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Impact of Foreign Health Aid to Health Targets Research Paper

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Introduction
Research Background

Foreign aid has become an important aspect of healthcare funding in many developing countries across the globe. As indicated in the Millennium Development Report of the year 2012, the average annual development aid from foreign donors is about $18 billion (Burfeind, 2014). In spite of huge amounts allocated to foreign aid, some experts have disputed their impacts on the health programs within the targeted countries. For instance, despite a series of Global Fund programs within Ukraine, the HIV prevalence has increased by 5% as compared to 0.5% in 1997 before foreign aid input (Gething et al., 2016). The disparity noted has raised serious concerns about whether foreign aid for healthcare is effective in the improvement of the health conditions of the targeted population or not. Moreover, the concern has been expanded to review how other factors such as transparency in aid accounts would determine the effectiveness of a health program funded by foreign aid.

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Preview of the Research Components

Several case studies have been carried out by different researchers to establish the underlying relationship between effective healthcare and foreign aid. Specifically, many studies have been done to link economic growth to foreign aid in developing countries. However, little research is in place on the effectiveness of different foreign aid programs targeting a specific health sector (Wroe, 2012). The available studies concentrate on the impact of foreign aid programs on education (Burfeind, 2014), environmental sustainability (Bendavid, 2014), and infant mortality reduction (Makuta &O’Hare, 2015) among others. Evidently, these studies indicate that there is a significant correlation between foreign aid programs and progress in the above sectors. Therefore, the proposed study will measure the effect of foreign health aid (bilateral and multilateral) on the avoidable mortality rate as an indicator of the quality of health. Specifically, avoidable mortality will be used as the dependent variable. Independent variables will consist of health aid (multilateral and bilateral) and the controlled variables such as public expenditures on health and education, access to water and sanitation, the completion rate of secondary education, the GNI ratio, the corruption perception index, and the population size.

Justification for the Study

Different from previous studies, the proposed research will concentrate on avoidable mortality as a measure of a health outcome. Avoidable mortality is preventable deaths in relation to modern medical development from technological and knowledge pillars. In most developing countries, programs such as STD/HIV/AIDS control, infectious disease control, and fertility control have been used as the channel for disbursing and using foreign aid in healthcare development. In relation to this study, the amount of foreign aid channeled to the healthcare program will be the explanatory variable. The researcher will use aid over GNI and aid per capita. Data to be used in the research analysis will be taken from the UN Aid data website, which has information on Accessible Information on Development Activities (AiDA) and Project-Level Aid (PLAID) (World Health Organization, 2017). The proposed research will attempt to establish a quantifiable relationship between foreign aid and the effectiveness of healthcare programs in different developing countries.

Research Question

Since the proposed case study is dynamic and result-oriented, the following question was created to address the topic of the impact of foreign health aid on health targets in developing countries.

Why do countries that receive health aid have different levels of health outcomes?

Research Objectives

Based on the question generated, the following research objectives were created to address the purpose of the study.

To establish how developing countries that receive foreign aid perform in terms of healthcare effectiveness.
To establish quantifiable impacts of foreign aid on the avoidable mortality rate within the healthcare sectors of different developing countries.
Literature Review
Foreign Aid for Healthcare Sector: Horizontal Versus Vertical Programs

Jablonski (2014) established empirical evidence indicating that foreign aid has a long-term positive impact on the healthcare sector in developing countries. According to Bendavid and Bhattacharya (2014), the primary issues discussed in forums on the effectiveness of aid are cost-effective approaches in using foreign aid for an optimal impact in the healthcare sector. For instance, the supporters of vertical aid, which is a donation that is directly injected towards disease eradication and specific healthcare issues, are convinced that this method is the most effective since it bypasses many hierarchies associated with poor governance and corruption. In most cases, the donors of vertical aid are charitable organizations such as the Melinda and Gates Foundation, Carter Foundation, and religion-based organizations. However, the CDCP, UNICEF, and the World Bank have supported such initiatives through proactive engagement. On the other hand, horizontal aid targets an entire sector of the health economy with an underlying assumption that the primary and preventive care programs would result in sustainable solutions (Taylor, Hayman, Crawford, Jeffery, & Smith, 2013).

