International Journal of Social EconomicsEmerald Article: The role of institutions in economic development: Evidence from 27 Sub-Saharan African countriesRasha Hashim Osman, Constantinos Alexiou, Persefoni Tsaliki

Article information:

To cite this document: Rasha Hashim Osman, Constantinos Alexiou, Persefoni Tsaliki, (2011),"The role of institutions in economic development: Evidence from 27 Sub-Saharan African countries", International Journal of Social Economics, Vol. 39 Iss: 1 pp. 142 - 160

Permanent link to this document: http://dx.doi.org/10.1108/03068291211188910

Downloaded on: 17-04-2012

References: This document contains references to 90 other documents

To copy this document: permissions@emeraldinsight.com

This document has been downloaded 117 times.

Access to this document was granted through an Emerald subscription provided by CRANFIELD UNIVERSITY For Authors: If you would like to write for this, or any other Emerald publication, then please use our Emerald for Authors service. Information about how to choose which publication to write for and submission guidelines are available for all. Additional help for authors is available for Emerald subscribers. Please visit www.emeraldinsight.com/authors for more information.

About Emerald www.emeraldinsight.comWith over forty years' experience, Emerald Group Publishing is a leading independent publisher of global research with impact in business, society, public policy and education. In total, Emerald publishes over 275 journals and more than 130 book series, as well as an extensive range of online products and services. Emerald is both COUNTER 3 and TRANSFER compliant. The organization is a partner of the Committee on Publication Ethics (COPE) and also works with Portico and the LOCKSS initiative for digital archive preservation.

*Related content and download information correct at time of download.

The role of institutionsin economic developmentEvidence from 27 Sub-Saharan

African countries

Rasha Hashim OsmanDepartment of Economics, Division of Development and Planning,

Aristotle University of Thessaloniki, Thessaloniki, Greece

Constantinos AlexiouCranfield School of Management, Cranfield University, Cranfield, UK, and

Persefoni TsalikiDepartment of Economics, Division of Development and Planning,

Aristotle University of Thessaloniki, Thessaloniki, Greece

Abstract

Purpose – The purpose of this paper is to explore the alleged link between institutional quality andeconomic performance in 27 Sub-Saharan Africa (SSA) countries during the period 1984-2003.

Design/methodology/approach – Four institutions’ quality indicators, namely governmentstability, corruption, ethnic tensions and socioeconomic conditions, along with other control andpolicy variables, are employed in a panel data analysis.

Findings – The institutional variables assume a key role in the process of economic developmentwhereas the control variables display a limited effect. Thus, the “conventional variables” of economictheory may not be able to fully explain the SSA experience.

Research limitations/implications – Future research efforts should explore how the vast changesexperienced by the countries in that region influenced their economic evolution during the lastdecades.

Practical implications – Policy makers should primarily focus on improving institutional quality,which is likely to positively affect economic performance in SSA countries.

Social implications – Improving institutional infrastructure (enhancing rule of law and qualityregulation, improving contract enforcement, securing property rights and reducing uncertainty) play akey role in delivering long-run economic development and social prosperity.

Originality/value – The paper analyzes the impact of institutional quality on economic performanceusing data from 27 SSA countries.

Keywords Sub Saharan Africa, National economy, Economic performance, Economic development,Institutional quality, Panel data

Paper type Research paper

The current issue and full text archive of this journal is available at

www.emeraldinsight.com/0306-8293.htm

JEL classification – 043, C33The authors are grateful to an anonymous referee for insightful and constructive comments.

The usual disclaimer applies.

IJSE39,1/2

142

International Journal of SocialEconomicsVol. 39 No. 1/2, 2012pp. 142-160q Emerald Group Publishing Limited0306-8293DOI 10.1108/03068291211188910

1. IntroductionResearch into the long-term determinants of economic growth and development hasenjoyed a resurgence over the past decades. Led initially by Barro (1991), economistshave been trying to pinpoint the key factors responsible for fluctuations in economicperformance among countries and regions in the world. A wide range of factors (tradeopenness, government size, income distribution, etc.) have been identified, along withthe more traditional influences (factors of production and technology), to be the majordeterminants of economic performance (Levine and Renelt, 1992). Recently, new growththeories suggested that additional factors, such as institutional quality, may constitutepossible causes of the variation of economic performances between countries andregions (Easterly et al., 2004; Acemoglu et al., 2003a).

Indeed, in recent years, the link between the quality of a country’s institutions and theirlevel of economic development has turned out to be an important and growing area ofresearch in economic analysis (Glaeser et al., 2004; Chong and Calderon, 2000; Hall andJones, 1999; Keefer and Knack, 1997). The main idea underpinning this field of research isthat institutions define the “rules of the game” and the conditions under which economicagents operate in an economy. Hence, attention has focused particularly on the role ofinstitutions as a determinant of a country’s economic growth. As a result, numerousempirical studies have emerged attempting to provide additional evidence of the influenceof institutions and the mechanisms by which institutions may affect development[1].

In the relevant literature, there is no clear evidence regarding the role of institutionson growth (Pistor, 1995; Eweld, 1995; Weder, 1995). Hence, it becomes imperative toexplore the extent to which institutional features and other macro variables employed inconventional growth analysis form an appropriate framework and platform upon whichthe inherent dynamics of the economies in the Sub-Saharan African (SSA) region shouldbe sought. Moreover, the quest for the development determinants in this poor region ofthe world may suggest that the “conventional” fundamentals of economic theory maynot fully explain the SSA experience (Easterly and Levine, 1995). Hence, it is worthexploring how, in recent decades, the economic performances of countries in the regionhave been influenced by the vast changes in their economic and institutionalenvironment[2]. Most of the research concerning the economic performance of theSSA countries has mainly focused on macroeconomic features, whereas very few studieshave examined the interactions between institutions and their long-run economicperformance. These studies either used a small sample of countries (Savvides, 1995;Ojo and Oshikoya, 1995) or they are significantly hampered by the quality of data andthe methodology used (Lal and Myint, 1996; Srinivasan, 1994).

The present article attempts to explore the alleged link between institutional qualityand economic performance in a group of 27 SSA countries for the period 1984-2003. Theempirical work employs panel data analysis and the data used for the constructionof institution quality indexes, control and policy variables were collated frominternationally reliable sources such as the World Development Indicators fromThe World Bank (2007)[3] and International Country Risk Guide (ICRG) published by thePolitical Risk Services Group (1999)[4]. The rest of the article is organized as follows:Section 2 presents an overview of the literature on institutions and economicperformance, with more emphasis on studies pertaining to the SSA region.Section 3 touches on the empirical methodology used, whereas Section 4 elaborates onthe results obtained. Finally, Section 5 provides some concluding remarks.

