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Assisted reproductive technology and women’s choice to pursue professional careers

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Abstract

We examine the impact of assisted reproductive technology on women’s choice to pursue professional careers. We hypothesize that the availability of assisted reproductive technology increases the expected benefits of a professional degree by allowing women to delay childbearing in their 20s and 30s while establishing their careers, thereby reaping greater financial benefit from human capital investment. State-level timing differences in the enactment of laws which mandated infertility treatment coverage in employer-sponsored health plans allow us to exploit state, year, and cohort variation in women’s ages at the time the laws are passed. These insurance mandates dramatically increase access to assisted reproductive technology. Using a triple difference strategy, we find that a mandate to cover assisted reproductive technology does increase the probability that a woman chooses to invest in a professional degree and to work in a professional career.

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Notes

  1. For example, women received only 5 % of law degrees conferred in the USA in 1970, but 47 % of new law degrees in 2010. (National Center for Education Statistics 2011)

  2. Other types of medical treatment included in ART are gamete intrafallopian transfer (GIFT), zygote intrafallopian transfer (ZIFT), embryo cryopreservation, egg or embryo donation, and gestational carriers (http://www.resolve.org/family-building-options/ivf-art/).

  3. For example: Clomid.

  4. http://www.resolve.org/family-building-options/insurance_coverage/the-costs-of-infertility-treatment.html. This figure does not appear to include the costs of ovulation medication and pre-cycle procedures.

  5. http://www.sart.org

  6. Resolve provides information about individual state specifications for insurance coverage for ART procedures, stating also that “Since most assisted reproductive technologies (ART) are not covered by insurance, the patient has to pay ‘out-of-pocket,’ often leading to increased stress as well as long-term financial burdens.” On the insurance coverage page at Resolve.org, infertile couples are told, “If the state you live in is not in the above list, there is no law in your state requiring insurance coverage for infertility treatment. Contact your local representative and ask them to introduce legislation to require infertility treatment coverage.” The website provides a link to www.contactingthecongress.org. In the absence of a mandate, there is no incentive for most for-profit insurance companies to cover ART procedures. A few firms now reimburse employees for egg freezing (and possibly ART) as an in-kind benefit, but this is not through insurance.

  7. Montana passed an ART coverage mandate in 1987; However, as noted in Hamilton and McManus (2012) there were no fertility clinics in the state during our study period. For this reason, we removed Montana from our study completely.

  8. Our coding of the mandate states is identical to the coding of Bitler and Schmidt (2012) for all states but Connecticut and Ohio. Connecticut passed a law on 2005 which replaces the preexisting law from 1989. Our coding reflects this change. According to Bitler and Schmidt, Ohio had a mandate to cover between 1990 and 1997; after 1997, this law was converted into a mandate to offer. Since we were unable to find other supporting documents for this statement, here we consider Ohio as having a mandate to offer IVF coverage since 1990.

  9. As a robustness check, we also check the results when all “weak mandate” states are included in the treatment group; these results are discussed in Section 6.

  10. Women with a higher socioeconomic status and more education are more likely to hold employer-based health insurance (Bitler and Schmidt 2012), and be able to afford the high out-of-pocket expenditure associated with the use of infertility treatments even with insurance coverage.

  11. The New York Times article can be found at http://www.nytimes.com/2015/07/23/upshot/more-than-their-mothers-young-women-plan-career-pauses.html. The original HBS study can be found at http://www.hbs.edu/women50/docs/L_and_L_Survey_2Findings_12final.pdf.

  12. A few articles that discuss these expectations can be found at: http://jezebel.com/5883016/young-people-totally-think-theyre-infertile; https://www.guttmacher.org/about/journals/psrh/2012/02/missed-conceptions-or-misconceptions-perceived-infertility-among; http://jezebel.com/5859741/the-fertility-denial-complex; http://www.today.com/id/45262603.

  13. National Longitudinal Survey of Youth 1979

  14. This is a strongly simplified model of reality in which we ignore all other costs of delayed fertility other than the cost of lost earnings. These ignored costs certainly include personal preferences for fertility timing.

  15. We exclude survey years before 1977 because prior to 1977 not all the states are identified individually.

  16. Prior to 1992, post-high school degrees cannot be differentiated in the CPS. For survey years 1977–1991, we define an individual as having a college degree if she has completed 4 years of college. From 1992 onwards, a respondent is said to have a college degree only if she has a bachelor’s degree or higher.

  17. A degree in veterinary sciences is an exception. However, most of those who apply for veterinary school have a bachelor’s degree.