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Unlike vertical aid, which is specific to an ailment, horizontal programs are focused on an entire community and oriented on the principles of changing social and economic structures. The primary aim of such programs is to improve healthcare conditions in an entire region or country. Specifically, horizontal programs are angled on policies that address current primary healthcare needs in order to ensure that projected long-term improvements are made possible. For instance, the International Conference on Primary Health Care, held in the year 1978 in Almaty, passed several resolutions on the need for donors and governments to concentrate on primary health, improvement of horizontal aid programs, and remodeling health sectors in developing countries (Wroe, 2012).

Primary Health and Foreign Aid

According to Burfeind (2014), the aim of primary healthcare is to better the general wellbeing through proactive education of the population, the prevention of common diseases, improving the water and food supply chain, effective family planning, and the implementation of policies for overall development. This means that primary health is holistic in its approach to the social, political, and economic needs of a community. Through the promotion of community participation and self-reliance, developing countries can effectively use foreign aid to create health programs that promote self-efficiency. As the global health development experience has improved over the years, most beneficiaries of health-related foreign aid in developing countries are hopeful that the increased funding of primary health would result in improved overall health (Gething et al., 2016). According to Bendavid (2014), the “stronger a country’s primary healthcare system, the higher the system’s quality and cost-effectiveness and the greater its impacts on health” (p. 22). This means that the integration of vertical and horizontal aid programs would be effective in improving primary health.

Despite the positive impacts of foreign aid on healthcare improvement in many developing countries, some experts have presented different reasoning following their mixed result findings. For instance, Bendavid and Bhattacharya (2014) and Wroe (2012) have doubted the actual benefits of international aid as they associate it with slow growth and poor governance, especially when the allocation is done on the basis of political affiliation. This means that there exists a complex relationship between foreign aid and improvements in the healthcare sector of a developing country. Moreover, the challenge of ‘endogeneity’ may arise as long as the effectiveness of an aid program is dependent on the level of societal development. Based on this argument, it is in order to state that richer developing countries are more likely to effectively allocate foreign aid than poor nations.

Foreign Aid and Specific Health Sector

Some past studies are devoted to reviewing the impacts of foreign aid on a specific health sector. The notable previous cases are how foreign aid impacts healthcare education (Bendavid & Bhattacharya, 2014), the existing link between foreign aid and environmental sustainability (Wroe, 2012), and the effect of different forms of foreign aid on democracy in developing countries (Bendavid, 2014). Global healthcare assistance volumes, in the form of foreign aid to developing countries, have expanded from $5.6 billion in the early 1990s to more than $25 billion in the year 2015 (World Health Organization, 2017). Following the adoption of the Accra Agenda for Action (AcAA), the Paris Declaration, and the Millennium Development Goals (MDG), the general interest in healthcare effectiveness has risen among different experts (Gething et al., 2016). For instance, Makutaand O’Hare (2015) observed that foreign assistance has had a significant impact on reducing infant mortality in developing countries. Through the use of a fixed-effect method, the researchers established that foreign aid is very effective in addressing infant mortality, especially when implementation authorities are accountable. In correcting any endogeneity in the study, through the use of lagged aid instrumentation, instrumental and fixed effect variables indicated that aid targeting healthcare is effective.

The findings of a study carried out by Bendavid and Bhattacharya (2014) on the impact of foreign aid on the mortality rate of infants indicated that it significantly lowers the death rate among infants. In research that reviewed 138 developing countries between the years 1994 and 2013, the findings revealed that increasing per capita healthcare-related aid by 100% results in a 2% or more reduction in the rate of infant mortality. Interestingly, there was a positive pattern in all the case study countries. However, Wroe (2012) suggested that there is a need to increase current health-related foreign aid up to 15 times in order to achieve the MDG targeted on infant mortality. In order to achieve the positive correlation result, the variables of infant mortality, health-related aid, and life expectancy were correlated for each of the case study countries. The improvement in healthcare as associated with foreign aid is directly dependent on good governance principles such as transparency, democracy, and sanitation (Gething et al., 2016).

