Thokozani Saulosi Economics Dissertation

HIV KNOWLEDGE AND RISKY SEXUAL BEHAVIOUR: A COMPARATIVE
ANALYSIS BETWEEN YOUTHS AND ADULTS IN MALAWI
Bachelors degree in social science dissertation
By
THOKOZANI MAXIN SAULOSI
BSoc. Sc. (University of Malawi)
Dissertation submitted to the Department of Economics, Faculty of Social Science, in partial
fulfilment of the requirements for a degree in Social Science
UNIVERSITY OF MALAWI
CHANCELLOR COLLEGE
SEPTEMBER 2014

ii
DECLARATION
I, the undersigned, hereby declare that this thesis is my original work and has not been submitted
to any other institution for similar purposes. Where other people’s work has been used
acknowledgements have been made.
THOKOZANI MAXIN SAULOSI
_______________________________
Signature
______________________________
Date

iii
CERTIFICATE OF APPROVAL
The undersigned certify that this thesis represents the student’s own work and effort and has
been submitted with my approval.
Signature: _________________________Date:_________________________
Levison Chiwaula, PhD (Lecture)

v
ACKNOWLEDGEMENTS
I thank God for the things He does in my life.
I would like to acknowledge and thank Dr Levison Chiwaula and Mr Gowokani Chijere
Chirwa for their insightful comments, constructive criticism and encouragement during the
development and writing up of this dissertation.
Special thanks are due to Maxin and Rose Saulosi for all the support. Words alone cannot
express how grateful I am to you guys. I also acknowledge the assistance rendered by Madalo
Saulosi, you the best sister I got.
All my cheerful friends: Faith Tsoka, Pilirani Mbedza, Lozindaba Mbvundula, Victor
Custom, Hannah Supply, Brian Numero, my mesho (Preston Matanda), Lumbiwe Zimba, etc.
I would like to thank you guys for your presence in my life. I appreciate what you have done.
It will be injustice not to acknowledge the assistance rendered by Lucious Cassim in doing
the Stata analysis.

vi
ABSTRACT
This thesis analyzes the impact the Knowledge of HIV and AIDS has on sexual behaviour.
Sexual behaviour is grouped into three categories which are Condom use, multiple sexual
partners and paid sex. Wealth status is used as a proxy of poverty. The thesis uses dataset
from the 2010 Malawi Demographic and Health Survey (MDHS). The sample size was 7175
and 23020 for men and women respectively. The decision to indulge in sexual behaviour
(Condom use and Paid sex) is modelled as a choice model and estimated using a Probit
model. Since multiple sexual partners is a count variable, the study uses the negative
binomial for the count variables.
The study found out that knowledge of HIV has an influence on sexual behaviour of men and
women. HIV knowledge has a positive influence the use of condoms by women but does not
have an influence on condom use by men. HIV knowledge also has a negative influence of
paid sex and multiple sexual partners. In addition, the study also found that other socio-
demographic factors influence sexual behaviour.

vii
TABLE OF CONTENTS
ABSTRACT........................................................................................................................... VI
TABLE OF CONTENTS.................................................................................................... VII
LIST OF TABLES................................................................................................................. X
CHAPTER ONE .................................................................................................................. 1
INTRODUCTION................................................................................................................ 1
1.0 background...................................................................................................................... 1
1.1 statement of the problem................................................................................................... 3
1.2 Research Questions........................................................................................................... 4
1.3 Research Objectives.......................................................................................................... 4
1.4 Research Hypothesis.......................................................................................................... 4
CHAPTER TWO................................................................................................................... 5
LITERATURE REVIEW..................................................................................................... 5
2.0 Introduction....................................................................................................................... 5
2.1 Theoretical literature......................................................................................................... 5
2.1.1 Baumeister Sexual Theory............................................................................................. 5
2.1.2 Rational Choice Theory................................................................................................. 5
2.2 Empirical Literature........................................................................................................... 7
2.2.1 Poverty and Risky Sexual Behaviour .............................................................................. 7
2.2.2 Early Sexual Debut........................................................................................................... 8
2.2.3 Condom use...................................................................................................................... 8

viii
2.2.4 Research done in Malawi on levels of HIV knowledge, risky sexual behaviour and
Vulnerability to HIV/AIDS....................................................................................................... 9
2.3 Conclusion......................................................................................................................... 10
CHAPTER THREE................................................................................................................. 11
METHODOLOGY.................................................................................................................. 11
3.0 Introduction...................................................................................................................... 11
3.1 Modelling Framework and econometric Specification ................................................... 11
3.2 Analytical Modelling......................................................................................................... 11
3.2.1 The Probit Model........................................................................................................... 11
3.2.2 The Negative Binomial Model....................................................................................... 12
3.3 Empirical Specification..................................................................................................... 13
3.4 Description of variables..................................................................................................... 14
3.5 Data Sources...................................................................................................................... 15
3.6 Data Analysis.................................................................................................................... 15
3.7 Diagnostic Tests................................................................................................................ 15
3.8 Conclusion......................................................................................................................... 16
CHAPTER FOUR................................................................................................................... 17
PRESENTATION AND INTERPRETATION OF RESULTS.............................................. 17
4.0 Introduction....................................................................................................................... 17
4.1 Descriptive analysis of the data......................................................................................... 17
4.2 Bivariate analysis of the data............................................................................................. 22
4.3 Multivariate analysis of the data....................................................................................... 26
4.4 Conclusion......................................................................................................................... 31
CHAPTER FIVE..................................................................................................................... 32

ix
CONCLUSION AND POLICY IMPLICATIONS................................................................. 32
5.0 Introduction....................................................................................................................... 32
5.1 Summary of results............................................................................................................ 32
5.2 Policy implications............................................................................................................ 32
5.3 Study limitations............................................................................................................... 33
REFERENCES........................................................................................................................ 35

x
LIST OF TABLES
Table 1.1: Socio-demographic characteristics of people aged 15-49 years............................ 17
Table 1.2: HIV related knowledge among people aged 15-49 years...................................... 18
Table 1.3: HIV related attitudes among people aged 15-49 years.......................................... 20
Table 1.4: Sexual behaviour among people aged 15-49 years................................................ 21
Table 2.1: Condom use and comprehensive knowledge among women................................ 22
Table 2.2: Condom use and comprehensive knowledge among men..................................... 23
Table 2.3: Multiple sexual partners and comprehensive knowledge among women.............. 23
Table 2.4: Multiple sexual partners and comprehensive knowledge among men................... 24
Table 2.5: Condom use among women who had multiple sexual partners............................. 24
Table 2.6: Paid sex and comprehensive knowledge among men............................................ 25
Table 2.7: Condom use and comprehensive knowledge among men who had paid sex.........25
Table 3: Results of Probit analysis on condom use and paid sex among people aged 15-49
years........................................................................................................................................ 26
Table 4: Negative Binomial results of multiple sexual partners............................................. 29

1
CHAPTER ONE
INTRODUCTION
1.0 background
Implementation of effective HIV prevention interventions still poses a challenge in the
national response to HIV and AIDS in Malawi. Although the national HIV prevalence is
declining, on average there are nearly 90, 000 new HIV infections each year with at least half
occurring among young people aged 15-24. The majority of people being infected are those
who were previously considered to be at low risk, for example, couples and partners in stable
sexual relationships (NAC 2009).
In Malawi, the response to HIV and AIDS pandemic relies on preventive strategies where
information on modes of transmission are provided to enable people identify and avoid risky
behaviour that could expose them to infection. Having accurate HIV and AIDS knowledge
about transmission and prevention is important for avoiding HIV infection and ending the
stigma and discrimination of infected and affected persons. However, 99 percent of women
and men in Malawi have heard of AIDS (NAC 2009, NSO and ORC MACRO, 2010).
Sexual behavior can be in various forms such as condom use and multiple concurrent
partnerships, among others. The outcomes of sexual behavior include HIV and AIDS
infection, gonorrhea, syphilis, and unwanted pregnancies, among others. Of these different
sexual behavior outcomes, HIV and AIDS has been the focus of public discussion as well as
policy initiative in the country due to its socio and economic impact. HIV and AIDS is a
socio-cultural, economic, political, development and health issue which has brought havoc to
all sectors of the economy in Malawi and other developing countries (GoM, 2006).
HIV and AIDS due to its negative consequences on communities and social structures is a
social problem. It is also a cultural issue because some cultural practices and beliefs fuel the
spread of the disease and mask positive traits of the system while encouraging stigma,
discrimination and denial (GoM, 2006). It is a political problem because a sick person will
not contribute to the political development of the country. It is also considered to be a health
issue because it affects directly a large number of people and the health-care system itself or

