Institutional Affiliation: Interdisciplinary Center (IDC) Herzliya
|The Macroeconomics of Automation: Data, Theory, and Policy Analysis|
with , , : w27122
During the last four decades, the U.S. has experienced a fall in the employment in middle-wage, "routine-task-intensive," occupations. We analyze the characteristics of those who used to be employed in such occupations and show that this type of individual is nowadays more likely to be out of the labor force or working in low-paying occupations. Based on these findings, we develop a quantitative, general equilibrium model, with heterogeneous agents, labor force participation, occupational choice, and investment in physical and automation capital. We first use the model to evaluate the distributional consequences of automation. We find heterogeneity in its impact across different occupations, leading to a significant polarization in welfare. We then use this framework as a laboratory to eva...
|How Exporters Grow|
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We show that in successful episodes of export market entry, there are statistically and economically significant post-entry dynamics of quantities, but no post-entry dynamics of markups. This suggests that shifts in demand play an important role in successful entry, but that firms do not use dynamic manipulation of markups as an instrument to shift demand. We structurally estimate two competing models of customer base accumulation to match these moments. In the first model, firms use marketing and advertising to acquire new customers and thereby shift demand and increase sales. In the second, they use temporarily low markups to do so. The marketing and advertising model fits the quantity and markup moments well, and implies that successful entry is associated with high selling expenses. Th...
|What Should I Be When I Grow Up? Occupations and Unemployment over the Life Cycle|
with , , : w20628
Why is unemployment higher for younger individuals? We address this question in a frictional model of the labor market that features learning about occupational fit. In order to learn the occupation in which they are most productive, workers sample occupations over their careers. Because young workers are more likely to be in matches that represent a poor occupational fit, they spend more time in transition between occupations. Through this mechanism, our model can replicate the observed age differences in unemployment which, as in the data, are due to differences in job separation rates.
Published: Martin Gervais & Nir Jaimovich & Henry E. Siu & Yaniv Yedid-Levi, 2016. "What Should I Be When I Grow Up? Occupations and Unemployment over the Life Cycle," Journal of Monetary Economics, . citation courtesy of
|Technological Learning and Labor Market Dynamics|
with , , : w19767
The search-and-matching model of the labor market fails to match two important business cycle facts: (i) a high volatility of unemployment relative to labor productivity, and (ii) a mild correlation between these two variables. We address these shortcomings by focusing on technological learning-by-doing: the notion that it takes workers time using a technology before reaching their full productive potential with it. We consider a novel source of business cycles, namely, fluctuations in the speed of technological learning and show that a search-and-matching model featuring such shocks can account for both facts. Moreover, our model provides a new interpretation of recently discussed "news shocks."
Published: Martin Gervais & Nir Jaimovich & Henry E. Siu & Yaniv Yedid‐Levi, 2015. "Technological Learning And Labor Market Dynamics," International Economic Review, Department of Economics, University of Pennsylvania and Osaka University Institute of Social and Economic Research Association, vol. 56, pages 27-53, 02. citation courtesy of