Download - THE IMPACT OF HIV/AIDS ON CHRONIC AND TRANSIENT POVERTY

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THE IMPACT OF HIV/AIDS ON CHRONIC AND TRANSIENT POVERTY♣

Federica Marzo♥

16TH MARCH 2004 First draft

Abstract

The aim of this article will be to isolate the impact of VIH/AIDS on poverty and on its transient and chronic components. Coping strategies, aiming to answer to sudden income fluctuations (transient poverty), often have more durable consequences and risk to perpetuate the poverty condition of affected households (chronic poverty). In this paper we will try to find the determinants of chronic and transient poverty and we will insert HIV/AIDS among the usual ones. The distinction between these two typologies of poverty is crucial to understand the socio-economics consequences of the epidemic and to elaborate adequate support policies. After a short theoretical part on the possible impacts of VIH/AIDS on affected household standards of living, we will tackle the empirical part of the study. The data, collected in rural and urban setting, encompass affected and non affected households. The Squared Poverty Gap Index will be used as a measure of poverty, and decomposed in a chronic and transient poverty indicator. The econometric methodology employed will be a simple censored regression model (Tobit) with random effects. In conclusion, the finding of a positive impact of HIV/AIDS on poverty, and especially on its chronic components, must to be taken into consideration for the formulation of policy recommendations and the elaboration of adequate responses to poverty and to the epidemic.

♣ Special thanks go to all the people who had collaborate, directly or indirectly, to the realisation of this study. In particular, the author wishes to thank Jean Claude Berthélémy for his support, Ms Marcela Villarreal and all the Population and Development Unit staff of the FAO, where part of this paper was written, and Mr Frederick Booysen for giving database. ♥ TEAM Pole développement - Université de Paris 1 and CNRS. MSE 106-112 Bd de l�Hôpital 75647 Paris Cedex 13. [email protected] 6 The word coping finds its origins in the literature concerning the individual and its capabilities to manage the stress (McCubbin,1979; McCubbin, 1980). This term has been used in famine literature as well (Watt,1983; Corbett,1988; De Waal, 1989 Devereux, 1993).

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1 Introduction

Nowadays, it is becoming more and more evident that the HIV/AIDS epidemic can no longer be considered simply as an international health issue. For its gravity and characteristics, the illness is reshaping the landscape of development, undermining most of the progress barely achieved during the last century in the developing world. UNAIDS accounts for 42 million people living with HIV/AIDS in the world, 95% of those belonging to developing countries and more than 70% living in Sub-Saharan Africa. If one considers that HIV/AIDS directly affects the life of four people other than the ill person, at least 160 million people are probably concerned by the illness on the African continent (Barnett and Whiteside; 2002).

A demographic shock, such as the one Africa is currently experiencing, cannot be without consequences on the standard of living of the most affected countries and on their economies. The socio-economic impacts of the illness are beginning to unfold and, if nothing is done, the most accredited scenario forecasts the collapse of those countries, the future of which is seriously threatened. This is why experts are now referring to HIV/AIDS as a dramatic development problem. The most emblematic indicator of this situation is the evolution of life expectancy: In the most affected countries of Sub-Saharan Africa life expectancy has fallen to the level of the fifties (UNDP 1997). In Malawi, for instance, in 1985 life expectancy was 45.2 years. Despite the demographic projections forecasting 57.4 years in 2000, today life expectancy in this country is about 36 years (Munthali 2002).

Thus, the illness is able to damage the most important determinants of growth and of socio-economic development: physical capital accumulation (decrease in savings and investments), human capital accumulation (reduction of school enrollment and teaching staff), and social capital accumulation (collapse of family and community structures).

Prevention still remains the most effective means to halt the spread of the illness. Nevertheless, traditional aspects of prevention (i.e. information campaigns aiming to teach people how to prevent infection, or free distribution of condoms) are not sufficient any longer. In fact, focusing only on this form of prevention would mean to accept the commonplace arguing that infection only occurs in the event of dangerous sexual behaviour, excluding all other co-factors which are, on the contrary, dramatically important. One of them is poverty. The reduction of poverty is a strategy of prevention, since in poverty structural determinants of such dangerous behaviors are engrained.

Specialists often say that HIV/AIDS is the illness of poverty. As a matter of fact, it is, in the first place, extreme poverty which makes people's lives a dangerous, unpredictable gamble: their perception of risk is affected, increasing the probability of extension of the epidemic. In order to cope with the scarcity of income, people get involved in risky professional activities.

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Prostitution is the most quoted example, but all work activities implying migrations or frequent travel are concerned.

The relation existing between poverty and HIV/AIDS is very complex, since it works also in the other direction. Thus, a double causality has to be noted: on the one hand, poverty fosters the spread of the illness; on the other hand, the illness increases poverty of affected people through complex socio-economic dynamics. The purpose of this paper is to start analysing this relation, focusing on the impact of HIV/AIDS on poverty. As a matter of fact, the illness, throughout a complex socio-economic process, can intensify poverty (by reducing households coping capabilities and thus increasing their vulnerability to the consequences of the illness). The decrease of income presumably concerns everybody, no matter what his or her ranking in income distribution is. Nevertheless, well-off people are likely to have at their disposal a bigger amount of resources, which enable them to cope, at least fairly well, with the consequences of the illness. On the contrary, people being already poor, or even middle income households, will risk falling further into poverty. A vicious circle of illness and poverty commences.

Hence, in this article we will focus mainly on the dynamic of poverty and AIDS. We will analyse the impact of the illness on the different components of poverty: transient poverty (short term income fluctuations) and chronic poverty (structural poverty). In order to tackle this issue, at first we will analyze theoretically the possible reasons why poverty increases among affected households. These different channels can be divided into direct and indirect. The former depend on the immediate consequences of illness in terms of decreasing productivity and loss of job/income; the latter are caused by coping choices of individuals (households). These short term choices, which aim to respond to sudden income fluctuations (transient poverty), may have dramatic and unwanted long terms effects. Those effects threaten to perpetuate the poverty status of affected households (chronic poverty) in time. The second part of the paper is empirical: we will test the usual determinants of poverty on our poverty indicator and on its transient and chronic components, by means of an already compiled dataset concerning South Africa. Variables concerning HIV/AIDS will be added to regressions. An introductory part concerning the description of the data and the econometric specification methodology will precede the exposition of results.

2 Transmission of poverty in affected households

The relation between poverty and HIV/AIDS is complex and bidirectional. As a matter of fact, we can record, on the one hand, an increase in susceptibility to the illness due to poverty, but on the other, we can also observe the progressive deterioration of the poverty status of affected households and an increase in their vulnerability towards the socio-

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economic consequences of the illness. For the rest of the article the second point will be developped.

Reviewing the existing literature on the impacts of the epidemic is useful to get a glance on the evidence coming from many developing countries. Nevertheless, most of existing works is still very descriptive, lacking in explanatory power. In particular, what is missing is the explanation of what makes people act in a risky way despite all prevention efforts being made so far. More precisely, it is necessary to put in evidence what the structural determinants of behaviour are, theoretically as well as empirically. The study of the dynamic of poverty is a first step towards this important goal, which remains crucial in order to fully understand the dynamic of the epidemic in itself and to halt it once and for all.