Conventional literature on the relationship between healthcare effectiveness and foreign aid indicates that good governance principles determine the effectiveness of each aid program in the promotion of healthcare improvement. For instance, a case study conducted by Bendavid (2014) reveals interesting results, suggesting that health aid is more effective in developing countries that are corrupt. The analogy of this case study indicated that compliance benefits and costs of effective allocation are the principles of engagement in developing countries that receive health-related foreign aid. Different from infrastructure or trade-related aid programs, foreign health-related aid is linked to relatively low compliance cost implications since lease-seeking is not as lucrative as is the case in the other industries. Since most horizontal and vertical health-related aid donations are channeled through different organizations that are not governmental, they do not depend on institutional challenges such as corruption (Wroe, 2012). In addition, most health-related donors concentrate on specific progress of the funded sector as opposed to the country’s institutional growth level.

Evidently, the most previous literature was directed at life expectancy or infant mortality as the determinants of a health outcome of foreign aid. In the proposed research, avoidable mortality will be the primary variable in establishing the relationship between healthcare effectiveness and foreign aid (Taylor et al., 2013). The merit of using the avoidable mortality indicator was informed by its ability to measure the quality and efficiency of a specific healthcare system of a region. Several channels can be used to improve the effectiveness of foreign aid in decreasing avoidable mortality in developing countries that receive such financial assistance. For instance, providing more hospitals in developing countries with well-trained medical personnel, modern healthcare technologies, and better patient handling equipment would result in improved chances of correct diagnoses and treatment (Bendavid & Bhattacharya, 2014). Specifically, support programs within the healthcare sector, such as STD control, infectious disease management, and continuous health education are attributed to reducing new infections where they are effectively executed.

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Concept of Avoidable Mortality

The concept of the avoidable mortality indicator has existed within the healthcare sector for more than four decades. As the Working Group on Preventable and Manageable Diseases (WGPMD) members, Rutstein, and others coined the term “preventable mortality” (Wroe, 2012). They further defined it as a measurement indicator for healthcare performance in a country through the elimination of unnecessary deaths. Rutstein and other proposed conditions that should not result in the death of a patient when timely and effective healthcare policies are in place (as cited in Wroe, 2012). Over the years, the conditions have been modified to accommodate advancements in healthcare provision across the globe. At present, there are thirty-three conditions that are universally accepted in the application of the preventable mortality indicator. Although there are different versions of the conditions, such as Holland, McKee, and Nolte, and Tobia lists, the underlying principles are the same (Bendavid & Bhattacharya, 2014).

The Gross Domestic Product (GDP) per capita has an influence on the avoidable mortality level in the healthcare sector. Specifically, the GDP per capita gives room for regulating structural divergences that exist in different economic development frameworks. For instance, in a study on how income affects health by Taylor et al. (2013), the findings indicated that the elasticity of income for the variable of infant mortality within developing countries ranges between 0.1 and 0.4. Education is another factor that determines the performance of different health indicators. For instance, the study on the relationship between effective healthcare and education by Gething et al. (2016) revealed that there is a positive correlation. Specifically, the findings of this research indicated that an extra year spent in school leads to a reduced chance of experiencing poor health in relation to hypertension, diabetes, and cancer prevalence. In addition, the population in a developing country where the government has significant expenditures on healthcare was found to experience better healthcare services. Moreover, Wroe (2012) established that health-related expenditures on education and the environment have a positive correlation with health outcomes in developing countries.

Foreign-Health Related Aid: Multilateral and Bilateral Aid

There are several psychological, economic, and political arguments that have been put forward to discuss different types of foreign aid allocation. These schools of thought indicate that foreign aid has the potential of enhancing growth in the GDP in the right conditions (Jablonski, 2014). However, the existence of any correlation between total foreign aid and GDP growth can be associated with the bilateral component of high magnitude. From a single composite aid regression, total aid can be separated into a series of multilateral and bilateral components. According to Taylor et al. (2013), multilateral aid is associated with slowed growth while bilateral aid results in increased economic growth. This means that any policy alterations implemented by a multilateral aid agency may constrict the rate of economic growth in the short run and improve the economy in the long run (Jablonski, 2014).