  18. Most traditional students will have completed college by age 25. See Goldin et al. (2006).

  19. These degrees correspond directly to the observations for which the variable educ is equal to 124 in the IPUMS March CPS data (King et al. 2010), and equal to 115 in IPUMS Census 1990, 2000, and ACS samples (Ruggles et al. 2015).

  20. Following the IPUMS documentation, we weight each observation by the variable wtsupp.

  21. This less restrictive treatment group is made up of both states that enacted legislation to mandate IVF coverage and also those passing mandates to offer ART coverage, or mandates to cover infertility treatments but explicitly excluding IVF-related procedures.

  22. This sample excludes all states that passed a mandate before 1992.

  23. The occupation sample includes eight mandate states (Arkansas, Connecticut, Hawaii, Illinois, Maryland, Massachusetts, New Jersey, and Rhode Island) and the CPS education sample includes two mandate states (Connecticut and New Jersey). This is because between 1992 and 2012, only Connecticut and New Jersey passed an insurance mandate to cover infertility treatments. The CPS education sample does not include states that passed a mandate before 1992 because there would be no pre-treatment period for these states.

  24. To clarify the timeline range: the starting point of t=-8 is because the first mandate event was in 1985 and our earliest data are from the 1977 CPS. The latest data from the 2012 CPS correspond to t=7 for the latest mandate event in 2005. The Treatment States value at t = 0 is the mean of the professional rate for Maryland in 1985, Arkansas in 1987, Hawaii in 1987, Massachusetts in 1987, Illinois in 1991, New Jersey in 2001, and Connecticut in 2005 (recall that Montana was dropped from our dataset). The subsequent value at t=1 is the mean professional rate 1 year after the mandate in each of these respective states. To construct the value for the Control States series, the variable on the vertical axis in a mandate year is given by the average of the value in the years in which treatment states passed a mandate. That is, the value at t=0 is the mean of the control group professional rate across the following years: 1985, 1987, 1987, 1987, 1980, 2001, and 2005. Notice that the 1987 rate is triple weighted since three states passed a strong mandate that year: Arkansas, Hawaii, and Massachusetts. In constructing the control group series, we follow the method used by Ayres and Levitt (1998).

  25. We test for pre-mandate differential trends using a difference in differences specification and data from the 1970 and 1980 Censuses. The data is limited but the results suggest there may be a pre-existing trend for degree outcomes. These results provide further support for the triple difference model and are available upon request.

  26. Gruber (1994) first used a DDD strategy to study the impact of insurance mandates to cover maternity benefits on women’s wages and employment. In the context of infertility insurance mandates, Schmidt (2007) first used a DDD strategy to study the effect of the mandates on fertility.

  27. Our identification strategy is close to Schmidt (2007), with two main differences. First, she uses older women as a treatment group and younger women as a control group. Second, Schmidt (2007) exploits variation across women’s age in the calendar year. Instead, here, we exploit variation across women’s age at the time of the mandates.

  28. This is conditional on having worked in the past 5 years. This is a necessary condition in order for us to use older women as a control group. If we only observed the data for working women, older women would form a poor control group given that the ART mandates have a negative effect on their labor supply, as shown by Buckles (2007).

  29. NSFG, 2003 and 2010. These statistics are for native-born women only.

  30. When choosing which controls to add, we followed previous research on the effect of the availability of the pill on women’s career. We include most of the controls in Table 2 of Bailey (2006).

  31. Our specification could be also written as: \(Pr(Y_{iskt}=1)=\alpha +\beta _{1}\text {Mandate}_{t} \times \text {35orYoungerMandate}_{sk} + \beta _{2}\text {Mandate}_{t}\\ + \beta _{3}\text {EverMandate}_{s}\times \gamma _{k}+ \beta _{4}X_{iskt} + \sum \limits _{kt} \mu _{kt}\\ + \sum \limits _{s}\delta _{s}\times t +\delta _{s}+\gamma _{k}+\tau _{t}+\epsilon _{iskt} \)where Mandate t is an indicator that takes value one after the mandate was passed in a mandate state. This notation is similar to the one used by Schmidt (2007).

  32. It should be noted that Abramowitz (2014) finds a relationship of the mandates on marriage timing. However, controlling for marital status does not change our results in the triple difference specification, as discussed in Section 6.

  33. Controlling for quadratic time trends, \(({\sum }_{s}\delta _{s}\times t^{2})\) produced identical results.

  34. Since there is no mandate year defined for states in the control group, we approximate the mandate year by defining t=0 in control group states as 1995, which is the midpoint of all the true mandate years.

