Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach

 

Andreas Gulyas.

(University of Mannheim).

Understanding the Sources of Earnings Losses After Job Displacement: A Machine-Learning Approach.

Abstract:

We document the sources behind earnings losses after job displacement adapting the generalized random forest due to Athey et al. (2019). Using administrative data from Austria over three decades, we show that displaced workers face large and persistent earnings losses. We identify substantial heterogeneity in losses across workers. A quarter of workers face cumulative 11-year losses higher than 2 times their pre-displacement annual income, while another quarter experiences losses less than 1.1 times their income. The most vulnerable are older high-income workers employed at well-paying firms in the manufacturing sector. Our methodology allows us to consider many competing theories of earnings losses prominently discussed in the literature. The two most important factors are the displacement firm’s wage premia and the availability of well paying jobs in the local labor market. Our overall findings provide evidence that earnings losses can be understood by mean reversion in firm rents and losses in match quality, rather than by a destruction of firm-specific human capital.

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Healthy Dietary Choices in Distorted Food Environments

 

 

Roberto Pancrazi.

(University of Warwick).

Healthy Dietary Choices in Distorted Food Environments.

We analyze how distortions in the food market limit the achievement of a healthy diet. Using Nielsen data we document a novel pattern on the relationship between healthy diet and income. We then build and estimate a tractable general equilibrium model in which difference in diet across income can be determined by preferences and/or by economic factors. We estimate the model so that it is able to account for the relevant stylized facts and we use as laboratory to study which policies could enhance healthy diets across different income levels.

 

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Unemployment Insurance and Occupational Switching

 

 

Anna Houstecka.

(Universitat Autònoma de Barcelona).

Unemployment Insurance and Occupational Switching.

Abstract: “In this paper, I study the relationship between unemployment benefits and occupational switching. Using US data, I document two new facts. First, unemployed individuals who are eligible to higher unemployment benefits are less likely to switch occupation. Second, I show that conditional on switching occupation, individuals with higher unemployment benefits exit to occupations with higher requirements of cognitive skills. I then build a search model with heterogenous workers and jobs to study how unemployment benefits affect skill requirements and wages for workers who experience employment-unemployment-employment transitions. I find that mismatch between the worker’s skill and requirements decreases as unemployment benefits increase.”

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