The informational effect of candidate’s traits on voter behavior. A survey experiment for the municipal elections of Rome (Italy).

Last registered on September 28, 2021

Pre-Trial

Trial Information

General Information

Title
The informational effect of candidate’s traits on voter behavior. A survey experiment for the municipal elections of Rome (Italy).
RCT ID
AEARCTR-0007763
Initial registration date
June 03, 2021
Last updated
September 28, 2021, 3:52 PM EDT

Locations

Region

Primary Investigator

Affiliation
Sapienza University - Department of Economics and social sciences

Other Primary Investigator(s)

PI Affiliation
Sapienza University - Deparment of Economics and social sciences
PI Affiliation
Sapienza University - Deparment of Economics and social sciences

Additional Trial Information

Status
In development
Start date
2021-09-29
End date
2021-10-16
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
We evaluate the effect of providing voters with a set of information about candidates' curriculum. The information set is comprised of basic information derived from candidates’ curriculum (education, work, and political experience). The set mimics transparency requirements introduced by the recently introduced Italian law against corruption (“Legge spazzacorrotti”).
To evaluate the informational effect on voting behavior we organized a dedicated campaign on transparency of the candidates. The evaluation is made trough a panel survey experiment where we will expose treatment group individuals to the campaign while covering the campaign to control group individuals. The informational treatment is an invitation to visit a website with information about candidates.
External Link(s)

Registration Citation

Citation
Galli, Emma, Giampaolo Garzarelli and Gabriele Pinto. 2021. "The informational effect of candidate’s traits on voter behavior. A survey experiment for the municipal elections of Rome (Italy).." AEA RCT Registry. September 28. https://doi.org/10.1257/rct.7763-2.1
Experimental Details

Interventions

Intervention(s)
To evaluate the impact of political transparency on voting behavior we organized a dedicated campaign on transparency of the candidates. The campaign reconstructs information from all curricula vitae presented by all the candidates for transparency requirements. Since the information comes in the form of long curriculum vitae, we extrapolate the more salient characteristics (education, job experience, political experience) and structure this information in a tabular format. This information is then converted into a simple and light website where the visitor can see information about the candidates (HIDDEN). To evaluate the effectiveness of the campaign, we organized a panel survey experiment where we will expose treatment group individuals to the campaign while covering the campaign to control group individuals.
The sample of our panel survey experiment is comprised of N=1000 eligible voters in the *HIDDEN* area.
We will ask respondents to fill two questionnaires before and after the election day. In the first questionnaire (3-4 days before the election day) we will ask the demographic and attitude characteristics of the respondents. In the second questionnaire (a week after the election day) we will ask all respondents to report their vote choice. The core of the experiment consists of the submission of an informational treatment to a sub-sample of treated units T=500 that we will randomly select from the main sample.
The informational treatment is an invitation to visit a website with information about candidates through a link. This treatment is submitted just after the last question of the first survey.
Intervention Start Date
2021-09-29
Intervention End Date
2021-10-04

Primary Outcomes

Primary Outcomes (end points)
-Turnout
-Expression of a preference for a candidate mayor/councilor
-Characteristic of the candidate mayor/councilor voted
-Consistency between characteristics of the ideal candidate and characteristics of the candidate actually voted (mayor and councilor)
Primary Outcomes (explanation)
After the experiment is concluded and we will have collected all the data, we will test the impact of the informational treatment on the vote choice reported in the second questionnaire. Besides, we will also test coherence between attitude reported in the first questionnaire and actual vote choice reported in the second questionnaire.
We will estimate the models using the Logistic model when the response variable is a binary indicator.
A – Impact on vote behavior
The impact on vote behavior will comprise a series of statistical tests where we will compare treated and untreated units' vote behavior. Vote behavior will be evaluated among several dimensions, including:
- A1: Turnout (vote extensity)
we regress an equation:
V_i= a_1*T_i+u_i
, for each individual i, where T_i is treatment and V_i(D2_1) is a dummy equal to 1 if individual i voted and 0 otherwise.
Are more informed individuals more/less likely to turn out?
H0: No, a_1=0
H1: Yes, a_1≠0


- A2: Expression of a preference for councilor candidate and mayor candidate (vote intensity)
we regress an equation:
P_i= a_2*T_i+u_i,
for each individual i, where T_i is treatment and P_i (D2_3) is a dummy equal to 1 if individual i expressed a preference and 0 otherwise.
Are more informed individuals more/less likely to cast a preference vote?
H0: No, a_2=0
H1: Yes, a_2≠0

- A3: Characteristic of the candidates that received the preference (vote choice): (D2_2-D2_4)
We will compare voter preferences of treated and untreated groups across several dimensions of the candidates, including:
-Gender, education, experience and, ideology.
we regress a set of equations V_i= a_3*T_i+u_i, for each individual i, where T is treatment and V can be Gender, Education, Experience and Ideology (party membership) of the candidates voted by the individual (both for mayors and councillors).
Do more informed individuals more/less likely to cast a preference vote for a Women/Educated/Experienced candidate?
H0: No, a_3=0
H1: Yes, a_3≠0
Mayors and councilors characteristics will be measured as follow:
-Gender (Female=0; Male=1)-Education (University Degree=1; Else =0)
-Experience (At least one previous political experience =1; Else=0)
-Ideology (Left (PAP, PRC and others at the left of PD); Centre-Left (PD and civic lists supporting Gualtieri ); 5 Star Movements; Centre-Right and Right)

