Technical efficiency impact of microfinance on small scale resettled sugar cane farmers in Zimbabwe

Figures & data

Table 1. Summary of technical efficiency distribution for the pooled sample

	Obs	Mean TE	Std Dev	Min TE	Max TE
Pooled	370	0.51430	0.18355	0.20281	1
Treatment	214	0.64443	0.12126	0.42335	1
Control	156	0.33577	0.06740	0.20281	0.65130
MFI Beneficiaries	110	0.63248	0.12526	0.38398	1
Bank Beneficiaries	104	0.65382	0.11968	0.43648	0.94565

Stata results from field survey 2018, bc_w (technical efficiency estimates according to Battese & Coelli, 1995; Wang, 2002).

Table 2. Technical efficiency distribution for smallholder resettled sugarcane farmers

	Technical Efficiency Range (%)	Number of DMUs	Percentage
Microfinance participants (214)	<40	14	6.54
	41 to 60	48	22.43
	61 to 80	71	33.18
	>80	81	37.85
Mean 64.44%		Minimum 38.4%	Maximum 100%
Non-participants (156)	<40	69	44.23
	41 to 60	53	33.97
	61 to 80	21	13.46
	>80	13	8.34
Mean 33.58%		Minimum 20.28%	Maximum 65.13%
MFI Participants (110)	<40	8	7.27
	41 to 60	26	23.64
	61 to 80	44	40
	>80	32	29.09
Mean 63.25%		Minimum 38.4%	Maximum 100%
Bank Participants (104)	<40	6	5.77
	41 to 60	22	21.15
	61 to 80	27	25.96
	>80	49	47.12
Mean 65.38%		Minimum 43.65%	Maximum 94.57%

Stata results from field survey 2018

Table 3. Log-likelihood tests for model

Null Hypothesis	LR Values	Critical Values	Degrees of Freedom	P-Value	Decision
Functional Form (OLS versus SFA ($H_0:\mathrm{{\rm Y}}=0$)	21.571	15.357	3	0.000***	Reject $H_0$
Functional Form (Cobb–Douglas versus Translog) ($H_0:{\beta }_{jk}=0$)	71.775	25.370	8	0.001***	Reject $H_0$

Table 4. Output elasticity of inputs (MLE^a of the translog stochastic production frontier model)

Number of observations			370
Wald chi2(20)			526.06
Prob > chi2			0.0000
Log likelihood			140.673
Variable/Regressor	Parameter	Coefficient	Std Error	z	P>|z|
Constant	${\beta }_0$	15.725	4.255	3.71	0.000***
Land	${\beta }_1$	1.126	0.599	1.88	0.005***
Labour	${\beta }_2$	1.564	0.399	3.92	0.000***
Seeds	${\beta }_3$	1.215	0.655	1.85	0.007***
Fertilizer & Chemicals	${\beta }_4$	−0.350	6.0358	−0.06	0.954
Irrigation	${\beta }_5$	0.363	0.109	3.34	0.000***
0.5 * Land^2	${\beta }_{11}$	0.064	2.525	0.03	0.980
0.5 * Labour^2	${\beta }_{22}$	−2.395	0.748	−3.20	0.001***
0.5 * Seed^2	${\beta }_{33}$	1.385	0.426	3.25	0.001***
0.5 * Fertilizer^2	${\beta }_{44}$	3.456	1.078	3.21	0.001***
0.5 * Irrigation^2	${\beta }_{55}$	−2.776	1.92	−0.838	0.053**
Land * Labour	${\beta }_{12}$	−0.253	0.984	−0.26	0.797
Land * Seed	${\beta }_{13}$	0.092	1.571	0.06	0.953
Land * Fertilizer	${\beta }_{14}$	0.765	0.284	2.69	0.007***
Land * Irrigation	${\beta }_{15}$	−0.688	1.985	−0.35	0.729
Labour * Seed	${\beta }_{23}$	1.320	0.708	1.86	0.062**
Labour * Fertilizer	${\beta }_{24}$	−2.58	0.680	−3.79	0.000***
Labour * Irrigation	${\beta }_{25}$	0.454	0.094	4.81	0.000***
Seeds * Fertilizer	${\beta }_{34}$	1.970	1.059	1.86	0.063**
Seeds * Irrigation	${\beta }_{35}$	0.297	0.083	3.58	0.003***
Fertilizer * Irrigation	${\beta }_{45}$	0.236	1.516	0.16	0.876

Source: Author’s own STATA computation from field survey 2018, Note: ***, **,* indicate significant at 1% (P < 0.01), 5% (P < 0.05), and 10% (P < 0.10) levels respectively.

^aMaximum likelihood Estimation technique.

