INTRODUCTION
1.1 Background of the Problem
For every nation to expand socio-economically, there is need to consider the welfare and health status of her population (Wang et al, 2011). Health economics has been applied widely in many fields of public health due to its ability to provide techniques and concepts that are helpful to policy makers to arrange for better planning, allocation and management of health resources to achieve equitable healthcare (Abel-Smith, 2018). From disciplines of health economics, evidence can be generated that useful amount of gross domestic product (GDP) of a country can be determined and allocated to health sector. Studies have been conducted on the relationship between public healthcare expenditure and GDP of a country.
Healthcare expenditure is witnessed to be continuously rising in all countries and is becoming an area of significance as far as financial sustainability of national health systems is concerned (Kutzin, O’Dougherty, and WHO, 2017. The issue of rising healthcare cost cuts across all countries including high-income countries. All countries are working to devise factors that could help manage public healthcare expenditure. Ever since the largest recession that invaded the world in 2008, the indicators that influence economic growth have been developed. Economic growth is one of these factors. Therefore, this study finds it important to gain an understanding of economic growth so that adaptable policies can be determined especially on allocating money to healthcare sector. Economic growth is mostly affected by financial crisis and this could lower the allocations to public healthcare facilities. The purpose of this study is to enrich the existing literature by contributing on how economic growth affects healthcare expenditure. The study is conducted with respect to United Kingdom.
1.2 History of Healthcare Financing in UK
Public healthcare in United Kingdom is provided to all the permanent residents. The coverage of healthcare is freely provided and paid for by general taxation. It is approximated that 18 percent of income tax from the citizens are used to finance healthcare. According to Stribling (2020), 8.5 percent of UK’s GDP is spent in providing healthcare. In addition, UK has private healthcare sector that is rapidly growing but it is much smaller than the public sector. The public healthcare in UK is provided by National Health Service (NHS) which was formed in 1946. Before NHS was found, the healthcare was only available to wealthy individuals unless one obtain free treatment through charitable hospitals. Several developments took place until NHS was formed.
Having a comprehensive health expenditure estimates in a country is a very important input in development of health policies and planning (Webber and Brown, 2018). From 1980s, total healthcare spending taken as a percentage of GDP in UK has been on a general rise. According to Towers Watson report, the private sector for health insurance took a very important role and accounted for 16.7 percent of total healthcare expenditure in 1999, an increase from 10.6 percent in 1980. The medical trend in 2006 was 6.0 percent, followed by 2009 (9.3%), 2010 (8.8%) and 2011(9.5%). In 2015, 2016, 2017 and 2018, the total healthcare expenditure of UK as a percentage of GDP was 9.9%, 9.9%, 9.8% and 10% respectively (Aikman, 2018).
The healthcare system in UK is one of the most efficient in the world when seven industrialized countries were analyzed (Osborn and Squires, 2012). The areas of performance that Commonwealth Fund Report identified include quality, access to care, efficiency, health lives and equity. According to the ranking accorded to each country, Netherlands was the first followed by UK and then Australia. When it came to access to care and quality of care, UK performed well. Efficiency was measured by examining the total healthcare expenditure as a share of GDP and UK emerged the first. According to the report, UK had short waiting times for basic medical care but long hours for specialist care.
In terms of expenditure, UK government spent in the financial year 2018/2019 around £153 billion on health related matters (World Bank, 2018). This represent an increase of 2 percent from the previous year. The total expenditure on healthcare in 2017 in UK was totaled at £149.94 billion which was an increment of 3.3 percent from the previous year 2016. On the other hand, this amount accounted for 9.6 percent of gross domestic product; a reduction from 9.7 percent in 2016. However, in real terms, (figures have been adjusted for inflation), in 2017, the healthcare spending in UK rose by 1.1 percent whereas the real healthcare per person rose by 0.5 percent. This trend represent the slowest growth rates from the start of series in 2013. Out of the total spending on healthcare in 2017, the government finances on the healthcare accounted for 79 percent. In the same year, the government financed-healthcare expenditure increased by 0.3 percent whereas the non-government-financed expenditure rose by 4.45 percent representing the lowest and the highest growth rates respectively from the beginning of the series in 2013. The government financed healthcare expenditure is made up of curative as well as rehabilitative care which is 65 percent representing 29 percent of non-government expenditure (World Bank, 2018). The figure below summarizes public spending inn UK.
Figure 1: UK Public Healthcare Spending
Despite the continuous increase in healthcare spending by the Department of health (DOH) inUK, the challenges that still face the national health system (NHS) include the ageing population, growing population, evolution in healthcare requirements including the cases of obesity, antibiotic resistance and diabetes. The advancement in medical technology has assisted in saving a lot of lives every year but it conversely push up the cost of healthcare.
Policies and Regulation
One of the main focus of National Health System (NHS) is to achieve quality care (Cooksey, 2006). Their goal is to ensure that there is an enhancement in quality and safety standards of both health and social care services. Several methods exists that are used to address the issue of quality medical care. UK has a number of bodies whose purpose is to monitor and access the quality of healthcare services provided by both public and private hospital facilities (Exworthy and Mannion, 2016). It involves service providers being assessed regularly/periodically and investigating the individual issues raised to the regulatory body in order to carry out careful consideration so that they can provide recommendation on the best practices to be carried out. Previously, the regulatory bodies responsible for healthcare insurance in UK were Mental Healthcare Commission, Healthcare Commission, and Commission for Social Healthcare Inspection. All these were in 2008 merged into Care Quality Commission. The quality on the healthcare delivered is not monitored by the named regulatory bodies alone but also by the Department of Health.
1.3 Problem Statement
There is a constant increment in the share of government expenditure on health witnessed over the years (Propper, 2001). This is due to the rapid increase in the demand for healthcare (Witter, Govender et al., 2017). Even though this is the case, the increment in healthcare expenditure are still not enough to meet all the needs of the population. This is due to other health complications such as obesity, antibiotic resistance and diabetes. Among the concerns that has caused inadequate allocation of share of public expenditure in UK have not been well linked to GDP per capita of the country.
