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HEALTH CARE FINANCING AND HEALTH OUTCOMES IN PACIFIC ISLAND COUNTRIES* Azmat Gani Macmillan Brown Centre for Pacific Studies University of Canterbury Christchurch, New Zealand Email: [email protected] ABSTRACT This paper provides empirical evidence on the relationship between per capita public health expenditure and three measures of health outcomes (infant and under five mortality rate and crude death rate) using cross-country data from seven Pacific Island countries for selected years between 1990 and 2002. The results of the fixed effects estimation procedure correcting for AR (1) errors provide strong evidence that per capita health expenditure is an important factor in determining health outcomes. The elasticity of infant mortality rate with respect to per capita health expenditure is -0.66. Based on this elasticity, a 10 percent increase in per capita health expenditure means that a country such as Papua New Guinea (PNG), with high infant mortality rate, would face a reduction of 3.6 infant deaths per 1,000 live births and a reduction of 2.0 infant deaths per 1,000 live births on average for the Pacific Island countries. The empirical results also provide strong evidence of per capita incomes and immunisation as additional core factors that determine health outcomes. Some policy implications are drawn.

Key words:

Pacific Island countries, health expenditure; incomes; immunisation; mortality.

This project is supported by the University of Canterbury’s Macmillan Brown Centre for Pacific Studies Research Scholarship awarded to the author. The author gratefully acknowledges this financial support. The findings and discussions herein are those of the authors. The author is responsible for all errors and omissions.

*

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1.0 INTRODUCTION Of the 11 million children dying annually, 90 percent of them happen to be under the age of 5 (Bokhari et. al., 2007) and in poor countries 30 percent of deaths are amongst children compared with less than 1 percent in rich countries (Cutler, et. al., 2006). At least as many as 10 million children under the age of five die each year, mainly from preventable (or curable) conditions that seldom kill children in rich countries (Jones et. al., 2003). These statistics provide clear evidence that poor health being is concentrated amongst poor people in poor countries (see also Bhalotra, 2007). Like developing countries elsewhere, the island countries in the South Pacific region are poor. Measured in terms of the per capita incomes, the Pacific Island countries fall in the low and lower-middle-income categories and on the basis of human development index; they fall in the low and medium human development categories. A number of countries in the region rank poorly in terms of their populations health status. For example, many countries reveal high incidence of infant and under five mortality and prevalence of preventable diseases. Children continue to die annually in the Pacific Island countries, majority of who are under the age of five (Figures 1 and 2). At the country level, under-five mortality rates in 2005 ranged from 18.0 per 1,000 in Fiji to 74.4 per 1,000 in PNG. In the same year, infant mortality rates ranged from 15.7 per 1,000 live births in Fiji to 55.2 per 1,000 live births in PNG. Neonatal causes have been the major cause of deaths among children under the age of five in several Pacific Island countries. In additions, diarrhoeal diseases, pneumonia and measles are other causes of deaths among children under the age of five (Table 1).

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Figure 1. Infant Mortality Rates - 2005 60

Per 1,000 Live Births

50 40 30 20 10

V a nu a t u

T u va lu

T o ng a

n d s la

oa

Is

on

S a m

ew

G

ui ne a

u P a la

N i u e

N a ur u

ne si a

nd s

ic r o

S o lo

P a pu a

N

m

M

Is

la

at i

K i rib

F i ji

M

C o ok

ar sh al l

Is

la

n d s

0

Countries

Source of data for Figure 1: World Health Organisation (2006).

Figure 2. Under Five Mortality Rate - 2005 80 70

Per 1,000

60 50 40 30 20 10 0

Fiji

Kiribati

Marshall Islands

Micronesia Papua New Guinea Countries

Samoa

Solomon Islands

Tonga

Vanuatu

Source of data for Figure 2: World Bank (2006).

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Table 1. Causes of death among children under five years of age in 2000 (percentages). Countries

Neonatal

HIV/AIDS Diarrhoeal Measles

Malaria

Pneumonia

diseases Cook Islands 96.1

0.0

0.7

0.5

0.0

1.1

Fiji

41.2

0.2

10.6

0.0

0.0

9.2

Kiribati

22.1

0.0

21.9

2.6

0.7

11.5

Marshall Is.

