The research, conducted by Pintu Paul of the Tata Institute of Social Sciences in Hyderabad, exploits two waves of the Understanding the Lives of Adolescents and Young Adults survey, known as UDAYA, a large-scale longitudinal study carried out in Uttar Pradesh and Bihar. The analytical sample pooled across the two survey waves comprised 12,924 girls aged between 10 and 23 years. Because the same girls were followed over time, the data allow researchers to distinguish between two very different kinds of statistical explanations: differences between girls who attend schools with and without toilets, and changes within the same girl as her school sanitation circumstances shift between survey waves. This distinction, central to modern causal inference in observational social science, turns out to be the crux of the entire study.

The mental health outcomes were captured through poor mental health scores, and the author applied a battery of regression techniques to interrogate the association from multiple angles. Pooled ordinary least squares models, which treat all observations as a single dataset, showed that girls in schools lacking toilet facilities had significantly higher poor mental health scores. Between-effects models, which focus on stable differences between individuals, and mixed-effects models, which combine within- and between-person information, produced the same pattern. Across these specifications, the association survived adjustment for sociodemographic characteristics, indicating that the link between absent sanitation and poorer mental health is not simply an artifact of poverty, age, or family background—at least as far as the measured covariates can capture those constructs.

Then comes the twist. When the author estimated an individual fixed-effects model—a specification that effectively compares each girl with herself across the two survey waves, sweeping out all time-invariant personal and household characteristics—the significant association disappeared. In other words, girls whose school sanitation situation changed over time did not show corresponding changes in their mental health scores. The fixed-effects result suggests that the observed relationship is driven primarily by differences between girls rather than by within-individual changes in school toilet availability. Statistically, this is a crucial diagnostic: it implies that the girls attending toilet-less schools are, on average, different in ways that both their schooling circumstances and their mental health reflect, rather than the toilets themselves exerting a direct, dynamic effect on any individual girl’s psychological state.

What kind of differences might these be? The author’s interpretation, offered in the study’s conclusions, is that the absence of school toilet facilities functions as a marker of broader educational and social disadvantage. A school without toilets is rarely a school that is otherwise well resourced; it signals underinvestment in infrastructure, weaker institutional support, and communities whose educational environments are stretched thin. Girls attending such schools may also come from households and neighborhoods facing compounding disadvantages that the regression covariates only partially capture. The toilet, in this reading, is not the poison but the symptom—a readily observable indicator that a girl’s learning environment, and the social context surrounding it, is failing her in multiple ways at once.

The subgroup analyses add texture to the headline finding. The association between missing school toilets and poorer mental health was more pronounced among girls residing in urban areas, among girls from Uttar Pradesh as opposed to Bihar, and among married girls, particularly those married early. The pattern was especially evident during the second survey wave. These heterogeneities hint at how context conditions the experience of inadequate sanitation. Urban schools, for instance, might be expected to meet basic infrastructure standards, making their absence particularly telling; married girls, meanwhile, may face layered constraints on mobility, privacy, and autonomy that make deficient school environments more consequential. The study reports these subgroup patterns as descriptive evidence, and they underscore that a one-size-fits-all sanitation intervention is unlikely to reach all girls equally.

Robustness was tested through sensitivity analyses based on a balanced panel—restricting the sample to girls observed in both survey waves—which reproduced the pattern seen in the main findings. This matters because longitudinal surveys inevitably lose participants between waves, and attrition can distort estimates if those who drop out differ systematically from those who remain. The consistency of results across the balanced panel strengthens confidence that the reported associations are not artifacts of sample composition. Together, the convergence of pooled, between-effects, and mixed-effects estimates, the null fixed-effects result, and the stable sensitivity checks form a coherent statistical picture that is more informative than any single coefficient could be.

The study also looked beyond mental health to two downstream outcomes in girls’ lives: continued school enrolment and subsequent marriage. Supplementary findings demonstrated that access to school toilet facilities was associated with higher odds of continued school enrolment, reinforcing a substantial body of evidence linking sex-specific sanitation to girls’ educational persistence. The mechanism is intuitive—adolescent girls managing menstruation without private, functional facilities face practical and dignity-related barriers to attending school, and many respond by staying home or leaving altogether. By contrast, no significant association was observed between school toilet access and subsequent marriage once sociodemographic characteristics were adjusted for, suggesting that the pathway from sanitation to marriage timing, if it exists, is entangled with the same background factors that drive both.

For policymakers, the implications cut in two directions simultaneously. On one hand, the persistent association between toilet-less schools and poorer mental health, alongside the enrolment findings, argues for prioritizing school sanitation as part of any serious effort to improve girls’ educational participation and psychological well-being. Toilets are cheap relative to most educational interventions, and their absence is a solvable engineering problem. On the other hand, the fixed-effects results caution against expecting sanitation alone to transform mental health outcomes. If missing toilets are a marker of broader disadvantage, then installing them in isolation, without addressing the surrounding deficits in school infrastructure, staffing, and supportive learning environments, may yield disappointing returns. The author’s conclusion is accordingly measured: strengthening school sanitation should be prioritized alongside broader investments in school infrastructure and supportive learning environments.

