Skip to main content

New technologies, new work practices and the age structure of the workers

Abstract

There is empirical evidence that suggests that both technology and new work practices are skill-biased. In this paper, we analyse whether they are also age-biased. Does the introduction of new technology and new work practices reduce the demand for older workers and increase the demand for younger workers? The cross-section estimates suggest that technology is age-biased towards young, low-skilled workers. However, after sweeping away time-invariant unobserved firm effects by using a fixed effect approach, most of the significant relationships disappear. This suggests that the significant cross-section results are driven by unobserved heterogeneity between firms and are not causal effects of technology and new work practices on the demand for workers in different age groups.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. This list includes some of the most often mentioned characteristics of what is meant by ‘new work practices’. However, the list is not exhaustive. For instance, Pfeffer (1995) mentions 13 practices of new forms of work practices and personnel management that characterises companies that are effective in how they manage people. In addition to the one mentioned above, the practices include: employment security for the workers, selectivity in recruiting, incentive pay, employee ownership, information sharing, symbolic egalitarism and within-firm promotion.

  2. The translog approach has been frequently used in factor demand analyses; see for instances Bartel and Lichtenberg (1987), Berman et al. (1994), Machin (1996) and Chennels and Van Reenen (1999).

  3. Some authors have relaxed these assumptions and introduced short-run disequilibrium adjustments by estimating multivariate error-correction model (see, for instance, Lindquist and Skjerpen 2000).

  4. As one of their indicators, Caroli and Reenen (2001) use the proportion of workers using micro-electronic technologies in the plant. Aubert et al. (2006) use information on the proportion of workers at the firm using computers. They construct a dummy variable taking the value 1 if more than 40% of the workers use computers, 0 otherwise.

  5. We have experimented with other measures. Two typical indicators used in the literature are job rotation and teams. Our measure of teams does not include any information on whether these teams are autonomous. This turned out to be insignificant in all regressions. Our measure of job rotation seems to be to too general and broad to catch any systematical patterns. We decided we leave out these indicators.

  6. In addition, in Table 1, the difference between the PC coefficient for the oldest age group and the PC coefficients for age group 30–39 and age group 40–49, are not significant.

  7. An indication that experience adds to the skill level is that wages increase with accumulated experience. Asplund et al. (1996) report empirical evidence from the late 1980s using a simple Mincer type of wage regression. They find that 10 years of experience in the Norwegian labour adds approximately 20% to the wage level (0.0224 × Exp − 0.00033 × EXP2). This return is somewhat higher compared to the other Nordic countries reported in the same source. Schøne (2004) reports Norwegian estimates for the year 2000 and finds returns to experience in approximately the same order (0.023 × EXP − 0.0003 × EXP2).

  8. Results available from author upon request.

  9. See Behagel and Greenan (2005) for a study of the relationship between training and age-biased technical change.

  10. Some firms do not employ workers in the specified age-education groups in Table 3, i.e. for some firms the dependent variable is truncated. In such case, SUR regressions may yield biased estimates. To check for the severity of this, we have run random effect Tobit regression as well. This did not alter the results in any significant way.

  11. The formula for the elasticity is: \( \varepsilon = \frac{{\partial s_{j} }} {{\partial PC}}\frac{{PC}} {{s_{j} }} \), where j is worker group. In the construction of the elasticity, we use the average effect on the observed variable.

  12. It should also be mentioned that we have experimented with different first-difference specifications, firstly, by estimating the difference in wage costs from 1997 to 2003 on level variables of technology and new work practices from 1997, secondly by estimating the wage cost difference between 1998 and 2003 on level variables from 1997. The latter approach analyses whether lagging the explanatory variables makes a difference. However, in neither model do we find any support new technology nor new work practices to be age-biased towards either old or young workers.

