Evaluating the Impact of Institutional Credit on Sustainable Income and Livelihoods in the Dairy Farming Sector: Evidence from Tamil Nadu, India

borrowing	not borrowing
Borrower	(a) E(Y_1i \| A_i = 1)	(c) E(Y_2i \| A_i = 1)	TT
Non-borrower	(d) E(Y_1i \| A_i = 0)	(b) E(Y_2i \| A_i = 0)	TU
Base heterogeneity	(e) BH¹	(f) BH²	TH

[i] (a) and (b) – represents observed expected outcome indicators; (c) and (d) – represents counterfactual expected outcome indicators; A_i = 1 if dairy farmers have taken out institutional credit; A_i = 0 if farmers have not taken out institutional credit; Y_1i – outcome indicators if dairy farmers have taken out institutional credit; Y_2i – outcome indicators if farmers have not taken out institutional credit; TT – the effect of the treatment on the treated (borrowers); TU – the effect of the treatment on the untreated (non-borrowers); BH_i – the effect of base heterogeneity for dairy farmers who have taken out institutional credit (i = 1), and farmers who have not taken it out (i = 2); TH = TT – TU (that is, transitional heterogeneity).

[ii] Source: own elaboration.

Table 2.

Descriptive statistics of borrower and non-borrower dairy households

Variable	Borrowers (Mean)	SD	Non-borrowers (Mean)	SD
1	2	3	4	5
Age (years)	59.89	5.05	55.38	4.99
Education level	3.67	1.30	3.63	1.47
Farming experience (years)	13.21	4.01	10.48	3.69
Annual income (₹ lakh)	3.23	0.79	4.48	1.49
Household size	4.82	0.87	4.85	1.02
Total landholding (ha)	1.13	0.65	1.27	0.58
Distance to nearest bank (km)	5.89	1.83	7.64	1.90
Membership in dairy cooperatives	0.78	0.33	0.69	0.37
Number of machineries used	4.13	0.78	2.89	1.14
Area under fodder cultivation (ha)	0.38	0.22	0.42	0.19
Area under dairy farm (ha)	0.57	0.32	0.64	0.29
Livestock insurance coverage	0.72	0.45	0.44	0.50
Farm income (₹/animal/day)	103.02	10.17	78.56	10.17
Milk productivity (L/animal/day)	8.68	1.30	7.83	1.30

[i] ₹ denotes Indian Rupees.

[ii] Source: own elaboration.

Table 3.

Impact of credit on farm income of farmers

Variables	Borrower = 1	Non-borrower = 0	Borrower =1 Non-borrower = 0
Variables	Farm income per day (per animal)		Selection
Dependent variable
Age of respondent	−0.098 (0.933)	0.175 (0.328)	0.107*** (0.033)
Education level	43.807*** (2.484)	20.853*** (2.195)	−0.089 (0.153)
Annual income (₹)	2.870*** (0.985)	0.498* (0.286)	−0.281** (0.112)
Number of machineries	7.418** (1.594)	2.546** (3.347)	0.291*** (0.100)
Farming experience	7.557*** (0.947)	−0.221 (0.411)	−0.067** (0.043)
Household size	3.067 (2.844)	2.833* (1.543)	0.017 (0.142)
Land holding (ha)	0.001 (0.001)	0.001*** (0.001)	0.001** (0.001)
Instrumental variables
Distance to nearest bank branch			−4.539*** (1.193)
Membership in dairy cooperatives			1.637*** (0.446)
Sigma 1	20.408*** (1.694)
Sigma 2		12.405*** (1.089)
Rho 1	0.254 (0.415)
Rho 2		0.898*** (0.098)
Constant	259.396*** (49.965)	369.405*** (23.294)	−1.727 (1.681)
Observations	100	100	200

[i] Robust standard errors in parentheses.

[ii] *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels.

[iii] Source: own elaboration.

Table 4.

Treatment and heterogeneity effect

Variables	Decision		Treatment effect	Percent change
Variables	Borrowing	Not borrowing	Treatment effect	Percent change
Borrower	103.02 (a)	94.30 (c)	ATT = 8.72	9.24
Non-borrower	85.42 (d)	78.56 (b)	ATU = 6.86	8.73
Base heterogeneity	BH = 17.60	BH = 15.74	TH = 1.86

[i] Source: own elaboration.

Table 5.

