Display options
Share it on

Environ Sci Pollut Res Int. 2022 Jan;29(1):444-456. doi: 10.1007/s11356-021-15458-1. Epub 2021 Jul 31.

Do credit constraints affect the technical efficiency of Boro rice growers? Evidence from the District Pabna in Bangladesh.

Environmental science and pollution research international

Md Ghulam Rabbany, Yasir Mehmood, Fazlul Hoque, Tanwne Sarker, Kh Zulfikar Hossain, Arshad Ahmad Khan, Mohammad Shakhawat Hossain, Rana Roy, Jianchao Luo

Affiliations

  1. College of Economics and Management, Northwest A&F University, Yangling, 712100, Shaanxi, China.
  2. Department of Agribusiness and Marketing, Faculty of Agribusiness Management, Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka, 1207, Bangladesh.
  3. Department of Social and Behavioral Sciences, National University of Medical Sciences, Rawalpindi, Pakistan.
  4. School of Economics and Finance, Xi'an Jiaotong University, Xi'an, 71004, China.
  5. Department of Agricultural Extension and Information System, Faculty of Agriculture, Sher-e-Bangla Agricultural University, Sher-e-Bangla Nagar, Dhaka, 1207, Bangladesh.
  6. World Vision Bangladesh, BleNGS Project, Jamalpur, 2000, Bangladesh.
  7. Department of Agroforestry and Environmental Science, Sylhet Agricultural University, Sylhet, 3100, Bangladesh.
  8. College of Economics and Management, Northwest A&F University, Yangling, 712100, Shaanxi, China. [email protected].

PMID: 34333750 DOI: 10.1007/s11356-021-15458-1

Abstract

This study analyzes the effects of credit constraints on technical efficiency of Boro rice growers in the district of Pabna in Bangladesh. Using a simple random sampling technique, the data was collected from 570 Boro rice growers from the Pabna district of Bangladesh. Before conducting a field survey, a theoretical model was designed to identify credit-constrained and non-constrained rice growers. We have analyzed the collected data in two phases: first, we investigated the technical efficiency of Boro rice growers using the stochastic frontier model (SFA); and second, we used an inefficiency effect model to estimate the influence of credit constraints on technical efficiency. Findings indicate that credit-constrained rice growers (CCRG) are 6.7% less technically efficient than credit non-constrained rice growers (CNRG). Findings further indicate that the education level of the household head, family size, certified seed, sowing time, access to extension services, off-farm income, and household savings have significant effects on the technical efficiency of both groups of rice growers. Furthermore, credit size has a significantly positive impact, whereas the interest rate imposed on the principal amount has a significantly negative impact.

© 2021. The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.

Keywords: Bangladesh; Credit constraints; Financial institution; Rice growers; Technical efficiency

