• Previous Article
    Weak nonlinear bilevel problems: Existence of solutions via reverse convex and convex maximization problems
  • JIMO Home
  • This Issue
  • Next Article
    Tail asymptotics for waiting time distribution of an M/M/s queue with general impatient time
July  2011, 7(3): 573-592. doi: 10.3934/jimo.2011.7.573

A three-stage DEA-SFA efficiency analysis of labour-owned and mercantile firms

1. 

Escuela de Administración y Contaduría Pública, Facultad de Ciencias Económicas, Universidad Nacional de Colombia, Carrera 30 No 45-03, Edificio 311 Oficina 305, Bogotá, Colombia

2. 

Emeritus Professor, University of New Brunswick, Canada

3. 

Departamento de Gestión de Empresas, Universidad Pública de Navarra, Campus de Arrosadía, 31006 Pamplona, Navarra, Spain

Received  November 2008 Revised  May 2011 Published  June 2011

This paper undertakes a three-stage DEA-SFA (Data Envelopment Analysis / Stochastic Frontier Analysis) efficiency analysis of labour-owned (LOF) and mercantile (PCF) firms to assess whether variations in the productive efficiency and in the total factor productivity of LOFs and PCFs are explainable by differences in their capital-ownership configuration. The model first purges, from each firm's performance, the impact of statistical noise and of environmental factors and yields increasing average efficiency estimates of the firms under study. Then, it tests the hypothesis that the average firm, be it LOF or PCF, is equally efficient and exhibits comparable levels of productivity growth. The evidence presented supports the proposition that differences in the capital-ownership configuration do not play a very significant role in their performance efficiency or in their productivity growth and whatever productivity growth occurs is attributable largely to innovation rather than to catching up to the efficient firms.
Citation: Zuray Melgarejo, Francisco J. Arcelus, Katrin Simon-Elorz. A three-stage DEA-SFA efficiency analysis of labour-owned and mercantile firms. Journal of Industrial & Management Optimization, 2011, 7 (3) : 573-592. doi: 10.3934/jimo.2011.7.573
References:
[1]

Z. J. Acs and J. E. Amorós, Entrepreneurship and competitiveness dynamics in Latin America,, Small Business Economics, 31 (2008), 305. doi: 10.1007/s11187-008-9133-y. Google Scholar

[2]

G. Ahuja and S. K. Majumdar, An assessment of the performance of Indian state-owned enterprises,, Journal of Productivity Analysis, 9 (1998), 113. doi: 10.1023/A:1018352415813. Google Scholar

[3]

J. M. Arauzo-Carod and A. Segarra-Blasco, The determinants of entry are not independent of start-up size: Some evidence from Spanish manufacturing,, Review of Industrial Organization, 27 (2005), 147. doi: 10.1007/s11151-005-8321-z. Google Scholar

[4]

F. J. Arcelus and P. Arocena, Measuring sectoral productivity across time and across countries,, European Journal of Operational Research, 119 (1999), 254. doi: 10.1016/S0377-2217(99)00129-0. Google Scholar

[5]

F. J. Arcelus and P. Arocena, Convergence and productive efficiency in OECD countries: A non parametric frontier approach,, International Journal of Production Economics, 66 (2000), 105. doi: 10.1016/S0925-5273(99)00116-4. Google Scholar

[6]

J. M. Argilés and E. J. Slof, The use of financial accounting information and firm performance: An empirical quantification for farms,, Accounting and Business Research, 33 (2003), 251. Google Scholar

[7]

N. K. Avkiran and T. Rowlands, How to better identify the true managerial performance: State of the art using DEA,, Omega, 36 (2008), 317. doi: 10.1016/j.omega.2006.01.002. Google Scholar

[8]

R. D. Banker, A. Charnes and W. W. Cooper, Some models for estimating technical and scale inefficiency in data envelopment analysis,, Management Science, 30 (1984), 1078. doi: 10.1287/mnsc.30.9.1078. Google Scholar

[9]

R. D. Banker and R. Morey, Efficiency analysis for exogenously fixed inputs and outputs,, Operations Research, 34 (1986), 513. doi: 10.1287/opre.34.4.513. Google Scholar

[10]

W. J. Baumol, Entrepreneurial enterprises, large established firms and other components of the free-market growth machine,, Small Business Economics, 23 (2004), 9. doi: 10.1023/B:SBEJ.0000026057.47641.a6. Google Scholar

