Master Program in Economic Demography

Department of Economic History Master Program in Economic DemographyIncome Inequality and HIV in ZambiaEKHR01 Master’s thesis (15 credits ECTS) Spring 2011Author: Caroline Ånmo Supervisor: Therese Nilsson AbstractPoverty has previously been viewed as one of the most important drivers of the HIV epidemic. However, recent studies have found that poor individuals are not more likely than wealthy to be HIV positive. Therefore, economic inequality together with gender inequality has instead been suggested as the main socioeconomic drivers of the spread of HIV. The studies analyzing the relationship between economic inequality and HIV are however few and most of them have used cross-country data and are thus likely to suffer from omitted variable bias and hide large variations in the individual data. This study has therefore, by using cross-sectional data of young women in Zambia, analyzed the relationship at an individual level. The empirical findings show a strong positive relationship between income inequality and risk of HIV infection. This relationship was also found to be related to riskier sexual behavior.Keywords: HIV, AIDS, Africa, inequality, wealth, poverty.2 AbbreviationsAIDS – Acquired Immunodeficiency Syndrome AIH – Absolute Income Hypothesis CHTTS – The CSPro HIV Test Tracking System DHS – Demographic and Health Surveys GCVL – Global Clinical Viral Laboratory HIV – Human Immunodeficiency Virus IIH – Income inequality Hypothesis OLS - Ordinary Least Square RIH – Relative Income Hypothesis LCMS – Living Condition Monitoring Survey STI – Sexually Transmitted Infection TDRC – Tropical Diseases Research Centre UNAIDS – United Nations AIDS organ WHO – World Health Organization ZDHS – Zambia Demographic and Health Study3 Table of Contents 1. Introduction...... 6 1.1 Background...... 6 1.2 Aim and scope...... 7 1.3 Outline of the thesis...... 7 2. Background...... 8 2.1 HIV and AIDS...... 8 2.2 Zambia...... 9 2.3 HIV/AIDS in Zambia...... 10 2.4 Inequality...... 11 3. Theoretical background...... 12 3.1 Income inequality and health...... 12 3.2 Income inequality and HIV...... 14 3.3 Additional determinants of HIV...... 15 4. Literature review...... 18 4.1 Income inequality and health...... 18 4.2 Income inequality and HIV...... 19 5. Data ...... 21 5.1 Description of the data...... 21 5.2 Type of source...... 21 5.3 Limitations of the data...... 21 6. Method...... 23 6.1 Sample...... 23 6.2 Research design...... 24 6.3 Time Horizon...... 24 6.4 Reliability...... 24 6.5 Validity...... 25 6.6 Statistical methods...... 26 6.7 Choice and definition of variables...... 27 7. Empirical analysis...... 31 7.1 Statistical result...... 31 7.2 The association between inequality and an increased risk of HIV infection...... 33 8. Discussion...... 37 9. Conclusion...... 40References Appendix 1 Appendix 2 Appendix 34 List of tables 1...... HI V/AIDS in sub-Saharan Africa...... 6 2...... Ex penditure inequality...... 9 3...... Res ult of measuring HIV...... 22 4...... Co mparison between refused and blood taken...... 22 5...... Blo od test result...... 27 6...... Ma in model...... 32 7...... Th e effects of inequality and income on sexual behavior ...... 34 8...... Th e effects of inequality and income on mobility ...... 365 1. Introduction1.1 Background The HIV/AIDS epidemic is affecting people all over the world. It was first discovered in 1981 and since then almost 60 million people have been infected and approximately 25 million have died of HIV-related causes. Although the HIV epidemic affects people all over the world, the burden of the disease is disproportional. Almost 95 percent of the 33.3 million people who were estimated to live with HIV in the end of 2009 were living in developing countries and approximately 68 percent of all the adults and 90 percent of all the children were living in sub-Saharan Africa (see table 1) (UNAIDS). Table 1: HIV/AIDS IN SUB-SAHARAN AFRICA Adults and Adults and AIDS-related % Adult Children children Adults living children deaths prevalenc Region living with newly with HIV living with among adults e (15-49 HIV infected HIV and children years) with HIV Sub- Saharan 20.3 million 2.3 million 22.5 million 1.8 million 1.3 million 5.0 AfricaWorld 30.8 million 2.5 million 33.3 million 2.6 million 1.8 million 0.8Sub-Saharan Africa's 65.91% 92.00% 67.57% 69.23% 72.22% burden Source: Own calculation using data from UNAIDSPoverty has previously viewed as one of the most important drivers of the HIV epidemic. However, recent studies have found that poor individuals are not more 6 likely than wealthy ones to be HIV positive. In fact, several studies have, by using individual data, found the opposite. Rich individuals are more or equally likely to be HIV positive as poor individuals. There are however limitations of such findings since wealthier individuals are likely to live longer with HIV, which will result in higher HIV prevalence for wealthy individual in cross-sections even if poor individuals have higher or equal incidence rates. There is strong empirical evidence of income inequality being associated with HIV prevalence at country level. This relationship is persistent after controlling for various indicators of poverty, economic development, gender inequality and urbanization, which indicates that it is more reasonable to describe HIV/AIDS as a disease of inequality than as a disease of poverty (Holmqvist, 2009). There is however still few studies that have studied the relationship between economic inequality and HIV/AIDS and most of them have used cross-country data and are therefore likely to suffer from omitted variable bias and hide large variations on the individual level (Durevall & Lindskog, 2009). 1.2 Aim and Scope The aim of this study is to analyze how income inequality affects the individual risk of HIV. Most previous research have analyzed the relationship using cross-country data and are therefore likely to suffer from omitted variable biases and hide large variations in the individual data (Durevall & Lindskog, 2009). This study will therefore study the relationship by using individual data. Since there are few studies that have analyzed this relationship, and even fewer that have used individual data, this study will fill the gap of knowledge that exists of how income inequality affects the individual risk of HIV. RQ: How does income inequality affect the individual risk of HIV?1.3 Outline of the thesis The first chapter of the paper provides the readers with necessary background information about HIV/AIDS and inequality. Thereafter follows a chapter with the theoretical background of the relation between income inequality and general health, 7 as well as the relation between income inequality and HIV prevalence. This is followed by summary of what previous research have found about the relationships. The chapter after that discusses the data and its limitations and is followed by a chapter of the methods used for the empirical analysis. The results of the empirical analysis is then presented and discussed and in the last and final chapter the paper is being summarized and concluded. 2. Background2.1 HIV and AIDS HIV is the abbreviation of “Human Immunodeficiency Virus” while AIDS is the abbreviation of “Acquired Immunodeficiency Syndrome”. These two are not the same thing. The HIV virus infects cells of the human immune system and destroys or impairs their function. It is first when a person’s immune system has been severely damaged by the HIV virus that AIDS arise. A person who has AIDS barely has an immune system and an otherwise harmless cold could be life treating and the risk of contracting a large number of infections and tumors is high (Moberg, 2007a; UNAIDS). When a person becomes infected with HIV, a battle between the virus and the immune system begins (UNAIDS). During this period a person’s HIV status cannot be detected and is therefore called “the window period”. The window period normally last three to six months (www.aids.gov). The progression of HIV is slow and there is a long incubation stage. Even without effective treatment it usually takes many years before the immune system becomes severely damaged. During the years between the infection and the occurrence of AIDS, people may have no or very few symptoms of the disease. The time between the two is usually long and is commonly said to be approximately ten years. Thus, people can carry the virus for many years without knowing that they are contagious. Therefore, it is extremely important to test for HIV (Moberg, 2007b). Every time the HIV virus comes in contact with cells it can infect, the so-called target cells, there is a risk of transmission. The transmission can occur through unprotected sexual intercourse, unprotected oral sex, blood transfusions, share of injection tools or8 mother-to-child transmission (Moberg, 2007a). There is no vaccine or cure against HIV, which means that once a person has been infected he/she will be contagious for the rest of his/her life. However, treatment almost completely blocks the replication of the virus, which means that the immune deficiency of AIDS is not irreversible and the immune system may recover (Moberg, 2007a). The treatment is however expensive and many poor countries are not able to treat all the people that have been infected. A lot of the focus has therefore been on prevention, which is crucial in the long run. There are plenty of prevention programs that emphasis on: abstain, being faithful and use of condom (UNAIDS). 