Table 5. Determinants of technical inefficiency

Variable/Regressor	Description	Marginal musigmas	Marginal usigmas
MFI	Microfinance Participation	−0.097***	−0.053***
MF	Microcredit (amount)	−0.032***	−0.035*
SEX	Gender	0.034	−0.006
HFA	Household Farming Assets	−0.088***	−0.034*
AGE	Age	0.010	0.002**
AGE^2	Age Squared	−0.0001*	−0.0001***
Eductn 2	Primary Education of Farmer	−0.007	0.007
Eductn 3	Secondary Education	−0.061***	−0.002**
Eductn 4	Tertiary Education	−0.093***	−0.012***
HHS	Household Size	−0.008	0.001
HHSS	Household Size Squared	−.01744	−.00051
EXP	Experience of farmer	−0.015***	−0.002***
FEW	Former Estate worker	−0.022***	−0.003**
NOF	Nature of Farming	0.090	−0.008
OFY	Off Farm Income	0.040***	0.004***
EXT	Extension Visits	−0.046*	0.001***
MFA	Association Membership	−0.013	0.005
MS	Marital Status	0.0126	−0.00003

Authors’ computations from STATA for 2018 field survey data, Note: ***, **,* indicate significant at 1% (P < 0.01), 5% (P < 0.05), and 10% (P < 0.10) levels respectively.

Table

Variable/Regressor	Parameter	Coefficient	Std Err	z	P>|z|	Marginal
musigmas
Constant	${\delta }_0$	8.275	2.392	3.46	0.000***
MFI	${\delta }_1$	−0.115	0.032	−3.64	0.000***	−0.097
MF	${\delta }_2$	−0.002	0.001	−2.68	0.007***	−0.032
SEX	${\delta }_3$	0.040	0.025	1.57	0.117	0.034
HFA	${\delta }_4$	−0.098	0.026	−3.78	0.000***	−0.088
AGE	${\delta }_5$	0.079	0.074	1.06	0.289	0.010
AGE^2	${\delta }_6$	−0.0001	0.0001	−1.66	0.098*	−0.0001
Eductn 2	${\delta }_7$	−0.024	0.037	−0.661	0.588	−0.007
Eductn 3	${\delta }_8$	−0.046	0.010	−4.64	0.000***	−0.061
Eductn 4	${\delta }_9$	−0.072	0.009	−7.93	0.000***	−0.093
HHS	${\delta }_{10}$	−0.054	0.060	−0.889	0.591	−0.008
HHSS	${\delta }_{11}$	−1.682	2.05	0.82	0.412	0.002
EXP	${\delta }_{11}$	−0.023	0.005	−4.75	0.000***	−0.015
FEW	${\delta }_{12}$	−0.012	0.004	−3.07	0.001***	−0.022
NOF	${\delta }_{13}$	0.090	0.069	1.30644	0.207	0.090
OFY	${\delta }_{14}$	0.040	0.020	1.99	0.007***	0.040
EXT	${\delta }_{15}$	−0.038	0.024	−1.61	0.090*	−0.046
MFA	${\delta }_{16}$	−0.015	0.026	−0.60	0.548	−0.013
MS	${\delta }_{17}$	0.020	0.020	1.08	0.282	0.0126
usigmas
Constant	${\delta }_0$	−1.135	0.216	−2.43	0.009**
MFI	${\delta }_1$	−0.183	0.061	−2.88	0.002***	−0.053
MF	${\delta }_2$	−0.0001	0.00006	−2.00	0.045*	−0.035
SEX	${\delta }_3$	−0.268	0.239	−1.12	0.261	−0.006
HFA	${\delta }_4$	−0.532	0.238	−2.23	0.026*	−0.034
AGE	${\delta }_5$	1.258951	0.669	1.88	0.009**	0.002
AGE2	${\delta }_6$	−0.024	0.006	−3.69	0.000***	−0.00001
Eductn 2	${\delta }_7$	0.294	0.340	0.86	0.402	0.007
Eductn 3	${\delta }_8$	−0.041	0.018	−2.32	0.018**	−0.002
Eductn 4	${\delta }_9$	−0.362	0.091	−3.97	0.000***	−0.012
HHS	${\delta }_{10}$	0.317	0.335	0.95	0.342	0.001
HHSS	${\delta }_{11}$	0.371	0.2948	−1.34	0.379	.00051
EXP	${\delta }_{11}$	−0.344	0.195	−3.51	0.000***	−0.002
FEW	${\delta }_{12}$	−0.178	0.086	−2.06	0.040**	−0.003
NOF	${\delta }_{13}$	−0.128	0.277	−0.46	0.649	−0.008
OFY	${\delta }_{14}$	0.109	0.029	3.77	0.000***	0.004
EXT	${\delta }_{15}$	−0.350	0.121	−2.88	0.005***	0.001
MFA	${\delta }_{16}$	0.203	0.257	0.72	0.471	0.005
MS	${\delta }_{17}$	0.061	0.105	0.58	0.561	−0.00003
Vsigmas		−32	9.795	−3.27	0.001***

Battese, G. E., & Coelli, T. J. (1995). A model for technical inefficiency effects in a stochastic frontier production function for panel data. Empirical Economics, 20(2), 325–332. https://doi.org/10.1007/BF01205442

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Wang, H.-J. (2002). Heteroscedasticity and non-monotonic efficiency effects of a stochastic frontier model. Journal of Productivity Analysis, 18(3), 241–253. https://doi.org/10.1023/A:1020638827640

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