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Nuanced Accountability: Voter Responses to Service Delivery in Southern Africa

Published online by Cambridge University Press:  13 December 2017

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Abstract

Various theories of democratic governance posit that citizens should vote for incumbent politicians when they provide good service, and vote for the opposition when service delivery is poor. But does electoral accountability work as theorized, especially in developing country contexts? Studying Southern African democracies, where infrastructural investment in basic services has expanded widely but not universally, we contribute a new empirical answer to this question. Analyzing the relationship between service provision and voting, we find a surprising negative relationship: improvements in service provision predict decreases in support for dominant party incumbents. Though stronger in areas where opposition parties control local government, the negative relationship persists even in those areas where local government is run by the nationally dominant party. Survey data provide suggestive evidence that citizen concerns about corruption and ratcheting preferences for service delivery may be driving citizen attitudes and behaviors. Voters may thus be responsive to service delivery, but perhaps in ways that are more nuanced than extant theories previously recognized.

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Articles
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© Cambridge University Press 2017 
Figure 0

Table 1 Key Aggregate-Level Variables

Figure 1

Fig. 1 South African basic service provision: 2001 and 2011 (household coverage within wards). Note: Each panel presents the distribution of service delivery coverage for households for a given service, at the ward level, in 2001 (solid line) and 2011 (dashed lines). The vertical lines are the year means (2001 is solid, 2011 dashed). The bottom right panel presents some key figures from the graphs. First, the percentage changes for each service. Second, the estimated number of new non-White households that have received services since 2001. This was estimated in a very simple fashion by taking the estimated number of households in South Africa (≈ 13 million – in 2001 there were roughly 12 million households in South Africa, and in 2011 roughly 15 million). This was then multiplied by the change in coverage.

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Table 2 Effect of Change in Service Delivery on Change on National ANC Vote Share

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Table 3 Heterogeneous Effects of Change in Service Delivery on Change on National ANC Vote Share

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Table 4 Heterogeneous Effects of Change in Service Delivery on Change on Local ANC Vote: ANC Municipalities Only

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Fig. 2 Service access effects on attitudes and voting intentions of Black South Africans. Note: Each panel presents estimated coefficients from five OLS regressions. In each regression, the titled service delivery item is the independent variable, and the survey response listed on the X-axis is the dependent variable. Each coefficient is estimated with survey wave and municipality fixed effects, and standard errors are calculated by clustering data at the municipality-level. Lines represent 95% cluster-robust confidence intervals. Individual-level controls include gender, age, education-level, dummy variables for home language (Xhosa or Zulu, with other as missing category) and urban residence. Note that data about own water were only contained in rounds 4 and 5; and data about own toilets were only contained in wave 5.

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Fig. 3 Household, enumeration-area service delivery and citizen attitudes and intentions, matching estimates (ANC-controlled municipalities). Note: Each panel presents ATT estimates of the listed service delivery item from calculations of difference in means between ‘treated’ and ‘untreated’ groups. Afrobarometer survey items listed on the X-axes are the outcome variables from separate analyses. Lines represent 95% confidence intervals.

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Table 5 Replicating Results in Other Southern African Democracies

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Fig. 4 Local-level service coverage effects on citizen attitudes and voting intentions. Note: Each panel presents estimated coefficients from five OLS regressions. In each regression, the titled service delivery item is the independent variable, and the survey response listed on the X-axis is the dependent variable. Each coefficient is estimated with survey wave fixed effects, and standard errors are calculated by clustering data at the municipality- or ward-level (depending on analysis). Lines represent 95% cluster-robust confidence intervals. Individual-level controls include gender, age, education-level, dummy variables for home language (Xhosa or Zulu, with other as missing category) and a dummy variable for urban residence; and at the municipal-level, we control for log of population. Recall that these regressions only include round 2, 2.5 and 5.

Figure 9

Table 6 Service Provision and Perceptions of Corruption (AB round 5 Data, Black Respondents in ANC-Controlled Municipalities)

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