The role ofinstitutions

143

2. Institutions and economic developmentThe “institutional quality hypothesis” contends that economic development is affectedby the institutional framework within which the economic agents interact to each otherin an economy[5]. According to this view, what matters most are the “rules of the game”in a society which are defined by the prevailing explicit and implicit behavioural normsand their ability to create appropriate incentives for desirable economic behaviour(Rodrik and Subramanian, 2003). Smith (1776) was the first to emphasize that nationswill prosper once they create the institutions that encourage entrepreneurship andsavings. However, most of the recent work on “institutional quality hypothesis” hasbeen associated with North’s (1990) effort to explore the relationship between economicperformance and institutional factors, such as political freedom, civil liberty, etc. Themajority of early studies focussed on the relationship between economic developmentand political institutions, which were measured through indices of political instabilityand violence. Over the years however, the development of new measures has highlighteda number of different institutional issues that will be addressed in the discussion thatfollows. .

One of the basic institutional characteristics addressed in the relevant literaturedeals with aspects of “economic freedom”. A group of studies by Gwartney et al. (1996)and Scully (1988) concluded that a country with economic freedom and policies thatprovide security of property, non-confiscatory taxes and enforcement of contractspromotes development and experiences better economic performance. Adkins andSavvides (2002), using data from 73 developed and developing countries for the period1975-1990, showed that institutions that promote economic freedom have a positiveeffect on economic performance. Similarly, Dawson (2003) concludes that, by positivelyaffecting investment, economic freedom promotes economic prosperity.

Another institutional characteristic which has been extensively introduced in therelative literature is “political freedom”. A strand of the extant empirical research hasscrutinized the extent to which more political freedom led to less income inequality andto economic prosperity. Studies by Easterly and Levine (2003), Sylwester (2002),Easterly (2001), Gradstein et al. (2001), Bourguignon and Verdier (2000), Barro (1999),Li et al. (1998), Granato et al. (1996) and Muller (1995), among many others, report thatcountries with greater civil liberties have lower levels of income inequality. In addition,Sokoloff and Engerman (2000) argue that high inequality provides high unbalancedaccess to economic opportunities, and that the direction of causality runs from inequalityto democracy and, in turn, to other institutions, without ruling out the reverse relation.In general, it is argued that high levels of inequality are incompatible with thedevelopment of a stable democracy in moderately developed countries[6].

In time, the debate on institutions moved beyond the measures of political instability orcivil liberties onto issues such as corruption, quality of bureaucracy, rule of law, etc. Withrespect to corruption, early works by Huntington (1968) and Leff (1964) supported theview that corruption may “grease the wheels” of bureaucracy and positively affectthe economy, whereas De Soto (1989) and Krueger (1974) argued the opposite. Althoughthese models gained some credibility in the 1980s, they suffered from a lack of empiricaljustification. Using survey data from the World Economic Forum (1997) and the TheWorld Bank (2007), Kaufmann and Wei (1999) and Wei (1999) found that corruptionactually increases the degree of regulatory burden on economic agents. Tanzi andDavoodi (1997) argued that corruption hampers also public spending on education, health,

IJSE39,1/2

144

maintenance of infrastructure, etc. whereas Wei (2000) and Hines (1995) observed thatforeign direct investment declines in the most corrupt countries[7].

With respect to rule of law, Knack and Keefer (1995) found a strong relationshipbetween economic growth and property rights (or rule of law in general), whereasSvensson (1998) established a triangular relationship between investment, propertyrights and political stability. Knack and Keefer (2002) followed a similar vein and arguedthat “social polarization” (income-land inequality and ethnic tensions) reduces contractand property rights, which in turn reduces growth. Barro (1996) has also been a stridentadvocate of the importance of property rights to economic performance. Moreover,Demirguc-Kunt and Detragiache (1998) found that the rule of law, quality of contractenforcement, quality of bureaucracy and the degree of corruption have a significantimpact in reducing the probability of banking crisis in 53 countries. However, it is worthpointing out that in the literature there is no consensus with respect to the relationshipbetween institution quality and growth performance. For instance, several works reportmixed results for the relation between economic prosperity and rule of law. Pistor (1995)argues that the two variables may be mutually reinforcing, and so causality may run inboth directions, whereas Eweld (1995) argues that both variables are autonomous.

The bulk of research on the determinants of economic performance emphasises therole of institutions. In fact, recently the focus of the research has shifted frommacroeconomic policies to institutions. Moreover, the empirical evidence of the researchsuggests that the positive correlation between good economic policies and development isthe result of good institutions. Acemoglu et al. (2003b) show that macroeconomicvariables (inflation, government spending, exchange rates, etc.) have no predictive powerin relation to economic performance, output volatility or cross-country variations inincome per capita, once institutional quality indexes are included in the analysis.Similarly, Easterly et al. (2004) notes that macroeconomic variables do not havea significant impact on economic development, after institutions are introduced into theanalysis. In the same line of argument, Rodrik et al. (2002), in their study of the impact ofinstitutions, geography and trade on income levels around the world show that thequality of institutions trumps all other variables. Once institutions are controlled, tradehas no direct effect on income, while geography has only weak direct effects at best.Moreover, they find that institutional quality has a positive and significant effect on tradeand vice versa, suggesting that trade can have an indirect effect on income levels byimproving institutional quality. Dollar and Kraay (2003), however, in supporting the roleof macroeconomic policies, claim that in the very long run better institutions and hightrade shares have a positive impact on economic growth, while in the short run, high tradeshares have a greater effect on economic performance than the quality of institutions.

2.1 SSA countries in economic growth literatureMost SSA countries got their independence in 1960s and immediately afterwards theystarted to build their own social and economic institutions and put together policies toaccelerate their economic prosperity. The region witnessed rapid economic developmentin the mid-1960s to early 1970s. Financial sectors operated under tight controls andinvestment was often directed into state-owned enterprises or “strategic” sectors. Thestate’s growing role in the economy was boosted by a big expansion of social services, aspost-colonial governments focused on narrowing the rural-urban income gap inheritedfrom colonial times. The oil-price shock in the 1970s and the debt crisis in early 1980s

The role ofinstitutions

145

exposed dramatically the structural economic and political weaknesses of SSAcountries. As a result, during this period the countries of the region witnesseddeterioration in their economic performance, sharp accumulation of external foreigndebt and persistent corrosion in trade terms. In the second half of the 1980s, many SSAcountries have started to implement economic reforms by introducing belt-tighteningpolicies such as devaluation, reduction in budget deficits, abolishment of food, utilitiesand transport subsidies, etc. Many countries tried for quick results, but due to the lack ofefficient political and economic environments, the outcomes of their applied policieswere weak and, at the same time, triggered domestic and neighbouring conflicts (AfricanDevelopment Report, 2006)[8].

In the growth literature, we observe that SSA countries usually form a part of thecross-country samples used in empirical work and, in general, the region plays adistinctive role (Aron, 2000) for two reasons: SSA development has been the slowest of anyregion in the world, while poverty is large and deepening; also SSA countries possess avery weak public and private institutional framework. A number of cross-country growthstudies treat SSA as a regional dummy variable. Hence, a stream of empirical work hasbeen generated in an attempt to explain the growth performance in SSA, with most ofthese studies focusing on macroeconomic policy factors with only a few attempting toinvestigate directly the role of the institutional quality. It is worth mentioning at this pointthat Easterly and Levine (1995) in their study suggested that the “conventional”fundamentals of economic growth theory may not fully explain the SSA experience.

Savvides (1995) covering 28 countries for the period 1960-1987 and using panelanalysis finds that their economic performance is correlated to trade openness,investment, initial income, schooling and growth of government. Ojo and Oshikoya(1995) studying the economic performance of 17 countries and applying ordinary leastsquares (OLS) and generalized least squares (GLS) techniques find that investment,external debt, population growth, human capital and proxies for macroeconomicenvironment significantly determine the long-run development in Africa.Gyimah-Brempong and Traynor (1999) explore the relationship between instability,investment and economic growth in the SSA region, and through a dynamic panelapproach they confirm the inverse relationship between political instability andeconomic growth. In addition, Gyimah-Brempong (2002), in a panel data context,observe that corruption affects negatively the investment and growth performance,whereas it affects positively the income inequality variable in a sample of 22 SSAcountries. From a historical perspective, Gennaioli and Rainer (2005) test the theory thatcountries whose ethnic groups were more centrally organized before colonization havebetter public goods provision and find the evidence to be supportive. Along the samelines, Bertocchi and Canova (2002), using factors such as legal origin and degree ofeconomic penetration of former colonial powers, found that colonial legacy explains thedifferent growth performances in Africa.

3. Methodology and dataDespite substantial improvements, the quality and quantity of data from the SSA regioncreate important barriers to advances in research. This may explain partially why thisregion remains marginalized in the academic literature, and why it is presented by a regiondummy in most cross-country development studies. The present work aims atcontributing to this literature by investigating the link between institutional quality and

IJSE39,1/2

146

other control and policy variables on the one hand, and economic performance on the other,in a group of 27 SSA Countries[9] for the period 1984-2003 using panel data analysis[10].

The general model specification envisaged in the undertaken analysis includes threecategories of variables which in the relevant literature appear to play an important rolein explaining economic performance. The three broad categories are: institutions,macroeconomic policies, and other control variables specific for each country. The modelspecification used in the analysis is set up as follows:

yit ¼ b0i þ b1 Instit þ b2 Policyit þþb4Controlit þ 1it ð1Þ

where i is the country dimension (i ¼ 1, . . . ,N); t is the time dimension (t ¼ 1, . . . ,T); yit isthe growth rate of GDP per capita income; Instit is a measure of institutional quality;Policyit is a measure that approximates macroeconomic environment; Controlit is a set ofother country-specific variables.

As far as the dependent variable is concerned this is reflected by GPGDP, whichstands for the growth rate of real per capita GDP in constant US$2,000. In various modelspecifications of our study, four different indexes of institutional quality are employed inan attempt to capture the effect of as many possible aspects of the effects of institutionson economic performance. The first index (SOEC) captures the degree of public’ssatisfaction with government economic policies, covering a broad spectrum of factorsranging from infant mortality and medical provision to housing and interest rates[11].According to conventional economic theory, countries with better socioeconomicconditions are expected to experience higher growth. The second index (CORPT) is anestimate of corruption which shows the extent to which government officials demandbribes connected with import and export licenses, exchange controls, tax assessment,police protection, loans, etc. On the basis of the relevant literature there is expected to bea negative relation between this index and the growth rate, although as mentionedabove, some authors and studies argued for the opposite relationship. The third index(GOVS) measures government’s ability to stay in office and depends upon factors suchas type of governance, cohesion of government and governing parties, elections,command of administration. It is expected that countries with better and longergovernments will experience higher growth. The final index for institutional qualityused in the analysis isETHNCwhich is a measure of the degree of tension attributable toracial, national or language divisions. The presence of tensions of any kind is expected tohave a negative impact on the growth performance of an economy.

Following the relevant literature, we introduce a number of macroeconomic policymeasures in our attempt to explain SSA cross-country differences in economicperformance. Among the policy variables we include inflation INF as an indicator ofmacroeconomic stability which is proxied by consumer price index (CPI). It is expectedthat high inflation distorts economic activity and reduces economic growth. In addition,government size (GOV) measured as a percentage of government spending to GDP isused in an attempt to capture the stimulating effect of an expansionary governmentpolicy. Given the existing theoretical approaches (Keynesian and mainstream),an ambiguous relationship is possible.

An additional set of explanatory variables (control variables in our analysis) oftenused in this type of research refers to a set of variables that describe the credit conditions,trade openness, population growth, etc. of the country. Among these variables weinclude an index of trade openness (TROP) measured by the sum of imports and exports

The role ofinstitutions

147

as a percentage of nominal GDP (Levine et al., 2000). A country’s open trade policies mayincrease profitability and, by extension, the incentives to invest and the growth of acountry. Also, foreign direct investment (FDI), measured as a share of net inflows toGDP, is considered an important influence on growth performance for these countriesand thus it is included in our work. An additional factor, i.e. credit provided by thebanking sector (CBSY) measured as a share of the domestic credit provided bythe banking sector to GDP included also in the pool of our control variables to capturethe domestic financial environment. A variable of population growth (POPG), the logdifference of population, is also introduced in our empirical work in order to capture itseffect on economic performance. The impact of the latter on growth is expected to be of anegative nature. Finally, foreign aid (AID) as a percentage to gross national incomewraps up the string of variables used to explain growth performance in SSA countries.Table AI in Appendix 2 contains all variables used in our model specifications, as well astheir expected signs in accordance with the traditional growth theory.

4. Empirical resultsA quick inspection of the correlation matrix of the institutional quality indices (Table AIIin Appendix 2) points towards a relatively high correlation between corruption, ethnictensions and socioeconomic conditions, while government stability exhibits a rather lowcorrelation.

In view of such a development, it was deemed essential that four different modelspecifications be set up, each one consisting of only one institutional variable. Severalestimated specifications following a general to specific approach, were estimated. Whatis reported in Table AIII in Appendix 2 is a presentation of the equations that wereselected on the basis of the Schwarz (SIC) and Akaike (AIC) information criteria.

From Table AIII, we observe that institutional quality variables are basically themain and significant ones in explaining the growth performance of the 27 SSA countriesduring the period 1984-2003. More specifically, socioeconomic conditions variable(model 1) is found to exert a positive and significant impact on economic performance.This indicates that the higher the satisfaction of the general public with respect tospecific government’s economic policies covering issues as health improvement, medicaland housing provision, etc. the better the growth performance of the economy. Withrespect to corruption (model 2), our results show that it has no significant impact togrowth performance. However, its positive sign may offer some support to thearguments of Huntington (1968) and Leff (1964) that corruption may “grease the wheels”of bureaucracy and positively affect the economy. Moreover, the positive coefficient ofcorruption is in accordance with the results of Li et al. (2000), Tanzi and Davoodi (1997)and Mauro (1995). It is also consistent with the result of Ndikumana (2007) andGyimah-Brempong (2002) in their studies for Africa.

With respect to government stability variable (model 3 in Table AIII) the regressionresults demonstrate that it exerts a statistically significant and positive impact on theeconomic performance of SSA region. This index, as already mentioned measuresthe government’s ability to stay in office and depends upon factors such as type ofgovernance, cohesion of government and governing parties, elections, and commandof badministration. In the relevant literature, it is expected that countries with better andlonger governments will experience higher economic performance, an idea confirmed bythe derived results.