37.1

0.3

14.1

0.5

0.0

13.5

Micronesia

49.2

0.3

8.0

1.5

0.0

11.3

Nauru

7.0

0.0

37.8

5.5

0.0

30.3

Palau

47.0

0.3

9.7

0.7

0.0

12.4

PNG

35.4

0.3

15.3

2.1

0.8

18.5

Samoa

49.2

0.3

9.7

0.1

0.1

10.2

Solomon Is.

49.5

0.3

8.8

0.5

0.1

9.5

Tonga

57.2

0.0

10.0

1.8

1.3

7.3

Tuvalu

40.0

0.3

13.2

1.2

0.0

13.5

Vanuatu

42.3

0.3

11.5

0.3

0.6

13.0

Source: World Health Organisation (2006).

While relevant interventions (immunisation and oral rehydration therapy) are found to cost low (see Deaton, 2006) and aids in minimising deaths among children, the incidence of under five mortality in the developing world as well as the Pacific Island countries raises the question whether national budgetary allocation to these forms of health care services among others are adequate and effective in terms of its impact on infant or under five mortality. Available evidence from the high-income

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countries provides strong support that main contributors to mortality decline in children were improved nutrition and medical technological progress (see Cutler and Miller, 2005 and Cutler et. al., 2006). Other than advances in medical science, improved immunisation, improved water, improved sanitation and education; public health expenditure is also considered to be an important factor that can influence health outcomes particularly in terms of reducing the incidence of infant and child mortality rates in developing countries. In their study on government health expenditure and health outcomes, Bokhari et. al., (2007) found that government spending is an important contributor to health outcomes. Similar effects of health spending were also noted by Bhalotra (2007) in her study on state health expenditure and infant mortality in India. Using lagged effects and controlling for trended unobservable and restricting sample to rural households on India, Bhalotra (2007) found significant effects of health expenditure on infant mortality rates. The examination of health care expenditure and health outcomes have been a subject of an ongoing inquiry (see for example, Newhouse, 1992; Hitris, 1997; Di Matteo and Di Matteo, 1998; Gerdtham and Jonsson, 2000; Hitris and Nixon, 2001; Gianonni and Hitris, 2002; Bokhari, et. al., 2007; and Costa-Font and Pons-Novell, 2007). It is clear from a number of these studies that fiscal policy and the composition of public spending are important ingredients to improving health outcomes. The emphasis on increasing public spending on primary health care is generally justified on the basis that such spending ameliorates the impact of disease on the productive life years of the population (Gupta, Verhoeven and Tiongson, 1999). It has been also shown that expenditure allocations in favour of health can also boost economic growth while reducing poverty (see for example, Barro, 1991 and Temple, 1999).

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While large developing country case studies provide evidence of health care spending improving health outcomes such as under five mortality rates (see for example, Bokhari, et. al., 2007 and Bhalotra, 2007), not much is written in terms of Pacific Islands health care financing and health outcomes. As such, an investigation of the relationship between per capita health expenditure and health outcome indicators becomes increasingly important in of appropriate policy intervention so as to ensure continued improvements in infant and under five mortality rates in the Pacific Island countries. Thus, the purpose of this study is to examine whether public expenditure allocations to health sector improves health outcomes in Pacific Island countries. In doing so, this study utilises cross-country data from seven Pacific Island countries (Fiji, Kiribati, PNG, Samoa, Solomon Islands, Tonga and Vanuatu) for selected years between 1990 and 2002 for public spending on health together with selected control variables that determine health outcomes. The empirical methodology utilises a fixed effects estimation procedure correcting for AR(1) errors. The results obtained from this method of estimation indicate that per capita health expenditure together with income and immunisation as core factors that determine infant and under five mortality rates and crude death rates. The rest of the paper is organised as follows. The next section discusses the patterns and trends in health expenditure in Pacific Island countries. Section three outlines the empirical model. Section four discusses the empirical findings. The limitations of this study are highlighted in section five. A summary and a discussion of policy issues follow.