2
fabric of society. HIV and AIDS is also an economic issue as it leads to reduction in
economic growth by reducing the productivity of the labour and draining investment
resources in all sectors. Lastly, HIV and AIDS is a development issue because it affects
negatively all sectors of the economy (GoM, 2006).
HIV/AIDS is a devastating human tragedy and the greatest humanitarian challenge of our
time. The pandemic is still a complex public health problem in sub-Saharan Africa which
accounts for more than 65% of HIV infections worldwide (UNAIDS and WHO, 2009). This
has been a painful reality, with noticeable impact on families, communities and the society at
large. There has been an intense debate in the last two decades on the relative roles of unsafe
sex and unsafe health care on HIV spread in Sub- Saharan Africa (Caldwell and Caldwell,
1996; Odebuyi and Vivekananda, 1991), but most public health experts believe that unsafe
sexual behaviors (unprotected sex and multiple and concurrent sex partners) are the
mechanism through which HIV is spreading in the region (Halperin and Epstein, 2004;
Leclerc- Madlala, 2008, 2003). According to these authors, multiple sexual partnerships—
particularly overlapping or concurrent partnerships—by both men and women lie at the root
of the persistence or the severity of the HIV epidemic in sub-Saharan Africa.
Malawi is one of the countries that have been affected by the HIV and AIDS pandemic. The
estimated prevalence rate for the adult population is 11 percent. Major factors in the
transmission of HIV in Malawi are poverty, low literacy levels, high rates of casual and
transactional unprotected sex in the general population, particularly among youth between the
ages of 15 and 24, low levels of male and female condom use, cultural and religious factors,
and stigma and discrimination (UNAIDS 2010). Malawi developed the National HIV and
AIDS Action Framework (NAF), which guided the national response for the period 2005-
2009 (NAC, 2004). The overall goal of the NAF is to prevent the spread of HIV, to provide
access to treatment for people living with HIV, and to mitigate the health, socioeconomic,
and psychosocial impact of HIV on individuals, families, communities, and the nation.
Empirical studies have been conducted to explain the relationship between poverty and
sexual risk-taking behavior. Fenton (2004), who studied how to prevent HIV/AIDS by
reducing poverty, argues that lack of knowledge, which results from poor access to relevant
information, is the major obstacle to practice of safer sexual behavior. Lack of knowledge
due to limited access to information is more common among the people of lower
socioeconomic status than among the people of higher socioeconomic status.

3
In Malawi, about three-quarters of women and men age 15-49 (72 and 73 percent,
respectively) know that consistent use of condoms prevents the spread of HIV. Eighty-seven
percent of women and 85 percent of men know that limiting sexual intercourse to one,
uninfected HIV negative partner can reduce the chances of contracting HIV. Sixty-six percent
of women and men know that using condoms and limiting sexual intercourse to one HIV-
negative partner can reduce the risk of HIV infection. Seventy-nine percent of women and 77
percent of men know that abstaining from sexual intercourse can reduce the risk of HIV
infection. Although there are variations in knowledge of HIV prevention methods across the
age groups, they are not consistent (NSO and ORC MACRO, 2010).
Cohen (1997) argues that the poor are more vulnerable to HIV/AIDS because they lack
access to methods for practicing safer sex, which might be more costly for them than for
people of higher socioeconomic status. Also, he argues that poverty influences women to
engage in early sexual relationships and informal prostitution. Moreover, women are less
empowered economically, legally, culturally, and socially compared with men, particularly in
Africa, which is a key factor in HIV transmission. Many women depend on their male
partners for income, food, clothing, and so forth, which can reduce their power to negotiate
for safer sex. In general, women may engage in risky sexual behavior out of economic need.
Poverty may raise the probability of contracting HIV in several ways: malnutrition, which in
turn increases susceptibility to any disease; poverty-related lack of education and information
may be a barrier to individuals changing their behaviors; while specific sexual behaviors
adopted by poor individuals in poor communities may directly increase vulnerability
(Dinkelman,2008; Baird, Chirwa, McIntosh, and Ozler, 2010).
1.1 Statement of the Problem
Many are quick to assert that poverty is a determinant of HIV status for women because poor
women are more likely to engage in risky sexual behaviour (Cohen, 1997). Others argue that
it is not women’s poverty but the relative wealth of men that is the cause of transactional sex
(Swidler and Watkins, 2007). Some have attributed it to cultural constructed beliefs and
ethnicity (Hickey, 1997). However these studies did not take into account the effect
comprehensive knowledge of HIV and AIDS has on risky sexual behaviour. With 41 percent
of women and 45 percent of men with comprehensive knowledge about HIV and AIDS it is
important to examine the bearing the levels of HIV knowledge has on sexual behaviour.

4
As such, the study will offer empirical evidence of the association between levels of HIV
knowledge and higher-risk sexual behavior. This can be for decision making in short-term
and long-term interventions, to help Malawians change their health lifestyles in order to
reduce HIV infection using their acquired knowledge about HIV and AIDS, especially among
vulnerable population. Second the study will contribute to the body of knowledge on how
levels of HIV knowledge and other socioeconomic factors influence higher-risk sexual
behavior.
1.2 Research Questions
 Main Question of the study
o Does level of HIV knowledge affect sexual behavior?
 Sub questions include
o Are there any differences between sexual behaviors of adults and
young people?
o Are there gender differences in knowledge?
1.3 Research objectives
 Overall objective of the study
o This study seeks to find out if HIV knowledge has an impact on sexual
behaviour.
 Specific objectives
o To find out if level of HIV knowledge affects sexual behavior.
o To find out if there are any differences between sexual behaviors of adults
and young people.
o To find out if there is gender differences in the knowledge of HIV.
1.4 Research Hypothesis
 To investigate the above objectives, the following null hypothesis will be
tested:
o HIV knowledge has an impact on sexual behaviour of women
o HIV knowledge has an impact on sexual behaviour of men

5
CHAPTER TWO
LITERATURE REVIEW
2.0 Introduction
This chapter presents a summary and discussion of what other researchers have done in the
area of HIV knowledge and sexual behaviour. The chapter has three sections. Section 2.1
provides the theoretical literature while section 2.2 gives the empirical literature done in other
countries and literature done in Malawi. Lastly section 2.3 gives a summary of the chapter.
2.1 Theoretical Literature
The theoretical literature contains economic, demographic and sociological theories and
schools of thought in line with sexual behaviour.
2.1.1 Baumeister Sex Theory
The theory treated sex as a valued good for which there is a marketplace in which women act
as sellers and men as buyers. The initiation of a sexual relationship corresponds to a
transaction in which men offer women other resources in exchange for sex. Those resources
correspond to the price of sex, which rises and falls with multiple factors, including the
balance of supply and demand across the marketplace, the competitive position of the woman
(especially her sex appeal relative to others), and how exclusive she has been in terms of
other sexual partners. The theory applies best to heterosexual interactions. It is less applicable
to same-gender sexual activity (because of the lack of marketplace-defined roles) and sex in
marriage (because commitment has already been made, because material property is jointly
owned and therefore not available for exchange, and because the marital contract regarding
sex removes the couple from the competitive marketplace). Sex is a precious good for which
demand generally exceeds supply, and so it would be surprising if marketplace dynamics and
economic principles were utterly absent.
2.1.2 Rational Choice Theory
Sexual behaviour is a decision which is assumed to be rational as such it can be explained
using rational choice theory. Rational choice theory examines how rational individuals make
consumption choices when faced with limited resources. The limited resources determine
what options an agent can afford. Given a set of available consumption bundle an individual
attempts to pick the best one that maximizes the utility of the agent. In this theory, it is
assumed that each decision maker is able to compare two alternatives “x” and “y” in the

