Reemployment Rates over the Life Course: Is there still Hope after Late Career Job Loss?

The labor market situation for elderly job searchers is more difficult than for their younger counterparts. To identify patterns in the reemployment of the elderly, we analyze the employment histories of about 113.000 male job searchers in West-Germany. The analysis is based on a hazard rate model with piecewise constant intensities. We focus on age-specific reemployment rates. Individual characteristics, labor market indicators as well as the influence of the previous employment history on reemployment are accounted for. As expected, reemployment rates decline with age. Between 1975 and 1995, the negative impact of age on reemployment chances increases significantly. The obsolescence of human capital seems to play a decisive role for reemployment, especially for engineering occupations: From age 50 on, the negative age effect is significantly stronger than for other occupations.late career job loss, reemployment, hazard rate models, elderly engineers

Foreign Nationality and Age - A Double Drawback for Reemployment in Germany?

We analyze reemployment prospects for Germans and non-Germans over the life course. Older foreigners may experience a double drawback due to health issues, discrimination or differences in occupational structure. This effect might be alleviated by accumulation of country-specific skills over time and selectivity effects. We apply a piecewise-constant hazard rate model on more than 270.000 unemployment episodes drawn from the social insurance register for male employees aged 25 to 65 years between 1975 to 2001. Foreign nationality lowers reemployment prospects by 7 percentage points. On average, the effect of aging on reemployment is stronger for non-Germans. The effect of nationality differs strongly between nationalities and ranges from minus 17 percentage points for Greeks up to plus 5 percentage points for people from Ex-Yugoslavia. Aging is particularly a problem for foreigners from Greece and Turkey: Until age 60, their prospects for reemployment are, on average, about 27 percent below that of natives.labor migration, aging workforce, reemployment, proportional hazard rate models, demographic change

Separating wheat and chaff: age-specific staffing strategies and innovative performance at the firm level

Adopting a dynamic perspective, this paper investigates age-related staffing patterns in German establishments and their effect on innovative performance. First, we investigate how establishments achieve the necessary workforce rejuvenation - from the inflow of younger or from outflows of older workers. In addition, we explore whether certain staffing patterns are more likely to appear under different economic regimes. In a second step, we analyse whether an establishment's innovative performance is related to the staffing patterns it experiences. The analysis of linked-employer-employee data shows that most of the 585 German establishments covered rejuvenate by inflows of younger workers. Half of the establishments also use the outflow of older workers. Furthermore, workforces are more likely to become more age-heterogeneous in growing establishments. Finally, we do not find evidence that a youth-centred human resource strategy (always) fosters innovation. --Workforce aging,staffing strategies,innovation

Attenuation bias when measuring inventive performance

Most previous results on determinants of inventive performance are biased because inventive performance is measured with error. This measurement error causes attenuation bias. More specifically, for example age and education as drivers of patenting success have biased coefficients and too high standard errors when inventive performance is measured in short observation periods. The reason for measurement errors in inventive performance is that patents are typically applied for in waves

Separating wheat and chaff: age-specific staffing strategies and innovative performance at the firm level

Adopting a dynamic perspective, this paper investigates age-related staffing patterns in German establishments and their effect on innovative performance. First, we investigate how establishments achieve the necessary workforce rejuvenation - from the inflow of younger or from outflows of older workers. In addition, we explore whether certain staffing patterns are more likely to appear under different economic regimes. In a second step, we analyse whether an establishment’s innovative performance is related to the staffing patterns it experiences. The analysis of linked-employer-employee data shows that most of the 585 German establishments covered rejuvenate by inflows of younger workers. Half of the establishments also use the outflow of older workers. Furthermore, workforces are more likely to become more age-heterogeneous in growing establishments. Finally, we do not find evidence that a youth-centred human resource strategy (always) fosters innovation

The power of individual-level drivers of inventive performance

Based on an established theoretical framework of the drivers of inventive performance, the so-called KSAO (Knowledge, Skills, Abilities, and Other) factors, this paper seeks to explain empirically the performance of inventors throughout their careers. We combine survey information spanning the inventors’ entire careers and psychometric test evidence, with patent history data for more than 1,000 inventors. We also control for variables that have traditionally been included in estimations of inventive performance such as inventor age and a broad list of applicant institution-, technology-, patent-, and period-related information. We show that educational level, skills acquired during the career, personality traits, career motivations, cognitive abilities, and cognitive problem-solving style are significantly related to inventive performance

Individual determinants of inventor productivity : report and preliminary results with evidence from linked human capital and patent data

This report offers new insights into the drivers of inventor productivity at the individual level. It includes well-known drivers, such as inventor age and education, and controls for inventor team size, and firm/applicant information, as well as period and technology field effects derived from patent data. In addition, it adds inventor characteristics that have been largely neglected in existing studies on inventor productivity, such as the breadth of work experience, divergent thinking skills, cognitive problem-solving skills, the use of knowledge sourced from networks within and outside of the inventors’ field of expertise, and personality traits. The empirical model draws on a new dataset that matches information about inventors’ human capital, such as creative skills, personality traits, networks, and career biographies (collected with a self-administered survey) with patenting histories for 1932 German inventors between the years 1978 and 2012 for clean technology, nanotechnology, and mechanical elements. Our results indicate that the additional inventor characteristics double the proportion of total variation of productivity explained by individual characteristics. Furthermore, we find differences in the importance of individual characteristics across industries and along the productivity distribution, between more and less productive inventors

Candidate screening for the recruitment of critical research and development workers - a report and preliminary results with evidence from experimental data from German high-tech firms

The report focuses on résumé-based screening strategies for the recruitment of highly qualified research and development (R&D) workers (critical R&D workers) in high-tech firms. We investigate which kinds of professional background, job-related experience, motivations, specific skills, and previous inventive activity make a candidate attractive for firms specializing in clean technology or mechanical elements. The report is based on a combination of survey and experimental data collected from 194 HR decision makers in German high-tech firms and from 89 technology experts in the clean technology and mechanical elements fields. A mixed logit model is used to analyse hiring preferences because this model allows us to deal with repeated choices. We find that HR decision makers prefer candidates with technology-specific patenting experience, an engineering background, analytical thinking skills, and a strong desire to develop path-breaking technologies. Furthermore, no one-size-fits-all candidate exists that is equally preferred in both technology fields. HR decision makers in mechanical element firms prefer specialists to generalists, whereas those in clean technology attach special importance to a candidate’s orientation towards environmental concerns and sustainability

The Innovative Capacity of an Aging Workforce

In times of sweeping demographic changes, policy makers and business executives in mature economies perceive workforce aging as one potential threat to the capacity for innovation and technological progress. However, evidence for age-dependency in innovative performance is still scarce. Pressing questions in this context are for example: - Does workforce age affect the innovative capacity of firms and regions, and if so, how and through which transmission channels do these effects occur? - What are the sources of possible age-dependency in innovative performance, in particular with respect to innovation-relevant human capital? - What are the policy implications of the interplay between workforce age and the capacity to produce technological advances in times of future workforce aging? Starting from a comprehensive survey and critical discussion of existing studies about the interplay between workforce age and innovation, this book suggests a new conceptual framework to study the age-dependency of innovation. Based on this, three empirical studies investigate how the age composition of a workforce affects inventive performance in European regions, to what extent certain staffing patterns experienced by German firms boost innovative performance and how a region’s entrepreneurial capacity relates to the age composition of its working-age population.aging workforce, innovation, entrepreneurship, human capital, demographic change