nep-ltv New Economics Papers
on Unemployment, Inequality and Poverty
Issue of 2020‒08‒31
six papers chosen by
Maximo Rossi
Universidad de la República

  1. Ten facts about income inequality in advanced economies By Lucas Chancel
  2. A Second Chance? Labor Market Returns to Adult Education Using School Reforms By Bennett, Patrick; Blundell, Richard; Salvanes, Kjell Gunnar
  3. Occupational Sorting and Wage Gaps of Refugees By Baum, Christopher F.; Lööf, Hans; Stephan, Andreas; Zimmermann, Klaus F.
  4. What Accounts for the Rising Share of Women in the Top 1%? By , Stone Center; Burkhauser, Richard V.; Herault, Nicolas; Jenkins, Stephen P.; Wilkins, Roger
  5. Labor Supply and Automation Innovation By Danzer, Alexander M.; Feuerbaum, Carsten; Gaessler, Fabian
  6. Genetic Risks, Adolescent Health and Schooling Attainment By Amin, Vikesh; Behrman, Jere R.; Fletcher, Jason M.; Flores, Carlos A.; Flores-Lagunes, Alfonso; Kohler, Hans-Peter

  1. By: Lucas Chancel (PSE - Paris School of Economics, WIL - World Inequality Lab)
    Abstract: This paper presents 10 basic facts regarding inequality in advanced economies. Income and wealth inequality was very high a century ago, dropped in the 20th century, and has been rising at different speeds across countries since the 1980s. The financial crisis of 2008 does not appear to have inverted this trend. At the global level, while between-country inequality mattered more than within-country inequality in the 1980s, it is the opposite today. The rise of inequality has not been counterbalanced by an increase social mobility. The reduction of gender pay gaps has tempered the rise of inequality in recent decades, but gender inequality remains particularly high among top income and wealth groups. Racial inequalities remain large as well. Evidence suggests that trade and technology alone cannot explain large inequality variations across rich countries. Shifts in tax and wage setting policies, as well as differences in educational and health systems matter a lot.
    Keywords: inequality,advanced economies,income inequality,wealth inequality,inequality data,Distributional National Accounts,DINA,inequality measurement
    Date: 2019
    URL: http://d.repec.org/n?u=RePEc:hal:wilwps:hal-02876982&r=all
  2. By: Bennett, Patrick (Centre for Applied Research, Norwegian School of Economics); Blundell, Richard (University College London and IFS); Salvanes, Kjell Gunnar (Dept. of Economics, Norwegian School of Economics and Business Administration)
    Abstract: Roughly one third of a cohort drop out of high school across OECD countries, and developing effective tools to address prime-aged high school dropouts is a key policy question. We leverage high quality Norwegian register data, and for identification we exploit reforms enabling access to high school for adults above the age of 25. The paper finds that considerable increases in high school completion and beyond among women lead to higher earnings, increased employment, and decreased fertility. As male education remains unchanged by the reforms, later life education reduces the pre-existing gender earnings gap by a considerable fraction.
    Keywords: Adult Education; Returns to Education; Fertility; Gender inequality
    JEL: I26 I28 J13
    Date: 2020–08–03
    URL: http://d.repec.org/n?u=RePEc:hhs:nhheco:2020_014&r=all
  3. By: Baum, Christopher F.; Lööf, Hans; Stephan, Andreas; Zimmermann, Klaus F.
    Abstract: Refugee workers start low and adjust slowly to the wages of comparable natives. The innovative approach in this study using unique Swedish employeremployee data shows that the observed wage gap between established refugees and comparable natives is mainly caused by occupational sorting into cognitive and manual tasks. Within occupations, it can be largely explained by differences in work experience. The identification strategy relies on a control group of matched natives with the same characteristics as the refugees, using panel data for 2003–2013 to capture unobserved heterogeneity.
    Keywords: refugees,wage earnings gap,Blinder—Oaxaca decomposition,employer-employee data,coarsened exact matching,correlated random effects model
    JEL: C23 F22 J24 J6 O15
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:glodps:562r&r=all
  4. By: , Stone Center (The Graduate Center/CUNY); Burkhauser, Richard V.; Herault, Nicolas; Jenkins, Stephen P.; Wilkins, Roger
    Abstract: The share of women in the top 1% of the UK’s income distribution has been growing over the last two decades (as in several other countries). Our first contribution is to account for this secular change using regressions of the probability of being in the top 1%, fitted separately for men and women, in order to contrast between the sexes the role of changes in characteristics and changes in returns to characteristics. We show that the rise of women in the top 1% is primarily accounted for by their greater increases (relative to men) in the number of years spent in full-time education. Although most top income analysis uses tax return data, we derive our findings taking advantage of the much more extensive information about personal characteristics that is available in survey data. Our use of survey data requires justification given survey under-coverage of top incomes. Providing this justification is our second contribution. (Stone Center on Socio-Economic Inequality Working Paper)
    Date: 2020–07–06
    URL: http://d.repec.org/n?u=RePEc:osf:socarx:wdt2r&r=all
  5. By: Danzer, Alexander M. (Catholic University of Eichstätt-Ingolstadt); Feuerbaum, Carsten (Catholic University of Eichstätt-Ingolstadt); Gaessler, Fabian (Max Planck Institute for Innovation and Competition)
    Abstract: While economic theory suggests substitutability between labor and capital, little evidence exists regarding the causal effect of labor supply on inventing labor-saving technologies. We analyze the impact of exogenous changes in regional labor supply on automation innovation by exploiting an immigrant placement policy in Germany during the 1990s and 2000s. Difference-in-differences estimates indicate that one additional worker per 1,000 manual and unskilled workers reduces automation innovation by 0.05 patents. The effect is most pronounced two years after immigration and confined to industries containing many low-skilled workers. Labor market tightness and external demand are plausible mechanisms for the labor-innovation nexus.
    Keywords: labor supply, automation, innovation, patents, labor market tightness, quasiexperiment
    JEL: O31 O33 J61
    Date: 2020–06
    URL: http://d.repec.org/n?u=RePEc:iza:izadps:dp13429&r=all
  6. By: Amin, Vikesh; Behrman, Jere R.; Fletcher, Jason M.; Flores, Carlos A.; Flores-Lagunes, Alfonso; Kohler, Hans-Peter
    Abstract: We provide new evidence on the effect of adolescent health behaviors/outcomes (obesity, depression, smoking, and attention deficit hyperactivity disorder (ADHD)) on schooling attainment using the National Longitudinal Study of Adolescent to Adult Health. We take two different approaches to deal with omitted variable bias and reverse causality. Our first approach attends to the issue of reverse causality by using health polygenic scores (PGSs) as proxies for actual adolescent health. Second, we estimate the effect of adolescent health using sibling fixed-effects models that control for unmeasured genetic and family factors shared by siblings. We use the PGSs as additional controls in the sibling fixed-effects models to reduce concerns about residual confounding from sibling-specific genetic differences. We find consistent evidence across both approaches that being genetically predisposed to smoking and smoking regularly in adolescence reduces schooling attainment. We find mixed evidence for ADHD. Our estimates suggest that having a high genetic risk for ADHD reduces grades of schooling, but we do not find any statistically significant negative effects of ADHD on grades of schooling. Finally, results from both approaches show no consistent evidence for a detrimental effect of obesity or depression on schooling attainment.
    Keywords: adolescent health,polygenic scores,education
    JEL: I21 I10
    Date: 2020
    URL: http://d.repec.org/n?u=RePEc:zbw:glodps:582&r=all

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