National Instituteof Economic and Social Research

WP8: Methodological Issues in Development Scenarios for Health Expenditures for EU

Ehsan Khoman and Martin Weale

Introduction

• Share of public health spending in GDP and in total expenditure has increased significantly in the EU since the 1960s. This trend is likely to continue over the coming decades.

• Health care projections are far more uncertain than pension expenditure projections since there are no rules for estimating future demand and supply of health care.

• The projections are based on a no policy change scenario and on existing legislation. Thus, a certain level of caution must be exercised the further the projections go into the future.

Data and Methodology

• A balanced panel dataset covering 26 countries was used. The data was separated into two sub-group of countries, (i) old EU15 and (ii) EU11 + 2 anticipated member states and Turkey.

• The panel has 24 yearly time periods (1980-2003) for the EU15, but only 8 yearly time periods for the EU11 (1996-2003).

• We attempt to model health care expenditure against a number of variables using the dataset provided by Christiansen et. al (2006) in work package 6.

• Also we have included a ‘mortality’ variable to account for the recent arguments made that the increase of health costs at higher ages may not be a function of age per se, but rather of individual proximity to death.

Data and Methodology

• The dependent variable used in this paper is the natural logarithm of total health care expenditure per capita (THEPC) measured in US nominal prices and adjusted for both PPP and inflation.

• The explanatory variables can be grouped into three categories: (i) economics variables, (ii) institutional variables and (iii) technology variables.

• We had hoped to include tobacco consumption as well but the data did not seem to be reliable. Female labour force participation rates were left out of the analysis as discussed later.

• The projections are in general made on the basis of no policy change, in that they reflect only existing legislation and not possible future policy changes.

The Model – Panel Unit Root and Co-integration Tests• Along with the ADF test (1979) and the Im, Pesaran and

Shin test (2003) four other unit root tests were performed.• Only UNEMP was rejected for the existence of a unit root.

• Following Breitung (2005) we next consider how many co-integrating vectors there may be present in our model.

• His test statistics for co-integrating spaces of up to rank 6 against alternatives of lower rank – we focus on THEPC, GDPPC, MORTALITY, LE65F, LE65M, AGE65-74, AGE75+ and PUHES.

• Our finding suggests that there is at most one co-integrating vector linking the variables in levels.

The Model – Arellano-Bond (1991) estimation• The model is constructed using the Arellano and Bond (1991)

estimation technique which allows for the use of lags of the dependent and explanatory variables to model health care.

• Consider:

(1)

• In order to get a consistent estimator of we first difference the equation above to eliminate the heterogeneous effects

(2)

• So Arellano and Bond (1991) derive a GMM estimator for and the other explanatory variables using lagged levels of the dependent variable and the predetermined variables.

, 1 1,..., ; 1,...,it i t it itTHEPC THEPC x u i N t T

, 1 , 1 , 2 , , 1 , , 1it i t i t i t i t i t i t i tTHEPC THEPC THEPC THEPC x x

The Model – Least-squares restriction

• From the Arellano-Bond method we therefore have a regression equation. This is defined as the unrestricted model.

• By applying least-squares estimation we use this unrestricted equation to estimate a restricted model by removing the insignificant coefficients.

• It is assumed that there is a parameter vector defined as the unrestricted set of variables which satisfies the constraint, .

• It is also observed that the parameters are distributed without bias around the true data with known variance matrix, .

• Our problem is to find a vector to satisfy the account constraint,

.

xAx r

*x

* Ax r

V

*x

The Model – Least-squares restriction

• The least-squares problem is that of minimising (3)

subject to the constraint (4)• The least-squares solution is given by

(5)• The estimator is just a linear combination of the unrestricted

parameters. • The mean of the estimator can be shown to be

(6)

• Balancing of the data also leads to a reduction in the data variance which can be demonstrated by evaluating the variance matrix of the balanced data

(7)

* 1 * x x V x x* Ax r

1* x x VA AVA Ax r

1*x x VA AVA Ax rE

1*V V VA AVA AV

Long-Run Elasticity Restrictions on the model

VariableUnrestrict

edBase

Restrictions

CAPGP 0.2708 0.2472

GLOBALHO -0.0049 0.0197

CASEHO -0.0004 0.0133

COPAYGP 0.0246 0.0000

COPAYHO -0.1119 -0.0844

FREEGP 0.3497 0.3157

FREEHO -0.0123 -0.0064

BEDS 0.0175 0.0000

MORTALITY 0.7415 0.7102

VariableUnrestrict

edBase

Restrictions

GDPPC 1.0776 1.0952

AGE0_5 0.0066 0.0027

AGE65_74 -0.0299 -0.0151

AGE75_ -0.0824 -0.0602

AVELE65 0.1438 0.1202

UNEMP -0.0097 -0.0156

ALCCON -0.0231 -0.0245

PUHES 0.0047 0.0076

SALARYGP 0.2058 0.2006

• We place restrictions on UNEMP(-1), PUHES(-1), GLOBALHO, COPAYGP, COPAYGP(-1), BEDS, BEDS(-1).

Long-Run Elasticities including and excluding FLFPR

Variable Unrestricted Models

GDPPC 1.0776 0.7802

AGE0_5 0.0066 0.0019

AGE65_74 -0.0299 -0.0199

AGE75_ -0.0824 -0.0565

FLFPR 0.0971

AVELE65 0.1438 0.0388

UNEMP -0.0097 -0.0095

ALCCON -0.0231 -0.0135

PUHES 0.0047 0.0094

SALARYGP 0.2058 0.1570

Variable Unrestricted Models

CAPGP 0.2708 0.1963

GLOBALHO -0.0049 -0.0526

CASEHO -0.0004 -0.0326

COPAYGP 0.0246 0.0710

COPAYHO -0.1119 -0.0347

FREEGP 0.3497 0.2475

FREEHO -0.0123 0.0296

BEDS 0.0175 0.0328

MORTALITY 0.7415 0.5595