2.2 Zambia Zambia is land-locked sub-Saharan country and boarders to Angola, Congo- Kinshasa, Malawi, Mozambique, Namibia, Tanzania and Zimbabwe (see appendix 1) (UNAIDS; WHO; ZDHS). The country makes up for about 2 to 5 percent of Africa’s land area of 752,612 square kilometers (ZDHS). There are approximately 12 million people living in the country and a just over 1 million of these are living in the capital, Lusaka (UNAIDS).The country is divided into nine provinces and 72 districts. Two of the nine provinces, Lusaka and Copperbelt, are predominantly urban and the remaining provinces, Central, Eastern, Northern, Luapula, Northwestern, Western, and Southern, are predominantly rural provinces (ZDHS). Zambia is, with its 38 percent living in urban areas, one of the most urbanized countries in sub-Saharan Africa (UNAIDS; WHO).The household per capita expenditures in Zambia are on average low and the monetary poverty levels are high. In 2004, approximately 68 percent of the population had consumption levels below the national poverty line and the poverty situation was most severe in the rural areas. When comparing the distribution of monetary means, Zambia is regarded as one of the most unequal societies in the world. As can be seen in table 2, the expenditure inequality at the national level is high and inequality has, to some extent, increased since the late 1990’s (Nilsson, 2009). 1996 1998 2004 Gini 0.518 0.533 0.5449 Table 2: EXPENDITURE INEQUALITYSource: Nilsson (2009)Zambia has for the last couple of years experienced an improving GDP growth rate. This has however not been sufficient to bring about significant changes in the standard of living and health status of the population (WHO). Considerable efforts have been taken over the past decade to improve the health situation in the country but due of a severe HIV/AIDS epidemic, the life expectancy at birth dropped from 45.8 years in 1990 to 38.4 years in 2005 (Nilsson, 2009). 2.3 HIV/AIDS in Zambia The first case of HIV/AIDS was reported in 1984 and was followed by a rapid increase in HIV prevalence (USAID). The HIV prevalence has however remained relatively stable since the 1990’s and the latest numbers of the situation showed that around 17 percent of the population was living with HIV and the most common cause of death was AIDS (UNAIDS; WHO). In Zambia the HIV prevalence among men and women in their most economically productive age, 15 to 49 years old, is high (WHO). Women are 1.4 times more likely to be HIV-infected than men and young women are especially vulnerable. Females in the ages 14 to 19 have six times higher HIV prevalence than males in the same age group. A reason for this is the existing gender inequality, women are, from an early age, taught never to refuse their husband sex regardless of if the husband has extra sexual partners or is unwilling to use a condom (UNAIDS). Other vulnerable groups in the country are orphans, military personnel, sex workers, truckers, fisheries workers and fishmongers (WHO).The knowledge of HIV remains low. A study conducted in Zambia showed that only 45 percent of people aged 15 to 24 could correctly identify the ways to protect oneself from sexual transmission of HIV and only 26 percent of the women and 38 percent of the men who have had sex with a casual sexual partner during the last 12 months reported condom use. Moreover, mother-to-child transmission is common in Zambia 10 and only 25 percent of pregnant women with HIV received a complete course of prophylactic antiretroviral drugs, which are drugs that reduce the risk of mother-to- child transmission. Of all the people in the country who are living with HIV, only 20 percent receive antiretroviral drugs and only 1 in 10 people have ever been tested for HIV and know their status (UNAIDS). The highest HIV rates can be found in urban areas such as Lusaka, Copperbelt and Southern provinces with a dense population. The urban areas have about twice as high HIV prevalence than rural areas and cities and towns with major transport routes tend to have the highest rate (UNAIDS) 2.4 Inequality Inequality refers to the distribution of resources such as income and wealth (Oluwatayo, 2008). It can also refer to the distribution of welfare among groups or individuals and can be measured using either the full distribution of groups or individuals or only the lower or upper parts (Nilsson, 2009; Oluwatayo, 2008). In recent years, there has been an increasing availability of data on living conditions in developed and developing countries. An important contribution to the increasing stock of information is the regular implementation of household budget surveys. There are practical problems of observing, identifying, and collection data on needs or opportunities that are significant. Therefore, applied economists often use measures of monetary indicators and indirectly address differences in needs using equivalence scales (Nilsson, 2009).Two commonly used monetary indicators are consumption expenditures and income. Consumption expenditures are often the preferred indicator, at least in a less developed context where people commonly rely on seasonal employment. The measurement errors differ between the indicators. The measurement error of income is generally larger than the measurement error of consumption and therefore often turns out to be greater than consumption inequality in a given distribution. It can thus be problematic to compare monetary inequality across or within countries over time if the unit bases are not the same (Nilsson, 2009). 11 Gini coefficient is a statistical measure that is commonly used to measure inequality. It can take any value between zero and one, with zero implying total equality and one total inequality (Nilsson, 2009). 3. Theoretical Background3.1 Income inequality and Health “The association between income inequality and HIV prevalence raises questions about the mechanisms involved” (Durevall & Lindskog, 2009, pp.5). Three main hypotheses have been suggested for why income inequality affects health in general. These are following: the absolute income hypothesis, the relative income hypothesis, and the income inequality hypothesis (Karlsson et al., 2010; Nilsson, 2009; Lorgelly & Lindley, 2008; Gravelle et al., 2002). Each of the hypotheses can exist in a union but they are not mutually inclusive, a hypothesis could exist in the absence of evidence of the others (Lorgelly & Lindley, 2008; Nilsson & Waldenström, 2011). The first hypothesis is called the absolute income hypothesis (AIH) and states that it is the individual’s own income that affects the health of the individual and not the distribution of it (Karlsson et al., 2010). A region with high average income but large income inequality may have bad health purely because there are many with low income. The AIH assume health to be a concave function of income, since the positive effect of an increase in income on health is suggested to be diminishing at higher incomes (Karlsson et al., 2010; Durevall & Lindskog, 2009; Gravelle et al., 2002; Lorgelly & Lindley, 2008). If this is the case, analysis of aggregate data would produce a relationship between health and income inequality even if income inequality has no casual effect on health (Durevall & Lindskog, 2009). The second hypothesis, relative income hypothesis (RIH), states that it is the economic situation relative to other individuals in some reference group that affects 12 the health of the individual (Karlsson et al., 2010; Gravelle et al., 2002; Lorgelly & Lindley, 2008). Income inequality is, according to the RIH, an indicator of social distance between individuals. The larger the distance is the more psychosocial stress and, as a consequence, the worse will the health of the individuals be (Durevall & Lindskog, 2009). According to Nilsson (2009, p. 66) “Poorer individual might for example feel stress, loss of respect, distrust and shame when comparing themselves to their richer counterparts in a society which in turn affect their health and well-being”. What reference groups to consider is an important issue that often has been overlooked. It has been suggested that the RIH should be tested with reference to average income within the subgroup of the population to which the individual belongs. One can for example define the