Literature Review Limitation and Delimitation

Several past case studies on the effectiveness of health-related aid to the healthcare sector in developing countries indicate a positive correlation, with a few exceptions. The past literature has also captured the discrepancies in the level of the effectiveness of bilateral and multilateral health-related foreign aid. However, there was no significant correlation in the donor fund allocation on the basis of the type of aid in place. As a result of these variations, the proposed case study will concentrate on the avoidable mortality indicator as influenced by the type of health-related foreign aid in developing countries.

Research Methodology
Research Hypothesis
Primary hypothesis

In the comparison of developing countries that receive health aid, those that receive more will have an increase in their health outcomes – the avoidable mortality rate – than those who receive less health aid.

Alternative hypothesis

In the comparison of developing countries that receive health aid, those that receive more aid will not experience an increase in their health outcomes – the avoidable mortality rate –as those who receive less health aid.

The null and alternative hypothesis will be proven wrong if the researcher establishes an insignificant correlation between the levels of health aid and health outcomes. This means that there must be a positive correlation in the variables within the null hypothesis for the purpose of the study to hold.

Research Conceptual Framework

In the conceptualization of the research, the avoidable mortality indicator was used to measure the healthcare system performance with the integration of foreign aid in developing countries. In the conceptual framework, preventable and treatable disorders were derived from the avoidable mortality indicator. The construction of the avoidable mortality measure was done using the most recent data on mortality and a list of exceptional conditions discussed earlier. The researcher excluded age-deaths pairs from the list to derive the annual avoidable rate of mortality for each case study country. In order to indicate that the avoidable mortality indicator is targeted by health-related foreign aid, the investigator used the non-avoidable mortality indicator as a dependent variable. The calculation of non-avoidable mortality is done by dividing the total mortality rate by the avoidable mortality rate (Wroe, 2012). Through the comparison of the two mortality rates, it would be possible to illustrate that non-avoidable mortality is not preventable and is not affected by any type of health-related foreign aid (Thomas, Tara, Cohen, & Dieleman, 2017). In order to draw a clear picture, the avoidable mortality indicator was modeled as a lagged health-related foreign aid function against a cluster of controls as summarized in the formula below.

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AM=α+β_Aid+β_CPI+β_CPI*Aid+β_C

Where:

AM = Specific health outcome that is measurable by avoidable mortality indicator
Aid = Actual health-related foreign aid in the last period
CIP = Corruption perception index
C = Vector(s) of a set of controls
β = Disturbance term

As indicated in the conceptual framework, health-related foreign aid is the independent variable. The researcher will employ aid-to-GNI and aid-per-capita rations as two standardized measures of the flow of foreign aid. In the contraction of the proposed measures, the investigator will use AidData site to collect the latest secondary data on foreign aid assistance in developing countries between the years 2000 and 2015. As a remedy for the avoidance of any potential endogeneity and lagged effect of different forms of health-related aids, the present avoidable mortality indicator will be regressed on aid assistance in the last three years. Moreover, foreign aid stock awarded in the three concurrent financial years will be integrated to measure the impact of aid on health (Thomas et al., 2017). The control vectors considered in the study are the GNI per capita, size of the population, education, sanitation, access to good water supply, and expenditure on education and health.

Despite the fact that the proposed conceptual framework may result in biased outcomes, especially when health-related aid is correlated with avoidable mortality determinants that cannot be observed, the researcher will use fixed effect regression. This approach is necessary in capturing the invariant of time that is peculiar to each case study country in correlation to health-related foreign aid. Moreover, the instrumentation of each variable will be necessary for reversing causality for each time-variant effect. This means that there is a need to establish a variable that correlates with foreign aid but does not associate directly or indirectly with avoidable mortality. Lastly, the researcher will ensure that instrumentation for foreign aid is standardized around population size, dummies for country-level effects, common language, country size, and a distance between the donor organization or nation and the recipient developing country.

Relating the Conceptual Framework to Hypothesis and Literature Review

The literature review indicates that several past studies examined aid flow effects as disaggregated on the basis of their sources. For instance, bilateral and multilateral aids are characterized by varying effects within any healthcare system, especially when different preferences are used in the allocation of health-related assistance. Thus, in order to test the null hypothesis, the investigator will introduce a more dynamic framework as summarized in the formula below.

