Skip to main content
Disciplines
Automotive Business IT + Informatics Construction + Real Estate Electrical Engineering + Electronics Energy + Sustainability Insurance + Risk Finance + Banking Management + Leadership Marketing + Sales Mechanical Engineering + Materials
Events
DE
EN
Books
Journals
Topic Page
Marketing
Start single access now
Access for companies
Automotive Acoustics Conference 2025

Call for Papers - Deadline: 01/13/2025. 

We would like to invite you to submit your proposals for a presentation and join us as we explore new practices in sustainable noise control. We are looking forward to your insights on innovative, eco-friendly, and smart ways to shape the future of automotive acoustics.
EXTENDED SEARCH
Log in
Top
Published in:
Read chapter Read first chapter

2024 | OriginalPaper | Chapter

Analysis of Agricultural Production in China and Measurement of Technical Efficiency Using Copula-Based Stochastic Frontier Model

Authors : Yueyi Chen, Paravee Maneejuk, Woraphon Yamaka

Published in: Applications of Optimal Transport to Economics and Related Topics

Publisher: Springer Nature Switzerland

Log in

Activate our intelligent search to find suitable subject content or patents.

search-config
loading …

Abstract

The purpose of this paper is to evaluate the efficiency of agricultural production in China, analyze China's production function, and identify the primary factor affecting efficiency. Methodologically, we employ the stochastic frontier model, accounting for the interrelation between the two-sided error term (U) and the one-sided inefficiency factor (V). To address this complex relationship, we introduce a recent approach known as the Copula-based stochastic frontier model. After obtaining technical efficiency (TE) values, we classify the 31 provinces in China into three distinct groups using the K-means clustering method, each exhibiting unique characteristics. Subsequently, we conduct a LASSO regression analysis to examine the key factors influencing TE values. Our findings reveal that the most significant factor impacting TE values is the average household size, which refer to the average number of laborers per rural household in each province. Furthermore, we observe a negative correlation between the level of education among the labor force and TE values. We analyze that this negative correlation primarily results from the agricultural sector's limited appeal to individuals with higher levels of education.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

  • über 102.000 Bücher
  • über 537 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Maschinenbau + Werkstoffe
  • Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 390 Zeitschriften

aus folgenden Fachgebieten:

  • Automobil + Motoren
  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Elektrotechnik + Elektronik
  • Energie + Nachhaltigkeit
  • Maschinenbau + Werkstoffe




 

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft"

Online-Abonnement

Mit Springer Professional "Wirtschaft" erhalten Sie Zugriff auf:

  • über 67.000 Bücher
  • über 340 Zeitschriften

aus folgenden Fachgebieten:

  • Bauwesen + Immobilien
  • Business IT + Informatik
  • Finance + Banking
  • Management + Führung
  • Marketing + Vertrieb
  • Versicherung + Risiko




Jetzt Wissensvorsprung sichern!

