INTRAHOUSEHOLD INEQUALITIES AND POVERTY IN SENEGAL Philippe De Vreyer (Université Paris-Dauphine et DIAL) Sylvie Lambert (Paris School of Economics INRA) Very preliminary April 2014 We thank IDRC (International Development Research Center), INRA Paris and CEPREMAP for funding data collection and UNICEF for supporting preliminary work.
Introduction When computing measures of poverty and inequality, most studies treat households as homogenous groups, wherein all members benefit from the same access to resources. Household surveys are based on nationally representative samples and allow a precise measurement of household consumption and provide with reliable indicators of the level of and changes in inequality and household poverty. However, these measurements lack of precision in particular because consumption is always measured at the household level and the individual well-being is measured by household per capita or per adult equivalent consumption or income. Indexes are computed on this basis. Independently, a large literature on intrahousehold allocation of resources now exists, that aims at understanding the mechanisms at play within household. This literature suggests that inequality within household could exist, depending on the bargaining power of each member. Very recently, a paper by Dunbar et al. (2013) tries to put numbers on the implication of potential within household inequalities on measures of poverty in a poor African country. Given they only have data on household consumption (as is generally the case), they do it based on the standard collective household model and using heavy structural hypotheses to estimate the actual sharing of resources. Their attempt is not completely satisfactory for a number of reasons, starting with the fact that it requires admitting the Pareto efficiency hypothesis which in Africa is fairly unlikely. Nevertheless, it is the first attempt to take seriously the fact that within household inequalities might hide the extent of poverty, and in particular for certain type of household members (they focus on children). In this paper we go one step in the direction of the true measurement of individual welfare. Our data are taken from the Pauvreté et Structure Familial Survey (Poverty and Family Structure - henceforth PSF, according to its French acronym) that has been conducted in Senegal in 2006-2007. This survey has been specifically designed to obtain a measurement of consumption at the "cell" level - a clearly identified budgetary decision unit within Senegalese households - that allows coming closer to true individual consumption than household consumption per capita. We evaluate inequality and poverty using household and cell measurements of consumption. In section 1 of this paper we present the principal features of the PFSS, give the precise definition of cells within households and explain why this level of observation is pertinent to analyse the extent and determinants of poverty.
Data and Context Context Senegal is a West African country, with a population of 13.7 million (World Development Indicators 1 ), predominantly Muslim (94% of the population, CIA world factbook 2 ; in the PSF survey 95% of adults 15+ are Muslim), and made up of over twenty ethnic groups, the largest being Wolofs/Lebous (43.3%) followed by Pulars (23.8%), Sereres (14.7%), Diolas (3.7%) and Mandinkas (3%) (CIA world factbook). 3, Senegal has a Human Development Index that ranked it 154 th in 2012 (UNDP, 2013). GDP grew at an average of 5% per year in the 10 years before the PSF survey (1995-2005) (Government of Senegal, 2013). At that date, nearly half of the population was still living with an income below the national poverty line (World Development Indicators). Having faced a series of economic shocks, the annual growth rate of GDP per capita staggered in the period 2005-2011 at an average of 0.5% per year and poverty only decreased modestly over the same period, from 48.3% to 46.7. More than half the population is rural (55%) and the challenges faced for poverty reduction are even more acute in rural areas (Government of Senegal, 2013). According to World Development Indicators, Senegal presents relatively high levels of inequality with a Gini index reaching 40.3 in 2011. Polygamy is legal in Senegal, the Islamic law allowing up to four wives. In practice, most polygamous marriages imply only two wives. Households tend to be large, due to polygamy, to the relatively high fertility level (about five children per woman, see Lambert and Rossi 2014), and also to the frequent presence of foster children (see Beck et al 2014) and extended family members. The Poverty and Family Structure Survey The data come from an original survey entitled Pauvreté et Structure Familiale (henceforth PSF) conducted in Senegal in 2006-7. The PSF survey results from cooperation between a 1 World Development Indicators, http://data.worldbank.org/data-catalog/world-development-indicators 2 CIA world factbook, https://www.cia.gov/library/publications/the-world-factbook/ 3 The respective figures in the PSF survey are 41%, 28%, 13%, 4% and 6%).
team of French researchers and the National Statistical of Senegal. 4 It is a nationally representative survey covering 1,800 households spread over 150 clusters drawn randomly from the census districts so as to insure a nationally representative sample. A total 1,781 records can be used. It is designed to allow a more precise measurement of individual access to resources in Senegalese households than traditional consumption surveys. Interviews conducted at the early stages of the PSF project showed that within Senegalese households, it is possible to distinguish sub-groups of household members (cells) that are at least partly autonomous in their budget management. Consumptions common to various cells in the household appeared clearly defined, as well as the responsibilities for paying for those consumptions, and cells own resources turned out to be not entirely pooled. This survey collects the usual information on individual characteristics, as well as a detailed description of household structure and budgetary arrangements. Households are divided into subgroups (or cells) according to the following rule: the head of household and unaccompanied dependent members, such as his widowed parent or children whose mother do not live in the same household, are grouped together. Then, each wife and her children and, potentially any other dependent under her care, make a separate group. Finally, any other family nucleus such as a married child of the household head with his/her spouse and children also form separate groups. Polygamous men other than the household head are treated in the same way as the head, with the husband and each wives in separate cells. This decomposition emerged from field interviews as being the relevant way to split the households in groups. This design corresponds well to the sociology of households in Senegal and, on the field it has never been difficult to master, neither by the enumerator who found it fairly natural, nor by the households themselves. The identification of cells allows us to hold account of their relative autonomy in terms of budget management. In the PSF sample, more than a third of households contain at least three cells. The grouping of household members in distinct cells allows collecting consumption expenditures made to benefit members of a given cell. Thus, without going up to the point where individual consumption is observed, PSF permits to approach a relatively individualized measure of consumption, which is almost never available in household surveys. Consumption expenditures are recorded in several parts: first all common expenditures are collected (housing, electricity bills etc). Food expenditures are compiled based on a detailed account of who shares which meal and how much money is specifically used to prepare the meal. These are the DQ or dépenses quotidiennes the name the Senegalese give to the 4 Momar B. Sylla and Matar Gueye of the Agence Nationale de la Statistique et de la Démographie of Sénégal (ANSD) on the one hand and Philippe De Vreyer (University of Paris-Dauphine and IRD-DIAL) Sylvie Lambert (Paris School of Economics-INRA) and Abla Safir (now with the World Bank) designed the survey. The data collection was conducted by the ANSD thanks to the funding of the IDRC (International Development Research Center), INRA Paris and CEPREMAP. The survey is described in detail in De Vreyer et al., 2008.
amount of money a woman has at her disposal to buy fresh ingredients for the meals of the day. Next individual consumption is collected at the cell level (such as clothing, mobile phone, transportation, and food outside the home expenditures). Finally, expenditures that are shared between several cells but not the whole household are collected. A measure of per capita consumption can then be constructed at the cell level allowing us to identify unequal consumption levels within households. Food expenditures for meals at home are often shared by the whole household. Nevertheless, in 17% of households, subgroups emerge that take some or all of their meals separately, making room for unequal food consumption among household members. In addition, some members take parts of their meal outside. In any event, non-food expenditures naturally offer wider possibilities of divergence within household. In what follows we measure consumption per capita at the cell level and provide comparison with what is obtained with the more "traditional" way of measuring individual consumption, when individuals' access to resources is estimated from the aggregate household measure of consumption. Senegalese households are large, with slightly more than eight members on average in the PSF. The families are typically multigenerational and extended both horizontally and vertically, with 36% of household members that are neither the head, nor one of his wives or children. Two thirds of households include such extended family members. Polygamous unions are common, with 24% of married men and 37% of married women engaged in such unions. Most of these comprise a husband and two wives (only 20% of polygamous unions have more than two wives). We find that 31% of polygamous men have non-cohabiting wives. In half of these cases, the husband is either considered the head of both households, or of one, while one of the wives is considered head of the other household. In the other half, a married polygamous woman lives in a separate household headed by a relative (mainly her father, brother or son). Measures of poverty in Senegal Poverty lines In this section we examine the poverty rates obtained from consumption observed at the cell and household levels in the PFSS. In order to compare with the results of previous surveys, we choose to use the same poverty lines as that of Ndoye et al. (2009), which presents a poverty profile established with data from the Enquête Suivi de la Pauvreté au Sénégal (ESPS), conducted in Senegal between December 2005 and April 2006. Two lines are retained, established following the basic needs approach. First a food poverty line is defined as the cost of the food basket that provides with at least 2400 kcal per day. The second line is the national poverty line in Senegal. It is obtained by augmenting the food poverty threshold with the amount of resources that is necessary to cover individual basic needs other than
