assess the effects of different shocks to the economy. However, this approach tends to significantly overestimate employment growth relative to GDP growth across sectors and countries.<sup>4</sup> The second method uses an econometric-based approach to estimate the relationship between employment growth and GDP growth. This follows various studies in the literature. For instance, Kapsos (2005) applies a panel estimation in levels with country and sectoral dummies, Sahin et al. (2015) estimate a cointegration relationship, while Crivelli et al. (2013) use a panel in levels with lags of the dependent variable and country specific dummies.<sup>5</sup> The estimated elasticities in these studies differ substantially, influenced by the specific country and sector of analysis and the chosen regression specification. While the results of these studies vary substantially, it is important to note that the key challenge of these approaches is the endogeneity bias in the estimators generated by the simultaneous causality of employment and economic growth.

The third estimation method is derived from distributional properties of employment and GDP. Following Klenow and Rodriguez (1997), it can be shown that the expected growth in employment conditional on observed growth in GDP equals the covariance of employment growth and economic growth divided by the variance of GDP growth.<sup>6</sup> Finally, the fourth estimation approach follows a direct and straightforward calculation of the GDP-employment elasticities as the average ratio of annual percentage growth in employment to the annual percentage growth in GDP. This method does not impose any structural assumptions on the underlying variables and can be implemented for time series of different length.<sup>7</sup> To evaluate these estimation methods’ predictive power, a cross-country data panel for developing economies in the period 2000-2016 was used based on the value added and employment data from the World Development Indicators and International Labor Organization. Within-sample as well as out-ofsample GDP-employment elasticity estimates were produced under each methodology and used to estimate employment growth based on observed GDP growth. The results of this analysis show that the fourth approach based on average ratio of annual percentage changes in employment and GDP exhibits

> 4 For example, following this approach (using macro data from Eurostat and the World Bank World Development Indicators), the observed 50 percent of GDP growth in European Union countries during 1995-2005 implies 50 percent growth in employment, in contrast with the 10 percent actual growth in employment observed over the same period.

> 5 Other works in the area include Kaldor (1966), Parikh (1978), Rowthorn (1975a,b), Saget (2000), Döpke (2001), Kapsos (2006), Pattanaik & Nayak (2014), or Olusoji (2016). 6 We thank Aart Kraay for his help on identifying this alternative estimation method. It is important to note that this approach is underlined by the assumption that the growth of employment and productivity are bivariate normal.

> 7 Okun’s law (Okun, 1962) offers an alternative way to compute employment elasticities based on the documented inverse relationship between the unemployment rate and GDP growth (see Ball et al., 2017 for a recent summary). While there have been sector-focused versions (Goto and Bürgi, 2020), one key challenge in applying this approach in the context of developing countries is that unemployment rates alone do not describe labor market supply-demand gaps, given sizable informal employment and inefficient labor use across different sectors. In this case, GDP growth leads to a combination of job creation and increased labor productivity. In developing countries significantly lagging behind the frontier economies, employment effects would be dampened.

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