Changing attitudes toward migrants in host societies to promote safe human mobility policies: a randomized online experiment in Mexico

Last registered on May 13, 2024


Trial Information

General Information

Changing attitudes toward migrants in host societies to promote safe human mobility policies: a randomized online experiment in Mexico
Initial registration date
May 04, 2024

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
May 13, 2024, 12:03 PM EDT

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


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Primary Investigator

Maastricht University / United Nations University

Other Primary Investigator(s)

Additional Trial Information

On going
Start date
End date
Secondary IDs
Prior work
This trial does not extend or rely on any prior RCTs.
This study aims to expand the knowledge into the efficacy of light touch interventions as tools for promoting more pro-social attitudes towards migrants framed in a long-term goal of cultural change. Specifically, two interventions in video format are tested: one focused on the perspective-taking approach, which posits that individuals envisioning themselves from others' standpoints can reduce negative attitudes towards the other group, and social categorization, which argues that people tend to hold more positive attitudes towards other groups when perceiving them as less heterogeneous.
External Link(s)

Registration Citation

Alejo Fernández, Inés. 2024. "Changing attitudes toward migrants in host societies to promote safe human mobility policies: a randomized online experiment in Mexico." AEA RCT Registry. May 13.
Experimental Details


This is a online survey experiment with a three group randomized design.

The first component of the treatment variable is derived from the perspective-taking approach, that predicts that when people reflect on the thoughts or emotions of someone from another group, they tend to be less prejudiced. This will be applied to the Mexican context through a video-message that conveys a first-person experience of a Venezuelan migrant who arrived to Mexico recently, emphasizing the vulnerabilities they face in their journey as well as their aspirations.

The second component of the treatment variable, derived from the line of thought that seeks to break the barriers of social groups (in and out-group), an intervention aligned to the theoretical framework of social categorization is proposed. This will be applied through a video message that breaks with the differentiation between “Mexicans” and “migrants”, emphasizing that the distinction is not clear, since the Mexican people have also been historically and continue to be migrant people. It will highlight the fact that Mexico is the country with the second largest number of migrants of origin in the world, so that, in one way or another, Mexicans, as well as individuals who cross through Mexico, are part directly or indirectly (either in first person or with relatives) of migration, either nationally or internationally, emphasizing common identity and seeking to blur the group distinctions.

The third component of the treatment variable is the control group, which will not watch any placebo video and will directly answer the rest of the survey.
Intervention Start Date
Intervention End Date

Primary Outcomes

Primary Outcomes (end points)
Attitudes of the host population toward migrants are conceptualized according to the ABC model, which states that attitudes comprise three components: affective, behavioral, and cognitive. This model is applied to the field of discrimination in the following manner: affective (prejudices), behavioral (intended behavior), and cognitive (stereotypes).
Primary Outcomes (explanation)

Secondary Outcomes

Secondary Outcomes (end points)
Heterogeneous effects among age groups and highest degree of education.
Secondary Outcomes (explanation)

Experimental Design

Experimental Design
To examine the extent to which perspective-taking and social categorization messages impact Mexican’s attitudes towards migrants, a randomized online experiment will be conducted. The experiment will take place on Prolific, a platform specialized in recruiting and managing participants for online research. It is to be noted that recent research indicates that Prolific stands out among similar online platforms for providing participants who consistently offer higher quality responses, adhere to instructions, work at a normal pace, and can verify their unique IDs, thus ensuring they are not bots (Douglas et al., 2023).
The population of interest is Mexican adults registered in Prolific, surpassing 2,800 individuals. To overcome one of the limitations encountered by previous experiments - which is that most of the research done has targeted college-age individuals (Paluck et al. 2021) the sample will consist of the adult population in general rather than only college-aged individuals. The experiment will involve a survey created using Qualtrics. Initially, participants will receive a brief explanation of the study, followed by a consent form. Upon agreeing to participate, they will be first asked a series of demographic questions. After this, participants will then be assigned to one of the videos or to the control group. Following the intervention, additional questions will be posed to evaluate the potential changes in attitudes regarding migrants both in regard to their impact in public safety perception as well as in regards to their inclusion in the labour market.

To determine whether the treatments impact the outcomes of interests, two statistical strategies will be employed: mean group differences and linear in means models.
Further, to ensure the comparability of the control and treatment groups, mean balance tests will be conducted to assess the baseline characteristics across the three groups. This process aims to discover whether any significant imbalances exist between the groups before the implementation of the treatment. If the balance tests reveal substantial imbalances between the treated and control participants across variables, a robustness check will be conducted using a linear regression that includes the treatment variable as well as the control variables that were found not to be balanced. This analysis will assess whether the treatment effect remains consistent when controlling for other variables that may influence the outcome. The mathematical expression for this robustness check is as follows:
Y=β+ ŤDi+X′B+ϵi
In the above equation, Y is the dependent variable, β is the intercept, ŤDi is the treatment variable, X′B is the variable or set of variables that were not balanced and ϵi corresponds to the error term.
After completing the robustness checks, and with the aim to answer Hypothesis 1, which suggests that receiving the treatment will increase positive attitudes toward migrants, the effect of the treatments will be analized with a saturated model, that given the three-group randomized design is expressed as:
Y=α+β1T1+β2T2 +ϵ
After this, heterogeneity analyses will be conducted to assess whether there are differences in effect across subgroups of interest. In this case, to answer hypothesis 3, which suggests that the effect will be lower in less educated individuals as well as in older people, a linear regression with interaction terms between treatment and both age and education respectively will be performed as follows:
yi = β+ ŤDi + agei + (ŤDi x agei) + Xi′B + ϵi
yi = β+ ŤDi +education + (ŤDi x education ) + Xi′B + ϵi
Experimental Design Details
Not available
Randomization Method
Survey designed in Qualtrics, utilizing the block randomization option to ensure an equal proportion of treatments.
Randomization Unit
Was the treatment clustered?

Experiment Characteristics

Sample size: planned number of clusters
No clusters
Sample size: planned number of observations
450 individuals
Sample size (or number of clusters) by treatment arms
150 aprox per treatment arm
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)

Institutional Review Boards (IRBs)