nutrition. This amount if obtained through the observation of the average non food consumption of households of which food consumption per adult equivalent is more or less than 5% of the food poverty threshold. In order to transpose this procedure to our data, we have to hold account of the fact that between 2005-2006 and 2006-2007 when the PFSS was on the field, the consumption price index increased. The PFSS has been conducted between November 2006 and April 2007, that is almost exactly a year after the ESPS. In order to estimate the average evolution of the price index between these two periods, we computed the average of the annual inflation rates calculated over the 6 twelve months periods going from November 2005 to November 2006, December 2005 to December 2006, January 2006 to January 2007, February 2006 to February 2007, March 2006 to March 2007 and April 2006 to April 2007. For total consumption this leads to an average one year inflation rate of 4.7% and 5% for food consumption. Table 1 shows the values of the two poverty lines for Dakar, Other Towns and Rural Areas separately. Table 1: Value of the poverty thresholds Food poverty line National poverty line Dakar 397 968 Other Towns 370 693 Rural Areas 357 588 Note: numbers correspond to daily expenditures per adult equivalent. Measurement unit is the CFA franc. Consumption measures The consumption aggregate used by Ndoye et al. (2009) includes all household consumptions on an annual basis, divided by the number of household members in adult equivalent. The equivalence scale is the following: adults aged 15 or over receive weight 1; children between 0 and 14 have weight 0.5. In order to preserve comparability we keep the same equivalence scale and all results are obtained for consumption by adult equivalent. Our consumption aggregate adds all household expenditures, including self consumption of household products, but excluding housing expenses. This choice results from the relatively small size of the PFSS sample. In order to obtain their consumption aggregate Ndoye et al. (2009) compute the imputed rent of owner households. Estimating the imputed rent bears no difficulty when the sampled number of households renting their housing is large enough, since the imputed rent of owner households can be estimated on the basis of the observation of rents paid by renter households for equivalent nearby housing. The ESPS sample size includes 13600 households allowing computing the imputed rent of owner households both in urban and rural areas. The small size of the PFSS sample only permits to make the same estimation in large cities of Senegal. For small cities and for rural areas, the number of renter households is too small to permit any meaningful estimation of imputed rents and for this reason we chose not to calculate them. In order to preserve comparability with Ndoye et al. (2009) we compute a consumption aggregate that excludes housing expenditures and then
apply a coefficient obtained from the share of housing expenditures which value depends on the residential area.5 Consumption is estimated at two different levels: at the household level, following the above described procedure, and at the cell level. Information about consumption comes from two different sources. The private consumption of each cell is recorded in a particular section of the questionnaire that is filled with answers obtained from the cell head or another adult cell member. Public consumptions and consumptions that are shared between cells are recorded in another section of the questionnaire. Common expenditures are then allocated between cells, following a sharing rule that allocate each cell with a share proportional to its size in adult equivalents. Comparing ESPS and PFSS. We start by comparing the estimated poverty rates obtained from the PFSS and ESPS data. Table 2 below shows the poverty rates estimated with PFSS data with both the food and national poverty lines and with ESPS for the national poverty line. In that table all poverty rates are estimated using the household as the unit of observation. The results show an important gap between estimated poverty depending upon the data source. For all residential areas and for Senegal as a whole, the PFSS data always conclude to a lower rate of poverty than Ndoye et al. (2009) with ESPS. This gap can receive several non exclusive explanations. First of all it could result from the continuous decrease in poverty that has been observed in Senegal over the early 2000 years. According to Ndoye et al. (2009) between 2001/2002 and 2005/2006 poverty in Senegal decreased by more than 6 percentage points from 57.1% to 50.8%. This decrease is particularly rapid in Dakar (-9.5 points) and in other towns (-7.3 points) but slower in rural areas (-3.3 points). If this trend was prolonged between 2005/2006 and 2006/2007 this could explain up to 1.6 percentage points in the difference between estimated poverty rates for Senegal. This is likely the higher bound in the estimation of what could be explained this way, since over the year 2006 growth in Senegal was lower than in the previous years (2% against an average of nearly 5% over the four previous years), and even with this much remains to be explained. The second reason for the gap between poverty rates can be searched in the difference between the average household size that is found different in the PFSS and the ESPS samples. According to the PFSS sample the average household in Senegal has 8 members, whereas it is 9 for the ESPS. One explanation of this difference in average household size is that in the PFSS sample the proportion of single person households is twice that in the ESPS sample (5.2% against 2.6%) because in Dakar sampling lead to the drawing of clusters in which these households are over-represented. Since they are less likely to be poor than others, this can explain at least part of the remaining gap. Finally, a positive difference between estimated poverty rates with ESPS data, on the one hand, and with PFSS data, on the other, was to be expected, since the way in which consumption expenditure are collected is very different between the two surveys, and since the PFSS questionnaire is less 5 See Ndoye et al. (2009) for a detailed repartition of household expenditures.