[11]

G. E. Batesse and T. J. Coelli, Frontier production function, technical efficiency and panel data: With application to paddy farmers in India,, Journal of Productivity Analysis, 3 (1992), 153. doi: 10.1007/BF00158774. Google Scholar

[12]

J. L. T. Blank and V. Valdmanis, A modified three-stage data envelopment analysis,, European Journal of Health Economics, 6 (2005), 65. doi: 10.1007/s10198-004-0260-3. Google Scholar

[13]

A. N. Bojanic, S. B. Caudill and J. M. Ford, Small-sample properties of ML, COLS, and DEA estimators of frontier models in the presence of heteroskedasticity,, European Journal of Operational Research, 108 (1998), 140. doi: 10.1016/S0377-2217(97)00101-X. Google Scholar

[14]

M. Callejón and A. Segarra, Business dynamics and efficiency in industries and regions: The case of Spain,, Small Business Economics, 1 (1999), 253. Google Scholar

[15]

J. L. Calvo, Testing Gibrat's law for small, young and innovating firms,, Small Business Economics, 26 (2006), 117. doi: 10.1007/s11187-004-2135-5. Google Scholar

[16]

S. B. Caudill and J. M. Ford, Biases in frontier estimation due to heteroskedasticity,, Economics Letters, 4 (1993), 17. doi: 10.1016/0165-1765(93)90104-K. Google Scholar

[17]

S. B. Caudill, J. M. Ford and D. Gropper, Frontier estimation and firm-specific inefficiency measures in the presence of heteroskedasticity,, Journal of Business & Economic Statistics, 13 (1995), 105. doi: 10.2307/1392525. Google Scholar

[18]

Y. Chen, L. Liang and J. Zhou, Equivalence in two-stage DEA approaches,, European Journal of Operational Research, 193 (2009), 600. doi: 10.1016/j.ejor.2007.11.040. Google Scholar

[19]

T. J. Coelli, "A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program,", CEPA WP8, (). Google Scholar

[20]

T. J. Coelli, D. S. P. Rao, C. J. O'Donnell and G. E. Batesse, "An Introduction to Efficiency and Productivity Analysis,", Springer-Verlag, (2005). Google Scholar

[21]

J. M. Cordero-Ferrera, F. Pedraja-Chaparro and J. Salinas-Jiménez, Measuring efficiency in education: An analysis of different approaches for incorporating non-discretionary inputs,, Applied Economics, 40 (2008), 1323. doi: 10.1080/00036840600771346. Google Scholar

[22]

M. A. Díaz, R. Sánchez, Firm size and productivity in Spain: A stochastic frontier analysis,, Small Business Economics, 30 (2008), 315. doi: 10.1007/s11187-007-9058-x. Google Scholar

[23]

C. Doucouliagos, The comparative efficiency and productivity of labor-managed and capital-managed firms,, Review of Radical Political Economics, 29 (1997), 45. doi: 10.1177/048661349702900203. Google Scholar

[24]

G. Dow, "Governing the Firm: Worker's Control in Theory and in Practice,", Cambridge University Press, (2003). Google Scholar

[25]

R. Färe, S. Grosskopf, B. Lindgren and P. Roos, Productivity developments in Swedish hospitals; A Malmquist output index approach,, in, (1994). Google Scholar

[26]

R. Färe, S. Grosskopf and C. A. K. Lovell, "Production Frontiers,", Cambridge University Press, (1994). Google Scholar

[27]

M. J. Farrell, The measurement of productive efficiency,, Journal of the Royal Statistical Society, 120 (1957), 253. doi: 10.2307/2343100. Google Scholar

[28]

M. Fritsch, U. Brixy and O. Falk, The effect of industry, region and time on new business survival - A multidimensional analysis,, Review of Industrial Organization, 28 (2006), 285. doi: 10.1007/s11151-006-0018-4. Google Scholar

[29]

H. O. Fried, C. A. K. Lovell, S. Schmidt and S. Yaisawarng, Accounting for environmental effects and statistical noise in data envelopment analysis,, Journal of Productivity Analysis, 17 (2002), 157. doi: 10.1023/A:1013548723393. Google Scholar

[30]

H. O. Fried, S. S. Schmidt and S. Yaisawarng, Incorporating the operating environment into a nonparametric measure of technical efficiency,, Journal of Productivity Analysis, 17 (1999), 249. doi: 10.1023/A:1007800306752. Google Scholar

