI-PING CHEN§

AbstractThis paper examines the catching-up (stochastic convergence in real per capita income) hypothesisfor 52 African countries with respect to the USA. over the 1969-2011 period, using a highlyflexible stationarity test. The empirical results show (i) that all African countries experienced atleast one break, switching between catching-up and divergence paths during the sample period; (ii)that structural breaks tend to coincide with political instability, trade liberalisation policies andterms of trade shocks; (iii) that among the 52 African countries studied, only five lie on thecatching-up path, while the remaining 47 diverge from the USA. Our results show thatthe economic performance of African countries fall far behind those of the USA and that theeconomic growth tragedy of Africa continues.JEL classification: O55, C51, P44Keywords: Catching up, stationarity test, structural breaks, Africa

1. INTRODUCTION

An important prediction of neoclassical growth theory is the income convergencehypothesis, where convergence is defined as the tendency of countries to move over timetowards equality in per capita income. In empirical studies, researchers have tested fourtypes of convergence hypotheses – absolute, conditional, stochastic (or catching up) andsigma convergence – by applying different empirical methodologies such as cross-sectional, time series and distribution approaches. The absolute convergence hypothesisrefers to the notion that all economies will converge towards the same per capita incomein a long-run steady state, so that there is no potential poverty at the international level.The conditional convergence hypothesis proposes that economies will eventuallyconverge to their own steady state. According to the conditional convergence hypothesis,per capita income across economies will not move to the convergence path, even in along-run steady state.

* Corresponding author. Professor, Department of Finance, Feng Chia University, No. 100,Wenhua Road, Seatwen, Taichung 40724, Taiwan. E-mail: [email protected]† Department of International Affairs, Ministry of Industry, Mine, and Trade, Tehran, Iran‡ Department of Finance, National Sun Yat-sen University, Kaohsiung, Taiwan§ Department of Accounting Information, National Taichung University of Science andTechnology, Taichung, TaiwanWe would like to thank the Editor, Associate Editor, and two anonymous referees for their highlyconstructive comments.

bs_bs_banner South African Journal of Economics

South African Journal of Economics Vol. 82:3 September 2014

© 2013 Economic Society of South Africa. doi: 10.1111/saje.12036371

mailto:[email protected]

Cross-sectional and time series approaches are used to test the absolute and conditionalnotions of the convergence hypothesis. Under the cross-sectional approach, the growthrate of per capita income is regressed on initial per capita income. A negative (partial)relation or reverse correlation between the two variables is interpreted as evidence ofabsolute (conditional) convergence.

In the time series framework, the convergence hypothesis is examined by employingunit root or stationarity tests. Hence, its empirical validity depends on advances in theeconometrics of unit root or stationarity tests. This approach does not includedeterministic terms (intercept and/or linear trend) in the unit root or stationarity test ofthe absolute convergence hypothesis. Sigma convergence is based on the cross-sectionaldistribution of per capita income among countries. Researchers have tested sigmaconvergence using dispersion indices as standard deviations and Gini coefficients as wellthrough a distribution dynamics approach. If standard deviations of per capita incomeacross countries decrease over time, then there is sigma convergence.

Numerous studies have empirically examined the income convergence hypothesis,with varying results. In tests of the null hypothesis of a unit root, i.e. divergence of percapita income, the literature validates the notion of income convergence in panel studies.Bénabou (1996), linking income inequality and growth, finds evidence for incomeinequality convergence across countries. Ravallion (2001) suggests that within-countryincome inequalities have slowly converged since the 1980s and that inequality tends tofall (rise) in countries in which inequality is initially high (low). Ezcurra and Pascual(2005), utilising European regional data from the European Community HouseholdPanel, find convergence in regional inequality, while Panizza (2001) investigatesinequality convergence in a study of the 48 contiguous states of the USA. Similarly,Gomes (2007) employs the Gini index to investigate the convergence of incomeinequality in a sample of 5,507 Brazilian municipalities, finding that Brazilianmunicipalities converged to an inequality level faster in 1991 than in 2000.

The contributions of the present paper are four-fold. First, while previous studies focusmostly on developing countries and the USA, few studies have examined incomeconvergence in Africa. For example, Holmes (2005) assesses long-run real per capitaincome convergence among selected African countries. Strong convergence is determinedon the basis of the first largest principal component in an examination of income levelswith respect to a chosen base country that is stationary. The qualitative outcome of thetest is invariant, depending on the base country chosen and the alternative methodologyused to test long-run convergence. Using annual data for the 1960-2000 period, strongconvergence is confirmed in the Communaute Financiere Africaine and South AfricanCustoms Union countries. An amended version of the test is unable to confirm weakerlong-run convergence for countries in the Economic Community of West African States.Cunado and Perez de Gracia (2006) test the real convergence of 43 African countries withthe USA, using Lagrange Multiple (LM) univariate unit root tests with one and twostructural breaks. They find that, when the augmented Dickey–Fuller (Dickey and Fuller,1979) tests are applied, the null hypothesis of the unit root is not rejected for mostcountries. However, when they apply the LM univariate unit root test with one and twostructural breaks, they observe convergence of the economies of Cape Verde, Egypt,Mauritius, Seychelles and Tunisia with that of the USA.

Second, the present paper investigates the catching-up hypothesis with respect to theUSA, using the per capita real gross domestic product (GDP) of 52 African countries.


© 2013 Economic Society of South Africa.

Because the USA has the highest level of per capita real GDP, we select the USA as theleader in our test of the convergence hypothesis.1 To this end, we utilise a new stationaritytest proposed by Carrion-i-Silvestre et al. (2005) to examine convergence of real incomebecause of specific advantages of this test relative to unit root tests. The Carrion-i-Silvestre, del Barrio-Castro and López-Bazo (CBL; Carrion-i-Silvestre et al., 2005)stationarity test is an extension test specification proposed by Kwiatkowski et al. (1992),who test the null hypothesis of stationarity. Becker et al. (2006:382) state that “tests withthe null of a unit root have low power with stationary, but persistent data. The problemof low power is exacerbated when a theory, such as the purchasing power parity or theconvergence of growth rates across nations, can be more naturally tested under the nullof stationarity.” As part of the analysis, we simulate the critical values of the CBL test,based on our specific panel sizes and time periods.

Third, it is now widely recognised as important in empirical work to allow forpotential structural changes in economic processes across countries (Lee, 2013; Lee et al.,2013a). Another advantage of the CBL stationarity test is that it can control for multiplestructural breaks with large numbers and various locations among countries. Africancountries have experienced various domestic and external shocks, such as war and termsof trade shocks, and most have implemented trade liberalisation policies. The CBL(2005) stationarity test permits us to investigate relationships of shocks and polices tostructural breaks that occur along African countries’ catching-up paths.

Fourth, this paper does not use panel unit root tests, as the null hypothesis of such testsis that all countries in a panel have a unit root, while the alternative hypothesis is that atleast one member of the panel exhibits stationarity. Therefore, the null of the unit rootcan be rejected even if only some countries in the panel exhibit stationarity (Lee et al.,2013b). As noted by Cunado and Perez de Gracia (2006), rejection of the unit root is anecessary condition for testing the convergence hypothesis. Thus, when the nullhypothesis of the unit root is rejected by panel unit root tests, we cannot decide whetherthe necessary condition of the convergence hypothesis holds for all members of the panel.