subgroup by geographical context, birth cohort or ethnicity (Karlsson et al., 2010). The third and last hypothesis, income inequality hypothesis (IIH), states that the income inequality in a society affects the health of everyone in that particular society (Karlssom et al, 2010; Nilsson, 2009). Three underlying mechanisms have been suggested as potential links between the IIH and health. The first of the three mechanisms relates to social capital and mutual trust. It says that there may be lower level of trust in societies with sharper inequalities, which in turn can have a negative effect on health due to less social interaction (Karlsson et al., 2010; Nilsson, 2009; Nilssom & Waldenström, 2011). Studies have for example found that people who are socially integrated have greater immunological resistance to certain diseases while social isolation have been found to be correlated with unhappiness (Nilsson, 2009). The second mechanism that the relationship mediates through is political (Nilsson, 2009; Nilsson & Waldenströn, 2011). Greater differences between poor and rich are believed to coexist with less common resources, which in turn have an effect on the individual health. Inequality may translate into less public spending as large differences in income often reflect heterogeneous interests between the rich and the poor (Nilsson, 2009).The third and final potential mechanism that the relationship mediates through is violent crime. Violence can, for obvious reasons, directly affect health but it can also 13 make people worry that they or somebody they know will become a victim, which will lead to more stress (Nilsson, 2009). It has been argued that the IIH and RIH are related to each other since low social status makes people feel disrespected, which in turn can generate violence (Karlsson et al., 2010). There is little agreement on the relative importance of the three hypotheses and it has been argued that it may be a third factor that affects both income inequality and health and not income inequality that affects health (Durevall & Lindeskog, 2009).3.2 Income inequality and HIV Since HIV is mainly transmitted through sexual intercourse, the potential mechanisms that link income inequality and the spread of HIV may differ from those that link income inequality and health in general (Durevall & Lindskog, 2009). “The main behavioral proximate driver of the HIV epidemics in Eastern and Southern Africa is believed to be the habit of having concurrent sexual partners and/or risky sex in general” (Durevall & Lindskog, p.6). Transactional sex is believed to be the main direct link between income inequality and HIV. In more unequal societies there tend to be more wealthy men who can afford sexual relationships and relatively poor women who have sexual relationships because of the aspiration of a better life (Dunkle et al., 2004; Durevall & Lindskog, 2009). According to Dunkle et al. (2004), sex in exchange for material gain is common in sub-Saharan Africa but very few of the women who engage in such transactions are being identified as sex workers. If inequality increases transactional sex, it would lead to larger risk of getting HIV infected for all the people in the sexual network (Durevall & Lindskog, 2009). There are good reasons to expect poverty to increase the risk of HIV infection. One view is that poverty makes people shortsighted, which in turn may lead to people taking more risks. Poverty may also result in women exchanging sex for goods or money in order to stay above the subsistence level and men to leave their families for long periods to work, which increase the probability of extra marital affairs. Moreover, a combination of external shocks, such as drought, and poverty is believed 14 to increase risky behavior substantially since poor people are more vulnerable to external shocks. Bad health caused by poverty is also believed to have an important effect on HIV since it increases the per contact transmission rate (Durevall & Lindskog, 2009). The relationship between economic inequality and the spread of HIV may also be connected to lack of social cohesion since lack of social cohesion will make it difficult to organize collective action to implement effective responses to the HIV epidemic (Durevall & Lindskog, 2009; Nilsson, 2009). Moreover, violence tends to be more widespread in unequal societies. This since inequality increases the burden of low social status and makes people feel disrespected and humiliated, which in turn is argued to trigger violence (Nilsson, 2009). In societies where violence is more widespread, gender violence is, as a consequence, believed to be more widespread as well. Gender violence can increase risky female behavior such as early sexual debut and increase the number of rapes, which are believed to increase the risk of HIV infection (Durevall & Lindskog, 2009). The relationship between inequality and HIV may also exist because inequality is associated with more mobility, which in turn is believed to increase the spread of HIV. “The most unequal societies in Sub-Saharan Africa tend to have an economic structure with large commercial farms and mines that generate geographical labour mobility. Prostitution and transactional sex relationships are common at these places, and it is well-known that infection rates are high among migrant workers, and that they might bring the disease to their home communities” (Durevall & Lindskog, 2009, p.7-8). There is also an increased risk of HIV infection for women who migrate since they are exposed to the risk of being forced to sexual activities at the border crossings and in exchange for services like protection (Türmen, 2003)3.3 Additional determinants of HIV The great gender inequality that exists in many of the African countries is believed to increase the risk of HIV infection. For many women, to refuse unwanted sexual advances is not a choice. Women often have very limited control over condom use when engaging in sexual activities. This gender inequality is likely to be larger the 15 younger the women are and therefore increases the risk of HIV infection for young women. Also, young women tend to have less access to information about HIV prevention, health care services, and access to condoms, which makes them even more vulnerable (Pettifor, 2004).It is a common view that better educated people have less risk of getting HIV infected. Senlling et al. (2007) have found a strong positive association between education and condom use. Lack of education may lead to a higher risk of HIV infection since it is associated with poverty and may, as mentioned before, lead to sex in exchange for money. However, according to Glynn et al. (2004), the relationship between education and HIV may be more complex than that since more educated people tend to travel more and postpone marriage, which may increase risky sexual behavior and thus increase the risk of HIV infection (Türmen, 2003). Unmarried and single individuals have been argued to have more casual relationships and sexual partners than their married counterparts. As more sexual partners increase the risk of HIV infection, unmarried women are said to meet a higher risk of HIV infection. However, according to Clark (2004), there are studies that have found higher rates of HIV prevalence among married adolescent girls and young women in Kenya and Zambia than among their sexually active unmarried counterparts. This has been argued to be due to the fact that marriage increases the frequency of sexual intercourse, decreases condom use and practically eliminates young women’s ability to abstain from sex.Male circumcision is one of the world’s oldest surgical procedures and can be performed because of medical, religious, social or cultural reasons. Men that are circumcised have been found to have a lower risk of being HIV infected. The rate of circumcision varies between as well as within countries. There is a link between religion and the culture of circumcision, which may be an explanation to the differences in HIV/AIDS infection within countries as between countries (WHO, 2009). There are studies that have found a relationship between urban residency and safer sexual behavior. A strong relationship has been found between living in urban areas 16 and condom use. Studies have also found that people living in the rural areas are less likely to use condoms, due to limited availability and religious beliefs (Snelling et al., 2007; Fylkesnes et al., 2001). Moreover, a decrease in sexual activities and number of partners has been found among people living in the urban areas while no similar change has been found among people living in rural areas. This indicates that there is likely to be a shift and an increase of HIV infection among rural population (Sandøy et al., 2006; Fylkesnes et al., 2001). However, in sub-Saharan Africa there is a flow of people coming from rural to urban areas, which is likely lead to problems such as over-crowdedness, lack of sanitation, and water in the urban areas, which are all factors that affect the rates of HIV/AIDS negatively (Todaro & Smith, 2009). 