for roughly 30% of cross-country total factor productivity differences. We now turn to the civil servants survey data to investigate the extent to which these findings are true in the public sector. The ‘factors’ we focus on are those emphasized in the growth accounting literature - physical and human capital, ‘technology’ - and those emphasized by civil servants themselves - politics, corruption and management.

To assess the correlates of productivity in the civil service, the best that a civil servant survey can do is regress estimates of civil service productivity by bureaucrats themselves on other characteristics of those bureaucrats. Since officials were asked to assess organizational productivity, we must make the organization the unit of analysis. Only in Ghana do we have both civil servants assessments of productivity (which we average at the organization level) and a sufficiently large number of organizations for quantitative analysis. Even here, we have to drop 8 of the 85 organizations as no official in the organization rated their productivity.

Specifically, the measure of productivity in Ghana is an organizational average of civil servant responses to ‘roughly to what degree are [the written performance standards of your organization] met?’ with potential answers approximating percentages. Only civil servants who knew of written standards of performance for their organization were asked this follow up question.<sup>35</sup> In the Ghana regressions I include ‘capital and noise controls’ which are measures of organization budgets and averages of assessments by the enumerators as to the reliability of the interviews at an organization.

A criticism of this approach is that civil servant reports of their organization’s productivity may not be an unbiased measure of the underlying truth. Thus, we complement the Ghana results with the use of independent audits of programs and projects undertaken at 63 of the 94 Nigerian organizations we have studied to this point. As outlined in Rasul and Rogger (2016), quantitative information was collected to measure the actual implementation success of 63 _federal_ ministries and agencies. The auditers were independent teams of engineers and members of civil society who provided assessments of completion rates (from 0 to 100% completed) for over 4700 public sector projects implemented by these 63 organizations.<sup>36</sup> The unit of observation in Nigeria is therefore a project implemented by one of the 63 organizations for which we have relevant data. Focussing on the project, rather than averaging to the organization-level, allows us greater power in assessing the impact of organizational characteristics on project outcomes. To absorb some of the project-specific variation, I include project type fixed effects (whether it was a dam, building, etc.) in all of the regressions, as well as project-level characteristics that include the log of the project budget, an indicator of whether it is a rehabilitation, and an assessment of its technical complexity. For consistency with the Ghanaian regressions I include capital controls that consist of organization-level measures of the log number of employees, total budget and the capital budget. I follow Rasul and Rogger (2016) by clustering the Nigeria regressions at the project type-organization level. In Column 8 I repeat our most extensive analysis at the organization level without the project-specific controls.

Since the Nigerian productivity data has multiple observations at each organization, we can begin by assessing the extent to which organizational, and other, fixed effects explain the underlying variation in completion rates. A regression of proportion completed on project type, organization, and local

> 35The exact questions were as follows. If an official answered positively to, “In many countries, public organizations are evaluated based on objective, measurable criteria of success, known as performance standards. An example of a performance standard for the Police might be ‘responding to 80 percent of all emergency telephone calls within 15 minutes’. Are there written standards of performance for your organization?”, they were asked “If yes, roughly to what degree are these performance standards met?” with potential answers ‘Has never (0%)’, ‘Almost no times (1-10%)’, ‘Less than half of the times (11-40%)’, ‘Around half of the times (41-60%)’, ‘More than half of the times (61-90%)’, and ‘Almost all the times (91-100%). The middle point of each percentage range was then applied to the respondent, and an average taken across respondents within an organization.

> 36Further details are provided in the paper. For example, the aggregate budget for these projects is US$800 million or 8% of all social spending in Nigeria during the study period. They are thus an important component of government activity and representative of an important component of public spending.

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