willingness to pay for public healthcare utilisation
TRANSCRIPT
MIDLANDS STATE UNIVERSITY
FACULTY OF COMMERCE
DEPARTMENT OF ECONOMICS
WILLINGNESS TO PAY FOR PUBLIC HEALTHCARE UTILISATION: CASE OF GWERU URBAN
BY
MITCHELL K. NAME
R114100J
SUPERVISED BY MR. NDLOVU
MAY 2015
THIS DISSERTATION IS SUBMITTED TO THE DEPARTMENT OFECONOMICS IN PARTIAL FULFILLMENT OF THE REQUIREMENTS OF THE BACHELOR OF
COMMERCE HONOURS DEGREEIN ECONOMICS
DECLARATION FORM
I, Mitchell Kuziwakwashe Name, do hereby declare that this research report presents my
own work and has not been copied or lifted from any source without the acknowledgement of
the source.
Signed……………………………………………….. Date………/………/……..
i
SUPERVISORS APPROVAL FORM
The undersigned certifies that they have supervised the student’s (Name Mitchell
Kuziwakwashe) dissertation entitled “Willingness to Pay for Public Healthcare Utilisation"
submitted in partial fulfilment of the requirements of the Bachelor of Commerce Honours
Degree in Economics (Midlands State University).
Supervisor’s Signature
Chapter 1 ……………………………….
Chapter 2 ……………………………….
Chapter 3 ……………………………….
Chapter 4 ……………………………….
Chapter 5 ……………………………….
Date ……………………………….
ii
APPROVAL FORM
The undersigned certify that they have supervised, read and recommend to Midlands State
University for acceptance a research project entitled:
WILLINGNESS TO PAY FOR PUBLIC HEALTHCARE UTILISAION IN ZIMBAMBWE:
CASE OF GWERU
Submitted by Name Mitchell Kuziwakwashe in partial fulfilment of the requirements of the
Bachelor of Commerce Honours Degree in Economics.
Signature of Student……….……………………… Date……/………/………….
Signature of Supervisor….……………………….. Date……/………/……........
Signature of Chairperson………………………….. Date……/………/………….
Signature of Examiner……………………………… Date……/……../…………..
iii
DEDICATIONS
I dedicate this to my lovely mother and sister whom I hope are proud of what I have
managed to achieve, you guys have been there for me through thick and thin and deserve
only the best from me and to God Almighty without whom nothing is possible.
iv
ACKNOWLEDGEMENTS
I’m very grateful to my supervisor Mr Ndlovu for his dazzling guidance and support during
the course of this research. I would also want to express my gratitude to the entire Economics
Department staff at Midlands State University for their contributions to the success of this
research. I also wish to thank my classmates for their academic support and the light
moments shared, which made the time spent valuable. This research would not have been a
success without the help from the Gweru citizens who took their time to accommodate me
and respond to my questionnaires.
To Jenifer and my great friends Rutendo, Belinda, Nyasha, Petty, Sandra, Tapiwa,Valerie,
Ashely, Chiedza, Tafadzwa, Lisa, and Rumbidzai you guys are the finest. Last but not least,
to my wonderful family; Ms Name, Mr and Mrs Mvundura, Mr and Mrs.P Name,
Annebolyne, Nigel, Kelly, Kelvin, Strive, Nigel, Tapiwa and everyone else; without you my
aspiration would have never come to pass. Thank you very much.
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ABSTRACT
Good health status should be part of everyone’s wellbeing and is an important component of
economic and social development. Public healthcare utilisation trends have been declining
and therefore this research paper seeks to explore the factors that affect willingness to pay
for public healthcare utilisation in Gweru Urban. The research thus analysed the effects of
age, gender, household income, level of education, proximity and severity of illness on
willingness to pay for public healthcare utilisation. The research used primary data to
estimate an OLS regression model. Regression results obtained showed that proximity,
severity and household income are important factors that influence the decision by
individuals on what amount they are willing to pay to access public healthcare in Gweru
Urban. Results of this study were consistent with other studies done in other countries. The
research suggested policies aimed at increasing awareness on importance of seeking
healthcare time and provision of public healthcare services at the shortest distance possible
through building of mobile clinics in every suburb. This study cannot be generalised to other
districts and provinces in Zimbabwe therefore the same research should be done in other
districts and provinces in Zimbabwe so as to generalise the results for the whole nation.
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TABLE OF CONTENTS
DECLARATION FORM.........................................................................................................i
SUPERVISORS APPROVAL FORM...................................................................................ii
APPROVAL FORM...............................................................................................................iii
DEDICATIONS......................................................................................................................iv
ACKNOWLEDGEMENTS.....................................................................................................v
ABSTRACT.............................................................................................................................vi
LIST OF TABLES...................................................................................................................x
LIST OF ACRONYMS.........................................................................................................xii
CHAPTER ONE.........................................................................................................................1
INTRODUCTION......................................................................................................................1
1.0 Introduction....................................................................................................................1
1.1 Background to the Study................................................................................................2
1.2 Problem Statement.........................................................................................................6
1.3 Objectives........................................................................................................................6
1.4 Significance of the Study................................................................................................7
1.5 Research Hypothesis......................................................................................................7
1.6 Organisation of the Study...............................................................................................7
CHAPTER TWO........................................................................................................................8
LITERATURE REVIEW............................................................................................................8
2.0 Introduction....................................................................................................................8
2.1 Theoretical Literature ...................................................................................................8
2.2 Empirical Literature ....................................................................................................11
2.3 Conclusion.....................................................................................................................13
CHAPTER THREE..................................................................................................................14
METHODOLOGY....................................................................................................................14
3.0 Introduction..................................................................................................................14
3.1 Model Specification.......................................................................................................14
3.2 Variable Justification....................................................................................................15
3.2.1 Age of respondent (A) and Age of respondent squared (A2)..........................................15
3.2.2 Gender of respondent (G).................................................................................................15
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3.3 Diagnostic Tests............................................................................................................17
3.3.1 Multicollinearity................................................................................................................17
3.3.2 Heteroskedasticity............................................................................................................18
3. 4 Data Types and Sources..............................................................................................18
3.5 Conclusion.....................................................................................................................19
RESULTS INTERPRETATION...............................................................................................20
4.0 Introduction..................................................................................................................20
4.1 Diagnostic Tests – Results..............................................................................................20
4.1.1 Multicollinearity................................................................................................................20
4.1.2 Heteroskedasticity............................................................................................................21
4.2 Regression Results........................................................................................................22
4.3 Interpretation of Results..............................................................................................23
4.4 Conclusion.....................................................................................................................26
CHAPTER 5.............................................................................................................................27
CONCLUSION AND POLICY RECOMMENDATIONS...........................................................27
5.0 Introduction..................................................................................................................27
5.1 Summary........................................................................................................................27
5.2 Policy Recommendations.............................................................................................28
5.3 Areas for Further Research..........................................................................................28
5. 4 Limitations of the Study..............................................................................................29
5.5 Conclusion.....................................................................................................................29
REFERENCES..........................................................................................................................30
APPENDICES........................................................................................................................34
viii
LIST OF APPPENDICES
APPENDIX Page
Appendix A: Questionnaire 34
Appendix B: Data Set 35
Appendix C: Correlation Matrix 42
Appendix D: Test for Heteroskedasticity 42
Appendix E: Descriptive Statistics 43
Appendix F: OLS Regression Results 43
ix
LIST OF TABLES
TABLE Page
Table 1.1: Changes in private hospital consultation fees 4
Table 1.2: Average consultation fees for public healthcare facilities 4
Table 4.1: Correlation Matrix 20
Table 4.2: Breusch-Pagan / Cook-Weisberg test for heteroscedasticity 21
Table 4.3: Descriptive Statistics 22
Table 4.4: Summary of the OLS regression results 23
x
LIST OF FIGURES
FIGURE Page
Fig 1.1: National OPD and impatient admissions 3
xi
LIST OF ACRONYMS
CLRM Classical Linear Regression Model
ICLS International Conference of Labour Statisticians
MDG Millennium Development Goal
NHS National Health Survey
OLS Ordinary Least Squares
UNAIDS United Nations programme for HIV/AIDS
WTP Willingness to Pay
ZIMSTAT Zimbabwe National Statistics Agency
xii
CHAPTER ONE
INTRODUCTION
1.0 Introduction
Good health is an essential part of people’s well-being and a key factor of economic and
social development (Kaija and Okwi, 2004). Zimbabwe’s health care quality has been
declining in line with political instability and economic decline. According to Muza 2011,
some state and public health services have been closed down due to shortages of drugs and
crucial medical equipment. PULSE a Cimas publication 2014, asserts that the country also
suffers severe threats in public health as medical tourism has become a common practice in
Zimbabwe.
