Automatic Reaction - What Happens to Workers at Firms that Automate?

Abstract

We provide the first estimate of the impacts of automation on individual workers by combining Dutch micro-data with a direct measure of automation expenditures covering firms in all private non-financial industries over 2000-2016. Using an event study differences-indifferences design, we find that automation at the firm increases the probability of workers separating from their employers and decreases days worked, leading to a 5-year cumulative wage income loss of about 8% of one year's earnings for incumbent workers. We find little change in wage rates. Further, lost wage earnings are only partially offset by various benefits systems and are disproportionately borne by older workers and workers with longer firm tenure. Compared to findings from a literature on mass layoffs, the effects of automation are more gradual and automation displaces far fewer workers, both at the individual firms and in the workforce overall.A broader empirical literature indeed makes clear that large-scale automation need not bring about labor displacement in aggregate, but rather, leads to labor reallocation. For one, even within the affected industry, automation can increase employment if industry demand is sufficiently elastic (Acemoglu and Restrepo (2018a,d); Bessen (2018)). Moreover, there is evidence that productivity gains generate employment increases in other industries through input-output linkages as well as final demand effects, offsetting any employment losses in automating industries (Autor and Salomons (2018); Gregory et al. (2018)). It should be noted that our analyses do not consider these countervailing forces: this implies our findings do not inform on the macroeconomic impacts of automation. However, to understand how automation affects work, it is critical to also study its effects on individual workers. After all, the absence of displacement in aggregate need not imply the absence of losses for individual workers directly affected by automation. These micro-level impacts are also of first-order importance for policymakers aiming to assuage adverse impacts out of distributional concerns. This paper is structured as follows. We first introduce our data source, Dutch matched employer-employee data which we link to a firm survey containing a direct measure of automation expenditures. Section 3 contains our empirical approach, outlining a definition of automation events and the resulting estimation framework using a combination of event study and differences-in-differences. Our results are divided into total impacts on workers' wage income (section 4), which we decompose into firm separation and employment impacts (section 5), and daily wage impacts conditional on employment (section 6). We next consider to what extent wage income losses are compensated by various benefit schemes (section 7), and how these losses differ across worker types (section 8). Lastly, in section 9 we consider the worker costs of automation conditional on displacement and compare these to income losses arising from mass lay-offs and firm closur...

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