The OPEN evaluation teams coded: (i) whether the project had started; (ii) its stage of completion; (iii) and in a subset of cases, the quality of the inputs and work. The main outcome variable is a continuous measure, from zero to one, of what proportion of the project has been implemented: zero refers to the project never having been started, one corresponds to the project being completed as specified in the original project description, and intermediate scores reflect part implementation.

Table 1 provides a summary of a key dependent variable in the analysis, the proportion of the projects implemented, across constituencies. Roughly 40 percent of projects are never started, while a third are completed.<sup>20</sup> Conditional on being started, a project has a 53 percent chance of being completed. There is therefore substantial scope for politicians to reduce bureaucratic inaction.

To gain a sense of the technical specifications of the projects we study, I hand-coded data on project-level characteristics such as the budget allocated to the project, whether it was a rehabilitation project, and a brief summary of its technical specifications from project documentation. I also coded which of 10 project types the projects fell into, with categories in both construction (water wells, buildings, and so on) and non-construction fields (procurement, financial projects, and so on). The project technical specifications were used to form engineer-approved measures of the technical complexity of each project and informational characteristics, in the sense of whether centralized or decentralized tiers had an informational advantage in project delivery. The Appendix: (i) details the construction of these indices; and, (ii) describes checks I put in place, using multiple engineers, to establish the validity of these complexity measures. There is heterogeneity across project types (boreholes, buildings, dams, procurement, roads, financial projects, canals, training, advocacy, and research) in terms of the quality of output. I therefore control for project type fixed effects throughout the analysis.

# **3.3 Bureaucrats**

A bureaucrat in this paper is a civil servant of the Federal Government of Nigeria. Each of the 3,008 projects I study in this paper were implemented by one of 54 bureaucratic organizations. To gain measures of the interactions between politicians and bureaucrats, I undertook surveys of a representative sample of roughly 10 percent of the staff at each of the organizations I study.<sup>21</sup> As part of this survey, I collected some of the first systematic measurements of interactions between politicians and bureaucrats.<sup>22</sup> Each of the officers I surveyed was asked the following question, “Rate the influence you think [members of the National Assembly] have on the success of a typical project implemented by your organization”, where responses were categorical and divided into ‘Most influence’, ‘Significant influence’, ‘Some influence’, ‘Less influence’ and ‘Least influence’. For each bureaucrat, I determine a binary variable that takes the value 1 if they responded ‘Most influence’ or ‘Significant influence’, and 0 otherwise. On average, roughly 50 percent of officials state that politicians have significant or most influence over the projects

> 20It is not possible for me to distinguish between whether projects were not started due to active or passive waste (Bandiera et al., 2009). Rather, the focus of this paper is on whether the decisions and actions of politicians can overcome bureaucratic inefficiency, whatever its nature.

> 21There is an emerging literature in political science on informal politics, such as the research described in Helmke and Levitsky (2006). This paper adds to that literature by providing large-scale and detailed measures of informal political pressures and a clear relationship of those measures to outputs.

> 22Survey data on politician interaction with bureaucrats around project implementation is rare. The closest numbers I have for comparison to this paper come from the World Bank’s ‘Public Officials’ Surveys’ (Manning et al., 2000). These surveys covered 16 countries, and there is significant heterogeneity in the average level of bureaucrat’s perception of politicians’ interference. Evidence from Bangladesh shows that when politicians stop interfering in day-to-day decisions, the perception of corruption in that organization will fall by 31 percent (Mukherjee et al., 2001). A similar impact of political interference was found for Bolivia (Manning et al., 2000). In Guyana, 42 percent of officials interviewed stated that politicians’ interference is frequently or very frequently a “significant problem” (Gokcekus et al., 2001). Thus, while these figures reflect varying contexts and survey questions, they are indicative that the magnitude of the issues studied here are of widespread significance.

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