Positive effects of ageing and age diversity in innovative companies – large‐scale empirical evidence on company productivity

• Author: Stephan Veen,Uschi Backes‐Gellner
• DOI: http://doi.org/10.1111/1748-8583.12011
• Published date: 01 July 2013
• Date: 01 July 2013

Positive effects of ageing and age diversity in

innovative companies – large-scale empirical

evidence on company productivity

Uschi Backes-Gellner, Institute for Business Administration, University of Zurich

Stephan Veen, Disney Research Zurich

Human Resource Management Journal, Vol 23, no 3, 2013, pages 279–295

This article investigates how age diversity within a company’s workforce affects company productivity.

It introduces a theoretical framework that helps integrate results from a broad disciplinary spectrum of

ageing and diversity research to derive empirically testable hypotheses on the effects of age diversity on

company productivity. It argues that first the balance between costs and benefits of diversity determines

the effect of age diversity on company productivity, and that second the type of task performed acts as

a moderator. To test these hypotheses, it uses a large-scale employer–employee panel data set. Results

show that increasing age diversity has a positive effect on company productivity if and only if a company

engages in creative rather than routine tasks.

Contact: Prof Uschi Backes-Gellner, Institute for Business Administration, Zurich University,

Plattenstrasse 14, CH-8032 Zurich, Switzerland. Email: backes-gellner@business.uzh.ch

INTRODUCTION

Previous literature from various disciplines has produced extensive evidence in respect to

the relationship between age and individual productivity; some studies finding negative

effects, others positive, and yet others none at all (for a meta-analysis, see Ng and

Feldman, 2008). However, it has not yet been studied how such individual ageing effects

transform into company productivity, which so far has, at most, been implicitly viewed as the

simple sum of individual productivities. Age differences among coworkers have not played a

role in such analyses. However, Riach (2009) already pointed out that it is important to focus

on ‘differences’ in organisations and to become more aware of the effects that age diversity may

have. Ennen and Richter (2010), meanwhile, have shown through their extensive literature

review that in a typical organisation, there are complementarities between workers, that is, ‘the

whole is more than the sum of its parts’. They also argue that the more diverse the ‘elements’

of an organisation are, the stronger is this complementarity effect. However, the ‘elements’ they

study do not include workers’ age diversity. They use a somewhat related ‘element’, knowledge

and capabilities, but found no consistent results for it. Our article argues that age-diverse

workers are one of these aforementioned ‘elements’ because they increase the value of the

‘whole’ by bringing in additional knowledge and capabilities, be they quantitatively or

qualitatively different. Therefore, it is not only workers’ individual ageing but also – perhaps

even more significantly – the interplay of different individual ageing effects that is relevant for

company productivity.

We provide an economic framework to conceptually model how age diversity may affect

company productivity and under what circumstances this productivity effect will be positive

or negative. Our theoretical framework consists of three components: diversity benefits,

diversity costs and the contextual factor of companies’ task requirements. To determine how

diversity benefits and diversity costs may look like, we survey literature on team, group and

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doi: 10.1111/1748-8583.12011

HUMAN RESOURCE MANAGEMENT JOURNAL, VOL 23 NO 3, 2013 279

© 2012 John Wiley & Sons Ltd.

Please cite this article in press as: Backes-Gellner, U. and Veen, S. (2013) ‘Positive effects of ageing and age diversity in innovative companies –

large-scale empirical evidence on company productivity’. Human Resource Management Journal 23: 3, 279–295.

organisational diversity, and its effect on performance, and find that results are rather

inconclusive so far (Horwitz and Horwitz, 2007; Bell et al., 2011). Recent research of Lauring and

Selmer (2012) further points out that results for demographic diversity (including age) are

different from results for cultural or linguistic diversity. Particularly, results are mixed for

studies that have produced evidence on how the interaction of age-diverse workers within an

organisation may influence organisational performance: some studies indicate positive effects

(Page, 2007; Backes-Gellner and Tuor, 2010), others negative effects (Cleveland and Lim, 2007),

and some find no consistent effects for age diversity (Leonard and Levine, 2006). We argue that

these inconsistent results are partly due to moderators that may differ from study to study; one

of the most important moderators being task requirements. We assume that task requirements

determine how large the benefits or cost effects are – and consequently, how large the total

effect will be. As task requirements may differ substantially between companies, we

hypothesise that having more creative versus more routine tasks determines whether in a

company positive, negative or no productivity effects occur as a result of increased age

diversity. If this hypothesis is correct, our results will help explain the inconsistency of previous

research on age diversity.

We test our hypotheses by using a matched employer–employee panel data set (LIAB –

Linked Institut für Arbeitsmarkt- und Berufsforschung). The employer side of this matched

data set includes more than 18,000 German companies and is representative for firms in the

private sector (Alda et al., 2005). The employee side of the data set provides employment and

socio-economic information on all workers in all of these companies (except for managers and

small part-time workers without social security coverage); it includes about two million

employees and spans a time period of 10 years. With this longitudinal data set, we overcome

one of the major problems of existing literature on the relation between HRM and performance

that Guest (2011) points out after his review of 20 years of research. He claims that even after

two decades of extensive research, ‘we are still unable to answer core questions about the

relationship between human resource management and performance’. According to him, ‘this

is largely attributed to the limited amount of research that is longitudinal’ (Guest, 2011: 3). The

major question that he sees unanswered is that of causation. With our data set, we hope to be

better able to solve this problem and contribute to the question on whether differences in HRM

cause differences in productivity. The data set also provides important control variables, such

as tenure, education, age or gender for all workers (more information is provided in the data

section). So we also try to address the second problem pointed out by Guest (2011: 7), namely

the lack of control variables.

From the employer side of the matched data set, we are also able to construct a quantitative

measure for company productivity (our dependent variable) and to introduce important

company-related control variables, such as size, capital stock or industry. And from the

matching of the employer and employee side of the data, we are able to construct our

quantitative measures for age diversity (our explanatory variable) because we know the ages

of all workers for all companies. We exploit the panel structure of the data to apply fixed effects

estimations, among others. We also provide a number of robustness checks, including ordinary

least square (OLS) and random effects estimations. All our estimations strongly support our

hypotheses.

Our article makes two main contributions. First, it makes a theoretical contribution by

providing a framework that is novel to the study of productivity effects of age diversity, and

particularly helps demonstrate the impact of contextual factors (such as different types of task

requirements). This framework is an analytical tool to aid in structuring the analysis of the

relationship between diversity and firm productivity. It provides a graphical analysis to

Productivity effects of age-diverse workforces

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© 2012 John Wiley & Sons Ltd.