positively vs. 27 percent in comparison areas. Possibly directly related to the efforts of the project, almost all officials in those areas say the LGA has a formal plan for capacity building vs. 62 percent in comparison areas; and conditional on having a plan, less than half of respondents in comparison areas could show the plan vs. three-quarters of respondents in project LGAs. While these differences are statistically significant, we do not observe significant differences in other indicators such as reporting of budget preparation delays, budget execution or share of unplanned settlements. On the topic of fiduciary systems, project areas also perform better: respondents are 16 percentage points more likely to report having the internal auditor position filled, and they are more likely to agree that the internal audit office is independent and effective in monitoring. We do not observe significant differences, however, on most indicators under the topics of infrastructure management and accountability and transparency.

Taken together, these results suggest that between comparison LGAs and those receiving the project, systematic differences exist in the initial survey between respondents. Given the project’s targeting rules, this is an expected result. Our identification strategy, a differencein-differences approach, is well placed to estimate the causal effect of the program under preexisting differences between treatment and comparison groups. We assess the main identification assumption behind a difference-in-differences approach by checking for parallel trends in state capacity levels before the start of the program. Specifically, we examine trends before the first round of data collection on a key measure of local state capacity, LGA’s own source revenues. An important limitation here is that we only have available own source revenue data at the LGA level for the years 2007 to 2011 and data is available for the entire period of 5 years for only 36 out of the 40 LGAs (16 project and 20 comparison).

In Figure C1 we plot the mean per-capita revenues for both project and comparison LGAs. (We normalize for LGA population size in 2011.) The trends follow a very similar path between 2007 and 2009, starting to show some divergence after 2009. Given the paucity of data, we cannot run a more thorough exercise which would include an event study (including the periods before and after the start of the program). However, when we run a simple statistical test of the difference between the mean per-capita revenue in project and comparison groups, we cannot reject the null of equality of the means.<sup>17</sup> To further explore the parallel trends assumption, we also use nighttime lights data. Nighttime lights data are highly granular and are available over a long period of time and so enables us to complement the pre-trend analysis of revenues. We focus on the period to 2000 to 2018.<sup>18</sup> However, while a good proxy of local economic activity,<sup>19</sup> nighttime light data might not be a good proxy for local state capacity. With this important caveat in mind, we show that trends in nighttime light are remarkably similar over the period 2000 to 2013 (which is when the program came into effect) between project and comparison areas (see panel (a) in Figure C1). This find-

> 17 Coefficients obtained estimating equation OwnPercapitaRevenue = α + β(ULGSP)+ year + ε with standard errors clustered at the LGA level.

> 18Details are in Appendix C.

> 19See Gibson et al. (2021) for a recent review.

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