[31]

F. C. Fu, C. P. C. Vijverberg and Y. S. Chen, Productivity and efficiency of state-owned enterprises in China,, Journal of Productivity Analysis, 29 (2008), 249. doi: 10.1007/s11123-007-0078-y. Google Scholar

[32]

T. García-Marco and M. D. Robles-Fernández, Risk-taking behavior and ownership in the banking industry: The Spanish evidence,, Journal of Business and Economics, 60 (2008), 332. doi: 10.1016/j.jeconbus.2007.04.008. Google Scholar

[33]

W. H. Greene, Maximum likelihood estimation of econometric frontier functions,, Journal of Econometric, 13 (1980), 26. doi: 10.1016/0304-4076(80)90041-X. Google Scholar

[34]

W. H. Greene, "Econometric Analysis,", 6th edition, (2008). Google Scholar

[35]

W. H. Greene, "LIMDEP, Version 9.0 - Econometric Modeling Guide,", Econometric Software, (2007). Google Scholar

[36]

J. F. Hair, B. Black, B. Babin, R. E. Anderson and R. L. Tatham, "Multivariate Analysis,", Prentice-Halle, (2006). Google Scholar

[37]

J. A. Hausman, Specification tests in econometrics,, Econometrica, 46 (1978), 1251. doi: 10.2307/1913827. Google Scholar

[38]

J. Hekcman, Sample selection bias as a specification error,, Econometrica, 47 (1979), 153. doi: 10.2307/1912352. Google Scholar

[39]

E. Huerta, La economía navarra: ¿por qué es importante la productividad?, Diario de Noticias, 5 (2007). Google Scholar

[40]

S. Jansson, Swedish labour-owned industrial firms,, Annals of Public and Cooperative Economics, 57 (1986), 103. doi: 10.1111/j.1467-8292.1986.tb01934.x. Google Scholar

[41]

J. Y. Lee, Application of the three-stage DEA in measuring efficiency - an empirical evidence,, Applied Economics Letters, 15 (2008), 49. doi: 10.1080/13504850600675435. Google Scholar

[42]

C.-C. Liu, Evaluating the operational efficiency of major ports in the Asian-Pacific region using data envelopment analysis,, Applied Economics, 40 (2008), 1737. doi: 10.1080/00036840600905126. Google Scholar

[43]

F.-H. F. Liu and P.-H. Wang, DEA Malmquist productivity measure: Taiwanese semiconductor companies,, International Journal of Production Economics, 112 (2008), 367. doi: 10.1016/j.ijpe.2007.03.015. Google Scholar

[44]

O. W. Maletta and V. Sena, Is competition really bad for cooperatives? Some empirical evidence for Italian producers' cooperatives,, Journal of Productivity Analysis, 29 (2008), 221. Google Scholar

[45]

B. B. Margari, F. Erbetta, C. Petraglia and M. Piacenza, Regulatory and environmental effects on public transit efficiency: A mixed DEA-SFA approach,, Journal of Regulatory Economics, 32 (2007), 131. doi: 10.1007/s11149-007-9025-0. Google Scholar

[46]

Z. Melgarejo, F. J. Arcelus and K. Simon, Type of ownership and the creation of new enterprises in Navarre, Spain: Differences in financial survival,, International Journal of Technology, 7 (2007), 225. doi: 10.1504/IJTPM.2007.015108. Google Scholar

[47]

M. Muñiz, Separating managerial inefficiency and external conditions in data,, European Journal of Operational Research, 143 (2002), 625. doi: 10.1016/S0377-2217(01)00344-7. Google Scholar

[48]

W. Nasierowski and F. J. Arcelus, On the efficiency of national innovation systems,, Socio-Economic Planning Sciences, 37 (2003), 215. doi: 10.1016/S0038-0121(02)00046-0. Google Scholar

[49]

W. K. Newey and K. D. West, A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix,, Econometrica, 55 (1987), 703. doi: 10.2307/1913610. Google Scholar

[50]

S. J. Nickell, Competition and corporate performance,, Journal of Political Economy, 104 (1996), 724. doi: 10.1086/262040. Google Scholar

[51]

M. Núñez-Nickel and J. Moyano-Fuentes, Ownership structure of cooperatives as an environmental buffer,, Journal of Management Studies, 41 (2004), 1131. doi: 10.1111/j.1467-6486.2004.00469.x. Google Scholar

