Son preference in India is not disappearing; it is shifting to earlier births. What appears to be a weakening of bias is, in fact, a change in when it operates.
Among mothers with one child, the gap in not desiring additional children – between those with a son and those without – has more than doubled between 2002–04 and 2019–21, from 5.1 percent to 12.1 percent. Today, families are far more likely to stop having children after a first-born son than they were two decades ago.
At higher parities, the pattern reverses. Among mothers with three or more children, the gap in not wanting additional children – between those with a son and those without – has fallen from 39.2 percent to 25.0 percent.
Son preference has not declined; it now appears earlier in the birth order, shifting from the third or higher order to the first. Once spread across higher order births, it is now most evident at the first birth – less visible in aggregate data, but more decisive from the outset.
Why Son Preference Is Evident at the First Birth
This shift is rooted in how families respond to declining fertility. When families were larger, son preference was expressed through continuation – couples would keep having children until they reached their desired number of sons.
However, that logic becomes harder to sustain at lower parities. As fertility falls, the margin for waiting for a son disappears. Consequently, the pressure to have a son intensifies at the first or second birth.
Fertility decline has not resolved gender bias, rather it has compressed it. This trend creates a hidden prejudice where the pressure for a son is heightened despite aggregate data suggesting a reduction in bias.
Why Son Preference Is Geographically Uneven
The shift in son preference is geographically uneven. State averages can obscure large differences within states.
Among mothers with one child, the gap between those with a son and those without – measured by whether they do not want additional children – ranges across districts from around –11 percent (indicating no son preference) to over 46 percent (strong preference). This variation exists not only across states, but also within: two districts within the same state can exhibit very different patterns of bias.
These differences are not random, but spatially dependent. Neighbouring districts often exhibit similar behaviours, creating clusters of both high and low son preference. Notably, clusters of high son preference can even extend across state borders. This shows that son preference is a locally embedded phenomenon, shaped by the sociocultural environment in which families make decisions.
These local patterns align with broader regional differences. Districts across parts of northern, western, and central India continue to show high – and in some cases expanding – levels of son preference, especially at lower parities. In contrast, much of the Southern and the Northeastern India consistently exhibit lower son preference.
These regional disparities are rooted in social structures. While areas with rigid patriarchal norms and low female autonomy maintain a strong son preference, regions with higher levels of women’s education and agency show much more balanced outcomes.
The Policy Mismatch: Why Current Interventions Are Failing
This uneven geography of son preference highlights a fundamental flaw in policy, as interventions remain uniformly focused on state-level campaigns and aggregate indicators. This approach fails to address the localised reality of bias, which has shifted to earlier birth orders and specific local contexts.
This mismatch operates along three dimensions.
First, scale. By focusing on state averages, interventions overlook local pockets where son preference remains entrenched, even within otherwise improving states.
Second, stage. By tracking outcomes such as sex ratios at birth, policy responds only after bias has already translated into demographic imbalance, rather than at the point where decisions are made.
Third, signal. Aggregate indicators mask behavioural shifts, making it harder to detect how son preference is adapting within smaller families.
In effect, policy is still responding to an earlier pattern of son preference – one that was spread across multiple births and visible at aggregate levels.
Rethinking Son Preference Policy: Are We Intervening Too Late
Closing this gap requires shifting policy focus to the earliest stages of family formation, particularly targeting the first birth where son preference is now concentrated.
Effective, tailored local interventions must replace broad state-level strategies, utilising behavioural indicators such as fertility intentions to address bias before it impacts demographic outcomes.
Interventions must match the scale of the problem. High-preference clusters persist across neighbouring districts, showing that state-level strategies alone are often too broad to be effective. Policies need to be tailored to local contexts, and coordinated across districts, even across state borders, where families share similar social environments.
Policy must also respond sooner, not later. Tracking fertility decisions – such as whether families plan to stop having children after the birth of a son – offers an early warning signal for son preference, far more actionable than waiting for shifts in aggregate sex ratios. Acting on these signals allows interventions to prevent imbalances before they become entrenched.
The Road Ahead
India’s fertility transition is not eliminating gender bias; it is changing how that bias is expressed. As family size becomes small son preference is no longer spread across multiple births but concentrated at the outset. This makes it less visible in aggregate data, but more decisive in shaping outcomes.
This shift also changes how we understand gender inequalities. The challenge is no longer only to reduce bias, but to track how it adapts to evolving demographic and social conditions.
Policy has yet to catch up. Programmes such as “Beti Bachao Beti Padhao” and “Sukanya Samriddhi Yojana” continue to operate at broader scales and later stages, even as the underlying behaviour has moved earlier and become more localised.
Son preference now operates early, locally, and through behaviour. Policy must respond accordingly, adapting to where and how bias actually shapes family decisions.