HIV/ AIDS has various channels through which it affects welfare, from the national to the individual level. At the macro-level, economic researchers indicate two main channels through which the HIV/ AIDS epidemic has negative macroeconomic effects. First, it kills people, which directly reduces welfare. From an perspective of the 'surviving' economy, the cost of HIV/AIDS, therefore, are mainly through the impact of HIV/AIDS on human capital. In the short run, HIV/ AIDS directly decreases human capital because it primarily affects the working age population, and higher mortality rates shrink the labor supply. Indirectly, and especially in the long run, HIV/ AIDS can hamper the accumulation of future human capital through higher child mortality and orphaned children (see e.g. Bell et al., 2003). Both effects can hamper economic development, especially if the relatively scarce skilled worker are more affected than unskilled worker. Second, the epidemic makes people ill. Longer and more frequent times absent from work as a result of the epidemic may lower labor productivity of infected workers. In addition, the epidemic leads to the rise of public health care expenditures both through the rise of people needing medical services and higher the cost of the antiretroviral **(ARV)** treatment of AIDS as compared to the treatment of other diseases (see e.g. Hellinger, 1993). Both impacts have negative effects on savings and investments leading to an overall decline in growth.

2This has important policy implications. The usual argument of political instruments to reduce HIV/AIDS is to improve the knowledge about the epidemic and its transmission channels and to spread the use of condoms. While this is a very important instrument, it might not be enough, especially when the very poor are the target group of such initiatives and if they are not willing to change their behavior. Recent data show that the very poor are relatively less likely to use condoms (UN, 2005a). Therefore, instruments to reduce HIV/AIDS through, for example, the use of condoms have to be accompanied by measures to reduce poverty and inequality.

During the last two decades, a growing body of literature on the macro-economic impact of **HIV/ AIDS** has developed. For example, Over ( 1992) estimates a decline in GDP of one third of a percentage point due to the effect of **HIV/AIDS** on savings and on skilled labor supply. Cuddington (1993) developed a Solow-type growth model and estimates a loss in GDP in 2010 by 15 to 20 percent for Tanzania. Arndt and Lewis (2000) find substantial divergencies in growth of GDP between two scenarios of AIDS and non-AIDS for South Africa. They estimate that the level of GDP is about 17 percent lower by the year 20 IO compared to the non-AIDS scenario, mainly as a result of higher public expenditures for health services and lower labor productivity, which results in lower growth of investments. Bonnel (2000) estimates an annual decline in GDP growth of 0. 7 percentage points using cross-country regression for Africa between 1990 and 1997. 3 More recently, Bell et al. (2003) emphasizes the importance of human capital and transmission mechanism across generation and argue that the long run impact of **HIV/AIDS** on GDP growth is even stronger and may even lead to a collapse of the economy of South Africa.

However, researchers also indicate some channels through which the epidemic has a compensating or even positive effect on economic growth, suggesting that the effect of HIV /AIDS on GDP per capita is generally overstated for several reasons (see e.g. Bloom and Mahal, 1997). First, the often existing labor surplus of unskilled worker could moderate the effect of lower labor productivity due to rising mortality and morbidity among the working age population as a result of AIDS. Second, social and economic adjustments could mitigate the rising public expenditures for health care services due to HIV/AIDS. Third, changes in behavior over time are usually not considered when forecasting the number of infected persons. Finally, higher infection rates among the poor could diminish the overall impact on average per capita terms of well-being. If the decline in GDP is absorbed by higher infection rates and resulting higher number of deaths among the poor, i.e. when the denominator of GDP per capita shrinks more than the nominator, this might lead to misleading welfare implications of only small shrinking or even rising GPD per capita.4

3Similar negative effects on GDP growth due to the epidemic are found, for instance, by Jamison, Sachs, and Wang (2001) and MacFarlan and Sgherri (2001).

4This problem of not taking into account premature mortality into analysis of per capita wellbeing is not only of particularly relevance in the case of the HIV/AIDS epidemic. In the context of the worldwide demographic change, the incorporation of variations in life expectancies, changes in population size, and mortality when performing aggregate welfare comparison over time and space is recently discussed in a growing literature. For theoretical implications see, for example, Kanbur and Mukherjee (2003), Becker, Philipson and Soares (2005), Blackorby, Bossert, and Donaldson (2005), and for empirical illustrations Ravallion (2005) and Grimm and Harttgen (2007).

Bloom and Mahal (1997) found only an insignificant impact of HIV/AIDS on GDP per capita, based on a cross-country analysis for 51 developing and developed countries. Young (2005) identifies decreasing fertility rates and higher per capita investments in human capital as another channel through which AIDS may affect the economy in a positive manner and which stands against the negative long term growth effect of a loss in human capital due to higher mortality rates. *5* Other studies find only a small or insignificant effect of the epidemic on the macro-economic performance of African countries. Botswana, where almost one in three persons is HIV positive, experiences a strong growth of GDP per capita (World Bank, 2005).

### **2.2.3 The Micro-Economic Impact of HIV/AIDS**

At the micro-level, the empirical evidence also shows the severe negative economic and social impact of HIV/ AIDS on household and individuals. Limited labor productivity of sick household members causes income and substitution effects. HIV/ AIDS-affected households experience a temporary loss of income if an income earner is not able to work. Finally, the death of an income earner leads to a permanent loss of income. Often, affected households have to sell assets to compensate the loss of income (Mutangadura, 2000; Bechu, 1998). Particularly poor households, which are more vulnerable to shocks, are most strongly affected. Poor households can cope, if at all, only to a limited extent with losses in income as they own no assets to sell, or cannot afford medical care for affected household members. In general, poverty increases both the infection risk and the impact of HIV/AIDS. For example, Booysen (2003) shows that in South Africa poor households that experienced an AIDS-related death were more than twice as likely to fall into long term poverty than non-affected households. In addition, high expenditures for medical care and the loss of income of sick or dead income earners leads to a reallocation of resources within **HIV/** AIDS-affected households. For example, affected households often experience a decline in consumption including a reduction in food consumption resulting in higher rates of undernutrition (Topouzis, 1994).6

5In particular, he compares a negative welfare effect of the decreasing accumulation of human capital through orphaned children with a positive welfare effect of a lower fertility. He states that when countries are affected by high infections rates, the fertility rate decreases directly via less unprotected sexual activities because of higher risks and indirectly via shrinking labor supply, which increases the value of the time of women. Using a Beckerian household model, he finds that the fertility effects dominated the effect of a shrinking human capital accumulation of orphaned children in South Africa resulting in a potentially higher future per capita income.

6In many countries, the death of the income earner leads in addition to high expenditure through high funeral costs (Menon et al., 1998).

Children living in HIV/ AIDS-affected households might face the heaviest burden resulting from the direct loss in income and intra-household resource reallocations. This paper focusses on three socio-economic channels of which HIV/ AIDS might affect childrens' welfare: the impact on child mortality, on undemutrition and on school enrollment. The empirical literature shows that **HIV/AIDS** is one of the leading causes of child mortality in Africa (see e.g. Hill et al., 2001).  The epidemic affects the mortality risk of children directly through mother-to-child-transmission during pregnancy, delivery and breastfeeding. 7 Without any interventions about 20 to 40 percent of HIV-infected mothers transmit the infection to their children (De Cock et al, 2000; World Bank, 1997) and the median age of death of such an HIV-infected child in Africa is about two years (see e.g. Spira et al., 1999). However, besides the direct effects of **HIV/AIDS** on child mortality, the epidemic may also have indirect effects on the mortality risk of children through the socio-economic consequences of HIV/AIDS-affected households that stem from reduced capacities of the infected parents to care for their children and from higher risk of illness, which is analyzed in this paper by estimating separately the effect of the HIV status of the mother and her partner on the mortality risk of children controlling for a set of socio-economic individual and household characteristics and environmental factors.

HIV/AIDS may have also direct and indirect effects on the nutritional status of HIV-affected children. Empirical evidence exists that HIV-infected mothers often directly affect the nutritional status of the children, since HIV infection increases the risk a low birth weight, which again leads directly to an increased risk of morbidity, mortality as well as chronic undemutrition (see e.g. Dreyfuss et al., 2001). In addition, undemutrition directly accelerates the progression of the disease towards AIDS-related death through effects on the immune system and its impact on nutrients intake, absorption and utilization (Piwoz and Preble, 2000).8 Indirectly, HIV/AIDS might affect the precondition of a secure nutritional status of a child and increases the risk of chronic undemutrition because the quantity and quality of food decreases as a result of reduced capacities to care, which plays an important role for the current and future development of the children, a channel, which has rarely been analyzed yet. Therefore, in this analysis, the focuss is

7Similarly, breastfeeding of HIV-infected mothers bears also considerable risk for the mother. The high energy demand of breastfeeding weakens the mother, which leads also to an acceleration of the progress of the disease. Mortality rates among mV-infected mothers who gave breast milk is three times higher than for infected mothers who did not (Nduati et al., 2001).

8In contrast, a good nutritional status, particularly of vitamin A reduces the infection risk (Haddad and Gillespie, 2001).

on the impact of HIV on chronic undemutrition of the children living an affected households.

There is also empirical evidence indicating severe negative effects of the epidemic on the educational attainment of children living in HIV/AIDS-affected households. Because of resource reallocations within households as the result of sick or deceased income provider, children often have to be taken out of school to reduce costs resulting in lower potential for future earnings. For instance, **Mu**tangadura (2002) finds for Zimbabwe that the share of children visiting school decreases by 20 percent after an AIDS-related death because of lack of money, or because the children have to go to work to compensate the loss in household income. Topouzis (1994) finds that only every fifth child remains in school after the death of a household member. The impact of HIV/ AIDS on education is even worse for children who become orphans, because it decreases strongly their future welfare perspective (see e.g. Bicego et al., 2003; Case et al., 2004). If orphans live with other adults, these adults might not invest in the children because they are not expected to care for them in retirement age (Ainsworth and Semali, 2000). However, since these studies are based on small-scale data sets with only few observations, and since these studies analyze the effect of AIDS-related deaths on children, no information is provided whether HIV-infected household members who are still alive already have an impact on the enrollment status of children living in those households. Only very limited empirical evidence exists on these possible indirect effects of HIV-infected household members on the enrollment status of children. For example, Graff Zivin et al. (2006) estimate the impact of antiretroviral treatment on childrens' schooling and nutritional status in Kenya and finds that the treatment of adult household members raises weekly schooling hours by 20 percent and also improves the nutritional outcome of children. However, also this study is based on a small-scale population survey and much scope for further research is left. This paper tries to fill this gap in the literature by using large scale DHS data for four Sub-Saharan African countries: Burkina Faso, Cameroon, Ghana, and Kenya.

# **2.3 Methodology**

### **2.3.1 Survival Model Framework**

To analyze the impact of HIV on child mortality, this paper applies a survival model framework. The idea of survival or hazard models is to analyze the time to the occurrence of an event, which is in this case the death of the child. 9 In partic-

91n general, survival analysis can be defined as the analysis of rates of the occurrence of the failure during a specific risk period (Yamaguchi, 1991 ). Survival analysis has become a common

ular, this paper employs a semi-parametric Cox proportional-hazard-continuoustime model (Cox, 1972), which is the most popular form of survival model when analyzing patterns of child mortality. Besides, taking the time to the event (i.e. the time to the death of a child) explicitly into account, an advantage of hazard rate models for the analysis of survival data as compared to standard cross-section regression models, is their capacity to deal with several kinds of censored observations. The most typical form of censoring when analyzing child mortality rates is right censoring, which means that the subject (child) has not had the event (death) when the observation time ends, e.g. the child is three years old when the observation time ends and one does not have information whether the child is going to die before she reaches the age of five. Standard regression models (in this case, for example, a logistic regression model) are then restricted to the events that occur within the observed follow-up period. But simply omitting the right-censored cases from the sample reduces the sample size a lot resulting in a loss of information and can generate serious biases in the parameter estimation. In survival analysis, one can include the information about the right-censored subjects up to the time of censoring without making any assumption about the date the event occurs in the future. 10

Based on Cox (1972), to illustrate the model, let *T* be the non-negative survival time, which is the time period between non-occurrence and occurrence of failure, i.e. the age between zero and five years. The immediate risk of failure of an individual i, which is alive at time *t,* is defined as the hazard rate or the or agespecific failure rate and expressed through the hazard function. 11 Let *h(t)* denote the hazard function of survival time *T* and *x;* = *(xii,X2i, ... ,Xpi)* be a vector of *p* independently observed covariates for individual i. The hazard function for individual i given the vector *x* can then be written as

$$h\_l(t|\mathbf{x}) = h\_0(t)\mathbf{g}(\mathbf{x}\_l),\tag{2.1}$$

where *g(x;)* is a function of the covariates and the term *ho(t)* is defined as the baseline hazard function, which is the hazard for the respective individual when all independent covariates are equal to zero. Assuming continuously distributed survival times and no ties, the hazard function can be written as

$$h\_i(t|\mathbf{x}) = h\_0(t) \exp\left(\beta\_1 \mathbf{x}\_{1i} + \beta\_2 \mathbf{x}\_{2i} + \dots + \beta\_P \mathbf{x}\_{pi}\right),$$

econometric instrument to analyze the detenninants of child mortality (see e.g. Ridder and Tunali, 1999; Van der Klaauw and Wang, 2004).