likely to miss important expenditures. Indeed, in the ESPS the household is the unit of observation and all questions about the household consumption are likely to be answered by one single person, most probably the head of the household assumed to be aware of all the expenses of household members. In the PFSS the cell is the unit of observation for all consumptions that do not benefit to the household as a whole. Information about the private consumption of a given cell is provided by the cell head, which is less likely to omit important expenses benefiting members of its cell than the head of the household. It is thus not surprising to observe higher levels of consumption in PFSS than in ESPS. This is in line with the findings of Beegle et al. (2012).
Table 2: Poverty rates obtained with PFSS and ESPS data PFSS 2006/2007 - PFSS 2006/2007 - ESPS 2005/2006 - Food poverty National poverty National poverty threshold threshold threshold Senegal 16,2% 43,5% 50,8% Dakar 1,4% 21,9% 32,5% Other Towns 7,1% 28,6% 38,8% Rural Areas 24,9% 56,7% 61,9% Source: our own estimates and Ndoye et al. (2009). Observation unit: household. It remains that even at a lower level than found in ESPS, poverty remains high in Senegal, particularly in the rural areas where it strikes nearly 57% of the population. Acute poverty, measured with the food poverty threshold, is concentrated in the rural areas, where it reaches alarming levels with about 25% of the population. These results are in line with those obtained by the 2005 Demographic and Health Survey (Ndiaye and Ayad, 2006), which finds that in rural areas 20.6% of children have a height for age z-score below the median WHO reference by more than two standard deviations. This percentage equals 8.5% in urban areas. The same survey shows that 76.8% of children and 54.4% of mothers in urban areas, 85.7% of children and 63.9% of mothers in rural areas were afflicted by a more or less severe form of anaemia. Thus in spite of real progress in the fight against poverty malnutrition remains an important problem in Senegal. Poverty of households, poverty of cells For each poverty threshold, knowing for each individual in our sample the household, and within this household the cell to which he/she belongs, we can compute the proportion of poor individuals following, on the one hand, the "aggregated" approach that retains the household as the observation unit for consumption and the "disaggregated" approach, where the wellbeing indicator is average cell consumption per adult equivalent. We first look at the proportion of poor individuals within the population at large and then focus on poverty rates among children less than 15. Population poverty Table 3 shows the poverty rates computed for the entire population for each poverty line and with the aggregated and disaggregated approach.6 The results show that the aggregated approach leads to an underestimation of poverty rates by 0.4 to 2.9 percentage points, depending on the poverty line and the residential area, that is in relative terms an underestimation of the prevalence of poverty between 4.8% (national poverty threshold) and 10.5% (food poverty threshold) at the national level. 6 All poverty rates are computed using the survey weights.
Table 3: Variation in poverty rates according to the poverty threshold and the unit of observation Aggregated approach (household level - A) Disaggregated approach (cell level - D) Difference (D-A) Relative difference (D-A)*100/D Food poverty threshold Dakar 1,4% 1,8% 0,4% 22,2% Other Towns 7,1% 8,2% 1,1% 13,4% Urban Areas 3,9% 4,7% 0,8% 17,0% Rural Areas 24,9% 27,6% 2,7% 9,8% Total Senegal 16,2% 18,1% 1,9% 10,5% National poverty threshold Dakar 21,9% 24,2% 2,3% 9,5% Other Towns 28,6% 31,5% 2,9% 9,2% Urban Areas 24,9% 27,5% 2,6% 9,5% Rural Areas 56,7% 58,6% 1,9% 3,2% Total Senegal 43,5% 45,7% 2,2% 4,8% Source: PFSS, our own estimates Inequality and Intrahousehold inequalities The difference between the aggregated and disaggregated approach comes from the fact than within Senegalese households large inequalities in the command over resources can be observed. There exists poor cells within non poor households and, inversely, non poor cells within poor households. This can be seen in table 4, where one finds the distribution of cells according to their poverty status and that of their household (columns 1 and 2). We observe that the proportion of poor cells belonging to a non poor household varies between 2.2% and 3.8% of cells depending on the poverty threshold. Looking now at the population percentages (columns 3 and 4) we observe that poor cells in non poor households represent a higher proportion of the population than that of cells. The opposite is true for non poor cells within poor households. In other words poor cells are more likely to be large cells which explains that one finds a higher poverty rate with the disaggregated approach. In total, following the national poverty threshold, nearly 11% of non poor households include at least one poor cell. This suggests that measuring poverty using a well-being measure computed at the household level can lead to a serious underestimation of the extent of poverty. In what follows we shall see that this is especially true for children.
