impact of macroeconomic, institutional and structural factors ......the poverty-growth-inequality...

Development, 2014, 57(3–4), (559–577)© 2015 Society for International Development 1011-6370/15www.sidint.net/development/

Development (2014) 57(3–4), 559–577. doi:10.1057/dev.2015.31

Local/Global Encounters

Impact of Macroeconomic, Institutional andStructural Factors on Inequality in South Africa

BABATUNDEOMILOLA ANDOLUSEGUN A.AKANBI

ABSTRACT Income inequality especially remains high in South Africa.This article investigates the impact of macro-economic, institutionaland structural factors on inequality across the nine provinces of SouthAfrica. Using a panel data econometric technique, the determinants/drivers of inequalities are estimated. The rate of openness(globalization) of an economy, the level of financial inclusion, thestatus of physical infrastructure, governance indicators, andsocioeconomic and institutional factors are explored as explanatoryvariables. The article concludes with a presentation of the challengesand policy options required to address social and economicinequalities in South Africa.

KEYWORDS income inequality; land inequality; education inequality;apartheid; governance; infrastructure

Introduction and background

The South African economy continues to face the triple challenge of high levels ofpoverty, unemployment and inequality; these dimensions being interlinked aspersistently high levels of unemployment and inequality could lead to poverty traps.Although the Millennium Development Goal (MDG) of halving poverty in the worldhas been met even before the set date, this positive development sits alongside therising level of inequality in societies, especially in developing economies (UNDP,2013). Unequal societies are more susceptible to social violence and are less cohesive.Inequality reduction therefore remains a crucial issue for the global economy toresolve in the next decade. For the developing economies where inequality is rising,this may be the manifestation of the predicted Kuznets Curve (1955) and Solow’seconomic growth model (1956). According to Kuznets, economic growth in poorcountries would initially lead to greater inequality, which would later decline as aneconomy continues to develop, while Solow’s prediction indicated that poor countrieswould tend to grow faster and eventually converge with the developed countriesthrough factor equalization.

There is evidence in the literature confirming that higher and sustainable inclusiveeconomic growth does translate into employment generation and eventually povertyreduction in a society. Whereas higher growth in and of itself is insufficient to eradicate

http://www.sidint.net/development/http://dx.doi.org/10.1057/dev.2015.31

extreme poverty, the distribution of growth mat-ters in achieving a substantial reduction in povertylevels. Equal access to or ownership of factors ofproduction will determine the pattern and extentof the distribution of income and wealth in asociety.

The poverty-growth-inequality triangle estab-lished in Bourguignon (2003) suggests that in asmuch as the elimination of absolute poverty isimportant, the country-specific combinations ofgrowth and distribution policies is crucial.Indeed, the level of inequality is a key driver ofpoverty in an economy and empirical literaturehas decomposed changes in poverty into agrowth component and redistribution compo-nent (Ravallion and Datt, 1991; Ravallion andChen, 1997; Adams Jr and Page, 2003; AdamsJr, 2004).

Over the past decade, the MDG on reducingpoverty has focused more on achieving higherand sustained economic growth, at the expense ofthe equally important factor of income distribu-tion. Consequently, poverty has fallen in tandemwith rising inequality, dampening the impact ofgrowth on poverty reduction (Balakrishnan et al.,2013). This trend indicates that the rising eco-nomic growth of the past decades has not beeninclusive enough and therefore, a more inclusive/pro-poor economic policy is needed to eradicaterelative poverty in the world (Saad-Filho, 2010).In this milieu, a shift in policy paradigm is needed.Government policies towards growing the econ-omy should be inclusive with more weightattached to the distributional effects of policyactions. Many other aspects of the economy havebeen linked to having major effects on inequality.In exploring the relationships that exist betweenfinancial development, globalization, and govern-ment expenditure and taxation, and level ofinequality, empirical evidence reveals mixed resultssince the causality strongly depends on the struc-ture of the economy (Greenwood and Jovanovic,1990; Cline, 1997; Atkinson, 2000; Winters et al.,2004; Classens and Perotti, 2005; Gersbach andSchmutzler, 2007; Piketty, 2007; Roine et al.,2009). Examining the causal relationship betweeninequality and a number of macroeconomic vari-ables altogether, Heshmati (2004) found that

income inequality is declining over time but thereare significant regional differences in the levels ofdevelopment.

