what do longitudinal data on millions of hospital visits tell us about the value of public health...
TRANSCRIPT
What Do Longitudinal Data onMillions of Hospital Visits Tell us About the Value of
Public Health Insurance as a Safety Net for the Young and Privately Insured?
Amanda E. KowalskiYale University and NBER
February 2015
What Do Longitudinal Data on Millions of Hospital VisitsTell us About the Value of Public Health Insurance as a Safety Net
for the Young and Privately Insured?
• Young, privately insured individuals with hospital visits for diagnoses that require more hospital visits in future years are more likely to transition to public health insurance in future years
• If we ignore the longitudinal transitions in the data, we miss over 80% of the value of public health insurance as a safety net
What Do Longitudinal Data on Millions of Hospital VisitsTell us About the Value of Public Health Insurance as a Safety Net
for the Young and Privately Insured?
• What the prior literature does not tell us• What my data can tell us – stylized facts• How I incorporate my data into a framework for
valuing public health insurance• What we learn from the framework, along with
robustness
The longitudinal value of health insurance is much greater than the sum of its cross-sectional parts
The current private health insurance system does not offer long-term protection against financial risk
• I consider the value of public health insurance as a “safety net” that fills gaps in the current private system– Previous literature focuses on design and regulation of
private health insurance contracts to mitigate this risk• Cochrane (1995), Pauly (1995)
• I use longitudinal data and a longitudinal framework– Previous literature focuses on the value of public heath
insurance for those who have already been caught by it, using cross-sectional data and a cross-sectional framework• Medicare: Finkelstein and McKnight (2008), Khwaja (2010),
Barcellos and Jacobson (2014)• Medicare Part D: Engelhardt and Gruber (2011)• Medicaid: Finkelstein, Hendren, Luttmer (2014)
What Do Longitudinal Data on Millions of Hospital VisitsTell us About the Value of Public Health Insurance as a Safety Net
for the Young and Privately Insured?
• What the prior literature does not tell us• What my data can tell us – stylized facts• How I incorporate my data into a framework for
valuing public health insurance• What we learn from the framework, along with
robustness
The longitudinal value of health insurance is much greater than the sum of its cross-sectional parts
I use longitudinal data on almost all hospital visits in New York from 1995-2011
• Using my SPARCS data and population data, I create a balanced panel to represent New York State population
• I focus on individuals who are young and privately insured in 1995 to isolate the value of the safety net
Just for the stylized facts, I focus on individuals with “persistent diagnoses,” likely to drive the value of private health insurance as a safety net
ICD-9 Diagnosis Code Description
Average SubsequentYears with Hospital Visits
through 2011403 Chronic Kidney Disease (hypertensive renal disease) 4.9282 Hereditary Anemia 4.7
277 Cystic Fibrosis 3.6
707 Ulcers 3.4
710 Lupus 3.4
340 Multiple Sclerosis 3.2
250 Diabetes 3.1
414 Heart Disease (Coronary Atherosclerosis) 3.1
595 Urinary Tract Infection 3.1
345 Epilepsy 3.0
295 Schizophrenic Disorders 2.9
555 Inflammation of Intestinal Tract 2.9
Young, privately insured individuals with persistent diagnoses have higher costs in future years, and they are more likely to transition to public insurance in future years
Even after imposing an annual upper bound of $30K, cumulative costs are $58K by 2011 for persistent diagnoses vs. 13K for other diagnoses
17.9% of individuals with persistent diagnoses have public coverage in 2011, in contrast to 3.7% with other diagnoses
What Do Longitudinal Data on Millions of Hospital VisitsTell us About the Value of Public Health Insurance as a Safety Net
for the Young and Privately Insured?
• What the prior literature does not tell us• What my data can tell us – stylized facts• How I incorporate my data into a framework for
valuing public health insurance• What we learn from the framework, along with
robustness
The longitudinal value of health insurance is much greater than the sum of its cross-sectional parts
Simple indifference condition for valuing public health insurance
• Rooted in frameworks used in the literatureWith public insurance Without public insurance
• Closed form solution for ρ under CARA
Goal: calculate how much we miss by using cross-sectional in lieu of longitudinal data• Special Cases of the Value of Insurance
• Ratio of interest
Contrast to other frameworks that produce the same value with cross-sectional or
longitudinal data• Kowalski (2015)
• Handel, Hendel, and Whinston (2013)
The cross-sectional value of insurance from our framework φ is almost the same as the alternative cross-sectional value of insurance μ
Implement the indifference condition empirically• Costs in actual world with public insurance:
where:
• Costs in counterfactual world without public insurance:
where:
• Uses all paths observed in the data, people with public coverage either gain private coverage or go uninsured
• Examine robustness to wide range of parameters
What Do Longitudinal Data on Millions of Hospital VisitsTell us About the Value of Public Health Insurance as a Safety Net
for the Young and Privately Insured?
• What the prior literature does not tell us• What my data can tell us – stylized facts• How I incorporate my data into a framework for
valuing public health insurance• What we learn from the framework, along with
robustness
The longitudinal value of health insurance is much greater than the sum of its cross-sectional parts
Longitudinal value of insurance much larger than cross-sectional value for a large range of risk aversion parameters
Red dashed line shows traditional framework that gives the same answer for longitudinalAnd cross-sectional data
Robustness to Parameters: αAcross a broad range, lose over 90% of value
Even though the levels vary a lot with α, the ratio stabilizes
Rather than calibrating one value, consider robustness to a large range
• All necessary parameters– α, coefficient of absolute risk aversion– M, upper bound for annual individual costs– T, number of years of data in sample– N, percentage of population in sample
• Private Information on Persistent Diagnoses• Robustness to Including Uninsured
Robustness to Parameters: MAcross broad range, lose over 90% of value
At very small values of M, there is not much risk, so all three values are close.We choose 30K as our baseline value because that is where the ratio stabilizes.As M gets really large, variability within a period increases while mean changes little,so cross-sectional value increases relative to risk-neutral valueBoth are small relative to longitudinal value – frequent visits dominate expensive visits
Robustness to Parameters: TAcross broad range, lose over 90% of value
Use all years as our baseline value.Ratio stabilizes after about 8 years. MEPS only has 2.5 – not long enough for values to diverge.
Robustness to Parameters: NAcross broad range, lose over 90% of value
Full sample contains 1.69 million individuals.MEPS is approximately 0.3% of our sample. Results at that size are highly variable.Results from 100 draws of the size of the MEPS range from 8.2% of 95.7% - too small for tails!Furthermore, this calculation assumes MEPS has 17 years of data, but it only has 2.5!
Robustness to Private Information on Persistent DiagnosesEven with extreme private info, results persist
Robustness to Including UninsuredThese are baseline, somewhat less intuitive
What Do Longitudinal Data on Millions of Hospital VisitsTell us About the Value of Public Health Insurance as a Safety Net
for the Young and Privately Insured?
• What the prior literature does not tell us• What my data can tell us – stylized facts• How I incorporate my data into a framework for
valuing public health insurance• What we learn from the framework, along with
robustness
The longitudinal value of health insurance is much greater than the sum of its cross-sectional parts
Appendix Slides
Hospital Count – SPARCS vs. AHA
Inpatient Cost – SPARCS vs. MEPS
Insurance Coverage – SPARCS vs. CPS
• Indifference condition with private information:
• Closed form solution for λ:
Robustness to Including Uninsured (cont.)
• Theoretically possible for the longitudinal value of insurance ρ to be equal to zero, even if the risk neutral value of insurance λ is positive