UK has had several achievements concerning public health in 20th and 21st century (Allsop, 2018). In the 20th century, UK managed to decrease the rates of food poisoning as well making improvements in childhood infections. For example, in 1970s, pertussis vaccine was introduced intended to increase vaccination and whooping cough among children fell. In addition, the influenza vaccination among the elderly of over 65 years of age was also introduced and increased the health status of its citizens (Allsop, 2018). The ten most public health achievement include (1) vaccination, (2) motor-vehicle safety, (3) safer work places, (4) control of infectious diseases, (5) reduction in deaths caused by coronary heart diseases and stroke, (6) safe and healthy food, (7) birth of healthier babies as well as mothers, (8) introduction of family planning, (9) fluoridation of drinking water, and (10) tobacco use recognized as health hazard.
Despite these tangible achievements in public healthcare in UK, public healthcare still need much. Life expectancy is increasing among all social groups but stark differences has remained the same and get worse. More than half of these differences have been attributed to consumption of tobacco as well as alcohol (Witter, Govender et al., 2017). This has been witnessed by the substantial rise in chronic liver disease and cirrhosis deaths particularly young women. Other areas to be eyed is nutrition; most particularly the childhood obesity, the rising cases of sexually transmitted diseases as well as rising burden of chronic diseases among the elderly. This put the question on whether enough funds have been channeled to healthcare in order to handle these cases.
The current study therefore is intended to fill the gap of knowledge existing about the impact of economic growth on the public health expenditure in UK. Determination of this effect of economic growth on the public health expenditure provides a good platform to start effective planning for financial resources that is sufficient for the healthcare sector in UK.
1.4 Objectives of the Study
The general objective of this study is to establish the relationship between the economic growth and public health expenditure in UK.
1.5 Specific Objectives
- To investigate the nature of relationship between economic growth and public healthcare expenditure in UK.
- To give an estimation of elasticity of public health with respect to growth rates in GDP using the UK data.
- With respect to the findings in (1) and (2) above, to produce policy options that are necessary for increasing public health expenditure in order to improve the health status of the population as the GDP grows.
1.6 Research Questions
- What is the nature of relationship between economic growth and public healthcare expenditure in UK?
- What is the estimated elasticity of public health with respect to growth rates in GDP using the UK data?
- With respect to the findings in (1) and (2) above, what policy options that are necessary for increasing public health expenditure in order to improve the health status of the population as the GDP grows?
1.7 Justification of the Study
The major problem that UK faces include the central funding that no longer keep the pace of the demand for healthcare. This does not only require traditional responses such as spending more, invoking market forces or looking for efficient savings but also requires new radical model that is negotiated openly between healthcare professionals and the policymakers. This radical model include involve the prevention of diseases, modification of health behavior as well as implementing changes in other fields such as food and transport. The ministry of health indicated the rising expenditure on healthcare but it does not meet the rising demand for healthcare service by the population and this may worsen the health status of the people especially the older population who are more prone to diseases. The inefficiency in the public health system too is another problem that increases the costs of service delivery as it has ability to weaken the sustainability of healthcare financing.
A wide literature exist concerning the healthcare financing strategies especially in developed nations. The impacts of GDP to the economy can be effectively applied in producing good plans for sufficient framework for healthcare expenditure. Based on this, there is still need to fill the knowledge gap existing between relationship between economic growth and public healthcare expenditure in UK especially as more and more chronic diseases continue emerging. This study therefore will contribute to assist the policymakers come up with efficient and sustainable healthcare financing.
1.8 Limitations and Assumptions of the Study
Every research is faced with limitations and this is not an exception. This is due to continuous technological advancement in the medical care sector as well as inability to easily get public expenditure on chronic illnesses in public hospitals. The public hospitals do not provide exhaustive data especially on chronic diseases.
1.9 Organization of the Study
The researcher has organized the study into five chapters, each with a number of sections and subsections. Chapter One, discuss Background of the study, problem statement, the research questions, research objective, limitation and assumption of the study. Chapter Two will discuss Literature review with a focus on empirical, theoretical framework of the study. Chapter Three will discuss Research Methodology. Chapter Four will present the results of data analysis focusing. Chapter Five discuss the summary of results of data analysis, recommendation for further research and conclusion. This is summarized by the following figure.
Figure 2: Organization of the Study
CHAPTER TWO
LITERATURE REVIEW
2.1 Introduction
This chapter presents literature review related to public health expenditure and economic growth. The chapter’s objective is to present a logical sequence of this study’s research questions. It captures both theoretical and empirical literature. The empirical literature captures the studies related to growth rate of GDP and public expenditure of healthcare. It also highlights various policies that have been developed relating to public health. On the other hand, the theoretical literature presents public expenditure theories that are used in providing platform to discuss issues of public expenditure on healthcare and economic growth that is relevant in this study. This chapter concludes by giving an overview of the literature reviewed.
2.2 Recent Economic Trends
Globally, the statistics have shown that economic growth slugged especially in 2019/2020 financial year. The slug has been contributed by several factors and one of them being the COVID-19 pandemic (McKibbin and Fernando, 2020). The disease has had far-reaching effects even beyond the spread of the disease itself and the efforts to contain it by initiating quarantine. As the virus spread round the globe, concerns have changed from issues relating to supply-side manufacturing to a witnessed decrease in service sector of the business (Fernandes, 2020). As far as history is concern, the pandemic has caused the largest global recession, as more than third of the world’s population put on lockdown. As at June 2020, the Global Economic Prospects has described the near-term overview of the effects of the pandemic and the long-term damages it has done to growth (McKibbin and Fernando, 2020). Using the market exchange rate weights, it was envisioned that the global GDP growth rate would contract by 5.2 percent, despite the tireless efforts put in place by governments to downturn it with various fiscal and monetary policies.
The global decline in GDP has not left UK unaffected. UK has had its own series of economic shocks since Great Slump of c. 1430 to c. 1490. In 1706, the country too experienced a War of the Spanish Succession that reduced the GDP growth by 15 percent. This war came out as a result of compounded failure of the harvest. This was followed by the Great Frost in 1709 that led too to failure of harvest brought about by Great Frost. This was followed by a series of depressions that included long depression of 1873 – 96 and the one from 1919 – 26 led by World War 1. The Great Depression of 1930 was led by US reducing the demand for the UK exports which took UK 16 quarters in order to recover. Another recession occurred in 1956 when there was uncompetitive motor industry, the squeezing of credit caused by high bank rates as well as inflationary pressures. In the Mid-1970s stagflation due to oil crisis affected UK. The inflation rate during this time went as high as 24.2 percent in 1975. The Great Recession of 2008 too led to unemployment and economic growth in UK. Recently, the COVID – 19 pandemic has reduced the GDP growth rate of UK by 20.4 percent as of Quarter 1 (Q2).