'°For a detailed description of survival models, see e.g. Lee (1992).

11 In the context of child mortality, the hazard rate can also be termed as the age-specific mortality rate (Ridder and Tunali, 1999). The mortality rate at time (age) *t* refers to the magnitude of the child mortality at this age, given that the child has survived to age *t.*

$$\mathbf{x} = h\_0(t) \exp\left(\sum\_{j=0}^{p} \beta\_j \mathbf{x}\_{j\bar{t}}\right). \tag{2.2}$$

Equation 2.2 shows that the underlying hazard rate is a function of a set of independent covariates. 12 To simplify the model, Equation 2.2 can be linearized by dividing both sides by *ho (t)* and then taking the logarithm of both sides:

$$\log\_{\epsilon} \frac{h\_l(t)}{h\_0(t)} = \beta\_1 x\_{1l} + \beta\_2 x\_{2l} + \dots + \beta\_p x\_{pi}$$

$$= \left(\sum\_{j=0}^p \beta\_j x\_{ji}\right). \tag{2.3}$$

The left-hand side of Equation 2.3 shows the hazard, i.e. the relative risk of individual i and the right-hand side is a linear function of the covariates *Xji* with their respective coefficients /3j, 13 To estimate Equation 2.3, a maximum likelihood approach is used. In contrast to parametric models, the semi-parametric Cox proportional hazard model does not require the specification of a parametric form of the hazard function *ho(t). 14*

One of the problems that arise is the possible existence of unobserved heterogeneity. The child mortality risk may also depend on unobserved individual and household characteristics and on unobserved biological frailties. It is important to account for this unobserved heterogeneity to avoid inefficient and inconsistent parameter estimation (Van der Klaauw and Wang, 2004). 15Therefore, the model is

12Given that there exist no left-censored observation, the likelihood function to estimate the hazard rate for a set of independent observations of duration *i* = I , ... *,I* can be expressed as TTf=t *h;(t;)6•S;(t;),* where I; is the duration of risk for individual *i,* S;(t;) is the survival function, defined as the probability that an individual survives longer than I (S(t) = *P(T* > 1)), and O; is a dummy whether the event occurred for *i* at time I; ( O; = I) or the observation was right censored at time I; (o; = 0). Both *h(t)* and S(t) depend on the values of the covariates of subject *i.* For a right-censored observation the contribution to the likelihood function remains S;(I;), i.e. the probability of not having the event between O and *t;.* Therefore, also the information of right-censored observation can be included into the model (Yamaguchi, 1991).

13The function *exp()* is simply chosen to avoid that the hazard function ever tum negative. Semi-parametric means that the analysis makes no assumption about the distribution of the hazard function, whereas the effects of covariates are still parameterized to affect the baseline hazard function in a specific way.

14Therefore, the semi-parametric Cox proportional hazard model is more robust than parametric models, because it is not vulnerable to miss-specification of the baseline hazard. The disadvantage of this approach, however, is a loss in efficiency. If one would know the true functional form of *ho(t)* one would obtain more efficient estimation results of the */Ji·*

15When unobserved heterogeneity exists and if it is not considered in the model, one either overestimates a negative duration effect or underestimates a positive duration effect (Yamaguchi, 1991).

extended to incorporate also unobserved heterogeneity. Thus the hazard function 2.3 becomes to

$$\log\_{\sigma} \frac{h\_i(t)}{h\_0(t)} = \sum\_{j=0}^{p} \beta\_j x\_{ji} + \alpha\_i,\tag{2.4}$$

where a; is the group *i* level frailty, which is assumed to be gamma distributed.

The impact of HIV is separately estimated both through the HIV status of the mother or the male partner. As the dependent variables for the analysis of the impact of **HIV** on child mortality, the hazard rate of children under five years of age is used. 16

### **2.3.2 Ordinary Least Squares and Logistic Model**

To analyze the impact of HIV on undemutrition and school enrollment, controlling for individual and household socio-economic and demographic characteristics and environmental factors, a standard ordinary least squares (OLS) and a logistic regression model are applied. For each country, the following equation is estimated:

$$\mathbf{y}\_{l} = \beta\_{1}\mathbf{x}\_{1i} + \beta\_{2}\mathbf{x}\_{2i} + \dots + \beta\_{p}\mathbf{x}\_{pl} + \mu\_{l}$$

$$= \sum\_{j=0}^{p} \beta\_{j}\mathbf{x}\_{ji} + \mu\_{i},\tag{2.5}$$

where *Yi* represents either the dependent variable to analyze child undemutrition or the dependent variable to analyze school enrollment for child *i.* As dependent variable to analyze the impact of HIV infected mothers or partners on child undernutrition the stunting z-score of children under five years of age is used. 17To analyze the impact on school enrollment, a dummy variable is used that shows whether all children aged between five and fifteen per household are enrolled in school. 18

16This paper does not separate between neonatal deaths, i.e the child dies within the first month of life and post-neonatal death, i.e. the child died between the second month and the first year of life, as **for** example proposed by Adebayo et al. (2004) because this did not change the estimation results.

17The z-score is defined as *z* = Al;-;IAI, where *Al;* refers to the individual anthropometric indicator (height for age - stunting, weight for height - underweight, weight for age - wasting), *MAI* refers to the median of the reference population, and *a* refers to the standard deviation of the reference population (see e.g. Klasen, 2003, 2007; Smith and Haddad, 2000). A child is considered as stunted if the stunting z-score (height for age) is below -2 standard deviations from the median of the reference category (WHO, 2006).

18For the logistic regression model, the dependent variable *y;* enters the regression as *log;;{':!:r,*  where *p;* = *pr(y;* = IJX), which is the probability that all children aged between 5 and IS living in the same household are enrolled in school, conditional on a vector of covariates *X.*

# **2.4 Empirical Analysis**

### **2.4.1 Data Description**

Fortunately, OHS conducted in recent years include HIV test results at the individual level for selected Sub-Saharan African countries. These OHS surveys are the first large-scale households survey data sets providing HIV testing results. 19 Thus, the data sets provide interesting scope for the analysis of the impact of HIV on the households' welfare and may give new insights into the causes and impact of the epidemic. Besides the information on HIV testing results, the OHS surveys provide also information on anthropometric outcomes of children, child mortality and socio-economic individual and household characteristics.

The Sub-Saharan African countries analyzed in this paper are Burkina Faso (2003), Cameroon (2004), Ghana (2003), and Kenya (2003). Concerning human development, Ghana is the only country of the sample that is classified as a 'Medium Human Development' country by the United Nations with a Human Development Index (HDI) rank of 138. The other three countries are classified as 'Low Human Development' countries with HDI ranks of 148 (Cameroon), 154 (Kenya) and 177 (Burkina Faso, which is only higher ranked than Sierra Leone and Niger) (UN, 2005b). In all countries, poverty rates20 are around 50 percent and GDP per capita is low. In 2003, Cameroon, Kenya, and Burkina Faso had a life expectancy between 46 and 48 years, whereas the situation in Ghana was better with a life expectancy of about 60 years (World Bank, 2005). In addition, all countries suffer from high incidence of child mortality of more than 100 per 1000 children and of child undemutrition of around 40 percent, compared to African average rates of 17 .1 percent for child mortality and 29.4 percent for malnutrition (weight for age) (World Bank, 2005). At present, it is not very likely that the four countries will reach the Millennium Development Goals (MDG) in 2015.21

The underlying theoretical framework to study the impact of HIV on childrens' welfare, controlling for socio-economic individual and households characteristics, the paper follows the theoretical framework for the study of child mor-

19 In particular, the OHS data sets provide a sub-sample including HIV infection testing results for males and females. As the OHS data sets are representative at the national level, the HIV sub-sample is compared to the full sample regarding the question if also the sub-sample is national representative. Table B. I compares the two data sets by estimating the probability of being in the full sample on a the set of variables that are used in the analysis of this paper. Almost all variables are not significant indicating that the results of the analysis using the lllV sub-sample can be interpreted as representative at the national level. However, the urban dummy is significant in Burkina Faso, Cameroon, and Kenya indicating the results are only to a limited extent interpretable as national representative.

20Considering the poverty line of below I USO PPP per day.

21 However, among these countries, Ghana has the best chance to reach the goals.

tality proposed by Mosley and Chen (1984), and for undemutrition by UNICEF ( 1990) and Engle et al. ( 1999). 22 In total, the data sets contain information on 13734 children living in 5629 households. 23 As independent variables, a set of household socio-economic and child characteristics are included into the regression models. In addition, to control for urban areas, the household size,24 the number of children, and a dummy whether the household is female headed are included. 25 As the DHS surveys provide no information on income or consumption, an asset-based approach is applied to obtain information about the material well-being of the households (Sahn and Stifel, 2001). For this, an index based on a principal component analysis, proposed by Filmer and Pritchett (2001), is derived. Assets to calculate the index are dummy variables whether household possesses a radio, TV, refrigerator, bike, motorized transport, low floor material, toilet, and drinking water.26

The sex of the child is included to control for a possible gender bias, which is often found in the literature (see e.g. Marcoux, 2002; Klasen, 1996). Other important determinants of child mortality and undemutrition are whether the child is the first born child, the preceding birth interval, and if the child was immediately breastfed after birth by the mother.27 In addition, the regression models capture also the access to health services, by including a dummy whether the child received all possible vaccinations,28 whether the child received vitamin A, and

22See also the data description of Section 1.3.1 in Essay I.

23 See Section 2.4.2 for descriptive statistics.

24 As the household size is arguably endogenous, i.e. households in which many children die tend to be larger to compensate the Joss of the dead children, the variable is not included directly into the regression. Instead in instrumental variable approach is applied, where as instrument variable the mean household size per cluster is used (see also Section 1.3.1 in Essay I).

25 HIV/ AIDS contributes to a rising share of female headed households, which potentially worsens the situation of food security for the children.

26 As already discussed in Section 1.3. J in Essay I, the reasons why one includes the asset index instead of separate dummies for each assets are, first that the index provides an income proxy of the household which can be used to analyze distributional differences of the impact of HIV or the distribution of HIV itself. Second, as the assets are correlated, their coefficients are likely to provide no significant effects if they are included separately, which would however lead to misleading interpretation of the estimation results.

27Breastfeeding in the first month of life plays an important role for the development of the child, because the breast milk meets most of the childs' nutritional needs and makes the child more resistent against diseases (see e.g. Ramalingaswami et al., 1996). However, breastfeeding is also a channel of mother to child transmission, which means that it might have a negative effect when the mother is HIV positive.

28 As described in Section 1.3.1 in Essay 1, to avoid the problem of endogeneity, i.e. that the number of vaccinations is an increasing function of the age of the child, the dummy whether the vaccinations process is completed is defined as follows: the first 2 month after birth are not considered as incomplete if no vaccinations were received, for the age between 3 and 6 months the dummy is one if the child has received at least 3 vaccinations, for the age between 7 and 9

whether the mother received prenatal care. Furthermore, concerning the status of the mother, the education of the mother is included to take into account the direct ability to acquire skills to take care of the children and indirectly the earning potential of the mother, which is expected to positively affect childrens' welfare outcomes. In addition, the nutritional status of the mother is included since a malnourished mother is expected to have a negative impact on the nutritional status of the child. 29

For the estimation of the impact of HIV on child education, the described household socio-economic characteristics enter the regression model as well. Apart from the educational level of the household head, the information on the educational status of the mother is included. Information whether the mother works for cash is also included, because it might have a positive effect on the probability that children are sent to school. This argument holds especially for female children. For example, in South Asian countries, the gender bias in education of children is found to be lower if the mother works and, therefore, strengthens her bargaining power position in the household (Alderman et al., 1996).

