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Factor-Augmenting Technical Change: An Empirical Assessment

Abstract

This paper estimates factor-specific technical change and input substitution using a structural approach. It contributes to the existing literature by introducing various technology drivers for factor productivities and by assessing the impact of endogenous technical change on the elasticity of substitution. The empirical results suggest that factor productivities are indeed endogenous. In addition, technology drivers are factor-specific. Whereas the R&D stock and machinery imports are important determinants of energy and capital productivity, the education stock is statistically related to labour productivity. The rate of energy-augmenting technical change is larger than that of either labour or capital. By contrast, the productivity of these two factors grows at similar rates. Estimates of the elasticity of substitution are within the range identified by previous literature. In addition, we show that endogenous technical change reduces substitution. Because the elasticity of substitution is lower than one, knowledge and human capital can ultimately have an energy-using effect. The estimated structure of endogenous technical change suggests that Integrated Assessment models focusing on energy-saving technical change might underestimate climate policy costs.

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Notes

  1. Hicks neutral technical change can be represented as a parallel shift in isoquants. Factor-biased technical change shifts the slopes of the isoquants, thereby affecting the relative marginal product of inputs. Technical change is factor-augmenting if it increases the productivity of factors.

  2. These are the models described by Edenhofer et al. [19], Goulder and Schneider [28] and Popp [43].

  3. Given the focus of the paper, which is the identification of the endogenous determinants of factor-augmenting technical change, we decided to start with one of the simplest CES structure that has an empirical foundation.

  4. Cost minimisation is also a standard assumption made in IA modelling literature. As in the IA modelling literature we also assume price-taking behaviour and therefore the unit cost function gives the price of final output, C(1; P K ,P L ,P E ) = P.

  5. Small letters denote percentage changes, e.g. \( x = dX\,/\,X = dlnX \).

  6. The time effect can also be made country-specific by interacting country dummies with the time trend. Although all of these specifications were estimated, the model with a common time trend was preferred because it is more parsimonious.

  7. Data available from http://www.sourceoecd.org/

  8. World Bank, 2006.

  9. ANBERD—R&D Expenditure in Industry 2006 available from http://www.sourceoecd.org/

  10. Data available from http://www.sourceoecd.org/

  11. Education Expenditures by Country, Nature, Resource Category, and Level of Education Vol. 2006 issue 01.

  12. A higher depreciation rate was also experimented, yielding very similar results.

  13. The correlation between these three variables is low and therefore they could be included simultaneously.

  14. We used an iterative selection technique that drops regressors one by one, selecting those with the lowest significance level, until all variables are significant.

  15. As in Table 2, we reject that labour and either capital or energy have the same rate of factor-augmentation, but we could not reject that energy and capital have the same growth rate.

  16. Bootstrap methods provide an alternative to inference based on parametric assumptions ,when those assumptions are in doubt.

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Correspondence to Enrica De Cian.

Appendices

Appendix I

Table 4 provides descriptive statistics of the main variables.

Table 4 Descriptive statistics of main variables

Appendix II

This Appendix reports the same results as in the main text from Tables 1 to 4, but with bootstrap standard errors. Results confirm the validity of the inference analysis carried out in the main text is valid.

Table 5 Exogenous technical change (constrained system estimation, FGLS estimator)
Table 6 Endogenous technical change (constrained system estimation, FGLS estimator)
Table 7 Endogenous technical change including only significant variables (constrained system estimation, FGLS estimator)

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Carraro, C., De Cian, E. Factor-Augmenting Technical Change: An Empirical Assessment. Environ Model Assess 18, 13–26 (2013). https://doi.org/10.1007/s10666-012-9319-1

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Keywords

  • Endogenous technical change
  • Integrated assessment models
  • Panel regression

JEL Classifications

  • C3
  • O47
  • Q55
  • Q56