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2.0 HEALTH EXPENDITURE IN PACIFIC ISLAND COUNTRIES: PATTERNS AND TRENDS In this section, the basic statistics on central government expenditures for key functional areas are summarised together with an overview of long-term trend in health care financing across the Pacific Island countries. The intention here is to provide an assessment of the trends and not to review the causes of progress or stagnation. The government expenditure as a share of total expenditure by functional category is summarised in Table 2. Not all of the countries listed in Table 1 appear in Table 2 due to incomplete or absence of published data. Health expenditure statistic for each country is shown relative to government expenditure in other key functional areas with the year of statistics indicated in parentheses. It is important to keep in mind that there are other functional areas such as defence, housing and social security which are not included in Table 2. It is clear from Table 2, that for all countries, health expenditure does not represent the largest share of total government expenditure. In Kiribati and Tonga, health is the second largest government expenditure while in the rest of the countries it is the third largest expenditure. It is also interesting to note that in all countries except Tonga, health expenditure is even less than education expenditures. While educational spending gets priority over health and economic services in Fiji and Kiribati, spending on economic services has priority over health and education in the remaining countries.

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Table 2. Government expenditure by function (percentage of total). Country

General public

Education Health Economic services

services Cook Islands (2005)

20.8

13.9

11.3

38.9

Fiji (2002)

26.1

29.4

14.3

18.3

Kiribati (2005)

5.0

14.0

9.3

8.7

PNG (2002)

11.3

10.0

5.7

12.1

Samoa (2005)

26.5

22.1

16.7

24.6

Tonga (2002)

40.8

12.9

13.9

18.8

Vanuatu (2005)

15.3

22.7

11.1

43.4

Source: Asian Development Bank (2007).

Figure 3 depicts the trends in health expenditure as a share of GDP for 19902001 for seven Pacific Island countries. Other than Kiribati, health expenditure as a share of GDP for the remaining six countries remained below 5 percent between 1990 and 1996. While health expenditure as a share of GDP rose slightly after 1997 for Fiji, PNG, Samoa, Solomon Islands and Tonga, it declined in case of Vanuatu. It is quite difficult to offer much more than a basic description of trends across each country. Indeed clear potential explanations for the observed trend is to do with governments budgetary allocation where other functional areas such as public services, economic services and education are given more priority and thus budgetary allocations done accordingly. Among the national policies that might explain the trends are economic growth, public sector reforms and the demand for health care services.

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Figure 3.

Percentage of GDP

Health Expenditure 20.0 15.0 10.0 5.0 0.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 Years

Fiji

Samoa Vanuatu

Kiribati

Solomon Islands

Papua New Guinea Tonga

Source of data for Figure 3: Asian Development Bank (2007).

Figure 4 depicts per capita health expenditure for seven Pacific Island countries. Using 2003 as the cut off year, per capita health expenditure ranged from a low of US$8 in PNG to a high of US$104 in Fiji. Of all the seven countries depicted in Figure 4, Fiji spends the most on a per capita basis while PNG spends the least. Between 1990 and 1994, per capita health expenditure remained below US$50 for all except Fiji. Kiribati, Samoa and Tonga’s per capita spending rose in the post-1995 period. For the Solomon Islands, per capita spending remained stagnant between 1990-2002. Surprisingly, PNG’s per capita health expenditure has been overt, falling gradually since 1997. Significant improvements in per capita spending are noted for Kiribati between 2001 and 2005. The statistics presented above provide confirmation that the Pacific Island governments have accorded low priority to improving health conditions of their populations. Every country has a ministerial portfolio, the Ministry of Health, but it has little bureaucratic effect and receives a smaller share of budget

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allocation. Many countries have regarded expenditure on economic services as an increasingly important for investment as opposed to investments in health care services that can have direct effect on national human capital formation.

Figure 4. Per Capita Health Expenditure 200.0 180.0 US Dollars (Current)

160.0 140.0 120.0 100.0 80.0 60.0 40.0 20.0

0.0

1990 1991 1992 1993 1994 1995 1996 1997 1998 1999 2000 2001 2002 2003 2004 2005 2006 Years

Fiji

Solomon Islands

Kiribati Tonga

Papua New Guinea Vanuatu

Samoa

Source of Data: Authors calculation based on data extracted from Asian Development Bank (2007).

