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How does the age structure of worker flows affect firm performance?

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Abstract

We develop a method for decomposing firm performance to impacts coming from the inflows and outflows of workers and apply it to study whether older workers are costly to firms. Our estimation equations are derived from a variant of the decomposition methods frequently used for measuring micro-level sources of industry productivity growth. By using comprehensive linked employer–employee data, we study the productivity and wage effects, and hence the profitability effects, of the hiring and separation of younger and older workers. The evidence shows that the separations of older workers are profitable to firms, especially in the manufacturing ICT-industries. To account for the correlation of the worker flows and productivity shocks we first estimate the shocks from a production function using materials as a proxy variable. In the second step the estimated shock is used as a control variable in our productivity, wage, and profitability equations.

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Notes

  1. There is a field of literature where the determinants of hiring and employing older workers are analyzed (e.g. Hutchens 1986, 1988; Aubert et al. 2006; Daniel and Heywood 2007; Ilmakunnas and Ilmakunnas 2015).

  2. One dictionary definition of the adjective dysfunctional is “characterized by a breakdown of normal or beneficial relationships between members of the group” (http://www.collinsdictionary.com).

  3. The positive influences of turnover have been emphasized more formally in models where the search and matching process allocates workers to their best uses in firms (e.g. Jovanovic 1979). Worker flows and the matching process may be particularly important for productivity when technological change is rapid (see e.g. Aghion and Howitt 1996).

  4. Vandenberghe (2010) has adopted our decomposition approach.

  5. Often the labor input is disaggregated by using parameters that measure the relative productivities of the worker types (e.g. Hellerstein, Neumark and Troske 1999). In this case, the production function is Y = f(θ 1 L 1 + ··· + θ M L M ) and the productivity terms are \( g_{j} = \left. {\theta_{j} f^{\prime}_{j} } \right|_{{X^{0} }} \).

  6. The decomposition is related to those used commonly in firm or plant-level productivity analysis (e.g. J. Haltiwanger 1997), but is closer to those used by Maliranta (1997), Vainiomäki (1999), Maliranta and Ilmakunnas (2005) as well as Diewert and Fox (2009) in different contexts.

  7. Our profit variable, the ratio of revenues and costs, is related to profitability measures used in productivity analysis (e.g. Balk 2010; Althin et al. 1996), where it is sometimes called “return to the dollar”. If we consider only value added and assume capital fixed in the short run, our measure is the ratio of net revenue (value added Y) and variable costs. It can also be interpreted as a mark-up. Assume that firms set price equal to unit labor cost multiplied by a mark-up 1 + m, so that we have revenue Y = (1 + m)W(1 + a). The operating margin can be written as OPM = Y − W(1 + a) = (1 + m)W(1 + a) − W(1 + a) = mW(1 + a), and the profit variable equals Π = 1 + m.

  8. The decomposition model is based on the assumption of constant returns to scale, but deviations from constant returns would show up as heteroscedasticity.

  9. We leave out the period 1990–1994, because our company data are substantially less comprehensive before the year 1995.

  10. These dummies also account for changes in the payroll taxes a, which were assumed fixed in the derivation of the formulas in Sect. 3.

  11. There is some measurement error because temporary agency workers or those with an atypical employment relationship, like service contract, are not included in the employment of the firms. According to the Finnish Private Employment Agencies Association, their employees accounted for only 1 % of total employment in 2010, which is below the European average. This share has been increasing in recent years, but it was still low in the period of our analysis. This kind of employment is more common in services. This is one reason why we estimate the models also by sector (industry and services). In principle, owner-managers can also cause measurement error. For small firms where the owner is working, he is included in the number of employees only if he takes at least half of his income as salary (as opposed to capital income). In any case, the restrictions we use (i.e., leaving out firms with less than ten employees and firms with missing employer-firm link) mean that this measurement error should not be large. We thank an anonymous referee for pointing out these issues to us.

  12. The proxy variable approach is based on the assumption that capital is inherited from the previous period, and the proxy variable (materials) and labor choices happen after the shock is realized. Therefore, the latter variables are endogenous, but capital stock itself is not.

  13. Some of the firms operate in more than one region. The region of a firm refers to the one where the employment share is the highest.