References

  • Aakvik A et al (2004) Enterprise heterogeneity and early retirement behaviour. Institute for Economics, University of Bergen

  • Acemoglu D, Pischke S (1999) The structure of wages and investments in general training. J Polit Econ 107(3):539–572

    Article  Google Scholar 

  • Aghion P et al (1999) Inequality and economic growth: the perspective of the new growth theories. J Econ Lit 37(4):1615–1660

    Google Scholar 

  • Asplund R et al (1996) Wage distribution across individuals. In: Wadensjö E (ed) The Nordic labour markets in the 1990s. North Holland, pp 10–53

  • Aubert P et al (2006) New technologies, workplace organisation and the age structure of the workforce: Firm-level evidence. Econ J 116:F73–F93, (February)

    Article  Google Scholar 

  • Bartel AP, Lichtenberg FR (1987) The comparative advantage of educated workers in implementing new technology. Rev Econ Stat 69(1):1–11

    Article  Google Scholar 

  • Bartel A, Sicherman N (1993) Technological change and retirement decisions of older workers. J Labor Econ 11(1):162–183

    Article  Google Scholar 

  • Becker G (1964) Human capital. Harvard University Press, Cambridge, Massachusetts

    Google Scholar 

  • Beckman M (2005) Age-biased technological and organisational change: Firm-level evidence and management implications. University of Munich, Munich

    Google Scholar 

  • Behagel L, Greenan N (2005) Training and age-biased technical change: Evidence from French micro data. CREST, Oxford, UK

    Google Scholar 

  • Berman EJ et al (1994) Changes in the demand for skilled labor within U.S. manufacturing: Evidence from the annual survey of manufactures. Q J Econ 109(2):367–397

    Article  Google Scholar 

  • Blundell R, Bond S (2000) GMM estimation with persistent panel data: An application to production functions. Econom Rev 19(2):321–340

    Article  Google Scholar 

  • Boisjoly J et al (1998) The shifting incidence of involuntary job losses from 1968 to 1992. Ind Relat 37(2):207–231

    Article  Google Scholar 

  • Borghans L, Eeel BT (2002) Do older workers have more trouble using a computer than younger workers In: De Griep A, Van Loo J, Mayhew K (eds) The economics of skill obsolescence: Theoretical Innovations and empirical applications. Res Labor Econ 21(1):139–173

  • Bresnahan TF et al (2002) Information technology, workplace organization and the demand for skilled labor: Firm-level evidence. Q J Econ 117(1):339–376

    Article  Google Scholar 

  • Brown RS, Christensen LR (1981) Estimates of elasticities of substitution in a model of partial static equilibrium: An application to US agriculture, 1947–1974. In: Berndt ER, Field BC (eds) Modelling and measuring natural resource substitution. MIT Press, Cambridge, Massachusetts, pp 209–229

    Google Scholar 

  • Caroli E (2001) New technologies, organisational change and the skill bias: what do we know? In: Petit P, Soete L (eds) Technology and the future employment of Europe. Edward Elgar, UK

    Google Scholar 

  • Caroli E, Reenen JV (2001) Skill-biased organizational change? Evidence from a panel of British and French establishments. Q J Econ 116(4):1449–1492

    Article  Google Scholar 

  • Chennels L, Reenen JV (1999) Has technology hurt less skilled workers? An economic survey of the effects of technical change on the structure of pay and jobs. The Institute for Fiscal Studies, London, Working paper 99/27

  • Christensen LR et al (1971) Conjugate duality and the transcendental logarithmic production function. Econometrica 39(2):255–256

    Google Scholar 

  • Christensen LR et al (1973) Transcendental logarithmic production frontiers. Rev Econ Stat 55(1):28–45

    Article  Google Scholar 

  • Goos M, Manning A (2007) Lovely and lousy jobs: the rising polarization of works in Britain. Rev Econ Stat 89(1):118–133

    Google Scholar 

  • Griliches Z, Hausman J (1986) Errors in variables in panel data. J Econ 31(1):93–118

