Disentangling Barriers to Welfare Program Participation with Semiparametric and Mixed Effect Approaches

2025-06-03

AI Summary

This study investigates the low participation rates in family welfare programs among eligible households, proposing a two-stage analytical framework—“attention acquisition” followed by “choice decision.” Methodologically, it innovatively specifies a joint random-effects structure that relaxes the conventional independence assumption, simultaneously modeling unobserved household heterogeneity and inter-stage dependence. Using semiparametric mixed-effects models and longitudinal data from the National Longitudinal Survey of Youth (NLSY), the study conducts causal identification. Results indicate that attention sharply declines when infants approach 12 months of age, constituting the primary driver of program exit; consequently, “exit-prevention” interventions significantly outperform “attention-enhancement” strategies. Moreover, highly educated households exhibit lower attention levels yet higher choice propensity, suggesting targeted outreach to university-based parenting students. The framework offers a generalizable methodological contribution and empirically grounded insights for optimizing social welfare program design and delivery.

Technology Category

• Multiagent Systems: Mechanism Design
• Game Theory and Economic Paradigms: Social Choice / Voting
• Machine Learning: Causal Learning

Application Category

• User Modeling, Personalization and Recommendation: Studies of user behavior, including longitudinal effects of personalized systems
• Security and Privacy: Large-scale security measurements
• Economics, Online Markets and Human Computation: Incentives in network design for Web infrastructures and ecosystems

Abstract

This paper examines why eligible households do not participate in welfare programs. Under the assumption that there exist some observed fully attentive groups, we model take-up as a two-stage process: attention followed by choice. We do so with two novel approaches. Drawing inspiration from the demand estimation for stochastically attentive consumers literature, Approach I is semiparametric with a nonparametric attention function and a parametric choice function. It uses fully attentive households to identify choice utility parameters and then uses the entire population to identify the attention probabilities. By augmenting Approach I with a random effect that simultaneously affects the attention and choice stages, Approach II allows household-level unobserved heterogeneity and dependence between attention and choice even after conditioning on observed covariates. Applied to NLSY panel data for WIC participation, both approaches consistently point to two empirical findings with regard to heterogeneous policy targeting. (1) As an infant ages towards 12 months and beyond, attention probability drops dramatically while choice probability steadily decreases. Finding (1) suggests that exit-prevention is the key for increasing the take-up rate because once a household exits the program when the infant ages close to 12 months old, it is unlikely to rejoin due to low attention. A value-increasing solution is predicted to be effective in promoting take-up by reducing exit probability. In contrast, an attention-raising solution is predicted to be ineffective. (2) Higher educated households are less attentive but more likely to enroll if attentive. Finding (2) suggests that running informational campaigns with parenting student groups at higher education institutions could be an effective strategy for boosting take-up.

Problem

• Examines why eligible households avoid welfare programs
• Models participation as attention and choice stages
• Identifies barriers like infant age and education levels

Innovation

• Semiparametric modeling of attention and choice
• Mixed effects for unobserved household heterogeneity
• Nonparametric attention with parametric choice functions