  14. The number of observations drops because of the reasons discussed in the text.

  15. Note that these figures underestimate actual turnover among the employees, since e.g. hiring of an employee after the start of a period and subsequent separation of the same employee before the end of the period is not included in the turnover rates.

  16. There are two main reasons why we prefer using employment weighted estimation. Firstly, we are ultimately concerned of productivity differences between different employment groups. Unfortunately we are unable to measure productivity at the level individuals but only at the level of firms so that in a sense we are using aggregated data. In order to give an equal weight for each individual in our analysis, we should give a larger weight to large firms than to smaller firms. Second, weighted estimation provides us with a more efficient procedure in the presence of heteroscedasticity.

  17. In the Finnish pension system the pension was until 1996 based on the last four years’ pay and until 2004 on the last ten year’s pay in each employment relationship, which gave incentives for obtaining a high pay at the end of the career. This combination of backloaded wage and a fixed retirement age is consistent with the deferred payment model of Lazear (1979), although it is a result of a quite different institutional setting. The system has been based on a mix of centralized negotiations between labor unions, employer organizations and the government, and firm-level wage setting. Lazear’s argument is somewhat difficult to use in the connection of labor flows, as it is an equilibrium model where the raising wage profile and unprofitability of the older employees is part of the “package”. However, even in this case unexpected increases in the costs of older employees or an increase in the share of older employees through aging may make the system unsustainable and give raise to incentives for separations.

  18. The probability of exit was modelled as a function of firm size, industry, employee characteristics (average tenure and education years, share of women), capital-labor ratio, productivity shock, and an indicator for growing firms. All of these variables were measured in the year prior to exit.

  19. In addition to the unemployment pension system, also disability pension gives incentives for laying off older employees. The larger firms are responsible for paying (part of) the disability pension until the normal retirement age. This can be avoided, if the worker goes to the unemployment pension tunnel before possible disability.

  20. See discussion in Daveri (2004). The results obtained by using the broad definition of ICT are available on request. It should be noted that “Financial intermediation” (ISIC 65–67) industries are excluded from our estimation sample.

  21. Also the econometric framework could be extended. We have used the proxy variable approach indirectly by estimating the shock from a level form and then using it in our difference form model. It remains an open issue how the approach could be directly applied to our model in one step. Also extensions to for example stochastic frontier models would be interesting.

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Acknowledgments

We are thankful to many individuals at Statistics Finland, and especially to Satu Nurmi and Elias Einiö for their guidance regarding the properties of the data. The data set is publicly available for research purposes, subject to terms and conditions of confidentiality, at the Research Services unit of Statistics Finland; contact tutkijapalvelut@stat.fi, for access to these data. We have benefited from the comments of Roope Uusitalo and seminar participants at the VATT Institute for Economic Research (Helsinki), Nordic Summer Institute in Labor Economics in Uppsala, EEA Congress in Vienna, CAED Conference in Chicago, Annual Meeting of the Finnish Society for Economic Research in Helsinki, World Aging and Generations Congress in StGallen, and Workshop on Labor Turnover and Firm Performance, Helsinki. We thank the referees and associate editor for useful comments. Financial support from the Yrjö Jahnsson Foundation is gratefully acknowledged. The SAS and Stata codes used in this study are available from the authors upon request.

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Authors and Affiliations

  1. Aalto University School of Business, P.O. Box 21240, 00076, Aalto, Helsinki, Finland

    Pekka Ilmakunnas

  2. The Research Institute of the Finnish Economy (ETLA), Arkadiankatu 28 B, 00100, Helsinki, Finland

    Mika Maliranta

  3. University of Jyväskylä, P.O. Box 35, 40014, Jyväskylä, Finland

    Mika Maliranta

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  1. Pekka Ilmakunnas
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  2. Mika Maliranta
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Correspondence to Pekka Ilmakunnas.

Appendix

Appendix

See Table 7.

Table 7 The number of observations in the estimation sample by branches
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Ilmakunnas, P., Maliranta, M. How does the age structure of worker flows affect firm performance?. J Prod Anal 46, 43–62 (2016). https://doi.org/10.1007/s11123-016-0471-5

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