Impact of credit on employment generation in dairy farms

Variables	Borrower = 1	Non-borrower = 0	Borrower = 1 Non-borrower = 0
Variables	Mandays per animal		Selection
Dependent variable = Mandays per animal
Age of respondent	0.002*** (0.001)	0.001*** (0.001)	−0.001 (0.001)
Household size	0.002*** (0.002)	0.007*** (0.005)	−0.156 (0.208)
Farming experience	0.021*** (0.001)	0.005*** (0.002)	−0.223* (0.082)
No of machineries	−0.001* (0.001)	−0.014*** (0.004)	0.631*** (0.166)
Annual income (₹)	0.001 (0.001)	0.008*** (0.003)	0.002*** (0.001)
Area under fodder cultivation (ha)	0.002** (0.005)	0.001 (0.000)	0.549*** (0.154)
Area under dairy farm (ha)	−0.007 (0.048)	0.058** (0.048)	15.673*** (2.803)
Instrumental variables
Livestock insurance coverage (0/1)			0.322*** (0.047)
Membership in dairy cooperatives (0/1)			0.185** (0.152)
Sigma 1	0.015*** (0.001)
Sigma 2		0.035*** (0.002)
Rho 1	−0.631** (0.206)
Rho 2		−0.836 (0.234)
Constant	−0.132*** (0.022)	−0.051 (0.035)	−0.737 (1.517)
Observations	100	100	200

[i] Robust standard errors in parentheses.

[ii] *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels.

[iii] Source: own elaboration.

Table 6.

Treatment and heterogeneity effect

Borrowing status	Decision		Treatment effect	Percent change
Borrowing status	Borrowing	Not borrowing	Treatment effect	Percent change
Borrower	0.182 (a)	0.132 (c)	ATT = 0.050	37.87
Non-borrower	0.172 (d)	0.129 (b)	ATU = 0.043	33.33
Base heterogeneity	BH = 0.010	BH = 0.003	TH = 0.007

[i] Source: own elaboration.

Table 7.

Impact of credit on farm mechanization of dairy farmers in Tamil Nadu using endogenous switching regression

Variables	Borrower = 1	Non-borrower = 0	Borrower = 1 Non-borrower =0
Variables	No of machineries		Selection
Dependent variable = Number of machineries
Age	−0.059* (0.034)	0.005 (0.006)	0.222 (0.069)
Gender	−0.040 (0.123)	−0.123 (0.093)	−0.637 (0.580)
Education level	0.021 (0.095)	0.025 (0.027)	0.820* (0.279)
Annual Income (₹)	0.891*** (0.158)	1.181** (0.049)	2.591*** (0.535)
Total Landholding (ha)	0.239** (0.117)	0.095** (0.038)	−1.694** (0.398)
Household size	0.004** (0.013)	−0.017 (0.008)	0.269* (0.126)
Farming experience	−3.236 (1.430)	0.916* (0.509)	14.989*** (3.653)
Instrumental variables
Membership in farmer cooperatives			0.195** (0.056)
Distance to nearest bank branch			−0.004*** (0.004)
Sigma 1	0.424*** (0.030)
Sigma 2		0.278*** (0.019)
Rho 1	−0.031 (0.486)
Rho 2		0.724 (0.471)
Constant	3.132 (2.515)	−1.669** (0.417)	−23.490** (6.503)
Observations	100	100	200

[i] Robust standard errors in parentheses.

[ii] *, **, and *** indicate statistical significance at the p < 0.1, p < 0.05, and p < 0.01 levels.

[iii] Source: own elaboration.

Table 8.

Treatment and heterogeneity effect

Variables	Decision		Treatment effect	Percent change
Variables	Borrowing	Not borrowing	Treatment effect	Percent change
Borrower	4.13 (a)	3.30 (c)	ATT = 0.83	25.15
Non-Borrower	3.33 (d)	2.89 (b)	ATU = 0.44	15.22
Base heterogeneity	BH = 0.80	BH = 0.41	TH = 0.39

[i] Source: own elaboration.

References

Authors

Metrics

Articles in this issue

DOI: https://doi.org/10.17306/j.jard.2026.3.00026r1 | Journal eISSN: 1899-5772 | Journal ISSN: 1899-5241

Journal RSS Feed

Language: English

Page range: 298 - 312

Accepted on: Aug 13, 2026

Published on: Sep 30, 2026

Published by: Poznań University of Life Science

In partnership with: Paradigm Publishing Services

Publication frequency: 4 issues per year

Keywords:

dairy sector,

institutional credit,

endogenous switching regression,

farm income,

employment

Related subjects:

Business and economics,

Political economics,

Political economics, other,

Business management,

Business management, other,

Life sciences,

Life sciences, other,

Social sciences,

Political science,

Political science, other

© 2026 Raj K Nithin, Ajay Verma, Biswajit Sen, Ajmer Singh, Anil Kumar Dixit, Gunjan Bhandari, published by Poznań University of Life Science
This work is licensed under the Creative Commons Attribution 4.0 License.

Volume 81 (2026): Issue 3 (September 2026)