openness, investment and growth in sub-saharan africa

Openness, Investment and Growthin Sub-Saharan AfricaHector Salaa,b,∗ and Pedro Trivına

aDepartament d’Economia Aplicada, Universitat Autonoma de Barcelona, Bellaterra, SpainbInstitute for the Study of Labor (IZA), Bonn, Germany

∗ Corresponding author: Hector Sala. E-mail: [email protected]

Abstract

This paper revisits the determinants of economic growth in Sub-Saharan Africaby looking at conditional and unconditional convergence, and by focusing onthe growth incidence of globalisation, domestic investment (DI), and foreigndirect investment (FDI). We use annual time-series to estimate dynamicpanel data models that exploit all sample information (i.e., we do not onlyuse 5-year averages as is standard in the literature). We find the rate of condi-tional convergence to be around 4%, and the growth impact of FDI and DI tobe greater the greater is the change in the degree of economic openness. Wealso find a net crowding out effect between both types of capital so that largeramounts of FDI reduce the impact of DI on economic growth (and vice versa).These results are obtained through the estimation of multiplicative interactionmodels which allows us to evaluate the interactions between changes in open-ness, DI and the net flows of FDI. This constitutes a novelty in the appraisal ofthe globalisation and investment impact on economic growth.

JEL classification: O55, O47, F43, E22

1. Introduction

This paper revisits the determinants of economic growth in Sub-SaharanAfrica (SSA) by looking at conditional and unconditional convergence,and by focusing on the growth incidence of two critical factors driving

# The author 2014. Published by Oxford University Press on behalf of the Centre for theStudy of African Economies. All rights reserved. For permissions, please email: [email protected]

Journal of African Economies, Vol. 23, number 2, pp. 257–289doi:10.1093/jae/ejt027 online date 2 January 2014

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today’s economic changes: globalisation and investment. Our analysis isbased on the estimation of multiplicative interaction models to accountfor the changing relationship between the degree of openness, domestic in-vestment (DI) and foreign direct investment (FDI) with respect to growth.

Significant studies concerned with the gruesome performance of Africahave outlined, directly or indirectly, the relevance of DI. Although in theseminal study by Sachs and Warner (1997) no role is given to investment,1

a reexamination of their model by Hoeffler (2001) shows that neglecting un-observed fixed-effects and endogeneity problems is the reason why invest-ment appears to be insignificant. In a subsequent study, Hoeffler (2002)shows that the Solow model provides useful insights into understandingthe poor growth performance of SSA countries, and states that it may be“worthwhile to focus on the continent’s low investment ratios and highpopulation growth rates, which we found to be sufficient to explainAfrica’s low growth rates” (Hoeffler, 2002, p. 156). Tsangarides (2001)differs and finds the Solow model to be inconsistent with the growth evidenceon Africa. However, he coincides in signalling investment (among otherfactors) as relevant. Bosker and Garretsen (2012) consider physical geog-raphy as key for economic performance, and focus on the importance ofmarket access. They find that market access positively affects income levelsand, despite seeing remoteness of many African economies as an importantburden, they state that there is room for policies fostering, for example, infra-structure investment. In the same vein, Calderon and Serven (2010) usequantity and quality indicators of infrastructure, and show that infrastruc-ture development has a positive impact on long-run growth (and a negativeone on income inequality). This evidence points to DI as a strategic factor forAfrica’s development and growth.

Another stream of literature is directly concerned with the growth effects ofFDI. Lipsey (2004) surveys empirical macro-research on this issue. Even ifthere is no clear consensus, the literature tends to favour the positiveimpact of FDI on the exports and growth of the host countries. On the con-trary, Herzer (2012) finds that FDI has negative effects on growth, on average,although he also acknowledges large differences in its effect across the 44

1 According to Sachs and Warner (1997), poor economic policies are critical. Easterly andLevine (1997) examine the hypothesis that the failure of public policies in SSA is substan-tiallyexplained byethnic diversity. They find that the fundamental role exerted byethnic div-ision on the poor performance of this region works indirectly through public policies,political stability and other economic forces. This line of reasoning has been endorsed byFosu et al. (2006), in clear contrast with Cinyabuguma and Putterman (2011) who findopposite results.

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developing countries examined. Regarding Africa, Adams (2009) examinesthe impact of FDI and DI on economic growth in SSA, finds both to besignificant and uncovers a net crowding out effect between the two. As inour study, he estimates a dynamic fixed-effects panel data model, and con-trols for the degree of openness. However, he works with a short sampleperiod (1990–2003) and his results are based on ordinary least squares(OLS) estimates liable to endogeneity problems.

The empirical strategy used this paper, as well as in Adams (2009), is atodds with the usual methodology consisting on the estimation of staticmodels with 5-year averages. We exploit all available information and useannual time-series to estimate dynamic panel data models where the time di-mension is similar to the cross-section dimension (i.e., T ≈ N). In addition,we contribute to the literature by providing new evidence based on the esti-mation of multiplicative interaction models (Brambor et al., 2006).Rodrıguez (2007) discusses empirical evidence regarding the link betweenopenness and growth in cross-country regressions. He concludes that stand-ard measures of trade policy are basically uncorrelated with growth, althoughhe clearly states that this does not imply that openness is irrelevant for growthbecause the problem is related to the oversimplification of growth regres-sions.2 In this paper, we take a step forward on this matter by estimatingstandard growth equations using multiplicative interaction models. Asexplained in Brambor et al. (2006), this is the appropriate tool wheneverthe hypothesis being tested is conditional in nature, as it is the case here(see Section 4).

Turning to our findings, and regarding the absolute convergence of percapita gross domestic product (GDP), we confirm and update the well-known SSA’s growth struggle. Economic catch-up towards the US (whichis taken as the reference economy) is perceived only in exceptional cases.When we classify our sample of 43 SSA economies into the ones thathave increased or reduced their international exposure to trade, we findno significant differences in their performance. In the 1980s, the ‘globali-sers’ grew substantially less than the ‘non-globalisers’, but these differencesare reverted and reduced in the 1990s, and virtually vanish in the 2000s.Moreover, when we compute the correlation coefficient between eachcountry’s per capita GDP normalised by the US one (as indicator ofconvergence) and the degree of openness (as indicator of economic global-isation), we find no patterns. About half of the SSA economies display

2 Along the same lines, Crafts (2004) concludes that the discussion on the relationshipbetween globalisation and growth is less than conclusive in many respects.

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positive (negative) correlations, but these are not forcefully related with apositive (negative) incidence of globalisation since a positive correlationmay arise from a situation of a widening GDP gap and a falling degreeof openness. We conclude that there is no relationship between absoluteconvergence and globalisation.

Regarding conditional convergence, we find that per capita incomes in SSA(in a sample of 33 economies having enough time-series data) converge totheir steady-state levels at a rate of approximately 4% per year. This resultis achieved in the presence of the standard controls in the literature, suchas the current account balance, shocks in terms of trade, price inflation,population growth, life expectancy, infrastructure development, financialmarkets depth and variables that control for the quality of the institutionalframework and the presence of armed conflicts.

