Report No. 12517-SE Senegal An Assessment of Living Conditions (In Two Volumes) Volume II: Annexes May 5, 1995 Africa Region Western Africa Department Country Operations '4~~~~~~~~~4 4_ ~ - _ _ Currency Equivalent Currency Unit: CFA Franc Period Average: 1992 - CFAF 264.69/USS Period Average: 1994 - CFAF 555.20/US$ ACRONYIS AND ABBREVIATIONS AGETIP AAgence d'Execution des Travaux d'IntEret Public BCEAO Banque Centrale de Etats de l'Afrique de F'Ouest CCF Christian Children's Fund CNCAS Caisse National de Credit Agricole du Senegal CONGAD Conseil des ONG de Developpement CPSP Caisse de Perequation et de Stabilisation des Prix CSA Commissariat a la Securite Alimentaire DAARA Islamic School teaching the Koran DPS Division de la Prevision et de la Statistique DRC Domestic Resource Cost FDEA Femmes, Developpement, Entreprise En Afrique FED Fonds Europeen de Developpement FONGS Federation de Organisations Non Gouvemementales du Senegal GDP Gross Domestic Product GOS Government of Senegal IBRD Intemational Bank for Reconstruction and Development IDA Intemational Development Agency IFRPI Intemational Food Research Policy Institute ISRA Senegalese Institute for Agricultural Research NGO Non-Governmental Organization NPI Nouvelle Politique Industrielle ONCAD Office National de Cooperation et d'Assistance pour le Developpement PAGD Programme d'Appui a la Gestion et le Developpement SAED Societe Nationale d'Amenagement et d'Exploitation des Terres du Delta du Fleuve Senegal et de Vallees du Fleuve Senegal et de la Falemee SAL Structural Adjustment Loan SAR Societe Africaine de Raffinage SMIG Salaire Minimum Interprofessionel Garanti UMOA Union Monetaire Ouest Africaine UNDP United Nations Development Program UNICEF United Nations Children's Fund USAID United States Agency for International Development ANNEX A - 1 TECHNICAL NOTE ON PROCEDURES AND DATA SOURCES FOR ESTIMATING REGIONAL POVERTY LINES FOR THE SENEGAL POVERTY ASSESSMENT 1. Methodology 1.1 Data sources. There are two primary sources of information on household income and expenditure in Senegal from which to estimate poverty lines: the first SDA Priority Survey in 1992 (PS), covering 10,000 households; and a much smaller sample (296 households) IFPRI/ISRA survey conducted over two agricultural years (1988-90 with some data from 1991) in the Groundnut Basin, Senegal Oriental (Tamabacounda) and Kolda. The PS represents the largest household survey ever conducted, and thus offers a high degree of confidence, even at the regional and sub-regional level of disaggregation, but collected information only on households' expenditures and not on quantities consumed or levels of auto-consumption. The ISRA/IFPRI survey did not cover the entire country and thus is not representative at the national level, but provides insights into consumption and production patterns not covered in the PS. The ISRA/IFPRI survey carefully calculated levels of auto-consumption, quantities consumed, and visited households more than once over a period of more than one year. 1.2 Approaches to defining poverty. One can estimate poverty lines based on relative terms - how one fares in relation to others in the same country/region - or on absolute terms - whether one attains a fixed "minimum" standard of living independent of the number of other people in the country who meet/do not meet this same standard. For example, using relative welfare one might look at households consuming less than one-third of average consumption levels in a country, and thus those falling into this category would not necessarily resemble each other from country to country (for example people spending less than one-third of average expenditure levels in the U.S. are certainly better off than a similar category of people in a lower-income country such as Nepal).' Therefore, as average consumption levels increased with growth, so would the poverty line thus making it difficult to tell if people actually were better off over time. In contrast, an absolute poverty line would stay relatively fixed, thus showing improvements in actual living conditions over time, and if definitions were similar, providing a point of comparison across countries. The drawback to this approach, however, is that determining what is a "minimum" implies numerous value judgements and possible error. 1.3 Overview of method adopted. The absolute approach to poverty lines adopted for the Senegal poverty profile is a variation of what Ravallion (1992) calls the "food energy method." The approach tries to minimize error and value judgements by focusing on one of the most universally accepted items of basic living standards - consumption of adequate food. We calculated how much it would cost (taking into account levels of auto-consumption) to attain a minimum caloric intake of 2,400 calories per adult equivalent per day, a level consistent with the level used in recent agricultural ' An example of the former is Boateng et al (1990) who fix a poverty line for Ghana as two thirds of the mean of the distribution of households by per capita expenditure, and a 'hard-core' poverty line as one-third of this mean. A-2 surveys. In reality, minimum requirements may vary substantially within gender/age cohorts according to level of physical activity (i.e. pregnant and lactating women, farmers during planting and harvest season, and manual laborers burn up more calories than urban residents working in an office). Sensitivity analysis (below) provides an indication of how changes in minimum caloric intake shift poverty line estimation and poverty incidence. Then, rather than estimating what households spend on non-food items, we took observed data on non-food expenditures from those households just spending enough to attain the minimum caloric intake and added this amount to the food poverty line to obtain the general poverty line. However, to get to this point, there were several adjustments that needed to be made. 1.4 Specific procedures followed. First, using the PS database we calculated total monthly adult equivalent expenditures for the food goods which account for the bulk of caloric intake - millet/sorghum, rice, peanuts, bread, sugar, and vegetable oil - dividing all people into twelve household expenditure groups (from those spending a total of 1,000 CFAF or less per capita per month to those spending over 25,000 CFAF per capita), and for both urban and rural areas in each of the ten administrative regions. Then, in order to understand how much food was purchased with these expenditures, the expenditures were divided by prices in each region obtained from other sources (see section 3 below on price data sources). The resulting quantities were then transformed into caloric equivalents, using coefficients from the Office de Recherches Sur I'Alimentation et la Nutriion Afticaine (ORANA) which is based in Dakar. This gave average total purchased calories and average expenditures on the six commodities for each expenditure group. Then, since the six commodities for which purchased calories were calculated represent roughly 85 percent of total calories consumed in Senegal, an additional 15 percent of these base calories was added to adjust for calories from other foods (a percentage supported by other studies on consumption in Senegal) for which prices were not available. The composition of the food basket required to satisfy a minimal level of caloric intake was varied by region to reflect the unique consumption patterns of each region. 1.5 Adjusting the Poverty Lines for Auto-Consumption. Purchased calories were then adjusted upwards to account for the fact that the PS did not estimate levels of auto-consumption (which would normally make households which spent less money yet consumed more home produced foods look poorer than they actually are). This was done by calculating coefficients for auto- consumption for urban and rural areas in each region using data from the IFPRI rural households surveys and other agricultural surveys detailed below. Thus the resulting poverty lines reflect the fact that in regions where residents rely more on auto-consumption, and where prices for basic food goods tend to be lower, the amount of expenditure required to achieve a minimum caloric intake is lower than in urban areas or regions (St. Louis for example) with lower levels of auto-consumption. 1.6 Non-Food Expenditures. Because estimates of the 'appropriate" expenditures on non-food items to achieve a minimum standard of living are likely to vary from region to region, and are often prone to mistakes, no judgement was made on a minimum level of non-food expenditure; rather, under the assumption that individuals first satisfy their basic food needs, actual observations on non-food expenditures of those just achieving the minimum caloric intake were taken directly from PS data and added to the required food expenditure to equal the region-specific poverty line. All of the above was calculated first on an adult equivalent basis, and then reconverted into per capita terms, so that the population distribution of households with per capita expenditures below the poverty line could be calculated for each region, urban and rural. Sensitivity analysis and regional poverty analysis were based on household level data. This slightly underestimates poverty in areas with larger A-3 than average household size (such analysis can be performed although this would require substantial data manipulation by the DPS while the regional differences if recalculated are probably not large). 1.7 Results and Comparisons with Relative Poverty Lines. Tables AL.1 and A1.2 show the results of regional and national poverty analysis. Relative poverty lines have also been calculated for comparison: at two-thirds of average expenditure in the same area of the country, 53.26% of the overall population could be considered poor (with 53% of the Dakar population falling below two-thirds of the average consumption in Dakar, 44% of the rest of the urban population, and 43% of the rural population). Alternatively, an even lower-bound poverty line of one-third mean expenditure would place approximately 25% of the population in poverty (13.8% of Dakar, less than 10.8% of other urban areas, and 16% of rural areas).2 The high level of relative poverty using an upper poverty line in Dakar, points to the substantially higher average consumption level in Dakar compared to the rest of the country. 1.8 The Gini index has been calculated based on expenditure data, and, as noted in chapter 1, this presents some problems as expenditures do not take into account levels of auto- consumption or price differences. Nonetheless, other available evidence and studies point to a gini coefficient of at least .4. The gini coefficient for Dakar is likely to be the most accurate (as home consumption and prices are uniform within Dakar), while the coefficient is probably overestimated for rural areas. 2. Caveats in Interpretation of Poverty Lines & Limitations of the Approach 1.9 Two difficulties present themselves in determining the relative welfare among rural regions based on income and/or expenditures from a period of a few months, as was done with the Priority Survey. The first difficulty encountered in generalizing about poverty from the "snapshot" measurement of the priority survey stems from the fact that rural consumption patterns change dramatically over the course of the agricultural cycle, from "hungry" season to harvest. The PS measured expenditures and revenues at the harvest season, when incomes and expenditures were likely to be at their highest, and prices at their lowest. Therefore, the rural poverty line presented above represents a bottom-line estimate with increases in the incidence of poverty likely at other times of the year. Sensitivity analysis presented below demonstrates that if all other factors are held constant, if auto-consumption levels decreased by 30%, as could be the case during the hungry season, poverty in rural areas could jump to 60% of households because of the large number of people around the poverty line. This highlights the dramatic variability of rural consumption levels, particularly in regions without access to remittance income. Sensitivity analysis also demonstrated the robustness of assumptions used for the model. Second, large interannual variations in rainfall patterns can affect the overall level of poverty or reverse the relative welfare ranking among rural regions from one year to the next, at least in regions with high dependence on rainfall and low remittance income. In the case of Senegal, the PS probably provides a fairly accurate representation of average poverty levels during harvest season over a period of years if one uses groundnuts, the 2 In Ghana and elsewhere, analysts have defined an 'upper' and 'lower' bound poverty line as 2/3 and 1/3 mean per capita expenditure. Here, the numbers are approximate as they represent interpolations of grouped data. A4 largest income source for the majority of the rural population, as a proxy for the 1991/1992 crop year; groundnut production during this crop year was fairly typical.3 1.10 Any methodology chosen for estimating poverty lines has its drawbacks.' One drawback of the "food energy intake" approach is that it is static in nature. While all poverty line estimation procedures are static (i.e. a snapshot at one point in time), this approach is hampered by the fact that food consumption patterns are determined in a complex and fluid way that combines demand responses to relative price movements, with changes in income levels and tastes and preferences. Another drawback is more philosophical in nature: this approach implicitly makes a normative assumption that people perceive meeting caloric requirements as their highest priority. With specific regard to Senegal, there is widespread anecdotal evidence that some ethnic groups consider other types of expenditure -- housing, ceremonies, religious tithes - as very high priorities and are willing to go deeply into debt to pay for them. There are also technical problems from a nutritional standpoint regarding choice of caloric intake levels, which may be different for a more sedentary urban dweller than for a physically active farmer. 1.11 This procedure has several other limitations that have more to do with data gaps encountered in Senegal than with the methodology itself. Because home consumption was not recorded under the PS, the analysis assumes constant proportions of home consumption versus purchases across expenditure groups. There are many reasons to believe that this varies across expenditure groups in rural areas. Recent survey work by ISRA/IFPRI5 clearly demonstrate great variability in household rates of cereals self-sufficiency within zones by income quartiles. There is a strong positive correlation between income level and cereals self-sufficiency rates. If this is true, taking a simple average for home consumption by region has the effect of underestimating rural poverty as poor people should have a lower actual home consumption coefficient than the regional average and should therefore be consuming fewer calories than the calculations indicate. A second issue is that taking coefficients for home consumption from other data sources may also introduce bias because none of these other surveys were designed to be representative on an administrative region basis. Third, with regard to prices, there are obvious problems related to taking regional averages as prices faced by all consumers in a given region. In addition, different expenditure groups may face different prices for the same goods (as poorer groups often purchase goods in smaller quantities at higher prices). 1.12 Despite these problems and "data headaches," it was felt that an absolute poverty line estimation approach had greater practical value and was potentially more credible to decision-makers than a relative poverty approach. This was because the poverty line was based on more meaningfiul criteria than setting the line at some proportion of mean national income or as some percentage of the population - essentially arbitrary cut-off points in terms of meeting basic human needs. I Groundnut yields and production were respectively 831 kg/ha and 701,000 MT, compared to 1980/81 to 1992/93 averages of 830 kg/ha and 730,000 MT (see Annex C, Table C. 1 for the data). ' See Ravallion (1992) for a summary discussion of the strengths and weaknesses of the various common approaches taken. 5 Kelly, Valerie A. Aspects Economiques de la Production el de la Commercialisation des Produits Agricols au Niveau des Mdnages du Bassin Arachidier et du Centre du SUneigal Oriental. March, 1993. A-5 Alternative Absolute Poverty Lines. Since this poverty line represents the minimum needed to meet daily caloric needs, one could also test the effect of a lower bound (ultra-poor) and an upper-bound poverty line of 90% and 1 10% of the base poverty line. Those who one might consider among the ultra poor, who spend 90% or less of the absolute minimum, constitute 29.7% of the population. Using a more generous poverty line of 110% of the base raises the percentage of the population that is poor to 37 %. 