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VOTER BEHAVIOUR IN THE METROS: TECHNICAL REPORT

This Brief done by Charles Simkins is the technical report which follows the Brief on voter behaviour in the metros.


This report accompanies a brief on voter behaviour in the metros in the 2016 local government election.  It details the calculations behind the interpretation contained in the brief.

 

Introduction

Table 1 sets out the allocation of council seats in the eight metros in 2011 and 2016.

 

Table 1 - Metro seat outcomes

       
             
             

2011

 

ANC

DA

EFF

Others

Total

             

Tshwane

 

118

82

 

10

210

Johannesburg

153

90

 

17

260

Ekuruhleni

125

62

 

15

202

Ethekwini

126

43

 

35

204

Cape Town

73

135

 

13

221

Nelson Mandela

63

48

 

9

120

Buffalo City

71

21

 

8

100

Mangaung

65

25

 

7

97

             

Total

 

794

506

 

114

1414

Per cent

 

56,2%

35,8%

 

8,1%

 
             

2016

 

ANC

DA

EFF

Others

Total

             

Tshwane

 

89

93

25

7

214

Johannesburg

121

104

30

15

270

Ekuruhleni

109

77

25

13

224

Ethekwini

126

61

8

20

215

Cape Town

57

154

7

13

231

Nelson Mandela

50

57

6

7

120

Buffalo City

60

24

8

8

100

Mangaung

58

27

9

6

100

             

Total

 

670

597

118

89

1474

Per cent

 

45,5%

40,5%

8,0%

6,0%

100,0%

 

On the face of it, the decline in support for the ANC seems precipitous, but if one adds together the number of seats commanded by the ANC and its three breakaway parties (COPE, EFF and UDM) the total comes to 824 (58.3%) in 2011 and 800 (54.8%) in 2016.  The drop of 3.5% in support for the group contrasts with a gain of 4.7% for the DA and a drop of 1.2% for the other small parties.  The change remains significant, but smaller. 

The purpose of this report is to analyse the change more closely and to bring the results of the 2014 national election into the picture.  The comparison with the national election is facilitated by the fact that the same voting stations were used in the 2014 national and the 2016 local government elections, except in Mangaung.   Some of the voting stations were split into two, or occasionally more, the number of registered voters in each split being separately identified.  This means that geographical areas in 2014 corresponding to the 2016 wards can be identified, since results by voting district are available for both years. 

 

Turnout analysis

For each of the three elections (2011, 2014 and 2016), the percentages of registered voters who voted in each voting district is reported.  The percentages of votes spoilt is also reported.  From these statistics, the net turnout – the percentage of registered voters who cast a valid vote – can be calculated.  It is the net turnout (simply referred as ‘turnout’ in what follows) that forms the basis for analysis in this section.

Table 2 sets out turnout rates for each metro in 2011, 2014 and 2016, separately for wards with an ANC plurality and wards with a DA plurality.  The difference between the ANC and DA ward turnout is calculated.

Not surprisingly, the turnout rate in the 2014 national election is markedly higher than in both the 2011 and 2016 local government elections in all metros.  In all metros except Ethekwini, DA wards saw a higher turnout than ANC wards in all three elections. 

In Tshwane, Johannesburg, Ekuruhleni, Cape Town, Ethekwini and Mangaung, the differences between the DA wards and ANC wards turnouts were higher in the 2014 national election than in both the 2011 and 2016 local government elections.  In Nelson Mandela Bay and Buffalo City, the turnout differences in the 2014 national election were lower than in the 2011 local goverment election.   In all metros except Mangaung, the turnout differential widened in favour of DA wards between the 2011 and 2016 local government elections.  This means that part of the swing towards the DA in the 2016 local government election compared with the 2011 local government election was a consequence of changing turnout differentials.

 

 

Table 2 - Turnout analysis

   
       
 

ANC wards

DA wards

Difference

Tshwane

     

2016

51,64%

67,91%

16,27%

2014

70,75%

80,22%

9,47%

2011

50,18%

62,36%

12,18%

Johannesburg

     

2016

50,79%

63,20%

12,40%

2014

71,56%

76,56%

5,00%

2011

50,95%

59,76%

8,81%

Ekuruhleni

     

2016

51,60%

65,27%

13,67%

2014

73,68%

77,25%

3,57%

2011

52,70%

60,39%

7,69%

Cape Town

     

2016

53,73%

66,57%

12,84%

2014

72,34%

75,83%

3,49%

2011

60,44%

65,42%

4,98%

Ethekwini

     

