Minimum detectable effect size for main outcomes (accounting for sample
design and clustering)
To approximate power, I simulate 100,000 datasets with the true theoretical treatment effect. On the simulated dataset, I estimate the key specification (regressing investment choice on treatment dummies (dimension II) within game (dimension I), clustering on simulated matching groups). I code an estimate as significant if the p-value is below a significance level of 5%, for a one-sided test, based on the theoretically motivated directional alternative hypotheses (for H1. to H4.). This shows significant estimates for 88.1% of simulations in substitutes (H1), 98.4% for complements (H2), as well as for 99.9% for the interaction effect (H3).