  35. This coefficient is represented by ß 1 in Eq. 1.

  36. The professional occupation rate among mandate state women rose from 0.0203 in 1985 to 0.0346 in 2012, an increase of 1.4 percentage points. A mandate effect of 0.012 and a change in exposure of 11.9 % would imply a predicted change of 0.012×0.119=.00143, or 0.14 percentage points. This accounts for 10 % of the observed increase.

  37. Goldin and Katz use two key occupation outcomes, and it is important to clarify that here we reference their results for a narrow group of occupations that matches our professional occupations outcome: lawyers, doctors, veterinarians, and dentists. See Goldin and Katz (2002) Table 5, column 4, row 3.

  38. As an alternative to state-specific linear trends, we also estimated the model controlling for state-specific quadratic trends. When including the full quadratic expression, STATA drops the squared term; the linear and squared term appear to be colinear.

  39. The 2010 ACS is a reweighted combination of the ACS surveys conducted in 2009, 2010, and 2011 (IPUMS ACS 2009-2011 3-year sample).

  40. As additional robustness checks, we replicate our results for both occupation and education within the unweighted CPS dataset. The unweighted CPS regression results are qualitatively very similar to the main results in Tables 4 and 5 although the magnitude of the mandate coefficients is a bit smaller. These results are available upon request.

  41. Documentation from the US Census urges caution in comparing rates, ratios, and distributions between Census and ACS, and between ACS samples of different years (guide available at: http://www.census.gov/programs-surveys/acs/guidance/comparing-acs-data/2010.html). Furthermore, professional degrees are not identified in the 1980 Census and therefore we defined them as having at least 7 years of education. While the triple difference estimate is still valid from this patch-worked sample (year fixed effects are included), we cannot accurately measure the change in the rate over the full time period.

  42. This is similar to the placebo test mentioned in footnote 25 and the results are available upon request.

  43. This difference is marginally significant at the 10% level, with a z-statistic of 1.36

  44. This variable is available for only a subset of years in our study period; these years are specified in the table note.

  45. We drop states that are assigned a weight equal to zero. The states included in the refined control group are Alabama, Delaware, District of Columbia, Michigan, Minnesota, Mississippi, Oklahoma, Pennsylvania, Utah, Vermont, Virginia, and Wisconsin.

  46. The category “non-traditional occupations” is taken from Bailey et al. (2012).

  47. Alternative outcomes that warrant consideration include MBAs and PhDs. MBAs satisfy the three requirements described above: high up-front educational investment, a demanding and inflexible schedule, and high earnings. The typical educational investment of an MBA—2 years—is shorter than the one required by professional degrees, but still carries a hefty tuition price tag, generally paid out of pocket. Careers in finance or consulting, which are a common placement for MBAs, require long working hours and pay high salaries. Unfortunately, looking at the effect of mandates on the probability of obtaining an MBA is not possible because neither the CPS nor the Census allow distinguishing MBAs from other Master degrees. Combining MBA and MA outcomes showed no impact of the mandates. The data do allow looking at PhDs separately. However, although a doctoral degree is a long investment, PhDs often have a flexible schedule and on average earn lower earnings relative to professionals. Analysis of PhD outcomes yields a coefficient on the triple interaction term that is small, negative, and statistically insignificant, suggesting that the mandates do not affect the probability that women obtain a PhD. These results are available upon request.

  48. For consistency, we exclude women aged 31 to 35 in 1995 in control states. 1995 is the midpoint of the years in which mandates were adopted in treatment states.

  49. Less precision is expected given the particularly small size of the “other race” sample.

  50. This practice is likely to grow more common with widely publicized announcements of such employee benefits by Facebook and Apple. http://www.nbcnews.com/news/us-news/perk-facebook-apple-now-pay-women-freeze-eggs-n225011

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Acknowledgments

The authors would like to thank Daniele Paserman, Claudia Olivetti, Lucie Schmidt, Mark Herander, Kevin Mumford, Osea Giuntella, Maria Navarro Paniagua, Colin Greene, Amalia Miller, Melanie Guldi, Joshua Wilde, Lisa Kahn, David Slusky, two anonymous referees, and numerous seminar and conference participants for helpful comments and suggestions.

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Correspondence to Sarah Kroeger.

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Responsible editor: Junsen Zhang

Appendix

Appendix

Table 9 Endogeneity of mandate timing
Table 10 Migration in the CPS
Table 11 Alternative age cutoff and men as additional control group
Table 12 Differential effects by race
Table 13 Triple difference regression with refined control group
Table 14 Alternative education outcomes
Table 15 Non-wage income

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Kroeger, S., La Mattina, G. Assisted reproductive technology and women’s choice to pursue professional careers. J Popul Econ 30, 723–769 (2017). https://doi.org/10.1007/s00148-016-0630-z

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