the full distribution of regions, and the distribution of choices closely matches population and district numbers.

The raw errors vary significantly, as documented in the main text. The distribution of the errors suggests that civil servants, on average, tend to overestimate the value of the indicator, with the average error of all but three of the indicators we study being positive (the three indicators with a negative actual error are: the proportion of rural inhabitants; the primary pupil-class ratio; and the hectares of land used for pastoral purposes). Since it is difficult to compare errors across different indicators, the overall skewness coefficient for the z-score of the actual error is 0.4. A test of skewness relative to the normal distribution is rejected at the usual levels. Individually, the indicators tend to be positively skewed (have distributions with longer right tails). Six of the 20 indicators have a negative skewness coefficent: proportion of rural inhabitants (-3.02); the antenatal care rate (-0.49); the rate of births delivered by a skilled attendant (-0.22); the share of households living on subsistence agriculture (-0.73); the proportion of income from regional block grants (0.64); and the proportion of income from own sources (-0.56).

In the core analysis, we aim to compare the claims of officials at multiple tiers of government relating to the same district. We therefore focus on districts for which we have subjective assessments from each tier of government. This requires a district to be one of the 25th/50th/75th percentiles for at least one of the indicators we study, since these are the districts over which federal and regional officials made assessments. We therefore drop roughly a third (24) of the 66 districts on which we collected data to ensure that each claim has counterparts at all three tiers of government.

We create a data set where the unit of observation is a claim of a bureaucrat about a district. As described, each federal and regional official is asked about three districts for each indicator (the 25th/50th/75th percentiles of that indicator), and so each bureaucrat-indicator-percentile is an observation for these officials. The claim of the district official about a specific indicator in her district is a single observation. Each response is associated with the corresponding district census ID.

In a very small number of cases, there seems to have been a data entry issue, where additional digits were included or excluded in the recording of responses to non-fractional variables. This leads us to some errors that are off by a very significant factor (of 94 at the 95th percentile of errors). These are not reflective of the wider variation in the data. Two-thirds of absolute errors are less than 1 in proportional terms. The most extreme values seem outside the realm of feasiblity for even the least informed bureaucrat. Since a plausible explanation is that these outliers reflect data entry issues rather than outsized errors, we therefore censor the top 5% of raw errors for each of the variables. The interpretation of the paper is therefore analysis of the lowest 95% of errors that public officials make, rather than of the top 5%.

Absolute values are then taken of all errors. At this stage, we have a data set that is not comparable 37