### **2.4.2 Descriptive Statistics**

The countries have experienced different histories regarding the development of the epidemic over the past 20 years. In Kenya, the first official AIDS case was reported in 1984, followed by Cameroon in 1985. In Burkina Faso and Ghana, the first cases were reported in 1986. Figure 2.1 shows the number of reported AIDS cases by year and country and describes the history and different stages of the epidemic for the countries over time. 30

30However, one should be careful when comparing the reported AIDS cases across countries. The number of reported AIDS cases is only a very crude indicator of the actual HIV/ AIDS prevalence and depends heavily on the existing and spatial distribution of reporting systems within countries. For example, if the share of people living in urban areas strongly differs between countries, than comparing the numbers of AIDS cases across countries can be misleading if, first, the prevalence of AIDS is higher in urban areas than in rural areas and, second, because reporting institutions are usually located in urban areas resulting in higher reporting in urban than rural areas. One should also be careful when interpreting the evolution of AIDS over time within a country,

months if the child has received at least 6 vaccinations and between 10 and 12 months if the child has received all 8 vaccinations.

29 As the measure of the nutritional status of the mother the body mass index (BMI) is used. A mother is considered *as* malnourished if the BMI is below 18.5 (see also Section l.3.1). The BMI enters also as the BMI squared into the regression to capture the possibility that the BMI of the mother affects the childs' nutritional status non-linearly. In particular, this captures the possibility that a very high BMI might effect the nutritional status of the child in a negative way, because the high value of the BMI might be simply due to the intake of many calories that are, however, nutritionally inadequate.

All countries exhibit the initial growth in AIDS prevalence as described in Section 2.2. l. During the first years of the epidemic, Kenya experienced the highest increase in AIDS cases in the sample. Already at the beginning of the 1990s, the number of new infections began to decline rapidly. Compared to Kenyas' development, Ghana, Cameroon and Burkina Faso are in an earlier stage of the epidemic but show a similar history of the epidemic. After a continuous rise, AIDS cases started to decline around the year 2000 in Ghana and Burkina Faso.

Table 2.1 shows the HIV infection rates by sub-groups for the four countries. Overall, the HIV prevalence in the DHS data sets is consistent with the prevalence published by UNAIDS (UNAIDS, 2006). In addition, in a recent paper, Oster (2006) provides a new methodology to estimate the HIV prevalence based on mortality data on siblings. By providing consistent estimates of HIV prevalence over time and across countries, she found that the HIV prevalence of the DHS surveys are not underestimated. Cameroon and Kenya are affected considerably stronger by the epidemic than Burkina Faso and Ghana. In Kenya and in Cameroon 7 and 6 percent, respectively, of all households have at least one HIVinfected person, compared to about 2 and 3 percent in Burkina Faso and Ghana, respectively. Whereas the age group between 25 and 59 years is more affected in Burkina Faso, Cameroon and Ghana, the opposite is found for Kenya. The distribution of **HIV** over age is also shown in Figure 2.2, which illustrate the results from Table 2.1.

The spatial distribution of the epidemic follows the usual pattern. HIV infection rates are considerably higher in urban than in rural areas in Burkina Faso, Cameroon, and Kenya, which might indicate more frequent changes in partnerships in urban areas. In Burkina Faso, HIV infection rates are more than 3 times higher in urban areas than in rural areas (4.01 compared to 1.22 percent). Only in Kenya is the HIV infection rate only slightly higher in rural than in urban areas, which might indicate that the rural population has less knowledge about the epidemic and the risk factors of HIV transmission. Looking at the infection rates of mothers, Table 2.1 shows a similar picture as for the total household. The highest rates are found in Kenya at 8.88 percent and the lowest infection rates are found in Burkina Faso, at 1.50 percent.

To get a picture of how HIV infections are distributed across welfare groups in the countries, Table 2.2 shows the HIV infection rates for the asset index quintiles. In Burkina Faso, Cameroon, and Ghana the poorest quintile has the lowest infection rate, whereas the richest quintile has the highest rate. This indicates that especially the wealthier population group is affected by the epidemic. For example, the ratio of infections of the first to the fifth quintile is 0.10 in Burkina

because the number and distribution of reporting institutions as well as the reporting behavior can change a lot over time.

#### Figure 2.1: Number of Reported AIDS Cases by Years

*Source:* WHO (2006a), own calculations.

*Source:* Demographic and Health Surveys (DHS); own calculations. *Note:* \*Positive tested for HIV type-I or HIV type-2.

Figure 2.2: HIV Infection by Age

*Source:* Demographic and Health Surveys (OHS); own calculations.

Faso. Interesting to note is, as Cogneau and Grimm (2006) already pointed out for Cote d'Ivoire, high infection rates are also observed for the second quintile, i.e. whom they call 'the rich of the poor'. For example, looking at the second and fourth quintile in Burkina Faso, the poorer quintile has considerably higher infection rates than the richer quintile (1.33 compared to 2.27). In contrast, Kenya shows the opposite picture. Here, Table 2.2 shows that the poorest quintile has the highest infection rates, which is reflected in a ratio of the first to the fifth quintile of 1.16. The distribution of HIV over asset index quintiles is also shown in Figure 2.3, which also indicates the slightly higher infection rates among the wealthier population groups.

Figure 2.3: HIV Infection by Asset Index

*Source:* Demographic and Health Surveys (OHS); own calculations.

This again indicates that Kenya has reached a different stage of the epidemic than the other countries. Comparing these findings about the socio-economic distribution of HIV infections to the findings of the empirical literature described in Section 2.2. I, only Kenya has yet reached the stage in the history of the epidemic where infection rates spread away from the wealthier to the poorer population.31 The other countries show, in spite of decreasing overall infection rates that still higher rates persist among the richer population group among the lower middle class. 32

Table 2.3 presents some descriptive statistics for child mortality, undemutrition, and education for the total data set and by asset index quintiles.  In contrast to HIV infections, Burkina Faso has the highest rates of child mortality at 164, as compared

31 As also found, for example, by Glynn et al. (2004).

32This was also found, for example, by Hargreaves et al. (2001) and Cogneau and Grimm (2006).

Table 2.2: HIV Infection by Asset Index

*Source:* Demographic and Health Surveys (DHS); own calculations.

*Notes:* The asset index is calculated based on a principal component analysis. As variables to calculate the asset index, dummies are included whether the following assets exist or not in a household: radio, TV, refrigerator, bike, motorized transport, low floor material, toilet, drinking water. Quintile one corresponds to the poorest and quintile five to the richest population sub-group.

to Ghana with the overall lowest child mortality rate of 106. 33 The percentage of stunted children are also considerably high in all four countries. Whereas more than one third of all children under five years of age are stunted in Cameroon, Ghana, and Kenya, even 44 percent suffer from chronic undemutrition in Burkina Faso. In addition, also the percentage of households in which all children between 5 and 15 are enrolled in school is worse in Burkina Faso. Here, only 7 percent households show complete school enrollment, which is more 7 times less than in Cameroon and Kenya, where 49 and 48 percent of households show complete school enrollment, respectively.

The distribution of child mortality, undemutrition, and complete school enrollment over the asset index shows a clear bias against the poor. On average, child mortality rates are about two times higher for the poorest quintile than for the richest quintile. Inequality is even worse in the case of undemutrition. In Ghana, half of all children in the first quintile are stunted (47 percent) compared to 'only' 15 percent in the fifth quintile. Cameroon, Ghana, and Kenya have quite similar total undemutrition rates of about 35 percent. Again, in Burkina Faso the situation is worse with a stunting rate of even 44 percent. The situation of school enrollment is also alarming. Concerning the distribution of educational opportunities for different welfare groups, Table 2.3 shows substantial inequalities between the quintiles in Burkina Faso, Cameroon, and Ghana. For example, the ratio of

33The level and evolution of the survival rates is also shown in Figure B.I in Appendix B. Figure B.1 illustrates the differences in the levels of survival rates over age between the four countries. During the first year of live, the survival rates do not differ very much between countries. But then, the survival rates begin to spread and Burkina Faso shows the lowest level of survival rates. In contrast, the highest survival rates are found for Ghana and Kenya.

*Source:* Demographic and Health Surveys (DHS); own calculations.

*Notes:* Child mortality shows the number of children per I 000 of under five years of age who died within the last 60 months, compared to all children under five years of age living in the respective quintile. The stunting rate shows the percentage of stunted children in the respective quintile compared to all children under five years of age. A child is considered as stunted if the height over age z-score is below -2 standard deviations from the new reference category (WHO, 2006). School enrollment refers the percentage of households where all children between five and fifteen are enrolled in school. The asset index is calculated based on a principal component analysis. As variables to calculate the asset index, dummies are included whether the following assets exist or not in a household: radio, TV, refrigerator, bike, motorized transport, low floor material, toilet, drinking water. Quin ti le one corresponds to the poorest and quintile five to the richest population sub-group.

the first to the firth quintile shows that in Cameroon and Ghana complete school enrollment is twice as high for the richest quintile than for the poorest quintile. Only in Kenya, the inequality in education is found to be lower.

To provide more specific insights into the situation in the countries, Table 2.4 provides descriptive statistics on specific household demographic, and socioeconomic characteristics, on sexual behavior, and on knowledge about **HIV/** AIDS. For example, Burkina Faso has the highest rates of malnourished mothers at 18.11 percent and the mean value of the **BMI** is almost 13 percent lower than in Cameroon (22.69 compared to 23.35). Cameroon has the highest school enrollment rates as well as has the highest rates of primary education of the household head (48.09 percent). The overall bad situation of access to piped drinking wa-

ter is also worth noting. In Burkina Faso, only 3.54 percent of households have piped drinking water and Ghana and Cameroon also show rates below IO percent. Only in Kenya the situation is slightly better where almost 14 percent of households have piped drinking water. Looking at the situation of knowledge about HIV/AIDS, Table 2.4 shows that nearly all respondents have heard of AIDS in all countries. For example, in Cameroon 46 percent know someone with AIDS and 62 spoke about AIDS with the spouse. However, the knowledge about specific transmission risk factors is still very limited. In Kenya, where 95.25 percent know about mother-to-child-transmission, only 12.47 percent know about other risk factors like prostitution or sharing razor blades with intra-venous drug users. The knowledge about risk factors of HIV-transmission is even worse in rural areas compared to urban areas in all four countries.

### Table 2.4: Summary Statistics for Individual and Household Characteristics and AIDS Knowledge

*Source:* Demographic and Health Surveys (DHS); own calculations.

*Note:* \*Dummy variable whether knowing about mother to child transmission of HIV/AIDS. \*\*Dummy variable whether knowing at least about one of the following risk factors: Prostitution, partner with many partners, sex with intra-venous drug users, sharing razor blades with AIDS patients.

### **2.4.3 Estimation Results**

Table 2.5 shows the regression results for child mortality. Overall, Table 2.5 shows the familiar pattern, which was also found in Section 1.3.3 in Essay 1 for countries in South Asia and other Sub-Saharan countries. Regarding the socio-economic characteristics of the child, very similar effects are found across countries. Three main determinants of child mortality can be identified: breastfeeding, prenatal care and a complete vaccination process. As expected, breastfeeding immediately after birth significantly reduces the mortality risk of children, which is in line with the general knowledge about the importance of the colostrum, which contains a large number of antibodies and basically works as a first immunization. In addition, a strong negative effect on child mortality is also observed if the vaccination process of the child is completed and if the mother has received prenatal care, which reflects the access to the medical care system. Differences between determinants are found across countries for the effect of female-headed households. Whereas in Ghana living in a female-headed household significantly increases the mortality risk, it significantly decreases the risk in Burkina Faso, and has no significant effect in the other two countries. Interestingly, no significant gender bias in the sense that girls have a higher mortality risk than boys could be identified in any of the four countries.  For example, the mothers' educational level, measured by the mother having secondary education, has only a significant mortality decreasing impact in Ghana. 35 The same holds for the nutritional status of the mother, measured by the BMI. Even more surprising is the low influence of wealth, measured by the asset index, on the reduction of the child mortality risk. Whereas no significant effect was found for Burkina Faso, Cameroon, and Kenya, the asset index even has a positive and significant effect in Ghana.36 In, addition, the percentage of children who recently suffered from fever only has a significant positive effect on mortality in Ghana. Although the percentage of access to piped drinking water show the right sign, it is insignificant in all countries.

