Does affirmative action work?

After four decades, we are still debating how much impact affirmative action can and should have on opportunities and outcomes at work.Discrimination in employment ; Sex discrimination against women ; Affirmative action programs

Commentary on Empirical Research on an Asset Building Policy: A Microeconomic Perspective

Commentary on Empirical Research on an Asset Building Policy: A Microeconomic Perspectiv

Publishing Trends in Economics across Colleges and Universities, 1991-2007

There is good reason to think that non-elite programs in economics may be producing relatively more research than in the past: Research expectations have been ramped-up at non-PhD institutions and new information technologies have changed the way academic knowledge is produced and exchanged. This study investigates this question by examining publishing productivity in economics (and business) using data from the Web of Science (Knowledge) for a broad set of institutions – both elite and non-elite – over a 17-year period, from 1991 through 2007. Institutions are grouped into six tiers using a variety of sources. The analysis provides evidence that non-elite institutions are gaining on their more elite counterparts, but the magnitude of the gains are small. Thus, the story is more of constancy than of change, even in the face of changing technology and rising research expectations.higher education, research productivity, publishing trends, inequality

The Struggle to Make Ends Meet: Teen Employment and the 1996 Federal Welfare Legislation

This study investigates the possibility that teens in more economically-disadvantaged families may have entered the labor market in response to the 1996 welfare legislation that replaced AFDC with TANF. Data are from the outgoing rotation groups of the Current Population Survey (CPS) from September 1995-May 1996 (pre-TANF) and from September 2000-May 2001 (post-TANF). To identify the policy\u27s effect, we compare changes in the employment of teens in economically-disadvantaged families over the study period with changes in the employment of their more advantaged counterparts (a difference-in-difference methodology). We find that teen employment significantly increased among those in economically-disadvantaged families relative to their more-advantaged counterparts, even after controlling for macroeconomic conditions, among other factors. Our results suggest that TANF\u27s pro-employment effects go beyond the effects previously identified for single mothers

Publishing Trends in Economics across Colleges and Universities, 1991-2007

Any opinions expressed here are those of the author(s) and not those of IZA. Research published in this series may include views on policy, but the institute itself takes no institutional policy positions. The Institute for the Study of Labor (IZA) in Bonn is a local and virtual international research center and a place of communication between science, politics and business. IZA is an independent nonprofit organization supported by Deutsche Post Foundation. The center is associated with the University of Bonn and offers a stimulating research environment through its international network, workshops and conferences, data service, project support, research visits and doctoral program. IZA engages in (i) original and internationally competitive research in all fields of labor economics, (ii) development of policy concepts, and (iii) dissemination of research results and concepts to the interested public. IZA Discussion Papers often represent preliminary work and are circulated to encourage discussion. Citation of such a paper should account for its provisional character. A revised version may b

The Impact of Information Technology on Scientists' Productivity, Quality and Collaboration Patterns

This study advances the prior literature concerning the impact of information technology on productivity in academe in two important ways. First, it utilizes a dataset that combines information on the diffusion of two noteworthy and early innovations in IT -- BITNET and the Domain Name System (DNS) -- with career history data on research-active life scientists. This research design allows for proper identification of the availability of access to IT as well as a means to directly identify causal effects. Second, the fine-grained nature of the data set allows for an investigation of three publishing outcomes: counts, quality, and co-authorship. Our analysis of a random sample of 3,771 research-active life scientists from 430 U.S. institutions over a 25-year period supports the hypothesis of a differential return to IT across subgroups of the scientific labor force. Women scientists, early-to-mid-career scientists, and those employed by mid-to-lower-tier institutions benefit from access to IT in terms of overall research output and an increase in the number of new co-authors they work with. Early-career scientists and those in top-tier institutions gain in terms of research quality when IT becomes available at their campuses.

Approaches to detect genetic effects that differ between two strata in genome-wide meta-analyses: Recommendations based on a systematic evaluation.

Genome-wide association meta-analyses (GWAMAs) conducted separately by two strata have identified differences in genetic effects between strata, such as sex-differences for body fat distribution. However, there are several approaches to identify such differences and an uncertainty which approach to use. Assuming the availability of stratified GWAMA results, we compare various approaches to identify between-strata differences in genetic effects. We evaluate type I error and power via simulations and analytical comparisons for different scenarios of strata designs and for different types of between-strata differences. For strata of equal size, we find that the genome-wide test for difference without any filtering is the best approach to detect stratum-specific genetic effects with opposite directions, while filtering for overall association followed by the difference test is best to identify effects that are predominant in one stratum. When there is no a priori hypothesis on the type of difference, a combination of both approaches can be recommended. Some approaches violate type I error control when conducted in the same data set. For strata of unequal size, the best approach depends on whether the genetic effect is predominant in the larger or in the smaller stratum. Based on real data from GIANT (>175 000 individuals), we exemplify the impact of the approaches on the detection of sex-differences for body fat distribution (identifying up to 10 loci). Our recommendations provide tangible guidelines for future GWAMAs that aim at identifying between-strata differences. A better understanding of such effects will help pinpoint the underlying mechanisms