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on Unemployment, Inequality and Poverty |
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Issue of 2009‒09‒05
four papers chosen by |
| By: | Steven E. Haugen (U.S. Bureau of Labor Statistics) |
| Abstract: | The Current Population Survey (CPS) has been the source of official labor force statistics for the U.S. since its inception in March 1940. The best-known statistic calculated from CPS data is the unemployment rate. To be classified as unemployed, a person must have had no employment during the survey reference week, been available for work, and made specific efforts to find employment during the 4-week period ending with the reference week. The unemployment rate represents the number unemployed as a percent of the labor force. The unemployment rate has proven to be a reliable indicator of overall labor market conditions and has performed quite well as a business cycle indicator. That does not mean, however, that everyone has been completely satisfied with the official figures. As a result, in the 1970s, a range of unemployment indicators known as U-1 through U-7 was introduced. In 1994, a redesigned CPS was fielded, and some of the survey changes affected series used as inputs in several of the U-1—U-7 measures. Consequently, BLS introduced a new set of “U’s” in 1995. The new U-1—U-6 range of alternative measures of labor underutilization offered an updated set of indicators that took advantage of newly collected information in the redesigned survey. This paper summarizes the rationale for the original and current ranges of alternative indicators. The paper also concludes that while the five alternatives to the official unemployment rate in the current U-1—U-6 range may represent varying views of labor resource underutilization, they show very similar patterns of change across the course of the business cycle. |
| Keywords: | Employment, unemployment, unemployment rate, underemployment |
| JEL: | E24 |
| Date: | 2009–04 |
| URL: | http://d.repec.org/n?u=RePEc:bls:wpaper:ec090020&r=ltv |
| By: | Judith Hellerstein; Melissa McInerney; David Neumark |
| Abstract: | We specify and implement a test for the importance of network effects in determining the establishments at which people work, using recently-constructed matched employer-employee data at the establishment level. We explicitly measure the importance of network effects for groups broken out by race, ethnicity, and various measures of skill, for networks generated by residential proximity. The evidence indicates that labor market networks play an important role in hiring, more so for minorities and the less-skilled, especially among Hispanics, and that labor market networks appear to be race-based. |
| Date: | 2009–01 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:09-01&r=ltv |
| By: | Stephen Jenkins; Richard Burkhauser; Shuaizhang Feng; Jeff Larrimore |
| Abstract: | To measure income inequality with right censored (topcoded) data, we propose multiple imputation for censored observations using draws from Generalized Beta of the Second Kind distributions to provide partially synthetic datasets analyzed using complete data methods. Estimation and inference uses Reiter’s (Survey Methodology 2003) formulae. Using Current Population Survey (CPS) internal data, we find few statistically significant differences in income inequality for pairs of years between 1995 and 2004. We also show that using CPS public use data with cell mean imputations may lead to incorrect inferences about inequality differences. Multiply-imputed public use data provide an intermediate solution. |
| Keywords: | Income Inequality, Topcoding, Partially Synthetic Data, CPS, Current Population Survey, Generalized Beta of the Second Kind distribution |
| JEL: | D31 C46 C81 |
| Date: | 2009–04 |
| URL: | http://d.repec.org/n?u=RePEc:cen:wpaper:09-05&r=ltv |
| By: | Petra E. Todd (Department of Economics, University of Pennsylvania); Kenneth I. Wolpin (Department of Economics, University of Pennsylvania) |
| Abstract: | This paper discusses the use of discrete choice dynamic programming (DCDP) methods for evaluating policies of particular relevance to developing countries, such as policies to reduce child labor and increase school attendance, to improve school quality, to affect immigration flows, to expand old age pension benefits, or to foster small business investment through microfinance. We describe the DCDP framework and how it relates to static models, illustrate its application with an example related to conditional cash transfer programs, consider numerous empirical applications from the literature of how the DCDP methodology has been used to address substantively important policy issues, and discuss methods for model validation. |
| Keywords: | development economics, policy evaluation, dynamic discrete choice models, schooling, migration |
| JEL: | J22 C21 H31 |
| Date: | 2009–07–24 |
| URL: | http://d.repec.org/n?u=RePEc:pen:papers:09-028&r=ltv |