However, despite the shocking factors, the World Bank group (2018) indicated that the nominal GDP of the country increased from $2.66 Trillion in 2017 to $2.861 Trillion in 2018. However, it slightly dropped to $2.827 trillion in 2019. The growth was led by several factors that include technological advancement, improvement in infrastructural development and reduction in inflation rates. Despite the increment in cost of living, the government has been increasing its allocations to the social sectors such as education and health. The figure below clearly shows the healthcare expenditure as percentage of economic growth in UK.
Figure 2: PHE as a percentage of GDP, 2000 to 2019
Recent Health Trends
UK like any other country has been experiencing hard times despite its improved health status witnessed by a rising life expectancy of its population. According to Department of Health and Social Care (2018), the health system of the country has delivered a good health outcome as compared to scale of income inequalities. The department also indicated the improvement National Health Service (NHS) that, the time that patients stay in the hospital has fallen significantly and the rate at which hospital services are utilized is 25 percent below the EU average. This witness efficiency in the healthcare facilities.
The recent decades have witnessed several improvements in many early-years of health. These include reduction of teenage conceptions, infant’s deaths, proportions of newborns with low birth weights and smoking in pregnancy (Skinner and Marino, 2016). However, the recent few years have seen these improvements slow down with no improvements on infant mortality rates or newborns with low weights. In addition, the tooth decay which is a serious problem among the children aged between 6 and 10 also have not been prevented. Some of the children do not enjoy life just like others. In some other deprived areas of England, it has been recently seen that teenage conceptions and tooth decay in children is around three times higher. In addition, infant mortality rate in the deprived areas is more than double.
Figure 3: England’s Life Expectancy
The figure above shows provisional 2018 data for life expectancy of England. As can be seen from the figure, the future trend of life expectancy of the country is uncertain. It indicates that life expectancy at birth is 79.6 years for males while it is 83.2 for females. This indicates that there is no improvement when compared to 2017 figures. When comparison is taken with other countries in the EU, UK has shifted its rank for the male life expectancy (Raleigh, 2018). In 2006 the country was 6th highest out of 28 countries but in 2017 it dropped to the 10th position. The women’s life expectancy stood constant at 17th in both years.
In UK, inequality in life expectancy has been witnessed in different areas. In 2015 -2017, the gap that existed in life expectancy is between the most deprived areas and those living in the least deprived areas was 9.4 years for men and 7.4 years for women which was a significant increase from 2011 – 2013. The increment in the life expectancy gap is mainly because of high death rates from respiratory and heart diseases, as well as lung cancer (Raleigh, 2018).
Improvement in health and wellbeing among the lives of the people results from having a good job (Sensenig and Donahoe, 2006). Good job boosts quality of life and gives protection against social exclusion. The employment rate of England has continued rising over the years. However, certain groups within the population do not find it easy to land in a meaningful job. In 2017/2018, people with secondary mental health issues and those with learning disability under employment were only 7 percent and 6 percent respectively. In 2016/17, it was captured that 29 percent of the population were living below the Minimum Income Standard. This standard is a base which is publicly accepted for social living today.
2.3 Theoretical Literature
The allocations of public health expenditure can be sourced from various decisions and from factors stemming up from economics and politics. The Buchanan’s theory of healthcare spending emerged in 1965 (Buchanan, 1965). At that time economists used to fear that if the government could provide total healthcare for its citizens, it would create an excess demand for healthcare and this could lead the government to spend excess money on health. The fear the Buchanan’s theory introduced was an encouragement for political decisions relating to public spending to be made that does not depend on demand. In this case, inefficiencies is witnessed not from lack of supply but the quality of services that is reduced as a result of congestion, unequal distribution of hospital personnel, and infrastructure (Buchanan, 1965). This theory describes the health status in UK as inefficiencies that are linked to healthcare is not on inability to adequately allocate funds but on the quality of the healthcare services in the country.
The results of the theory above is dangerous as the private sector will better the quality of their health services they provide and this result in expensive healthcare costs which then deems healthcare to be unequitable to the public hence going contrary to its own aim. Based on this, healthcare financing will be made politically and not automatically because the supply and demand are dictated by the price of a good. According to (Jowett (2003), the GDP projections could be the solutions for the political decisions regarding the healthcare financing.
According to Leu (1986), and other researchers the wealthier the nation is, the more they incur high healthcare cost per head. In UK, the World Bank statistics shows that the nominal GDP of UK is ranked the 6th in the world and therefore it is expected that the country will spend much on healthcare (Crafts, 2017). It is expected that a country like UK will not opt to slush the public healthcare costs. In any healthcare system, the ultimate goal is the provision of adequate welfare to the public through maximization of welfare using the given resources as well as adjusting these resources to be equitably valuable (Barr, 2020).
Harding and Pritchard, (2016) in a study on comparing UK and other twenty countries on healthcare cost, a study that intended to find out the best way of determining expenditure on health in a country specifically with health systems that are publicly oriented rather than private, found out that so as to address the problem related to demarcation of adequate finance in every country, when the private healthcare is limited, is by ascertaining the economic strength per individual. The advice of the study was that the GDP per capita is considered to be the main determinant of the spending on healthcare because per capita income is more likely to relate to political decisions that influence the public expenditure. Hence, based on this the best estimating variable for public health expenditure is the GDP per capita.
There are other studies that have analyzed the healthcare expenditure and economic growth. The study conducted by Drastichová and Filzmoser, (2020) on medical care expenditure with an aim to attempt answering the question, ‘what are the factors that determine the quantity of resources devoted by a country to medical care?’ using compositional data analysis discovered that, if a country’s GDP is high, this is not an indication or meaning that the doctors in that country earns high salaries, or the sense that the health status in that country will improve, but is a revelation that a higher allocation of finances to medical sector is required to be improved in that country. The improvement of medical care include the following; 1) reduction of anxiety, symptoms such as pain and itching due to improved ambulatory services, 2) doctors improve their decision making because they can undertake the tests that are funded by government as they see fit. In the UK quality services are demanded not even the salaries for doctors. Therefore, if the GDP per capita has an ability to explain the public healthcare financing, this will help UK to undertake proper planning for public healthcare expenditure.
