Paying for Security: Public Support and Policy Priorities in the Weimar Triangle

Last registered on November 17, 2025
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Pre-Trial

Trial Information

General Information

Title
Paying for Security: Public Support and Policy Priorities in the Weimar Triangle
RCT ID
AEARCTR-0017170
Initial registration date
November 11, 2025

Initial registration date is when the trial was registered.

It corresponds to when the registration was submitted to the Registry to be reviewed for publication.

First published
November 17, 2025, 7:17 AM EST

First published corresponds to when the trial was first made public on the Registry after being reviewed.

Locations

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Primary Investigator

Name
Salmai Qari
Affiliation
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Other Primary Investigator(s)

PI Name
Tim Lohse
PI Affiliation
BCEE, HWR Berlin
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PI Name
Tobias Börger
PI Affiliation
BCEE, HWR Berlin
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PI Name
Jürgen Meyerhoff
PI Affiliation
BCEE, HWR Berlin
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PI Name
Andreas Stotland
PI Affiliation
BCEE, HWR Berlin
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Additional Trial Information

Status
In development
Start date
2025-11-20
End date
2026-10-31
Keywords
Crime, Violence, & Conflict, Governance
Additional Keywords
Defense spending, willingness to pay, contingent valuation, policy priorities, public preferences, stated preferences, option price, subjective probability
JEL code(s)
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Following Russia's invasion of Ukraine, NATO members have committed to unprecedented increases in defense spending. This study measures individuals’ preferences and willingness to pay for strengthening NATO defense capabilities in Germany, France, and Poland using the experimental contingent valuation methodology (CVM). In each of the three countries, a representative sample of approximately 2,000 will be randomly assigned to scenarios with different time horizon (2028 vs. 2033 for NATO readiness) and annual costs (€10-€500 per household). Respondents cast binary votes on additional taxation for defense spending. The study further elicits subjective probabilities of successful defense that allow (in combination with votes concerning additional taxation) to calculate option prices representing expected valuations under uncertainty. Before entering the CVM part of the study, individuals’ policy priorities concerning public expenditures are measured using best-worst-scaling (BWS). After the CVM, various attitudinal questions are asked, for example, attitudes toward conscription and European defense integration. This research provides a first systematic within-national and cross-national analysis of European defense preferences in the post-2022 security environment.
External Link(s)

Registration Citation

Citation
Börger, Tobias et al. 2025. "Paying for Security: Public Support and Policy Priorities in the Weimar Triangle." AEA RCT Registry. November 17. https://doi.org/10.1257/rct.17170-1.0
Sponsors & Partners

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Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2025-11-20
Intervention End Date
2025-12-31

Primary Outcomes

Primary Outcomes (end points)
Willingness to Pay, Mean and Aggregate WTP, Subjective Defense Capability, Option Value Estimates, Defense Priority Ranking
Primary Outcomes (explanation)
1. Willingness to Pay (Binary Vote): Respondent's vote on the proposed annual household tax at the randomly assigned cost level and time horizon, enabling WTP estimation.
2. Mean and Aggregate WTP: Estimated mean willingness to pay per household per year for NATO defense strengthening, calculated separately by time horizon (2028, 2033) and country as built-in scope test (both inter- and intra-subject), with aggregation to societal level.
3. Subjective Defense Capability: Respondent's probability assessment (0-100%) that NATO can successfully defend against a Russian large-scale attack on European NATO territory by the treatment-assigned year (2028 or 2033).
4. Option Value Estimates: Derived by integrating willingness to pay with subjective probability assessments of successful defense, representing expected valuations accounting for citizen uncertainty regarding NATO's defensive success. This approach, established in environmental economics for valuing public goods under uncertainty, accounts for heterogeneity in citizen valuations and beliefs about defense capability.
5. Defense Priority Ranking: Best-worst scaling scores for the relative importance of defense spending compared to other public expenditure categories.

Secondary Outcomes

Secondary Outcomes (end points)
Support for conscription; attitudes toward European defense integration and NATO trust; preference heterogeneity by socioeconomic characteristics (age, education, income, region) and political orientation; cross-national comparisons; threat perception measures
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This study employs a contingent valuation design with two factors and individual-level randomization. Respondents are randomly assigned to: (1) Time Horizon (NATO readiness by 2028 vs. 2033), and (2) Cost Level (€10-€500 per household per year). For each randomized scenario, respondents vote ("in favor" vs. "opposed") on a policy proposal for additional taxation to fund defense strengthening. Each respondent responds to two independently randomized valuation questions. The survey uses professional translations (German, French, Polish) and is administered online via SurveyEngine with quota sampling for age, gender, education, and region. Following best-practice contingent valuation, the survey includes consequentiality statements, budget reminders, comprehension checks, and attention filters.
Key methodological innovation: Before the valuation question, respondents provide subjective probability assessments (0-100%) of NATO successfully defending European territory within the scenario's timeframe. These probabilities enable calculation of option prices, representing expected valuations under uncertainty about NATO's defensive success.
Experimental Design Details
Not available
Randomization Method
Individual-level computerized randomization via SurveyEngine platform
Randomization Unit
Individual respondent
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
N/A
Sample size: planned number of observations
6,000 (2,000 per country)
Sample size (or number of clusters) by treatment arms
Each respondent receives both time horizons (2028 and 2033, order randomized) and one randomly selected cost level per scenario
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
German Association for Experimental Economic Research e.V.
IRB Approval Date
2025-10-31
IRB Approval Number
vaXrILVW
Analysis Plan