1 earnings inequality in south africa since the end of apartheid phillippe g. leite terry mckinley...

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Earnings Inequality in South Africa since the End of Apartheid

Phillippe G. LeiteTerry Mckinley

Rafael G. Osório

IPC-BrasíliaJuly 17th, 2006

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Phillippe G. Leite, Terry McKinley and Rafael G. Osório

MotivationWhy study inequality in South Africa?Our paper studies the evolution of earnings inequality in SA combining different sources of data covering 1995-2004. Why earnings? In SA earnings inequality was responsible for around 70% of the total income inequality in 2000 and also because of the availabilty of the data.

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ContributionSA has a large literature on poverty and inequality focusing on 1995 and 2000.Because earnings is the largest component of income inequality, if we understand its determinants, we can have a clearer picture of the evolution of SA inequality over a 10 year period: 1995-2004 .We can also apply static and dynamic decomposition techniques in order to identify how structural changes in SA might have affected inequality .

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Main results

Figure 5: Earning's Inequality in South Africa 1995-2004: GINI

0.50

0.53

0.55

0.58

0.60

0.63

0.65

0.68

0.70

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Gini Urban Rural

Source: Author's Calculation. OHS 1995-1999; LFS 2000:2;LFS 2001:2; LFS 2002:2; LFS 2003:2; LFS 2004:1

Gini Peak: 0.623 representing 10% for 1995-2000

2004 Gini: 0.598 representing 3.9% decrease for 2000-2004 and 5.7% increase from 1995-2004.

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Main resultsSuch trend is a hopeful sign but whether it will continue or not depends on the dynamics of factors driving inequality.Earnings inequality has risen or remains high across various categories of workers, particularly based on Race, Education or Rural/Urban differentiations.Demographic changes in the size of sub-groups are functioning to reduce inequality.However, the income gap across sub-groups is increasing.Occupational changes emerge as an influential factor driving inequalities.

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Data SourcesHousehold surveys collected by STATS SA.OHS for 1995-1999 LFS for 2000-2004.IES for 1995 and 2000 in order to link our study with the literature.Many challenges because we are merging different surveys with different concepts and definitions.

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Inequality MeasuresGini: Most popular – Income Gap between 2 individuals randomly selected from the populationGE(): Satisfy all desirable axioms of inequalityHigh values of implies more weight on the top of the distribution.GE(0): Theil-L *GE(1): Theil-TGE(2): Half of the square of CV

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Trends in Household Income Inequality

0.59

0.6

0.61

0.62

0.63

0.64

0.65

0.66

0.67

0.68

1993 1995 2000 2004

Gini

?

4.0%

8.0%

3.9%

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Figure 2: Density Function of the Logarithm of Household Per Capita Income

-0.05

0

0.05

0.1

0.15

0.2

0.25

0.3

0.35

0.4

4 5 6 7 8 9 10 11 12 13 14

Log of Household Per Capita Income

1995 2000

Source: Income and Expenditure Survey, 1995-2000.

Increase the density of the population with lower levels of income in 2000

Hollows out the middle of distribution in 2000

Similar density of the population with higher levels of income in

1995 and in 2000

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Trends in Household Income InequalityA. Labour Market ChangesFacts: Increased movement of people previously classified as

inactive to the labor market, mainly African women and former agricultural workers (small farmers). Rural migration towards urban areas.

Skill biased labor market due to liberalization of SA trade and increased openness to foreign investment (Priority of the Government’s strategy).

Recent literature, Milanovic (2005b), shows evidence that at the beginning of trade liberalization in developing countries, high-income households have been the main beneficiaries, not low-income or middle-income households.

Labor market changes can intensify between-group inequality. Increase polarization.

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Figure 1: Evolution of the Ratio of Returns to Schooling 1995-2000 in South Africa

-3.00

-2.50

-2.00

-1.50

-1.00

-0.50

0.00

Years of schooling

Returns

1995 2000

1995 -2.40 -2.15 -1.66 -0.78

2000 -2.68 -2.38 -2.01 -1.05

2000/1995 12% 11% 22% 34%

1 2 3 4

Source: IES 19995 and 2000; Author's calculationNote: Omitted category 11+

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Total Household Per Capita

Income

Total Household Per

Capita Earnings

Total Household Self-Employment / Employeer Per Capita Income

Total Household Per Capita Income

Total Household Per

Capita Earnings

Total Household

Self-Employment / Employeer Per

Proportionate factor contribution (Si) to total income

100% 62% 12% 100% 72% 5%

Concentration Index (Gini if y>0) 0.612 0.716 0.663 0.765

Ci times Si 0.379 0.089 0.480 0.035

Changing shares of different income sources

-1.5% 7.0% -6.0%

Change due tochanging concentration coefficients of income sources

4.1% 3.7% 0.2%

Interaction changes 0.1% 0.5% -0.4% Gini 2.7%Source: Author’s calculations from IES 1995 and 2000.

Table 1: The Contribution of Income Types to Gini inequality 1995-2000.