17 4. Literature review Income inequality and health The idea that health and income inequality are related to each other became popular in the beginning of the 1990s. Since then, over 200 articles have been published on the topic and although the results vary, many find a strong association between various health indicators and income inequality across countries or regions as well as within countries (Durevall & Lindskog, 2009; Gravelle, 2002). According to Wagstaff & Doorslaer (2000), Rodgers (1979) was probably the first one to draw attention to the relationship by examining three mortality-based population health measures and income inequality. In his study, a relationship between inequality and mortality was found for all the 56 countries that were included in the study as well as for a subsample of less-developed countries (Rodger, 1979; Wagstaff & Doorslaer 2000).Wilkinson & Pickett (2006) conducted a study including 168 analyses in 155 papers that report findings on the association between income distribution and population health. A large majority of the studies, 70% of them, suggested that poor health was more common in societies where income differences were bigger. Also, a substantial difference in the proportion of supportive findings was found between studies that measure inequality in large and small areas, with studies that measured income inequality in large areas being the more supportive ones (Wilkinson & Pickett, 2006).18 The significant variables for health at individual level have been found to vary depending on the used hypotheses (Wagstaff & Doorslaer, 2000; Nilsson & Waldenström, 2011). For the AIH it is the individual income that is most important, for the RIH it is the income of the individual as well as the income in the reference group, and for the IIH it is the income inequality that is most significant. Moreover, what other variables that are important for the individual’s health often vary between individuals. Thus, since aggregated studies disregard differences between individuals, it is problematic to make any conclusions about the effect of inequality from these kinds of studies (Nilsson & Waldenström, 2011). Previous studies that have used individual data do not report as strong support for a relationship between income inequality and health as studies that have used cross- country data (Lorgelly & Lindley, 2008). Moreover, there are several studies have found inequality to be related to ill health at the individual level. However, according to Nilsson (2009), studies that analyze the relationship between inequality and health in developed countries often find no association between the two. Income inequality and HIV There is also strong empirical evidence of an association between income inequality and HIV prevalence at the country level. Over (1998) was probably the first to show such relationship, by analyzing the HIV prevalence in urban areas across developing countries (Durevall & Lindskog, 2009). Since then there have been several studies that have with help of cross-country data found a relationship between income inequality, which is normally measured by the Gini coefficient, and HIV prevalence. This relationship is persisting when controlling for various other indicators of poverty, economic development, gender inequality and urbanization. The relationship has been found for a global sample, a sample of Sub-Saharan Africa, as well as global sample excluding Sub-Saharan Africa. It has also been found in studies on national level that are based on state or province data (Holmqvist, 2009). The size of the effect of inequality changes depending on the specification but a change from an equal society to an unequal society have been found to raise the HIV prevalence by 0.5 to 1 percent. When samples are restricted to developing countries 19 however, there is usually no impact of GDP per capita or poverty on the spread of HIV. In fact, relatively rich African countries have been found to have higher infection rates than poor ones (Durevall & Lindskog, 2009).There are also various studies that have by using individual data challenged the view that poor individuals have a higher risk of HIV infection. Using mainly DHS data for a number of Sub-Saharan countries they often find the opposite, that wealthy individuals are more or equally likely to be HIV positive. However, a possible caveat for such findings is that wealthier people are likely to survive longer with HIV and thus the HIV prevalence in cross-sectional would be higher for richer people even if poor people have higher or equal incidence rates (Durevall & Lindskog, 2009). There are two previous studies that have analyzed the role of poverty at the regional level within a country: Lauchad (2007) on Burkina Faso, and Msisha et al. (2008) on Tanzania. They measure poverty by the headcount ratio and find it to be inversely related to HIV (Durevall & Lindskog, 2009). Both studies find a positive relationship between HIV prevalence and living standards of individuals (Lauchad, 2007; Msisha, 2008). Hence, several studies find that income inequality is important, while most studies on income and poverty, at individual, communal and country levels, fail to find support for the hypothesis that HIV is more common among the poor (Durevall & Lindkog, 2009).Durevall & Lindskog (2009) is probably the first and only study to analyze the relationship between inequality and HIV on individual-level data for a particular country. In their study they find a strong positive association between communal inequality and the risk of HIV infection and that individual absolute poverty does not increase the risk of HIV infection. They also find the relationship between HIV and inequality to be related to riskier sexual behavior, gender violence, and close links to urban areas, measured by return migration.20 5. Data5.1 Description of the data The empirical analysis is based on quantitative data of Zambia. The reasons for choosing this particular country are good access to data and high HIV prevalence. Two nationally representative population-based surveys have been used, the Zambia Demographic Health Survey (ZDHS) and the Living Condition Monitoring Survey (LCMS). The data from the LCMS is from 2000 and is measured at province level, while the data of the ZDHS is from 2007 and is measured at province as well as individual level. The respondents have been randomly selected and the target groups for the surveys were men aged 15 to 59 and women aged 15 to 49. The main advantage of these surveys is that the datasets consist of large samples.5.2 Type of source Since the data was collected in the past and for other purposes than for the purpose of this particular study, the strategy that is being used is archival. When using an archival research strategy it is basically inevitable to use secondary data (Saunders et al., 2009). Thus, there were no data collected solely for the purpose of this study.5.3 Limitations of the data A limitation of the data is that it does not contain all the relevant variables related to HIV prevalence and income inequality. Another limitation is that, due to the nature of the data, it is not possible to make comparisons over time. Thus, it is not possible to say anything about changes and developments. The most important limitation is that it was not possible to collect blood samples from all the women who were selected to 21 participate. As can be seen in table 3, HIV status data was successfully collected from 78 percent of the 3080 women who were interviewed (in the ages 15 to 24). This means that 22 percent of the women refused to give blood samples. This will be problematic and lead to bias result if the refusal is related to wealth. This since the impact of wealth and its distribution on the risk of being HIV infected is being studied (Durevall & Lindskog, 2009).Table 3: RESULT OF MEASURING HIV Freq. Percent Blood taken: 2.419 0.78 Refused: 666 0.22 Total: 3085 100.00However, it does not seem like there is any huge differences in wealth between those whose blood was collected and those who refused (see table 4). Therefore, the relatively high refusal rate is not believed to cause any serious bias. Table 4: COMPARISION BETWEEN REFUSED AND BLOOD TAKEN Poorest Poorer Middle Richer Richest Total Blood taken 327 381 449 630 632 2419 % 13.52 15.75 18.56 26.04 26.13 100.0 0 Refused 90 114 140 169 153 666 % 13.51 17.12 21.02 25.38 22.97 100.0 0Finally, due to the nature of the data it was not possible to include district measures of inequality. This would have been preferred over region since it would give better and more detailed information about the relationship between inequality and HIV. 22 6. Method6.1 Sample To collect data from