PROPSC — Treatment Effects on Proportions (Bogatyrev & Stoetzer 2026)

Estimator:

PROPSC — Treatment Effects on Proportions with Synthetic Controls — mlsynth.PROPSC

Source:

Bogatyrev, K., and L. F. Stoetzer (2026), “Estimating Treatment Effects on Proportions with Synthetic Controls,” Political Analysis (doi:10.1017/pan.2026.10046).

Reference code:

the authors’ R package propsdid (lstoetze/propsdid), a GPL fork of synthdid extending it to compositional outcomes.

Replication type:

Path A — the two published empirical applications (Spain and Poland) reproduced cell by cell — and cross-validation against the authors’ R package to numerical precision.

Status:

Fully verified — both empirical tables and the R package matched.

Validation strategy

The paper’s contribution is a coherence property, not a headline point estimate: common weights across the components of a composition make the estimated effects sum to zero, which separate synthetic controls violate. The natural target is therefore the whole vector of effects in each application, checked two ways.

First, cross-validation. Because the authors ship a runnable R package, the strongest evidence is a value-by-value diff against it on the same panel. mlsynth’s PROPSC is a faithful port of the package’s common-weights estimator (the synthdid Frank-Wolfe solver with the two-round sparsify pass, stacked across the \(K\) components), and reproduces the package cell by cell — estimates, jackknife standard errors, unit weights, and time weights — to roughly \(10^{-11}\) (floating-point reordering).

Second, Path A. That same run reproduces the paper’s published Table 2 (Spain) and Table 3 (Poland) common-weights columns to the printed two decimals.

Path A — Spain “Just Transition” (Table 2)

Bogatyrev and Stoetzer re-examine Bolet, Green and González-Eguino (2024): the electoral effect of a compensatory “Just Transition Agreement” in Spanish coal-mining municipalities, estimated on the full vector of party vote shares rather than one party at a time. The panel (basedata/spain_propsc.csv — 525 municipalities over five elections 2008–2019, 109 treated in 2019, party shares in percentage points with VOX coded zero before its 2013 founding) is exported from the article’s Harvard Dataverse archive (doi:10.7910/DVN/MPUEIC).

import pandas as pd
from mlsynth import PROPSC

df = pd.read_csv("basedata/spain_propsc.csv")
parties = ["psoe", "pp", "podem", "cs", "vox", "others"]
res = PROPSC({
    "df": df, "outcomes": parties, "treat": "coalXpost",
    "unitid": "munid", "time": "year", "method": "sdid",
}).fit()

for party, att, se in zip(parties, res.att_vector, res.se_vector):
    print(f"{party:8s} {att:+.2f} ({se:.2f})")
print("sum:", round(res.sum_constraint, 12))

The synthetic-DID common-weights estimates reproduce Table 2 exactly:

Party	PROPSC (SDID, common)	Paper Table 2
PSOE	+1.30 (0.68)	+1.30 (0.68)
PP	+0.98 (0.82)	+0.98 (0.82)
PODEMOS	+0.30 (0.36)	+0.30 (0.36)
Citizens	+0.94 (0.67)	+0.94 (0.67)
VOX	−3.43 (0.53)	−3.43 (0.53)
Others	−0.09 (0.35)	−0.09 (0.35)
Sum	0	0

Substantively, modelling the full composition changes the reading of the original study: the effect is concentrated in a decline for the far-right VOX (the mainstream gains lose significance under common weights), and the six effects sum to zero, whereas separate synthetic DID leaves a net of +0.55 percentage points that the composition forbids.

Path A — Poland anti-LGBTQ resolutions (Table 3)

The second application revisits Haas et al. (forthcoming): the effect of municipal anti-LGBTQ resolutions before the 2019 Polish parliamentary election on a three-way composition of the electorate — government turnout, opposition turnout, and abstention. With the same call (method="sdid", the three outcomes as the composition), PROPSC reproduces Table 3’s common-weights column: government turnout −0.25 (0.20), opposition turnout −1.13 (0.17), abstention +1.39 (0.14), summing to zero.

Cross-validation harness

The durable check is benchmarks/cases/propsc_spain.py. It fetches propsdid at the pinned commit (benchmarks/R/install_propsdid.sh), runs the package on basedata/spain_propsc.csv via benchmarks/R/propsdid_spain.R, and diffs PROPSC.fit() against the live-R output cell by cell. R is not run in CI: the gate compares against a frozen capture of the R output by default and re-runs the R reference when PROPSDID_LIVE=1 is set. The measured discrepancy is at the floating-point-reordering level (\(\sim 10^{-11}\) on the effects and the jackknife standard errors).

References

Bogatyrev, K., and L. F. Stoetzer (2026). “Estimating Treatment Effects on Proportions with Synthetic Controls.” Political Analysis.

Bolet, D., F. Green, and M. González-Eguino (2024). “How to Get Coal Country to Vote for Climate Policy: The Effect of a ‘Just Transition Agreement’ on Spanish Election Results.” American Political Science Review 118(3).

Arkhangelsky, D., S. Athey, D. A. Hirshberg, G. W. Imbens, and S. Wager (2021). “Synthetic Difference-in-Differences.” American Economic Review 111(12).