CREDIT CONSTRAINTS AND RURAL FARMERS WELFARE IN AN AGRARIAN ECONOMY

Subject Area: Education / Adult Learning / Economatric

Modified: 29th August 2025

Credit constraints limit rural farmers’ access to essential financial resources, hindering agricultural productivity and overall welfare. This study explores the impact of these constraints in agrarian economies, focusing on factors like age, land ownership, interest rates, and family size that affect access to formal credit. Through various econometric models, we aim to understand how these factors influence farmers’ credit application and their welfare.

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Table Of Content

Referencing Tools

S. No

1 Farmer characteristics

2 Association between credit constraints and credit application status

3 Ordinary Least Square (OLS)

6

4 Logistic regression

7

5 Heckman Selection model

7

6 Endogenous switching regression model

7 Table 1: Farmer characteristics

	Frequency (n)		Percentage (%)
Gender
Female	2		0.3
Male	573		99.7
Marital status
Married	559		97.2
Unmarried	16		2.8
Educational level
No	62		10.8
Primary	105		18.3
Middle School	120		20.9
High School	127		22.1
College	70		12.2
University	91		15.8
Farming
No	4		0.7
Yes	571		99.3
Job
No	457		79.5
Yes	118		20.5
Dairy
No	559		97.2
Yes	16		2.8
Rented/Own
Rented	3		0.5
Own	545		94.8
Lease	5		0.9
Farming	22		3.8
Migrated
Yes	9		1.6
No	566		98.4
Farming Experience
Yes	575		100.0
If yes, no. of years
<=11	196		34.1
12-20	262		45.6
>=21	117		20.3
Religion
Muslim	575		100.0
Family health status
Healthy	572		99.5
Unhealthy	3		0.5
Livestock holding
Yes	166		28.9
No	409		71.1
	Mean	Maximum	Minimum
Family members	10.45	39	3
People over sixty years	1.45	4	1

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Table 2: Association between credit constraints and credit application status

Number of credit constrained households	Credit application status		Total
	Household who applied for formal credit	Household who did not applied for formal credit
	n(%)
No	153 (59.1)	136 (43.0)	289 (50.3)
Yes	106 (40.9)	180 (57.0)	286 (49.7)
Total	259 (100.0)	316 (100.0)	575 (100.0)

Chi-square value: 14.640, p-value: 0.000<0.05

Table 3 Ordinary Least Square (OLS)

Credcont2	Coefficient	SE	R-squared	t-value	p-value
age	0.000	0.001	0.923	0.840	0.401
landownn1	-0.007	0.028		-0.240	0.814
lnintrate	-0.326	0.004		-73.670	0.000**
lntotarea	-0.022	0.013		-1.680	0.093
famsize2	-0.003	0.013		-0.220	0.826
accesset	0.062	0.018		3.400	0.001**
lnagrinc	0.019	0.010		1.950	0.052*
Dadu	-0.095	0.021		-4.570	0.000**
Shikarpur	-0.090	0.021		-4.190	0.000**
Jacobabad	-0.087	0.021		-4.170	0.000**
Nawabshah	-0.073	0.021		-3.450	0.001**
Sanghar	-0.070	0.022		-3.180	0.002**
Constant	0.785	0.106		7.390	0.000

SE- Standard Error, **p<0.01, *p<0.05

Table 4 Logistic regression

credcont2	Coefficient	SE	Pseudo R²	z-value	p-value
age	0.030	0.028	0.902	1.080	0.282
landownn1	-0.353	1.227		-0.290	0.773
lnintrate	-3.527	0.450		-7.830	0.000**
lntotarea	-0.985	0.579		-1.700	0.089
famsize2	-0.372	0.760		-0.490	0.624
accesset	3.737	2.757		1.360	0.175
lnagrinc	0.824	0.407		2.020	0.043*
Larkana	2.600	0.771		3.370	0.001**
Jacobabad	-0.848	1.465		-0.580	0.562
Shikarpur	-1.023	1.415		-0.730	0.468
Nawabshah	-0.995	1.597		-0.620	0.533
Constant	-7.199	5.420		-1.330	0.184