[52]

R. Park, D. Kruse and J. Sesil, Does employment ownership enhance firm survival?,, in, 3 (2004). Google Scholar

[53]

S. Rho and J. An, Evaluating the efficiency of a two-stage production process using data envelopment analysis,, International Transactions in Operations Research, 14 (2007), 395. doi: 10.1111/j.1475-3995.2007.00597.x. Google Scholar

[54]

J. Ruggiero, Non-discretionary inputs in data envelopment analysis,, European Journal of Operational Research, 111 (1998), 461. doi: 10.1016/S0377-2217(97)00306-8. Google Scholar

[55]

J. M. Sarriegui, El reto de la pequeña empresa,, El País, 30 (2006). Google Scholar

[56]

J.-K. Shang, W.-T. Hung and F.-C. Wang, "Service Outsourcing and Hotel Performance: Three-Stage Dea Analysis,", Applied Economics Letters, (2008). doi: 10.1080/13504850600993523. Google Scholar

[57]

J.-K. Shang, W.-T. Hung and F.-C. Wang, Ecommerce and hotel performance: Three-stage DEA analysis,, The Service Industries Journal, 28 (2008), 529. doi: 10.1080/02642060801917679. Google Scholar

[58]

R. W. Shephard, "Theory of Cost and Production Functions,", Princeton Studies in Mathematical Economics, 4 (1970). Google Scholar

[59]

H. A. Simon and C. P. Bonini, The size distribution of business firms,, American Economic Review, 58 (1958), 607. Google Scholar

[60]

J. Surroca, M. A. García-Cestona and L. L. Santamaría, Corporate governance and the Mondragón cooperatives,, Management Research, 4 (2006), 99. doi: 10.2753/JMR1536-5433040202. Google Scholar

[61]

E. Taymaz, Are small firms really less productive?,, Small Business Economics, 25 (2005), 429. doi: 10.1007/s11187-004-6492-x. Google Scholar

[62]

H. Tulkens and P. V. Vanden Eeckaut, Non-parametric efficiency, progress and regress measures for panel data: Methodological aspects,, European Journal of Operational Research, 80 (1995), 474. doi: 10.1016/0377-2217(94)00132-V. Google Scholar

[63]

H. White, A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity,, Econometrica, 48 (1980), 817. doi: 10.2307/1912934. Google Scholar

[64]

R. Yaffee, "Primer for Panel Data Analysis,", Available from: \url{http://www.nyu.edu/its/statistics/Docs/pda.pdf}., 2005 (). Google Scholar

show all references

References:
[1]

Z. J. Acs and J. E. Amorós, Entrepreneurship and competitiveness dynamics in Latin America,, Small Business Economics, 31 (2008), 305. doi: 10.1007/s11187-008-9133-y. Google Scholar

[2]

G. Ahuja and S. K. Majumdar, An assessment of the performance of Indian state-owned enterprises,, Journal of Productivity Analysis, 9 (1998), 113. doi: 10.1023/A:1018352415813. Google Scholar

[3]

J. M. Arauzo-Carod and A. Segarra-Blasco, The determinants of entry are not independent of start-up size: Some evidence from Spanish manufacturing,, Review of Industrial Organization, 27 (2005), 147. doi: 10.1007/s11151-005-8321-z. Google Scholar

[4]

F. J. Arcelus and P. Arocena, Measuring sectoral productivity across time and across countries,, European Journal of Operational Research, 119 (1999), 254. doi: 10.1016/S0377-2217(99)00129-0. Google Scholar

[5]

F. J. Arcelus and P. Arocena, Convergence and productive efficiency in OECD countries: A non parametric frontier approach,, International Journal of Production Economics, 66 (2000), 105. doi: 10.1016/S0925-5273(99)00116-4. Google Scholar

[6]

J. M. Argilés and E. J. Slof, The use of financial accounting information and firm performance: An empirical quantification for farms,, Accounting and Business Research, 33 (2003), 251. Google Scholar

[7]

N. K. Avkiran and T. Rowlands, How to better identify the true managerial performance: State of the art using DEA,, Omega, 36 (2008), 317. doi: 10.1016/j.omega.2006.01.002. Google Scholar

[8]

R. D. Banker, A. Charnes and W. W. Cooper, Some models for estimating technical and scale inefficiency in data envelopment analysis,, Management Science, 30 (1984), 1078. doi: 10.1287/mnsc.30.9.1078. Google Scholar

