Robots and Redistribution

Last registered on March 04, 2022

Pre-Trial

Trial Information

General Information

Title
Robots and Redistribution
RCT ID
AEARCTR-0009045
Initial registration date
March 03, 2022

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
March 04, 2022, 9:16 AM EST

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

Locations

Region

Primary Investigator

Affiliation
University of Cambridge

Other Primary Investigator(s)

PI Affiliation
PI Affiliation

Additional Trial Information

Status
In development
Start date
2022-03-03
End date
2022-03-24
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
This project considers the factors influencing political preferences, and preferences for redistribution. The point of departure is that people’s beliefs on job automation can affect their views on a range of issues. The primary goal of the project is to investigate how respondents’ perceived likelihood of job automation affect their political preferences, preferences for redistribution, and views on populism using new large-scale survey data.
External Link(s)

Registration Citation

Citation
Boneva, Teodora, Marta Golin and Christopher Rauh. 2022. "Robots and Redistribution." AEA RCT Registry. March 04. https://doi.org/10.1257/rct.9045
Sponsors & Partners

Sponsors

Experimental Details

Interventions

Intervention(s)
Online questionnaires will be sent to a representative sample of United States labor force participants aged 25-54 years old. The target sample is 8,000 respondents. We will use quota-based sampling to ensure representativeness of the sample across broad age categories, regions in the United States, gender, occupational groups, and educational attainment. Respondents will answer a range of survey questions detailing their background (i.e. individual characteristics/personality traits, education, work experience, location etc.) and their beliefs on the likelihood of their job being automated/taken over by robots. The respondents then will be grouped into two treatment and one control group. Randomization will be performed at the individual level, and study participants will have a roughly 33% chance of being assigned to the control group or either one of the two treatment groups. Participants assigned to the two treatment groups will be given truthful information on the average expectation of job automation in their occupation obtained from a previous survey, and how this compares with their own expectations, which we will elicit before treatment assignment. The material from the information treatment comes from surveys conducted over March – May 2020 as part of the “Covid Inequality Project”, with representative samples of members of the labor force in the United States. The two treatments will differ in whether the information will be phrased as coming from other people from the labor force in the United States (`Treatment 1’) or from expert economists (`Treatment 2’). The control group will not receive any information. Then, the respondents will be asked various questions on their political preferences, views on populism, and support for redistribution. This methodology enables the analysis on how a change in perception effect views on populism and political preferences by controlling for gender, age, occupation, industry, municipality, education, income, and personality traits.

Experimental details (hidden): We will estimate the average treatment effect of the treatments on each outcome by running OLS regressions of the outcome on the treatment dummies. To increase precision of our estimates, we will control for baseline characteristics (gender, age, occupation, industry, municipality, education, income, and personality traits). In addition, we will look at several dimensions of heterogeneity. More specifically, we are interested in whether the treatments are differentially effective for (i) men vs women, (ii) old vs young workers, (iii) people in different occupational groups, (iv) people from different socio-economic backgrounds, and (v) people whose expectations about job automation are below / above the numbers from the previous representative study.
Intervention Start Date
2022-03-03
Intervention End Date
2022-03-24

Primary Outcomes

Primary Outcomes (end points)
o Support for Universal Basic Income
o Beliefs about unfairness of inequality and unemployment
o Support for capital gains tax
o Support for taxes on income generated by robots
o Support for spending on adult retraining and unemployment benefits
o Support for unions
o Earnings expectations
o Views on political ideology
o Trust in the government and experts
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
Hypothesis: If the respondent receives information that the probability of their job being automated is greater (lower) than what they perceive, they will be more (less) likely to favor Universal Basic Income (UBI), redistribution, or adult training programs. The treatment intensity might differ depending on whether the given information is said to come from university experts or from people working in the same type of job.
Experimental Design Details
Randomization Method
Randomization performed by Qualtrics, the survey software we use to conduct the survey.
Randomization Unit
Randomization is performed at the individual level and we do not have any clusters.
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
8,000 individuals. Participation criteria: age 25-54, residence in the US and either employed or actively looking for employment, having had previous work experience.
Sample size (or number of clusters) by treatment arms
Sample size by treatment arm: 2667 individuals ‘Control’, 2666 individuals ‘Treatment 1’, 2666 individuals ‘Treatment 2’ (approximately).
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
IRB Approval Date
IRB Approval Number

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