Skip to main content

An evaluation of subsidy policy impacts, transient and persistent technical efficiency: A case of Mongolia

Abstract

Agricultural sector is the backbone of economy in an developing country. Farm households living in rural areas are particularly small-scale farmers and mainly rely on agriculture. Agriculture sector is the highly supported sector by governmental agencies in Mongolia. Although the subsidy–efficiency relationship has been extensively studied in other country contexts, limited studies have discussed that how farm efficiency and productivity are influenced by subsidies; in all studies subsidies are treated as exogenous. In order to fulfill the research gap, this paper specifically analyzes the subsidy payment effects on technical efficiency (TE) of wheat farmers in Mongolia. An unbalanced farm-level data of central arable region farmers from 2013 to 2018 were utilized in this study. We use a four-component stochastic frontier analysis approach that specifically separates the persistent and transient components, by controlling the heterogeneity. The findings of production frontier results indicate that wheat sown area, seed and labor were the main driving inputs for production growth. The investment in machinery has no impact on the output with insignificant technical changes. The overall estimated mean TE was 60% and mean persistent and transient TE were 0.778 and 0.765 respectively, that implies the possible growth in production without increasing the inputs under current technology. Our results further reveal that cash incentives and soft loans for the purchase of inputs have positive affect on overall TE and its transient components. The farm households technical skills toward efficient use of inputs and farming practices need to be improved. The government should take necessary measures related to technology innovations and disaster risk management and further promote the current subsidy policy, efficiency and technical up-gradation in wheat farming.

This is a preview of subscription content, access via your institution.

Fig. 1

Notes

  1. 1.

    can follow either truncated, half-normal or exponential distribution.

  2. 2.

    the true fixed-effect or true random-effect model, which separate firm heterogeneity from inefficiency.

  3. 3.

    model that separate persistent and time-varying inefficiency.

  4. 4.

    As stated in Battese (1997), dropping these observations or substituting these values by using the value of one /or arbitrarily small number greater than zero/ then the procedure may result in seriously biased estimators of the parameters in the production function.

  5. 5.

    There are four crop-farming regions. Among these regions, the central region has the most convenient climate and geographical condition for wheat growing.

  6. 6.

    consumer price indices with 2010 as the base year was used to express all values in real terms.

  7. 7.

    For more detail about systems, see the “Soil Bulletin”; FAO (2013). The latter three systems are all considered as conservation tillage.

  8. 8.

    However, the farm heterogeneity is taken into account.

  9. 9.

    In case of the households, manager is the head of the household.

References

  1. Acosta, A., & De los Santos-Montero, L. A. (2019). "What is driving livestock total factor productivity change? A persistent and transient efficiency analysis. Global Food Security, 21, 1–12.

    Article  Google Scholar 

  2. ADB. (2020). Mongolia's economic prospects: Resource-rich and landlocked between two giants. M. Helble, H. Hill and D. Magee. Manila.

  3. Adom, P. K., & Adams, S. (2020). Decomposition of technical efficiency in agricultural production in Africa into transient and persistent technical efficiency under heterogeneous technologies. World Development, 129, 104907.

    Article  Google Scholar 

  4. Aigner, D. J., Lovell, C. A. K., & Schmidt, P. (1977). Formulation and estimation of stochastic frontier production function models. Journal of Econometrics, 6(1), 21–37.

    Article  Google Scholar 

  5. Battese, G. E., & Coelli T. J. (1995). A model of technical inefficiency effects in a stochastic production function for panel data. Empirical Economics, 20, 325–332.

  6. Battese, G., Smale, M., & Nazli, H. (2017). Factors Influencing the productivity and efficiency of wheat farmers in Punjab, Pakistan. Journal of Agribusiness in Developing and Emerging Economies, 7(2), 82–98.

    Article  Google Scholar 

  7. Berisso, O. (2019). Analysis of factors affecting persistent and transient inefficiency of ehiopia's smallholder cereal farming

  8. Bojnec, S., & Latruffe, L. (2013). Farm size, agricultural subsidies and farm performance in Slovenia. Land Use Policy, 32, 207–217.

    Article  Google Scholar 

  9. Boris, E.B.-U., Solis, D., López, V. H. M., Maripani, J. F., Thiam, A., & Rivas, T. (2007). Technical efficiency in farming: A meta-regression analysis. Journal of Productivity Analysis, 27, 57–72.