3.0 HEALTH EXPENDITURE AND HEALTH OUTCOMES: LITERATURE AND THE EMPIRICAL MODEL Public spending for health care is important for health outcomes of the poor as the poor are more likely to obtain health care from publicly provided facilities (Gwatkin, 2000). Past studies show that the poor are significantly less healthy than the rich (see Wagstaff, 2000 and Gwatkin, 2000) and that the rich are more likely to obtain medical

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care when sick (Makinen et. al., 2000). Hence, it is the level of health care financing that can bridge the gap between the health status of the poor and the rich. A number of studies on health care financing and health status reveal that public expenditure on health care minimizes the poor rich differences in health outcomes. In a study involving 70 developing and transition economies, Gupta, Verhoeven and Tiongson (2001) found that the poor are more strongly affected by health care in comparison with the non poor and that the difference in the impact of spending between the poor and non-poor could be substantial. In the study by Gakidou and King (2000), health expenditures per capita, among other variables were found to be negatively correlated with health inequality. The effect of public spending on health is usually measured by health outcome variables such as infant or child mortality rates and life expectancy. The effect of government health expenditure on infant and under five mortality has been investigated by several researchers. While some studies do not find any support for health expenditure reducing mortality rates, other reveal that health care spending has beneficial outcomes in terms of reducing infant and under five mortality. For example, Anand and Ravallion (1993) using cross-sectional data for 22 developing countries in 1985 find that health expenditure raises life expectancy. In a study on Philippines, the World Bank (1995) reported that public expenditure on health in the Philippines contributed to the reduction in infant mortality rates in the poorer regions, but not in the richer regions. In their study involving 50 developing countries, Gupta, Verhoeven and Tiongson (1999) found empirical evidence to support the claim that greater public spending decreased infant and child mortality rates. In a further study by these authors (see Gupta et al., 2001) relating to public spending on health care for a larger sample (70 developing countries), the authors find some evidence that health

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expenditure reduces childhood mortality. Hojman’s (1996) study involving Central American and Caribbean countries revealed that public health spending has a statistically significant effect on health status. The study by Bidani and Ravallion (1997) also find that public spending has beneficial impact on health condition for the poor. There are studies that do not find any support or statistically insignificant support for health expenditure reducing mortality rates. Kim and Moody (1992) and Musgrave (1996) found that the effect of public spending on health status as measured by health outcome variables, infant and child mortality rates, to be statistically insignificant. Le Grand (1987) found weak and a negative correlation between health inequality and share of public spending in health care. Filmer, Hammer and Pritchett (1998) attempted to address the issue of allocations within the health sector by including a measure of government spending on primary health care in their crosssection analysis of the causal factors of infant mortality and fail to find a statistically significant impact of primary health care spending on infant mortality rates. Filmer and Pritchetts’s (1999) further study on government health expenditure on infant and under five mortality in 98 developing countries reveal statistically insignificant effect. Deolalikar (2005) using a state panel for 1980-99 for India found no effect of current health expenditure on mortality rates. While the above review provides evidence of some studies support for health expenditure reducing mortality rates (and some studies without any support), no such study exists for Pacific Island countries. The effect of health care financing on health outcomes in the Pacific Island countries certainly deserves an investigation in light of the prevalence of preventable diseases, high incidence of infant and under five mortality rates and low levels of budgetary allocations to the health sector. The case

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of Pacific Island countries health expenditure and health outcome links can be examined through an empirical framework where the key issue relating to health outcomes and per capita health expenditure is unfolded. Thus, the structural equation to examine the impact of public spending on health care in the Pacific Island countries takes the following general form:

Yit = f ( H it , X it )