As noted, however, the focus of our analysis is placed on the quantitativerole exerted by DI and FDI on economic growth. And the novelty here isthe attempt to evaluating this quantitative role in a context in which thesetwo variables, together with the changing degree of openness, are assumedto depend on one another.

Even in this context, though, a standard interpretation of our empiricalresults would likely outline the relevance of international trade, the findingof a positive influence of DI on growth, and the finding of a positive influenceof FDI on growth, all of them being evaluated unconditionally. Our analysis,which certainly confirms these findings, allows us to go much beyond them inassessing the complexity of these effects. First of all, we uncover the existenceof significant complementarities between (i) the impact of DI on growth anda growing exposure to international trade and (ii) the impact of FDI ongrowth and a growing exposure to international trade. In other words, thegreater the increase in trade openness, the larger the impact of both kindsof investment on growth. Second, we provide new evidence on the substitut-ability between DI and FDI regarding theircontribution to economic growth.That is, we document a net crowding out effect by which the growth impact ofDI becomes lower the larger is the availability of FDI (and vice-versa). Third,this trade-off between the effects of both types of investment varies withchanges in the degree of openness. In particular, we find that the largerthese changes are, the greater this trade-off is (that is, the growth impactof DI and FDI becomes more sensitive to the availability of the other typeof investment).

Given that the effects of DI, FDI and globalisation are closely intertwined,and given their significant consequences on growth, it is crucial for SSAcoun-tries to identify the best way to combine these factors and exploit efficiently



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any possibility given by their changing exposure to the internationalisationprocess.3

Next section briefly reviews the main theoretical and methodologicalissues underlying economic growth regressions. Section 3 documentsthe poor results achieved by Sub-Saharan countries in terms of absoluteconvergence. Section 4 examines conditional convergence. Section 5concludes.

2. Background: key issues in the analysis of growth

2.1 Theoretical underpinnings

Two crucial concepts of economic convergence refer to unconditional (or ab-solute) convergence and conditional (or relative) convergence.4 Unconditionalconvergence implies that poor economies grow faster than wealthy economiesindependently of the particular political and socioeconomic structure of eachparticular country. Conditional convergence, in turn, refers to the catching-upprocess of any economy (or group of economies) in its own steady-state,which is determined by the political and socioeconomic structure of thateconomy (or group of economies).

The existence of unconditional convergence can be empirically tested bychecking whether the coefficient of initial income is negative in a univariateregression of the form:

Dyi, t = ai, t + byi, t−1 + 1i, t, (1)

whereDyi, t is the log difference of real GDP per capita, and yi, t−1 is the laggedlogarithm of real GDP per capita. The estimation of this equation, however, isgenerally disregarded due to the inherent problem of omitted-variables bias.

Conditional convergence is usually estimated on the basis of a multivariateregression analysis with a set of conditioning variables that control for thesteady state or long-run per capita income towards which the economy even-tually converges. This may be augmented with cross-section and/or period

3 Note that, by exploiting the trade-offs between the effects of DI and FDI on growth, ourresults encompass the possibility of a larger impact of one of these types of investmenteven in the case of a reduction in the degree of trade openness.

4 These concepts relate to the notion of b-convergence (see Sala-i-Martin, 1996). Otherstudies, however, focus on the fall over time in the dispersion of per capita incomeacross countries, which is known as s-convergence (for a recent example see Younget al., 2008).



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fixed-effects so that the standard model ends up taking the form:5

Dyi, t = a+ byi, t−1 + dXi, t + hi + jt + 1i, t, (2)

whereb and d are parameters to be estimated, Xi,t is a row vector of determi-nants of economic growth, hi is a vector of country fixed-effects controllingfor time-invariant differences across countries that are not controlled by Xi, t,jt are time fixed-effects controlling for temporary shocks common acrosscountries and 1i, t is a vector of zero-mean white-noise residuals.

2.2 Caveats on the standard practice of taking five-year averages

The estimation of growth models was initially developed within a cross-section context—for example, the seminal paper by Barro (1991) amongthe many others that followed. This practice, however, entailed a twofoldproblem, one related to the potential endogeneity of the regressors, andthe other to the existence of country-specific effects. A second wave ofstudies (for example, those of Beck et al., 2000; and Barro, 2000) switchedto the estimation of panel data models so that the unobserved country-specificities could be accounted for by the estimated fixed-effects.

The standard practice became working with five-year averages to allow forthe use of some variables available only at this frequency (human capital, forexample), and to clean the information from business cycle noise so that theanalysis would only focus on long-run relationships. In that case, equation(2) changes to

Dyi, t = a+ b0yi, 0 + dXi, t + hi + jt + 1i, t, (3)

where y0, i is the initial value of per capita income (of each five-years period),and the estimation of b0 proxies the convergence rate.

The advantages of using five-year averages can be put into perspective onthe following grounds. First of all, variables available only for five-yearperiods are quite often indicators (or indices) that proxy a true unobservedvariable. Second, the concept of ‘business cycle’ is generally defined adhoc, as the five-year periods usually start at the beginning or in the middleof a decade (in the worst case, their limited availability from a particularyear may even end up conditioning the initial year of the five-yearperiods). Moreover, this method also implies relying on the assumption

5 As the goal of this paper is mainly empirical, we do not enter into well-known theoreticaldetails that can be found, for example, in Mankiw et al. (1992) and Barro andSala-i-Martin (1995).



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that the length of the business cycle is always the same and, as a consequence,business cycles are considered identical (in time and length) across countries.

Here we follow a different strategy and use yearly data. One of the historicallimitations that studies on SSA had to face was the lack of sufficient data, bothin quantity and quality. This limitation has become progressively less bindingand it is fair to make full use of the current information at hand. Moreover,business cycles are inherently part of the economic behaviour: to understandhow the different economic forces affect growth we cannot neglect the role ofthe former. Finally, making full use of the long time series available is helpfulfor the precise identification of the estimated coefficients. This is what leadsus to use the available yearly data as individual observations.

2.3 Fixed-effects vs GMM estimation methods

The advantages of using panel data are well known. As shown in equation (2),we control for cross-section and time fixed effects to avoid as much as pos-sible an omitted variable bias that could arise, for example, from country-specific factors (e.g., amenities) that might be correlated with the explanatoryvariables. Cross-section fixed effects control for time-invariant differencesacross countries that are not controlled by the explanatory variables,whereas time dummies control for shocks that are common across countries(e.g., worldwide economic crises).

However, as we estimate a dynamic equation and the lagged dependentvariable appears as an explanatory variable, the use of a standard fixed-effectsmodel could yield inconsistent coefficients even if the residuals were notserially correlated. To overcome this potential endogeneity problem,Arellano and Bond (1991) developed the Difference Generalized Methodof Moments (GMM) method in which the levels of the explanatory variableslagged two and more periods are used as instruments.

Some authors, however, considered that this method could have problemsfor persistent variables such as many of those generally used in economicgrowth models. This additional concern can be addressed through the useof the System GMM method, developed by Arellano and Bover (1995) andBlundell and Bond (1998), which better deals with the presence of persistentvariables.6 The System GMM method consists in the estimation of two

6 For details on System GMM and its advantages over Difference GMM, see Bond et al. (2001)and Roodman (2009). For their application on economic growth models, seeCastello-Climent (2010).



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equations, one in differences and one in levels such that:

Dyi, t = gDyi, t−1 + dDXi, t + D1i, t,

yi, t = gyi, t−1 + dXi, t + hi + 1i, t,(4)

where yi, t is the dependent variable, yi, t−1 is the lagged dependent variable,Xi, t is a row vector of explanatory variables, hi is a fixed-effect term, g and d

are parameters to be estimated and 1i, t is a disturbance (i and t represent thecross-section and time dimensions, respectively).

The key contribution of this method is that it uses further moment condi-tions than the Difference GMM estimator. Not only the levels of the variableslagged twice and more are used, as in the Difference GMM method, but alsothe lags of the variables in differences which are now added as instruments inthe level equation of the system.

In Section 4, we present empirical versions of equations (2) and (3),estimated using both the fixed effects and System GMM methods.

2.4 The addition of interactions

Beyond the choice of the econometric technique, one of the contributions ofthis paper is the estimation of multiplicative interaction models, which willhopefully allow us to better disentangle the relationship between DI, FDI andthe degree of economic openness. Next, we explain how the estimatedcoefficients need to be interpreted in this context.

A simple linear additive model can be expressed as follows:

Y = a0 + a1X + a2Z, (5)where Y is the estimated value of the dependent variable, X and Z are theexplanatory variables and the a ′s are estimated parameters. In turn, asimple multiplicative interaction model takes the form:

Y = b0 + b1X + b2Z + b3XZ, (6)where the b′s are estimated parameters, and XZ is an interactive term (fordetails see Aiken and West, 1991; Brambor et al., 2006).

The presence of the interactive term alters the interpretation of the esti-mated parameters in a fundamental way. The reason is that in model (5) Xand Z are considered independent of one another, whereas in model (6) Xand Z are not. In other words, in the additive model the effect of X on Y isconsidered to be constant while, in the multiplicative interaction model,this effect depends on the values taken by variable Z. Therefore:



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† a1 is the unconditional marginal effect of X on Y, whileb1 is the conditionalmarginal effect of X on Y when Z ¼ 0.(The same interpretation holds for a2 and b2 regarding Z).

† b3 captures the impact of X on Y for different values of the modifyingvariable Z and allows this impact to vary. That is, the overall conditionalmarginal effect of X on Y is

∂Y

∂X= b1 + b3Z. (7)

Most of the literature uses linear additive models and disregards the possibil-ity that the explanatory variables may be conditioned by one another.Moreover, Brambor et al. (2006) show that most of the literature consideringmultiplicative interaction models is subject to one (or both) of the followingproblems: omitted constitutive terms and a lack of computation of marginaleffects and standard errors across a substantively meaningful range of themodifying variable (Brambor et al. 2006, p. 78).

To minimise the possibility of omitted constitutive terms, in this study wespecify a richer multiplicative interaction model with three interactive terms(details on the empirical justification of this procedure are provided below).This implies an augmented version of model (6) such that

Y = b0 + b1X + b2Z + b3W + b4XZ + b5XW + b6ZW

+ b7XZW, (8)

in which case the overall conditional marginal effect of X on Y is

∂Y

∂X= b1 + b4Z + b5W + b7ZW . (9)

This specification is used in Section 4 to compute marginal effects and stand-ard errors of X (in our case, domestic or FDI) across a substantively meaning-ful range of the modifying variable Z (changes in the degree of openness) forgiven values of W (FDI or DI).

2.5 Validity of instruments in System GMM estimation of modelswith interactions

A crucial issue in our analysis is the use of GMM models in the presence ofinteraction terms giving rise to nonlinearities. Next we show that the logic



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of the System GMM estimation method can be safely applied to the estima-tion of multiplicative interaction models.7

Arellano and Bover (1995) showed that lags of predetermined stationaryvariables are appropriate instruments to control for endogeneity bias indynamic (error correction) models. Analogously to their model,8 let us con-sider the following simple interactive case:

yit = b′(xit ∗ zit) + uit, (10)where uit = hi + yit . If the product xt ∗ zt is a predetermined stationary vari-able, then the difference of the products (xt ∗ zt) − (xt−1 ∗ zt−1) is a valid in-strument for the error term expressed in levelshi + yit . This is so because thereasoning developed in Arellano and Bover (1995) applies to the difference ofthe products rather than to the product of the differences. And it is indeedthe difference of the products that allows a mechanic application of SystemGMM to an interactive regression model (if it was the product of the differ-ences, the resulting nonlinearities would hinder the use of this econometricmethodology, but this is not the case).

3. Unconditional convergence

Table 1 displays information on the economic catching-up process ofSub-Saharan economies between 1980 and 2009.9 The first block in thetable shows income per capita in each country relative to that of US. Forexample, real GDP per head in 1980 in Angola was 8.3% of that of US andprogressed to 11.6% in 2009. This implies that Angola has tended to convergeand in the last three decades has shortened its gap by 3.3 percentage-points(of US real per capita GDP in PPP).

The conclusion we obtain from reading this table is twofold. First of all, percapita income in the vast majority of economies is less than 15% of that of USand in many of them it is below 5%. Among the 43 Sub-Saharan countries,the exceptions are Botswana (close to 22% in 2009), Equatorial Guinea(near 54%), Gabon (25%), Mauritius (23%), Seychelles (almost 58%) and

7 We owe this point to kind advice received from Manuel Arellano.8 This model is represented by equation (19) in Arellano and Bover (1995, p. 38):

yit = ayit−1 + b′xit + g ′ fi + uit,where uit = hi + yit , and E[yit | xi0, ..., xiT, fi, hi] = 0.In this setting, given the unobservable individual effect hi, and under the assumptionthat xit and fi are strictly exogenous, b can be identified, but not g. See Arellano andBover (1995) for details.

9 The countries with an asterisk are those that have enough information to be included in theeconometric analysis conducted in Section 4.



