government fixed effects explains 49% of the variation in project completion rates. organization fixed effects on their own explain 33% of the variation. However, in an analysis of variance, the partial sum of squares on the organization variable is about half as large as that on the local government variable, which picks up both local political dynamics and constituency characteristics. These both dwarf the explanatory power of project type, bringing these results in line with the claims by Nigerian officials that technical characteristics of projects and programs do not dictate their success, but the organizational and political dynamics around them do.

Turning now to the correlations, Table 6 presents regressions of a subset of the variables we have discussed in this paper on measures of public sector productivity, at the organization level in Ghana and the project level in Nigeria. The explanatory variables chosen are those on which we have data for both Ghana and Nigeria. Columns 1 and 5 restrict analysis to basic demographics. There is little evidence of demography being an important driver of productivity. In Nigeria, the longer the average service tenure among officials, the lower their productivity; a result significant at the 10% level that disappears in all other specifications.

Columns 2 and 6 introduce measures of the experience of the service. In Ghana, the longer officials stay at an organization, the more likely they are to claim that it is productive. Postgraduate education is strongly correlated with reports of organizational productivity in Ghana, and in Nigeria the coefficients are generally positive but insignificant at the usual levels. In neither country does the gender of officials seem to correlate with organizational productivity. With regard to how job satisfaction affects productivity, the results from the two countries are divergent. Job satisfaction correlates strongly with reports of productivity in Ghana, but is potentially negatively related to actual productivity measures in Nigeria.

The contrasting findings from columns 2 and 6 question the value of bureaucrat assessments of productivity. That more tenured, educated and satisfied officials rate their organization as performing more effectively may be because they are more aware of the relevant outputs or because they are happy with their own work. These may be true drivers of productivity, but the fact that the most significant drivers of the self-reported measures of productivity turn out to be highly consistent with a positive responsebias threatens the credibility of such an approach. The challenges to interpreting the Nigeria results are not insignificant despite the productivity assessments being independently measured. Adding the additional complexity of self-assessment makes interpretation demanding.

Columns 3 and 7 extend the analysis to include measures of political interference, corruption and management, the three factors highlighted by public officials themselves as critical to productivity. One concern related to the criticisms of self-reporting is that there may be selection in which respondents provide an assessment. In our context, different proportions of officials across organizations felt there were performance standards they could judge their organization by. This may therefore lead to greater measurement error across organizations. In Column 4, we re-run the specification in Column 3 using weighted least squares, where the weights are the proportion of civil servants who make an assessments of their organization’s productivity.

Interestingly, in both specifications we see a significant negative correlation between claims of political interference, and reported productivity. Even if we interpret the Ghana data as suffering from response bias, this may be one area where public officials can see a clear link. If they personally experience the detrimental effects of political interference, they may factor this in to their assessments of organizational productivity.

Given the mixed evidence arising from the analysis of self-assessments of productivity in Ghana, we might fare better by focusing on the independent measurement of civil service outputs in Nigeria. Here, we replicate the first three columns of the Ghana specifications in Columns 5 to 7 respectively. We see 29