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Business Cycles, Medicaid Generosity, and Birth Outcomes

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Abstract

Birth outcomes influence many aspects of later life health and wellbeing, making healthcare access during pregnancy a policy priority. Low-income mothers often depend on Medicaid, for which eligibility is determined by their income relative to state eligibility thresholds. The prevalence of adverse birth outcomes is known to exhibit cyclical variation, due in part to changes in the composition of women giving birth in response to changing economic conditions. However, cyclical variation in adverse birth outcomes also varies with respect to Medicaid eligibility thresholds. Our analysis uses birth-records data for 2000 through 2013, aggregated into 173,936 county-by-quarter observations and linked to county-level unemployment rates and state-level parental Medicaid thresholds. Using fixed-effects negative binomial models, we examine the role of Medicaid generosity in influencing birth outcomes across business cycles. We test for interactions between Medicaid and unemployment, hypothesizing that the negative effects of recessions are worse where Medicaid thresholds are more restrictive. We find that higher Medicaid generosity dampens the negative effects of recessions on birth outcomes. The extent to which Medicaid interacts with unemployment also varies according to the age and race composition of mothers; in particular, Black mothers are both most affected by unemployment and most responsive to Medicaid generosity. Given current concerns about racial gaps in both infant and maternal mortality, our findings suggest that Medicaid may be an important feature of a strategy to close gaps in the prevalence of adverse birth outcomes across racial groups, especially during bust years.

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Fig. 1

Source Authors’ estimates using data provided by the National Center for Health Statistics, Bureau of Labor Statistics, Kaiser Family Foundation, and Hamersma (2013)

Fig. 2

Source Authors’ estimates using data provided by the National Center for Health Statistics, Bureau of Labor Statistics, Kaiser Family Foundation, and Hamersma (2013)

Fig. 3

Source Authors’ estimates using data provided by the National Center for Health Statistics, Bureau of Labor Statistics, Kaiser Family Foundation, and Hamersma (2013)

Fig. 4

Source Authors’ estimates using data provided by the National Center for Health Statistics, Bureau of Labor Statistics, Kaiser Family Foundation, and Hamersma (2013)

Fig. 5

Source Authors’ estimates using data provided by the National Center for Health Statistics, Bureau of Labor Statistics, Kaiser Family Foundation, and Hamersma (2013)

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Notes

  1. The implications differ because poor birth outcomes have been linked to ongoing health challenges that can be costly on a per-birth basis. A number of studies, summarized in Black et al. 2007), have documented the negative effects of low birth weight on both short-term health (e.g., infant mortality) and long-term success (e.g., IQ, education, and earnings). Low-birthweight cohorts have manifested increased levels of glucose intolerance, high blood pressure, obesity, stress sensitivity, coronary heart disease, and breast cancer relative to adjacent cohorts (Roseboom et al. 2006). Similarly, premature birth is associated with higher risks of asthma as well as developmental and sensory disabilities that can persist into adulthood (see, for example, Moster et al. 2008).

  2. Among the individual-level variables known to be correlated with these outcomes are maternal nutritional status, body composition, and gestational weight gain (Abu-Saad and Fraser 2010; Tyrell et al. 2015; Goldstein et al. 2017), as well as the type (e.g., Cesarean) and the location (in or out of hospital) of delivery (Snowden et al. 2015; Molina et al. 2015).

  3. We classify birth records in which one of the two measures is missing as “adverse” if the non-missing measure confirms either pre-term or low birth weight. For those with only one measure that indicates a healthy birth, we cannot ascertain whether the missing measure was adverse, so we exclude them from the sample, along with those missing both measures; this affects only about 0.5% of the sample.

  4. Note that the county-level data cannot be further disaggregated by demographic group; the overall quarterly county-level unemployment rate is used for all demographic groups in the analysis.

  5. Policy data were gathered from a variety of sources; see Hamersma (2013) for details of data sources through 2007. Data for 2008 through 2012 were gathered from more recent Kaiser Family Foundation reports (www.kff.org) as well as numerous state-specific data sources identifying eligibility limits at various points in time alongside effective dates of legislation changing these limits; details are available upon request.

  6. We exclude Alaska and Hawaii among the 50 states. We also excluded three counties due to problems in aligning their birth data with unemployment data. The three counties are as follows: Broomfield, Colorado; Clifton Forge; and Bedford, Virginia.

  7. For example, if a county is quite small and racially homogeneous, it is possible that in a given quarter there are no women in the population in a particular age category in a racial minority group.

  8. The marginal changes presented in Tables 3 and 4 have been multiplied by 100 (i.e., expressed in percentage terms). Changes in λA (or λH) can be interpreted as percentage changes in the number of births in an at-risk population of size 1, i.e., as a per-capita change.

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Authors and Affiliations

  1. Department of Public Administration and International Affairs, Maxwell School of Citizenship and Public Affairs, Syracuse University, Eggers 205, Syracuse, NY, 13244-1020, USA

    Sarah Hamersma, Yilin Hou, Yusun Kim & Douglas Wolf

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  1. Sarah Hamersma
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Correspondence to Douglas Wolf.

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Hamersma, S., Hou, Y., Kim, Y. et al. Business Cycles, Medicaid Generosity, and Birth Outcomes. Popul Res Policy Rev 37, 729–749 (2018). https://doi.org/10.1007/s11113-018-9483-3

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