[9]

R. D. Banker and R. Morey, Efficiency analysis for exogenously fixed inputs and outputs,, Operations Research, 34 (1986), 513. doi: 10.1287/opre.34.4.513. Google Scholar

[10]

W. J. Baumol, Entrepreneurial enterprises, large established firms and other components of the free-market growth machine,, Small Business Economics, 23 (2004), 9. doi: 10.1023/B:SBEJ.0000026057.47641.a6. Google Scholar

[11]

G. E. Batesse and T. J. Coelli, Frontier production function, technical efficiency and panel data: With application to paddy farmers in India,, Journal of Productivity Analysis, 3 (1992), 153. doi: 10.1007/BF00158774. Google Scholar

[12]

J. L. T. Blank and V. Valdmanis, A modified three-stage data envelopment analysis,, European Journal of Health Economics, 6 (2005), 65. doi: 10.1007/s10198-004-0260-3. Google Scholar

[13]

A. N. Bojanic, S. B. Caudill and J. M. Ford, Small-sample properties of ML, COLS, and DEA estimators of frontier models in the presence of heteroskedasticity,, European Journal of Operational Research, 108 (1998), 140. doi: 10.1016/S0377-2217(97)00101-X. Google Scholar

[14]

M. Callejón and A. Segarra, Business dynamics and efficiency in industries and regions: The case of Spain,, Small Business Economics, 1 (1999), 253. Google Scholar

[15]

J. L. Calvo, Testing Gibrat's law for small, young and innovating firms,, Small Business Economics, 26 (2006), 117. doi: 10.1007/s11187-004-2135-5. Google Scholar

[16]

S. B. Caudill and J. M. Ford, Biases in frontier estimation due to heteroskedasticity,, Economics Letters, 4 (1993), 17. doi: 10.1016/0165-1765(93)90104-K. Google Scholar

[17]

S. B. Caudill, J. M. Ford and D. Gropper, Frontier estimation and firm-specific inefficiency measures in the presence of heteroskedasticity,, Journal of Business & Economic Statistics, 13 (1995), 105. doi: 10.2307/1392525. Google Scholar

[18]

Y. Chen, L. Liang and J. Zhou, Equivalence in two-stage DEA approaches,, European Journal of Operational Research, 193 (2009), 600. doi: 10.1016/j.ejor.2007.11.040. Google Scholar

[19]

T. J. Coelli, "A Guide to DEAP Version 2.1: A Data Envelopment Analysis (Computer) Program,", CEPA WP8, (). Google Scholar

[20]

T. J. Coelli, D. S. P. Rao, C. J. O'Donnell and G. E. Batesse, "An Introduction to Efficiency and Productivity Analysis,", Springer-Verlag, (2005). Google Scholar

[21]

J. M. Cordero-Ferrera, F. Pedraja-Chaparro and J. Salinas-Jiménez, Measuring efficiency in education: An analysis of different approaches for incorporating non-discretionary inputs,, Applied Economics, 40 (2008), 1323. doi: 10.1080/00036840600771346. Google Scholar

[22]

M. A. Díaz, R. Sánchez, Firm size and productivity in Spain: A stochastic frontier analysis,, Small Business Economics, 30 (2008), 315. doi: 10.1007/s11187-007-9058-x. Google Scholar

[23]

C. Doucouliagos, The comparative efficiency and productivity of labor-managed and capital-managed firms,, Review of Radical Political Economics, 29 (1997), 45. doi: 10.1177/048661349702900203. Google Scholar

[24]

G. Dow, "Governing the Firm: Worker's Control in Theory and in Practice,", Cambridge University Press, (2003). Google Scholar

[25]

R. Färe, S. Grosskopf, B. Lindgren and P. Roos, Productivity developments in Swedish hospitals; A Malmquist output index approach,, in, (1994). Google Scholar

[26]

R. Färe, S. Grosskopf and C. A. K. Lovell, "Production Frontiers,", Cambridge University Press, (1994). Google Scholar

[27]

M. J. Farrell, The measurement of productive efficiency,, Journal of the Royal Statistical Society, 120 (1957), 253. doi: 10.2307/2343100. Google Scholar

[28]