Thus, this paper represents one of the first attempts to analyse the impact of HIV/AIDS under this point of view. In the recent past, another article tried to emphasize the relation between the illness and transient and chronic poverty. Booysen (2003) analysed the same dataset on South Africa employed in this article, but he adopted a completely different definition of the two poverty components, as well as a completely different econometric method. The most important difference with regard to Booysen�s work is that, although working on the same database, we will exploit its longitudinal dimension instead of doing a cross section analysis. This aspect is very important since panel dataset are very rare in general, and longitudinal datasets containing epidemiological and socio-economics variables at the same time are even rarer, especially in the contest of HIV/AIDS.

HIV/AIDS impoverishes people. This is a fact. What has not been precisely defined yet are the mechanisms through which this occurs. For this reason, it is of great interest to cut this process of impoverishment in several phases. This would permit to underline the evolution of the different socio-economic impacts. The scale of these impacts depends on many factors, in particular on household endowment of resources (well-off people will react to a decrease in income much better than poor people); on the presence and reaction of the community; on the surrounding environment (access to treatment; services availability; public grants, support and infrastructures�).

Many attempts have been made to define what these impacts are. Their definition must encompass a social dimension as well as a temporal one. The former concerns the effect lived by households, by their structures and resources. The latter concerns its evolution in time. According to these dimensions, specialists have split impacts into two main groups and named them in various ways: long term and short term impacts, gross and net impacts, direct and indirect impacts. For this research, we have decided to use the last classification which seems to better suit our understanding of the phenomenon.

2.1 Direct and indirect impacts

We call direct impacts all the consequences coming from the illness in terms of morbidity and mortality. Even if morbidity and mortality are spaced out in time, we can consider that their consequences are short term. In fact, the time gap between the onset of the symptomatic phase of AIDS

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and the death of the ill is about one year/one year and a half in African countries (Stillwaggon 2000). Nevertheless, it is difficult to estimate the impact of what we call morbidity, since this could go from a simple feeling of tiredness to the complete inability to work. At any rate, in order to simplify the picture, we can say that the direct economic consequences for the household are a decrease in productivity of the ill and, most of the time, of its entourage, and, consequently, a sharp decrease in income.

The decrease in productivity is very important since most of the people living with HIV/AIDS belong to the 15-35 age band, which constitutes the active work force of society. Moreover, it often happens that (at least) some of the relatives stop working, in order to look after the ill. The main consequence of this fall of productivity is felt in income. As a matter of fact, this can decrease to an amount equal to 67% of mean income (Morris, Burdge and Cheevers (2000). The most serious situations are found in rural settings, where most of the time agriculture represents the main source of income but also the first means of subsistence. Income decreases start at the beginning of the symptomatic phase of AIDS and worsen in time, coming to a halt with the death of the ill person. This evolution is due to the diminution of available resources, but it is worsened by a sensible distortion in consumption with a sharp increase in sanitary expenditure. After the death, and if the deceased was the only ill person in the family, households could start recovering little by little. Unfortunately, even in this case, indirect impacts can seriously affect recovery.

Indirect impacts are those deriving from household reactions to more direct consequences of the illness. Human societies tend to develop reaction mechanisms (coping strategies6) and risk management strategies in order to answer to difficult situations. What is important is that these short term strategies, aiming to give some immediate relief, often have dramatic long term effects. The most typical example concerns instruction: if children are prevented from attending school, this will have immediate positive consequences on income, since they will go working, but the opportunity cost of them attending school cannot be calculated just in terms of immediate work remuneration, because long term consequences need to be taken into consideration as well (loss in human capital accumulation and productivity for the future). Moreover, another dramatic consequence is with regard to gender issues, which are crucial in the evolution of the HIV/AIDS epidemic: girls are the first to suffer from this choice, since their withdrawal from school represents a missed opportunity of emancipation. The consequences of this are important for every aspect of the development of African society.

As for AIDS, the crisis is managed in very different ways, according to the social status of the households concerned. The affected population being heterogeneous, indirect impacts are heterogeneous as well and in general they have as a consequence the increase in poverty for the poor and, hence, of inequality (Barnett, Whiteside (2002)7. To be more precise, households� reactions and choices are answers to the existence of a risk whose

7 Barnett, Whiteside (2002).

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distribution is unequal: for this reason we say that �poor people cope, rich people don�t cope, they manage�8.

In figure 1, the category of poor includes also households ranked just above the poverty line, or so close to it that a downward fluctuation of their income can push them below the poverty line. The starting point of the scheme is the infection by HIV of both poor people and non-poor people. In order to simplify the model, both categories appear to be infected and fall ill in the same way: in reality, it is proven that poor people are more likely to get infected since their immune system is weaker. This happens because of malnutrition, because of more frequent and untreated STI and because of risky behaviour dictated by poverty. Moreover and for the same reasons, once infected, the evolution of the illness is much more rapid for the poor than for the rich.

Figure 1: The vicious circle of AIDS and poverty

We can assume that the illness has the same direct impacts on both categories. Nevertheless, well off people are probably more able to mitigate these effects.

8 Panafrican Emergency Training Center, Addis Abbeba, july 1998 �Emergency health training programme for Africa� WHO/EHA.

POOR NOT POOR

HIV/AIDS DIRECT IMPACTS

Coping strategies Management of the crises using the available resources.

INDIRECT IMPACTS INCREASED POVERTY

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The important link is the following one: poor people, in order to cope with the direct effects, are engaged in coping choices which may have various longer term indirect effects. These ones can, on the one hand, increase susceptibility to the illness (through the engagement in risky behaviours), and on the other, they can increase poverty.

Households� choices affect consumption, human capital and physical capital investments, savings, and assets in general. The following sequence has been developed: 1- Use of savings; 2- Asset sale (livestock, work instruments�); 3- Debt; 4- Dependent work; 5- Community support; 6- Inaction (which can end up with the disappearance of the household).

Every point of this sequence goes with a progressive reduction in expenditures, such as education and food. Empirical studies produced so far can hardly show the real scale of those impacts, since it is very difficult to have access to useful database, if existent at all.

Indirect impacts of these progressive coping strategies can be summarized as follow:

Distortion in consumption: the first consequence is a strong distortion in the pattern of expenditure. Households react straight away by redistributing resources in favour of the person/s (Ainsworth and Dayton (2003) living with HIV/AIDS. Nevertheless, consumption will not decrease immediately because of the use of savings. Once savings are exhausted, consumption will start decreasing, except for sanitary and burial expenditures and especially food consumption. This can cause an increase in malnutrition, which is related both to the quantity and quality of the available food.

Reduction in physical capital investments: another consequence of the reduction of income and of the re-orientation of expenditure is a reduction in infrastructure maintenance: the house, the soil, work instruments� For this reason, infrastructures and instruments deteriorate and become unusable: houses become hazardous to health, soil loses fertility and instruments rot.

Reduction in human capital investment: the education sector is one of the most affected by the epidemic. Both supply and demand are concerned: supply is affected because the teaching staff has been seriously struck by the illness, recording one of the highest prevalence rate; demand is affected because households tend to withdraw children from school sending them to work or making them look after the ill. Nevertheless it is very difficult to estimate the impact of the epidemic on school attendance, since it is hard to know the real reason why children are withdrawn from school (Coombe (2002). As a matter of fact, sending children to work has always been a way

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to cope with income reduction and with poverty in general, at the same time it is a fact that, in consequences of the HIV/AIDS epidemic, this phenomenon is getting more and more important. Moreover, human capital formation does not concern uniquely school attendance, but every knowledge trnsmission, such as traditional knowledge. HIV/AIDS threatens all type of knowledge transmission (World bank (2003).