Table 4: Distribution of cells and households according to their poverty status % of cells % of the population Food poverty line National poverty line Food poverty line National poverty line Poor cells in non poor households 2,18 3,84 3,25 4,55 Poor cells in poor households 10,4 31,7 14,1 39,3 Non poor cells in poor households 1,52 3,68 1,36 2,23 Non poor cells in non poor households Source: PFSS, our own estimates 85,9 60,8 81,3 53,9 Poverty among children Since large cells are those in which the number of children is likely the largest, one can suspect that the gap in the estimated poverty rates following the aggregated and disaggregated approach will increase when looking at poverty amongst children aged less than 15 years old. This intuition is confirmed by the results presented in table 5. For rural areas and the food poverty threshold the difference between both poverty rates reaches 3.6 percentage points and for urban areas outside Dakar and the national poverty line 4.4 percentage points. As a consequence, anti-poverty policies targeted on poor households identified as such by their average expenditure by adult equivalent will miss between 6.2% (national poverty line) and 13.7% (food poverty line) of poor children at the national level, because of lack of precision in the measurement of individual consumption levels. Results in table 5 suggest that the size of the gap between estimated poverty rates following one approach or the other depends on the position of the poverty threshold. In order to better assess the robustness of our results figures 1 to 4 show the estimated difference (D-A) between the poverty rates obtained with the disaggregated and with the aggregated approach depending on the position of the poverty line.7 7 Graphs have been drawn using Stata command cfgts2d from the DASP Package and available on line at http:// http://dasp.ecn.ulaval.ca/ (Araar and Duclos, 2007).
Table 5: Variation in children poverty rate according to the poverty threshold and the unit of observation Aggregated approach (household level - A) Disaggregated approach (cell level - D) Difference (D-A) Relative difference (D-A)*100/D Food poverty threshold Dakar 1,4% 1,9% 0,5% 26,3% Other Towns 6,5% 8,2% 1,7% 20,7% Urban Areas 4,0% 5,1% 1,1% 21,6% Rural Areas 24,9% 28,5% 3,6% 12,6% Total Senegal 17,6% 20,4% 2,8% 13,7% National poverty threshold Dakar 25,2% 28,2% 3,0% 10,6% Other Towns 30,6% 35,0% 4,4% 12,6% Urban Areas 27,9% 31,6% 3,7% 11,7% Rural Areas 57,3% 60,8% 3,5% 5,8% Total Senegal 47,1% 50,6% 3,5% 6,9% Source: PFSS, our own estimates Figure 1 to 3 show the results obtained for Dakar, the other towns and the rural areas respectively, while figure 4 show those for Senegal as a whole.8 As we can see the difference between poverty rates is significant for a large range of poverty lines. For rural areas and for Senegal as a whole the maximum gap is obtained for a poverty line that lies between the food and national poverty thresholds. For Dakar and other towns, the graph shows that a larger gap would be obtained with higher poverty thresholds. 8 The poverty lines that are drawn on this graph are the rural food poverty line, on the left, and the national poverty line for Dakar, on the right.
