Designing Subsidy Reforms When Trust is Low: A Survey Experiment from Latin America

Last registered on April 16, 2024

Pre-Trial

Trial Information

General Information

Title
Designing Subsidy Reforms When Trust is Low: A Survey Experiment from Latin America
RCT ID
AEARCTR-0012384
Initial registration date
April 11, 2024

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
April 16, 2024, 3:13 PM EDT

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

Locations

Region
Region
Region
Region
Region
Region
Region
Region
Region

Primary Investigator

Affiliation
IFPRI

Other Primary Investigator(s)

PI Affiliation
Inter-American Development Bank
PI Affiliation
Inter-American Development Bank

Additional Trial Information

Status
On going
Start date
2023-12-08
End date
2024-12-31
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
For decades, economists have given standard advice on advancing Pareto improving economic reforms that have redistributive consequences: implement the reform while using savings to compensate losers from reform, ensuring that all are better off. Fossil fuel subsidies are a quintessential example of a distortionary economic policy prevalent in many developing countries. By pricing fuel below market value and failing to price in externalities, they promote over-consumption of fossil fuels and affect the adoption of clean energy alternatives, not to mention strain government budgets and crowd out forms of public spending that would promote economic growth. Although fuel subsidies are generally regressive—with richer households with higher consumption benefitting more in absolute terms—poor households still benefit from these subsidies, and any reform would affect them. Standard economic advice is thus to repeal the subsidies while compensating poor households for higher fuel prices.

Yet, citizens often have good reason to mistrust promises to compensate them for the costs of reform. If governments have a poor track record of implementing redistributive programming, compensation promises are unlikely to be credible. Similarly, if trust in government is low, citizens may be loathe to entrust governments with any ‘savings’ from lowering public spending on fuel subsidies. Using a survey experiment implemented across nine Latin American countries, we consider which groups may be most receptive to compensation and how different compensation strategies are most effective at reducing opposition to fuel subsidy reform.
External Link(s)

Registration Citation

Citation
Ardanaz, Martin, Jordan Kyle and Carlos Scartascini . 2024. "Designing Subsidy Reforms When Trust is Low: A Survey Experiment from Latin America." AEA RCT Registry. April 16. https://doi.org/10.1257/rct.12384-1.0
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Experimental Details

Interventions

Intervention(s)
We have three study arms. All survey participants will receive a brief survey vignette outlining some of the general costs and benefits of eliminating subsidies on fuel, elecricity, and transport. Treatment group 1 (T1) receives an additional portion of the vignette designed to test whether adding a promise of compensating the poor for some of the costs of reform will affect support for reform, and among which groups. Treatment group 2 (T2), on the other hand, tests whether changing the institution responsible for implementing the promised compensation affects support for reform.

Intervention (Hidden)
We have three study arms. All survey participants will receive a brief survey vignette asking them to consider that when subsidies on fuel, electricity, or transport are eliminated, prices on these factors rise. The vignette then notes that eliminating subsidies can help to reduce public spending, which allows governments to invest in other sectors like health, education, and public safety and to reduce impacts on the environment by reducing consumption of fossil fuels. The placebo group is then asked about whether they support eliminating these subsidies. Exact wording below:

When subsidies are eliminated, like those on fuel, electricity, or transportation, the price of fuel, electricity, or transportation rises. But the elimination of subsidies can help to: (1) reduce public spending, allowing for these resources to be invested in other sectors like health, education or security, or (2) mitigate environmental impacts through lower consumption of energy or fuel. Without a doubt, such rises in prices affect poor households the most.

Treatment group 1 (T1) receives an additional portion of the vignette, stating that the government would propose to compensate poor households for higher prices with a transfer that would be implemented by the national government, like most such transfer program. T1 serves as a very standard reform formula and allows us to test whether offering compensation increases support for Pareto improving economic reform, and among which groups. Exact wording below:

[Introductory text same as placebo] To help those households to adjust to the increase in prices, the government proposes to compensate them with a transfer. Like the majority of these programs, the national government would be responsible for identifying who would be the beneficiaries of the transfer and of implementing the program.