The study also carries methodological lessons for the growing field of research on water, sanitation, and adolescent health. Much of the literature in this area rests on cross-sectional data, where the direction of causality is hopelessly entangled and confounding is rampant. By deploying longitudinal data and explicitly comparing between-person and within-person estimates, this study models a standard of transparency that other researchers would do well to emulate—and it demonstrates that the answer to a seemingly simple question can be genuinely two-sided. The association is real and policy-relevant, yet its causal interpretation demands humility. For the 12,924 girls of Uttar Pradesh and Bihar whose experiences underpin these findings, the message is ultimately one of visibility: the state of a school toilet block, mundane as it seems, is legible in the statistical record of their minds, and attending to it means attending to the whole architecture of disadvantage in which it stands.

Subject of Research: The association between school toilet availability and mental health outcomes among adolescent and young adult girls in Uttar Pradesh and Bihar, India

Article Title: Inadequate school sanitation and mental health outcomes among adolescent and young adult girls in Uttar Pradesh and Bihar, India

Article References: Paul, P. (2026). Inadequate school sanitation and mental health outcomes among adolescent and young adult girls in Uttar Pradesh and Bihar, India. Humanities and Social Sciences Communications. https://doi.org/10.1057/s41599-026-09323-0

Image Credits: AI Generated

DOI: 10.1057/s41599-026-09323-0

Keywords: school sanitation, adolescent mental health, girls' education, Uttar Pradesh, Bihar, UDAYA survey, longitudinal study, fixed-effects models, school toilets, India, educational disadvantage, public health

The study was motivated by a practical question with high stakes. Policymakers across the world rely on measures of socio-economic status, or SES, for two main purposes. First, they use them to decide which schools should receive additional resources aimed at levelling the playing field for students from poor backgrounds. Second, researchers use them to understand how family and school circumstances shape achievement, and to monitor whether equity reforms are working. If the underlying measures are flawed, both the flow of funding and the scientific conclusions built on them are compromised.

The research team, led by Lorraine Gilleece of the Educational Research Centre in Dublin, together with Anastasios Karakolidis and Aidan Clerkin, compared four sets of indicators. Two came from international large-scale assessments: the 2019 Trends in International Mathematics and Science Study, or TIMSS, which tested Grade 8 students, and the 2022 Programme for International Student Assessment, or PISA, which tested 15-year-olds. From each assessment, the researchers extracted a school-average SES index, aggregated from individual student responses, together with principals’ own estimates of the percentage of their students coming from disadvantaged homes. These were set against two national indicators: the Pobal HP Deprivation Index, a continuous measure derived from census data on small residential areas, and a binary flag for whether a school participates in Ireland’s Delivering Equality of Opportunity in Schools programme, known as DEIS, which directs extra resources to schools with high concentrations of disadvantage.

Technically, the analyses were extensive. The team computed correlations, ran linear and logistic regressions, and fitted two-level multilevel models using survey weights and plausible values for achievement, in line with the statistical protocols of the International Association for the Evaluation of Educational Achievement and the OECD. The TIMSS sample covered 4,118 students in 149 schools, while the PISA 2022 sample included 5,569 students in 170 Irish schools. The HP index, built from census indicators such as educational attainment, social class composition, unemployment rates and demographic vitality, was matched to schools using student home addresses, meaning school-average values were based on almost the entire enrolled cohort rather than only the assessed students.

The convergent validity results were emphatic. Correlations between the international and national indicators were large by conventional statistical standards, with coefficients of roughly 0.7 among indicators derived from aggregated student-level data, such as school-mean SES and school-mean HP scores, and around 0.6 among inherently school-level indicators such as principals’ reports and DEIS status. When both TIMSS-derived indicators were entered together in a regression predicting the school-mean HP index, they explained 55 percent of its variance; the equivalent PISA model explained 50 percent. DEIS schools were also shown to differ markedly from non-DEIS schools on every international indicator, with large effect sizes throughout.

One of the most reassuring findings concerned school principals. Because principals’ estimates of the share of disadvantaged students rest on impressions that cannot be directly verified, they have long been treated with suspicion. Yet in this study, principals’ judgements aligned well with both the census-based HP index and the aggregated student-level SES indices. In the logistic regression predicting DEIS status, schools whose principals reported that more than half of their students came from disadvantaged homes had 65 times higher odds of DEIS categorisation than schools reporting up to a quarter, a pattern the authors read as reflecting the policy’s deliberate targeting of schools with the highest concentrations of disadvantage. Principals, the study concludes, appear to know their schools remarkably well.