Turning to the impact of HIV on child mortality, Table 2.5 shows a strong and significant effect of the **HIV** status of the mother in all four Sub-Saharan African countries. The mother being HIV positive considerably increases the mortality risk. For example, simulating for all HIV-infected mothers in the sample being HIV negative yields a reduction in the hazard rate by about 8 percent in Cameroon

34This can partly be explained because girls are less vulnerable to diseases than boys in the first months of live.

35However, the educational level of the mother influences other determinants of child mortality, which directly affects child mortality like fertility or feeding practices, which are separately considered in the regression model.

36One possible explanation of this questionable result is the problem of underreporting. Especially the very poor and bad educated population sub-group is very likely to conceal the death of a child, which then might lead to distorted estimation results.

and Kenya and by around 3 percent in Ghana and Burkina Faso. The question is, how can this result be interpreted in the light of the question through which channel HIV affects the welfare of the children? Does this result shows only the mother-to-child-transmission of the epidemic, or can we also draw conclusions about the indirect effect of lower capacity to care of HIV-infected mothers? The main effect of this variable seems to be due to mother-to-child-transmission. This can be verified by including the variable whether the male partner is HIV-infected, instead of considering the status of the mother. If the HIV status of a male household member has a significant impact on child mortality, this would suggest that the socio-economic consequences of HIV are also captured and would also play an important role for the mortality risk.37

371n addition, interesting to note is that if the variable whether the child was breastfed immediately after birth is excluded from the model, the negative effect of the HIV status increases, which reflects the higher risk of mother-to-child-transmission due to breastfeeding.

*Source:* Demographic and Health Surveys (OHS); own calculations.

*Notes:* \*P-value<O. I. \*\*P-value<0.01. The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used.

*Source:* Demographic and Health Surveys (OHS); own calculations.

*Notes:* \*P-value<O. I. \*\*P-value<O.O I. The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used.

As shown in Table 2.6, the HIV status of a male household member (where the mother is not infected) has no significant effect on child mortality in all countries. This result seems to indicate that the main effect of HIV is then through motherto-child-transmission rather due to the socio-economic impact of HIV. In other words, if a child lives in a HIV-affected household, and if the infected person is not the mother, this does not seem to automatically increase the mortality risk of the child. The effect of the HIV status of the partner might be mitigated by the asset index because **HIV** worsens the material well-being as a consequence of less ability to work and, therefore, reduce the capacity to care and might increase the child mortality risk. Indeed, if the asset index is excluded from the regression, the coefficient becomes also positive in Ghana Faso and Kenya, but still remains insignificant. However, the effect of the HIV status of the mother could also include the effect of less care capacity and not only the effect of mother-to-childtransmission, even if the HIV status of the male partner shows no significant result. In general, the mother is the main care provider of the child. Therefore, if a HIV-infected mother suffers from the epidemic, resulting in reduced care for the children, this is expected to have a much higher impact on the survival probability than less care capacities of an HIV-infected male household member.

In addition, being HIV positive is clearly not the same as the household member already having started to suffer from AIDS, which is expected to have a clearer and significant negative impact on the household and on the welfare of the child. One possible way to statistically separate HIV-infected mothers among those who already suffer from AIDS is to compare them by their nutritional status. However, including only those mothers with a BMI less then 18.5 instead of HIV positive mothers did not change the results significantly.

It is most likely that the effect of HIV on child mortality is underestimated. As the information one child mortality rates are based on retrospective data collected from mothers that were alive at the time of the survey, the analysis leaves out the mortality among children whose mother had already died from AIDS. The higher risk of dying for children whose mother died from AIDS is not evaluated, resulting in a possible underestimation of the impact on HIV on child mortality.38

An additional regression is implemented based on a combined data set of all children in the four countries. The results of this global regression are shown in Table B.2 in Appendix B and confirm the results from Table 2.5 and Table 2.6. Again, the HIV status of the mother has a strong impact on the mortality risk of the child, whereas a positive test result of a male household member has no significant effect.

38However, this problem may be weakened by the fact that fertility rates are much lower among HIV-infected women (see e.g. Gray et al., 1998; UN, 2005a).

Table 2.7 shows the regression results for stunting. The coefficients of the stunting z-score follow the usual pattern across the four countries. 39 Besides the age of the child, which effects the nutritional status of the child in the well known non-linearity way, again, three main determinants are identified that strongly reduce the risk of children to be stunted. First, in contrast to the impact on child mortality, the material welfare, proxied by the asset index shows a strong and significant decreasing effect on child undernutrition in all four countries. Second, the educational attainment of the mother also has a significant positive impact on the nutritional status of the child in all four countries. Third, the same holds for the nutritional status of the mother. A higher **BMI** of the mother significantly increases the z-score of her children. Interesting to note is that the impact of the nutritional status has a significant non-linear impact on the child in Ghana. As was already found in the regression results for child mortality, no gender bias in the sense that girls have a worse nutritional status than boys could be identified. In Burkina Faso, Cameroon, and Kenya being a female child significantly increases the z-scores. In contrast to that, the preceding birth interval significantly decreases the stunting z-scores of children. In addition, being the first born child in the household significantly increases the risk of being stunted in Cameroon and Ghana. Quite surprisingly, a significant positive effect of breastfeeding is found only for Ghana.

Considering the impact of HIV on undemutrition, no significant effect is found in all four countries.40 Whether the mother is **HIV** positive or not seems to be of low importance for the nutritional status of the child when controlling for other socio-economic characteristics. These finding holds even if the nutritional status of the mother is excluded, which might capture the effect of **HIV** on the the mother. Also in this case, the HIV status has no significant impact on the nutritional status of the child. In addition, also excluding the asset index from the regression does not change this results. These results seem to indicate again that being HIV positive does not automatically decrease the nutritional status of the child.41 Furthermore, no significant effect is found on the stunting z-scores if the male partner is HIV positive, which is shown in Table B.3 in Appendix B.

Table 2.8 shows the results for school enrollment. Again, the coefficients show the expected directions. For example, urban households are more likely to show complete school enrollment than rural areas. In contrast to the results for child mortality and stunting, Table 2.8 shows a significant gender bias in education. In Cameroon and Kenya, girls are less likely to be enrolled than boys, whereas the no

39Overall, the results of the regression reflect the results that are found in Section 1.3.3 in Essay I for Uganda, Mali, and Zambia.

40This results is also confirmed by the results of the global regression of the HIV status of the mother or male partner on undemutrition, which are shown in Table B.4 in Appendix B.

41 However, an underestimation of the impact of HIV is again very likely.

significant effect is found for Burkina Faso and Ghana. The asset index has also a significant increasing effect on the probability of complete school enrollment reflecting that richer households have better access to the educational system and invest more resources into the education of their children. Quite surprisingly, the educational level of the household head plays no significant role for the probability of complete school enrollment, whereas the educational level of the mother has a significant positive impact of the school enrollment of the children. The same result is found for female headed households, which strongly increases the probability of complete school enrollment. The socio-economic status of the mother is not only captured in her educational level but also in the variable whether the mother works for cash, which has a significant positive influence on the enrollment status of the children in Ghana and Kenya.

Turning to the effect of the HIV status of the mother on school enrollment, two different results are found. For Cameroon and Kenya, no significant effect of HIV on school enrollment is found, which tends to confirm the previous results. However, for Burkina Faso and Ghana, a significant negative impact of **HIV** on school enrollment is found.42 Concerning the question of the impact of **HIV** on the welfare of children this negative effect is a very interesting result. Whereas HIV seems to have no impact on undernutrition, and whereas the impact on mortality was identified as mainly due to mother-to-child-transmission, this result seems to indicate that already being HIV-infected, without any information whether the individual already suffers from AIDS, has also a negative impact on childrens' education. A significant effect on school enrollment is also found if a male household member is **HIV** positive (where the mother is not infected), which is shown in Table B.5 in Appendix B.

42The results of the global regression of the impact of the HIV status on school enrollment is shown in Table B.6 in Appendix B.

### Table 2.7: Regression Results of Stunting (OLS Regression)

*Source:* Demographic and Health Surveys (DHS); own calculations.

*Notes:* \*P-value<O. l. \*\*P-value<O.O I. The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used.

### Table 2.8: Regression Results of School Enrollment (Logistic Regression)

*Source:* Demographic and Health Surveys (DHS); own calculations.

*Notes:* \*P-value<0. l. \*\*P-value<0.01. The household size enters via an instrumental variable into the model. As instrument the mean household size per cluster is used.

### **2.5 Conclusion**

This paper analyzed the effects of HIV-infected household members on child mortality, undernutrition, and educational attainment for Burkina Faso, Cameroon, Ghana, and Kenya. All four Sub-Saharan African countries are strongly affected by the HIV/ AIDS epidemic and suffer from high rates of child mortality, undernutrition, and from an overall low rate of school enrollment. The aim of the paper was to shed more light on the effects of the **HIV/ AIDS** epidemic on the welfare of children caused by direct vertical transmission and by worsening socio-economic conditions as a result of the epidemic.

The results show strong evidence for a severe direct negative impact of HIV on child mortality through mother-to-child-transmission in all four countries. When controlling for other individual and household characteristics, no indirect negative socio-economic effect of **HIV** was found for child mortality and undernutrition. One possible explanation for the limited socio-economic effects of HIV found is that the data cannot sperate between those mothers who are HIV positive and those who already suffer from AIDS. This could lead to underestimation of the indirect impact of the HIV status of the mother or the male partner on children. In addition, another reason for the overall low indirect impact might be that the analysis captures only those household members that were alive at the time of the survey and, therefore, omits the effect on children whose mother or other infected household member had already died from AIDS. A negative relationship between HIV and school enrollment is found in Burkina Faso and Ghana, however. This seems to confirm that negative socio-economic impact of **HIV** on the welfare of children do exist that go beyond the direct transmission of the epidemic.

One should be very careful when drawing any policy implications from the result of an overall low indirect impact of HIV on childrens' welfare outcomes. Again, the analysis cannot distinguish who already suffers from AIDS, which clearly lead to reduced capacities to care and, therefore, to indirect negative effects on children. However, the negative impact of HIV on child mortality and education, which was also recently found by GraffZivin et al. (2006), strongly argues for better treatment opportunities for HIV-infected persons. Better treatment opportunities would not only reduce the impact of the epidemic on the infected person, the results show that this also would have a positive impact on the situation of other household member, especially on the children. The future development of the **HIV/AIDS** epidemic and its impact depends heavily on improved education regarding the infection risks in order to reduce further spreading. The socioeconomic impact depends heavily on appropriate policy instrument that mitigate the reduced care capacities of households affected by the epidemic. Therefore, future research on the impact of HIV/ AIDS on households and children should further focus on the indirect impact of the epidemic, but will also depend heavily on data availability regarding AIDS at the individual level.

# **Essay 3**

# **Measuring Pro Poor Growth in Non-Income Dimensions**

**Abstract:** In order to track progress on MDG 1 and explicitly link growth, inequality, and poverty reduction, several measures of pro-poor growth have been proposed in the literature. However, current concepts and measurements of propoor growth are entirely focused on the income dimension of well-being, which neglects the multidimensionality of poverty and well-being. There are no corresponding measures for tracking progress on non-income dimensions of poverty. In this paper, we propose to extend the approach of pro-poor growth measurement to non-income dimensions of poverty by applying the growth incidence curve to non-income indicators. The approach allows a much more detailed assessment of progress towards MDGs 2-6 by focusing on the distribution of progress, rather than simply focusing on mean progress. Moreover, this extension allows the assessment of the linkage between progress in income and non-income dimensions of poverty. We illustrate this empirically for Bolivia between 1989 and 1998. We find that growth was pro-poor both in the income and in the non-income dimension, but results for the non-income dimensions are less clear when the poor are ranked by income.

based on joint work with Melanie Grosse and Stephan Klasen.

## **3.1 Introduction**

Pro-poor growth has recently become a central issue for researchers and policy makers, especially in the context of reaching the Millennium Development Goals (MDG). The various proposals to measure pro-poor growth have also allowed a much more detailed assessment of progress on reducing poverty as they explicitly examine growth along the entire income distribution.

