Technological Change and Preferences for Redistribution

Last registered on August 04, 2022

Pre-Trial

Trial Information

General Information

Title
Technological Change and Preferences for Redistribution
RCT ID
AEARCTR-0009719
Initial registration date
July 08, 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
July 08, 2022, 10:00 AM EDT

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

Last updated
August 04, 2022, 12:57 PM EDT

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

Locations

Region

Primary Investigator

Affiliation
King's College London

Other Primary Investigator(s)

PI Affiliation
King's College London
PI Affiliation
King's College London

Additional Trial Information

Status
Completed
Start date
2022-07-12
End date
2022-07-18
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
Abstract
Fairness perceptions are important in shaping redistributive preferences , as well as support for taxing the rich. Evidence from laboratory experiments finds that demand for redistribution is higher when the better off are seen to have gained their incomes through luck rather than effort. This luck vs. effort distinction misses something important, however, as most real-world distributive decisions are between incomes earned through different types of work. In this study, we provide a first experimental test of whether fairness views and preferences for taxing top earners differ when their income is earned through luck, routine work, or (non-routine) complex work. This set up also mirrors the changing nature of tasks in the US labour market in recent decades as a result of routine-biased technological change, which has seen a substantial shift towards the type of abstract problem-solving tasks that are complementary to information and communications technologies. In three novel incentivised belief elicitations, we also test the underlying causes for the differences in perceived deservingness of top incomes across treatments. Specifically, we test whether such differences are due to a change in perceived cognitive cost, agency, or uniqueness of the required skills across tasks of varying complexity.
External Link(s)

Registration Citation

Citation
Hope, David, Julian Limberg and Nina Weber. 2022. "Technological Change and Preferences for Redistribution ." AEA RCT Registry. August 04. https://doi.org/10.1257/rct.9719
Experimental Details

Interventions

Intervention(s)
Intervention Start Date
2022-07-12
Intervention End Date
2022-07-18

Primary Outcomes

Primary Outcomes (end points)
Individuals that have performed the same task are randomly assigned to groups of 5, with the best performing individual (in the case of the routine and complex work treatments) receiving a payment of $5. For the group receiving the luck treatment, the payment is simply randomly assigned. Respondents (top earners, other workers, and impartial observers) then have the possibility to redistribute the $5 among the group of 5. More specifically, they can choose how much money should be taken away from the top earner and redistributed equally among the other group members. Choices range from $0 (top earner keeps all of the payoff) to $5 (top earner receives no payoff and each of the other four workers receives $1.25). We then measure the extent of this elicited redistributive preference by looking at the amount that is taken away from the top earner as a share of the top earner’s total additional payment. Simply put, this can be seen as a measure capturing the tax rate on top earners, ranging from 0% (no payoff is redistributed) to 100% (all of the payoff is redistributed).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
The experimental design consists of 4 parts.

Part I: Workers are randomly assigned to the luck, routine work, or complex work treatment. They are then randomly allocated to a group of five. Each worker within the group has been allocated to the same treatment. The randomly chosen worker (in the luck treatment) or the best performer (in the routine and complex work treatments) is allocated an initial bonus of $5. This amount will, however, only be paid after the decisions in part II and III.

Part II: Workers are provided with the payoff information for their group (i.e., which group member was allocated the $5). They then have the option to redistribute the $5 allocated to the top earner, to be equally distributed across the other group members. Given there is a 50% chance the decision of one of the five workers will be implemented and that worker is chosen at random, there is a 10% chance an individual worker’s decision will be implemented.

Part III: Spectators each make three allocation decisions, one for each treatment. The order in which they make decisions across the three treatments is randomised. For the routine and complex work decisions, spectators are asked to participate in the respective task themselves for one minute without being informed of their own performance. This stage aims to provide spectators with a better idea of the complexity of each task and allows us to compare spectator and worker decisions while holding task experience constant. For each treatment, spectators are then provided with the payoff information for a group and have the option to redistribute the $5 allocated to the top earner, to be equally distributed across the other group members. There are three spectators for each group and a 50% chance the decision of one of the three spectators will be implemented. As that spectator is chosen at random, there is a 17% chance an individual spectator’s decision will be implemented. Figure 1 illustrates a potential scenario spectators may face for a group in the routine work treatment condition. Spectators receive no information on the preferences expressed by the workers in part II.