The picture changed when the researchers turned to criterion-related validity, asking how well each family of indicators predicted mathematics achievement. The international indicators were consistently stronger. Together, school-mean SES and the principal-reported share of disadvantaged students explained 16 percent of the variance in TIMSS mathematics scores and 9 percent in PISA. The national indicators on their own explained less, at 11 percent for TIMSS and 6 percent for PISA. Crucially, in a combined model, only the ILSA-derived school-mean SES remained a statistically significant predictor; adding the national indicators to the international ones barely improved predictive power at all. Multilevel models told a consistent story, with the final predictor sets explaining 87 percent of between-school variance in TIMSS and 76 percent in PISA, despite relatively modest intraclass correlations of 21 percent and 11 percent respectively.

The authors are careful to note that the national indicators should not be dismissed as redundant simply because they add little explanatory power once international measures are controlled for. Loss of statistical significance in a combined model does not mean two variables measure the same construct, and the national indicators each capture unique variance consistent with their different conceptual origins. The PISA ESCS index reflects a gradient approach to SES, combining parental occupation, parental education and home possessions; the TIMSS Home Educational Resources scale draws on books in the home, study supports and parental education; the HP index is an area-level census composite; and DEIS status is an administrative designation shaped by policy history. Each is a different lens on the same underlying phenomenon.

Nevertheless, the findings carry a clear practical message for governments: wherever feasible, SES data should be collected at the student level rather than relying solely on school-level designations. Indicators aggregated from individual data tracked each other more tightly and predicted achievement more powerfully than inherently school-level labels. This matters because higher-level designations can blur over time, as Ireland’s own experience with successive waves of DEIS designation illustrates, complicating efforts to monitor outcomes. The lesson also echoes international research: in Sweden, register-based SES explained less variance in mathematics achievement than TIMSS measures and added almost nothing beyond them, while French work on TIMSS 2023 found the national Social Position Index correlated less strongly with performance than the international home resources indices.

For a field increasingly dependent on large-scale assessment data to guide equity policy, this study provides a methodological template. By linking TIMSS and PISA data to national administrative records, countries can audit their own indicators of disadvantage and decide, on evidence rather than habit, which measures deserve a place in funding formulae and research. Given that the OECD has urged Ireland to refine and validate the indicators underpinning its resource allocation model, and that similar debates over free school meals, Universal Credit eligibility and register-based measures are playing out in the United Kingdom, the Netherlands and Belgium, the Irish findings arrive at an opportune moment. Principals’ perceptions, census indices and international survey composites all tell substantially the same story about which schools serve disadvantaged communities. But if the goal is to understand and predict how socio-economic context shapes learning, the richest measures remain those built from the experiences of students themselves.

Beyond its immediate policy relevance, the study speaks to a long-standing methodological puzzle in comparative education research: the fact that no single operational definition of socio-economic status exists. Because SES is a latent construct, every measure — whether built from survey questionnaires, census small-area statistics, or administrative designations — is an imperfect proxy. This is precisely why validity evidence matters more than any one indicator’s apparent sophistication. A census-based index can be statistically elegant yet blunt at the level of individual schools, while a survey composite can capture family circumstances that area-level averages smooth over.

The choice of mathematics achievement as the criterion variable is also worth noting. Mathematics performance is often regarded as particularly sensitive to family resources, since out-of-school support — private tutoring, study materials, parental familiarity with the curriculum — tends to track household advantage more closely than in some other subjects. This may partly explain why the home-resources-based international measures showed such consistent predictive strength across both TIMSS and PISA cohorts in the study.

The multilevel modelling approach adopted by the researchers also deserves attention from an international audience. Because achievement data in assessments such as TIMSS and PISA are nested within schools, analysing them with single-level regression risks overstating the precision of school-level effects. By explicitly modelling between-school variance and reporting intraclass correlations alongside explained variance at each level, the study offers a transparent account of how much of the socio-economic gradient operates between rather than within schools. The relatively high proportion of between-school variance explained suggests that school-level SES indicators, whatever their construction, capture a substantial share of the structural differences among institutions.

Finally, the study’s timing is significant. As education systems grapple with rising data expectations, integrating international assessment data with national administrative records remains rare in practice, often blocked by technical or legal barriers rather than analytical ones. The Irish example demonstrates that such linkage is feasible and can be undertaken with existing public-use and administrative datasets, lowering the threshold for other countries to subject their own equity instruments to comparable empirical scrutiny.

Subject of Research: Comparing the validity of school socio-economic status measures from TIMSS and PISA against Irish national administrative indicators and their power to predict mathematics achievement

Article Title: The validity of school socio-economic status measures: how do indicators from TIMSS and PISA compare to national administrative data sources?

Article References: Gilleece, L., Karakolidis, A., & Clerkin, A. (2026). The validity of school socio-economic status measures: how do indicators from TIMSS and PISA compare to national administrative data sources?. Large-scale Assessments in Education, 14(1), Article 45. https://doi.org/10.1186/s40536-026-00305-x

Image Credits: AI Generated

DOI: 10.1186/s40536-026-00305-x

Keywords: socio-economic status, TIMSS, PISA, educational disadvantage, Ireland, DEIS, Pobal HP Deprivation Index, mathematics achievement, validity, large-scale assessments, education policy, administrative data