We provide causal evidence on how people's beliefs about the labor market effects of immigration affect their attitudes toward immigration. In the study, we plan to elicit people's beliefs about how the Mariel boatlift, which caused a large influx of low-skilled Cuban immigrants to Miami, Florida, affected local labor market outcomes. Then, drawing on the results from Card (1990), half of the participants will receive information about the actual labor market consequences of the Mariel boatlift. Subsequently, we will measure the participants' support for immigration with self-reported and behavioral measures. In the attached pre-analysis plan, we outline our plan for analysis of the data, including the main specifications of interest, the dimensions of heterogeneity, and corrections for multiple hypothesis testing.
External Link(s)
Citation
Haaland, Ingar and Christopher Roth. 2018. "Beliefs about the Labor Market Impact of Immigration." AEA RCT Registry. May 23. https://doi.org/10.1257/rct.2247-4.0.
1) Attitudes towards low-skilled immigrants:
We compute an index of people's support for increasing the number of low-skilled immigrants based on the following two questions:
- Do you think the US should allow more or less of low-skilled immigrants that are highly familiar with American values and traditions to come and live here?
- Do you think the US should allow more or less of low-skilled immigrants that are not familiar with American values and traditions to come and live here?
2) Attitudes towards high-skilled immigrants: We compute an index of people's support for increasing the number of high-skilled immigrants based on
the following two questions:
- Do you think the US should allow more or less of high-skilled immigrants that are highly familiar with American values and traditions to come and live here?
- Do you think the US should allow more or less of high-skilled immigrants that are not familiar with American values and traditions to come and live here?
3) Intention to sign the petitions:
This variable takes the value minus 1 for people who said they want to sign the petition in favor of decreasing the number of H-2B visas; value 1 for people who said they want to sign the petition in favor of increasing the number of H-2B visas; and value 0 for people who did not want to sign any of the two different types of visa.
4) Actual signing of the petitions:
This variable is only available at the group level. We compute the "net support for increasing H2-B visas" as the number of actual signatures for the petition in favor of increasing the number of H2-B visas minus the number of actual signatures for the petition in favor of decreasing the number of H2-B visas. We then compare the proportion of positive minus negative signatures for the treatment and control group. To do so, we will employ the "Mann-Whitney U test".
Secondary Outcomes (end points)
Secondary Outcomes (explanation)
Experimental Design
We first elicit people's beliefs about the effects of the Mariel boatlift, an unexpected mass immigration of Cubans to the Miami, Florida, which increased the size of the workforce in Miami by 8 percent almost at once. We ask our participants what they think happened to wages and unemployment in Miami relative to wages and unemployment in other comparable US cities that did not experience large inflows of low-skilled immigrants. Specifically, we ask our participants what they think happened to the wages and the unemployment of (i) high-skilled workers and (ii) low-skilled workers.
In a between-subject design, we then inform subjects in the treatment group about the results from a seminal study about the labor market consequences of the Mariel boatlift (Card, 1990). Specifically, we truthfully inform the subjects that this study found that the mass immigration of Cubans to Miami had virtually no adverse effects on the labor market. By contrast, subjects in the control group do not receive any information and go straight from the belief elicitation questions to the outcome questions.
Experimental Design Details
Randomization Method
Randomizer in Qualtrics with the option "evenly present elements".
Randomization Unit
Individual
Was the treatment clustered?
No
Sample size: planned number of clusters
3000 individuals.
Sample size: planned number of observations
3000 individuals.
Sample size (or number of clusters) by treatment arms
1500 subjects in each treatment.
Minimum detectable effect size for main outcomes (accounting for sample design and clustering)
3000 participants give us 0.8 power to detect an effect size of 0.10 of a standard deviation between the treatment and the control group in the main study at a .05 significance level.