6
choice set. If “x” is strictly preferred to “y” the decision maker either prefers “x” to “y” or is
indifferent. The ranking that occurs with the various options is what defines individual’s
utility.
In terms of sexual behaviour, it is assumed an individual compares alternative sexual
behaviour (“x” to “y”) in order to maximize utility. This depends on the preferences and the
expected utility from each, subject to the costs. The costs in this case among others are things
such as being infected with diseases. The benefits may include the sexual pleasure derived
from the acts as well as the income earned from transactional sex. However, one can still
criticise the theory in as far as sexual behaviour is concerned since it does not fully explain
every aspect of sexual behaviour. This is so since it includes only rational sexual decisions,
where as some sexual decisions may be irrational such as fulfilling cultural obligations such
as ritual sexual cleansing, which may be imposed on the individual and not out of free will.
Furthermore, practices such as rape may not be explained by this theory.
Furthermore, Philipson and Posner (1993) try to explain risky sexual behaviour subject to
HIV constraint. In this framework, an individual makes a choice between safe and unsafe sex.
“Safe sex” means sex with condoms and is completely safe, and “unsafe sex” denotes all
other forms of sex and is equally unsafe. Individuals engaged in sexual trade with each other
are denoted as,𝑚, for male, and 𝑓, for female. The decision to engage in unsafe sex is
modelled as a problem of making a rational choice under condition of uncertainty. The
expected utility ( 𝐸𝑈) of risky sex for males and females is equivalent to the benefits ( 𝐵)
minus the expected costs ( 𝐶) of risky sex. Therefore the utility functions are defined as:
𝐸𝑈 𝑚 = 𝐵 − 𝐶(𝑃𝑡𝑓(1 − 𝑃𝑡𝑓)𝑃𝑓) ............................................... (1)
𝐸𝑈𝑓 = 𝐵 − 𝐶( 𝑃𝑡𝑚(1 − 𝑃𝑡𝑚 ) 𝑃𝑚) ............................................... (2)
Where, 𝐸𝑈 = expected utility of the sexual behaviour, 𝐵= benefit of unsafe sex, C = cost of
becoming infected with HIV, 𝑃𝑡𝑖 =probability of transmission, 𝑖=𝑚, 𝑓, 𝑃𝑖 = probability that 𝑚
or 𝑓 is already infected, 𝑖 = 𝑚, 𝑓. Only when the expected utility of both individuals are
positive is when the exchange will occur. The benefit (B) of unsafe sex is synonymous to the
disutility of using a condom as per assumption. The benefit is assumed to be mutual although
the utilities may be different. This means that sexual pleasure with no condom is not the same
for the two (Philipson and Posner, 1993).

7
However, the theories are weak when it comes to explaining sexual behaviour when it is
irrational and when the economic aspect is ruled out. They also ignore the ways in which
cultural inequalities such as ethnicity, gender, class, and tribe may systematically bias safe
sex market negotiations, including those over safe sex. They also ignore the social and
political factors that affect sexual behaviour. In addition, the theories are much more centred
on the costs of unsafe sex and ignore the costs of safe sex.
2.2 Empirical Literature
2.2.1 Poverty and Risky Sexual Behaviour
Bloom and Sevilla (2001) established that poverty has a direct link with HIV/AIDS. Their
findings make two important observations. First, the poorest women start sexual activities at
early ages compared with wealthier women, thus having relatively more exposure to the risk
of HIV infection. Second, the poorest women are less likely to engage in safe sex compared
with wealthier women, making them more vulnerable to HIV infection.
Furthermore, empirical evidence shows that poverty hinders people from practicing safe sex
because they lack access to means of protection. This argument is supported by a survey
study by MacPhail and Campbell (2001) in Khutsong, South Africa. The survey included a
group of young people age 13-25. Results indicated that lack of access to condoms due to
their inability to afford the costs of acquiring condoms was the main reason that they
practiced unprotected sex. Results also indicated that economic hardship was the main reason
for young women engaging in sexual relationships at an early age.
Collins and Rau (2000) have observed that poverty is likely to be associated with lack of
education, and lack of education implies that messages regarding the risk of contracting
HIV/AIDS and prevention measures are often inaccessible. Nattrass (2002) argues that not
only does poverty cause young women to engage in commercial sex activities to support their
livelihood, which thus exposes them to the risk of HIV infections, but also that HIV/AIDS
can cause further poverty. Once a person contracts HIV/AIDS as a result of poverty, the sick
person will need costly treatments, so that over time the situation will worsen and may even
cause the family to lose all their resources and end up in absolute poverty.
Although some evidence shows that HIV prevalence is associated with poverty, as measured
by per capita income (Bloom and Sevilla 2001), in other parts of sub-Saharan Africa,
HIV/AIDS is often associated with wealth. At the macroeconomic level, South Africa and

8
Botswana, which are regarded as the strongest economies or as rich countries in sub-Saharan
Africa, have the highest rates of HIV prevalence compared with poorer countries in the
region (UNAIDS 2005). At the micro level, Shelton and colleagues found wealth and positive
HIV serostatus to be positively related (Shelton et al. 2005).
2.2.2 Early Sexual Debut
Early initiation of intercourse poses potential risks for unintentional pregnancy, abortion, and
STDs, especially HIV, among young people. A systematic review about the early sexual
debut as a risk factor for HIV infection among women in sub-Saharan Africa showed
significant association between early sexual debut and HIV infection (Stockl, 2013). Two
studies in Zimbabwe demonstrated that when young people are having first sex intercourse
before the age of 15 years had an increased risk for HIV transmission (Pettifor et al., 2004;
2009).
2.2.3 Condom Use
A systematic review regarding condom use in sub-Saharan Africa (Maticka-Tyndale, 2012)
identified that condom use in the region was generally rare, and the factors including poverty;
relationships with parents, peers and partners; limited, insufficient or absent information;
gender and sexual norms, and gender/power dynamics; and beliefs and attitudes about HIV,
condoms and sexuality, were barriers to condom use for a large proportion of African people.
Nevertheless, the study found the increasing trends of condom use among single women in
many countries, increasing acceptance and condom use among some university students,
successes in producing potentially sustainable condom use resulting from select
interventions, and resistance to succumbing.
Awuso-Asare and Annim (2008) explore the determinants of sexual risk-taking behavior
especially the effects that variations in household wealth status, gender and different sub-
population groups have on this behavior in Kenya and Ghana. Wealth quintiles were used as
a proxy for economic status, while non-marital and non-cohabiting sexual partnerships were
considered indicators for risky sexual behavior. The results were mixed. For females, there
appeared to be an increasing probability of sexual risk taking by wealth status in Kenya;
while in Ghana, an inverted J-shaped relationship is shown between wealth status and sexual
risk taking. When controlled for other variables, the relationship between wealth status and
sexual risk-taking behavior disappears for females in the two countries. For males, there was

9
no clearly discernable pattern between wealth status and sexual risk-taking behavior in
Ghana, while there is a general trend towards increasing sexual risk-taking behavior by
wealth status in Kenya. In general, for both Ghana and Kenya, men in the highest wealth
quintile were found to be more likely to have multiple sexual partners than the other groups.
2.2.4 Research done in Malawi on levels of HIV knowledge, risky sexual behavior and
Vulnerability to HIV/AIDS
A study of risky sexual behavior and condom use in Malawi (Madise and Chanon, 2004),
established that 12.6 percent of sexually active females in the sample were seen to have had
risky sexual intercourse. Most were classified as risky due to the presence of STI in the last
12 months indicating large levels of passive exposure. Condom use with a marital partner, a
girlfriend or fiancée or a casual partner showed no variation at the cluster or district level.
Matrilineal ethnicities were, in general, seen to be more likely to engage in risky sexual
intercourse and less likely to use a condom.
Madise, Zulu, and James in 2003 using logistic regressions looked at the link between
poverty and risky sexual behavior in four countries by examining the effect of wealth status
on age at first sex, condom use, and multiple partners using nationally representative
adolescents’ data from the Demographic Health Surveys of Burkina Faso, Ghana, Malawi,
and Uganda. Wealth status measured using wealth quintiles derived from information on the
presence or absence of household assets and amenities as proposed by Filmer and Pritchett
(2001). Results showed that the wealthiest girls in Burkina Faso, Ghana, and Malawi had
later sexual debut compared with their poorer counterparts but this association was not
significant for Uganda. Wealth status was weaker among males and significant only in
Malawi, where those in the middle quintile had earlier sexual debut. Wealthier adolescents
were most likely to use condoms at the last sexual act, but wealth status was not associated
with number of sexual partners (Madise, Zulu and James, 2003).
A study done in Balaka, Chirwa (2012) found that income does not influence sexual
behavior. He also found out that knowledge of HIV/AIDS does not affect sexual behavior.
From this study, the variables that were found to have an impact were primary education,
employment status, condom beliefs, and religious beliefs held by Catholics and adherence of
traditional religions. On the bases of these findings it can be said that other background
factors which are non money metric measures are important predictors of risky sexual
behavior. However, the results of the study are prone to error due to the fact that

10
measurement of behavior usually relied on verbal reports, which can suffer from a number of
biases, both intentional and unintentional.
2.3 Conclusion
The chapter has reviewed different studies on HIV knowledge and risky sexual behaviour.
However, individual demographic and economic characteristics have shown mixed results as
far as their impact on HIV knowledge and sexual behaviour is concerned.