However, one existing shortcoming of current pro-poor growth concepts and measurements is that they are completely focused on income, thus focused only on MDG I with the aim to ha! ve the incidence of poverty until 2015. 1 The shortcoming of the one-dimensional focus on income is that a reduction in income poverty does not guarantee a reduction in non-income dimensions of poverty, such as education or health. This means that finding pro-poor growth in income does not automatically mean that non-income poverty has been also reduced (see e.g. Klasen, 2000; Grimm et al. 2002). In this context, Kakwani and Pemia (2000) note that it would be 'futile' if one operationalizes poverty reduction via pro-poor growth using just one single indicator because poverty is a multidimensional phenomena, and thus pro-poor growth is also multidimensional. For this reasons, multidimensionality of poverty and pro-poor growth as two main research areas have to be combined. While non-income indicators have recently received more and more attention in the concept and measurement of poverty they have not in the concept of pro-poor growth and no attempts have been made to measure pro-poor growth on the basis of non-income indicators. 2. Also international organizations point to the importance of the direct outcomes of poverty reduction such as health and education (see e.g. World Bank, 2000; UN 2000; UN 2000a).

The aim of this paper is to introduce the multidimensionality of poverty into the pro-poor growth measurement. The basic idea of implementing the multidimensionality of poverty into the pro-poor growth concept goes back to Sen's capability approach (Sen, 1987, 1988). Defining human well-being in terms of functionings and capabilities3, Sen ( 1987, 1988) considers poverty as a multidimensional phenomenon and focusses on direct outcomes of human well-being. Since money-metric indicators of poverty reflect only the ability to achieve functionings, it serves only as an indirect measure of the standard of living, whereas direct measures are, for example, the status and access to health and education.

1In this paper, we only consider income as the money-metric measure of living standard and do not distinguish between income and consumption.

2Examples for recent studies examining the multidimensional casual relationship between economic growth and poverty reduction are Bourguignon and Chakravarty (2003), Mukherjee (2001), and Summer (2003)

3Where functionings are the achievements of human well-being and capabilities reflect the ability to achieve these functionings.

Based on this approach, many poverty assessments including social indicators have been done using aggregate data or household-level data (see e.g. UN, 1996; Klasen, 2000; Grimm et al., 2002). However, non-income indicators have not been considered in the pro-poor growth measurement so far.

We introduce the multidimensionality of poverty into to the pro-poor growth measurement by applying the growth incidence curve (GIC) by Ravallion and Chen (2003) to non-income indicators and call our resulting graphs non-income growth incidence curves (NIGIC). We illustrate this approach using micro-data for Bolivia for 1989 and 1998. We distinguish between ranking the sample by each non-income indicator and ranking the sample by income and investigate based on this income ranking the changes of the non-income indicator, with respect to the position in the income distribution. In addition to investigating growth rates, we investigate absolute changes of the non-income indicators. We find that growth was pro-poor both in the income and in the non-income dimension, but results for the non-income dimensions are less clear for the non-income development when the poor are ranked by income.

The paper is organized as follows. Section 3.2 briefly gives an overview of the concept of pro-poor growth and the need to investigate it in a multidimensional perspective. Section 3.3 explains our methodology to apply the GIC to non-income indicators and discuss some limitations. Section 3.4 presents the results of the GIC and the NIGIC for selected variables and for a composite welfare index. Section 3.5 summarizes and gives an outlook for future research.

### **3.2 The Concept of Pro-Poor Growth**

### **3.2.1 Definition of Pro-Poor Growth**

According to some, pro-poor growth is simply economic growth that benefits the poor (e.g. UN, 2000; OECD, 2001, 2006). This definition, however, provides little information how to measure or how to implement it. What remains to be specified is, first, if economic growth benefits the poor and, second, if yes to what extent. For example, Klasen (2004) provides more explicit requirements that a definition of pro-poor growth needs to satisfy. The first requirement is that the measure differentiates between growth that benefits the poor and other forms of economic growth, and it has to answer the question by how much the poor benefited. The second requirement is that the poor have benefited disproportionately more than the non-poor. The third requirement is that the assessment is sensitive to the distribution of incomes among the poor. The fourth requirement is that the measure allows an overall judgement of economic growth and not focuses only

on the gains of the poor. Besides this approach there exist several other attempts conceptualizing pro-poor growth.4

Categorizing the different and conflicting definitions, we speak of three definitions of pro-poor growth in our paper: weak absolute pro-poor growth, relative pro-poor growth, and strong absolute pro-poor growth (see also Klasen, 2005). Pro-poor growth in the weak absolute sense means that the income growth rates are, on average, above 0 for the poor. Pro-poor growth in the relative sense means that the income growth rates of the poor are higher than the average growth rates, thus that relative inequality falls (i.e. in which some indicator considering the relative gap between the rich and the poor falls). Pro-poor growth in the strong absolute sense requires that absolute income increases of the poor are stronger than the average, thus, that absolute inequality falls (i.e. some measure considering the absolute gap between the rich and the poor falls, e.g. Klasen, 2004).5

The different definitions of pro-poor growth are illustrated in Table 3.1, which is taken from Klasen (2005). Table 3.1 shows a country in which the poor earn \$100 per capita and the non-poor \$500 per capita in the initial period. In year 1, the income of the poor grow by 3 percent and the income of the non-poor grow by 2 percent. In terms of the pro-poor growth definitions, this is pro-poor in the weak absolute sense (i.e. growth rates are above 0) and in the relative sense (i.e. growth rate for the poor is higher than for the non-poor). In year 2, the income of the poor grow by 1 percent and the income of the non-poor also by 1 percent. This is pro-poor only in the weak absolute sense, since the the poor have benefited from growth, which illustrates the importance of the the relative and absolute definition of pro-poor growth in order to reduce inequality. In year 3, the income of the poor grow by 6 percent and the income of the non-poor by 9 percent. This illustrates the advantage of the weak absolute definition of pro-poor growth. Even if the benefit is not pro-poor in the relative sense, only the weak absolute definition captures that the poor also have been made improvements (even if inequality rises). In year 4, the income of the poor grow more than the income of the non-poor showing pro-poor growth in the weak absolute and relative sense. Moreover, the growth is

4For a detailed review on the different definitions and measures of pro-poor growth, see, for example, Son (2003). Other approaches to define pro-poor growth are provided, for example, by White and Anderson (2000), Ravallion and Datt (2002), Klasen (2004), Hanmer and Booth (2001). The most common measures that have evolved in pro-poor growth measurement are the 'poverty bias of growth' of McCulloch and Baulch (2000), the 'pro-poor growth index' of Kakwani and Pemia (2000), the 'poverty equivalent growth rate' of Kakwani and Son (2002), the 'poverty growth curve' of Son (2003), and the 'growth incidence curve' of Ravallion and Chen (2003).

5Most inequality measures, including the Gini, Theil, and Atkinson measures as well as decile or quintile ratios are relative inequality measures, but these measures can also be turned into absolute measures of inequality, e.g. absolute Gini coefficients (Ravallion, 2005a). For a discussion of the merits of also considering absolute inequality measures, see Atkinson and Brandolini (2004).

also pro-poor in the strong absolute sense since the absolute increase in income for the poor (\$20) is higher than for the non-poor (\$15).

Table 3.1: Illustration of Pro-Poor Growth Definitions

*Source:* Klasen, 2005.

Table 3.1 illustrates that the definition of strong absolute pro-poor growth is obviously the strictest definition of pro-poor growth and the hardest to achieve, which is also shown empirically by White and Anderson (2000). This is why most researchers concentrate, in general, on the weak absolute and relative definition. But this ignores that decreases in relative inequality might be - and often are - accompanied by increases in absolute inequality, which is seen as undesirable by many and can be an important source of social tension (e.g. Atkinson and Brandolini, 2004; Duclos and Wodon, 2004; Klasen, 2004). Conversely, growth that is associated with falling absolute inequality would be particularly pro-poor and, therefore, it is useful to consider this strong absolute concept as well. This is particularly important when examining pro-poor growth in the non-income dimension of poverty where even pro-poor growth in the relative definition might not be seen as sufficiently pro-poor. Consider the case where the poorly educated increased their education level from I to 2 years, an increase of I 00 percent while the rich increased their education levels from IO to 12 years, an increase of 20 percent. This would be pro-poor growth in the relative definition as relative inequality falls, but most observers would also note the rise in absolute inequality and might, therefore, not consider this type of educational expansion 'pro-poor' since no degree is achieved. Besides, concentrating only on percentage changes in education misses that the poor should catch up to the non-poor regearing specific degrees in education. Concentrating also on absolute changes allows one to examine, for example, whether a poor individual achieved the level of primary or secondary education.6

6See also the discussion below in Section 3.3.4.

### **3.2.2 Multidimensionality of Pro-Poor Growth**

The most glaring shortcoming of all attempts to define and measure pro-poor growth is that they rely exclusively on one single indicator, which is income. This means that they are only focussed on MDG 1 but leave out the multidimensionality of poverty, which is taken into account in the other MDGs.

Income enables households and/or individuals to obtain functionings. This means, income serves to expand peoples' choice sets (capabilities) (Sen, 1987, 1988) and is, therefore, an indirect measure of poverty. In contrast, certain nonincome indicators measure the functionings of households and individuals directly. Measuring poverty only with income assumes that income growth is accompanied by non-income growth. However, the problem of focussing only on MDG 1 is that an improving income situation of households need not automatically imply an improving non-income situation, thus, reaching the other MDGs is not automatically guaranteed (for example, as shown in Klasen (2000) or Grimm et al. (2002)). While non-income indicators have recently received more and more attention in the concept and measurement of poverty 7 they have not in the concept of pro-poor growth and no attempts have been made so far to measure pro-poor growth on the basis of non-income indicators.

Following Sen ( 1987, 1988), our conceptual approach to introduce non-income indicators in the pro-poor growth measurement starts with the selection of nonincome indicators determining the most important functionings of human welfare. In line with the MDGs (UN, 2000) we select education, health, nutrition, and mortality as non-income indicators of poverty and, therefore, follow the spirit of the most prominent multidimensional poverty indices such as the Human Development Index, the Human Poverty Index, and the Physical Quality of Life Index by UN (1991) and UNDP (2000). After having selected the indicators and defined related variables we investigate whether non-income growth was pro-poor between two periods. We do this exemplarily in applying the methodology of the growth incidence curve (GIC) to non-income indicators, but non-income pro-poor growth can also be applied to other pro-poor growth measures. We also compare the results based on non-income indicators with those based on income.

7Examples for recent studies examining the multidimensional casual relationship between economic growth and poverty reduction are Bourguignon and Chakravarty (2003), Mukherjee (2001) and Summer (2003). Also international organizations point to the importance of the direct outcomes of poverty reduction such as health and education (see e.g. World Bank, 2000; UN, 2000; UN, 2000a).

## **3.3 Methodology**

### **3.3.1 The Growth Incidence Curve**

To answer the question if and to what extent growth was pro-poor one can investigate the growth rates of the poor, i.e. those who were below the poverty line in the initial period. A useful tool for this purpose is the GIC (Ravallion and Chen, 2003), which shows the mean growth rate *g,* in income *y* at each percentile *p* of the distribution between two points in time, t-1 and *t.* The GIC links the growth rates of different percentiles and is given by

$$\text{GIC}: \mathfrak{g}\_t(p) = \frac{\mathfrak{y}\_t(p)}{\mathfrak{y}\_{t-1}(p)} - 1. \tag{3.1}$$

By comparing the two periods, the GIC plots the population percentiles (from **1-** 100 ranked by income) on the horizontal axis against the annual per capita growth rate in income of the respective centile. If the GIC is above 0 for all percentiles *(g,(p)* > 0 for all *p),* then it indicates weak absolute pro-poor growth. If the GIC is negatively sloped it indicates relative pro-poor growth. It is important to note that we assume anonymity throughout, i.e. we consider the growth rates of percentiles, even though they contain different households in the two periods.8 For a discussion of this and results when the anonymity axiom is lifted, see Grimm (2007).

