The growth of temporary employment is one of the most important transformations of labor markets in the past decades. Theoretically, firms' exposure to short-term workload fluctuations is a major determinant of employing temporary workers when employment protection for permanent workers is high. The authors investigate this relationship empirically with establishment-level data in a broad comparative framework. They create two novel data sets by merging 1) data on 18,500 European firms with 2) measures of labor-market institutions for 20 countries. Results show that fluctuations increase the probability of hiring temporary workers by 8 percentage points in countries with strict employment protection laws. No such effect is observed in countries with weaker employment protections. Results are robust to subgroups, subsamples, and alternative estimation strategies. Temporary employment accounts for a considerable share of the European Union (EU-27) workforce-approximately 14%, 60% of which is involuntary (Eurostat 2012). Workers on temporary contracts are one of the groups most vulnerable to economic downturns (Boeri 2011: 1207), which implies a large risk of incurring losses in well-being (Frey and Stutzer 2002; Lucas 2007). Moreover, temporary employment comes with fewer training opportunities, lower wages, and higher job insecurity compared to permanent employment (Booth, Francesconi, and Frank
Although the negative economic effects of temporary employment are widely discussed, cross-country research on firms' demand for temporary employment is rare. National studies indicate that workload fluctuations are one major motive for firms to employ temporary workers. By studying a novel data set of 18,500 firms from 20 countries, we show that workload fluctuations increase the probability of hiring temporary workers by eight percentage points in rigid labour markets, but no such effect is observed in flexible labour markets. This conditioning effect of employment protection is in line with a recently developed search-and-matching model. Our results are robust to subgroups, subsamples and alternative estimation strategies.
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