Propensity Score

i. We will estimate the propensity score matching using the control variables described in the previous section. We will use a Probit specification to estimate the propensity score for each sample member. The propensity score can be translated into β€œthe probability of being selected into the treatment”. The Probit model is as follows: 𝑃𝑃𝑆𝑆𝑖𝑖 = 𝐡𝐡𝑋𝑋′ + πœ–πœ–, where PSM is the propensity score for each i participant, which can be predicted by a matrix of X predictors that include all of our control variables. We will not include either participation in the program, the outcome variable, or age in this regression.

ii. Once we have the PS for each participant, we want to find area of common support. This means that we want to look for participants who have similar propensity scores, but who belong to both Cohort 1 and Cohort 2. The area of common support guarantees that our groups are comparable, since they are statistically the same in almost everything except for the participation in the program.

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iii. After we have common support, we will use Stata to non-parametrically estimate both nearest neighbor match average treatment effect and kernel density. We will estimate both matching methods for robustness. The outcome of this estimation will yield the difference between our dummy variables of being enrolled in a STEM field at the end of freshman year. Since the outcome is a variable between 0 and 1, we can interpret our results as how much participating in the program affects the probability of being enrolled in a STEM degree at the end of freshman year.

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