The prevention, treatment and management of illness and the safeguarding of mental and
physical well-being through the service offered by the medical and allied health professions
is known as medical care (McGraw-Hill, 2002). The Business Dictionary (2003) defines
healthcare as the act of taking defensive or necessary medical care procedures to improve a
person’s well-being. According to McGraw-Hill, (2002) utilisation is the extent to which a
given group of people uses a particular service or the extent to which the members of a
covered group use a certain program. Health care utilisation is therefore the usage of medical
services that improves people’s well-being. Medical tourism is defined as the rapidly-
growing practice of travelling across international borders to obtain healthcare services (Pulse
Magazine, 2014). It is also referred to the practice of healthcare providers travelling
internationally to deliver healthcare. Due to the challenges that the public health sector has
been facing and the rise in private health care fees the researcher sought to determine factors
that influence people’s/households’ Willingness to Pay (WTP) for public healthcare
utilisation. The research seeks to reveal to what extend do these factors affect households’
willingness to pay for public healthcare utilisation.
1
1.1 Background to the Study
The Millennium Development (MDGs) have identified more access and utilisation of
healthcare services as an integral part of the strategies for poverty reduction (Mugilwa, 2005).
One of the objectives of Zimbabwe’s health system is to attain higher utilisation of healthcare
services especially by the vulnerable segments of the population (Ministry of Finance, 2010).
Although substantial improvement has been made in expanding healthcare services in Sub-
Saharan Africa in recent years, the consumption of healthcare services in the region has
remained low predominantly in rural areas (Kevany et al., 2011).
Pulse Magazine (2014), identified medical tourism as one of the major problems that
Zimbabwe is facing. Factors that have led to the increasing recognition of medical travel for
most Zimbabweans are high cost of healthcare services, long wait ques for certain
procedures, shortages of specialist doctors locally, distress of waiting for specialists from
outside the country and improvements in both technology and standards of care in other
countries.
The 2000- 2008 economic crisis period saw a severe underutilisation of healthcare facilities
due to shortages of drugs and health care professionals in the country in both private and
public health (National Health Survey, 2011). According to the National Health Survey
(NHS), (2011), the fall in use of healthcare services has been indicated by lessening in
outpatient and inpatient admissions at health facilities. Year after year healthcare services
utilisation has continued to be part of government’s goals and money has been allocated to it
but somehow there has not been much of an improvement. The National Health Statistics,
(2013) indicated a decline in healthcare service utilisation during the period despite higher
prevalence of diseases. This decline is as a result of medical tourism, unavailability of
medication in public health centres and high cost of private health services. Fig 1.1
summarises the trends of total national utilisation of health care services from 2001 to 2013
on a yearly bases.
Fig 1.1 shows that there was a general decline in the trend but 2004, 2007, 2009 and 2010
show some increase. In 2009 and 2010 this may have been due to availability of money and
better standards of living. It can be noted that healthcare consumption was high in 2001; this
2
is shown by high number of cases of outpatient and inpatient admissions. From 2002 to 2008
admissions have been continuously decreasing. In 2009 after the introduction of the multi-
currency system the cases of outpatient and inpatient admissions increased showing a better
healthcare utilisation compared to 2008. Healthcare utilisation continued to get better in 2010
though it started to decrease in 2011 and continued in the decreasing trend in 2012 and 2013.
Pulse magazine (2014) asserted that this was due to medical tourism. Lower cases of
outpatient and inpatient admissions to a larger extent reflected inadequate provision of
healthcare services and barriers to accessibility of health care services.
1 2 3 4 5 6 7 8 9 10 11 12 130
2000000
4000000
6000000
8000000
10000000
12000000
14000000
years (2001 - 2013)
num
ber o
f cas
es
Fig 1.1: Zimbabwe Outpatient (OPD) attendance and Inpatients admissions, 2001-2013
Source: Ministry of Health and Child Welfare (2014) - National Health Statistics
With an unemployment rate (after subtracting both formal and informal employment) of
10.7% in Zimbabwe (ICLS 2014) private health care services consultation fees were
increased and this happened without a corresponding increase in people’s income. According
to Mathuthu (2014), in May 2014 health services became very expensive as some
consultation fees were increased by 100%. This was because they wanted to make a
significance difference in public and private sector as private health care is meant for few
people and it should not be crowded. General practitioners can now charge $30 for
consultations, up from the previous $15, while physicians and paediatricians can charge $70.
At public hospitals, patients only pay $10 for a consultation as shown in Table 1.1. Although
a global report by UNAIDS noted that Zimbabwe managed to reduce the number of new HIV
infections by 34 percent between 2005 and 2013, the country still accounted for 3 percent of
3
new HIV infections worldwide which is still high. Below is a table that summarise changes
that were done in consultation fees.
Table 1.1: Changes in Consultation Fee 2014
BEFORE AFTER
Item Before May
2014
After May 2014
General practitioner consultation $15 $30
Consultation at a hospital $20 $40
Weekend and night doctors
consultation
$60 $70
General practioners room $20 $35
Source: @AllAfrica.com
Good health is a necessity and an increase in consultation fees made health services less
affordable. These changes made Zimbabwe medical care cost the highest in the region. It
matches that of Australia and France which are developed countries. Zimbabwe is a
developing country and its medical cost is as that of developed countries level. This was
another factor that has been causing medical tourism.
Table 1.2: Average consultation fees (US $) by facility level, 2010 - 2014
Facility Level Consultation Fee
Central Hospital $10
Provincial Hospital $6
District hospital $4
Mission hospital $3
Rural health centre/ clinic $1
Source: Ministry of Health and Child Welfare (2014)
The Cabinet allocated the health sector US$330 million (down from $407 million in 2013),
which amounted to 8 percent of the 2014 budget, while public hospitals were given $25
4
million for operations even though, by January 2014, they owed various suppliers $33 million
(Mathuthu, 2014). Mathuthu, (2014) went on to say that as more people have been pushed
into joblessness or working in the informal economy, the country’s tax base has dwindled and
government is struggling to collect sufficient revenue to pay for public programmes and civil
servants’ salaries.
According to Mathuthu (2014) the Ministry was struggling to raise funds to pay health
workers. Services of health workers were crucial and the country is critical need their
services. According to Mathuthu (2014), essential drugs were in short supply at public
hospitals and clinics in urban and rural areas. Also the public health centres were operating
below 40 percent of their capacity due to government failure to buy drugs and fund other
operations. Rural facilities were slightly better but lacked 50 percent of needed medicines.
Public hospitals were said not to have fully recovered from the economic crisis that
Zimbabwe experienced before 2009, there is still a critical shortage of pharmacists, nurses
and doctors (Mathuthu, 2014).
Since 2000, Zimbabwe has been experiencing political unrest and economic downturn which
resulted in increase in emigration by the majority of human capital to neighbouring countries
and overseas in search of greener/better fortunes. According to Sunday Mail (2010)
Zimbabwe has lost more than one million of its skilled workers who occupied a wide range of
jobs in education and health sectors to overseas and nearby countries. Brain drain levels
remained high even after the development of the inclusive government in 2009 as evidenced
by United States AID and United Nations, which were reported to be on a massive drive to
recruit lecturers, teachers, doctors, engineers and nurses among other professionals from
Zimbabwe to Lesotho and other countries in the region to resuscitate their economies
(Sunday Mail, 2010).
According to Mambo (2014), doctors in public health facilities were on strike demanding
more money yet the country is facing a liquidity crunch and the government has been
struggling to pay civil servants. The strike went on for about seventeen days (Mathuthu,
2014). Most services that require doctors attention were on hold and patients had to look for
other services else were like private doctors or seek medical attention outside the country
5
1.2 Problem Statement
Medical services should be easily accessible in cases where one needs medical attention.
Zimbabwe public health care services are facing challenges of underfunding, medical
tourism, doctors striking and brain drain. One of Zimbabwe’s national health development
objectives is to achieve high accessibility and utilisation of health care services targeting the
poor and vulnerable segments of the population (Ministry of Finance, 2010). The Millennium
Development Goals (MDGs) have also identified increasing utilisation of health care services
as one of the ways to eradicate poverty (Mugilwa, 2005). This is because good health status is
part of people’s well-being and an important component of economic and social advancement
(Kaija and Okwi, 2004). The Zimbabwean health sector has been experiencing a consistent
decline in outpatient and inpatient attendances and therefore this research seeks to find out
which particular factors affect household’s willingness to pay for healthcare utilisation
considering the problems that the health sector has been facing.
