Much of the literature on political behavior in Africa’s new semi-democracies has treated partisan affiliation as weak, purely pragmatic, or a proxy for other, more meaningful identities such as ethnicity. In this article, I dispute these conceptions by demonstrating that partisanship in an African context, like partisanship in established democracies, is a psychologically meaningful identity that can inspire voters to engage in motivated reasoning. By combining survey data with an original dataset of objective indicators of local public goods quality in Uganda, I show that supporters of the incumbent president systematically overestimate what they have received from government, while opposition supporters systematically underestimate. Partisan support precedes, rather than results from, this mis-estimation. I also show that partisans of the incumbent (opposition) are significantly more (less) likely to attribute any bad outcomes they observe to private actors rather than the government. I argue that these findings are consistent with the predictions of social identity theory: the conflict that marked many African political transitions, and the mapping of African parties onto existing social cleavages, are sufficient conditions for the creation of strong political-social identities like those that characterize partisanship in the West. My findings indicate that Africanists should take partisanship seriously as a predictor of political behavior and attitudes.
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Bullock et al. (2013) show by incentivizing respondents to give correct factual information that this effect is less likely to be about true misperceptions and more about “cheerleading” their party to an interviewer. In this study, however, I don’t have a good way of separating out the two effects and so will continue in the vein of the literature that treats these as true misperceptions. Nevertheless, even if voters don’t actually hold erroneous beliefs, intentionally misrepresenting one’s views to avoid disparaging one’s party is arguably still evidence of a strong psychological attachment to a party.
The precise date of the party’s foundation is unclear. Though the party itself claims its founding was in 1986, the military organization from which it sprang was founded several years earlier.
Two parties from the immediate post-independence period—the DP and the UPC—still exist, but both are popular among only a very small subset of the population. Between them, they win about 3 % of the vote.
At the core of the divide is Buganda, Uganda’s largest traditional kingdom, whose territory includes a large portion of the southern half of the country. Baganda were favored by the colonists, and particularly targeted for repression by Milton Obote.
In the 2011 election, Museveni won a large number of northern districts previously aligned with the opposition. Some observers treat this massive shift as genuine and attributable to the end of the war with the LRA and the subsequent economic recovery (Conroy-Krutz and Logan 2013). Others argue that sentiment in the north still lay with the opposition, and attribute the shift to large-scale fraud or coercive campaigning by Museveni (EUEOM 2011).
Unlike the NRM, the FDC has now outlasted its founder. Besigye stepped down as party president in 2012 and the FDC will run someone else as their presidential candidate in 2016.
Though Ugandan parties may be extreme in this regard, politics is personalized to some extent in all party systems. Many authors conceive of personalization as a continuum (Carey and Shugart 1995; Krauss and Nyblade 2005; Garzia 2012; Bean and Mughan 1989). Coattails effects, which are common in developed democracies, imply a conflation of a charismatic candidate with the party he represents (Thorson and Stambough 1995; Hogan 2005; Meredith 2013). In the United States, partisanship and approval of the president and his performance are so intertwined that attempts to determine the direction of causation comprise an entire literature (see e.g. Gerber and Huber 2010).
We could imagine a situation in which straight ticket voting was purely rational, such as if voters know that MP’s will only distribute government resources to local officials of their own parties.
The median Ugandan has completed primary school, or 7 years, of education.
There is recent evidence that even in the US voters’ assessments of macroeconomic indicators are influenced by local-level outcomes (Ansolabehere et al. 2012), making misperceptions of local-level indicators an important measure to capture.
Most likely, this is the frame of reference that most respondents were using anyway, which means that I would have gotten similar results had I simply asked respondents to rate absolute quality.
Examples of their statements include: “The government is not helping us the way we want, it is only helping [the president’s regional stronghold]”; “There is a lot of segregation in the government whereby other regions are favored.”; “Government should equitably serve all regions”; “There is a lot of imbalance where people are not treated equally and the resource distribution is not the same.”
