|**Coverage rate**<br>Direct|0.971|0.853|0.409|0.215|
|---|---|---|---|---|
|SAE|0.988|0.990|0.952|0.976|
|In-sample municipalities|1,072|1,072|1,072|1,072|

Only in-sample municipalities are included. The coverage rate is equal to one in a given municipality if the actual census value is within the calculated confidence interval (point estimates +/- 1.96 times the standard error). MSE with equalized coverage rates refer to MSEs after multiplying the estimated variance of the direct estimates by a constant to match the coverage rate of the small area estimates. Relative SE is missing for unemployment due to the large number of zeros in the direct estimates.

We note that both male and female unemployment rates are not normally distributed in our sample, even after transforming them, due to the mass at zero. The empirical best predictor method used here assumes a normal distribution. This may contribute to uncertainty if anything being slightly overestimated, as indicated by coverage rates that lie between 95 and 99 percent.

We next present statistics intended to measure the accuracy of the estimates, relative to results in the full census. We start with estimated relative bias and absolute deviation in Table 5 . These two measures differ along two key dimensions: The former is both relative, in the sense that it gives deviations from low true values greater weight than deviations from high true values, and directional, in the sense that negative and positive bias will cancel out. Estimated absolute deviation on the other hand is both measured in absolute terms and uses an absolute value and is therefore non-directional. It is also important in both cases to distinguish means from medians. Small (census) values of unemployment can lead to very large estimates of relative bias, since the bias formula contains the true value in the denominator.<sup>23</sup> For most policy applications relating to targeting, relative bias may be a misleading measure of accuracy for unemployment rates. This is because relative bias gives absolute discrepancies greater weight in municipalities with low true rates, which does not necessarily correspond to the objective of allocating resources to minimize unemployment.

For labor force participation, both estimated mean and median relative bias is modestly higher in the SAE estimates participation than in the direct estimates, though median relative bias is under 1 percent in both cases. For unemployment rates, estimated mean relative bias is much higher, reflecting the lack of explanatory power of the unemployment models, which in turn leads to large relative bias in areas where true unemployment rates are low. However, when it comes to absolute deviation the SAE estimates outperform the direct estimates by a substantial margin, for both LFP and unemployment. For female LFP, for example, the SAE procedure reduces estimated mean absolute error by about 45 percent, from 0.069 to 0.039, and for female unemployment rates the estimated absolute deviation falls by over half.

To get a further sense of the accuracy of the estimates, the bottom rows of the table report estimated rank and Pearson correlations. For the labor force participation estimates, the small area estimation increases rank correlation by 0.11 points for men, and by 0.13 points for women, from 0.59 to 0.73. For Pearson correlations the improvement is slightly smaller, 0.08 points for men and 0.11 points

> 23 For example, the smallest non-zero value for female unemployment is 0.0006349. An estimated unemployment rate of 0.005 would lead to a bias value of 688.

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