1.3 Objectives
The general objective of the study is to reveal the factors affecting willingness to pay for
healthcare utilisation in Zimbabwe using household data from Gweru urban in Midlands
region.
The specific objectives of the study are:
I. To explore factors that affect willingness to pay for public healthcare utilisation by
households.
II. To assess the extent to which these factors shape households willingness to pay for
public healthcare utilisation.
1.4 Significance of the Study
Many studies that concern healthcare use have been done in Gweru. Most of these studies
focused on demand for healthcare and factors that affect demand. Mwaiyana (2011) did a
study on determinants of healthcare choice providers in Zimbabwe case of Gweru. His study
6
did not look at the value that people place on healthcare use and therefore this current study
aims to evaluate households’ value for healthcare use. This current research therefore seeks to
provide an insight on factors that affect households’ willingness to pay for healthcare
utilisation regardless of the healthcare choice provider in Gweru Urban. Muzundar and
Guruswany (2009) did a study on demand and willingness to pay for healthcare utilisation for
public healthcare services in India. They focused on a public healthcare use only. This
current research will focus on a community that has a variety of options of healthcare
services providers. Also this research seeks to find out if results of the studies done earlier in
India by Muzundar and Guruswany (2009) are consistent in a different geographical area.
The study is significant to the regulatory authorities, households and academia. To the
regulatory authorities, vital information may be generated to be used in policy planning and
implementation. In academia this study will generate vital information and also be used in
future studies.
1.5 Research Hypothesis
Willingness to pay for health care utilisation is not dependent on age, gender, educational
level, household income, severity and proximity.
1.6 Organisation of the Study
The next chapters are as follows: Chapter Two offers theoretical and empirical literature
review whereas Chapter Three shows the methodology used in this study. Chapter Four will
show results of the study and interpretations. The last chapter (Chapter Five) will give the
recommendations and the conclusion.
7
CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
This chapter is going to give details on the literature that exist on healthcare utilisation. This
will be divided into two different categories which are theoretical and empirical review. The
theoretical literature review will elaborate on what theory says about this research whilst the
empirical will focus on studies done by others which relate to this research. There are various
theories that explain healthcare utilisation and these include Andersen’s behavioural,
Parson’s sick role, Mechanic’s general theory of help seeking and Suchman’s stages of illness
and medical care.
2.1 Theoretical Literature
The first theory that explains health care utilisation is that of Anderson which was first
developed in 1968. The model is called Andersen’s behavioural model of healthcare demand.
The model gives an overview of relevant social determinants for seeking healthcare services.
The theoretical framework describes the process of healthcare utilisation as a casual
interaction of three different levels which are societal, healthcare system (programme factors)
and individual determinants. The societal determinants include the current state of knowledge
as well as people’s attitude and beliefs about health and illness. According to Andersen
(1968), the healthcare system in turn allocates available resources to healthcare institutions
and forms the organisational framework to provide healthcare services. The model asserts
that there are particular factors that predispose people towards healthcare service utilisation.
These factors influence an individual’s level of willingness to pay for healthcare utilisation.
For example, the basic demographic characteristics such as age, sex and past illness may have
an influence on the individual’s or household’s willingness to pay for healthcare services.
The social structure factors such as education, household size, occupation and race are also
important predisposing factors. More so, beliefs, values and knowledge about health and
medical care services can affect a decision to seek healthcare services and amount one is
willing to pay for health services. One of the weaknesses of Andersen’s model is that it does
not directly consider distance as a factor which affects healthcare service use (Rajaram et al,
1999).
8
Another theory of healthcare utilisation is the sick role as propounded by Parson in 1951.
According to this theory, when an individual is sick they adopt the role of being ill. This sick
role can be best explained in four main categories which are 1) the individual is not
responsible for their state of illness and is not expected to be able to heal without assistance;
2) the individual is excused from performing normal roles and tasks; 3) there is general
recognition that being sick is an undesirable state; and 4) to facilitate recovery, the individual
is expected to seek for medical assistance and to comply with medical treatment. Parson’s
theory assumes that households are more willing to pay for health care utilisation when the
sickness is severe. Parson’s sick role postulates a positive correlation between willingness to
pay and severity of the disease. Parson’s theory ignores other factors that affect household’s
willingness to pay for health care utilisation. Parsons’ theory attempted to identify typically
seen behaviour in individuals who are ill. However, the sick role failed to account for
variability in illness behaviour.
Mechanic (1978) came up with a theory that better explained healthcare utilisation which was
termed general theory of help seeking. The theory has ten decision points which determine
illness behaviour and this behaviour influences amount one is willing to pay for healthcare
utilisation. These decisions points are; 1) the salience of deviant signs and symptoms; 2) the
individual’s perception of symptom severity. Decision points one and two suggest that
severity of the illness determines individuals or household’s willingness to pay for healthcare
utilisation. 3) The disruption of the individual’s daily life as caused by the illness.4)
Frequency of symptoms and their persistence. 5) The individual’s tolerance of symptoms.
The way females and males respond to illness is different; males tolerate diseases more
compared to females. Man are stereotyped to be strong by the society and therefore most men
are likely less willing to pay for healthcare utilisation compared to females. 6) The
individual’s knowledge and cultural assumptions of the illness. 7) Denial of illness as a result
of basic needs; 8) whether or not response to the illness disrupts needs; 9) alternative
interpretations of symptom expression; and 10) treatment availability via location, economic
cost, psychological cost (stigma and humility), and treatment resources. The last point bases
on proximity to the healthcare facility. It assumes that those that stay close to a medical
centre they are more willing to pay for healthcare services. Beyond these ten points,
Mechanic’s theory allowed for illness response to be influenced by either the individual or a
person who makes decisions for the individual.
9
Suchsman (1980) summarized the healthcare utilisation in five stages. He stated that stages of
illness and medical care indicates five stages of the individual’s decision process in
determining whether or not to utilize health care: 1) the individual’s symptom experience,
including pain, emotion and recognition of experience as symptomatic of illness. As an
individual gets familiar with certain symptoms they are like to be willing to pay more if the
symptom has resulted in severe sickness in the periods before. 2) The individual’s assumption
of a sick role. During this second stage, the individual also explores his or her lay referral
system for validation of the sick role and for exploration of treatment options. This is
generally affected by age, income and educational level. Suchman’s theory third stage is
medical care contact. During this stage the individual seeks a professional health care system.
The major factor here is distance to the nearest medical centre. People that stay close to the
medical centre are likely more willing to pay for health care utilisation. 4) The assumption of
a dependent-patient role via acceptance of professional health care treatment. It is possible for
this stage to be disrupted if the individual and the professional health care provider have
differing opinions of the illness. 5) The individual’s recovery from illness. The individual
recovers upon relinquishing their role as patient. However, if an illness is not curable, a
person may assume a chronically ill role.
Based on the healthcare utilisation theories age, income, proximity, gender, severity and
educational level are the factors that affect willingness to pay.
The Contingence Valuation Method (CVM) is an established research approach which uses
survey techniques to elicit consumers’ valuation of non-market goods including health
(Russel et al 1995). The health benefit has been widely evaluated based on the concept of
willingness to pay since it is consistent with the principle of welfare economic theory and
cost benefit analysis. According to the theory of welfare economics the benefit to an
individual of a service or an intervention is defined as the maximum amount that the
individual is willing to pay for such a service or intervention. Among the various methods
used for measuring willingness to pay, the contingence valuation method is a flexible
approach providing a conceptually correct and complete measure of willingness to pay since
individuals state how much they value the product in question. This is the reason why the
study adopts this approach. This method can be applied through interviews or asking people
the maximum they are willing to pay through distribution of questionnaire. In this current
10
research the researcher will use an open ended questionnaire where one would answer the
question of the maximum amount they are willing to pay.
2.2 Empirical Literature
Bukola (2013) did a study on willingness to pay for community based healthcare utilisation
among rural and urban households in Osum state in Nigeria. A cross-sectional comparative
study was done among 450 urban and equal numbers of rural households in Osum State using
multistage sampling. After running the data in a logistic regression the findings of this
research showed that rural households 373 (82.8%) were more willing to pay for community
based health care usage than the urban households 232 (51.6%). Major factors identified that
contributed to willingness to pay in the households are level of education, income, distance to
the health centre, marital status, age and male gender. Women, the poor and the people with
low level of education were less willing to pay.