Savings: households make their saving choices with respect to the perception of risk and life expectancy. If people forecast that they will not have a future, they are unlikely to save money. A simulation produced in 2002 on South African data showed that saving rate in 2015 will be 15% lower than in a scenario without HIV/AIDS (Freire (2002). This reduction is caused mainly by the diminishing life expectancy. Beside the rational nature of this kind of choice, we have to consider another aspect, which is equally important but hard to be modelled: the horrible pain and suffering generated by the illness and death of a relative. Most probably, in such a situation, people would not think about the future, on the contrary they would let go everything they have in order to give hope or a short relief to a child or relative.

Assets sale: Like for savings, assets become a source of income as well. Many studies have shown that households at a certain point start selling assets. This sale can concern every kind of assets, such as cattle and livestock in general, transport means, furniture, work instruments, radios� Sometimes farmers even raise a mortgage on their future crops in order to get cash immediately15.

All these indirect factors will result, in the medium and long term, in a sharp increase in poverty in all its dimensions (social and monetary). A simulation realized in 2000 on the impact of HIV/AIDS on poverty in Botswana showed that in 2010 at least one quarter of the population will see its income reduced as a consequence of the illness and the percentage of poor will increase by 4%-6%. This means that half of the progress made in terms of poverty reduction during the last years will be erased.

In conclusion, HIV/AIDS sets lose impoverishment, in every social class since available income is reduced. Rich and poor families experience huge emotional and economic suffering. The difference lies in the initial availability of resources: well-off families will have at their disposal the means to manage income reduction, thus being able to escape from the indirect impacts experienced by poor households and generated by dangerous economic choices.

As a matter of fact, even if the illness is at first at the origins of temporary income fluctuations (transient poverty), poor households do not have the resources to cope with the new situation and to come back to their previous living standards. Evidence from Tanzania showed that well-off people often rely on private credits, made inside the community and based on mutual trust: lenders know that the household will service the debt sooner or later. It is evident that poverty concerns social capital as well: nobody will

15 Rugalema (1999), SAfAIDS.

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take the risk to lend money to a poor family without any collateral. For this reason, those households are obliged to take formal credits, with high interests and required collateral.

Poor people risk becoming poorer and poorer and the threat of a vicious circle of illness and poverty becomes a reality. This is because, on the one hand, poverty makes people more susceptible to the infection and, on the other, because in response of direct effects of the illness, people can get involved, as a coping strategy, in risky professional activities, such as prostitution.

In the next section of the article, before tackling the empirical part of the study, we will introduce the poverty indicators employed in the econometric regression. A preliminary empirical analysis on the impacts of HIV/AIDS on poverty will follow, with a special focus on chronic and transient poverty indicators.

2.2 Construction of the poverty indicators

The poverty indicator to be used in this study must comply to precise

characteristics: it must be additive (both over time and across households); the individual poverty function )( ityp must be the same for all households and for every period; this function must be convex (in order to comply with the transfert axiom of Sen (1976) and it must decrease while approaching the poverty line, becoming zero thereafter. Following the example of Ravaillon Jalan (1998), the only indicator satisfying all these criteria is employed: the squared poverty gap (SPG).

SPG is calculated and taken as dependant variables in the regressions. Moreover, this indicator can give the description of the sample in terms of chronic and transient poverty.

2)1()( itit yyp −= if ity < 1

= 0 otherwise

Where )( ityp represents the poverty indicator and ity is normalized with respect to the poverty line and takes value of one for households which are on the poverty line and beyond it16.

Once each household SPG is calculated for each period, the transient and chronic part of it we can be defined. As figure 2 illustrates, the sample can be split in several groups: the always poor (persistent poor), whose income/consumption is always below the poverty line and fluctuates around

16 [ ]

2

1yi)/z-(z1/n SPG ∑=

q

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the mean; the chronic transient poor, whose mean income/consumption is under the poverty line, even if sometimes it moves above it; the transient poor, whose mean income is above the poverty line, but it moves under it sometimes; the never poor, whose income/consumption is always above the poverty line.

The measure of transient poverty is obtained as follow:

−

− )()( iit ypyp

Where −

)( iyp is the chronic component of the SPG and is calculated on the

mean iy , if −

y <z.

Thus, our poverty indicator is:

)()()()(__

ypypypp itity +⎥⎦⎤

⎢⎣⎡ −=

Nevertheless, in order to simplify the econometric exercise, transient poverty will be calculated on mean spg for the entire panel. These operations will not harm to the capability of the model to capture transient poverty (Ravallion and Jalan 1998). Figure 2: The distinction between chronic and transient poor

Transient Chronic Poor Persistent Chronic

Poor

Transient Poor

It may happen that the entire sample is poor in all periods. In this case, the transient poverty measure for household i will be the variance in time of ity .

zy <_

Never PoorMean y > z

zy >_

Always PoorMean y <z

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According to this kind of model, chronic poverty occurs when mean income/consumption falls below the poverty line and transient poverty is simply the difference between total poverty (SPG) and chronic poverty. It is important to note that this definition of chronic poverty goes further than the always poor definition, since only a mean y below the line is required in order to be listed in this category. This allows us to take into consideration the transient part always existing among the chronic poor.

The SPG and its components of chronic and transient poverty have been used for the first regression. Afterwards, the dependant variable has been modified. As a matter of fact, using the indicators as calculated in the previous equations would not be satisfying: If the general SPG automatically gives a rank (going from 0 to 1), the two components tell very little on their relative importance as part of total poverty. For example: if household a has an SPG of 0,9, we know that this family is very poor. By looking at the two components of the general indicator we will notice that the chronic part is 0,3 and that the transient one is 0,6. Let�s consider another household whose SPG is 0,3. Its chronic part is now 0,09 an its transient part 0,21. Looking at those data one would assume that transient poverty is very important for the first household but, in relative terms, it is more important for the second one. In order to take into account this aspect, we have calculated the chronic and transient part of poverty as percentage of the general SPG and used these new variables as the dependant variables of the regressions.

The poverty indicators obtained, have been used in econometric regressions with the goal of finding the determinants of poverty, of chronic poverty and transient poverty, and their relative impact. Among the usual variables generally used in such regressions, we have introduced some HIV/AIDS related variables, such as the serostatus of households (affected if at least one person is living with HIV/AIDS; not-affected otherwise), the number of orphans, and the number of ill or deceased persons during the interview or just before.

3 Empirical approach: Poverty and AIDS in South Africa The second part of the article concerns a preliminary empirical study of the impact of HIV/AIDS on monetary poverty17 and on its dynamic. The following analysis is composed of two sections: the description of setting and dataset and a methodological point and

3.1 Setting and Dataset

17 For description of monetary and non monetary poverty see « L�impact de VIH/SIDA sur la pauvreté chronique et transitoire: une étude de cas en Afrique du Sud� Federica Marzo, master final dissertation.

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The HIV/AIDS epidemic is a dangerous threat for socio economic progress in the developing world. South Africa unfortunately is no exception. The country, recently liberated from the criminal regime of apartheid, has a population of 43.1 millions people, strong social instability and enormous income distribution inequalities. As a matter of fact, despite being generally classified as a middle income country, South Africa has a high level of absolute poverty, which is due to the uneven distribution of income among households (national Gini coefficient 59.3% (UNDP 2003)).