References

  1. Afrad SI, Wadud F, Babu SC (2019) Reforms in agricultural extension service system in Bangladesh. In: Agricultural Extension Reforms in South Asia, pp 13–40. https://doi.org/10.1016/b978-0-12-818752-4.00002-3 - PubMed
  2. Afrin S, Haider MZ, Islam MS (2017) Impact of financial inclusion on technical efficiency of paddy farmers in Bangladesh. Agric Financ Rev 77:484–505. https://doi.org/10.1108/AFR-06-2016-0058 - PubMed
  3. Ahmed Z, Guha GS, Shew AM, Alam GMM (2021) Climate change risk perceptions and agricultural adaptation strategies in vulnerable riverine char islands of Bangladesh. Land Use Policy 103:1–10. https://doi.org/10.1016/j.landusepol.2021.105295 - PubMed
  4. Aigner D, Lovell CAK, Schmidt P (1977) Formulation and estimation of stochastic frontier production function models. J Econ 6:21–37. https://doi.org/10.1016/0304-4076(77)90052-5 - PubMed
  5. Amanullah, Lakhan GR, Channa SA et al (2020) Credit constraints and rural farmers’ welfare in an agrarian economy. Heliyon 6:e05252. https://doi.org/10.1016/j.heliyon.2020.e05252 - PubMed
  6. Arshad M, Amjath-Babu TS, Kächele H, Müller K (2016) What drives the willingness to pay for crop insurance against extreme weather events (flood and drought) in Pakistan? A hypothetical market approach. Clim Dev 8:234–244. https://doi.org/10.1080/17565529.2015.1034232 - PubMed
  7. Arshad M, Amjath-Babu TS, Krupnik TJ, Aravindakshan S, Abbas A, Kächele H, Müller K (2017a) Climate variability and yield risk in South Asia’s rice–wheat systems: emerging evidence from Pakistan. Paddy Water Environ 15:249–261. https://doi.org/10.1007/s10333-016-0544-0 - PubMed
  8. Arshad M, Kächele H, Krupnik TJ, Amjath-Babu TS, Aravindakshan S, Abbas A, Mehmood Y, Müller K (2017b) Climate variability, farmland value, and farmers’ perceptions of climate change: implications for adaptation in rural Pakistan. Int J Sustain Dev World Ecol 24:532–544. https://doi.org/10.1080/13504509.2016.1254689 - PubMed
  9. Attipoe SG, Jianmin C, Opoku-Kwanowaa Y, Ohene-Sefa F (2020) The determinants of technical efficiency of cocoa production in Ghana: an analysis of the role of rural and community banks. Sustain Prod Consum 23:11–20. https://doi.org/10.1016/j.spc.2020.04.001 - PubMed
  10. Ayaz S, Hussain Z, Sial MH (2010) Role of credit on production efficiency of farming sector in Pakistan (A Data Envelopment Analysis). World Acad Sci Eng Technol 42:1028–1033 - PubMed
  11. Balcombe K, Fraser I, Rahman M, Smith L (2007) Examining the technical efficiency of rice producers in Bangladesh. J Int Dev 19:1–16. https://doi.org/10.1002/jid.1284 - PubMed
  12. Bashir MK, Mehmood Y (2010) Institutional credit and rice productivity: A case study of District Lahore, Pakistan. China Agric Econ Rev 2:412–419. https://doi.org/10.1108/17561371011097722 - PubMed
  13. Battese GE, Coelli TJ (1995) A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empir Econ 20:325–332. https://doi.org/10.1007/BF01205442 - PubMed
  14. BER (2019) Bangladesh economic review. Ministry of Finance. Government of the People’s Republic of Bangladesh, Dhaka 1–358 - PubMed
  15. Beyhaghi M, Firoozi F, Jalilvand A, Samarbakhsh L (2020) Components of credit rationing. J Financ Stab 50:1–14. https://doi.org/10.1016/j.jfs.2020.100762 - PubMed
  16. Bhattacharya M, Inekwe JN, Valenzuela MR (2020) Credit risk and financial integration: an application of network analysis. Int Rev Financ Anal 72:1–14. https://doi.org/10.1016/j.irfa.2020.101588 - PubMed
  17. Bhattacharyya A, Mandal R (2016) A generalized stochastic production frontier analysis of technical efficiency of rice farming: a case study from Assam, India. Indian Growth Dev Rev 9:114–128. https://doi.org/10.1108/IGDR-10-2015-0041 - PubMed
  18. Bibi Z, Khan D, Haq I u (2020) Technical and environmental efficiency of agriculture sector in South Asia: a stochastic frontier analysis approach. Environ Dev Sustain 23:9260–9279. https://doi.org/10.1007/s10668-020-01023-2 - PubMed
  19. Bidisha SH, Hossain MA, Alam R, Hasan MM (2018) Credit, tenancy choice and agricultural efficiency: evidences from the northern region of Bangladesh. Econ Anal Policy 57:22–32. https://doi.org/10.1016/j.eap.2017.10.001 - PubMed
