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Homework1-AK - Homework 1 ECO 7427 Spring 2011 ANSWER KEY...

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1 Homework # 1 ECO 7427, Spring 2011 ANSWER KEY Prof. Sarah Hamersma 1. The key to answering this question is to note that (1) the word “significant” should be used with “statistically” when that is the proper meaning and (2) one should not talk about the direction o f an “effect” if the standard errors make the direction of the effect uncertain. An ideal answer recognizes the relationship between the three variables: the change in Medicaid coverage plus the change in private coverage must add up to the change in “any coverage , and so the set of estimates taken together provide insight about crowd-out. My answer: The coefficients on the interaction between the target-group dummy and the post-expansion dummy presented in the second row provide our regression estimates of the effects of the parental-eligibility expansions. On average, the expansions increased maternal Medicaid coverage by a statistically significant 2.7 percentage points, which is a large increase from the prior level of participation. Given this increase, we expect to find that either total coverage (measured by “any”) incre ased as well, or it was crowded out by a decrease in private coverage. We are unable to precisely measure the effect on private coverage; while the coefficient is -.013, we cannot be confident that the effect is negative since the coefficient has a t-value of only about 1. Given this uncertainty in measuring crowd out, there is also uncertainty about the effects on “any coverage.” The coefficient on “any coverage” is positive, as expected based on the other point estimates, but we cannot be confident that the true effect is positive due to its large standard error. Thus the key result that we can state with confidence is that the expansions did appear to increase Medicaid coverage, but we do not know the extent to which crowd-out may have dampened these gains overall. 2. [ directions omitted] Using STATA, produce a table of descriptive statistics (means, standard deviations, mins and maxes) for each of the two data sets. Briefly indicate your reasoning for including these particular variables in your dataset. . clear . use CPS1993 . sum Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- OCCURNUM | 14609 1.088028 1.428057 0 14 H_IDNUM | 14609 4.46e+11 3.30e+11 1.04e+07 1.00e+12
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2 PHF_SEQ | 14609 1.229721 .4906753 1 7 A_HGA | 14609 39.37799 2.74167 31 46 A_RACE | 14609 1.316038 .6872655 1 5 -------------+-------------------------------------------------------- FOWNU18 | 14609 .6000411 .9943722 0 9 A_FTPT | 14609 .1119858 .3534364 0 2 A_CIVLF | 14609 .7110001 .4533134 0 1 A_AGE | 14609 37.02587 13.79849 16 65 HG_ST60 | 14609 51.67657 26.43724 11 95 -------------+-------------------------------------------------------- A_SEX | 14609 2 0 2 2 A_MARITL | 14609 5.952769 1.159967 4 7 FKIND | 14609 3 0 3 3 year | 14609 1993 0 1993 1993 . use CPS2000 . sum Variable | Obs Mean Std. Dev. Min Max -------------+-------------------------------------------------------- OCCURNUM | 13624 41.59681 1.08178 41 52 H_IDNUM | 13624 4.99e+14 3.21e+14 1.82e+10 1.00e+15 PHF_SEQ | 13624 1.241853 .5197484 1 12 A_HGA | 13624 39.63733 2.703849 31 46 A_RACE | 13624 1.303509 .6511438 1 4 -------------+-------------------------------------------------------- A_FTPT | 13624 .1207428 .3657488 0 2 FOWNU18 | 13624 .5620229 .9549644 0 9 HG_ST60 | 13624 54.62243 26.75401 11 95 A_AGE | 13624 37.3441 13.80067 16 65 A_CIVLF | 13624 .7598356 .4271989 0 1 -------------+-------------------------------------------------------- A_MARITL | 13624 6.037801 1.129946 4 7 A_SEX | 13624 2 0 2 2 FKIND | 13624 3 0 3 3 year | 13624 2000 0 2000 2000 (Note that I recoded the “state” variable in 2000 from it’s original name,
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