Five main findings emerge from this figure: <mark>(i) In most countries, the employment projections tend to overestimate the actual employment level of 2020, and therefore underestimate the magnitude of the employment contraction. This is contrary to our expectation that employment projections would overestimate employment losses</mark> as explained above.

<mark>(ii) The magnitude of the bias of the employment projections differs greatly between countries, ranging from close to zero for some countries and methods, such as Indonesia, to more than 20%,</mark> as in Peru when using any of the elasticity methods.

<mark>(iii) Within each country and with only some exceptions, there is little difference in the performance of the different elasticity methods, resulting in employment projections’ biases of similar magnitude. Exceptions include Albania, Türkiye, Romania, and Ecuador, in which the average elasticity method performs more poorly than the others, and South Africa, where using the mean regression method controlling for GDP interacted with labor informality leads to less</mark> accurate estimates.

<mark>(iv) In 11 of the 15 countries, the differences between actual and projected 2020 employment levels were less than 5%, regardless of the method and direction of the bias, while in 4 countries the differences were larger than 5%. The first group includes Albania, Vietnam, North Macedonia, Indonesia, Türkiye, Bulgaria, Romania, Ecuador, Colombia, Mexico, and the Philippines. The second group includes Brazil, South Africa, Costa Rica, and Peru.</mark> (v) The most accurate method differs between countries. In Albania, Brazil, Mexico, South Africa, and Vietnam, the best method is the average of annual elasticities. In Costa Rica, North Macedonia, the Philippines, and Romania, it is the average elasticity without outliers. In Bulgaria, Colombia, and <mark>Türkiye,</mark> the best method is the median regression. In Indonesia, it is the mean regression controlling for the logarithm of total GDP. Finally, in Ecuador and Peru, the method with the best performance is the mean regression controlling for the logarithm of total GDP interacted with the informality rate. Table A2 in the Appendix shows the percentage difference between our employment projection for each country and method and the actual employment level, highlighting the method with the best performance. In general, the mean regression, median regression, and the average without outliers methods perform well in all cases. In many cases, there is little difference between the performances of the different methods.

In Figure A1 in the Appendix, we present the comparison between the employment projections for 2020 and the observed employment levels disaggregating by country, estimation method and economic sector.<sup>9</sup> We find that the magnitude of the bias is larger in the agriculture sector in 8 of 14 countries regardless of the estimation method and direction of the bias. In 4 countries, the bias is larger in the service sector, while in the remaining two countries, the largest biases appear in the industry sector. Another important finding to highlight is that in 8 of the 14 countries included in the analysis, the direction of the bias differs between sectors. In four of these 8 countries, the sector where the elasticity estimations lead to a bias of a different sign is the agriculture sector. In Costa Rica, Peru, Philippines, and South Africa, the bias for the agriculture sector is negative, meaning

> 9 Sectoral employment data for 2020 is not available for Albania.

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