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 PRESENTATION
INTRODUCTION
 THE PROBLEM
 OBJECTIVES OF THE STUDY
 DATA & METHODS
RESULTS & 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 PROBLEM
Higher 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 rate
Health 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.
HSI t  w1HIVt  w2 BASK t  w3 IMM t  w4CONTt  w5 ISFt  w6 IWFt  w7 LEBt  w8 MMRt
W 9MALt  w10 PWPCT
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
p 1
X t     i X t 1  X t  p   t
i 1
• Where  is the first difference operator,
i  (  A1...  Ai ) is coefficient matrix
representing short-run dynamics
 is Rank and is nxn matrix,  t is error term
Two different likelihood ratio tests were developed by Johansen for testing
the number of Cointegration vectors (r): the trace test and maximum eigene value
test given respectively by
and
g
trace (r )  T  ln( 1  i )
i  r 1
max (r, r  1)  T ln( 1  i1 )
RESULTS
Long 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% level
B-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, SLSierra 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.
THANK YOU FOR
COMING