IJSE39,1/2

148

Model 4 (Table AIII, Appendix 2) reports positive and statistically significant resultsfor the variable of ethnic tension. The index ETHNC, as mentioned above, is a measureof the degree of tension attributable to racial, national or language divisions. Out of the27 countries in our sample, seven[12] experienced ethnic conflicts during the periodunder investigation. The presence of tensions of any kind is expected to exert a negativeimpact on the growth performance of an economy and our result is incompatible withthese a priori economic postulations. In his study of African countries, Collier (2000)finds that the probability of conflict is highest (28 per cent) in countries with a dominantethnic group (45-90 per cent) of the population. This finding is echoed also in Knack andKeefer (2002) who suggest that the ethnic tension is highest when the largest ethnicgroup in a country has about a 37 per cent share of population. The result derived in ouranalysis indicates that ethnic conflict has many and probably contradictoryramifications for economic development, which have to be introduced into theanalysis in order to arrive at a conclusion regarding how and why ethnic tension mayaffect growth performance and in what direction. In passing, it should be stressed thatthe positive impact established in our analysis might be attributed to a certain extent tothe nature of our empirical-panel analysis, as well as to the small number of countriesexperiencing ethnic tension in our sample. We also feel that in all likelihood a negativeimpact is evident in country-specific regression models.

With respect to policy and control variables that we included in our analysis, theresults confirm the argument posed by Acemoglu et al. (2003a) according to whichmacroeconomic variables (i.e. inflation, government spending, exchange rates, etc.)have no predictive power in relation to growth, output volatility or cross-countryvariations in income per capita, once institutional quality indexes are included in theanalysis. Easterly et al. (2004) have also arrived to the conclusion that macroeconomicpolicies do not affect economic performance after accounting for institutions in theanalysis. Similarly, Rodrik et al. (2002) show that once institutions are introducedinto the analysis, macroeconomic variables, such as trade, have no direct effect onincome.

In our analysis, the only control variable which is consistently significant in all fourmodels is CBS, which measures the share of private sector credit and money depositrelative to GDP. The empirical findings with regard to the variable capturing thefinancial sector developments indicate that the coefficient of credit to the private sectorby banks is negative and significant in all model specifications of our panel analysis. Thisresult implies that an increase in the credit or deposits of the private sector does not boostprivate investment as the conventional economic theory suggests which in turn willpropel economic development. The specific finding of the non-positive impact of CBS forSSA countries may imply that the institutional environment surrounding their privatesector is characterised by a lack of strong business and professional organisations, lackof bank’s ability to channel credit to productive private activities, lack of bank’spersonnel with experience and expertise in credit analysis, etc. In addition, the negativeand significant relation between CBS and growth found in SSA countries allows us toinfer that the existing financial capacity of the private sector is directed to consumption ofluxury goods which are usually imported, exerting an overall negative impact on thegrowth performance in these underdeveloped countries. Studies of the effect of financialdevelopment on growth for the SSA region yield mixed results. Some studies suggest thatfinancial factors have enhanced economic growth by positively influencing investment

The role ofinstitutions

149

(Ndikumana, 2000); others indicate that the impact of financial development on growthhas been rather negligible (Adjasi and Biekpe, 2006; Anoruo and Ahmad, 2001). In thisrespect, our empirical results also add to the findings of Collier and Gunning (1999) whofind that deficiencies in SSA financial systems lead to a negligible impact of finance ondevelopment, and to those of Reinhart and Tokatlidis (2003) which detect no positiveeffects of financial liberalization on economic growth in SSA.

Continuing to other macro variables employed in the analysis, we see that althoughthe coefficient of inflation variable has the negative sign one would expect from thetheory, it remains insignificant for all models. This result may be attributed to largestructural fiscal deficits and to erratic monetary and exchange rate policies that weakenthe financial system in many SSA countries. Similarly, the government consumptionvariable displays a negative relation with economic growth, and remains not significantfor all model specifications in our panel analysis. The negative sign indicates that anincrease in government lowers the economic performance of an economy, which in a waycontradicts the Keynesian proposal that government spending exerts a positive growtheffect on an economy by boosting effective demand. However, another argument basedon economic theory may propose that increases in government spending deter privateinvestment (crowding out effect) and thus have a negative impact on growthperformance. The non-significant negative effect of government spending in our work isin accordance with the results obtained by Ghura (1995) and Nelson and Singh (1994).More specifically, testing the relation between government consumption and economicperformance for developing countries, Ghura (1995) finds a negative relation, whereas,Nelson and Singh (1994) find no relation between an increase in government spendingand the growth rate in GDP.

Furthermore, we observe that for all model specifications in Table AIII the controlvariable of trade openness does not exert any significant effect on economic performance,although all the estimated coefficients are positive. This outcome is in accordance withRodrik et al. (2002), whose shows that, once institutions are introduced in the analysis,trade variables have no direct effect on income. In addition, we may argue that there is avery weak trade structure in SSA economies which cannot propel their growth. Thecoefficient of the population control variable is found to be negative, as expected, butstatistically not significant.

The variables FDI andAID exert no influence whatsoever as the results in Table AIII(Appendix 2) report. More specifically, FDI is found to have a positive impact in allmodel specifications but with no statistical significance. Studies for the region haveshown that SSA countries do not form a group of countries which attract high levels ofFDI and its limited effect could be attributed to the lack of synergies between foreign anddomestic investment (Ndikumana and Verick, 2008). Similarly, the foreign aid variableis found to exert a positive but not significant impact on SSA growth performance,a result which may be partly explained by the fact that these countries lack a goodmacroeconomic environment which forms the prerequisite for foreign aid to havea positive impact on growth. As Leonard and Scott (2003) argue over 50 per cent of aidflows in the region is directed toward debt repayment, which has become a great barrierfor many SSA countries.

Table AIV in Appendix 2 provides a list of the key factors that appear to conditioneconomic development. An inspection of Table AIV suggests that the institutionalvariables assume a key role in the process of economic development.

IJSE39,1/2

150

5. Conclusions and policy recommendationsOur investigation of the determinants of economic performance in a group of 27 SSACountries during 1984-2003 using panel data analysis has showed that amonginstitutional quality and macroeconomic variables, the former displayed a consistentinfluence on the performance of this highly underdeveloped region of the world.Variables purported to capture government stability and socioeconomic conditionsappear to be significant and positively signed, suggesting that both factors are crucial toensuring regional growth. Corruption, on the other hand, does not appears to play animportant role in the region as it found to be not significant, bearing a positive sign. Withrespect to the ethnic tensions institution quality index, our results show that it exerts astatistically significant and positive influence on economic performance. Even thoughthis outcome stands at stark contrast to prior economic postulates (Knack and Keefer,2002), it may also indicate that ethnic tensions might have multidimensional economicconsequences and, thus, we cannot draw safe conclusions regarding how and why ethnictensions may affect the growth performance of an economy.

