The 2018 ASSA Annual Meeting January 6, 2018 Medicaid Crowd-Out of Long-Term Care Insurance With Endogenous Medicaid Enrollment The 2018 ASSA Annual Meeting Geena Kim Health, Retirement, and Long-Term Analysis Division The information in this presentation is preliminary and is being circulated to stimulate discussion and critical comment as developmental work for analysis for the Congress.

Introduction Long-Term Care Cost, Usage, and Private Insurance Average annual cost of nursing home care was $82,128 in 2016 About half of Americans turning 65 were estimated to develop a disability that requires long-term care (ASPE, 2016) Few (about 10 percent) have private long-term care insurance (LTCI)

Introduction (Continued) Long-Term Care and Medicaid In 2015, 62 percent of nursing home residents relied on Medicaid Total nursing home care spending was $157 billion in 2015 Medicaid accounted for about a third of that spending Long-term care costs accounted for about 30 percent of total Medicaid expenditures in 2015

Introduction (Continued) Goals To estimate the effect of lowering premiums of private long-term care insurance on LTCI demand To estimate the Medicaid crowd-out effect

Introduction (Continued) Approach Develop and estimate a stochastic dynamic model of decisions on: LTCI purchase Medicaid enrollment Nursing home use Savings

Introduction (Continued) Preview of Findings Price elasticity of LTCI demand is small Medicaid crowd-out of LTCI demand is small

Existing Literature Price Elasticities of LTCI Demand Goda (2011) Coutemanche and He (2009) Cramer and Jensen (2006) Johnson, Schaner, Toohey, and Uccello (2007) Medicaid Crowd-Out of LTCI Demand Pauly (1989, 1990) Brown and Finkelstein (2008)

Model Stochastic Dynamic Decision Model Each period, shocks are realized (e.g., shocks to health, income, medical care cost, and preference) Observing the shocks, individuals make decisions Choices: On health insurance, nursing home use, and savings State: Given their own state (realized at each period) Objective: In order to maximize expected lifetime utility Constraints: Subject to budget constraints, health transition functions, Medicaid eligibility rules, etc.

Model (Continued) Medicaid eligibility rules Categorically needy (CN) and medically needy (MN) CN: All states have a categorically needy program that specifies an income limit (Īs) and an asset limit (Ws) for Medicaid eligibility MN: In some states, people may enroll in Medicaid even if their income/assets exceed categorical thresholds, provided that their income and wealth net of their total medical care cost are at or below certain limits Post-eligibility rules: personal needs allowance pnas: To maintain eligibility, a Medicaid beneficiary in a nursing home cannot have income exceeding the personal needs allowance

Model (Continued) Individual’s Problem (*1 period = 2 years) where Ca is consumption, (Ws, Īs, pnaa) are determined by Medicaid rules, Ia is income, mca is medical care cost, pa is LTCI premium, k is type

Model (Continued) Choice Set Da = { hia, nha, sra } hia ϵ { 0, 1, 2 }: health insurance hia = 1: LTCI (long-term care insurance) hia = 2: Medicaid hia = 0: neither nha ϵ { 0, 1 }: nursing home nha = 1: nursing home use nha = 0: no nursing home sra : savings rate

Model (Continued) Dynamic Decisions Buying private long-term care insurance enables usage of a nursing home sometime in the future at low cost Nursing home use affects the probability of surviving to the next period Current savings decision affects Medicaid eligibility in the next period

Model (Continued) State Space Ωa = { s , a , e , Ha , Wa , hia-1, dra, nha-1 ; ϵa , k } Ha ϵ {  0, 1, 2 }: health status (ADL: activities of daily living) Ha = 1: #ADL < 3 (Good) Ha = 2: #ADL ≥ 3 (Poor) Ha = 0: (Dead) s: state of residence e: education Wa: assets carried over to age a dra: duration of LTCI ownership at age a ϵa: vector of shocks k: type (unobserved permanent heterogeneity)

Model (Continued) Per-Period Utility

Model (Continued) Budget Constraints hia ≠ 2 (no Medicaid) Wa+1 = (1 + r ) Wa + Ia − Ca − mca − pa · I ( hia = 1 ) hia = 2 & nha = 0 (Medicaid and no nursing home) Wa+1 = (1 + r )  min{ Wa, Ws } + min{ Ia, Īs  } − Ca hia = 2 & nha = 1 (Medicaid and nursing home) Wa+1 = (1 + r )  min{ Wa, Ws  } + min{ Ia, pnas  } − Ca

Data and Estimation Data Health and retirement study (1998–2004) Single elderly women living in CA, TX, MI, and FL Estimation Simulated maximum likelihood estimation

Solution Method Dynamic Programming Estimation procedure Iterates between the solution of the dynamic programming and the calculation of the likelihood

Model Fit Health Insurance Choices by Age LTCI (%) Medicaid (%) Neither (%) Age Actual Pred. 73–74 15.9 13.8 18.2 11.7 65.9 74.5 75–76 15.7 13.2 18.1 12.9 66.2 73.9 77–78 12.0 12.1 17.7 14.2 70.3 73.7 79–80 11.6 10.2 17.3 16.1 71.1 81–82 7.5 8.2 15.5 16.4 77.0 75.4 83–84 6.5 8.3 17.0 78.0 74.7 85–86 9.4 7.1 23.0 20.0 67.6 72.9 87–88 27.4 22.3 66.1 69.4 Percentage holding private long-term care insurance, being on Medicaid, and having neither Chi-square goodness-of-fit statistic: 4.31 Critical value of chi-square distribution with 2 degrees of freedom at 5 percent significant level: 5.99

Model Fit (Continued) Nursing Home (NH) Choice by Age NH (%) No NH (%) Actual Pred. 73–74 1.1 1.5 98.9 98.5 75–76 1.6 98.4 77–78 2.5 97.5 79–80 4.0 3.2 96.0 96.8 81–82 4.6 4.1 95.4 95.9 83–84 7.1 7.9 92.9 92.1 85–86 10.8 11.8 89.2 88.2 87–88 12.1 15.1 87.9 84.9 Percentage choosing nursing home and not choosing nursing home Chi-square goodness-of-fit statistic: 1.17 Critical value of chi-square distribution with 1 degree of freedom at 5 percent significant level: 3.84

Model Fit (Continued) Mean Assets by Age

Findings Price Elasticity of LTCI Demand If the premium is reduced by half, the LTCI demand increases by 4.2 percent Price elasticity of LTCI demand: -0.08 Medicaid Crowd-Out of LTCI Demand In the absence of Medicaid: LTCI demand would increase by 5.3 percent Median assets would increase by 15.3 percent Nursing home care use would decrease by 24.4 percent

Limitations The findings from this study cannot be generalized to the entire U.S. population Those findings are specific to unmarried elderly women living in four states: CA, TX, MI, and FL Married couples’ incentives for LTCI needs are different from unmarried individuals’ Informal care is not explicitly modeled Some individuals might prefer family-provided nursing care at home rather than in nursing home Those people would be less price-sensitive

Conclusion Contributions Provide a new estimate of price elasticity for a population at risk of future long-term care shock Use a new approach where both price elasticity and Medicaid crowd-out are examined Future work Explicitly model informal care

Thank You