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Table 1: Economic catching-up in Sub-Saharan Africa: 1980–2009

Real GDP per capita in PPP as % of US real GDP per capita in PPP

Levels Differences (percentage-points)

1980 1990 2000 2009 80–90 90–00 00–09 80–09

Angola 8.3 7.4 6.0 11.6 20.9 21.5 5.6 3.3Benin∗ 3.8 3.39 2.94 2.71 20.45 20.45 20.22 21.1Botswana∗ 11.9 18.34 21.89 21.58 6.4 3.56 20.32 9.7Burkina Faso 2.65 2.08 2.03 2.20 20.56 20.05 0.17 20.45Burundi∗ 1.47 1.41 1.01 0.90 20.05 20.41 20.11 20.6Cameroon∗ 7.89 6.00 4.31 4.41 21.89 21.69 0.10 23.5Cape Verde∗ 4.43 5.11 6.33 9.19 0.69 1.22 2.86 4.8CAF∗ 3.58 2.34 1.62 1.58 21.24 20.72 20.04 22.0Chad∗ 2.31 2.35 1.87 3.11 0.04 20.48 1.24 0.8Comoros∗ 5.59 4.11 2.50 2.23 21.47 21.62 20.27 23.4D.R. Congo 3.07 2.29 0.30 0.56 20.78 21.99 0.26 22.5R. Congo 7.00 7.70 5.84 5.40 0.70 21.86 20.44 21.6Cote d’Ivore∗ 6.15 5.11 3.86 3.27 21.04 21.26 20.59 22.9Equatorial Guinea 2.97 1.98 14.88 53.57 20.99 12.89 38.69 50.6Eritrea∗ n.a. n.a. 2.1 1.4 n.a. n.a. 20.7 n.a.Gabon∗ 52.1 34.5 28.2 25.0 217.67 26.28 23.18 227.1The Gambia∗ 3.4 2.7 2.05 3.6 20.69 20.63 1.52 0.2Ghana∗ 3.2 2.7 2.6 3.0 20.55 20.40 0.75 20.2Guinea∗ 3.5 2.7 1.9 2.0 20.78 20.79 0.08 21.5Guinea-Bissau∗ 1.5 1.35 1.2 2.0 20.16 20.19 0.83 0.5Kenya∗ 4.58 3.75 2.89 2.9 20.82 20.86 0.04 21.6Lesotho∗ 3.13 2.87 2.82 3.2 20.26 20.05 0.37 0.1Liberia 7.04 1.64 1.34 1.0 25.40 20.30 20.38 26.1Madagascar∗ 3.23 2.90 2.07 1.8 20.33 20.83 20.24 21.4Malawi∗ 3.64 1.88 1.46 1.6 21.76 20.41 0.12 22.05Mali∗ 2.25 2.18 1.91 2.4 20.07 20.27 0.52 0.2Mauritius∗ 11.86 15.84 19.20 23.07 3.98 3.35 3.88 11.2Mozambique∗ 1.87 1.28 1.12 1.85 20.59 20.16 0.73 20.0Namibia∗ 17.84 12.13 10.18 11.52 25.70 21.96 1.34 26.3Niger∗ 2.74 1.60 1.21 1.30 21.14 20.38 0.09 21.4Nigeria∗ 5.79 3.73 2.90 4.95 22.06 20.83 2.05 20.8Rwanda∗ 3.14 2.44 1.69 2.51 20.69 20.76 0.82 20.6Sao Tome and P. 6.08 3.26 2.59 4.09 22.81 20.67 1.50 22.0Senegal∗ 4.52 3.56 3.38 3.63 20.96 20.18 0.25 20.9Seychelles 39.09 41.19 44.66 57.91 2.11 3.47 13.24 18.8Sierra Leone 4.10 3.31 1.35 2.12 20.79 21.95 0.77 22.0South Africa∗ 24.42 17.04 15.04 18.46 27.38 21.99 3.42 25.95Swaziland∗ 8.88 9.74 8.10 8.37 0.85 21.63 0.27 20.5

(continued on next page)



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South Africa (above 18%). Second, only in exceptional cases there has beensome economic catch-up. For example, in some of the rich countries justmentioned—but not in Gabon, and South Africa—as well as in Angolaand Cape Verde. Overall, the African experience in the last three decades iscertainly not successful.

Table 2 classifies our set of countries into two groups, one where inter-national exposure has increased in the last decades, and another one whereit has fallen. International exposure is measured by the trade ratio overGDP (i.e., exports + imports/GDP). We call the former ‘globalisers’ andthe later ‘non-globalisers’.

There are significant differences in the 1980s, when the non-globalisersincreased per capita income by 10.5% while the globalisers lost 2.4% of it.The 1990s, on the contrary, show little differences between the two groups,with the globalisers growing (at 3.5%) and the non-globalisers losing track(22.3%). No differences, either, characterised the 2000s in spite of thisbeing the most successful period in terms of economic growth with a risein per capita GDP close to 30%.

Table 1: Continued

Real GDP per capita in PPP as % of US real GDP per capita in PPP

Levels Differences (percentage-points)

1980 1990 2000 2009 80–90 90–00 00–09 80–09

Tanzania∗ 2.69 2.10 1.84 2.89 20.59 20.26 1.06 0.2Togo∗ 5.32 3.32 2.14 1.79 22.00 21.18 20.35 23.5Uganda∗ 2.16 1.75 2.09 2.80 20.41 0.35 0.71 0.6Zambia∗ 6.62 3.81 2.12 4.29 22.80 21.70 2.18 22.3Zimbabwe 1.41 1.16 0.94 0.35 20.25 20.22 20.60 21.1

Source: Penn World Table 7.0.∗See footnote 9 for explanation of asterisk.

Table 2: Economic growth in Sub-Saharan Africa

Cumulative growth of real GDP per capita in PPP

1980s 1990s 2000s exports + importsGDP × 100

( )

Globalisers 22.4 3.5 30.0 34.8Non-globalisers 10.5 22.3 28.3 221.2All 3.5 0.8 29.2 8.9

Source: Penn World Table 7.0.



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The connection between per capita GDPand the degree of openness can befurther explored by examining the relationship between the two country bycountry. Figure 1 shows the correlation coefficient between per capita realGDP of each country, as a ratio of that of US, and the degree of openness(measured as imports plus exports of goods and services over domesticoutput). Normalisation with respect to the US GDP provides a synthetic in-dication of convergence, which we connect with the degree of internationalexposure. In this way, countries with a positive correlation are successful inthe sense that they have been capable of managing the inexorable globalisa-tion process without harming economic growth. On the contrary, countrieswith a negative correlation are unsuccessful in the sense that changes in theirexposure to international markets are negatively correlated with their eco-nomic progress.10

Figure 1: Correlation between per capita GDP (as % of that of US) and openness.Source: Data from Penn World Table 7.0.

10 Note that both groups of countries may display this correlation irrespective of the signstaken by both variables. For example, an economy with a widening GDP gap withrespect to the US and a falling degree of openness will have a positive correlation coefficient.



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Figure 1 shows a clear ambiguous relationship, where approximately halfof the countries display positive and negative correlation coefficients.We conclude that openness does not exert a clear influence on absolute con-vergence and is thus not able, at least at a first glance, to explain the poor per-formance of Sub-Saharan countries in terms of absolute convergence. Next,we carefully explore this issue when applied to conditional convergence.

