Abstract
The purpose of this study is to identify the role of automatization in increasing wage inequality, by comparing the United States to Portugal. Using the PSID and Quadros de Pessoal (Personnel Records), we find that labor income dynamics are strongly determined by the variance of the individual fixed component. This effect is drastically reduced by adding information on workers’ occupational tasks, confirming that a decreasing price of capital and the consequent replacement of routine manual workers have deepened wage inequality. During the current crisis, we find that the ability to keep working is strongly related with the kind of occupation. As such, we foster the impact of a permanent demand shock using an overlapping generations model with incomplete markets and heterogeneous agents to quantitatively predict the impact of Covid‑ 19 and lockdown measures on wage premium and earnings inequality. We find that wage premia and earnings dispersion increase, suggesting that earnings inequality will increase at the expense of manual workers.
Highlights
Technological progress is considered one of the main drivers behind earnings inequality
We estimate the impact of COVID‐19 outbreak by applying the drop in working hours aggregating the drop in demand for each sector and weigthing occupations by teleworking ability, as we expect firms to adapt to the new social distancing norms
In this paper we study the role of task complementarity in explaining an important component of earnings inequality, namely the task wage premia
Summary
Technological progress is considered one of the main drivers behind earnings inequality. Factor‐biased technological change and skill‐biased technological change represent two main sources of wage inequality. To this extent, we explore empirically the differences between workers in different categories, according to their occupation tasks, to assess how labor market has been impacted by task premia changes. We implement an overlapping generations model with incomplete markets to study the role of skill‐based technological change in increasing wage inequality and to assess the potential impact of Covid‐19 when people ability to continue working is mostly determined by the type of task they perform. We calibrate the model in order to match US and Portuguese economies using 2010 as benchmark year and we repeat the exercise targeting different working hours ratio per cognitive and manual workers in order to simulate the impact of demand side shocks
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