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Skill-Biased Innovation, Growth, and Inequality

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Abstract

Parallel to the widespread inequality phenomena in the advanced countries, there has been a growing literature about skill-biased technology and inequality. Among them, having analyzed the dynamics of technical unemployment in the framework of the induced factor bias of innovation, Stiglitz (2014) argued that the formulation of induced skill-biased innovation is one of the promising researches for analyzing the various inequalities prevailing in the OECD countries. One of the implications of the induced innovation framework in line with Kennedy (1964) and Samuelson (1966) is that relatively increasing of the factor share can induce firms to introduce its own factor-augmenting technical progress in the maximization of the instantaneous cost reduction rate of change on the concavity of the innovation frontier.

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Notes

  1. 1.

    See Acemoglu (2002) and Hornstein et al. (2005). Recently, there are literature on capital-augmenting technical progress as an improvement in an Automation and Artificial Intelligence. See Acemoglu and Restrepo (2017a), Acemoglu and Restrepo (2017b), Kotlikoff and Sachs (2012), Graetz and Michaels (2015), and Korinek and Stiglitz (2017). In this paper, as an exogenous parameter, we analyze the impact of capital-augmenting technology on skill-biased innovation and income inequality.

  2. 2.

    See Krusell et al. (2000) and Duffy et al. (2004).

  3. 3.

    See Acemoglu (2010), Korinek and Stiglitz (2017).

  4. 4.

    See Nordhaus (1973) and Acemoglu (2015).

  5. 5.

    For pioneer works, see Griliches (1969) and Sato (1967).

  6. 6.

    Caselli and Coleman (2006) considered the same type of frontier in capital-skill complementarity. However, they did not analyze the induced biased innovation.

  7. 7.

    Samuelson (1966) suggested that the capital share of income can be one of the shift parameter of the innovation possibility frontier.

  8. 8.

    See Ciccone and Peri (2005). In the two-factor case, if the elasticity of substitution between skilled and unskilled labor is larger than unity, the steady state may always be unstable and thus the induced skill-biased innovation can make the income of skilled labor increasing over time.

  9. 9.

    Defining \(c_{ij} {\equiv } F_{ij}F/F_{i}F_{j}\) as the partial elasticity of complementarity between i and j, capital-skill complementarity is indicated as an inequality whereby the elasticity of complementarity between capital and skilled labor is larger than that between capital and unskilled labor \(c_{1K}(=F_{1K}F/F_{1}F_{K})>c_{2K}(=F_{2K}F/F_{2}F_{K})\). In our two-level CES production technology, \(\sigma _{2}>\sigma _{1}\) implies \(c_{1K}>c_{2K}\) since \(c_{1K}-c_{2K}=(\sigma _{1}^{-1}-\sigma _{2}^{-1})/(1-F_{2}L_{2}/F)\). Thus, \(\sigma _{2}>\sigma _{1}\) implies capital-skill complementarity. Note that in our three-factor case, the elasticity of substitution is not always equal to the inverse of the elasticity of complementarity. Specifically, \(c_{1K}=(\sigma _{1}^{-1}- \sigma _{2}^{-1})/(1 -F_{2}L_{2}/F)+ \sigma _{2}^{-1}\ne \sigma _{1}^{-1}\) although \(c_{2K}=\sigma _{2}^{-1}\).

  10. 10.

    See Duffy et al. (2004) and Hornstein et al. (2005).

  11. 11.

    As noted before, Samuelson (1966) suggested that the capital share of income is one of the shift parameter of the innovation possibility frontier that has a tradeoff between capital-augmenting and labor -augmenting technical progress.

  12. 12.

    In our weak separable two-level CES production technology, \(f_{ij} l_{i} /f_{j} (i,j=1,2)\) are specified as follows: \(f_{11} l_{1} /f_{1} =-(\kappa \sigma _{1}^{-1}+ab\sigma _{2}^{-1})/(1-b)<0\),\(f_{12} l_{2} /f_{1} =b\sigma _{2}^{-1}>0\), \(f_{21} l_{1} /f_{2} =a\sigma _{2} ^{-1}>0\), and \(f_{22} l_{2} /f_{2} =-(1-b)\sigma _{2}^{-1}<0\). Thus, these specifications give Eq. 3.21.

  13. 13.

    See Ciccone and Peri (2005). As noted in footnote 8, if the elasticity of substitution between skilled and unskilled labor is larger than unity, the steady state may always be unstable in the two-factor case.

  14. 14.

    \(\sigma ^{A}\) is derived in the appendix.

  15. 15.

    See Fig. 3.1.

  16. 16.

    The details are available on request.

  17. 17.

    See Acemoglu and Restrepo (2018), Berg et al. (2018).

References

  • Acemoglu, D. 2002. Technical change, inequality, and the labor market. Journal of Economic Literature 40: 7–72.

    Article  Google Scholar 

  • Acemoglu, D. 2010. When does labor scarcity encourage innovation? Journal of Political Economy 118: 1037–1078.

    Article  Google Scholar 

  • Acemoglu, D. 2015. Localised and biased technologies: Atkinson and Stiglitz’s new view innovation, and directed technological change. Economic Journal 125: 443–463.

    Article  Google Scholar 

  • Acemoglu, D., and P. Restrepo. 2017a. The Race between man and machine: Implications of technology for growth, factor shares and employment. American Economic Review (forthcoming).

    Google Scholar 

  • Acemoglu, D., and P. Restrepo. 2017b. Robots and jobs: Evidence from US labor markets. NBER Working Papers, 23285.

    Google Scholar 

  • Acemoglu, D., and P. Restrepo. 2018. Modeling automation. American Economic Review, Paper and Proceedings.

    Google Scholar 

  • Adachi, H., T. Nakamura, K. Inagaki, and Y. Osumi. 2019. Technological progress, income distribution, and unemployment -Theory and empirics-, Springer Briefs in Economics.

    Google Scholar 

  • Berg, A., E. Buffie, and F. Zanna. 2018. Should we fear the robot revolution? (The correct answer is yes), IMF Working Paper, 18/116.

    Article  Google Scholar 

  • Caselli, F., and W.J. Coleman II. 2006. The world technology frontier. American Economic Review 96: 499–523.

    Article  Google Scholar 

  • Ciccone, A., and G. Peri. 2005. Long-run substitutability between more and less educated workers: Evidence from a panel of countries. Review of Economics and Statistics 87: 652–663.

    Article  Google Scholar 

  • Drandakis, E.M., and E.S. Phelps. 1966. A model of induced invention, growth, and distribution. Economic Journal 76: 823–840.

    Article  Google Scholar 

  • Duffy, J., C. Papageorgiou, and F. Perez-Sebastian. 2004. Capital-skill complementarity? Evidence from a panel of countries. Review of Economics and Statistics 86: 327–344.

    Article  Google Scholar 

  • Graetz, G., and G. Michaels. 2015. Robots at work. CEPR Discussion Paper 1335.

    Google Scholar 

  • Griliches, Z. 1969. Capital-skill complementarity. Review of Economics and Statistics 51: 465–468.

    Article  Google Scholar 

  • Hornstein, A., P. Krusell, and G. L. Violante. 2005. The effects of technical change on labor market inequalities. In Handbook of economic growth, vol. 1B, eds. Aghion, P. and S.N. Durlauf, 1275-1370, North-Holland.

    Google Scholar 

  • Kennedy, C. 1964. Induced bias in innovation and the theory of distribution. Economic Journal 74: 541–547.

    Article  Google Scholar 

  • Kotlikoff, L., and J.D. Sachs. 2012. Smart machines and long-term misery. NBER Working Paper, 18629.

    Google Scholar 

  • Korinek, A., and Stiglitz, J.E. 2017. Artificial intelligence and implications for income distribution and unemployment. NBER Working Paper 24174.

    Google Scholar 

  • Krusell, P., L.E. Ohanian, J.-V. Rios-Rull, and G.L. Violante. 2000. Capital-skill complementarity and inequality: A macroeconomic analysis. Econometrica 68: 1029–1053.