South Africa has high-income inequality as wellas other social dimensions of inequality seenin skewed access to education and land owner-ship, creating structural constraints to economicgrowth and development. However, specific ana-lysis of the drivers of inequality in South Africa isvery limited. Bhorat and Van der Westhuizen(2012) carried out a thorough and critical ana-lysis of poverty, inequality and the nature ofeconomic growth. Their analysis suggests that,even though absolute and relative poverty havefallen in the post-apartheid era, income inequal-ity in South Africa remains one of the highest inthe world and is most visible across racial group-ings. Economic growth has mostly benefitedthose in the top income echelons. Kerr and Teal(2012) investigated the determinants of incomeinequality using the Kwazulu-Natal provinceincome dynamics study data. Their results sug-gest that human capital and individual abilityexplain much of the earnings differentials withinthe private sector but cannot explain the largepremiums for public sector workers. Empiricalevidence related to health inequality is found inAtaguba et al. (2011) when investigating thesocioeconomic-related health inequality in SouthAfrica using general household surveys of self-reported illness and disability indices. Their find-ings reveal a huge inequality in the health systemin terms of access and usage of health facilities.

Very few studies have examined the structuraland institutional factors that could determine thelevel of inequality, even if levels of governance andphysical infrastructure have been identified asmajor drivers of both social and economic inequal-ities (UNDP, 2013).

In this article, we look at the macroeconomic,structural and institutional determinants of socialand economic inequalities, examining theirimpact across the nine provinces of South Africa.The analysis employs a panel data econometrictechnique to estimate the determinants/drivers ofinequalities in the country (see Appendix A and Bfor clarifications on the theoretical framework,methodology and data analysis).

Development 57(3–4): Local/Global Encounters

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Econometric investigation of the impactof macroeconomic, institutional andstructural factors on inequality in SouthAfrica

Empirical results

Balanced panel estimations were carried out withdifferent pooled observations after the necessaryadjustments. After solving for all the possibleestimation problems that could render the coeffi-cients invalid, the explanatory variables examinedin all estimations were found to be statisticallysignificant determinants of inequality except forsome few instances like investment spending,financial inclusion, openness and physical infra-structure variables in the education, income andland inequality equations, respectively. As shown

in Table 1, these variables explained significantlywell the level of economic and social inequalitieswithin the country.1

The results show that a one percent increase ingross domestic product (GDP) per capita will leadto a decrease of about 0.23, 0.35 and 0.86 percentin the level of income, education and land inequal-ity, respectively. Rising GDP per capita seems tohave a better redistributive tendency towards landinequality. This can be attributed to the strongwelfare effect this type of inequality portrays.Income and education inequalities will decline byabout 0.45 percent each when physical infrastruc-ture improved by one percent and this will lead toabout a 0.15 percent decline in the case of landinequality. In terms of governance, an improve-ment in the level of overall governance by a unit

Table 1. Estimated coefficients for the determinants/drivers of inequality

Dependent variables: Gini Index

Independent variables Income Education Land

Real GDP per capita −0.23** −0.35* −0.86**(−1.75) (−1.71) (−2.61)

Physical infrastructure index −0.45*** −0.45*** −0.15(−4.86) (−2.92) (−0.88)

Governance −0.33*** −0.38** −0.40*(−5.15) (−2.46) (−1.69)

Investment spending −0.31*** −0.08 0.40**(−3.30) (−0.94) (2.58)

Openness 0.06 0.06** 0.08(1.76) (1.73) (1.39)

Financial inclusion 1.08*** 0.02 −1.06***(9.18) (0.17) (−5.00)

Tax rate −0.48*** 0.36* 0.54*(−4.11) (2.34) (1.81)

Income Gini index 0.14* 0.38*(1.91) (2.06)

Constant 4.96*** 4.71** 4.62(3.43) (1.80) (1.45)

Number of observations 126 144 90R2 0.86 0.89 0.87Durbin-Watson Statistic 1.10 1.39 0.49

Note: *** significant at 1 percent level; ** significant at 5 percent level; * significant at 10 percent level. T-statistics arerecorded in parenthesis ()Source: Authors’ calculation from Eviews 8

Omilola and Akanbi: Inequalities in South Africa

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will, respectively, lead to about a 0.33, 0.38 and0.4 percent decline in the levels of income, educa-tion and land inequality, respectively.

Investment spending will not have any positiveeffect on land inequality if a minority in the societycontinues to hold the majority of the land; instead,land inequality will continue to rise by about 0.4percent when investment spending increases by 1percent. Since land remains a fixed and an inevitablefactor of production, interest on any capital invest-ment will continue to benefit the very few owners ofland. On the other hand, an increase in investmentspending by 1 percent will lead to a decrease ofabout 0.31 percent and 0.1 percent in the levels ofincome and education inequality, respectively.