1995

Decomposition

2000

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Trends in Earnings InequalityA. Effect of Unemployment

Figure 3: Official Unemployment Rates in South Africa, 1994-2004

15

17

19

21

23

25

27

29

31

33

1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

Years

%

Source: October Household Survey 1994-1999; Labor Force Survey September round; General Household Survey.

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Trends in Earnings InequalityB. Job creation

1995 2000

Agriculture 943,800 809,600 -14%Domestic Worker 708,400 788,200 11%Self Employed 702,600 1,334,300 90%Employees 7,137,300 7,272,300 2%More than one activity 139,100 106,200 -24%

Total 9,631,200 10,312,600 7%Source: Casale, Muller and Posel (2005)

Table5: Employment Trends of workers aged 15-65 years old, 1995-2000

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Trends in Earnings Inequality

Figure 4: Employees and Self-Employed/Employers - An Historical Review

0

500,000

1,000,000

1,500,000

2,000,000

2,500,000

1995 1999 2000

Years

To

tal

of

Wo

rker

s

Mining and quarrying Manufacturing Trade Agriculture

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Evolution of Earnings InequalityFigure 6: Earning's Inequality in South Africa 1995-2004 : GE(0)

0.50

0.53

0.55

0.58

0.60

0.63

0.65

0.68

0.70

0.73

0.75

0.78

0.80

0.83

1995 1996 1997 1998 1999 2000 2001 2002 2003 2004

GE(0) Urban Rural


0.733 GE(0) Peaks: 0.735 representing 19% increase compared to

2004 GE(0): 0.619 representing 3.8% decrease compared with Peaks and 14% increase from 1995-2004.

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Evolution of Earnings InequalityFigure 7: Differences between the Lorenz Curves of Earnings - 1995, 2000 and 2004

-0.05

-0.04

-0.03

-0.02

-0.01

0

0.01

0.02

0.03

0.04

0.05

0 0.1 0.2 0.3 0.4 0.5 0.6 0.7 0.8 0.9 1

Cumulative share of people

Cum

ulat

ive

inco

me

diff

eren

ce

2000-1995 2004-2000

Source: Author's Calculation. OHS 1995; LFS 2000:2;LFS 2004:1

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Converging Towards and Diverging away from the mean

Employment Status 2000 2001 2002 2003 2004 2000 2001 2002 2003 20041 = Legislators, senior officials and managers 4.6% 5.7% 5.8% 5.8% 6.3% 3.43 3.24 3.15 3.32 3.222 = Professionals 4.4% 4.2% 3.9% 4.0% 4.1% 3.34 3.14 3.34 3.22 3.183 = Technical and associate professionals 9.0% 10.2% 10.6% 9.9% 9.5% 1.90 1.70 1.89 1.80 1.784 = Clerks 8.9% 9.8% 9.9% 9.7% 9.9% 1.25 1.24 1.28 1.20 1.255 = Service workers and shop and market sales workers 12.0% 13.0% 11.1% 11.8% 11.8% 0.79 0.72 0.71 0.74 0.766 = Skilled agricultural and fishery workers 9.3% 4.4% 6.4% 3.0% 2.6% 0.21 0.51 0.35 0.28 0.357 = Craft and related trades workers 13.0% 13.4% 12.6% 12.6% 12.3% 0.90 0.81 0.77 0.82 0.808 = Plant and machine operators and assemblers 9.9% 10.3% 10.1% 10.4% 10.6% 0.78 0.71 0.69 0.75 0.709 = Elementary Occupation 20.0% 20.1% 20.9% 23.3% 23.9% 0.42 0.37 0.35 0.37 0.3510 = Domestic workers 8.7% 8.9% 8.5% 9.5% 8.9% 0.21 0.19 0.17 0.20 0.20

Table 11: Characteristics of employment status and Sector of activitities (industry)Composition Relative means

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% Relative % Relative

8% 0.38 7% 0.1616% 0.42 19% 0.2122% 0.62 23% 1.1249% 1.28 48% 1.255% 2.69 4% 2.57


62% 1.09 54% 1.3738% 0.85 46% 0.56


65% 0.72 71% 0.8611% 0.72 11% 0.9621% 2.11 14% 1.703% 1.41 4% 0.89

1995 2000Schooling01-45-78-1011+

GenderMaleFemale

Population GroupAfricanColouredWhitesOther

Table 4: Participation share and relative wages of workers in South Africa

African Schooling % Relative % Relative

0 11% 0.38 9% 0.161-4 21% 0.42 23% 0.205-7 26% 0.58 26% 1.238-10 39% 0.94 39% 1.0911+ 2% 2.05 2% 2.69

Coloureds Schooling % Relative % Relative

0 7% 0.39 4% 0.161-4 21% 0.41 18% 0.235-7 33% 0.58 29% 0.628-10 38% 0.97 46% 1.5111+ 2% 1.92 3% 1.89

Whites Schooling % Relative % Relative

0 0% - 0% -1-4 0% 0.56 0% 0.325-7 3% 1.74 4% 0.468-10 82% 1.94 82% 1.3111+ 16% 3.30 14% 3.13

1995 2000

Source: Author’s calculations from IES 1995 and 2000.