African nations provide an interesting arena of research for the following reasons. First,African economies have rebounded from the slump occasioned by the latest globalrecession. In 2010, Africa’s average economic growth rate was 4.9%, up from 3.1% in2009. Political events in North Africa likely depressed the continent’s growth rate to3.7% in 2011, while that economic normality has returned to these countries. Africa’saverage growth rate reached 5.8% in 2012. The current economic recovery in Africa islikely to reduce the cyclical component of unemployment; however, structuralunemployment remains high in many countries.

Second, Africa has undergone impressive changes with respect to the volume andcomposition of financial flows over the last decade. Between the years 2000 and 2010, thetotal amount of foreign direct investment (FDI), portfolio investment and officialdevelopment assistance have increased almost five-fold, from US$27 billion in 2000 to anestimated US$126 billion in 2010 (OECD/DAC, 2010; UNCTAD, 2010; IMF, 2010).The share of global FDI that flows into Africa has risen over the last decade, from 0.7%in 2000 to 4.5% in 2010. These numbers offer impressive evidence of change in Africa

1 Previous studies, such as Cunado and Perez de Gracia (2006), Datta (2003) and Giles andStroomer (2006), have selected the USA as the benchmark country.

373South African Journal of Economics Vol. 82:3 September 2014


and its increasing potential to take advantage of the opportunities associated withglobalisation. Nevertheless, some challenges remain.

Third, the wave of regionalism in the 1990s spurred academic and professional interestin the economic effects of regional integration agreements (RIAs). Among the mostdebated issues is whether RIAs stimulate convergence of per capita incomes acrossparticipants. Since the beginning of the 1990s, several African countries have establishedstock exchanges to satisfy their needs for capital inflows and to incorporate marketsystems into their economies (Singh, 1999). Thus, sufficient data are available forresearchers to evaluate the effects of economic liberalisation on economic growth.

The remainder of the paper proceeds as follows. Section 2 describes the convergencehypothesis. Section 3 describes the data used in this study. Section 4 discusses the methodof CBL (2005). Section 5 presents our empirical results. A final section then concludes.

2. THE CONVERGENCE HYPOTHESIS IN REAL INCOME

The time series approach to the convergence hypotheses is introduced by Carlino andMills (1993) and developed by Bernard and Durlauf (1995), Evans and Karras (1996)and Li and Papell (1999). According to this approach, country i will converge towardscountry j (as the leader or benchmark country) if and only if

lim |, ,n i t n j t n tI aI→∞ + +−( ) =ξ 0 (1)

where I represents the logarithm of the per capita real GDP, ξt is the information set attime t, and i and j denote country i and country j, respectively.

We define three versions of the convergence hypothesis using equation (1). If a = 1,there is absolute convergence. To test this hypothesis, researchers utilise the unit rootand/or stationarity test without inclusion of an intercept term or a linear trend. If a ≠ 0,and the series (Ii,t-Ij,t) has level stationarity, then there is conditional or deterministicconvergence. However, if a ≠ 0, and the series (Ii,t-Ij,t) exhibit trend stationarity, then acatching-up process prevails. Li and Papell (1999) note that significant evidence of thecatching-up process is difficult to obtain with time series data.2

As noted in the previous section, we use the CBL (2005) stationarity test to test thecatching-up hypothesis. The CBL test method is extended by the Hadri (2000) test andallows us to test hypotheses using two types of multiple structural break: one thatincorporates an intercept term (without a linear trend) and one that incorporates both anintercept term and a linear trend. Tomljanovich and Vogelsang (2002) and Cunado andPerez de Gracia (2006) note that, for the deterministic and catching-up hypotheses tohold, level stationarity and trend stationarity are necessary conditions, respectively. Toinvestigate the sufficient condition for catching up, we follow the models of Tomljanovichand Vogelsang (2002), Cunado and Perez de Gracia (2006), and Carrion-i-Silvestre andGerman-Soto (2009) by utilising equation (2) to further test the null of trend stationarity:

RI T DU DTi t i k i k tk

m

i k i k tk

m

i t

i i

, , , , , , , ,= + + + += =

∑ ∑α β θ ρ ε1 1

(2)

2 “This hypothesis definition, however, is open to criticism because the presence of a time trendallows for permanent per capita output differences” (Li and Papell, 1999:268).



In equation (2), RI is the logarithm of relative real per capita GDP, and t and m aretime and the optimal number of breaks, respectively. We define the regressors as follows:

DUif t TB

otherwisei k t

i k

, ,

,=>⎧

⎨⎩

1

0(3)

DTt TB if t TB

otherwisei k t

i k i k

, ,

, ,=− >⎧

⎨⎩0

(4)

Here, TBi,k denotes the k-th break location for the i-th country, with k = {1, . . . , mi}, andmi ≥ 1. Because real per capita GDP in each African country was less than real per capitaGDP in the USA in 1969, according to Carrion-i-Silvestre and German-Soto(2009:318), there is evidence of a catching-up process if the coefficients of the DU andDT parameters in each regime are significant at least at the 10% level of significance andhave opposite signs, i.e. θk < 0 and ρk > 0, or θk > 0 and ρk < 0. If the coefficients of thetwo parameters in each regime have the same sign and are significant at the 10% level,then we can conclude that there is divergence. If both parameters (θk and ρk) areinsignificant, this suggests that a catching-up process has occurred. If a catching-upprocess has occurred, but only one of the parameters is significant, then we conclude thatweak catching up has been detected. When both parameters have the same sign, but onlyone parameter is significant, weak divergence has occurred.

3. DATA

We collect annual per capita real GDP (2005 = 100) for 52 African countries and the USAfor the 1969-2011 period, using the World Economic Outlook Database as the data source.Table 1 provides average relative real per capita GDP (in logs) and average real per capitaGDP growth rates for each decade. The dynamics of the per capita real GDP datasets overthe past four decades (1970s, 1980s, 1990s and 2000s) indicate that Burundi, the Republicof Congo, Eritrea, Ethiopia, Malawi, Mozambique and Zimbabwe have the lowest relativeper capita real GDP levels, while Gabon, Libya, the Republic of South Africa andthe Seychelles have the highest relative per capita real GDP levels over all four decades.Ten countries – Angola, Sierra Leone, Mozambique, Ethiopia, Chad, Tanzania, Rwanda,Ghana, the Cape Verde Islands and Nigeria – experienced negative growth over the decadesof the 1970s, 1980s and 1990s but showed strong growth in the 2000s.

An examination of average relative per capita real GDP (in logs) over the four decadesshows that average per capita real GDP has increased on the African continent over the1969-2011 period. However, the dispersion of real per capita GDP (in logs) amongAfrican countries and the USA shows an increasing trend, rising from 3.82 in the 1970sto 4.27 in the 2000s. This finding indicates sigma divergence between African countriesand the USA.