    Article  Google Scholar 

  10. Caudill, S. B., & Ford, J. M. (1993). Biases in frontier estimation due to heteroscedasticity. Economics Letters, 41, 17–20.

  11. Caudill, S. B., Ford, J. M., & Gropper, D. M. (1995). Frontier estimation and firm-specific inefficiency measures in the presence of heteroscedasticity. Journal of Business & Economic Statistics, 13, 105–111.

  12. Centre for Sustainable Agriculture Mechanization. (2017). Country presentations: Bangladesh, Cambodia, China, Nepal, India, Indonesia. The 5th Regional Forum on Sustainable Agricultural Mechanization in Asia and the Pacific - Promoting Sustainable Agricultural Mechanization Strategy, Kathmandu, Nepal Centre for Sustainable Agriculture Mechanization of United Nations.

  13. Chen, H., Wang, X., & Singh, B. (2020). Transient and persistent inefficiency traps in Chinese provinces. Economic Modelling, 97, 335–347.

    Article  Google Scholar 

  14. Chidmi, B., Daniel, S., & Cabrera, V. E. (2011a). Analyzing the sources of technical efficiency among heterogeneous dairy farms: A quantile regression approach. Journal of Development and Agricultural Economics, 3(7), 318–324.

    Google Scholar 

  15. Chidmi, B., Solís, D., Cabrera, V. E. (2011b). Analyzing the sources of technical efficiency among heterogeneous dairy farms: A quantile regression approach. Journal of Development and Agricultural Economics.

  16. Coelli, T., D. S. Rao and G. Battese (1998). Review of production economics. In: An introduction to efficiency and productivity analysis, Springer, Boston

  17. Coelli, T. J., Rao, D. S. P., Donnel, C. J. O., & Battese, G. E. (2005). An introduction to efficiency and productivity analysis. Berlin: Springer.

    Google Scholar 

  18. Daniel, C. M., Chen, Z., & Bonaparte, Y. (2010). Explaining production inefficiency in China’s agriculture using data envelopment analysis and semi-parametric bootstrapping. China Economic Review, 21(2), 346–354.

    Article  Google Scholar 

  19. DMKNL. (2016). Experiences and recommendations of the German-Mongolian sustainable agriculture project: 2013 - 2015. Ulaanbaatar, 1–54.

  20. Fahad, S., Bai, D., Liu, L., & Baloch, Z. A. (2021). Heterogeneous impacts of environmental regulation on foreign direct investment: do environmental regulation affect FDI decisions. Environmental Science and Pollution Reserach. https://doi.org/10.1007/s11356-021-15277-4

    Article  Google Scholar 

  21. Fahad, S., & Wang, J. (2018a). Evaluation of Pakistani farmers’ willingness to pay for crop insurance using contingent valuation method: the case of Khyber Pakhtunkhwa province. Land Use Policy. https://doi.org/10.1016/j.landusepol.2017.12.024

    Article  Google Scholar 

  22. Fahad, S., & Wang, J. (2018b). Farmers’ risk perception, vulnerability, and adaptation to climate change in rural Pakistan. Land Use Policy, 79, 301–309.

    Article  Google Scholar 

  23. Fahad, S., & Wang, J. (2019). Climate change, vulnerability and its impacts in rural Pakistan: A Review. Environmental Science and Pollution Research, 27, 1334–1338.

  24. FAO. (2003). Optimizing soil moisture for plant production: The significance of soil porosity. FAO Soils Bulletin

  25. FAO. (2017). Productivity and efficiency measurement in agriculture: literature review and Gaps Analysis. Improving agricultural and rural statistics: Technical report series.

  26. FAO. (2018). Guidelines for the measurement of productivity and efficiency in agriculture. The global strategy to improve agricultural and rural statistics, Food and Agriculture Organization of the United Nations.

  27. Farrell, M. J. (1957). The measurement of productive efficiency. Journal of the Royal Statistical Society Series A (General), 120(3), 253–290.

    Article  Google Scholar 

  28. Ferjani, A. (2008). The relationship between direct payments and efficiency on swiss farms. Agricultural Economics Review, 9, 93–102.