Starting from the GIC, Ravallion and Chen (2003) define the pro-poor growth rate (PPGR) as the area under the GIC up to the poverty headcount ratio *H.* The PPGR is formally expressed by

$$PPGR = \mathbf{g}\_t^p = \frac{1}{H\_t} \int\_0^{H\_t} \mathbf{g}\_t(\mathbf{p})d\mathbf{p},\tag{3.2}$$

which is equivalent to the mean of the growth rates of the poor up to the headcount. What is normally done in poverty assessments is to compare the PPGR with the growth rate in mean (GRIM). The GRIM is defined by

$$GRIM = \chi = \frac{\mu\_t}{\mu\_{t-1}} - 1,\tag{3.3}$$

where *µ* is mean income. If the PPGR exceeds the GRIM, growth is declared to be pro-poor in the relative sense.

8One should be cautious when deducing policy implications from the GIC when assuming anonymity. In particular, the GIC allows not to show if, for example, specific policy measures were beneficial to those who where poor in the initial period, but can show if the poor in both periods have benefited more from the measures than the non-poor.

Examining pro-poor growth in the strong absolute sense, one has to concentrate on the absolute changes in income of the population percentiles between the two periods. We define the absolute GIC or by

$$\text{GIC}\_{absolute} : c\_t(p) = \wp\_t(p) - \wp\_{t-1}(p), \tag{3.4}$$

which shows the absolute changes for each percentile. By comparing the two periods, the absolute GIC plots the population percentiles on the horizontal axis against the annual per capita change in income of the respective percentile on the vertical axis. If the absolute GIC is negatively sloped it indicates strong absolute pro-poor growth.

Starting from the absolute GIC, we define 'pro-poor change' (PPCH) as the area under the absolute GIC up to the headcount *H.* The PPCH is formally expressed by

$$PPCH = c\_t^p = \frac{1}{H\_l} \sum\_{l}^{H\_l} c\_l(p),\tag{3.5}$$

which is equivalent to the mean of the changes of the poor up to the headcount. We compare the PPCH with the change in mean (CHIM), which is defined by

$$CHIM = \delta\_t = \mu\_t - \mu\_{t-1} \,. \tag{3.6}$$

If the PPCH exceeds the CHIM, growth is declared to be pro-poor in the strong absolute sense.

### **3.3.2 The Non-Income Growth Incidence Curve**

The calculation of the non-income growth incidence curves (NIGIC) broadly follows the concept of the GIC. Instead of income (y), we apply Equations (3.1) through (3.6) to selected non-income indicators to measure pro-poor growth directly via outcome-based welfare indicators. Thus, the NIGIC measures pro-poor growth not in an income sense but in a non-income sense, e.g. the improvement of the health status or the educational level between two periods for each percentile of the distribution.

We calculate the NIGIC in two different ways. The first way we call the unconditional NIGIC in which we rank the individuals by each respective non-income variable and generate the population percentiles based on this ranking. For example, using average years of schooling of adult household members, the 'poorest' percentile is now not the income-poorest percentile but the one with the lowest average household educational attainment.

The second way, we call conditional NIGIC in which we rank the individuals by income and calculate based on this income ranking the population percentiles of the non-income variable. With the conditional NIGIC, we capture the problem that the assignment of the households to income percentiles on the one hand (GIC) and to non-income percentiles on the other hand (unconditional NIGIC) might not be the same. For example, the income-poorest group might not be the education-poorest group at the same time. This means that, in the conditional NIGIC, the percentiles are income percentiles, thus that the 'poorest' percentile is the one with lowest income, but that the growth rates are non-income growth rates, thus, are calculated for, e.g. years of schooling of the income percentiles. With the conditional NIGIC, we measure how the development of the non-income indicators is distributed across income groups.

Both ways of calculating the NIGIC are of particular relevance for policy making. The unconditional NIGIC mirrors the development of the social indicators that are relevant for human welfare. Thus, it can monitor how the non-income MDGs (especially MDGs 2-6) have developed over time for different points of the non-income distribution. In order to reach the MDGs, improvements will be particularly important for those at the lower end of the non-income achievements and the NIGIC allows such an assessment. The conditional NIGIC give an additional tool to investigate how the progress in non-income dimensions of poverty was distributed over the income distribution. This is also of relevance when evaluating distributional impacts of aid and public spending. Standard benefit incidence studies, for example, analyze the impact of public spending by calculating shares of the total spending to each percentile and comparing the shares of the income poorest with the income richest centile (see e.g. Van de Walle, 1998; Van de Walle, 1995; Lanjouw and Ravallion, 1998; Roberts, 2003). But the share of public spending for the poor serves only as a proxy for a real welfare impact in terms of non-income achievements. With the conditional NIGIC, it is than possible to analyze the actual improvements in the particular social indicator over the income distribution. For example, it provides an instrument to assess if public social spending programs has reached the targeted income-poorest population groups and if the public resources are effective allocated and used. For example, Berthelemy (2005) shows that education policies in Sub-Saharan Africa are biased against the poor. On average, policies favor the non-poor because they are concentrated on improvements in secondary and tertiary education and only little attention is paid to improvements in primary eduction, i.e. to the poor population. In this respect, the conditional NIGIC might be a useful tool in the pro-poor spending analysis to understand who benefits from public spending and to what extent.

When interpreting the NIGIC, three issue need to be discussed. First, in comparing the GIC and the NIGIC, one cannot deduce any causality between income and non-income indicators. For example, from the curves, we can neither say that an improvement in income causes an improvement in the health status nor that an improvement in the health status causes an improvement in income. They simply show how improvements in income and non-income indicators are related to each other, which might be due to causal or spurious correlations. Second, one cannot compare the absolute values of the growth rates of income and non-income variables because the variables are measured in different dimensions such as monthly income and years of schooling. One can only compare if the growth rates are positive or negative and by how much the PPGR exceeds the GRIM. Lastly, due to the different dimensions of the income and non-income indicators, and the fact that many of the non-income indicators are bounded above (i.e. there is an upper limit to survival prospects or to educational achievements)9, it may well be plausible that different definitions of 'pro-poor growth' would be appropriate for different indicators. While one may be satisfied that income growth was pro-poor if it met the relative definition (i.e. the poor had higher income growth rates than the rich), one may only call growth in educational achievements pro-poor if the poor had higher absolute increments than the non-poor. 10

### **3.3.3 Specification of the Non-Income Indicators**

We calculate the unconditional and conditional NIGIC for education, health, nutrition, and for a composite welfare index (CWI) as described below. We are working with DHS data for Bolivia from the years 1989 and 1998 that do not contain information on income or consumption due to its focus on demographics, health, and fertility. However, in our DHS data set, we use simulated incomes based on a dynamic cross-survey micro-simulation methodology introduced by Grosse et al. (2004 ). 11 The basic idea of this simulation methodology is the following. The authors use two kinds of surveys: first, the DHS (of 1989 and 1998)

9See discussion in Section 3.3.4 below.

JO A different way to deal with this problem would be to re-scale the non-income variables by, for example, transforming the education indicator into a percentage shortfall from a maximum level, say 16 years of education, and then define growth as the percentage reduction in that shortfall, which was also discussed by Sen (1981) and Kakwani (1993). With such an indicator, one may well decide to choose the relative definition as sufficient to define pro-poor growth. As discussed below, this issue will also arise when comparing the Gini coefficients of incomes with Gini coefficients in non-income indicators. We do not apply this approach in this paper, because we do not want to give achievements at higher levels more weight than achievements at the lower levels in education since we are interested into the question whether the poor can catch-up to the non-poor. See Section 3.3.4 for a more detailed discussion on this particular issue.

11 For the calculation of the PPGR in the next chapter, we use the headcount of 77 percent as found in Klasen et al. (2004) for the moderate poverty line. We use the same headcount for the calculation of the PPGR of all non-income indicators.

and, second, the Bolivian household surveys (the 2nd EIH of 1989 and the ECH of 1999). Then they estimate an income correlation in the household survey, apply the coefficients to the DHS and predict, i.e. simulate, incomes in the DHS. 12

For each non-income indicator, we identify alternative variables to capture particular aspects of the non-income dimension in question. For education, we specify eight different variables. We calculate average years of schooling for all adult household members and for males and females separately. 13 Age plays an important role when analyzing changes in non-income indicators, especially for education. In particular, not much improvements in education can be expected among the adult population (the education of 30-40 year olds in 1989 should not be be very different from the education of the 40-50 year olds in 1999). To avoid misleading conclusion from potential low improvements, we, therefore, restrict the sample to women aged between 20 and 30 as only this age group is likely to have experienced a change in their educational achievement (the 20-30 year olds in 1999 represent a new cohort of women who were educated later than the other cohorts). In addition, we calculate the maximal education per household instead of the average for all adults, males, females, and females aged between 20 and 30. The idea behind using these variables as an indicator is that it might be sufficient that one household member is well educated to generate income for the whole household and to invest in education of other household members (i.e. intra-household externalities) (Basu and Foster, 1998). 14 To take into account possible intra-household inequalities in education, we also calculate gender gaps

12To provide some more detail, the authors estimate an income/consumption expenditure model in the 1999 Living Standard Measurement Survey (LSMS) data restricting the set of covariates to those which are also available in the 1998 OHS data and interacting all variables with a rural dummy. They then use the regression to predict incomes in the OHS and add a randomly distributed error term. They then repeat the procedure for the EIH of 1989, which is only available in urban areas. When imputing incomes in rural areas, they use the model for urban areas in 1989 and add the results of the rural interaction terms from 1999, thus assuming that the difference in the impact of income correlates between 1989 and 1999 did not change over time. While the results work well in a validation test for 1999, there is a tendency that the simulated income growth is higher than the observed one. This overprediction should not bias the results in this paper, but it might be useful to test the results generated here with a survey that contains detailed information both on income and on non-income variables.

13The OHS only includes households with at least one woman in reproductive age, i.e. aged between 15 and 49 who serve as respondents in the OHS. The education for the male household members has to be taken from the memory of the respondents concerning the education of their husband or partner (with the age of the men being unknown). Households without women in reproductive age are excluded as well as unmarried men.

14 An important issue is to be noted here: An overall problem of years of schooling as a variable for educational attainment is that years of schooling do not say anything about educational quality and, therefore, the indicator should be treated with some caution. This problem might be solved by using other data such as education test scores (like Pisa scores). However, these data are not always available and certainly not in the same data sets.

in eduction within households. In particular, we calculate the female minus male education in the households (in years of education).

For health, we specify three different variables. We calculate infant survival rates of children aged under 1 years and also for children aged under 5 year. 15 Furthermore, we take the average vaccinations of children aged between 1 and 5 per household, with a maximum of 8 possible vaccinations for each child. 16 The vaccination rate is a variable that represents access to health care and preventive medicines. A similar variable has, for example, been used in the monitoring of the health sector reform project in Bolivia in 1999 Montes (2003).

For nutrition, we use stunting z-scores as the variable that measures chronical undernutrition for children aged between 1 and 5 years. The stunting z-scores are defined as the difference of height at a certain age and the median of the reference population for height at that age divided by the standard deviation of the reference population. 17 It takes values between approximately -6 and 6, where values below -2 are considered as being moderately undernourished and below -3 as being severely undernourished (see e.g. Klasen, 2003, 2007). Problematic might be that the z-score contains a lot of 'genetic noise' in the sense that, for example, a low z-score interpreted as being undernourished might simply appear because the parents are genetically short but the child is small but well nourished and vice versa.

An alternative possibility to address the issue of the multidimensionality is to aggregate several indicators to a composite welfare index (CWI). 18 Here, we follow the methodology of the Human Development Index (HDI) to address the problem of difference scales of the variables (UN, 2000). Each variable that enters the index is normalized to be between 0 and 1 in subtracting the individual value from the minimum value observed in the data set divided by the range

$$CWI = \frac{1}{n} \sum\_{i=1}^{n} \frac{individual\_n - minimum}{maximum - minimum} \,\text{s}\,\tag{3.7}$$

The CWI is constructed by simply averaging the sum of the selected variable scores *n.* It includes four of the above explained variables: average education

151n our calculation, we use household child survival rates instead of child mortality rates. An improvement in child mortality comes out as a lower value but this lower value is mathematically interpreted as a deterioration. The linear transformation used is: survival rate= (mortality rate - I) \* ( -1). This means, for example that a reduction of child mortality from 80 percent to 60 percent is transformed into an increase in child survival from 20 percent to 40 percent.

