nep-ltv New Economics Papers
on Unemployment, Inequality and Poverty
Issue of 2023‒10‒23
four papers chosen by
Maximo Rossi, Universidad de la República


  1. The effects of pension reforms on physician labour supply: Evidence from the English NHS By Carol Propper; George Stoye; Max Warner
  2. Inequality of Opportunity in Wealth: Levels, Trends, and Drivers By Daniel Graeber; Viola Hilbert; Johannes König
  3. The unexpected power of negative awards By Jérémy Celse; Bruno Frey; Gilles Grolleau; Naoufel Mzoughi
  4. Generation Next: Experimentation with AI By Gary Charness; Brian Jabarian; John A. List

  1. By: Carol Propper (Institute for Fiscal Studies); George Stoye (Institute for Fiscal Studies); Max Warner (Institute for Fiscal Studies)
    Date: 2023–09–29
    URL: http://d.repec.org/n?u=RePEc:ifs:ifsewp:23/26&r=ltv
  2. By: Daniel Graeber (DIW Berlin, IZA Bonn, CEPA); Viola Hilbert (DIW Berlin, BSE); Johannes König (DIW Berlin, BSE)
    Abstract: While inequality of opportunity (IOp) in earnings is well studied, the literature on IOp in individual net wealth is scarce to non-existent. This is problematic because both theoretical and empirical evidence show that the position in the wealth and income distribution can significantly diverge.We measure ex-ante IOp in net wealth for Germany using data from the Socio-Economic Panel (SOEP). Ex-ante IOp is defined as the contribution of circumstances to the inequality in net wealth before effort is exerted. The SOEP allows for a direct mapping from individual circumstances to individual net wealth and for a detailed decomposition of net wealth inequality into a variety of circumstances; among them childhood background, intergenerational transfers, and regional characteristics. The ratio of inequality of opportunity to total inequality is stable from 2002 to 2019. This is in sharp contrast to labor earnings, where ex-ante IOp is declining over time. Our estimates suggest that about 62% of the inequality in net wealth is due to circumstances. The most important circumstances are intergenerational transfers, parental occupation, and the region of birth. In contrast, gender and individuals’ own education are the most important circumstances for earnings.
    Keywords: inequality, wealth, inequality of opportunity, decomposition
    JEL: D63 J62 D31 J24
    Date: 2023–10
    URL: http://d.repec.org/n?u=RePEc:pot:cepadp:69&r=ltv
  3. By: Jérémy Celse (ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers); Bruno Frey (Unibas - Université de Bâle = University of Basel); Gilles Grolleau (ESSCA Research Lab - ESSCA - Ecole Supérieure des Sciences Commerciales d'Angers); Naoufel Mzoughi (ECODEVELOPPEMENT - Unité de recherche d'Écodéveloppement - INRAE - Institut National de Recherche pour l’Agriculture, l’Alimentation et l’Environnement)
    Abstract: We characterize negative awards. Their pervasiveness in various domains as well as the objectives of their designers and promoters are documented. We discuss the outcomes generated by negative awards and provide some rationales explaining why individuals and organizations may be interested in getting them. Several issues deserve further exploration.
    Date: 2022
    URL: http://d.repec.org/n?u=RePEc:hal:journl:hal-03599550&r=ltv
  4. By: Gary Charness; Brian Jabarian; John A. List
    Abstract: We investigate the potential for Large Language Models (LLMs) to enhance scientific practice within experimentation by identifying key areas, directions, and implications. First, we discuss how these models can improve experimental design, including improving the elicitation wording, coding experiments, and producing documentation. Second, we discuss the implementation of experiments using LLMs, focusing on enhancing causal inference by creating consistent experiences, improving comprehension of instructions, and monitoring participant engagement in real time. Third, we highlight how LLMs can help analyze experimental data, including pre-processing, data cleaning, and other analytical tasks while helping reviewers and replicators investigate studies. Each of these tasks improves the probability of reporting accurate findings.
    JEL: C0 C1 C80 C82 C87 C9 C90 C92 C99
    Date: 2023–09
    URL: http://d.repec.org/n?u=RePEc:nbr:nberwo:31679&r=ltv

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