4. Conditional convergence

4.1 Data

We use annual data running from 1980 to 2009 collected from differentsources. Per capita GDP, DI, the degree of economic integration (proxiedby openness) and population growth are obtained from the Penn WorldTable; data on the current account balance and price inflation from theIMF World Development Outlook; information on net inflows of FDI,terms of trade, life expectancy, infrastructure development (proxied by tele-phone lines density) and financial markets depth (proxied by domestic creditto private sector) comes from the World Bank’s Development Indicators.Variables that control for the institutional framework and armed conflictsare obtained, respectively, from the Polity IV project (we use the polity2 vari-able) and the UCPD/PRIO databases (for the latter, see Gleditsch et al., 2002)(Table 3).

Our dynamic panel estimation includes the 33 SSA economies for whichlong enough time-series data are available (recall that these economies aredenoted with an asterisk in Table 1). The rest cannot be taken into accountfor different reasons—for example, Burkina Faso has no data on institutionalindicator, Zimbawe on the current account, Sao Tome and Prıncipe on FDI,so on and so forth.

4.2 Estimated equations

We present results broken down on a threefold dimension: (i) based on theestimation of a standard linear additive model—where all explanatory vari-ables are considered independent—and of a multiplicative interactionmodel—where some explanatory variables are considered dependentamong them; (ii) based on the estimation of a two-way fixed-effects modelthrough OLS and System GMM and (iii) based on estimates using five-yearaverages and yearly data. Our reference results will be those obtained through



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the estimation of a multiplicative interaction model on yearly data by SystemGMM.

4.2.1 Results

Although the results based on the estimation of linear additive models are notcentral to our analysis, it is important to have them at hand for comparisonpurposes (see Table A1 in Appendix 1). Some features of these results are thefollowing. First of all, the estimated coefficients of yt−1 and yi0 are negativeand significant across all regressions and confirm the existence of conditionalconvergence, which ranges from 2 to 13% depending on the methodology.Regarding the role played by the explanatory variables, the results are alsodiverse depending on the econometric methodology and the specificationof the database in five years or annual frequencies. One significant featureis that DI appears as a robust growth driver. Another one is that a numberof growth factors appear to be non-significant when the estimation iscarried out using five-year averages. This may be due to the inherent limita-tions (and imposed restrictions) of the estimation methodology, and/or tocross-country heterogeneity resulting on an average null effect (the latter

Table 3: Definitions of variables

Variables Sources Subindices

yit Real per capita GDP in PPP PWT i = 1, ..., 33 countriesyi0 Initial real per capita GDP in PPP PWT t = 1, ..., 30 yearsdiit Domestic investment (%GDP) PWTfdiit Net foreign direct investment (%GDP) WBopit Openness exports + imports

GDP ∗ 100( )

PWT

cait Current account balance (%GDP) IMFtotit Terms of trade: log price exports

price imports

( )WB

pit Price inflation (GDP deflator growth rate) IMFDpoit Population growth PWTlexit Life expectancy (log) WBinstit Institutional indicator (polity2) PolityIVinfrit Telephone lines per 100 people WBfinit Domestic credit to private sector (%GDP) WBarmedit Armed conflicts indicator UCDP/PRIO

Note: PPP, purchase power parity; PWT, Penn World Table 7.0; WB, World Bank; IMF,International Monetary Fund; UCDP/PRIO, Uppsala Conflict Data Program/InternationalPeace Research Institute, Oslo; version 4-2009.



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would be consistent, for example, with Herzer’s, 2012, finding of heteroge-neous FDI effects in developing countries).

In contrast, the estimation of the multiplicative interaction model yieldsmore conclusive results. This is due to the improvement in the model’sspecification that occurs when unconditional marginal effects are estimatedin the presence of cross-dependencies among explanatory variables thatrequire the estimation of conditional marginal effects (i.e., the impact ofsome explanatory variables on the dependent variable depend on the valueof other explanatory variable). We argue that FDI, DI and openness are notindependent of one another and, therefore, require their growth impact tobe assessed through the correct estimation of the conditional marginaleffect of these variables on economic growth.

These results are presented in Table 4. The first two columns present thetwo-way fixed effect estimates by OLS (using five-year and yearly data),whereas the last two columns show the same specifications estimated bySystem GMM. We directly focus on the results displayed in the lastcolumn, which correspond to our preferred estimation (yearly data,dynamic model, System GMM methodology).11

The first salient outcome of this regression is that the speed of conditionalconvergence is placed at a rate of 4%. The second is that DI and FDI appearas the most important conditional driving forces of economic growth inSSA countries, while openness enters as a difference with a significant nega-tive sign.12

The rest of the variables behave as standard in existing literature. Thecurrent account balance, shocks on the terms of trade (that is, the first differ-ence in the log terms of trade), life expectancy and the institutional frame-work have a positive marginal effect on growth, whereas, as expected,population growth, the inflation rate and the presence of armed conflictsexert a negative impact. Finally, our controls for infrastructure and financialmarkets depth are not significant in the System GMM estimation, although

11 It is important to emphasise that the results obtained through the fixed-effects estimationdeliver very similar outcomes regarding the marginal effects of our key variables on growth(see Figures A1, A2, A3 and A4, in Appendix 2, which allow a systematic compare and con-trast between both sets of results).

12 There is evidence in the literature of a J-curve type response of openness to growth, consist-ing on a current negative impact of openness on growth, and a subsequent positive effect(see, for example, Greenaway et al., 1998). Although, our results provide support to thiseffect, the current and lagged estimated coefficients on openness are restricted to enter asa difference. Since this restriction cannot be rejected, this is the variable used in the analysisof the marginal effects.



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Table 4: Dynamic two-way fixed effects models with interactions

Dependent Variable Dyit

OLS System GMM

Five-years Yearly Five-years Yearly

Coeff. [Prob.] Coeff. [Prob.] Coeff. [Prob.] Coeff. [Prob.]