(1)

where, Y is a health outcome indicator reflecting health status of country i, H is per capita public spending on health care; X is a vector of socio-economic control variables, i is the country and t is the time. The infant mortality, under five mortality and crude death rates are considered to be better indicators of the health status of the population (see also Sen, 1998). Changes in national health policies and in particular budgetary allocations to health sector that directly affect investment in health capital are likely to show quicker effects in terms of health status of the population. It has been noted that the long-term improvements in the health status of populations are best reflected in infant mortality and life expectancy rates (see for example, Gupta and Mitre, 2004). Health outcomes are presumed to be primarily a function of per capita health expenditure as well as several other variables. The variables tested are predominantly the main conventional variables as used in many cross-country studies. The control variables include per capita income, immunization rates, urbanisation rates and calorie intake. In the regression analysis, equation (1) is expressed in three sets of reduced forms as follows:

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ln IMRit = α 0 + α 1 ln Yit + α 2 ln PCH it + α 3 ln IMU it + α 4 ln URBit + ln α 5 CI it + υ it (2) ln U 5M it = β 0 + β1 ln Yit + β 2 ln PCH it + β 3 ln IMU it + β 4 ln URBit + ln β 5 CI it + υ it (3) ln CDRit = χ 0 + χ 1 ln Yit + χ 2 ln PCH it + χ 3 ln IMU it + χ 4 ln URBit + ln χ 5 CI it + υ it (4)

where, IMR

=

U5M =

infant mortality rate; under five mortality rate;

CDR

=

crude death rate;

Y

=

per capita income;

PCH

=

per capita health expenditure;

IMU

=

immunisation (against measles);

URB

=

urbanisation;

CI

=

calorie intake;

ln

=

logs;

i

=

country;

t

=

time;

The error term in the above equation is υ it with the assumption that υ it ≈ iid ( 0, σ 2 ) . The expected effects are: Y (-); PCH (-); IMU (-); URB (-); and CI (-). All variable names and their definitions are presented in Table 3. The sample years for health outcome indicator infant mortality rate were 1990, 1995, 1998, 2000 and 2002. For the health indicator outcome under five mortality rates, the sample years were 1990, 1995, 2000 and 2002. For the health indicator crude death rate, the

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sample years were 1990, 1992, 1995, 1997, 2000 and 2002. All data were were extracted from various publications from a number of sources as indicated in Table 3.

Table 3. Variable definition and data sources. Variable Infant mortality rate

Definition The number of infants dying before reaching one year of age per 1,000 live births in a given year

Source of data World Health Organisation

Under five mortality rate

The probability that a new born baby will die before reaching age five expressed as a rate per 1,000

Crude death rate

The number of deaths occurring during the year per 1,000 population estimated at mid-year.

World Bank

Per capita income

The gross national income per capita (adjusted for purchasing power parity) in US dollars

World Bank

Per capita health expenditure

The per capita health expenditure in US dollars

Asian Development Bank

Immunisation

The percentage of children ages 12-23 months who received one dose of vaccine against measles before 12 months

World Bank

Urbanisation

The estimated urban population as a percentage of total population

World Bank and Asian Development Bank

Calorie intake

The average daily per capita total calorie supply in grams

World Health Organisation and Asian Development Bank

World Bank

4.0 ESTIMATION PROCEDURE, RESULTS AND DISCUSSION Equation (2) includes 7 countries and 5 time periods; equation (3) includes 7 countries and 4 time periods; and equation (4) includes 7 countries and 6 time periods. While equations (2) to (4) can be estimated with ordinary least squares, the result is likely to 15

be biased if the error terms are correlated within each time series unit and are heteroscadastic across each cross sectional unit given that the data utilised here is cross-country. Combining these assumptions means estimating a cross-sectionally heteroskedastic and time-wise autoregressive model. Hence, the initial estimation procedure begins with the estimation of a cross-sectionally heteroskedastic and timewise autoregressive model. This procedure of estimation is also equivalent to the generalised least squares (GLS) estimation. The results of this estimation procedure are recorded in Table 4. It should be noted that the GLS equivalent estimation does not take into account of the country-specific factors. While the sample of countries falls in same geographical latitudes and somewhat similar socio-economic structures, the extent of health outcomes differs from one country to another. To take into account of countryspecific differences, a fixed effects estimation procedure including country-specific dummy variables is adopted. In total there are 7 dummies, D1 to D7, (see Table 5) for each of the equations (2) to (4). The no-constant option is adopted in the estimation procedure so as to avoid the commonly known dummy variable trap. Given the nature of data, the possibility of AR (1) errors are likely and so the fixed effects estimation procedure corrected for AR (1) errors is adopted. The results of this procedure of estimation is reported in Table 5 and considered to be most robust as the statistical significance and R-square improved significantly compared to the GLS estimation in Table 4. The discussion of the results of the right hand side variables of equations (2) to (4) follows.

