The Twenty Percent Border: a living research note
Does EU-wide fast-track asylum processing change application volume, timing, or destination?
Preliminary. Data through 2026-05. Last successful update: 2026-08-04 07:31 UTC.
1 Setting and research idea
Under the EU’s new Asylum Procedure Regulation (2024/1348), a nationality group is routed into accelerated examination whenever its EU-wide, prior-year recognition rate is 20% or lower. The rule creates a sharp, numerical, and publicly announced eligibility threshold: origins just below 20% face a faster, more precarious procedure than origins just above it. This note tracks whether that eligibility change shifts how many people apply, when they apply, and where in the EU they apply – three behavioral responses (deterrence, timing, and diversion) that a single-country policy evaluation cannot separate, because it cannot tell a reduction in arrivals from relocation to a neighboring destination. Because the rule applies EU-wide, aggregate EU applications are largely robust to within-EU diversion by construction, turning that usual confound into a directly testable, secondary outcome. The rule enters into application in Q2 2026, so everything shown here is still pre-policy: the exercise validates that treated and control origins were not already trending apart before the cutoff bites.
2 Research design
The note runs two complementary designs side by side. The first is a difference-in-differences: origins with a 2025 recognition rate between 12.5% and 20% (treated by the cutoff) are compared to origins between 20% and 27.5% (control), using a Poisson (PPML) event study with origin and quarter fixed effects. The second is a dynamic difference-in-discontinuities: at each quarterly horizon, it compares the change in EU-wide applications for origins just below the 20% cutoff against origins just above it, relative to the most recent (reference) quarter, using a fixed 20-percentage-point bandwidth with triangular-kernel local-linear and local-quadratic fits on each side. The first design is simpler and does not depend on local smoothing; the second uses a wider 0-40% support and treats the cutoff itself, rather than the 12.5-27.5% band, as the source of identification. Agreement between the two is a stronger pre-policy check than either alone.
Technical details
The running variable is the 2025 EU-wide recognition rate minus 20. The sample keeps origins with a 2025 recognition rate strictly above 12.5% and at or below 27.5%, and at least 200 EU-wide applications in 2025 (13 treated, 12 control origins as of this update). For quarterly horizon l, the outcome is the change in that origin’s applications relative to the reference quarter r = 2026 Q2, normalized by the origin’s average quarterly 2025 applications: (Y_r - Y_l) / (Y_2025 / 4) for horizons before the Pact’s application date, and (Y_l - Y_r) / (Y_2025 / 4) for horizons at or after it – so a pre-policy value above zero and a post-policy value above zero both read as movement in the same, policy-consistent direction. Both Y_l and Y_r are rescaled to a full-quarter-equivalent by 3 / months observed whenever a quarter is incomplete, so a partial reference or partial current quarter is not mechanically under-counted relative to complete quarters. The PPML event study applies the same exposure adjustment as a regression offset, log(months observed / 3), rather than rescaling counts directly. Standard errors are clustered by origin country throughout. The data vintage advances automatically to the latest month for which at least 90% of EU Member States have reported any observations, so the sample window, quarter count, and axis extent all update on their own as new Eurostat releases arrive – nothing here is pinned to a calendar date except the Pact’s own Q2 2026 application date.
3 Preliminary results
Figure 1 shows where the 25 origins in the 12.5-27.5% window actually sit relative to the 20% cutoff – the dashed line marks the threshold, with treated origins (accelerated) below it and control origins above it. Figure 2 plots the raw monthly application totals for the two groups; both have been drifting down over 2025-26 without an obvious break yet, but volumes and composition differ, so this alone is not evidence of parallel trends. Figure 3 is the difference-in-differences design’s quarter-fixed-effects event study: the treated-control gap is close to zero in every pre-reference quarter and never statistically distinguishable from it. Figure 4 is the dynamic difference-in-discontinuities design’s main estimate, plotted on the same y-axis scale as Figure 3 for direct comparison: linear and quadratic fits agree that the gap has been converging toward zero as the reference quarter approaches, with wide, overlapping confidence bands throughout.
Figure 1: origin countries around the 20% cutoff
Explainer: what’s behind Figure 4 (the local RDD fits)
Each panel of the figure below is one quarterly horizon’s cutoff comparison: origin-level normalized changes plotted against the 2025 recognition rate, with application-weighted bins and the triangular-kernel linear/quadratic fits that Figure 4 summarizes into a single treated-minus-control estimate per quarter. It’s the diagnostic view behind the headline numbers, useful for checking that no single origin or outlier is driving a quarter’s estimate.
4 Conclusion
With 13 treated and 12 control origins, this design has limited power: the most recent complete pre-reference quarter’s PPML estimate is -0.053 (SE 0.049), a confidence interval wide enough to miss all but a large treated-control divergence. That is the right benchmark for reading every figure above – a flat pre-trend here is consistent with parallel trends, but it is also consistent with a moderate divergence the sample is simply not powered to detect. The way forward is mechanical rather than exploratory: once Q2 2026 and later data clear the 90% reporting-coverage bar, the last uncontaminated pre-policy quarter freezes as the reference, the same specification re-estimates the post-cutoff break, and this page updates automatically. Until then, this note exists to make that pre-policy validation step – and any future revision to it – fully public and reproducible as the data arrive.
Sources: Eurostat migr_asydec1pc (first-instance decisions) · Eurostat migr_asyappctzm (first-time applications) · Code: update.R · Contact: j.a.h.adema94@gmail.com