nep-ltv New Economics Papers
on Unemployment, Inequality and Poverty
Issue of 2023‒11‒27
six papers chosen by



  1. The Fundamental Properties, Stability and Predictive Power of Distributional Preferences By Fehr, Ernst; Epper, Thomas; Senn, Julien
  2. Vacancy Duration and Wages By Ihsaan Bassier; Alan Manning; Barbara Petrongolo
  3. Inequality of opportunity and intergenerational persistence in Latin America By Brunori, Paolo; Ferreira, Francisco H. G.; Neidhöfer, Guido
  4. How and Why the Gender Pension Gap in Urban China Decreased between 1988 and 2018 By Gustafsson, Björn Anders; Zhang, Peng; Jia, Hanrui
  5. The unexpected compression: Competition at work in the low wage labor market By David Autor; Arindrajit Dube; Annie McGrew
  6. Artificial intelligence and jobs: evidence from online vacancies By Acemoglu, Daron; Autor, David; Hazell, Jonathon; Restrepo, Pascual

  1. By: Fehr, Ernst (University of Zurich); Epper, Thomas (CNRS); Senn, Julien (University of Zurich)
    Abstract: Parsimony is a desirable feature of economic models but almost all human behaviors are characterized by vast individual variation that appears to defy parsimony. How much parsimony do we need to give up to capture the fundamental aspects of a population's distributional preferences and to maintain high predictive ability? Using a Bayesian nonparametric clustering method that makes the trade-off between parsimony and descriptive accuracy explicit, we show that three preference types - an inequality averse, an altruistic and a predominantly selfish type - capture the essence of behavioral heterogeneity. These types independently emerge in four different data sets and are strikingly stable over time. They predict out-of-sample behavior equally well as a model that permits all individuals to differ and substantially better than a representative agent model and a state-of-the-art machine learning algorithm. Thus, a parsimonious model with three stable types captures key characteristics of distributional preferences and has excellent predictive power.
    Keywords: distributional preferences, altruism, inequality aversion, preference heterogeneity, stability, out-of-sample prediction, parsimony, Bayesian nonparametrics
    JEL: D31 D63 C49 C90
    Date: 2023–10
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp16535&r=ltv
  2. By: Ihsaan Bassier; Alan Manning; Barbara Petrongolo
    Abstract: We estimate the elasticity of vacancy duration with respect to posted wages, using data from the near-universe of online job adverts in the United Kingdom. Our research design identifies duration elasticities by leveraging firm-level wage policies that are plausibly exogenous to hiring difficulties on specific job vacancies, and control for job and market-level fixed-effects. Wage policies are defined based on external information on pay settlements, or on sharp, internally-defined, firm-level changes. In our preferred specifications, we estimate duration elasticities in the range -3 to -5, which are substantially larger than the few existing estimates.
    Date: 2023–08–03
    URL: http://d.repec.org/n?u=RePEc:oxf:wpaper:1020&r=ltv
  3. By: Brunori, Paolo; Ferreira, Francisco H. G.; Neidhöfer, Guido
    Abstract: How strong is the transmission of socio-economic status across generations in Latin America? To answer this question, we first review the empirical literature on intergenerational mobility and inequality of opportunity for the region, summarizing results for both income and educational outcomes. We find that, whereas the income mobility literature is hampered by a paucity of representative datasets containing linked information on parents and children, the inequality of opportunity approach – which relies on other inherited and pre-determined circumstance variables – has suffered from arbitrariness in the choice of population partitions. Two new data-driven approaches – one aligned with the ex-ante and the other with the ex-post conception of inequality of opportunity – are introduced to address this shortcoming. They yield a set of new inequality of opportunity estimates for twenty-seven surveys covering nine Latin American countries over various years between 2000 and 2015. In most cases, more than half of the current generation’s inequality is inherited from the past – with a range between 44% and 63%. We argue that on balance, given the parsimony of the population partitions, these are still likely to be underestimates.
    JEL: D31 I39 J62 O15
    Date: 2023–09–01
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:120555&r=ltv
  4. By: Gustafsson, Björn Anders (Göteborg University); Zhang, Peng (Zhejiang University); Jia, Hanrui (Shanghai Administration Institute)
    Abstract: In urban China, gender gaps in employment and earnings have steadily increased since the 1990s. Such gender gaps are important because pension rights and amounts are based on labor force participation and wages. However, as this study demonstrates, despite the rise in gender differences in the urban labor market, the average gender pension gap decreased between 1988 and 2018. In the paper, we describe the evolution of the fragmented pension system in urban China using a quantitative approach that distinguishes between pension coverage rates and average benefit amounts. Additionally, we conducted a birth cohort analysis to gain further insights into the reasons for changes in the gender pension gap. We utilized data from the China Household Income Project, focusing on individuals aged 60 years and older. Therefore, this study demonstrates how changes in China's pension system have benefited women more than men during the aforementioned period.
    Keywords: gender pension gap, pension reforms, time effect, cohort effect, urban, China
    JEL: H55 J14 J26 P36
    Date: 2023–10
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp16558&r=ltv
  5. By: David Autor; Arindrajit Dube; Annie McGrew
    Abstract: Labor market tightness following the height of the Covid-19 pandemic led to an unexpected compression in the US wage distribution that reflects, in part, an increase in labor market competition. Rapid relative wage growth at the bottom of the distribution reduced the college wage premium and counteracted approximately one-quarter of the four-decade increase in aggregate 90-10 log wage inequality. Wage compression was accompanied by rapid nominal wage growth and rising job-to-job separations - especially among young non-college (high school or less) workers. At the state-level, post-pandemic labor market tightness became strongly predictive of price increases (price-Phillips curve), real wage growth among low-wage workers (wage-Phillips curve), and aggregate wage compression. Simultaneously, the wage-separation elasticity - a key measure of labor market competition - rose among young non-college workers, with wage gains concentrated among workers who changed employers and industries. Seen through the lens of a canonical job ladder model, the pandemic increased the elasticity of labor supply to firms in the low-wage labor market, reducing employer market power and spurring rapid relative wage growth among young non-college workers who disproportionately moved from lower-paying to higher paying and potentially more-productive jobs.
    Keywords: compression, competition, low wage, labor market
    Date: 2023–07–03
    URL: http://d.repec.org/n?u=RePEc:cep:poidwp:076&r=ltv
  6. By: Acemoglu, Daron; Autor, David; Hazell, Jonathon; Restrepo, Pascual
    Abstract: We study the impact of artificial intelligence (AI) on labor markets using establishment-level data on the near universe of online vacancies in the United States from 2010 onward. There is rapid growth in AI-related vacancies over 2010–18 that is driven by establishments whose workers engage in tasks compatible with AI’s current capabilities. As these AI-exposed establishments adopt AI, they simultaneously reduce hiring in non-AI positions and change the skill requirements of remaining postings. While visible at the establishment level, the aggregate impacts of AI-labor substitution on employment and wage growth in more exposed occupations and industries is currently too small to be detectable.
    Keywords: artificial intelligence; displacement; labor; jobs; tasks; technology; wages
    JEL: J23 O33
    Date: 2022–04–01
    URL: http://d.repec.org/n?u=RePEc:ehl:lserod:113325&r=ltv

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