The level of openness (globalization) will havea negative impact on both economic and socialinequalities, but at low magnitudes. This trend isin line with the global evidence reported in UNDP(2013), that: globalization processes have largelybeen responsible for the increasing incomeinequality in the developing world. However,the economic impact of globalization (0.06,0.06, and 0.08 percent, respectively) on income,education and land inequalities is found to beinsignificant. This suggests that openness inthe aspect of exchange of services is still at a lowlevel in South Africa.

The level of financial inclusion in the countryremains very low and has been exacerbating thelevels of income and education inequalities.When financial inclusion increases by 1 percent,income inequality will rise by about 1.1 percent,while education inequality will only increase byabout 0.02 percent. South Africa has one of themost sophisticated financial systems in sub-Saharan Africa, but only a very small fraction ofthe population have access to it.

With regard to land inequality, the financialsector has played a redistributive role over theyears. The positive and significant impact thefinancial inclusion variable exerts on land inequal-ity can be attributed to the participatory role playedby the financial sector in providing funds to thelanded property markets to carry out their opera-tions. However, an increase in financial inclusionby 1 percent will lead to a decrease of about 1.1percent in the level of land inequality.

Average tax rate has a positive effect only onincome inequality while other social inequalitiesare found to have negative impacts. When theaverage tax rate increases by 1 percent, incomeinequality will decline by about 0.5 percentsince income will be redistributed from the topincome percentile to the bottom income percentile.On the other hand, this will lead to increasinglevels of social inequality as the tax increases willlimit access to education facilities, and land usagewithin the country. A 1 percent increase in the taxrate will lead to an increase of about 0.36 and 0.54percent in the levels of education, and landinequality, respectively.

Looking at the inclusion of the incomeinequality as an additional explanatory variablein the social inequality equation, the effectsare consistent. A decline in income inequalitywill directly be translated into a decline ineducation, and land inequality. This indicatesthat equal distribution of income is a necessaryfirst step (but not sufficient) in achieving strongsocial cohesion. When the level of incomeinequality increases by 1 percent, education andland inequalities will rise by about 0.14 and 0.38percent, respectively.

Policy implications: identifying a ‘sustainable’level of inequality

This article contends that reducing both economicand social inequalities in a developing countrysuch as South Africa requires a higher and sus-tained income per capita, productive investmentspending, an improved level of physical infrastruc-ture and good governance.

Regression analysis has clearly shown thatincreases in income (GDP per capita), and invest-ment spending matter for the reduction of eco-nomic and social inequalities in South Africa.Government intervention through increases intaxation will only help in reducing incomeinequality but will not directly translate into equalaccess to education and land. Although in the longrun the indirect effects of taxation can impactsocial inequality positively. Financial inclusionand globalization (openness) have led in most casesto rising economic and social inequalities. On the


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other hand, governance and physical infrastruc-ture matter for reducing the level of inequality.

Furthermore, thresholds for economic (income)and social inequalities are derived as functions ofper capita income. However, given the dearth ofempirical relevance of the impact of governance(institutions) and physical infrastructure (struc-tural impediments) on inequality, this analysistakes a further step to derive thresholds for eco-nomic and social inequalities as functions of thesetwo variables.2

The thresholds presented in Figure 1 revealthat rising per capita income alone is not enoughin eradicating inequality, especially incomeinequality.3

This is an indication that the trend in percapita income is partially reflected in the distri-bution of income in the society. In other words,rising income per person does not necessarilytranslate into equal distribution across all spec-trums of the society. Income inequality willremain at 0.73 when average income per capitain the economy is about US$3,000 per annum.This will fall to about 0.62 Gini (income) indexpoint as income per capita rises to US$6,000 perannum. Between US$6,000 and US$15,000 percapita income, income inequality will onlydecline to about 0.5 index points. This slowdownin the decline rate of inequality continues asaverage per capita income increases and startingfrom about US$36,000 onward, changes in

income inequality remain relatively unchanged.This reveals an important fact: that per capitaincome may matter in reducing income inequal-ity (when all other factors remain unchanged),and only to a certain extent. A similar trend isobserved for the social inequality index, but atlower levels than income inequality. Risingincome per capita seems to be very effective withland inequality, as the index will fall to about 0.1when average annual income per capita is aboutUS$30,000. At this point, education and incomeinequality will stand at 0.31 and 0.43 indexpoints, respectively.4

The governance scores range from −2.5 to 2.5and in order to categorize governance at differentlevels: the range between −2.5 and −1 corre-sponds to ‘poor governance’; between −1 and+0.5 corresponds to ‘average governance’; and,between +0.5 and +2.5 corresponds to ‘goodgovernance.5