We will explore the heterogeneity of the treatment effect along several dimension including:
-Political participation (party-member), civic participation (a member of the activist group), education level. (D1_6, D1_7, D1_3)
we regress a set of equations:
V_i= b_1*T_i+b_2*P_i+a_4*P_i*T_i+u_i,
for each individual i, where T is treatment and V can be Gender, Education, Experience and Ideology (party membership) of the candidates voted by the individual. P can be the level of political participation, civic participation, or education level of the individual.
Does a prior level of political participation influence the informational effect of casting a preference vote for a Women/Educated/Experienced candidate?
H0: No, a_4=0
H1: Yes, a_4≠0

B – Attitudes and vote behavior
The second part of the results will analyze voters’ perception about transparency and demand for candidates’ quality and their actual voting behavior. We will thus carry a series of statistical tests comparing answers reported in the first survey compared to answers reported in the second survey.
B1 - Ideal Candidate and vote behavior
We will compare the matching between:
-the profile of the ideal candidate reported by the interviewer and the actual preference expressed.
we regress an equation:
〖AC〗_i= b_1*T_i*〖AI〗_i+b_3*〖AI〗_i+u_i,
where AI (D1_11) is the attribute of the ideal candidate as reported by the individual i. And AC is the same attribute of the candidate voted by the individual. The attribute can be the level of education or the level of experience. For example, we compare how the individual responds to the following question “Do you think the ideal candidate should have at least a university degree?”, and compare their answer with the level of education of the candidate for which they voted. Or, for example, we ask “Do you think women should be more represented in Politics?” and compare if the voter actually expressed a preference for a women candidate. T_i is a dummy if the individual belongs to the treated units.
Does the informational effect influence the matching between preference expressed and actual vote behavior?
H0: No, b_1=0
H1: Yes, b_1≠0

Does preference expressed predict actual vote behavior?
H0: No, b_3=0
H1: Yes, b_3≠0

B2 – Demand for transparency and use of transparency.
We will compare the matching between
-the demand for transparency expressed by voters and the actual use of the information.
we regress an equation:
R_i= b_2*D_i+u_i,
where D is the answer of the individual to the question “are you interested in political transparency”, “do you think political transparency is important” (D1_16), and R is a dummy on whether the respondent opened the link with the information set.
Does the revealed demand for transparency predicts the use of transparency?
H0: No, b_2=0
H1: Yes, b_2≠0
Representativity of the sample
We will analyze and discuss the representativity of the final sample (respondent of the first and second questionnaire) with respect to:
Sample of all the respondents of the first questionnaire
Total population of Rome (using the survey “Aspetti della Vita Quotidiana” from the National Institute of Statistics (ISTAT) that contains the same questions)
The analysis will comprise balance tests and computation of selection propensity score (probability of being in the final sample) among sex, age, Education, Area of residence (Municipio), Political Participation, Participation in the electoral campaign (D1_1 to_D1_9 of the baseline questionnaire).
After having computed the propensity scores we will repeat and provide results of testing the hypothesis of the experiment using the propensity scores as weight in the regression models (as suggested by Stuart et al. 2011).

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
A panel survey with two rounds before and after the elections.
Target:
a representative sample of 1000 voters in the Municipality of *HIDDEN* over the age of 18 with gender quotas. Interviews carried in CAWI.
Date:
The first phase of the municipal elections of *HIDDEN*. 3-4 October 2021
First Round:
To be held within 4 days before the election date.
The interviewees will be subjected to a questionnaire of about 18 questions, lasting about 6 minutes.
Treatment:
At the end of the questionnaire, 50% of the interviewees, chosen randomly, will receive via text message or e-mail a link to a website containing basic information about candidates, such as education, previous political experience, age, and criminal record.
Second Round
To be carried out within ten days following the date of the elections.
The interviewees will be subjected to a questionnaire of about 5 questions, lasting about 2 minutes. The questionnaire will also ask for the choice of the vote for the candidate for mayor and candidates for the city council
Experimental Design Details
A panel survey with two rounds before and after the elections.
Target:
a representative sample of 1000 voters in the Municipality of Rome over the age of 18 with gender quotas. Interviews mixed CAMI and CAWI.
Date:
The first phase of the municipal elections of Rome. The date will be defined by the Ministry of the Interior, in a period between April and June 2021. In case of postponement due to a pandemic, between September and December 2021.
First Round:
To be held within 4 days before the election date.
The interviewees will be subjected to a questionnaire of about 16 questions, lasting about 6 minutes.
Treatment:
At the end of the questionnaire, 50% of the interviewees, chosen randomly, will receive via text message or e-mail a link to a website containing basic information about candidates, such as education, previous political experience, age, and criminal record.
Second Round
To be carried out within ten days following the date of the elections.
The interviewees will be subjected to a questionnaire of about 9 questions, lasting about 2 minutes. The questionnaire will also ask for the choice of the vote for the candidate for mayor and candidates for the city council
Randomization Method
randomization done by survey software
Randomization Unit
individual
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
1
Sample size: planned number of observations
1000 +200 additional control interviewed only for the second questionnaire.
Sample size (or number of clusters) by treatment arms
500 treated individuals, 500 control individuals
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
Hypothesis Variable of Interest Source for the prior Prior Probability Minimum Detectable effect (80% power, 5 % C.I.) Percentage Minimum Detectable effect (80% power, 5 % C.I.) The difference in Percentage Points A1 Turnout Election results 2016 62.47 % 18% 11 (73.47%) A2 Preference Election results 2016 13.68 % 65% 9 (22.5%) A3 Gender of candidate Election results 2016 1.45% 293% 4.5 (6%) A3 Education of candidate (University Degree) Author’s estimate 10.00% 80% 8 (18%) A3 Experience of the candidate (political experience) Author’s estimate 5.00% 125% 6 (11%)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number
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