M. Fritsch, U. Brixy and O. Falk, The effect of industry, region and time on new business survival - A multidimensional analysis,, Review of Industrial Organization, 28 (2006), 285. doi: 10.1007/s11151-006-0018-4. Google Scholar

[29]

H. O. Fried, C. A. K. Lovell, S. Schmidt and S. Yaisawarng, Accounting for environmental effects and statistical noise in data envelopment analysis,, Journal of Productivity Analysis, 17 (2002), 157. doi: 10.1023/A:1013548723393. Google Scholar

[30]

H. O. Fried, S. S. Schmidt and S. Yaisawarng, Incorporating the operating environment into a nonparametric measure of technical efficiency,, Journal of Productivity Analysis, 17 (1999), 249. doi: 10.1023/A:1007800306752. Google Scholar

[31]

F. C. Fu, C. P. C. Vijverberg and Y. S. Chen, Productivity and efficiency of state-owned enterprises in China,, Journal of Productivity Analysis, 29 (2008), 249. doi: 10.1007/s11123-007-0078-y. Google Scholar

[32]

T. García-Marco and M. D. Robles-Fernández, Risk-taking behavior and ownership in the banking industry: The Spanish evidence,, Journal of Business and Economics, 60 (2008), 332. doi: 10.1016/j.jeconbus.2007.04.008. Google Scholar

[33]

W. H. Greene, Maximum likelihood estimation of econometric frontier functions,, Journal of Econometric, 13 (1980), 26. doi: 10.1016/0304-4076(80)90041-X. Google Scholar

[34]

W. H. Greene, "Econometric Analysis,", 6th edition, (2008). Google Scholar

[35]

W. H. Greene, "LIMDEP, Version 9.0 - Econometric Modeling Guide,", Econometric Software, (2007). Google Scholar

[36]

J. F. Hair, B. Black, B. Babin, R. E. Anderson and R. L. Tatham, "Multivariate Analysis,", Prentice-Halle, (2006). Google Scholar

[37]

J. A. Hausman, Specification tests in econometrics,, Econometrica, 46 (1978), 1251. doi: 10.2307/1913827. Google Scholar

[38]

J. Hekcman, Sample selection bias as a specification error,, Econometrica, 47 (1979), 153. doi: 10.2307/1912352. Google Scholar

[39]

E. Huerta, La economía navarra: ¿por qué es importante la productividad?, Diario de Noticias, 5 (2007). Google Scholar

[40]

S. Jansson, Swedish labour-owned industrial firms,, Annals of Public and Cooperative Economics, 57 (1986), 103. doi: 10.1111/j.1467-8292.1986.tb01934.x. Google Scholar

[41]

J. Y. Lee, Application of the three-stage DEA in measuring efficiency - an empirical evidence,, Applied Economics Letters, 15 (2008), 49. doi: 10.1080/13504850600675435. Google Scholar

[42]

C.-C. Liu, Evaluating the operational efficiency of major ports in the Asian-Pacific region using data envelopment analysis,, Applied Economics, 40 (2008), 1737. doi: 10.1080/00036840600905126. Google Scholar

[43]

F.-H. F. Liu and P.-H. Wang, DEA Malmquist productivity measure: Taiwanese semiconductor companies,, International Journal of Production Economics, 112 (2008), 367. doi: 10.1016/j.ijpe.2007.03.015. Google Scholar

[44]

O. W. Maletta and V. Sena, Is competition really bad for cooperatives? Some empirical evidence for Italian producers' cooperatives,, Journal of Productivity Analysis, 29 (2008), 221. Google Scholar

[45]

B. B. Margari, F. Erbetta, C. Petraglia and M. Piacenza, Regulatory and environmental effects on public transit efficiency: A mixed DEA-SFA approach,, Journal of Regulatory Economics, 32 (2007), 131. doi: 10.1007/s11149-007-9025-0. Google Scholar

[46]

Z. Melgarejo, F. J. Arcelus and K. Simon, Type of ownership and the creation of new enterprises in Navarre, Spain: Differences in financial survival,, International Journal of Technology, 7 (2007), 225. doi: 10.1504/IJTPM.2007.015108. Google Scholar

[47]

M. Muñiz, Separating managerial inefficiency and external conditions in data,, European Journal of Operational Research, 143 (2002), 625. doi: 10.1016/S0377-2217(01)00344-7. Google Scholar

[48]

W. Nasierowski and F. J. Arcelus, On the efficiency of national innovation systems,, Socio-Economic Planning Sciences, 37 (2003), 215. doi: 10.1016/S0038-0121(02)00046-0. Google Scholar