To more precisely analyse the dynamics of real per capita GDP in African countriescompared with the USA, we estimate the well-known β-convergence equation

GRIRI

RIi

i

USAi= + ⎛

⎝⎜⎞⎠⎟ +α β εln (5)



Table 1. Relative real per capita GDP over four decades

Country Average relative income (in logs) Average relative income growth rates

1970s 1980s 1990s 2000s 1970s 1980s 1990s 2000s

Algeria 0.101 0.101 0.074 0.074 1.866 0.003 −0.162 4.175Angola 0.091 0.065 0.042 0.056 −0.096 −0.247 −0.980 17.748Benin 0.021 0.018 0.014 0.013 −0.222 −0.179 0.543 1.257Botswana 0.051 0.087 0.114 0.132 7.792 3.827 1.338 3.917Burkina Faso 0.011 0.010 0.009 0.009 0.727 0.453 1.135 3.911Burundi 0.006 0.005 0.004 0.002 1.159 0.774 −1.547 −0.718Cameroon 0.038 0.046 0.028 0.025 2.325 0.500 −0.639 1.649Cape Verde Islands 0.030 0.039 0.042 0.054 2.619 1.859 1.635 8.398Central African Republic 0.022 0.015 0.010 0.008 −0.193 −0.467 −0.433 −1.250Chad 0.015 0.011 0.010 0.011 −1.289 1.534 −0.212 10.319Comoros Islands 0.037 0.025 0.019 0.015 −0.733 0.105 −0.543 −0.796Congo, Republic of 0.003 0.004 0.003 0.002 1.129 0.880 −0.881 3.545Cote D’Ivoire 0.062 0.044 0.029 0.021 1.415 −0.896 −0.052 −2.619Djibouti 0.048 0.026 0.034 0.026 −1.426 −0.723 −1.785 5.630Egypt 0.024 0.029 0.031 0.033 1.951 1.123 1.218 4.663Equatorial Guinea 0.061 0.043 0.057 0.137 1.736 −0.860 15.669 3.801Eritrea 0.008 0.007 0.007 0.005 −0.169 0.054 1.732 −2.660Ethiopia 0.007 0.005 0.004 0.004 −0.440 −0.363 −0.193 10.434Gabon 0.330 0.262 0.212 0.151 2.331 −0.480 −0.286 0.217Gambia 0.012 0.011 0.008 0.007 0.825 −0.061 −0.279 2.351Ghana 0.019 0.012 0.011 0.012 −0.273 −0.492 0.859 9.229Guinea 0.012 0.010 0.008 0.008 0.484 −0.296 0.487 1.122Guinea Bissau 0.020 0.016 0.015 0.010 0.499 0.997 −0.710 −0.838Kenya 0.020 0.019 0.015 0.013 1.524 0.129 −0.321 1.919Lesotho 0.015 0.015 0.015 0.018 2.967 0.085 1.246 6.383Liberia 0.042 0.025 0.004 0.005 0.011 −2.131 −0.583 −2.899Libya 0.182 0.097 0.147 0.179 −0.801 −1.298 5.811 −5.971Madagascar 0.019 0.012 0.008 0.007 −0.421 −0.876 −0.682 −0.774Malawi 0.009 0.007 0.006 0.005 1.374 −0.781 0.742 3.566Mali 0.014 0.011 0.010 0.011 1.418 −0.393 0.799 5.155Mauritania 0.027 0.022 0.017 0.016 0.302 −0.072 0.139 2.646Mauritius 0.071 0.080 0.105 0.126 2.954 2.184 1.973 5.550Morocco 0.047 0.049 0.046 0.049 1.556 0.661 0.377 6.989Mozambique 0.008 0.006 0.006 0.008 0.710 −0.222 1.202 11.786Namibia 0.156 0.102 0.082 0.087 −0.857 −0.706 0.803 6.334Niger 0.017 0.012 0.008 0.006 −0.213 −0.811 −0.506 1.384Nigeria 0.031 0.021 0.019 0.019 1.801 −0.755 −0.022 7.424Republic of South Africa 0.206 0.167 0.125 0.127 0.297 −0.388 −0.032 4.999Rwanda 0.010 0.010 0.006 0.007 0.896 −0.228 −0.916 9.312Sao Tome and Principe 0.019 0.015 0.012 0.011 −0.249 −0.112 −0.085 4.344Senegal 0.033 0.026 0.020 0.019 0.077 0.096 0.212 2.380Seychelles 0.218 0.233 0.276 0.274 4.066 0.641 1.759 2.001Sierra Leone 0.012 0.010 0.006 0.006 0.397 −0.545 −1.580 12.465Sudan 0.022 0.017 0.015 0.018 0.246 0.188 1.292 6.449Swaziland 0.044 0.048 0.052 0.048 1.562 1.815 0.167 0.583Tanzania 0.012 0.010 0.008 0.009 −0.006 0.033 −0.057 9.464Togo 0.021 0.016 0.011 0.009 −0.105 −0.771 −0.203 −0.187Tunisia 0.055 0.059 0.059 0.071 2.491 0.212 1.617 5.650Uganda 0.009 0.007 0.007 0.009 −0.203 0.037 1.848 7.200Zaire 0.095 0.061 0.028 0.015 −0.965 −0.450 −2.694 1.994Zambia 0.041 0.027 0.017 0.015 −0.535 −0.725 −0.962 5.483Zimbabwe 0.002 0.002 0.002 0.001 0.578 0.184 0.163 −4.909Average standard deviation of African countries’ real

per capita GDP (in logs) around the USA.3.817 3.989 4.214 4.268

Average African countries real per capita GDP (inlogs) relative to the USA.

7.004 7.029 7.143 7.381

Notes: Standard deviation of African countries’ real per capita GDP (in logs) around US real per

capita GDP is calculated as SDGDPPER GDPPER

nt

i

n

i t USA t

=∑ −( )

−

, ,2

1. Here, GDPPER is real per

capita GDP, and the average standard deviation in any decade is calculated as∑=

=

t

t

tSD1

10

10.

GDP: gross domestic product; GDPPER: gross domestic product per capita.



where GRIi is the average relative per capita real GDP growth rate for country i, and RIis per capita real GDP. We estimate equation (5) for each of the past four decades (1970s,1980s, 1990s and 2000s) separately, using two approaches: pooled ordinary least squares(OLS) and quantile regression. As noted in the economic growth literature, theestimation of the β-convergence equation with pooled OLS ignores Galton’s fallacy(Friedman, 1992; Quah, 1993). Hence, in using OLS to estimate equation (5), a negativevalue of β indicates that economies in our sample do not converge to the same long-runsteady state. This may occur because some countries converge towards the balancedgrowth path, while others do not. However, a quantile regression (introduced by Koenkerand Basset, 1978) can be used to estimate the parameter β with each conditional quantileas the dependent variable, i.e. the average relative per capita real GDP growth rate. Thus,as noted by Koenker (2000), the quantile regression approach can resolve Galton’s fallacyand identifies various convergence or divergence patterns in a sample of countries.

Figure 1 presents the results of the pooled OLS and quantile regressions. We plot thefitted values for the OLS regression as the dotted line. Fitted values for 50% quantile(median) are shown by the black line, and fitted values for other quantiles, i.e. 10%, 25%,80% and 95%, are indicated by grey lines. As observed, convergence patterns differbetween countries over the decades. We observe convergence with respect to the USAamong countries in the 10%, 20% and 50% quantiles during the 1970s and 1980s, but

Panel A: 1970s Panel B: 1980s

0.008

Initial real GDP per capita relative to the USAGro

wth

rat

e of

rel

ativ

e re

al G

DP

per

cap

ita


wth

rat

e of

rel

ativ

e re

al G

DP

per

cap

ita

Panel C: 1990s Panel D: 2000s


wth

rat

e of

rel

ativ

e re

al G

DP

per

cap

ita


wth

rat

e of

rel

ativ

e re

al G

DP

per

cap

ita

–0.004

–0.010

–0.005

0.000

4 5 6 7 8

4 5 6 7 8 9

4 5 6 7 8 9

4 5 6 7 8 9

0.000

0.005

0.010

0.004

0.006

–0.004

–0.002

0.000

0.002

0.004

–0.005

0.000

0.005

Figure 1. Fitted values for ordinary least squares (OLS) and quantile regressionsNotes: Dotted line: Fitted values for the OLS estimation. Black line: Fitted values for the50% quantile (median). Grey lines: Fitted values for the 10%, 25%, 80% and 95%quantiles.



divergence with respect to the USA among countries in the 80% and 95% quantiles. Inpanel C, we find a divergence trend among African countries in the 1990s, but thatconvergence patterns vary among African countries in the 2000s. Despite divergencepatterns observed among countries in the high quantiles (80% and 95%) during the1970-1999 period, in the 2000s, we find a convergence trend among these countries withrespect to the USA.