-.05 0.05.1 Difference between poverty rates as a function of the poverty threshold Dakar Food and national poverty lines Value of the poverty threshold 95% confidence interval Estimated difference Figure 1 -.05 0.05.1 Difference between poverty rates as a function of the poverty threshold Other towns Food and national poverty lines Value of the poverty threshold 95% confidence interval Estimated difference Figure 2
-.02 0.02.04.06.08 Difference between poverty rates as a function of the poverty threshold Rural areas Food and national poverty lines Value of the poverty threshold 95% confidence interval Estimated difference Figure 3 Difference between poverty rates as a function of the poverty threshold Senegal 0.02.04.06 Food and national poverty lines Figure 4 Value of the poverty threshold 95 % confidence interval Estimated difference
One result of this study is to shed light on the importance to come as close to the individual as possible when measuring welfare, in order to obtain adequate measures of poverty and help anti-poverty policies to efficiently target the poor. When households are large and of a complex structure, as in Senegal, where several relatively autonomous budgetary units cohabit, it is not the case that everyone has access to the same level of resources. In such context children poverty is most likely to be underestimated when based on aggregate household welfare since they belong to the largest budgetary units. The correlates of poverty Beyond poverty measurement we try to identify the observable characteristics that are correlated with the risk of being poor. This is done in several steps. First, we adopt a conventional approach, that aims at identifying the households that are the most at risk of being poor. Then we repeat the same exercise, this time using cells rather than households as units of observation. This is done first for the population at large and then for cells that belong to households that are not identified as poor by usual standards. This allows to identify the groups that are at risk of being poor, but that are missed by anti-poverty policies that target households. Poverty profile In this section we estimate the probability of being poor using a probit model. This can be done for both poverty lines. We have done this and obtained qualitatively similar results. In what follows we restrict the presentation to the results obtained with the national poverty line for which the most significant estimates are obtained. When we estimate the risk of poverty at the household level, only household characteristics are included in the list of explanatory variables: sex, age and education of the household head, household composition by age and sex, residential area. When cells are the unit of observation included variables are: age, sex and education of the cell head, cell composition by sex and age of its members, residential area and a series of dummies measuring the blood link between the cell and the household heads. To these variables we add the sex, age and education of the household head, together with the composition of other cells in the household. These household level variables are included so as to compare their impact with that of the cell characteristics and control for the possibility that cell level variables capture an omitted household level fixed effect. Since we observe the welfare and characteristics of more than one cell in a large enough number of households, these household level fixed effects can be removed using a conditional logit approach. This is done in what follows to test the robustness of our estimates.
Poverty of households and cells Table 6 show the results of regressions runs when taking the household (column 1) or the cell (columns 2 to 4) as unit of observation. Columns 1 and 2 show the results obtains in simple probit regressions. In column 3 the results of a conditional logit estimation are shown with the cell as the observation unit. In column 4 the same approach is employed but this time restricting the sample to those cells that are part of non poor households in which at least one cell is poor. Results in columns 1 and 2 are marginal effects: that is the reported coefficients show the change in the probability of being poor for a one unit change in the independent variables. For a dummy variable, this corresponds to the change in probability when the dummy equals one rather than zero. Such marginal effects could not be computed for conditional logit estimates, and the coefficients in columns 3 and 4 are odds ratio. Household results At the household level (column 1) three correlates of poverty can be identified: a low level of education, a high dependency ratio and the residential area. Households in which the head has received less than 4 years of primary education have a much higher risk of poverty: when compared with those which head has at least a secondary level of education (reference category) their poverty rate is found higher by 20 percentage points. The risk of poverty is "only" increased by 13 to 15% when the head has been to Koranic school or has received 4 to 5 years of primary education. Household with a large number of children are also at a higher risk of poverty: one more child aged less than 15 is associated with a poverty rate higher by 4 percentage points. We also observe a positive and significant correlation between the number of men aged between 16 and 20 and, to a lesser extent, of those aged 21 to 25 years old and the risk of poverty. This could result from the difficulties that young workers experience in finding a job. Without work they are constrained to live in their parents household and increase the dependency ratio. The number of cells in the household has a negative and significant coefficient: holding the household composition constant, one more cell is associated with a decrease of 7% in the risk of poverty. As cells are necessarily headed by adults that are likely to be active or, at least, to have some sort of independent resources, this result can be interpreted as the positive impact of having a larger number of income sources. But it is also possible that the wealthiest households attract a larger number of people, which would increase their number of cells. At this stage we have no way to disentangle between these two possibilities, but the result remains that extended families have a lower rate of poverty. At last, the residential area is also found to be an important correlate of the risk of poverty. Holding everything else equal, households that live in rural areas have a probability of being poor 19.5 points higher than those living in Dakar or in other cities of the country. Results in
table 3 show that the gap in poverty rates between rural and urban areas is about 35 percentage points. Regressions results show that part of this difference come from household characteristics: less educated and larger in rural areas, households have also a high risk of being poor. But this is not the whole story and a substantial part of the difference remains.
