the role of housing and financial wealth on healthcare ... 3.pdf · the role of housing and...

Research Topic and MotivationTheoretical FrameworkData and Methodology

Econometric Methodology and Preliminiary FindingsFuture work

The Role of Housing and Financial Wealth onHealthcare Expenditure

Nayan Krishna Joshi

Department of EconomicsWayne State University

Sep 19, 2013

Nayan Krishna Joshi The Role of Housing and Financial Wealth on Healthcare Expenditure



Outline

1 Research Topic and Motivation

2 Theoretical Framework

3 Data and Methodology

4 Econometric Methodology and Preliminiary Findings

5 Future work




Focus on the influence of housing and financial wealth onhealthcare expenditure.

Only few studies have provided empirical evidence on theeffect of wealth on healthcare spending. These includeOkunade, Suraratdecha, and Benson (2010), Marshall,McGarry, and Skinner (2010), and Kim and Ruhm (2012).




Motivation1 Okunade, Suraratdecha, and Benson (2010): the first

study to analyse the effect of wealth (using proxies such as airconditioner, motorbike, car, television etc) on the householdhealthcare expenditure, but they focus on data of Thailand.

2 Marshall, McGarry, and Skinner (2010): use the Healthand Retirement Survey and find that the expenditures arestrongly positively related to wealth but weakly related toincome (near the end of life).

3 Kim and Ruhm (2012): use the Health and RetirementSurvey and inheritances as an instrument for positive wealthshock and find positive and small but insignificant effect ofshocks on the probability of positive expenditures.




Research gap and Contribution1 Previous studies on U.S. focus only on elderly population.

2 Previous studies donot analyze the independent role offinancial and housing wealth, though the literature on wealthand consumption indicates that such effects could exist (e.g.,Bostic, Gabriel, and Painter, 2009; Case, Quigley, and Shiller,2012).

3 Previous studies donot include the most recent data (2010).




Research gap and Contribution1 Previous studies on U.S. focus only on elderly population.2 Previous studies donot analyze the independent role of

financial and housing wealth, though the literature on wealthand consumption indicates that such effects could exist (e.g.,Bostic, Gabriel, and Painter, 2009; Case, Quigley, and Shiller,2012).

3 Previous studies donot include the most recent data (2010).




Outline





5 Future work




Grossman human capital model (1972, 2000)1 Individuals derive utility from consumption and health:

U = U(C ,H)

2 Health is produced by combining time inputs, t, withhealthcare expenditure, M: H = H(t,M)

3 An individual works Tw hours and faces the time constraint T :T = Tw + tM and budget constraint:wTw + R + S = Pm(1− r) + C where, R is housing wealthand S is financial wealth

4 M = M(w ,T , t,R,S ,Pm, r)5 ∂M

∂R � 0 if UMC � 0 and ∂M∂S � 0 if UMC � 0





U = U(C ,H)2 Health is produced by combining time inputs, t, with

healthcare expenditure, M: H = H(t,M)

3 An individual works Tw hours and faces the time constraint T :T = Tw + tM and budget constraint:wTw + R + S = Pm(1− r) + C where, R is housing wealthand S is financial wealth

4 M = M(w ,T , t,R,S ,Pm, r)5 ∂M







healthcare expenditure, M: H = H(t,M)3 An individual works Tw hours and faces the time constraint T :

T = Tw + tM and budget constraint:wTw + R + S = Pm(1− r) + C where, R is housing wealthand S is financial wealth

4 M = M(w ,T , t,R,S ,Pm, r)5 ∂M









4 M = M(w ,T , t,R,S ,Pm, r)

5 ∂M∂R � 0 if UMC � 0 and ∂M

∂S � 0 if UMC � 0








4 M = M(w ,T , t,R,S ,Pm, r)5 ∂M





Outline





5 Future work




Household healthcare expenditure data are obtained fromConsumer Expenditure Survey (BLS)

1 Survey also provides information on demographiccharacteristics and assets and liabilities

2 Healthcare expenditure is the sum of four components: healthinsurance, medical services, prescription drugs, and medicalsupplies

3 Each household is interviewed five consecutive times, once perquarter. Focuses only on the households who completed allfive interviews.

4 Uses the Survey of Consumer Finance (SCF) to impute thewealth data (Bostic, Gabriel, and Painter, 2009). SCF hasrelatively good coverage of wealth data compared to othersurveys conducted in U.S. ( Kennickell and Woodburn, 1999)




Use the non-parametric statistical matching technique toimpute the wealth data from SCF to the CES data (Bostic,Gabriel, and Painter, 2009; Galama et al.,2012)

Table : Dataset A (Recipient) and B (Donor)

a MS Age HCE b MS Age Wealth1 U 27 22 1 U 54 222 M 35 19 2 M 21 173 U 41 47 3 U 48 154 U 61 41 4 U 33 145 U 52 17 5 U 63 13