3. Data Sources 3.1 Price Data 1.13 Price data used in the poverty profile were collected from three sources: the Commissariat a la Securite Alimentaire (CSA) market price information system; the GOS Direction de Commerce; and the 1988-90 ISRA/IFPRI survey work. 1.14 Data from the CSA were used for millet/sorghum and broken rice. The CSA collects cereals price data6 in 24 urban and 31 rural markets around Senegal on a weekly basis. The regional distribution of these markets is presented in Table A 1.5.7 Prices are collected at the retail, wholesale, semi-wholesale and producer price level. 1.15 For this analysis, a simple average of retail prices were taken for urban and rural markets by region for the four month period that data were collected under the PS (October 1991 to January 1992). For regions where no rural price data were collected (Ziguinchor and Dakar), the urban average was taken. This results in a downward bias in prices for imported cereals (either from overseas or other regions of the country), while the prices of commodities produced and traded locally are biased upwards. 1.16 For bread, sugar, and vegetable oil, Direction de Commerce prices were used. These are official prices (adjusted slightly for distance from Dakar), not prices actually observed in the market. However, with the exception of sugar, official prices are more or less respected in most parts of the country. The official sugar price was adjusted downwards (from the official price of 340 CFAF/kg to 250 CFAF/kg) for regions bordering The Gambia (Tambacounda, Ziguinchor, Kolda, Kaolack) to reflect widespread availability of smuggled sugar. 1.17 Finally, average groundnut prices (shelled) were used from the ISRA/IFPRI data for those regions in which the survey operated (Groundnut basin, Kolda, Senegal Oriental). For all other regions, the average of the ISRA/IFPRI prices were used. While this method leaves a lot to be desired, the PS data show that expenditures on groundnuts is very small in the purchased food budget (both in value and caloric terms), and altering it would have little effect on poverty line estimation. 6 Millet, sorghum (local and imported), rice (paddy, local milled, imported whole rice and broken), and maize. The PS did not collect data on maize expenditures, so these CSA data were not used. 7 In recent years, coverage has been very limited in Ziguinchor due to the political situation. A-6 3.2. General Caloric Intake and Home Consumption Patterns 1.18 The most important sources for calculating home consumption coefficients are Kelly et al, Volume II, Part II (1992) for the Groundnut Basin and Senegal Oriental, various surveys cited in Kite et al (1992) for Oriental and the Casamance, Horowitz et al. (1992) for St. Louis. This is supplemented with Martin (1988) who calculated cereals self-sufficiency coefficients for all regions. 1.19 Although most of these data emanate from detailed and well-executed surveys, their reliability is limited by the fact that sample sizes are small. In addition, most samples were determined based on stratification by agro-climatic zone. For purposes of estimating poverty lines, the PS data are organized using administrative boundaries as criteria, so this creates adds an additional complication to the issue of comparability between data sets. That said, home consumption coefficients are considered to be reasonable "first guesses" that hopefully can be refined and improved over time. 1.20 Table A1.6 presents aggregate caloric intake patterns in Senegal from Food and Agricultural Organization (FAO) food balance sheets for 1984-86. Although these estimates are somewhat dated and national aggregates are of limited usefulness, they serve as a rough reference point that allows us to get a sense of the importance of different food sources for caloric intake. The six commodities included in estimating caloric intake with PS data represent roughly 85 percent of total calories consumed according to FAO data. This figure has been used to adjust caloric intake for the six commodities (purchased and home production) upwards by 15 percent to derive total caloric intake. 1.21 As part of his work on representative farm models for Senegal, Martin (Table Al.7) presents figures for cereals self-sufficiency in major agro-climatic zones for average and poor rainfall years. These are used as a rough check on other data sources, and as will be seen below are used to replace some of the counter-intuitive home consumption rates from other surveys. 3.2.1. The Groundnut Basin and Senegal Oriental 1.22 The main source of home consumption for these regions is the ISRAIIFPRI rural household survey. Kelly et al (1992) calculated caloric intake on an adult equivalent basis by source (purchases, home production, gifts) for the following commodities: millet, sorghum, maize, local rice, imported rice, cowpeas (niebe), peanuts, vegetable oil, wheat bread, milk, and tubers. Tables A1.8 and A 1.9 take the ISRA/IFPRI tabular data to derive home consumption coefficients by survey zone, and then apply them to administrative regions. For zones in which data were collected over a two year period, a weighted average of home and purchased consumption was taken. Taking an average was considered reasonable as in most survey zones, 1988/89 was a somewhat poor rainfall year, while 1989/90 was better than average. 1.23 For the most part, the ISRA/IFPRI figures correspond to what one would expect: higher home consumption appears fairly well-correlated with higher rates of rainfall. Geographically, as one proceeds from north to south, home consumption rates rise. There are a few exceptions, such as the very high rate of home consumption recorded by the ISRA survey in the city of Niakhar which is in the Fatick region but also near the border of Thies which is a well monetized region. However, in some cases, cultural traditions (i.e. the Serrer ethnic group are often reported to place a high value A-7 on food security gained through growing one's own crops) can cause variations from village to village in the same region. Since Thies is well monetized part of the country with somewhat irregular rainfall (at least relative to areas such as Tambacounda, Kolda, and parts of the Casamance), one would assume a home consumption coefficient more along the lines of Colobane (around 50 percent) if one were searching for a figure for the entire Thies region. Martin uses a figure of 70 percent for cereals self-sufficiency in the Central Groundnut Basin. That figure is used for Thies cereals home consumption in the poverty line analysis.8 3.2.2. Ziguinchor 1.24 Data in Table Al.10 are from Jolly et al, with citation on page 11-15 of Kite (vol 11). Data are old (1983), but presumably, any trends in deficits would only have gotten worse, due to declining rainfall, increased salt intrusion (especially in lowland villages south of the Casamance River), population pressure, and political unrest. Sample size is also small (9 villages and 196 households). Nearly all farms were cereals deficit (5 of nine were 40 percent or more deficit, 40 percent had stocks sufficient to cover more than 6 months of need, and only 5 percent held supplies in excess of annual needs) with farms south of the Casamance River having a greater structural deficit than those north of the River. The worse situation south of the river appears to be strongly influenced by land availability and productivity. Southern farmers cultivated only 0.378 ha each while the figure was more than double for Northern farms (0.863 ha/worker). Southern yields were also substantially lower than those in the North (maize, 221 kg/ha versus 838 kg/ha, rice, 1511 kg/ha versus 888 kg/ha, and groundnuts, 621 kg/ha versus 954 kg/ha). As a result, farms south of the River have turned increasingly to non-farm income. For their sample villages, Posner et al estimated that southern farms received the majority of their income (59 percent) from outside of agriculture. North of the River, this figure was only 20 percent. 1.25 Because the situation has probably gotten worse in the last ten years for the reasons cited above, and one surplus village skews results somewhat, the unweighted percent (rather than the weighted figure of 18 percent), rounded to 30 percent is chosen as the cereals deficit for Ziguinchor, giving a home consumption coefficient of 70 percent (this is 5 percent higher than the figure cited in Martin). 3.2.3. Kolda 1.26 Kite (Volume II) discusses a Canadian-financed survey implemented in 1989 as part of a forestry project in the Department of Kolda. 269 interviews were conducted in 52 randomly selected villages. In addition, all 7 quartiers of the city of Kolda were visited (however, the number of interviews is not known). Survey results are only indicative, due to the small sample size. I Incomes (and associated food consumption patterns) may be highly variable within the Thies region relative to other regions of the country. There is anecdotal evidence of severe pockets of poverty in rural Thies, while there are other areas where incomes are quite high due to participation in fruit tree cultivation, truck gardening, and maritime fishing. Judging by the low levels of caloric intake registered by Kelly et al, and their judgment that diets were not well-diversified due to low levels of cash on hand (especially in 1988189), the Niakhar zone is probably one of the poorer zones of the Fatick/Thies regions. A-8 1.27 The one interesting result for purposes of deriving home consumption coefficients is that urban residents grow much of their own food. Based on an annual standard of 2,000 kg/household (10 family members at 200 kg/yr each), and reported cereals purchases of 1,025 kg/yr, Kite gives a ballpark figure of 50 percent as cereals home consumption. According to the PS, average household size in urban Kolda is 8.7. Therefore, we can revise this down to 40 percent home consumption (8.7x200= 1740, 1025/1740=59 percent purchased).9 1.28 Concerning rural home consumption of cereals, Kite derives a rough figure of 75 percent. This is approximately in line with Kelly et al's home consumption figure (incorporating a wider array of commodities) of 77 percent. The Kelly et al figure of 77 percent is retained, as it is the more reliable of the two data sources. 1.29 Eighty-two percent of the families interviewed were not cereals self-sufficient, with the bulk of purchases occurring in the soudure (lean season). Cereals produced are largely for home consumption, as only 2-3 percent of cereals were marketed. Proportions marketed were higher for other crops (vegetables 33 percent and cassava/sweet potato 20 percent marketed). The main on-farm cash crops are cotton (97 percent marketed) and groundnuts (67 percent marketed), while total cash revenues were highest from groundnuts (30 percent of family revenues). 1.30 Womens' incomes are especially low, reflecting limited access to land and inputs, and their traditional concentration on rice production (little of which is marketed). Fifty percent of the women had cash incomes of less than 5,000 CFA/yr. 1.31 Forest products (construction wood, fuelwood and charcoal, are also an important secondary source of cash revenue for some households, as is also hunting and gathering. However, these sources (as well as agriculture) are increasingly threatened due to over-exploitation of the natural resource base, population pressure, declining soil fertility and erosion. Fallow periods have shortened as 58 percent of households fallow only 4 years and only 15 percent fallow more than 5 years (while 8 years is appropriate). 3.2.4. Saint Louis 1.32 IDA data collected by Horowitz et al (1992) show that households in Saint Louis derive a relatively low proportion of food from home production, around 20-25 percent of the value of food consumed according to preliminary results from 3 villages. More extensive data in the table below (from IDA Phase II which covered 9 villages) appear to indicate a slightly higher home consumption coefficient on the order of 35 percent. These are not necessarily inconsistent as figures in the table are on a calorie equivalent basis. Because purchased calories are generally more expensive, (major expenditure items include oil, sugar, fish, etc. while home consumption consists of low value cereals), this makes sense. As Table A 1.7 shows, Martin uses home consumption figures for cereals in the range of 50 to 80 percent in average years and 20 to 50 percent in bad years. Using the figures in Table Al. 11 from Horowtiz et al. (1992), if cereals consumption represents approximately 60 percent of Fleuve caloric intake, and all non-cereals calorie sources are purchased, I It is possible that urban home consumption in a number of other regions is substantially higher than the one percent figure used. This is probably true for regions where 'urban' centers (other than perhaps the regional administrative seat) are really towns bordering agricultural areas. A-9 this works out to 30 percent to 48 percent of food consumed is produced at home (50% x 60% and 50% x 80%) in average years. On this basis, a home consumption coefficient of 35 percent is chosen for the St. Louis region, roughly similar to the IDA figure for 9 villages. 1.33 Using IDA household consumption data for the Fleuve, the six commodities covered in the estimation of regional poverty lines (using PS data) account for 86 percent of total caloric intake. This coincides closely with the 15 percent figure chosen to adjust total caloric intake to account for those commodities not explicitly considered in calculating poverty lines. 4. Sensitivity Analyses 1.34 Sensitivity analysis can provide an indication of the robustness of the analysis performed to changes or revisions in assumptions. It can also shed light on how higher prices occuring at other times of the agricultural cycle (other than harvest when the PS was conducted) might alter the poverty profile. The following tests were performed: prices of all six food commodities were increased by 10 percent; the minimum daily caloric requirement was lowered by 10 percent (from 2,400 to 2,160 calories per adult-equivalent); the "other foods" coefficient was raised and lowered by 10 percent (from 15 percent to 16.5 and 13.5 percent); and the rural home consumption coefficients were raised and lowered by 10 percent for each region. 1.35 Table A1.12 presents the results of the sensitivity analyses in terms of levels of expenditure needed to maintain a minimum level of caloric intake.'° Taking the millet price change in Dakar as an example, the percentage figures are interpreted as follows: a 10 percent increase in the price of millet results in a 0.86 percent increase in the amount of total expenditure required for urban Dakar consumers to attain the poverty line (from a base case of 5,610 to 5,658 CFA/per capita), ceteris paribus. For the same example, the incidence of poverty remains unchanged at 12.5 percent. The variables most sensitive to moving the poverty line are changes in the minimum calorie level and the rice price. Millet, peanut, and sugar price changes cause little movement in the expenditure necessary to reach the poverty line. 1.36 If one were to assume that in bad harvest years (or at other times of the year other than during the harvest) production and home consumption decline, rural poverty would increase by 4% for each 10% decline in home consumption levels, all else being held constant (i.e. from 36.6% poor to 38.4% poor households). Changing minimum caloric requirements from 2400 to 2160 calories would decrease from 12.5% of households to 7.3% in Dakar; in other urban areas from 13.2% of households to 8.8%; and in rural areas from 36.6% of households to 31.3%. 1.37 Of the data series altered, the price series for rice, bread, and vegetable oil, and the minimum caloric requirement were considered to be fairly accurate, as these products are subject to fairly uniformly respected price controls. Less confidence was placed in the prices of millet, peanut, and sugar, the "other foods" coefficient, and the rural home consumption coefficients. The price of sugar was not deemed completely accurate because, although subject to price control, it is extensively smuggled. Millet and peanut consumer prices are not subject to price controls, and although their prices come from actually observed market prices, they may not be exactly the same prices faced by '0 The analysis will also be done for changes in percentages of population falling below the poverty line. A-10 Priority Survey participants because they are only representative regional prices. Reservations about the 'other foods" coefficient and the rural home consumption coefficients were detailed above. 1.38 In general, variables in which we have less confidence do not appear to change results significantly. That said, results are moderately sensitive to changes in rural home consumption coefficients, and further work to improve their reliability remains important. S. Recommended Improvements in Poverty Analysis and Poverty Line Estimates 1.39 There are a number of ways in which these initial poverty line estimates (and hence any policy analysis done employing this database) could be improved. These include the following: 1.40 Seasonal adjustments. As the PS was a single visit survey, it was unable to account for seasonal fluctuations in consumption and expenditure. Adjustment coefficients could possibly be estimated (at least for some regions) using ISRA/IFPRI data and perhaps data from ORSTOM (which has done some longitudinal work on nutrition). 