AM=α+β₁Ba+β_Ma+β_CPI+β_CPI*Ba+β_CPI*Ma+β_C+

Where:

Ma = Quantity of multilateral health-related aid
Ba = Quantity of bilateral health-related aid

For consistency, bilateral and multilateral health-related aid will be measured in relation to per capita aid, the GNI ratio, and a three-year aid stock.

The mortality data were collected from the WHO database which summarizes comprehensive data on deaths by cause, age, sex, and country. The investigator then normalized the collected data on the basis of the flows in foreign aid and the GNI ratio as a health-related aid indicator. The data set included nine developing countries between the years 2000 and 2015. The study relied on secondary data as the only source of the necessary material for support. The collected secondary data were properly coded and passed through the SPSS software for the ease of interpretation. Moreover, the researcher used cross-tabulation to aid in data interpretation and analysis as discussed in the next chapter.

Empirical Results and Analysis

Using the SPSS software, the researcher got results for different regressions. Specifically, the OLS were pooled as measured using the Breush-Pagan test. As captured in table 1, the regression was carried out for non-avoidable mortality and avoidable mortality. As captured in table 7, it is apparent that there is a positive correlation between health-related aid and avoidable mortality. However, there is no correlation between non-preventable mortality. The coefficient of health-related aid (as captured in table 9) indicates that an increase in aid by 1% has an effect of improving healthcare effectiveness by up to 59 lives for every 100,000 patients (World Health Organization, 2017). Apparently, the control variables also indicated a positive correlation by lowering the avoidable mortality rate. For instance, increasing the public educational expenditure by 1% results in the reduction of the avoidable mortality rate by 0.08% (as captured in table 3, 7, and 11). As demonstrated in tables 4, 6, 9, and 10, the control variables have a similar effect on the uncontrolled variable. This is an indication that foreign aid has a positive impact on healthcare effectiveness as summarized in figure 1 below. In summary, the empirical results for fixed effect confirm the null hypothesis that health-related foreign aid has a positive effect on avoidable mortality but does not affect non-avoidable mortality.

