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arxiv: 2509.22714 · v3 · submitted 2025-09-24 · 📊 stat.AP

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

Fabio I. Martinenghi , Mesfin Genie , Katie Attwell , Huong Le , Hannah Moore , Aregawi G. Gebremariam , Bette Liu , Francesco Paolucci
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Christopher C. Blyth
This is my paper

Pith reviewed 2026-05-18 14:46 UTC · model grok-4.3

classification 📊 stat.AP
keywords vaccine mandatepull-forward vaccinationinduced vaccinationregression discontinuityadolescentsCOVID-19vaccination timingcoverage
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The pith

A time-limited low-coercion vaccine mandate raised short-term uptake only by advancing vaccinations that would have happened anyway.

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

The paper asks whether time-limited vaccine mandates raise overall coverage or simply shift the timing of shots people would get later. It studies a low-coercion deadline for graduating high-school students in Western Australia and uses regression discontinuity designs on full population records to separate the two channels. Short-run uptake rose by 9.3 percentage points, yet the groups converged over the longer term, showing the entire gain came from students moving their vaccinations forward by as much as 80 days. The authors supply a present-bias model that predicts when deadlines produce timing shifts rather than new vaccinations.

Core claim

The mandate increased short-run first-dose uptake by 9.3 percentage points (12.7 percent) among the targeted cohort. Dynamic evidence from regression discontinuity designs shows 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. A present-bias model interprets the patterns and derives conditions under which time-limited mandates are more likely to pull forward rather than induce vaccinations.

What carries the argument

Dynamic regression discontinuity design that tracks first-dose uptake over time around strict school-age eligibility cutoffs, separating temporary timing shifts from permanent increases in total coverage.

If this is right

  • Mandates with deadlines should be judged by long-run coverage rather than short-run windows.
  • Deadlines are more likely to produce only pull-forward effects when individuals already plan to vaccinate eventually.
  • The mix of pull-forward and induced effects depends on mandate features and the surrounding public-health setting.
  • Students moved their vaccinations forward by as much as 80 days.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • Similar deadline rules in other domains such as health screenings may also shift timing without raising totals.
  • Combining a deadline with ongoing incentives could achieve both earlier action and net increases in uptake.
  • Even pure timing shifts could still cut transmission during active outbreaks by closing the gap sooner.

Load-bearing premise

That long-run convergence in vaccination rates between eligible and ineligible cohorts means zero induced vaccinations, with no other time-varying differences between the groups.

What would settle it

Follow-up data showing that the ineligible cohort's vaccination rate remains permanently below the eligible cohort's post-deadline rate for reasons unrelated to the mandate.

Figures

Figures reproduced from arXiv: 2509.22714 by Aregawi G. Gebremariam, Bette Liu, Christopher C. Blyth, Fabio I. Martinenghi, Francesco Paolucci, Hannah Moore, Huong Le, Katie Attwell, Mesfin Genie.

Figure 1
Figure 1. Figure 1: Vaccination rates in WA at the policy deadline by date of birth [PITH_FULL_IMAGE:figures/full_fig_p008_1.png] view at source ↗
Figure 2
Figure 2. Figure 2: Student vaccination rates in WA Notes. This figure plots the vaccination rate of WA students by month-year cohort (a) at the time of the mandate’s deadline for vaccinating (21 November 2021), and (b) at the date of the first public mention of the policy (1 October 2021). Students are identified via 2021 Census. Equivalently, the denominator counts the number of WA students within a given month-year cohort,… view at source ↗
Figure 3
Figure 3. Figure 3: McCrary test for continuity in the density of the running vari [PITH_FULL_IMAGE:figures/full_fig_p019_3.png] view at source ↗
Figure 4
Figure 4. Figure 4: Dynamic RDD treatment effects of the policy [PITH_FULL_IMAGE:figures/full_fig_p020_4.png] view at source ↗
read the original 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.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit. Tearing a paper down is the easy half of reading it; the pith above is the substance, this is the friction.

Referee Report

1 major / 2 minor

Summary. The paper studies the effects of a low-coercion, time-limited COVID-19 vaccine mandate for graduating high-school students in Western Australia. Using static and dynamic regression discontinuity designs on population-wide administrative first-dose records that exploit a strict school-age eligibility cutoff, it reports a 9.3 percentage point (12.7%) short-run increase in uptake that is entirely attributable to pull-forward behavior, with uptake converging in the long run and thus implying zero net induction. Students advanced vaccination by up to 80 days. A simple present-bias model is developed to interpret the patterns and derive conditions under which time-limited mandates are more likely to pull forward rather than induce vaccinations.