Following the approach used in Akanbi (2013,2015), the physical infrastructure index (PII)derived for the South African economy rangesfrom 0.6 to 1.25. However, Calderón and Servén’s(2010) derivation, which incorporates otherregions such as India and the East Asia Pacific(EAP), found a range between −1.5 and +2. BothIndia and the EAP are regarded as having anaverage infrastructure stock based on the WorldEconomic Forum (WEF) rankings on quality ofinfrastructure. WEF rankings range from one to

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seven, with one being the worst (i.e. Haiti) andseven the best (i.e. Switzerland) in infrastructurequality. To capture the lowest performers (under-developed regions) and best performers (developedregions) in infrastructure stock, the scale is broa-dened and rearranged to align with the WEFrankings. Therefore, in our analysis physical infra-structure ranges from −2.5 to +6.5. Also, tocategorize the PII at different levels: the rangebetween −2.5 and 0 corresponds to ‘poor’ infra-structure; between 0 and +3 corresponds to ‘aver-age’ infrastructure; and between +3 and +6.5corresponds to ‘good’ infrastructure.

It is also interesting to detect further if per capitaincome matters in achieving perfect equality in thesociety. Therefore, for a given average GDP percapita ranging from US$2,000 to US$8,000(Figure 2 A–F), provinces with poor governanceand poor infrastructure ratings will sustain ahigher level of inequality while those with goodgovernance and good infrastructure ratings will beable to sustain a lower inequality level. In general,per capita income seems to matter in achievinglower inequality especially with poor governanceand poor infrastructure ratings but inequalitytends to converge in some cases when good infra-structure ratings are established.

From Figure 2, operating at a higher level ofGDP per capita will bring about a lower inequalitylevel. In relation to income and educationinequality, governance and physical infrastruc-ture in Panels A–D, there seems to be an existenceof convergence in inequality as physical infra-structure improves but divergence as governanceimproves. In other words, irrespective of the levelof GDP per capita, income and education inequal-ity tend to converge at a lower level as goodinfrastructure are attained but at different levelswhen good governance is pursued. This meansthat per capita income matters in reducingincome and education inequality through enfor-cement of good governance but as good physicalinfrastructure is established, lower inequality canbe achieved irrespective of the level of per capitaincome.

The distinctive difference between Panels A/Cand B/D (operating from the baseline scenario) is thefact that for inequality to start declining, a province

must have achieved at least an average governancerating while at a poor infrastructure rating, inequal-ity could decline as physical infrastructure improves.This implies that a higher level of inequality isfeasible even with a good governance rating.In addition, given the baseline GDP per capita level,the income and education inequality indices will fallfrom 1 to 0.3 as better governance is pursued, andfall from 1 to 0.03 with improved infrastructure(Panels A–D).

With regard to land inequality, governance andphysical infrastructure in Panels E and F, conver-gence could not be achieved; instead, inequalityseems to remain at different levels as GDP differs.This indicates that for land redistribution to beeffective across the country, rising level of incomeper capita is needed. Provinces with higher levelsof GDP per capita will continue to reduce landinequality at a lower rate even when infrastruc-ture improvements and better governance are thesame as others.

In contrast to income and education inequalityin Panels A–D, inequality will start declining asgovernance and infrastructure ratings improve.In addition, lower levels of land inequality canbe achieved with the poorest physical infrastruc-ture ratings if a higher level of income per capitacan be attained. With the baseline GDP per capitalevel, the land inequality index will fall from 1 to0.23 as better governance is pursued, and fallfrom 0.8 to 0.21 with improved infrastructure(Panels E and F).

Conclusions and policy recommendations

This analysis has found that the macroeconomicvariables (trade liberalization, financial develop-ment, investment spending and taxation) playan important role in both social and economicinequalities. However, this will not be enough toachieve the desirable distribution of income andsocial inclusion if majority of the population stilllack basic physical infrastructure or are governedpoorly. However, augmenting the estimations withthese two variables has provided more robust andsignificant parameter estimates. To quantify theseeffects, a given level of inequality is derived inrelation to a particular level of per capita income,


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physical infrastructure rating and governance rat-ing after controlling for and averaging out otherexplanatory variables in the model. For instance, aparticular province operating at a given level ofinfrastructure will be at a particular level ofinequality or Gini index. Therefore, as infrastruc-ture improves after controlling for all other factors,inequality declines. This could be useful for inform-ing policy directions and targets.

The main findings reveal the following:

● Income inequality remains very high with anestimated 0.72 average Gini index recorded forthe overall economy over the period 1999–2012. The Lorenz curve distribution revealsthat a small percentage of the populationreceives the largest portion of national income.About 60 percent of the household populationonly earned about 10 percent of nationalincome, while 20 percent of the householdpopulation takes about 70 percent of thenational income. The econometric specifica-tion of the determinants of income inequalityreveal that higher tax rates, more investmentspending, better physical infrastructure andgood governance will redistribute incomewithin the society and provinces more thanrising per capita income itself.

● Education inequality recorded for the overalleconomy is at an estimated 0.73 average Giniindex in 2012 from 0.56 index point in 1995.The Lorenz curve distribution reveals that onlyabout 1.5 percent of the population haveattained at least a bachelor’s degree as at 2012and this trend has not changed since 1995. Thetrend shows that apart from financial con-straints, poor quality of basic education is amajor hindrance in accessing higher education.The econometric specification of the determi-nants of education inequality reveal that betterphysical infrastructure and good governancewill redistribute education (human capital)within the society and provinces more thanrising per capita income itself.

● Land inequality recorded for the overall econ-omy is at an estimated 0.74 average Gini index.The Lorenz curve reveals a very highly skeweddistribution with only a small percentage of the

population having access to land for productionpurposes. As at 2012, a cumulative 40 percentof the household population owns about 10percent of the country’s land, while 8 percentof population holds about 70 percent of thecountry’s land. The econometric specification ofthe determinants of land inequality reveal thatrising level of per capita income and goodgovernance will eventually redistribute landequally within the society and provinces.

● Gauteng and Western Cape provinces remainthe best performing provinces in the country interms of both economic and social distribution ofresources, while Limpopo and Eastern Capeprovinces are the worst performing provinces(Appendix A).

● Apart from per capita income, investmentspending, trade liberalization, financial inclusionand taxation, the level of governance and phy-sical infrastructure are also significant determi-nants of economic and social inequalities in thecountry. Therefore, inequality-reducing strate-gies should focus more on structural and institu-tional impediments but without underminingthe macroeconomic factors.

● Rising income per capita either through smallerhouseholds’ population or higher productivitylevels will lead to much lower social inequa-lity than economic inequality. There exists aconvergence in income inequality and educa-tion inequality, but only when there is improve-ment in physical infrastructure. Other than this,income per capita really matters in reducingeconomic and social inequalities.

Policy directions

Good governance and increased investment in amore equitable physical infrastructure appear tohave critical importance. A more stable socio-economic and political environment, corrupt-free society, an effective public service, goodregulatory environment, and a transparent lea-dership structure should be considered at leastequally important than other more conventionaleconomic objectives. Equally distributed andqualitative physical infrastructure across all the


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provinces in the country could create a con-ducive environment for private agents or inves-tors to locate their investments in any partof the economy without fear of infrastructureconstraints.

Investment in education is a crucial element tothe development of South Africa. The quality ofbasic education (especially in public educationalinstitutions) needs an urgent revamp. To addressskill gaps in the economy, a strong educationalfoundation is needed. However, since the appro-priate skills to drive the economy to its fullpotential is lacking, it is appropriate for govern-ment to set-up a medium- to long-term skillstransfer mechanism for South African citizens.To revamp the quality of basic education, there isan urgent need to invest in qualified trained tea-chers, especially in the areas of science and mathe-matics, and generate the appropriate knowledge andskills that the economy needs. In addition, computa-tion of skill-needs per sector or field of specializationis required in order to guide government efforts toboost adequate matching skills development.

Education can also be used as a tool forredistributing land in the country. Governmentcould create a medium- to long-term mentor-ship/apprenticeship programme to facilitate thetransfer of land ownership for agricultural pro-duction. Farmers’ training programmes shouldbe established at every level of educationalattainment. On completion of such training pro-grammes, each prospective farmer could beposted to the located farms where he/she willreceive practical training with the aid of experi-enced farmers; alongside this, the trainee willbegin to own a percentage of land and itsproduce. This will also depend on the period oftime spent on a particular farm. However, pro-grammes of this nature would require strongpolitical and societal will.

Moreover, boosting skill levels through qualityeducation, pursuing good governance by puttingin place the appropriate structures, and creatingequal distribution of physical infrastructure couldincrease the pool of entrepreneurs and eventuallylead to declining income inequality in the country.

Notes1 It should be noted that in the case of a TSLS estimation, the coefficient of variations (R2) presented are no

more valid since we can no longer decompose the variation in inequality into different independent components(Wooldridge, 2010: 89–115).

2 The computed inequality indices from the estimation results are about 0.72, 0.65 and 0.53 for income, educationand land measures, respectively (based on the mean of the pooled sample of all provinces). The computed inequalityindices are very close to the actual values from the mean of the pooled sample.

