significant at the 5% level.

Alignment between the mission of an organization and its employees is a feature of the principalagent relationship frequently discussed in the literature. Table A4 includes our complementary measures of alignment. The measure based on the management practices approach explains substantially more variation than that based on direct questioning. However, the coefficients on neither variable are significant at the usual levels in column 2 with p-values of 0.138 and 0.431 respectively.

# **Robustness of Core Results**

To assess the robustness of the core descriptive results to our methods of measurement, we present a series of robustness checks in Table A5. The first column of Table A5 re-runs our preferred regression, but disaggregating the central tier of government binary into the federal and regional tiers, with region as the excluded category. The coefficient on district government is -0.31 with a p-value of 0.00, equivalent to that in our preferred regression, and the federal government binary has a value of 0.04 and is insignificant at the usual levels, implying that there is no statistical difference between the errors made by federal and regional officials. As described above, the federal and regional organization structure is equivalent and functionally distinct to the district. In columns 2 to 5 of Table A5, we return to a binary district variable and vary the level at which we cluster the standard errors in our preferred regression. Taking the conservative perspective that information regarding a specific district may be freely shared among officials across government, we cluster at the ‘claimed about’ district in column 2. The district coefficient continues to be significant at the 1% level. Similarly, we may be concerned that errors are correlated across the respondent’s claims, within their organization, or across the region they work in through their regional sector offices (who provide them with regulations and technical assistance). We therefore cluster at the respondent level, the organization-of-respondent level, and the region-sector level in columns 3, 4 and 5 of Table A5 respectively. In each case, the coefficient on the district government binary is significant at the 1% level. We can also rerun our preferred specification using proportional rank as an alternative measure of the magnitude of errors. Column 6 of Table A5 presents the result, which implies a district official makes an error that is 10 percentage points lower than a central bureaucrat. Returning to comparison with the poverty rate, this magnitude is equivalent to 37 times the magnitude of the coefficient on the poverty rate using this dependent variable, and thus akin to making claims over the 25th percentile of poor districts rather than the 62nd. As outlined in section 2.4, we may be concerned about using absolute errors in the presence of skewness of the underlying indicators. We therefore re-run our preferred specification on the ‘skewness-adjusted z-score’ and find that the coefficient on district government is even larger in magnitude with a coefficient of -0.39, and a p-value of 0.00. Distinct measurement approaches produce qualitatively 42