    Google Scholar 

  29. Giannakas, K., Richard, S., & Vangelis, T. (2001). Technical efficiency, technological change and output growth of wheat farms in saskatchewan. Canadian Journal of Agricultural Economics, 49, 135–152.

    Article  Google Scholar 

  30. GoM. (2016). Concepts of Mongolian sustainable development goals 2030. Ulaanbaatar.

  31. Hadri, K. (1999). Heteroscedasticity in stochastic frontier cost function. Journal of Business and Economics and Statistics 17, 359–363.

  32. Hadley, D. (2006a). Patterns in technical efficiency and technical change at the farm-level in England and wales, 1982–2002. Journal of Agricultural Economics, 57, 81–100.

    Article  Google Scholar 

  33. Hadley, D. (2006). Patterns in technical efficiency and technical change at the farm-level in England and Wales, 1982–2002. Journal of Agricultural Economics, 57, 81–100.

    Article  Google Scholar 

  34. Huang, C. J., & Liu, J. T. (1994). Estimation of a non-neutral stochastic frontier production function. Journal of Productivity Analysis 5, 171–180.

  35. Karagiannis, G., Sarris, A. (2002). Direct subsidies and technical efficiency in greek agriculture.

  36. Karagiannis, G., & Tzouvelekas, V. (2009). Parametric measurement of time-varying technical inefficiency: Results from competing models. Agricultural Economics Review, 10, 50–79.

    Google Scholar 

  37. Kisan, G., Charles, A.-F. (2014). Review, estimation and analysis of agricultural subsidies in Mongolia. Understanding the constraints to agriculture in Mongolia L. Gannal, World Bank.

  38. Kumbhakar, S. C., and Gudbrand, L. (2010). Impact of subsidies on farm productivity and efficiency. In B. V. Eldon, F. Roberto, & G. Luciano (Eds.), The economic impact of public support to agriculture: An international perspective (pp. 109–124). Springer.

    Chapter  Google Scholar 

  39. Kumbhakar, S. C., & Heshmati, A. (1995). Efficiency measurement in Swedish dairy farms: An application of rotating panel data, 1976-88. American Journal of Agricultural Economics, 77, 660–674.

  40. Kumbhakar, S. C., & Sun, K. (2013). Derivation of marginal effects of determinants of technical inefficiency. Economics Letters, 120, 249–253.

    Article  Google Scholar 

  41. Kumbhakar, S. C., Ghosh, S. & McGuckin, J. T. (1991). A generalized production frontier approach for estimating determinants of inefficiency in US dairy farms. Journal of Business and Economic Statistics, 9, 279–286.

  42. Kumbhakar, S., Tsionas, M., & Sipilainen, T. (2009). Joint estimation of technology choice and technical efficiency: An application to organic and conventional dairy farming. Journal of Productivity Analysis, 31, 151–162.

  43. Kumbhakar, S. C., Wang, H.-J., & Horncastle, A. P. (2015a). A practitioner’s guide to stochastic frontier analysis using stata. Cambridge University Press.

    Book  Google Scholar 

  44. Kumbhakar, S. C., Wang, H.-J., & Horncastle, A. P. (2015b). A practitioner’s guide to stochastic frontier. Cambridge University Press.

    Book  Google Scholar 

  45. Lachaal, L. (1994). Subsidies, endogenous technical efficiency and the measurement of productivity growth. Journal of Agriculture and Applied Economics, 1, 299–310.

    Article  Google Scholar 

  46. Lai, H.-P., & Kumbhakar, S. C. (2018). Panel data stochastic frontier model with determinants of persistent and transient inefficiency. European Journal of Operational Research, 271(2), 746–755.

    Article  Google Scholar 

  47. Lee, Y. H., & Schmidt P. (1993). A production frontier model with flexible temporal variation in technical efficiency. In H. Fried, C. A. K. Lovell & S. Schmidt (Eds.), The measurement of productive efficiency. New York: Oxford University Press.

  48. Latruffe, L., & Desjeux, Y. (2016). Common agricultural policy support, technical efficiency and productivity change in French agriculture. Review of Agricultural, Food and Environmental Studies, 97(1), 15–28.

    Article  Google Scholar 

  49. Latruffe, L., Guyomard, H., Mouël, C. (2009). The role of public subsidies on farms' managerial efficiency: An application of a five-stage approach to France. Working Paper SMART-LERECO 09.