3 The scale of the per capita income broadened to a high of $60,000 for better comparison with high-income countries.4 The regression results presented in Table 1 are used to calculate the thresholds. The thresholds for economic and

social inequality in relation to GDP per capita (Figure 1) are obtained by controlling for average investmentspending levels, infrastructure index, financial inclusion, openness, governance, tax rate and where necessaryincome equality. A similar procedure was carried out in Figure 2 by controlling for average GDP per capita,investment spending, financial inclusion, openness, tax rate and where necessary income equality. This is based onthe mean of the pooled sample of selected provinces. In addition, the obtained thresholds also controlled for all otherunobserved variables in the estimations, which is captured through the error term.

5 Similar idea was adopted in Kraay and Nehru (2006) when categorizing weak, medium and strong policy using theWorld Bank’s Country Policy and Institutional Assessment.

6 The derived Lorenz curves and Gini index for economic and socio-inequality per province are presented inAppendices A and B.

7 According to Statistics South Africa’s General Household Survey of 2012, on average, there are about four membersin a particular household in the country.

8 The following are codes to access the data from Statistics South Africa via Quantec Easydata database: ‘Q413hect’for land distribution, ‘Q16hiedu’ for education distribution, and ‘totmhinc’ for income distribution.

9 To derive the Lorenz curves, the cumulative distribution of the income or social variables is plotted against thecumulative distribution of the population variable after specifying the line of equality. This is done for every yearwhere data are available between 1995 and 2012 (see Appendix A).


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Appendix A

Theoretical framework, methodology anddata analysis

The distribution of a country’s economicresources is vital in achieving its major nationaldevelopment objectives. Income inequality inSouth Africa is closely inter-linked to socialinequality. For instance, access to quality basicand higher education, and access to land forproduction are all associated with the level ofincome (UNDP, 2013).

The study presented in this article looks at themacroeconomic, structural and institutionaldeterminants of social and economic inequal-ities, examining their impact across the nineprovinces of South Africa. The analysis employsa panel data econometric technique to estimatethe determinants/drivers of inequalities in thecountry.

Structural factors are linked to the level orstock of physical infrastructure, while institu-tional factors are associated with the level ofgovernance embedded in the system. Therefore,three distinct panel estimations were carried outto determine the factors responsible for inequal-ity in South Africa over the period 1999–2012,1995–2012 and 2002–2012 (based on availabledata) for income, education and land inequality,respectively.

The econometric models are presented alongsidetheir a priori sign below:

ln gini incit = β1 - β2 ln gdp pcit - β3 ln invit± β4 ln openit ± β5 ln creditit

- β6 ln piiit - β7govit ± ln taxit ± eit ðA1Þ

ln gini eduit = β1 - β2 ln gdp pcit - β3 ln invit± β4 ln openit ± β5 ln creditit

- β6 ln piiit - β7govit ± ln taxit+ ln gini incit ± eit ðA2Þ

ln gini landit = β1 - β2 ln gdp pcit - β3 ln invit± β4 ln openit ± β5 ln creditit

- β6 ln piiit - β7govit ± ln taxit+ ln gini incit ± eit ðA3Þ

Where gini_inc is the level of gini (income) index;gini_edu is the level of gini (education) index;gini_land is the level of gini (land) index; inv is theinvestment spending; gdp_pc is the gross domesticproduct (GDP) per capita; open is the level ofopenness (globalization); credit is the level of finan-cial inclusion/deepening; tax is the average taxrates; pii is the physical infrastructure index (PII);gov is the level of governance index; e is theerror term and the subscript (it) refers to prov-ince and time period respectively. All variables arepresented in their natural logarithm (ln_) forms


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http://www.anst.uu.se/h/danwa175/Research_files/The%20LongRun%20Determinants%20of%20Inequality%20%20What%20Can%20We%20Learn%20from%20Top%20Income%20Data.pdfhttp://www.anst.uu.se/h/danwa175/Research_files/The%20LongRun%20Determinants%20of%20Inequality%20%20What%20Can%20We%20Learn%20from%20Top%20Income%20Data.pdfhttp://www.anst.uu.se/h/danwa175/Research_files/The%20LongRun%20Determinants%20of%20Inequality%20%20What%20Can%20We%20Learn%20from%20Top%20Income%20Data.pdf

except for the governance index, which has nega-tive values.

The GDP per capita is expected to pose anegative relationship with inequality, as evidencedin the literature. Therefore, an increase in GDP percapita should reduce the level of both income andnon-income (social) inequality, given that the causeof the rising GDP is pro-poor otherwise a positiverelationship may ensue. Similarly, the level of invest-ment spending (public and private) is expected tohave a negative relationship with inequality. Therate of openness (globalization) of an economy, thelevel of financial inclusion/deepening and tax ratesare ambiguous in relation to the level of inequality.An open economy may reduce inequality, withmore equal access to economic activity. Also, equalaccess to finance or credit is expected to lead toreduction in inequality. Tax rates could lead toredistribution of income in the economy if taxrevenue collected by government benefits the poorin the society. A rising tax level may not be effectivein granting equal access to quality education,healthcare facilities and land especially in a mixedeconomy with huge structural constraints such asSouth Africa. Therefore, increased taxation couldlead to continuing social inequality.