Studies have also been developed concerning the appropriate determinants that affects medical care spending of a country. In the study on health policy planning and financing in UK, Abel Smith (2018) stated that the population’s state of health is influenced directly by the attention that is given to the healthcare system and is even more efficient when there is high correlation between the national economic growth per capita and public healthcare expenditure. In specific, the study’s empirical results indicated that there is a strong correlation of 0.85 between public healthcare expenditure and GDP per capita. Hence, if GDP per capita is used as an estimator of Public healthcare expenditure, it will be easier to achieve feasibility of healthcare expenditure.
The existing literature on healthcare expenditure directs this study to use the GDP as the main determinant to decide the allocation to public health in a country. ThEre are several thoughts that we consider important. According to Harding and Pritchard, (2016), almost 92 percent of all changes in public health care expenditure can be explained due to the changes in economic growth. The study was conducted in developed countries and implied that the best indicator for the resources that a country affords is to allocate funds to healthcare sector. In order to make projections concerning the healthcare expenditure of a country, elasticity of demand of public healthcare need to be looked at with respect to growth in GDP of a country. In a country like UK, there are several studies that have been conducted relating how responsive public health care is to national incomes. These studies include Jowett (2003), Dai and Tayur (2019), and Cooksey, D., 2006. The following major reasons have been highlighted by these studies concerning the variation in public health expenditure.
Firstly, population growth: the growth in population leads to an increase in amount of expenditure on healthcare in the country as a result of salinity rate of the population that are brought about factors such as chronic illnesses as well as severe illnesses that tend to increase over time. Secondly, advancement in technology which tend to reduce the amount of expenditure on public health in the long-run following heavy investment at initial stages of medical technology by public healthcare in the short-run. Thirdly, expansion of medical infrastructure which include more dispensaries, hospitals, roads as well as health clinics hence this necessitates a rapid increase in public health expenditure. Finally, economic growth which is referred to as an increase in GDP of a country which would lead to an increase in public health expenditure however the magnitude of the increment depends on the elasticity of demand for public health care in country in question. Therefore, in order to plan and make effective allocation on the healthcare expenditure in a particular country, there is need to get an understanding of the effects of economic growth on government expenditures on health.
2.4 Empirical Literature
Various studies have investigated the relationship that exist between GDP per capita and healthcare spending but they have ended up with different conclusions. With respect to the fact that public healthcare is an annualized fiscal social government entity that require adequate allocation with time, this factor is considered random (Propper, 2001). This means that the expenditure of today varies over time and across areas. Even though several studies have indicated the GDP per capita is the best factor, the challenge that exist is that there is existence of variability. In order to tackle this challenge, a model was developed by (Grossman, 1972) that assumed sure correlation between public healthcare expenditure and GDP per capita. In that model Grossman indicated that public health expenditure is an investment in human capital. This is because economic production require an input in form of human capital. Therefore, if there is an increase in expenditure on healthcare it will result in an increase in GDP and an increase in GDP increases healthcare spending.
Theory suggests that there is a lot of risks associated to this assured relationship between the economic growth and public healthcare expenditure especially in cases when there is emergencies such as outbreak of diseases like COVID-19 as well as political instability. However, as per the suggestion by Grossman, we can accept this relationship if we take the standard of living of a country as necessary factor but not necessarily a sufficient estimator. In order to carry out this effect, the condition set by Grossman for each variable to be independent only caters for necessity but not sufficiency as an estimator. The current study therefore, carried out a test on the extent to which current and lagged values of healthcare expenditure as function of economic growth can be applied in predicting the future GDP values. The result indicated that the correlation holds at 0.87 between the two variables. Through this context, it can be said that GDP per capita is 87 percent important to determine the public healthcare expenditure.
The system of healthcare delivery is dynamic as a result of various technological advancement as well as varying measures to control diseases. The challenge that can be witnessed here is on the measurement of how increased expenditure such as on new technology affects public healthcare expenditure status of a country. Sensenig and Donahoe (2006) attempted to present a description of policies that have been developed on national health system of UK. The study applied the relation model of healthcare expenditure and GDP in determining whether GDP per head sufficiently address variability in technology as well as other programs intended to improve the healthcare system. The data that Sensenig and Donahoe used were obtained from NHEA and carried out comprehensive measure of total health expenditures versus investment in medical sector capital. The model became a success and Donahoe used to predict that a 17 percent increase in public healthcare expenditure in 2004 in USA and also found that the elastic relationship between increased finding for advancement of medical funding and technology and GDP is 0.05 in healthcare system of US. This means that GDP per capita component as a determinant of public healthcare expenditure sufficiently caters for 0.05 for variability in technological; advancement.
The studies that have been carried out in most developed countries have indicated that GDP per capita estimates well the projections for public healthcare expenditure (Fölster and Henrekson, 2001). The question that is of concern is determination of how far the GDP per capita sufficiently determines the long-run expenditure on public healthcare. While testing for error correlation, co-integration and unit root testing on stationarity of real per capita GDP and real per capita public healthcare expenditure, Jowett (2003) found that GDP is an appropriate tool to predict short-run as well as long-run capital outlay on public healthcare because unit root and stationarity are present. The presence of stationarity approves long term relationship between variables.
In addition, recent studies too have been used in empirically ascertaining whether the GDP per capita is the best method that estimates the public healthcare expenditure rather than other variables. Caley and Sidhu, (2011) indicated in their study that healthcare spending cannot necessarily explained by age of the population, share of public finance and number of public practicing doctors. Based on this, the question to ask is what directly affects public healthcare expenditure? The study concluded using public healthcare expenditure and GDP per capital co-integration that GDP per capita of a country influences adequate amount of public healthcare expenditure.
The results from a study conducted by Wang et al., (2011) using the UK data indicated that income could directly influence the health spending at individual level. The study concluded that there is direct relationship between GDP per capita and total expenditure on health. From the studies done in Turkey concerning expenditure on healthcare, mixed results were obtained. Some showed positive correlation between GDP and healthcare expenditure while other had negative results.
Hitiris, (2014) analyzed the relationship between economic growth and healthcare expenditure in emerging markets in Europe. The results indicated GDP is a very important factor that can explain the variation in healthcare expenditure among the countries. GDP defines the elasticity of income with respect to expenditure on health. Hitiris, (2014) indicated that in European countries, the elderly population impacts the healthcare expenditure and healthcare is considered as a normal good. In addition, Mehrara (2012) found on the other hand that in MENA countries the share of GDP on expenditures reduces as GDP declines which indicates too that healthcare is a normal good.