4. CARRION-I-SILVESTRE ET AL. (2005) STATIONARY TEST

This study applies the CBL (2005) stationarity test because of its advantages noted inSection 1. Under the CBL stationarity test, the data generation process under the null ofstationarity is based on the following model:

RI T DU DTi t i k i k tk

m

i k i k tk

m

i t

i i

, , , , , , , ,= + + + += =

∑ ∑α β θ ρ ε1 1

(6)

In equation (6), α, T and m are the intercept, linear trend and optimal number of breaks,respectively; and DU and DT are defined as in equations (3) and (4) above, respectively.The LM test statistic is computed as in the Kwiatkowski et al. (1992) test with multiplebreaks:

LM T Si tt

T

λ ω( ) = −

=∑ˆ ˆ

,2 2

1(7)

Here, ˆ,Si t is the partial sum of the estimated OLS residuals from equation (6), ω̂ denotes

a heteroskedasticity and autocorrelation consistent estimate of the long-run variance of εt,and λ represents the locations of breaks relative to the entire time period (T). The teststatistic relies on λ, and with the LM statistic, we can correctly identify the locations ofbreaks.

CBL (2005) recommend using the Bai and Perron (1998) procedure for analysis. Thisprocedure is based upon the global minimisation of the sum of squared residuals asfollows:

TB TB SSR TB TBmi TB TB mimi

ˆ , , ˆ ˆ , , ˆˆ , , ˆ1 11

… ……( ) = ( )( )argmin (8)

We select the optimal number of breaks in accordance with the modified Schwarzinformation criterion of Liu et al. (1997). The finite sample critical values are computedthrough Monte Carlo simulations, using 20,000 replications.

5. EMPIRICAL RESULTS

5.1 Univariate Unit Root Test (Without Breaks) ResultsTo test the catching-up hypothesis for African countries, we first apply the unit root tests,then use five univariate unit root tests – namely, the augmented Dickey–Fuller test (Dickeyand Fuller, 1979), Elliott et al. (1996, Dickey Fuller generalized least squares (DF-GLS)),Ng and Perron (2001), Phillips and Perron (1988), and Kwiatkowski et al. (1992) –without concern for structural breaks. Finally, to determine structural breaks, we utilise theCBL (2005) stationarity test by including an intercept term and a linear trend. In Table 2,



Table 2. The simple univariate unit root test results for relative real per capita GDP

Country ADF DF-GLS Ng-Perron PP KPSS

stat. stat. stat. stat. stat.

Algeria −0.708 [1] −0.834 [1] −4.265 [4] −1.73 (0) 0.123* (4)Angola −0.578 [1] −1.148 [1] 0.385 [0] −1.641 (3) 0.115 (5)Benin −1.854 [1] −1.991 [1] −4.686 [3] −1.62 (4) 0.112 (5)Botswana −3.171 [1] −1.466 [1] 0.278 [0] −2.021 (3) 0.164** (5)Burkina Faso −1.31 [0] −1.642 [0] −4.567 [4] −1.911 (3) 0.13* (5)Burundi −1.456 [1] −1.27 [1] −3.248 [2] 0.479 (3) 0.174** (5)Cameroon −4.371*** [4] −3.486* [4] −3.786 [4] −1.354 (3) 0.179** (4)Cape Verde Islands −1.1 [0] −1.218 [0] −4.751 [3] −0.997 (4) 0.179** (4)Central African Republic −1.107 [0] −1.484 [0] −5.098 [1] −1.43 (3) 0.108 (5)Chad −1.456 [0] −1.312 [0] −3.274 [2] −2.548 (3) 0.091 (5)Comoros Islands −1.631 [0] −1.773 [0] −5.803 [2] −1.381 (1) 0.121* (4)Congo, Republic of −2.443 [1] −2.254 [1] −5.125 [2] 1.178 (2) 0.21** (5)Cote D’Ivoire −2.874 [3] −2.28 [3] −5.559 [3] −0.981 (0) 0.151** (5)Djibouti −2.081 [0] −1.936 [0] −5.922 [2] −2.957 (2) 0.117 (4)Egypt −2.803 [4] −1.74 [0] −5.354 [0] −1.614 (4) 0.114 (5)Equatorial Guinea −1.687 [1] −1.627 [1] −3.113 [3] −0.596 (2) 0.175** (5)Eritrea −1.501 [0] −1.569 [0] −6.225 [3] −1.612 (0) 0.161** (4)Ethiopia 1.77 [2] 0.012 [0] 2.013 [7] −1.261 (4) 0.163** (5)Gabon −3.132 [0] −2.121 [0] −3.569 [6] −0.555 (18) 0.193** (5)Gambia −1.116 [0] −1.269 [0] −4.807 [1] −0.594 (2) 0.136* (5)Ghana 1.092 [0] −0.613 [2] 1.327 [2] −1.989 (2) 0.147** (4)Guinea −0.981 [0] −1.77 [1] −3.274 [0] 0.597 (0) 0.169** (5)Guinea Bissau −3.019 [0] −2.907 [0] −10.627 [2] −4.514*** (0) 0.215** (5)Kenya −4.376*** [1] −2.533 [1] −5.017 [3] −2.593 (2) 0.166** (5)Lesotho −1.791 [0] −1.891 [0] −6.67 [0] −1.807 (4) 0.123* (5)Liberia −1.651 [1] −1.816 [1] −6.116 [4] −1.018 (1) 0.152** (4)Libya −2.214 [1] −2.098 [1] −4.562 [3] −1.518 (2) 0.174** (5)Madagascar −0.86 [0] −1.165 [0] −1.866 [2] −1.569 (2) 0.182** (4)Malawi −0.827 [0] −1.163 [0] −4.626 [3] −2.04 (2) 0.137* (5)Mali −0.68 [0] −0.997 [0] −2.403 [2] −2.073 (2) 0.127* (4)Mauritania −1.681 [0] −1.937 [0] −6.443 [1] −1.39 (3) 0.171** (5)Mauritius −3.998** [5] −3.815** [5] −10.831 [2] −1.749 (3) 0.111 (5)Morocco −0.648 [1] −1.136 [1] −6.984 [3] 1.822 (7) 0.196** (5)Mozambique −1.011 [1] −1.327 [1] −0.405 [0] −3.108 (6) 0.09 (4)Namibia 0.669 [0] −0.681 [1] 1.954 [16] −2.535 (3) 0.123* (4)Niger −1.612 [0] −1.85 [0] −7.038 [0] −1.791 (0) 0.119 (4)Nigeria −1.31 [4] −2.47 [3] −4.788 [4] −0.421 (2) 0.199** (5)Republic of South Africa 0.925 [3] −1.792 [6] −0.184 [3] −1.08 (3) 0.143* (4)Rwanda −1.708 [0] −1.906 [0] −5.889 [3] −1.474 (1) 0.175** (5)Sao Tome and Principe 1.81 [0] −0.839 [1] 2.572 [9] −2.528 (2) 0.049 (4)Senegal −0.67 [1] −1.272 [0] −2.747 [18] −0.078 (0) 0.197** (5)Seychelles −3.157 [1] −2.495 [1] −8.01 [1] 2.001 (16) 0.213** (5)Sierra Leone −0.33 [0] −0.742 [0] −2.618 [2] −1.51 (3) 0.118 (5)Sudan 0.533 [3] −2.037 [1] −0.544 [24] 2.704 (9) 0.188** (5)Swaziland −3.392* [1] −1.801 [2] −5.219 [7] −3.116 (1) 0.087 (4)Tanzania 0.56 [1] −0.889 [1] 2.263 [5] −0.176 (24) 0.196** (5)Togo −1.982 [0] −2.184 [0] −8.899 [2] −3.199* (7) 0.183** (4)Tunisia −1.673 [0] −1.515 [0] −5.327 [3] 2.223 (5) 0.185** (5)Uganda −1.053 [1] −1.163 [1] 0.308 [4] −0.315 (4) 0.212** (5)Zaire −3.641** [7] −2.158 [1] −4.542 [4] −1.274 (4) 0.098 (5)Zambia 1.289 [0] −0.356 [1] 1.012 [5] 1.343 (5) 0.176** (5)Zimbabwe −2.121 [1] −1.879 [1] −4.536 [1] −2.677 (1) 0.168** (5)