Significance. If the dynamic RDD identification is valid, the paper provides valuable evidence on the composition of mandate effects by distinguishing pull-forward from induction using both short-run and long-run windows on full-population data. This contributes to the literature on behavioral responses to deadlines and has direct policy relevance for designing time-limited vaccination policies. The empirical strategy and the theoretical framework are strengths that allow falsifiable predictions about when mandates induce versus merely accelerate uptake.

major comments (1)
  1. [Dynamic RDD analysis] Dynamic RDD section: The central claim that the short-run 9.3 pp effect is entirely pull-forward with no induced vaccinations rests on long-run convergence of uptake rates between the eligible and ineligible cohorts. This interpretation requires the assumption that the two cohorts would have followed identical counterfactual trajectories absent the mandate (no differential long-term trends, selection, or time-varying confounders such as differential information campaigns). The manuscript does not report tests for parallel post-mandate trends, cohort-by-time interactions, or placebo cutoffs at other dates; without such checks the convergence result cannot rule out induced vaccinations that are offset by other factors.
minor comments (2)
  1. [Abstract] Abstract: Clarify whether the 12.7% figure is a relative increase computed from the ineligible cohort's baseline rate or from another reference; this affects interpretation of the magnitude.
  2. [Theoretical model] The present-bias model section would benefit from an explicit statement of the key parameters and how they map to the empirical patterns (e.g., which parameter governs the pull-forward window of up to 80 days).

Simulated Author's Rebuttal

1 responses · 0 unresolved

We thank the referee for their constructive feedback and positive assessment of the paper's contribution. We address the major comment on the dynamic RDD below and will incorporate revisions to strengthen the robustness of our identification.

read point-by-point responses

  1. Referee: Dynamic RDD section: The central claim that the short-run 9.3 pp effect is entirely pull-forward with no induced vaccinations rests on long-run convergence of uptake rates between the eligible and ineligible cohorts. This interpretation requires the assumption that the two cohorts would have followed identical counterfactual trajectories absent the mandate (no differential long-term trends, selection, or time-varying confounders such as differential information campaigns). The manuscript does not report tests for parallel post-mandate trends, cohort-by-time interactions, or placebo cutoffs at other dates; without such checks the convergence result cannot rule out induced vaccinations that are offset by other factors.

    Authors: We appreciate this point and agree that explicit robustness checks would strengthen the interpretation of long-run convergence. The dynamic RDD exploits a sharp, administratively determined eligibility cutoff based on exact birth date for school entry, which generates two adjacent cohorts that are otherwise highly comparable in a population-wide administrative dataset. Differential long-run trends or time-varying confounders (such as information campaigns) are unlikely to align precisely with this cutoff. Nevertheless, we will add the suggested checks in revision: tests for parallel pre-mandate trends, cohort-by-time interactions in the post-mandate window, and placebo cutoffs at other non-mandate dates. These will be reported in the main text and an expanded appendix. We expect the results to corroborate the original findings. revision: yes

Circularity Check

0 steps flagged

No circularity: empirical RDD results and interpretive model remain independent of inputs

full rationale

The paper's central claims rest on static and dynamic regression discontinuity designs applied to external administrative vaccination records, exploiting a strict school-age eligibility cutoff for identification. The finding that the 9.3 pp short-run increase is entirely pull-forward follows directly from observed long-run convergence in uptake rates between cohorts; this is an empirical pattern, not a definitional equivalence or fitted parameter renamed as a prediction. The present-bias model is introduced separately to interpret those patterns and derive theoretical conditions on mandate features, without the empirical estimates reducing to model parameters by construction or via self-citation chains. No load-bearing steps invoke uniqueness theorems from the authors' prior work, smuggle ansatzes through citations, or rename known results. The analysis is self-contained against external benchmarks and standard econometric methods.

Axiom & Free-Parameter Ledger

0 free parameters · 2 axioms · 0 invented entities

The paper rests on standard econometric assumptions for causal identification and a simple present-bias behavioral model; no new entities are introduced.

axioms (2)
  • domain assumption Continuity of potential outcomes at the school-age eligibility cutoff
    Required for valid local causal identification in the RDD design.
  • domain assumption No differential long-run trends between cohorts in the absence of the mandate
    Needed to interpret long-run convergence as evidence of zero induction.

pith-pipeline@v0.9.0 · 5860 in / 1219 out tokens · 67329 ms · 2026-05-18T14:46:01.085447+00:00 · methodology

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Works this paper leans on

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