11
CHAPTER THREE
METHODOLOGY
3.0 Introduction
This chapter presents the methodology of the study. Section 3.1 to section 3.5 presents
the modeling framework and econometric specification. Data description is presented
in section 3.6. In section 3.7 data sources and sample size are presented. Section 3.8
presents the data analysis of the study and lastly section 3.9 gives the conclusion of
the section.
3.1 Modeling Framework and econometric Specification
The definition of the behaviour which constitutes risky sexual behaviour has varied
between studies, with the obvious result of difficulty in comparisons between
investigations (Madise, 2007). Risky sexual behaviour includes early sexual debut (
age less than 18), unprotected sexual activity, inconsistent use of condoms, high-risk
partners , sex with a partner who has other partners or more than one partner at a time
, survival sex (sex in exchange for money, drugs, food, or shelter), (Taylor-Seehafer
and Rew, 2000; Hallman, 2004; Madise, 2007;Warren, 2010), among others. In this
study risky sexual behavior is defined as paid sex, non use of condom at last sex and
having multiple sexual partners (NSO and ORC MACRO, 2010).
3.2 Analytical Modeling
In this study there are two types of dependent variables which are the count and non
count variables. The non-count variables in this study are condom use and paid sex,
the count variable in this study is multiples sexual partners.
3.2.1 The Probit model
Since the non-count variables follow a choice, hence this study will use the Probit
model. The choice model is formulated as (Gujarati, 2003);
𝑦∗
𝑖
= 𝛼𝑖 𝑋𝑖 + 𝜀𝑖
Where 𝑦𝑖 = {
1 𝑖𝑓 𝑦𝑖
∗
> 1
0 𝑖𝑓 𝑦𝑖
∗
< 0

12
𝑦𝑖 =1 if individual has paid sex; 𝑦𝑖= 0 if individual does not have paid sex, 𝑋𝑖 are
explanatory variables and 𝜀𝑖 is an error component. The Probit model is based on the
following cumulative standardized normal distribution;
𝐹( 𝑧) = Φ( 𝑧) = ∫
1
√2𝜋
ℯ
1
2
𝑧2
𝑧
−∞
So that the change in the probability of an individual having paid sex given their
characteristics, 𝑋𝑖 would be given as follows;
𝜕𝑃(𝑦𝑖 = 1)
𝜕𝑥
= 𝑓( 𝑧) ∝𝑖= Φ(𝑧)𝛼𝑖
Where 𝑋𝑖 are the demographical characteristics of the individual.
3.2.2 The Negative Binomial Model
The negative Binomial Model will be used for the count variable, multiple sexual
partners.
Given a discrete random variable Y, and observed frequencies 𝛾𝑖, 𝑖 = 1,… . , 𝑁
where 𝛾𝑖 ≥ 0 and regressors𝑋𝑖.
𝑃𝑟𝑜𝑏( 𝑌 = 𝛾𝑖) =
ℯ−𝜆 𝑖 𝜆𝑖
𝛾𝑖
𝛾𝑖!
, 𝛾𝑖 = 0,1,…
In this model (Poisson model) 𝜆 𝑖 is both mean and variance of 𝛾𝑖 . The negative
binomial model allows the variance of the process to differ from the mean such that
𝜆𝑖 is respecified so that 𝑙𝑛 𝜆𝑖 = 𝛽′
𝑋𝑖 + 𝜀
Where exp (𝜀) is a gamma distribution with mean and variance𝛼1. The resulting
probability distribution is:
Prob( 𝑌 = 𝛾𝑖| 𝜀) =
ℯ−𝜆 𝑖exp(𝜀)
𝜆 𝑖
𝛾𝑖
𝛾𝑖!
, 𝑦 = 0,1,….,
Integrating ε out of the expression produces the unconditional distribution of 𝛾𝑖. The
formulation of this distribution is given by;
Prob[ 𝑌 = 𝛾𝑖] =
Γ( 𝜃+𝛾𝑖)
[Γ( 𝜃) 𝛾𝑖 !] 𝜇𝑖
𝜃(1−𝜇𝑖) 𝛾 𝑖
1 This is one of the several variants of the negative binomial model discussed by Cameron and Trivedi (1986).

13
Where 𝜇 𝑖 = 𝜃
( 𝜃 + 𝛾𝑖 )⁄
𝜃 = 1
𝛼⁄
This model has an additional parameter α such that
𝑉𝑎𝑟( 𝛾𝑖 ) = 𝐸[ 𝛾𝑖]{1 + 𝛼𝐸[ 𝛾𝑖 ]}
This is an actual form over dispersion in that the over dispersion rate is:
𝑉𝑎𝑟[ 𝛾𝑖]
𝐸[ 𝛾𝑖]
= 1 + 𝛼𝐸[ 𝛾𝑖]
Signs of coefficients and marginal effects of the independent variables, likelihood test
ratio and a parameter measuring over dispersion are used to interpret the results in this
study. Signs of the coefficients indicate the direction of the effect of one unit change
in the independent variable over the number of multiple sex partners. The marginal
effects show the magnitude of the impact of the independent variable of a particular
independent variable. The likelihood test ratio (chi square) tests whether all estimates
in the model are insignificant whereas the parameters measuring over dispersion tests
whether the model is statistically different from zero.
3.3 Empirical specification
This study adopts the specification by Booysen and Summerton (2002) However, this
paper uses a slightly different methodology than the study by Booysen(2002), which used
the concentration index approach to measure health inequalities at the household level
and used the wealth index as the only indicator of socioeconomic status. This paper does
not use the concentration index approach. Instead this paper uses the wealth index as an
indicator of poverty and uses other socioeconomic indicators (education, age, marital
status, urban-rural residence) at the individual level. Thus the specification becomes;
𝑦𝑖 = 𝛽1 + 𝛽2 𝑋𝑖 + 𝛽3 𝑊𝑖 − 𝛽4 𝐻𝐼𝑉𝑖 + 𝜀𝑖
Where,𝑦𝑖 is the dependent variable (sexual behaviour), 𝑋𝑖 is a set of individual-level
covariates (age, education, etc), and W is the wealth index measuring poverty, 𝜋 𝛼 is a
variable which assess the level of comprehensive knowledge of HIV and AIDS, 𝜀𝑖 is the
error variable.
3.4 Description of variables

14
Sexual behaviors
These are dependent variables. The proxy used for risky sexual behavior is condom use with
non marital sexual partners (NSO, 2005). Sexual behaviour in this study will be; condom use,
multiple sexual partners and having paid sex which are considered risky sexual behaviors
(NSO and ORC MACRO, 2010).
Poverty
This study uses wealth index as a measure of poverty. The relationship between wealth and
risky sexual behavior can be positive, negative or neutral.
HIV Knowledge
Knowledge essentially is the recall recognition of specific and universal elements in a subject
area. In the context of HIV and AIDS, having knowledge implies ability to recall facts
concerning causes, transmission and prevention concerning HIV and AIDS. It is expected
that when one has the knowledge of HIV and AIDS, the accompanying behavior would be
rational. That is, having the knowledge of prevention, transmission and other facts would
motivate rational safe sex behavior.
Residence
This variable captures type of place of residence. This variable is used as a controlling
variable to see how urban or rural residence is associated with HIV/AIDS knowledge and
higher-risk sexual behaviors.
Age
Refers to number of years lived since birth and ranges from 15-49 in this study. Age is used
to compare sexual behaviour among men and women in different age groups so as to identify
tangible policy actions focused on certain age groups. Age is recoded into four groups for
both men and women (15-19, 20-29, 30-39, and 40-49), and are all included in the model
Level of education
This is one of the key variables, which captures socioeconomic characteristics of the
population. This variable is recoded into four categories: no education, primary education,
secondary and higher education.