2016

57,09%

58,89%

1,80%

2014

78,81%

73,11%

-5,70%

2011

58,45%

57,20%

-1,25%

Nelson Mandela

     

2016

57,08%

68,51%

11,43%

2014

71,62%

76,15%

4,53%

2011

62,05%

65,81%

3,76%

Buffalo City

     

2016

52,03%

67,32%

15,29%

2014

72,59%

76,85%

4,26%

2011

54,69%

57,58%

2,89%

Mangaung

     

2016

53,22%

62,70%

9,47%

2014

72,30%

75,67%

3,37%

2011

51,52%

62,13%

10,61%

 

 

Swing analysis

For both the 2104 national election and the 2016 local government election, it is possible to calculate the percentage distribution of votes gained by the ANC, DA, EFF and smaller parties together in each geographical areas corresponding to a 2016 ward.  Can a valid swing analysis be based on these percentages, given the usually substantial different turnouts in general and local government elections?

Two conditions taken together are sufficient for validity:

(i) As a general rule, no-one votes in a local government election without simultaneously being willing to vote in a national election

(ii) As a general rule, the probability of voting in a local government election, given willingness to vote in a national election is independent of party affiliation.

The first condition is likely to be true with few exceptions.  Between a national election and the following local government election, however, the probability of voting in the local government election, given willingness to vote in the national election may well depend on party affiliation.  The question is then this:  Is this dependence a consequence of the violation of condition (ii) or not?  If it is, the comparison will be distorted.  If not, then the differential probability is a result of changing patterns of political behaviour.  In the latter case, suppose the probability dips for supporters of a particular party.  This will indicate a drop in average enthusiasm of that party’s supporters and is validly represented in swing statistics.  It is impossible to tell from available data whether condition (ii) is violated or not and, if its, the extent of the violation.  So the results below should be interpreted with caution. 

What does not matter is the differential turnout across wards as indicated in Table 2, since the analysis is carried out ward by ward.  

There is a further difficulty.  In local government elections, voters have to vote in the voting district in which they are registered.  The rule in national elections is that voters are expected to vote at the voting station where they are registered to vote.  However, voters outside their voting districts on the day of the election may vote at another voting station.  From a swing analysis point of view, this introduces noise into the system, more pronounced at the voting district than at the ward level.

There were changes in pluralities between the ANC and DA in 1.8% of the 2016 ward areas between 2011 and 2014 and 3.6% between 2014 and 2016.  Table 3 sets the changes out by metro.

 

 

Table 3 - Ward plurality shifts, 2014 to 2016

     
           
 

2011-2014

2014-2016

 
 

ANC to DA

DA to ANC

ANC to DA

DA to ANC

Total wards

           
           

Tshwane

1

4

5

1

107

Johannesburg

1

1

6

1

135

Ekuruhleni

1

0

0

0

112

Ethekwini

0

2

9

1

110

Cape Town

0

0

0

0

116

Nelson Mandela

1

0

1

0

60

Buffalo City

0

0

0

1

50

Mangaung

0

2

2

0

50

           

Total

4

9

23

4

740

 

The swings, in percentages of total votes cast, are set out in Table 4.

 

Table 4 – Distribution of wards by swing between 2014 and 2016

 

Swing away

Swing towards

 

More  than

4% to 8%

Less than

Less than

4% to 8%

More  than

 

8%

 

4%

4%

 

8%

             

Tshwane

           

2014-2016

           

ANC

32

47

25

2

 

1

DA in ANC wards

 

1

 

11

45

15

DA in DA wards

 

1

 

2

5

27

EFF in ANC wards

   

14

47

9

2

EFF in DA wards

   

26

9

   

2011-2016

           

ANC

66

13

25

2

 

1

DA in ANC wards

1

3

5

17

29

14

DA in DA wards

3

1

18

11

4

1

EFF in ANC wards

       

3

66

EFF in DA wards

     

22

10

6

Johannesburg

           

2014-2016

           

ANC

29

77

25

3

 

1

DA in ANC wards

   

2

70

10

7

DA in DA wards

1

 

6

4

16

19

EFF in ANC wards

1

1

10

61

15

1

EFF in DA wards

   

23

23

   

2011-2016

           

ANC

94

25

12

2

 

2

DA in ANC wards

 

1

10

60

13

5

DA in DA wards

4

5

8

24

5

 

EFF in ANC wards

     

1

4

84

EFF in DA wards

     

17

23

6

 

 

 

 

Swing away

Swing towards

 