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Table 4. Empirical results based on generalised least squares estimation. Ln IMR -0.705 (0.199)

ln U5M 0.518 (0.169)

Ln CDR 0.331 (0.109)

ln Y

-0.483 (7.922)*

-0.154 (3.005)*

-0.105 (1.632)***

ln PCH

-0.611 (6.185)*

-0.005 (0.092)

0.036 (0.615)

ln IMU

-0.490 (2.077)**

-0.478 (3.183)*

-0.506 (3.244)*

ln URB

0.068 (0.434)

-0.110 (0.841)

-0.125 (1.058)

ln CI

1.562 (3.090)*

0.656 (1.538)

0.644 (1.528)

Number of observations

35

28

42

R-square

0.88

0.49

0.46

Durbin Watson 1.80 statistic

1.75

1.81

Constant

t - statistics are in parentheses. *, **, and *** indicates significant at the 1, 5 and 10 percent levels respectively.

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Table 5. Empirical results of fixed effects estimation corrected for AR (1) errors. Ln IMR -0.409 (8.416)*

Ln U5M -0.416 (2.425)**

Ln CDR -0.047 (0.753)

ln PCH

-0.655 (9.897)*

-0.069 (1.089)

0.123 (1.969)**

ln IMU

-0.884 (5.888)*

-0.446 (2.216)**

-0.643 (3.863)*

ln URB

0.139 (0.979)

-0.098 (0.716)

-0.163 (1.319)

ln CI

1.983 (4.390)*

0.793 (1.619)***

0.855 (1.784)***

D1

-2.850 (0.905)

-0.474 (0.133)

-1.049 (0.308)

D2

-2.863 (0.905)

-0.558 (0.156)

-1.137 (0.333)

D3

-2.989 (0.945)

-0.488 (0.136)

-1.125 (0.328)

D4

-2.826 (0.902)

-0.540 (0.150)

-1.184 (0.346)

D5

-2.898 (0.917)

-0.614 (0.171)

-1.244 (0.363)

D6

-2.902 (0.917)

-0.585 (0.163)

-1.217 (0.355)

D7

-3.280 (1.033)

-0.692 (0.193)

-1.337 (0.389)

Number of observations

35

28

42

0.54

0.51

ln Y

R-square 0.94 t – statistics are in parentheses.

*, **, and *** indicates significant at the 1, 5 and 10 percent levels respectively.

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Per capita health expenditure The coefficient for the per capita health expenditure has the correct sign as expected, negative for infant and under five mortality rates (Tables 4 and 5). The coefficient for infant mortality rate is statistically significant at the 1 percent level. The best point estimates for the coefficients of infant and under five mortality are -0.66 and -0.07, respectively. There is a large difference between the estimated coefficients of the infant and under five mortality rates. This suggests that government health care funding does impact infant mortality rate more strongly than the under five mortality rate. This outcome is consistent with the pattern of the governmental budgetary allocation to health care in the Pacific Island countries which are largely targeted for basic primary health care services such as immunisation, control of diarrhoea, nutritional training programs for mothers and some antenatal care. Based on the elasticity of infant mortality rate, a 10 percent increase in per capita health expenditure would mean a reduction in infant mortality rate by approximately 6.6 percent. For a country such as PNG with high infant mortality rate, it means a reduction in approximately 3.6 infant deaths per 1,000 live births and an average reduction of 2.0 infant deaths per 1,000 live births for the Pacific Island countries. The coefficient for the crude death rate is positive and statistically insignificant and indicates that health care expenditure does not matter much.

