Did the US infertility insurance mandates affect the time of firs

Transcription

1 Did the US infertility insurance mandates affect the time of first birth? Department of Economics University of Warwick January 13, 2010

2 Motivation and Research Summary Impaired fecundity affects 7.3 million women and her partners in the U.S. (NSFG, CDC (2002)). Infertility treatments in the U.S. are costly. $50-$5000 per cycle for a drug treatment (Babycenter(2009)). $12400 for an IVF cycle (ASRM(2009)). Several U.S. states introduced legislation between and mandated insurance providers to offer coverage for infertility treatment.

3 Motivations and Research Summary Continued Several studies (Schmidt (2005; 2007), Bundorf et al. (2007), Bitler (2008)) have focused on the policy impacts among older women. However, these mandates may have had an unintended effect on younger women.

4 Motivation US women have been struggling to Have It All Phipps, Burton, and Lethbridge (2001) US women, on average, have more job interruptions than men. 80 percent of these interruptions are related to motherhood. Career interruptions seem to have a detrimental impact on women s future wages (for example, see Buckles, 2007; Miller, 2008; Eckstein and Wolpin, 1989; Korenman and Neumark, 1992) US women with higher educational attainment have been delaying birth (Rindfuss, Morgan, and Offutt, 1996).

5 Motivations Birth at an older age leads to... Infertility rate (46% increase in the rate of infertility (age 33 to 35)) Treatment success rates ( 22% decline in the ART success rates (age 33 to 35)). Health outcomes of mothers and children. Down syndrome Miscarriages Pregnancy and labour complications Health care costs (which are approximately between $3-5 billion for infertility treatment).

6 Theoretical framework Introductions of these state-level infertility insurance mandates lead to.. Delay through the decline in the opportunity cost of having a child in the future Delay through the moral hazard Speeding up through the increase in insurance premiums Mixed effect through the reduction in wages

7 Literature Buckles (2007) Natality Detail Files and Current Population Survey (Repeated cross sectional data) A non-linear difference-in-differences estimator. Women aged between increased the probability of first birth by 40 percent after 5 years of coverage. Young women aged between and each decreased birth rate by 26 percent after 5 years of coverage.

8 Contributions Compared to Buckles(2007), this study uses longitudinal data which allows me to follow the same individuals over years. Controlling for the cohort effect, observe how the probability of first birth changed over years. Identify who resided in mandated states long enough to be affected by the legislation. Can go back each individual s birth history to ensure the correct sample selection. Can distinguish between whether women are delaying or not having children at all.

9 Discrete-time proportional hazard model 1 h i (j x i (j), ɛ i ) = P[T = j T > j 1, x i (j), ɛ i ] = 1 exp[ exp(x i (j) β + Mandate i (j 2) δ 2 + γ(j) + ɛ i )] (1) γ(j) : A piecewise constant baseline hazard. ɛ i : Unobserved heterogeneity. Mandate i (j 2) : 1 if the individual resides in a state which introduced the mandate for at least two years.

10 Discrete-time proportional hazard model 2 h i (j x i (j), ɛ i ) = P[T = j T > j 1, x i (j), ɛ i ] = 1 exp[ exp(x i (j) β + γ(j) + Mandate i (j 2) α(j) + ɛ i )] (2)

11 Results Table: Estimates of policy effects (1) (2) (3) Mandate_white ** (0.14) (0.27) (0.19) Mandate_black (0.21) (0.36) (0.29) Mandate_hispanic (0.41) (1.57) (0.59) Gamma Frailty 0.97*** (1.05)*** 0.55* (0.20) (0.26) (0.30) Number of women observed Observations Standard errors are in parenthesis. *** p<0.01, ** p<0.05, * p<0.1.

12 Results 2 Table: Estimates of policy effects Periods (t) Coefficients Periods(t) Coefficients Mandate_period1-0.61** (0.27) period Mandate_period2-0.59** (0.17) (0.30) period3 0.43* Mandate_period3-0.59* (0.23) (0.31) period4 0.56* Mandate_period4-1.05** (0.30) (0.41) period5 0.75** Mandate_period5-0.77* (0.37) (0.42) period Mandate_period (0.45) (0.54) period Mandate_period (0.49) (0.42) period Mandate_period (0.55) (0.67) period Mandate_period (0.61) (0.55) period Mandate_period (0.64) (0.60) period Mandate_period (0.78) (0.86) period Mandate_period (0.83) (0.89) period Mandate_period (0.83) (0.68) period Mandate_period (1.26) (1.43) Standard errors are in Asako parentheses. Ohinata *** p<0.01, ** p<0.05, Did * the p<0.1us infertility insurance mandates affect the time of firs

13 Results 4 Figure: Survival function for highly educated white women

14 Results 5 Figure: Survival function for highly educated white women

15 Conclusions The US state-level infertility insurance mandates affected the highly educated white young women to delay their first birth. If women were exposed to the mandates at the age of 23, the median delay is approximately 1.5 years (from age 26.5 to 28). If women were exposed to the mandates at the age of 28, the median delay is approximately 2 years (from age 33 to 35). Preliminary estimation of macro externality Approximately 14.5% increase in the number of miscarriages. Approximately 20.6% increase in the use of ART. Increase in the health care costs as large as $0.6 to $2 billion.