Eliciting Preferences of Party Leaders for Candidate Characteristics

Last registered on August 15, 2025
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Pre-Trial

Trial Information

General Information

Title
Eliciting Preferences of Party Leaders for Candidate Characteristics
RCT ID
AEARCTR-0015917
Initial registration date
June 09, 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
June 13, 2025, 7:04 AM EDT

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

Last updated
August 15, 2025, 4:32 PM EDT

Last updated is the most recent time when changes to the trial's registration were published.

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Primary Investigator

Name
Lukas Leucht
Affiliation
University of Oxford
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Other Primary Investigator(s)

PI Name
Janne Tukiainen
PI Affiliation
University of Turku
Contact Investigator
PI Name
Tuomas Kari
PI Affiliation
Aalto University
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Additional Trial Information

Status
In development
Start date
2025-06-11
End date
2028-07-31
Keywords
Electoral, Governance
Additional Keywords
Personnel, Recruitment, Political Selection
JEL code(s)
D72, C93, P00, M50
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This study investigates the preferences of local party elites in Finland regarding the selection of municipal election candidates. Using a series of conjoint survey experiments, we elicit local party leaders’ evaluations of hypothetical candidate profiles varying across key attributes, including education, labor market experience, political experience, policy positions, age, and gender. This design allows us to isolate the importance that political leaders assign to each attribute in candidate selection, without the concern of confounding by potentially correlated candidate characteristics. We vary the framing of the choice tasks to shed light on how party leaders evaluate the importance of the attributes differently for electoral performance and the capacity to govern, and how these preferences depend on the composition of the rest of the candidate list. The results will provide novel evidence on the implicit trade-offs party actors make when balancing representational goals with other candidate characteristics. By explicitly modeling these preferences, the study offers new insights into intra-party decision-making and the micro-foundations of political representation in a proportional, multi-party democracy.
External Link(s)

Registration Citation

Citation
Kari, Tuomas, Lukas Leucht and Janne Tukiainen. 2025. "Eliciting Preferences of Party Leaders for Candidate Characteristics." AEA RCT Registry. August 15. https://doi.org/10.1257/rct.15917-1.2
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Experimental Details

Interventions

Intervention(s)
Respondents complete 30 conjoint tasks in total, divided evenly across three sections. The first section includes 10 choices over candidate lists. The order of the two sections focusing on individual candidate evaluations is randomized between participants.
Intervention Start Date
2025-06-11
Intervention End Date
2025-09-01

Primary Outcomes

Primary Outcomes (end points)

List Choice in the List Conjoint

Candidate Choice in the Electoral Success Conjoint
Candidate Choice in the Competence Conjoint

Candidate Choice in the Pooled Sample of all Conjoints

Differences in Candidate Choice between the Conjoints.

Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Our experiment is designed to estimate average marginal component effects (AMCEs) to assess the impact of each candidate attribute on respondents’ choices, averaged over the distribution of the other attributes. Additionally, conditional AMCES will be examined based on respondent characteristics, such as the demographic composition of their municipality. The study will also distinguish between preferences for candidates depending on the type of choice respondents have to make in the three types of conjoint experiments.
Experimental Design Details
Not available
Randomization Method
Randomization is done by the survey company via computer.
Randomization Unit
We randomize on the level of each individual hypothetical candidate profile or list profile that respondents (i.e. local party leaders) are presented with to compare and choose from.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
We expect to receive around 6300 evaluations of candidate or list profiles by local party leaders. We will send the survey to 317 local party leaders, who will each be faced with 30 decisions among pairs of candidates or candidate lists (19020 = 317 x 30 x 2). We expect a response rate of less than a third of all respondents, based on existing studies of comparable populations (6340 = 19020 / 3).

August Update: We received contact information for two additional local party leaders. We will add them to the study and send them an invitation to evaluate candidate and list profiles. At this moment, when we received the additional contact information and decided to include them, we do not yet have access to any of the data from the conjoint experiment. The survey company will share the data with us in September. We do not expect any significant changes to the resulting number of evaluations and our power calculation.
Sample size: planned number of observations
6300 evaluations.
Sample size (or number of clusters) by treatment arms
Since this study employs a conjoint design, the concept of treatment arms with exact sample sizes for each treatment arm is not straightforwardly applicable. For each of the three types of conjoint experiments, we randomize each binary candidate attribute with equal probability. In expectation we should therefore observe 3150 evaluations for each possible candidate attribute, with 1050 evaluations for each attribute per type of conjoint experiment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
The minimum detectable AMCE (average marginal component effect) for each individual conjoint experiment analyzed separately is 0.0607 (at 80% power. For the analysis of the pooled sample of all three types of conjoint experiments, the minimum detectable AMCE is 0.0351 (at 80% power). For the pair-wise comparison of evaluations across any two types of conjoint experiments, the minimum detectable difference in AMCEs is 0.0852 (at 80% power). All power calculations were performed calculations via https://m-freitag.github.io/cjpowR_shiny/.
Supporting Documents and Materials
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
Analysis Plan