March 1, 2023

Labor market insurance policies in the XXI century
By: Boeri, Tito; Cahuc, Pierre

Abstract:
The recovery from the Covid-19 crisis will force governments to accelerate transformation in their menu of labor market policy tools. The crisis was a stress test for unemployment insurance schemes as it involved a sudden and unexpected shutdown of a very large set of activities. This forced countries to introduce, often from scratch, income support schemes for workers under new forms of employment, and the self-employed. There was also a considerable expansion of short-time work schemes notably towards the small business. The challenge ahead of us is perhaps even harder as post-Covid19 labor markets are likely to be characterized by substantial labor reallocation. Major innovations in labor market policy are required to smooth consumption of workers involved in this reallocation. We survey the large body of research on schemes reducing the costs of reallocation complementary to unemployment insurance. Our attention is on short-time work (preventing layoffs by subsidizing hours reductions), partial unemployment insurance (enabling workers to combine unemployment benefits with low-income jobs), and wage insurance (offering a temporary wage subsidy to workers changing jobs). The properties of these new schemes are first presented and compared to those of standard unemployment benefits. Next the main results of the empirical literature on the effects of wage insurance, partial unemployment insurance and short-time work are presented. A final section is devoted to discussing directions for further research.

Keywords: partial unemployment insurance; wage insurance; short-time work

URL: http://d.repec.org/n?u=RePEc:ehl:lserod:118000&r=ltv

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March 1, 2023

2022: A SUMMARY OF ARTEFACTUAL FIELD EXPERIMENTS ON FIELDEXPERIMENTS.COM: THE WHO’S, WHAT’S, WHERE’S, AND WHEN’S
By: John List

Abstract: 2022 Summary of Artefactual Experiments
Date: 2023
URL: http://d.repec.org/n?u=RePEc:feb:artefa:00767&r=ltv


March 1, 2023

What makes a satisfying life? Prediction and interpretation with machine-learning algorithms

By: Clark, Andrew E.; D’Ambrosio, Conchita; Gentile, Niccol├│; Tkatchenko, Alexandre

Abstract: Machine Learning (ML) methods are increasingly being used across a variety of fields and have led to the discovery of intricate relationships between variables. We here apply ML methods to predict and interpret life satisfaction using data from the UK British Cohort Study. We discuss the application of first Penalized Linear Models and then one non-linear method, Random Forests. We present two key model-agnostic interpretative tools for the latter method: Permutation Importance and Shapley Values. With a parsimonious set of explanatory variables, neither Penalized Linear Models nor Random Forests produce major improvements over the standard Non-penalized Linear Model. However, once we consider a richer set of controls these methods do produce a non-negligible improvement in predictive accuracy. Although marital status, and emotional health continue to be the most important predictors of life satisfaction, as in the existing literature, gender becomes insignificant in the non-linear analysis.
Keywords: life satisfaction; well-being; machine learning; British cohort study
JEL: I31 C63

URL: http://d.repec.org/n?u=RePEc:ehl:lserod:117887&r=ltv