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The inter-industry employment effects of technological change

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Abstract

This paper analyzes the inter-industry effects of a technological change on the employment in the Turkish manufacturing industry case over the 1985–1998 period. The inter-industry effects are calculated using input-output tables; the technological change is estimated as the growth of total factor productivity. The calculated simple employment multipliers show that the textile and garment industry has the highest capacity to generate employment in all manufacturing industries via intra- and inter-industry linkages. The study suggests that there is a labor substitution on the production employees whereas the forward linkages induced by technological change in other industries have positive effects. However, there is no evidence regarding the backward linkages. The technological change has a positive effect on the administrative employees but a negative effect on the production labor in the skilled labor intensive industries.

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Notes

  1. These multipliers are based only on backward linkages. As forward linkages can also be important in inter-industry employment effects and can be traced out only by econometric analysis, this study takes an econometric approach.

  2. Neary (1981) proposed that a labor saving technological change will reduce demand for labor. But, the negative effect of technological change will be minimized if real wages do not significantly increase (Sinclair 1981).

  3. For the inter-industry effects on wages, see Sicherman and Bartel (1999).

  4. The form of technological change, process and product innovation, matters for backward linkages. If process innovation leads to only a cost advantage as less inputs used, but not an increased output of industry j, a negative effect via backward linkages can be expected. However, process innovation which replaces labor in user industries may increase the employment in machine producer industries (Vivarelli 2014). When process innovation causes expanded output, a positive employment effect through backward linkages can be observed. In the case of product innovation, output volume would rise, so would employment, through backward linkages. Both process and product innovation are supposed to reduce output prices, which would generate positive employment effects via forward linkages. Due to the lack of data for the period covered in this study, product/process innovation distinction will not be made in the empirical analysis.

  5. Domestic in this expression refers to the exclusion of imported inputs.

  6. Output coefficient matrix should be used (Jones 1976) for forward linkages.

  7. A vector of n×1 technological change of industries, T, was turned into n×n matrix t where n is the number of industries.

  8. The capital price is approximated by the following equation: \(r\, = \,\alpha Y/K\, = \,\partial F/\partial K\) where r is the price of capital, Y real value added, K is capital stock. The idea is based on the standard intuition, i.e., firms will rent capital until the marginal product of capital becomes equal to the rental price. A Cobb-Douglas production function is assumed for the calculation of marginal product. Note that α multiplies the capital productivity ratio, therefore the estimated coefficient of this variable does not reflect the exact magnitude but the sign. K here was calculated by the standard capital accumulation equation and the initial capital stock was calculated as \({K_0}\, = \,I/(g\, + \,\delta )\) where I is real investment, g is the average growth rate of investment, and δ is depreciation rate (for the initial capital stock calculation see, Hall and Jones 1999).

  9. See appendix for the classification of industries.

  10. See the footnote 8 for the calculation of capital.

  11. Output data in Turkish Lira was converted into US Dollars for a comparison purpose.

  12. Total Factor Productivity was estimated as the Solow residual of a standard Cobb-Douglas production function. More specifically, \(TFP\, = \,VA - \alpha * K - \beta * L\) where TFP, VA, K and L stand for total factor productivity, value added, capital stock, and man-hour worked, respectively. α and β are the elasticity of capital and labor, respectively. In order to approximate these parameters, a Cobb-Douglass production function was estimated for the 77 four-digit industries over the period between 1982 and 2000 by using data obtained from Turkstat (varios years). A system GMM method used to control for endogeneity in capital.

  13. The reason why this period was studied is that the input-output tables and the manufacturing industry statistics are compatible only in this period. The methodology of both the input-output tables and the manufacturing industry statistics changed in 2002 in Turkey; so, the industries and their content changed. The manufacturing industries were defined according to the International Standard Industrial Classification (ISIC) prior to 2002; and according to the statistical classification of economic activities in the European Community (NACE- Nomenclature statistique des activités économiques dans la Communauté Européenne) following 2002.

  14. Since some industries included in the 1998 input-output table were merged to be compatible with the 1985 and the 1990 tables, 24 industries were left for the analysis. The forward and backward linkages were calculated for the years 1985, 1990, and 1998, for which the input-output tables are available; the linkages were interpolated for missing values.

  15. Greene (2005) proposes to compare 3SLS estimation results with 2SLS.

  16. This estimator was developed for datasets with large time dimension. Although the time dimension is 13 in the dataset, Hoechle (2007) states that small sample properties are much better than other robust estimators when cross-sectional dependence exists. So, this estimator was preferred.

  17. Using the normalized values of other control variables in the model lead to variance inflation factor to increase above 5, therefore, these variables were not normalized.

  18. Please note that variables are standardized, therefore, interpretations are made on the basis of standard deviation rather than percentage change.

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Acknowledgments

I am grateful to Erol Taymaz and Anıl Duman for their valuable comments and advices, and to Ebru Voyvoda, who generously shared her work on the harmonization of industries between the input-output tables and the international standard classification (ISIC). The financial support provided by The Scientific and Technological Research Council of Turkey (TUBITAK) with project no. 107K384 is greatly acknowledged. Earlier versions of this paper were presented in North American Productivity Workshop VI, June 3–5, 2010, Houston, USA, and at a departmental seminar Oviedo University, Spain. I also thank the participants of both events for their comments. The usual disclaimer applies.

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  1. Department of Economics, Ege University, Bornova Izmir, 35040, Turkey

    Aykut Lenger

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Appendıx

Appendıx

Table 6

Table 6 Skill intensity of industries
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Lenger, A. The inter-industry employment effects of technological change. J Prod Anal 46, 235–248 (2016). https://doi.org/10.1007/s11123-016-0485-z

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