1.41 Additional work on estimation of home consumption coefficients by region. This might include econometric approaches to rural home consumption estimation. Simply correlating home consumption with rainfall levels may be the best approach (except in the Fleuve due to irrigated agriculture). With regard to urban areas, adjustment of current home consumption parameters is appropriate for regions where "urban" centers are predominantly large towns bordering agricultural areas. In addition, manipulating the raw data from the two surveys could provide a more accurate correlation between zone and home consumption level. 1.42 Adjustments for different levels of home consumption by expenditure group. The ISRA/IFPRI shows that lower income groups in rural areas have lower home consumption shares of caloric intake than do better-off groups. The initial estimates in this report of rural poverty may therefore be underestimated (as a single regional average assumes higher levels of home consumption for poor groups and lower levels of home consumption for richer groups). 1.43 More refined food policy analysis. Analysis in Chapter 2 only scratched the surface of the types of food policy analysis that could be done incorporating this database. Additional work could be done on tax incidence and income effects. In the absence of solid empirical elasticity estimates (especially cross-price elasticities on both the supply and demand side), "stylistic" analysis that traced out cause and effect relationships of price changes using plausible ranges for elasticities could be employed. A-1l Table Al.1 Regional Distribution of Povert at the Household Level Zone Urban Rural Poven Percen $har of Concen- Poverty perent: Otare of Concen- yLub BR Pow0 ToWal tration of Line * - --ToJal R tration Region *Potry ( RH Poo Poverty (%) of HH Diouwbd 4,569 7.9 0.05 2.1 3,210 -22.7 5.9 6.6 Patick 4,330 ;4.6 0,02 0.8 2,248 46.7 115 6.2 Kaolack 4,808 17.4 0.19 2.8 2,746 48.3 15.5 8.0 Tanbacounda 4,267 17.1 0.02 1.0 2,525 44.3 8,4 4.7 Saint Louis 5,503 10.8 0.12 2.6 3,072 14,7 4.2 7.2 Thics 4,449 12.3 0.42 4.6 2,637 25.8 -8. 8.6 Ziguinchor 4,876 22.5 0.28 3.0 2,526 53.6 7.3 3.4 Louga 3,996 14.0 0.02 1.1 3,393 45.8 9.9 5.4 Kolda 2,917 24.9 0.05 1.3 2,269 57.1 15.5 6.8 Daks.r (Urban 5,610 12.5 11.8 23.7 na as na na & Rural) Total 4,334 j13.0 14.3 43.1 2,651 36,6 87.G 56.9 (') Monthly per capita CFAF expenditurm required to purchase mminmum tood (2400 cilones) and observed non- food baskct (converted from adul equivalents). Table A1.2 N Poor Rural Houscholds Prevalence Percentage Regional Severity Population Bclow Poverty Line Lesr than 10,000 St. Louis 20% or less St. Louis 10,000-16,000 Diourbel, Ziguinchor 20%-29% Diourbel, Thies 16,999-20,000 Tambacounda, Thies 30%-39% 20,999-25,000 Louga, Fatick, (Urban & 40%-49% Fatick, Kaolack, 25,999-33,000 Kolda, Kaolack 50% - 59% Ziguinchor,Kolda A-12 Table Al.3 Gini Coefficient (0 = perfect equality) Rwanda 28.90 Indoneuia 33.18 India 32.27 Cote D'Ivoire 34.55 Senegl na - Dakar 47.4 - Other Cities 40.9 - Rural 42.0 Columbia(i) 51.32 Brazil (1) 63.42 Source: (other thn Senegal) Is Poverty Increasing in the Developing World?, Chen et al., World Bank, Policy Research Department Working Paper, June 1993. (i) indicates income measures instead of consumption. Table A1.4 Per Capita Poverty Mean Mean (CFAFI Line Expenditure Expenditure mouth) Poor Dakar 5,610 16,094 4,825 Other Urban 3,971 10,335 3,348 Rural 2,651 4,154 1,845 -. .. . .......... Table A1.5 Table A1.6 Geographic Breakdown of Markets Covered by the CSA Cereals Senegal Calork Consumption, 198486 Price Information System calores Paced Per Capita Of Region Urban RuraL TotaL Per Day TOOa TOWa 2336 100.00% Ziguinchor 2 0 2 Thies 1 3 4 Plan 2166 92.72% Saint Louis 4 7 11 Animal 171 7.32% D i ourbe 3 4 7 Toald (net of alcohol) 2330 9g.74% Dmi~aourbel 3 4 7 Tmbcounds 3 4 7 Cereals 1609 6S.31% Dakar 4 0 4 Roots and ubbrs 12 0O51% Koida 3 1 4 Suprandbon-y 115 4.92% Louga 1 3 4 Legumineux 30 1.28% Kaolack 1 7 8 Nuts 96 4.11% Fatick 2 3 5 Fn 13 0 60 Fnails 14 0.60% TotaL 24 31 55 Meat 69 2.95% Source: Coaissariat de Securit6 ALimentaire. Egp 69 0.13% Fiuh 31 1.33% Milk 54 2.31% Oils and ft 275 11.77% Oilsandveg. Fat 263 11.2 Oils and anim. Fats 12 0.51% Spices 7 0.30% Sti-malants 1 0.04% Ach bev. 6 0.26% 6 co_modidein povertyfine estimnts 5.1% Adjusntctt factor for othr foods - 14.9% Soure: FAO as cted i Laval Unvenity (1991). Table A1.7 Table A1.8 Cereals Self-SufficiencY Rates for Representative Fanns by ISRA/IEFRI Caloric Intake Data by Zone, Average Per Day Per Agrodiimatic Zone Adult Equivalent (1988189 - 89/90) Self-Sufficiency Rate Zone (Percent) Cer/PuL etc. Total Percent Average Year Poor Year va, SAGATTA* Central Groundmut Basin 70 30 8S/A9 Northern Groun'ic*twt Basin 60 20 Own prod. cal 323 56 379 16.3X Purchased cal. 1812 512 3224 83.7X Southwest Grouxkwut Basin 75 40 HIAKHAR Southeast Groundnut Basin 80 50 Own prod. cal. 3485 131 3616 79.0X Purchased cal. 4282 295 4577 21.01 Oriental Center 75 40 COLOBANE Upper Casamance 65 25 Own prod. cal. 2482 208 2690 52.9X Purchased cal. 4423 663 5086 47.1X Middle Casamence 65 25 SS PASSY Lower Casamince 60 20 Average Serwgat River Lower VaLley 80 50 ~~~~~Own prod. cat. 3209 40 3249 66.22 Senegal River Lower Valley 80 50 Purchased cal. 4655 250 4905 33.8X Senegal River Middle Valley 50 30 >GANDA-DtOLY Senegal River Upper Valley 60 20 Own proda ca 3001 34 3035 75g3X Purchased cal. 3936 94 4030 24.71 Source: Martin (1968). MISSIRAH Average Own prod. cal. 3094 162 3256 78.32 Purchased cal. 3985 175 4160 21.72 KAOLACK (URBAN)** Own prod. cal. 19 1 20 1.01 Total cal. 1530 569 2099 99.02 TAMBACOUNDA (URBAN)** Own prod. cal. 149 0 149 6.41 Total cal. 1823 505 2328 93.6X * Unless otherwise specified, caloric intake data are sunmd over 2 years. Sagatta fs for 1988/89 only, while Kaolack and Taba urban are for 1990/91 only. Source: Kelly et al Votum 2, Part 2 (December 1992). A-15 Table A1.9 Rural and Urban Home Consunption Coefficients Chosen, Based on ISRAIIFRI Survey Data Region Survey Zone Rurale Kaolack, Fatick Southwest Groundnut Basin (Passy) 66.2X Louga Northern Groundnut Basin (Sagatta) 16.3X Thies Central Groundnut Basin (Niakhar) 79.0X Diourbet Central Groundnut Basin (Colobane) 52.91 Tamba, KoLda Central OrientaL (Hissirah, Nganda) avg. 76.81 Urban: Kaolack, Rest of Senegal 1.0X Tambacounda 6.0X Kolda (see section 3.2.3) 40.0X Table A1.1O Ziguinchor Cereals Balances, 1983 ! ~~~~~~~~~~~~~~Surplus/ | ~~~~ ~ ~~~Prod Deficit Percent !oussouye Vittage 1 150.0 -50.0 -25.0X Oussouye Village 2 76.4 -123.6 -61.8X Blouf Village 1 2109.0 -178.1 -7.8X BLouf VilLage 2 67.2 -132.8 -66.4X Niaguis Village 1 91.0 -109.0 -54.51 Niaguis Village 1 151.6 -48.4 -24.21 Sindian-Katounayes VilLage 1 120.6 -79.4 -39.71 Sindian-KaLounayes ViLLage 2 253.2 53.2 26.61 Fogny-Combo ViLlage 1 177.3 -22.7 -11.41 Total (weighted percent) 3196.3 -690.8 -17.81 Unweighted percent 29.31 Source: Jolly et at (1991) as cited in Kite et aL, (1992) Part 11, Page 11-17. A-16 Table A1.11 Senegal River Basin Monitoring Activity Home Consumption and Total Consumption, 1991/92 Home Total NC as X rwu.vfriit rPlr IMg rnn@ P rnc- nf Tr Paddy 2405 267.6 586.7 15.0 Milled local rice 3620 59.5 132.5 3.4 Imported rice 3700 431.3 746.8 19.0 Millet 2822 56.3 89.5 2.3 Flood plain sorghum 2822 28.7 15.0 0.4 Rainfed sorghum 2822 144.6 1678.7 4.6 Dry corn 3000 60.5 103.4 2.6 Imported sorghum 2822 211.0 418.2 10.7 Niebe 3420 6.9 40.3 1.0 Ocean fish 1000 2.0 91.4 2.3 River fish 1000 3.4 12.6 0.3 Dry fish 2700 5.4 112.3 2.9 Peanuts 5490 19.2 206.7 5.3 Sugar 4000 42.9 386.5 9.9 Milk powdered 5020 3.2 131.0 3.3 Milk fresh 790 10.6 19.3 0.5 Milk sour 790 4.6 27.4 0.7 Milk sour 790 4.6 27.4 0.7 Total calories a 1437.4 3922.4 Own-production coefficient = 36.6 Cereals as X of total a 57.9 6 Commodities as X of totaL 86.3 Table A1.12 Sensitivity Analyses Results Dakar - Rural- -Other Urban- Pr Ma _ P__n__f_CAn PC Ma P'n PrW M. i Prn Bans 5,610 12.5 2,651 36.6 4,334 13.2 Milet price increase 5,658 0.86 12.5 2.691 1.51 37.1 4,430 2.22 14.2 Peanut price increa 5,634 0.43 12.5 2,658 0.26 36.7 4m383 1.13 13.5 Ric price increase 6,012 7.17 14.5 2,793 5.36 39.3 4,686 8.12 16.7 Rd pnrce incrsae 5,704 1.68 12.6 2,656 0.19 36.7 4,390 1.29 13.6 Sugr price increa 5,657 0.84 12.5 2,679 1.06 37.0 4,405 1.64 13.8 Vegoil prce increae 5,802 3.42 13.1 2,699 1.81 37.2 4,475 3.25 17.5 Min. caloric decrea 4,742 -15.47 7.3 2,383 -10.86 31.3 3,753 -13.41 8.8 Odwrfoodscal. Increa 5,485 -2.23 11.7 2,614 -1.40 36.0 4,285 -1.18 12.9 Oiter foods cal. Decrease 5,725 2.05 12.8 2,689 1.43 37.1 4,442 2.49 14.3 Runl HC coeffincase ne Oa no 2,554 -3.66 35.1 no na Bs Pu,..I W' rftAxln.. n-l nfl na 7sX g1 0o 1Rt A ,. n, n ANNEX A-2 POVERTY, GROWTH AND THE IMPORTANCE OF EQUITY ANNEX A-2 POVERTY, GROWTH, AND THE IMPORTANCE OF EQUITY 1.44 One often hears of the need to "grow" out of poverty. However, this statement masks a large number of factors which influence how much the poor will benefit from growth. In some regions of the world (Asia) economic growth has resulted in dramatically higher incomes for the vast majority of the population; in others, growth has increased average incomes but some groups have not gained much in the process. What exactly does this mean for Senegal and how much growth would it take to eliminate poverty? 1.45 Based on the current distribution of poor and their current level of expenditure, eliminating poverty for one year would require 26.9 billion CFAF, the equivalent of 1.7% of GDP. This theoretical estimate assumes that this money is perfectly targeted, and that there are no transaction costs associated with this targeting." In contrast, if assistance (or the benefits of economic growth) was not targeted and if each percentage growth in GDP results in an equivalent percentage growth in per capita consumption (that all benefit from growth equally), then per capita consumption would need to grow by 375% in Dakar, 545% in other urban areas, and 395% in rural areas in order to lift the poorest person above the poverty line for just one month and thus eradicate poverty. Another means to illustrate the cost between targeting the benefits of growth and transfers versus a completely non-targeted approach is to look at the poverty gap index. The poverty gap index of 13.9 tells us that it would cost 14 times more to alleviate poverty through a non-targeted approach than through perfect targeting, and thus this is another way to express the potential savings of seeking more efficient means of targeting. This comparison also illustrates the importance of ensuring that the poor are able to benefit equally from growth, that taxation policies are not regressive, that public services are equitably available to poor and noni-poor alike. 1.46 Another method to project the impact of growth on poverty over time would be to use the elasticity of the headcount index (percentage of population that is poor) with respect to per capita consumption (here measured as expenditures). Assuming that increases in per capita private household consumption follows the same trends as overall GDP growth, then, using various assumptions on population growth and growth in per capita consumption, one can estimate the impact on poverty over the long term (see Table A.2. 1 attached). 12 If Senegal continues to face economic stagnation, and thus per capita consumption does not increase, poverty would increase to 60% of the population by the year 2015. If the growth rate of real per capita consumption reaches 3% per annum, but population growth is slowed by 20%, poverty would drop to 25% of the population in 2015. If Senegal were to enter into a very high growth phase, and real per capita consumption grew by 4.5% per annum, poverty would decrease to 21 %. 1.47 This analysis assumes that the poor and non-poor benefit equally from growth. However, as chapter 3 demonstrated, this is likely not to be the case as lack of access to education, credit, land, and other inputs means that while the poor are likely to benefit, they may not benefit to the same degree as those with access to such inputs. Thus, either more economic growth would be 11 This rough calculation is derived from grouped data. It is simply the sum of the distance of avcrage expcnditure for each expenditurc class (there are 12) from the poverty line. 12 Growth rates are derived from the average growth in household consumption from the GOS national accounts for 1988- 92, with the exception of 1993 which is taken directly from GOS projections. All figures are in rcal terms. A-19 required to produce the same degree of poverty alleviation, or more equitable investment and macroeconomic policies should be put in place. In addition, systems and programs intended to benefit the poor would need to be improved to produce more accurate targeting than is currently the case. How Much Would it Take to Grow Out of Powrty Assuming Growth Bnefitted the Poor? POVGAP XIS Parmucier JSceario I - Continustion Present Trends 1992 1993 2000 2005 2010 2015 Data (com Natia Account tfi Naal AN t cun (lt 19S7 mu 'fa 1266900 1,267,500 1,304,200 1,304,200 1,304.200 1,304,200 Asuming so ral groww ia per capa t (cnie with S7-91 treads) 1.00 poputaio nMa (Cowth 2.53%) - baud a Nit. Accts. 1.02S 7.70 7.89 9.40 10.65 12.07 13.68 piul Hit Coasuiption (toat 1987 mu efa) pei capitA 164,532 160,549 138,690 122,403 t08,028 95,341 Prcent c g ia Rel Per Capita Ca cion (Mcca Consumpion) -2.42% -2.47% -2.47% -2.47% -2.47% Ittasiety at Nteaot tudcx to Mea Cosupto -1.12S H eadcotyz HndeoD33% 33.90% 39.78% 46.60% 52.28% 59.94% >>>Pow,tylncrewn Scenario 2- Ppulation Growth decreases 10'N Growth improves 0 1992 1993 2000 2005 2010 2015 Data from Natioa Awcounts Finat HH tsiutim (coant 1987 ma cfa) 1266900 1,267,500 1.557,283 1,05,31S 2,092,858 2,426,196 3 S ea growth (consistrt with 19S8 -1992 avg.) 1.03 PopuVatic. Mn (Ormwtb 2.277%) 1.023 7.70 7.88 9.22 10.32 11.55 12.92 pial NH N ConAapti (conta 1987 urn cfa) pcr capita 164,532 160,946 16S,909 174,964 181,236 187,733 Percea. Cbge in Rea Per Capita CoMnptin (=Ma CoUsumto) -2.18% 0.71% 0.71% 0.71% 0.71% Plasicity d Headcount ledex to Mean Constumpton -1.12% Ncadount itex 31% 33.81% 32.01% 30.76% 29.56% 28.40% >>>Powv,y Decrmu Scenario 3 Population Growth Rate Decreas 20'/./Growth Improves 0 1992 1993 2000 2005 2010 2015 Data from Nationa Acctunts Pial HNN Consmption (cat 1987 m, cfa) 1266900 1,267,500 1,557,283 1,805,31S 2,092,858 2,426,196 3S real growth (conistwS with 198 -1992 avg.) 1.03 Pqup ien, Mn (Gowth 1.8%) 1,018 7.70 7.84 8.90 9.73 10.65 11.66 Fina H1 CosnAnpejo (conuue 1987 m. ef) per cphita 164,532 161,668 175,070 IS5,453 196,451 208,101 Perceto Chage ic Real Pr Capita CmsWqtko ( Mea Consuaption) -1.74% 1.16% 1.16% 1.16% 1.16% lassicity df Headeouit Idex to Mca C::ns 4ui-1 .12% H el Index 33% 33.65% 30.73% 28.78% 28.96% 25.25% > > >Poverty Decreases |Scawsio 4 -Medium Real Growth Rate 1992 1993 2000 2005 2010 2015 Data from Nationl Accounut 0.05S Final 11H Cn ption (colamnt 1987 mu cfs) 0 1266900 1,267,500 1,426,069 1,536,281 1,655,011 1,7S2,917 1.5 S Red (Oowth Rate popuion, Mn (o wth 2.53%) 1.2 7.70 7.89 9.40 10.65 12.07 13.68 FsildHH Catmmupt(ios(eot"S1987macfa)per apita 164,532 160,549 151,650 144,184 137,086 130,337 lth. ane i e PeroCapita Cmaptin (=Man Consumption) -2.42% -1.00% -.0D0% -1.00% -L.OD% Esticity of Hr eount Inex to Mean Cmsaa:pti -. Ha lIndex ount ... 33% 33.90% 38.11% 38.20% 40.40% 42.73% >Po"elxtcreS I lows luch Would it Take bo tmow out or Povty Assuning Growlh Eanlritted the Poor? POVGAP.XLS |Sccoano 5 - Real Growth Rale Incresses 192 1993 2000 2005 2010 2015 Pa1e from Nadonat Accounts ttll5 Imal II" Ctrnetion (coant 1917 c(a) 0 1266900 1.267.,5t 1,698.401 2,116.525 2.637.573 3.26.199 4.5S Re.alOCowh Rat LOO ?opmRut,. Ma (row1h 2.531% 1.025 7.70 7,89 9.40 10.65 12.01 13.68 Indl HlH Cmuarpion (contan m"7 oet ) per capia 164,532 160,349 130.611 I9".642 211,473 240.213 lerce C_Age i Rest Per Capia CamAtin (- Mean Consumption) -2.42% L.9T 1.92h 1.9Th 1.92% PeAIkiy t Hiaesttcet hdex to Mec Consmption -1.22% 4eedeamm iten 33% 33.90% 29.62% 26.56% 23.81% 21.35% > > >. _e lSeawo 6 - Red irowth Rate Incrcses A Population rose dereaxs 20% 192 I23 2000 200t5 2010 2025 Dots (mmn Natnal Account 0.043 Fid 1H1 CsonAim (ceattl 1917 m ecs)- 0 1266900 1,267,3C0 i,698.4o8 2J6,6e525 2,637.57S 3.216,199 4.5% Ret t3o11 Rale L#45 I'opuei,. Ml (oowtI 1.822S l.O3 7.70 7.84 8.90 9.73 10.65 11.66 F_at HH Cousmtitn (contu 1987 m c(s) per capita 164,532 161,663 190,935 217,421 247.532 281.926 Pet Chge i Real Per Cai Co _.mioa (=Mea Cmsuptioa) -1.74% 2.63% 2.63% 2.63% 2.63% esiicity of Hedount Index to Meaw Com_bioa -1.12% lkadeouati Iex 33% 33.r6% 27.77% 23.90% 20.57% 17.70% >>> Pw, Dee"vas Scenario 7 (thoretical) - Retl Growth Rate Increases to 39%- pr *aum 2992 1"3 2000 2005 2010 2015 I)de tn Natiol Accounts 0.045 FitHalHl e Canp o (cmut 29I7 cm cfa) 0 1266900 1.267,5C0 9,406,607 48.809,797 253,268,396 1,314,210.447 Pam Real Orwtb Racs - Avere 19U -1992 1.39 Popatim, Ma (Orowtb 2.53%) 1.025 7.70 7.89 9.40 10.65 12.07 13.68 Fiaal 4H1 tstation (c0tmat 1297 _ dfa) per capita 164,532 160,549 1,000.310 4,580,932 20,978,433 96,070,988 remesa Che ge it Real Per CaptA Cmmption (= Mea Coomoptio) -2.42% 35.57% 35.57% 35.57% 35.57% lasticiyo dHe owtkt tdx to t Ca im *1.12S Headkunt hb ex 33% 33.90% 1.58% 0.12% 0.01% 0.00% > > > ow,fhakoau ElstikIty with respect to: S ngal Mean Con G40i UdCX Hlceaut -1.123 I.5l Po"(loGap hde -1.402 4.39 Foter Orcer Tborbeeke 2 ,1.593 7.07 ANNEX A - 3 REVENUE SOURCES OF THE POOR NnPoor_Z AAA.I_I0E - ACTIVTtS NON AOaICOL S oO 0~~~~~~~~~~~~~~~~ A - TRANSFERITS ^ lACTIUTES NON ACAWCOLtS_ ACIMTES NON AGRICOLES SALAIRES PRIVES TRANSfERTS SALAIRES PRIVES ACTIVITES NON AGRICOLES_ SAL AIRES PNIVES TRAsFERrTS COTON VIRSEMENTS -VfStfI vERsEMENTs ~~~~~~~~~~VERSIMINIS SALAIRES SECTEUHP_S-- o ACTIVITES NON AGRICOLES CULTURES MARMCIHERES to ACTIVITES NON AGfSCOLIS SALAIRES SECTEUR PUWLIC AuTHES SOURCES S AUlRES RACTIUTES NON ARICOS CULTURfS MARMCHERES AUTRtS REVENUS FOVMEL C L S MIL nOARSRAUTRES REVENUS REA4 LOY(RSO AUTRES RN VENUS U [ AC IMIES N ATRC ORUS AGfLCOLES Z -a ACTIVITES NON AGAACALES C O RCHE CH VERSEAAENTS ? 