Notes: The number in brackets indicates the lag order selected based on the Schwarz informationcriterion. The number in the parentheses indicates the truncation for the Bartlett Kernel, assuggested by the Newey–West test (1987).ADF: augmented Dickey-Fuller; DF-GLS: Dickey Fuller generalized least squares; GDP: grossdomestic product; KPSS: Kwiatkowski et al. 1992; PP: Phillips and Perron 1988.***, ** and * indicate that the null hypothesis is rejected at the 1%, 5% and 10% levels,respectively.



where we present the results of the univariate unit root tests, we observe that all univariateunit root tests do not reject the null hypothesis for most countries, a result that is consistentwith the existing literature and shows the low power of tests when relative real per capitaGDP is highly persistent.3 This result implies that relative per capita real GDP for mostAfrican countries follows a random walk process during the sample period.

5.2 CBL (2005) Stationarity Test ResultsAs noted in section 2, the CBL (2005) stationarity test enables us to test for structuralbreaks with an intercept term and a linear trend. The results, presented in Table 3, showthat the null hypothesis of stationarity is rejected for 18 countries: Angola, Botswana,Burkina Faso, Chad, the Comoros Islands, the Republic of Congo, Equatorial Guinea,Gabon, Kenya, Lesotho, Liberia, Libya, Morocco, Sao Tome and Principe, Swaziland,Tanzania, Zaire and Zambia. For the remaining 34 countries, the null of stationarity isnot rejected at the 10% level.

We further classify the break dates into the four decades of the 1970s, 1980s, 1990s and2000s, presented in columns 6-9 of Table 3. In each cell of columns 6-9, the first and thirdterms in brackets indicate the 95% confidence interval, while the second term indicates theestimated break point. The C and D letters in the table denote the catching-up anddivergence processes, respectively.The estimated number of breaks inTable 3 shows that allcountries experienced at least one statistically significant structural break, indicating theimportance of confirming structural breaks by use of stationarity tests.

The distribution of breaks shows that they occurred in all decades, with most occurringin the 1970s and 1980s. We measure 58, 64, 56 and 50 break points in the 1970s, 1980s,1990s and 2000s, respectively. Our procedure detects only 1 country (Mauritania) with1 break; 5 countries (Morocco, Algeria, Sierra Leone, Madagascar and Rwanda) with 2breaks; 11 countries (Cape Verde Islands, Ghana, Guinea, Senegal, Burundi, Cameroon,Central African Republic, Djibouti, Malawi, the Seychelles and Uganda) with 3 breaks;8 countries (Benin, Cote D’Ivoire, Gambia, Guinea Bissau, Mali, Eritrea, Kenya andNamibia) with 4 breaks; 17 countries (Egypt, Libya, Tunisia, Liberia, Niger, Nigeria,Togo, Chad, the Comoros Islands, Ethiopia, Lesotho, Mauritius, Mozambique, Sudan,Swaziland, Zaire and Zambia) with 5 breaks; 3 countries (Angola, Equatorial Guinea, andSao Tome and Principe) with 6 breaks; and 7 countries (the Republic of South Africa,Burkina Faso, Botswana, the Republic of Congo, Gabon, Tanzania and Zimbabwe) with7 breaks.

5.3 Analysis of the Results According to the Catching-Up ProcessTo test the null of stationarity, we test the sufficient conditions and estimate equation 2for the 34 countries, following the methodology described in section 2. To conserve space,we do not report the pooled OLS results of equation 2 and only report the resultsregarding convergence or divergence for each structural break. In Table 3, which alsoreports the results for the sufficient conditions, catching up and divergence are denotedas C and D, respectively, and presented in the parentheses next to the breaks. To visualise

3 We specify any univariate series as an AR(p) model – X Xt ip

i t i t= + ∑ += −α β ε1 , where α isconstant, βi is parameters, εt is the residual, and p is the lags decided by SIC, and then calculatethe AR coefficients and the half-lives of all univariate series. Our results confirm that they arehighly persistent.



Tabl

e3.

Car

rion

-i-S

ilves

tre,

delB

arri

o-C

astr

oan

dLó

pez-

Baz

o(2

005)

statio

nari

tyte

stre

sults

for

rela

tive

real

GD

Ppe

rca

pita

Cou

ntry

Tes

tsC

riti

cal

valu

esB

efor

efi

rst

brea

k

Bre

akda

tes

[95%

L,

esti

mat

edbr

eak,

95%

U]

(Con

verg

ence

?)

90%

95%

1970

s19

80s

1990

s20

00s

Alg

eria

0.03

580.

067

0.07

7C

[81,

83,8

4](D

)[9

5,97

,98]

(C)

Ang

ola

0.03

94**

*0.

024

0.02

6[7

2,74

,75]

[84,

86,8

7][9

0,92

,93]

[95,

98,9

9][0

1,03

,04]

[05,

07,0

8]B

enin

0.02

000.

042

0.05

4D

[75,

77,7

8](C

)[8

0,82

,83]

(D)

[86,

88,8

9](D

)[0

5,07

,08]

(C)

Bot

swan

a0.

0467

***

0.01

80.

02[7

0,72

,73]

[76,

78,7

9][8

1,83

,84]

[85,

87,8

9][8

9,91

,92]

[94,

96,9

8][9

9,01

,02]

Bur

kina

Faso

0.03

95**

*0.

018

0.01

9[7

0,72

,73]

[74,

76,7

8][8

0,81

,82]

[83,

85,8

9][8

8,90

,91]

[93,

95,9

7][0

4,06

,07]

Bur

undi

0.03

060.

125

0.15

7D

[90,

92,9

3](D

)[9

4,96

,97]

(D)

[03,

05,0

6](C

)C

amer

oon

0.02

970.

039

0.04

2D

[71,

73,7

4](C

)[8

3,85

,86]

(D)

[91,

93,9

4](D

)C

ape

Ver

deIs

land

s0.

0265

0.05

30.

063

C[8

0,82

,83]

(C)

[89,

91,9

2](C

)[0

1,03

,04]

(C)

Cen

tral

Afr

ican

Rep

ublic

0.02

300.

