ANALYSING THE RELATIONSHIP BETWEEN HEALTH CARE EXPENDITURE AND HEALTH OUTCOMES IN

WEST AFRICA

BY

RAZAK M. GYASI1, CHARLOTTE M. MENSAH2 & ADAM M. ANOKYE3

1&2 DEPARTMENT OF GEOGRAPHY & RURAL DEVELOPMENT, KNUST, KUMASI

3 SCHOOL OF BUSINESS, UNIVERSITY OF CAPE COAST, CAPE COAST

GGA Annual Conference, August, 2012, KNUST, Kumasi, Ghana

OUTLINE OF PRESENTATIONINTRODUCTION THE PROBLEM OBJECTIVES OF THE STUDY DATA & METHODSRESULTS & DISCUSSION POLICY IMPLICATIONS REFERENCES

INTRODUCTION Health is an indicator of development and the

mechanism for achieving development (Buor, 2008).

Strong health systems are fundamental to improving health outcomes and accelerate progress towards health-related MDGs (Sen, 1999; WHO, 2009).

The enjoyment of the highest attainable standard of health is fundamental right of every human being (WHO/UN, 2000; Human Right Council, 2002).

THE PROBLEMHigher government expenditure on human

health creates three-tier-benefit (Barro, 1996).

Increasing life expectancy at birth by 10% increases economic growth rate by 0.35% a year (Comm. Macroeconomics & Health, 2001).

Studies are conducted to argue this relationship.

THE PROBLEM CONT’D

To see health expenditure-health status relationship, studies are not comprehensive, eg. Castro-Leal (2000), Anyanwu and Erhijakpor (2007), Buor (2008).

Is there any linkages between health care spending and health status in West Africa?

OBJECTIVES OF THE STUDY

To estimate the long-run relationship between health care expenditure and health status.

To estimate the short-run relationship between health care expenditure and health status in West Africa.

DATA & METHODS

• Time series data of three variables from 1990 to 2010 were used:

- Health status Index - Health expenditure- Literacy rateHealth expenditure and literacy rate were

extracted from World Bank WDI online Database, June 2012

DATA & METHODS

Health status index was constructed from 10 health indicator measures using principal component Analysis(PCA)

- Prevalence of HIV, total (% of population ages 15-49)-HIV- Births attended by skilled health staff (% of total)-BASK- Contraceptive prevalence (% of women ages 15-49)-CONT- Immunization, measles (% of children ages 12-23 months)-

IMM - Improved sanitation facilities (% of population with access)-ISF- Improved water source (% of population with access) -IWS- Life expectancy at birth, total (years) -LEB- Malaria cases reported -MAL- Maternal mortality ratio (national estimate, per 100,000 live

births)-MMR - Pregnant women receiving prenatal care (%) -PWPC

DATA & METHOD CONT’D• Eigenvector with lager eigenvalue was used to

construct the index. All components used explained above 60% of the variation in HSI.

Tt

ttttttttt

PWPCwMALW

MMRwLEBwIWFwISFwCONTwIMMwBASKwHIVwHSI

109

87654321

TIME SERIES PROPERTIES OF THE DATA

There are many different unit root test used in the literature, however we use two most commonly used test

• Augmented Dickey Fuller test (ADF)• Phillips and Perron test (PP) The results of both tests indicate that all the

variables are of I(1). Under such circumstance Johansen Multivariate Cointegration approach is appropriate.

• The Multivariate Cointegration approach is base on error correction representation of the p order Vector Autoregressive model with Gaussian error

• Where is the first difference operator, is coefficient matrix representing short-run dynamics

is Rank and is nxn matrix, is error term

Two different likelihood ratio tests were developed by Johansen for testingthe number of Cointegration vectors (r): the trace test and maximum eigene value

test given respectively by and

tptt

p

iit XXX

1

1

1 )...( 1 ii AA

t

)1ln()(1

g

riitrace Tr )1ln()1,( 1max iTrr

RESULTSLong Run Test & Relationship

B BF CD CV GA GH GU GB LB M N NG SG SL TG

Trace x x x x Max x x x x

EXP +* +* -* +* +* + + +* +* +* +*

LIT +* +* - +* + +* +* - +* +* +

+*(-*) is positive (negative) and significant at 5% level, +(-) is positive (negative) and insignificant at 5% levelB-Benin, BF-Burkina Faso, CD- Cote d’Ivoire, CV-Cape Verde, GA-The Gambia, GH-Ghana, GU-Guinea, GB-Guinea Bissau, LB-Liberia, M-Mali, N-Niger, NG-Nigeria, SG-Senegal, SL-Sierra Leone, TG-Togo

POLICY IMPLICATIONS

Governments should be committed to increase health care spending in West Africa.

General health education and awareness should be prioritised in West Africa.

As closer look should be taken to see weather health care resources are channelled to areas meant for.

REFERENCES Barro, Robert J. (1996a), “Determinants of Economic Growth: A Cross-Country Empirical

Study”, NBER Working Paper No. 5968 (Cambridge, Massachusetts: National Bureau of Economic Research).

Buor, D. (2008). Analysing the socio-spatial inequities in the access of health services in sub-Saharan Africa: Interrogating geographical imbalances in the uptake of health care. Professorial Inaugural Lecture. Great Hall, KNUST, Kwame Nkrumah University of Science and Technology, Kumasi, Ghana. October 9, 2008.

Sen, Amartya (1999), Development as Freedom. Oxford: Oxford University Press.

WHO (2004) Comprehensive Community- and Home-based Health Care Model. World Health Organization Regional Office for South-East Asia. New Delhi, India SEARO Regional Publication No. 40.

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