2 Lb EL D W l S LOYFnuTSAU RES SOURCES MIL ET AUR HS CEREALE E AUTRES SOURCES AUTRES REVENUS fORMEL C CULTURES MARN4EES AUTRES FRUITS 0 CUfIU.Mf- cl~~~~** ACTIVITES NON AGfICOLUS AUTFiES ACTIVITES NON CUZILLETTI ~~~~~~~~~~~~~~~0 AGRICOLES C LbN/NE " S zUT CUILLTE N ACTIMTES NON AGRICOLES AUTRES ACTIMTES NON.L URSACME O AGPJCOLES ~~~~~~~~AGRICOLES El b 0 AUTOES REVENUS t g AUTRES REVENUS SALAIRES SECTEUR PU8UC AG8O8 0 0 AC F° COTON MANGUES a LOYERS 0 0~~~~~~~~~- CD LOYERS Lb ~~~~~~~~~~~~~~~~~PirARACHIDE - u MANGUES I 00 IIANANEST Lb A CTIM TES NO N A OF.ICOL ESA T IL R NS_ C OlONA S rtiz- ~~~~~~~~MIL ET AUTRES CEREALES. MIL IT AUTRES CEREALES- ORANGE -MANGUfS. FMZ UMAS ORANO( MAIS AUTFRES fRUITS MANS ORANGE - SAMANES ~~~~~~~~~~CUEILLETTE CUEILLETTE 0 ~~~~~~0 00 000 0 gill~~~~ 800 0 0000 0 Table A3.2 D)m&_ CFAF MUCn PKr iourbol.d (P. W_brnld hwe gwr simpicit HH inps.n I. lb- 4" Lc* r Pare. Pr medi Lian S Tout a , Poor NH (3Im) I Poor HH Poor S TeSl Ravw.. Non-Poor (Srin) U Non-Poor Non-Poor RC.Wou ArGi4 Ui06L4476 si550 262 0.03% $23.14i,iis.9 IOA 6 II 000 Calm 0.00 5503 0 0.*D% 0.00 166456 0 0.00% MiU A. A re Co( 0.00 15503 0 o.0os 0.00 166456 0 0.00% .k 0.00 15503 0 0.00% 0.00 166456 0 0.00% McI 0.00 15503 0 0.00% 1,015.316.68 166456 6 0.00% Qiar U.mibmaa $47,4,.630.00 15503 5466 6.17% 1,432.3f7.402.60 166456 8005 0.50% MAPPIN 10.552A453.40 15503 662 0.00% 15,359,220.20 166456 92 0.02% Omng. 0.00 25503 0 0.00% 0.00 166456 0 0.ees Remain. m_9.309. I 25503 23 0.00% 0.00 166456 0 0.00% Aiim Fr.iA 14,301,36.40 15503 9567 1.05% 441,730.363.90 166456 2696 0.16% CUla.e 0.00 25503 0 0.00% 29.802,00196 166456 179 0.02% A.MzeR ew.Apeocbm 53,321.995.66 25503 3362 0.42% 302.181,293.96 166456 5325 0.11% A N _iLm -AvksW A 3,416,11,513.2 15503 220312 14.n8% 63.093.953,269.00 166456 379043 21.93% Aedvb. 4NwAfr0WB 1.017,332,07.90 i5503 65622 7.41% 10.628.279.599.00 166456 63850 3.69s Adlvham-Ag.Wab C 238,145.fn.30 15503 16651 I.Us 2.76,m7.537.00 166456 26613 0.96% Advb Ni.-Aguba D IX25,437.49 15503 5742 0.65% 799,972,716.70 166436 4806 0.23% AO,em Avba 14in-AgIoob 115,710,015.30 21503 7464 0.54% 663,757,567.89 266456 4000 0.23% Sabin. Suear PI*Me 9951tA0,2 05.0 15503 641U 7.25% 62.30.,626.022.00 166456 375418 21.73% Solla r e 1,216,09"3.37 15303 31456 3.4a% 92,126.1s32.50.00 266456 553462 32.01% Aae PRaweme FoI 2303,013.264.4 15503 13091 1.46% 3.40o,394,895.20 166456 20423 2.2ls L_ 410.962,465.0 5503 26309 2.99% 2,155,139,07.00 166456 48993 2.13% Tbm 4. 1.593P73,45.09 15503 202759 11.40% 8.MS,4107,130.00 166456 223336 6.56% vewmmmu 1,120,911.709.58 is553 72303 3.16% 16,327,960.773.00 266456 112510 6.52% Aitrus Saware 227.61325U 2.3 15503 24662 1.66% 3.70%,027.$40.40 166456 22763 2.32% - NWI,732,506.796.23 665682 100.00% F237.761,948.294.30 2723756 200.001 Table A3.3 (H _nd b 3999 per on" pE -W mh) Mm S Tln HH Min * T't RH Smmz Poo HH (Sum) I Pow POr 1 Non-Poor (Sum) I Non-Poor NonI-Poo leone Amiid $168.996,.39.71 16791 510,064.71 2.38% S515.617.591.00 133117 $3,733.19 0.39S Ceon 50.904,160.50 16791 3.031.63 0.72% 24.072,129.33 138117 174.29 0.02% Mile AllmbCeJnd 2,150,861.54 16791 12S.10 0.03% 67.4053.3.42 13S117 486.03 0.05% liz 431,965.55 16791 29.06 0.01% ND,7S4,323.77 138117 594.68 0.06% Mg" 0.00 16791 0.00 0.00% 36.327.978.19 136117 263.02 0.03% QuIUm Maicbua SO,417,576.17 16791 4,719.33 1.13% 253.810.271.26 13117 1,13.35 0.19% Mneg 1.002.537.24 16791 9.71 0.01% 24,210,249.88 133117 173.29 0.02% DI-F .19S370. 0 16791 11.64 0.00% 0.00 138117 0.00 0.00% B_mw 0.00 16791 0.00 0.00% 7,231.370.27 138117 52.36 0.01% Anw Fmniu 11.168.942.60 16791 665.17 0.16% 361,226.23 13S117 6.24 0.00% mdkae 0.00 16791 0.00 0.00% 23.133.542.29 138117 203.73 0.02% AtutRewca w uAgla* 64.021.417.90 16791 3,812.U 0.90% 357.391.053.86 133117 2,591.22 0.27% AI em A N -rnim A 2.083.669.639.33 16791 124,094.44 2936% 34,686,476.215.00 138117 251,136.43 2M3 AacivlaeeNao-AgIokles l 611,335.039.13 16791 36,420.41 8.62% 7,604,326,637.60 13S117 55,060.76 5.72% AhdvIisNom-ApbknleeC 183.352.644.63 16791 10,919.70 2.58% 1.447,557.361.70 138117 10,4O.66 1.09% AdvIu NowArdoonD 72.433,.11.90 16791 4,313.85 1.02% 616.658,S64.40 133117 4,464.76 0.46% Am= AalwIseN4-Agrlole 53.478.67n.00 16791 3,196.96 0.75% *02.247.511.60 138117 3,30.46 0.60% S.rnS.dku rfbUt 626,210.7365.00 16791 49.20S.57 11.64* 35.708,414,SI.00 138117 215,537.43 26JS% Sleh bn 4WOM 6.550.I13.90 16791 43.0)4.67 1137% 21,066,482,467.00 138117 152.526.35 I5.84% Autmleae Forows 228.532,963.00 16791 13,610.44 3.22S 1.643,89.796.20 13S117 11,902.20 1.24% Lotu ".544.202.90 16791 5.690.20 1.35% 2.761,074,590.00 138117 I9.90.94 2.06% > T _UNWW 1.074.939,391.50 16791 64,013.73 15.I35 12.457,014.034.00 138117 90.191.75 9.37S | V ou 4491.365M72.00 16791 29,263.64 6.2% 11,946.215,447.00 133117 36.493.45 .96% Qn lAueaSaors 139.031,659.10 16791 11.257.92 2.66% 369.096,224.60 138117 6,292.46 0.65% -- 57,n05.990.223.40 S422.606.77 S133.011.290.299.59 963.033.44 ItO.OOS Table A3.4 Rural CFAF mean (defleed hen asHH spe_km t a 299 df1 per pn pe no"l_ ) Pao HH % d HH 1U421101 source Pow (HH (Sum) I Poor Poor 1one Non-Poor (Sum) I Non- Por NO&-Poor AnKW& S12,034,336.107.00 133913 $63,703.06 27.37% 12553792006 258135 $43,623.24 Cd.. I,S)9,005,552.00 168913 9.575387 4.19% 949133325 258135 3,671.09 Ml e AUutm Ceelbe 524,546,447.so 3339)3 2,776.66 1.21% 12215364 253135 3,145.7 Riz 157,369,618.80 IsM193 333.03 0.36% 1636454643 253135 6,333.30 Mali 105.301,766.95 U18913 557.41 0.24% 300111610 253135 1,165.10 Culium Mwudcwi 909,733,933.00 131913 4.315.91 2.11% 3403539668 253135 13,20209 Meagure 197.012,492.50 133913 1.04324 0.46% 266544351 25Mss I,032.38 OW° 153,378.391.70 I1393 811.90 0.36% 4272493 258315 16295 BM_=$ 40.689,430.79 1S913 215.39 0.09% 11662636 253135 45.17 AuaerFruits 104,729.397.73 133913 S54.33 0.24% N193413 253135 341.59 Cidle1I 320,449,068.70 13913 1,696.23 0.74% 225235730 253s5 372.57 Awm Rgygm Ag oolfs 630,631,190.70 133913 4,396.90 1.92% 362658636 253135 3,341.24 AdjaSm Nsms bl A 1I.794.512.230.00 13913 62,433.57 27.n% 44066551301 253135 170,676.20 AaSvjI Nam-Aariooia 3 l.3,691,604.00 13913 9,637.3 4.22% 7743111084 253135 29,99.SS P.alvhm t4i-Agrclec C 432.712,205.50 13913 2,290.54 1S.0% 1394679755 253135 7,331.46 Aaivhm Nom-Agrioiew D 161.250.514.30 133913 653.57 0.37% 34536130 253135 3,1.59 Auu Aldvilu Noa-Agkiole 292.203,385.30 133913 1,546.79 0.63% 128049)103 25miss 4,959.59 SWb Socku. Public 267,733,185.00 133913 1,417.23 0.62% 7616437485 253)65 29,499.92 S.11. Pdtw 1,325,237.341.10 1313 9,662.05 4.23% l365l211318 2581)5 52,373.76 Autme Rlc_am Forels 30,354,312.00 111913 4,607.)7 2.02% 2015203519 253815 7,905.27 Lm)e 219,921.324.41 133913 1,164.14 0.51% 1360549193 253135 5,269.67 Tamufel 6.661.,031.00 1IS913 35,264.81 15.4% 26537M33334 253)95 102,786.14 £ Venum 767.325,S90.50 163913 4,061.30 1.7S% 5356S16843 258135 20,747.91 ON Aute Source 376,737,310.70 ItS913 4,640.96 2.03% 2436425S52 258135 9,436.74 143.l78,024,281.48 S228,560.37 100.00% 13S,941.958,211 A-27 Table A3.5:Annual Transfer Revenue Per Household Institutional % of Inter- % of Revenue Total (CFAF) Revenue Household Transfers/ (CFAF) total Revenue Dakar Non-Poor 114,438 6.5% 111,740 6.3% 12.8% Poor 98,376 10.6% 90,446 9.7% 20.3% Other Urban Non-Poor 100,370 9.7% 92,672 8.9% 18.6% Poor 67,268 12.8% 60,091 11.5% 24.3% Rural Non-Poor 96,072 19.0% 19,686 3.9% 22.9% Poor 38,574 16.8% 3,902 1.7% 18.5% Senegal Non-Poor NA 10.4% A; 6.4%9 16.8% Poor, 14.3% 5.7% 20% Source: PS data Chart A3.6 Senegal Distribution of 70 bn fda in Transfers, 1992 (source: PS) 17% Dakar 30% OSL Louis 10% Diourbel ~~~~~ E~~~~~~~~Thies 13% Loup 13% 17% A other regons A3. 7 Annual Transfers by Expenditure Group and by Gender Household Head | M-lnstitutional * F-Institutional U M-lnter-HH O F-Inter HH 350000O-_ 300000- _ loss 1000 1,999 3,999 4.999 5,999 7,499 9,999 14999 19999 24999 25000 Expenditure Group (Source: PSI ANNEX A - 4 CONSUMPTION PATTERNS/OTHER CHARACTERISTICS OF THE POOR Socioeconomic Characteristics and Poverty 1.48 The following Table A4.1 provides a ranking of different socioeconomic groups according to their mean level of per capita monetary expenditures. It should be interpreted with substantial caution since the Priority Survey did not measure auto- consumption, and this model cannot correct for lack of this data based on socioeconomic groups. It does, however, provide some relevant comparisons (i.e. between groups likely to have similar levels of auto-consumption such as upper level cadres and service employees). It is based on a regression with the logarithm of the level of households' monetary consumption expenditure per capita as a dependent variable (with a sample size about 10,000 data points). The explanatory variables represent the effect of belonging to certain groups, relative to a specified reference group, holding all other variables constant. As the dependent variable is in log form, all the estimated coefficients, except that of household size, which is a simple elasticity, may be approximately interpreted as percentage difference of consumption level relative to the reference group. 1.49 Table A4.2 examines the correlation between low levels of household expenditures and education. The figure of 6,000 CFAF has been chosen here as the cutoff for low levels of consumption as it represents the equivalent of US$1 per day, with a 20% adjustment to account for some degree of auto-consumption (this is a very rough approximation). While this global level of auto-consumption is subject to substantial error, this would not likely change the relevant comparisons within regions. These figures show that achievement of an upper secondary level of education makes a substantial impact on consumption levels, although in rural areas the sample size is small at the higher educational levels (secondary and above). In addition, primary level education in rural areas has less of an impact on consumption levels than it does in urban areas (although there are other health benefits). In rural areas, the most powerful protection against low levels of consumption is to attain an upper level education, likely because at that level people can join the public or formal sector, which has a dramatic impact on wages. A-31 Table A4.1:Monetary Expenditures of Varous Groups (Dependant variable: Log, of per capita level of monetary expenditure Coeff, t-ratio Constant 4.88 (1839.13) Household Size: -0.016 (251.15) AgMlimatic Arms (Reference Group: Dakar) Pikine-Rufisque -0.17 (117.55) Other Urban: -0.16 (119.45) Thits: -0.36 (213.82) Louga: -0.39 (204.17) Diourbel: -0.31 (167.28) Sine: -0.52 (279.48) Irrigated Zone (north Fleuve): -0.24 (125.43) Upper Casamance: -0.62 (322.65) Arid Zone: -0.37 (196.21) (east Fkeuve and South-East) Lower Casamance: -0.61 (332.20) Saloum: -0.48 (248.60) Socio-Economic Grouns (Reference Group: Upper Level Cadres) Middle Level Cadres: -0.08 (29.41) Service Employees: -0.13 (48.37) Farmers: -0.41 (150.15) Craftsmen: -0.20 (74.86) Other Independent Workers: -0.21 (76.70) Non Specified: -0.23 (87.31) Mignt (Reference Group: Non Migrants) Migrants into Towns: 0.02 (17.10) Migrants to Rural Areas: 0.04 (30.02) Aae of the Houswhold Head (Reference Group: > 55) Adult (<55): 0.0003 (0.37) Young (<30): 0.04 (26.82) Maximum Education Level (Refrence Group: Higher Education) No Education: -0.42 (170.47) Primary -0.35 (131.81) Lower Secondary: -0.25 (94.55) Upper Secondary: -0.13 (45.94) R2=O.57, F=39 899.96. 32 Table A4.2 Fraction of Housebolds Spending les than 6000 CFAF per month per capita (%) per agro-dliatic area and educational level Upper Secondary Lower Primary Without Secondary Education Dakw. 1.9 2.8 4.8 11.4 Pikle-Rufisque: 5.9 9.0 14.3 23.9 Odher Urban: 0 10.1 17.0 27.7 Rwaul Azas Thids: 0* 70.6 63.1 70.1 LOUga CC a, o0 77.4 Diourol: - 42.8 84.4 58.3 Sing: O' 28.8- 85.9 90.0 Irigpted Zone: (r (r 38.5 52.9 Uppet Casamance: 37.1- 71.3* 88.7 89.0 Arid Zone: ar 39.2 59.3 62.8 Lower Casmance: 18.5- 50.5 83.9 87.2 Saloumn 37.8* 68.9- 67.9 84.2 Note: *: Insufficient number of observations **: Not Revelant A-33 Table A4.3 Children Aged HH Size 0-14 in the HH Dakar Poor 12.0 6.0 Non-Poor 7.7 3.2 Rural Poor - 5.4 Non-Poor - 4.9 Senegal Poor 10.7 5.5 Non-Poor 8.3 3.9 Table A4.4:Annual Educational Expenditure (CFAF) by Student According to Pbverty Levd Mean/Student #Household DAKAR 24208 107451 Non-Poor 27014 93732 Poor 5033 13729 OTHER CITES 7604 91801 Non-Poor 8370 78297 Poor 3159 13505 RURAL 3213 112393 Non-Poor 3700 78071 Poor 2465 44323 1992 PS Table A4.5 :Annual Educational Expenditure pe Student and by Level of Total Pe Capta Household Expeniditure Moysn. sanwucU Mean/student Nbr. do m.nagos Moina de 1000 2160.2 10122 5000 * 1999 2369.4 19399 2000 b 2999 2593.7 24893 30001 3999 2981.5 23769 40001 4999 3839.5 25411 50001 5999 3991.1 26246 60001 7499 5877.7 30297 75001 9999 7657.5 42094 100001 14999 11020.9 42640 150001 19999 17055.4 23624 200001 24999 28057.0 13113 2SOOOou + 50371.3 30048 _9 P A-34 Table A4.6: Expenditures on Medicine vs. Visits by Expenditure Group Conrumptiton | Dakasr | Other Ciict i | Rural Quintacs Medecines (1) I = lowest 298 273 53 2 416 261 98 3 561 485 197 4 915 603 198 5 = highest 1716 1002 402 MEAN 781 525 190 Visits (2) - (1)/(2) _(2) (11(2) I= lowest 74 4 35 7.8 18 2.9 2 89 4.6 25 10.4 17 5.7 3 118 4.7 72 6.7 19 10.3 4 210 4.3 74 8.1 25 7.9 5 = highest 724 2.3 266 3.7 41 9.8 Mt0HEAN;00000000000 tl00 243 3.2 94 _5. 6 24 7.9 Baseon data from 19 PPS Table A4.7:Senegal Energy Use - Percent used by Poor/Non-Poor Dakar Other Urban Rural % Poor HH 12% 11% 37% Fuel Source (% Poor) Wood Petrol 60% 29% 40% Gas 15% 12% 49% Blectricity,Solar 6% 2% 2% Charcoal 0% 7% 0% Other 17% 9% 1 % 5% 18% 47% Lighting Source Wood na 0% 51% Petrol 31% 25% 38% Electricity,Solar 6% 5% 4% Candle 22% 13% 8% Other 43% 67% 51% Based on data from 1992 PS Table A 4.8 Food Expenditure Shares by Expenditure Group, Dakar DAKAR DEPENSES TOTALES ALINENTAIRES SELON LE PRODUIT El LA NIVEAU DE DEPENSE MENSULLE PER CAPITA NIL RIZ ARACHDE HUILE lOMATE CONDIMENT POISSON VIANDE SUCRE CAFE PAIN IIlE BOISSON N0OISSON APRODUITS fRUITS TOTAL 1,291 9,629 769 5,323 2,044 6,028 7,201 5,520 3,703 1,077 6,531 1,567 646 513 2,557 885 55,283 moins de 0 0 0 0 0 0 0 0 182 0 0 100 0 0 0 0 282 1000 A 0 0 0 0 0 0 0 0 89 0 323 0 0 173 0 0 585 2000 A 27 589 26 175 26 55 64 17 152 26 306 46 0 0 29 17 1,557 3000 i 75 697 33 276 59 139 IS0 61 229 36 428 55 7 3 61 2 2,311.. 4000 i 65 832 47 381 93 253 300 82 225 59 487 82 4 5 64 4 2,983 5000 i 121 859 41 417 109 316 353 105 244 66 521 103 7 4 89 9 3,364 6000 i 112 940 74 498 139 408 444 192 305 93 560 115 13 8 159 12 4,071 7500 a 141 1,013 71 555 191 551 597 293 333 99 607 134 23 10 184 26 4,828 10000 i 162 1,098 101 646 255 741 865 487 396 131 673 174 42 17 277 41 6,105 15000 i 193 1,162 98 696 313 915 1,126 821 441 151 n30 215 80 67 340 88 7,435 20000 i I3 1,172 128 796 383 1,067 1,427 1,093 486 155 840 256 109 49 489 123 8 745 2500 * 220 1,268 151 885 475 1,583 1.874 2,369 622 260 1,056 288 361 III 865 563 -13,016 X IJ. DAKAR DEPENSES TOTALES ALINENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE NENSULLE PER CAPITA HIL RIZ ARACHDE HUILE lOHATE CONDIMENT POISSON VIANDE SUCRE CAFE PAIN IHE SOISSON N0OISSON APRODUITS FRUITS TOTAL 2.3% 17.4% 1.4% 9.6% 3.7% 10.9% 13.0% 10.0% 6.7X 1.9% 11.8. 2.8% 1.2% 0.9% 4.6% 1.6% 100.0% Moins de 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 64.6% 0.0% 0.0% 35.4% 0.0. 