and electricity also improved between the two survey rounds: the share of individuals that agree that citizens should pay taxes so local government develops increased from 41% to 50%, and those who think it is wrong not to pay electricity improved by 9 p.p.

Similar improvements were observed in the survey of government officials, presented in Table 7 and Table 8. The share of respondents affirming the LGA has a plan that is up to date improved from 27% to 44%; the share reporting a plan approved in the last 2 years increases by 26 p.p.; and the share of LGAs with formal capacity building plans increased by 30 p.p. Improvements were also observed in responses about budget delays and execution, agreements with independence of internal audits, and responses on both staff capacity and staff morale.

# **4.4 The impact of the project on household responses and government official reports of institutional quality**

In order to estimate the causal effect of the project on areas that received it, as discussed above, we use areas that did not receive the program as counterfactuals. Under the assumption that these areas are valid counterfactuals (i.e., they would have followed similar trajectories in the absence of the program), assessing whether the project had an effect on the outcomes of interest is equivalent to examining whether areas that received the program had a differential change in outcomes, when compared to the comparison areas. The simplest (and most transparent) way to make this difference-in-differences estimation is to compare columns (5) and (6) in Tables 4 through 8: were the changes in project areas larger than those in comparison areas? Simple visual inspection suggests that, overall, this is not the case: project LGAs do not seem to consistently outperform comparison areas, which often presented larger improvements.

To formally test whether changes in project areas were different from changes in comparison areas, we estimate equation 1 above, including a series of individual level controls.<sup>22</sup> Figure 3 - Figure 5 present the results for these regressions for the household survey, while Figure 6 and Figure 7 present results for government officials. The left panel in each figure presents the point estimate and 95% confidence interval for the coefficient γ, indicating the change in outcome,<sup>23</sup> between 2016 and 2018, for comparison LGAs. The right panel presents the same for coefficient β, and it is the difference-in-differences coefficient: it represents the differential change in outcome among project respondents, when compared to non-project respondents.

Focusing first on the left-hand panel, we observe that for a vast number of indicators, as discussed above, comparison areas recorded improvements between 2016 and 2018. All indicators are constructed such that higher values indicate normatively better outcomes or improved views, and very few estimates indicate worsening performance between first and second rounds, in

> 22Controls include household size, age, gender, marital status and education level. Note that we are not estimating individual fixed-effect models, so we can include time-invariant comparisons. Furthermore, as discussed above, some households and officials are replaced in the second round survey so controls would still vary over time for some units. All standard errors are clustered at the LGA level. Due to the small number of clusters, we also report 95% confidence-interval for our estimates using wild-bootstrapping. Results are not sensitive to the inference method.

> 23For ease of reading, all dependent variables were standardized before the regressions, so coefficients are to be interpreted as changes in standard-deviation units of the dependent variable.

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