035

0.04

1D

[70,

73,7

4](D

)[8

9,91

,92]

(D)

[01,

02,0

3](C

)C

had

0.02

77*

0.03

0.03

[72,

74,7

6][7

7,78

,79]

[90,

92,9

4][9

7,99

,00]

[02,

03,0

4]C

omor

osIs

land

s0.

0695

***

0.02

60.

026

[72,

74,7

5][7

9,81

,82]

[88,

91,9

2][9

8,00

,02]

[04,

06,0

7]C

ongo

,Rep

ublic

of0.

0528

***

0.01

60.

018

[73,

74,7

5][7

6,78

,79]

[80,

82,8

3][8

5,87

,88]

[91,

93,9

4][9

5,98

,99]

[04,

06,0

7]C

ote

D’Iv

oire

0.02

380.

040.

049

C[7

7,79

,80]

(D)

[81,

83,8

5](D

)[9

3,95

,96]

(D)

[04,

07,0

8](D

)D

jibou

ti0.

0198

0.04

10.

05D

[75,

77,7

8](D

)[8

7,89

,90]

(D)

[98,

00,0

1](C

)E

gypt

0.02

700.

026

0.02

9C

[70,

72,7

3](C

)[7

5,77

,80]

(C)

[79,

81,8

2](D

)[8

7,89

,92]

(C)

[03,

04,0

5](C

)E

quat

oria

lGui

nea

0.03

75**

*0.

021

0.02

5[7

2,74

,75]

[76,

78,8

0][8

6,88

,89]

[92,

94,9

5][9

7,99

,00]

[02,

04,0

5]E

ritr

ea0.

0325

0.09

90.

135

D[8

7,89

,90]

(C)

[92,

93,9

4](D

)[9

7,99

,01]

(D)

[05,

07,0

8](C

)E

thio

pia

0.02

930.

065

0.07

8D

[81,

82,8

3](D

)[8

4,86

,88]

(D)

[89,

91,9

2](C

)[9

5,97

,98]

(C)

[00,

02,0

3](C

)G

abon

0.05

92**

*0.

018

0.01

9[7

1,73

,74]

[75,

77,7

8][8

0,82

,83]

[84,

86,8

7][8

9,91

,92]

[96,

98,9

9][0

4,05

,06]

Gam

bia

0.02

240.

027

0.02

8D

[72,

74,7

6](D

)[8

2,84

,89]

(D)

[89,

94,9

5](D

)[0

3,05

,06]

(C)

Gha

na0.

0240

0.04

20.

052

D[7

2,74

,79]

(D)

[80,

82,8

3](D

)[0

3,04

,05]

(C)

Gui

nea

0.02

740.

050.

06D

[80,

81,8

2](D

)[8

3,85

,86]

(D)

[98,

00,0

1](D

)G

uine

aB

issa

u0.

0172

0.03

20.

037

D[7

3,76

,77]

(D)

[81,

85,8

8](C

)[9

5,97

,98]

(D)

[03,

05,0

6](C

)K

enya

0.02

55*

0.02

70.

03[7

0,72

,76]

[81,

83,8

4][8

9,91

,92]

[01,

03,0

4]Le

soth

o0.

041*

**0.

023

0.02

5[7

0,72

,73]

[74,

76,7

7][8

5,86

,87]

[91,

92,9

2][0

2,04

,05]

Libe

ria

0.05

27*

0.04

0.05

2[7

8,80

,81]

[86,

88,8

9][8

9,92

,93]

[94,

96,9

7][0

0,02

,03]

Liby

a0.

0287

**0.

026

0.02

9[7

1,73

,74]

[78,

80,8

1][8

5,87

,88]

[92,

94,9

5][0

6,07

,08]

Mad

agas

car

0.04

270.

060.

076

D[8

0,83

,84]

(D)

[00,

01,0

2](C

)M

alaw

i0.

0255

0.04

30.

049

C[7

6,78

,79]

(D)

[92,

94,9

6](D

)[0

2,04

,05]

(C)

Mal

i0.

0245

0.03

20.

034

C[7

1,72

,73]

(C)

[74,

76,7

7](D

)[8

2,84

,85]

(D)

[98,

00,0

1](C

)M

auri

tani

a0.

0421

0.24

0.25

4D

[99,

01,0

2](C

)M

auri

tius

0.01

850.

028

0.03

1C

[73,

75,7

6](D

)[7

8,79

,80]

(C)

[81,

83,8

4](C

)[9

0,92

,93]

(C)

[03,

04,0

5](C

)M

oroc

co0.

0549

*0.

053

0.06

6[7

2,74

,75]

[97,

98,9

9]M

ozam

biqu

e0.

0215

0.04

90.

07D

[80,

82,8

3](D

)[8

4,86

,87]

(C)

[89,

91,9

2](D

)[9

3,95

,96]

(C)

[98,

99,0

0](C

)N

amib

ia0.

0232

0.04

80.

058

D[8

0,81

,82]

(D)

[83,

85,8

6](D

)[8

8,90

,91]

(D)

[98,

00,0

1](C

)N

iger

0.01

670.

024

0.02

7D

[70,

72,7

3](D

)[7

5,77

,79]

(D)

[81,

83,8

4](D

)[8

9,91

,93]

(D)

[03,

05,0

6](C

)N

iger

ia0.

0184

0.02

30.

026

C[7

0,72

,74]

(D)

[78,

80,8

1](D

)[8

2,84

,85]

(C)

[88,

90,9

1](D

)[9

9,01

,02]

(C)

Rep

ublic

ofSo

uth

Afr

ica

0.01

650.

018

0.01

9D

[68,

73,7

4](D

)[7

5,77

,78]

(C)

[79,

81,8

2](D

)[8

3,85

,86]

(D)

[90,

91,9

2](D

)[9

6,98

,99]

(C)

[03,

05,0

6](C

)R

wan

da0.

0321

0.04

90.

059

D[7

6,79

,80]

(D)

[91,

93,9

4](C

)Sa

oTo

me

and

Prin

cipe

0.02

87**

0.02

20.

027

[73,

74,7

5][7

7,78

,78]

[81,

83,8

5][8

8,90

,91]

[98,

00,0

1][0

5,07

,08]

Sene

gal

0.03

350.

045

0.05

3D

[70,

72,7

3](C

)[7

4,76

,77]

(D)

[96,

98,9

9](C

)Se

yche

lles

0.02

580.

050.

062

C[7

8,80

,81]

(D)

[86,

88,8

9](C

)[9

9,02

,03]

(C)

Sier

raLe

one

0.03

290.

059

0.06

6D

[79,

81,8

2](D

)[9

8,00

,01]

(C)

Suda

n0.

0211

0.02

90.

037

D[7

0,72

,73]

(C)

[75,

77,7

8](D

)[7

9,81

,82]

(D)

[83,

85,8

6](C

)[9

9,01

,02]

(C)

Swaz

iland

0.03

27*

0.03

0.03

3[7

2,74

,75]

[77,

79,8

0][8

0,83

,85]

[89,

91,9

2][0

5,07

,08]

Tanz

ania

0.04

81**

*0.

019

0.02

1[7

3,74

,75]

[77,

78,7

8][8

1,83

,84]

[88,

89,8

9][9

1,93

,94]

[98,

00,0

1][0

5,07

,08]

Togo

0.01

880.

046

0.05

3D

[77,

79,8

0](D

)[8

2,84

,85]

(D)

[91,

92,9

3](C

)[9

5,97

,98]

(D)

[03,

05,0

6](C

)Tu

nisi

a0.