Cell results Column 2 shows the estimates obtained with the cell as the observation unit.9 One important difference with the previous results is the sign and significance of the household head sex on the risk of poverty. Household headed by women appear to have a much lower risk (-9%). This average effect may cover very different situations: household headed by women might be those of migrants living abroad and that help their origin household through remittances, or it could be those of independent and active women. On the opposite, they could be those of mothers that are single, widowed or divorced and for which the risk of poverty is likely much higher. Apart from this effect, household characteristics have qualitatively the same impact on the risk of poverty than when the household is the unit of observation. Cells belonging to a household which head has no education have a higher risk of poverty, but the effect is relatively small (+7%) when compared with its effect in the household regression. On the other hand the cell head's education has a strong and significant coefficient: everything else equal, and in particular holding account of the household head's education, cells which head has no education have a risk of poverty higher by 15 percentage points. The cell composition also has a significant impact: counting among its members one more child less than 15 years or more than 65 years old is associated with a 5 to 6 percentage points higher risk of poverty. These results, obtained while controlling for the household head's characteristics and for the composition of other cells, testify to the fact that the situations vis à vis poverty inside Senegalese households are not equally shared. In particular, even if the household head is educated, cells headed by uneducated individuals have a higher risk of being poor. In such context one can expect the link between the cell and household heads to play a role. This is partly confirmed by the estimated coefficients of the variables measuring this link. Compared to cells headed by the first spouse of the household head (reference category), cells headed by a child, a brother or sister or the second spouse of the household head have a higher risk of poverty. However the effects are not very significant. Conditional logit estimates In column 3 we use a conditional logit to remove all cell common unobserved characteristics that could have an impact on the risk of being poor while being correlated with cell characteristics, such as past positive or negative shocks that have affected the entire household. In this regression we estimate the determinants of the probability of being a poor cell in a household that includes both poor and non poor cells. All households in which all cells are either poor or non poor are dropped from the regression. This reduced sample size is the price to pay to obtain estimates that are robust to the presence of household fixed effects. The results confirm what has been found in column 2. Cells for which the head has no 9 As mentioned previously the regression includes the household composition excluding the cell as a set of control variables. This is necessary to ensure that the variables measuring the cell composition do not act as proxies for the household composition. As these variables have not direct interpretation and in order to allege presentation we do not report their coefficients.
education have a risk of poverty twice as large as otherwise comparable cells. Also the presence of one more child aged less than 15 is associated with a risk of poverty multiplied by a factor that lies between 1.38 to 1.69. The number of young women aged between 16 and 25 and the number of adults aged more than 65 also seem to increase the risk of being poor. The risk of poverty is also higher for the household head's second spouse. Table 6: Risk of poverty in household and cells VARIABLES (1) (2) (3) (4) Household head's characteristics Woman -0.052-0.088*** (0.035) (0.030) Age (years) 0.002-0.001 (0.001) (0.001) Koranic education 0.127*** 0.041 (0.042) (0.034) No education 0.200*** 0.072** (0.041) (0.033) Primary 1 to 3 years 0.198** 0.094 (0.088) (0.067) Primary 4 to 5 years 0.151*** 0.074* (0.055) (0.041) Cell head's characteristics Woman 0.013 1.521 1.848 (0.034) (0.490) (1.090) Age (years) 0.003*** 1.040*** 1.063*** (0.001) (0.012) (0.020) Koranic education 0.090** 1.423 0.798 (0.036) (0.496) (0.381) No education 0.154*** 2.061** 1.244 (0.031) (0.693) (0.591) Primary 1 to 3 years 0.084 1.023 0.204** (0.053) (0.585) (0.155) Primary 4 to 5 years 0.075* 1.023 0.464 (0.040) (0.496) (0.272) Household composition Nb of children 0 to 5 years 0.039*** (0.011) Nb of children 6 to 15 years 0.041*** (0.008) Females 16 to 20 years 0.024 (0.019) Females 21 to 25 years 0.010 (0.021) Females 26 to 65 years 0.024 (0.017) Males 16 to 20 years 0.054*** (0.019) Males 21 to 25 years 0.035* (0.020) Males 26 to 65 years 0.014 (0.015) Adults 66 years and more 0.021 (0.027)