Huang et al (2011) did a study on willingness to pay for obesity prevention in Taiwan. This
study estimated consumers’ willingness to pay (WTP) and investigate factors that affect
participation in therapy to reduce weight or prevent obesity. A population of 5690 as targeted
and a sample of 578 was selected. A simple ordinary least squares regression analysis was
used to run the data obtained after distributing questionnaires in a proportional stratified
random sampling. The amount one is willing to pay to prevent obesity was dependent
variable. The results showed that the price charged for therapy was a key factor. Furthermore
education, income, gender and health conditions were found to be important and significant
determinants of level of willingness to pay for obesity prevention. The results of the profile
analysis suggest that obese females with high education, high income, who think that obesity
affects work achievement and who have tried to control their weight are the most likely to be
willing to pay the greatest amount for weight reduction therapy.
In Bangladesh, Howlader et al., (2000) analysed health care demand using the willingness to
pay approach. The use of the willingness to pay approach was motivated by the fact that there
was no market for the majority of health care services in Bangladesh. One of the specific
objectives of the study was to derive demand for health care function for three health care
types namely child immunisation, curative care for children and women’s health care in the
11
context of rural Bangladesh. The study utilised primary data which was collected from a total
sample of 2210 households using a structured household questionnaire. Sampling was carried
out using cluster sampling. The study used a dichotomous dependent variable for measuring
demand for health care where willingness to pay for health care took value of one (1) whereas
zero (0) represented not willing to pay for health care. Among the explanatory variables
which were used included household income, duration of illness as a proxy for severity of
disease and education level of respondents. A logit model was employed to examine the
statistical significance of the factors. Household income and education of the household head
were statistically significant at 1% and 5%, respectively.
Linnosmaa and Rissanen (2006) did a related study on willingness to pay for online physician
services. Open ended questions were used to establish consumers’ willingness to pay.
Ordinary least squares regression was used and willingness to pay the dependent variable
represented by the amount one is willing to pay for online physician services. The results
obtained were that income, distance to the nearest physician and general interest in
information technology significantly explains willingness to pay for online physician
services. Healthier and younger individuals were also willing to pay more than less healthy
and older individuals, but these results were not statistically significant.
Frick et al (2000) carried out a study on household willingness to pay for azithromycin
treatment for trachoma control in the United Republic of Tanzania. 394 households in six
villages located in Tanzania were used for the survey. A random sample was used and it was
then followed by interviews. Data was gathered on risk factors for trachoma and ordered
probit regression analysis was used to test for statistically significant relationships. Findings
were that 38% of the responded households were not willing to pay anything for
azithromycin treatment although they were willing to participate in the treatment. A proxy for
cash availability was positively associated with household willingness to pay for future
treatment.
Measure of willingness to prepay was conducted using bidding game techniques on 471 rural
households in the centre region of Cameroon by Poinam et al (2000). Questionairres were
used and an ordered probit model was used. Results of the ordered probit model of
willingness to prepay suggested that some attributes such as the level of revenue, the gender,
the habit of frequenting the health service centre, the associative experience, the household
health status, the availability of the basic drugs at the health service centre and the regular or
12
periodical attendance of the physician at the health service centre due to the health service
centres had a significant impact on the willingness to prepay value.
Muzundar and Guruswamy (2009) did a study on demand and willingness to pay for health
care utilisation in rural west Bengal of India. The researcher used simple ordinary least
squares regression analysis. Multi stage sampling was used by the researcher and contingence
valuation method to determine one’s willingness to pay for public healthcare service. An
OLS regression was done with age, education, occupation and economic level as some of its
independent variables. The maximum amount which the respondents were willing to pay was
the dependent variable. Willingness to pay varied considerably depending on the economic
status of households. Severity had a positive relationship with willingness to pay for
healthcare use whilst education, occupation and healthcare provider had a negative sign with
willingness to pay for healthcare use. The results indicated that education was insignificant
whilst age, household income and healthcare provider were significant
2.3 Conclusion
From empirical literature discussed, there is evidence to suggest that willingness to pay for
healthcare utilisation, especially in urban contexts is influenced by socio demographic,
economic and institutional factors. These factors include age, gender, education, household
income, severity of illness, proximity to the nearest medical centre distance among others.
13
CHAPTER THREE
METHODOLOGY
3.0 Introduction
This chapter explains the methodology to be used in the research. It will specify the sources
and data type to be collected that is secondary or primary data which will be used to regress
the model. Willingness to pay for healthcare utilization is affected by age, gender, household
income, education level, proximity to the medical center and severity. An analysis of the
methods used to establish the relationship between the above mentioned variables will be
made. Variable justification will also be done in this chapter.
3.1 Model Specification
The researcher will borrow the model from Mazumdar and Guruswamy (2009). A simple
(OLS) ordinary least squares regression was also used. Their willingness to pay function was
as follows:
WTP =f (age squared, severity, education, occupation, provider of health care service)
The researcher will modify the above model to
WTP= f (age, age squared, gender, household income, educational level, proximity to the
medical centre, severity).
WTP=α 0+α1 A+α 2 A2+α3 G+α 4 HHY +α5 EDUC+α 6 PRO+αS+E
WHERE:
WTP– amount that the responded is willing to pay for healthcare utilisation. It is measured
by the amount one is willing to pay for health access at the healthcare centre they usually
visit.
A – Age of the respondent.
A2 – the squared value of the respondent’s age.
G – Gender of the respondent measured by the respondent’s sex (female = 1 and male = 0).
HHY - Household income measured by the amount the household earns by end of each
month.
14
EDUC- Educational level measured by the cumulative number of years spent at school.
PRO – proximity to the nearest medical centre measured by the estimated distance from
one’s home to the nearest medical centre.
S – Severity of the illness measured by when the individual seeks medical attention
(Immediately they fall sick = 0 and when severe = 1)
3.2 Variable Justification
3.2.1 Age of respondent (A) and Age of respondent squared (A2)
This is a continuous variable which captures the age of the respondent in years. Mechanic
(1978) states that as one grows to old age they demand more healthcare as their immune
system deteriorates. This therefore implies a negative relationship between willingness to pay
for healthcare use and the economically active age group. The study expects higher
willingness to pay for utilisation of health care services in households and individuals who
are old. The study uses age squared to capture the effects of old age of an individual’s
willingness to pay for health care utilisation.
3.2.2 Gender of respondent (G)
The gender of responded is defined as a dummy variable. A value of zero (0) and one (1) will
be assigned to a male and female respectively. Grossman (1972) viewed females to be the
ones that consume health services more compared to man. The argument is that females by
nature are risk averse and they prefer to be attended to by health professionals during illness.
However, the influence of gender on seeking health care has remained inconclusive in the
empirical literature. Therefore, the variable has a positive sign.
3.2.3 Household income (HHY)
The level of household income reflects the economic status of the household. Several studies
have found that household income increases the likelihood of seeking treatment from health
care facilities (Lawson 2004, Fredrickx, 1998). When income increases healthcare services
will become easy to access and households will be ready to pay any amount to access
15
healthcare services. Therefore, the study expects the probability of seeking health care
services by an individual to increase with higher household income. The monthly income
earned will be used as a proxy for household income. The variable will be continuous. Both
the theoretical and empirical evidence have confirmed a positive relationship between income
and willingness to pay for healthcare utilisation. A positive sign is expected in this case.
3.2.4 Education level attained (EDUC)
In the study, education is a continuous variable that is measured by the number of years one
spent at school. Education increases efficiency in health production and thus reduces the price
of health investment and returns on health are likely to be higher for the more educated
(Grossman, 1972). Education of an individual may affect the recognition of symptoms and
link them with presence of a disease. This will affect the perception of illness, its degree of
severity and consequently the probability of visiting a health care provider (Hjortsberg,
2003). Some empirics discussed above showed education is inversely related to willingness
to pay for healthcare use. However, a priori expectation is that the more years the individual
spent at school, the higher the probability of seeking health care services by an individual.
Hence, the study expects a positive or negative sign on education.
3.2.5 Distance/ proximity to the nearest health facility (PRO)
Distance to the nearest health facility will be measured in kilometres and the variable will be
continuous. Distance to the nearest facility measures physical accessibility of health services
in the community. Acton (1975) stated that the longer the distance the higher the cost. A
longer distance implies more cost and this means that individuals will be reluctant to seek
healthcare services. Muhofa (2010) asserts that willingness to pay for healthcare utilisation
tends to decline with distance. Thus, from evidence, the study expects an inverse relationship
between willingness to pay for healthcare use and distance to the nearest health care facility.