inform now
previous chapter Economic Growth in Sub-Saharan Africa: An Analysis of the Technical Efficiency of Natural Resource Rent and Institutions Based on a Copula Stochastic Frontier Model
next chapter Analysis of Factors Affecting China’s Demographic Fertility Transition: A Random Forest Algorithm Approach
Appendix
Available only for authorised users
Literature
1.
Aigner, D.J., Lovell, C.A.K., Schmidt, P.S.: Formulation and estimation of stochastic frontier production function models. J. Econ. 6, 21–37 (1977)CrossRef
2.
Ayuttaya, K.N.: Planting season choice and technical efficiency in Thailand’s arabica coffee and macadamia nut production: case study Northern of Thailand, Diploma thesis, Chiang Mai University (2011)
3.
Battese, G.E., Coelli, T.J.: Frontier production functions, technical efficiency and panel data: with application to paddy farmers in India. J. Product. Anal. 3, 153–169 (1992)CrossRef
4.
Bezat-Jarzębowska, A., Rembisz, W.: Efficiency-focused economic modeling of competitiveness in the agri-food sector. Procedia. Soc. Behav. Sci. 81, 359–365 (2013)
5.
Bhat, C.R., Eluru, N.: A copula-based approach to accommodate residential self-selection effects in travel behavior modeling. Transp. Res., Part B, Methodol. 43(7), 749–765 (2009)
6.
Bojnec, Š, Fertő, I., Jámbor, A., et al.: Determinants of technical efficiency in agriculture in new EU member states from Central and Eastern Europe. Acta Oeconomica 64(2), 197–217 (2014)CrossRef
7.
Boyer, B.H., Gibson, M.S., Loretan, M.: Pitfalls in tests for changes in correlations, IFS Discussion Paper No 597R, Federal Reserves Board (1999)
8.
Brechmann, E.C., Schepsmeier, U.: Modeling dependence with C- and D-Vine Copulas: the R package CDVine. J. Stat. Softw. 52(3), 1–29 (2013)CrossRef
9.
Burns, R.: The simulated Maximum Likelihood Estimation of Stochastic Frontier Models with Correlated Error Components. The University of Sydney, Sydney (2004)
10.
11.
Chaovanapoonphol, Y., Singvejsakul, J., Sriboonchitta, S.: Technical efficiency of rice production in the Upper North of Thailand: clustering copula-based stochastic frontier analysis. Agriculture 12(10), 1585 (2022)CrossRef
12.
Choi, I.: Unit root tests for panel data. J. Int. Money Financ. 20(2), 249–272 (2001)
13.
Coelli, T.J., Rao, D.S.P., Battese, G.E.: An Introduction to Efficiency and Productivity Analysis. Kluwer, Boston (1998)CrossRef
14.
Coelli, T.J., Rahman, S., Thirtle, C.: Technical, allocative, cost and scale efficiencies in Bangladesh rice cultivation: a non-parametric approach. J. Agric. Econ. 53, 607–626 (2002)CrossRef
15.
Cullinane, K., Song, D.W.: Estimating the relative efficiency of European container ports: a stochastic frontier analysis. Res. Transp. Econ. 16, 85–115 (2006)CrossRef
16.
Đokić, D., Zekić, S., Jurjević, Ž., & Matkovski, B.: Drivers of technical efficiency in agriculture in the Western Balkans and New EU Memeber States. Custos Agronegocio Online, 16, 2–15 (2020)
17.
Dou, H.W., Ming, T.Z., Xu, J., et al.: Numerical simulation of pollutant propagation characteristics in a three-dimensional urban traffic system. China Environ. Sci. 38(1), 51–58 (2018)
18.
Greene, W.: Distinguishing between heterogeneity and inefficiency: stochastic frontier analysis of the World Health Organization’s panel data on national health care systems. Health Econ. 13, 959–980 (2004)CrossRef
19.
Greene, W.H.: Econometric Analysis, 6th edn. Prentice Hall, Upper Saddle River, NJ, USA (2008)
20.
Greene, W.: A stochastic frontier model with correction for sample selection. J. Product. Anal. 34(1), 15–24 (2010)CrossRef
21.
Farrell, M.J.: The measurement of productive efficiency. J. R. Stat. Soc: series A (General) 120(3), 253–281 (1957)
22.
Jondrow, J., Lovell, C.A.K., Materov, I.S., Schmidt, P.: On the estimation of technical inefficiency in the stochastic frontier production function model. J. Econ. 19, 233–238 (1982)CrossRef
23.
Justice, G., Djokoto, F.K., Gidiglo, F.Y., Srofenyoh, K.A., Agyei-Henaku, A.-O., Afrane Arthur, A.A., Badu-Prah, C.|John Fry (Reviewing editor).: Sectoral and spatio-temporal differentiation in technical efficiency: a meta-regression. Cogent Econ. Financ. 8, 1 (2020)
24.
Kao, C., Lee, L., Pitt, M.M.: Simulated maximum likelihood estimation of the linear expenditure system with binding non-negativity constraints. Ann. Econ. Financ. 2, 203–223 (2001)
25.
Kumar, P.: Probability distributions and estimation of Ali–Mikhail–Haq copula. Appl. Math. Sci. (Ruse) 4(14), 657–666 (2010)
26.
Kumbhakar, S.C., Lovell, C.A.K.: Stochastic Frontier Analysis. Cambridge University Press, Cambridge, UK (2000)CrossRef
27.
Latruffe, L., Bravo‐Ureta, B.E., Carpentier, A., et al.: Subsidies and technical efficiency in agriculture: Evidence from European dairy farms. Am. J Agric. Econ. 99(3), 783–799 (2017)
28.
Lei, C., Qi, Q., Jiang, L., Fu, Y., & Liang, Q.: Analysis of current and qualified major grain producing areas in China in the last 30 years. Sustainability, 14(5), 2909 (2022)
29.
Litster, A.L., Buchanan, J.W.: Radiographic and echocardiographic measurement of the heart in obese cats. Vet. Radiol. Ultrasound 41(4), 320–325 (2000)CrossRef
30.
