in average years of schooling, taken from the Barro and Lee (2015) dataset.

Variables are taken as averages during the period 1990-2018, primarily due to widespread sectoral data availability only starting in the 1990s. This is a shorter horizon than over which the VARs are estimated. However, results are robust to using longer averages including the 1980s for non-sectoral data and shorter averages since 2000. The overall explanatory power of the covariates is modest but material for the employment impact in year 1, with an adjusted-R<sup>2</sup> of 0.18 in the best-fitting specification, particularly given the wide range of factors that will influence this outcome across economies. There are many statistically significant determinants of the employment effect found despite the simple nature of the proxies for structural and technological change (Table 2).

**_Rising industrial employment_** . An increasing share of employment in the industrial sector since 1990 is strongly correlated with the negative employment effects of technology shocks at all horizons.<sup>6</sup> A 10 percentage point rise in the industrial employment share over this period is associated with a 0.1-0.2 percentage point larger negative impact on employment on average from a technology shock. The effect is statistically significant at the 1% or 5% level in each of the horizons. The average _level_ of industrial employment during this period is also separately a statistically significant determinant of the employment impact at the 5 and 10-year horizons but it loses significance when included in the same estimation as the _change_ in employment share. As will be discussed in the following section, all advanced economies in the sample lost employment share in the manufacturing sector since 1990. However, a smaller loss of employment share in the industrial sector, relative to a large loss, remains a significant determinant of the size of the negative employment impact. Changes in the share of employment in agriculture and services are not statistically-significant determinants of the employment impact.

An interpretation of this finding is that economies that have made productivity gains that are more centered around the industrial sector have experienced less job intensive (or even job-reducing) productivity growth. Economies with increasing employment shares in the industrial sector since the 1990s will have been at the highest risk from automation. In addition, those economies with increasing employment shares in this industry may have had the least success in increasing employment in other sectors following job losses in the industrial sector due to automation. This finding links directly to much of the existing literature which has found that routine manufacturing jobs (although to a lesser extent routine service sector jobs) have been at the highest risk of automation.

> 6While industrial production can include types of production other than manufacturing, such as mining and extraction industries, there is no relationship between the proportion of exports made up of commodities and the employment impact, suggesting that the manufacturing component of industry is the key driver of the employment dislocation following technology shocks.

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