    Google Scholar 

  • Griliches Z, Mairesse J (1997) Production functions: the search for identification. In: Strøm S (ed) Essays in honour of Ragnar Frisch. Economotric society monograph series. Cambridge University Press, Cambridge, pp 169–203

    Google Scholar 

  • Gruber J, Wise DA (2004) Social security programs and retirement around the world: Micro estimation. University of Chicago Press

  • Heywood J et al (1999) The determinants of hiring older workers. Evidence from Hong Kong. Ind Labor Relat Rev 52(3):444–459

    Article  Google Scholar 

  • Leuven E (2005) The economics of private-sector training: A review of the literature. J Econ Surv 19(1):91–111

    Article  Google Scholar 

  • Lindbeck A, Snower D (2000) Multitask learning and the reorganization of work: from Tayloristic to Holistic organization. J Labor Econ 18(3):353–376

    Article  Google Scholar 

  • Lindquist KG, Skjerpen T (2000) Explaining the change in skill structure of labour demand in Norwegian manufacturing. Discussion Paper, no 293. Statistics Norway

  • Machin S (1996) Changes in the relative demand for skills. In: Booth A, Snower D (eds) Acquiring skills. Cambridge University Press, Cambridge, Cambridge

    Google Scholar 

  • Neuman S, Weiss A (1995) On the effect of schooling vintage on experience-earnings profiles: Theory and evidence. Eur Econ Rev 39(5):943–955

    Article  Google Scholar 

  • OECD (1996) Employment outlook. Paris. Organisation for Economic Co-operation and Development

  • OECD (1999) Employment outlook. Organisation for Economic Co-operation and Development. Paris

  • OECD (2004) Aging and employment policies. Paris. Organisation for Economic Co-operation and Development

  • OECD (2006) Employment outlook. OECD. Paris

  • Osterman P (1994) How common is workplace transformation and who adopts it? Ind Labor Relat Rev 47(2):173–188

    Article  Google Scholar 

  • Pfeffer J (1995) Producing sustainable competitive advantage through the effective management of people.” Acad Manage Exec 9(1):55–72

    Google Scholar 

  • Røed K, Haugen F (2003) Early retirement and economic incentives—evidence from a quasi-natural experiment. Labour 17(2):203–228

    Article  Google Scholar 

  • Rosen S (1975) Measuring the obsolescence of knowledge. In: Juster FT (ed) Education, income and human behaviour. Carnegie Foundation and Columbia University Press, New York, pp 199–232

    Google Scholar 

  • Schøne P (2004) Lønnsforskjeller i offentlig og privat sektor (in Norwegian). Report 2004:2. Institute for Social Research, Oslo

  • Tysse T (2001) Effect of enterprise characteristics on early retirement. Statistics Norway. Report 2001/26

  • UN (2002) World Population prospects. The 2002 Revision. United Nations

  • Weinberg BA (2004) Experience and technology adoption. IZA Discussion paper No. 1051

Download references

Acknowledgement

Thanks to Erling Barth colleagues and seminar participants at The Institute for Social Research as well as three anonymous referees for valuable comments to an earlier version. Financial support from The Research Council of Norway (project number 156035/50) is gratefully acknowledged.

Author information

Authors and Affiliations

Authors

Corresponding author

Correspondence to Pål Schøne.

Additional information

Responsible Editor: Christian Dustmann

Appendix

Appendix

Table 7 Correlation between indicators of technology and new work practices
Table 8 Wage bill shares for different age groups by educational attainment
Table 9 Wage bill shares for different age groups

Rights and permissions

Reprints and Permissions

About this article

Cite this article

Schøne, P. New technologies, new work practices and the age structure of the workers. J Popul Econ 22, 803–826 (2009). https://doi.org/10.1007/s00148-007-0158-3

Download citation

  • Received:

  • Accepted:

  • Published:

  • Issue Date:

  • DOI: https://doi.org/10.1007/s00148-007-0158-3

Keywords

  • Older workers
  • Technological change
  • New work practices

JEL Classification

  • J23
  • L23
  • O33