  20. Bond EW, Tybout J, Utar H (2015) Credit rationing, risk aversion, and industrial evolution in developing countries. Int Econ Rev (Philadelphia) 56:695–722. https://doi.org/10.1111/iere.12119 - PubMed
  21. Boucher SR, Carter MR, Guirkinger C (2008) Risk rationing and wealth effects in credit markets: theory and implications for agricultural development. Am J Agric Econ 90:409–423. https://doi.org/10.1111/j.1467-8276.2007.01116.x - PubMed
  22. Cabrera VE, Solís D, del Corral J (2010) Determinants of technical efficiency among dairy farms in Wisconsin. J Dairy Sci 93:387–393. https://doi.org/10.3168/jds.2009-2307 - PubMed
  23. Cao S, Leung D (2020) Credit constraints and productivity of SMEs: evidence from Canada. Econ Model 88:163–180. https://doi.org/10.1016/j.econmod.2019.09.018 - PubMed
  24. Carrer MJ, Maia AG, de Mello Brandão Vinholis M, de Souza Filho HM (2020) Assessing the effectiveness of rural credit policy on the adoption of integrated crop-livestock systems in Brazil. Land Use Policy 92:1–10. https://doi.org/10.1016/j.landusepol.2020.104468 - PubMed
  25. Chandio AA, Jiang Y, Gessesse AT, Dunya R (2019) The nexus of agricultural credit, farm size and technical efficiency in Sindh, Pakistan: a stochastic production frontier approach. J Saudi Soc Agric Sci 18:348–354. https://doi.org/10.1016/j.jssas.2017.11.001 - PubMed
  26. Charnes A, Cooper WW, Rhodes E (1978) Measuring the efficiency of decision making units. Eur J Oper Res 2:429–444. https://doi.org/10.1016/0377-2217(78)90138-8 - PubMed
  27. Chiu LJV, Khantachavana SV, Turvey CG (2014) Risk rationing and the demand for agricultural credit: a comparative investigation of Mexico and China. Agric Financ Rev 74:248–270. https://doi.org/10.1108/AFR-05-2014-0011 - PubMed
  28. Das S, Munshi M, Kabir W, Biswas J (2017) Intervention of ICTs in rice production in Bangladesh: an impact study. Bangladesh Rice J 20:67–72. https://doi.org/10.3329/brj.v20i2.34130 - PubMed
  29. Diana F, Guirkinger C, Boucher S (2010) Risk, credit constraints and financial efficiency in Peruvian agriculture. J Dev Stud 46:981–1002. https://doi.org/10.1080/00220380903104974 - PubMed
  30. Dong F, Lu J, Featherstone AM (2012) Effects of credit constraints on household productivity in rural China. Agric Financ Rev 72:402–415. https://doi.org/10.1108/00021461211277259 - PubMed
  31. Drehmann M, Sorensen S, Stringa M (2010) The integrated impact of credit and interest rate risk on banks: a dynamic framework and stress testing application. J Bank Financ 34:713–729. https://doi.org/10.1016/j.jbankfin.2009.06.009 - PubMed
  32. Duong PB, Thanh PT (2019) Adoption and effects of modern rice varieties in Vietnam: micro-econometric analysis of household surveys. Econ Anal Policy 64:282–292. https://doi.org/10.1016/j.eap.2019.09.006 - PubMed
  33. Ekinci MF, Omay T (2020) Current account and credit growth: the role of household credit and financial depth. North Am J Econ Financ 54:101244. https://doi.org/10.1016/j.najef.2020.101244 - PubMed
  34. Elahi E, Abid M, Zhang L, ul Haq S, Sahito JGM (2018) Agricultural advisory and financial services; farm level access, outreach and impact in a mixed cropping district of Punjab, Pakistan. Land Use Policy 71:249–260. https://doi.org/10.1016/j.landusepol.2017.12.006 - PubMed
  35. Escobal J (2001) The determinants of nonfarm income diversification in rural Peru. World Dev 29:497–508. https://doi.org/10.1016/S0305-750X(00)00104-2 - PubMed
  36. FAO (2012) Food and agricultural commodities production from http://www.fao.org/faostat/en/#data/QC . - PubMed
  37. Fatemi M, Atefatdoost A (2020) The alternative model to predict adoption behavior of agricultural technologies. J Saudi Soc Agric Sci 19:383–390. https://doi.org/10.1016/j.jssas.2020.04.003 - PubMed
  38. Galema R (2020) Credit rationing in P2P lending to SMEs: do lender-borrower relationships matter? J Corp Finan 65:101742. https://doi.org/10.1016/j.jcorpfin.2020.101742 - PubMed
  39. Haryanto T, Talib BA, Salleh NHM (2016) Technical efficiency and technology gap in Indonesian rice farming. Agris On-line Pap Econ Inform 08:29–38. https://doi.org/10.7160/aol.2016.080303 - PubMed