Per Capita Income The sign on the coefficient of income is as expected, negative for infant and under five mortality rates and death rates. Elasticity with respect to per capita income is 0.41 for infant and under five mortality rates and -0.05 for crude death rate (Table 5). The income coefficient is statistically significant at the 1 percent level, both for infant

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and under five mortality rates. There are large differences between the estimated coefficients for per capita income for infant and under five mortality rates and death rates. This outcome also suggests that governmental expenditure is more so targeted towards primary health care. The elasticity of the under five mortality rate of -0.41 is very close to that obtained by Bokhari et. al. (2007) in a study involving several large developing countries where the elasticity of income was -0.40. The results obtained for the income variable provides strong support that the level of income matters strongly for infant and under five mortality and death rates: rising income means falling mortality rates. On the basis of elasticity’s obtained for infant and under five mortality rates, it can be argued that a 10 percent increase in per capita incomes implies a reduction in approximately 3.0 under five deaths per 1,000 live births in PNG and an average reduction of approximately 1.7 deaths per 1,000 live births for the Pacific Island countries.

Immunisation The coefficient immunisation has the expected negative sign for infant and under five mortality rates and crude death rates. In all these three health indicator outcomes the coefficients of immunisation are statistically significant at the 1 percent (infant mortality and death rates) and 5 percent (under five mortality rate) levels. The elasticity of infant and under five mortality rates is -0.88 and -0.45 respectively. The elasticity’s obtained indicate that a 10 percent increase in immunisation rates would reduce infant and under five mortality rates by 8.8 and 4.5 percent respectively. On the basis of the elasticity of infant mortality rate, a 10 percent increase in immunisation rates implies a reduction of approximately 4.8 infant deaths per 1,000 live births in PNG and an average reduction of approximately 2.6 infant deaths per

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1,000 live births for Pacific Island countries. These are in fact large effects for countries such as Kiribati, Marshall Islands, Micronesia and PNG as they have high infant and under five mortality rates. The results obtained for the immunisation variable provides strong evidence that immunisation programs are vital and it certainly contributes towards reducing mortality rates. Comparing the size of the elasticity’s of per capita health expenditure, per capita income and immunisation; it is clear that immunisation has a much stronger impact in reducing mortality rates.

Urbanisation and calorie intake The model here also controls for urbanisation and calorie intake. In all the three health outcome indicators, the coefficient of urbanisation is statistically insignificant. For under five mortality rates and crude death rates, the sign on the coefficient is as expected, negative. However, the sign on the coefficient of urbanisation for infant mortality rate is positive and contrary to the prior expectations. Similarly, calories intake as the positive sign on its coefficient and contrary to a priori expectations.

5.0 LIMITATIONS AND SUGGESTIONS FOR FUTURE RESEARCH The study has its limitations and it is worthy to point some of the specific drawbacks. The major limitation surrounds the choice of variables and data. Published data on some of the key variables of interest largely to do with socio-economic characteristics that may affect health outcomes were not available. For example, a potential limitation lies in the fact that this study does not control for private health expenditure. Past studies have indicated that private health expenditure and private health insurance does play a role in supplementing public health care (see for example, Costa-Font and Garcia, 2003). Some Pacific Island countries do host private

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health care facilities through private hospitals or private health practitioners. Data on these forms of private health care services is not available. Hence, the models tested here do not include private health care expenditure. Another variable that is not controlled for is the level of education or literacy rates. Past studies have shown that literacy, particularly of females,’ impacts on health status of infants and children (see for example, Shultz, 1961 and Deolalikar, 2005). Previous studies analysing the effect of health expenditure on health outcomes have controlled for literacy or education levels (for example, Gupta, et. al., 1999 and Bokhari, et. al., 2007). The models here do not include any measures of adult female literacy or education. Data on level of education is available for some countries and in some countries this data is missing. Given the nature of cross-country estimations, consistent set of data was needed for the specified time periods. Due to missing data for some countries, it was impossible to control for this variable. It is worthy to note that while infant mortality is expected to be lowered by improvements in education, the recent study by Bhalotra (2007) testing a model with a very large sample from India and controlling for state education expenditure while revealing the expected negative effect was statistically insignificant. Further, the empirical analysis here does not compare health care outcomes between rich and the poor. It can be strongly argued that the rich may be able to access better health care services than the poor when governmental budgetary allocations to health care are squeezed. On the contrary, the poor may access health care services in times of budgetary cuts through foreign aid or forms of in-kindtransfers from family and friends and charity groups. The data utilised here are national aggregates that do not differentiate between rich and the poor. Hence, such data limitations constrain further analysis in this line.

