Movin’ on up? A Survey Experiment on Mobility Enhancing Policies

Last registered on July 10, 2020


Trial Information

General Information

Movin’ on up? A Survey Experiment on Mobility Enhancing Policies
Initial registration date
July 09, 2020

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 10, 2020, 10:02 AM EDT

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


Primary Investigator

California State University Channel Islands

Other Primary Investigator(s)

PI Affiliation
Bryant University

Additional Trial Information

Start date
End date
Secondary IDs
We use a nationwide survey experiment in the United States to measure whether information on intergenerational economic mobility or policy-specific arguments influence support for six pro-mobility policies advocated by political entrepreneurs. We find the information treatments do not affect support, but the argument treatments significantly increase support for three of the policies. We also include a behavioral measure by allowing respondents the opportunity to write their U.S. Senators. We find argument treatments significantly increase the likelihood that letters address economic mobility and significantly promote advocacy for that policy in the letter, but no increase in advocacy from the information treatments.
External Link(s)

Registration Citation

Barton, Jared and Xiaofei Pan. 2020. "Movin’ on up? A Survey Experiment on Mobility Enhancing Policies." AEA RCT Registry. July 10.
Experimental Details


Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
1. the difference in support (measured by a 5-point likert scale pre- and post-intervention) at the individual level
2. whether the individual wrote anything at all in the U.S. Senator letter prompt
3. whether the individual wrote a letter to their U.S. Senators
4. whether that letter mentioned economic mobility
5. whether that letter mentioned, supported, or opposed each of the policies discussed in the intervention above
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
We posted the survey on Amazon Mechanical Turk (mTurk) with a description stating that the survey would take roughly 10 minutes, and respondents would receive $1.50 for completing the survey (a $9.00 hourly wage, approximately). Respondents were free to drop out at any time or take up to 24 hours to work on all the questions. We restricted our sample to those workers Amazon has verified as U.S. residents. We did not, however, choose to work with only “masters qualified” mTurk workers to avoid frequent (if otherwise reliable) survey experiment participants in favor of less experienced respondents. We launched our survey during U.S. business hours to discourage ineligible international respondents. Respondents only receive payment contingent upon completing the survey and are required to provide a unique password that is only visible at completion. Finally, aside from the voluntarily answered question on whether to write a letter to their U.S. Senator, we required subjects to answer all questions, and pop-up windows reminded them to complete all questions in each section before continuing.

Our experimental design includes eight treatments (two “information” treatments and six “argument” treatments) and a control condition. In all of these, respondents begin by indicating whether they favor or oppose six policies that one or more political actors (elected official or advocacy groups) have linked to improving economic mobility. These six polices are: 1) raising the minimum wage, 2) increasing cash assistance to the poor, 3) providing housing vouchers to move poor people into middle class neighborhoods, 4) universal pre-kindergarten, 5) marriage tax credits to encourage two-parent families for children, and 6) reducing immigration (both legal and illegal). These are not necessarily policies developed by economists nor found to be effective to promote intergenerational economic mobility, but have been framed in terms of current or intergenerational mobility by one or more political entrepreneurs. Following these questions, respondents are randomly assigned into one of the nine conditions.

The information treatments—Relative and Absolute—use a short interactive task to elicit respondents’ beliefs about either relative mobility or absolute mobility, respectively, though they do not elicit any information on the respondents themselves. The Relative task asks respondents to indicate what fraction of children born in the bottom 20 percent of the income distribution in the 1980s end up in each income quintile as an adult today, and also what fraction of children born in the top 20 percent end up in each income quintile today. The Absolute task, in contrast, asks respondents to estimate what fraction of children born in each income quintile in the 1980s earn more today than their parents earned when the children were born. Following the respondents’ guesses, we show them how their estimates compare to the actual data from Chetty et al. (2014) for relative mobility and Chetty et al. (2017) for absolute mobility, with the data presented both textually and graphically.

In addition to the two information treatments, we have six argument treatments. Each treatment is an explicit argument for one of the six policies styled similarly to how proponents of that policy tie it to economic mobility or poverty reduction in policy briefs, op-eds, or speeches, alongside a graphical presentation of evidence for the policy drawn from policy entrepreneurs’ arguments. Note that we are not claiming these policies will have the effect of increasing social mobility or decreasing income inequality. Rather, there are proponents of these policies who have framed arguments for each policy in these terms, and we have adapted their arguments and evidence into six treatments. Each of these treatments is similar to the argument for the estate tax in Kuziemko et al. (2015), except that the accompanying visual is always a graph (rather than, e.g., a picture) and the accompanying argument is always framed in terms of increasing social mobility. All treatments are standardized for length (between 159 and 162 words, about the length of a typical abstract).

To ensure that it is the content of each treatment, and not the type of activity (interactive guessing or reading arguments) or the length of time that drives the result, we pair each information treatment (Relative and Absolute) with a placebo argument and each argument treatment with a placebo interactive information task. Following Nickerson’s (2008) use of recycling as a “placebo” (as opposed to an uncontacted control group), we also employ an argument for recycling as a placebo argument for the two information treatments. Similarly, we use an interactive information task about recycling as a placebo for the six argument treatments (specifically, subjects guess what fraction of various products, like lead batteries or newsprint, are recycled). We chose recycling as a relatively “neutral” issue, both on the basis of Nickerson (2008) and of its overwhelming popularity across partisan lines (Pew Research Center 2009), though other survey evidence shows a partisan divide on recycling consistent with differences in the parties toward environmental issues generally (Coffey and Joseph 2013). To ensure consistency in terms of the order of activities, each treatment begins with an interactive task and ends with an argument.
Experimental Design Details
Randomization Method
Randomization done by a computer (Qualtrics-provided randomization into treatment).
Randomization Unit
Individual respondent
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clustering. 1 control and 8 treatments.
Sample size: planned number of observations
2,400 individuals
Sample size (or number of clusters) by treatment arms
~260 individuals per treatment arm (and control)
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)

IRB Name
Study has received IRB approval. Details not available.
IRB Approval Date
Details not available
IRB Approval Number
Details not available


Post Trial Information

Study Withdrawal

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Is the intervention completed?
Intervention Completion Date
April 12, 2019, 12:00 +00:00
Data Collection Complete
Data Collection Completion Date
April 12, 2019, 12:00 +00:00
Final Sample Size: Number of Clusters (Unit of Randomization)
2399 individuals
Was attrition correlated with treatment status?
Final Sample Size: Total Number of Observations
2399 individuals
Final Sample Size (or Number of Clusters) by Treatment Arms
267 Control, 266 Absolute, 263 Relative, 267 Minimum Wage, 268 Cash Assistance, 266 Housing Voucher, 268 Universal Pre-K, 269 Marriage Tax Credit, 265 Less Immigration
Data Publication

Data Publication

Is public data available?

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Program Files

Program Files
Reports, Papers & Other Materials

Relevant Paper(s)

Reports & Other Materials

Movin’ on up? A Survey Experiment on Mobility Enhancing Policies
Barton, Jared and Xiaofei Pan. 2020. "Movin’ on up? A Survey Experiment on Mobility Enhancing Policies." AEA RCT Registry. July 10. 2020. "Registration Entry Title: Movin’ on up? A Survey Experiment on Mobility Enhancing Policies." AEA RCT Registry. July 09

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SHA1: d6e6739f0b9265f5edb4d3bfbbff45e5bd0791ab

Uploaded At: July 09, 2020