analysis on the errors, a missing value is inserted in place of the 0 for the error variables. Furthermore, a ‘Don’t Know’ binary indicator is created that takes the value 1 whenever a respondent does not know the answer to a question.

Our benchmark data is regarded as the best available in the Ethiopian context. One 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. For example, if the ratio of pupils per school has a high variance from one year to the next within the same district, this could be seen as a warning sign as to the quality of measurement. 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. Column 4 shows the estimated autoregressive parameter from a Dickey Fuller Regression, controlling for district fixed effects and column 5 shows the estimated autoregressive parameter from a Dickey Fuller Regression, controlling for district fixed effects and districtspecific linear time trends. The estimated coefficients are less than 1, which indicate stability in the trend, for all indicators except for three: the amount of land used for agricultural purposes; the amount of land used for pastoral purposes; and the number of business licenses issued. In addition, column 6 reports the z-statistics and column 7 the associated p-value from a unit-root test based on Harris and Tzavalis (1999) where there are sufficient data to do so. Column 8 reports the fixed-N critical value at the 5% level and column 9 the fixed-N test statistic from Im, Pesaran and Shin (2003), allowing for linear time trends. These tests are appropriate for panel data settings with short timeframes. The results of the tests provide significant evidence against the existence of a unit-root process, and therefore a random walk, for the tested series. These results provide support to the notion that the benchmarking data are consistent over time.

For each statistic we study, we subtract the corresponding benchmark data sourced from survey or administrative data from the claim of the respondent. For questions related to the rules of the service, which are uniform across all respondents regardless of sector and tier, the claim is subtracted from the benchmark data and the absolute value generated. For the demographic and sector claims, the format of these differ slightly by tier. District officials were asked directly about their district, and the relevant benchmark indicators subtracted from these responses. The regional officials were asked about the 25th, 50th and 75th percentile districts of individual variables separately. The federal officials were asked, “Which region, out of the following options, do you work on most/think that you know best/feel most comfortable answering questions about?” They were then asked about the 25th, 50th and 75th percentile districts of individual variables within that region. This ensures that each official is responding to questions on the districts that they are most familiar with, and work most closely on. Federal officials across sectors chose 36