The test above confirms that if GDP per head is well utilized in predicting the extend of expenditure on healthcare of a country, the country may have a good chance to provide funds that equitably meet the health needs of its citizens.
2.5 Overview of Literature
The literature that have been reviewed by this study provides that GDP per capita can be used as the best benchmark to make viable decisions concerning financial matters relating to public healthcare expenditure of a country.
CHAPTER THREE
RESEARCH METHODOLOGY
3.1 Introduction
This chapter gives detailed information and description of research methodology that was used in this study. It entails the research design, research philosophy, and research approach, the sampling method and various methods to be used in data collection and also provide understanding of data analysis.
3.2 Research Design
Research studies utilize different research designs depending on the topic under investigation since the topic affects researcher’s data collection method (Creswell, 2014). The design as mentioned earlier can either be quantitative or qualitative which are also referred to as fixed and flexible designs (Creswell, 2014). The use of quantitative method requires comparison and establishing causal relationship among variables. However, the quantitative or fixed research method is not suitable in cases where the aim of the research is to investigate individuals as well as complex behavior (Matthews, 2014). On the other hand, flexible designs are suitable in analyzing phenomena in a complex context (Kvale & Brinkmann, 2014). Matthews, (2014) indicated that the main purpose of the qualitative method is to understand a phenomenon to a greater extent through analysis, interpretation and description of the collected data by use of words rather than using numbers.
In this study, the quantitative design will be used since the study aims to understand the phenomena of relationship between economic growth and public healthcare expenditure in UK. The use of quantitative research is meant to provide authenticity and an understanding of the situation in Pakistan as opposed to using the policies concerning uncertainty set by other researches that focused on other countries. The situation in UK might be different, and the conclusions made from research conducted by other quantitative researches might not be the real portrayal of how the public healthcare expenditure may be influenced by the rate of GDP growth of the country. Given this reality, the quantitative research that this paper conducts provides an opportunity to have an understanding of how GDP per head can affect public healthcare expenditure.
3.3 Research Philosophy
Realism interpretivism and positivism are the most critical perspectives used by researchers. As one of the approaches, Interpretivism suggests that common occurrence, in reality, can be used in the interpretation process that can help us understand reality. As it may, this method recommends that a study be conducted among people (Hughes, 2016). It involves collecting data concerning the social construction of people to provide their interpretation. Realism philosophy recommends that what our sense tells us is deemed to be the truth in reality (Mayer, 2015). The current study will adopt positivistic philosophy which permits assembling of statements trough verified evidences and experience founded on empirical data with the researcher restricted from acting as an influencer (Matthews, 2014). Using this approach in our study will help in a valid comparison of statistical data and different variables.
3.4 Research Approach
Research approach is defined by (Moen, 2006) as a pathway of obtaining a conscious scientific reasoning. Hyde, 2000 indicated two ways of research approaches that include deductive and inductive. According to Hyde, deductive research approach is a theory based method that scans the already established theory followed by construction of a theoretical framework and derives a logical conclusion while trying to gain new information with respect to a specific scenario. In other words, deductive research approach is begins with the development of a theory as well as hypothesis and then test the hypothesis. Deductive research approach is a construct based on strong theoretical framework. It starts from a general point of view to a specific case or cases. On the other hand, an inductive research approach is a theory development process that starts with an empirical observation and phenomenon generalization (Spens & Kovacs, 2006). Inductive research approach starts from an argumentation process to understanding the overall theme.
In order for this study to explore the phenomena under investigation, it will be prudent to develop a theoretical platform which will facilitate the main assumptions of the study to be built, make suggestion for proposition and then draws the final conclusion. Therefore, deductive research approach best fits this study to explore how GDP per capita can impact the public healthcare expenditure in UK. This study is not an exploratory but rather explanatory and considering that the theories of uncertainty already exist, the deductive approach best this paper, unlike the inductive one which best fit where hypotheses are being formulated. As such, the study uses real option theory and probability theory besides the use of other concepts such as company size and industry. This will enrich the theoretical framework for the research and enhance understanding. Based on the above arguments, we can say that the current study moves from the general understanding about concepts to understanding the specific scenario.
3.5 Sample Selection
Due to constraints such time and resources, it is argued that it is almost impossible to test or study an entire population (Denscombe, 2009). The study has the freedom to include all the data in this study, but it is restricted by constraints such as time. Furthermore, the selection criteria for this study is annual reports for their most recent source are studied. The motivation behind the use of the public bodies is because their data are readily availab. The sample was selected using stratified random sampling. This is sampling technique where the population is classified to relevant strata based on identified attributes, and then a simple or systematic random sample is selected form each of the strata.
3.6 Data Collection Method
The data for this study was obtained from secondary sources (economic policies). In order to get a justified data for this thesis, we selected more than one firm. Using different sources gives a more accurate perception of reality (DiCicco – Bioom and Grabtree, 2006). Hence, by selecting more than one company and industry validates the collected data. The data was collected from the websites of the sampled listed companies using the information provided either in their annual reports. The table below shows the sources of our data.
Table 3.1: Data Sources
| Variable | Sources of the Data |
| Gross Domestic Product per capita | World Development Indicators (1989 – 2019), |
| Public Healthcare Spending | Public Expenditure Estimates (1989 – 2019) from Department of Health and Social Care |
| Inflation rate | Bank of England Annual Reports (1989 – 2019) |
| Population Growth rate | United Kingdom (UK) Office for National Statistics (1989 – 2019) |
| Physician per population of 100,000 | Statistical Abstracts (1989 – 2019) from United Kingdom (UK) Office for National Statistics |
3.7 Data Analysis
Data analysis is the process in which both written and verbal data is systematically analyzed to measure the variables quantitatively (Yaffee, 2002). Data in this study were analyzed through the use of E-Views. Several statistical tests including t-test, Pearson’s correlation test to test association between the dependent and independent variables. Again, multiple regression was also undertaken in testing the hypothesis formulated in the research study. The sample size for the study was covered the period between 1989 and 2019. A sample size larger than 30 is, in most cases, assumed to be normally distributed (Yaffee, 2002).
