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Does Publicly Provided Home Care Substitute for Family Care?

Does Publicly Provided Home Care Substitute for Family Care?. By Liliana E.Pezzin, Peter Kemper, and James Reschovsky; Journal of Human Resources , Summer 1996, v. 31, n.3. Presented by Mark L. Trueman. Introduction.

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Does Publicly Provided Home Care Substitute for Family Care?

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  1. Does Publicly Provided Home Care Substitute for Family Care? By Liliana E.Pezzin, Peter Kemper, and James Reschovsky; Journal of Human Resources, Summer 1996, v. 31, n.3. Presented by Mark L. Trueman

  2. Introduction • Elderly population in U.S. expected to double by 2030. Demand for long term care (LTC) increasing. • Types of LTC for the poor: • In kind, by unpaid “informal” caregivers (IC). • Institutional arrangements: e.g., nursing homes (N)- thru Medicaid. • “Formal” home care (FC)- Subsidy. Limited. • Concern: subsidies may increase LTC expenditures w/o lowering nursing home use. • Purpose of paper: gain a better understanding of the extent to which public subsidy of formal home care substitutes for family care. • Key feature: estimate how living arrangement (LA) choices and hours of IC (HIC)/LA differ in presence/ absence of public subsidy program.

  3. Decompose the Effects of Subsidy (FC) • Direct effect (aka “hours effect”): the induced marginal change in behavior of informal caregivers : • change in hours of IC to individuals in a given LA, weighted by probability of choosing a particular LA. • Indirect effect (aka “LA effect”): change in the probability of choosing a specific LA: • this change in probability is weighted by E(HIC/ LA). * Note: Policy making may be more effective in countering the direct effects- tailor interventions through case management guidelines on IC and respite care.

  4. Theoretical Model • Max U=U(X, L, F; τ) s.t. an “arrangement specific budget constraint” • X is a vector of private goods, or a composite commodity • L is leisure • F is a measure of disabled, elderly person’s functioning; May be produced in: • Community setting: Either Separate/ Joint households • Requires Compensatory LTC: either HFC or HIC where H is hours • F = F(HFC, HIC ; D) where D is the level of disability • Institution: Nursing home services (N), exclusively • F = F(N; D) • τ is a taste parameter- captures family preferences for privacy and independence (affects utility of LA options) • Step 1:Choose optimal X, L, and F/ on each LA • { X*, L*, F*;H*FC, H*IC} are the cond’l commodity and input demands. • Step 2: Compare values, then choose type of care (IC and/or FC; or N)andLA (separate/ joint households) which maximizes overall utility, U*. • Let j = index of LA options: • j = 0, independent living (separate household) • j = 1, shared living (joint “intergenerational” household) • j = 2, institutional living (nursing home)

  5. Theoretical Model Continued • By substitution, we get a set of indirect utility functions, Vj , where: • I = family’s unearned income • PIC = shadow price of informal caregiving time • PFC = price of formal home care • PN = price of nursing care • Px = 1 (nummeraire) • V0 = V0(I, PIC, PFC) = U*[X0*, L0*, F0*(HIC,0*, HFC,0*; D); τ] • V1 = V1(I, PIC, PFC) = U*[X1*, L1*, F1*(HIC,1*, HFC,1*; D); τ] • V2 = V2(I, PN) = U*[X2*, L2*, F2*(N; D); τ] • Chosen living arrangement will be: • j* = argmax[Vj(I, PIC, PFC, PN; t, D)] for j = 0,1, 2 • Implied (conditional) demands: • HIC* = HIC(I, PIC, PFC, PN; D/ j*) • HFC* = HFC(I, PIC, PFC, PN; D/ j*) • N* = N (I, PIC, PFC, PN; D/ j*) • Eq’n (2) implies that a subsidy program that reduces PFC affects family’s conditional demand functions but also LA choices. • əE(HIC)/əPFC = Σ{ə E(HIC / j)/ əPFC * Prob(j) + [əProb(j)/ əPFC]* E(HIC / j) (1) (2) j Overall Effect = Direct Effect + Indirect Effect (3)

  6. Channeling Experiment & Data • National test of expanded public financing of home care, 1982-1985. • Aim: Test whether a managed system of home and community based services could be a cost effective alternative to institutionalization. • 5 communities/ 6,236 eligible applicants • individuals channeled into one of 3 groups, which had case managers (CMs) • Group 1/ “Basic”: CMs determine needs/ services under existing system (i.e., limited grants, $, to finance home care services) • Group 2/ “Financial”: Direct provision of home care subject to a CM’s authorization & cost limits >>>> substantial increase in use of HFC ($$$)! • Group 3/ Control Group • Screening interviews to establish eligibility (disability & unmet need) & follow-up interviews to collect data (service use, LA, # of “visiting”/ “resident” hrs.). • Average age: 79, most w/ multiple functional limitations. • Average monthly income: < $ 530. • At 1-Year follow-up interview: 28% died; 15% nonrespondents; 57% analyzed.