every single member of the population is often not possible. Therefore, sampling techniques are often being used (Saunders et al., 2009). The sample for the 2007 ZDHS survey consist of 8.000 representative households and is designed to provide estimates of population and health indicators at the national as well as provincial level. The sample design allows indicators to be specifically calculated for each of the nine provinces (Central, Copperbelt, Eastern, Lusaka, Luapula, Northern, Northwestern, Southern, and Western). The sampling frame of the survey was adopted from the Census of Population and Housing of the Republic of Zambia (CPH) conducted in 2000, provided by the CSO (ZDHS, 2009). There are two types of errors that affect the estimates from a sample survey: non- sampling errors and sampling errors. Non-sampling errors are the results of mistakes that were done during the implementation and the process of the data. Examples of such mistakes are: failure to locate and interview the correct household, misunderstanding of the questions, and data entry errors. A numerous of efforts were made to minimize this type of error during the implementation of the ZDHS 2007. It is however not possible to completely avoid non-sampling errors and it is very difficult to evaluate them statistically (ZDHS, 2009).23 It is however possible to consider sampling errors in a different manner as they can be statistically evaluated. In the ZDHS of 2007 the sample of respondents, that was ultimately utilized, was one of many that could have been selected using the same design and size. The results generated by each set of samples would be expected to have some degree of variance from the results of the actual sample selected. Sampling errors are a measure of the variability between all possible samples. The degree of variability, which is not exactly known, can be estimated from the survey results. The ZDHS 2007 sample is a result of multi-stage stratified design and thus complex formulae had to be used to calculate the sample error, for more information see the ZDHS 2009 (ZDHS, 2009). Research design Depending on how the research question is being asked, a research can either be exploratory, explanatory or descriptive. Exploratory studies aim to find new insight into a problem. Explanatory studies also involve finding insight into a problem but focuses on studying a situation or a problem through statistical testing on quantitative data. Descriptive studies on the other hand aim to produce an accurate presentation of people, events or situations and are often used as an extension of, or as a forerunner to, explanatory or exploratory research (Saunders et al., 2009). This study is an explanatory study since statistical testing is performed on quantitative data in order to provide a better understanding of the relationship between income inequality and the individual risk of HIV.6.2 Time horizon According to Saunders et al. (2009), there are two types of time horizons that can be used: cross-sectional and longitudinal. Cross-sectional studies are called “snapshot” studies since they capture a particular phenomenon at a particular moment. They are usually conducted using survey strategy or qualitative methods. Longitudinal studies on the other hand have more of a diary perspective and studies developments and changes over time (Saunders et al., 2009). In this study the relationship between income inequality and HIV will be studied at a specific point in time and is therefore a so-called cross-sectional study. 24 6.3 Reliability There are three requirements that a study need to fulfill to be reliable. The first requirement is that the same result should be possible to attain on other occasions. The second requirement is that other observers should reach similar observations. The third and final requirement is that there should be transparency in how sense was made from the raw data (Saunders et al., 2009). There are four possible threats to the reliability of a study. The first treat is subject or participant error, for example if a questionnaire is conducted during different days of the week it may lead to different results. The second threat is called subject or participant bias and will arise when the respondents say what they think the researcher wants to hear. This error can be limited by promising the respondents anonymity. The third threat is observer error and could occur if several people are conducting the interviews but are using different methods. Structured interviews have been argued to limit this kind of error. The forth and last threat is observer bias and may arise when there are mot than one interviewer since the people conducting the interviews might interpreted the replies differently (Saunders et al., 2009).The reliability of the data is considered to be rather high. The respondents were promised anonymity, which have been argued to limit the second threat to the reliability of a study. Anonymity is probably crucial for the result of this study to be considered as reliable since blood samples were taken and some very sensitive questions were included in the survey. Moreover, the surveys were very structured and interviewers had to follow a script (see ZDHS, 2009), which also have been argued to limit the threat to the reliability of the data. 6.4 Validity The validity of a study concerns whether the findings are truly about what they appear to be about and whether the relationship between two variables is causal or not There are six threats to the validity of a study: history, testing, instrumentation, mortality, maturation, and ambiguity about causal direction. The first threat, history, arises when a study has been influenced by something that happened in the past. The second threat, testing, may affect the result of the study if the respondents believe that the 25 results of the research may disadvantage them in some way. The third threat, instrumentation, may affect the result of the study if there is a change in the middle of the testing. The fourth treat, mortality, arises when participants decide to drop out of the study. The fifth treat, maturation, could be a problem if there are events happening that have an effect on the study. The sixth and last threat, ambiguity, arises if there are difficulties in knowing which of the factor that is causing one (Saunders et al., 2009).No obvious threat to the validity could be thought of except, as already mentioned, the rather high refusal rate for blood sample. The DHS has a worldwide reputation for collecting and distributing accurate, nationally representative data and thus the validity of the data could be expected to be rather high. Two forms of quality control were employed to ensure the quality and validity of test results. The first quality control was an internal quality control, which was established during the testing. The second quality control was an external quality control and was performed by sending ten randomly selected percent of all the HIV samples collected during the survey to Global Clinical Viral Laboratory (GCVL). The sample included both HIV positive and HIV negative samples. The CSPro HIV Test Tracking System (CHTTS) program then randomly selected ten percent of the sub-sample for re- testing. Out of these, approximately 60 percent were positives and 40 percent negatives. The result of the external quality control testing yielded a 99 percent agreement with the results of the first testing, which was conducted by the Tropical Diseases Research Centre (TDRC) (ZDHS, 2009).6.5 Statistical methods Since the independent variable of the study is a binary variable, a binomial logistic regression is being used for the analysis of the data. The logistic regression shares some common features with ordinary least square regression (OLS). For example, it provides you with pseudo R2 statistics and the logit coefficients correspond to the beta coefficients. However, unlike OLS regression, logistic regression does not require normality, does not assume linearity between the independent and dependent variables or homoscedasticity of error terms (Garson, 2008). Logistic regression is used to determine or predict the probability of the occurrence of the event of the dependent variable on the bases of explanatory variables by fitting a given data to a 26 logit function. The logistic regression can either be binary or multinomial depending on the number of category of the dependent variables (Long & Freese, 2006). Logistic modelLogit(p) = log(p/1-p)= β0+ β1X1+ β2X2+ βkXk p = probability of outcome p/1-p = oddsβ0 = intercept β1…. βk = regression coefficients The probability of the outcome of the dependent variable is influenced by the magnitude of the regression coefficient of the given explanatory variables, i.e. high regression coefficient means the explanatory variables affects the probability of that outcome strongly, while smaller regression coefficient has an opposite effect. The logit (p) can take any value and the corresponding probability (p) is constrained to lie between 0 and 1 (Long & Freese, 2006). 