More  than

4% to 8%

Less than

Less than

4% to 8%

More  than

 

8%

 

4%

4%

 

8%

             

Ekuruhleni

           

2014-2016

           

ANC

9

60

36

6

1

 

DA in ANC wards

 

1

7

64

5

1

DA in DA wards

1

 

1

2

23

7

EFF in ANC wards

   

8

65

5

 

EFF in DA wards

     

27

7

 

2011-2016

           

ANC

67

20

22

2

 

1

DA in ANC wards

 

1

5

63

9

 

DA in DA wards

1

3

13

9

6

2

EFF in ANC wards

     

1

9

68

EFF in DA wards

     

19

8

7

Ethekwini

           

2014-2016

           

ANC

42

32

20

8

1

6

DA in ANC wards

1

1

22

40

9

13

DA in DA wards

2

2

2

5

4

7

EFF in ANC wards

   

16

67

 

3

EFF in DA wards

   

7

16

   

2011-2016

           

ANC

29

16

23

26

8

7

DA in ANC wards

 

3

14

47

11

11

DA in DA wards

   

3

9

2

9

EFF in ANC wards

     

47

36

3

EFF in DA wards

     

23

   

 

 

 

 

Swing away

Swing towards

 

More  than

4% to 8%

Less than

Less than

4% to 8%

More  than

 

8%

 

4%

4%

 

8%

             

Cape Town

           

2014-2016

           

ANC

25

33

56

1

1

 

DA in ANC wards

     

17

12

6

DA in DA wards

2

 

13

44

16

6

EFF in ANC wards

   

6

27

1

1

EFF in DA wards

   

4

77

   

2011-2016

           

ANC

40

26

45

4

1

 

DA in ANC wards

     

13

15

7

DA in DA wards

1

 

60

28

14

8

EFF in ANC wards

     

2

21

12

EFF in DA wards

     

76

5

 

Nelson Mandela

           

2014-2016

           

ANC

18

16

22

2

2

 

DA in ANC wards

   

2

29

5

2

DA in DA wards

 

2

1

9

6

4

EFF in ANC wards

   

6

27

4

1

EFF in DA wards

   

8

14

   

2011-2016

           

ANC

33

7

11

8

1

 

DA in ANC wards

   

1

17

19

1

DA in DA wards

 

3

7

11

 

1

EFF in ANC wards

     

4

12

22

EFF in DA wards

     

21

1

 

 

 

 

 

Swing away

Swing towards

 

More  than

4% to 8%

Less than

Less than

4% to 8%

More  than

 

8%

 

4%

4%

 

8%

Buffalo City

           

2014-2016

           

ANC

23

18

8

   

1

DA in ANC wards

   

5

29

1

9

DA in DA wards

1

     

1

4

EFF in ANC wards

   

3

28

12

1

EFF in DA wards

   

1

5

   

2011-2016

           

ANC

31

7

7

4

 

1

DA in ANC wards

1

1

9

23

6

4

DA in DA wards

1

1

1

3

   

EFF in ANC wards

     

4

10

30

EFF in DA wards

     

6

   

Mangaung

           

2014-2016

           

ANC

18

10

17

2

1

1

DA in ANC wards

1

1

4

28

2

1

DA in DA wards

 

1

 

3

4

4

EFF in ANC wards

 

1

15

18

3

 

EFF in DA wards

   

5

7

   

2011-2016

           

ANC

34

2

6

4

1

2

DA in ANC wards

1

2

11

22

 

1

DA in DA wards

2

5

3

1

1

 

EFF in ANC wards

       

3

34

EFF in DA wards

     

8

3

1

 

 

 

 

Swing away

Swing towards

 

More  than

4% to 8%

Less than

Less than

4% to 8%

More  than

 

8%

 

4%

4%

 

8%

             

All

           

2014-2016

           

ANC

196

293

209

24

6

10

DA in ANC wards

2

4

42

288

89

54

DA in DA wards

7

6

23

69

75

78

EFF in ANC wards

1

2

78

340

49

9

EFF in DA wards

   

74

178

7

 

2011-2016

           

ANC

394

116

151

52

11

14

DA in ANC wards

3

11

55

262

102

43

DA in DA wards

12

18

113

96

32

21

EFF in ANC wards

     

59

98

319

EFF in DA wards

     

192

50

20

 

The swing away from the ANC is evident everywhere between 2011 and 2016, with the ANC sustaining losses in 90% of 2016 ward areas.  There was a swing towards the DA in wards with an ANC plurality in 2011 in 86% of such ward areas, compared with 51% in ward areas with a DA plurality in 2011.

