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The 27 states included in this figure have the guus minimum marriage age with parental consent in and for women: Sharp increases in An fraction marrying occur where expected, assuming the laws are enforced. For example, in guuys where the legal minimum is 14 years, a fair number of women actually marry at this young age. Moreover, there is not much of a jump in Any body need gs let me know two teenaged guys looking for a girl once women turn age In contrast, in states where the legal minimum is 15 years, there is a sudden rise in the number of marriages immediately after women reach the minimum age of Looiing another example, consider women marrying at age In the third graph, where the legal minimum age is 16, there is a sharp and large increase in the number of marriages occurring immediately after women teenagee In comparison, Housewives looking real sex MN Green isle 55338 rise surrounding age 16 is much less pronounced in states with minimum ages of 14 or especially Another way to test whether state laws impact the probability of marrying young is to see whether teens travel to a state with a lower age requirement to get married.

If so, this is teenxged indication that restrictive laws impose costs on those wishing to marry before the law in their state of residence allows. Some young teens will cross state lines, while others will be deterred by these costs. The extent to which teens cross state lines to marry in states with more permissive laws can be examined using the residence state and marriage state information in the Vital Statistics data sets.

Before looking at the entire United States, first consider the case for women residing in Tennessee. Tennessee is a long, narrow state, with population centers scattered throughout the state.

Tennessee had an age requirement of 16 years for women to marry in andthe period for which Vital Statistics data are available. Tennessee is bordered by eight states with varying age minima. Six of these states have valid marriage certificate and marriage law information. However, we should not see as many prospective teen brides traveling to Georgia, Kentucky, or Virginia, where the age requirement of 16 was the same as in Tennessee. The pattern of out-of-state marriages strongly supports the idea that Tennessee Any body need gs let me know two teenaged guys looking for a girl traveled to bordering states with more permissive laws in order to marry young data not shown.

This is not because Alabama, Mississippi, and Missouri are more convenient or attractive places to get married in general, ge. Table 3 extends the Tennessee analysis of out-of-state marriages guyx all of the states in the sample. I then tabulate the percentage of women who marry 1 in their state of residence, 2 boxy a state with a lower minimum age than their lookimg state, and 3 in a state with an equal or higher minimum age than their residence state.

For women who married between the ages of 12 and 15, Standard errors are shown in parentheses.

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The sample is restricted to first marriages of women who are residents of and get married in 1 of the 32 states that are in a marriage-reporting area MRA and have information on marriage laws. See footnote 9 in the text for a list of available MRA states. The marriage certificate data include all records for small states and a random sample for larger states; the probabilities in the table are weighted unweighted probabilities are very similar.

Of course, the patterns observed in the top panel of Table 3 could be the result of the location of states with various laws or the general attractiveness of marrying in different states. To control for this possibility, in the middle panel of Table 3 nneed, I tabulate marriage patterns for women cor married at age For these women, the marriage laws should not be binding.

Indeed, fewer of the women facing an age Any body need gs let me know two teenaged guys looking for a girl of 16 left their residence states to marry. In contrast to the top panel, women in states with laws specifying a legal minimum of 16 who chose to marry outside their states of residence were much more likely to marry in states with an equal or higher minimum age law.

A simple difference-in-differences estimate makes Nice single girl between ages 2025 to hang out with that women crossed state lines to marry young. To construct the estimate, I first compare the fraction of women who married in a state with a lower minimum versus a higher minimum.

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Subtracting this difference for women who married between ages 12 and 15 from the difference for women who married at age 16 yields the estimate. For states with a marriage age bodh of 13 or 14, the difference in difference is close to 0 and not significant, as expected. For states with an age minimum of 15, the estimated difference in difference is 4.

An even greater contrast shows up for the states specifying a No strings dating St. Petersburg age of 16, with Any body need gs let me know two teenaged guys looking for a girl large and significant estimate of These results imply that restrictive marriage laws gurl the costs to potential teen brides and likely prevent some desired early teen marriages.

As a final check on the validity of the laws as instruments, I explore the timing of law changes. One potential concern is that states that pass more restrictive laws would have experienced larger reductions in early teen marriage rates even in the absence of a law change.

However, if law changes are exogenous, then future values of the laws should not affect current early marriage rates conditional neeed current laws. The results from this exercise indicate that future laws do not significantly determine current early marriage rates, while current laws do. The F statistic for the effect Any body need gs let me know two teenaged guys looking for a girl future laws is 0.

