1. Causal Inference [20 points] Many conditional cash transfer (CCT) programs like the one we discussed in Mexicotransfers conditional on child enrollment and regular health checkupsare introduced in a staggered manner. That is, one group of locations get the program today, another gets the program in 1 year, and so on. Suppose you are asked by your country's government to evaluate the impact on health status of a conditional cash transfer (CCT) scheme that was provided to region A in January 2020, region B in January 2021, region C in January 2022, and region D in January 2023. Suppose that (i) you have data on health indicators in all four regions in January of each year 2019-2022, and (ii) if CCTs have any impact on health, it will occur within 1 year. Note that for the sake of the exercise, we presume that cash transfer programs improve heaith status. Later in the course, we wilt see whether this is actaaiiy the case. (a) Describe a difference-in-difference procedure to estimate the impact of the CCT on health. What will you measure? When? What will the treatment group(s) and control group(s) be? (b) Suppose that in January 2020, households in regions B, C and D were notied that they would not receive the program until the later scheduled date. As a result, these households began to work even harder, earn more income, and engage in better health practices than before they learned about the forthcoming program. This is an example of what type of bias/effect? Will this effect lead you to over- or under-estimate the true impact of CCTs on health? What would be one solution for addressing this bias? (c) Suppose that region A is poorer than region B, which is poorer than region C, which is poorer than region D. This suggests that policymakers chose to place the program in poorer regions rst. This is an example of endogenous program placement, which can bias our estimates of program impact. Suppose that you can go back in time and design a randomized control trial (RCT) to study the impact of the CCT. How would the design compare to the approach in part (a)? ((1) Suppose that region B is adjacent to region A and some households in region B gain access to the program as early as January 2020. This is an example of what type of problem in program evaluation? How will this affect the estimated impact of the program if we just focused on the difference-in-difference estimate for regions A and B? [Hint: how will YB;- Ygo look in this case compared to a case where region B is very far from region A and no households in B receive the program until January 2021?] (e) Suppose we are evaluating a CCT program that randomly assigned cash transfers to half of the poor households in one region. This led to a large increase in the use of public health clinics by program recipients, which, in turn, put pressure on scarce health infrastructure and supplies and reduced the available medicines for non-recipients. This is an example of what type of challenge in program evaluation? How will this affect the estimated impact of the program? (t) Suppose that it is now the year 2025, the program exists in every region, and you are interested in studying the impact of CCTs on household members\" medium-term health behavior. Someone notices that the implementing agency in region B only provided CCTs to households with landholdings less than 1 hectare. How would you use this rule to estimate the program impact? Describe the empirical strategy: dene the treatment and control groups, state the relevant assumptions, etc