    Article  Google Scholar 

  • Nordhaus, W.D. 1973. Some skeptical thoughts on the theory of induced innovation. Quarterly Journal of Economics 87: 208–219.

    Article  Google Scholar 

  • Samuelson, P. 1966. A theory of induced innovation along Kennedy-Weizacker lines. Review of Economics and Statistics 33: 133–146.

    Article  Google Scholar 

  • Sato, K. 1967. A two-level constant-elasticity-of-substitution production function. Review of Economic Studies 34: 201–218.

    Article  Google Scholar 

  • Solow, R.M. 1956. A contribution to the theory of economic growth. Quarterly Journal of Economics 43: 65–94.

    Article  Google Scholar 

  • Stiglitz, J.E. 2014. Unemployment and innovation. NBER Working Paper 20670.

    Google Scholar 

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Acknowledgements

The author is grateful to Hideyuki Adachi, Professor Emeritus of Kobe University. He is also grateful to Tamotsu Nakamura, Takeshi Nakatani, Atsushi Miyake, and Hideaki Uchida for their valuable comments on an earlier version of this paper.

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Appendix

Appendix

1.1 Aggregate Elasticity of Substitution

We can derive the endogenous elasticity of substitution as follows. w / ris given by the following equation:

$$\begin{aligned} w/r= & {} \{\textit{uw}_{1}+(1-u)w_{2}\} /\{f(l_{1},l_{2})-f_{1}(l_{1},l_{2})l_{1}-f_{2}(l_{1},l_{2})l_{2}\}\nonumber \\= & {} v (l_{1}, l_{2}, u) \end{aligned}$$
(3.60)

In this case, \(l_{1}= u/k\) and \(l_{2}=(1-u)\) where \(k\equiv K/L\). Denoting \(\theta _{l1k}\equiv (k/l_{1})(dl_{1}/dk), \theta _{l2k}\equiv (k/l_{2})(dl_{2}/dk)\), we have the following equations:

$$\begin{aligned} \theta _{l1k}=-1, \theta _{l2k}=- 1 \end{aligned}$$
(3.61)

Moreover, defining \(\eta _{1}\equiv (l_{1}/v)\partial v/\partial l_{1}\) and \(\eta _{2}\equiv (l_{2}/v)\partial v/\partial l_{2}\), the aggregate elasticity of substitution \(\sigma ^{A}\) is then written as

$$\begin{aligned} \sigma ^{A}\equiv & {} [(w/r)/k]dk/d(w/r)\nonumber \\= & {} \{(k/v)dv/dk\}^{-1}\nonumber \\= & {} (\eta _{1}\theta _{l1k} + \eta _{2}\theta _{l2k})^{-1}\nonumber \\= & {} (-\eta _{1}-\eta _{2})^{-1} \end{aligned}$$
(3.62)

Calculating each \(\eta _{1}\equiv (l_{1}/v)\partial v/\partial l_{1}, \eta _{2}\equiv (l_{2}/v) \partial v/\partial l_{2}\), and specifying these in our two-level CES production function yield

$$\begin{aligned} \eta _{1}= (b\sigma _{2}^{-1}-\sigma _{1}^{-1})a/(a+ b\kappa ), \eta _{2}=-\sigma _{2}^{-1}b/(a+b)<0 \end{aligned}$$
(3.63)

Thus, the aggregate elasticity of substitution is given by

$$\begin{aligned} \sigma ^{A}= & {} (-\eta _{1 }-\eta _{2})^{-1}\nonumber \\= & {} (a+b\kappa )/(a\sigma _{1}^{-1}+ b\kappa \sigma _{2}^{-1}) \end{aligned}$$
(3.64)

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Osumi, Y. (2019). Skill-Biased Innovation, Growth, and Inequality. In: Hosoe, M., Ju, BG., Yakita, A., Hong, K. (eds) Contemporary Issues in Applied Economics. Springer, Singapore. https://doi.org/10.1007/978-981-13-7036-6_3

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