6.6 Choice and definition of variables The variables that are included in the empirical analysis are characteristics that have been identified by previous research to be associated with the risk of HIV infection. A table of descriptive statistics can be found in appendix and explanations for the coding of the variables can be found below.Dependent variable HIV status The dependent variable for this study is HIV status. It takes the value one if the respondent is HIV positive and the value zero if the respondent is HIV negative. Blood samples were collected from men aged 15 to 59, and women aged 15 to 49 and provide nationally representative estimates of HIV prevalence rates. In this study however only the HIV status of women aged 15 to 24 are included (see table 5). The reason for this is to avoid possible mortality bias. Including the blood sample for all women may lead to higher prevalence rates among richer women even if they have lower incidence rates than poorer women since richer people are suspected to survive 27 longer with HIV than their poorer counterparts. Men were not included in the analysis due to lack of data.Freq. Percent Blood test result HIV negative 2.145 90.58 HIV positive 223 9.42 Table 5: BLOOD TEST RESULTIndependent variables Age It is reasonable to assume that the HIV prevalence is higher the older the women are since they have been exposed to the risk for longer time. Therefore, to measure the effect of age, the actual age of the respondents as well as its squared form have been included in the analysis. The squared form is included since the relationship between HIV prevalence and age is suspected to be non-linear. Education Education has often been found to be both theoretically and empirically significant in HIV research. However, the findings have gone in different directions. The variable has been categorized into four different dummy variables of schooling corresponding to the educational system in Zambia: none (no years of schooling), primary (1-7 years of schooling), secondary (8-12 year of schooling) and higher education (more than 12 year of schooling). Each of the categories takes the value one if the respondent belongs to the specific category and zero otherwise. The first of these categories, no education, is used as reference category.Income To measure income, and test for the AIH, the household wealth quintile is used. The wealth quintile is based on a wealth index, which is a composite measure of the cumulative living standard of a household. To calculate the wealth index, data on a 28 household’s ownership of selected assets such as televisions and bicycles, materials used for housing construction, and types of water access and sanitation facilities is being used. Each of the household assets is assigned a weight or factor score, which is generated through principal components analysis. The scores are then standardized in relation to a standard normal distribution with a mean of zero and a standard deviation of one and used to create the breaking points that define the different wealth quintiles: poorest, poorer, middle, richer and richest (www.measuredhs.com). These wealth variables take the value one if the respondent belongs to the specific wealth quintile and zero otherwise. The wealth quintile called poorest is used as reference group. Marital status Marital status has been proven to be empirically significant for this kind of research. Unmarried and single individuals have been argued to have more casual relationships and more sexual partners than married individuals. Therefore, a dummy has been introduced to measure marital status and takes the value one if the respondent is married and zero otherwise. Gender inequality Gender inequality is considered to be an important driver of HIV. To measure gender inequality, the proxy variable “wife beating justified if she refuses to have sex with him” is being used. The variable takes the value one if the woman believes that it is justified to beat the wife if she refuses to have sex and zero otherwise.Residence Urban residency has been found to be both positive and negative related to the risk of HIV infection. Therefore, a dummy variable that takes the value one if the respondent is living in the urban areas and zero otherwise is included in the analysis. Age at first intercourse Women who have sex at an early age have been argued to engage more in risky sexual activity and have been found to meet a higher risk of HIV infected. Therefore, to measure the effect the variable age at first intercourse is included in the analysis. Religion 29 To measure the effect of religion, a dummy variable is being used. The dummy variable takes the value one if the respondent is catholic and zero otherwise. The reason for not including a dummy variable for each religion that exists within the country is because most of the respondents were Catholics or Protestants, only 0.43% of the respondents were Muslims and 1.03% belonged to other religions.Inequality To measure expenditure inequality, and test for the IIH, the Gini coefficient for each of the provinces is used. A Gini coefficient is a statistical measure of inequality and can take any value between zero and one, with zero implying total equality and one total inequality (Nilsson, 2009). According to the IIH, the relationship between inequality and the dependent variable, HIV status, is believed to be positive. Expenditures According to the RIH, the relationship between average expenditure and HIV status is believed to be negative. In order to measure expenditures, the logs of average expenditures of each province are included in the model. Male circumcision Men who are circumcised have been found to have a lower risk of being HIV infected than men who are not. Since women’s risk of HIV infection is affected by the male infection rate, a variable for the mean male circumcision in each province has been included in the analysis. Density Population density is often used as an indication of living standard and has previously been found to be negatively correlated with HIV prevalence. To measure population density, the density per square kilometer in each province from year 2000 is included in the model.30 7. Empirical AnalysisWhen a regression contains multiple explanatory variables, there is a possibility of some of these being highly correlated. If this is the case, there will be problems with multicollinearity, which in turn may create problems to distinguish the effect of the explanatory variables on the dependent variable. Therefore, to check whether there are problems with multicollinearity, a correlation matrix was created. The matrix showed that density and expenditures were highly correlated with each other (a value of 0.9074). However, the correlation between density and expenditures did not seem to cause any problems to the model and both variables were therefore included in the model. Logit coefficients are not very informative about the size of the impact of the explanatory variables (Durevall & Lindskog, 2009). Therefore, to get a better sense for the magnitude of the effects, the predicted probabilities of HIV infection were computed (see appendix 4). By comparing the predicted probabilities of inequality 31 when equal to its mean minus half a standard deviation to its mean plus half a standard deviation, the effect of one standard deviation increase in inequality around its mean is obtained. The same procedure is done to get the effect of one standard increase in median expenditures. Also the predicted probabilities of household wealth were compared for when household wealth was set to the poorest, second poorest, middle, second richest, and richest quintile. 7. 