The swing away from the ANC is also evident between 2014 and 2016, with the ANC sustaining losses in 95% of 2016 ward areas.  There was a swing towards the DA in wards with an ANC plurality in 2014 in 94% of such ward areas, compared with 86% in wards with a DA plurality in 2014.

The EFF improved its position overall, though there was a swing away from it in 152 wards, and in most of the remaining wards it gained between 0% and 4% of votes cast. 

A further analysis is possible.  One can standardise 2011 results by reweighting votes cast in each 2016 ward area in such a way that (i) the 2014 turnout by 2016 ward area is reproduced and (ii) the 2016 turnout by 2016 ward area is reproduced.  The purpose of this standardisation is to remove the effect of differential turnouts on outcomes.  A similar exercise is carried out for the 2014 and 2016 results.  The results are reported in Table 5.

 

 

Table 5 - Standardised turnouts all metros

   
         
 

ANC

DA

EFF

OTHER

2016 standard

       

2011

52,74

38,37

0,00

8,88

2014

49,48

37,33

6,62

6,56

2016

43,30

41,71

7,64

7,36

2014 standard

       

2011

55,52

35,41

0,00

9,07

2014

52,00

34,38

7,03

6,59

2016

45,57

38,80

8,13

7,50

2011 standard

       

2011

54,02

37,03

0,00

8,94

2014

50,72

36,12

6,62

6,54

2016

44,33

40,50

7,67

7,50

         

Improved turnout 2016

 

1,34

   

Per cent

 

39%

   

 

Standardised results are based on the turnouts in each of the three elections.  Thus, the 2016 standardised results indicate what the results in each of three elections would have been if the turnout in each election had followed the 2016 pattern.  Similarly, the 2014 standardised elections indicate what the results in all three elections would have been if all elections had followed the 2014 election turnout pattern.  The 2011 standardised results indicate what the results in all three elections would have been if all elections had followed the 2011 election turnout pattern.

Three features of Table 5 stand out.  The first is that the DA did slightly worse in the 2014 national election than in the 2011 local government election on all three standards.  This contrasts with the gains between the 2014 national election and the 2016 local government election. 

Secondly, the effect of the increased differential in turnout between DA and ANC wards on the increase in the DA vote between 2011 and 2016 can be assessed by comparing the difference between the 2016 DA vote in the 2016 standardisation and the 2016 DA vote in the 2011 standardisation.   This indicates that a swing of 1.34 can be attributed to the increased differential in turnout, 39% of the total DA gain between 2011 and 2016.

Thirdly, while the absolute swing towards the EFF between 2014 and 2016 was smaller than the absolute swing towards the DA in both the 2014 and 2016 standardisations, the swing relative to the 2014 share of the vote was higher than towards the EFF.  

Where does the 2016 local government election imply about ward contestability?  Table 6 presents the number of wards which satisfy the following conditions:

·         ANC wards where the vote for the ANC was less than 60% of the total

·         DA wards where the vote for the ANC was less than 60% of the total

·         ANC wards in which the DA vote was less than 10% of the total

·         ANC wards in which the EFF votes was less than 10% of the total.

Just over a fifth of ANC and DA wards had ANC votes and DA votes respectively that were below 60%.  On the other hand, in 57% of ANC wards, the DA had less than 10% support, and in 42% of ANC wards the EFF had less than 10% support.

 

Table 6 - Contestability of wards, 2016

       
             
 

ANC

DA

ANC

ANC

ANC

DA

 

wards

wards

wards

wards

wards

wards

 

ANC<60%

DA<60%

DA<10%

EFF<10%

   
             

Tshwane

17

5

18

8

68

39

Johannesburg

22

18

50

15

83

52

Ekuruhleni

17

8

63

20

77

35

Ethekwini

20

13

52

76

79

29

Cape Town

4

1

21

29

81

35

Nelson Mandela

10

2

25

23

37

23

Buffalo City

15

0

28

28

45

5

Mangaung

4

3

32

13

38

12

             

All

109

50

289

212

508

230

Per cent of ANC wards

21,5%

 

56,9%

41,7%

   

Per cent of DA wards

 

21,7%

       

 

 

Race, class and voting behaviour

The unit of analysis in this section is the 2016 ward.  We have no data on individuals.