0.0% 0.0% 0.0% 100.0% 1000 A 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 0.0% 15.ZX 0.0% 55.2% 0.0% 0.0% 29.6% 0.0% O.OX 100.0% 2000 b 1.8% 37.81 1.7% 11.3% 1.7% 3.6% 4.1% 1.1% 9.8X 1.6% 19.7% 2.9X 0.0% 0.0% 1.9% 1.1% 100.0% 3000 A 3.3% 30.2% 1.4% 11.9% 2.6% 6.0% 6.5% 2.6X 9.9% 1.6% 18.5% 2.4% 0.3% 0.1% 2.6% O.I 100.0% 4000 i 2.2X 27.9% 1.6% 12.8% 3.1% 8.5% 10.1% 2.8% 7.5% 2.0% 16.3% 2.r7 0.1% 0.2% 2.1% 0.1% 100.0% 5000 A 3.61 25.5% 1.2% 12.4% 3.2% 9.4X 10.5% 3.1% 7.2% 2.0% 15.5% 3.1% 0.2% 0.1% 2.6% 0.3% 100.0% 6000 i 2.8% 23.1% 1.8% 12.2% 3.4% 10.0% 10.9% 4.7% 7.5% 2.3% 13.7% 2.8% 0.3% 0.2% 3.9X 0.3X 100.0% 7500 i 2.9% 21.0% 1.5% 11.5% 4.0% 11.4% 12.4% 6.1% 6.9X 2.0% 12.6% 2.8% 0.5% 0.2% 3.8% 0.5% 100.0% 10000 i 2.6% 18.0% 1.7% 10.6% 4.2% 12.1% 14.2% 8.0% 6.5% 2.1% 11.0% 2.8% 0.7% 0.3% 4.5% 0.7% 100.0% 15000 j 2.6% 15.6% 1.3% 9.4% 4.2% 12.3% 15.1% 11.0% 5.9% 2.0% 9.8% 2.9% 1.1% 0.9% 4.6% 1.2% 100.0% 20000 i 2.0X 13.4% 1.5% 9.1% 4.4% 12.2% 16.3% 12.5% 5.6% 1.8% 9.6% 2.9% 1.2% 0.6% 5.6% 1.4% 100.0% 2500 * 1.7% 9.7% 1.2% 6.8% 3.6% 12.2% 14.4% 18.2% 4.8% 2.0% 8.1% 2.2% 2.8% 1.4% 6.6% 4.3% 100.0% Table A 4.9 Food Expenditure Shares by Expenditure Group, Other Urban AUTRES VILLES DEPENSES TOTALES ALIMENTAIRES SELON LE PRODUIT El LA NIVEAU DE DEPENSE MENSULLE PER CAPITA NIL RIZ ARACHIDE NUILE TOMATE CONDIMENT POISSON VIANDE SUCRE CAFE PAIN THE BIOSSON NBOISSON A LAIT FRUIT TOTAL 2,404 10,688 1,071 5,642 2,283 6,838 6,573 4,691 4,517 1,080 5,004 2,276 445 186 2,142 627 56,468 Moins de 0 29 20 0 0 44 0 19 189 0 3 200 0 0 9 0 515 loooa 47 462 9 144 30 127 79 30 77 13 22 55 0 4 12 1 1,111 2000 A 125 659 27 193 58 183 170 51 159 12 49 52 0 2 33 1 1 773 3000 a 85 783 44 288 72 257 213 68 217 39 206 98 3 3 49 2 2,429 4000 a 176 857 56 356 102 275 292 140 269 51 234 107 2 4 68 14 3,004 sooo a 174 953 67 452 155 401 377 179 359 62 323 127 4 3 101 18 3,756 6000 a 222 1,032 82 511 184 507 462 222 361 72 436 142 10 5 113 11 4,370 7OO a 339 1,043 111 580 222 599 543 323 412 96 504 192 19 8 171 24 5,186 10000 & 280 1,118 131 678 295 812 785 s5o 466 119 647 227 33 18 225 42 6,384 15000 a 303 1,269 155 765 335 910 975 744 582 135 732 271 47 18 330 62 7,634 20000 a 343 1,255 223 830 419 1,292 1,218 954 737 228 856 430 82 1d 414 137 9,435 2500 * 310 1,228 146 845 411 1,431 1,459 1,451 690 252 991 374 244 105 618 316 10,871 ' AUTRES VILLES cn DEPENSES TOTALES ALIMENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE HENSULLE PER CAPITA NIL RIZ ARACHIDE HUILE 1OHATE CONDIMENT POISSON VIANDE SUCRE CAFE PAIN THE BIOSSON NBOISSON A LAIl FRUIT TOTAL 4.3 '18.9% 1.92 10.0% 4.0% 12.1X 11.61 8.3x 8.0% 1.9% 8.9% 4.0X 0.8% 0.3% 3.8% 1.1% 100.0X Moins de 0.0% 5.7% 4.0% 0.0% 0.0% 8.6% 0.01 3.8% 36.7% o.ox 0.6% 38.9% 0.0% 0.0% 1 7% 0.0% 100.0% 1000 i 4.2% 41.5% 0.8% 12.9% 2.7% 11.4% 7.1% 2.7% 6.9% 1.2% 2.0X 4.9% 0.0% 0.4% 1.1. 0.1% 100.0% 2000 a 7.0% 37.2% 1.5% 10.9% 3.31 10.3% 9.61 2.9% 8.9% 0.7% 2.8% 2.9% 0.0% 0.1% 1.8x 0.1% 100.0% 3000 A 3.51 32.2% 1.8% 11.8% 2.91 10.6% 8.8% 2.8% 8.9% 1.6% 8.5% 4.1% 0.1% 0.1% 2.0% 0.1% 100.0X 4000 6 5.9% 28.5s 1.9% 11.9% 3.41 9.2% 9.71 4.71 9.0% 1.7% 7.8% 3.6% 0.1% 0.1% 2.3% 0.5% 100.0% 5000 a 4.6% 25.41 1.8% 12.01 4.11 10.7% 10.01 4.8% 9.sx 1.6% 8.6% 3.4% 0.1% 0.1% 2.7% 0.5% 100.0% 6000 A s.1% 23.6% 1.91 11.7% 4.2% 11.61 10.61 5.1% 8.3% 1.6% 10.0% 3.2% 0.2% 0.1% 2.6% 0.2% 100.0% 7500 A 6.51 20.1X 2.1% 11.2X 4.31 11.51 10.5% 6.21 8.0% 1.9% 9.7% 3.rx 0.4% 0.1% 3.3% 0.5% 100.0% o0000 a 4.41 17.51 2.0% 10.6% 4.61 12.7% 12.31 8.01 7.3% 1.9% 10.1% 3.6% 0.5% 0.3X 3.5% 0.7% 100.01 15000 a 4.01 16.6% 2.0% 10.0% 4.41 11.9% 12.81 9.7% 7.6% 1.8% 9.6% 3.6% 0.6% 0.2% 4.3% 0.8% 100.0% 20000 & 3.6% 13.3% 2.41 8.8% 4.41 13.7% 12.91 10.11 7.81 2.4% 9.1% 4.6% 0.9% 0.2% 4.4% 1.4% 100.0% 2500 + 2.9% 11.3% 1.3% 7.8% 3.81 13.21 13.41 13.31 6.3% 2.3% 9.1% 3.4% 2.2% 1.0% 5.7% 2.9% 100.0% Tabte A 4. 10 Food Expenditure Shares by Expenditure Group. Rural ZONE RURALE DEPENSES TOTALES ALIKENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE MENSULLE PER CAPITA NIL RIZ ARACHIDE HUILE TOHATE CONDIMENT POISSON VIANDE SUCRE CAFE PAIN THE BIOSSON NHOISSON A LAIT FRUIT TOTAL 3,446 11,406 972 6,458 1,676 6,584 4,944 3,948 5,391 1,302 3,583 2,612 214 72 1,860 239 54,707 lNoins de 5 51 4 30 6 71 53 15 73 3 7 30 0 1 8 1 357 1000 i 36 260 12 91 20 113 107 41 153 17 26 67 1 4 21 1 970 2000 a 96 555 19 191 37 152 155 83 227 33 45 103 0 3 27 2 1,729- 3000 a 164 693 36 299 50 243 217 98 308 60 98 141 1 4 48 3 2,463 4000 a 224 821 48 382 79 290 293 ĥ60 331 72 171 148 1 3 68 4 3,095 5000 a 256 910 62 442 97 342 339 178 385 75 246 207 2 4 91 8 3,644 6000 a 326 986 107 519 142 495 417 244 435 105 320 221 2 3 137 21 4,478 7500 A 411 1,218 116 617 165 532 460 292 491 118 357 259 7 1 155 28 5,228 10000 a 420 1,421 121 704 191 683 588 463 548 137 447 279 9 4 198 31 6,244 15000 a 623 1,528 158 1,143 319 1,326 734 747 721 227 499 341 25 7 374 83 8,854 20000 A 396 1,574 196 1,270 316 1,305 760 828 747 187 571 372 46 6 197 17 8,787 2500 * 489 1,392 94 770 254 1,034 822 798 972 268 797 442 118 33 535 40 8,858 ZONE RURIALE DEPENSES TOTALES ALIMENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE HENSULLE PER CAPITA NIL RIZ ARACHIDE HUILE TOMATE CONDIMENT POISSON VIANDE. SUCRE CAFE PAIN THE BIOSSON NBOISSON A LAIT FRUIT TOTAL 6.3% 20.8% 1.8% 11.8% 3.1% 12.0% 9.0% 7.2% 9.9% 2.4% 6.5% 4.8% 0.4% 0.1% 3.4X 0.4% 100.0% M4oins de 1.4% 14.2% 1.1% 8.4% 1.7% 19.8% 14.8% 4.3% 20.4% 0.8% 1.9% 8.5% 0.0% 0.2% 2.3% 0.2% 100.0% 1000 i 3.7% 26.8% 1.2% 9.4% 2.1% 11.6% 11.0% 4.2% 15.8% 1.8% 2.6% 6.9% 0.17. 0.4% 2.2% 0.1% 100.0% 2000 i 5.6% 32.1% 1.1% 11.0% 2.1% 8.8% 9.0% 4.8% 13.2% 1.9% 2.6% 6.0% 0.0% 0.2% 1.5% 0.1% 100.0% 3000 i 6.7% 28.1% 1.5% 12.2% 2.0X 9.9% 8.8% 4.0% 12.5% 2.4% 4.0% 5.7% 0.0% 0.2% 1.9% 0.1X 100.0% 4000 i 7.2% 26.5% 1.5% 12.4% 2.5% 9.4% 9.5% 5.2% 10.7% 2.3% 5.5% 4.8% 0o0% 0.1. 2.2X 0.1% 100.0% 5000 A 7.0% 25.0% 1.7% 12.1% 2.7% 9.4% 9.3% 4.9% 10.6% 2.1% 6.%r 5.7% 0.1X 0.1% 2.5% 0.2% 100.0% 6000 i 7.3% 22.0% 2.4% 11.6% 3.2% 11.1% 9.3% 5.4% 9.7% 2.3% 7.1% 4.9% 0.0% 0.1% 3.I1X 0.5% 100.0% 7500 A 7.9% 23.3% 2.2% 11.8% 3.2% 10.2% 8.8% 5.6% 9.4% 2.3% 6.8% 5.0% 0.1% 0.0% 3.0% 0.5% 100.0% 10000 h 6.7% 22.8% 1.9% 11.3% 3.1% 10.9% 9.4% 7.4% 8.8% 2.2% 7.2% 4.5% o01x 0.1% 3.2% 0.5% 100.0% 15000 a 7.0% 17.3% 1.8% 12.9% 3.6% 15.0% 8.3% 8.4% 8.1% 2.6% 5.6% 3.9% 0.3% 0.1% 4.2% 0.9% 100.0% 20000 a 4.5% 17.9% 2.2% 14.4% 3.6% 14.8% 8.6% 9.4% 8.5% 2.1% 6.5% 4.2% 0.5% 0.1% 2.2X 0.2% 100.0% 2500 * 5.5% 15.7% 1.1% 8.7% 2.9% 11.7% 9.3% 9.0% 11.0% 3.0% 9.0% 5.0% 1.3X 0.4% 6.0X 0.5% 100.0% TO. A 4.11 food Expenditure Swares by Expenwiture Group, Senegal SENEGAL DEPENSES TOTALES ALI1ENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE MENSULLE PER CAPITA NIL RI1 ARACHIDE INUILE TOMATE CONDIMENT POISSON VIANDE SUCRE CAFE PAIN THE BIOSSON NBOISSON A LAIT FRUIT TOTAL 2,282 10,348 922 5,711 2,075 6,655 6,951 5,421 4,417 1,166 4,845 2,096 539 273 2,380 771 56,852 Moins de 5 51 4 30 6 70 52 1S 74 3 7 31 0 1 a 1 358 1000l 36 263 12 91 20 113 106 40 151 17 26 67 1 4 21 1 970 2000 i 97 564 20 191 38 153 155 79 221 31 51 98 0 3 27 2 1,732 3000 i 149 707 37 298 53 238 213 92 293 56 135 130 2 4 49 3 2,459 4000 i 189 835 50 379 86 283 295 144 303 66 237 129 2 3 68 6 3,075 5000 i 208 908 58 438 113 350 351 162 348 70 323 166 4 4 93 11 3,605 6000 i 240 985 91 510 153 475 436 224 379 92 414 170 7 5 135 16 4,334 7500 a 293 1,089 99 584 194 563 538 304 410 104 496 193 17 6 171 26 5,089 10000 a 261 1,173 116 668 254 750 771 488 453 128 610 216 31 14 240 39 6,211 15000 A 292 1,254 126 786 322 976 1,022 787 529 158 699 252 62 43 342 79 7,730 20ooo i 247 1,240 161 858 385 1,157 1,290 1,022 587 179 811 318 94 35 434 115 8 939 2500 * 263 1,280 147 877 450 1,527 1,722 2,062 669 261 1,035 323 320 151 791 473 12:351 P L.), SENEGAL DEPENSES TOTALES ALIENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE NENSULLE PER CAPITA NIL RIZ ARACHIDE NUILE TOHATE CONDIMENT POISSON VIANDE SUCRE CAFE PAIN THE BIOSSON NBOISSON A LAIT FRUII TOTAL 4.0X 18.2X 1.6X 10.0 3.7% 11.71 12.21 9.51 7.8X 2.1X 8.5X 3.7X 0.9% 0.5% 4.2% 1.4% 100.0 Moins de 1.4X 14.1X 1.2X 8.3X 1.7X 19.71 14.71 4.31 20.6X 0.81 1.9% 8.8% 0.0X 0.2% 2.3% 0.2X 100.01 1000 a 3.81 27.1X 1.21 9.4X 2.11 11.61 10.9X 4.11 15.61 1.81 2.7% 6.9% 0.1% 0.4% 2.2% 0.1% 100.01 2000 i 5.61 32.6% 1.2% 11.0% 2.2X 8.81 8.9% 4.61 12.8% 1.8% 2.9X 5 . rx 0.0% 0.1% 1.6% 0.11 100.02 3000 A 6.11 28.71 1.51 12.1X 2.21 9.71 8.71 3.81 11.9% 2.3% 5.5% 5.31 0.11 0.2% 2.0% 0.1% 100.0% 4000 a 6.21 27.2X 1.61 12.31 2.81 9.21 9.61 4.7% 9.91 2.1% 7.7% 4.2% 0.1% 0.1% 2.2% 0.2% 100.0% 5000 i 5.81 25.21 1.61 12.2% 3.11 9.71 9.71 4.51 9.61 1.9% 8.9% 4.6% O.1% 0.1% 2.6% 0.3% 100.0% 6000 i 5.5% 22.71 2.11 11.8% 3.5X 11.01 10.11 5.21 8.81 2.1% 9.6% 3.9% 0.2% 0.1% 3.1% 0.4% 100.0O 7500 i 5.81 21.41 1.91 11.5X 3.8X 11.11 10.61 6.01 8.1% 2.0% 9.7% 3.8% 0.3% 0.17 3.4% 0.5% 100.0% 10000 i 4.2% 18.91 1.9% 10.7% 4.11 12.1X 12.41 7.9 7.31 2.1% 9.81 3.5% 0.5% 0.2% 3.9% 0.61 100.0% 15000 i 3.8% 16.2% 1.6% 10.2% 4.2% 12.62 13.22 10.21 6.8% 2.0% 9.0% 3.3% 0.8% 0.6% 4.4% 1.0% 100.01 20000 i 2.8% 13.9% 1.8% 9.6% 4.3% 12.9% 14.41 11.41 6.61 2.0% 9.1% 3.6% 1.1% 0.4% 4.9% 1.3% 100.01 2500 * 2.1% 10.4% 1.21 7.1% 3.61 12.41 13.9% 16.71 5.4% 2.1% 8.4X 2.62 2.6X 1.2% 6.4% 3.82 100.0% Table A 4.12 Nouseholds Spending/Not Spending an E&aation HOUSEHOtDS SPENDING/NOT SPENDING ON EDUCATION DAKAR OTHER URBAN RUQAL SENEGAL DON'T PERCENT DON'T PERCENT DON'T PERCENT DON'T PERCENI SPEND SPEND SPENDING SPEND SPEND SPENDING SPEND SPEND SPENDING SPEND SPEND SPENDING Noins de 1000 50 0 100.0% 149 121 55.2% 8823 23908 27.0X 9022 24029 27.3% 1000 i 1999 42 0 100.0% 675 882 43.4% 20091 57274 26.0% 20808 58156 26.4% 2000 i 2999 01 577 58.1% 3410 2353 59.2% 21811 57006 27.7% 26022 59936 30.jX 3000 i 39 2937 1722 63.0% 4781 4420 52.0X 17490 50548 25.7% 25208 56690 30.8X 4000 4999 5498 3875 58.7X 7868 5317 59.7% 13023 35198 27.0% 26389 44390 37.3% 5000 i 5999 6652 3877 63.2% 7962 4620 63.3X 11850 23328 33.7% 26464 31825 45.4% 6000 a 7499 9848 6940 58.7% 11654 7371 61.3X 9117 25853 26.1X 30619 40164 43.SX 7500 i 9999 18278 8914 67.2% 17261 10504 62.2% 8008 23692 25.3X 43547 43110 50.3X 10000 i 14999 19355 15293 55.9% 18168 9853 64.8% 6597 16796 28.2% 44120 41942 S1.SX 15000 i 1999 13082 8355 61.0% 7946 5096 60.9% 2118 5959 26.2% 23146 194)0 54.4% 20000 a 24999 8320 5638 59.6% 3855 3344 53.5% 729 2859 20.3% 12904 11841 52.1X 2500 * 22227 19676 53.0% 7229 10068 41.8% 1441 3758 27.7X 30897 33502 48.0% IOIAL 107090 74867 58.9% 90958 63949 58.7% 121098 326179 27.1% 319146 464995 40.7X Tame A 4 .13 an -Food Expaiditure Shaee by Eupmuiiture Group, Dakar DAKAR PLUSIERS DEPENSES NON-ALINENTAIRES SELON LE PRODUII ET LA NIVEAU DE DEPENSE HENSULLE PER CAPITA TOTAL HABILLE14ENT TRANSPORT SANTE EDUCIATION (FtNf) 7,985 5,593 6,650 3,813 139,243 Noins de 0i0 0 40 0 44 981 1000 A1999 0 262 0 79 1,264 2000 A 2999 35 134 59 27 2,617 3000 A 3999 38 167 134 82 3,551 4000 i 499 154 212 158 45 4,506 5000 & 5999 117 238 265 80 5,478 6000 A 7499 213 365 248 116 6,775 7500 & 9999 400 437 482 163 5,695 10000 A 14999 744 574 665 257 12,155 15000 A 19999 1,138 794 1,005 471 17,200 20000 A 24999 1,550 847 1,383 098 22,429 2500 * 3,596 1,524 2,251 1,551 53,592 DAKAR PLUSIERS DEPERSES NON-ALINENTAIRES SELON LE PRODUIT ET LA WIVEAU DE DEPENSE HENSULLE PER CAPITA ALIHENI 2 HABILLENENT TRANSPORT SANTE EDUCATION TOTAL DU IOIAL 5.7X 4.02 4.8X 2.7X 100.0X 39.7X Moins de 1000 0.0X 4.1X 0.0X 4.5X 100.0X 28.7X 1000 A 1999 0.0X 20.8X 0.0X 6.2X 100.02 46.3X 2000 & 2999 1.32 5.1X 2.3X 1.0X 100.02 59.52 3000 A 3999 1.12 4.72 3.8X 2.32 100.0X 65.12 4000 & 4999 3.4X 4.72 3.52 1.02 100.02 66.2X 5000 A 5999 2.12 4.3X 4.8X 1.52 100.0X 61.42 6000 A 7499 3.12 5.4X 3.7X 1.7X 100.0X 60.1X 7500 A 999 4.62 5.0X 5.5 1.9X 100.02 55.52 10000 A 14999 6.1% 4.72 5.52 2.1X 100.02 50.22 15000 A 1999 6.62 4.62 5.80 2.72 100.02 43.22 20000 A 2499 6.92 3.82 6.22 4.02 100.0X 39.0X 2500 + 6.7X 2.82 4.2X 2.9X 100.0X 24.32 Tame A 4.14 man-Food Expenditure Shares bV Expenditure Group, Other Urban AUTRES VILLES PLUSIERS DEPENSES NON-ALIIENTAIRES SELON LE PRODUIT El LA NIVEAU DE DEPENSE MENSULLE PER CAPITA NASILLENENT TRANSPORT SANTE EDUCATION IOTAL 9.957 2,420 6,050 1,627 133,078 Hoins de 1000 27 0 10 a 822 1000 i 1999 53 29 44 24 1,cll/ 2000 A 2999 75 28 112 32 Z,506 3000 i 3999 76 54 162 37 3,566 4000 a 4999 175 95 197 59 4,506 5000 5999 193 66 245 60 5,511 6000 & 7499 288 112 256 84 6,735 7500 & 9999 530 197 493 110 8,726 10000 A 14999 1,042 199 699 161 12,279 15000 i 19999 1,914 337 919 286 16,925 20000 i 24999 1,577 388 1,302 324 22,640 2500 . 4,008 916 1,609 442 47,254 AUIRES VILLES PLUSIEIS DEPENSES NON-ALIHENTAIRES SELON LE PRODUIT El LA NIVEAU DE DEPENSE HENSULLE PER CAPITA ALIHENI 2 NABILLEMENT TRANSPORT SANTE EDUCATION TOTAL DU lOlAt 7.52 1.82 4.5% 1.2X 100.0X 42.42 Noins de 1000 3.2X O.OX 1.3X 1.0 100.0 62.7Z 1000 & 1999 3.3X 1.8X 2.8X 1.5X 100.0 69.12 2000 i 2999 3.0% 1.1X 4.5X 1.32 100.02 70.72 3000 i 3999 2.1X 1.52 4.5X 1.02 100.0X 68.12 4000 A 4999 3.9X 2.1X 4.4X 1.32 100.0X 66.72 5000 A 5999 3.5X 1.22 4.4X 1.1X 100.0X 68.12 6000 A 7499 4.3% 1.72 3.82 1.22 100.0X 64.92 7500 4 999 6.12 2.32 5.62 1.3X 100.0X 59.42 10000 A 14999 81.5% 1.62 5.7X 1.32 100.02 52.0X 15000 & 1999 11.3X 2.02 5.4X 1.72 100.0X 45.12 20000 A 24999 7.02 1.71 5.82 1.4X 100.0 41.7x 2500 * 8.52 1.9X 3.42 0.92 100.02 23.02 Table A 4.15 Mon-food Expenditure Shares by Expenditure Group, Rural ZONE RURALE PLUSIERS DEPENSES NON-ALI1ENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE NENSULLE PER CAPITA HABILLEHENT TRANSPORT SANTE EDWCATION TOTAL 9,192 2,980 6,914 389 126,172 Noins de 1000 28 14 35 9 680 1000 A 1999 50 41 92 12 1,512 2000 A2999 75 63 132 14 2,477 3000 i3999 109 79 195 1i 3,491 4000 & 4999 182 101 176 15 4,449 5000 A 5999 251 175 204 25 5,450 6000 & 7499 312 175 334 30 6,630 7500 A 9999 524 237 467 26 8,551 10000 & 14999 941 364 428 68 11,8J8 15000 i 19999 2,382 489 1,082 43 17,357 20000 A 249"9 1,767 307 918 42 22,446 2500 . 2,570 935 2,850 93 41,240 ZONE RURALE PLUSIERS DEPENSES NON-ALIIENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE MENSULLE PER CAPITA ALIENEJT K J MADILLEIENI IRANSPOUl SANTE EDUCATION TOTAL DU TOTAL 7.3X 2.4X 5.5X 0.3X 100.0X 30.2X Mains de 1000 4.2X 2.0X 5.1X 1.3X 100.0X 34.4X 1000 i 1999 3.3X 2.7X 6.1X 0.8K 100.0X 39.1X 2000 A 2999 3.OX 2.5X 5.3X 0.6X 100.0 41.1X 3000 i 3999 3.1X 2.3X 5.6X 0.3X 100.0 41.4X 4000 A 4999 4.1K 2.3X 4.0X 0.3X 100.0K 41.0X 5000 59s 4.6X 3.2X 3.7X 0.5X 100.0X 40.1X 6000 A 7499 4.7K 2.6o 5.0K 0.5 100.0X 40.3K 7500 A 99 6.1X 2.8K 5.5 0.3X 100.0X 37.9X 10000 A 14999 * 7.9X 3.1X 3.6X 0.6K 100.0K 34.4X 15000 A 19999 13.7X 2.8K 6.2K 0.2X. '100.0 34.8K 20000 A 24999 7.9X 1.4K 4.1K 0.21 100.0X 28.1K 2500 . 6.21 2.3X 6.9X 0.2X 100.0X 17.7X hable A 4.16 Non-Food Expenditure Shares bV Expenditure Gromp, Senegal SENEGAL PLUSIERS DEPENSES NON-ALIHENTAIRES SELOIN LE PRODUIT El LA NIVEAU DE DEPENSE HENSULLE PER CAPITA MABILLENENT TRANSPORT SANTE EDUCATION lOTAL 9,123 3,977 6,691 2,659 137,063 Noins de 1000 28 13 35 9 680 1000 A199 50 41 90 12 1,510 2000 2999 75 62 129 15 2,488 3000 a3999 100 83 188 20 3,520 4000 A 49 177 118 179 29 4,496 5000 A 5999 209 163 226 45 5,464 6000 A7499 279 206 289 68 6,686 7500 & 999 484 294 482 103 8,686 10000 k 1499 891 391 622 180 12,090 15000 1i 19999 1,576 603 991 349 17,183 20000 i 2499 1,580 653 1,306 636 2Z.469 2500 . 3,673 1,349 2,153 1,194 51,792 SENEGAL PLUSIERS DEPENSES NON-ALINENTAIRES SELON LE PRODUIT ET LA NIVEAU DE DEPENSE HENSULLE PER CAPITA A IHE14i X HABILLEHENT TRANSPORT SANTE EDUCATION TOTAL DU IOIAL w 6.7X 2.9X 4.92 1.9° 100.0X 41.5X 1Moins de 1000 4.2X 2.0X 5.1X 1.3X 100.02 52.62 1000 & 1999 3.3X 2.7X 6.02 0.82 100.0X 64.2X 2000 A 2999 3.0X 2.52 5.2X 0.6X 100.02 69.6X 3000 A 3999 2.8X 2.3X 5.32 0.6X 100.02 69.9X 4000 A 4999 3.9X 2.6X 4.0X 0.6X 100.0X 68.4% 5000 A 5999 3.8X 3.0X 4.1X 0.82 100.02 66.0X 6000 & 7499 4.2X 3.12 4.32 1.0X 100.0X 64.82 7500 & 999 5.62 3.42 5.62 1.22 100.02 58.62 10000 i 14999 7.4X 3.22 5.12 1.52 100.02 51.42 15000 A 19999 9.22 3.52 5.82 2.0X 100.0X 45.02 20000 h 24999 7.02 2.92 5.86 2.82 100.02 39.82 2500 * 7.1X 2.6X 4.22 2.3X 100.0X 23.82 A-44 Table A 4.17 Senegal, 1992: Average Expenditure Levels 100%I 90% : 80% 70% 00 .A 60% 1 50% s 40% Z 30% f 20% b 10% 0% ~ ~ 0 % 0 00 0 0 ~ ~ 0 00 !; - ° C o ° - X0 ClE % V1- C- - cs 'i %0 0 o x c- rb 0s CFAF Per Capita Per Month (source: Priority Survey) Table A 4.18 Population Distribution by Expenditure Level and Region 25.00% |- 20.00% __\___\\_________ ˘ 150.00% / Dakar % .2 0- ~~~~~~~~~~~~~~~~~~~~~~ural % 10.00% / Other Urban % C 5.00% a 0.00% 1 2 3 4 5 6 7 8 9 10 11 12 Per Capita Expenditure Categories Itop categories compressed) Tableau A 4.19: Prevalence de la pauvrete selon certaines caracteristiques du Chef de Menage (CM) N O N P AV R E S P A U V a E S X P A U V R E S DAKAR AUTRE VILLES RURAL DAKAR AUTRE VILLES RURAL DAKAR AUTRE VILLES RURAL TOTAL ..................... 159264 131475 277464 22695 23432 169612 12.5X 15.1X 37.9X Etat matrimonial Celibatair ....... ...... 7239 5541 67m 119 638 2186 1.6X 10.3X 24.4X Marle(e) I conJ ......... 877M 68155 151393 10814 11903 83180 11.0X 14.92 35.5X Merie 2 epouses ......... 30m 26059 73151 6841 6063 58860 18.22 18.92 44.62 Mario 3 epouses ......... 9953 9070 20973 2111 1323 15160 17.5X 12.72 4Z.02 Marie 4 epouses et +.... 2817 4065 8689 658 721 3682 18.92 15.1X 29.82 Veuf(ve) ................ 13445 14386 12637 1290 1610 5250 8.82 11.22 29.4X Divorce(e) .............. 6793 4055 3726 864 884 1214 11.3X 17.91 24.6X Autre ................... 471 143 93 90 100 0.02 38.62 51.82 Sexe duk CM masculin ................ 121028 93359 231129 18556 18501 160551 13.3X 16.5X 41.0X FeaInin ................. 38236 38116 46335 4140 4931 9082 9.82 11.52 16.4X Source eCu potable > Riviere. cours eau, lace.. 1621 13309 232 4888 12.52 26.92 Pults ................... 4249 25809 174789 2086 9061 130519 32.92 26.02 42.72 Forage .................. S7466 40961 66111 15175 10210 31716 20.92 20.02 32.42 Robinet Inter. propre .... 66185 50379 13779 4107 3012 1175 5.82 5.62 7.92 Robinet Inter. portage.. 28672 11767 3247 1132 511 299 3.82 4.22 8.4X Autre ................... 2 691 939 6229 196 405 1035 6.82 30.1X 14.22 Eciairage os S..515 27604 28566 0.02 50.92 Petrole .........23322 54476 227212 10644 17745 131357 31.32 24.62 37.7X Electricite, soltair e... 117980 64440 11184 7021 3679 429 5.62 5.42 3.72 ugle .................. 17806 11926 9104 4912 1767 826 21.62 12.92 8.32 Autres .................. 155 118 2360 119 241 2454 43.42 67.12 51.02 CoWbustible Bois .................... 1897 38662 253800 2814 15924 166616 59.72 29.2X 39.62 Petrole ................. 543 880 1791 96 118 1733 15.02 11.82 49.2X Gaz ..................... 80308 28985 6497 4803 657 116 5.62 2.22 1.82 Electrfclte, sotaire .... 1135 862 223 61 0.0X 6.62 0.02 Charbon de bols ......... 