[49]

W. K. Newey and K. D. West, A simple positive semi-definite, heteroskedasticity and autocorrelation consistent covariance matrix,, Econometrica, 55 (1987), 703. doi: 10.2307/1913610. Google Scholar

[50]

S. J. Nickell, Competition and corporate performance,, Journal of Political Economy, 104 (1996), 724. doi: 10.1086/262040. Google Scholar

[51]

M. Núñez-Nickel and J. Moyano-Fuentes, Ownership structure of cooperatives as an environmental buffer,, Journal of Management Studies, 41 (2004), 1131. doi: 10.1111/j.1467-6486.2004.00469.x. Google Scholar

[52]

R. Park, D. Kruse and J. Sesil, Does employment ownership enhance firm survival?,, in, 3 (2004). Google Scholar

[53]

S. Rho and J. An, Evaluating the efficiency of a two-stage production process using data envelopment analysis,, International Transactions in Operations Research, 14 (2007), 395. doi: 10.1111/j.1475-3995.2007.00597.x. Google Scholar

[54]

J. Ruggiero, Non-discretionary inputs in data envelopment analysis,, European Journal of Operational Research, 111 (1998), 461. doi: 10.1016/S0377-2217(97)00306-8. Google Scholar

[55]

J. M. Sarriegui, El reto de la pequeña empresa,, El País, 30 (2006). Google Scholar

[56]

J.-K. Shang, W.-T. Hung and F.-C. Wang, "Service Outsourcing and Hotel Performance: Three-Stage Dea Analysis,", Applied Economics Letters, (2008). doi: 10.1080/13504850600993523. Google Scholar

[57]

J.-K. Shang, W.-T. Hung and F.-C. Wang, Ecommerce and hotel performance: Three-stage DEA analysis,, The Service Industries Journal, 28 (2008), 529. doi: 10.1080/02642060801917679. Google Scholar

[58]

R. W. Shephard, "Theory of Cost and Production Functions,", Princeton Studies in Mathematical Economics, 4 (1970). Google Scholar

[59]

H. A. Simon and C. P. Bonini, The size distribution of business firms,, American Economic Review, 58 (1958), 607. Google Scholar

[60]

J. Surroca, M. A. García-Cestona and L. L. Santamaría, Corporate governance and the Mondragón cooperatives,, Management Research, 4 (2006), 99. doi: 10.2753/JMR1536-5433040202. Google Scholar

[61]

E. Taymaz, Are small firms really less productive?,, Small Business Economics, 25 (2005), 429. doi: 10.1007/s11187-004-6492-x. Google Scholar

[62]

H. Tulkens and P. V. Vanden Eeckaut, Non-parametric efficiency, progress and regress measures for panel data: Methodological aspects,, European Journal of Operational Research, 80 (1995), 474. doi: 10.1016/0377-2217(94)00132-V. Google Scholar

[63]

H. White, A heteroskedasticity-consistent covariance matrix estimator and a direct test for heteroskedasticity,, Econometrica, 48 (1980), 817. doi: 10.2307/1912934. Google Scholar

[64]

R. Yaffee, "Primer for Panel Data Analysis,", Available from: \url{http://www.nyu.edu/its/statistics/Docs/pda.pdf}., 2005 (). Google Scholar

[1]

Cheng-Kai Hu, Fung-Bao Liu, Cheng-Feng Hu. Efficiency measures in fuzzy data envelopment analysis with common weights. Journal of Industrial & Management Optimization, 2017, 13 (1) : 237-249. doi: 10.3934/jimo.2016014

[2]

Habibe Zare Haghighi, Sajad Adeli, Farhad Hosseinzadeh Lotfi, Gholam Reza Jahanshahloo. Revenue congestion: An application of data envelopment analysis. Journal of Industrial & Management Optimization, 2016, 12 (4) : 1311-1322. doi: 10.3934/jimo.2016.12.1311

[3]

Mahdi Mahdiloo, Abdollah Noorizadeh, Reza Farzipoor Saen. Developing a new data envelopment analysis model for customer value analysis. Journal of Industrial & Management Optimization, 2011, 7 (3) : 531-558. doi: 10.3934/jimo.2011.7.531

[4]