  50. Mabe, F. N., Donkoh, S. A., & Seidu, A. .-H. (2018). Accounting for rice productivity heterogeneity in ghana: The two-step stochastic metafrontier approach. International Journal of Agricultural and Biosystems Engineering, 12, 223–232.

    Google Scholar 

  51. Meeusen, W., & Broeck, J. V. D. (1977). Technical efficiency and dimension of the firm: some results on the use of frontier production functions. Empirical Economics, 2(2), 109–122.

    Article  Google Scholar 

  52. Ministry of Food Agriculture and Light Industry. (2018). Overview of the agriculture sector in Mongolia, from. http://mofa.gov.mn/exp/blog/8/998

  53. Minviel, J. J., & Latruffe, L. (2016). Effect of public subsidies on farm technical efficiency: A Meta-analysis of empirical results. Applied Economics, 49(2), 213–226.

    Article  Google Scholar 

  54. National Statistics Office. (2007–2019). Agricultural sector overview.

  55. OECD. (2016). OECD’s producer support estimate and related indicators of agricultural support: Concept, calculations, interpretation and use (the PSE Manual).

  56. Oumer, B. (2019). Analysis of factors affecting persistent and transient inefficiency of Ethiopia’s smallholder cereal farming. In N. Pia & H. Almas (Eds.), Efficiency, equity and well-being in selected African countries (pp. 199–228). Springer.

    Google Scholar 

  57. Rasmussen, D., Frempong, C. (2017). Agricultural productivity and marketing: Mongolia World Bank Mongolia Agriculture Productivity and Marketing Study.

  58. Rizov, M., Pokrivcak, J., & Ciaian, P. (2013). CAP subsidies and productivity of the EU farms. Journal of Agricultural Economics, 64(3), 537–557.

    Article  Google Scholar 

  59. Serra, T. (2006). Effects of decoupling on the mean and variability of output. European Review of Agricultural Economics, 33(3), 269–288.

    Article  Google Scholar 

  60. Serra, T., Zilberman, D., & Gil, J. M. (2008). Farms’ technical inefficiencies in the presence of government programs. The Australian Journal of Agricultural and Resource Economics, 52, 57–76.

    Article  Google Scholar 

  61. Tang, J., Folmer, H., & Xue, J. (2015). Technical and allocative efficiency of irrigation water use in the Guanzhong Plain, China. Food Policy, 50, 43–52.

    Article  Google Scholar 

  62. Wang, H.-J., & Schmidt, P. (2002). One-step and two-step estimation of the effects of exogenous variables on technical efficiency levels. Journal of Productivity Analysis, 18(2), 129–144.

    CAS  Article  Google Scholar 

  63. Wang, H. J. (2002). "Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. Journal of Productivity Analysis, 18(3), 241–253.

    Article  Google Scholar 

  64. Wilson, P., Hadley, D., & Asby, C. (2001). The influence of management characteristics on the technical efficiency of wheat farmers in eastern England. Agricultural Economics, 24, 329–338.

    Article  Google Scholar 

  65. Zhu, X., & Alfons, O. L. (2010). Impact of CAP subsidies on technical efficiency of crop farms in Germany, the Netherlands and Sweden. Journal of Agricultural Economics, 61(3), 545–546.

    Article  Google Scholar 

  66. Zhu, X., & Lansink, A. O. (2010). Impact of CAP subsidies on technical efficiency of crop farms in Germany, the Netherlands and Sweden. Journal of Agricultural Economics, 61(3), 545–564.

    Article  Google Scholar 

Download references

Funding

Not applicable.

Author information

Affiliations

Authors

Corresponding author

Correspondence to Hua Li.

Ethics declarations

Conflict of interest

All authors declare that there is no conflict of interest.

Additional information

Publisher's Note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Ganbold, N., Fahad, S., Li, H. et al. An evaluation of subsidy policy impacts, transient and persistent technical efficiency: A case of Mongolia. Environ Dev Sustain (2021). https://doi.org/10.1007/s10668-021-01821-2

Download citation

Keywords

  • Crop farming
  • Persistent efficiency
  • Stochastic frontier analysis (SFA)
  • Time-varying efficiency
  • Subsidy policy