Quality physical infrastructure and good govern-ance structures set a strong foundation for a moreinclusive economy where every individual canengage in meaningful economic activity. Therefore,physical infrastructure and governance are expectedto pose a negative relationship with inequality.

Income inequality index is included as an addi-tional explanatory variable to the two measures ofsocial inequality (access to education and land).Income inequality is expected to pose a positiverelationship with social inequality (UNDP, 2013).Therefore, a reduction in income inequality shouldalso lead to a reduction in the level of socialinequality.

The specification of the models reveals a possibleendogeneity problem among the regressors andthis has rendered the use of ordinary least squaresinappropriate in the estimations. This implies thatthe explanatory variables in the model could bedetermined by each other and hence can becorrelated with the error term. In this scenario,an instrumental variables regression is appropriate

to derive robust estimates of the parameters inEquations (1) – (3). Following similar steps as inAkanbi (2013, 2015), a two-stage least squares(TSLS) estimation method is adopted and inorder to correct for possible omitted variables anderror-in-variables, suitable instruments that areassumed to be highly correlated with the observedexplanatory variables and uncorrelated with theerror term are used (Wooldridge, 2010: 89–115).The instruments used are one to two period lag ofall the dependent and independent variables in theestimations.

In addition, province-specific characteristicswere considered in the estimations under theassumption that although these provinces mayhave similar economic structures, there still existmajor differences in their patterns of inequality.Time-specific characteristics could not be captureddue to the limited time period used. Given this, thestudy carried out the TSLS with one-way (cross-section-specific) fixed effect estimation techniques.

Data description and analysis

The data used in the analysis are obtained from theStatistics South Africa, South African Reserve Bank,Quantec; EasyData, World Bank; World Databank,IMF; International Financial Statistics, and World-wide Governance Indicators database. It covers theperiod between 1995 and 2012 for the nine pro-vinces. Where necessary, all data are measured inlocal currency (South African rand). Data availabil-ity and weaknesses limited the analysis, especiallywith regard to the survey data from Statistics SouthAfrica employed in deriving the inequality variables.

Deriving the Lorenz curves and Gini index

Social inequality is analyzed in terms of the dispa-rities in the distribution of human capital – accessto education and land ownership, while economicinequality refers to unequal income distribution inthe society.

Economic inequality is analyzed on the basis ofgeneral household income and expenditure distri-bution and categorized as the percentage of thehousehold population belonging to a particular


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income bracket in the nine provinces of SouthAfrica.6

Social inequality is analyzed as follows:

Education inequality

This refers to the percentage of the total popula-tion who have completed a particular levelof education per province using an outcome-based approach. The education sector has regis-tered poor outcomes over the past two decadesdespite increased spending in the sector withapproximately 20 percent of the country’s bud-get devoted to education. There is a huge dis-parity between the quality of public and privateeducation, especially at the basic educationlevel. Only middle- to high-income earners canaccess quality private education. This setbackhas hindered many South Africans from pursu-ing higher education. Therefore, access to edu-cation is a key indicator of social inequality inSouth Africa.

Land inequality

This refers to the percentage of household popula-tion who have access to land for productionpurposes per province.7 As a factor of production,land plays a crucial role in any economy andtherefore when it is evenly distributed amongcitizens, this enhances production efficiency. Landdistribution remains one of the most debatablesocio-economic issues in South Africa. The legacyof apartheid continues to hinder the equal distribu-tion of land. Majority land ownership remains inthe hands of a minority, and this presents a majorsocial threat. The analysis spotlights the land sizethat is accessible to each household for productionpurposes.

Lorenz curves are derived for the nine provincesusing the Statistics South Africa Household Sur-vey.8 The Lorenz curves and Gini coefficients arecalculated following Slack and Rodrigue (2009)and presented below:

G=1 -Xz

i=0

σYz -1 + σYzð Þ σXz -1 - σXzð Þ (A4)

where σY is the cumulative distribution of theincome or social variables, for i=0,…z with Y0=0and Y1=1; and σX is the cumulative distributionof the population variable, for i=0,…z with =0and X1=1.

9

The Gini coefficient is a ratio between zero andone, where zero implies that each individual orhousehold in the economy receives the sameincome or social capital and one implies that onlyone individual or household receives all theincome and social capital (Benson, 1970).

