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 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 (H IC )/LA differ in presence/ absence of public subsidy program.

3 Decompose the Effects of Subsidy (FC) 1.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. 2.Indirect effect (aka “LA effect”): change in the probability of choosing a specific LA: this change in probability is weighted by E(H IC / 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 H FC or H IC where H is hours –F = F(H FC, H IC ; 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) and LA (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, V j, where: –I = family’s unearned income –P IC = shadow price of informal caregiving time –P FC = price of formal home care –P N = price of nursing care –P x = 1 (nummeraire) V 0 = V 0 (I, P IC, P FC ) = U*[X 0 *, L 0 *, F 0 *(H IC,0 *, H FC,0 *; D); τ ] V 1 = V 1 (I, P IC, P FC ) = U*[X 1 *, L 1 *, F 1 *(H IC,1 *, H FC,1 *; D); τ ] V 2 = V 2 (I, P N ) = U*[X 2 *, L 2 *, F 2 *(N; D); τ ] Chosen living arrangement will be: –j* = argmax[V j (I, P IC, P FC, P N ; t, D)] for j = 0,1, 2 Implied (conditional) demands: –H IC * = H IC (I, P IC, P FC, P N ; D/ j*) –H FC * = H FC (I, P IC, P FC, P N ; D/ j*) –N* = N (I, P IC, P FC, P N ; D/ j*) Eq’n (2) implies that a subsidy program that reduces P FC affects family’s conditional demand functions but also LA choices. ə E(H IC) / ə P FC = Σ { ə E(H IC / j)/ ə P FC * Prob(j) + [ ə Prob(j)/ ə P FC ]* E(H IC / j) (1) (2) Direct Effect + Indirect EffectOverall Effect =(3) j

6 Channeling Experiment & Data National test of expanded public financing of home care, 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 H FC ($$$)! –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.

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8 Empirical Model (1) Eq’n (2) counterparts of LA choice & H IC: –V ijt * = ß j0 + Σß jg T ig,t + Σß jm A im,t-1 + Σß jk Z ik,t-1 + ε ijt (4) –ln H IC ijt = γ j0 + Σ γ jg T ig,t + Σ γ jk Z ik,t-1 + μ ijt (5) –V ijt * = latent variable, value to family i choosing jth LA, at time t, Where: –ln H IC ijt = 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,P IC, P N, 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 ε ijt and μ ijt are distributed BVN (0,0,σ ε 2, σ μ 2,ρ) where ρ is the correlation between LA choices and hours of informal care (H IC ). ß and γ are the vectors of coefficients to be estimated in the model. V ijt * is an indicator of which of the “j” LA alternatives is chosen. An elderly person will be observed in a particular LA, V ijt = 1 iff V it * falls into a particular interval α j-1 < V it * < α j. [α j ] is a vector of (J +1= 2+1=3) thresholds in the latent variable index, where α 0 =0. gmk gk “H FC: subsidy”

9 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 V it * is chosen is given by: –Prob [V ijt = 1] = Φ[ (α j –β'Y i )/ σ ε ] - Φ[ (α j-1 –β'Y i )/ σ ε ] (6) Φ() represents a cdf, Y is a matrix of all nonstochastic explanatory variables in eq’n (4), Assumes and σ ε = 1.

10 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(α j –β'Y i ) - Φ(α j-1 –β'Y i )] Vijt (7) –Step 2: Estimate the effect on H IC / LA choices by applying OLS to a new version of Eq’n (5): E(ln H ICit / j) = γ j0 + Σ γ jg T ig,t + Σ γ jk Z ik,t-1 + b j λ ij + μ ijt * where, –H IC represents either “visiting” or “resident” care hrs, –b j λ ij 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. (8) ij gk

11 Results: LA Choices (1) Rows 1 and 2 coefficients represent Channeling’s impact on V it *, 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.

12 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.

13 Results: H IC /LA choice (1) Table 4 presents estimated experimental impacts on H IC, 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.

14 Results: H IC /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.

15 Results: Overall, Direct, & Indirect Program Effects (1) Table 6 presents the results of a simulation: an experimental analog of Eq’n (3): ə E(H IC) / ə P FC = Σ { ə E(H IC / j)/ ə P FC * Prob(j) + [ ə Prob(j)/ ə P FC ]* E(H IC / 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, H IC -hat, each would receive had he/she chosen each LA, according to the estimated hours equation; average of these predictions: E(H IC / j) in the presence and absence of each treatment Overall program impact is given by: (1/N) Σ Σ (P T if H T ICif – P C if H C ICif ) (9) LA effect is given by (1/N) Σ (P T if – P C if ) H C ICif (10) Hours effect is given by (1/N) Σ (H T if – H C if ) P C ICif (11) j i j i i

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17 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.

18 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?