The Apartheid regime was officially abolished in June 1991 and the first multiracial elections took place in 1994. Despite this democratization, many indicators show that inequality between black and white is still important. This kind of regime has generated strong political isolation and disapproval, which explains the interruption of relations with international organizations and the difficulties encountered to find reliable demographic data, at least for black people and during the apartheid period.

The destiny of South Africa in terms of HIV/AIDS is the same as the one of many Sub-Saharan countries. Infact, the environment in which the illness has found fertile grounds is, mostly, the same. Nevertheless, the dramatic past of the country, the caracteristics of its society in terms of poverty and inequalities and the delicate relation existing between its black population and the white developed world can in part explain why South Africa has taken so long to react to the pandemic and why it has taken such enormous proportions. As a matter of fact, the country accounts for the highest number of people living with HIV/AIDS in the world (more than 5 million people). The impact of HIV/AIDS on poverty is estimated using the results of a survey compiled in South Africa between 2000 and 2002. Interviewed people belong, on the one hand, to affected households and, on the other, to a control group of non-affected households. Households were defined in terms of the standard definition employed by Statistics of South Africa in the October Household Survey (OHS), i.e. "a person or a group of persons who live together at least four nights a week at the same address, eat together and share resources". The goal of the survey was to explore the quality of life of affected and non-affected households. To do so, questionnaires have been sent four times to the same households. The interval between each wave was of six months. Interviews were made always with the person �responsible for the daily organization of the household, including household finances". The survey was carried out in two different settings of the Free State province: an urban district (Welkom) and a rural one (Qwaqwa). In both communities the epidemic of HIV/AIDS is particularly rife.

Three of the characteristics of this database render it particularly interesting: its longitudinal dimension, the opposition between rural and

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urban setting, and the epidemiological information, generally very difficult to collect because of the delicacy of the questions.

i) Rural vs urban setting:

According to the report19 entitled Measuring Poverty published by Statistics SA in early 2000, the Welkom magisterial district, situated in the Goldfields, is the third richest in the Free State province, with a headcount poverty ratio of 0.34 and average monthly household expenditure of R2364. The magisterial district of Witsieshoek, which is within the boundaries of the former Qwaqwa, is the poorest in the Free State province and also ranks amongst the poorest in the country. The headcount poverty ratio in this district is 0.69, while average monthly household expenditure amounts to R807. Thus, the particular selection of study sites also allows one to compare the household impact of HIV/AIDS between communities that differ substantially in terms of the general standard of living (Statistics South Africa, 2000). Moreover, the distinction between rural and urban setting is made on a governance bases (traditional vs modern) and not on the differences in dwellings equipments and infrastructure endowments.

ii)Affected vs not-affected households:

This distinction is probably the most important in this study since it is related to the definition of affected. All the impact evaluation derives from this definition. Affected households are those in which, at the time of the interview, someone has declared being HIV positive. Households belonging to this group have been selected through NGO�s and public services working in the field of HIV/AIDS. Informed consent prior to the utilisation of the data has been given by concerned people or by their relatives. An important aspect needs to be underlined: HIV positive people who accepted to participate in the study have not necessarily informed their family about ther serostatus. This choice has been respected by fieldworkers. Nevertheless, under a statistical point of view, we can easily expect the answers of those families to be biased. For this reason, it would have been interesting to split the affected group into two sub-groups: the aware, and the non-aware. This could have given a better idea of the impact of the illness on individual and household choices. Unfortunately we do not dispose of this information.

As for non-affected people, they have been selected among the neighbours of affected households by asking them whether someone in the household suffered or had suffered from TB or pneumonia. If the answer was no, the household was selected as non-affected and added to the control group. Thus, the definition of affected and not-affected is very limited: as a matter of fact, taking into account the geographic proximity of the two groups, it is difficult to believe that the non-affected group do not suffer from any indirect consequences linked to the illness.

This variable appears to be very problematic, most of all because of a

possible problem of endogeneity. It is important to note that this is a preliminary study and we are aware of the limitations of it. The necessity to

19 �The Socio-Economic Impact of HIV/AIDS on Households in South Africa: Pilot Study in Welkom and Qwaqwa, Free State Province� Booysen, F. le R., Bachmann, M., Matebesi, Z. & Meyer, J. October 2003.

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create an instrument for this variable and run simultaneous equations is under consideration at present, despite the difficulties encountered in finding of an adequate instrument for a health variable such as affected/non affected.

iii) Panel data:

Having a panel dataset means disposing of the same information, for the very same people, for a period of time. The importance of such a database lies in the possibility of monitoring the evoution in time of relevant variables. Part of our regressions will exploit the longitudinal dimension of data. The four waves of data collection were completed respectively in May/June 2001 (wave I) and November/December of 2001 (wave II), in July/August 2002 (wave III) and November/December of 2002 (wave IV). To date, the baseline sample has suffered an attrition rate of 13.5 percent (or 55 households). In almost 90 percent of cases the reasons for attrition are related to migration.This illustrates the manner in which migration and the disintegration of households, which are important effects of the epidemic, can act to erode the sample population.

Due to sampling design and small sample size, results coming form the analysis of this household impact study are not statistically relevant to represent the overall South African population. As a matter of fact, selected households are most of all poor (two third of black population in South Africa are poor), African households that utilize public health care services (Booysen et al., 2002c). Thus, the research is indicative only (but nevertheless revealing) of the socio-economic impact of HIV/AIDS on South African households, a characteristic shared by most other HIV/AIDS household impact studies (Booysen and Arntz, 2003)20.

3.2 Econometric specification

The econometric method used is the simple Tobit, since the dependant variable is latent and censured. In our case, y is represented by the SPG which has the value of 0 (limit value) for households whose income is more than R500, being the poverty line R250 (Booysen 2002).

In the Tobit model

y = y* si y* > 0 with y*= x�β+µ = 0 si y* <= 0

Where y is the dependant variable, x� is a vector of explanatory variables always observed, β is the parameter vector to be estimated and µ is the error term normally distributed (N(0,1)). What is important is that the variable of interest y* is not observed once it goes above the chosen limit, in this case R500.

20 For a more accurate description of the sampling, the survay modalities, the questionnaire emplyed and the difficulties encountered during filedwork, see �The Socio-Economic Impact of HIV/AIDS on Households in South Africa: Pilot Study in Welkom and Qwaqwa, Free State Province� Booysen, F. le R., Bachmann, M., Matebesi, Z. & Meyer, J. October 2003.

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Thus the model appears as follow21:

*)()( itit ypyp = if 0)*( >− ityz with µβ += ')*( xyp it

Before showing results, some precisions about our method are necessary. Namely, we will explain the choice of the indicator of standard of living used and its characteristics, and the choice of the censure at R500. In order to measure poverty, we have to choose a standard of living indicator: generally, as for monetary poverty, the choice is done between income and consumption. Income reflects households/individual consumption possibilities and, for this reason, it is often employed to measure poverty (Hagenaars (1991)). In the database used for this study, the information on income comprises employment income and non-employment income, including remittances. A total estimate of income has been obtained by summing all these figures up.

An alternative source of information is consumption. During the survey, volunteers in charge of the delivery of the questionnaire collected information on households� pattern of expenditure, as the interviewed persons were those in charge of the management of the household. Data concern expenditure for food, education, health, debt repayment, cloths� Like for income, the measure of total consumption is obtained by summing all these figures.