3.8 Theoretical Framework
The aim of this study was to investigate the relationship between economic growth and public healthcare expenditure by carrying out the following tests; Augmented Dickey Fuller (ADF) test, Granger causality test and Johansen tests so as to determine the properties of healthcare expenditure, physicians per population of 100,000, population growth rate and income per capita as a ratio of GDP per capita time series (Singh and Sahni, 1984).
It is considered that time series data has trends that has to be removed when estimation is undertaken. The study carries out the elasticity between the public healthcare expenditure and GDP per capita using linear approach. The assumption of linear approach is that other factors are kept constant. The measurement of price elasticity is application of inflation rate. Population growth rate accounts for population progress. Physician per population 100,000 is to capture the medical intensity in the country.
Our main area of interest is to identify the simple form of public health expenditure model. With our assumption that public healthcare expenditure depends on GDP per capita as well as its explanatory variables, we employ the Cobb-Douglas production function. We investigate the impact of GDP on healthcare expenditure using the following equation.
LnPHE = α0 + α1lnGDPPC + α2lnPoP + α3lnINFL + α4lnPHY + ω
Where;
αi – measures elasticity of independent variables changes to healthcare expenditure
LnPHE – natural logarithm of expenditure on public healthcare
lnGDPPC – natural logarithm of GDP per capita
lnPoP – natural logarithm of growth rate of population
lnINFL – natural logarithm of inflation rate
lnPHY – natural logarithm of Physicians per population of 100,000
ω – Error term
The model shown above uses the OLS regression model to establish the linear relationship between dependent and independent variables. The aim of this study is to establish the impact of economic growth on public healthcare expenditure amid other factors in UK.
CHAPTER FOUR
DATA ANALYSIS AND RESULTS
4.1 Introduction
This chapter discuss the major findings that were analyzed using secondary data obtained from various bodies including World Development Indicators, Public Expenditure Estimates from Department of Health and Social Care, Bank of England Annual Reports, United Kingdom (UK) Office for National Statistics, Statistical Abstracts from United Kingdom (UK) Office for National Statistics and covered the period between 1989 and 2019. This period is particularly interesting because of the Great Recession that took place in 2008 and affected GDP growth rate and allocations to public healthcare facilities. The data were analyzed using E – Views software. This software is mainly used for econometric analysis that are time oriented. This was done in line with the objective of this study which was to investigate the impact of GDP per capita on public healthcare expenditure in UK.
The study carried out several tests before establishing linear relationship. The test were meant to ensure that the results are not spurious (Yaffee, 2002). The first test is unit root test that tests the stationarity of the time series data. This is to ascertain whether the variable changes or do not change overtime. Regarding the causality effect of the parameters in our model, it is necessary to test the Granger causality in order to determine whether the independent variable fits to be endogenous variable. The next step is to test the co-integration which helps to check the long term relationship of the variables. Once these tests have been done, it is good to estimate the relationship of the variables.
4.2 Unit Root Test
The study used Augmented Dickey Fuller’s test to test the existence of unit root and the following are the results.
4.2.1 GDP per Capita
Table 4.1: Testing the presence of unit root in GDPPC
| Null Hypothesis: GDP Per Capita has a unit root | |||||
| Lag Length: 0 (Automatic – based on SIC, maxlag=6) | |||||
| t-Statistic | Prob.* | ||||
| ADF test statistic | -6.9011 | 0 | |||
| Test of critical values: | 1% level | -4.339 | |||
| 5% level | -3.5875 | ||||
| 10% level | -3.229 | ||||
| *MacKinnon (1996) one-sided p-values. | |||||
| ADF Test Equation Dependent Variable: GDPPC,2Method: OLS | |||||
| 1989 – 2019 | |||||
| Summary of observations: 27 (adjusted figure) | |||||
| Variable | Coefficient | Std. Error | t-Statistic | Prob. | |
| GDPPC(-1)) | -1.3224 | 0.1916 | – 6.9011 | 0 | |
| Constant | 6.3973 | 22.2710 | -0.28724 | 0.7764 | |
| 1982 | -0.3235 | 1.25045 | -0.25874 | 0.798 | |
| R-squared | 0.6650 | Mean dependent variance | -1.6356 | ||
| Adjusted R-squared | 0.6371 | S.D. dependent variance | 83.844 | ||
| S.E. of regression | 50.503 | Akaike info criterion | 10.7864 | ||
| Sum squared residual | 6121. | Schwarz criterion | 10.9304 | ||
| Log likelihood | -142.616 | Hannan-Quinn criter. | 10.829 | ||
| F-statistic | 23.8302 | Durbin-Watson stat | 1.9189 | ||
| Prob(F-statistic) | 0.000002 |
The table above indicates that the t statistic; α= -6.901132 is larger than the critical value at 1 percent = -4.339230, at 5%= -3.587627 and at 10%= -3.229130. Therefore we reject the null hypothesis (GDPPC series has unit root) and conclude that the GDP per capita is stationary at Lag (0).
4.2.2 Public Healthcare Expenditure
Table 4.2: Testing the presence of Unit root in PHE
| Null Hypothesis: PHE has a unit root | ||
| Exogenous: Constant, Linear Trend | ||
| Lag Length: 0 (Automatic – based on SIC, maxlag=7) | ||
| t-Statistic | Prob.* | |
| ADF test statistic | -6.61654 | 0 |
| Test critical values: | 1% level | -4.3399 |
| 5% level | -3.48023 | |
| 10% level | -3.12434 | |
| *MacKinnon (1996) one-sided p-values. |
| Summary of observations: 28 after adjustments | ||||
| Variable | Coefficient | Std. Error t-Statistic | t-statistic | Prob. |
| PHE(-1)) | -1.431879 | 0.20405 | -6.61064 | 0 |
| Constant | -1.36E+05 | 7.01E+07 | -1.83174 | 0.0658 |
| TREND(1989) | 2.40E+08 | 49415708 | 4.847138 | 0.0001 |
| R2 | 0.642311 | Mean dependent var | 3.01E+08 | |
| Adjusted R2 | 0.613695 | S.D. dependent var | 2.70E+09 | |
| S.E. of regression | 1.58E+09 | Akaike info criterion | 45.43271 | |
| Sum squared residual | 7.01E+19 | Schwarz criterion | 45.56644 | |
| Log likelihood | -632.9319 | Hannan-Quinn criter. | 45.46734 | |
| F-statistic | 22.44652 | Durbin-Watson stat | 1.651672 | |
| Prob (F-statistic) | 0.000003 |
It can be seen from the table above that t statistic; α= -4.310727, is larger than the critical value at 1 percent = -3.711457, at 5%= -2.981038 and at 10%= -2.629906. Therefore we reject the null hypothesis (Population growth has unit root) and conclude that the population growth is stationary at Lag I (1).