16The possible vaccinations are 3 against polio, 3 against DPT, I against measles, and I BCG. 17See also Section 1.3.1 and 2.4.1 in Essay I and Essay 2.

18For a detailed overview about several composite welfare indices and how they are calculated, see e.g. UN (2006).

of all adult household members, stunting z-scores, under I survival, and average vaccinations. 19

As not all variables are given for all households (e.g. health and nutrition variables are only available for households who have children), we calculate the CWI for two different samples. The first sample, called small sample, is the one for which all variables are available for all households. This reduces the sample size enormously (in 1989, e.g. from 6,053 to 1,306 households) and, more importantly, in a non-random fashion. 20The second sample, called big sample, includes all households, but the index is averaged over fewer variables for those households, which do not have data for nutrition and/or health variables. The advantage of creating the CWI based on the big sample is the higher number of observations but the disadvantage is that the results for some percentiles are driven by very few, or even only one variable. The smaller sample has fewer observations but contains for all households the same number of variables. For both, the small and the big sample, we also augment the indices by also including simulated income as a fourth indicator.

### **3.3.4 Limitations of the Indicators**

While we show below that these indicators yield important information, there arise also a number of problems when analyzing non-income indicators of welfare, which also are important to note for the use of the NIGIC, but can also be seen as general inherent limitations of non-income indicators of human well-being to be aware of and which we want to highlight. The first limitation is the informational value of the calculated growth rates of the NIGIC, where we interpret an ordinal relation in a cardinal fashion. Examining an ordinally scaled variable one can say that 6 years of schooling is better than 3 years but one cannot be sure to that the household is twice as well educated.21 This ordinal scaling leads to two different kinds of interpretation problems.

First, averaging an ordinally scaled variable leads to a ranking problem when assuming that education is one of the most important determinants to generate income and reduce poverty (Osberg, 2000). For example, comparing two households, A and B, with two adults in each household where the household members of A have O and 12 years of schooling and of B have 6 and 7 years of schooling,

19The latter two variables do not enter separately but form a health sub-index as the simple average of the two scores. In contrast to the HDI, we use the maximum and minimum values defined by the data sets and do not use fixed maximum and minimum values.

20This reduction in observations translates into the calculation of the percentiles resulting in higher standard errors than for the large sample.

21 The same problem exists when interpreting income in a cardinal fashion, despite the Jacking foundation for such an interpretation, but this issue is normally neglected in applied discussions.

household B has a higher average education than A. Now, when B is ranked higher than A, one ignores any kind of educational degrees and the resulting differentials in returns to education. This means that the person with 12 years of schooling might earn disproportionally more income than both members of household B together, thus, household A should be ranked higher than B. We address this problem in also using maximal education per household.

In addition, averaging the years of schooling over the household ignores also possible intra-household inequalities in education. Taking into account the distribution of education within the household and, therefore, taking into account possible intra-household inequalities in education, we additionally focus on the individual educational attainment (instead of only on the average of the household) and on the potential gender gap in education of households.

Second, concerning the usual problem of absolute versus relative changes, increases in years of schooling, just comparing growth rates might be misleading and might not reflect their true achievements. For example, Table 3.2 shows for average education an increase of 80 percent for the 2nd decile compared to 6 percent of the 9th decile, which might be overstating the improvement for the poor because the years of schooling of the poor increase from 1.31 to 2.37 years of schooling and those of the non-poor from 11.73 to 12.43. In addition, improvements in tertiary education mighty be harder to achieve than improvements in primary education, which should also be taken into account. This problem is related to the fact that many of the non-income indicators are bounded above, i.e. there are firm or likely upper limits on such achievements. 100 percent survival in the first year is the upper limit for health, more than 18 or 19 years of education is very rare, more than 8 vaccinations is not recommended, done, or measured, etc. One may assume 'declining marginal returns' to improvements in non-income indicators, which would suggest that a marginal year of schooling or another vaccination is less valuable when the level of schooling is already high.

This problem is also discussed by Kakwani (1993). He derives an achievement function for non-income indicators based on the assumption that the value of the achievement increases non-linearly with the achievement level, i.e., an increase in 1 year of tertiary education reflects greater achievement than I year of primary education.22 However, the value of this increase is based on the effort made to achieve but does not consider the value of the outcomes of this achievement. Since we are interested in the question whether the poor can catch-up to the non-poor and, therefore, rather interested in investigating improvements in direct outcomes of social indicators than in the effort of these achievements, we do not

221n particular, based on the achievements function Kakwani (1993) derives an improvements index, which takes into account both the asymptotic limit of non-income indicators of standard of living and the non-linearity of the values of achievements.

weight the improvements in relative achievements.23 Besides, in addition to the relative changes, we calculate the absolute NIGIC and pro-poor changes examining directly the absolute improvements in years of education. However, even when we use absolute changes, which equal approximately 1, a further question remains open. An increase of 1.06 years of schooling of the th decile might be less beneficial because perhaps the persons are still more or less illiterate, compared to the increase of 0. 70 years of schooling in the 9th decile, which might mean completing secondary schooling and getting a degree.

An another issue that arise due to the bounded above problem of social indicators is that it may be the case (and indeed is the case in Bolivia) that some households have reached the upper limit and further growth is not possible. However, our main focus is on the bottom of the distribution. Even if we observe improvements in, for example, education only for the lower deciles, we still can interpret these findings regarding the pro poorness of improvements in the educational system, particularly whether the poor have benefited from these improvements.

The third type of problem in comparing relative changes relates to the stunting z-score. In our data sets, it ranges roughly from -6 to 6. Relative changes in the stunting z-score cannot be calculated because of the coexistence of negative, positive and 0 values in the variable range. For example, how to compare the relative improvement from -2 to -1 with an improvement from 1 to 2 from the year 1989 to 1998? We reduce this problem by transforming the z-score in such a way that all values are positive, which means by adding the minimum value of both data sets (in our case -5.89) to each z-score to get a range of only positive numbers.

Another limitation is the problem of weighting, which we illustrate with the example of child mortality. For example, comparing two households, A and B, where A has 1 child and B has 10 children the households should be weighted differently when in each of the two households 1 child dies. Household A has a child mortality rate of 100 percent whereas B of 'only' 10 percent. From an intrinsic point of view, it is obvious that both deaths are equally lamentable. In this case, one could think of just counting the death per household independently of the total number of children. However, **it** is less obvious from an economic point of view where children can be partly considered as investment goods. Here, a higher mortality rate mirrors the more heavy loss of one child in the one-child household A compared to the IO-children household B. The investment-good character comes from absence or lack of social security systems in which case the children care for the parents in the cases of unemployment, sickness, and old age (see e.g. Ehrlich

23 Another way to address this would be a logarithmic transformation of non-income achievements as is done for the income component of the HDI.

and Lui, 1997).24 Following these two extreme points of view, one might think of weighting the death of children in households talcing both arguments somehow into account. But any weighting would, however, be quite arbitrary and induce difficulties in justifying it with economic or welfare-theoretical judgments. Keeping this critical issue in mind we use unweighted child survival rates.

Weighting problems are also difficult with the nutrition indicator. A negative stunting z-score indicates malnourishment. But the z-score should not be interpreted as a linear variable in the sense that an increasing z-score is always equivalent to an improvement in the nutritional status. From a certain threshold onward, increasing z-scores might no longer reflect improvements of the nutritional status but indeed quite the opposite. For example, a child with a very high z-score of 3 might not be better off as one with O because she might be too tall for her age. This problematic holds even stronger if one would consider wasting z-scores (weight over age). Here, increasing z-scores strongly above O reflect obesity that negatively affects the health status (see e.g. De Onis and Blossner, 2000).25

Another limitation when calculating the NIGIC is that some variables of the non-income indicators do not vary much between households. This holds especially for under 5 and under 1 survival, which is very low in Bolivia at the household level. For both years, Table I shows that from the 2nd decile upwards, the maximum value of 100 percent survival is already reached in both years, so that no improvement is possible any more. This translates into growth rates of 0, so that the unconditional NIGIC becomes flat and takes the value of O from the 2nddecile onward. The problem of flat curves always arises when the variable values are bounded above (as for example a maximum of 19 years of schooling or 8 vaccinations).

Dealing with this limitation in a more general way, the discussed variables have a more discrete character (in the sense that one either has survived or not, which makes it difficult to observe relative differences among individuals, households, and over time. This is why these indicators (such as mortality rates) are mostly generated and interpreted at an aggregate level. The only, but small, variation evolves from taking household averages instead of individual data. This is why these variables - and all kinds of dummy variables - show little variation for the pro-poor growth analysis using the NIGIC.

24One complicating aspect arises when taking gender preferences for the children into account. The loss of one child when considered as an investment good might depend on the cultural habits (e.g. labor market opportunities for females and males, marriage agreements, and the question who takes care of the parents in old age).

25 In particular, several studies show that obesity in childhood negatively affects the development of the child and which is an increasing concern in developing countries (see e.g. Dietz, 1998; Martorell, 1998).

More interesting to examine in these cases is the conditional NIGIC, in which we link the non-income variables to income. Here, low variation is less problematic than for the unconditional NIGIC because the variables are ranked by income. As Table 3.3 and all figures show, there is no flat part any more. Now we generate interesting information regarding the changes on the non-income indicators when ranked according to their income situation and how improvements are distributed.

## **3.4 Empirical Analysis**

### **3.4.1 Inequality**

Bolivia is one of the countries with a very unequal income distribution in Latin America. Table 3.2 shows the distribution of income and the non-income indicators (unconditional) and Table 3.3 shows the distribution of the non-income indicators for the conditional case, i.e. when the non-income indicators are ranked by income.

We find high and persisting income inequality as measured with the Gini coefficient that falls from 0.56 in 1989 to 0.54 in 1998. This high inequality is also reflected in the high and only slightly falling l 00: l 0 ratio. Turning from inequality to growth, we find that all deciles increased their incomes. Especially in the 1990s, Bolivia experienced relatively high growth rates (which also were pro-poor in urban and rural areas). However, Bolivia was and is one of the poorest countries of the region, and the positive economic trend has reversed since 1999 combined with some episodes of social and political turmoil. Bolivia used to show much worse outcomes in social indicators than other countries in the region. However, there have been notable and sustained improvements in many social indicators since the late 1980s, which continued to improve during the recent economic slowdown (see e.g. Klasen et al., 2004).

Locking at the unconditional case (Table 3.2), the Ginis for education variables are all in the range of0.40 to 0.50.26 As stated above, due to the boundeness of the variable, one cannot infer directly from this that educational inequality is

26 It is important to note that the values for the Gini coefficients can not be compared between income and non-income indicators and not among non-income indicators, because the values of the indicators have different ranges. For example, whereas the values for income can take values in a nearly infinite range, all non-income indicators are bounded above resulting in generally lower Gini coefficients. As Klasen (2007) notes, this is only the case for countries (like Bolivia), where the education richest groups are already close to the upper bound. This is, for example, not the case for many African countries as shown by Thomas et al. (2000) who calculate Gini coefficients for education.

in some sense substantively smaller than income inequality.27 For all educational variables, the Ginis fall between 1989 and 1998, which is likely due to the fact that the rich have already reached high levels of education and the poor are catching up. Interesting to note is that the highest Ginis exist for the group of all respondents both for average and maximal education indicating a gender bias in educational achievements. These findings are also reflected in the JOO: JO ratio. The conditional deciles, which are shown in Table 3.3 also show that the level of schooling increases with increasing income for all educational variables, but the JOO: IO ratio is much lower than in the unconditional case. We find that an improvement has been made for all educational variables in all deciles for both the unconditional and the conditional case (Tables 3.2 and 3.3). However, as already both tables shows, improvements where much higher in the unconditional than in the conditional case indicating that the improvements in non-income indicators of poor, when they are ranked by income, are less clear than if the improvements are linked to the initial level in the respective non-income indicator.

The extremely low Ginis for the under l and under *5* survival rates (both for the unconditional and conditional ranking) can be explained by the overall low incidence of child mortality in Bolivia at the household level. For both age groups, child mortality is below 10 percent. The conditional deciles indicate that mortality seems to be more or less randomly distributed over the income distribution (Table 3.3).28 For vaccination, the Gini falls strongly from 1989 to 1998, and we find clear improvements, especially for the lower deciles (except the lowest decile), which is also due to the fact that the best vaccinated deciles had only limited room for improvements. The inequality of the stunting z-score is relatively low and falls slightly. Malnutrition decreases with an increasing position in the income distribution, but the differences for the income deciles are quite low.

