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The big committee out of Dining table 4 merchandise the latest baseline results for the latest instrumented poverty regression

The big committee out of Dining table 4 merchandise the latest baseline results for the latest instrumented poverty regression


Very early teen relationship and you can losing from high-school each other possess sizable outcomes towards chances a woman will end up from inside the poverty. The fresh new quotes imply that marrying younger try for the a 30.six fee section rise in the chances of staying in poverty. Shedding from senior high school was with the a keen eleven.2 payment area rise in poverty.

I now establish several choice estimate approaches to assess new robustness of the standard impact. Table 1 revealed that aggregation produced a massive difference to own OLS estimates: the person-top performance advised a tiny impact from very early teenager relationships toward poverty, because the category-height OLS prices suggested a huge feeling. The initial column inside the Dining table 5 repeats the newest baseline IV studies, however, this time around that have categorized data. The information and knowledge are aggregated within state off birth ? 12 months from birth ? census 12 months top. The new classified-data IV estimates try amazingly just like the private-peak IV estimates (0.314 as opposed to 0.306 to own very early teenager relationships and 0.112 in place of 0.112 to own dropout). New resemblance of your own coefficient estimates is not stunning because tool was ongoing for all someone in a condition-cohort category, efficiently aggregating both personal-top and you will category-height rates. The product quality errors including transform hardly any when using the classified study, growing because of the throughout the 10% to own very early teen relationship and not at all getting dropout. Because the aggregated study supplies much the same area estimates and you will slightly far more conventional simple mistakes, with what pursue, We establish outcomes for aggregated studies unless or even listed. 16

Desk 5

Notes: Important mistakes, adjusted to have clustering from the state away from beginning, receive within the parentheses. All of the regressions are dummy parameters to have census seasons, race, years, state regarding birth, and you may cohort away from delivery, and you will region of beginning manner. Analysis was aggregated to state off delivery ? year out of delivery ? census seasons phone function. Comprehend the cards so you’re able to Table step 1 . The fresh migration-adjusted approach are discussed on text message as well as the control function means are described regarding text in addition to appendix.

The next activity will be to gauge the feeling migration has on the fresh new assignment away from state laws for relationships, schooling, and you may work therefore the subsequent IV estimates. Because the a reminder, statutes try assigned considering good woman’s condition from birth, even in the event preferably you want to make use of the county a woman lived-in from the ages fifteen. Given that certain females features moved out of their beginning state and you may with the your state having a different https://datingmentor.org/escort/fairfield/ gang of guidelines by the decades fifteen, the various tools are counted having mistake. We evaluate exactly how that it impacts the latest IV rates inside column step three of 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.

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