There is a higher probability that an individual will seek healthcare services during illness if
the household is close to a health care facility, therefore the researcher expects a negative
sign on proximity.
16
3.2.6 Severity of illness (S)
According to Andersen’s behavioural model, a perception about the severity of illness is an
important factor that influences willingness to pay for healthcare utilisation. In the empirics
by Muzundar and Guruswamy (2009) severity has a positive correlation with willingness to
pay for healthcare use. The study expects a positive relationship between severity of illness
and willingness to pay for healthcare utilisation. This variable will be used as a dummy. A
value of one will be assigned when the individual seeks medical attention when bedridden
and zero (0) when he or she is not bedridden. Severity is expected to have a positive sign
based on the theoretical and empirical literature review above.
3.3 Diagnostic Tests
Diagnostic tests to be carried in this research will be based on regression model run using an
economic statistical package called STATA 11. The purpose of these tests is to check for the
validity of the parameters obtained. The researcher will test for econometric problems namely
multicolleniarity and heteroscedasticity. The researcher may also perform maximum
likelihood tests. These tests are carried out such that data obtained might be in harmony with
a priori conditions and hence do not therefore violate economic theories and laws.
3.3.1 Multicollinearity
This problem arises when two or more variables or combinations of variables are highly but
not completely correlated with each other. The term came up from the findings of Frisch
(1993).According to Blanchard (1987), if multicollinearity is not serious we adopt the do
nothing school of thought. One of the penalty of multicollinearity is that it makes it difficult
to obtain coefficient estimates with small standard error. This is going to be measured by
generating a correlation matrix showing the relationship existing between independent
variables.
H0: There is no perfect linear relationship among regressors
H1: There is a perfect linear relationship among regressors
17
3.3.2 Heteroskedasticity
According to Gujarati (2004), this is a situation whereby the error variances are not constant
thus violating the assumption of constant variance (homoskedasticity). One major reason why
this problem has to be tested is to avoid misleading predictions and inferences. The
researcher is going to adopt the Breusch-Pagan/Cook Weisberg test for the purposes of
testing for this econometric problem. This can be corrected by application of weighted least
squares method in which OLS is applied to transform values of dependent and independent
variables.
H0: The model is homoscedastic
3. 4 Data Types and Sources
The study relies on primary data. The data shall be gathered by way of questionnaires
administered on the sample. The area covered by the research is Gweru urban, in the
Midlands province of Zimbabwe. The 2012 census established that Gweru has 12642
households and each household is assumed to have an average of 3 people. The sample size
was arrived at using the Krejcie and Morgan Table for Determining Sample Size, which
assumes a standard error of 0.05 (Krejcie and Morgan 1970). Two strata (high density and
low density) were selected and systematic random sampling technique was be used for this
research. This sampling method is ideal for ensuring that most sections of the population are
represented. It takes representative households from each stratum in the population. The
population was divided into two strata which are high density and low density. From the two
strata chosen the researcher randomly picked a ward in each stratum and questioners were
distributed to these randomly picked wards in a systematic way. The first house was chosen
randomly in each ward. Sample of 381 was chosen basing on Krejcie and Morgan Table for
Determining Sample Size. High density population constitutes 68% of the sample size whilst
the remaining was for low density. The questionnaires were given to every 15 th house in high
density and to every 8th household in the low density.
18
3.5 Conclusion
This chapter presented the research methodology which will be used to examine empirically
the willingness to pay for healthcare utilisation. The estimation and presentation of results
will appear in the next chapter.
19
CHAPTER FOUR
RESULTS INTERPRETATION
4.0 Introduction
The results of this study are presented in this chapter and examined. The researcher will
basically analyze the results to compare with a priori theory and empirical results discussed
in Chapter 2. The tables showing results are derived using STATA 11 commands and all the
essential parameters for interpretation are provided. In this chapter the researcher also
summarized the results of the tests of multicollinearity and heteroskedasticity and model
specification tests. The response rate of this research was 80.7% which is 310 out of 384.
4.1 Diagnostic Tests – Results
4.1.1 Multicollinearity
According to Gujarati (2004), multicollinearity is a situation where there exists a linear
relationship amongst explanatory variables. This is measured by generating a correlation
matrix which shows the correlations existing between exogenous variables in the model as
shown below:
Table 4.1: Correlation Matrix. (Appendix C)
VARIABLES A A2 G HHY EDUC PRO SA 1A2 0.9198 1G -0.212 -0.2616 1HHY 0.1367 0.1315 -0.0157 1EDUC 0.1814 0.151 -0.1267 0.4088 1PRO 0.0239 0.0247 -0.0362 -0.2138 -0.2495 1S -0.0675 -0.0987 -0.045 -0.2473 -0.1189 0.3228 1
20
As a rule of thumb, if the cross correlation is less than 0.8, we do not reject the null
hypothesis of no perfect linear relationship among regressors against the alternative
hypothesis that there is a perfect linear relationship among regressors. The results from the
above matrix show the existence of pair-wise correlation which exceeds 0.8 only between
AGE and its squared value (0.9198). This is expected since the values are perfectly related
with age2 simply being the square of the given age values. The variable age2 was included in
the model to account for the non-linearity behavior of the age with respect to willingness to
pay for public healthcare utilisation. As age increases, individuals are expected to have higher
willingness to pay for public healthcare utilisation but only up to a certain age over and above
which individuals would consider any form of sickness to be due to chronicity and hence
willingness to pay will start declining and also in Zimbabwe anyone above 65years of age do
not pay to access medical attention at public health facilities. However, besides that one case,
the results show no other correlation between the regressors, therefore, all variables can be
incorporated in the regression equation. Not accounting for the correlation between age and
age2, the results show no correlation between the regressors, therefore, all variables can be
incorporated in the regression equation. The least pair wise correlation (-0.2616) is between
age squared (Age2) and gender (G) while the highest pair wise correlation (0.4088) is
between number of years spent at school (EDUC) and total household income (HHY).
4.1.2 Heteroskedasticity
The researcher adopted the Breusch-Pagan / Cook-Weisberg test to test whether the variables
are homoskedastic or not. Homoskedasticity is a case where the regressors have constant
variances and if not, this results in the violation of one of the Classical Linear Regression
Model assumptions of homoskedasticity. The test is conducted on the null hypothesis of
constant variance across the regressors against an alternative hypothesis that there is no
constant variance across the regressors. The table below shows the results obtained for the
test:
Table 4.2: Breusch-Pagan / Cook-Weisberg test for heteroscedasticity (Appendix D)
chi2(7) 248.84 Prob > chi2 0.0000
21
The test showed the presence of heteroscedasticity. The decision rule is that if the prob-value
is preferably 0.05 or smaller, then the null hypothesis is rejected and there is significant
evidence to conclude that heteroscedasticity exists. As evidenced above, since the prob-value
(0.0000) is less than 0.05 we conclude that the regressors suffer from heteroscedasticity,
therefore, we reject the null hypothesis. In order to rectify this, the researcher decided to run
the regression model with robust standard errors, which in essence corrects the problem of
heteroscedasticity in a given model.
4.1.3 Descriptive Statistics
Table 4.3: Average, Min and Max (AppendixD)
Variable Obs Mean Std. Dev. Min MaxWTP 310 7.887097 5.427083 0 50
G 310 0.545161 0.498761 0 1A 310 38.55806 8.577923 20 70
EDUC 310 16.76129 4.028352 5 27HHY 310 759.0323 710.8394 20 7000PRO 310 4.287419 3.525075 0.1 20
S 310 0.6 0.49069 0 1A2 310 1578.394 695.4989 400 4900
The minimum amount that people in Gweru are willing to pay to access public healthcare is
nothing (zero dollars). Some people want free access to public healthcare. On average people
in Gweru urban are willing to pay US$7.89 which be rounded off to US$9.00. This amount is
below the maximum that people are currently paying to access public healthcare which is
US$10.00. In terms of value system people place low value on public healthcare system.
There are a few that were willing to pay a maximum of US$50.00 to access public healthcare.
The other means, min and max are as shown in the Table 4.3.