73543 60590 13947 14886 6353 114 16.8X 9.52 0.82 Autres .................. 1837 1496 1206 97 319 1053 5.02 17.62 46.62 Plus haut niveeu d Instruction du CM Sans Instruction ........ 87935 90821 256125 19098 20117 161821 17.82 18.12 38.72 Ecole prl_iare .......... 25289 15933 12927 2357 2238 7109 8.52 12.32 35.52 Secondbire ler cycle .... 17868 10714 3696 868 637 592 4.62 5.6X 13.82 Secondaire 2ece cycle ... 10793 6304 2833 216 331 110 2.02 5.02 3.72 En efgnement superleur.. 17379 7703 1883 157 108 0.92 1.42 0.02 Tableau A 4.19: Prevalence de la pauvrete selon certaines caracteristiques du Chef de Menage (CM) ) N ON P A V R E S P A U V R E S X P A U V R E S DAKAR AUTRE VILLES RURAL DAKAR AUTRE VILLES RURAL DAKAR ALJTRE VILLES RURAL Stetut d'occup stion dua CM Occupe .................. 117770 97648 251670 14510 170 159776 11.0% 15.Z% 38.8% Chaoeur ................. 8580 4608 1402 2093 1145 19.6% 19.9% 0.0% Eleve etudiont .......... 302 256 0.0X 0.02 ERR Au foyer ...... . ...... .......s11125 12139 9437 1342 1154 669 10.3% 8.7% 6.62 Retarsite ............... 17272 10095 1931 3566 1527 1403 17.1% 13.1% 42.1% Autres Inctifs ......... 3437 6055 12934 1184 2116 7523 25.6% 25.9X 36.6X Age du CM Noins de 30 ans ......... 9716 8747 24895 527 968 7648 5.1% 10.0% 23.5% 30 a 59 ens ............. 121359 94222 185825 16214 15053 114148 11.6% 13.8% 38.1% 60 en ou *.............. 28189 28506 66744 5954 7411 47837 17.4% 20.6% 41.7% Possede mison oui ..................... 76905 83068 225366 14356 17005 137375 15.7% 17.0% 37.9% Mon ..................... 82359 48407 52096 6U40 6427 32258 9.2% 11.7% 38.Z% Posede terrain & betir ui ..................... 1896 27590 377U3 1108 4028 11454 5.5X 12.7% 23.3% Won ..................... 140296 103885 239681 21587 19404 158178 13.5% 15.7% 39.8% Possede terrain cultivable Oui ..................... U86 19025 204075 2386 6097 147875 21.9% 24.3% 42.0% > Man. 150778 112450 7389 20310 17335 21758 11.9% 13.4% 22.9% Possede charrue ou ..663 6160 132080 164 2627 110461 19.8% 29.9% 45.5% Non .158601 125315 145384 22532 20805 59172 12.4X 14.2% 28.9% Possede charrette Oui ..................... 1415 6482 88576 670 3143 60841 32.1% 32.7% 40.7% Non .157849 124993 18888 22026 20289 108791 12.2% 14.0% 36.5% Possede Pirogue Oui ..................... 782 4220 6142 81 162 2015 9.4% 3.7% 24.7% Non .15482 127255 271322 22615 23270 167618 12.5% 15.5% 38.2% Possede Filet Oui . 352 1446 2618 190 1421 0.0% 11.6% 35.2% Man .158912 130029 274846 22695 23241 168212 12.5% 15.2% 38.0% Possede Nobylette out .................... 2786 6900 5844 58 349 1831 2.0% 4.8% 23.9% Mn. 156478 124575 271620 22637 23083 167802 12.6% 15.6% 38.2% Possede Vehicule out .................... 18381 7884 3644 315 194 115 1.7% 2.4% 3.1% Mn .140884 123591 273820 22380 23238 169517 13.7% 15.8% 38.2% Possede Televiseur Oi ..72358 38595 8356 3736 1330 408 4.9% 3.3% 4.7% Mon . 86906 9280 269108 18960 22101 169224 17.9% 19.2% 38.6X Tablea A 4.19:. Prcyi.uc de la Psuvrete aelom CWtai W2rcteitquu du Clii de Manae (CM) M o M PA PEE S P A UV E E S ZPAUVIE S tlCAR MITRE VltLES EMAI. DDAR AUPtE VILLES auRAL OAK" MITtE VILLES RUAL Passeda Kadin, A coudre OuI ......................24546 19474 m1 1344 1446 2944 5.2Z 6.9X 27.62 Non .....................1 4718 112001 29n 21351 219" 16668 13.7X 16.42 38.2X Passede Ref r twretr OuI ..................... S1196 22973 3605 1016 4S2 125 2.1X 1.9X 3.4X Man ..................... 10066 106502 273859 21610 22979 169508 16.72 17.5X 38.22 Possed. Culsuniere Oul ..................... 17567 4930 565 34 123 O.ZX 2.4X O .OX thn ..................... 141697 126545 276899 22661 23309 169632 13.8J 15.62 38.02 POssed tltleur. Oui ..................... 6440 1170 213 53 0.0O 4.3X 0.02 4 Non ..................... 1ZZ824 t30305 27TZSi 2269S Z3379 169632 12.9X 15.Z 36.0X Passed Telephowe out ..................... 16465 S331 456 37 0.2 0.OX 0.OX Mon ....... . ............142799 12614 277006 22658 23432 169632 13.72 15.72 38.0X Tableau A 4.19: Prevalence de la pauvrete sdon cartaines rUsques du Chef de Menage (CM) WMO PAUVJtES PALNRES DAKAt AUTPES MILIEU DAKAR AUTES NILIEU VI LLES MAtAL VILLES UAAL Taltte nycarvw cu manag g *eno ........e...... 159264 131475 27746 22695 23432 169632 Noyewie ................ ?.7 8.5 8.6 12.2 11.0 10.4 Mo bre *owen de noypsa # *eq ..............m . 159264 131475 27746 22695 23432 169632 MoysM ................ 1.4 1.5 1.6 1.9 1.7 1.7 oabre pieces A usg d'habitatfon *a*erSr.................. m g159264 131475 277464 22695 23432 169632 M oys.3.1 3.7 4.0 3.6 3.8 4.3 Nambre de persoanes/plece * *a nag .............. 159264 131475 277464 22695 23432 169632 Noy w .. ................ 2.9 2.5 2.3 4.0 3.3 2.6 Sambre enf onts 0 5 wn * manage . 159264 131475 277464 22695 23432 169632 Moymma. 1.4 1.6 1.9 2.7 2.4 2.4 Nmbre enfants 0 14 *n OD * mages. . 159264 131475 277464 22695 23432 169632 No v.r. 3.2 3.9 4.2 6.0 5.8 5.4 obre adultes 15-64 *mnaes* .............. 159264 131475 277464 22695 23432 169632 Norm n .4.3 4.3 4.0 5.9 4.9 4.6 O bfr de vlsux 65 *w au + * manage . 159264 131475 277464 22695 23432 169632 NoYwie................ .2 .3 .3 .3 .4 .4 abre gleves a m .nage. 159264 131475 277464 22695 23432 169632 Noyenn .1.7 1.9 .5 1.6 1.8 .5 NoUbre cansemateurs 9 manage. 159264 131475 277464 22695 23432 169632 Noysru .6.0 6.5 6.4 9.1 8.1 7.7 Tableau A 4.19: Prevalence de la pauvrete selon certaines caracteristiques du Chef de Menage (CM) N PAtNYtES PAWtES DAKARt AUTRES MILIEU DAKAR AUTRES MILIEU VILLES hEAL VILLES RURAL WoSbres d ho em f U gena es .............. 159264 131475 277464 22695 23432 169632 Nowmn ................ 3.8 4.1 4.0 5.9 5.4 S.1 Iombre de fos U *n9 ................. 159264 131475 27744 22695 23432 169632 Nopui ................ 4.0 4.4 4.5 6.3 5.7 5.3 hmbre * tf m a manages.............. 159264 131475 277464 229S 23432 169632 NoYWw ................ 4.0 4.4 4.S 6.3 5.7 5.3 mnuet lt/p erswm ee ..............mag 159264 131475 277464 22695 23432 169632 Noyamrw ................ 28786.1 16588.2 7051.4 4433.0 3444.3 16K4.6 >-s A-50 Table A 4.20: Urban Non-Food Expenditures by Expenditure Level soo 450 400 350 300 _ - a -transport 300 250 - clothing 200 f health 1 SO0 - oducadon 100 50 0 1000 1999 2999 3999 4999 5999 7499 1999 NH Sanding Lao than ... Pcr Capita per Month Table A 4.21: Rural: Non-Food Expenditures Per Capita by HH Total Expenditure Level Soo Soo_ 400 -- tranwport 3- clothing 300 ~~~~~~~~~~~~~~--health 200 . oducation 100 0S o= 1000 1999 2999 3999 4999 5999 7499 9999 NH Spending La" than ... Per Capita Per Month ANNEX B POLICY ISSUES AND MISCELLANEOUS TABLES Table Bl RuMI Cost of UsiAg InM for SaMetal OR Grouadmu Rioe Rati Index Groundnt Groundnt Yldd Yid. Ws. (1) (2) (1Y(2) (8081-100) Production Yedd Index Index W0e81 44 80 0.55 94.4 520 489 57.0 53.8 81/82 60 103 0.58 100.0 866 858 100.0 100.0 82/83 S0 130 0.38 66.0 1139 997 116.2 76.7 83/84 50 130 0.38 66.0 S70 528 61.5 40.6 84/85 60 160 0.38 64.4 669 780 90.9 58.5 85/86 90 160 0.56 96.6 590 993 115.7 111.8 86N87 90 160 0.56 96.6 821 1041 121.3 117.2 87/88 90 140 0.64 110.4 946 1139 132.8 146.5 8mm89 70 130 0.54 92.4 717 810 94.4 87.3 89/90 80 130 O.S4 92.4 819 1072 124.9 115.5 90/91 80 135 0.59 101.7 678 766 89.3 90.8 91/92 80 13S 0.59 101.7 701 831 96.9 98.5 92/93 80 135 0.59 101.7 457 485 56.5 57.5 Avrage 8WS1 -92/93Yield- 830 Average 80181-92/93 PFoduction 730 (1981 cboen as bane year became yied for thrA year i cle to avempge 81-93 yied Sousces: Kke (March 1993) for groundmi pnre and yield daft; Diroebon de Commerce uad Maeconomic Updae Repoit for broken nre pnce. B-2 Table B2 Fertilizer and Groundnut Seed Purchases by Households in Five Southern Regions, 1992 Average Quantity AvergP Percent Purchasing Purchased (kg) Area Planted in 199 Region Fertilizer Seed Fertilizer Seed (ha) Fatick 19.2 40.3 430 288 7.0 Kaolack 34.2 49.9 379 290 8.1 Tambacounda 40.0 29.1 277 214 6.0 Kolda 44.9 23.4 228 205 3.7 Ziguinchor 8.3 6.9 198 115 1.9 Total 30.0 34.9 315 264 6.0 Source: Kite et al, 1993, (page 44). Table B3: Trends in Fertilizer Use Crop/Fertilizer Type 1980/81 1985/86 1989/90 Millet/Sorghum/Maize 26,820 8,582 3,119 Irrigated Rice 8,290 6,400 2,365 Urea 9,763 Total Cereals 35,110 14,982 15,247 Groundnuts/Cowpeas 29,600 4,200 2,966 Cotton 5,100 7,900 4,536 Other 2,425 Total 74,680 27,082 26,345 Farm Price (CFA/kg) 25 105 80 Percent Subsidy 61% 16% 0% Source: USAID/Senegal. 'Senegal Agricultural Sector Analysis." 1990. Page 64. B-3 Table B4: Faumers' First Choice on How to Spend Money First or Second Choice Kelly Goetz Buy food 67.8 75.3 Buy groundnut seed 82.2 47.5 Buy/repair equipment 16.7 25.3 Hire labor () 20.2 Buy fertilizer 7.8 9.1 Livestock 8.9 (*) Other 11.1 37.9 No. of observations 90 198 Source: Kelly (1988) and Goetz (1990) as quoted in USAID/Senegal. 'Senegal Agricultural Sector Analysis.' 1990. Page 67. TABLE B5 SUMMARY OF PRICING POLICIES ON KEY COMMODITIES (PRIOR TO DEVALUATION) NOJQFS TRANSFERS W.O M IUCE *Prices et by Goverunev. OConsumers pay the GovementA CFAF *In theory, rice producers in the Fleuve 'Urban confmner who represent *Producer priCe - 5 CFA/kg 11 billion in the form of taxes. Te Region who represent about SSI of the approxinutely 38% of the population'. OConumr priCe - 135 CPA/kg. average tax and perequation per person is total population. In practice, in rect 66% of the urban population resides in *Total ain perquation taxes - 39 % of equal to CFAF 1467, and 2161 if one years producers did not receive full Dakar nd Thice. CIP price' (19.3X import duty and 20% includs ovcrhead. nubsidy. In 89/90, rice poduction perequatio to CPSP which will vary accounted for les than IS of total *Rice purchass repteant about 17% depending on world prices) *A portion of CPSPs revenue goes to cultivated aea.' of very low inclome houshokd' budgets * In addiion, CPSPs overhead coa are SAED, which cuAetly operated under a in urean rama. extremely high - 20% of the CIF price or deficit. In 1991, SAED received CFAF 3 OThe Tresury and CPSP. 14.6 CFAP per kilo and one could billion in subsidie for lcal rice oRice accouau for 4045% of caloric consider e ponion of ths onopoly production. intake in some urban area'. rents. MILLET/ *Neither retail or wholesale prices re set *None *None SORGHUM by the Government. However, CSA, is Sub-optimal production of millet and mAndated to intervene if producer prices sorbum. drop below 55-65 CFA/kg. High rice pricesm e as implicit protection for milt/shum ahhough increasing evidenwe point to nonprice fwAor in theo consumntioof rice as oppoaed to local cereals. ONo formal taxation. IWorid Ban} Senegal Country Team estmae. 2Based on total population of 7.4 million. 3Population as of 1988. USAID Agrcutural Sector Analysis, Septemnber 1990, p.22. 4USAID Agricukund Sector Analysis, p. 1 SWorid Bank, Senegal Macroeconomic Update Report, July 1992, SA Table 1. 6USAID, Agriculural Sector Analysis, Septenber 1990, p. 21. 7Priority Survey data. Other esimates for all utban households have been higher, in the range of 37% to 66% budget shares according to; Pearson, Scott R., J. Dirck Stryker, Charles P. Humphreys, Rie in W. Afri, Stanford University Press, Stanford, California, 1981, p. 277. RBased on the Final Report IPPRIISRA Study on Consumption and Supply Impact of Agricukural Price Policies, funded by USAID, 1993. LC:UFS TRANSFES MLS MAIZE *CSA, Commissariat I la S6curit None None None Alimientaire, responsible for maintaining producer floor price at 55-65 CFAAkg. *No formal taxation. GROUND- *Producer prices arem rateed by *Fron taxpayen to groundnut producer. *Peanutproducerswhoprimtrilymreidein Taxpayers. NUTS Government nd are above the word the Groundnut Basin (including regions of market price. However, ineffliciences and Diourbel, Kaolack, Thiea, and Loug&), high fixed coat in the opeation of the accounting for low than 32% of the rural grund-t processing p , population'. SONACOS, cut into producer pices. *In 1992, producer prics wete raised fkom 70 to 30 CFA/kg. *Official government mbdies for seed, ferilizer and equipment were abolished in 1986. WHEAT *CPSP has a monopoly on wheat ipomu. *CFAF S Billion from cormers to the *Govemrnent *Mosly Urban Consumners who A French milling company has a Government. represent 38% of the total population. 'convention speciale for the procesn *French nilling monopoly. and marketing of whest. OSome portion of the 5 billion miling fee *Urban consumrer derive between 4- to milling comnpny. An indication of the 7% of calories from wheat and spend as 6Oovernuient fixes regionul prices which excessive coats is evident in the fact that much as 13 % of their budgets on ane mome than doubk the wodd price, the cost for milling whet is 80%45% tie wheat'o. U price of landed wheat. *Inport duty on wheat iryst is approximately 46% *Tax per urban inbabitant (not including dte mnopoly rent) is CFAF 1778; with the satire milling fee itclded this inceases to 3556. 9USAID, Agriculur Sector Analysis, September 1990, p. 22. lOWorld Bank, Senegal Macroeconomic Update Report, July 1992, SA Table 1. B-6 o  I o :i a j iii ! I- I 1111 * * a I a*eI I hilt 00 I  ___________ I 1I*I*I S F ii . ii jI jIJ ii ill .10 1 * L *Ii 1.11 *; 1i1 - .5. (.4 I.i  fl ii __________ _________ 11 B-7 Potential Impact of Pricing Policy Modifications on Poverty (Pre-devaluation Scenarios) 2.1 The previous section demonstrated the regressive nature of much of the consumption taxes in Senegal. What would happen if some of these taxes, monopoly rents were reduced? Table 3.6 shows how the poverty line and the incidence of poverty shift when prices for rice, sugar, and vegetable oil are changed under six different scenarios.' Much of the analysis focuses on rice price changes as rice represents the single most important source of calories for many Senegalese, as well as the most important expenditure item. Moreover, as was detailed in Chapter 1, rice constitutes a larger share of total caloric intake for lower income groups than for the relatively well-off, who have more varied diets. 2.2 Dakar is most striking as poverty is cut by two thirds under the first scenario in which rice, sugar, and vegetable oil are reduced to their nominal border prices. The lion's share of this change stems from a change in the rice price. Removing sugar and vegetable oil and only considering a rice price reduction to its nominal border equivalent (Scenario II) only changes the Scenario I expenditure results by one percent for each expenditure group. The differences in poverty incidence are also minor between Scenarios I and II. 2.3 Because of the overwhelming importance of rice, the remaining scenarios deal only with modifications of the rice price. Scenario II assumes a demand elasticity for rice of -0.6. Although inelastic, some observers question whether such a coefficient is inelastic enough. Although low world prices have played a role in raising rice demand in Senegal and elsewhere in West Africa, the interrelated effects of urbanization, ease of preparation (relative to locally-produced cereals), and rising incomes may have contributed to greater price inelasticity than was perhaps true ten or twenty years ago.2 For these reasons, Scenario III incorporates a more inelastic demand coefficient of -0.3. The effects of a rice price reduction are still substantial, especially for Dakar. In Dakar, the poverty line falls by 15 percent, while it falls by 6 to 8 percent in other parts of the country. The other two scenarios highlight the importance of the rice perequation, more than taxes or tariffs on poverty. ' 2.4 Scenarios IV and V deal with changing different parts of consumer rice pricing policy - the pbrfquation and other taxes and tariffs. Using CPSP-provided data for a period roughly concurrent with the Priority Survey data collection period, Kite (1992) calculated that the pgrequation on a ton of rice with a retail price of 135,000 CFA/MT in Dakar and an exchange rate of 280 CFA to US$1 amounted to 31,937 CFA/MT, and taxes and tariffs totalled 14,077 CFA/MT. The p9r,0quauion represents 24 percent of the final retail price while other taxes and tariffs account for I Although we have figures for taxation of wheat imports, no analysis was performed incorporating changes in the bread price because we do not have any milling budgets that allow us to estimate costs of wheat acquisition as a portion of total bread costs. 2 Very possibly, the own-price elasticity of demand for rice may be more elastic in rural Senegal than in urban areas depending on the season and availability of millet. During certain times of the year miet is not available, and rural residents rely on imported rice. At other times of the year, demand may be very elastic because of good harvests of local cereals. This analysis does not dcal with thi issue. I Own-price demand elasticities should only be viewed as ranges, not precise estimates. While the rice demand elasticity used (-0.6) in all scenarios except Scenario III was estimated in an econometric model by Delgado and Reardon (1992), elasticities for sugar nd vegetable oil are only assumed. While demand for these staples is almost certainly inelastic, precise coefficients have never been estimated for Senegal. Given this, a highly inelastic figure of -0.3 was chosen as a cautious minimum ('cautious as the more inelastic the coefficient, the less responsive the effects of price changes on quantities consumed). B-8 roughly 10 percent of the retail price. Removing the pgrgquation alone (Scenario IV) lowers the poverty line by nearly 21 percent in Dakar and poverty incidence from 12.5 to 5.6 percent. It is therefore the component of the consumer rice price tax structure with the most influence on poverty, especially in Dakar. This points to the potential gains from increasing the efficiency or reducing the role of CPSP. In Scenario V, the pbriquation is retained, while other taxes and tariffs are removed. Although the effect is substantial, removing the ptrgquation is more significant. 