Luo, J.: Stepwise estimation of D-Vines with arbitrary specified copula pairs and EDA Tools, Diploma thesis, Technische Universität München (2010)
31.
Meeusen, W., van den Broeck, J.: Efficiency estimation from Cobb-Douglas production functions with composed error. Int. Econ. Rev. 18, 435–444 (1977)CrossRef
32.
Nelsen, R.B.: An Introduction to Copulas, 2nd edn. Springer, New York (2006)
33.
Njeru, J.: Factors influencing technical efficiencies among selected wheat farmers in Uasin Gishu District, Kenya. In: AERC Research paper, p. 206 (2010)
34.
Rahman, S., Wiboonpongse, A., Sriboonchitta, S., Chaovanapoonphol, Y.: Production efficiency of jasmine rice producers in Northern and North-eastern Thailand. J. Agric. Econ. 60(2), 419–435 (2009)CrossRef
35.
Rahman, S., Wiboonpongse, A., Sriboonchitta, S., Kanmanee, K.: Total factor productivity growth and convergence in Northern Thai agriculture. Afr. J. Agric. Res. 17(7), 2689–2700 (2012)
36.
Rahman, S., Hasan, M.K.: Impact of environmental production conditions on productivity and efficiency: a case study of wheat farmers in Bangladesh. J. Environ. Manag. 88(4), 1495–1504 (2008)CrossRef
37.
Rahman, S.A., Umar, H.S.: Measurement of technical efficiency and its determinants in crop production in Lafia local government area of Nasarawa State, Nigeria. Agro-Sci. 8(2) (2009)
38.
Sklar, M.: Fonctions de repartition à n dimensions et leurs marges. Publ. Inst. Stat. Univ. Paris 8, 229–231 (1959)
39.
Smith, M.D.: Stochastic frontier models with dependent error components. Econ. J. 11, 172–192 (2008)
40.
Sriboonchitta, S.: Evaluation of cost efficiency of Thai public universities. Int. J. Intell. Syst. Technol. Appl. 5, 361–374 (2012)
41.
Sriboonchitta, S., Liu, J., Wiboonpongse, A., Denoeux, T.: A double-copula stochastic frontier model with dependent error components and correction for sample selection. Int. J. Approx. Reason. 80, 174–184 (2017)
42.
Sriboonchitta, S., Nguyen, H.T., Wiboonpongse, A., Liu, J.: Modeling volatility and dependency of agricultural price and production indices of Thailand: static versus time-varying copulas. Int. J. Approx. Reason. 54, 793–808 (2013)CrossRef
43.
Sriboonchitta, S., Wiboonpongse, A.: On the estimation of stochastic production frontiers with self-selectivity: jasmine and non-jasmine rice in Thailand. CMU J. Nat. Sci. 4, 105–124 (2005)
44.
Sriboonchitta, S., Wiboonpongse, A.: Technical efficiency of rural micro and community enterprises in the Upper North of Thailand. In: Asia-Pacific Productivity Conference, pp. 17–19. Seoul National University, Seoul, Korea (2006)
45.
Sriboonchitta, S., Wiboonpongse, A.: The effects of production inputs, technical efficiency and other factors on jasmine and non-jasmine rice yields in Thailand, In: Asia-Pacific Productivity Conference, pp. 14–16. Brisbane, Australia (2004)
46.
Tesema, T.: Determinants of production efficiency of maize‐dominated farmers in western parts of Ethiopia in Gudeya Bila district: evidence under shifting cultivation area. Sci. World J. 2022(1), 3355224 (2022)
47.
Trivedi, P., Zimmer, D.: Copula modeling: an introduction for practitioners. Found. Trends Econ. 1(1), 1–111 (2005)
48.
Tiedemann, T., Latacz-Lohmann, U.: Production risk and technical efficiency in organic and conventional agriculture–the case of arable farms in Germany. J. Agric. Econ. 64(1), 73–96 (2013)CrossRef
49.
Wiboonpongse, A., Liu, J., Sriboonchitta, S., et al.: Modeling dependence between error components of the stochastic frontier model using copula: application to intercrop coffee production in Northern Thailand. Int. J. Approx. Reason. 65, 34–44 (2015)CrossRef
50.
Wiboonpongse, A., Sriboonchitta, S,: Agricultural production growth assessment for agroeconomic zones in Northern Thailand using statistical data. In: Asia Pacific Productivity Conference 2008. Taipei, Taiwan, Academic Sinica (2008)
51.
Wiboonpongse, A., Sriboonchitta, S.: Impact of technical skill on potato production efficiency in Thailand. In: Asia Pacific Productivity Conference 2008. Taipei, Taiwan, Academic Sinica (2008)
52.
Wiboonpongse, A., Sriboonchitta, S., Battese, E.G.: The effects of production input, technical inefficiency and biological risk on jasmine and non-jasmine rice yields in Thailand. Empir. Econ. Lett. 4(3) (2005)
53.
Wiboonpongse, A., Sriboonchitta, S., Rahman, S., Calkins, P.: Thanes Sriwichailumphun, Joint determination of the choice of planting season and technical efficiency of potato in Northern Thailand: a comparison of Greene’s versus Heckman’s sample selection approach. Afr. J. Bus. Manag. 6(12), 4504–4513 (2012)
54.
Zellner, A., Kmenta, J., Drèze, F.: Specification and estimation of Cobb-Douglas production functions. Econometrica 34, 784–795 (1966)CrossRef
Metadata
Title
Analysis of Agricultural Production in China and Measurement of Technical Efficiency Using Copula-Based Stochastic Frontier Model
Authors
Yueyi Chen
Paravee Maneejuk
Woraphon Yamaka
Copyright Year
2024
Book
Applications of Optimal Transport to Economics and Related Topics

Print ISBN: 978-3-031-67769-4

Electronic ISBN: 978-3-031-67770-0

Copyright Year: 2024

DOI
https://doi.org/10.1007/978-3-031-67770-0_31

Premium Partner

    Image Credits