Generally, researchers prefer to choose consumption as the best indicator of the standard of living to measure poverty. Many reasons exist in favour of this choice: firstly, income can fluctuate considerably, especially in the short term. On the contrary, consumption is smoothed thanks to the use of savings. Moreover, since consumption is financed by savings, looking at expenditure gives information on past and future income. Secondly, even if it was easier to think that income is used by all members of the household, in reality there are still important inequalities and discrimination within the family. As for our data, estimations for income are higher than the ones for expenditure, even though one would expect the opposite. The likely explanation is that the interviewed person has knowledge about collective expenditures and not about individual consumption, as individuals seldom record expenditures in detail. On the contrary, the interviewed person is probably better informed about the job and income of every household member. If we do not consider this last consideration, specifically related to our database, consumption seems a better indicator. Nevertheless, the special case of HIV/AIDS provokes another important consideration: literature on the subject suggests that the illness affects consumption and income (Booysen (2003)) in different ways. We have already noticed in the previous section that households� supply of labour can fall, causing a sharp income

21 The model is estimated with the command xttobit, by the software STATA8. This model estimates the coefficients with a Maximum of log likelihood method. Thus, we obtain the estimators of Max log likelihood, which maximizes the probability of obtaining the observed values of y. The tobit is estimated in panel (four waves ) with random effects.

16

reduction. At the same time, expenditures for health care and burial services increase. For this important reason, income estimations give a better picture of households� standards of living and of the impact of HIV/AIDS on it.

Therefore, income is used in the calculation of the SPG. Nevertheless, some modifications are necessary: on the one hand, we have to consider the size of the family using an equivalence scale; on the other, it is necessary to correct data with respect to the country price index. As for the latter operation, real income has been derived from the estimation of total income adjusted for the Consumption Price Index published by the statistics office of South Africa (Statistics South Africa 2003).

For the first operation, we have calculated the real adult equivalent household income using the OECD equivalence scale. The calculation is the following:

)3,0)1(5,01( ba nny

+−+

Where y is the real total monthly income of the household. The denominator is the term which considers the size of the family ( an being the number of adults and bn the number of children) and the equivalence scale. Originally, the operation would be

αny

Where n is the number of persons in the household and the α is the adjustment for scale economies. In our case, different people in the households have different scale economy adjustments: the head of the family accounts for 1, the other adults account for 0,5 and children account for 0,3. This decomposition allows us to describe the situation existing in the household with more precision. We have chosen to fix the censure at R500 for several reasons. First of all, HIV/AIDS affects the income of households belonging to every social class. Nevertheless, as we have previously seen, well off people dispose of the necessary resources to manage the crisis avoiding majors long terms consequences. On the contrary, other households will suffer more from the falling of income, and will make economic choices which will have an influence on their future. Poor people are the first to be concerned by this. Anyway, also people having an income slightly superior to the poverty line risk not having the necessary resources to cope. Thus, we have chosen R500 because it is close enough to the median values and because it is precisely the double of the poverty line indicated by South African Statitical organs (250 rands).

Moreover, the second reason is a technical one. In order to avoid the problem of heavy censoring (the excessive presence of censured values) the

17

number of zeros in the regression must not represent more than 50% of the sample. The bias linked to a non-normal distribution of residuals (which represents the main criticism advocated against this kind of model) can be substantial, depending on the level of censorship (Arabmazar and Schmidt (1982)). The limit of R500 satisfies this condition.

4 Results

4.1 Descriptive statistics

The descriptive analysis of data confirms what previously announced by theory28.

Direct impacts concern demographic modifications as well as income modifications, namely employment income. Both are strictly related, the composition of the household being an indicator of its work supply. As for demographic aspects, the analysis of our sample confirms that the most affected part of the population is comprised between 20 and 40 year olds. This means that the most active part of the population, in terms of labour force, as well as reproductive capabilities, is at stake.

Previous analysis showed that 72% are treated at home: the probability of being home-treated is appreciably higher for affected households (75%) than for non-affected (62%). The same is true for the duration of treatment (the average is of 20 days for affected people and it falls to 14 for non-affected) and for the time spent by household members to treat the ill relative (4 hours per day in affected households and 3 hours in the others). This evidence shows that the impact on employment income must be higher for affected households, since more time and energy is lost by people affected by AIDS as well as by their caregivers. Nevertheless, data concerning caregivers also show that the number of lost working days is

28 For a detailed analysis of the data see �The Socio-Economic Impact of HIV/AIDS on Households in South Africa: Pilot Study in Welkom and Qwaqwa, Free State Province� Booysen, F. le R., Bachmann, M., Matebesi, Z. & Meyer, J. October 2003.

18

the same for affected and non-affected households29 (7 days per month). Besides, in the terminal phase of the illness (AIDS), people caring for the ill are most of all the inactive members of the family, namely women. In this case, even if, on the one hand, the opportunity cost of caregivers is lower in terms of income, on the other, all other traditional female activities, e.i. reproductive and domestic activities, are, at least in part, abandoned.

Affected households31 suffer from a permanent drop in labour

supply, which chronically diminishes their potential income. In order to look at potential labour supply, one can consider the family structure, namely the number of people in the household and the dependency ratio. A comparison of these data over the entire panel could be revealing as well. Results can be found in the table below (table1).

Table 1: dependency ratios and mean number of people in household with respect to serostatus and setting

May/June 2001

(wave I) Nov/Dec 2001

(wave II) Jul/Aug 2002

(wave III) Nov/Dec 2002 (wave IV)

dpr Nber people dpr Nber

people dpr Nber people dpr Nber

people Affected 37,5 5,5 38,1 5,3 37,2 5,2 36.1 5.2

Non affected

33,8 4,4 35,6 4,4 34,3 4,5 34.9 4,5

Our results show that affected households are larger than non-affected. This is true most of all for the urban community, where the average number of people is slightly superior (6 persons). This evidence could make one think that affected households have a higher labour force at their disposal. Nevertheless, a glance at data concerning dependency ratios (inactive people/total number of people) is enough to change this view: this ratio is, as a matter of fact, largely superior in affected households. On average for the four waves, the ratio is of 37% for affected and 34% for not affected. Once again, this difference is bigger for the urban community. This new evidence tells us that there is a relatively high number of inactive people among household members, namely children under 15 and elderly persons. Even if they are bigger, affected households risk not having enough labour supply. No significant evolution is registered from the first to the last wave of observations.

As for income (table 2), affected household are poorer than not affected households. This is true both for urban and rural communities, even if rural incomes are much lower than urban, even among the affected. This

29 When not affected people are home-treated 31 Our analysis was done on 315 households, the ones ofr who information on income was available for every wave of the panel.

19

datum supports national statistics outlining that the poorest South African are African, rural and women (J.May 2000). Table2: Mean real equivalent per capita income (in rands)

May/June 2001



(wave III) Nov/Dec 2002

(wave IV) Total affected 488 428 440 445

Total non affected 867 687 615 595

The poverty line established by the statistical service of South Africa is

of R250. Nevertheless, for our analysis we have set it at R50033.

Table3: Real equivalent per capita income for affected households in terms of not affected household income (in percentage).