1 Statistical results The results of the main regression are presented in table 6. As the table shows, the standard error estimations and t-values for each of the coefficient are included in the model as well. The significance of the variables is based on the calculated t-values and is marked with stars. One star indicates significant at the ten percent level, two stars at five percent level, and three stars at the one percent level. As the table report, the effect of inequality is statistically significant at a one percent level and is positively related to HIV. An increase in inequality by one standard deviation around the mean raises the mean risk of HIV infection by 3.9% (see appendix 3). Median expenditure seems to be negatively associated with the risk of HIV and an increase by one standard deviation around the mean raises the mean risk of HIV infection with 4.3% (see appendix 3). However, in the model there was no statistically significant effect found for median expenditure. Table 6: THE MAIN MODEL Variable Coefficient Std. Err. t-value Age -0.074 0.442 -0.17 Age (squared) 0.006 0.011 0.59 Residence 0.553** 0.239 2.32 Marital status -0.073 0.199 -0.37 Wealth Poorer 0.077 0.389 0.20 Middle 0.767** 0.389 2.20 Richer 1.029** 0.386 2.66 Richest 0.786* 0.428 1.83 Age at first intercourse -0.035 0.053 -0.65 Education Primary -0.181 0.327 -0.55 Secondary -0.144 0.342 -0.4232 Higher -0.132 0.513 -0.26 Religion 0.041 0.199 0.20 Gender inequality 0.398** 0.166 2.40 Inequality 6.259*** 2.304 2.72 Mean province expenditures (ln) -0.849 0.646 -1.31 Male circumcision 0.714 0.480 1.06 Density 0.010 0.009 1.06The results also show that women belonging to the second richest wealth quintile seem to have the largest risk of HIV infection while women belonging to the poorest wealth quintile seem to have the lowest risk. Moreover, if all women belonged to the quintile with the highest risk (the second richest) rather than one with the lowest risk (the poorest one), it would increase the mean risk of HIV infection with 7.1% (appendix 3). This indicates that absolute poverty is not related to higher risk of HIV infection on the individual level. Looking at the control variables, there do not seem to be a relationship between age and the risk of HIV infection. A relationship was however found between living in urban areas and the risk of HIV infection, with young women living in urban areas having a higher risk of HIV infection. Gender inequality was found to increase the risk of HIV infection, which is in line with what have been found by previous research. There seems to be a smaller risk of HIV infection for married women than unmarried women. However, this effect was not statistically significant. Finally, no statistically significant effect was found for the following controls: education, sex at first intercourse, male circumcision, religion, and population density.7.2 The association between inequality and an increased risk of HIV infection To investigate the relationship between HIV infection and inequality even further, regressions with different sexual behavior indicators as dependent variables were created. In this part of the analysis, the behavior of men and older women were also considered when appropriate. The reason for this is that the risk of HIV infection is not only affected by the sexual behavior of oneself but also the sexual behavior of one’s sexual partners and others in a common sexual network. It has been argued that survey data on sexual behavior is likely to have serious issues with bias. However, 33 there is no reason to believe this to be systemically related to inequality or wealth. The result should therefore only contain classical measurement error problem with probable attenuation bias (Durevall & Lindskog, 2009). The first three specifications in table 7 are ordered logistic estimations of the number of sexual partners other than the spouse in the last 12 months. There are three possible outcomes: 0, 1, and 2 or more. Since the outcomes are ordinal and more than two, ordered logistic models are used. In the table below, one can see that inequality is clearly associated with a larger number of non-spousal sexual partners. The probability of reporting non-spousal sexual intercourse increases with 7.3% for young women, 5.8% for all women, and 6.8% for men when inequality increases by one standard deviation around its mean. The result shows no effect of absolute poverty on the number of sexual partners. In fact, when looking at the estimation with all women, the women in the poorest wealth quintile seem to have less non-spousal partners than the women in any of the other wealth quintiles.Table 7: THE EFFECTS OF INEQUALITY AND INCOME ON SEXUAL BEHAVIOUR (1) (2) (3) (4) (5) (6) Dependent Non- Non- Non- Never Condom Paid for variable: spouse spouse spouse had sex use sex partners partners partners Method: Ordered Ordered Ordered Logit Logit Logit logit logit logit Sample: Young Women Male Young Young Men women women women Wealth Poorer 0.299 0.164 -0.045 -0.035 -0.218 -0.277 (0.210) (0.122) (0.107) (0.234) (0.275) (0.226) Middle 0.235 0.256** 0.153 0.024 0.124 0.019 (0.209) (0.120) (0.099) (0.231) (0.284) (0.200) Richer 0.143 0.193 0.328*** 0.635** -0.278 -0.048 (0.241) (0.118) (0.117) (0.334) (0.317) (0.256) Richest -0.167 0.213* 0.099 1.776*** -1.025*** -0.311 (0.327) (0.124) (0.139) (0.298) (0.363) (0.333) Median -1.540*** -1.458*** 0.024 0.230 1.115* -0.115 expenditures (ln) (0.443) (0.293) (1.025) (0.519) (0.575) (0.286) Inequality 6.752*** 6.983*** 5.137*** -5.185** -10.123*** -1.14934 (1.587) (1.057) (0.0.845) (1.770) (2.170) (2.305) The effect an one standard deviation increase in inequality around the mean 7.3% 5.8% 6.8% 4.8% 10% 0.4% Controlls for age, education, population density and residency were also included in all the specifications.The fourth specification in table 7 is a logistic estimation on abstinence among young women. The dependent variable takes the value one if the respondent has never had sex and zero otherwise. The result shows that inequality is associated with a statistically significant higher probability of an earlier sexual debut. The probability of abstinence decreases with 4.8% when inequality increases by one standard deviation around its mean. Young women in the richer wealth quintiles are more likely to report abstinence than young women in the poorest quintile. Thus, it seems like poverty is related to riskier sexual behavior. The fifth specification is a logistic estimation on condom use at last non-spousal sexual intercourse. The dependent variable takes the value one if the respondent used condom and zero otherwise. The result shows that inequality is associated with a statistically significant smaller probability of reporting condom use. When inequality increases inequality increases by one standard deviation around its mean, the probability of reporting condom use decreases with 10%. Moreover, young women belonging to the richest quintile are less likely to report condom use than the ones in the poorest quintile.The last specification is a logistic estimation of whether men have paid for sex in the last twelve months. The variable takes the value one if the respondent has paid for sex in the last twelve months and zero otherwise. No statistically significant effect was found but men in more unequal societies seem to pay less often for sex. There is a chance however that man may have prostitutes in mind and not transactional sexual relationships when answering the question (Durevall & Lindskog, 2009). It has been argued that the relationship between inequality and HIV may also exist because inequality is associated with more mobility, which in turn is believed to increase the spread of HIV. Table 8 reports whether the respondent has been away for more than one month the last twelve months. The dependent variable takes the value 35 one if the respondent has been away for more than one month and zero otherwise. As can be seen in table 8 below, there seems to be a negative relationship between mobility and the risk of HIV infection. A one standard deviation increase in inequality around its mean decreases the probability of being mobility with 9.3% among young women, 83% among all women and 5.9% among men.Table 8: THE EFFECTS OF INEQUALITY AND INCOME ON MOBILITY Dependent variable: Mobility Mobility Mobility Method: Logit Logit Logit Sample: Young women Women Men Wealth Poorer 0.184 0.310** 0.390*** (0.245) (0.145) (0.143) Middle 0.410* 0.231 0.259* (0.235) (0.141) (0.139) Richer 0.563** 0.341** 0.008 (0.221) (0.135) (0.168) Richest 0.490** 0.063 -0.050 (0.222) (0.137) (0.196) Median expenditures (ln) 0.319 -0.041 -0.329** (0.500) (0.320) (0.163) Inequality -5.363*** -4.952*** -3.636*** (1.793) (1.145) (1.373) The effect an one standard deviation increase in inequality around the mean 9.3% 8.3% 5.9% Controlls for age, education, population density and residency were also included in all the specifications.36 8. DiscussionThe result of the empirical analysis shows a strong relationship between inequality and the risk of HIV infection. When inequality increases with one standard deviation around its mean, the risk of HIV infection increases with 3.9% (see appendix 3). This effect can be considered to be substantial given an infection rate of 9.42% among the women in the sample (see table 5). This relationship has been well established before on cross-country level. There seems however only to be one other study that has established the relationship using individual-level data. In the previous study, a strong relationship between inequality and the individual risk of HIV infection was also found, which indicate that there is a relationship between inequality and HIV on the individual level as well. Young women belonging to the poorer quintiles were not found to meet a higher risk of HIV infection than young women belonging to the richer. Instead, the empirical analysis showed that women from