Part IV: To determine the underlying mechanism for potential differences in redistributive choices across treatments, we elicit spectator as well as worker beliefs at the end of the experiment. Apart from demographics and distributive and political questions, we specifically include three incentivised belief elicitations that allow us to directly test three potential competing mechanisms:

Perceived Cognitive Cost: Subjects are asked whether they would be willing to perform the task (again) within their treatment condition and, if so, what the minimum amount of payment would be they would want to receive for their participation. To put restrictions on subjects’ required minimum payments, we inform them that 10 subjects with the lowest suggested amount will be selected to actually complete the task at their proposed rate. Although this introduces a strategic element, it will be held constant across treatments. A higher average required minimum payment in the complex work treatment as opposed to the routine work treatment would suggest that the perceived cognitive cost of the complex problems task is higher. Alternatively, if the required minimum payment in the complex work treatment is lower, it would suggest that the cognitive cost is perceived to be lower. This may be the case because the intrinsic motivation to perform the complex problems task is perceived to be higher. This belief elicitation will allow us to test for these possibilities.

Perceived Agency: Subjects (within treatment conditions) are asked to provide incentivised estimates of the average performance of workers in two previous studies that differed (only) in the size of the prize given to the best performer. If subjects believe there to be a larger difference in performance for the slider task than for the complex problems task under different prizes, it would suggest that they perceive workers to have more agency over their effort level in the slider task as compared to the complex problems task. While the absolute estimates for these questions will, of course, be affected by their own performance in the task, the difference between the two prize scenarios should still capture their perception of agency. If subjects are within +/-5 percentage points of the correct answer for each estimate, they will receive an additional payment of 20 cents.

Perceived Uniqueness of Skill: We ask subjects (within treatment conditions) to provide incentivised estimates of the number of workers out of 100 randomly selected ones who were able to perform the task above a certain performance threshold. If subjects are within +/-5 percentage points of the correct answer, they will receive an additional payment of 20 cents. If subjects estimate the number of workers being able to perform very well in the complex problems task to be lower than in the slider task, that would suggest that skills needed for the complex problems task are perceived to be more unique than those needed to perform well in the slider task. The performance threshold is set based on worker performance in the pilot study and corresponds to the number of sliders/complex problems only the top 20% of workers in the pilot were able to complete within 3 minutes.
Experimental Design Details
Randomization Method
Individuals will be randomised by a computer.
Randomization Unit
Individual randomization
Was the treatment clustered?
No

Experiment Characteristics

Sample size: planned number of clusters
2400 workers and 480 spectators in the first wave, and, depending on available funding, a second wave with 600 workers and 120 spectators.
Sample size: planned number of observations
2400 workers and 480 spectators in the first wave, and, depending on available funding, a second wave with 600 workers and 120 spectators.
Sample size (or number of clusters) by treatment arms
3 treatment arms and 800 workers per treatment in the first wave, and, depending on available funding, a second wave with 200 workers per treatment. Each spectator makes one distribution decision per treatment, i.e. there will be 480 spectator distribution decisions per treatment in the first wave, and, depending on available funding, 120 spectator distribution decisions per treatment in the second wave.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
IRB

Institutional Review Boards (IRBs)

IRB Name
King's College London Research Ethics Committee
IRB Approval Date
2022-04-22
IRB Approval Number
MRSP-21/22-29830
Analysis Plan

Analysis Plan Documents

Pre-Analysis Plan: Technological Change and Preferences for Redistribution

MD5: 4c7d9d51c9be6b47f25fce002a9b1e29

SHA1: 7fb49d6aa36111a7cfe0a2ae1fbe1688380392e0

Uploaded At: July 08, 2022

Post-Trial

Post Trial Information

Study Withdrawal

There is information in this trial unavailable to the public. Use the button below to request access.

Request Information

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