The big panel away from Dining table 4 presents the newest baseline results for the new instrumented poverty regression

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The big panel away from Dining table 4 presents the newest baseline results for the new instrumented poverty regression


Very early teenager relationship and dropping off twelfth grade both possess large effects into probability a female will become inside impoverishment. The fresh new rates indicate that marrying younger are of a thirty.6 percentage section increase in the probability of residing impoverishment. Dropping away from twelfth grade is actually of an enthusiastic 11.2 percentage section escalation in poverty.

We now establish a series of choice estimate remedies for determine the new robustness of one’s baseline effect. Dining table step 1 indicated that aggregation produced a giant distinction for OLS estimates: the individual-height overall performance recommended a tiny feeling out of very early teenager relationships into the poverty, once the classification-height OLS prices recommended a massive impact. The initial line during the Dining table 5 repeats brand new standard IV data, however, now that have labeled investigation. The information is aggregated within state from birth ? year away from delivery ? census season peak. Brand new labeled-analysis IV estimates try amazingly just as the individual-peak IV prices (0.314 rather than 0.306 to have early teenager matrimony and you may 0.112 instead of 0.112 to possess dropout). The similarity of the coefficient quotes is not surprising once the instruments is actually constant for all someone in a state-cohort group, effortlessly aggregating both individual-level and you will classification-peak quotes. The product quality mistakes together with transform little or no when using the grouped investigation, broadening by throughout the ten% getting very early teenager relationships and not at all to possess dropout. Since the aggregated research produces comparable area estimates and you will a bit alot more traditional simple errors, with what employs, We expose outcomes for aggregated analysis unless of course otherwise noted. sixteen

Table 5

Notes: Practical errors, adjusted for clustering of the state off delivery, are offered within the parentheses. All of the regressions are dummy details getting census seasons, race, years, condition out of delivery, and cohort off beginning, and you will region of birth fashion. Data is actually aggregated to say off beginning ? seasons out-of delivery ? census season phone mode. See the notes so you can Desk step one . The new migration-adjusted means are demonstrated on the text message and the manage means means is described on text and also the appendix.

Next task will be to assess the impact migration has on this new assignment out of state laws and regulations for relationships, schooling, and you can performs plus the next IV estimates. Because the a reminder, legislation is assigned based on a good female’s county out-of delivery, regardless if ideally you want to utilize the condition a woman stayed in in the age fifteen. Due to the fact specific female keeps moved out of their birth state and for the a state which have an alternative gang of statutes because of the age fifteen, the equipment are mentioned having error. I evaluate how that it affects the fresh IV prices within the column step 3 regarding Desk 5 .

To see how I examine the issue, notice that the expected value of the ideal (but unobserved) state laws can be calculated if migration probabilities are known. Let zij * be a dummy variable indicating the state law woman i faces at age 15, given she was born in state j. The asterisk indicates that this variable is not observed, given that she may have moved from her birth state by age 15. However, if migration probabilities are known, the expected value of this variable can be calculated as

where pjk represents the probability that a woman will live in state k at age 15 given that she was born in state j, and wk is the law in force in state k for the relevant year. The same logic applies when there are several variables for the state laws.

It is straightforward to show that substituting in E[zij * ] for zij * yields consistent estimates in an IV framework. The remaining issue is how to consistently estimate the conditional migration probabilities, pjk. Although this information is not available for all women, the migration patterns for women who were age 15 at the time of the census enumeration can be estimated because the census records both state of birth and state of current residence. I use 15-year-old women in the 1960 census to estimate these migration probabilities. I then calculate the expected value of the laws based on the state a woman lived in at age 15 as outlined above and use these expected laws as instruments. 18 The migration-adjusted estimates in column 3 of Table 5 are very similar to the baseline estimates, indicating that the assignment of state marriage laws based on state of birth is a reasonable approach.