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Dynamic DecompositionFLL(2006) shows that the Rb term can be also decomposable into 3 categories using a dynamic decomposition based on Mookherje and Shorrocks (1982) and Jenkins (1995):

a) within inequality part b) allocation effect: within inequality constant but not

shares c) allocation effect: relative means constant but not

shares d) income effect: captures changes in mean income

across sub-groups.

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Percentage change in GE(0)

a b c d a b c d a b c dUrban/rural 12.2% -0.6% -0.6% 4.2% -2.1% 0.1% 0.4% 1.0% 9.5% -0.2% -0.3% 5.5%Region 15.7% 0.3% -0.2% -0.6% -2.8% -0.7% 0.0% 2.8% 12.5% -0.5% -0.1% 2.6%Age 15.5% 0.5% -0.4% -0.4% -0.7% 0.2% -0.1% 0.0% 14.6% 0.9% -0.5% -0.5%Education 15.6% -0.3% 0.1% -0.1% -2.0% 0.6% -1.4% 2.2% 13.4% 0.3% -1.5% 2.4%Gender 16.2% -0.2% 0.3% -1.1% 1.1% -0.1% 0.0% -1.7% 17.1% 0.2% 0.2% -2.9%Race 12.9% 1.1% -4.4% 4.7% 1.5% 1.2% -1.6% -1.2% 14.9% 2.3% -6.1% 3.1%Occupation 14.9% 0.6% -1.9% 1.6% -3.8% -2.5% 2.6% 3.1% 6.8% 0.0% 1.5% 6.2%Industry - - - - -1.3% -0.3% 0.0% 0.8% - - - -Note: term a is the pure inequality effect; terms b and c are the allocation effect; term d is the income effect.


Table 13: The Percentage of Inequality Explained Over Time by Household Characteristics.

14.5%

1995-2004

15.3%

1995-2000 2000-2004

-0.7%

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Macroeconomic TrendsGovernment Policies:

focusing on macroeconomic stability and exports in order to promote growth;

making labour markets more flexible; improving productivity; increasing training and employment for unskilled and

low-skilled workers; increasing the proportion of non-whites and females

at all levels of employment; providing a right to annual leave; and imposing rules and procedures that prevent

unfair dismissal.

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According to the prevailing literature: Policies have failed to generate growth rapid enough

and employment-intensive enough to counteract rising unemployment

However, most of these judgments are based on evaluating trends only up to 2000.

Human development outcomes have been more encouraging due to more equitably distributed fiscal expenditures and the expansion of access to public services.

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YearEconomic

growthInflation

rate1

Food Prices - Changes in

Metrop

Exchange rate (R/$US),

year end

1994 3.4% 9.8% 17.5% R3.54 1995 3.3% 6.9% 3.7% R3.65 1996 3.5% 9.3% 9.9% R4.68 1997 1.7% 6.2% 6.5% R4.87 1998 0.1% 9.0% 6.4% R5.901999 4.0% 2.2% 5.1% R6.15 2000 4.7% 7.0% 5.4% R7.64 2001 2.1% 4.6% 11.4% R11.682002 4.3% 12.4% 16.1% R8.942003 2.4% 0.3% 2.6% R6.532004 5.8% 3.4% 1.5% R5.72

Source: Reserve Federal Bank; Statistics of South Africa1SA STATISTICS, 2004/05

Table 7: Socio Economic Picture from 1994-2004 in South Africa

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Correlation with Macroeconomics factorsInflation was negatively correlated with inequality but not statistically significant.The correlation of the Gini coefficient of earnings with the Unemployment rate is positive (0.708) and significant (0.02 p-value). Contrary to expectations, Growth has also been associated with rising inequality. When we hold unemployment constant, we find that rising inequality is associated with a fall in average earnings (-0.575 sig at 10%).

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ConclusionSA earnings inequality rose sharply initially until 1999/2000 and then fell marginally.Such a trend is a hopeful sign but whether it will continue or not depends on the dynamics of factors driving inequality.Rising unemployment was the principal immediate factor.Higher rates of participation of Africans, rural residents and women pushed wages down, increasing inequality (rising labour supply increasing bargain power of employers).

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ConclusionExport-led growth strategy has benefited higher income workers who possess education and skills.Decline in inequality will continue?

Earnings of higher-income workers are decreasing relative to mean earnings.

But lower-income workers are also losing in relative terms.

Employment Status has emerged as an influential factor in driving earnings inequality, especially during the 2000s. On a sectoral basis, agriculture has declined in importance and services have risen. Also a higher level of formalisation for employees.

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ConclusionEarnings inequality has risen or remains because of sharp differentials across various categories of workers, particularly based on Race, Education or Rural/Urban differentiations.Demographic changes in the size of such groups are functioning to reduce inequality.However, the income gap across sub-groups is increasing.Occupational changes emerge as an influential factor driving inequalities. Earnings inequality likely to remain high as long as unemployment is high.Will increased openness generate widespread employment and rising earnings for low-skill workers?

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