The top panel regarding Desk cuatro presents the latest standard outcomes for new instrumented impoverishment regression

Estimates

Early adolescent marriage and you may shedding of high-school both has actually substantial effects towards chances a woman might be during the poverty. This new quotes indicate that marrying more youthful is actually for the a 30.6 percentage area rise in the chances of living in impoverishment. Shedding out-of twelfth grade is of an 11.dos commission area increase in poverty.

I now present some option estimation solutions to evaluate the new robustness of the standard result. Dining table step 1 showed that aggregation made a big change to own OLS estimates: anyone-level results ideal a little impact out of early adolescent marriage to your poverty, due to the fact classification-level OLS quotes ideal a large perception. The original line from inside the Desk 5 repeats this new baseline IV research, but this time that have labeled studies. The information and knowledge was aggregated at state of beginning ? 12 months regarding delivery ? census year height. This new grouped-studies IV estimates is actually surprisingly much like the personal-top IV quotes (0.314 as opposed to 0.306 getting very early teen matrimony and 0.112 as opposed to 0.112 to possess dropout). The new similarity of one’s coefficient rates isn’t surprising while the instruments try ongoing for all somebody in a state-cohort category, efficiently aggregating both personal-level and group-level rates. The standard problems along with transform hardly any with all the grouped research, expanding from the on the ten% getting early teenager relationships and not whatsoever for dropout. While the aggregated study supplies quite similar area rates and you will quite even more conservative simple errors, with what comes after, I establish results for aggregated study until if you don’t listed. 16

Desk 5

Notes: Simple errors, adjusted getting clustering because of the county off birth, are provided in parentheses. All regressions is dummy parameters having census 12 months, battle, age, state out of birth, and cohort regarding beginning, and you will area for beginning trend. Study try aggregated to express away from birth ? seasons out-of birth ? census 12 months phone form. Understand the notes in order to Dining table step one . The fresh migration-adjusted means is revealed on the text message together with control mode approach try discussed throughout the text together with appendix.

Next activity is to assess the effect migration has on the latest task from county laws having marriage, schooling, and you can functions and also the subsequent IV quotes. Because a reminder, legislation was tasked based on an excellent female’s county regarding beginning, regardless if if at all possible we should utilize the state a lady stayed in from the years 15. Because particular people has actually migrated from their delivery condition and towards your state which have a separate set of statutes from the many years fifteen, the equipment is measured with mistake. We assess just how it impacts the new IV estimates within the line step 3 out-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 Foot Fetish dating service 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 * ] 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.