May/June 2001



(wave III) Nov/Dec 2002

(wave IV)

Total affected 56.28 62,29 71.54 74,78

Total non-affected 100 100 100 100

In percentage terms (table 3), affected income equals for 64% of non-affected household income, on average for the entire panel. More precisely, it passes from 56% of the first wave, to 75% for the last one. It is important to note that this improvement is not caused by an increase in affected households� income but by a sharp drop recorded for non-affected households, especially urban. Besides, it is useful to note that, as for mean values, the decrease of income occurred both for affected and non-affected. Nevertheless, data concerning median values reveal that income has slightly grown, since median income has passed from R340 of the first wave, to R352 of the fourth. This evidence could stand for a reduction of income belonging to the highest quartile. Moreover, median values reveal that most of the households have a very low income (affected household are just above the poverty line with R297 and that distribution of income is very uneven, reflecting its aggregated data. A transition matrix will shed further light on this aspect.

In conclusion, the evidence shown by these data tells us that affected households are poorer than non- affected ones, and that they dispose of fewer means to make a living, since their active labour force is weakened, if

33 For a detailed explanation of our choice, look at the previous section on methodology.

20

not destroyed by the illness. The econometric exercise, will clarify the role of HIV/AIDS in explaining poverty.

Indirect impacts are longer term, and derive from economic choices taken by households. In this section we will have a look at the different ways in which affected and not affected households spend their income, on the one hand, and react to financial crises, on the other. Namely, we will briefly check if affected households act differently in terms of debt, sale of assets, and use of saving. These are some among the co-factors that could make them fall in the vicious circle of poverty and illness.

As for the pattern of expenditure, previous analysis34 on data has shown that affected households in general, but particularly affected households that have experienced morbidity and mortality in two or more periods, allocate relatively MORE of their resources to food, health care, and rent and relatively LESS to education, transport, clothing, and personal items when compared to non-affected households. Of particular importance is the apparent crowding out of household expenditure on education, personal items and durables in affected households in favour of expenditure on health care, food and other basis necessities.

Moreover, a dramatic drop in expenditure on food is to be noted concerning affected households. For them, the mean adult equivalent per capita food expenditure represents, on average, 76% of non-affected households� expenditure (R108 vs. R141). This ratio reaches 50% for affected households that have experienced morbidity and mortality in two or more periods. In the longer run, this distortion in expenses may contribute to malnutrition among household members and to a series of negative effects as for the evolution of the illness (or new infections).

On average, the amount of savings used by affected and non-affected households is of R3451 and R3049 respectively. The relative amplitude of this dissaving is considerable, particularly for affected households who experienced morbidity or mortality in two or more waves of the panel. In percentage of average annual income, utilized savings represented 21.3% for affected households, and 12.9% for not affected households. Moreover, this amount is even more telling if we consider the evidence that affected household save half of the amount saved by non-affected (R167 vs R348 on monthly average), which is in part normal since their income accounts for almost a half of not affected households�35. It is worth noting that this dissaving was lowest in affected households that have experienced morbidity or mortality in one period only or not at all: this tells us that the utilization of savings, as argued above, represents a possible response to relatively severe and ongoing financial crises.

The main reasons that urge household to use their savings are different for affected and non-affected households. The most quoted reasons for affected households were to pay for expenses on funerals (50%) and medical expenses (18%), followed by food (12%), clothing (9%) and transport (6%).

34 see �The Socio-Economic Impact of HIV/AIDS on Households in South Africa: Pilot Study in Welkom and Qwaqwa, Free State Province� Booysen, F. le R., Bachmann, M., Matebesi, Z. & Meyer, J. October 2003. 35 See previous paragraph for data on income.

21

In non-affected households in turn the most often quoted reasons for utilizing savings were to pay for food (24%), education (21%) and funerals (21%), followed by maintenance of assets (15%), investments in house reparations (9%), and the purchase of durables (6%).

On average, households belonging to our sample own relatively few assets. Moreover, asset ownership is relatively lower in affected households in general and, in particular, in affected households that have experienced morbidity or mortality more often. A decline in asset ownership all over the periods is experienced by affected households that suffered from morbidity or mortality in each period. A possible explanation could be that these households are relatively more likely to have sold assets than non-affected households. At any rate, the assets index, representing assets holdings, is relatively low probably because very few households of the sample are able, or willing, to exercise this financial strategy. The absolute value of the gains coming from the assets sale and their relative importance were sensibly higher for affected households than was for non-affected households. This suggests that asset sales, although relatively uncommon, represent a substantial source of resources in times of financial crises. However, the loss of any asset has a consequence on the overall wealth of that particular household. This means that those households would be, in the longer term, more vulnerable to the impact of the epidemic.

After having turned to savings and assets sale, in case of income fluctuations households may recur to borrowing. The levels of mean real household debt are considerably higher for non-affected households than for affected households (R10416 vs R8161), being the lowest in affected households that have experienced morbidity or mortality. This evidence can be explained in part by the fact that higher levels of income (as it is the case for non affected households) makes it possible to borrow larger sums of money with lower interest rates. Nevertheless, these differences are not as pronounced in terms of the median of real household debt. In fact, median household debt of affected households often exceeds that of non-affected households (R3383 for affected vs R3000 for not affected). Affected household may need to take on new debt to pay for health care and/or funerals. New borrowing is very dangerous for household suffering from chronic illness, because morbidity (and mortality) could make it difficult, if not impossible, the repayment of the new debt. For this, new borrowing could play an important role in perpetuating or worsening the poverty status of affected households. The last aspect that we have examined before passing on to the econometric regressions is the observation of income dynamics thanks to the construction of a transition matrix. The matrix permits to see whether households� income has moved upwards or downwards. Income has been divided in quartiles. For calculations (descriptive statistics as well as econometric regressions), 315 (out of 402) households of the sample have been taken into considerations, that is to say the ones for which information on income was available.

22

Table 4: Income transition matrix for the entire sample (percentage)

Wave1/wave4

Quartile 1

Quartile 2

Quartile 3

Quartile 4 Total

Quartile 1 44 28 24 4 78 Quartile 2 29 39 28 5 80 Quartile 3 17 22 33 28 78 Quartile 4 11 11 14 63 79

The results of table 4 show that most of the households have a stable income, especially those belonging to the highest quartile (63%). As for moving incomes, percentages are similar, although upward incomes are more numerous (29% vs 26%). This supports the evidence that incomes in general have slightly increased. Table 5: Income transition matrix for affected households (percentage)

Wave1/wave4 Quartile 1

Quartile 2

Quartile 3

Quartile 4 Total


Table 6: Income transition matrix for non affected households

Wave1/wave4 Quartile 1

Quartile 2

Quartile 3

Quartile 4 Total


Comparing affected (table5) and non-affected (table 6) households, it is remarkable that the evolution of income is different. Affected households have a much more stable income (almost always over 40% of the sample). This percentage reaches 50% for those belonging to the highest quintile. Nevertheless, this value is still below that of the non-affected households: for the latter, households belonging to the highest quartile have a more stable income (68%). Hence, 50% of the richest people among the affected have experienced an income decrease: this can explain why the mean values of income have fallen whereas the median values have increased. In fact, the intermediate bands of income, especially for non- affected households, have risen. On the contrary, the major trend characterizing affected households is downward sloping, especially for the upper quartiles. As for the lower quartile, 44% of the people remain in the

23

same quartile. This could mean that income has moved within the quartile, either up- or downwards. Further investigation on income movements inside this quartile is likely to shed further light on this.

