For each question, public officials were permitted to state, “Don’t Know”, though the enumerator did not suggest this as a response. In 23% of questions, the respondent stated that they simply did not know and could not provide a valid guess. This is in contrast to the fact that the quantities assessed are the core variables required to make policy, planning and distribution decisions in accordance with the national sector plans.

# **2.4 Objective Assessments of Citizen Characteristics**

For each indicator, we have administrative or survey data that can benchmark the responses by officials. The specific sources for each variable are outlined in Table A2 and are the latest Census, the latest available service delivery data from the education and health management information systems, and the Woreda and City Benchmarking Survey (WCBS). Each data set is created by sourcing information directly from service providers and aggregating it at the center of government. The sources we use are generally regarded as the highest quality large-scale measurement undertaken in Ethiopia on the topics outlined above. For example, in education, the Education Management Information System is comprised of school-level data that are processed through each tier of government, officials of which undertake quality checks to ensure that the data are as accurate as possible. The WCBS data was collected in collaboration with the World Bank and the Ministry of Public Service and Human Resource Development as a monitoring tool over five separate waves of data collection. All of this data is available to the officials we study through the relevant directorates of their tier of government at the minimum, and in some cases online.<sup>10</sup> We take the administrative and survey data as a proxy for the underlying state of the world. We use these variables to benchmark the claims of public officials. For each indicator, we subtract the corresponding value in the benchmark data from the relevant claims in our data set, such that a positive error is an overestimate.

Our core analysis aims to bring together the full set of claims in a unified framework. However, individual errors are not comparable across indicators. Making an error of 0.5 in a district’s pupil-class ratio is large for that indicator, but tiny for the population indicator. To jointly assess the scale of errors across multiple indicators, we undertook a number of transformations of the raw error data. First, we took absolute values of the errors so that deviations of different signs are comparable.<sup>11</sup> Second, we trimmed

> eral/regional level respondents are asked about the value of the 25th percentile district in their (chosen) region, the 50th percentile district in their (chosen) region, and the 75th percentile district in their (chosen) region.

> 10One proxy of the quality of the benchmarking data is their stability over time. By assessing the extent to which indicators vary within districts over data collection rounds, we aim to understand whether the data reflect a noisy collection process. Table A2 presents summary statistics and tests for the presence of a random walk for each of the indicators used in the analysis where panel data are available, and the Appendix describes the results. We find evidence that the benchmarking data is consistent over time.

> 11The probability of a negative (as opposed to a positive) error in the data we use for our core analysis is 0.404, such that the likelihood of over- and under-estimates is relatively equal. We have also transformed the errors to be percentages

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