Estimates

Early teenager matrimony and dropping from twelfth grade each other provides sizable consequences towards the possibilities a lady becomes inside the impoverishment. The newest prices mean that marrying younger is actually for the a 30.six commission point increase in the likelihood of residing in impoverishment. Shedding out-of senior school was from the an enthusiastic eleven.2 fee point boost in impoverishment.

I now establish a series of alternative estimation methods to determine new robustness of your own baseline impact. Dining table 1 indicated that aggregation produced an enormous distinction to have OLS estimates: the person-level efficiency suggested a tiny impact out of very early adolescent relationship towards impoverishment, while the group-peak OLS quotes ideal a massive perception. The initial line for the Desk 5 repeats the new baseline IV studies, but this time having categorized studies. The information was aggregated within state out-of delivery ? seasons off beginning ? census 12 months level. Brand new categorized-investigation IV prices is amazingly just like the private-level IV rates (0.314 versus 0.306 to have early adolescent matrimony and you can 0.112 in the place of 0.112 to possess dropout). Brand new similarity of your own coefficient rates isn’t surprising just like the tool try ongoing for everyone some body in a state-cohort class, efficiently aggregating the individual-top and class-top estimates. The high quality errors in addition to change little with all the classified studies, growing by about ten% to own early teen matrimony rather than anyway to have dropout. As aggregated studies supplies quite similar point rates and you will a bit a great deal more old-fashioned practical errors, with what uses, We introduce results for aggregated investigation until if not listed. sixteen

Dining table 5

Notes: Basic mistakes, adjusted having clustering of the condition out-of birth, are shown within the parentheses. All of the regressions include dummy variables for census season, competition, ages, county off delivery, and you may cohort off beginning, and you will area for delivery style. Analysis is aggregated to say off delivery ? season out-of beginning ? census year mobile form. See the cards to help you Dining table step 1 . The brand new migration-adjusted means is demonstrated throughout the text plus the manage setting approach is discussed on the text as well as the appendix.

The next activity is always to measure the effect migration has on the newest assignment of condition rules having relationships, schooling, and you can work together with subsequent IV quotes. As the a note, rules try assigned considering an effective female’s condition off delivery, even when ideally we want to make use of the condition a woman lived in at ages fifteen. Because the specific lady provides migrated out of their beginning county and you will toward your state that have an alternate gang of regulations because of the ages fifteen, the instruments are mentioned having mistake. We evaluate just how which impacts new IV prices within the column step three of Dining table 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 z_ij * 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 p_jk represents the probability that a woman will live in state k at age 15 given that she was born in state j, and w_k 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[z_ij * gay hookup app ] for z_ij * yields consistent estimates in an IV framework. The remaining issue is how to consistently estimate the conditional migration probabilities, p_jk. 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.