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Skill Biased Technical Change and Misallocation. A Unified Framework and a country-sector exercize

Author

Listed:
  • Michele Battisti

    (University of Palermo, Italy; CeLEG, LUISS Guido Carli, Italy; Rimini Centre for Economic Analysis)

  • Massimo Del Gatto

    (“G.d’Annunzio” University, Italy; CRENoS, Italy)

  • Christopher F. Parmeter

    (University of Miami, USA)

Abstract

Due to strict reliance on perfectly competitive labor markets, standard approaches estimating skill biased technical change (SBTC) conflate ‘true’ SBTC and labor market distortions preventing firms from choosing the efficient skilled to unskilled labor ratio. To overcome this limit, we present a unified framework to estimate SBTC, net of factor accumulation (FA) effects, and quantify the discrepancy between skilled to unskilled marginal rate of technical substitution (MRTS) and wage ratio (i.e., relative misallocation). The methodology takes advantage of recent developments in nonparametric estimation methods (i.e., generalized kernel regression) that allow us to estimate the marginal productivity of inputs at the country-sector level directly from country-sector data. Using 1995-2005 data, we find a 3% yearly growth rate for the MRTS between skilled and unskilled labor and show such change to be mostly driven by SBTC, rather than FA. We then show that MRTS changes does not match the evolution of the wage ratios (quite stable over time), thus yielding substantial heterogeneity in terms of relative misallocation patterns, for which we report a 6% increase, overall (1.5% in manufacturing, against a rough 12% in Non-Manufacturing sectors). Finally, we show evidence that relative misallocation increased less in country-sectors in which it was larger at the beginning of the period and grew more in country-sectors characterized by: higher skill-intensity; lower bargaining power of skilled over unskilled workers; lower FA effects.

Suggested Citation

  • Michele Battisti & Massimo Del Gatto & Christopher F. Parmeter, 2019. "Skill Biased Technical Change and Misallocation. A Unified Framework and a country-sector exercize," Working Paper series 19-08, Rimini Centre for Economic Analysis.
    • Handle: RePEc:rim:rimwps:19-08
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    Citations

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    Cited by:

    1. Gary M. Koop, 2013. "Forecasting with Medium and Large Bayesian VARS," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 177-203, March.
    2. Karlsson, Sune, 2013. "Forecasting with Bayesian Vector Autoregression," Handbook of Economic Forecasting, in: G. Elliott & C. Granger & A. Timmermann (ed.), Handbook of Economic Forecasting, edition 1, volume 2, chapter 0, pages 791-897, Elsevier.
    3. Dimitris Korobilis, 2013. "Var Forecasting Using Bayesian Variable Selection," Journal of Applied Econometrics, John Wiley & Sons, Ltd., vol. 28(2), pages 204-230, March.

    More about this item

    Keywords

    Skill Biased Technical Change; Misallocation; Production Function Estimation; Technology; Nonparametric Estimation;
    All these keywords.

    JEL classification:

    • C14 - Mathematical and Quantitative Methods - - Econometric and Statistical Methods and Methodology: General - - - Semiparametric and Nonparametric Methods: General
    • D24 - Microeconomics - - Production and Organizations - - - Production; Cost; Capital; Capital, Total Factor, and Multifactor Productivity; Capacity
    • O41 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - One, Two, and Multisector Growth Models
    • O47 - Economic Development, Innovation, Technological Change, and Growth - - Economic Growth and Aggregate Productivity - - - Empirical Studies of Economic Growth; Aggregate Productivity; Cross-Country Output Convergence

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