0207

0.02

50.

029

C[7

0,72

,74]

(C)

[74,

76,7

8](C

)[7

9,81

,82]

(D)

[87,

89,9

0](C

)[9

9,02

,03]

(C)

Uga

nda

0.03

340.

058

0.06

7D

[80,

81,8

2](D

)[8

4,86

,87]

(C)

[03,

05,0

6](C

)Z

aire

0.02

76**

0.02

20.

024

[71,

73,7

4][7

7,79

,80]

[87,

89,9

0][9

2,94

,95]

[99,

01,0

2]Z

ambi

a0.

0301

*0.

026

0.02

9[7

4,76

,77]

[81,

83,8

5][9

0,93

,94]

[97,

98,9

9][0

3,05

,06]

Zim

babw

e0.

0183

0.02

20.

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the break dates and the catching-up or divergence paths that follow breaks, we plot the logof relative per capita real GDP and its broken estimated linear trend in Figure 2.

The results for the sufficient conditions, presented in Table 3, show that, of the 58breaks in the 1970s, 23 result in catching up to the USA, while 35 result in divergencefrom the USA. Among the 64 structural breaks in the 1980s, 19 result in convergenceand 45 result in divergence. Among the 56 structural breaks in the 1990s, 24 result incatching up and 32 result in divergence. Among the 50 structural breaks in the 2000s,43 result in catching up and 7 result in divergence. In the 2000s, African countriesexperienced the largest number of catching-up processes relative to the USA among thedecades considered. The pooled OLS estimation results from equation 2 show that, ofthe 58 structural breaks in the 1970s, 32 correspond with a decrease in real per capitaGDP and 26 correspond with an increase in real per capita GDP. Among the 64structural breaks in the 1980s, 45 correspond with a decrease in real per capita GDP,and 19 correspond with an increase in real per capita GDP. Among the 56 structuralbreaks in the 1990s, 39 correspond with a decrease in real per capita GDP, and 17correspond with an increase in real per capita GDP. Among the 50 structural breaks inthe 2000s, 34 correspond with a decrease in real per capita GDP, and 16 correspondwith an increase in real per capita GDP. Overall, approximately 70% of structural breaksamong African countries over the 1980-2011 period correspond with a decrease in realper capita GDP.

To determine the catching-up or divergence paths for each country over the entire1969-2011 period, we estimate the linear trend function for the relative real per capitaGDP series to test the null of stationarity. If the intercept of the trend function has anegative value and its slopes are positive, then we conclude that country i could catch upwith the USA over the 1969-2011 period. However, if its slopes have negative signs, thenwe conclude that country i diverges from the USA over that period. To save space, we donot report the details but simply divide countries into two classes. Our results show thatthe linear trend functions of all 34 countries’ relative real per capita GDP series, in whichthe null of stationarity is not rejected, have negative intercepts. Only five countries –Egypt, the Cape Verde Islands, Mauritius, the Seychelles and Tunisia – have trendfunctions with positive slopes and thus could catch up with the USA over the 1969-2011period, while the others diverge from the USA over the sample period. We emphasise thatAfrican countries do not exhibit uniform catching-up or divergence paths in the 1969-2011 period. The countries catching up with the USA experienced divergence in somesub-periods. For example, Egypt and Tunisia diverged from the USA during the 1980s,and the Seychelles experienced divergence over the 1980-1988 period. In contrast, somecountries that diverged from the USA over the 1969-2011 period exhibited catching upfollowing some break dates. Algeria, for example, experienced catching up over the twobreaks of 1969-1983 and 1997-2011.

5.4 Discussion and Interpretation of Estimated Structural BreaksIn section 5.2, all countries experienced at least one structural break in their catching-upor divergence paths. The same events might have affected different countries although notsimultaneously. To check the accuracy of our identification of breaks, we compute 95%confidence intervals for the estimated break points, with results presented in Table 3. Thefirst and third terms in the brackets are the 95% confidence intervals, while the secondterm is the estimated break point. There is clear evidence of clustering patterns of breaks,



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based on economic shocks such as booms and busts of primary commodity prices, warsand macroeconomic policies (e.g. trade liberalisation).4

As noted in section 5.3, most divergence processes occur in the 1980s and 1990s.A possible reason for this is that, in the period from 1960 to the mid-1970s,industrialised countries had strong economic performance, with high GDP growthrates. Thus, their demand for primary goods and raw materials was high. As mostAfrican countries specialise in the production and export of primary goods and rawmaterials and supplied their goods to the world market at high prices during thisperiod, the strong economic performance of industrialised countries may have increasedtheir per capita income levels. However, this trend did not continue. From the mid-1970s to the early 1980s and again in the early 1990s, industrialised countriesexperienced recessions, resulting in declining terms of trade for primary goods and rawmaterials; thus, the African countries experienced negative shocks during these periods.The dispersion of structural breaks shows that most breaks detected in relative percapita GDP of African countries can be associated with terms of trade shocks, war,political instability and trade liberalisation policies. Although multiple explanations forsome of the break dates are plausible, we focus here on break dates coinciding with thethree types of shocks mentioned, i.e. terms of trade shocks, war and political instability,and trade liberalisation policies.

First, most African countries were involved in military conflicts during the studyperiod, events that affected their economic performance, as noted by Cook (2002).Moreover, war damage to capital plants induced sharp divergences in output per worker.5

Our findings show that some break points coincide with years of military conflicts, warsand political instability in Angola (1992 and 1974), Burundi (1992), Ethiopia (1990),Liberia (1994), Mozambique (1982, 1986, 1991), Niger (1991), Rwanda (1993) andUganda (1981), which experienced sharp decreases in relative real per capita GDP duringsuch periods. Countries such as Burundi, Mozambique, Niger and Uganda diverged fromthe USA.

Second, some African countries implemented trade liberalisation polices during the1969-2011 period. As observed in the income convergence hypothesis literature, onesource of income convergence is international trade. Hence, some researchers haveanalysed the impact of trade openness and trade liberalisation on income convergence(Sachs and Warner, 1995; Ben-David, 1996; Williamson, 1997; Ben-David, 1998;Slaughter, 2001; Galor and Mountford, 2006; Giles and Stroomer, 2006) but haveobtained mixed results. While some studies find that international trade serves as a potentmeans by which poor countries can catch up to developed countries through the transferof technology, knowledge and intermediate goods, Slaughter (2001) and Galor andMountford (2006), by contrast, empirically challenge the hypothesis that international

4 We find some anecdotal evidence for a coincident relationship between break dates and tradeliberalisation and/or political instability, but an investigation of such links is beyond the scope ofthis paper.5 Gyimah-Brempong and Traynor (1999) find that political instability has a statistically negativeeffect on economic growth and that it indirectly decreases economic growth by reducing long-runcapital accumulation. Gyimah-Brempong and Corley (2005) find that the incidence and severityof a civil war have a robust, negative and significant effect on the growth rate of per capita income.



trade contributes to income convergence, arguing that international trade increases theincome gap between poor and rich countries.6

We find evidence of a coincident relationship between some of the break points andtrade openness in African countries. Cameroon (1993), the Cape Verde Islands (1991),Ethiopia (1994), Gambia (1984), Mozambique (1995), the Republic of South Africa(1991), Sierra Leone (2000), Tunisia (1989) and Zambia (1993) all experienced breaks inthe catching-up process that coincided with trade openness dates in these countries.7 Ourresults indicate that, following the trade openness dates in the above-noted countries(except for the Cape Verde Islands, Ethiopia and Tunisia), relative real per capita GDPdecreased; Cameroon, Gambia and the Republic of South Africa diverged from the USA,and the Cape Verde Islands, Ethiopia, Mozambique, Sierra Leone and Tunisia were on thecatching-up path with respect to the USA.