15
Marital Status
This variable captures the marital relationships between men and women. This variable is
recoded into three categories: never-married, currently married, and formerly married.
3.5 Data Sources and sample size
The 2010 Malawi Demographic and Health Survey, with a national stratified probability
sample of 13,574 individuals. Analysis is based on respondents age 15-49. The sample
included a total of 7175 men and 23020 women. Data was collected by the National
Statistical Office in collaboration with ICF Macro.
3.6 Data Analysis
The study will use a statistical package STATA 12. Firstly, Univariate analysis will be done
for each dependent and independent variable. Univariate analysis is done to analyze
observations included in each variable as well as the number of missing values. Secondly,
Bivariate analysis will be done between each dependent variable and independent variables to
show how each dependent variable varies by each independent variable. Lastly multivariate
analysis will be done to analyze the effects of each independent variable on the dependent
variables
3.7 Diagnostic Tests
Multicolinearity
Multicolinearity is one of the problems encountered in regressions. Multicolinearity among
the explanatory variables can be assessed using the pair-wise correlations or Variance
Inflation Factor (VIF). Using the VIF, multicolinearity is a serious problem if the VIF is in
excess of 10 (Gujarati, 1993). If multicolinearity is evident, the process of transforming
variables into their first difference form will be used. This method entails running the
regression, not on the original variables but on the differences of successive values of the
variable.
Heteroscedasticity
Heteroskedasticity problems often arise from cross-sectional differences; the simplest way to
deal with this is to take group means. The Breusch-Pagan / Cook-Weisberg test for
heteroskedasticity is the test for higher order heteroskedasticity and this test will be used to
test for heteroskedasticity in this study.

16
In most cases, where there is heteroscedasticity, models are usually fitted with estimated or
feasible generalized least squares (EGLS or FGLS). However in this study any potential
heteroscedasticity in the probit models is resolved by using robust standard errors.
Correct Model Specification and Overall Significance of the Model
To test the likelihood of incorrect model specification, that is to say, whether the model has
omitted certain variables, has incorrect functional form, or there is correlation between
explanatory variables and the residuals, the Ramsey RESET can be used. However, it must be
noted that it is difficult to determine what the exact problem between the two is exactly
indicated by the RESET.
Endogeneity
Endogeneity is when there is a correlation between the parameter or variable and the error
term. This occurs as a result of measurement error, simultaneity, omitted variables, and
sample selection errors. This results in biasness of the regression coefficient in an Ordinary
Least Squares (OLS), however if the correlation is not contemporaneous, then it may still be
consistent (Woodridge, 2002; Cameron and Trivedi, 2005). There can be a possibility of
causality between poverty and risky sexual behaviour. This is because poverty can cause a
person to indulge into risky sexual behaviour such as survival sex. Likewise risky sexual
behaviour such as outcomes such as HIV and AIDS, among others leads to ill health which in
turn reinforces aspects of poverty by undermining labour capabilities and eroding human
capital potential. Due to lack of instruments in the literature, estimation is done without
testing for endogeneity.
3.8 Conclusion
The chapter has provided a detailed description of the methodology used in the estimation of
various relationships in the study. The chapter has also explained the variables and data used
in the study. In addition to these data sources have been explained.
CHAPTER FOUR

17
PRESENTATION AND INTERPRETATION OF THE RESULTS
4.0 Introduction
This chapter presents and interprets the results of the study. The chapter is presented in four
sections. Section 4.1 presents descriptive statistics of the variables used, section 4.2 presents
the bivariate analysis of the dependent variable with some selected variable. Section 4.3
presents the econometric analysis and the interpretation. Lastly, section 4.4 concludes the
chapter.
4.1 Descriptive analysis
4.1.1 Socio-demographic characteristics
Table 1.1 Socio-demographic characteristics of people aged 15-49
Men Women
Background
characteristics
Number Percent Number Percent
Age groups
15-19 1757 24.49 5040 21.89
20-24 1217 16.96 4392 19.08
25-29 1064 14.83 4313 18.74
30-34 942 13.13 3290 14.29
35-39 777 10.83 2575 11.19
40-44 552 7.69 1777 7.72
45-49 866 12.07 1633 7.09
Education
No education 488 6.24 3390 14.73
Primary 4629 64.52 15339 66.63
Secondary 1894 26.40 3970 17.25
Higher 204 2.84 321 1.39
Wealth quintiles
Poorest 1138 15.86 4539 19.72
Poor 1458 20.32 4506 19.57
Middle 1475 20.56 4721 20.51

18
Richer 1547 21.56 4699 20.41
Richest 1557 21.70 4555 19.79
Residence
Urban 1014 14.13 3068 13.33
Rural 6161 85.87 19952 86.67
Table 1.1 describes the socio-demographic characteristics including age, residence, education
and wealth index among people aged 15-49 years men and women. The population of age 15-
19 represented most of the people for both men and women (24.49 and 21.89 percent
respectively). Moreover, both men and women had more respondents in rural areas than in
urban areas. Highest proportion of people among educational levels for men and women was
those with primary education (64.52 and 66.63 percent respectively). For the wealth index,
compared to women, men had lower proportions of people in the Lowest and higher
proportions of people in the poor, middle, richer and richest wealth quintiles. Women had the
lowest proportion of people in the poor wealth quintile. However, they are no significant
variation in the distributions of men and women in the wealth quintiles.
4.1.2 HIV related Knowledge, attitudes and behaviour
Table 1.2 HIV related knowledge among people aged 15-49
Women Men
1. Knowledge of HIV prevention
Having one faithful and
uninfected partner
19839 86.64 6185 86.78
Not having sex at all 18310 79.96 5631 79.01
Always using a condom 16706 72.96 5212 73.13
Knowledge of
prevention(score 3)a
13645 59.27 4106 57.23
2. Beliefs about HIV and AIDS
Healthy person can have
AIDS
19883 86.83 6573 92.23
Cannot get AIDS from 16604 75.51 5282 74.11

19
mosquitoes
Cannot get AIDS by
sharing food
20794 90.81 6669 93.57
Cannot get AIDS by
supernatural ways
19403 84.74 6322 88.70
3. Comprehensive knowledge about HIV and AIDS
Comprehensive
knowledge(score 7)b
8174 35.51 2682 37.38
4. Mother to child transmission of HIV and AIDS
During pregnancy 17202 75.12 4978 69.85
During delivery 19415 84.79 5723 80.30
During breastfeeding 20906 91.30 6170 86.57
Knowledge of MTCT
(score 3)
15930 69.20 4155 57.91
Prevention of MTCT
Can be prevented
through ARV
19487 90.12 5464 80.97
a. one faithful uninfected partner+ always use a condom+ not having sex at all
b. One faithful uninfected partner+ always use a condom+ not having sex at all+ cannot get AIDS from
mosquitoes+ cannot get AIDS from sharing food with an infected person + cannot get AIDS from supernatural
ways.
Table 1.2 presents the descriptive characteristics of HIV-related knowledge among people
aged 15-49 years between men and women. For HIV prevention methods, women had lower
percentage of respondents who said that people could protect themselves from contracting
HIV by having sex only with one faithful, uninfected partner (86.64%), but higher percentage
of respondents who said that people could protect themselves from contracting HIV by not
having sex at all and using a condom when having intercourse (79.96% and 79.96
respectively), compared to men. However, the percent of respondents who knew all HIV
prevention methods (using condoms, having sex only with one faithful, uninfected partner
and no sex at all) for men was (57.23%) was significantly lower than that for women
(59.27%).

20
For beliefs about HIV, 86.83 percent of women said that a healthy-looking person could have
the AIDS virus, which were significantly lower than the percent of men who said so
(92.23%). Only 75.51 percent of people in women and 74.11 percent of men believed that
they could not get HIV from mosquito bites. Men had significant higher percentage of
respondents who knew that HIV could not be transmitted by sharing food with a person who
has AIDS (93.57%), and who rejected that they could get HIV by supernatural means
(84.74%) than women (90.81%, 84.74%, respectively). Men had a better comprehensive
correct knowledge of HIV/AIDS with score 7 (37.38%) than women (35.51%).
Regarding MTCT of HIV, women knew significantly better than men. There were about
75.12 percent of women who knew that HIV could be transmitted during pregnancy and
delivery (84.79%), while about 69.85 and 80.30 percent of men realized about these
respectively. Moreover, up to 91.30 percent of women, but only 86.57 percent of men, knew
that HIV could be transmitted through breastfeeding. Women had about 69.20 percent of
people who knew all the three methods of MTCT, which were higher than men (57.91%).
Likewise, women had higher percentage of respondents who knew that MTCT could be
prevented by ART (90.12%), compared to men (80.97%).
Table 1.3 HIV related attitudes among people aged 15-19
Men Women
1. Stigma and Discrimination associated with HIV
Buy fresh vegetables 6387 89.62 18787 82.05
No secretive 2843 39.89 6740 29.43
Willing to care 6970 97.80 22216 97.05
HIV teacher allowed
to continue teaching
6546 91.85 20079 87.69
Accept all attitudes 2394 33.37 4669 20.28
2. Attitudes towards negotiating safer sex
Refuse to have sex na na 11672 75.57
Ask partner to use a
condom
na na 12204 79.02