SE- Standard Error, **p<0.01, *p<0.05

Table 5 Heckman Selection model

	Coefficient	SE	z-value	p-value
credcont2
age	0.002	0.001	2.640	0.008**
landownn1	-0.199	0.049	-4.090	0.000**
lnintrate	-0.334	0.007	-47.570	0.000**
lntotarea	0.048	0.014	3.410	0.001**
famsize2	0.074	0.020	3.680	0.000**
accesset	0.070	0.030	2.330	0.020*
Dadu	-0.246	0.034	-7.310	0.000**
Shikarpur	-0.170	0.033	-5.170	0.000**
Jacobabad	-0.108	0.031	-3.490	0.000**
Nawabshah	-0.152	0.032	-4.760	0.000**
Sanghar	-0.174	0.034	-5.160	0.000**
Constant	0.757	0.058	13.120	0.000
Consumption
age	0.011	0.005	2.400	0.016*
landownn1	-0.406	0.253	-1.600	0.109
lnintrate	0.119	0.038	3.170	0.002**
lntotarea	0.274	0.095	2.880	0.004**
famsize2	0.419	0.106	3.940	0.000**
accesset	0.381	0.158	2.410	0.016*
lnagrinc	-0.016	0.025	-0.640	0.521
lnprdcst	0.005	0.058	0.090	0.931
Dadu	-0.625	0.175	-3.580	0.000**
Shikarpur	-0.887	0.176	-5.040	0.000**
Jacobabad	-0.543	0.166	-3.270	0.001**
Nawabshah	-0.801	0.171	-4.690	0.000**
Sanghar	-0.907	0.179	-5.060	0.000**
Constant	-1.008	0.748	-1.350	0.178
/athrho	3.492	0.243	14.360	0.000**
/lnsigma	-1.647	0.043	-38.130	0.000**
rho	0.998	0.001
sigma	0.193	0.008
lambda	0.192	0.008
LR test of indep.eqns. : Chi²=263.76 Prob>Chi² = 0.000

SE- Standard Error, **p<0.01, *p<0.05

Table 6 Endogenous switching regression model

	Coefficient	SE	z-value	p-value
credcont2_1
age	0.002	0.001	2.520	0.012**
landownn1	-0.203	0.058	-3.480	0.001**
lnintrate	-0.334	0.007	-45.360	0.000**
lntotarea	0.044	0.014	3.130	0.002**
famsize2	0.068	0.021	3.200	0.001**
accesset	0.068	0.029	2.340	0.020*
Dadu	-0.247	0.037	-6.690	0.000**
Shikarpur	-0.165	0.032	-5.130	0.000**
Jacobabad	-0.108	0.030	-3.560	0.000**
Nawabshah	-0.146	0.031	-4.640	0.000**
Sanghar	-0.167	0.034	-4.970	0.000**
constant	0.777	0.055	14.010	0.000
credcont2_0
age	0.001	0.001	1.430	0.152
landownn1	0.049	0.024	2.030	0.043*
lnintrate	-0.335	0.004	-75.970	0.000**
lntotarea	-0.001	0.009	-0.080	0.936
famsize2	-0.002	0.015	-0.140	0.887
accesset	0.045	0.017	2.620	0.009**
Dadu	-0.052	0.022	-2.400	0.016*
Shikarpur	-0.033	0.024	-1.380	0.168
Jacobabad	-0.041	0.024	-1.740	0.082
Nawabshah	-0.011	0.023	-0.470	0.637
Sanghar	-0.009	0.023	-0.390	0.696
constant	0.943	0.035	26.930	0.000
Consumption
age	0.012	0.005	2.580	0.010**
landownn1	-0.260	0.289	-0.900	0.368
lnintrate	0.124	0.043	2.890	0.004**
lntotarea	0.234	0.077	3.050	0.002**
famsize2	0.362	0.106	3.410	0.001**
accesset	0.371	0.160	2.330	0.020*
Dadu	-0.568	0.180	-3.150	0.002**
Shikarpur	-0.878	0.181	-4.850	0.000**
Jacobabad	-0.549	0.165	-3.320	0.001**
Nawabshah	-0.778	0.167	-4.650	0.000**
Sanghar	-0.892	0.175	-5.100	0.000**
lnprdcst	-0.002	0.030	-0.070	0.944
constant	-1.117	0.436	-2.560	0.010
/lns1	-1.670	0.064	-25.910	0.000**
/lns2	-2.347	0.042	-56.250	0.000**
/r1	3.911	0.191	20.480	0.000**
/r2	0.052	0.140	0.370	0.711
sigma_1	0.188	0.012
sigma_2	0.096	0.004
rho_1	0.999	0.000
rho_2	0.052	0.140
LR test of indep.eqns. : Chi²=262.98 Prob>Chi² = 0.000

SE- Standard Error, **p<0.01, *p<0.05

Conclusion:

The study reveals that credit constraints significantly affect rural farmers’ ability to access formal credit, with factors like interest rates, land ownership, and regional location being key determinants. Credit-constrained households are less likely to apply for formal credit, impacting their welfare and agricultural investment. The findings suggest that policies aimed at reducing interest rates, improving collateral access, and enhancing financial literacy can alleviate these constraints, boosting rural farmers’ welfare and contributing to broader economic development.