C 0.09 [0.791] 0.66 [0.000] 20.12 [0.558] 20.06 [0.734]yit−1 20.13 [0.000] 20.04 [0.000]yi0 20.08 [0.000] 20.02 [0.114]diit 0.12 [0.030] 0.23 [0.000] 0.11 [0.020] 0.17 [0.004]fdiit 0.41 [0.279] 0.46 [0.042] 1.27 [0.001] 0.61 [0.007]Dopit 20.13 [0.082] 20.60 [0.007] 20.08 [0.251] 20.64 [0.001]Dtotit 0.02 [0.033] 0.03 [0.058] 0.02 [0.220] 0.03 [0.046]cait 0.03 [0.548] 0.12 [0.006] 0.05 [0.500] 0.10 [0.002]Dpopit 20.28 [0.020] 20.27 [0.433] 20.24 [0.091] 20.60 [0.088]pit 20.02 [0.030] 20.06 [0.007] 20.002 [0.818] 20.09 [0.004]lexit 0.13 [0.154] 0.05 [0.137] 0.07 [0.310] 0.07 [0.130]instit 0.002 [0.027] 0.002 [0.036] 0.001 [0.023] 0.002 [0.016]infrit 0.03 [0.833] 0.25 [0.023] 0.05 [0.738] 0.03 [0.846]finit 0.05 [0.156] 0.04 [0.145] 20.01 [0.764] 0.004 [0.901]armedit 20.02 [0.098] 20.02 [0.050] 20.001 [0.958] 20.01 [0.174]diit ∗ Dopit 0.14 [0.658] 1.38 [0.024] 0.11 [0.712] 1.38 [0.009]fdiit ∗ Dopit 3.93 [0.203] 9.12 [0.038] 2.37 [0.396] 8.81 [0.014]diit ∗ fdiit 20.96 [0.303] 20.67 [0.219] 23.03 [0.003] 21.17 [0.033]diit ∗ fdiit ∗ Dopit 29.21 [0.396] 223.42 [0.050] 28.35 [0.452] 221.06 [0.028]

(continued on next page)

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Table 4: Continued

Dependent Variable Dyit

OLS System GMM



R2 0.48 0.31AR(1) 0.006 0.000AR(2) 0.657 0.358Hansen 1.00 1.00Obs. 156 856 156 856N 33 33 33 33

Endogenous variables: diit, fdiit, Dopit, Dpopit, cait, infrit, finit, diit ∗ Dopit, fdiit ∗ Dopit, diit ∗ fdiit, diit ∗ fdiit ∗ Do pit ; predeterminedvariables: yit−1(or yi0), Dtotit, pit, lexit, instit ; exogenous variables: armedit .Notes: OLS,ordinary least squares; GMM, generalized method of moments.

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they are, and with the expected sign, when the model is estimated by fixed-effects. This discrepancy, together with the one affecting D pop (which isnot significant in the fixed-effects estimation) raise an issue related to thedegree of endogeneity of these variables (which are of course treated as en-dogenous in the System GMM estimation) and related, also, to their abilityto provide sound empirical counterparts of the phenomena they are intendedto proxy (for a discussion on infrastructure indicators, for example, seeCalderon and Serven, 2010).

Regarding these caveats, it is worth mentioning the severe trade-offs weface in terms of the number of available variables versus the sample periodthey cover. However, since our research is based on a time series analysis,sample coverage was favoured when building up the database (this caused,for example, the exclusion of education enrolment, which would have other-wise required massive interpolation under pretty heroic assumptions). Inthis context, and given that our key variables of interest deliver robustresults, we next examine carefully the economic interpretation of the inter-action coefficients and their standard errors.

4.2.2 Interpreting the interactions

Table 5 documents the changes in the relevant estimated coefficients whendifferent interactions are considered. We start from simple specifications,with just one interaction, and sequentially add interactive terms. The speci-fication reached in Interactions 5 corresponds to the model presented in thelast column of Table 4, with all interactions included in the estimation. This isthe one used to compute the marginal effects.

The interpretation of the scenarios considered is the following. InInteractions 1 the only interaction is dit ∗ Dopt . This implies that the effectof DI on growth is considered to be dependent on changes in the degree ofopenness (this is accounted for by the interaction), but independent ofFDI since there is no further interaction considered. FDI, in turn, is also in-dependent of the change in the degree of openness. In this simple scenario,the marginal effect of DI (di) on growth (Dy) is

∂Dy

∂di= 0.14

[0.023]+ [ 1.07

[0.026]∗ Dop].

This implies, first, that when Dop = 0, the marginal effect of DI is 0.14; andsecond, given the positive sign of the interactive term, that there is a potentialcomplementarity between di andDop regarding their growth effects. The sig-nificance of this relationship, however, has to be evaluated for each value



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Table 5: Estimated interactions—System GMM

diit fdiit Dopit diit ∗ Dopit fdiit ∗ Dopit diit ∗ fdiit diit ∗ fdiit ∗ Dopit

Interactions 1 0.14[0.023]

0.23[0.051]

−0.47[0.009]

1.07[0.026]


0.19[0.060]

−0.26[0.010]

1.80[0.119]


0.43[0.157]

−0.18[0.062]

−0.78[0.259]


0.25[0.012]

−0.50[0.004]

0.99[0.025]

1.41[0.079]


0.61[0.007]

−0.64[0.001]

1.38[0.009]

8.81[0.014]

−1.17[0.033]

−21.06[0.028]

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of Dop. This is done in Figure A1.a in the Appendix, where the marginaleffects of DI on growth, and their corresponding 95% confidence intervals,are evaluated for a range of values of Dop between 0 and 30.13 It can beseen that there is indeed a significant complementary effect between di andDop so that the more intensive the change in trade exposure, the larger theeffect of DI on growth, which ranges from 0.14 (when Dop = 0) to 0.45(when Dop = 30).

In Interactions 2, with fdit ∗ Dopt , the effect of FDI ( fdi) on growth is con-sidered to be dependent on changes in the degree of openness, but independ-ent of DI (which is also independent of the change in the degree of openness).The marginal effect of FDI on growth is thus

∂Dy

∂ fdi= 0.19

[0.060]+ [ 1.80

[0.119]∗ Dop].

This implies that when Dop = 0, the marginal effect is 0.19 (significant at a6% critical value). In this case, Figure A1.c shows a positive correlationbetween the impact of FDI on growth and the changing degree of exposureto international trade.

In Interactions 3, with dit ∗ fdit, the effect of DI is considered to be depend-ent on FDI (and vice versa), and both kinds of investment are independent ofchanges in the degree of openness. The marginal effect of DI on growth isthus:

∂Dy

∂di= 0.09

[0.123]− [ 0.78

[0.259]∗ fdi].

This implies that when fdi = 0, the marginal effect is 0.09 (significant only ata 12% critical value). Following Figure A1.e in the Appendix, the marginaleffect of FDI on DI (for a range of values of FDI between 0 and 30) evolvesalong a negative path. This would indicate that the impact of DI on growthdecreases the larger is FDI. This effect, however, is non-significant at standardcritical values of 5%.

Interactions 4 contains two interactive terms, dit ∗ Dopt and fdit ∗ Dopt .Therefore, the effect of DI on growth is considered to be dependent ofchanges in the degree of openness, but independent of FDI; whereas thatof FDI depends on changes in the degree of openness, but not on DI. The

13 A range of values between 0 and 30 implies that our exercise considers situations in whichthe degree of openness may not change (value 0) or may increase by as much as30 percentage-points.



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marginal effects of DI and FDI on growth are, respectively:

∂Dy

∂di= 0.14

[0.017]+ [ 0.99

[0.025]∗ Dop];

∂Dy

∂ fdi= 0.25

[0.012]+ [ 1.41

[0.079]∗ Dop].

