Experimental Design Details
Structure of the experiment:
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The first 3 of 5 parts consist of the investment game (Bergemann & Morris, 2019), with 20 periods per part. Each of these 3 parts uses a different level of recommendations (within-subject treatment variation III). Part 4 elicits beliefs (see secondary outcomes). In part 5, I elicit (i) lottery-type choices, capturing behavior in an individual-decision making transformation of the investment game; (ii) risk preferences (Eckel & Grossman, 2002); and (iii) inequity aversion parameters (Fehr & Schmidt, 1999) using the method by Yang, Onderstal & Schram (2016).
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I test comparative static predictions on (i) how the optimal structure depends on the receivers’ strategic environment and (ii) whether recommendations from the information structures are being followed and how this depends on their obedience.
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Null hypotheses: <br>
H1. In games of strategic substitutes, private structures lead to equal investment frequencies as public structures. <br>
H2. In games of strategic complements, public structures lead to equal investment frequencies as private structures. <br>
H3. Diff-in-diff of hypotheses H1 & H2: Equal investment frequencies in public structures compared to private structures for complements vis-a-vis the same comparison for substitutes. <br>
H4. Frequency of following recommendations is equal across the levels of recommendations (treatment dimension III). <br>
H5. Beliefs (levels and correctness) on the state and others' choices are not affected by the type of information structure (private vs. public, level).
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As the main test of hypotheses H1-H3 I will use the intermediate level ("optimal") of the level of recommendation, but will also perform the tests on the pooled data.
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As the key specification, I use regressions of the dependent variables on treatment dummies, using each choice as an observation. I cluster standard errors on the matching group level. I will also estimate these models with additional controls: part fixed effect, a linear period trend within each block, subject controls (risk preferences, social preferences, gender, age) and, for the pooled data, level fixed effects.
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Additional analyses: <br>
- Robustness of main results using non-parametric tests (Mann-Whitney U-tests/Wilcoxon signed-rank tests) with data averaged on the matching group level. <br>
- I will classify subjects into types based on following behavior at each level of recommendation (treatment dimension III). For example, group 1 is classified as "never follower", group 2 as "follow only low", group 3 as "follow low and optimal", etc. I will classify a subject as following a level (treatment dimension III) if at least 15 out of 20 recommendations are being followed, and classify a subject as weakly following if at least 12 recommendations are being followed. I will study correlates of the additional elicitations (risk/inequality preferences, beliefs) with the different types. <br>
- Testing behavioral predictions on a subject level (on risk aversion, inequality aversion).
- Structural estimation of the behavioral parameters (inequity aversion as in Fehr & Schmidt, 1999, and risk aversion, quantal response equilibrium), to be compared with the separately elicited parameters in part 5. <br>
- To study the effect of strategic uncertainty, I compare behavior in parts 1-3 to choices in the first task of part 5.<br>
- To study the impact of imperfect best responses, I will compare behavior to empirical best responses based on elicited beliefs in part 4.<br>
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Data use: <br>
- If participants in the online experiment drop out during the experiment, I will use all data of groups who can complete the experiment, including the dropped out participants for all available data. In the experiment, participants matched with a drop-out see that their participant has not made an active choice and receive the maximum payment in this period. <br>
- To account for learning effects within each part, I will do the analysis both for (i) the full dataset and (ii) dropping the first 7 periods within each part (I continue using 13 periods to ensure power in the restricted sample with experience). <br>