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on Unemployment, Inequality and Poverty |
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Issue of 2017‒06‒18
five papers chosen by |
| By: | Burda, Michael C; Genadek, Katie R.; Hamermesh, Daniel S. |
| Abstract: | We use the American Time Use Survey (ATUS) 2003-2012 to estimate time spent in non-work on the job. Non-work is substantial and varies positively with local unemployment. Time spent in non-work conditional on any positive amount rises, while the fraction of workers reporting positive values declines with unemployment. Both effects are economically important, and are consistent with a model in which heterogeneous workers are paid efficiency wages. That model correctly predicts the relationship between the incidence of non-work and unemployment benefits in state data linked to the ATUS, and is consistent with estimated occupational differences in non-work incidence and intensity, as well as the cyclical behavior of aggregate labor productivity. |
| Keywords: | efficiency wages; labor productivity; non-work; time use |
| JEL: | E24 J22 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:cpr:ceprdp:12087&r=ltv |
| By: | Erling Barth; James C. Davis; Richard B. Freeman; Andrew J. Wang |
| Abstract: | This paper uses linked establishment-firm-employee data to examine the relationship between the scientists and engineers proportion (SEP) of employment, and productivity and labor earnings. We show that: (1) most scientists and engineers in industry are employed in establishments producing goods or services, and do not perform research and development (R&D); (2) productivity is higher in manufacturing establishments with higher SEP, and increases with increases in SEP; (3) employee earnings are higher in manufacturing establishments with higher SEP, and increase substantially for employees who move to establishments with higher SEP, but only modestly for employees within an establishment when SEP increases in the establishment. The results suggest that the work of scientists and engineers in goods and services producing establishments is an important pathway for increasing productivity and earnings, separate and distinct from the work of scientists and engineers who perform R&D. |
| JEL: | D24 J21 J31 O3 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23484&r=ltv |
| By: | George J. Borjas |
| Abstract: | Card’s (1990) study of the Mariel supply shock remains an important cornerstone of both the literature that measures the labor market impact of immigration, and of the “stylized fact” that immigration might not have much impact on the wage of workers in a receiving country. My recent reappraisal of the Mariel evidence (Borjas, 2017) revealed that the wage of low-skill workers in Miami declined substantially in the years after Mariel, and has already encouraged a number of re-reexaminations. Most recently, Clemens and Hunt (2017) argue that a data quirk in the CPS implies that wage trends in the sample of non-Hispanic prime-age men examined in my paper does not correctly represent what happened to wages in post-Mariel Miami. Specifically, there was a substantial increase in the black share of Miami’s low-skill workforce in the relevant period (particularly between the 1979 and 1980 survey years of the March CPS). Because African-American men earn less than white men, this increase in the black share would spuriously produce a drop in the average low-skill wage in Miami. This paper examines the robustness of the evidence presented in my original paper to statistical adjustments that control for the increasing number of black men in Miami’s low-skill workforce. The evidence consistently indicates that the race-adjusted low-skill wage in Miami fell significantly relative to the wage in other labor markets shortly after 1980 before fully recovering by 1990. |
| JEL: | J0 J61 |
| Date: | 2017–06 |
| URL: | http://d.repec.org/n?u=RePEc:nbr:nberwo:23504&r=ltv |
| By: | Audrey Pereira; Sudhanshu Handa; Goran Holmqvist |
| Abstract: | Target 2.1 of the Sustainable Development Goals calls for an end to hunger, in all its forms, by 2030. Measuring food security among children under age 5, who represent a quarter of the world’s population, remains a challenge that is largely unfeasible for current global monitoring systems. The SDG framework has agreed to use the Food Insecurity Experience Scale (FIES) to measure moderate and severe food insecurity. The FIES is an experience-based metric that reports food-related behaviours on the inability to access food due to resource constraints. We present the first global estimates of the share and number of children below age 15, who live with a respondent who is food insecure. |
| Keywords: | child poverty; child well-being; developed countries; food expenditures; |
| Date: | 2017 |
| URL: | http://d.repec.org/n?u=RePEc:ucf:inwopa:inwopa900&r=ltv |
| By: | Nicholas W. Papageorge (Johns Hopkins University); Kevin Thom (New York University) |
| Abstract: | Recent advances have led to the discovery of specific genetic variants that predict educational attainment. We study how these variants, summarized as a genetic score variable, are associated with human capital accumulation and labor market outcomes in the Health and Retirement Study (HRS). We demonstrate that the same genetic score that predicts education is also associated with higher wages, but only among individuals with a college education. Moreover, the genetic gradient in wages has grown in more recent birth cohorts, consistent with interactions between technological change and labor market ability. We also show that individuals who grew up in economically disadvantaged households are less likely to go to college when compared to individuals with the same genetic score, but from higher socioeconomic status households. Our findings provide support for the idea that childhood socioeconomic status is an important moderator of the economic returns to genetic endowments. Moreover, the finding that childhood poverty limits the educational attainment of high-ability individuals suggests the existence of unrealized human potential. |
| Keywords: | Human capital, inequality, education, genes |
| JEL: | I24 J24 |
| Date: | 2017–05 |
| URL: | http://d.repec.org/n?u=RePEc:upj:weupjo:17-273&r=ltv |