Fig 1: Conceptual framework.
Regression Output
Table 1: Descriptive Statistics.
Country Count Mean Standard Deviation Coefficient of Variation
Bangladesh logmortality 16 2.037562E0 .4055368 19.9%
population age 16 3.327076E1 2.4009807 7.2%
schlenrollment 16 1.080595E2 10.3206552 9.6%
GDPperCapita 16 2.792560E0 .1622007 5.8%
improvedwatersource 16 8.156875E1 3.4648172 4.2%
undernourishment 16 1.703750E1 1.3088799 7.7%
labourforce 16 5.719560E1 .3934851 .7%
populationdensity 16 1.132068E3 70.2551403 6.2%
HIVprevalene 16 .100000 .0000000 .0%
healthexp 16 7.549478E0 1.0931995 14.5%
Brazil logmortality 16 1.948850E0 .1661985 8.5%
populationage 16 2.621547E1 2.4272808 9.3%
schlenrollment 16 1.281319E2 11.4189287 8.9%
GDPperCapita 16 3.820056E0 .2454063 6.4%
improvedwatersource 16 9.599375E1 1.5373001 1.6%
undernourishment 16 4.700000E0 3.1757414 67.6%
labourforce 16 6.571146E1 1.2004909 1.8%
populationdensity 16 2.291082E1 1.1560480 5.0%
HIVprevalene 16 .475000 .0856349 18.0%
healthexp 16 6.042195E0 1.5263339 25.3%
Burkina logmortality 16 3.803869E0 .1275152 3.4%
populationage 16 4.635210E1 .3655338 .8%
schlenrollment 16 6.267361E1 17.9632630 28.7%
GDPperCapita 16 2.656879E0 .1687458 6.4%
improvedwatersource 16 7.300625E1 7.7857535 10.7%
undernourishment 16 2.288125E1 2.4205285 10.6%
labourforce 16 7.215813E1 3.6585565 5.1%
populationdensity 16 5.342938E1 7.5743319 14.2%
HIVprevalene 16 1.418750E0 .5205366 36.7%
healthexp 16 1.354950E1 2.9094903 21.5%
Burundi logmortality 16 2.962888E0 .3675350 12.4%
populationage 16 4.564850E1 1.6704475 3.7%
schlenrollment 16 9.859649E1 32.7178605 33.2%
GDPperCapita 16 2.272995E0 .1510201 6.6%
improvedwatersource 16 7.395625E1 1.3391384 1.8%
undernourishment 16 3.170000E1 6.7161497 21.2%
labourforce 16 8.042199E1 1.7582527 2.2%
populationdensity 16 3.173544E2 47.9608391 15.1%
HIVprevalene 16 2.012500E0 .7675719 38.1%
healthexp 16 1.053195E1 2.9773978 28.3%
Chile logmortality 16 1.028063E0 .6679969 65.0%
populationage 16 2.368293E1 2.0219279 8.5%
schlenrollment 16 1.037707E2 3.2922988 3.2%
GDPperCapita 16 3.961925E0 .2040011 5.1%
improvedwatersource 16 9.731250E1 1.3932576 1.4%
undernourishment 16 4.075000E0 .2516611 6.2%
labourforce 16 5.809772E1 3.2644222 5.6%
populationdensity 16 2.226287E1 1.0711320 4.8%
HIVprevalene 16 .343750 .1030776 30.0%
healthexp 16 1.416748E1 1.1705782 8.3%
Estonia logmortality 16 1.406812E0 .1826791 13.0%
populationage 16 1.570777E1 .7846310 5.0%
schlenrollment 16 1.002157E2 1.9692456 2.0%
GDPperCapita 16 4.063358E0 .2350152 5.8%
improvedwatersource 16 9.933125E1 .2120338 .2%
undernourishment 16 3.700000E0 1.2236557 33.1%
labourforce 16 6.008901E1 1.4936258 2.5%
populationdensity 16 3.176036E1 .6239674 2.0%
HIVprevalene 16 .362500 .0500000 13.8%
healthexp 16 1.192386E1 1.0362524 8.7%
Ghana logmortality 16 3.464819E0 .1544578 4.5%
populationage 16 4.019854E1 .9648009 2.4%
schlenrollment 16 9.632631E1 12.4255469 12.9%
GDPperCapita 16 2.902943E0 .3049225 10.5%
improvedwatersource 16 7.991875E1 5.7855243 7.2%
undernourishment 16 8.668750E0 3.2007746 36.9%
labourforce 16 7.577265E1 .7191581 .9%
populationdensity 16 1.015027E2 12.1819654 12.0%
HIVprevalene 16 2.587500E0 .8883505 34.3%
healthexp 16 1.149602E1 3.3982361 29.6%
India logmortality 16 2.936094E0 .1664785 5.7%
populationage 16 3.180932E1 1.9072051 6.0%
schlenrollment 16 1.069519E2 10.4961568 9.8%
GDPperCapita 16 2.955869E0 .2086371 7.1%
improvedwatersource 16 8.780625E1 4.5047336 5.1%
undernourishment 16 1.733750E1 2.2925604 13.2%
labourforce 16 5.725988E1 2.6045397 4.5%
populationdensity 16 3.987395E2 27.4693988 6.9%
HIVprevalene 16 .337500 .0500000 14.8%
healthexp 16 4.384908E0 .3772490 8.6%
Indonesia logmortality 16 2.620856E0 .2818297 10.8%
populationage 16 2.936680E1 .8971914 3.1%
schlenrollment 16 1.072982E2 1.9582856 1.8%
GDPperCapita 16 3.286392E0 .2456907 7.5%
improvedwatersource 16 8.280625E1 3.0127438 3.6%
undernourishment 16 1.449375E1 4.6713979 32.2%
labourforce 16 6.745221E1 .4678915 .7%
populationdensity 16 1.295560E2 8.2232770 6.3%
HIVprevalene 16 .287500 .1204159 41.9%
healthexp 16 5.241800E0 .6643258 12.7%
Total logmortality 144 2.467757E0 .9350550 37.9%
populationage 144 3.247247E1 9.7936428 30.2%
schlenrollment 144 1.013360E2 21.5704114 21.3%
GDPperCapita 144 3.190331E0 .6337582 19.9%
improvedwatersource 144 8.574444E1 10.1552066 11.8%
undernourishment 144 1.384375E1 9.6214120 69.5%
labourforce 144 6.601763E1 8.4023183 12.7%
populationdensity 144 2.455093E2 340.8803893 138.8%
HIVprevalene 144 .880556 .9486301 107.7%
healthexp 144 9.431911E0 3.9907549 42.3%
Table 2: Model Dimensionb
Number of Levels Covariance Structure Number of Parameters Subject Variables
Fixed Effects Intercept 1 1
Random Effects countrya 9 Variance Components 1 country
Residual 1
Total 10 3
Table 3: Information Criteriaa
-2 Restricted Log Likelihood 128.826
Akaike’s Information Criterion (AIC) 132.826
Hurvich and Tsai’s Criterion (AICC) 132.912
Bozdogan’s Criterion (CAIC) 140.752
Schwarz’s Bayesian Criterion (BIC) 138.752