21
Negotiating towards
safer sex(score 2)a
na na 10216 44.38
a. refuse to have sex+ ask partner to use a condom.
Table 1.3 demonstrates the descriptive characteristic of HIV-related attitudes among people
aged 15-49 years. For stigma and discrimination associated with HIV, overall men had more
positive attitudes toward people living with HIV than women. The percentages of men, who
would buy vegetables from a vendor with HIV is 89.62 percent which is higher than that of
women (82.05%). For those willing to care for their relative who is infected, the percentages
of men and women were not significantly different from each other (97.80% and 97.05%
respectively). However, women are more reluctant to allow an HIV infected person to
continue teaching than men (87.69% and 91.85% respectively). The overall percentage of
women who accepted all the methods of safe sex was low at 44.38 percent.
Table 1.4: Sexual behaviour among people aged 15-49 years
Men Women
1. First sex intercourse
Median age at first
intercourse
16 years 18 years
Sex before age of 15 years 2191 30.54 7013 30.46
2. Sexual partnership
Multiple sexual partners(>1) 1417 19.75 1505 6.54
Condom used with recent
partner
1073 20.18 1533 9.02
3. Commercial sex
Paid sex in the last 12 months 285 5.41 na na
Condom used every time paid
for sex
168 84.42 na na
The median age at first sex for men is lower than that for women (16 and 18 years
respectively). However, the percentage of men and women who had sex before the age of 15
years is not significantly different (30.54% and 30.46% respectively). The highest percentage

22
of people that have multiple sexual partners is men with 19.75 percent which is significantly
lower than women (6.54%). The percentage of men who reported to have used a condom
with their last sexual partner was higher than the percentage of women who said the same
(20.18% and 9.02% respectively). However, only 5.41 percent of men reported to have paid
for sex in the last 12 months prior the survey and out of those men 84.42 percent of them
reported to have used a condom every time they had paid sex.
4.2 Bivariate analysis
4.2.1 Condom use and Comprehensive knowledge
Having the knowledge of prevention, transmission and other facts relating to HIV and AIDS
would motivate logical safe sex behaviour (Odu B and Akanle, 2008).
Table 2.1 Condom use and Comprehensive knowledge among women
CONDOM USE AT LAST SEX INTERCOURSE
NOT USED USED Total
Comprehensive knowledge N Percent N Percent N Percent
Not knowledgeable 13939 93.89 907 6.11 14846 100
Knowledgeable 7548 92.34 626 7.66 8174 100
Table 2.1 and table 2.2 show the relationship between the use of the condom and
comprehensive knowledge among women and men respectively. Out of 14846 women that
had comprehensive knowledge of HIV and AIDS, 92.34 percent did not use a condom during
their last sexual intercourse while 94.15 percent of men did not use a condom despite having
comprehensive knowledge.
Table 2.2 Condom use and Comprehensive knowledge among men
CONDOM USE AT LAST SEX INTERCOURSE

23
NOT USED USED Total
Comprehensive N Percent N Percent N Percent
knowledge
Knowledgeable 2268 94.15 141 5.85 2409 100
Despite having comprehensive knowledge, there is still low usage of condoms. Thus the
knowledge is not being put into practice (Munthali et al., 2004). These findings are not
different from those established by NSO and ORC MACRO (2005), where condom usage
was established to be low mostly among ages 15-24 years.
4.2.2 Multiple sexual partners and comprehensive knowledge
It is well established that having multiple sexual partners increases the risk of getting infected
with HIV and other sexually transmitted infections (STIs) (Shelton et al. 2004). Table 2.3 and
table 2.4 show the relationship between multiple sexual partners and comprehensive
knowledge.
Table 2.3 Multiple sexual partners and comprehensive knowledge among women
Multiple sexual partners(>1)
No multiple
partners
Multiple partners Total
Comprehensive
knowledge
N Percent N Percent N Percent
Knowledgeable 7572 92.08 651 7.92 8223 100
The percentage of women and men who have comprehensive knowledge and have multiple
sexual partners was lower than those with no multiple sexual partners (7.92% and 19.87%).
However, compared to women, the percentage of men with multiple sexual partners is higher
than the percentage of women. Mishra et al (2005) found out that multiple sexual

24
partnerships remain common in sub-Saharan Africa, with men having more lifetime partners
than women have and being less faithful to their spouse(s).
Table 2.4 Multiple sexual partners and comprehensive knowledge among men
Multiple sexual partners(>1)
No multiple
partners
Multiple
partners
Total
Comprehensive knowledge N Percent N Percent N Percent
Knowledgeable 2149 80.13 533 19.87 2682 100
However, among women who had more than sexual partners and had comprehensive
knowledge of HIV and AIDS, only 45.47 percent of them used a condom at their last sexual
intercourse. Table 2.5 shows the percentages of women who have multiple sexual partners
and had comprehensive knowledge of HIV and AIDS and used a condom at their last sexual
intercourse.
Table 2.5 Condom use among women who had multiple sexual partners
Condom used at last sexual intercourse
Not used Used Total
Comprehensive
knowledge
Knowledgeable 355 54.53 296 45.47 651 100
4.2.3 Paid sex and comprehensive knowledge
Male respondents in the 2010 MDHS were asked if they had ever paid anyone in exchange
for sex. This type of sexual intercourse is associated with a greater risk of contracting HIV
and other STIs because of compromised power relations and the likelihood of having
multiple partners as a result (NSO, 2010). From table 2.6 among those that had

25
comprehensive knowledge of HIV and AIDS only 3.24 percent reported to have paid for sex
in the last 12 months and 96.76 percent did not pay for sex.
Table 2.6 Paid sex and comprehensive knowledge among men
Paid sex in the last 12 months
Did not pay for sex Paid for sex Total
Comprehensive
knowledge
N Percent N Percent N Perce
nt
Knowledgeable 2595 96.76 87 3.24 2682 100
However, it could be noted from table 2.7 below, that among the men who had paid sex and
have comprehensive knowledge of HIV and AIDS only 27.59 percent used a condom at their
last sexual intercourse. This means that there is low condom use among men who are
involved in high risk sexual behaviour (paid sex).
Table 2.7 Condom use and comprehensive knowledge among men who had paid sex in
the last 12 months
Condom used
Not used Used Total
Comprehensive
knowledge
Knowledgeable 63 72.41 24 27.59 87 100

26
4.3 Econometric Results
4.3.1 Diagnostic results
Diagnostic tests were carried out to check that estimation, hypotheses testing and statistical
inferences of the model are made with accuracy. By allowing for robust standard errors in the
command, any potential heteroscedasticity in the probit models is resolved. There was no
serious case of multicolinearity amongst the explanatory variables since the correlations do
not exceed the suggested rule of thumb of 10. However, we note that the McFadden R2 were
low in all models. This is nevertheless a key concern as cross sectional data analysis normally
exhibits low R2 values compared to time series.
4.3.2 Estimation results
4.3.2.1 Probit model analysis
Table 3 Results of Probit analysis on condom use and paid sex among people aged 15-49
Marginal effects on condom use and paid sex
Condom use Paid sexa
Men Women Men
Age (adults aged 24-49)
Youths (15-24 years) -0.017(0.6317) 0.003 (0.0318) 0.005 (0.0935)
Education (no education)
Primary 0.404 (0.0921)** 0.023 (0.0445)* 0.002 (0.1161)
Secondary 0.111 (0.0975)* 0.047 (0.0533)* -0.012 (0.1319)
Higher 0.0934 (0.1385)* 0.101 (0.0900)* -0.023 (0.2422)
Marital status (never married)
Married -0.092 (0.0633)* -0.014 (0.0348)* -0.015 (0.0930)**
Formerly married 0.0273 (0.1261) 0.027 (0.0510)* 0.023 (0.1692)**
Comprehensive knowledge ( no comprehensive knowledge)
Comprehensive knowledge 0.0117 (0.0657) 0.006 (0.0270)** -0.009 (0.0580)**
Wealth (poorest)
Poor 0.011 (0.0657) 0.006 (0.0444) 0.011 (0.0884)
Middle 0.014 (0.0651) 0.010 (0.0436)** 0.006 (0.0900)

