Occupational sex-segregation, specialized human capital and wages: evidence from Britain

Female-dominated occupations are poorly paid, but there is disagreement about why. Sociological explanations argue that pay in such occupations is low because society undervalues 'women's work', while economic theory argues that this is due to scant requirements for specialized skills. This article sheds light over these debates by examining the impact of occupational feminization on wages in Britain and exploring the mechanisms that produce it, using innovative statistical models that account for both observable and unobservable skill. Results confirm that occupational sex-segregation explains a sizeable portion of the gender wage gap and that wages in female-dominated occupations are lower than wages in other occupations. Inconsistent with human capital theory, low pay in female-dominated occupations cannot be explained fully by low skill specialization or by observable or unobservable characteristics of their workers. Remaining wage penalties in such occupations are consequently taken as evidence of institutional devaluation of 'women's work'

Enduring Legacy? Charles Tilly and Durable Inequality

This article assesses Charles Tilly’s Durable Inequality and traces its influence. In writing Durable Inequality, Tilly sought to shift the research agenda of stratification scholars. But the book’s initial impact was disappointing. In recent years, however, its influence has grown, suggesting a more enduring legacy

Unveiling Participant Level Determinants of Unit Non-Response in Business Tendency Surveys

Business Tendency Surveys (BTS) continue to be an important source of timely information on business cycles in many countries. We address quality of economic survey data by uncovering the relation between unit non-response and participant characteristics on company respectively respondent level. We use a unique, matched dataset that merges rich business tendency survey panel data with data from an exclusively conducted meta survey. Our meta information enhances the set of firm characteristics by information such as valuation of business tendency surveys or perceived response burden. We use different count data models to explain non-response count. Our models include weighted count data regressions as well as a two part hurdle model. We find that response burden, a company's survey track record, timeliness and participation mode are the strongest and most robust predictors of unit non-response. We also find a weaker negative effect of the business situation on unit response. Remarkably we do not find a significant influence of neither company size nor valuation of BTS on the propensity to respond to periodical qualitative BTS

Convergence or Divergence? Immigrant Wage Assimilation Patterns in Germany

Using a rich German panel data set, I estimate wage assimilation patterns for immigrants in Germany. This study contributes to the literature by performing separate estimations by skill groups and controlling for a wide range of socio-economic background variables. It aims to answer the question whether Germany can be considered an attractive host country from an immigrant's perspective. Comparisons with similar natives reveal that immigrants' experience earnings profiles are flatter on average, although clear differences show up among skill groups. The effect of time spent in the host country is significantly positive for all skill groups and thus partly offsetting the diverging trend in the experience earnings profiles. Still, wage differences between natives and immigrants remain. They are particularly noticeable for highly skilled immigrants, the group needed most in Germany's skill intensive labor market. Separate estimations for immigrant subgroups confirm the general validity of the results

Missing Links: Referrer Behavior and Job Segregation

How does referral recruitment contribute to job segregation, and what can organizations do about it? Current theory on network effects in the labor market emphasizes the job-seeker perspective, focusing on the segregated nature of job-seekers’ information and contact networks, and leaves little role for organizational influence. But employee referrals are necessarily initiated from within a firm by referrers. We argue that referrer behavior is the missing link that can help organizations manage the segregating effects of referring. Adopting the referrer’s perspective of the process, we develop a computational model which integrates a set of empirically documented referrer behavior mechanisms gleaned from extant organizational case studies. Using this model, we compare the segregating effects of referring when these behaviors are inactive to the effects when the behaviors are active. We show that referrer behaviors substantially boost the segregating effects of referring. This impact of referrer behavior presents an opportunity for organizations. Contrary to popular wisdom, we show that organizational policies designed to influence referrer behaviors can mitigate most if not all of the segregating effects of referring