Training, Automation, and Wages: International Worker-level Evidence
Oliver Falck, Yuchen Guo, Christina Langer, Valentin Lindlacher, Simon Wiederhold
IWH Discussion Papers,
No. 27,
2024
Abstract
Job training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether job training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without job training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that job training reduces workers’ automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to job training. Job training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Women tend to benefit more from training than men, with the advantage becoming particularly pronounced at older ages.
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Training, Automation, and Wages: International Worker-level Evidence
Oliver Falck, Yuchen Guo, Christina Langer, Valentin Lindlacher, Simon Wiederhold
CESifo Working Papers,
No. 11533,
2024
Abstract
Job training is widely regarded as crucial for protecting workers from automation, yet there is a lack of empirical evidence to support this belief. Using internationally harmonized data from over 90,000 workers across 37 industrialized countries, we construct an individual-level measure of automation risk based on tasks performed at work. Our analysis reveals substantial within-occupation variation in automation risk, overlooked by existing occupation-level measures. To assess whether job training mitigates automation risk, we exploit within-occupation and within-industry variation. Additionally, we employ entropy balancing to re-weight workers without job training based on a rich set of background characteristics, including tested numeracy skills as a proxy for unobserved ability. We find that job training reduces workers’ automation risk by 4.7 percentage points, equivalent to 10 percent of the average automation risk. The training-induced reduction in automation risk accounts for one-fifth of the wage returns to job training. Job training is effective in reducing automation risk and increasing wages across nearly all countries, underscoring the external validity of our findings. Women tend to benefit more from training than men, with the advantage becoming particularly pronounced at older ages.
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The Distribution of National Income in Germany, 1992-2019
Stefan Bach, Charlotte Bartels, Theresa Neef
IWH Discussion Papers,
No. 25,
2024
Abstract
This paper analyzes the distribution and composition of pre-tax national income in Germany since 1992, combining personal income tax returns, household survey data, and national accounts. Inequality rose from the 1990s to the late 2000s due to falling labor incomes among the bottom 50% and rising incomes in the top 10%. This trend reversed after 2007 as labor incomes across the bottom 90% increased. The top 1% income share, dominated by business income, remained relatively stable between 1992 and 2019. A large share of Germany’s top 1% earners are non-corporate business owners in labor-intensive professions. At least half of the business owners in P99-99.9 and a quarter in the top 0.1% operate firms in professional services – a pattern mirroring the United States. From 1992 to 2019, Germany’s top 0.1% income concentration exceeded France’s and matched U.S. levels until the late 2000s.
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Disentangling Stock Return Synchronicity From the Auditor's Perspective
Iftekhar Hasan, Joseph A. Micale, Qiang Wu
Journal of Business Finance and Accounting,
No. 5,
2024
Abstract
This paper investigates a firm's stock return asynchronicity through the auditor's perspective to distinguish whether this asynchronicity can proxy for the company's firm-specific information or the quality of its information environment. We find a significant and positive association between asynchronicity and audit fees after controlling for auditor quality and other factors that affect audit fees, suggesting that stock return asynchronicity is more likely to capture a company's firm-specific information than its information environment. We also find that asynchronous firms are more likely to receive adverse opinions on their internal controls over financial reporting, but are associated with lower costs of capital and auditor litigation, providing further evidence in support of the firm-specific information argument. Asynchronicity's positive association with audit fees is driven by firms with higher accounting reporting complexity, suggesting stock return asynchronicity captures a firm's complexity, resulting in more significant efforts by the auditor.
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