4.2 Regression Results
After running the OLS regression model between willingness to pay for public healthcare
utilisation and its exogenous variables, the following results were obtained:
22
Table 4.4:Summary of the OLS regression results (Appendix E)
RobustWTP Coef. Std. Err. t P>t [95% Conf. Interval]
AGE -0.07473 0.041458 -1.8 0.072 -0.15631 0.006857AGE2 0.001141 0.000538 2.12 0.035 0.000082 0.002201
G -0.11479 0.434685 -0.26 0.792 -0.97019 0.740601HHY 0.005513 0.000927 5.95 0 0.00369 0.007336
EDUC -0.03066 0.070033 -0.44 0.662 -0.16848 0.10715PRO -0.34702 0.050786 -6.83 0 -0.44696 -0.24708
S 1.404888 0.521684 2.69 0.007 0.378292 2.431484_cons 6.003866 1.470992 4.08 0 3.109175 8.898558
Number of obs =310
F (7. 302) =22.56
Prob > F =0.0000
R-Squared =0.5858
Adj-R Squared =0.5762
Root MSE =3.5329
Using the results obtained from running the regression and the specified model from Chapter
Three, the following model was found significant
WTP=6.003866−0.07473 AGE+0.0 01141 AGE SQUARED−0.11479G+0.005513 HHY−0.03066 EDUC−0.34702 PRO+1.404888 S
4.3 Interpretation of Results
The interpretations of the results that relate to the individual coefficients of variables used in
this model were analysed at 5% significance. The researcher compared the outcome signs
against the expected signs basing the outcome on theory and empirics as discussed earlier in
Chapter 2. The interpretations are given as follows:
23
4.3.1 Age (A)
The first variable represents age of the respondent. The variable was measured as a
continuous variable. The researcher expected a negative sign denoting an inverse relationship
between the age of the respondent and willingness to pay for public healthcare utilisation of
individuals. The variable was not significant at 5% significant level and had a sign as
expected. A unit change in age will result in a 7% change in willingness to pay. Since it has a
negative sign a unit increase in age will result in a 7% change decrease in the amount one is
willing to pay for public utilisation.
4.3.2 Age2 (A2)
AGE2 refers to the squared value of the respondent’s age and was incorporated into the model
in order to capture for non-linearity of the behavior of the age variable as people tend to be
getting free access to public healthcare as they grow old. As they grow old people tend to be
reluctant to their health status and therefore have a low willingness to pay for public
healthcare utilisation. The variable was significant at 5%. This implies that the variable is
important in affecting willingness to pay for public healthcare use.
4.3.3 Gender (G)
The other variable is gender. This variable was measured as a dummy variable with one (1)
representing female and zero (0) for male. It had a negative sign which was contradictory to
Grossman’s 1972 view whilst it concurred with the findings of Huang et al (2011). This
variable was found to be insignificant at 5% significant level. The change in gender from
female to male will decrease willingness to pay for public healthcare utilisation by 11.5%.
This implies that females have a high willingness to pay for public healthcare compared to
men.
4.3.4 Household Income (HHY)
Household income (HHY) was found to have a positive sign implying a positive relationship
between household income and willingness to pay for public healthcare utilisation as per
expectations of the researcher. This variable was found to be significant at 5% level. Income
is important in determining willingness to pay for public healthcare utilisation. A unit
24
increase in household income will result in a 0.5% change in willingness to pay for public
healthcare utilisation.
4.3.5 Education (EDUC)
Education (EDUC) was found to be insignificant and had a negative sign which implies a
negative relationship with individual’s willingness to pay for public healthcare utilisation.
This concurred with a research by Muzundar and Guruswamy (2009).The negative sign may
have been due to the fact that in Zimbabwe as individuals learn more they adapt methods of
self-treatment and they prepare their bodies to be immune against diseases through health
eating habits, exercising and others rely on medical aids. This in turn results in educated
people having low willingness to pay for public healthcare utilisation. A unit increase in
number of years spent at school results in a 3% decrease in the amount one is willing to pay
for public healthcare utilisation.
4.3.6 Proximity to the nearest public healthcare (PRO)
Proximity to the nearest public healthcare had a negative sign which is a prior to expectations
of the researcher. The negative sign implied that people that stay far from medical centres are
willing to pay less for public healthcare utilisation. This variable was found to be significant
at 5% level of significance. A unit increase in distance will cause a 34.7% decrease in the
amount one is willing to pay in order to access public healthcare. This then implies that
people that stay close to medical centres have very high willingness to pay for use of public
healthcare services.
4.3.7 Severity of illness (S)
Severity was significant at 5% significant level and had positive sign which is as per
expectations of the researcher. This sign is in line with that of Muzundar and Guruswamy
(2009). Willingness to pay has a positive relationship with severity implying that individuals
are willing to pay more when bedridden. Severity of illness is relevant in determining
willingness to pay for public healthcare utilisation. As illness moves towards severity
willingness to pay for public healthcare increases by 140%. Severity greatly affects amount
one is willing to pay to access public healthcare.
25
4.4 Conclusion
The researcher found 4 of the 7 explanatory variables to be statistically significant. These
were age squared, household income, proximity to the nearest public healthcare facility and
severity of the illness when one seeks medical attention. This implies that these significant
variables have higher chances of influencing the amount households are willing to pay in
order to utilise public healthcare. The insignificant variables, gender and educational level,
should, however, not be disregarded in making inferences as this is somewhat relevant.
The next and final chapter will outline the researcher’s summary of the research, conclusion
and policy recommendations which are based on the findings presented in this chapter.
26
CHAPTER 5
CONCLUSION AND POLICY RECOMMENDATIONS
5.0 Introduction
This chapter gives the conclusions drawn from the research as well as policy
recommendations on the analysis of gender, educational level, household income, distance to
the nearest public medical center and severity of illness in influencing willingness to pay for
public healthcare utilisation in Gweru Urban. The chapter first gives highlights of major
findings, then conclusions that address the research objectives and finally the
recommendations on what can be done to improve willingness to pay for public healthcare
utilisation.
5.1 Summary
The main purpose of this study was to reveal factors affecting willingness to pay for publicly
provided healthcare utilisation in Zimbabwe. The study was carried out in different suburbs
in Gweru Urban. An OLS regression technique was used with the Stata 11 software package
since the dependent variable was continuous. The explanatory variables used were individual
characteristics, socioeconomic and household characteristics. The variables used in this study
were namely gender, level of education, household income, proximity and severity of illness.
A sample size of 384 respondents was chosen for the study of which the response rate for the
study was 80.7%. From the regression results, age squared, household income, distance to
the nearest public healthcare facility and severity of illness are significant factors which
influence willingness to pay for public healthcare utilisation in Gweru Urban and these
results conform to economic theories. However, gender of respondent and educational level
or years spent at school had a negative sign and had an insignificant impact on willingness to
pay for public healthcare utilisation.
27
5.2 Policy Recommendations
The investigation made in this study is important for policy purposes to improve the
economic advancement of the country. There are several policy insights that can be derived
from the empirical findings of this study. From the results of the study, distance is one of the
factors that decrease the willingness to pay for public health care services. Therefore, the
policy implication of this finding is that policies that aim to reduce distance to the nearest
health care facility are likely to increase the probability of seeking public health care services.
In light of the above, policy makers should implement policy interventions that aim to
shorten the distance which people travel to access public health care services. Such
interventions include increasing the number of health care facilities. This can be done by
introducing community based mobile clinics and increasing the number of public healthcare
facilities.
From the study it shown that number of years spent at school has a significant effect on
household’s willingness to pay for public healthcare utilisation. Policies aimed at increasing
knowledge/education may help in reducing a number of cases were healthcare attention is
sought for at bedridden stage. People can be taught skills of disease management so as to
enable them to avoid certain illnesses becoming severe. Government can also provide
incentives to those that practice home bases disease management programs so as to
encourage others to practice health living.
Household income determines one’s ability to pay for a given service. An increase in income
will mean that healthcare services will be more affordable, thus imply that government may
need to subsidies healthcare services to those living below the poverty datum line so that they
can access healthcare services whenever there is a need.
5.3 Areas for Further Research
The study needs a larger sample selection maybe encompassing the whole province of
midlands or other geographical areas and also time to come up with appropriate results. Due
to the time constraint the researcher faced while carrying out this study and the inadequate
resources, the researcher also suggests the use of interviews in data collection to be
encompassed in the methodology so as to get more unambiguous responses on some sensitive
28
questions which some respondents may feel insecure about answering, for example education
or age as women tend to be reluctant to provide such information.