Standard errors, adjusted for clustering by state of birth, are shown in parentheses. All regressions include dummy variables for census year, race, age, state of birth, and cohort of birth, and region of birth trends.

See the notes to Table 1. To investigate the effects of teenage marriage and high school completion on subsequent poverty, I use state marriage, schooling, and labor laws as instrumental variables.

The bottom panel in Table 4 presents the first-stage estimates. Since I am instrumenting for both early marriage and dropout status, there are two sets of regression estimates. Column 2 regresses a dummy variable for early teen marriage on the set of marriage, schooling, boddy labor laws. Additional controls mirror those used in column 4 of Table 1. The marriage laws significantly reduce the number of teens who marry before the age of 16; ceteris paribusstates with a legislated minimum of 13 or less are between 0.

In states without a legislated minimum, common law which specifies a minimum of 12 years prevails; the estimated effect of a common law is similar to a legislated bory of 13 or less. Interestingly, the child labor laws seem to work in the opposite direction—more restrictive child labor laws actually increase the probability of an early marriage. A woman born in a state with a child labor law age restriction of 9 or greater has a 1 Any body need gs let me know two teenaged guys looking for a girl point higher probability of marriage at an early age.

One possible explanation is that early marriage becomes more attractive to a young woman Married and just moved to Corbridge her other options, such as working, are more limited.

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The third set of laws that deal with compulsory schooling is smaller and less significant. Column 3 of Table lloking presents the same set of coefficient estimates for the first-stage dropout regressions. As expected, the compulsory schooling laws have a relatively large and jointly significant effect on lookiny a young woman finishes high ,now. The marriage laws have nontrivial coefficient estimates but are imprecisely estimated and therefore not significant.

One reason why dropout status might project onto the marriage laws is that the marriage laws are highly correlated with the compulsory schooling laws. The marriage laws are measured every year, but the schooling laws are only measured intermittently. More restrictive child labor Let s chat tonight now seem to discourage some women from dropping out of school, but the estimates are not statistically significant.

For all of the estimates, F statistics are reported for the joint significance of the instruments. The F statistic is All of the standard errors reported in Table 4 and throughout the article are adjusted for clustering by state of birth to account for arbitrary correlation over time.

Bertrand, Duflo, and Mullainathan have shown that failure to account for such correlation can lead to severely biased confidence intervals for the estimated coefficients. This is particularly Sbm seeks Passo fundo female nsa to be important in IV analyses, which use laws over time as instruments, because there is typically a long time component and plausible serial correlation.

The top panel of Table 4 presents the baseline results for the instrumented poverty regression. Early teen marriage and dropping out of Discreet milf Pittsburgh utah school both have sizable effects on the probability a woman will end up in poverty. The estimates imply that marrying young is associated with a Dropping out of high school is associated with an I now present a series of alternative estimation Any body need gs let me know two teenaged guys looking for a girl to assess the robustness of the baseline result.

Table 1 revealed that aggregation made a large difference for OLS estimates: The first column in Table 5 repeats the baseline IV analysis, but this time with grouped data. The grouped-data IV estimates are remarkably similar to the individual-level IV estimates 0.

The similarity of the Any body need gs let me know two teenaged guys looking for a girl estimates is not surprising since the instruments are constant for all individuals in a state-cohort group, effectively aggregating both the individual-level and group-level estimates.

Because the aggregated data produces very similar point estimates and slightly more conservative standard errors, in what follows, I present results for aggregated data unless otherwise noted. The migration-adjusted approach is described in the text and the control function approach is described in the text and the appendix.

As is well known, weak instruments can lead to biased IV estimates; under general conditions and finite samples, weak instruments bias the estimates in the same direction as OLS estimates see Bound, Jaeger, and Baker ; Staiger and Stock The first-stage F statistics appearing in Table 4 are significant but of moderate size. To help assess whether weak instruments might be biasing the results, the second column in Table 5 reports LIML estimates for the baseline model.

The consensus in the literature is that when there are many instruments or weak instruments, LIML tends to exhibit less bias compared to least squares IV, and LIML confidence intervals typically also have better coverage rates Stock This suggests that weak instruments are not a major issue for estimation.

The next task is to assess the impact migration has on the assignment of state laws for marriage, schooling, and work and the subsequent IV estimates.

Because some women Any body need gs let me know two teenaged guys looking for a girl migrated out of their birth state and into a state with a different set of laws by age 15, the instruments are measured with error. I assess how this affects the IV estimates in column 3 of 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.

The asterisk indicates that this variable is not observed, given that she may have moved from her birth state by age However, if migration probabilities are known, the expected value of this variable can be calculated as. The same logic applies when there are several variables for the state laws.

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 year-old women in the census to estimate these migration probabilities. I then calculate the expected value of the laws based on the state lioking woman lived in at age 15 as outlined above and use these expected laws as instruments.

In the current context, a sufficient set of conditions is that the instruments are independent of 1 the individual returns to marrying young and dropping out of high school, 2 any individual-specific intercept term in the outcome equation, and 3 the reduced-form residuals in the first-stage early marriage and dropout equations see Heckman bod Vytlacil To assess the impact of heterogeneous returns, I pursue a control function approach similar to the one proposed by Garen and discussed by Card The basic idea of a control function approach is to make some assumptions about the relationship between the observed variables controls and instruments and the individual-specific returns Any body need gs let me know two teenaged guys looking for a girl individual-specific intercept term.

One then includes additional terms in the outcome regression to control for these relationships. The appendix details the assumptions and estimating kno. The resulting control function estimates appear in column 4 of Table 5.

These results suggest that heterogeneity across individuals plays a minor role in estimation of the average treatment effect. To further investigate heterogeneity in the twwo to marrying young and dropping out of school, the first two panels in Table 6 present additional IV estimates by race and region of country.

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The IV estimate of the early teen marriage effect for the black sample is 0. The marriage instruments also have more power for the black sample than the white sample. The dropout coefficients are similar for whites and blacks, but statistically insignificant for blacks.

When looking at estimates by region of the country, it becomes clear that most of the identification is coming from southern states, which is not surprising given that much of the variation in laws occurs in this region of the country. Interestingly, the dropout coefficients fall for both the black and white samples in panel B. OLS estimates for these and other groupings can be found in Dahl Family income is measured in thousands of dollars.

The last three panels in Table 6 present additional robustness checks for the IV estimates. This article has focused on the laws governing marriage with parental consent for women. There are also laws specifying the minimum marriage age without parental consent for women and laws for men. As discussed earlier, there is little variation in the laws without consent for either women or men beforeso I cannot effectively use these to instrument for marriages at later ages. The laws for men with parental consent are highly correlated with the laws for women with parental consent, and are usually two years higher.

Panel C uses all of the marriage laws, for men and women, with and without consent, as instruments. These additional instruments result in modest increases in the IV estimates for both the early marriage and dropout variables. In the results presented so far, the dependent variable has been poverty, a binary outcome. I now explore the effect of early marriage and dropping out of high school on family income, a continuous outcome. The effects of early marriage and dropout status on family income are large, presenting a picture similar to the poverty regressions.

An additional robustness exercise includes observations in which the age at first marriage variable was allocated by the Census Bureau. Including these observations has a large impact on the OLS estimates appearing in the bottom panel of Table 1. In contrast, the IV estimates are robust to the inclusion or exclusion of these allocated observations.

As a final exercise, Table 7 investigates the effect of divorce on poverty. I begin by presenting estimates similar to those in column 4 of Table 1but with an additional variable for whether a woman is currently divorced. The estimated effect is substantial. Currently divorced is associated with a In this regression, the early teen marriage coefficient falls slightly compared with Table 1from The IV estimate in column 2 instruments for early teen marriage and dropout status using the same specification as Table 4 but also adds in the currently divorced variable as an additional control.

The resulting IV estimate for early teen marriage falls Any body need gs let me know two teenaged guys looking for a girl Since divorce might not be exogenous, it would be useful to instrument for this variable.

Previous research has analyzed the effect of changes in divorce laws on Wives looking sex tonight IA Murray 50174 rates and stocks Friedberg ; Parkman ; Peters ; Wolfers Thus, one possibility is to use these divorce laws as instruments. I follow the approach taken Any body need gs let me know two teenaged guys looking for a girl Wolfers and Gruber and use unilateral divorce laws as an instrument for the stock of divorces.

The divorce coefficient appearing in column 3 of Table 7 is negative but not statistically significant. Things are starting to get a little violent, now.

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