Problem 1 - Interpreting Regression Results: Recycling [26 points] Entrepreneurs introducing new technologies often have a hard time driving adoption. Entrepreneurs often try to drive adoption of their technologies by stressing their social benefits. Household recycling is a technology that seems amenable to this approach, as its social benefits are especially clear. An organization in Peru conducted an experiment to encourage households to recycle more. The results are described in this article. This problem will focus on interpreting the results reported in the article. This problem focuses just on Table 4 of that article and does not require downloading any data. The paper contains two experiments, a "participation study" and a "participation intensity study." In this problem, we will focus on the latter study, whose results are contained in Table 4 in the paper, which is on page 20. You may need to read the relevant section of the paper (starting at the bottom of page 17 of the manuscript until the bottom of page 19) in order to understand the experimental design and variables. (Note that "indicator variable" is a synonym for "dummy variable," in case you haven't seen this language before, which means a variable that always either takes a value of 0 or 1.) In Table 4, each column gives the results of a separate regression where the authors analyze the effects of the same treatments on different outcome variables. The header row (heading of each column) tells you which outcome variable they are using in the regression reported in that column. The header column (the column to the left to column 1) lists out all the independent variables that the authors included on the right hand side of regression equations. Each coefficient estimate consists of two parts in the table: the estimate itself with stars indicating its statistical significance, and the standard error of the estimates reported in the parentheses right underneath the estimates. Note that they also never report the intercept of the regression because they don't think it is meaningful. Instead, they write "mean of dependent variable," since they think that is more useful to know than what the intercept tells you (the expected value of the outcome when all the covariates are zero). "Dependent variable" is a synonym for the outcome. a. In Column 3 of Table 4A, what is the estimated ATE of providing a recycling bin on the average weight of recyclables turned in per household per week, during the six-week treatment period? Provide a 95% confidence interval. [5 points] b. In Column 3 of Table 4A, what is the estimated ATE of sending a text message reminder on the average weight of recyclables turned in per household per week? Provide a 95% confidence interval. [5 points]

c. Which outcome measures in Table 4A show statistically significant effects (at least the 5% level) of providing a recycling bin? [4 points] d. Which outcome measures in Table 4A show statistically significant effects (at least the 5% level) of sending text messages? [4 points] e. Suppose that the covariate "percentage of visits turned in bag, baseline" had been left out of the regression reported in Column 1. [Note: by "baseline," they mean, "before the experiment started." So, "percentage of visits turned in bag, baseline" just means "percentage of weeks in which the household turned in a bag in the weeks before the treatment began." The outcome variable in this regression, by contrast, is the same measure, but collected after the experiment started.] What would you expect to happen to the results on providing a recycling bin? In particular: Would you expect an increase, decrease, or no change in the estimated ATE? Would you expect an increase or decrease in the standard error? Explain your reasoning. [4 points] f. In column 1 of Table 4A, would you say the variable "has cell phone," which was measured before the start of the experiment, is a "bad control" that should not be included in the regression? Explain your reasoning. [4 points]

Here is the table 4A: