Very early teen matrimony and you will dropping out-of high school both keeps large outcomes towards chances a lady will end up in the poverty. The new estimates mean that marrying young are of a thirty.6 commission section boost in the possibilities of located in poverty. Dropping away from senior high school is actually from the an eleven.2 fee section boost in impoverishment.
We now present several choice estimation remedies for determine brand new robustness of your own standard result. Table 1 showed that aggregation made a large huge difference to own OLS estimates: the person-height efficiency suggested a tiny impact of very early teen relationship into the impoverishment, once the category-top OLS rates recommended a massive feeling. The initial line from inside the Dining table 5 repeats the newest standard IV analysis, but this time having grouped research. The knowledge are aggregated at condition out-of beginning ? seasons away from birth ? census year top. The latest categorized-analysis IV rates was remarkably similar to the personal-height IV rates (0.314 in the place of 0.306 to possess very early adolescent marriage and you can 0.112 as opposed to 0.112 to own dropout). The newest resemblance of coefficient rates is not stunning since the tool are constant for everyone somebody in a state-cohort category, efficiently aggregating both the personal-top and you may class-top prices. The product quality mistakes including alter hardly any when using the labeled research, growing from the regarding 10% getting very early teenager marriage and never after all to possess dropout. As aggregated research produces much the same point quotes and you may a little a great deal more conservative standard errors, in what employs, I expose results for aggregated data unless or even listed. 16
Notes: Basic problems, modified to own clustering because of the condition of birth, receive in the parentheses. Every regressions include dummy variables getting census season, battle, many years, county regarding beginning, and cohort from birth, and you will area for beginning styles. Study is actually aggregated to say from birth ? 12 months of delivery ? census 12 months telephone form. See the notes so you can Dining table step one . The newest migration-adjusted approach was explained on text message and the control function means are demonstrated regarding text message therefore the appendix.
Next task would be to gauge the impression migration is wearing this new task from state laws and regulations to possess relationship, schooling, and you can work and the further IV prices. Since the a note, rules are assigned based on a good female’s county away from delivery, regardless of if essentially you want to utilize the county a woman lived-in on many years 15. Since particular lady has actually migrated from their beginning state and you will with the a state having a unique gang of statutes by the age 15, the instruments was counted having error. I assess just how it has an effect on the latest IV quotes during 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 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.