  40. Hasnain MN, Hossain ME, Islam MK et al (2016) Determinants of technical efficiency of rice farms in northcentral and north-western regions in Bangladesh. J Dev Areas 45:73–94. https://doi.org/10.1016/j.wdp.2017.12.001 - PubMed
  41. Heriqbaldi U, Purwono R, Haryanto T, Primanthi MR (2015) An analysis of technical efficiency of rice production in Indonesia. Asian Soc Sci 11:91–102. https://doi.org/10.5539/ass.v11n3p91 - PubMed
  42. Hossain MK, Kamil AA, Baten MA, Mustafa A (2012) Stochastic frontier approach and data envelopment analysis to total factor productivity and efficiency measurement of Bangladeshi rice. PLoS One 7:1–9. https://doi.org/10.1371/journal.pone.0046081 - PubMed
  43. Jalilov S, Mainuddin M (2019) Efficiency in the rice farming: evidence from Northwest Bangladesh. Agriculture 9:1–14. https://doi.org/10.3390/agriculture9110245 - PubMed
  44. Jana J (2015) Prague economic papers / online first money market equilibrium. Prague Econ Pap 25:321–334. https://doi.org/10.18267/j.pep.564 - PubMed
  45. Jin M, Zhao S, Kumbhakar SC (2019) Financial constraints and firm productivity: evidence from Chinese manufacturing. Eur J Oper Res 275:1139–1156. https://doi.org/10.1016/j.ejor.2018.12.010 - PubMed
  46. Kabir MJ, Cramb R, Alauddin M, Gaydon DS (2019) Farmers’ perceptions and management of risk in rice-based farming systems of south-west coastal Bangladesh. Land Use Policy 86:177–188. https://doi.org/10.1016/j.landusepol.2019.04.040 - PubMed
  47. Kabir J, Cramb R, Alauddin M, Gaydon DS, Roth CH (2020) Farmers’ perceptions and management of risk in rice/shrimp farming systems in South-West Coastal Bangladesh. Land Use Policy 95:104577. https://doi.org/10.1016/j.landusepol.2020.104577 - PubMed
  48. Kattel RR, Regmi PP, Sharma MD, Thapa YB (2020) Factors affecting adoption of improved method in large cardamom curing and drying and its impact on household income in the Eastern Himalayan road-corridor of Nepal. Technol Soc 63:1–13. https://doi.org/10.1016/j.techsoc.2020.101384 - PubMed
  49. Kjenstad EC, Su X, Zhang L (2015) Credit rationing by loan size: a synthesized model. Q Rev Econ Financ 55:20–27. https://doi.org/10.1016/j.qref.2014.08.001 - PubMed
  50. Koirala KH, Mishra AK, Mohanty S (2013) Determinants of rice productivity and technical efficiency in the Philippines. South Agric Econ Assoc Annu Meet 1:1–15. https://doi.org/10.13140/2.1.3275.1360 - PubMed
  51. Komicha H, Öhlmer B (2008) Effect Of credit constraint on production efficiency of farm households in Southeastern Ethiopia. Ethiop J Econ 15:2–32. https://doi.org/10.4314/eje.v15i1.39816 - PubMed
  52. Kumar A, Takeshima H, Thapa G, Adhikari N, Saroj S, Karkee M, Joshi PK (2020) Adoption and diffusion of improved technologies and production practices in agriculture: Insights from a donor-led intervention in Nepal. Land Use Policy 95:104621. https://doi.org/10.1016/j.landusepol.2020.104621 - PubMed
  53. Kumbhakar SC, Lovell CAK (2000) Stochastic frontier analysis. Cambridge University Press. https://doi.org/10.1017/cbo9781139174411 - PubMed
  54. Li C, Lin L, Gan CEC (2016) China credit constraints and rural households’ consumption expenditure. Financ Res Lett 19:158–164. https://doi.org/10.1016/j.frl.2016.07.007 - PubMed
  55. Li YA, Liao W, Zhao CC (2018) Credit constraints and firm productivity: microeconomic evidence from China. Res Int Bus Financ 45:134–149. https://doi.org/10.1016/j.ribaf.2017.07.142 - PubMed
  56. Li W, Clark B, Taylor JA, Kendall H, Jones G, Li Z, Jin S, Zhao C, Yang G, Shuai C, Cheng X, Chen J, Yang H, Frewer LJ (2020) A hybrid modelling approach to understanding adoption of precision agriculture technologies in Chinese cropping systems. Comput Electron Agric 172:105305. https://doi.org/10.1016/j.compag.2020.105305 - PubMed
  57. Lin L, Wang W, Gan C, Nguyen QTT (2019) Credit constraints on farm household welfare in rural China: evidence from Fujian Province. Sustain 11:1–19. https://doi.org/10.3390/su11113221 - PubMed