Socio-economic data at the ward level are only available from the 2011 census.  These data are therefore five years out of date.  Yet it is likely expect that the conditions have not changed greatly on average, though there will have been sharper changes in some individual wards, particularly those which have seen a substantial change in the racial composition of the population.

There is also the problem of relating 2016 wards to 2011 wards.  However, one can map 2016 wards on to 2011 wards using voting station numbers.  The criterion for a match is whether a majority of voting stations in 2016 map on to a particular ward in 2011.

Dropping the two wards with IFP pluralities in 2016, 635 (86%) wards out of 740 in 2016 meet this criterion, and it is on this subset that the analysis is based.  In party plurality terms, the subset has roughly the same distribution as the entire set: the ANC has a plurality in 61.6% of the wards in the subset, compared with 62.4% in the entire set.

The race variable is simply the proportion of the ward population age 18 and over that is Black/African.  The class variable is composed from three variables:

·         The employment rate

·         The average years of education among people over the age of 25

·         The logarithm of the median individual income

These variables are strongly correlated.  They have been fed into a factor analysis, and the first factor is interpreted as the class indicator.

Two analyses are then conducted.  The first divides that the race variable into three categories:

·         More than 90% black

·         Between 50% and 90% black

·         Below 50% black,

 

and the class variable (which is measured in terms of z-values, i.e. values from a normal distribution with mean zero and standard deviation one) is also divided into three categories:

 

·         z less than -0.5  (low)

·         z between -0.5 and 0.5  (medium)

·         z above 0.5 (high).

 

These two classifications produce a table with nine cells.  The wards with ANC pluralities are distributed across the cells in Table 7, as are the wards with DA pluralities. 

 

Table 8 considers all votes cast.  It carries out a logit regression[1], with each of the percentage of votes gained by the ANC, DA and EFF as dependent variables, and the race and class variables as independent variables.  It considers three regression models: Model 1 with race only as an independent variable, Model 2 with class only as an independent variable, and Model 3 with both as independent variables.

 

Both tables lead to the conclusion that race continues to outweigh class in determining the voting pattern in wards.  In Table 7, the ANC has pluralities in 97% of the wards that are over 90% black, 68% in wards that are 50% - 90% black and 8% in wards which are less than 50% black.  By contrast, it has pluralities in 91% of the wards that have low socio-economic scores, 69% in wards with medium socio-economic scores and 14% in wards with high socio-economic scores.  Table 7 more strongly indicates the same conclusion.  For each party, the proportion of the variance explained by race is higher than the proportion explained by class, and in the case of the DA, the coefficient of the class variable in Model 2 has the wrong sign.  There is a correlation between the race and the class variables of -0.67, negative because the higher the proportion of black people in the population, the lower tends to be the socio-economic score.  In Model 3, the class coefficients have the expected signs for the ANC and the DA.  The class coefficient in Model 3 for the EFF is positive.  But the addition of the class variable to the race variable adds little to explanatory power (as measured by the increase in variance explained), while the addition of the race variable to the class variable adds considerably more.

 

 

Table 7 – Race and class as determinants of voting behaviour, 2016

Pluralities

 

Per cent black

     
   

More than 90

50 to 90

Less than 50

Total

ANC

         
 

Low

168

26

2

196

Class

Medium

121

43

7

171

 

High

5

11

8

24

 

Total

294

80

17

391

DA

         
 

Low

5

0

14

19

Class

Medium

3

18

56

77

 

High

0

19

129

148

 

Total

8

37

199

244

           

Per cent ANC

       
 

Low

97%

100%

13%

91%

Class

Medium

98%

70%

11%

69%

 

High

100%

37%

6%

14%

 

Total

97%

68%

8%

62%

 

 

Table 8 – Race, class and votes, 2016

Regression

       

Dependent variable

     

y = ln(anc/(100-anc))

     
         
   

Model 1

Model 2

Model 3

Coefficients

       

Race

 

4,13

 

3,45

Class

   

-1,20

-0,37

R-squared

 

0,85

0,55

0,88

         

Dependent variable

     

y = ln(da/(100-da))

     
         
   

Model 1

Model 2

Model 3

Coefficients

       

Race

 

-5,28

 

-4,29

Class

   

-0,95

0,53

R-squared

 

0,86

0,58

0,90

         

Dependent variable

     

y = ln(eff/(100-eff))

     
         
   

Model 1

Model 2

Model 3

Coefficients

       

Race

 

2,39

 

3,17

Class

   

-0,35

0,42

R-squared

 

0,55

0,09

0,62

         

All coefficients significantly

   

different from zero at

     

the 5% level

       

 

 

Conclusions

The conclusions from this study can be summarised as follows:

1. When one considers the ANC alone as a party, the loss of share of seats in all the metros was 10.7% between 2011 and 2016.  When one considers the ANC along with the parties which have split from it since 1994, the loss drops to 3.5% for the group.  The DA has been the beneficiary primarily of both the decrease in share of ANC and splinter parties, and secondarily the decrease in the share of smaller parties other than the ANC and splinters.  The EFF won 8.0% of the seats in all the metros in 2016.

