Background: The National Family Health Survey (NFHS) is a vital source of demographic and health statistics in India, yet concerns remain about the accuracy of self-reported fertility data, particularly the number of living children. Discrepancies in reporting can arise from recall bias, social desirability, and proxy reporting, potentially distorting fertility and health indicators. Methods: Using NFHS-IV (2015–16) and NFHS-V (2019–21), this study analyzed women who were both household heads and eligible for the women’s questionnaire. Data from household and women’s files were merged to compare the number of living children reported by household heads and individual women. Matched and unmatched cases were categorized, and discrepancies were examined across age, residence, education, religion, caste, and wealth index. Logistic regression was used to identify predictors of mismatches, while spatial autocorrelation (Moran’s I and LISA cluster analysis) was applied to detect geographic patterns of reporting inconsistencies. Results: In NFHS-IV, 65.3% of reports matched, compared to 63.2% in NFHS-V, with mismatches increasing with women’s age. Women aged 40 and above had over 20 times higher odds of mismatch compared to those under 29. Rural women consistently showed higher odds of discrepancies than urban women (OR = 1.27 in NFHS-IV; OR = 1.32 in NFHS-V). Education was a strong protective factor: women with higher education had 63–64% lower odds of mismatch compared to those with no education. Wealthier women reported more accurately, while religion and caste showed only modest differences. Spatial analysis revealed clusters of high mismatches in central and southern states, while districts in the Northeast and Jammu & Kashmir displayed strong consistency. Conclusion: Reporting discrepancies in NFHS fertility data are strongly associated with age, education, residence, and wealth. Older, less educated, rural, and poorer women are particularly vulnerable to misreporting. These findings underscore the need for targeted survey improvements, enhanced enumerator training, simplified tools, and validation mechanisms to strengthen the reliability of fertility data and ensure more equitable representation across demographic groups.
Gupta et al. (Wed,) studied this question.