6 U 29 157 M 36 198 M 55 249 U 27 18




Table : Matched dataset

Marital Staus Age HCE donor id Wealth1 U 27 22 9 182 M 35 19 7 193 M 41 47 7 194 U 61 41 5 135 M 52 17 1 22

Marital Status : donor class/stratification variables on whichexact matching is done

Age: matching variable on which each record in the recipientdata is matched with the closest record in the donor data,according to a distance measure




Stratification variables1 Race: white, black, other2 Marital status: married/living with partner, other3 Education: twelfth grade or less, high school, some college or

more4 Occupation: not working, managers and professionals,

technicians, services, operators, other5 Family size: one, two, three, four or more6 Tenure: homeowner, other

Matching variables: Age and Income

Mahalanobis distance is used:dab = (xa − xb)′

∑−1(xa − xb)




Table : Correlation coefficients for variables across the surveys

CES SCFlog(income) log(income)

CES log(income) 1.000 0.906SCF log(income) 0.906 1.000CES healthcare expenditure 0.232 0.232SCF wealth variablesGross financial wealth 0.216 0.24Houses 0.368 0.398Other real estate 0.129 0.141Net financial wealth 0.209 0.233




Outline





5 Future work




Double Hurdle Model: About seven percent of sample for2010 have zero values for healthcare expenditures. So, needto use appropriate model. The model assumes that thehouseholds must overcome two hurdles when purchasing thehealthcare (Aristei, Perali, Pieroni, 2008)

1 whether to consume the health care (participation decision)2 the level of consumption of the health care conditional on the

participation (consumption decision).





1 whether to consume the health care (participation decision)

2 the level of consumption of the health care conditional on theparticipation (consumption decision).





1 whether to consume the health care (participation decision)2 the level of consumption of the health care conditional on the

participation (consumption decision).




1 Observed consumption

yi =

{y∗2i ⇐⇒ y∗1i > 0 and y∗2i > 0

0 otherwise(1)

2 Participation equation

y∗1i = x′1iβ + vi (2)

3 Consumption equation

y∗2i = x′2iγ + ui (3)




[ui vi

]∼ BVN(0,Σ) (4)

Σ =

[1 σ12σ12 σ

](5)

Sample likelihood for observed consumption (with independencebetween u and v: σ12 = 0): likelihood function for the sample withzero consumption (denoted 0) times likelihood function for thesample with positive consumption (denoted +)

L =∏0

[1− Φ(x

′1iβ)Φ(x

′2iγ)/σ

]∏+

[Φ(x

′1iβ)

1

σφ((y − (x

′2iγ))/σ)

](6)




2007 2010Variables APE SE p-value APE SE p-valueage 0.023 0.01 0.09 0.002 0.01 0.39agesqu 0.000 0.00 0.10 0.000 0.00 0.01marit 0.558 0.02 0.00 0.554 0.11 0.00edu2 0.674 0.05 0.00 0.804 0.01 0.00edu3 0.895 0.01 0.00 1.061 0.05 0.00hhsize2 0.372 0.00 0.00 0.428 0.02 0.00hhsize3 0.653 0.07 0.00 0.510 0.07 0.00hhsize4 0.470 0.12 0.00 0.310 0.06 0.00race2 -0.540 0.04 0.00 -0.257 0.01 0.00race3 -0.450 0.31 0.13 -0.336 0.30 0.21region2 0.120 0.09 0.15 0.247 0.04 0.00region3 -0.066 0.15 0.36 0.116 0.06 0.08region4 -0.203 0.07 0.01 0.067 0.18 0.37




2007 2010Variables APE SE p-value APE SE p-valueoccup2 0.174 0.14 0.18 0.262 0.23 0.21occup3 -0.018 0.03 0.35 0.059 0.17 0.38occup4 -0.251 0.12 0.05 -0.288 0.29 0.24occup5 -0.177 0.05 0.00 -0.244 0.00 0.00occup6 -0.362 0.20 0.08 -0.056 0.15 0.37log income 0.211 0.07 0.00 0.262 0.06 0.00log financial 0.012 0.01 0.09 0.001 0.00 0.38log housevalue 0.025 0.00 0.00 0.028 0.00 0.00log oth.real estate -0.005 0.00 0.20 0.001 0.00 0.00

# of observations 4715 2816# censored obser. 281 175




Survey of Consumer Finance (SCF) handles missing data problemby employing multiple imputation method. Under this method,SCF provides five different estimates (called implicates) resulting ina total data set with five times the actual number of householdsand hence five different matched datasets. The results providedearlier are only for the first implicate. However, these results areconsistent with the results for other four implicates.




Outline





5 Future work




1 The analysis needs to be conducted separately for thecalendar years 2004, 2001, 1998, 1995, 1992, and 1989 andthen for the pooled sample (1989-2010).

2 Examine the robustness of results to using net wealthmeasures (net financial wealth), home equity, and otherresidential real estate equity.

3 Examine whether the credit/liquidity constraints will haveeffect on the households healthcare spending.

4 Use Box Cox hurdle model to take into account non-normalityin health care expenditure.


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