2. In Tshwane, Johannesburg, Ekuruhleni, Cape Town, Ethekwini and Mangaung, the differences between the DA ward and ANC ward turnouts were higher in the 2014 national election than in both the 2011 and 2016 local government elections.  In Nelson Mandela Bay and Buffalo City, the turnout differences in the 2014 national election were lower than in the 2011 local government election.   In all metros except Mangaung, the turnout differential widened in favour of DA wards between the 2011 and 2016 local government elections.  This means that part of the swing towards the DA in the 2016 local government election compared with the 2011 local government election was a consequence of changing turnout differentials.

3. A swing analysis between the 2014 national election and the 2016 local government election and between the 2011 local government election can be carried out on a ward by ward basis.  Its validity depends on the assumption that, as a general rule, the probability of voting in a local government election, given willingness to vote in a national election is independent of party affiliation.  This assumption is impossible to check on the basis of existing information, and swing calculations must be interpreted with caution.  However, carrying out the analysis ward by ward eliminates the effect of differential turnouts within the metros.

4. There were changes in the relative number of votes for the ANC and DA in 13 out of 740 wards between 2011 and 2014, 4 towards the DA and 9 towards the ANC. Between 2014 and 2016 there was a swing of 23 towards the DA and of 4 to the ANC.

5. Between 2011 and 2016, there was a swing away from the ANC in 661 wards.  Within wards with an ANC plurality in 2011, there was a swing towards the DA in 408 wards.  Within wards with a DA plurality in 2011, there was a swing towards the DA in 149 wards.  Between 2014 and 2016, there was a swing towards the EFF in 398 wards with an ANC plurality in 2014 and in 185 wards with a DA plurality in 2014. 

6. Standardisation of results in each election across all metros to 2011, 2014 and 2016 turnouts yields three new insights.  First, compared with the 2011 local government election, the DA lost ground slightly between 2014 national election on all standardisations, while increasing it substantially between 2014 and 2016.  Secondly, nearly 40% of the DA gain between 2011 and 2016 can be attributed to an increase in differential turnouts in wards with ANC and DA pluralities in 2011.  Thirdly, although the absolute percentage swing towards the DA was higher than the swing towards the EFF on both the 2014 and 2016 standardisations, the swing relative to the 2014 vote share was higher for the EFF than for the DA.

7. Four indices of the contestability of wards can be calculated.  Just over one-fifth of the wards with ANC pluralities are held with less than 60% of the vote going to the ANC, and the DA is in virtually the same position.  On the other hand, the DA received less than 10% of the vote in 57% of wards with ANC pluralities.  The corresponding figure for the EFF is 42%, indicating that the EFF achieved more than 10% of the vote in 58% of wards with ANC pluralities.

8. When the composition of wards by race and by class (measured by a factor extracted from three variables: the employment rate among people over 25, the average years of education among people over 25, and the logarithm of median individual income) is considered, it is evident that the racial composition of wards outweighs their class composition.  When both race and class measures are included in a regression analysis, class has a secondary impact on outcomes, in the expected direction in the case of the ANC and DA, and a positive impact in the case of the EFF.  Again these results should be interpreted with caution since the socio-economic information is five years out of date, and it has been possible to match only 57% of 2016 wards with 2011 wards.

9. Looking forward to the national election in 2019 and the local government election to 2021, socio-economic improvement is likely to lead to an improvement in the DA’s performance.  But the effect will be very small, partly because of the secondary role of class in determining electoral outcomes, and partly because expected growth in real per capita income will be very low.  What will matter much more will be (i) the states of the ANC, DA and EFF at the national level in the coming years and (ii) the extent to which there is an improvement or deterioration in service delivery in each metro and which party or parties get the credit or blame.

 

The co-operation of Anele Mtwesi and Kimera Chetty in assembling information from the 2016 local government election is gratefully acknowledged.

 

Charles Simkins
Head of Research
charles@hsf.org.za

 

 



[1] A logit regression is an appropriate form of regression when the dependent variable is a percentage