households in the second richest and richest wealth quintiles have the largest risk of HIV infection. This indicates that absolute poverty is not related to higher risk of HIV infection at the individual level and is in line with what was found by Durevall & Lindskog (2009) in their study “Economic Inequality 37 and HIV in Malawi”. The aforementioned study found that women from households in the middle and second richest wealth quintiles to have the largest risk of HIV infection and women from the two poorest household wealth quintiles to have the lowest risk of HIV infection in Malawi. One can therefore argue that the relationship between inequality and the individual risk of HIV does not seem to be due to an effect of absolute income on health.The empirical analysis also showed that there seems to be an association between lower mean expenditure and higher risk of HIV infection. However, this effect was not statistically significant. Thus, as the RIH suggests that the risk of HIV infection should increase with mean province expenditure, no evidence of this hypothesis could be found for young women in Zambia.The main proximate behavioral driver of the HIV epidemics in Eastern and Southern Africa have been suggested to be the habit of having concurrent sexual partners and/or risky sex in general (Durevall & Lindskog, 2009). To investigate this relationship, different sexual behavior indicators were used as dependent variables. For the first indicator, non-spousal partners, a statistically significant effect of inequality was found for all the three estimations. For the second indicator, abstinence, inequality was found to be associated with a statistically significant higher probability of an earlier sexual debut. The result of the third indicator, condom use, however showed that inequality was associated with statistically significantly higher probability of condom use. Thus, two of these three indicators showed that inequality affects the risk of HIV infection through increased risky sexual behavior. One can therefore suspect the relationship between income inequality and risk of HIV infection to be related to riskier sexual behavior.While Durevall & Lindskog (2009) found an effect of absolute poverty on sexual behavior in Malawi, the results found for young women in Zambia were somewhat mixed. Young women in the poorer wealth quintiles were found to be less likely to report abstinence but more likely to report condom use than women belonging to the richest quintile. This result may be connected to the negative association between inequality and condom use and an explanation for it may be that individuals that are 38 living in unequal societies engage more in risky sexual behavior and thus have a greater need to use condom than other individuals. Moreover, it has been argued that the relationship between inequality and HIV may also exist because inequality is associated with more mobility, which in turn is believed to increase the spread of HIV. However, the empirical result shows that inequality seems to be associated with less mobility. An explanation to this could be the way mobility is being measured. By using the variable “away for more than one month the last twelve months” one can be expected to capture the effect of people who can afford to go on vacation for more than one month as well. Therefore, it would be preferred to examine this relationship using a different variable. According to Durevall & Lindskog (2009), there are reasons to believe economic inequality to be related to more transactional sex. Women in more unequal societies may engage in sexual relationships not only to ensure their survival but because of the aspiration of a better life. In more unequal societies there are also likely to be relatively wealthy men who can afford transactional sex. If transactional sex is related to inequality, it does not only increase the risk of HIV infection of the individuals that engage in transactional sex but for all the individuals in the sexual network. To measure this relationship the variable ever paid for sex was included. However, no statistically significant effect could be found. One could argue that the variable only measures one side of the coin since it only include the ones who buy sex and not the ones who sell sex. Finally, it has been argued that under-nutrition is related to HIV prevalence and inequality since health increases the per-contact transmission. According to Durevall & Lindskog (2009, pp. 25) “less healthy HIV positive people may be more infectious due to higher viral loads and less healthy HIV negative people may be more vulnerable to infection”. This hypothesis could however not be investigated due to lack of data. 39 9. ConclusionThe aim of the study was to analyze how inequality affects the individual-level risk of HIV infection. The study was carried out using data from Zambia. The reason for choosing Zambia was because of its high HIV prevalence and good access to data. The two main sources of were the Zambia Demographic and Health Survey (ZDHS), carried out in 2007, and the Living Condition Monitoring Survey (LCMS), carried out in 2000. For the analysis of the data, a logistic model of the individual risk of HIV infection was estimated. To avoid possible mortality bias only women aged 15 to 24 was included in the study. This is due to the assumption that richer individuals can survive longer with HIV, which would lead to higher HIV prevalence rates among richer individuals even when the incidence rates are not. A strong association between inequality and the risk of HIV infection was found in the study. An increase by one standard deviation around the mean in inequality was found to increase the risk of HIV infection by 3.9%, which can be considered to be substantial given an infection rate of 9.42%. Moreover, the relationship between HIV and inequality was also found to relate to riskier sexual behavior. The probability of 40 non-spousal sexual intercourse increase with 7.3% for young women, 5.8% for all women, and 6.8% for men when inequality increases by one standard deviation around its mean. The probability of young women reporting of abstinence decreases with 5.6%, and the probability of reporting condom use decreases with 9.9% when inequality increase with one standard deviation around its mean.There are some restrictions, such as data limitation and time, to the result of the study. It would be preferable for future research to use district or neighborhood data instead of province data since it would provide better and more detailed information about the relationship. Moreover, since there are very few studies that have analyzed the relationship between income inequality and HIV on the individual level, it would be useful if similar studies were conducted on different countries in order to increase the knowledge of the relationship.ReferencesClark, S. (2004) Early Marriage and HIV Risks in Sub-Saharan Africa. Population Council, Vol. 35, No 35, pp. 149-160. Dunkle, K-L., Jewkes, R-K., Brown, H-C., Gray, G-E., McIntryre, J-A., Harlow, S-D. 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Zambia Demographic and Health Survey 2007 43 Appendix 1MAP OF ZAMBIA44 Source: http://www.bcnn5.com/zambia-map.jpgAppendix 2DESCRIPTIVE STATISTICS Variable Obs. Mean Std. Dev. Min Max45 HIV status 2368 0.094 0.292 0 1 Age 2368 19.296 2.916 15 24 Age (squared) 2368 380.820 113.515 225 576 Urban 2368 0.498 0.500 0 1 Married 2368 0.411 0.481 0 1 Poorest 2368 0.136 0.343 0 1 Poorer 2368 0.154 0.362 0 1 Middle 2368 0.186 0.389 0 1 Richer 2368 0.263 0.440 0 1 Richest 2368 0.260 0.439 0 1 Age at first intercourse 2361 16.138 1.292 8 23 No education 2368 0.064 0.244 0 1 Primary 2368 0.487 0.500 0 1 Secondary 2368 0.424 0.494 0 1 Higher 2368 0.024 0.152 0 1 Religion 2330 0.192 0.394 0 1 Gender inequality 2084 0.349 0.477 0 1 Inequality 2368 0.527 0.036 0.477 0.570 Density 2368 22.468 20.282 4.6 63.5 Mean province expenditures (ln) 2368 11.664 0.285 11.268 12.226 Male circumcision 2368 0.200 0.174 0.034 0.481 Young women Non-spouse partner 2368 0.217 0.449 0 3 Never had sex 2368 0.296 0.456 0 1 Condom use 1418 0.802 0.398 0 1 Mobility 1062 0.502 0.5002 0 1 All women Non-spouse partner 7146 0.149 0.383 0 3 Mobility 3041 0.412 0.492 0 1 Men Non-spouse partner 6482 0.336 0.611 0 3 Mobility 2902 0.383 0.486 0 1 Paid for sex 4781 0.0529 0.224 0 1Appendix 3Mean46 Province inequality at its mean -0.5 std. dev. 0.082 Province inequality at its mean +0.5 std. dev. 0.121 Province median expenditures at its mean -0.5 std. dev. 0.125 Province median expenditures at its mean +0.5 std. dev. 0.082 Household wealth quintile poorest 0.045 Household wealth quintile poorer 0.048 Household wealth quintile middle 0.092 Household wealth quintile richer 0.116 Household wealth quintile richest 0.093 MEANS OF PREDICTED PROBABILITIES OF HIV INFECTION47

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