This implies that when Dop = 0, the marginal effect of DI on economicgrowth is 0.14, while that of FDI is 0.25. In addition, both are positively cor-related with changes in the degree of openness. The marginal effects of thesegrowth drivers for different values of Dop are given, respectively, in FiguresA2.a and A2.c in the Appendix. Note that the marginal effects of both DIand FDI are significant for all considered values of Dop, and quantitativelysimilar to those obtained in the previous scenarios.

Overall, this set of interactions show that DI and FDI exert a significantpositive effect on economic growth. Nevertheless, our results are not yet con-clusive about the possible crowding out effect on growth of these two types ofcapital. For this purpose, we use a model with all interactions considered—Interactions 5—which provides the central result of our analysis.

4.3 Marginal effects

The last scenario in Table 5 considers a situation of multiple interactive termscomprising di ∗ Dop, fdi ∗ Dop, dit ∗ fdi, and dit ∗ fdit ∗ Dop. This corre-sponds to equation (8). The analysis of the conditional marginal effects isequivalent to that presented in equation (9) for a variable X. Here,however, we have two variables to be considered: domestic and FDIs.

4.3.1 Case 1: domestic investment

When DI is examined, the empirical version of equation (9) is the following(note that X denotes di, Z denotes Dop, and W denotes fdi):

∂Dy

∂di= 0.17

[0.004]+ [ 1.38

[0.009]∗ Dop − 1.17

[0.033]∗ fdi − 21.06

[0.028]∗ (Dop ∗ fdi)].

In this case, we consider that both domestic and FDIs depend on changes in glo-balisation and, also, that the two types of investment depend on each other.

As we have three different variables, what we now study is the conditionalmarginal effect of DI on economic growth across a range of changes in open-ness, and for given values of fdi. In the absence of changes in the degree ofopenness and FDI, the impact of DI is positive, with a coefficient of 0.17



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(this is the case in which Dop = fdi = 0, and the terms within the bracketsare null). In turn, when Dop . 0, fdi . 0, or both, the terms in bracketsbecome relevant. In particular, the impact of DI on economic growth isenhanced in the presence of changes in the degree of openness, but isreduced by larger amounts of fdi. The extent to which this impact is signifi-cant across the different values of Dop and fdi is plotted in Figure 2.

As noted before, the range of values for Dop is 0–30, as presented in thehorizontal axis. For this range, we simulate the impact of di on growth usingfive different trajectories of fdi comprising, on the one hand, its minimum,maximum and average values computed over the 33 countries in thesample; and, on the other hand, the upper and lower bounds of the 95% con-fidence intervals. These sets of values are denoted, respectively, FDI minimum,FDI maximum, FDI average, FDI upper bound and FDI lower bound.

Figure 2 shows that DI exerts a positive impact on economic growth. Thereference case is the one in which FDI takes the average value. Given thisaverage value, the larger the change in the degree of openness the larger theimpact of di on growth. This impact is significant for all values in the consid-ered range.14 Quantitatively, this impact goes from 0.15 (when Dop = 0 andfdi = 2.1) to 0.42 (when Dop = 30 and fdi = 2.1) implying that a 1

Figure 2: Marginal effects of DI on economic growth. System GMM.

14 Larger changes in short periods of time as the ones being evaluated are not realistic. Forexample, the most important change in the degree of openness in a single year occurredin Botswana and attained 30 percentage points (from 138.07 in 1988 to 108.85 in 1989).



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percentage-point increase in the ratio of DI to GDP generates between 0.15 and0.42 extra percentage-points of per capita growth depending on the accelerationin the globalisation process (always evaluated at average levels of fdi ).

The upper and lower bound lines represent the effects of DI on growth (fora given change in openness) when fdi takes values at the end of the 95% con-fidence interval. In the upper bound case, fdi = 5.9 and the effect of DI oneconomic growth is significant for changes in openness between 0 and 12. Incontrast, in the lower bound case, fdi = −1.815 and the growth impact of DIis larger than for the average value of fdi. This larger impact is significant forall changes in openness, and ranges from 0.19 (when Dop = 0 andfdi = −1.8) to 0.70 (when Dop = 30 and fdi = −1.8).

There is extensive literature on the crowding out effects between DI and FDI(for recent examples, see Adams, 2009; Morrissey and Udomkerdmongkol,2012). In general, however, these effects are not evaluated with respect to theirdirect influence on economic growth as our analysis allows us to do. In particu-lar, observe that the sequence of effects of DI on growth (i.e., represented by thevarious lines in Figure 2) is progressively shifted downwards with higher valuesof FDI. This reveals the existence of substitutabilities between the two types ofinvestment or, in other words, a significant crowding out effect from largerflows of FDI on the DI effects on growth.

Let us refer, also, to the fact that when FDI takes maximum values (that is, thehighest FDI value of the country with the highest FDI) the impact of DI ongrowth can be significantly negative. Of course, this result should be seen as ahighly unrealistic extreme case, but it is reported because it reinforces thepattern just described regarding the relationship between DI and FDI in theireffect on growth. What this extreme case shows is that, for extremely highlevels of FDI, there would be no point in detracting resources from consumptionor other uses to be invested nationally (although of course, our is an aggregateanalysis, and we do not control for the always existing scope for domestic tar-geted policies).

4.3.2 Case 2: foreign direct investment

When FDI is examined, the empirical version of equation (9) takes the form(note that now X denotes fdi, Z denotes Dop, and W denotes di):

∂Dy

∂ fdi= 0.61

[0.007]+ [8.81

[0.014]∗ Dop − 1.17

[0.033]∗ di − 21.06

[0.028]∗ (Dop ∗ di)].

15 Recall that the variable fdi accounts for the net inflows of FDI. Therefore, a negative value inthis variable implies that the outflows of FDI have become larger than the inflows.



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As before, both domestic and FDI depend on changes in globalisation, andthe two types of investment depend on each other. Now, however, weexamine the conditional marginal effect of FDI on economic growth acrossa range of changes in openness, and for given values of di.

In the absence of changes in the degree of openness and DI, the impact ofFDI is positive, with a coefficient of 0.61 (i.e., Dop = di = 0, and the termswithin the brackets are null). In turn, when Dop . 0, di . 0 or both, theterms in brackets become relevant. In particular, the impact of foreign invest-ment on economic growth is enhanced in the presence of changes in thedegree of openness, but is reduced by larger amounts of DI. The extent towhich this impact is significant across the different values of Dop and di isplotted in Figure 3. As before, we distinguish five different trajectories ofFDI comprising the minimum, maximum, and average values computedover the 33 countries in the sample, and the upper and lower bounds ofthe 95% confidence intervals (denoted, respectively, as DI minimum, DImaximum, DI average, DI upper bound and DI lower bound).

