Suddhasil Siddhanta | Searcharxiv

arXiv2026

"Unmatched" From Skewed Births to a Structural Surplus of Grooms

Data on marriage flows are not available in most developing countries, making marriage market imbalance difficult to measure. Existing measures use crude fertility rates and do not account for early-life mortality, overstating the number of births surviving to marriageable ages. This paper develops the Surplus Groom Index to quantify marriage market imbalance under monogamy using census age structure, vital registration of births and deaths, and marriage timing data. The index incorporates effective fertility-total births adjusted for under-five mortality - to reflect actual cohort progression from birth to marriageable ages. This adjustment matters in settings where child mortality shapes the supply of marriage partners. Using India's 2011 Census data, we find that eleven percent of men aged 15-54 cannot marry due to bride shortage, approximately 39 million men. Marriage imbalance is widespread rather than regionally concentrated. Punjab records the highest deficit at 33 percent, but states considered demographically progressive show substantial imbalance: Kerala 18 percent, West Bengal 14 percent, Karnataka and Tamil Nadu 11 percent each. Declining fertility has produced smaller female cohorts unable to absorb male-heavy cohorts from earlier birth years. Balanced sex ratios at birth do not ensure marriage market equilibrium once fertility declines and marriage is delayed.

econ.GN↗

arXiv2025

Marrying Up or Matching Even? Socioeconomic Drivers of Spousal Age Gaps in India

This study examines the determinants of the spousal age gap (SAG) in India, utilizing data from the 61st and 68th rounds of the National Sample Survey (NSSO). We employ regression analysis, including instrumental variables, to address selection bias and account for unobservable factors. We hypothesize an inverted U-shaped relationship between educational assortative mating and SAG, where, keeping the husband's education constant at the graduation level, the SAG first widens and then narrows as the wife's education level increases from primary to postgraduate. This pattern is shaped by distinct socio-economic factors across rural and urban settings. In rural India, increasing prosperity, changes in family structure, and educational hypergamy contribute to a wider age gap, with the influence of bride squeeze further exacerbating this disparity. Conversely, in urban areas, while the growth of white-collar jobs initially contributed to a narrowing of the SAG in 2004-05, this trend did not persist by 2011-12. Specifically, the influence of income on SAG becomes nonlinear, showing declining trends beyond the 7th income quantile, reflecting limited marriage mobility opportunities for females and hinting at a possible threat to the institution of marriage among the urban upper class. To the best of our knowledge, this is the first study to provide empirical evidence on how specific social, economic, and cultural dynamics influence the spousal age gap in Indian society. This increasing and persistent spousal age gap has significant implications for the treatment of women, power dynamics, and violence within marriage.

econ.GN↗