Table 3.4 shows the distribution of the composite welfare index. The CWI reflects the findings from above where the Gini coefficients decrease for the selected variables. For the CWI (both excluding and including income), the Gini coefficient is higher for the big sample than for the small sample indicating betweengroup inequality.29 Table 3.4 also illustrates the difference in the values of the indices if income is included and excluded. If income is included into the index,

27 One should also be aware of the fact that the calculation of the Ginis of the social indicators are based on discrete variables. Although income also is strictly discrete, it has a much more continuous character then social indicators like years of education. Thus, it is much more difficult to calculate a Lorenz curve for years of schooling given the lower boundary (0) and upper boundary (I 8), and the Ginis should be interpreted with caution. An attempt to face this problem as addressed by Thomas et al. (2000).

28 As explained below, reasons for this might be the overall low mortality risk in Bolivia and the tendency for underreporting among poorer population groups.

29This between-group inequality is driven by the higher degree of homogeneity in the small sample.

the level of values decreases, both in the unconditional and the conditional case, which is driven by the high and persisting income inequality in Bolivia.

Table 3.2: Income and Non-Income Indicators (Unconditional)

(Bolivia, 1989 and 1998)

Table continues on next page.

(Unconditional, Bolivia, 1989 and 1998)

*Notes:* The explanation for the variables is the following. Income: Real household income per capita in Bolivianos per month (CPI of 1995=100). Education: All variables for education are measured in average single years per household. Respondents and partners are only couples, for which the respondent knows the education of her partner. Health: Under *5* (1) survival rates are estimated with life table estimations taking the sample of children born 10 (5) years prior to the sample. Survival rates are averaged over the household. Vaccinations: Average vaccinations of the children in the household older than I, where the possible vaccinations are 3 against polio, 3 against DPT, I against measles, and l BCG. Nutrition: Stunting z-score of the last born child of each respondent (averaged over the household). A a child is defined as stunted if her z-score is below -2. *Source:* Demographic and Health Survey (DHS); own calculations.

Table 3.3: Income and Non-Income Indicators (Conditional)

(Bolivia, 1989 and 1998)

Table continues on next page.

~ tI1

**z**

(/)

*0*

**z**

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*Notes:* **The** explanation for the variables is the following. Income: Real household income per capita in Bolivianos per month (CPI of 1995= I 00). Education: All variables for education are measured in average single years per household. Respondents and partners are only couples, for which the respondent knows the education of her partner. Health: Under 5 (I) survival rates are estimated with life table estimations taking the sample of children born IO (5) years prior to the sample. Survival rates are averaged over the household. Vaccinations: Average vaccinations of the children in the household older than I, where the possible vaccinations are 3 against polio, 3 against DPT, I against measles, and I BCG. Nutrition: Stunting z-score of the last born child of each respondent (averaged over the household). A a child is defined as stunted if her z-score is below -2. *Sourc,:* Demographic and Health Survey (DHS); own calculations.

#### Table 3.4: Deciles of the Composite Welfare Index

(Bolivia, 1989 and 1998)

*Notes:\*The* composite welfare index includes average education per household, under five survival rate, average vaccination per child (age>=!) and stunting. *Source:* Demographic and Health Survey (DHS); own calculations.

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### **3.4.2 Pro-Poor Growth**

Figure 3.1 shows the relative and absolute GIC for income. The relative GIC plots the annual growth rates in monthly household per capita income for each household of the distribution. The absolute GIC plots absolute increases in real Bolivianos for the whole period 1989-1998 for each percentile. Included are (also in all other figures) the bootstrapped 95% confidence intervals30 and the moderate and extreme poverty headcounts for 1989 of 77 and 56 percent, respectively. As can be seen from the position and slope of the GIC, income growth was pro-poor in the weak absolute and relative sense since the curve is above O for all and negatively sloped for nearly all percentiles. As expected, we do not find strong absolute pro-poor growth since the absolute GIC is positively sloped meaning that absolute increases in income were much higher for the non-poor than for the poor.

*Source :* Demographic and Health Survey (DHS); own calculations.

Figure 3.2a shows the relative and absolute unconditional NIGIC for average education per household. Figure 3.2b shows the relative and absolute conditional

30In particular, based on the households in both surveys, the bootstrap draws 200 random samples with replacement for each period and calculates the respective percentiles and growth rates (absolute changes) so that we obtain 200 values for each indicator per household. Based on these 200 values, we draw the mean and the standard deviation and calculate based on this the respective lower and upper 95% confidence interval.

(smoothed31 ) NIGIC for this variable. Note that the confidence intervals of the unconditional **NIGIC** lie very tight around the **NIGIC.** The reason for this lies in the discrete character of the social indicator. Each percentile contains households with nearly the same level of years of education, which results in low variations within percentiles and which leads to the very tight confidence intervals around the unconditional growth rates (absolute changes).

Whereas for the unconditional NIGIC the growth rates and absolute changes are shown for percentiles (1-100), for the conditional NIGIC the growth rates and absolute changes are shown for vintiles (1-20). The reason for using vintiles instead of percentiles is to get a higher number of observations four each group when households are ranked by income. For example, if a percentile contains only 50 households (ranked by income) and if we assign to these households the respective mean years of education, then it is possible to obtain huge variations within each percentile, which results in very wide confidence intervals between the growth in the two periods, and we **will** miss to show the income gradient.

For the unconditional NIGIC 3.2b, we find pronounced weak absolute as well as relative pro-poor growth. 32 The relative pro-poorness of average education is reflected comparing the **PPGR** with the **GRIM** where the **PPGR** for moderate poverty is 3.89 percent and the PPGR for extreme poverty 4.88, both much higher than the GRIM of 1.80 percent (Table 3.5).

The conditional NIGIC is more volatile than the unconditional NIGIC and also shows weak absolute and relative pro-poor growth but to a lower extent. Thus, the conditional NIGIC shows that the income-poor have experienced slightly higher educational growth than the average. This is also reflected in the higher PPGR (2.00 percent for moderate and 2.24 percent for extreme poverty) compared to the **GRIM** (l.80 percent).

31 As the conditional are very volatile, we only include the smoothed conditional NIGIC in the figures to show the major trend of the curves. 32 A noteworthy point appears when looking at the upper part of the unconditional NIGIC and

their absolute changes. In the range of the ih and 8th decile, all curves for the education variables fall below O and become positively sloped afterward. This reduction might not be a deterioration but might be due to a reform of the schooling system, i.e. in the number of years necessary to complete schooling grades.

Figure 3.2: NIGIC for Average Education

*Source:* Demographic and Health Survey (OHS); own calculations.

Figure 3.3: **NIGIC** for Individual Education

*Source:* Demographic and Health Survey (OHS); own calculations.

#### Table 3.5: Pro-Poor Growth Rates ~

(Bolivia, 1989 and 1998)

Kenneth Harttgen - 978-3-631-75358-3

Downloaded from PubFactory at 01/11/2019 05:57:50AM

via free access

*Notes:* Notes: For the explanation of the variables, see Table 3.2. We are using two poverty lines. Toe moderate poverty line leads to an income headcount of 77 percent and the extreme poverty line to an income headcount of *56* percent, which we also use for the non-income indicators. CHIM: Change in mean. PPCH: Pro-poor change. Changes are for the entire period and not annualized. *Source:* Demographic and Health Survey (DHS); own calculations.

To take into account also possible intra-household inequalities, in addition, Figures 3.3a and 3.3b show the unconditional and conditional NIGIC for individual education in single years. Both figures reflect the picture that was found for average education per household showing relative pro-poor growth both for the unconditional and for the conditional case. This indicates that intra-household inequalities have not a substantial impact on the pro-poorness in the improvements in education attainment in Bolivia between the two periods.

Turning to the absolute growth incidence curves, the absolute GIC in Figure 3.1 clearly shows that income growth in Bolivia was strongly anti-poor using the strong absolute definition. The absolute increments of the rich far exceed those of the poor.

We do not find strong absolute pro-poor growth for the absolute unconditional NIGIC for education as the slope of the absolute curves in Figure 3.2 and 3.3 is not negative, but even positive for the poorest deciles. This is quite interesting because it puts the findings of the relative unconditional NIGIC in Figure 3.2a in perspective where we have found high relative pro-poor growth for the first 3 deciles. This seemingly contradictory finding is largely due to the high growth rates for the lower deciles which results from the very low base in 1989. The absolute conditional NIGIC is virtually flat, meaning that the income-poor have not been able to improve their educational attainment by more than the average. These findings are also reflected in comparing the PPCH with the CHIM. As Table 3.5 shows, the unconditional pro-poor change is still larger than the change in mean, however, only slightly: the average years of schooling only increased by 1.18 years in mean and by 1.30 years for the moderately poor and 1.34 for the extremely poor. For the absolute conditional changes and for both poverty lines, the CHIM is higher than the PPCH of 1.01.

Another way to look at intra-household inequalities is to look at the gender gaps in education within households. To remind, we calculate the female minus male education in the households (in years of education). Ranking the households by this gap, we plot in Figure 3.4 the unconditional and conditional absolute change in the gender gap. We find that the intra-household gender gaps were reduced for nearly all households except for those between the 10th and the 20th percentile. Again, especially households in the middle of the distribution showed the strongest reductions (3.4a). When looking at the conditional NIGIC, we find no clear trend meaning that the reduction in gender gaps is equally distributed across all groups (3.4b).

Figure 3.4: NIGIC for Gender Gap in Education

*Source:* Demographic and Health Survey (DHS); own calculations.

Figures 3.5a and 3.5b show the results for average vaccination. The unconditional NIGIC shows pro-poor growth in the weak absolute and is also slightly negatively sloped. Table 3.5 confirms the pro-poorness in the relative sense. Here, both PPGR exceed the GRIM. However, improvements are relatively low, which was also shown in Table 3.2.33

The conditional NIGIC shows no clear pro-poor growth trend, also visible in the wide confidence intervals. In addition, the PPGR are lower than the GRIM and for some deciles we even find a deterioration. The same findings also hold for the absolute curves. This reveals that relative pro-poor growth might not be enough for the poor and that absolute increases (the amount of additional vaccinations) are of particular weight. Finally, it is essential for the health status of children to have all possible vaccinations. The conditional absolute NIGIC shows that the improvements are relatively equally distributed among the income groups.

When examining the high relative growth in the unconditional NIGIC for education and vaccinations, Figures 3.2a and 3.5a do not report growth rates for the very poor deciles. This is due to two reasons. First, the very poor began and ended with no education and no vaccinations (see discussion below). Second, the slightly between off started with no education or no vaccination and ended up having positive levels of education and vaccinations in the second period. But in this case the growth rate is not defined and, thus, not reported. Remember that the very high growth rates that appear on the graphs at the left are, therefore, based on percentiles who had some small amount of education and vaccinations, and even a moderate absolute expansion translates into a very high growth rate.

Examining the absolute unconditional NIGIC for education and vaccinations also reveals an important finding regarding the very low tail of the distribution. As Figures 3.2a, 3.3a, and 3.5a show, the very education-poor and vaccination-poor had no education (vaccinations) in the first period and this continued to be the case in the second period. This is true for the first few deciles in the education indicator and nearly the entire first decile in the vaccination indicator. Thus, whatever expansion has taken place in non-income improvements, it bypassed a core group of very poor. 34

33Interesting to note is the bump around the 70th percentile. Whereas the flat parts of the curves before and after the bump show the percentiles that had 7 and 8 vaccinations in both periods respectively, the bump shows the improvements of those who had vaccinations between 7 and 8 in the initial period.

34The findings with the education indicator have to be treated with some caution as they may simply say that adult women that had no education in the first survey continue to have no education in the second survey, which is to be expected in the absence of adult education programmes. This is not the case, however, with the vaccination indicator as it refers to children between ages 1 and S and, thus, it is indeed worrying that a new cohort of children has grown up without any vaccinations.

For all the other educational variables, we confirm the findings above. Comparing the results for females with males, we find some signs for gender inequality, which are most obvious in the lower percentiles. But we find that the gender inequality seems to have been reduced because the average and maximal education for females increased by more years than for the other groups, especially for males (Tables 3.2 and 3.5). However, the women in the all respondents sample started from a lower level and are on average still worse educated.