1. our colleague asks for advice on a study she is planning. She Wants to test whether providing young men (ages 15—25) with a bicycle in Chile increases their probability of employment. Her hypothesis is that a lack of transportation is a serious impediment to employment for these individuals. Being a good researcher who understands causal inference, you immediately chime in that she should randomly assign which study participants get a bicycle and which ones don’t get a bicycle. She replies that randomization is too risky. She previously ran a small-scale pilot study and when she randomized her participants into treatment and control groups. the treatment group on average had older boys. She continued, I can’t risk a had randomization, so I’m going to pick and choose which participants get the bikes and which ones don’t. I won’t have leftover money to run another study if I botch this one.”
What would you recommend she do if she wants to estimate a causal quantity, she has a serious budget constraint, and she is concerned about the risk of bad randomization Briefly explain. [Ipt)
2. She runs her study. A year later she reports back to you with the data she collected. Each figure below is a mean based on her sample of n — 500 study participants (half assigned to treatment, half assigned to control). Estimated standard errors are reported below in parentheses [2ptsj
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