With respect to other macroeconomic variables, our empirical findings showed thatonly CBS exerts a statistically significant but negative influence, an outcome which is inaccordance with other studies indicating the impact of financial sector on growthis either negligible (Adjasi and Biekpe, 2006; Anoruo and Ahmad, 2001; Collier andGunning, 1999) or not positive (Reinhart and Tokatlidis, 2003). The other policyand control variables engaged in the analysis displayed the expected influence, althoughthey turned to be not statistically significant. Hence, the empirical evidence of thisresearch suggests that once institutions are introduced in the analysis, the correlationbetween good policies and growth disappears. This result is also found in Easterly et al.(2004), Acemoglu et al. (2003a) and Rodrik et al. (2002), whose studies of the impact ofinstitutions onto income levels around the world show that the quality of institutionsvariable trumps everything else. Once institutions are controlled, other macroeconomiccharacteristics turn out to have no significant effect on income.

Additionally, the present work suggests that the quest for the developmentperformance determinants in this poor region of the world may not follow the“conventional” fundamentals of economic theory which, ultimately, may not be able tofully explain the SSA experience (Easterly and Levine, 1995). Hence, it is worthexploring how the vast changes that the countries of the region experienced in theireconomic and institutional environments influenced their economic evolution in recentdecades. In terms of policy implications, this study suggests that policy makers shouldfocus primarily on improving institutional quality, which is likely to affect positivelyeconomic performance in SSA countries. Several methods of improving institutionalinfrastructure play key roles in delivering long-run economic development: for example,enhancing rule of law and quality of regulation, improving contract enforcement,securing property rights, and reducing uncertainty.

Notes

1. Brunt (2007), Kostevc et al. (2007), Butkiewicz and Yanikkaya (2006), Vukotic and Bacovic(2006). Different variables (i.e. political instability, corruption, bureaucracy, characteristics ofpolitical regimes, rule of law, property rights protection, etc.) have been employed to explorethe effect of institutional quality on growth. The wide range of indicators for the “institution

The role ofinstitutions

151

quality” variable is due to that ‘institution’ is a broad concept which is defined differently bythe various strands (i.e. political, social, economic, etc.) of science.

2. In recent decades, the majority of the SSA countries dramatically changed their policies;from high protectionism and state-led import substitution industrialization policies, theyreduced trade barriers, applied market-friendly reforms, applied privatizations, reduceddomestic competitive barriers, etc.

3. WDIs provide the most reliable and comprehensive set of hard and soft data on the SSAcountries. They document the problem of insufficient data for SSA countries over the past 20years. All data on per capita GDP growth rate, population growth, trade openness, inflation,bank credit, FDI, Aid as a percent of GNI and government spending are taken from WorldBank’s Development Indicators 2007 CD-ROM.

4. On a monthly basis since 1980, ICRG has produced political, economic and financial riskratings for countries important to international business. ICRG now monitors 140 countries.Data on institutional quality variables come from this source (www.prsgroup.com/ICRG.aspx).

5. According to Rodrik et al. (2002) the institutional characteristics of an economy have a muchbroader impact on its development than the geographic and natural resource characteristics.

6. Acemoglu and Robinson (2000), looking at the historical record of several Western countries,argue that society democratizes due to social pressure that emerges when inequality is risingdue to the impossibility of the poor to invest in human capital. Once inequality reaches acritical threshold and a threat for a revolution intensifies, the elite is forced to extend politicalrights to the masses. Once they are free to vote, they do chose to implement policies aimed atproducing a more fair income distribution and increased schooling (Savoia et al., 2004).

7. Using a variety of institutional measures, Lambsdorff (2003) divided total investment into itsdomestic component (savings) and its overseas component (net capital inflows). While hecould not determine a significant relationship with respect to domestic savings, he find thatproperty rights (rule of law) was a significant factor in capital inflows, with the rationale beingthat overseas firms would not invest in countries with a poor system of property rights.

8. Further readings on Africa’s growth performance can be found in Collier and Gunning(1999), Easterly and Levine (1995), Elbadawi and Ndulu (1995), Sachs and Warner (1995) andRavallion (1995).

9. They are: Angola, Botswana, Burkina Faso, Cameroon, Congo, Rep., Congo, Dem. Rep., Coted’Ivoire, Ethiopia, Gabon, Gambia, Ghana, Guinea, Guinea-Bissau, Kenya, Madagascar,Malawi, Mali, Mozambique, Nigeria, Niger, Senegal, Sierra Leone, Sudan, Tanzania, Togo,Zambia and Zimbabwe.

10. Panel models have a wide range of economic applications including empirical models ofeconomic growth, since they provide information about the variation across individual units(SSA countries in our case) over time (Verbeek, 2004; Greene, 2003; Hsiao, 2003) and allow us tocontrol for unobserved (cross-sectional) heterogeneity in the adjustment dynamics betweencountries (Bond, 2002). Hence, the panel methodology makes allowances for individual effects,uses more observations and more degrees of freedom. The standard models, i.e. pooledcross-sectional time series, fixed and random effects, are estimated. In Appendix 1, we brieflypresent the panel data approach.

11. This index contains several other important factors to growth theory, such as the existenceor not of human capital, etc.

12. Angola, Congo, Rep., Cote d’Ivoire, Ethiopia, Guinea-Bissau, Sierra Leone, Sudan.

IJSE39,1/2

152

References

Acemoglu, D. and Robinson, J. (2000), “Why did the west extend the franchise: democracy,inequality and growth in historical perspective”, Quarterly Journal of Economics, Vol. 115,pp. 1167-99.

Acemoglu, D., Aghion, P., Griffith, R. and Zilibotti, F. (2003a), “Technology, hold-up, and verticalintegration: what do we learn from micro data?”, mimeo, IFS, London.

Acemoglu, D. et al. (2003b), “Institutional causes, macroeconomic symptoms: volatility, crisesand growth”, Journal of Monetary Economics, Vol. 50, January, pp. 49-123.

Adjasi, C.K.D. and Biekpe, N.B. (2006), “Stock market development and economic growth:the case of selected African countries”, African Development Review, Vol. 18 No. 1,pp. 141-61.

Adkins, M. and Savvides, A. (2002), “Institutions, freedom, and technical efficiency”, SouthernEconomic Journal, Vol. 69 No. 1, pp. 92-108.

African Development Report (2006), Aid, Debt, Relief and Development in Africa, AfricanDevelopment Bank Group, Abidjan.

Anoruo, E. and Ahmad, Y. (2001), “Causal relationship between domestic savings and economicgrowth: evidence from seven African countries”, African Development Review, Vol. 13No. 2, pp. 238-49.

Arellano, M. and Bond, S.R. (1991), “Some tests of specification for panel data: Monte Carloevidence and an application to employment equations”, Review of Economic Studies,Vol. 58, pp. 277-97.

Arellano, M. and Bover, O. (1995), “Another look at the instrumental variable estimation oferror-component models”, Journal of Econometrics, Vol. 68, pp. 29-45.

Aron, J. (2000), “Growth and institutions: a review of evidence”, World Bank Research Observer,Vol. 15 No. 1, pp. 99-135.

Barro, R.J. (1991), “Economic growth in cross-section of countries”, Quarterly Journal ofEconomics, Vol. 106 No. 2, pp. 407-43.

Barro, R.J. (1996), “Democracy and growth”, Journal of Economic Growth, Vol. 1 No. 1, pp. 1-27.

Barro, R.J. (1999), “Determinants of democracy”, Journal of Political Economy, Vol. 107 No. 2,pp. 158-83.