5. 4 Limitations of the Study
Due to time and financial constraints, the study only focused on Gweru, used 310 households
to draw conclusions on the factors which influence willingness to pay for public healthcare
utilisation. These results cannot be generalized for Zimbabwe since Gweru is not
representative of the Zimbabwean nation.
5.5 Conclusion
The study analyzed willingness to pay for public healthcare utilisation using a model adapted
from Muzundar and Guruswamy (2009). The research looked at healthcare utilisation
international perspectives looking at theories developed on the subject together with tests
done in many different countries. The results showed that household income, proximity and
severity of illness are significant variables and increase willingness to pay for public
healthcare utilisation. Gender and educational level were found to be insignificant. It can be,
however argued that this research conforms to earlier researches done elsewhere in the world.
The policies and suggestions given in this paper are believed to improve the people’s
perception of public healthcare service so as to encourage local utilisation of healthcare
services and avoid cases of medical tourism, if implemented well.
29
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in Determinants of Health Care Services in Rural Zimbabwe 2013.
ZIMSTAT, (2012) Census Preliminary report. Zimbabwe National Statistics Agency,
Harare,Zimbabwe
Zimbabwe Health System Assessment (2013), Ministry of Health and Child welfare.
Available at www.health systems 2020.org (accessed 14-10-2014).
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APPENDICES
APPENDIX A: QUESTIONNAIRE
My name is Mitchell Name and I am a fourth year student studying an Honours Degree in Economics with Midlands State University. I am carrying out a research on the factors affecting willingness to pay for local public healthcare utilisation in Gweru Urban and this questionnaire is meant to assist with the study. Information provided in response to the questions asked is going to be kept confidential and shall be solely used for purposes of the study and that only.
Please fill in the questionnaire by ticking where appropriate or filling in the given space
1. Gender male female
2. How old are you? ..................................................
3. How many cumulative years have you spent at school? E.g grade seven is 7yrs,
form four is 11 yrs……………………..
4. What is your total monthly household income? .........................
5. Approximately how far is your home to the nearest medical centre?....................km
6. Do you normally visit public healthcare facilities? Yes No
7. If Yes, how much are you willing to pay to access public healthcare at any public healthcare facility? .....................................
8. If No, how much would you be willing to pay to access public healthcare?..................
9. When do you visit a medical centre? Immediately when you fall ill
When the illness is severe e.g bedridden
THANK YOU FOR YOUR COOPERATION!!!!
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APPENDIX B: DATA SET
OBS WTP G A EDUC HHY PRO S A21 10 0 39 19 905 0.5 1 15212 10 0 37 24 500 1 1 13693 5 0 31 20 800 2 0 9614 3 1 42 19 1500 4 1 17645 7 1 33 17 400 5 0 10896 1 1 22 11 500 7 1 4847 15 1 29 22 1000 2 1 8418 5 1 39 17 450 7 1 15219 6 0 35 19 400 7 1 122510 10 0 22 11 300 7 1 144411 9 1 43 18 700 3 1 184912 10 1 27 11 250 2 1 72913 10 0 29 11 900 2.5 0 168114 5 1 25 11 300 2 1 62515 30 1 46 27 1750 0.5 1 211616 3 1 37 7 200 5 1 136917 2 0 35 17 350 3 1 122518 5 0 41 20 450 2.5 1 168119 5 1 33 17 400 3 0 108920 5 0 30 22 900 2 1 90021 5 1 42 11 400 0.5 0 176422 3 0 50 17 750 0.7 0 211623 10 0 33 15 600 0.5 1 108924 10 1 40 11 700 1 1 160025 1 1 43 7 200 7 1 184926 10 0 29 20 900 6 1 324927 10 1 46 22 950 5 1 211628 10 1 28 21 800 7 0 136929 10 0 38 17 950 1 0 270430 10 1 34 14 800 1 0 115631 5 1 52 11 300 3 1 122532 4 1 33 23 350 3 1 108933 3 1 39 15 400 4 1 152134 5 0 47 17 450 0.5 0 220935 3 0 25 11 250 7 0 280936 3 1 29 13 500 4 1 84137 5 1 50 11 300 6 1 250038 1 0 42 20 450 7 1 176439 10 0 47 17 300 3 1 220940 5 1 45 17 500 3 0 202541 5 0 33 20 200 1 0 108942 10 1 30 22 350 2 0 90043 7 0 44 23 550 1 0 1936
35
44 10 0 35 20 600 0.5 0 122545 50 0 39 17 5670 7 1 152146 5 1 20 11 800 1 0 40047 10 1 33 17 950 1 0 108948 4 1 47 13 450 2 0 220949 7 1 32 20 750 2 0 102450 10 0 56 27 800 2 0 313651 2 1 33 20 250 4 1 108952 10 1 22 11 600 7 1 84153 5 0 44 20 450 2 0 193654 10 0 37 15 900 1 0 136955 3 1 39 17 550 1 0 152156 5 1 45 23 700 7 0 202557 10 0 32 22 1000 0.5 0 168158 10 0 33 20 250 3 1 108959 2 1 27 17 700 1 0 72960 10 1 37 19 700 5 1 136961 35 0 49 20 3000 1 1 240162 20 0 52 23 900 1 1 270463 25 1 39 17 650 1 0 152164 15 1 37 24 1500 2 0 136965 5 0 56 20 700 0.5 1 313666 10 1 45 22 900 1 0 202567 10 1 58 17 1000 2 1 313668 10 0 45 23 2000 7 0 202569 5 1 47 17 500 0.5 1 220970 35 1 59 27 4000 0.5 1 348171 3 0 33 20 600 2 1 108972 10 1 29 17 900 0.5 1 84173 15 1 42 23 1500 7 1 176474 15 0 40 22 1000 1 1 160075 3 1 50 20 450 4 1 250076 3 1 52 17 500 7 1 122577 3 1 36 17 300 4 1 129678 6 0 40 18 400 4 1 160079 7 0 48 19 250 5 1 230480 1 1 36 17 450 4 1 129681 1 0 28 17 600 4 1 78482 10 1 50 20 700 5 0 250083 3 0 45 17 200 5 1 202584 3 0 37 19 300 5 1 136985 1 1 29 17 500 5 0 84186 5 1 37 17 450 17 0 136987 3 0 33 20 400 3 0 108988 3 0 35 17 500 3.5 1 122589 3 0 38 19 550 4 0 1444
36
