Saving Babies: The Efficacy and Cost of Recent Changes in the Medicaid Eligibility of Pregnant Women The Journal of Political Economy, Vol. 104, No.6 (Dec., 1996), 1263-1296 Authors: Janet Currie; Jonathan Gruber. Presented by (Jason) Chia-cheng Liao April, 2004. The paper try to answer:
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Saving Babies: The Efficacy and Cost of Recent Changes in the Medicaid Eligibility of Pregnant WomenThe Journal of Political Economy, Vol. 104, No.6 (Dec., 1996), 1263-1296 Authors: Janet Currie; Jonathan Gruber
Presented by (Jason) Chia-cheng Liao
*the result for ‘the broad’ is only 2.2 percent decrease of the infant mortality rate in part C.
*The column 5 and 6 are the y incidence combined both the low birth weight and the infant mortality rate.
The only significant changes is the overall results for low birth weight are no longer statistical significant.
This table was regressed by Linear Probability Models included state and time dummies. The CPS data set consists of 526,830 observations over 14 years.
The Medicaid policy makes an extra woman covered will raise the odds that she will be covered by 3.9 percent.
Relative to the baseline, this is a take-up rate of 34 percent (34% = 3.9/11.4).
The overall take-up rate is 34%.
The ‘targeted’ take-up rate is 49%, and the ‘broad’ take-up rate is only 16%.
The authors give two reasons why the ‘broad’ take-up rate is low:
1st, the ‘broad’ population was less needy.
2nd, the broader policy changes may have been less effective. It may be difficult to bring women who have never received any social assistance into the Medicaid program, either they do not know about it or the stigma effects. Previous research report that many low-income families and their physician are unaware that they can qualify for Medicaid.
The authors also ran the cost regression analysis as the following:
the authors normalize and deflate health expenditure by Consumer Price Index and use the dollars in 1986.
For example, the coefficient in the col. 2 refers to the the actual fraction made eligible of all expenditure under the targeted change. (notice that: .301= .092+ .171+ .038)
From the previous table, the majority of the spending comes through inpatient hospital costs.
The most striking finding (from the simulated models) is that spending per ‘broad’ eligible is actually higherthan spending per ‘target’ eligible. (the richer use more than the poorer)
Among targeted eligible, only about half of the spending is on inpatient hospital services, whereas among broad eligible, over 90 percent of spending is on these services.
The cost of saving a infant’s life:
The authors also estimated the increase of actual expenditure is $202 per year per additional eligible women. $224 per targeted eligible per year.
This leads the cost of saving a life through targeted eligibility changes was $840,000. (if interested, check with the footnote 22 for details)
Is this worthy?
According to the other research (Tengs et al, 1995) , the child restraint system in cars costs about $5.5 million per child life saved.
The similar prenatal care program also costs $1.06 million to save a life according the Institute of Medicine.
OLS in col. 1 says the targeted Medicaid-eligible women are likely to delay prenatal care, but the instrumental models say otherwise.
Actually, the targeted group reduced the probability of delay by almost the half when the simulated instrument is used (in col. 3).
Overall, the Medicaid is quite efficient especially in the ‘targeted’ group. The efficiency includes the higher take-up rate, more cost-effectiveness, and good utilization of the prenatal care.
The authors conclude the targeted expansions were clearly more cost-effective than the broad eligibility changes, these results could be good references for the improvement of future health insurance policies.
Simulation Method: J. Cook and L. A. Stefanski. A simulation extrapolation method for parametric measurement error models.
Journal of the American Statistical Association, 89: 1314-1328, 1995