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The Effect of Technological Change on the Task Structure of Jobs and the Capital-Labor Trade-Off in US Production

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Abstract

This paper calibrates the Cobb-Douglas (CD) model of US aggregate production with empirically derived values of the relative shares of capital and labor. A conceptual framework is developed to show how technological progress can affect the relative importance of these shares in US production. To identify and track the transmission of technical change to the macro-economy, the CD model is transformed into the Solow growth model. Specific examples of different types of technical change are developed to illustrate their effect on the task structure of jobs, the capital/labor ratio, total factor productivity (TFP), and US aggregate output. These examples are supported with graphical and flowchart analyses providing intuition concerning the implications of technical change on the production process and the relative importance of the input shares of capital and labor. Some broader aspects of technical change are discussed including the task structure of future jobs and the importance of aligning the skills workers acquire with task requirements of US industry.

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Notes

  1. Other assumptions of the CD model include: constant-returns-to-scale (CRS), profit maximization, competitive input and output markets, Hicks-neutral technical change (see “The Nature of Technological Progress”), capital and labor income shares are constant and the elasticity of substitution between capital and labor equals unity in the long run (Ơ = 1). The (Ơ) is a measure of the responsiveness of the optimal capital/labor combination to a change in the relative prices of the two inputs. It measures the ease in which capital can be substituted for labor. In general form, Ơ = (% change in K* / L*)/% change in PL/Pk. If Ơ > 1, the two inputs are gross substitutes. If Ơ < 1, the two inputs are gross complements (Boskin and Lau 2000; Ramskov and Munksgaard 2001; Acemoglu 2002).

  2. This study does not address income inequality issues in great detail. See Woods (2007) for a study on regional income equality. Also, see Autor et al. 2008; Autor and Dorn 2013 and Piketty (2014) for additional treatment of distribution topics.

  3. This section is partially adapted from McGahagan (2000) and Woods (2007).

  4. For simplicity, I will refer to output per capita as “output.”

  5. These influences include work practices, unions, the elasticity of demand for goods and services, literacy of workforce, training and education of workers, government regulations, omitted variables, model misspecification, aggregation bias, measurement error among others (Prescott 1997; Hulton 2001).

  6. This subsection is partially adapted from Hatipoglu et al. (2014).

  7. The benefits of capital-augmented technical change are higher with greater capital accumulation in the economy (Boskin and Lau, 2000).

  8. See Golden and Katz (2008) (Chap. 9) for a discussion of the low international ranking of US 15-year-olds in mathematics, literacy, and problem-solving skills.

  9. Capital-saving technical change requires the rise in the absolute share of labor be offset by the decline in the absolute share of capital in order to produce a constant level of output. If each factor is paid its MP, then Euler’s Theorem holds where Ǭ = MPL (L) + MPK (K). In this equation, MPL(L) = the absolute share of labor and MPK(K) = the absolute share of capital.

  10. To produce a constant level of output, the absolute share of capital would rise by the same amount of decline in the absolute share of labor.

  11. Note that Euler’s Theorem guarantees higher output levels resulting from Hick-neutral technical change. As the MP of all inputs rise with a constant capital/labor ratio, the absolute shares of capital and labor rise corresponding to higher output.

  12. With skill-biased technical change, the rise in the absolute share of capital exceeds the absolute share of labor leading to higher output.

  13. See endnote 10.

  14. For recent estimates of the elasticity of substitution (Ơ) in US production, see Antras (2004), Klump et al. (2007), and Raurich et al. (2011).

  15. See Frey and Osborne (2013) for estimates of the degree of susceptibility various occupations are to computerization.

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Acknowledgments

I thank Emily Gipson for technical research assistance.

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  1. Department of Economics, Indiana University Purdue University - Indianapolis, Indianapolis, IN, 46202, USA

    Jeffrey G. Woods

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Woods, J.G. The Effect of Technological Change on the Task Structure of Jobs and the Capital-Labor Trade-Off in US Production. J Knowl Econ 8, 739–757 (2017). https://doi.org/10.1007/s13132-015-0275-2

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