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


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.

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Fig. 1


  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.


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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.

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Correspondence to Pål Schøne.

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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

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Schøne, P. New technologies, new work practices and the age structure of the workers. J Popul Econ 22, 803–826 (2009).

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  • Older workers
  • Technological change
  • New work practices

JEL Classification

  • J23
  • L23
  • O33