Treatment group 2 (T2), on the other hand, changes the promised reform formula slightly: rather than stating that compensation would be implemented by the national government, it notes that unlike past transfers, this transfer would be implemented by local governments, local churches, and NGOs, institutions that tend to be more trusted by citizens compared to national governments. This enables us to test whether channeling compensation through more trusted institutions meaningfully changes patterns of support for reform. Exact wording below:
[Introductory text same as placebo] To help those households to adjust to the increase in prices, the government proposes to compensate them with a transfer. Unlike other similar programs implemented in the past, this time municipal governments, local churches, and neighborhood organizations would be in charge of identifying who would be the beneficiaries of the transfer and of implementing the program.
Intervention Start Date
2023-12-08
Intervention End Date
2023-12-18

Primary Outcomes

Primary Outcomes (end points)
After each vignette, respondents are asked the extent to which they agree with the reform on a 4-part scale. We plan to both look at the 4-point scale as well as a binary variable indicating whether the respondent broadly “agrees” or “disagrees” with the proposed reform.
Primary Outcomes (explanation)
We plan to look at two versions of the dependent variable: (1) As a 4-part Likert-scale going from strong agreement to strong disagreement and (2) as a binary variable where the top two categories are coded as "agreement" and the bottom 2 categories are coded as "disagreement"

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
This survey experiment will be implemented among 9,000 online respondents in nine Latin American countries (Colombia, Peru, Brazil, Chile, Argentina, Mexico, Costa Rica, Guatemala, Panama), with 1,000 respondents targeted in each of the 9 countries. These countries span different ideological orientations, average levels of trust in government, and types of subsidy programs. The online survey will be implemented by LAPOP. The sample will be drawn from a standing online panel, which is representative of the online-population in these countries.

The experimental design randomly varied whether individual respondents were exposed to the placebo, T1, or T2 vignette. The vignettes were designed to vary the institutions responsible for implementing a transfer to compensate poor households for the higher prices that come with eliminating subsidies. Following the vignette, we measured an attitudinal outcome for respondents’ level of support for subsidy reform. .
Experimental Design Details
This survey experiment will be implemented among 9,000 online respondents in nine Latin American countries (Colombia, Peru, Brazil, Chile, Argentina, Mexico, Costa Rica, Guatemala, Panama), with 1,000 respondents targeted in each of the 9 countries. These countries span different ideological orientations, average levels of trust in government, and types of subsidy programs. The online survey will be implemented by LAPOP. The sample will be drawn from a standing online panel, which is representative of the online-population in these countries.

The experimental design randomly varied whether individual respondents were exposed to the placebo, T1, or T2 vignette. The vignettes were designed to vary the institutions responsible for implementing a transfer to compensate poor households for the higher prices that come with eliminating subsidies. Following the vignette, we measured an attitudinal outcome for respondents’ level of support for subsidy reform. .

We will analyze the average treatment effects of (1) introducing compensation for reform and (2) of varying the institution responsible for implementing the compensation using a specification as follows:

Y_it=β_0+β_1 〖T1,2〗_i+γX_(i,t-1)+δ_c+ϵ_ic (1)

Y_it=β_0+β_1 〖T1〗_i+β_2 〖T2〗_i+γX_(i,t-1)+δ_c+ϵ_ic (2)

where i indexes women, c indexes countries, and t indexes whether the measurement is pre- or post-treatment (t-1 is pre-treatment, t is post-treatment). Equation (1) pools treatments 1 and 2 to test the overall effect of introducing compensation for reform, regardless of implementation designation. Treatment effects are relative to the base group of assignment to the placebo group. Equation (2) separately tests the effects of T1 and T2. We would then test the difference between β_1 and β_2 in Equation (2) to test the effect of assigning implementation to local governments, churches, and NGOs rather than to national government. Both specifications include country fixed effects. X_(i,t-1) is a vector of control variables, including: educational attainment, income level, gender, age, frequency of public transportation use, and support for current president / head of state.

In addition to testing the effects in the pooled sample across all countries, we may test treatment effects in specific countries in order to support more specific policy recommendations and analysis relevant to countries considering reforms.

Randomization Method
randomization embedded into survey design, assigning treatment status with 1/3 probability to placebo, T1, or T2
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
9,000 individuals
Sample size: planned number of observations
9,000
Sample size (or number of clusters) by treatment arms
3,000 individuals per treatment arm (placebo, T1, T2)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
In our sample of 9,000, we have 80 percent power to detect an effect size of 0.06 standard deviations and 99 percent power to detect an effect size of 0.10 standard deviations when comparing the combined effects of T1 and T2 (adding in compensation policies) to the placebo group. When comparing T1 to T2, we have 80 percent power to detect an effect size of 0.07 standard deviations and 99 percent power to detect an effect size of 0.11 standard deviations. If we want to test hypotheses within countries rathe than using the full sample pooled across countries, we have 80 percent power to detect an effect size of 0.19 standard deviations when comparing the combined effects of T1 and T2 versus placebo, and 80 percent to detect an effect size of 0.22 standard deviations when comparing the effects of T1 versus T2.
IRB

Institutional Review Boards (IRBs)

IRB Name
Vanderbilt University
IRB Approval Date
2020-03-25
IRB Approval Number
200472
Analysis Plan

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Post-Trial

Post Trial Information

Study Withdrawal

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Intervention

Is the intervention completed?
No
Data Collection Complete
Data Publication

Data Publication

Is public data available?
No

Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials