Abstract
This paper examines the within-industry distributions of jobs created and destructed across plants in terms of technical efficiency, technical efficiency change, scale effect, and technical change. It further investigates how these distributions vary with economic activity. By applying the stochastic frontier analysis to plant-level longitudinal data on Taiwan’s 23 two-digit manufacturing industries spanning the period 1992–2003, we find that jobs created (destructed) are disproportionately clustered at plants with lower technical efficiency but higher rate of technical change. A fall in economic activities is associated with a statistically significant decrease (increase) in the fraction of newly created (destructed) jobs accounted for by plants with a higher rate of technical change, indicating that creative destruction is more pronounced during economic contractions.
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Notes
In an earlier version of this paper, we used a production function that restricts the patterns of TE change to be the same for all firms—which is too restrictive for our intended purpose of analyzing the rate of technical efficiency change across plants. We thank the associate editor for pointing this out and suggesting to switch to this more flexible model.
For the derivation of the likelihood function, see Aigner et al. (1977) and Chapter 3 of Kumbhakar and Lovell (2000). The maximum likelihood estimation of the half normal production function is carried out using the computer program Frontier 4.1, written by Professor Tim Coelli. Frontier 4.1 is available at http://www.uq.edu.au/economics/cepa/frontier.htm.
According to Kumbhakar and Lovell (2000), TFP change consists of four as opposed to three terms. Specifically, \( T\dot{F}P = {\text{T}}\Updelta + {\text{SC}} + \sum\nolimits_{k} {\left[ {\left( {\frac{{\varepsilon_{k} }}{\varepsilon }} \right) - S_{k} } \right] + \dot{X}_{k} + {\text{TE}}\Updelta .} \) That is, there should be a fourth term that captures the effect of allocative inefficiency in Eq. (3-3), but because data on the price of capital service are unavailable, we are unable to empirically calculate the allocative inefficiency term. We follow Kumbakhar and Lovell (2000, p. 284) to assume that factors are paid the value of their marginal product, i.e., plants attain allocative efficiency so that the allocative inefficiency term vanishes. We are indebted to the associated editor’s suggestion to include the above discussion about the allocative inefficiency term in this paper.
Note that calculating the output elasticity of capital as one minus labor’s share in output implicitly assumes that the product market is perfectly competitive such that there is no markup. As a result, factor income shares add up to one and the production technology exhibits constant-returns-to-scale.
For an excellent review of the advantages and drawbacks on the index number approach and the SFA approach, see Chapter 12 of Coelli et al. (2005).
The main drawback of the SFA approach is its requirement of specifying a particular functional form for a production or a cost function, despite that the true functional forms are not known a priori.
We are indebted to the associate editor for pointing this out.
The two-digit industry classification codes have been changed twice during the sample period. The four-digit codes are used to retrieve a consistent two-digit industry classification.
The average exchange rate over the period 1993–2003 was NT$30.47/US$1.
For brevity the same statistics for individual industries are not shown but are available upon request.
See Sect. 2.2 for a brief review of the theoretic debate on the effects of downturns on the effectiveness of the restructuring process.
Industry-level real output is calculated by the authors by summing up plant-level real output across plants in an industry.
The mean, standard deviation, minimum, and maximum of real per capita GDP growth over the period 1992–2003 are 5.26, 2.68,–2.17, and 7.85%, respectively.
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Lin, YC., Huang, TH. Creative destruction over the business cycle: a stochastic frontier analysis. J Prod Anal 38, 285–302 (2012). https://doi.org/10.1007/s11123-012-0273-3
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DOI: https://doi.org/10.1007/s11123-012-0273-3