Search arXiv⌕ Search

arXiv · 2509.22714

Pull-Forward and Induced Vaccination Under Time-Limited Mandates: Evidence from a Low-Coercion Mandate

Abstract

Vaccine mandates featuring a deadline, i.e. time-limited, can raise uptake either by pulling forward vaccinations that would have occurred later or by inducing additional vaccinations that would not have occurred absent the mandate. This paper asks how such mandates change vaccination behaviour, how the overall effect decomposes into the pull-forward and induction components, and which features of the mandate and public-health context drive that composition. Empirically, we study a low-coercion time-limited mandate targeting graduating high-school students in Western Australia and identify its causal effects using regression discontinuity designs based on strict school-age eligibility rules, applied to population-wide administrative records on first-dose COVID-19 vaccinations. We estimate both a static RDD at the deadline and a dynamic RDD that estimates the treatment effect over time. The mandate increased short-run first-dose uptake by 9.3 percentage points (12.7%) among the targeted cohort, but the dynamic evidence shows that this effect is entirely driven by pull-forward behavior: uptake converges in the long run, implying no vaccinations were induced. Students advanced vaccination by up to 80 days. Theoretically, we develop a simple present-bias model of vaccination under deadlines. We use it to interpret the empirical patterns and to derive, among other results, conditions under which time-limited mandates are more likely to pull forward vaccinations rather than inducing them. Our findings highlight the importance of evaluating mandates beyond short-run windows and provide a framework for designing and interpreting time-limited vaccination policies. Keywords: mandate; vaccination; incentives; uptake; adolescents; timing; coverage. JEL: I12; I18.

Explore related subjects

Keep this discovery

Explore connections, maps & timelines

BibTeXRIS

Fabio I. Martinenghi, Mesfin Genie, Katie Attwell, Huong Le, Hannah Moore, Aregawi G. Gebremariam, Bette Liu, Francesco Paolucci, Christopher C. Blyth. 2026-04-08. Pull-Forward and Induced Vaccination Under Time-Limited Mandates: Evidence from a Low-Coercion Mandate. https://arxiv.org/abs/2509.22714

Cite the original work for its findings. Save a collection to share your selection of sources.

KEEP EXPLORING

Related papers

Auditing Bayesian Graph Alignment: Diagnostic Comparisons and Reference Failure

Bayesian graph alignment estimates correspondence probabilities, but convergence of an alignment-score trace need not imply accurate correspondence marginals. We audit this gap on 240 new exact graph pairs from four source families, 240 larger pairs with 20-100 vertices, and a separate 60-case exact implementation check. Under an explicit edge-flip likelihood, we compare three samplers and score, marginal, indicator, categorical, and classifier-based diagnostics. Marginal disagreement improves error discrimination over score R-hat for the exact informed sampler, but its improvement for vanilla local sampling is uncertain. Assignment-based R* and short indicator panels are competitive; no diagnostic dominates across samplers and endpoints. At larger sizes, diagnostics predict subsequent marginal changes, not posterior error, and classification performance depends on the drift threshold. Disjoint-window and held-out-chain checks attenuate but preserve positive associations. Only 22 of 240 original reference sets pass an agreement screen. On forty failure-selected cases, eightfold SMC particle escalation does not resolve disagreement, whereas additional rejuvenation helps. Longer informed runs remain unstable. An elementary feasible-alignment bound demonstrates severely unrepresentative SMC and informed-chain scores in concentrated 100-vertex cases, independently of approximate reference consensus. We also exhibit common-start chains with near-zero disagreement despite exact marginal error near .967. These results support assignment-sensitive auditing while identifying limits of finite budgets, diagnostic rankings, and reference agreement as evidence of accuracy.

stat.AP↗

GeoDose-CP: Graph-Local Conformal Inference for Continuous-Treatment Earth Observation

Reliable intervention-oriented uncertainty quantification from Earth observation (EO) remains challenging when continuous treatment shifts, spatial dependence, limited support, and satellite-outcome uncertainty must be addressed simultaneously. Existing causal, conformal, and spatial approaches address parts of this problem, but their direct combination does not generally recover the appropriate interventional reference law because candidate reassignment jointly alters treatment likelihood, standardized residuals, and graph-dependent residual likelihood. This study presents GeoDose-CP, a support-aware conformal framework for localized stochastic potential outcomes under continuous or mixed continuous-atomic treatment. Its central methodological contribution is a graph-local target-orbit law that jointly represents intervention-induced treatment shift, the inverse outcome-scale Jacobian, and spatial residual dependence. The framework further provides exact weighted candidate inversion, a scalable sparse approximation with explicit discrepancy accounting, and refusal under inadequate support. Evaluation used controlled known-truth experiments, MineDoseBench, treatment-density sensitivity analysis, external conformal comparators, and a multi-mine New South Wales (NSW) study. In MineDoseBench, GeoDose-CP achieved mean selective coverage of 0.9692 across 27 configurations and a minimum local q0.05 of 0.8951; exact-sparse auditing produced nine inclusion disagreements over 2,700 targets. In the NSW study, the absence of an auditable longitudinal rehabilitation treatment rendered treatment-dependent inference nonoperational rather than forcing inference through a proxy exposure.

stat.AP↗

Interpreting relative utility for probabilistic predictions

At a fixed threshold, relative utility (RU) measures the net-benefit gain of a prediction model over the better of treat-all and treat-none relative to the corresponding gain under perfect outcome classification. We illustrate that RU equal to 1 therefore represents perfect outcome classification at that fixed threshold, not perfect probabilistic prediction. However, even when every predicted probability equals the true probability, observed RU can equal 0. In a simple constant-risk setting, this occurs with probability approaching 1 as the sample size increases. Consequently, the distance from observed RU to 1 should not in general be interpreted as improvement achievable by a better prediction for binary probabilities.

stat.AP↗