Bertocchi, G. and Canova, F. (2002), “Did colonization matter for growth? An empiricalexploration into the historical causes of Africa’s underdevelopment”, European EconomicReview, Vol. 46 No. 10, pp. 1851-71.

Bond, S.R. (2002), “Dynamic panel data models: a guide to micro data methods and practice”,Portuguese Economic Journal, Vol. 1 No. 2, pp. 141-62.

Bourguignon, F. and Verdier, T. (2000), “Oligarchy, democracy, inequality and growth”, Journalof Development Economics, Vol. 62 No. 2, pp. 285-313.

Brunt, L. (2007), “Property rights and economic growth: evidence from a natural experiment”,CEPR Discussion Papers No. 6404.

Butkiewicz, J.L. and Yanikkaya, H. (2006), “Institutional quality and economic growth:maintenance of the rule of law or democratic institutions, or both?”, Economic Modelling,Vol. 23 No. 4, pp. 648-61.

Chong, A. and Calderon, C. (2000), “Causality and feedback between institutional measures andeconomic growth”, Economics and Politics, Vol. 12 No. 1, pp. 69-81.

Collier, P. (2000), “Rebellion as a quasi-criminal activity”, Journal of Conflict Resolution, Vol. 44,pp. 839-54.

The role ofinstitutions

153

Collier, P. and Gunning, J.W. (1999), “Explaining African economic performance”, Journal ofEconomic Literature, Vol. 37 No. 1, pp. 64-111.

Dawson, J. (2003), “Causality in the freedom-growth relationship”, European Journal of PoliticalEconomy, Vol. 19 No. 3, pp. 479-95.

Demirguc-Kunt, A. and Detragiache, E. (1998), “The determinants of banking crises in developedand developing countries”, IMF Working Paper, No.106.

De Soto, H. (1989), The Other Path: The Invisible Revolution in the Third World, HarperCollins,New York, NY.

Dollar, D. and Kraay, A. (2003), “Institutions, trade and growth”, Journal of Monetary Economics,Vol. 50 No. 1, pp. 133-62.

Easterly, W. (2001), “The middle class consensus and economic development”, Journal ofEconomic Growth, Vol. 6 No. 4, pp. 317-35.

Easterly, W. and Levine, R. (1995), “Africa’s growth tragedy: a retrospective, 1960-89”,World Bank Policy Research Working Paper, No. 1777.

Easterly, W. and Levine, R. (2003), “Tropics, germs, and crops: how endowments influenceeconomic development”, Journal of Monetary Economics, Vol. 50 No. 1, pp. 3-39.

Easterly, W., Levine, R. and Roodma, D. (2004), “Aid, policies, and growth: comment”, AmericanEconomic Review, Vol. 94 No. 3, pp. 774-80.

Elbadawi, I. and Ndulu, B. (1995), “Long-term development and sustainable growth inSub-Saharan Africa”, in Lundahl, M. and Ndulu, B. (Eds), New Directions in DevelopmentEconomics: Growth, Environmental Concerns and Governance in the 1990s, Routledge,London.

Eweld, W. (1995), “Comparative jurisprudence (II): the logic of legal transplants”, AmericanJournal of Comparative Law, Vol. 43 No. 4, pp. 489-510.

Gennaioli, N. and Rainer, I. (2005), “The modern impact of pre-colonial centralization in Africa”,available at: http://ssrn.com/abstract¼848164

Ghura, D. (1995), “Macro policies, external forces, and economic growth in Sub-Saharan Africa”,Economic Development and Cultural Change, Vol. 43 No. 4, pp. 759-78.

Glaeser, E.L., La Porta, R., Lopez-de-Silanes, F. and Shleifer, A. (2004), “Do institutions causegrowth?”, Journal of Economic Growth, Vol. 9 No. 2, pp. 271-303.

Gradstein, M., Milanovic, B. and Ying, Y. (2001), “Democracy and income inequality: an empiricalanalysis”, Paper No. 2561, Country Economics Department, World Bank, Washington, DC.

Granato, J., Inglehart, R. and Leblang, D. (1996), “The effect of cultural values on economicdevelopment: theory, hypotheses, and some empirical tests”, American Journal of PoliticalScience, Vol. 40 No. 3, pp. 607-31.

Greene, W. (2003), Econometrics Analysis, 5th ed., Prentice-Hall, New York, NY.

Gwartney, J., Lawson, R. and Block, W. (1996), Economic Freedom of the World: 1975-95,The Fraser Institute, Vancouver.

Gyimah-Brempong, K. (2002), “Corruption, economic growth, and income inequality in Africa”,Economics of Governance, Vol. 3 No. 3, pp. 183-209.

Gyimah-Brempong, K. and Traynor, T.L. (1999), “Political instability, investment and economicgrowth in Sub-Saharan Africa”, Journal of African Economies, Vol. 8 No. 1, pp. 52-86.

Hall, R. and Jones, C. (1999), “Why do some countries produce so much more output per workerthan others?”, Quarterly Journal of Economics, Vol. 114 No. 1, pp. 83-116.

Hines, J.R. (1995), “Forbidden payment: foreign bribery and American business after 1977”,NBER Working Paper, No. 5266.

IJSE39,1/2

154

Holtz-Eakin, D. et al. (1990), “Estimating vector autoregressions with panel data”, Econometrica,Vol. 56, pp. 1371-95.

Hsiao, C. (2003), Analysis of Panel Data, 2nd ed., Cambridge University Press, New York, NY.

Huntington, S.P. (1968), Political Order in Changing Societies, Yale University Press,New Haven, CT.

Kaufmann, D. and Wei, S. (1999), “Does ‘grease payment’ speed up the wheels of commerce?”,NBER Working Paper, No. 7093.

Keefer, P. and Knack, S. (1997), “Why don’t poor countries catch up? A cross national test ofinstitutional explanation”, Economic Inquiry, Vol. 35 No. 3, pp. 590-602.

Knack, S. and Keefer, P. (1995), “Institutions and economic performance: cross-country testsusing alternative institutional measures”, Economics and Politics, Vol. 7 No. 3, pp. 207-27.

Knack, S. and Keefer, P. (2002), “Polarization, politics, and property rights: links betweeninequality and growth”, Public Choice, Vol. 111 Nos 1/2, pp. 127-54.

Kostevc, C., Redek, T. and Susjan, A. (2007), “Foreign direct investment and institutionalenvironment in transition economies”, Transition Studies Review, Vol. 14 No. 1, pp. 40-54.

Krueger, A.O. (1974), “The political economy of the rent-seeking society”, American EconomicReview, Vol. 64 No. 3, pp. 291-303.

Lal, D. and Myint, H. (1996), The Political Economy of Poverty, Equity and Growth, ClarendonPress, Oxford.

Lambsdorff, J. (2003), “How corruption affects persistent capital flows”, Economics ofGovernance, Vol. 4 No. 3, pp. 229-43.

Leff, N. (1964), “Economic development through bureaucratic corruption”, American BehavioralScientist, Vol. 8 No. 3, pp. 6-14.

