disruption in supply chains in many industries and the implementation of social distancing policies that reduced mobility and promoted remote work.<sup>5</sup> <mark>In this section we obtain estimates of employment elasticities for the agriculture, industry, and services sectors using different model specifications. Using these elasticities, we project the level of employment for 2020 and compare them against actual employment data. We cover 15 countries in four regions. The sample includes Albania, Bulgaria, North Macedonia, Romania, and Türkiye from the Europe and Central Asia region; Brazil, Colombia, Costa Rica, Ecuador, Mexico, and Peru from Latin America and the Caribbean; Indonesia, the Philippines, and Vietnam from East Asia and the Pacific, and South Africa from Sub-Saharan Africa. These countries were selected based on the availability of post-crisis employment data from Labor Force Surveys or other official</mark> sources.

<mark>To calculate employment elasticities, we use data on sectoral employment and sectoral GDP levels for the period 2010 to 2018</mark><sup>6</sup> <mark>from ILO and World Development Indicators, respectively. To project the 2020 employment level, we use the 2019 employment level from ILO and the 2020 estimated GDP change from the World Bank’s Macro Poverty Outlooks. Finally, we compare our employment projections with actual data on 2020 employment levels from National Statistical</mark> Offices.

# **2.1 Employment elasticity models and results**

<mark>We compare estimated elasticities from six different estimation methods. These models are estimated for each country separately. First, we calculate sectoral</mark> _<mark>average elasticities</mark>_ <mark>. The average sectoral elasticities are defined as the average among annual elasticities obtained as the ratio between the percentage change of employment and the percentage change of GDP between consecutive years. Second, we calculate sectoral</mark> _<mark>average elasticities without outliers</mark>_ <mark>by excluding from previous calculations those annual elasticities which fall outside of a bound defined as Q</mark> 1 <mark>(1.5*IQR) and Q</mark> 3 <mark>+(1.5*IQR), where Q</mark> 1 <mark>and Q</mark> 3 <mark>are the first and third quartiles of the annual elasticities and IQR is the interquartile range. Third, we apply a</mark> _<mark>mean regression</mark>_ <mark>method to obtain sectoral point elasticities by estimating OLS models of the logarithm of employment on the logarithm of GDP. Fourth, we calculate sectoral employment elasticities by estimating</mark> _<mark>median regression</mark>_ <mark>models. Fifth, we expand the mean regression method by adding the logarithm of total GDP as a control variable to consider the fact that sectoral employment levels depend not only on the sectoral GDP but also on the movements of the economy as a whole. Last, we expand the mean regression method by including the logarithm of total GDP multiplied by the sectoral informality rate as a control variable. The objective is to control for the fact that in some of the countries in</mark>

> 5 The available evidence on the relationship between home-based work and productivity is not conclusive. Studies finding a negative association have indicated that reduced uninterrupted working hours at home, some tasks not being able to be performed at home, poor telecommunication environment, and loss of face-to-face interaction with coworkers are the main reasons for productivity reductions (Behrens et al., 2021; Gibbs et al., 2021).

> 6 At the time of undertaking the work to estimate the employment elasticities, the 2019 data was not yet available and 2018 data was the latest available. Nevertheless, it is not expected that the elasticities will differ much.

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