|Title||Behavioral effects of descriptive representation -- Lab edition -- Vol 2|
|C1 Background and Explanation of Rationale||
How can diverse societies guarantee inclusiveness? Descriptive representation – that is, whether an individual shares a social characteristic with her elected representative – is advocated as an effective part of the solution. It has been shown to foster political stability, trust, participation, and efficacy, particularly among groups that have been historically marginalized (Gay, 2002; Mansbridge, 1999; Schwindt- Bayer and Mishler, 2005; West, 2017). The effects of descriptive representation have been said to go beyond the political realm and may empower disadvantaged social groups in all aspects of life. Empowerment arises with the existence of role models, a change in the level of expected discrimination, and altered beliefs about how much governmental institutions enforce anti-discrimination policies (among others, Marx, Ko and Friedman (2009)).
Our study (1) experimentally varies whether women’s descriptive representation in national parliaments around the world is framed as positive achievement (positive frame treatment) or reason for concern (negative frame treatment) or not at all (no frame treatment) and, then, in a laboratory experiment, (2) elicits subjects’ willingness to complete a math task for a fixed piece rate (1 token per correctly solved addition) or a variable piece rate (0.5 to 1.5 token as chosen by the male experimenter upon reviewing subjects’ demographic information and performance in a math pre-test). The positive frame treatment illustrates the increase of 13% in female members of national parliaments around the world from 1990 to 2018 as success while the negative frame illustrates the 76% male members in 2018 as vast underrepresentation of women).
|C2 What are the hypotheses to be tested?||Main hypotheses: (H1) We predict an increase of women’s preference for completing the math task under the variable piece rate when receiving the positive frame treatment over negative frame and no frame treatments. We further expect (H2) women’s performance in pre- and main math task to be better as well as their belief in their relative ability to be stronger in the positive frame than the negative and no frame treatments. (H3) Finally, we expect an decrease in women’s belief that they are likely to be discriminated against, and only a slight increase (magnitude less than discrimination belief) in their beliefs about their relative performance (H4). Heterogeneous treatment effects: Treatment effects (on men and women) are expected to vary with subjects’ gender identification, gender role models, collective self-esteem, and beliefs in gender group marginalisation. Mechanism: The effects of the positive frame treatment on women will run through either of the following mechanism: Increased self-efficacy, increased self-esteem, decreased expectation of discrimination, or increased presence of female role models.|
|C3 How will these hypotheses be tested? *||Upon entering the laboratory, subjects answer a range of socio-demographic questions (including gender) and political attitudes questions, and we elicit their risk preference using a standard Holt/Laury (2002, AER)-list. Then, they are randomly assigned to the positive frame, negative frame, or no frame treatments (random assignment blocked on gender). Subjects in the no frame treatment advance directly to the two math tasks while we elicit from subjects in the positive/negative frame treatment further self-reported measures on subjects’ beliefs about gender identification, gender role models, collective self-esteem, and gender group marginalisation (moderator variables) as well as about self-efficacy, expected discrimination in society, role models, and self-esteem (Mediator/Mechanism) variables. The treatment screen (positive/negative frame) is shown between moderator and mediator variables. We recruit women and men from the subject pool at a ratio of 2:1. After the survey, subjects in the positive/negative frame treatment advance to the two math tasks. Conversely, subjects in the “no frame” condition take the survey after they complete those two tasks (so as not to prime them). The two tasks are monetized and contain adding two two-digit numbers. In the pre-math task, subjects receive 1 token for each correctly added number. In the main math task, they can choose between receiving 1 token or a by the experimenter chosen rate from the range 0.5 to 1.5 (outcome measure 1). We also elicit their belief about the percentage of other participants who scored lower than they did (outcome measure 2) and look at their score in both math tasks (outcome measure 3). The average treatment effect of the positive frame treatment is evaluated by a difference-in-proportions (choice fixed rate vs experimenter-set variable rate) and difference-in-distribution tests (performance, belief about relative performance) as well as appropriate regression models which include moderator, mediator, order of task variables, and risk preferences..|
|C4 Country||United Kingdom|
|C5 Scale (# of Units)||100|
|C6 Was a power analysis conducted prior to data collection?||Yes|
|C7 Has this research received Insitutional Review Board (IRB) or ethics committee approval?||Yes|
|C8 IRB Number||n/a|
|C9 Date of IRB Approval||05/06/2019|
|C10 Will the intervention be implemented by the researcher or a third party?||not provided by authors|
|C11 Did any of the research team receive remuneration from the implementing agency for taking part in this research?||No|
|C12 If relevant, is there an advance agreement with the implementation group that all results can be published?||not provided by authors|
|C13 JEL Classification(s)||not provided by authors|