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The relationship between age and creative performance has been found to follow a hump-shaped profile in the arts and sciences, and in great technological achievement. Accordingly, accelerating workforce aging raises concern about whether future capacity to innovate is endangered. This paper provides a literature survey and critical discussion of existing studies exploring the effects of age on innovative performance, both at the individual and the firm level. However, as individual-level evidence and some of the firm-level studies are of purely cross-sectional nature, the existing results have to be interpreted with caution owing to the presence of selectivity biases and unobserved heterogeneity. Studies at the aggregate level of firms address some of these issues. In particular, the scarce longitudinal evidence reveals that it is very likely that older workers fare much better in innovation than previous cross-sectional evidence suggests. Moreover, up to now, the literature survey does not find conclusive evidence that a youth-centred human resource strategy (always) fosters innovation. Apart from integrating the existing empirical evidence on different levels of aggregation, a strong focus of this paper is on methodological challenges in the empirical study of workforce age and innovation and it aims to offer a sound conceptual and methodological basis for further studies in this field of management research.

Die Dis cus si on Pape rs die nen einer mög lichst schnel len Ver brei tung von neue ren For schungs arbei ten des ZEW. Die Bei trä ge lie gen in allei ni ger Ver ant wor tung der Auto ren und stel len nicht not wen di ger wei se die Mei nung des ZEW dar.Dis cus si on Papers are inten ded to make results of ZEW research prompt ly avai la ble to other eco no mists in order to encou ra ge dis cus si on and sug gesti ons for revi si ons. The aut hors are sole ly respon si ble for the con tents which do not neces sa ri ly repre sent the opi ni on of the ZEW. AbstractThis 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.

Age, human capital and the geography of innovation Abstract An aging labor force is often associated with a decreasing innovative performance on aggregate, firm or individual level. Using a regional knowledge production function to explain patenting activity in German districts, we propose to include the effect of age in a twofold specification: First, we account indirectly for age by including the aggregate, age-heterogeneous human capital available in each district and estimating its effect on patenting performance. Second, we assume that there is an age effect that is independent of human capital and therefore include the age structure of the districts' labor force directly, too. Possible explanations for an independent age-effect are age-dependent differences in the ability to exploit innovation-relevant human capital or age-specific motivation to lead creative ideas to successful inventions. Departing from these conceptualizations provided by economics and I-O psychology, we estimate a negative binomial regression model appropriate for count data. Results on German district level indicate that engineering knowledge in the younger as well as the prime age group significantly enhances patenting performance, whereas we do not find any effect for the age group 50+. However, for older ages, the stock of experience has a positive influence. On aggregate level, we find a positive independent age effect.

The relationship between age and creative performance has been found to follow a hump-shaped profile in the arts and sciences, and in great technological achievement. Accordingly, accelerating workforce aging raises concern about whether future capacity to innovate is endangered. This paper provides a review of existing studies exploring age effects on innovative performance, both at the individual and the macro levels. Empirical evidence confirms the hump-shaped relationship between workers' ages and innovative performance, with the highest levels of performance seen between ages 30 and 50, depending on the domain. Industrial invention in knowledge-intensive fields, and great invention in general, seem to be a young man's game. Yet in more experiencebased fields, innovative performance peaks later, and remains stable until late in the career. Moreover, the quality of invention remains rather stable at older ages. However, individual-level evidence has to be interpreted with caution due to the presence of selectivity biases and unobserved heterogeneity. Studies at the levels of firms, regions, and countries address some of these issues. Results of these studies have indicated that young professionals drive knowledge absorption, innovation, and technological progress, whereas more experienced workers are more relevant in mature technological regimes. Apart from integrating the existing empirical evidence on different levels of aggregation, a strong focus is on methodological issues and conceptual challenges. This review therefore provides a sound basis for further studies on the impact of workforce aging on innovative performance. In addition, promising directions for future research are proposed.