fractionalization across enumeration areas is quite low, with greater fractionalization in the middle areas of the country.

To control for respondents’ political identity, we use data from the 2008 NPS survey, which includes a question about respondents’ self reported voting behavior for the national elections. Specifically, respondents were asked which party they voted for in the 2007 national elections. The NPS sample is limited to respondents who could verify their claim of voting by producing a voter identification card with the corresponding hole punches made by polling station staff (IRCBP Evaluation Unit, 2010). We code respondents’ political identity as either a supporter of the ruling party, the APC, or as a supporter of an opposition party.<sup>10</sup>

## **4.4 Methods**

The NPS data is multilevel and includes repeated measurements of household and enumeration area characteristics. Households are also nested within enumeration areas, which are nested within wards.<sup>11</sup> We estimate hierarchical, logistic longitudinal models using the lmer function in R (see Equation 1). Ignoring the multilevel structure of our data can generate a number of statistical problems (Gelman and Hill, 2007; Guo and Zhao, 2000). In addition to correcting for biases in parameter estimates and standard errors, multilevel models offer two additional advantages. First, they also allow us to examine how covariates measured at the enumeration and ward levels influence our outcome variable. For example, we include EA-level measures of distance and war violence. Second, this type of model allows us to test whether slopes are random, e.g., the effect of individual-level measures on our dependent variable differs across enumeration areas (Guo and Zhao, 2000, p. 444). We test, for example, whether the effect of ethnicity on trust in local government officials varies across EAs. Taking into account the multilevel nature of our data, we estimate random intercepts for the households and enumeration areas and treat our dependent variable as a binary outcome.

# **5 Results**

In this section, we present the results of our model on perceived trustworthiness of local government officials. We discuss regression results of trust in local government officials using predicted probabilities and first differences (King, 1989). In a multilevel logit equation,

> 10We are not completely satisfied with this control. According to Casey (2010), in Sierra Leone voters tend to cross the party lines for local elections. We therefore believe that a variable indicating whether respondents voted for the winning party/candidate in the 2004 and/or 2008 elections will be more relevant for our purposes. We are in the process of collecting this information, and will include this variable in the next version of the paper.

11A complication in our data is that 80 percent of wards were redistricted between the 2007 and 2008 rounds of the NPS. There does not appear to be any systematic differences between wards that were re-districted and wards that were not. Nonetheless, we estimated separate models using the 2008 ward boundaries and the 2004 boundaries. Unfortunately, we had to re-create the latter boundaries from other data sources and we could not determine the boundaries for 23 percent of the wards. Because the results were similar for both estimation strategies, we focus our discussion on the results that use the 2008 ward boundaries.

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