VARIABLES (1) (2) (3) (4) Number of cells -0.069*** (0.022) Cell composition Nb of children 0 to 5 years 0.062*** 1.690*** 1.553** (0.011) (0.233) (0.345) Nb of children 6 to 15 years 0.049*** 1.377*** 1.446** (0.008) (0.133) (0.208) Females 16 to 20 years 0.026 1.663** 1.709 (0.019) (0.373) (0.600) Females 21 to 25 years 0.037* 2.204*** 2.755** (0.021) (0.535) (1.089) Females 26 to 65 years 0.021 1.152 0.519* (0.018) (0.264) (0.189) Males 16 to 20 years 0.085*** 1.445 1.087 (0.018) (0.344) (0.264) Males 21 to 25 years 0.048** 1.388 0.647 (0.022) (0.407) (0.200) Males 26 to 65 years 0.012 1.031 0.641 (0.017) (0.227) (0.213) Adults 66 years and more 0.058** 2.233** 1.300 (0.029) (0.810) (0.578) Cell head's relationship with household head Household head 0.007 0.669 0.481 (0.036) (0.232) (0.269) Child 0.087* 1.673 1.122 (0.048) (0.757) (0.672) Cospouse -0.064 0.423 0.137* (0.093) (0.340) (0.162) Father/mother 0.083 0.364 0.122* (0.084) (0.327) (0.149) Brother/sister 0.107** 0.768 1.256 (0.052) (0.341) (0.876) Other parent 0.047 1.460 3.010* (0.041) (0.529) (1.882) No link -0.033 0.414 (0.080) (0.453) Spouse rank 2 0.074* 1.722* 0.871 (0.041) (0.550) (0.447) Spouse rank 3 or + 0.089 1.076 0.177 (0.082) (0.542) (0.189) Residential area Dakar -0.054-0.051** (0.035) (0.024) Rural 0.195*** 0.201*** (0.034) (0.023) Observations 1,745 4,257 907 375 Pseudo R-squared 0.144 0.146 0.289 0.281 Robust standard errors in parentheses
VARIABLES (1) (2) (3) (4) *** p<0.01, ** p<0.05, * p<0.10 Sources: PFSS, our own estimates. Variable list in column 2 includes the composition of other cells. Coefficients are not shown to allege presentation. In column 4 we use a conditional logit, this time to examine the probability of being the poor cell in a household that is not poor, and for which we know that at least one cell is poor. The goal is to identify those groups of individuals that are likely to be missed by anti-poverty policies that use an aggregate household measure of consumption to target the poor. The main significant effects are found for the cell composition: once again the number of children aged less than 15 is associated with a higher risk of poverty. To resume our results point out the unequal repartition of resources within households and the necessity to better identify individual situations. Even inside non poor households, cells which head is not educated or only went to Koranic school, and those that include young children have a higher risk of being poor. This the second important result of this study: it invites to target anti-poverty policies in the direction of uneducated adults and those in charge of children even if they live in households that do not show apparent signs of being poor. Conclusion This paper shows that there exists within Senegalese households an unequal repartition of resources that leads to an underestimation of poverty when the household is the unit of observation, when compared with what is found when consumption is measured for subgroups of household members that are relatively autonomous in their budgetary arrangements. The gap is found most important in the sub-population of children less than 15. This is explained by the fact that the composition of households and cells have a strong and robust effect on the risk of poverty: the highest the number of children, the highest will be the poverty level, showing the impact of the dependency ratio on the budgetary constraint. The poor cells are thus those with the highest number of children, which explains that the gap between poverty rates when measured at the household or the cell levels is at its highest for children. The other main correlates of poverty are the residential area and the lack of education. Unsurprisingly this study shows that poverty is much concentrated in the rural areas of the country. In these areas acute poverty, defined as having less resources than what is necessary to obtain a dietary intake of at least 2400 kcal per day, strikes more than a quarter of the population. The persistence of malnutrition in such a large proportion of the population is a major impediment to the development of Senegal given its short and long term impacts on infant mortality, high fertility rates and the reduction in cognitive capacities of children and adults. The lack of education is the second major factor explaining poverty. In spite of persistent difficulties in access to the labour market for educated young workers, education remains a
shield against poverty, which justifies the resources that are dedicated to the education system and pleads to increase its efficiency in a context where the demographic pressure remains high. Finally the study invites to redesign the targeting of anti-poverty policies. Within non poor households, there can exist groups of poor individuals. These groups are more likely to count a relatively large number of children and to be headed by uneducated individuals. Antipoverty policies should not retain the level of poverty of the household as a unique mean of targeting. Children that live under the responsibility of an uneducated adult, in large households and in rural areas are particularly exposed to the risk of being poor.
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