Since the distribution in the household sur-veys changes frequently over time, this createsvolatility in the calculated annual Gini index. Toreduce this noise between these years, a five-yearmoving average for the Gini index is calculatedusing the initial year Gini index as the bases forthe previous five years. This is based on theassumption that significant changes in inequal-ity or other development issues will only bevisible over the medium-term period (i.e. fiveyears).

Measuring infrastructure

The measure of infrastructure is updated fromAkanbi (2013) derived index, which includesSouth Africa. Infrastructure is represented by acomposite PII, which is based on three infra-structure stocks of (i) roads, (ii) telecommunica-tion, and (iii) electricity. Empirical literature onthe link between infrastructure and economicdevelopment has been plagued by single-infra-structure stock (Calderón, 2009). To avoid thisshortcoming, Calderón and Servén’s (2004)approach was adopted in building an aggre-gate index that combines the three infrastruc-ture stocks. In addition, the aggregation of theinfrastructure stocks will help to reduce themeasurement error associated with a single-infrastructure indicator.

Measuring governance

The worldwide governance indicators developedby Kaufmann et al. (1999) were utilized in thisanalysis. The indices cover a broad range of policyand institutional outcomes for a large number of


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countries, and include the rule of law, corruptioncontrol, government effectiveness, regulatory qual-ity, voice and accountability, and political instability.These indices are also employed in Globerman andShapiro (2002) and Akanbi (2012) as a measureof governance.

Governance is captured in a broader contextby taking the average value of the six elementsin the governance indicators as a measure ofgovernance. The governance indicators’ seriesis only available from 1996 onward, with miss-ing values for 1997, 1999 and 2001. To circum-vent this problem, an interpolation techniquewas adopted in order to fill in the missing datapoints.

Other variables

Other variables included in the specifications are:real GDP per capita, measured as the ratio of realGDP to population per province; investment spend-ing is measured as the gross capital formation perprovince; openness/globalization is measured asthe sum of exports and imports in relation to GDPper province; financial inclusion is measured asthe ratio of broad money supply to GDP; and, taxrate is measured as the ratio of GDP to total taxrevenue in the economy. The financial inclusionand tax rate variables use the aggregated (overall)values for all the provinces due to unavailability ofdisaggregated data.


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Total Economy Western Cape Eastern Cape

Northern Cape Free State Kwazulu-Natal

North West Gauteng Mpumalanga

Limpopo

Figure A1: Total economy and provincial Lorenz curves – based on income distribution


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Limpopo

Figure A2: Total economy and provincial Lorenz curves – based on education distribution


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Limpopo


Figure A3: Total economy and provincial Lorenz curves – based on land distribution


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0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Gin

i In

dex

(In

com

e)

Total WesternCape EasternCape NorthernCape

FreeState

KwazuluNatal NorthWest Gauteng Mpumalanga

Limpopo

Figure B1: Total economy and provincial Gini (income) index 1999–2012

0.2

0.3

0.4

0.5

0.6

0.7

0.8

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 2007 2008 2009 2010 2011 2012

Gin

i In

dex

(E

du

cati

on

)

Total WesternCape EasternCape NorthernCape

FreeState

KwazuluNatal NorthWest Gauteng Mpumalanga

Limpopo

Figure B2: Total economy and provincial Gini (education) index 1995–2012

Appendix B


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Downward trending provinces Upward trending provinces

0.5

0.55

0.6

0.65

0.7

0.75

0.8

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

Gin

i In

dex

(L

and

)

Total EasternCapeKwazuluNatal Limpopo

0.2

0.3

0.4

0.5

0.6

0.7

0.8

Gin

i In

dex

(L

and

)

WesternCape NorthernCapeFreeState NorthWestGauteng Mpumalanga

2002

2003

2004

2005

2006

2007

2008

2009

2010

2011

2012

a b

Figure B3: Total economy and provincial Gini (land) index 2002–2012Panel A: Downward trending provinces;Panel B: Upward trending provinces


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Impact of Macroeconomic, Institutional and Structural Factors on Inequality in South AfricaIntroduction and backgroundEconometric investigation of the impact of macroeconomic, institutional and structural factors on inequality in South AfricaEmpirical resultsPolicy implications: identifying a ‘sustainable’ level of inequality

Conclusions and policy recommendationsPolicy directions

NotesReferencesAppendix ATheoretical framework, methodology and data analysisData description and analysisDeriving the Lorenz curves and Gini indexEducation inequalityLand inequalityMeasuring infrastructureMeasuring governanceOther variables

Appendix B

impact of macroeconomic, institutional and structural factors ......the poverty-growth-inequality...

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