4.2 Econometric regressions

The last part of this article concerns a series of preliminary econometric regressions which have been run with STATA8 software. Firstly, we have run a regression of our explanatory variables on the poverty indicators suggested by Ravaillon Jalan (1998); namely, on the general SPG, on the chronic SPG and on the transient one. The aim of this exercise was to test if the usual determinants of poverty, together with HIV/AIDS variables, had an impact on dependent variables. Besides, using the transient poverty indicator, we have tested whether the same poverty determinants were statistically significant for both chronic and transient poverty. Secondly, we have modified poverty indicators, expressing the chronic and transient SPG as the percentage of the general SPG36. In this way, we had the possibility to explain the relative importance of the two elements with respect to the general SPG/figures.

Explanatory variables used in our regressions have been selected among the determinants commonly employed to explain poverty37.

More specifically, we have used variables describing households� characteristics (the sex and age of the household chef), human capital variables (total years of schooling); physical capital variables (number of employed members in households); setting variables (rural vs. urban).

All variables concerning the composition of households (number of household members, number of children, and number of elderly) have been excluded because of the use of the equivalence scale in the calculation of poverty indicators. The variable of medical access, previously added to the regressions, has been excluded because of its ambiguity (the access to treatment may stand for the social status of people having a good job). Variables concerning asset sales, previously included in the regressions, have been withdrawn, too, because of a problem of endogeneity.

Variables linked to HIV/AIDS are the ones suggested by Booysen (2003), namely households' serostatus, the number of deaths and ill persons in the households, the presence of orphans in the households.

36 For a more detailed explanation, please refer to the methodological paragraph.

37 Ravallion et Jalan (1998) ; Haddad et Ahmed (2003) ; Hulme and Shephard (2003).

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Table 7: Regression on general SPG

explanatory variables SPG

setting -0,287 (0,3545756) Age Head of the hh -0,018 (0.119) N.ber employed -1,831*** (0,235) educ 0,051 (0,039) orphans 0,560** (0,185) sex head hh -0,717** (0,363) affected/non aff. 1.111*** (0,349) cons -0,006

(0,753) Wald test 94,39***

822 uncensored observations; 438 left-censored observations;

0 right-censored observations

The results of this first regression are not surprising: the probability

of being poor is higher for rural households (urban=1; rural=0), even if this variable is not statistically significant. In the same way, the variable �mean years of schooling per household member� (educ) is not significant. Striking is the result concerning the other variables, especially the ones related to HIV/AIDS. Usual determinants of poverty appear significant and with the right sign. Namely, the higher the number of employed people in the household, the less likely is this household to be poor; if the head of the household is a woman (man=1; woman=0), the household has higher chances to be poor. As for HIV/AIDS variables, they are statistically very significant and have the expected sign: affected household have more chances to be poor (affected=1; non affected=0). In the same way, households sheltering orphans are more likely to be poor since resources have to share among more people and because it means that the household has suffered (or is suffering) from the consequences of HIV/AIDS.

25

Table 8: Regression on chronic SPG

The second regression was made on chronic SPG. Results are even

clearer. Maybe, this depends on the fact that general SPG comprises also households that are poor only for one period, being not poor for the rest of the panel. Hence, a structural variable such as setting was not statistically significant for our previous regression. On the contrary, the chronic poverty indicator includes all those households whose mean income lies above the poverty line. Structural variables necessarily have more impact.

Looking at results, we notice that now rural households have more chances to be chronically poor, as well as households whose head is very young. The fact that this last variable has become significant may also explain the fact that the HIV/AIDS variable orcnt is not significant anymore. In fact, if the head of the household is very young, he or she is likely to be an orphan. Orcnt variable captures, in part, the impact of Age Head of the hh38. Besides, the older the head of the household, the more likely is the household not to be poor: this result can be explained by the fact that the elderly in South Africa have important income stemming from governmental pensions. The variable expressing households� serostatus is still significant and negative. We have run the same regression, just changing the dependent variable for transient poverty. Unfortunately, none of the explanatory variables turned out to be significant. There are two possible explanations for this: the first one is that transient and chronic poverty cannot have the same determinants

38 For correlation estimations, see at the end of the article.

Explenatory variables SPG Chronic Setting -0,242*** -0,003 Age Head of the hh -0,001*** (0,000) N.ber employed -0,005** -0,002 Educ 0,000 (0,000) Orphans 0,000 -0,001 Sex head hh 0,001 -0,003 Affected/non aff. 0,173*** -0,003 Cons 0,123*** -0,006 Wald test 7870,59*** 815 uncensored observations, 444 left-censored observations; 0 right-censored observations

26

since the latter indicates something structural and needs structural explanation. Secondly, transient poverty indicates income fluctuations in the very short-run: those fluctuations can be caused by any short term shock, which is very difficult to identify with a variable. We have then tried to find out determinants for transient poverty, creating transition variables and limiting the panel to three waves. In addition, we have also substituted the HIV/AIDS variable, choosing a more flexible proxy (impact= the sum of ill people and deceased people in the household, the number of ill people and the number of deaths separately). Unfortunately, results are not more telling. Table3: Regression on chronic SPG expressed as a percentage of general SPG

Explenatory variables

Chronic SPG percentage

Setting 0,9.802*** N.ber employed 0,1.689*** Educ 0,.246*** Orphans -0,115 Sex head hh -1.905*** Affected/non aff. 8,767*** Cons 58,210*** Wald test 535,77*** 816 uncensored observations; 444 left-censored observations

For the third regression we have employed a modified indicator: chronic SPG is now expressed in terms of percentage of total SPG. This way we can test the impact of variables relative to the importance of the chronic element inside the general indicator. Results are very encouraging: this time the variable representing education is significant and has a negative sign. This means that the more the households� members have attended school, the less the chronic part of poverty is important. As for HIV/AIDS, the regression shows that being affected increases the chronic part in total poverty.

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Table5: Regression on transient poverty expressed as a percentage of general SPG

Explainatory variables

Transient SPG percentage

Setting 14.218*** (0,765) N.ber employed -1,501*** (0,386) Educ 0,083 (0,0619) Orphans 0,715** (0,320) Sex head hh 0,949 (0,723) Affected/non aff. -16.928*** (0,796) Cons 43.926*** (0,.106) Wald test 1336.68*** 1044 uncensored observations,216 left-censored observations

Table number 4 show results for the regression on transient poverty,

expressed in terms of general SPG. Living in Welkom increases transient poverty: people are richer but their income is more volatile. The number of employed members reduces poverty in general, both its transient and chronic elements. When the head of household is a man, the chances of experiencing transient poverty instead of chronic poverty are higher. As for HIV/AIDS variables, being affected reduces the part of transient poverty. The reason for this is probably because the panel is very short, covering just two years. Hence, it is difficult to record income fluctuations related to AIDS.

6 Conclusions

The purpose of this paper was to explore which are the possible channels of transmission of monetary poverty in households affected by HIV/AIDS. After having theoretically analyzed all direct and indirect impacts, the empirical study of a longitudinal datasets of South Africa has been attempted. Its aim was to test whether the usual determinants of poverty, together with variables linked to HIV/AIDS, have an impact on monetary poverty, as well as on its chronic and transient components.

The results have shown that usual determinants of poverty (setting, household characteristics, human capital endowments, physical capital

28

endowments) have an impact on general SPG and chronic SPG. Moreover, the variable concerning HIV/AIDS was always significant and negative, meaning that affected households are more likely to be poor, and especially chronically poor. Testing the same determinants on the transient poverty indicator was more delicate: any of the variables used in previous regressions were significant. This is understandable, since transient poverty, by its very definition, needs transient explanatory variables, and not structural ones. Accordingly, new transition variables were created and tested only for three of the four waves of the panel. But without success. As a matter of fact, the short length of the panel (each wave is six months long) makes it very difficult to find variables that capture short term variations of income, which can be caused by any short term shock. Another explanation could be that HIV/AIDS can make income fall, even as far as high income households are concerned and as descriptive statistics had shown. In fact, income had sharply decreased for affected households belonging to the upper quartile.

