Our primary focus in this study is to investigate the impact of income and insurance coverage on a couple's likelihood of pursuing medical intervention for infertility and on their choice of treatment option. Beyond the expected positive effects of these “financial access” factors, however, lie less obvious access and utilization issues. Less-educated couples may be less informed about available infertility treatments, even holding income constant. Couples of different races, ethnic origins, or religions may be more or less willing to pursue medical assistance for infertility problems. Couples who already have at least one child may feel less compelled to undergo invasive procedures to conceive a child. Thus, even with similar insurance and incomes, different couples may make very different treatment choices. As different treatments have different likelihoods of success, differences in treatment choice by demographic characteristic may result in significant differences in which infertile couples ultimately are able to bear children.
Several previous studies, all using different cycles of NSFG data, have estimated the effects of individual characteristics on the likelihood of seeking medical help for infertility. Kalmuss (1987
, using NSFG cycle III) and Stephen and Chandra (2000
, NSFG cycle V) both examine the choice between seeking help to get (and remain) pregnant or seeking no assistance, where help can be defined anywhere from simply getting advice to undergoing in-vitro fertilization. Wilcox and Mosher (1993
, NSFG cycle IV) also estimated a dichotomous choice, but defined the choice between using “specialized fertility services” (ovulation drugs, surgery, ART) and using no treatments beyond advice and testing (including using no treatments of any sort).
For comparison with these previous studies, we first estimate a simple logit model with a dichotomous dependent variable indicating whether the respondent ever sought medical help to get pregnant.5
We use the same data set as Stephen and Chandra (2000
, NSFG Cycle V) similarly restricted to infertile/subfecund women over the age of 21; however, unlike that study, we do not count as service-seekers those infertile women whose only treatment was “medical help to prevent miscarriage.” The explanatory variables are largely the same, with a few slight differences in specification. We include age, education, parity, race, marital status, education, income, and insurance coverage.6
One notable specification difference between our model and those in previous studies is in measuring parity. Rather than classifying women in two categories as either having no children or at least one (as in Stephen and Chandra 
and Wilcox and Mosher 
) or defining parity as a continuous variable measuring number of live births (as in Kalmuss 
), we define parity using three categories—no previous live births, one live birth, or more than one. The multiple births group is the omitted reference category in the estimation of the model. We expect women with no children to be most likely to seek treatment but want to allow for the possibility that women with one may be more likely than those with several children to pursue medical help should they develop fertility problems.
Using the same explanatory variables as in our binomial logit, we next estimate the effects of these characteristics on choice of treatment type using the multinomial logit model.7
In this model, the choice of not seeking any treatment is the reference category. Thus, for every other treatment category (advice, testing, drugs, surgery, ART) estimation of the model yields coefficients expressing the effect of the independent variables on the log of the probability of choosing that treatment type relative to the probability of choosing no treatment at all.
Based on previous studies (and simple economics), we would expect the ability-to-pay or “financial access” variables of income and insurance to be positively related to the choice of service seeking. However, we hypothesize that the importance of these factors to the likelihood of seeking service varies significantly across the type of treatment considered. For instance, income might be expected to have more of an impact on the choice to pursue relatively high-cost options (ART) than on less costly treatments such as seeking advice.
The effects of insurance coverage on service seeking and choice of treatment type will clearly depend to a large extent on which, if any, infertility treatments are covered by insurance. Unfortunately, that information is not available in the NSFG data. The survey includes a question about whether the respondent's insurance covers infertility diagnosis and treatment, but it makes no distinction among treatment types and, much more problematic, it is asked only of women who reported seeking such treatment. The only insurance information available is whether or not the respondent was covered by some insurance. Admittedly, this variable provides no information about explicit infertility coverage; nonetheless, Stephen and Chandra found it had a significant positive effect on the probability of service seeking.
The significance of private insurance coverage on the likelihood of seeking infertility treatments, despite no information on fertility coverage per se, has several potential explanations. First, while respondents with insurance may or may not have coverage for infertility treatments, respondents with no insurance definitely do not. Having any insurance clearly increases the likelihood that at least some infertility services are covered. Indeed, the data suggest a strong relationship between seeking infertility treatment and having insurance coverage for such treatment. In our data, 76 percent of the infertile couples seeking help reportedly had insurance that covered “help getting pregnant.” Further, of help-seekers who had private insurance, 82.5 percent had policies that covered “help getting pregnant.” Such “help” is unlikely to extend equally to all treatment options. A study from this same time period found that about 30 percent of private insurance plans did not cover “most” infertility diagnosis or treatment and over 80 percent did not cover IVF (Millsap 1996
Additionally, couples who have insurance, even if it does not cover infertility services, may be able to get advice, basic testing, and some treatments (such as surgery for endometriosis) covered by having a doctor agree not to code the treatments as infertility related. If this is the case, one would expect insurance coverage to have a greater effect on the probability of choosing these options than on the probability of pursuing ART, which would be impossible to disguise as not infertility related. A final explanation for the potential significance of the private insurance variable is that even if insurance does not cover infertility treatments and services utilized cannot be coded so that they might be covered, there remains the fact that having insurance serves as a gateway into the realm of medical possibilities. A woman with coverage may be more likely to visit doctors on a regular basis, during which visits infertility issues may be raised and paths of inquiry suggested that might not occur to an uninsured woman having similar difficulties in conception.