Third, one of the important characteristics of African countries is that they are highlyspecialised in producing and exporting a few primary goods and raw metals and materialssuch as oil, copper, uranium, phosphate, iron ore, manganese, gold, diamonds, coffee,cocoa, tea, sugar, tobacco, cotton, etc. Hence, their economies and growth performanceare strongly affected by trade shocks,8 and indeed, some of the break points coincide withterms of trade shocks. The results show that following positive terms of trade shocks, realper capita GDP of some countries rose, placing them in on a catching-up path, a patternthat contrasts with negative terms of trade shocks that have caused some countries todiverge from the USA. For example, falling prices of mineral and metal materials duringthe 1980s and early 1990s caused negative economic shocks for Egypt, Ghana, Guinea,Namibia, Sierra Leone and South Africa.

There is a clear pattern among oil-producing countries, specifically, that break pointsin some nations (Cameroon (1973), Egypt (1972 and 1977) and Nigeria (1972)) areclustered approximately 1973-1974, 1979 and the mid-2000s. Some countries such asAlgeria (1983), Cameroon (1985), Egypt (1981and 1989) and Nigeria (1980 and 1984)are clustered around the steep decline in oil prices over 1981-1985. By contrast, countriesthat produce and export diamonds, such as Angola (1986 and 1992), the Central AfricanRepublic (1991), Namibia (1981 and 1990), South Africa (1981 and 1991) and SierraLeone (1981), experienced divergence approximately 1982, 1989 and 1991. Amongthese countries, only South Africa was on the catching-up path, coinciding with thediamond boom in 1971.9

We find evidence of divergence for Ghana (after 1982), Guinea (after 1981), Namibia(after 1981 and 1990), South Africa (after 1981) and Zimbabwe (after 1982),

6 Galor and Mountford (2006:299) note: “The rapid expansion of international trade in thesecond phase of the industrial revolution . . . has been a prime cause of the ‘Great Divergence’ inincome per capita across countries in the last two centuries. International trade enhanced thespecialisation of industrial economies in the production of industrial, skilled intensive goods, andstimulated technological progress. In non-industrial economies, in contrast, international tradegenerates an incentive to specialise in the production of unskilled intensive, nonindustrial goods.”7 In order to determine whether an African country has gone through a trade liberalisationepisode, we use the trade liberalisation period compiled by Wacziarg and Welch (2008), which isan updated version of Sachs and Warner’s (1995) liberalisation dates.8 For more details on this debate, see Cashin and Pattillo (2006), Bidarkota and Crucini (2000)and Deaton and Miller (1996).9 For Namibia, the negative breaks in 1981 may be related to the fall in copper prices.



developments that may be related to falling gold prices in 1982 and 1989. There is alsoclear-cut evidence of divergence for Niger after 1983 and 1991 and for Namibia after1981, 1985 and 1990, events that are related to falling uranium prices in the early 1980sand 1990s. Our results show that the uranium boom during the 1973-1981 periodimproved Niger’s relative real per capita GDP in 1972 and 1977 but did not place thecountry on a catching-up path. Finally, falling uranium prices in the early 1980s and1990s caused Niger’s relative real per capita GDP to fall sharply over the 1980-2005period.

Higher aluminium and phosphate prices over the 1971-1978 period may explain whySenegal (1972-1976), Togo (after 1979) and Tunisia (1972-1981) appeared to be oncatching-up paths. However, falling prices beginning in the 1980s may have been anothersource of divergence for Senegal (after 1976), Togo (1979-1992), Tunisia (1981-1989),Guinea (after 1981) and South Africa (after 1981 and 1985). Our results show thatGuinea experienced divergence over the 1981-1985 period, a period associated withfalling prices of bauxite.

Benin lies on the catching-up path over the 1977-1982 period, coinciding withsubstantial cocoa production over the 1976-1979 period. By contrast, Benin (after 1982and 1988), Eritrea (after 1993), Ethiopia (after 1982 and 1986), Ghana (after 1974 and1982), Madagascar (after 1983), Sierra Leone (after 1981) and Togo (after 1979 and1984) experienced divergence from the USA. These break points may be related to fallingcocoa prices in 1974, the 1980s and the early 1990s.

Negative break points appear with respect to relative real per capita GDP in Burundi(1992 and 1996), Cameroon (1985), the Central African Republic (1991), Eritrea (1999),Ethiopia (1982 and 1986), Madagascar (1983), Rwanda (1979), Togo (1979 and 1984)and Uganda (1981), coinciding with the dramatic decline in coffee prices over most of the1980s (except for 1986) and the 1990s (except for 1993-1994). In contrast, the coffeeboom in Africa in the early 1970s, 1976-1979, 1993-94 and 1986 is strongly consistentwith catching up observed in Cameroon (after 1973), Togo (after 1992) and Uganda (after1986). Relative real per capita GDP of Cameroon and Eritrea exhibit a break in 1993,coinciding with the coffee boom of 1993-1994, although neither nation lies on thecatching-up path. The slope of the divergence path of Cameroon with respect to the USAdeclines after 1993, whereas relative real per capita GDP of Eritrea increases in 1993.

Cotton-producing and -exporting countries experienced a catching-up phase in themid-1970s and 1994 (i.e. Benin (1977), Mali (1972), Mozambique (1995) and Sudan(1972)). Cote d’Ivoire and Malawi experienced breaks in 1995 and 1994, respectively,coinciding with the cotton boom in 1994. Although relative real per capita GDP rose forboth countries, neither country lies on the catching-up path. We also find evidence ofdivergence for Benin (1982 and 1988), the Central African Republic (1991), Coted’Ivoire (1979 and 1983), Malawi (1978), Mali (1976 and 1984), Mozambique (1982and 1991), Sierra Leone (1981), Sudan (1977 and 1981), Togo (1979 and 1992) andZimbabwe (1978, 1984, 1987 and 1991), related to the sharp decline in cotton pricesduring the early and late 1970s and the early 1980s and 1990s.

6. CONCLUSIONS

One of the oldest controversies in the economic growth literature concerns theconvergence hypothesis, which states that inequality of per capita income will disappear



in the long run. This paper has examined the catching-up hypothesis with respect to theUSA by measuring real per capita GDP in 52 African countries over the 1969-2011period. We used the Carrion-i-Silvestre et al. (2005) stationarity test, which enabled us totest for structural breaks, using an intercept term and a linear trend slope. Our findingsshow that convergence patterns vary among African countries. In addition, we findconvergence towards the USA in low-income African countries during the 1970s and1980s but divergence from the USA in the 2000s only high-income African countriesexhibited catching-up with respect to the USA. Our findings show that most break pointscoincide with political instability, trade policies and terms of trade volatility. Due to weakinstitution and poorly developed social infrastructure, most African countries cannot usepositive terms of trade shocks to make better policy decisions. Trade liberalisation policiesusually resulted in negative shocks and placed some countries on divergence paths.Generally, our findings show that, of the 52 African countries, only 5 (9.6%) lie onthe catching-up path over the 1969-2011 period, while other countries experienceddivergence following break points. Our findings thus imply that African countries muststill exert considerable efforts to escape the poverty trap.

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