2.5 It should be noted that although the piriquation is nominally a tax, consumers were effectively subsidized through exchange rate overvaluation, and the pjriquadon recaptured some of this implicit subsidy.' In 1993 the GOS consumer pricing policy for rice was designed to maintain a Dakar wholesale price of 122 CFA/kg and a retail price of 135 CFA/kg in the face of world price and exchange rate fluctuations.5 Kite (1992) calculates that the pdr'quation "tax" would be wiped out if the local currency price of imported rice rose by 50 percent. This could be occasioned by either a rise in the dollar-denominated world price, exchange rate depreciation, or some combination of the two amounting to a 50 percent increase in the CFA-denominated landed price.6 2.6 This analysis should be viewed with caution for several reasons. First, the analysis is very partial and does not consider income effects of the various scenarios on overall ability to purchase a minimum daily caloric intake. The income effects of any significant change in the rice price (primarily on consumers as there are relatively few farmers selling rice) may be quite important as it is the single largest expenditure item for Senegalese households. Second, while own-price effects are taken into account, cross-price effects (both in consumption and production) are ignored. Prior econometric analysis of food demand and supply response in Senegal has generally had disappointing results in terms of coming up with statistically significant cross-price relationships between the price of rice and other crops such as millet production. This stems largely from the difficulty of performing statistical analysis in a price-controlled environment where neither spatial nor temporal price variability can be readily observed for a number of major purchased food items, especially rice. Changing the rice price may affect other producer prices (and hence farm incomes) which render results that look solely at consumers more ambiguous for rural residents who are net food sellers.! However, recent work by Kelley (based on IFPRI/ISRA houshold survey) showed that higher income rural residents not in market towns were more likely to sell large amounts of cereals than low income residents. 4 Various analys have estimated CPA overvaluation at 30 to 50 percent for Senegal (Ks and van de Walle [1991] u quoted in Reardon et a [1992]). ' A transport subsidy (approximately 4 percaet of the Dakar wholesale price) im also appLed to even out regional price variability although this is in the process of being phased out. ' For this reason, no scenario was run altering the exchange rte. Such a scenario would only make sene if the authorities simultaneously decided to raise controled wholesale and consumer price by a commensurate amount 7 An added complication concern urban-nual tranfer. These have become very importnt income sources in some rural areas. Changes in urban income due to fluctuating food prices could be felt in rural areu through changes in the ability of urban income euaner to send transfers to rural dcpendents. B-9 Table B6 Changes in Povert Incidence and Average Monthly Per Capita Expenditure Required to Attain the Povert Line Resulting from Major Food Commodity Price Changes Percent of l Percent of Percent Dakar Percent Households | Percent Households Percent Households Urban Change Poor er Urb Change Poor Rural Change Poor Bae Scenario 5,610 12.5% 4,334 13.2% 2,651 36.6% 1 I 4,165 -25.8% 4.3% 3,523 -18.71% 7.5% 2,326 -12.26% 30.5% n 1 4,232 -24.6% 4.7% 3,602 -16.89% 7.7% 2,355 -11.17% 31.2% i m 4,755 -15.2% 7.3% 4,016 -7.34% 10.9% 2,486 -6.22% 33.8% o IV 4,455 -20.6% 5.6% 3,855 -11.05% 9.4% 2,432 -8.26% 32.9% S V 5,168 -7.9% 9.7% 4,165 -3.90% 11.9% 2,557 -3.55% 35.2% S o VI 6,195 10.4% 15.6% 4,847 11.84% 18.2% 2,858 7.81% 40.2% Scario 1: Consumer rice, sugar, and vegetable oil prices reduced to their nominal border prices; Scari ii: Consumer rice price reduced from a protected priee to its nominal border price and assuming an own- rice elasticity of demand for rice of -0.6; ScmnarZo m Same as Scenario n, only the own-price elasticity of demand for rice is lowered to -0.3; Scenario IV. CPSP rice price stabilization (pdrdquation) policy removed, but no other price changes; Scenario V: All rice taxes and tariffs removed, but the CPSP stabilization tax is retained; and senario VWl Consu B-10 SENEGAL'S EMPLOYMENT POLICY 2.7 The labor market in Senegal is highly segmented between the formal and the informal sector. In spite of efforts to reform labor regulations, the formal sector remains subject to a rigid system of regulations which result in burdensome transaction costs for firms which are subject to regulation on hiring, firing, setting wages, working conditions and agreeing employment contracts. In the formal sector a minimum wage (SMIG) is determined by a tripartite commission consisting of the Ministry of Public Affairs and Labor, CNTS1, and various other employer associations. This commission also determines the bonuses and supplements employers will add to the base salary, and demonstrates the key role played by unions in determining working conditions and guarantees in the formal sector. Bonuses and supplements represented 25% of nominal wages for unskilled workers and up to 80% for professional and managerial employees between 1980 and 19902. Thus, supplements are determined not as much by performance as by legislation. For example, Article 44, awards a bonus for night work, continuous work of 10 hours and overtime work exceeding 3 hours. Article 46 grants a transportation bonus equal to cost of 95 trips on the 1st three sections of public sector bus transport, or approximately 9,000 CFA per worker in 1987/88. 2.8 Wage Differential Between Formal/Informnal Sector. In contrast to the formal sector, In 1990, average monthly revenues in the informal sector averaged between 21,000 CFAF and 40,000 CFAFI. Civil service salaries for unskilled workers were 30% greater than informal sector wages, while salaries for management level employees exceeded average informal sector wages by more than 8 times4. The wage differential between the informal and modern sectors demonstrates that wage policies do not benefit low-income groups. The government mandated minimum wage (SNUG) and other restrictions on hiring and firing has resulted in fewer employment opportunities, with higher salaries for those fortunate to obtain jobs in the modern sector. As a result, employment levels between 1980-85 (stabilization period) increased at an average annual rate of 2.7%, substantially lower than growth in the overall supply of labor'. In 1990, the size of the informal sector had grown dramatically, representing 21 % of the active labor force'. 2.9 Layoffs. Before being able to lay workers off, employers must seek the opinion of the labor inspector, who determines whether the layoff is warranted. The Labor Office does not consider economic hardship as a valid reason for laying off workers. The inspector has up to 45 days to approve or deny a proposed layoff. Employers and workers have the option of appealing a ruling by 1Confederation Nationale des Travailleurs du Senegal, a principal employer association. 2Terrell, Katherine, Jan Svejnar, The Industrial Labor Market and Economic Performance in Senegal. A Study in Enterprise OwnershiR. Export Orientation, and Government Recommendation. Westview Press, Boulder, 1989, p.14. 3Niane, Thiemo Seydou, Situation Economiaue. Conditions de Vie et Strategies de Survie au Senegal. p. 6. The monthly SMIG in 1990 was approximately 30,000 CFAF. 4World Bank, Senegal Macroeconomic Update Report, July 1992, SA Table 22. 5Confederation Nationale des Travailleurs du Senegal. 6Direction de la Statistique, Recensement et Svnthese des Etudes sur Le Secteur Informel. May 1991. B-il principle, the decision of the Labor Directorate can be appealed to the Supreme Court, but often represents the final verdict. 2.10 The Labor Code permits three types of employment contracts: fixed term, seasonal and permanent. In order to encourage long-term employment, the government has discouraged the use of fixed term and seasonal contracts. If an employer extends a fixed-term contract more than twice for the same employee or if an employer abuses the use of seasonal contracts then the contract is automatically converted to an open-ended contract. Temporary labor contracts was significantly relaxed in 1989. Temporary contracts can now be extended up to 5 years. For firms in the industrial free zone, the length of temporary contracts is unlimited. B-12 Table B7: Senegal - Composition of Active Labor Force Professional Occupation (PS) Agriculture 65.27% Commerce 13.80% Public Service 9.57% Mechanic 2.34% Transport 2.12% Public Works 1.98% Wood 1.43% Food 1.27% extile 0.64% Undefined 0.59% Construction 0.24% Bank 0.17% Chemicals 0.15% NGO/Politics 0.12% Diplomat 0.11% Extractive 0.07% Printer 0.07% Drinks & Tobacco 0.05% Chart BS: Constant GDP Per Capita 600000 500000 ,. 400000 W 300000 , U U - U - -url . 200000 * * * * Rural/2 100000 , Urban/1 0 amo A 1970 1979 1985 1989 1990 1991 Source: GOS, Tableau du Bord B-13 Figure B.1 Evolution of Public Employment in Senegal, 1981- 1992 90 '0 40 10 91 92 93 94 95 95 97 89 99 90 91 92 Year Figure B.2 Evolution of Public Sector Wages, 1980/81 to 1989/90 (In Constant 1987 CFA) 210 200- 190- 499- ISO 190 140 110 60Si 0V12 IV83 OW4 04/N SS'10 0387 07V0 0/0 0W00 Y_ Table B9: Selected Macroeconomic Indicators, Senegal, 1981/82 to 1990/91 Indicator 1981/82 1990/91 1. Fisal deficit as % of GDP 10.8 1.7 2. Current Account deficit as % of GDP 24.3 7.8 3. Resource Gap (CFAF billion) 130.2 63.4 4. Total public expenditure as % of GDP 28.1 18.7 5. Revenues and grants as % of GDP 20.0 18.2 6. Tax revenues as % of GDP 18.5 15.2 7. Non-tax revenues as % of total rcvenues 8.0 19.8 8. External finance as % of total public expenditures 31.8 41.0 9. Non-capital extenal finance as % of total extemal finance @0.0 59.3 10. Non-capital external finance as % of total public expenditures @0.0 25.0 11. Investment as % of total public expenditures 40.0 32.0 Sources: For itaems 1,2, and 4-7, Macroeconomic Update (1992); for items 3 and 8-11, Public Expenditure Review (1992). Items from the PER use 1989190 data for the latter year (except no.9, for which data are from 1987/88). Table B10: Comparative Costs of Factors of Production (Dollars per Unit) w Fringe Indutrial Conatnietio Sea Freight Wage Rate Benefits (% Electricity Water Telephone Rates a Coat (20 ft Air Freight Regular Petrol (hr) of wage) Rates (Kwh) Rates (m3) (per mimute) (m2) conaainer) (kg) (iter) Average Competitor I.08 38 0.10 0.75 2.07 160 1,680 1.56 as Median Competitors 0.69 35 0.07 0.52 1.60 150 1,400 1.80 Da CFA Average 0.69 37 0.18 0.71 3.90 233 1,537 2.35 1.01 Non-CFA Average 0.50 37 0.07 0.86 1.69 172 1,593 1.55 0.37 Senega1 1.02 35 0.26 0.93 3.14 31S 1,700 2.50 1.31 Ghana 0.45 25 0.03 0.70 1.61 na 1,500 0.85 0.51 Nos: Compettors incu in the frst two lines are: Dominican I epublic, Honduras, Gutitemgla, P uritius, Cost Rica, I ny Ua,Wy`ia, Hong Kong, an ThailandC Senegal and Ghaa figure. are for 1992, while those of conpetiton ae for 1989/90. Telephone ratea are to the major trading partner. Source:IFC, 'Senegal: Private Sector Asessment. October 1992. (Page 20). B-15 Table Bll: Dakar Infonnal Sector Growth Percent 1975 1991 Change Number of Enterprises: Production 5,456 14,978 174.5% Services 1,226 3,730 204.2% Handicrafts 1,065 1,997 87.5% Total 7,747 20,705 167.3% Number Employed: Production 17,267 32,033 85.5% Services 3,988 9,326 133.0% Handicrafts 2,388 4,333 81.4% Total 23,643 45,692 93.3% Average Number of Employees: Production 3.16 2.14 Services 3.25 2.50 Handicrafts 2.24 2.17 Weighted Average * 3.05 2.21 * Weighted by numbers of firms in each category. Sources: For 1991, Direction de la Commerce, Recensement National de PArtisinat. For 1975, Ministere des Finances et Affaires Economiques, Direction de la Statistique, Enquese sur les Structures et l'Exploilation de l'Artisinat en Mileu Urbain 1973-77. June 1977. (as quoted in Lubell, 1990). Table B12: Reason Why Associations Granted Credit Percent of Responses Reason Urban Rural Total Social Needs 73 78 76 Lean Season 20 43 34 Lodging 17 4 9 Production 40 17 26 Commerce 27 9 16 Livestock 17 4 9 Source: IBRD, Le Secreur Financier Informel au Sn'nigal et Ses Implications pour le Developpement du Secteur Financier. 1991. Table B13: Regional Social Indicators RFgion hTuunization Average Real Pcr '000 '000 Acccss to Gross Students Malnutrition (6-59 months) (0- 1) Pet Capita Capita pewpic pe/ Potable Prinur /Clab Annual Public Reath Health Water y (1988) Private Health Post Hut (1988) School Health Expenditure Earoll Expenditu ment ._ Meas DP13 Stunting % Wasting % les I_(chronic) (seasonal) %- -= ===== 70 Male Femalc Mal Fernle Dakar 68 71 8,961 705 19 4 91% 95% 58 26.1 28.0 6.8 5.3 Ziguinchor 77 80 4,087 1,158 6 a 30% 104% 62 26.1 20.7 8.4 L.' Diourbel 64 62 4,496 714 13 6 86% 27% 50 46.4 48.4 5.2 1.7 St. Louis 47 49 3,065 1,105 6 2 78% 50% 42 28.0 27.7 10.4 10.7 Tambacounda 35 23 2,957 797 6 2 25% 35% 58 30.3 40.9 8.6 1.1 Kaolack 58 56 3,418 660 13 20 87% 38% 65 37.6 27.2 2.0 2.6 _~~~~~~___ ___ _ ' Thies 63 69 3,587 792 13 18 88% 59% 57 28.8 17.8 3.1 1.4 Louga 56 56 2,699 753 8 2 85% 31% 53 37.7 24.3 11.9 12.4 Fatick 63 54 2,596 500 9 4 85% 49% 46 19.2 16.1 8.0 4.8 Kolda 43 50 2,403 312 12 17% 42% 59 28.8 40.0 3.0 3.9 f Rutal _ _ _ | 38% 351| 32.1 6.5 4.1 i Urban ______ I _______ ____ _____ _____ Jover | 2300 21.8 6.4 5.1 ~~~~~~~~~~~~~~~~90X i- I - I lbEpniucft Pro S d | 58% | 30.2 28.0 6.5 -45 Annual Per Capita ea dtures from ririty Survcy data. Public Health Expenditures are total current expenditures for 1989/90. From Statistical Annex, Table 19, Issues in Health Care Fnancing, IBRD, 1992. Immunization is % of childrmn under one year that rcceived two of the target diseases of UNICEF Expanded Program of Immunization: Measls and D131 (third sho of Diptheria, Pertusais, Tetanus); Malnutrition: a child ias clasified as nalnourished if his anhropometric measurement falls at or below - 2 standard deviation units frorn the median of the reference population. Stunting (Height-for Age) indicates chronic nutrition problems. Wasting (Weight for height) indicat evecre nutrition problems and is sensitive to seasonal changes of food availability. Priority survey data which covered the period November, 1991 - January, 1992 considerd the pte-harvest season. Table B14: Regional Human Development Index REGIONAL IIUMAN DEVELOPMENT INDEX: Perceniatpe Difference fromn Best Reeional Indicator Malnutrition 0-5 Potable Poverty Wasting Stunting Measles In Illiteracy HH/School Water Urban Rural HDI HD12 HDU3 HD14 0 Best Rank I 2 3 4 5 6 7 8 ( ...8) (2,3,4,6,7,8) (2,3,5,6,8) (2,3,4,5,6) Dakar 186% 53% 12% 0% 243% 0% 67% 560% 198% 374% Ziguinchor 129% 32% 0% 25% 0% 67% 200% 265% 717% 589% 364% 124% Diourbel 57% 168% 17% 90% 262% 5% 0% 54% 654% 335% 508% 543% St. Louis 400% 58% 39% 71% 68% 14% 44% 0% 693% 226% 178% 249% Tamubacounda 148% 98% 55% 88% 39% 73% 128% 201% 829% 642% 466% 352% Kaolack 10% 84% 25% 71% 129% 4% 132% 229% 683% 544% 470% 313% Thies 0% 27% 18% 55% 113% 3% 64% 76% 356% 243% 237% 216% Louga 476% 77% 27% 90% 126% 7% 87% 212% 1102% 499% 449% 327% Fatick 205% 0% 18% 73% 55% 7% 228% 218% 803% 543% 297% 152% Kolda 62% 93% 44% 87% 20% 81% 232% 288% 907% 826% 526% 325% I\Since urban and rural poverty in Dakar are grouped together, the same rate of household poverty has been used for Dakar urban and rural for the purpose of this index. w *YEIGHTED Malnutrition 0-5 Potable Poverty Wasting Stunting Measles In Illiteracy HH/School Water Urban Rural HDI HD12 HDU3 HDI4 Dakar 0.11 0.08 0.05 0.00 0.23 0.00 0.06 0.04 0.56 0.24 0.39 0.35 Ziguinchor 0.08 0.05 0.00 0.04 0.00 0.26 0.17 0.16 0.75 0.68 0.47 0.34 Diourbel 0.03 0.24 0.07 0.14 0.25 0.02 0.00 0.03 0.79 0.47 0.61 0.72 St. Louis 0.24 0.08 0.15 0.11 0.06 O.05 0.04 0.00 0.74 0.48 0.36 0.46 Tamnbacounda 0.09 0.14 0.21 0.14 0.04 0.28 0.11 0.13| 1.131 0.99 | 0.80 | 0.81 Kaolack 0.01 0.12 0.10 0.11 0.12 0.02 0.11 0.14 0.73 0.57 0.50 0.47 Thies 0.00 0.04 0.07 0.08 0.11 0.01 0.05 0.05 0.42 0.32 0.28 0.31 Louga 0.28 0.11 0.11 0.14 0.12 0.03 0.07 0.13 0.99 0.53 0.50 0.50 Fatick 0.12 0.00 0.07 0.11 0.05 0.03 0.19 0.14 0.71 0.59 0.28 0.26 Kolda 0.04 0.13 0.17 0.13 0.02 0.31 0.20 0.18F 1.18, 1.15 0.82 | 0.77 a/ Calculations equal percentage difference from lowest (best) indicator, which is then weighted. B-18 Table B1S: Percentage of Population Malnourished I I 0.35 0.3 Ura 0.25 Chronic severe- height for age Weight for age Weight for Height Table B16 :Regional Profiles Dakar (urban) Ziguinchor Diourbel St. Louis Tambacounda Kaolack Thies Louga Fatick Kolda % Households 12.5% Urban: 22% Urban:8% Urban: 11% Urban: 17% Urban: 17% Urban:12% Urban: 14% Urban:25% Urban:25% (HH) Poor Rural: 54% Rural:23% Rural: 