Fixed Effect
Table 4: Type III Tests of Fixed Effects.
Source Numerator df Denominator df F Sig.
Intercept 1 8.000 63.363 .000
Table 5: Type III Tests of Fixed Effectsa
Source Numerator df Denominator df F Sig.
Intercept 1 .000 2.988E3 1.000
country 8 . 47.154 .
Table 6: Estimates of Fixed Effectsb
Parameter Estimate Std. Error df t Sig. 95% Confidence Interval
Lower Bound Upper Bound
Intercept 2.620856E0 .135440 .000 19.351 1.000 -87.850958 93.092671
[country=Banglade] -.583294 .191540 .000 -3.045 1.000 -128.529761 127.363174
[country=Brazil] -.672006 .191540 .000 -3.508 1.000 -128.618474 127.274461
[country=Burkina] 1.183013E0 .191540 .000 6.176 1.000 -126.763455 129.129480
[country=Burundi] .342031 .191540 .000 1.786 1.000 -127.604436 128.288499
[country=Chile] -1.592794E0 .191540 .000 -8.316 1.000 -129.539261 126.353674
[country=Estonia] -1.214044E0 .191540 .000 -6.338 1.000 -129.160511 126.732424
[country=Ghana] .843963 .191540 .000 4.406 1.000 -127.102505 128.790430
[country=India] .315238 .191540 .000 1.646 1.000 -127.631230 128.261705
[country=Indonesi] 0a 0 . . . . .
Table 7: Estimates of Covariance Parametersa
Parameter Estimate Std. Error Wald Z Sig. 95% Confidence Interval
Lower Bound Upper Bound
Residual .106002 .012902 8.216 .000 .083504 .134561
country [subject = country] Variance .011719 3.026978E5 .000 1.000 .000000 .

Covariance
Table 8: Estimates of Covariance Parametersa
Parameter Estimate Std. Error Wald Z Sig. 95% Confidence Interval
Lower Bound Upper Bound
Residual .106002 .012902 8.216 .000 .083504 .134561
country [subject = country] Variance .858364 .432495 1.985 .047 .319731 2.304401
Table 9: Correlation Matrix for Estimates of Covariance Parametersa
Parameter Residual country [subject = country]
Variance
Residual 1 -.002
country [subject = country] Variance -.002 1
a. Dependent Variable: logmortality

Table 10: Coefficientsa
Model Unstandardized Coefficients Standardized Coefficients t Sig.
B Std. Error Beta
1 (Constant) -.094 .153 -.612 .542
populationage .079 .005 .826 17.474 .000
2 (Constant) -.067 .148 -.450 .653
populationage .081 .004 .851 18.358 .000
populationdensity .000 .000 -.152 -3.283 .001
3 (Constant) .309 .167 1.847 .067
populationage .084 .004 .879 19.793 .000
populationdensity .000 .000 -.215 -4.637 .000
healthexp -.045 .011 -.191 -4.145 .000
4 (Constant) .130 .174 .750 .455
populationage .100 .007 1.050 14.527 .000
Populationdensity .000 .000 -.182 -3.906 .000
Healthexp -.053 .011 -.226 -4.870 .000
undernourishment -.021 .007 -.220 -2.946 .004
5 (Constant) 4.299 1.108 3.878 .000
Populationage .065 .011 .677 5.652 .000
Populationdensity .000 .000 -.288 -5.486 .000
Healthexp -.041 .011 -.175 -3.780 .000
undernourishment -.040 .008 -.411 -4.716 .000
GDPperCapita -.877 .230 -.594 -3.804 .000
6 (Constant) 5.887 1.093 5.387 .000
populationage .069 .011 .722 6.421 .000
populationdensity -.001 .000 -.428 -7.397 .000
healthexp -.031 .010 -.131 -2.950 .004
undernourishment -.051 .008 -.529 -6.190 .000
GDPperCapita -1.292 .234 -.876 -5.525 .000
HIVprevalence -.280 .061 -.284 -4.571 .000
Table 11: Model Summary.
Model R R Square Adjusted R Square Std. Error of the Estimate
1 .826a .683 .680 .5286723
2 .840b .705 .701 .5113562
3 .859c .737 .732 .4843183
4 .868d .753 .746 .4715539
5 .881e .776 .768 .4502450
6 .898f .806 .797 .4209226

Conclusion
Research Implications

The findings indicated that there is a positive correlation between health-related foreign aid and the avoidable mortality rate. Moreover, the controlled variables also exhibited a positive correlation with foreign aid. This means that governments who receive foreign aid have more resources to support the healthcare sector and reduce the avoidable mortality rate. In summary, the coefficient of determination in the final regression model is 80.6%. The value is high and it is an indication of a good fit. This implies that the explanatory variables show a significant percentage of variations in the dependent variable. Further, the overall regression line is also statistically significant as indicated by the results of the F-test and the t-test. Therefore, developing countries should integrate good governance in order to ensure that the impact of bilateral and multilateral aid is optimized. The findings will help donors and implementers of foreign aid programs to integrate specific and controlled indicators for the optimal level of healthcare effectiveness.

Research Limitations

Since the research relied on secondary data from different developing countries to establish the existing relationship between foreign aid and healthcare effectiveness, it would be ineffective to solely rely on the findings in policy modeling since the data used are more than two years old. Moreover, the research was carried out on a specific sector using the avoidable mortality indicator, which may not be applicable in other health-related sectors.

Area for Future Research

Given that the research was based on a single indicator, which is avoidable mortality and its correlation with health-related foreign aid, the findings may not present a true picture in the healthcare sector. Although several control variables were included in the case study, the current findings are limited to the avoidable mortality rate as an indicator for healthcare effectiveness. Therefore, there is a need to carry out further research on how the avoidable mortality rate indicator related to foreign aid in at least two health sectors for the ease of a comprehensive comparative analysis.

References

Bendavid, E. (2014). Is health aid reaching the poor? Analysis of household data from aid recipient countries.PLoS One, 9(11), 84-125.

Bendavid, E., & Bhattacharya, J. (2014).The relationship of health aid to population health improvements.JAMA Intern Med, 4(174), 881–7.

Burfeind, L. (2014). The effects of foreign aid for health on health outcomes in developing countries.Electronic Theses and Dissertations, 7(2), 1-70.

Gething, P. W., Casey, D. C., Weiss, D. J., Bisanzio, D., Bhatt, S., & Cameron, E. (2016). Mapping plasmodium falciparum mortality in Africa between 1990 and 2015. New England Journal of Medicine, 375(25), 2435–2445.

Jablonski, R.S. (2014). How aid targets votes: The impact of electoral incentives on foreign aid distribution. World Politics, 66(4), 293–330.

Makuta, I., & O’Hare, B. (2015). Quality of governance, public spending on health and health status in Sub-Saharan Africa: A panel data regression analysis. BMC Public Health, 5(15), 932.

Taylor, E. M., Hayman, R., Crawford, F., Jeffery, P., & Smith, J. (2013). The impact of official development aid on maternal and reproductive health outcomes: A systematic review. PLoS ONE, 8(2), 562-571.

Thomas, J. B., Tara, T., Cohen, M., &Dieleman, J. (2017).Lower-income countries that face the most rapid shift in non-communicable disease burden are also the least prepared. Health Affairs, 36(11), 1866-187.

World Health Organization. (2017). General government expenditure on health as a percentage of total government expenditure: 2000-2015. Web.

Wroe, D. (2012). Donors, dependency, and political crisis in Malawi.African Affairs, 5(111), 135–135.

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