27
Rich 0.033
(0.0604)**
0.016 (0.0405)* 0.005 (0.0849)
Residence (urban)
Rural -0.011 (0.0560) 0.009 (0.0382)** -0.004 (0.0908)
*p value<0.05; ** p-value<0.1; figures in parenthesis are robust standard errors; words in parenthesis are
reference points. a. Data for women not available.
Interpretation of results
Marginal effects are used instead of the actual coefficients in the interpretation of the probit
models. This is because marginal effects captures the probability and are more meaningful
than the actual coefficients. These are obtained by taking the derivative of the dependent
variable with respect to a specific regressor, holding other regressors constant. (Gujarat,
2003; Cameron and Trivedi, 2005).
Age: Age determines the point at which an individual enters into the sexual market. This
variable has been found to be insignificant at the 5 percent and 10 percent level for both men
and women. This suggests that age does not influence the use of a condom and does not
determine the risky behaviour of having paid sex.
Education: The no education variable was set as a benchmark for the education variable.
From the results education does not influence the behaviour of having paid sex. This is so
because education is found to be insignificant at all levels. The likelihood of women with
primary education using condom is 2.35 percentage points as compared to the no education
group. The implication is that primary education attainment has a positive effect on condom
use, implying low risky sexual behaviour. The likelihood of women using a condom
increases as education levels increases as compared to those women with no education. This
result proves that some minimum level education is important in as far as reducing risky
sexual behaviour is concerned.
However, for men the likelihood of using a condom decreases as education levels increase
from primary to higher as compared to women (40.4%, 11.11%, and 9.34% for primary,
secondary and higher respectively). From the results, men are more likely to use condom than
women.
Marital status: Marital status was defined as been married, formerly married and never
married. Never married were set as a benchmark for marital status. From the results, the

28
likelihood of a married woman using a condom decrease by 1.4 percentage points and the
likelihood of women who were married at some point in time using a condom increases by
2.7 percent. The likelihood of married men using a condom increases by 9.2 percent as
compared to the never married. The variables are significant at the 5 percent level.
The meaning of this is that married individuals are not risky as compared to the unmarried, as
defined these results are consistent with United Nations (2005) who also conclusively
established that condom use outside marriage is increasing.
Comprehensive knowledge: An individual is considered to have comprehensive knowledge
if they answered the administered question on HIV methods of transmission and prevention
correct. Thus it takes the value of 1 if they answered all question correctly and zero
otherwise. From the results, at 10 percent significance level, women who have
comprehensive knowledge are likely to use a condom at 0.6 percent. Comprehensive
knowledge of HIV and AIDS does not influence condom use among men. Men who have
comprehensive knowledge are less likely to have paid sex with percentage points of 0.9.
Despite high levels of HIV knowledge and awareness, there is still low use of condom.
Similar results were established with the NSO and ICF MACRO (2011), where despite the
high awareness of HIV and AIDS, there is still low condom use. It is hard to know what the
specific reasons that are still making condom usage to be low despite the HIV and AIDs
message being disseminated.
Wealth: An analysis of wealth indicates that the probability of rich men and women to use a
condom is 3.3 percent and 1.6 percentage points at 10 percent and 5 percent significance
level respectively as compared to those poorest men and women. This could be to the reason
which Cohen (1997) argued that the poor are more vulnerable to HIV/AIDS because they
lack access to methods for practicing safer sex, which might be more costly for them than for
people of higher socioeconomic status. The likelihood of women in middle wealth quintile to
use a condom is 1 percent. However, it was noted that wealth does not influence paid sex
since its variables were found to be insignificant at all levels.
Residence: Rural women were more likely to use a condom than urban women at 10 percent
significant level. However, the difference on the likelihood of rural women compared to
urban women is not statistically significant. This means that residence does not influence the
use of a condom. For men, residence does not influence paid sex and condom use since the
variables are found to be insignificant at all levels.

29
4.3.2.2 Negative Binomial model results
Table 4 below presents the estimated marginal effects after running the negative binomial
regression.
Table 4 Negative binomial results of multiple sexual partners.
Variables Men Women
Age (adults 25-49)
Youths (15-24 years) -0.317 (0.1408)* 0.009 (0.3382)
Education (no education)
Primary 0.013 (0.6987) -0.079 (0.3963)
Secondary -0.138 (0.7254) -0.024 (0.5045)
Higher 0.202 (0.8903) 0.033 (0.7239)
Marital status (never
married)
Married -0.488 (0.1358)* -0.248 (0.3162)*
Formerly married -0.262 (0.2168)** -0.089 (0.2824)
Comprehensive knowledge
(no comprehensive
knowledge)
Comprehensive knowledge -0.410 (0.1512)* -0.141 (0.2379)**
Wealth (poorest)
Poor 0.020 (0.4156) 0.089 (0.4813)
Middle 0.266 (0.4233) 0.005 (0.4989)
Rich 0.118 (0.3878) 0.151 (0.4529)
Residence (urban)
Rural 0.315 (0.3088) 0.212 (0.3699)
** p-value <0.1, * p-value < 0.05; figures in parenthesis, standard errors; words in parenthesis are reference
points(base for dummy variables).
Interpretation of the results
Age: Age as already said determines the point at which an individual enters into the sexual
market. In this case, the youths compared to the adults are less likely to have multiple sexual

30
partners by 31.7 percentage points. This is analysis is only for men since age does not have
an influence on multiple sexual partners for women.
Education: Education does not have an influence on the number of sexual partners’ men and
women have. This is because the variables are insignificant at all significance levels. This is
similar to what Silas (2013) found his study in Tanzania that having multiple sexual partners
is not associated with wealth or education among either men or women.
Marital status: Married men and women are less likely to have multiple sexual partners as
compared to the never married me and women. At 5 percent significance level, married men
and women reduce the probability of having multiple sexual partners by 48.8 and 24.8
percentage points respectively. Formerly married men are less likely to have multiple sexual
partners by 26.2 percent at 10 percent significance level.
Comprehensive knowledge: comprehensive knowledge of HIV and AIDS has an influence
on multiple sexual partners. At 5 percent significance level, men who have comprehensive
knowledge are less likely to have multiple sexual partners. The probability reduces by 41
percent. Women are less likely to have multiple sexual partners when they have multiple
sexual partners at 14.1 percentage points which compared to men is lower. This could be
because women are less empowered economically, legally, culturally, and socially compared
with men, particularly in Africa, which is a key factor in HIV transmission. Many women
depend on their male partners for income, food, clothing, and so forth, which can reduce their
power to negotiate for safer sex. In general, women may engage in risky sexual behaviour out
of economic need (Cohen 1997).
Wealth: Wealth was found to be insignificant in influencing multiple sexual partners for both
men and women. This is different from what Silas (2013) found that wealth has an influence
on men having multiple sexual partners including having sex with non marital partners.
Residence: Residence has an influence on multiple sexual partners among men but on the
contrary it does not influence multiple sexual partners of the women. Rural men are less
likely than urban men to have multiple sexual partners by 31.5 percentage points at 10
percent significance level.

31
4.4 Conclusion
This chapter has presented the study findings and interpretations were in two forms;
descriptive statistics and marginal effects of the probit regressions results and the negative
binomial regression results. The next chapter concludes the study by presenting summary
results, policy implications, limitations of the study and areas for further research.

32
CHAPTER FIVE
CONCLUSION AND POLICY RECOMMENDATIONS
5.0 Introduction
This chapter concludes the study by presenting the summary of findings in section 5.1, policy
recommendations in section 5.2 and limitations and areas for further study in section 5.3
5.1 Summary of Findings
The study set out to find the link between HIV and AIDS knowledge and sexual behaviours.
Knowledge in this study was a person having comprehensive knowledge of the disease and
not only been aware of the disease. The study sort to find out if there are differences in sexual
behaviours of men and women and these was done by separating men from women in the
analysis. Furthermore, the study sort to find out if youths and adults differ in sexual
behaviour and using the MDHS 2010 the youth were described as those people aged 15-24
while adults are those 25 above. Sexual behaviour in this study is limited to three forms;
condom use, paid sex and multiple sexual partners.
The study hypothesised that HIV knowledge has an impact on the sexual behaviour of men
and women. In the study it was found out that comprehensive knowledge of HIV and AIDS
has no impact on the use of a condom by men but had an impact on the use of condoms by
women and the risky behaviour of having paid sex.
Other socio economic and demographic variables were added in the study to see how they
influence sexual behaviour. Age which was used to separate the adults from the youth,
residence which had to measure the differences in sexual behaviour between rural and urban
areas, education which is considered a socio economical determinant of human behaviour,
marital status and wealth of individual were among the variables which were included to
analyse how they affect sexual behaviour.
The study found that age does not influence condom use and paid sex but influences multiple
sexual partners for men only. Education and wealth have an influence on condom use and
paid sex but not multiple sexual partners. There is difference in sexual behaviour when it
comes to sexual behaviour except for the use of a condom by women.

