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Notes: The values in parenthesis specify standard errors Significant degree at ***p< 0.01; **p< 0.05, and *p< 0.10 Table 3 Parameter estimates of inefficiency effects model

Table 3 Notes: The values in parenthesis specify standard errors Significant degree at ***p< 0.01; **p< 0.05, and *p< 0.10 Table 3 Parameter estimates of inefficiency effects model

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Responsible Editor: Nicholas Apergis
Responsible Editor: Nicholas Apergis
Fig. 1 Theoretical model of credit constraints rice growers (CCRG) and credit non-constraints rice growers (CNRG)
Fig. 1 Theoretical model of credit constraints rice growers (CCRG) and credit non-constraints rice growers (CNRG)
non-negative random variables allied with the inefficiency model for rice growers in production with a mean (u) vari- ance (0, ) (which is | N (u, o | ), as well as treated as a half- normal distribution (Kumbhakar and Lovell 2000). The var- normal distribution (Kumbhakar and Lovell 2000). The var
non-negative random variables allied with the inefficiency model for rice growers in production with a mean (u) vari- ance (0, ) (which is | N (u, o | ), as well as treated as a half- normal distribution (Kumbhakar and Lovell 2000). The var- normal distribution (Kumbhakar and Lovell 2000). The var
Table 1 Maximum likelihood estimates of production frontier Notes: The values in parenthesis specify standard errors. Significant level at ***p< 0.01; **p< 0.05, and *p: Variance parameters Credit unconstrained: log likelihood = 538, Wald 7 = 1426, \ = ¢,,/0, = 1.08, 0” =0.0013 Credit constrained: log likelihood = 619, Wald ,? = 561, \ = 0,,/o, = 0.112, 07 = 0.0017 Full sample: log likelihood = 946, Wald - = 1081, A =40,,/c, =0.008, o” = 0.0009
Table 1 Maximum likelihood estimates of production frontier Notes: The values in parenthesis specify standard errors. Significant level at ***p< 0.01; **p< 0.05, and *p: Variance parameters Credit unconstrained: log likelihood = 538, Wald 7 = 1426, \ = ¢,,/0, = 1.08, 0” =0.0013 Credit constrained: log likelihood = 619, Wald ,? = 561, \ = 0,,/o, = 0.112, 07 = 0.0017 Full sample: log likelihood = 946, Wald - = 1081, A =40,,/c, =0.008, o” = 0.0009
Table 2 Efficiency estimates of credit constrained, credit non-constrained, and full sample size Source: author’ calculation significant, which indicates the success of Bangladesh’s agri- cultural development corporation (Afrad et al. 2019). Similarly, the wing of traction facilities and the availability of compost for livestock agriculture in rural Bangladesh have greatly improved farmers’ productivity and helped minimize overall production costs. The coefficient value of livestock is significant and positive for CNRG, pointing out that those who have livestock facilities are typically more efficient than other farmers. results concur with Musaba and Bwacha’s (2014) analytical work. The coefficient of own irrigation system for CCRG is significant at 10% probability level, with a positive impact on rice producers’ technical efficiency, while for CNRG, it is vice versa and insignificant. The presence of severe electricity is- sues in rural areas of Bangladesh is always a challenge, and diesel and petrol prices are extremely costly. As a result, most farmers in rural areas rely on external irrigation sources. Therefore, logically, we should not conclude that the CNRG community ofrice growers with their irrigation system are less efficient than those who did not.
Table 2 Efficiency estimates of credit constrained, credit non-constrained, and full sample size Source: author’ calculation significant, which indicates the success of Bangladesh’s agri- cultural development corporation (Afrad et al. 2019). Similarly, the wing of traction facilities and the availability of compost for livestock agriculture in rural Bangladesh have greatly improved farmers’ productivity and helped minimize overall production costs. The coefficient value of livestock is significant and positive for CNRG, pointing out that those who have livestock facilities are typically more efficient than other farmers. results concur with Musaba and Bwacha’s (2014) analytical work. The coefficient of own irrigation system for CCRG is significant at 10% probability level, with a positive impact on rice producers’ technical efficiency, while for CNRG, it is vice versa and insignificant. The presence of severe electricity is- sues in rural areas of Bangladesh is always a challenge, and diesel and petrol prices are extremely costly. As a result, most farmers in rural areas rely on external irrigation sources. Therefore, logically, we should not conclude that the CNRG community ofrice growers with their irrigation system are less efficient than those who did not.
Table 4 Descriptive statistics of output and inputs variables used in SFA analysis Notes: Standard deviations are presented in parentheses Significant degree at ***p < 0.01; **p < 0.05; and *p < 0.10 1 US$ = 85 BDT when the study was conducted Source: Authors’ field survey in 2018 Notes: Standard deviations are presented in parentheses
Table 4 Descriptive statistics of output and inputs variables used in SFA analysis Notes: Standard deviations are presented in parentheses Significant degree at ***p < 0.01; **p < 0.05; and *p < 0.10 1 US$ = 85 BDT when the study was conducted Source: Authors’ field survey in 2018 Notes: Standard deviations are presented in parentheses
Table 5 Descriptive statistics of explanatory variables used in the inefficiency effects model Notes: Standard deviations are specified in parentheses Significant level at *p< 0.10, **p< 0.05, and ***p< 0.01 1 US$ = 85 BDT when the study was conducted Source: Authors’ field survey in 2018
Table 5 Descriptive statistics of explanatory variables used in the inefficiency effects model Notes: Standard deviations are specified in parentheses Significant level at *p< 0.10, **p< 0.05, and ***p< 0.01 1 US$ = 85 BDT when the study was conducted Source: Authors’ field survey in 2018
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