  58. Long LK, Van Thap L, Hoai NT (2020) An application of data envelopment analysis with the double bootstrapping technique to analyze cost and technical efficiency in aquaculture: do credit constraints matter? Aquaculture 525:735290. https://doi.org/10.1016/j.aquaculture.2020.735290 - PubMed
  59. Ma S, Wu X, Gan L (2019) Credit accessibility, institutional deficiency and entrepreneurship in China. China Econ Rev 54:160–175. https://doi.org/10.1016/j.chieco.2018.10.015 - PubMed
  60. Mallick D (2012) Microfinance and moneylender interest rate: evidence from Bangladesh. World Dev 40:1181–1189. https://doi.org/10.1016/j.worlddev.2011.12.011 - PubMed
  61. Mariyono J (2018) Productivity growth of Indonesian rice production: sources and efforts to improve performance. Int J Product Perform Manag 67:1792–1815. https://doi.org/10.1108/IJPPM-10-2017-0265 - PubMed
  62. Mehmood Y, Rong K, Arshad M, Bashir MK (2017) Doliquidity constraints influence the technical efficiency of wheat growers? Evidence from Punjab, Pakistan. J Anim Plant Sci 27:667–679 - PubMed
  63. Mehmood Y, Rong K, Bashir MK, Arshad M (2018) Does partial quantity rationing of credit affect the technical efficiency of dairy farmers in Punjab, Pakistan?: An application of stochastic frontier analysis. Br Food J 120:441–451. https://doi.org/10.1108/BFJ-03-2017-0162 - PubMed
  64. Min SHI, Paudel KP, Feng-bo C (2020) Mechanization and efficiency in rice production in China. J Integr Agric 19:2–15. https://doi.org/10.1016/S2095-3119(20)63439-6 - PubMed
  65. Musaba E, Bwacha I (2014) Technical efficiency of small scale maize production in Masaiti District, Zambia: A Stochastic Frontier Approach. J Econ Sustain Dev 5:104–111 - PubMed
  66. Okoruwa VO, Abass AB, Akin-Olagunju OA, Akinola NA (2020) Does institution type affect access to finance for cassava actors in Nigeria? J Agric Food Res 2:1–8. https://doi.org/10.1016/j.jafr.2020.100023 - PubMed
  67. Rana MMP, Moniruzzaman M (2021) Transformative adaptation in agriculture: a case of agroforestation in Bangladesh. Environ Challenges 2:1–11. https://doi.org/10.1016/j.envc.2021.100026 - PubMed
  68. Reardon T, Taylor JE, Stamoulis K, Lanjouw P, Balisacan A (2000) Effects of non-farm employment on rural income inequality in developing countries: an investment perspective. J Agric Econ 51:266–288. https://doi.org/10.1111/j.1477-9552.2000.tb01228.x - PubMed
  69. Roy R, Chan NW, Rainis R (2014) Rice farming sustainability assessment in Bangladesh. Sustain Sci 9:31–44. https://doi.org/10.1007/s11625-013-0234-4 - PubMed
  70. Sarkar A, Abdul J, Al A et al (2021) Structural equation modeling for indicators of sustainable agriculture : prospective of a developing country’s agriculture. Land Use Policy 109:1–12. https://doi.org/10.1016/j.landusepol.2021.105638 - PubMed
  71. Shew AM, Durand-Morat A, Putman B, Nalley LL, Ghosh A (2019) Rice intensification in Bangladesh improves economic and environmental welfare. Environ Sci Policy 95:46–57. https://doi.org/10.1016/j.envsci.2019.02.004 - PubMed
  72. Tipi T, Yildiz N, Nargeleçekenler M, Çetin B (2009) Measuring the technical efficiency and determinants of efficiency of rice (Oryza sativa) farms in marmara region, Turkey. New Zeal J Crop Hortic Sci 37:121–129. https://doi.org/10.1080/01140670909510257 - PubMed
  73. Tuihedur Rahman HM, Robinson BE, Ford JD, Hickey GM (2018) How do capital asset interactions affect livelihood sensitivity to climatic stresses? Insights from the northeastern floodplains of Bangladesh. Ecol Econ 150:165–176. https://doi.org/10.1016/j.ecolecon.2018.04.006 - PubMed
  74. von Cramon-Taubadel S, Saldias R (2014) Access to credit and determinants of technical inefficiency of specialized smallholder farmers in chile. Chil J Agric Res 74:413–420. https://doi.org/10.4067/S0718-58392014000400006 - PubMed
  75. Wang J, Etienne X, Ma Y (2020) Deregulation, technical efficiency and production risk in rice farming: evidence from Zhejiang Province, China. China Agric Econ Rev 12:605–622. https://doi.org/10.1108/CAER-11-2019-0197 - PubMed
  76. Zhao J, Barry JP (2014) Effects of credit constraints on rural household technical efficiency. China Agric Econ Rev 6:654–668. https://doi.org/10.1108/caer-10-2012-0115 - PubMed

Publication Types