Ferrara, Giancarlo and Vidoli, Francesco and Canello, Jacopo and Campagna, Arianna (2013): Labour-use Efficiency in the Italian Machinery Industry: a Non-parametric Stochastic Frontier Perspective. Published in: Qds - Journal of Methodological and Applied Statistics , Vol. 16, (2014): pp. 61-75.
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Abstract
Firms’ efficiency is a mainstream in the study of economic growth. Within this broad research area, the present work, conducted as part of the research activities of SOSE S.p.A., analyses the labour use efficiency in the Italian machinery industry through the application of a non-parametric stochastic frontier model with the aim of suggesting new insights to better understand the recent dynamics of the Italian manufacturing system. An extended panel data of manufacturing Small and Medium Enterprises (SMEs) operating in the mechanical industry for the period 2002-2012 has been extracted (in anonymous form) from the Italian Ministry of Economy and Finance annual survey and used for the implementation of the proposed method. Results show the presence of a persistent level of labour-use inefficiency in the sample used for the analysis: this issue is particularly evident for the subset of firms using non standard jobs, while firms entitled to access to wage redundancy fund appear to have achieved higher levels of efficiency in labour input use on average. The analysis also shows that the inefficiency gap between the two subsets of firms tends to reduce in absolute terms over time
Item Type: | MPRA Paper |
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Original Title: | Labour-use Efficiency in the Italian Machinery Industry: a Non-parametric Stochastic Frontier Perspective |
Language: | English |
Keywords: | Labour-use efficiency; Stochastic frontier; SMEs; GAM; Splines |
Subjects: | C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General C - Mathematical and Quantitative Methods > C1 - Econometric and Statistical Methods and Methodology: General > C14 - Semiparametric and Nonparametric Methods: General H - Public Economics > H2 - Taxation, Subsidies, and Revenue J - Labor and Demographic Economics > J2 - Demand and Supply of Labor > J24 - Human Capital ; Skills ; Occupational Choice ; Labor Productivity |
Item ID: | 94359 |
Depositing User: | Giancarlo Ferrara |
Date Deposited: | 10 Jun 2019 09:03 |
Last Modified: | 27 Sep 2019 11:19 |
References: | Aigner D.J., Chu S.F. (1968), On estimating the industry production function. American Economic Review, 58, 826–839 Aigner D., Lovell C.A.K., Schmidt P. (1977), Formulation and estimation of stochastic frontier production function models, Journal of Econometrics, 6, 21–37. Bassanini A., Nunziata L., Venn D. (2009), Job protection legislation and Productivity growth in OECD Countries. Econ. Policy, 24, 349–402. Boeri T., Garibaldi P. (2007), Two tier reforms of employment protection: a honeymoon ends?, Economic Journal, 117, 357–385. Charnes A., Cooper W.W., Rhodes E. (1978), Measuring the Inefficiency of Decision Making Units, European Journal of Operational Research, 2, 429–444. Congia M.C., Pacini S. (2012), La stima da fonti amministrative di indicatori retributivi congiunturali al netto della cassa integrazione guadagni, Rivista di Statistica Ufficiale, 14, 19–40. Cooper W., Seiford L., Zhu J. (2000), A unified additive model approach for evaluating inefficiency and congestion with associated measures in dea. Socio-economic planning science, 34, 1–25. Deprins D., Simar L., Tulkens H. (1984), Measuring labor inefficiency in post offices, In: Marchand, M., Pestieau, P., Tulkens, H. (eds.) The Performance of Public Enterprises: Concepts and Measurements, North-Holland, Amsterdam. De Steis S., Fonseca R. (2007), Matching efficiency and labour market reform in Italy: a macroeconomic assessment, Labour, 21(1). Fan Y., Li Q., Weersink A. (1996), Semiparametric estimation of stochastic production frontier models. Journal of Business & Economic Statistics, 14, 460–468. Fare R., Grosskopf S., Kokkelenberg E.C. (1989), Measuring plant capacity, utilization and technical change: A nonparametric approach. International Economic Review, 30(3), 655–666. Farrell M.J. (1957), The Measurement of Productive Efficiency. Journal of the Royal Statistic Society , 120, 253–281 Federmeccanica (2006), Indagine congiunturale. Ferrara G., Vidoli F. (2015), semsfa: semiparametric estimation of stochastic frontier models. R package version 1.0 Greene W. (2008), The econometric approach to efficiency analysis. In: Fried, O., Lovell, C.A.K., Schmidt, S.S. (eds.) The measurement of productive efficiency and Productivity change, Oxford University Press, New York. Grosskopf S. (1996), Statistical inference and nonparametric efficiency: a selective survey. Journal of Productivity Analysis, 7, 161–176 Hastie T., Tibshirani R. (1990), Generalized additive models, Chapman & Hall/CRC. Jondrow J., Lovell C.A.K., Materov I.S., Schmidt P. (1982), On the estimation of technical inefficiency in the stochastic frontier production function models, Journal of Econometrics, 119, 233–238. Kumbhakar S.C., Park B., Simar L., Tsionas E. (2007), Nonparametric Stochastic Frontiers: A Local Maximum Likelihood Approach, Journal of Econometrics, 137, 1–27 Kumbhakar S.C., Hjalmarsson L. (1995), Labour-use efficiency in Swedish social insurance offices, Journal of Applied Econometrics, 10, 33–47. Kumbhakar S.C., Lovell C.A.K. (2000), Stochastic Frontier Analysis, Cambridge University Press, Cambridge. Kumbhakar S.C., Zhang R. (2013), Labour-use efficiency and employment elasticity in chinese manufacturing, Journal of Industrial and Business Economics, 40, 5–24. Lucidi F., Kleinknecht A. (2010), Little innovation, many jobs: an econometric analysis of the Italian labour productivity crisis, Cambridge Journal of Economics, 34, 525–546. Meeusen W., van den Broeck J. (1977), Efficiency estimation from Cobb-Douglas production functions with composed error, International Economic Review, 18, 435–444. Olson J.A., Schmidt P., Waldman D. M. (1980), A monte carlo study of estimators of stochastic frontier production functions, Journal of Econometrics, 13, 67–82. Picchio M. (2006), Wage differentials between temporary and permanent workers in Italy, Quaderni del dipartimento di Economia dell’Universita’ Politecnica delle Marche, 257. R Core Team (2013), R: A language and environment for statistical computing, R Foundation for Statistical Computing, Vienna, Austria. Schindler M. (2009), The Italian labour market: recent trends, institutions and reform options, IMF Working Paper n. 47. Tone K., Sahoo B. K. (2004), Degree of scale economies and congestion: A unified DEA approach, European journal of operational research, 158(3), 755–772. Vidoli F., Ferrara G. (2014), Analyzing Italian citrus sector by semi-nonparametric frontier efficiency models, Empirical Economics. http://dx.doi.org/10.1007/s00181-014- 0879-6 Wood S.N. (2003), Thin plate regression splines, Journal of the Royal Statistical Society series B, 65, 95–114. Wood S.N. (2004), Stable and efficient multiple smoothing parameter estimation for generalized additive models, Journal of the American Statistical Association, 99, 673– 686. Wood S.N. (2006), An Introduction to Generalized Additive Models with R, Boca Raton, FL: CRC Press. |
URI: | https://mpra.ub.uni-muenchen.de/id/eprint/94359 |