33
5.2 Policy implications
The results have various policy implications. Programmes which will not only improve the
literacy levels, but have the out-curriculum effect of reducing sexual behaviour, must be put
in place in addition to the existing ones. Programmes which aim at changing the attitudes or
perceptions on condoms must continue. Positive attitudes towards condom use will in turn
affect the rate at which safe sex is practiced. Increasing access to condoms must continue for
example distribution of condoms in schools, HIV and AIDS awareness campaigns should be
scaled up.
Increase investments in education for all Malawian youth. This may decrease early sexual
debut and increase HIV/AIDS knowledge. In as far as the impact of education is concerned;
the programmes which improve the education of women must be scaled up. Policy initiatives
should focus on the creation of income-generating activities among the poor. Programmes
which aim at economically empowering women for example soft loans for small scale
business should also be put in place. This will reduce their dependency level on their partners
which will in turn boost their negotiating powers.
Behaviour change efforts should be put in place that is based on the necessity of
understanding and addressing the factors that sustain the culture of transactional sex. There is
a necessity of a community-driven decentralized approach to behavior change efforts guided
by a cohesive message that can be a powerful means through which values, norms and
meanings associated with permissive and liberal sexual practices can be disrupted and
dismantled.
5.3 Limitations of the study
First, DHS surveys do not collect data on household income or expenditure, which would be
used to assess wealth status. The asset-based wealth index used in the study is the only proxy
indicator of household economic status. This index is not preferable in terms of comparability
of wealth status due to differences in the level and distribution of wealth across the country.
Secondly, due to the nature of a cross sectional study, causality could not be analyzed. Thus,
the study could only look for associations, not causes and effects.
Thirdly, analysis is based on individual responses to survey questions. There are cases of
misreporting, especially when dealing with issues related to sexual behaviour. For instance,

34
women tend to under-report involvement in sexual activity, and men tend to over-report and
exaggerate their involvement. Therefore, the findings may be biased due to the fact that men
and women included in the sample misreport their number of sexual partners, age at first sex,
condom use, and so forth.
The results of this study should be treated with caution as no endogeneity test was done.
However, despite these limitations there is reasonable confidence that the data used in this
study are valid, since the information on the variables in the analysis is credible and reflects
expectations.
Further research is required to establish what factors influence HIV knowledge in Malawi. In
addition, a study looking at the determinants of paid sex could be done since prostitution in
Malawi is increasing as such becoming a public health problem since it increases the risk of
HIV transmission.

35
REFERENCES
Awuso –Asare, K. and Annim, S. K. (2008). Wealth Status and Risky Sexual Behaviour in
Ghana and Kenya. Applied Health Econ Health Policy, 6 (1), 27-39.
Cohen, D. (1997). Socio-Economic Causes and Consequences of the HIV Epidemic in
Southern Africa: A Case Study of Namibia. UNDP Issues Paper No. 31. HIV and
Development Programme Issues. New York, NY, USA: UNDP.
Collins, J., and B. Rau. 2000. AIDS in the Context of Development. UNRISD Programme on
Social Policy and Development, Paper No. 4. Geneva, Switzerland: UNRISD/UNAIDS.
Dinkelman, T., Lam, D. and Leibbrandt, M. (2008). Linking Poverty and Income Shocks to
Risky Sexual Behaviour: Evidence from a Panel Study of Young Adults in Cape Town. South
African Journal of Economics, 76(S1), 51-74.
GoM (2011) Malawi Health Sector Strategic Plan, Ministry of Health
GoM (2008) Malawi National Health Policy, 2012, Ministry of Health
GoM. (2006). Malawi Growth and Development Strategy: From Poverty to Prosperity 2006-
2011. Lilongwe, Malawi: Ministry of Economic Planning and Development.
Greene W.H, (2003). Econometric Analysis (5th ed). Upper Saddle River, New Jersey
Gruber, J. (2000). Risky Behaviour among Youth. Accessed May 05, 2014, from:
http://economics.mit.edu/files/59
Gujarati, D. (2003). Basic Econometrics (4th ed). Sidney: McGraw-Hill.
Lema, L.A., Katapa, R.S., and Musa, A.S. (2008). Knowledge on HIV/AIDS and Sexual
Behaviour among Youths in Kibaha District, Tanzania. Tanzania Journal of Health
Research (2008), 10(2), 79-83.
Limwame, K. and Kumwenda, M. (2008). Multiple and Concurrent Sexual Partnerships in
Malawi: A Target Audience Research Report. Blantyre: Pakachere Institute Madise, N., Zulu,
E. and James C. (2007). Is Poverty a Driver for Risky Sexual Behaviour? Evidence from
National Surveys of Adolescents in Four African Countries. African Journal of Reproductive
Health, 11(3), 83-98.

36
Madise, N., Zulu, E. and James C. (2007). Is Poverty a Driver for Risky Sexual Behaviour?
Evidence from National Surveys of Adolescents in Four African Countries. African Journal
of Reproductive Health, 11(3), 83-98
NAC. (2009). National HIV Prevention Strategy 2009 to 2013. Lilongwe, Malawi: NAC.
NAC. (2010). Malawi HIV and AIDS: Monitoring and Evaluation. Lilongwe, Malawi : NAC
NSO and ORC MACRO. (2001). Malawi Demographic and Health Survey (MDHS) 2000.
Maryland: National Statistical Office and ORC Macro.
NSO and ORC MACRO. (2005). Malawi Demographic and Health Survey (MDHS) 2004.
Maryland: National Statistical Office and ORC Macro.
NSO. (2005). Integrated Household Survey (IHS2), 2004. Zomba, Malawi: NSO.
NSO. (2010). Welfare and Monitoring Survey 2009. Zomba: NSO:
NSO and ORC Macro (2011). Malawi Demographic and Health Survey (MDHS) 2010.
Maryland: National Statistical Office and ICF Macro.
Mishra V, Thaddeus S, Kafuko J, Opio A, Mermin J, Hong R, Kirungi W, Cross A, Bunnell
R. 2009. Fewer lifetime sexual partners and partner faithfulness reduce the risk of HIV
infection: evidence from a national sero-survey in Uganda.
Odu, B. K. and Akanle F, F. (2008).Knowledge of HIV/AIDS and Sexual Behaviour among
the Youths in South West Nigeria. Humanity and Social Sciences, Journal, 3(1), 81-88
Olley, B. O., S. Seedat, F. Gxamza, H. Reuter, and D. J. Stein. 2005. "Determinants of
Unprotected Sex among HIV-Positive Patients in South Africa." AIDS Care 17(1): 1-9.
Ray, R. and Sinha, K. (2010), “Measuring the Multi Dimensional Knowledge Deprivation of
HIV/AIDS: A New Approach with Indian Evidence on its Magnitude and Determinants”,
forthcoming in Journal of Biosocial Science.
Stoneburner RL, Low-Beer D. Population-level HIV declines and behavioral risk avoidance
in Uganda. Science. 2004a; 304(5671):714-8.
Uchudi J, Magadi M and Mostazir M, 2010, “A multilevel analysis of the determinants of
high risk sexual behavior (multiple sexual partners) in Sub-Saharan Africa”, Working paper

37
UNAIDS. 2005. Annual Report 2004. Johannesburg, South Africa: UNAIDS Regional
Support Team for East and Southern Africa.
UNAIDS (1997). Impact of HIV and Sexual Health Education on the Sexual Behaviourof
Young People: a Review Update. Geneva: UNAIDS
UNAIDS. 2006. Report on the Global AIDS Epidemic. Geneva, Swaziland: UNAIDS.
UNAIDS. 2010. Towards Universal Access to PMTCT. Geneva, Swaziland: UNAIDS.
Wooldridge, J.M. (2002). Econometric Analysis of Cross Section and Panel Data.
Cambridge: MIT Press

Thokozani Saulosi Economics Dissertation

Recommended

Recommended

More Related Content

Viewers also liked

Viewers also liked (11)

Similar to Thokozani Saulosi Economics Dissertation

Similar to Thokozani Saulosi Economics Dissertation (20)

Thokozani Saulosi Economics Dissertation