#60659 | AsPredicted

'Does information about comparative vaccination performance matter?'
(AsPredicted #60659)

Created:       03/11/2021 02:12 PM (PT)

This is an anonymized version of the pre-registration.  It was created by the author(s) to use during peer-review.
A non-anonymized version (containing author names) should be made available by the authors when the work it supports is made public.

1) Have any data been collected for this study already?
No, no data have been collected for this study yet.

2) What's the main question being asked or hypothesis being tested in this study?
This experiment studies whether and how citizen hold democratic governments accountable during the Covid-19 epidemic with a focus on cross-national benchmarking concerning vaccinations with the possibility of selective exposure. Is there evidence of political biases in the choice of benchmarks, such that people more (less) inclined to support the government are more (less) likely to select a benchmark favorable to their views? What is the effect of exogenous information conditional on prior self-selection into news?

3) Describe the key dependent variable(s) specifying how they will be measured.
1) Choice of a benchmark text among 2 possibilities: for half of the sample, either a neutral or a positive headline; for the other half of the sample, either neutral or negative headline.
(2a) Assessment of government performance in the crisis: Respondents are asked “Can you tell us how strongly you agree or disagree with the following statement? All in all, the government has handled coronavirus better than most other countries.” [Translation from country’s language.] Answers are recorded on a 11-point scale (0 = “strongly disagree”, 10 = “strongly agree”). Denoted by COMPGOV from now on.
(2b) Vote intentions: the variable is a standard vote intention question, which records which party the respondent would vote for if an election were held next week/Sunday. The resulting measure will be equal to 1 if respondents are inclined to vote for the party or parties currently in government, 0 otherwise.
(3) Spending preferences: Respondents are asked “Should there be more or less public expenditure in each of the following areas? Vaccination campaign against COVID19”. Answers are recorded on a 5 point scale : 1. “Much less than now”, 2. “Somewhat less than now” 3. “The same as now” 4. “Somewhat more than now” 5. “Much more than now”.

4) How many and which conditions will participants be assigned to?
Between-subject design. Two stages: headline selection and randomization.
STAGE 1: respondents choose benchmark case by selecting one of two headlines for further reading; Two pairs of headlines are randomly allocated: a neutral one and a positive one (subsample pair1); a neutral one and a negative one (subsample pair2).
STAGE 2: Random allocation of short vignettes with the exact same text (around 1000 characters) and a table comparing France with 4 other OECD countries conditional on selected headline.
Subsample pair1:
- T1. Table with balanced information (France as a middle case among 5 OECD countries).
- T2. Comparatively positive information in the table (France ahead of 5 OECD countries).
Subsample pair2
- T1. Table with balanced information (France as a middle case among 5 OECD countries)
- T3. Comparatively negative information in table (France lagging among 5 OECD countries).

In all treatment conditions respondents are asked to evaluate if text was (i) informative, (ii) credible, and (iii) if the they would share/recommend it.

5) Specify exactly which analyses you will conduct to examine the main question/hypothesis.
(1) To test the hypothesis concerning the biased choice of benchmark information, we estimate regression models with choice of the positive benchmark (relative to neutral) or the negative (relative to neutral) as dependent variables. Test if pre-treatment satisfaction with how the executive has handled the coronavirus is related to selective exposure.

(2) Analysis of benchmarking hypothesis by self-selected strata in stage 1. Depending on subsample, the test concerns the difference between COMPGOV between T2 (T3) and T1 conditional on headline choice. The expectation is that positive (negative) benchmarking information leads to an increase (decrease) in COMPGOV. We also test if there is effect heterogeneity across strata.

(3) To test the corollary hypothesis regarding vote choice and spending preferences on vaccination campaign, we will replicate the analyses described before using vote choice and spending preferences as outcome variables.

6) Describe exactly how outliers will be defined and handled, and your precise rule(s) for excluding observations.
No cases will be classified or excluded as "outliers".

7) How many observations will be collected or what will determine sample size?
No need to justify decision, but be precise about exactly how the number will be determined.

The experiment is embedded in an opt-in online panel in France for the project on Citizens’ attitudes to Covid-19 run by the international survey company Ipsos. Ipsos will attempt to balance the panel sample to be representative of each country’s population of eligible voters.

Target sample sizes:
N=2,000 France

8) Anything else you would like to pre-register?
(e.g., secondary analyses, variables collected for exploratory purposes, unusual analyses planned?)

Treatment effect heterogeneity: Does effect of exogenous benchmark treatments on COMPGOV vary by trust in media (4-point scale), pre-treatment satisfaction with executive, pre-treatment satisfaction with how democracy is working in the country, pre-treatment attitudes towards vaccination?