4.2.3 Inflation Rate
Table 4.3: Unit root in INFL
| Null Hypothesis: D(INF) has a unit root Exogenous: Constant | |||
| Lag Length: 1 (Automatic – based on SIC, maxlag=7) | |||
| t-Statistic | Prob.* | ||
| ADF test statistic | -5.60185 | 0.0001 | |
| Test of critical values: | 1% level | -3.69971 | |
| 5% level | -2.97663 | ||
| 10% level | -2.62742 | ||
| *MacKinnon (1996) one-sided p-values. | |||
| Adjusted sample: 1989 – 2016 | |||
| Summary of observations: 28 (adjusted) | |||
| Variable | Coefficient | Std. Error:t-Statistic | Prob. |
| (INF(-1)) | -1.57258 | 0.28659 -5.60185 | 0 |
| (INF(-1),2) | 0.40510 | 0.18887-2.15336 | 0.0415 |
| Constant | -0.07936 | 1.80711 -0.04347 | 0.9654 |
| R2 | 0.6254 | Mean dependent var | 0.41346 |
| Adjusted R2 | 0.59118 | S.D. dependent variance | 14.7654 |
| S.E. of regression | 9.3574 | Akaike info criterion | 7.21567 |
| Sum squared residual | 2129.708 | Schwarz criterion | 7.32549 |
| Log likelihood | -97.1506 | Hannan-Quinn criter. | 7.26381 |
| F-statistic | 21.02904 | Durbin-Watson stat | 1.973372 |
| Prob(F-statistic) | 0.000009 |
The t statistic; α= -5.60185 is larger than the critical value at 1 percent = -3.69971, at 5%= -2.97663 and at 10%= -2.62742. Based on this, we reject the null hypothesis (inflation has unit root) and conclude that the inflation is stationary at Lag I (1).
4.2.4 Physician per 100,000 population
Table 4.4: Unit root in PHY
| Null Hypothesis: (PHY) has a unit root | |||
| Exogenous: Constant | |||
| Lag Length: 1 (Automatic – based on SIC, maxlag=6) | |||
| t-Statistic | Prob.* | ||
| ADF test statistic | -7.2137 | 0 | |
| Test critical values: | |||
| At 1% | -3.71146 | ||
| At 5% | -2.98103 | ||
| At 10% | -2.62906 | ||
| Summary of Observations: 26 (adjusted figure) | |||
| Variable | Coefficient | Std. Error andt-Statistic | Prob. |
| (PHY(-1)) | -2.70295 | 0.37464 -7.21372 | 0 |
| (PHY(-1),2) | 0.67911 | 0.221814 3.063430 | 0.0055 |
| Constant | 0.00807 | 0.00652 1.22539 | 0.2329 |
| R2 | 0.86796 | Mean dependent variance | 0.0012 |
| Adjusted R2 | 0.86441 | S.D. dependent var | 0.08575 |
| S.E. of regression | 0.03249 | Akaike info criterion | -2.9074 |
| Sum squared residual | 0.024279 | Schwarz criterion | -2.7649 |
| Log likelihood | 53.79898 | Hannan-Quinn criter. | -2.8812 |
| F-statistic | 75.5724 | Durbin-Watson stat | 1.1216 |
| Prob(F-statistic) | 0 |
The table above indicates that the t statistic; α= -7.2137 is larger than the critical value at 1 percent = -3.71146, at 5%= -2.9810 and at 10%= -2.6299. Based on this, we reject the null hypothesis that (Physician per 100,000 population has unit root) and conclude that the series of Physicians per 100,000 population is stationary at Lag I (1).
As shown by the test above that the variables are stationary at lag 1 (1) we can then proceed to test for co-integration between the variables so as to check the long-term relationship existing between the variables being studied.
Before co-integration analysis on the variables are done, there is need to know that there can be correlation between the GDP per capita and public healthcare expenditure in that other can cause the other. This take us to the next analytical model that is granger causality. This is to test whether the dependent variable fits to be independent variable or should exist as dependent variable only.
The following table presents the granger causality test results;
4.3 Pairwise Granger Causality Tests
Table 4.5: Pairwise Granger Causality Tests
| Sample: 1989 – 2019 | |||
| Lags: 2 | |||
| Null Hypothesis: | Obs | F-Statistic | Prob. |
| GDP per capita does not Cause healthcare expenditure | 30 | 3.1902 | 0.0699 |
| Healthcare expenditure does not Cause GDP per capita | 10.527 | 0.0002 |
The above results indicate that P-values 3.1906 and 11.528 are larger than the critical values at levels 1%, 5%, and 10%. Based on this, we fail to reject the null hypothesis and conclude that public healthcare expenditure does not granger-cause the GDP per capita and GDP per capita on the other hand does not granger cause the public healthcare expenditure. Therefore, we are confident to say that public healthcare expenditure is an appropriate dependent variable.
By considering the results in the unit root test already done, it shows that both series are integrated I (1), then we need to test co-integration using the Johansen procedure (1988, 1991). Testing the co-integration equals showing that the residual’s vector is stationary.
4.4 Test for Co-integration
Using the methodology that was developed by Johansen (1991, 1995) E-Views supports the VAR based co-integration. The following shows the tests for co-integration.
Table 4.6: Test for Co-integration
| Number of co-integrating relations | ||||
| Unrestricted Co-integration Rank Test (Trace) | ||||
| Hypothesized No. of CE(s) | Trace Statistic | 0.05 | ||
| Eigenvalue | Critical Value | Prob.** | ||
| None * | 0.91016 | 119.269 | 68.8189 | 0 |
| At most 1 * | 0.51473 | 49.294 | 48.8513 | 0.0464 |
| At most 2 | 0.41179 | 28.335 | 28.7907 | 0.0947 |
| At most 3 | 0.32119 | 10.479 | 16.4941 | 0.1737 |
| At most 4 | 0.00144 | 0.0321 | 3.84466 | 0.8504 |
Trace test indicates 2 co-integrating eqn(s) at the 0.05 level
* reject hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
| Hypothesized No. of CE(s) | Max-Eigen Statistic | 0.05 | ||
| Eigenvalue | Critical Value | Prob.** | ||
| None * | 0.91041 | 69.9647 | 34.8768 | 0 |
| At most 1 | 0.51457 | 20.9591 | 28.5843 | 0.2887 |
| At most 2 | 0.42117 | 15.8561 | 22.1316 | 0.2434 |
| At most 3 | 0.32611 | 11.4463 | 15.2646 | 0.1432 |
| At most 4 | 0.00114 | 0.03321 | 3.81466 | 0.8654 |
Max-eigenvalue test indicates 1 co-integrating eqn(s) at the 0.05 level
* reject hypothesis at the 0.05 level
**MacKinnon-Haug-Michelis (1999) p-values
From the empirical results above there is an existence of at least 1 co-integration equation. Based on this, we reject null hypothesis that non-integration does not exist between variables and conclude that there is an existence of co-integration relationship and this implies that the selected variables that is public healthcare expenditure, GDP per capita, inflation rate, population growth rate, and physician per population of 100,000 are stationary. In addition, the results show that at most 2 of the variables are significantly co-integrated at 5 percent level of significance. This also implies that the variables show linear long-run relationship especially the public healthcare expenditure and GDP per capita.
4.5 Estimation of Regression Equation
Table 4.7: Regression Equation
| Dependent Variable: ln(PHE) Method: OLS | |||
| Sample (adjusted): 1989 – 2018 | |||
| Summary of observations: 30 | |||
| Variable | Coefficient | Std. Error t-Statistic | Prob. |
| Intercept | 7.47165 | 1.47019 – 5.08207 | 0 |
| LN(GDPPC) | 0.02354 | 0.21608 – 0.10897 | 0.9241 |
| LN(PoP) | -3.4331 | 0.38665 -8.87897 | 0 |
| LN(INFL) | 0.03573 | 0.04613- 0.77464 | 0.4448 |
| LN(PHY) | 1.35040 | 0.19972 -6.76119 | 0 |
| R2 | 0.97650 | Mean dependent variance | 2.1347 |
| Adjusted R2 | 0.97274 | S.D. dependent variance | 1.1149 |
| S.E. of regression | 0.18405 | Akaike info criterion | -0.3961 |
| Sum squared residual | 0.84693 | Schwarz criterion | -0.1625 |
| Log likelihood | 10.9417 | Hannan-Quinn criter. | -0.3214 |
| F-statistic | 259.758 | Durbin-Watson stat | 1.0086 |
| Prob(F-statistic) | 0 |
From the above table, we can estimate the regression equation as follows:
lnPHE = 7.471 + 0.0235 lnGDPPC – 3.433 lnPOP + 0.0357 lnINF + 1.350 lnPHY
Income elasticity of public healthcare expenditure can be estimated using the above results which is defined as percentage change in PHE divided by percentage change in GDP per head. From the equation above, it can be deducted that an increase of GDP per capita by 1 percent means that public healthcare expenditure has to increase by 0.0235 percent.
The R-Squared (R2) is used to measure the extent that the regression can predict the dependent variable in the sample. R-Squared is interpreted as all variations in dependent variable that is explained by independent variables. If the regression is perfectly fit, the R-squared will equals 1 and zero if it does not fit. If the R-Squared is above 0.97 it shows high authenticity of variables of data being analyzed.
CHAPTER FIVE
SUMMARY, CONCLUSION AND RECOMMENDATION
5.1 Introduction
This chapter presents the summary, conclusions and policy recommendations of the study. Section 5.2 presents summary of the study; section 5.3 presents conclusions; section 5.4 gives the policy recommendations.
5.2 Summary of the Study
After testing for co-integration properties, the study did an analysis of the model. It can be seen from the results that the healthcare expenditure has negative relationship with GDP per capita when the long-term relationship is established by adjusting the parameters. The fact that all the variables do not have the unit roots at the first lagged difference I (1), it enable the model to estimate the stationarity at mean (0) and variance (1).
From the regression equation the coefficients can be described as follows; the model predict that a 1 percent increases in GDP per capita will lead to an increase in public healthcare expenditure by 0.025 percent, when other variables are analyzed; an increase in population growth by 1 percent will decrease the public healthcare expenditure by 3.4 percent. In UK, the population growth rate has been decreasing overtime however, it is expected that with growth in population, public healthcare expenditure has to be much higher. The results presented by this study have gone contrary to the theory may be because of limited amount of data used. In our case, as the population keeps on dwindling lesser and lesser expenditure on public healthcare is expected to increase. With respect to matters relating to prices, an increase in inflation by 1 percent, increases public healthcare expenditure by 0.036 percent. In addition, if the medical staff; doctors, registered nurses, medical officers and lab technicians in UK increases by 1 percent, the public healthcare expenditure will increase by 1.35 percent when value for money is taken into account as time goes by.
5.3 Conclusion
Determining how GDP per capita impacts the public healthcare expenditure is very important as far as financial planning is concern. When predicting the values of public healthcare expenditure in future, it can be seen that an increase in GDP per capita by 1 percent results in an increase in public healthcare expenditure by 0.025 percent. Based on this and given that the IMF World Economic Outlook 2019 forecast the GDP per capita of UK to rise with the rate of 3.6 percent in 2024, as per our model, this attracts an increase of 0.9 percent or in nominal terms, it will be expected to increase from 44,311 in 2023 to 45, 934 in 2014.
It can be deducted that the healthcare expenditure in UK is a normal. The price elasticity of demand for healthcare expenditure is 0.025 meaning that the demand is almost perfectly inelastic. As income rises, the demand for healthcare almost remain the same. But an increase in healthcare expenditure is less than proportion increase in income.
5.4 Policy Recommendations
This study’s results give a clear benchmark on making decisions regarding the financial matters on public healthcare expenditure in UK. The following are among the policy recommendations that this study have considered necessary for UK; given that the healthcare is a normal good in the country; policymakers should eye pushing for healthcare especially the elderly population to be as a necessary benefit. The policymakers of the country should also devise policies to reduce health inequalities. This can be achieved by having a more progressive income distribution strategies through increasing investment in deprived areas.
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