Fiscal Accountability: Institutional Discipline and Behavioral Frictions

Last registered on April 15, 2026

Pre-Trial

Trial Information

General Information

Title
Fiscal Accountability: Institutional Discipline and Behavioral Frictions
RCT ID
AEARCTR-0016567
Initial registration date
August 13, 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
August 18, 2025, 6:40 AM EDT

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

Last updated
April 15, 2026, 9:36 AM EDT

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

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Primary Investigator

Affiliation
Tianjin University

Other Primary Investigator(s)

PI Affiliation
University of Manchester

Additional Trial Information

Status
On going
Start date
2024-10-15
End date
2026-05-15
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We study fiscal accountability in a laboratory election environment in which candidates compete over a full fiscal platform consisting of a budget and its allocation between a public good and private rents. The theory predicts that more disproportional power-sharing rules raise the electoral stakes of winning and therefore discipline rent extraction. The experiment is designed to test five hypotheses. First, greater power-sharing disproportionality should reduce corruption, increase public-good provision, and improve aggregate voter welfare. Second, if voters behave fully rationally, human voters should not differ from automated utility-maximizing voters. Third, explicit disclosure of rents should not matter when voters can infer rents from the budget constraint. Fourth, greater disproportionality should weaken the rent-increasing effect of stronger partisanship. Fifth, away from equilibrium, a candidate may sustain higher rents by bundling them with higher public-good provision when the benchmark platform provides too little of the public good relative to the socially efficient benchmark. We implement seven laboratory treatments at Tianjin University with 346 student subjects. Main outcomes are candidate policy choices, vote choice and vote share, and aggregate voter welfare.
External Link(s)

Registration Citation

Citation
Saporiti, Alejandro and Yizhi Wang. 2026. "Fiscal Accountability: Institutional Discipline and Behavioral Frictions." AEA RCT Registry. April 15. https://doi.org/10.1257/rct.16567-2.0
Experimental Details

Interventions

Intervention(s)
This is a laboratory experiment implementing a three-stage election game. In each of 25 periods, two candidates simultaneously propose a fiscal platform. The platform consists of a budget or tax rate and an allocation of that budget between a public good that benefits all voters and a private good interpreted as political rents. Five voters then observe the platforms and vote for candidate A or candidate B. The electoral outcome determines political influence through a power-sharing rule.

The design includes seven treatments:

Treatment T1 (Baseline) implements the model with a proportional power-sharing rule, low partisan intensity, human voters, and partial fiscal transparency, where voters observe only the proposed tax and public good levels and must infer corruption from the budget constraint.

Treatment T2 (Automated Voters) is identical to the baseline, except that the five human voters are replaced by five automated agents programmed to vote sincerely for the party offering them the highest utility. Comparison between T1 and T2 tests Hypothesis 2.

Treatment T3 (Transparency) is identical to the baseline, except that voters are explicitly shown the level of rents proposed by each candidate. Comparison between T1 and T3 tests Hypothesis 3.

Treatment T4 (Disproportional Rule) is identical to the baseline, except that it implements a more disproportional power-sharing rule. The comparison between T1 and T4 provides a direct test of Hypothesis 1.

Treatments T5--T6 (Disproportionality--Partisanship Interaction) replicate the proportional and disproportional power-sharing rules under stronger partisan intensity. Together with T1 and T4, they form a two-by-two factorial design that varies the electoral rule and the strength of partisan bias, providing a direct test of Hypothesis 4.

Treatment T7 (Public-Good Gambit) is a scripted-candidate election treatment in which the candidates' platforms are pre-programmed by the computer. In each round, five human voters observe the two platforms under full transparency and cast their votes, so the electoral outcome continues to depend on aggregate voter support. The scripted platform pairs are organized around two benchmark environments: one in which the benchmark platform provides the socially efficient amount of the public good, and one in which the benchmark platform provides too little of the public good. In each environment, the opposing scripted platform combines a fixed increase in rents with different amounts of additional public-good provision.


The experiment uses a common theoretical calibration in which both private consumption and public-good benefits are valued with diminishing marginal returns, there is no office rent or valence advantage built into the candidates, income is fixed across participants, and partisan bias is introduced through an individual-specific predisposition toward one of the candidates. Some treatments use weaker partisan bias and others use stronger partisan bias.

Hypothesis 1 (H1):Greater power-sharing disproportionality is expected to promote stronger fiscal discipline. Under the disproportional rule treatment, relative to the proportional baseline, we predict that candidates will propose lower levels of corruption, greater provision of public goods, and, as a result, higher aggregate voter welfare.

Hypothesis 2 (H2): Policy payoffs lead voters to electorally punish rent-seeking behavior and reward public-good provision. Under the assumption of voter rationality, we predict no significant differences between a treatment with automated utility-maximizing voters and a baseline with human voters.

