Can microcredit help improve the health of poor women? Some findings from a cross-sectional study in Kerala, India

Table 6 Models for exposure to health risks. Binomial logistic regression: odds ratios with 95% confidence intervals and goodness of fit statistics^a

Dependent variable: exposed to health risk (yes, n = 206, no, n = 722)	M0	M1	M2
*Socioeconomic characteristics and caste*
Education (ref = high school+) Primary		1.24 [0.80–1.93]	1.27 [0.82–1.97]
No education		1.43 [0.92–2.23]	1.50 [0.96–2.35]
Employment (ref = not engaged) Engaged		0.96 [0.66–1.39]	0.95 [0.65–1.37]
Landholdings (ref = more than 50 cents) 50 cents or less		1.46 [0.96–2.21]	1.45 [0.95–2.20]
Caste of head (ref = forward) OBC		0.67 [0.43–1.05]	0.67 [0.43–1.05]
SC/ST		0.72 [0.46–1.14]	0.72 [0.45–1.14]
*SHG participation*
SHG (ref = not member) Early joiner (> 2 years)	1.27 [0.81–1.97]		1.35 [0.84–2.18]
Late joiner (< = 2 years)	0.90 [0.64–1.27]		0.99 [0.69–1.42]
Chi square (df) -2 log likelihood	2.3(2) 980.2	51.6(9)*** 930.9	53.6(11)*** 928.9
Deviation		4.0(2)

^a Notes: Models are adjusted for age and women's household position. Results in bold are statistically significant, *p < 0.05, **p < 0.01, ***p < 0.001
OBC = Other Backward Caste, SC/ST = Scheduled Caste/Scheduled Tribe.

ISSN: 1475-9276