Leonard, D. and Scott, S. (2003), Africa’s Stalled Development, Lynne Reinner Publishers,Boulder, CO.

Levine, R. and Renelt, D. (1992), “A sensitivity analysis of cross-country regressions”, AmericanEconomic Review, Vol. 82 No. 2, pp. 942-63.

Levine, R. et al., (2000), “Financial intermediation and growth: causality and causes”, Journal ofMonetary Economics, Vol. 46 No. 1, pp. 31-77.

Li, H., Xu, L.C. and Zou, H. (2000), “Corruption, income distribution and growth”, Economics andPolitics, Vol. 12 No. 2, pp. 155-85.

Li, H., Xu, L.C., Squire, L. and Zou, H. (1998), “Explaining international and intertemporalvariations in income inequality”, Economic Journal, Vol. 108 No. 446, pp. 26-43.

Mauro, P. (1995), “Corruption and growth”, The Quarterly Journal of Economics, Vol. 110 No. 3,pp. 681-712.

Muller, E.N. (1995), “Democracy, economic development, and income inequality”, AmericanSociological Review, Vol. 60 No. 6, pp. 966-82.

Ndikumana, L. (2000), “Financial determinants of domestic investment in Sub-Saharan Africa:evidence from panel data”, World Development, Vol. 28 No. 2, pp. 381-400.

Ndikumana, L. (2007), “Corruption and pro-poor growth outcomes: evidence and lessons forAfrican countries”, Governance and Pro-Poor Growth, African Economic ResearchConsortium, Nairobi.

Ndikumana, L. and Verick, S. (2008), “The linkages between FDI and domestic. Investment:unravelling the development impact of foreign investment in Sub-Saharan Africa”, IZAWorking Paper, No. 3296.

The role ofinstitutions

155

Nelson, M.A. and Singh, R.D. (1994), “Deficit-growth connection: some recent evidence fromdeveloping countries”,EconomicDevelopment andCultural Change, Vol. 43 No. 1, pp. 167-91.

North, D. (1990), Institutions, Institutional Change and Economic Performance, CambridgeUniversity Press, Cambridge.

Ojo, O. and Oshikoya, T. (1995), “Determinants of long term growth: some African results”,Journal of African Economies, Vol. 4 No. 2, pp. 163-91.

Pistor, K. (1995), “Law meets the market: matches and mismatches in transitioneconomies”, World Development Report 1996: From Plan to Market, World Bank,Washington, DC.

Political Risk Services Group (1999), International Country Risk Guide, Brief Guide to the RatingSystem, available at: www.icrgonline.com

Ravallion, M. (1995), “Measurement social welfare with and without poverty lines”, AmericanEconomic Review, Vol. 84 No. 2, pp. 359-64.

Reinhart, C.M. and Tokatlidis, I. (2003), “Financial liberalisation: the African experience”, Journalof African Economies, Vol. 12 No. 2, pp. 53-88.

Rodrik, D. and Subramanian, A. (2003), “The primacy of institutions”, Finance & Development,Vol. 40 No. 2, pp. 31-4.

Rodrik, D., Subramanian, A. and Trebbi, A. (2002), “Institutions rule: the primacy of institutionsover geography and integration in economic development”, National Bureau of EconomicResearch Working Paper No. 9305.

Sachs, J.D. and Warner, A. (1995), “Economic reform and the process of global integration”,Brookings Papers on Economic Activity, Vol. 1 No. 1, pp. 1-118.

Savoia, A., Josly, E. and Andrew, M. (2004), “The relationship between inequalityand institutions: an empirical analysis”, working paper, University of Bath, Bath.

Savvides, A. (1995), “Economic growth in Africa”, World Development, Vol. 23 No. 3, pp. 449-58.

Scully, G.W. (1988), “‘The institutional framework and economic development’”, Journal ofPolitical Economy, Vol. 96 No. 3, pp. 652-62.

Smith, A. (1776), The Wealth of Nations, The Modern Library, New York, NY.

Sokoloff, K.L. and Engerman, S.L. (2000), “History lessons: institutions, factor endowments, andpaths of development in the new world”, Journal of Economic Perspectives, Vol. 14 No. 3,pp. 217-32.

Srinivasan, T.N. (1994), “Database for development analysis: an overview”, Journal ofDevelopment Economics, Vol. 44 No. 1, pp. 3-27.

Svensson, J. (1998), “Investment, property rights and political instability: theory and evidence”,European Economic Review, Vol. 42 No. 7, pp. 1317-41.

Sylwester, K. (2002), “Democracy and changes in income inequality”, International Journal ofBusiness and Economics, Vol. 1 No. 2, pp. 167-78.

Tanzi, V. and Davoodi, H. (1997), “Corruption, public investment and growth”, IMFWorking Paper No. WP/97/139.

Verbeek, V. (2004), A Guide to Modern Econometrics, 3rd ed., Wiley, New Work, NY.

Vukotic, V. and Bacovic, M. (2006), “Economic freedom and economic growth in South EastEurope”, Transition Studies Review, Vol. 13 No. 1, pp. 81-91.

Weder, B. (1995), “Legal systems and economic performance: the empirical evidence”,in Mowat, M., Malik, W.H. and Dakolias, M. (Eds), Judicial Reform in LatinAmerica and the Caribbean, World Bank Technical Paper, No. 280, World Bank,Washington, DC.

IJSE39,1/2

156

Wei, S. (1999), “Corruption in economic development: beneficial grease, minor annoyanceor major obstacle?”, World Bank Discussion Paper, No. 2048, World Bank,Washington, DC.

Wei, S. (2000), “How taxing is corruption on international investors?”, Review of Economics andStatistics, Vol. 82 No. 1, pp. 1-11.

(The) World Bank (2007), World Development Indicators, The World Bank, Washington, DC.

World Economic Forum (1997), Global Competitiveness Report, Oxford University Press, Oxford.

Appendix 1Models of panel dataFor the estimation of our model, we use a data-set which consists of N cross-sectional units,denoted i ¼ 1, . . . , N, observed at each of T time periods, denoted t ¼ 1, . . . , T. We have a total ofTN observations and y is a (TN £ 1) vector of endogenous variables and X is a (TN £ k) matrixof exogenous variables which does not include a column of units for the constant term. In ourcontext, we use annual data for 27 SSA countries from 1984-2003 (so N ¼ 27; T ¼ 19).

The generalized regression model provides our basic framework:

yit ¼ ai þ bixit þ 1it ; where 1it , i:i:d:ð0:s 2i Þ ðA1Þ

where ai is a scalar, and bi is a (k £ 1) vector of slope coefficients. We assume similar variancesbetween countries, i.e. s 2

i ¼ s 21 ;i; and zero covariances between countries, i.e.Covð1it; 1jsÞ ¼ 0

for i – j. We distinguish three cases of (A1).The pooled model. When both a and b are common between regions, we get the pooled model:

y ¼ iaþ Xbþ 1; ðA2Þ

where i is a (TN £ 1) column vector of ones. For this simple model, the GLS estimator reduces topooled OLS.