Jinying Ma, Honglei Xu. Empirical analysis and optimization of capital structure adjustment. Journal of Industrial & Management Optimization, 2017, 13 (5) : 1-11. doi: 10.3934/jimo.2018191

[5]

Mohammad Afzalinejad, Zahra Abbasi. A slacks-based model for dynamic data envelopment analysis. Journal of Industrial & Management Optimization, 2019, 15 (1) : 275-291. doi: 10.3934/jimo.2018043

[6]

Jiang Xie, Junfu Xu, Celine Nie, Qing Nie. Machine learning of swimming data via wisdom of crowd and regression analysis. Mathematical Biosciences & Engineering, 2017, 14 (2) : 511-527. doi: 10.3934/mbe.2017031

[7]

Saber Saati, Adel Hatami-Marbini, Per J. Agrell, Madjid Tavana. A common set of weight approach using an ideal decision making unit in data envelopment analysis. Journal of Industrial & Management Optimization, 2012, 8 (3) : 623-637. doi: 10.3934/jimo.2012.8.623

[8]

Angela Cadena, Adriana Marcucci, Juan F. Pérez, Hernando Durán, Hernando Mutis, Camilo Taútiva, Fernando Palacios. Efficiency analysis in electricity transmission utilities. Journal of Industrial & Management Optimization, 2009, 5 (2) : 253-274. doi: 10.3934/jimo.2009.5.253

[9]

Wei Li, Yun Teng. Enterprise inefficient investment behavior analysis based on regression analysis. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 1015-1025. doi: 10.3934/dcdss.2019069

[10]

Wu Chanti, Qiu Youzhen. A nonlinear empirical analysis on influence factor of circulation efficiency. Discrete & Continuous Dynamical Systems - S, 2019, 12 (4&5) : 929-940. doi: 10.3934/dcdss.2019062

[11]

Deren Han, Xiaoming Yuan. Existence of anonymous link tolls for decentralizing an oligopolistic game and the efficiency analysis. Journal of Industrial & Management Optimization, 2011, 7 (2) : 347-364. doi: 10.3934/jimo.2011.7.347

[12]

Zhenhua Peng, Zhongping Wan, Weizhi Xiong. Sensitivity analysis in set-valued optimization under strictly minimal efficiency. Evolution Equations & Control Theory, 2017, 6 (3) : 427-436. doi: 10.3934/eect.2017022

[13]

Qiang Du, M. D. Gunzburger, L. S. Hou, J. Lee. Analysis of a linear fluid-structure interaction problem. Discrete & Continuous Dynamical Systems - A, 2003, 9 (3) : 633-650. doi: 10.3934/dcds.2003.9.633

[14]

Zhouchen Lin. A review on low-rank models in data analysis. Big Data & Information Analytics, 2016, 1 (2&3) : 139-161. doi: 10.3934/bdia.2016001

[15]

Pankaj Sharma, David Baglee, Jaime Campos, Erkki Jantunen. Big data collection and analysis for manufacturing organisations. Big Data & Information Analytics, 2017, 2 (2) : 127-139. doi: 10.3934/bdia.2017002

[16]

Tyrus Berry, Timothy Sauer. Consistent manifold representation for topological data analysis. Foundations of Data Science, 2019, 1 (1) : 1-38. doi: 10.3934/fods.2019001

[17]

Nina Yan, Tingting Tong, Hongyan Dai. Capital-constrained supply chain with multiple decision attributes: Decision optimization and coordination analysis. Journal of Industrial & Management Optimization, 2019, 15 (4) : 1831-1856. doi: 10.3934/jimo.2018125

[18]

Yihong Xu, Zhenhua Peng. Higher-order sensitivity analysis in set-valued optimization under Henig efficiency. Journal of Industrial & Management Optimization, 2017, 13 (1) : 313-327. doi: 10.3934/jimo.2016019

[19]

Hong Zhang, Fei Yang. Optimization of capital structure in real estate enterprises. Journal of Industrial & Management Optimization, 2015, 11 (3) : 969-983. doi: 10.3934/jimo.2015.11.969

[20]

Serge Nicaise, Cristina Pignotti. Asymptotic analysis of a simple model of fluid-structure interaction. Networks & Heterogeneous Media, 2008, 3 (4) : 787-813. doi: 10.3934/nhm.2008.3.787

2018 Impact Factor: 1.025

Metrics

  • PDF downloads (12)
  • HTML views (0)
  • Cited by (1)

[Back to Top]