90 2 1 44 17 450 3.5 1 193691 10 1 24 11 600 1 1 57692 9 0 38 17 900 7 1 144493 1 1 22 13 200 7 0 48494 10 0 29 15 550 6 1 84195 10 1 20 14 300 6 1 40096 5 0 32 11 400 6 1 102497 10 0 42 20 700 5 1 176498 10 1 35 13 800 7 1 122599 10 0 39 12 950 6 0 1521100 2 1 63 6 200 10 1 3969101 10 1 27 13 450 7 1 729102 7 1 33 11 500 5 1 1089103 5 1 39 13 100 6 1 1521104 10 1 42 14 700 6 1 1764105 10 1 36 16 400 6 1 1296106 10 0 41 17 500 7 1 1681107 1 0 62 5 20 14 1 3844108 10 1 36 19 400 6 1 1296109 10 1 35 20 500 7 1 1225110 9 0 50 15 1000 7 1 2500111 7 0 49 24 550 1 1 2401112 7 0 45 17 400 7 1 2025113 5 0 29 15 450 6 1 841114 5 0 33 17 300 6 1 1089115 10 1 42 11 400 1 1 1764116 5 1 44 13 300 7 1 1936117 10 1 26 13 450 6 1 676118 10 1 30 16 450 1 1 900119 10 1 39 14 600 6 1 1521120 9 0 37 17 700 7 1 1369121 1 1 29 20 300 5 1 841122 7 0 33 17 600 6 1 1089123 10 0 47 23 800 7 1 2209124 1 0 40 23 700 7 1 1600125 7 1 35 24 800 6 1 1225126 3 1 36 20 750 6 1 1296127 5 1 30 17 500 11 1 900128 10 1 33 19 400 5 1 1089129 10 1 39 20 350 1 1 1521130 7 1 30 17 700 3 0 900131 7 1 42 17 450 7 1 1764132 7 0 30 13 400 1 1 900133 7 1 37 15 400 6 1 1369134 5 1 31 17 400 5 1 961135 5 1 30 17 1000 14 1 900
37
136 4 1 34 13 500 5 0 1156137 5 1 45 11 600 5 1 2025138 9 1 53 11 750 3 0 2809139 10 1 44 15 200 2 0 1936140 9 0 49 17 300 5 1 2401141 8 0 41 11 450 5 1 1681142 8 0 52 17 900 5 0 2704143 7 0 50 15 250 4 0 2500144 9 1 37 13 300 3 0 1369145 10 1 32 11 500 2 0 1024146 7 0 42 11 400 5 1 1764147 7 0 48 11 600 4 0 2304148 10 1 40 15 700 3 0 1600149 10 0 34 11 400 2 0 1156150 10 0 51 11 350 1 0 2601151 10 0 27 13 800 6 1 729152 10 1 47 17 900 5 1 2209153 5 1 50 15 500 15 1 2500154 7 1 44 16 400 6 1 1936155 6 0 35 17 800 5 1 1225156 7 1 30 15 800 7 1 900157 6 1 29 13 400 5 0 841158 5 1 34 17 500 5 0 1156159 4 0 28 19 450 5 1 784160 4 0 53 13 300 9 1 2809161 5 0 44 20 300 6 1 1936162 4 0 47 25 350 6 1 2209163 5 0 52 13 250 6 1 2704164 3 0 41 15 400 11 1 1681165 1 1 38 11 50 7 1 1444166 3 1 31 19 450 15 1 961167 4 0 39 20 700 7 1 1521168 3 0 46 22 300 17 1 2116169 6 0 40 20 400 7 1 1600170 3 1 32 7 100 7 1 1024171 3 1 35 23 650 7 1 1225172 5 1 37 20 700 5 0 1369173 5 1 30 20 800 5 1 900174 3 0 38 11 400 4 0 1444175 3 0 46 13 500 13 0 2116176 2 1 44 15 400 5 0 1936177 2 1 40 20 500 5 1 1600178 2 0 41 13 300 7 1 1681179 3 1 52 11 400 7 1 2704180 2 1 36 13 450 7 1 1296181 20 0 40 18 1554 2 0 1600
38
182 15 0 45 17 1220 1 0 2025183 15 0 56 20 1000 2 0 3136184 30 1 41 25 7000 2 0 1681185 10 1 43 13 950 2 0 1849186 10 1 51 22 1000 2 0 2601187 10 1 20 20 1500 1.5 0 400188 10 1 29 17 1000 2 0 841189 10 1 35 19 1000 2 1 1225190 10 0 41 19 1500 2 1 1681191 10 0 53 22 1000 1.5 1 2809192 10 0 35 20 1500 1.5 1 1225193 15 1 37 21 2000 1.5 1 1369194 10 1 30 23 2000 2 0 900195 10 1 41 20 2013 2 0 1681196 10 1 33 24 900 2 0 1089197 10 0 47 20 1200 2 0 2209198 10 0 55 19 1500 2 0 3025199 13 0 54 25 1600 2 0 2916200 10 0 63 20 1000 2 0 3969201 10 0 70 13 500 2 0 4900202 10 0 60 20 2000 2 0 3600203 10 0 35 13 2000 2 0 1225204 10 0 38 13 1500 1.5 0 1444205 10 1 31 15 1000 1.5 0 961206 10 1 37 17 1020 1.5 0 1369207 10 1 36 19 1100 0.3 0 1296208 10 1 50 20 1000 1.5 0 2500209 10 0 56 23 1500 0.5 0 3136210 10 1 39 20 900 0.5 0 1521211 23 1 29 17 1500 2 0 841212 10 1 33 18 1000 2 0 1089213 10 1 35 20 900 2 0 1225214 10 0 39 21 1000 2 0 1521215 10 0 52 20 1000 1 1 2704216 10 1 30 20 1000 1 1 900217 10 0 37 21 1500 1 0 1369218 15 1 38 21 2000 1 0 1444219 10 0 39 23 550 2 1 1521220 10 1 44 20 2000 2 1 1936221 10 0 56 20 776 2 0 3136222 10 1 44 19 2000 2 0 1936223 30 1 30 15 3300 0.5 0 900224 10 1 41 16 2000 2 0 1681225 10 1 47 17 2500 2 0 2209226 10 0 38 19 1000 2 1 1444227 10 1 42 17 1500 7 1 1764
39
228 10 0 36 19 1000 2 0 1296229 10 1 30 17 1000 2 0 900230 15 0 33 19 1500 0.5 0 1089231 10 0 39 20 1000 0.5 0 1521232 10 0 35 21 2000 2 0 1225233 15 0 45 22 2500 2 0 2025234 10 0 47 20 1000 2 1 2209235 10 0 48 23 1000 0.5 1 2304236 10 0 40 20 1000 2 0 1600237 10 1 40 20 2000 2 0 1600238 10 0 51 27 2500 2 0 2601239 10 1 33 23 2000 2 0 1089240 10 1 38 20 2000 2 0 1444241 10 1 33 17 600 1 1 1089242 10 1 29 13 400 5 1 841243 10 0 34 14 400 6 1 1156244 3 0 30 17 500 10 1 900245 7 0 25 11 550 6 1 625246 8 0 39 17 300 7 0 1521247 8 0 43 17 300 6 1 1849248 0 0 49 11 600 16 1 2401249 10 0 42 14 600 0.5 1 1764250 5 1 40 15 700 7 1 1600251 3 1 38 19 650 4 0 1444252 4 1 36 17 500 5 0 1296253 2 0 35 13 400 6 1 1225254 3 0 36 17 450 10 1 1296255 10 1 30 15 150 6 1 900256 10 1 24 17 250 0.5 1 576257 10 0 33 16 300 5 1 1089258 5 1 34 17 600 6 1 1156259 9 0 30 18 300 1 1 900260 8 1 20 11 300 0.5 1 400261 7 1 34 17 250 6 1 1156262 6 1 35 17 200 1 1 1225263 0 1 30 7 300 5 0 900264 6 1 31 13 500 4 0 961265 7 0 33 13 500 6 1 1089266 8 0 39 14 450 7 1 1521267 9 0 42 17 400 6 1 1764268 10 1 31 17 450 1 1 961269 9 1 39 13 500 1 1 1521270 7 1 42 15 700 7 1 1764271 9 0 33 17 800 1 1 1089272 4 1 34 11 500 10 1 1156273 7 1 39 13 400 5 1 1521
40
274 5 1 43 15 500 4 0 1849275 6 0 40 11 400 5 1 1600276 7 1 38 13 400 1 0 1444277 7 1 30 15 450 1 0 900278 7 0 42 15 450 4 0 1764279 3 0 40 14 500 17 1 1600280 7 0 35 13 600 5 1 1225281 8 1 35 13 500 1 1 1225282 9 1 37 14 450 0.5 1 1369283 10 1 47 14 300 4 0 2209284 5 1 43 13 360 9 0 1849285 4 1 40 17 300 4 1 1600286 3 1 40 13 300 15 1 1600287 3 0 47 17 350 5 1 2209288 3 0 43 17 370 16 1 1849289 6 0 48 15 400 1 1 2304290 1 0 21 7 150 6 1 441291 5 1 30 15 200 6 1 900292 5 1 32 13 600 7 1 1024293 5 1 39 11 200 4 0 1521294 6 0 30 13 300 1 1 900295 7 0 37 14 250 4 0 1369296 6 0 23 15 300 1 1 529297 5 1 50 17 200 4 0 2500298 6 1 42 17 300 1 1 1764299 5 1 41 15 400 7 1 1681300 4 0 35 13 500 4 0 1225301 9 0 30 15 700 1 0 900302 10 0 42 21 1232 3 0 1764303 5 1 28 16 640 14 1 784304 6 1 40 13 550 2 0 1600305 10 0 34 18 1300 6 1 1156306 2 1 22 16 200 0.5 0 484307 3 1 30 17 500 0.1 0 900308 4 0 40 13 600 20 1 1600309 1 1 52 15 20 7 1 2704310 10 0 42 17 1800 19 0 1764
APPENDIX C: CORRELATION MATRIX
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APPENDIX D: TEST FOR HETEROSKEDASTICITY
APPENDIX E: DESCRIPTIVE STATISTICS.
42
AGE2 310 1578.394 695.4989 400 4900 S 310 .6 .49069 0 1 PRO 310 4.287419 3.525075 .1 20 HHY 310 759.0323 710.8394 20 7000 EDUC 310 16.76129 4.028352 5 27 AGE 310 38.55806 8.577923 20 70 G 310 .5451613 .4987614 0 1 WTP 310 7.887097 5.427083 0 50 Variable Obs Mean Std. Dev. Min Max
. sum
APPENDIX F: OLS REGRESSION RESULTS
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