|
Field
Intervention (Public)
|
Before
The experiment will be embedded into the survey questionnaire.
The experimental block will follow a quasi-prepost design. This means that the outcome variables are measured before and after the treatment, but with similar (rather than identical) questions. This is a form of repeated measurement design which aims to minimize the consistency pressure arising from measuring the outcome twice (Clifford et al., 2021). Eliciting a prior first-stage outcome variable allows the researcher to test the heterogeneity of treatment effect dependent on prior beliefs (Stantcheva, 2023). For example, one can check whether the treatment effects are greater for those who received a larger “shock” from the treatment (ibid.). Pre-testing also allows the researcher to control for individual fix-effects, contributing to greater measurement precision (Clifford et al., 2021).
Pre-test. In the pre-test, respondents are asked to rank government expenditure areas in order of their importance. Supporting families with children will be listed alongside other conventional functions (such as health care, infrastructure, pensions for the elderly) and functions not typically associated with government duties (e.g., supporting international corporations). The multiple choice question will avoid drawing explicit attention to our topic of interest, which minimizes priming, experimental demand effect, and consistency pressures.
The treatment. In the experimental section, respondents will be randomly assigned to three equally sized groups (one control and two treatment arms). This will be done using Qualtrics’ built-in randomize function. Each experimental arm will be shown a short (2-minute) video about child and family policies. No video will be presented to the control group.
The video treatment administered to one treatment arm will emphasize the immediate benefits of spending on children and families, such as reducing the burden of parents and improving the well-being of children. The expectation is that this will prime respondents to think about their immediate self-interest, which will depend on whether they (intend to) raise children. This frame may also increase support among non-parents and the elderly who are altruistic (i.e., utility through altruism).
The other video treatment will introduce the notion of endogeneity of future pensions to current child benefits. It will thus emphasize the long-term benefits for the economy and for pension sustainability. It is expected to increase support particularly among childless, but young individuals, for whom the direct utility from child benefits is zero. In terms of our utility model developed in the theoretical section, the treatment aims to activate the forward-looking term. Because of discounting or bounded rationality, the intertemporal link may be undervalued without the provision of information on it.
Post-test. Following the treatment, I will measure both the first-stage and second-stage outcomes. It is important to measure the first-stage outcome because it is the only opportunity to check if the treatment has worked (successfully shifted perceptions). The second-stage outcome refers to my main variables of interest, i.e., respondents’ policy preferences. For more details, see the operationalization of key variables in the Analysis Plan section.
|
After
The experiment will be embedded into the survey questionnaire.
The experimental block will follow a quasi-prepost design. This means that the outcome variables are measured before and after the treatment, but with similar (rather than identical) questions. This is a form of repeated measurement design which aims to minimize the consistency pressure arising from measuring the outcome twice (Clifford et al., 2021). Eliciting a prior first-stage outcome variable allows the researcher to test the heterogeneity of treatment effect dependent on prior beliefs (Stantcheva, 2023). For example, one can check whether the treatment effects are greater for those who received a larger “shock” from the treatment (ibid.). Pre-testing also allows the researcher to control for individual fix-effects, contributing to greater measurement precision (Clifford et al., 2021).
Pre-test. In the pre-test, respondents are asked to rate government expenditure areas based on their importance. Supporting families with children will be listed alongside other conventional functions (such as health care, infrastructure, pensions for the elderly) and functions not typically associated with government duties (e.g., supporting international corporations). Asking about multiple expenditure functions will avoid drawing explicit attention to our topic of interest, which minimizes priming, experimental demand effect, and consistency pressures.
The treatment. In the experimental section, respondents will be randomly assigned to three equally sized groups (one control and two treatment arms). This will be done using Qualtrics’ built-in randomize function. Each experimental arm will be shown a short (2-minute) video about child and family policies. No video will be presented to the control group.
The video treatment administered to one treatment arm will emphasize the immediate benefits of spending on children and families, such as reducing the burden of parents and improving the well-being of children. The expectation is that this will prime respondents to think about their immediate self-interest, which will depend on whether they have children. This frame may also increase support among non-parents and the elderly who are altruistic (i.e., utility through altruism).
The other video treatment will introduce the notion of endogeneity of future pensions to current child benefits. It will thus emphasize the long-term benefits for the economy and for pension sustainability. It is expected to increase support particularly among childless, but young individuals, for whom the direct utility from child benefits is zero. In terms of our utility model developed in the theoretical section, the treatment aims to activate the forward-looking term. Because of discounting or bounded rationality, the intertemporal link may be undervalued without the provision of information on it.
Post-test. Following the treatment, I will measure both the first-stage and second-stage outcomes. It is important to measure the first-stage outcome because it is the only opportunity to check if the treatment has worked (successfully shifted perceptions). The second-stage outcome refers to my main variables of interest, i.e., respondents’ policy preferences. For more details, see the operationalization of key variables in the Analysis Plan section.
|