  7. Empirical Model (1) g m k • Eq’n (2) counterparts of LA choice & HIC: • Vijt* = ßj0 + ΣßjgTig,t + ΣßjmAim,t-1 + ΣßjkZik,t-1 + εijt (4) • ln HICijt = γj0 + Σ γjgTig,t + Σ γjkZik,t-1 + μijt(5) • Vijt* = latent variable, value to family i choosing jth LA, at time t, • Where: • ln HICijt = ln of IC hours in family i, choosing jth LA, at time t, • t is at 1- year follow up evaluation; t-1 is at initial screening, • T is a (1 X g) vector of dummies for treatment status (“basic”, “financial”) • Z is a (1 X k) vector of variables proxying remaining elements which affect family’s utility & cond’l demand functions: (I,PIC, PN, D). Preexperimental measures of family’s economic, demographic, & health status, prior service use, and “site” dummies. • A is a (1 X m) vector capturing family’s transactions costs and τ (prior LA) • Assumes εijt and μijt are distributed BVN (0,0,σε2, σμ2,ρ) where ρ is the correlation between LA choices and hours of informal care (HIC). • ß and γ are the vectors of coefficients to be estimated in the model. • Vijt*is an indicator of which of the “j” LA alternatives is chosen. • An elderly person will be observed in a particular LA, Vijt = 1 iff Vit* falls into a particular interval αj-1 < Vit* < αj . • [αj]is a vector of (J +1= 2+1=3) thresholds in the latent variable index, where α0=0. g k “HFC: subsidy”

  8. Empirical Model (2) • Model is operationalized by assuming that LA choices can be ordered in hierarchy corresponding to higher levels of assistance: • Independent, shared, institutional living • Eq’n (4) reduces to an ordered probit model. • Elderly person will attempt to live independently as long as possible, until a threshold is reached; then switches. • Probability of any level Vit* is chosen is given by: • Prob [Vijt = 1] = Φ[ (αj –β'Yi)/ σε] - Φ[ (αj-1 –β'Yi)/ σε] (6) • Φ(•) represents a cdf, • Y is a matrix of all nonstochastic explanatory variables in eq’n (4), • Assumes and σε = 1.

  9. Empirical Model (3) • Use a 2-step estimation procedure that provides estimates of the effects of Channeling on LA choices and IC provision. • Step 1: £ = Π Π [Φ(αj –β'Yi) - Φ(αj-1 –β'Yi)]Vijt (7) • Step 2: Estimate the effect on HIC / LA choices by applying OLS to a new version of Eq’n (5): • E(ln HICit/ j) = γj0 + Σ γjgTig,t + ΣγjkZik,t-1 + bjλij + μijt* where, • HIC represents either “visiting” or “resident” care hrs, • bjλij is a selectivity bias correction due to nonrandom selection of LA choice. • Model is estimated separately for each person’s marital status at time of follow-up, t. • Married/ unmarried have substantially different endowments of potential care. Presence of spouse affects role of others/incentives. • We expect unmarried to be more responsive to intervention. i j (8) g k

  10. Results: LA Choices (1) • Rows 1 and 2 coefficients represent Channeling’s impact on Vit*, after controlling for differences in preexperimental LA. • Financial intervention increased probability of being in more independent LA. • Married folks have lower threshold value by which they will switch LA.

  11. Results: LA Choices (2) • Table 3 shows predicted probabilities of each LA choice in presence and absence of each interventions (from equation (6)). • Financial interventions effect on LA choices of unmarried individuals was substantial.

  12. Results: HIC/LA choice (1) • Table 4 presents estimated experimental impacts on HIC, adjusted for LA choices: Eq’n (8). • Overall, there is no evidence that the intervention had a significant impact on conditional hours of care, net of its impact thru LA. • Strong & significant effect of the LA correction term on both visiting & resident hours for the unmarried sample. • Authors assert that it is important to control for the endogeneity of the LA choices when analyzing use of LTC.

  13. Results: HIC/LA choice (2) • The coefficients in the previous table were used to find the predicted hours in table 5, based on Eq’n (8). • Greater reductions in informal care are observed for those receiving more generous financial intervention.

  14. Results: Overall, Direct, & Indirect Program Effects (1) • Table 6 presents the results of a simulation: an experimental analog of Eq’n (3): əE(HIC)/əPFC = Σ{ə E(HIC / j)/ əPFC * Prob(j) + [əProb(j)/ əPFC]* E(HIC / j)} Overall Effect = Direct (or Hours) Effect + Indirect (LA) Effect • For each individual: • calculate predicted probability of choosing each LA, P-hat, assuming treatment, then control status; average of these predictions: E[Prob (j)]. • calculate predicted hours of visiting and resident care, HIC-hat, each would receive had he/she chosen each LA, according to the estimated hours equation; average of these predictions: E(HIC / j) in the presence and absence of each treatment • Overall program impact is given by: (1/N) ΣΣ (PTifHTICif – PCifHCICif) (9) • LA effect is given by (1/N) Σ (PTif – PCif) HCICif (10) • Hours effect is given by (1/N) Σ (HTif – HCif) PCICif (11) j j i i i

  15. Conclusions • Public home care provision results in: • only small reductions in overall amount of care provided by informal caregivers to unmarried persons; • No reductions for married persons. • Implication: benefits of such programs will flow primarily to disabled elderly recipients rather than to informal caregivers. • Decomposition suggests that the direct effect on hours of care assuming no change in LA, is likely to dominate any overall effect of subsidized care. • Policymakers concerned with potential substitution should focus their attention on specific measures designed to minimize caregiver’s behavioral responses rather than on discouraging effects due to LA choices. • Channeling financial intervention had both sizable and statistically significant effects on LA decisions of unmarried persons. • Increased probability of living independently by 7.1% relative to control group. • Increase was associated with corresponding significant reduction in probabilities of living with others (2.4%) and of living in a nursing home (4.7%) • The more generous intervention appears to have enabled unmarried elderly persons with disabilities to live more independently.

  16. Questions Raised • Are the benefits from increased independence and associated quality of life sufficient to justify the cost of expanded public home care coverage for this group? • If so, can the targeting of benefits to unmarried persons be justified on equity grounds?

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