Hypothesis 3 (H3): Greater fiscal transparency does not necessarily improve voter accountability. Under the assumption of voter rationality, we predict that equilibrium policy will be similar in a treatment where political rent levels are explicitly disclosed to voters and in a baseline where corruption must be inferred from the budget constraint.

Hypothesis 4 (H4): Greater disproportionality weakens the rent-increasing effect of stronger partisanship. When the electoral rule and partisan intensity are varied independently, we predict that the increase in corruption caused by stronger partisanship will be smaller under the disproportional rule than under the proportional baseline.

Hypothesis 5 (H5): The electoral viability of the public-good gambit depends on whether public-good provision at the benchmark platform is already socially efficient. Comparing candidate vote shares across scripted platform pairs, we predict that combining higher rents with additional public goods should not improve electoral support when the benchmark platform already provides the efficient amount of the public good, but can improve support when the benchmark platform provides too little of the public good and the increase in public-good provision is large enough.
Intervention Start Date
2024-10-15
Intervention End Date
2026-05-15

Primary Outcomes

Primary Outcomes (end points)
Candidate policy choices: proposed political rents or corruption, proposed public-good provision, and proposed budget or tax rate. Electoral outcomes: candidate vote share and individual voter choice. Aggregate voter welfare. In the scripted-candidate treatment, vote share and individual support for the deviating platform.
Primary Outcomes (explanation)
The primary outcomes map directly to the five hypotheses in the paper. Candidate policy choices test whether institutional rules affect rent extraction, public-good provision, and budget size. Vote choice and vote share test voter accountability, the effects of transparency, the comparison between human and automated voters, and the electoral viability of the public-good gambit. Aggregate voter welfare tests whether the disproportional rule improves welfare relative to the baseline.

Secondary Outcomes

Secondary Outcomes (end points)
Responsiveness of voting to utility differences and to differences in rents, public-good provision, and taxes. Interaction between partisan bias and policy responsiveness, which captures the partisan-shield mechanism. Treatment interactions between disproportionality and partisan intensity. Time trends and learning in candidate policy choices across rounds.
Secondary Outcomes (explanation)
These outcomes are used to study behavioral frictions that mediate accountability, including whether human voters are less punitive toward corruption than automated voters, whether transparency changes the weight voters place on policy differences, whether stronger partisanship weakens accountability, and whether candidates learn over time to adjust the composition of the budget.

Experimental Design

Experimental Design
The experiment implements the election game from the model over 25 repeated periods. Treatments T1 through T6 involve two candidates and five voters in each electoral interaction, except that T2 replaces the five human voters with automated voting agents. In T1 and T3 through T6, subjects are randomly re-matched each period into groups of seven, consisting of two candidates and five voters. In T2, each group consists of two human candidates paired with five computer algorithms. In T7, subjects are randomly re-matched each period into groups of five voters, while the two candidates' platforms are scripted by the computer. The election environment is preserved in T7 because aggregate voter support still determines the electoral outcome between the scripted platforms.

The treatment structure is designed to identify the effect of a disproportional versus proportional power-sharing rule, the difference between human and automated voters, the effect of explicit rent disclosure, the interaction between disproportionality and partisanship, and the off-equilibrium public-good gambit.

The design combines between-treatment variation with repeated within-subject play and random rematching across periods. The main comparison for institutional discipline is between T1 and T4. The comparison between T1 and T2 tests voter rationality. The comparison between T1 and T3 tests transparency. Treatments T1, T4, T5, and T6 form a two-by-two factorial design in the power-sharing rule and partisan intensity. T7 isolates voter responses to scripted platform pairs in a fully transparent environment.
Experimental Design Details
Not available
Randomization Method
Computerized randomization. Within treatments, participants are randomly re-matched across periods by the experimental software. Treatment assignment should be recorded at the session level to avoid contamination across treatments; if the current registry already lists the exact session-level procedure, retain that wording.
Randomization Unit
Primary treatment variation: experimental session. Within-session rematching: group composition is randomized each period. In T7, scripted platform pairs are assigned by the computer across rounds.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
56 groups
Sample size: planned number of observations
1200 observations at the candidate's level, and 6250 observations at the voter level.
Sample size (or number of clusters) by treatment arms
Each treatment has at least two sessions with 56 subjects (16 candidates and 40 voters) and 25 rounds. Therefore, the minimum sample size is (8*25*6=)1200 (winners of the elections) for the candidate analysis and (20*25*10+25*25*2=)6250 for the voting analysis.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
Ma Yinchu School of Economics
IRB Approval Date
2024-09-15
IRB Approval Number
MYSOE-2024003