Code of variable Definition of variables Expected sign

Dependent variableGPGDP Growth rate of real per capita GDPIndependent variablesInstitutional qualitySOEC Socioeconomic conditions þCORR Corruption –GOVS Government stability þETHNT Ethnic tensions –

Policy variablesINF Inflation –GOV Government spending (% GDP) 2 or þ

Control variablesTROP Trade openness (% GDP) þCBSY Bank credit (% GDP) þPOPG Population growth –FDI Foreign direct investment (% GDP) þAID Foreign aid (% GNI) þ

Table AI.Variables and

expected signs

The role ofinstitutions

157

The fixed effects modelThe fixed effects (or least squares dummy variables model, or within model) isbased on the notion that differences across countries can be captured in differences in theconstant term:

yit ¼ ai þ b 0xit þ 1it; ðA3Þ

The fixed model is a reasonable approach when we can be confident that the differences betweencountries can be viewed as parametric shifts of the regression function.

Relationships and tests between modelsEquations (A2) and (A3) are restricted versions of (A1), whereas (A2) is a restricted form of (A3).Under the assumption that the 1it are independently normally distributed over i and t, with meanzero and variance s 2

1, the F-statistics can be used to test the linear restrictions postulated by (A2)and (A3).

The Random effects modelIf we believe that sampled cross-sectional units are drawn from a large population, itmay be more appropriate to use the random effects model (or variancecomponents model), in which individual constant terms are randomly distributed acrosscross-sectional units:

yit ¼ aþ b 0xit þ mi þ 1it ; ðA4Þ

where E(Eðmi ¼ 0Þ; Eðm2i Þ ¼ s2

m; EðmimiÞ ¼ 0 for i – j, and E(1it mj) ¼ 0, for all i, t, and j.Thus, mi is a random disturbance which characterizes the ith observation andis constant through time; it can be regarded as a collection of factors that arespecific to region i and are not included in the regression. The above model can be estimatedby GLS.

The approach used in this study draws on the one developed by Holtz-Eakin et al.(1990),Arellano and Bond (1991) and Arellano and Bover (1995). In particular, ageneralized-method-of-moments dynamic panel procedure has been utilized. In our context,consider the following regression equation:

yit ¼ ai þ b 0xit þ gyit21 þ 1it; ðA5Þ

where y is the growth rate of GDP, x denotes the set of explanatory variables, a is an unobservedcountry-specific effect, 1 is the error term, and the subscripts i and t represent country andtime period, respectively.

Appendix 2

Governmentstability Corruption

Ethnictensions

Socioeconomicconditions

Government stability 1.000Corruption 0.441 1.000Ethnic tensions 0.345 0.765 1.000Socioeconomic conditions 20.104 0.891 0.672 1.000

Table AII.Correlation matrix ofinstitutional qualityindices (1984-2003)

IJSE39,1/2

158

Dep

end

ent

var

iab

le:

gro

wth

rate

ofre

alp

erca

pit

aG

DP

Var

iab

les

Mod

el1

Mod

el2

Mod

el3

Mod

el4

ETHNC

0.62

1(2

.115

)**

GOVS

0.28

7(2

.631

)**

*–

CORR

0.11

7(0

.339

)–

–SOEC

0.41

2(2

.138

)*–

INF

20.

000

(20.

728)

20.

000

(20.

878)

20.

000

(20.

702)

20.

000

(20.

874)

GOV

20.

099

(21.

528)

20.

107

(21.

630)

20.

094

(21.

446)

20.

090

(21.

373)

TROP

0.02

7(1

.637

)0.

025

(1.5

00)

0.01

6(0

.958

)0.

018

(1.0

79)

CBSY

20.

167

(23.

331)

**

*2

0.14

9(2

3.00

5)*

**

20.

133

(22.

691)

**

*2

0.16

4(2

3.29

1)*

**

POPG

20.

348

(20.

990)

20.

181

(20.

517)

20.

070

(20.

206)

20.

048

(20.

140)

FDI

0.09

6(1

.247

)0.

067

(0.8

79)

0.00

9(0

.127

)0.

069

(0.9

13)

AID

0.03

4(1

.215

)0.

038

(1.3

52)

0.05

3(1

.869

)0.

043

(1.5

48)

c0.

424

(0.2

59)

1.60

8(0

.959

)2

0.28

8(2

0.16

6)2

0.02

8(2

0.01

6)O

bse

rvat

ion

s53

853

853

853

8

Notes:

Sig

nifi

can

tat

:* 1

0,*

* 5an

d*

** 1

per

cen

t;ro

bu

stt-

stat

isti

csin

bra

cket

s

Table AIII.Results from models

selected on the basis ofthe SIC and AIC

The role ofinstitutions

159

About the authorsDr Rasha Osman received her BSc from Afad University for Women and her PGD and MA fromKhartoum University -Sudan. She received her PhD from University of Juba-sudan. Dr Rasha iscurrently an Assistant Professor at the Department of Political Science College of Social& Economic Studies, University of Juba-Sudan. Her research interests are in the fields ofDevelopment and Growth, Political Psychology, Political Economy, and Personality and PoliticalLeadership. She has presented many papers at national and international conferences. Dr Rashais a member in Sudanese Association of Political Science and Association of Sudanese WorkingWomen. She has also given many lectures and seminars, as invited speaker, for manyinstitutions, Mass Media and universities.

Dr Constantinos Alexiou is a Senior Lecturer in the School of Management at CranfieldUniversity, UK. After completing his studies (BSc, Pg.Dip., MSc, PhD) he worked at various Britishuniversities, such as Queen Mary’s College, London Guildhall University, and Open University. Asan academic, he has participated in a number of conferences and has produced papers published ininternational economic journals, such as the Journal of PostKeynesianEconomics,Contributions toPolitical Economy, Ekonomia, International Review of Applied Economics, etc.

Dr Persefoni Tsaliki received her BA from Aristotle University of Thessaloniki and her MAand PhD from New School for Social Research, New York. She is currently an AssistantProfessor in the Department of Economics, Aristotle University of Thessaloniki. Her researchinterests are in the fields of political economy, profitability, accumulation, productivity andgrowth. She has published a book The Greek Economy: Sources of Growth in the Postwar Era(Praeger Publications) and many scientific articles in journals such as Review of IndustrialOrganization, Review of Radical Political Economy, International Review of Applied Economics,etc. and she has presented many papers in international conferences. Dr Tsaliki is a member ofmany professional international and European economic associations and has given lectures andseminars, as invited speaker, for many institutions and universities. Persefoni Tsaliki is thecorresponding author and can be contacted at: ptsaliki@econ.auth.gr

Code of factors Definition of factors Impact

CBSY Bank credit (% GDP) HighGOVS Government stability HighETHNT Ethnic tensions HighSOEC Socioeconomic conditions HighTROP Trade openness (% GDP) LowGOV Government Spending (% GDP) LowAID Foreign Aid (% GNI) LowFDI Foreign direct investment (% GDP) WeakINF Inflation WeakPOPG Population growth WeakCORR Corruption Weak

Note: Factors have been arranged in a descending order of impact (i.e. based on the t-statistics)Table AIV.Impact of factors

IJSE39,1/2

160

To purchase reprints of this article please e-mail: reprints@emeraldinsight.comOr visit our web site for further details: www.emeraldinsight.com/reprints