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Once data becomes available, future research should examine the effects of some of the core control variables mentioned here such as the effect of literacy rates and private per capita health expenditure. In addition, it would be useful to examine the effects of health care funding on the rich and poor segments of the population in future once data becomes available. Household survey data would be more useful for this kind of inquiry and the collection of such data is highly encouraged. Further, more country-specific studies are encouraged so as to account for the in-country variations in the socio-economic structures that determine health status with greater strength and meaning.

6.0 SUMMARY AND POLICY IMPLICATIONS The central focus of this study was to examine whether governmental expenditure allocations to health sector improves health outcomes in Pacific Island countries (Fiji, Kiribati, PNG, Samoa, Solomon Islands, Tonga and Vanuatu). In doing so, this study utilised cross-country data for these countries for selected years between 1990 and 2002 for public spending on health on a per capita basis together with selected control variables that determine health outcomes. Three indicators of health outcomes: infant mortality rate, under five mortality rate and crude death rate were chosen for empirical analysis. The regression results of a fixed effects model correcting for AR (1) errors provide strong confirmation that per capita health expenditure matters for health outcomes in Pacific Island countries. Based on the elasticity of infant mortality rate, it is argued that a 10 percent increase in per capita health expenditure would mean a reduction in infant mortality rate by approximately 6.6 percent. For a country such as PNG with high infant mortality rate, it means a reduction in approximately 3.6 infant

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deaths per 1,000 live births and an average reduction of 2.0 infant deaths per 1,000 live births for the Pacific Island countries. The regression results also provide strong evidence that other than per capita health expenditure, per capita incomes and immunisation rates are other two core factors that determine health outcomes in the Pacific Island countries. Based on the elasticity’s obtained, it is also argued that a 10 percent increase in per capita incomes would mean a reduction in approximately 3.0 under five deaths per 1,000 live births in PNG and an average reduction of approximately 1.7 deaths per 1,000 live births for the Pacific Island countries. In terms of the elasticity’s obtained for immunisation, it is argued that a 10 percent increase in immunisation rates would mean a reduction of approximately 4.8 infant deaths per 1,000 live births in PNG and an average reduction of approximately 2.6 infant deaths per 1,000 live births for the Pacific Island countries. While the limitations of this study is acknowledged and results may be interpreted with caution, the empirical findings of this study nevertheless strongly indicates that governments per capita health expenditures do matter much for improvements in health outcomes across the Pacific Island countries. This study points to the fact that governments of the Pacific Island countries certainly need to boost their budgetary allocations to health care. The findings here also reveal that health outcomes are also determined by factors other than just per capita health expenditure. Per capita incomes and immunisation rates are found to be other two core variables that influence health outcomes. Given that the Pacific Island governments have faced hard long-term budget constraints, there is an urgent need to devote more resources in terms of uplifting per capita incomes and immunisation levels.

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Like elsewhere, Pacific Island governments are always pressed to bring budget into balance and raise productivity. In this process government budgetary allocation may be cut without much thought to health sector specific outcomes. Human health is a major determinant for national human capital formation among other factors. Like several of the governmental functional areas of spending, the budgetary allocation to the health sector need to be guarded against expected national revenue shortfalls and unexpected downward revisions of budgets. Pacific Island governments should account for the match between health expenditures and the associated health outcomes over the long-term. This should be of concern for policy makers and Finance Ministers. While Pacific Island government’s annual budgets are usually intended to ensure the macroeconomic sustainability of total government expenditures, measuring outcomes is a serious problem and budgets don’t take these into consideration. There is an urgent need to take into account of health expenditures and health outcomes in budgetary formulation process and formulate budgetary allocations to the health sector accordingly.

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