The Ugandan government has records of how resources are distributed to sub-national governments for the purpose of providing local public goods, but these data may or may not be reliable. They are prone to missingness, and, given the political salience of distribution, they may even be intentionally manipulated. Data generated at the central level also may not reflect the quality of goods on the ground, since resources can leak at the local level. Much of the government’s own data on the quality of goods at the local level is generated through surveys, which is unhelpful in determining if respondents’ perceptions are accurate.
Respondents were recruited from every fifth house by enumerators traveling on foot in different directions from the center of the village. The age and gender of the respondent to be recruited at each house was pre-randomized.
About 15 % of respondents insisted that they knew nothing about any local government facility. This is approximately the same percentage as those saying they “don’t know” about local facilities on Round Three on the Afrobarometer, which asked about the quality of local goods provision. Additionally, some called the facility by an alternate name, or by the name of one of its staff members (e.g. Sarah’s clinic). Unless the enumerator was able to establish that the facility the respondent was citing was in fact one of the facilities I had inventoried, I did not use these responses in the sample.
There is also a significant positive correlation between the number of pupils per teacher and respondents’ quality ratings. However, it seems less likely that respondents value crowding and more likely that students prefer to attend class with a teacher who is regarded as better— unlike in the US, there is no constraint on which public school a child may attend. This measure may therefore serve as a proxy for teacher quality.
All three variables remain robustly significant under different specifications and the resultingly different sample sizes.
It is almost certain that some respondents reported that they supported Museveni when they did not: reported support for Museveni in the survey is higher than it should be, even for core areas. This is a problem for inference if voters also feel compelled to be overly positive about the goods they have received from government. However, Afrobarometer respondents who think they are being interviewed by the government under-report their access to local public goods, presumably because they are hoping to attract more resources. This patterns works against the finding here and suggests that the effect I find is a lower bound.
Vote choice and party support are conceptually different. But they are strongly correlated and the results of the analysis when vote choice is replaced with NRM party support are the same.
These results are robust to two alternative specifications: a model interacting support for Museveni with indicators of quality, which shows no impact from the interaction terms, and a model that introduces facility fixed effects, rather than controlling for indicators of quality at the facility level.
Some facilities were dropped as they had no variation on partisanship within their patients.
Non-supporters report an average wait of 2.4 h, while supporters report an average wait-time of 3.6 h, significant at the 0.09 level.
One possibility is that the attachment is still an ethnic one, but to a larger group than the individual ethnic group. In this case, Uganda’s north-south cleavage roughly correlates with a division between Bantu and non-Bantu language groups. A Bantu language dummy added to the model is statistically insignificant, and of the wrong sign.
The opposition showcases these failures as evidence that the central government is failing to govern. The government media presents such stories as evidence that there is rampant corruption on the ground which the president is working to reduce.
Those who choose to listen to the news are certainly different than those who do not, so the model includes controls for age, gender, wealth, urban residence, and education.
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Appendix 1: Complete List of Indicators Gathered
Distance from facility to respondent’s home
Quality of maintenance (subjective scale)
Recent improvement (subjective scale)
Facility has electricity
Facility has running water
Facility has permanent walls and roof
Facility has back-up water tank
Number of toilets
Tier of clinic/hospital
Number of inpatient beds
Current number of inpatients
Number of doctors/physician assistants assigned to facility
Number of doctors/physician assistants currently at work
Number of nurses/nursing assistants/midwives at facility
Number of nurses/nursing assistants/midwives currently at work
Anti-malarials/antibiotics/immunizations in stock
Lab with microscope?
Number of patients served daily
Primary Schools (2 classrooms each)
Number of complete classrooms
Number of incomplete classrooms
Number of teachers assigned to facility
Number of teachers currently at work
Classroom has door?
Classroom has chalkboard?
Classroom has windows that can close?
Count of pupils in classroom
Count of textbooks in classroom
Count of desks in classroom
Number of students sitting on floor
Number of students sitting 2008 leaving exam
Number of students passing 2008 leaving exam
Appendix 2: Indicators, By Partisanship
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Carlson, E. Finding Partisanship Where We Least Expect it: Evidence of Partisan Bias in a New African Democracy. Polit Behav 38, 129–154 (2016). https://doi.org/10.1007/s11109-015-9309-5
- Partisan bias
- New democracies