Figure 3 shows that FDI exerts a positive impact on economic growth. Asbefore, the reference case is the one in which DI takes the average value. Giventhis average value, the larger the change in the degree of openness the largerthe impact of FDI on growth. This impact is significant for all values of Dopconsidered (i.e., between 0 and 30 percentage-points). Quantitatively, thisimpact goes from 0.38 (when Dop = 0 and di = 20.1) to 1.71 (when

Figure 3: Marginal effects of FDI on economic growth. System GMM.



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Dop = 30 and di = 20.1) implying that a 1 percentage-point increase in theratio of FDI to GDP generates between 0.38 and 1.71 extra percentage-pointsof per capita growth depending on the acceleration in the globalisationprocess (as usual, evaluated at average levels of DI).

In this case, the upper and lower bound lines represent the effects of FDI ongrowth for a given change in openness when DI takes the values at the end ofthe 95% confidence interval. In the upper bound case, di = 30.9 and thegrowth effect of FDI is smaller than for the average value of DI. Thissmaller impact is significant for the whole range of values in Dop, and goesfrom 0.25 (when Dop = 0 and di = 30.9) to 0.91 (when Dop = 30 anddi = 30.9). In contrast, in the lower bound case, di = 9.4 and the growthimpact of FDI is larger than for the average value of DI. This impact is signifi-cant for changes in openness between 0 and 30 percentage-points, and rangesfrom 0.51 (when Dop = 0 and di = 9.4) to 2.51 (when Dop = 30 anddi = 9.4).

Regarding the substitutabilities between DI and FDI, Figure 3 shows ananalogous relationship than that shown in Figure 2, and confirms—fromthe other side of the analysis—the existence of significant crowding-outeffects between these two types of investment. More precisely, the sequenceof effects of FDI on growth (as before in Figure 2 those from DI) is progres-sively shifted downwards with higher values of DI. Moreover, extremely largevalues of DI (that is, an evaluation of this impact at the highest DI value of thecountry with the highest DI) imply that the impact of FDI on economicgrowth is in general not significant, with the only exception of changes inopenness between 11 and 12 percentage-points (observe that, beyond this ex-ception, the bottom dotted line shows no significant marginal effects). Whatthis extreme case shows is that, when DI takes place so intensively, smaller orbigger flows of FDI are irrelevant in terms of growth. This reinforces the ideaof full-crowding between these two types of investment.

5. Conclusions

We have examined the growth pattern of SSA economies across the last threedecades. In terms of unconditional convergence, we have shown that mostcountries have not progressed and its situation today is worse, in relativeterms, than three decades ago. We have tried to establish some pattern thatconnects their evolution in per capita output to their degree of economicopenness, but no clear conclusion could be reached.

In view of this result, we study the conditional convergence of a selection of33 SSA economies through the estimation of a multiplicative interaction



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model. Conditional convergence is found significant and progressing at anaverage rate of around 4%, whereas DI, FDI and the change in the degreeof openness are identified as crucial drivers of this process. Interactionmodels allow the assessment of the cross-dependencies among explanatoryfactors. We find that the impact of FDI and DI on growth is greater thegreater is the change in the degree of economic openness, but we also finda net crowding out effect between both types of capital: larger amounts ofFDI reduce the impact of DI on economic growth, and vice versa.

These new insights into the substitutabilities between different types ofcapital need to be interpreted with caution. First of all, neither DI nor FDIis negative for economic growth by itself. Second, SSA economies need acareful design of growth policies so that their precise effect in terms of com-plementarities or substitutabilities between capitals can be identified. Whatwe learn from this paper is that the key to success does not lie in arbitrarilyopening the economy, in attracting as much FDI as possible, or in rising gen-erically DI. The potential success of any country, and especially of developingeconomies, relies very much on their ability to combine correctly all thesefactors (and, of course, others). This does not seem to be happening at themoment in most SSA economies.

Future research should aim at a more accurate establishment of the specificrelationship between globalisation and investment forces. Conditioned on avail-able data, country-specific analyses should lead to precise policy measureshelping to transform capital crowding-out effects on crowding-in effects; allin all, helping the countries use FDI and economic openness to complementtheir DI so that economic growth is boosted as efficiently as possible.

Acknowledgements

We are grateful to an anonymous referee for insightful comments on a pre-vious version of this paper. We acknowledge financial support from theSpanish Ministry of Economy and Competitiveness through grantECO2012-13081.

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Appendix 1: Estimated results in the absence of interactions

The first two columns of Table A1 present the two-way fixed effect estimatesby OLS (using five-years and yearly data), whereas the last two columns showthe analogous System GMM estimates

Table A1: Dynamic two-way fixed effects models without interactions

Dependent variable: Dyit

OLS System GMM



c 0.19 [0.553] 0.73 [0.000] 20.05 [0.809] 20.23 [0.278]yit−1 20.13 [0.000] 20.04 [0.000]yi0 20.08 [0.000] 20.02 [0.105]diit 0.11 [0.008] 0.18 [0.012] 0.11 [0.028] 0.10 [0.100]fdiit 0.14 [0.292] 0.22 [0.041] 0.16 [0.441] 0.19 [0.149]Dopit 20.07 [0.008] 20.22 [0.020] 20.01 [0.834] 20.20 [0.033]Dtotit 0.02 [0.038] 0.02 [0.137] 0.02 [0.216] 0.02 [0.101]cait 0.01 [0.769] 0.12 [0.006] 0.08 [0.292] 0.10 [0.013]Dpopit 20.27 [0.029] 20.23 [0.520] 20.20 [0.213] 20.98 [0.001]pit 20.02 [0.017] 20.06 [0.002] 20.01 [0.490] 20.11 [0.002]lexit 0.11 [0.182] 0.04 [0.167] 0.06 [0.463] 0.13 [0.022]instit 0.001 [0.080] 0.002 [0.042] 0.001 [0.578] 0.001 [0.184]infrit 0.004 [0.972] 0.24 [0.034] 20.01 [0.960] 20.10 [0.484]finit 0.03 [0.315] 0.03 [0.254] 0.01 [0.790] 0.03 [0.296]armedit 20.03 [0.037] 20.02 [0.016] 20.0002 [0.986] 20.02 [0.041]R2 0.44 0.25AR(1) 0.003 0.001AR(2) 0.759 0.304Hansen 1.00 1.00Obs. 156 856 156 856N 33 33 33 33

Endogenous variables: diit, fdiit, Dopit, Dpopit, cait, infrit, finit ; predetermined variables:yit−1(or yi0), Dtotit, pit, lexit, instit ; exogenous variables: armedit .Notes: OLS, ordinary least squares; GMM, generalized method of moments.



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Appendix 2: Individual marginal effects and robustness checks

See Figures A1–A4.

Figure A1: Individual marginal effects on economic growth.



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Figure A2: Marginal effects of DI and FDI on economic growth.

Figure A3: Marginal effects of DI on economic growth. Fixed effects model.



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Figure A4: Marginal effects of FDI on economic growth. Fixed effects model.



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openness, investment and growth in sub-saharan africa

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