The last regressions were run on a modified poverty indicator: chronic and transient poverty indicators expressed as percentages of total poverty SPG. These regressions mean to explain the relative importance of transient and chronic elements in total poverty. Results have shown that structural determinants of poverty increase the chronic part of it, together with the variable linked to HIV/AIDS. As for transient poverty, its relative importance is increased by living in urban setting. As for the HIV/AIDS variables: sheltering orphans increases transient poverty, maybe because of exceptional expenses. On the contrary, the presence of someone living with HIV/AIDS diminishes transient but increases chronic poverty.

This paper, thanks to a case study, has shown what the consequences of the illness for household welfare are. The evidence that HIV/AIDS increases chronic poverty is a very important one, and rich in lessons. Affected households risk drifting into, and remaining in, chronic poverty, whereas non- affected households can get out of it. A vicious circle linking illness and poverty is triggered, further pushing the affected families in poverty and distress.

The relation between poverty and AIDS is of great complexity which is why a deeper investigation on the topic is recommended as this study represents merely a preliminary approach. In particular an exploration of the relation existing between poverty and HIV/AIDS is urgently needed: in which way can poverty cause new infections? What are the structural determinants for �dangerous behaviour�? Can poverty be considered as one of those? The answer to these questions could give a solution to the main limitation of the present study, which is the aforementioned problem of endogeneity of our affected not affected variable.

29

Annex 1

Correlation matrix | spg

spg chron

spg transi orcnt educ nbre empl age sex setting aff/naff

spg chron 0.4953 1.0000

spg transi 0.5008 0.9673 1.0000

orcnt 0.0521 0.0936 0.0762 1.0000

educ 0.1100 0.0063 0.0316 -0.0096 1.0000

nbre empl -0,0466 -0,0594 0,0079 -0.0345 0.1387 1.0000

age -0,0176 -0.0332 -0.0327 0.1959 -0.0394 -0.2015 1.0000

sex -0,0368 -0,1101 -0.0514 -0.1907 0.0529 0.1723 -0.1357 1.0000

setting 0.0219 0.0140 0.0580 -0.0252 0.0616 0.1512 0.0208 0.1688 1.0000

aff/naff 0.0378 0.0988 0.0624 0.1236 -0.0614 -0.0883 0.0608 -0.0163 -0.0037 1.0000

Correlation between residuals and affected/non aff. Variable. Regression on general SPG

resid aff/naff

resid | 1.0000

aff/naff | 0.3897 1.0000

Correlation between residuals and affected/non aff. Variable. Regression on chronic SPG

resid1 aff/naff

resid1 | 1.0000

aff/naff | 0.4947 1.0000

Bibliography Ainsworth, M., Dayton, J., �The impact of the AIDS epidemics in the health of older persons in northen Tanzania�, World development n.31(1), 2003 pp 131-148. Ainsworth, M., Semali, I., �The impact of adult death on children�s health on northen Tanzania� � in Ainsworth, M., Fransen, L., Over, M., eds � Confronting AIDS; Evidence

30

from the developing world: selected background papers for the worldbank policy research report. European Commission.

Arambazar, A., Schidt, P., � Notes and comments: An investigation on the robustness of the tobit estimator to non-normality�, Econometrica, Vol. 50, No. 4, 07-1982. Atkinson, A.B., �Measuring Poverty and Differences in Family Composition�, Economica,Vol 56, 1-16, 1991. Atkinson, A.B., �On the measurement of poverty�, Econometrica, Vol. 55, No 4, 07-1987. Barnett, Whiteside �Poverty and HIV/AIDS:Impact, Coping and Mitigation Policy� in �AIDS, PUBLIC POLICY AND CHILD WELL-BEING�, Unicef 2002. Booysen, F. le R. & Bachmann, M. �HIV/AIDS, Poverty and Growth: Evidence from a Household Impact Study conducted in the Free State province, South Africa� Paper presented at the Annual Conference of the Centre for Study of African Economies (CSAE), St Catherine.s College, Oxford, 18-19 March 2002 . Booysen, F. le R. �Chronic and transitory poverty in the face of HIV/AIDS-related morbidity and mortality: Evidence from South Africa� Paper presented at international conference on "Staying Poor: Chronic Poverty and Development Policy", University of Manchester, 7-9 April 2003, United Kingdom. Booysen, F. le R., �HIV/AIDS and Poverty: Evidence from a Household Impact Study conducted in the Free State province, South Africa�, Paper presented at DPRU Conference, Johannesburg, 22-24 October 2002. Booysen, Frederik le R. et Arntz, Tanja � The methodology ofHIV/AIDS impact studies: a review of current practices�, Social Science & Medicine 56 (2003) 2391�2405.

Coombe, Carol « Mitigating the Impact of HIV/AIDS on Education Supply, Demand and Quality�, in �AIDS, PUBLIC POLICY AND CHILD WELL-BEING�, Unicef 2002. Freire, S., �HIV/AIDS affected households: status and temporal impacts� in �Economics of AIDS and access to HIV/AIDS care in developing countries. Issues and challenges�, ANRS 2003. Freire,S. �Impact of HIV/AIDS on saving bhaviour in South Africa�,TEAM, Université Paris I Sorbonne, First Draft June 7, 2002. Haddad, L., Ahmed, A. �Chronic and transitory Poverty: Evidence from Egypte, 1997-99�, World development, Vol 31, No.1, pp.71-85, 2003. Hagenaars, A., « A class of poverty indices », International Economic Review, Vol 28, No 3, 1987, Jalan, J., Ravallion, M., �Is Transient Poverty Different? Evidence for Rural China�, 1998. Johnston, J., Dinardo,J., �Méthodes économétriques�, Economica 1999. May, J., I. Woolard, and S. Klasen, 2000, "The Nature and Measurement of Poverty and Inequality", in J. May, ed., Poverty and Inequality in South Africa: Meeting the Challenge, Cape Town: David Philip. Morris , Burdge, Cheevers � Economic Impact of HIV Infection in a Cohort of Male Sugar Mill Workers in South Africa� The journal of South African economics, 2000. . Munthali, Alister C. �Adaptive Strategies and Coping Mechanisms of Families and Communities Affected by HIV/AIDS in Malawi�, Department of Anthropology Rhodes

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University, Grahamstown 6140, South Africa .Draft paper prepared for the UNRISD project HIV/AIDS and Development: Ravallion, M., Jalan,J. (1998)�Determinants of Transient and Chronic Poverty: Evidence from Rural China�, Workd bank working paper. Ravallion, Martin (1997) « COMPARAISONS DE LA PAUVRETE :Concepts et méthodes » Ravallion, Martin, �Expected Poverty Under Risk-induced welfare variability� The economic Journal, december 1982. Rugalema, G.,�HIV/AIDS and the commercial agricultural sector of Kenya. Impact, Vulnerability, susceptibility and coping strategy� FAO and UNDP 1999. Stillwaggon, Eileen �HIV Transmission in Latin America: Comparison with Africa and Policy Implications �2000 SAJE v68(5) p985 .