15% Rural:44% Rural:48% Rural:26% Rural:46% Rural:47% Rural:57% %of Tota HH 12% (urban) 7% 6% 4% a % 15% 9% 10% 11% 15% Poverty (Rural) _ E; i___E__Ei_ Poverty Line 5610 (urban) 2526 3210 3072 2525 2746 2637 3393 2248 2269 (Monthly CFAF per capita) at 1992 prices Health/Educatio * Highest level * High primary * Very low * Higher incomes. But, * Low level of * Low * Sensitive to * Suprisingly * High levels of n of primary school female literacy those without remittance infrastructure levels of seasonal, low rate of malnutrition education but cnrollment rates rate/high chronic income arc very much at * Low access primary internittent stunting * Lowest level of crowded *Low access to malnutrition rate risk (women whose to potable school malnutrition (chronic public investment classes/few potable water husbands have failed to water (25 %, enrollment (as opposed malnutrition) in health health posts to (30%) send money, possibly * Lowcst level * High rate to chronic) * Low dispense pastoralists, the very poor of both DPT3 of AIDS DPT3/Measles generic drugs who cannot even get and measles * Drought vaccination merchant credit) vaccination in recent years Characteristics Poverty * Severe poverty * Well monetized, * Well monetized with ISRA/IFPRI Many NGOs 1993 was concentrated in possibly because substantial substantial remittance study found arc third peri-urban of civil strife, internal/external income that lack of hcadquartered consecutive areas * Salinization of trade * Benefits from numerous infrastructure and active in year of (Guedewayc, soils presents donor/NGO interventions constrained this area, drought - Dalifor, problem around irrigated perimeters agricultural * Community incomes Pikine..). productivity, mobilization depleted Specific target causing forced generally groups. migration. strong Possible Targeted Increased overall Interventions on * Small number of very * Investment * Monitoring * Increased * Increased levels Poverty-Focused intervention for investment for nutrition and well targeted interventions in improved of nutrition investment in of investment in Options the poorest. the region litcracy targeted at to women/displaced infrastructure(r indicators soil health, female women pastoralists without ural roads), prcscrvation, edueation, off- rem ittances (merchants health, off-farm farm aetivitics often serve as channel for education activities remittances & may by able to identify poor) during the hungry sason (probably already some in place). Land (persons Constraint- Somewhat Constraint Low density but other than Not immediate Salinization Constraint Low density Constraint, Salinization per) High density Constrained perimeters low quality soil eonstraint of soils but generally partly beeause of poses poor rains- beeause slinization constraint little arabIc salinization on rabie land of soils land Persons Perkr2 2707 54 142 IS 6 51 143 17 64 28 B-20 Table B17: Change in class size by Region, 1983184 and 1988/89 Percent 83/84 88/89 Change Dakar 63 70 11.4% Ziguinchor 53 58 9.2% Diourbel 56 62 10.2% Saint Louis 45 50 10.1% Tambacounda 41 42 2.2% Koalack 56 58 3.3% Thies 56 65 16.4% Louga 48 57 18.1% Fatik 51 53 3.6% Kolada 47 46 -2.8% Total 54 59 10.0% Source: Ministere de l'Education Nationale, Actions d'nseigne-ment et e ormation. August 1990 (p.43). Table B18 Net Elementary School Enroll. 1990/91, Ministry of Education 100 90 80 70 60 so 40 30 20 4 10 0 3 Boys --Girls B-21 Graph B19 Inhabitants ('000) per Health Personnel 1988 300 250 PHAR - -- - / SAGEF 200- 1\\ / \ 200 NURSES-0----- NU S 100 0 0~~~~~ Graph B20 Access to Basic Hemth Carer Households Per... 160 r---;- --- ------- --......... 1400 - . --\ .- .- . 1000 --- .. -.-- .-.....-. i 800--- _ Health Cente 800 ~.-..-... 600 -H- a- H--lth Huts & Maħenitieg 40 -7 --------- -.-.^;- -.-.-. _ 400 .... . ... 200---. ; ....... 0 'S Y8 a , , y .SS I Households B-22 Table B21: Senegal: Selected Health Indicators, 1979 to 1990 Indicator 1979 1985 1989 1990 Vaccination' Mesles 41.2% 49.2% 61.1% 63.5% Tetanau 6.1% 12.1% 28.9% 33.9% Tuberculosis 64.0% 71.7% 82.8% 85.0% Whooping Cough 3.5% 7.4% 20.3% 24.1% Chid Morlity (0-4 yrs per 1,000) Urban-SaintLouis 155.9 123.1 68.9 na Rural - Niakhar * na 353.0 185.0 178.0 Rural - Ngayohemc 280.1 334.0 178.0 174.0 Hospitals Number 12 16 16 17 Persons per Unit 460,829 408,163 444,444 440,529 Hospital Beds Number 4,311 4,813 4,516 4,680 Persons per Unit 1,283 1,357 1,575 1,600 Doctos Number 413 395 407 407 Persons per Unit 13,398 16,533 17,472 18,437 Health Centers Number 35 47 47 48 Persons per Unit 157,999 138,949 151,300 156,300 Health Poss Number 471 580 659 665 Persons per Unit 11,741 i 1,260 10,791 11,262 Health Huts Number 626 1,301 1,490 1,665 Persons per Unit 8,834 5,020 4,773 4,498 * Vaccination figures and rural child mortality figures in the 1989 column are actually for 1988. Source: Adapted from Ministry of Economy, Finance, and Plan, Tableam de Bord Annuel de la Situadon Sociale am Snigak: Edidon 1991. B-23 Table B22: The Benefits of Educating Women Milieu Educational Level Literacy Indicator Urban Rural None [ Primary Secondar Illitratec Literatc Pertility 5.4 7.1 6.8 5.2 3.7 6.8 4.6 0-5'. with diarrhea last 30.6 42.0 40.0 32.2 20.1 40.3 25.0 2 wks At sistan ic "dur in delivery ______ _______ ___ __ _________ None 1.5 8.2 6.5 3.3 0.9 6.6 1.6 Doctor 1.7 0.4 0.5 0.8 6.1 0.5 3.1 Midwife 46.2 8.1 16.8 39.1 52.8 17.2 45.3 Health Center 34.5 11.3 16.9 30.9 32.5 16.8 34.0 Matron Assistance 8.1 8.0 7.8 10.5 5.7 7.9 9.0 Traditional Assistanc 2.6 26.2 20.9 5.5 0.9 20.5 3.5 Other Assistance 5.4 37.5 30.3 10.0 0.9 30.3 3.4 Pr na a VS i sits _ _ _ _ __; _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ _ __0 t it X . . None 4.5 52.5 41.7 8.0 1.9 40 9 4.8 At least one (*) 95.1 46.8 57.4 | 91.6 97.6 58.3 94.6 Post-Natal Prcventivo Child care (Percent of Children-0-5) __________ With Camets de Sante 44.5 12.1 19.1 41.0 52.3 19.0 48.9 Vaccinated (acc. to 42.4 11.4 17.9 39.7 51.8 17.8 47.9 Camets) _ Vaccinated (acc. to 38.8 39.1 38.9 39.0 39.7 38.7 39.0 mothers) (*) With either doctor, midwife or nurse, health center, or matrone. Source: Adapted from: IBRD, "Senegal: Women in Development Country Assessment and Strategy.' July 1992. B-24 Table 1'7?. -otal Expenditure by Function Classification 1981/83 - 1989/90 (Billion CFAF) 1981/83* 1986/87 1987/88 1988/89 1989/90 Public Service 46.6 62.7 63.2 60.1 66.2 Defense 23.2 28.5 29.6 31.0 31.4 Education 40.2 54.4 57.3 57.2 69.0 Health 10.7 13.9 11.2 12.7 15.1 Comm. Services 117.9 19.3 20.1 27.4 18.9 Economic Services 107.8 117.8 101.4 118.3 103.5 Agriculture 24.6 37.2 35.2 48.2 53.5 Transport 35.5 12.9 11.7 10.9 Unallocable 32.2 48.1 55.6 54.9 50.8 Total 278.5 344.6 338.4 351.5 354.9 VPeriod Averagc Table B24 :Public Expenditures on Education Year Itemn 1986/87 1987/88 1988/89 1989/90 1990/91 1991/92 Total Education Expenditure (Bn. CPAF) 54.4 57.3 57.2 69.0 NA NA Education Exp. as % Total Expenditure 15.8 16.9 16.3 19.4 NA NA Real Growth Rate NA 3.2 -1.8 18.0* NA NA Percent of Expenditure: _ Central Servies 4 4 4 NA S S Primary 46 45 44 NA 46 48 Middle & Secondary 25 26 27 NA 21 21 Teacher Training 2 2 1 NA 1 1 Professional Training 4 4 5 NA 2 0 Higher Education 1S 16 17 NA 24 24 Per Student Expenditure (In Thousands of Constant 1987 CFAF): Primary 39 37 37 NA NA 40 Secondary 119 125 118 NA NA 85 Higher496 496 466 581 NA 695 752 Higher/Primary Per Student Exp. Ratio 13 13 16 NA NA 19 -no ae ral speOing iDCrease in this year was ue to cvil service pay increases. Source: IBRD. 'Republic of Senegal: Second Humsan Resources Development Project (Education V). Staff Appraisal Report. Febnrary 1993. Page 33. B-25 Table B25: Public Expenditure Allocation to the Primary Sector, Actual 1990/91 Expenditures and Proposed 1995 Allocations (In Constant 1990/91 CFA billions) Function Actual 1990/91 Proposed 1995 Amount I Percent Amount Percent General Administration 1.3 1 1.00 1 Rainfed Crops 22.2 23 15.00 22 Imfigated Crops 37.6 39 15.65 22 Livestock 2.7 3 2.25 3 Fisheries 4.6 5 3.20 5 Forestry/Environment 7.7 6 13.00 19 Rescarch 4.6 5 7.00 10 Crop Protection 1.1 1 1.07 2 Rural Water 15.1 16 11.50 17 :3Tota-i$000000i}000;0-0$;0;0;0:$0 96.00000-0j$00;000: .W00;;009 100 60000t0|t0$0$9.67 100 Source: PER, pages 29 and 31. Proposed levels are rrom te PER. ANNEX C REVIEW OF THE PORTFOLIO Annex C: Review of the Portfolio SELECTED ONGOING BENEFIT/FOCUS ON POOR Targeted Paticpation PROJECTS (** means priority Intervention? Component? for poverty alleviation) 1. HUMAN RESOURCES DEVELOPMENT Human Resources Development - Promotes the status of women through adult literacy, building case foyers, support for women's groups. Only No No I (PDRHI), Credit No. 2255-SE targeting criteria used other than gender is to provide assistance to groups which have never received assistance. Closing Date: December 31, - Support for healh reform and use of generic drugs represents necessary program to alleviate large burden on poor 1995** households. Careful monitoring/assessment of cost recovery on rural populations should be carried out, and the possibility that matching public funds be geared towards income levels on a regional basis could be explored here. - project includes IEC campaign on generic drugs. We propose that special efforts are made to ensure that this campaign reaches rural areas with low monetary income (Fatick, Ziguinchor, Kolda, Tambacounda). Primary Education in Targets programs to help educate girls by selecting regions/areas where girls education is particularly low. Also, Yes, target Yes - loal Development (PDRHII, Credit important innovation in providing local control over budgets and over ideas to &prove educational quaity and girls indicators are school present No. 1735-SE enrolbnent may alleviate the burden of materials and supplies on private households. Will increase pupil/teacher and educational proposals to Closing Date: June 30, 1995** classrooms in regions - particularly in rural areas - where education levels are low and which have been and female improve disadvantaged from a public investment viewpoint in the past (Diourbel, Louga, Kolda). enrollment,not educational income quality indicators but poorest regions do benefit. H. AGRICULTURE l Agriculturl Services, Credit As proposed in the WID assessment, it is recommended that this project assist in developing an innovative No No No. 2108. Closing Date: agricultural extension program for women farmers. In light of regional differences, such a program might start in (i) Deocember 31, 1994 poorer rural areas and (ii) areas where women are increasingly holding primary responsibility for farming due to mak outmigration. Second Small Rural Operations OveraU project objective is to encourage local initiative and broaden popular participation and decision-making in No Yes, design Credit No. 2108, Closing date, rural investments. This was to include encouraging the formation of Economic Interest Groups (GlEs), supporting mc*nt to June 30, 1998** women's agricultural production, creating employment in rural arcas, trgthening the existing capacity for encourage identification and prepartion of small projects. At present, substantial progres has bcen achieved in restructuring paticpation and the project after some financial management problems and poor performance in inciting loal participation. The recent mid-term project wm now cxperiment with creating an autonomous private mnangement structure to improve bureaucratic review processes and transparcncy. The WID asessment for Scnegal proposcs that the project also support women's recommended agricultural marketing activities as weU as a wider range of activities in the rural infonral sctor that offer good that beneficiaries income opportunities (food conservation perhaps). We propose that the project consider supporting activities in be included in poorer regions, particularly during the hungry sason (if it is not already doing so). sub-project design. Agricultural Research Credit Proposed that thi research also cover the crops that women farmers are responsible for (WID assessment). No No No. 2107, Closing Date: December 31, 1995 Irrigation IV , credit 1855-SE, Meant to modernize and expand irrigation in the Senegal River Valley. To assist with the restructuring of SAED and No Yes, local Closing Date: June 30, 1994 the devolution of responsibilities to local organizations, and responsibility of credit from SAED to CNCAS organizations taking over responsibiities of SAED Hm. INSTITUTIONAL Improve SDA analysis and the institutional base to analyze poverty and to makc this data opertionally available. No - but wil No DEVELOPMENT This would requirc someone to assist users with the data and to provide local level information and analysis if scrve as the Development Management possibk. It might also involve aUowing the statistics office to charge for some such services, tool for better Project, Credit No. 1910-SE targeting Closing date: June 30, 1994** IV. INFRASTRUCTURE, |ENERGY, |TELECOMMUNICATION Municipal Housing One objective of this project is to improve the operations of the urban land market, through annual supply of a No No Development Project, Credit number of serviceable plots and to privatize the delivery of plots. It is important to understand the impact this might No. 1884-SE, Closing Date: have on poorer residents (including poorer female headed households). Towards this end, a beneficiary assessment involving consulting some of the poorer and squatter residents could shed some light on possible costs and benefits. Second Public Works and The project objective is to decentralize AGETIP to secondary cities, develop pilot activities to see whether AGETIP Yes - self No - Employment Project** can be applied to other sectors, train municipal governments and micro-enterprises, make AGETIP sustainable and targeting information able to recover its costs through commissions. Several very important pilot components of AGETIP include: through wage campaign and addressing the delivery of materials for public ends - this could be school supplies (desks) which are badly needed; rates paid for sensitizing possiblk nutrition interventions, monitoring activities, and education by using AGETIP to contract out (with NGOs labor community but perhaps or those most qualified); exploration of using AGETIP in rural areas. This last component should be intensive no hard accorded high priority, particularly during the hungry season. Finally, AGETIP components in urban areas should public works, participation explore the potential benefit of participation of the local community in project selection, and targeting communities which are poorest as identified in the priority survey. Beneficiary assessments not only of those who have worked for AGETIP, but those who benefit from its services and those who do not (poor neighborhoods which do not yet have an AGETIP project) would be useful in this regard. Transport Sector To promote greater efficiency and beter management of road maintenance, the Port of Dakar, and rail services. No No Adjustment/lnvestment Given the higher income levels of those with access to market towns (12% higher), road maintenance and Operation infrastructure, particularly in Tambacounda and Kolda, is one key element in increasing income opportunities for the rural poor. PROPOSED PROJECTS l l HUMAN RESOURCES The project will further strengthen the family planning and basic health services in urban and rural areas. One area Not for the Not for the DEVELOPMENT which requires such strengthening is in training health personnel in the early detcction and treatment of malnutrition. moment moment 1. Health Sector Development We propose that this project also consider training women's groups active in rural areas in identifying some of the (FY97)** signs of malnutrition. In addition, one issue which should be closely examined is the itmpact of cost recovery measures on poorer rural households (and how to track this) AGRICULTURE The project will address restructuring of the agricultural seor. It is key that this project also examine the cost of the No No Agricultural SECAL I current rice pricing policy, access to credit by poorer rural residents and means of providing incentives for non- FY95 traditional credit societies to lend in the rural sector (perhaps for off-farm income activities), as well as fostering I expansion of high value crops (onions, green beans, etc.) Natural Resource Management Objective is to promote local participation in land-use and decision-making with a view towards reform of the land- No Yes - meant to (PICOGERNA)** tenure system to encourage more sustainable land use and farming practices. Lack of progress on the preparation of increase local a National Environmental Action Plan had stalled the dialogue and had led to the cancellation of the Norwegian grant control over for such preparation. natural resources and local decision- making INFRASTRUCTURE Objective is to provide financial support, technical assistance, and incentives for cities to undertake their own No No Urban IV invcstment programs, and to generate their own revenues. Project currently under preparation. It would be important in this project, and in the process of decentralization in general that an eye be kept towards overall levels of taxation; in other words, as responsibilities are devolved from the state to more local levels, resources should also be transferred in order to avoid an increase in overall lvel of taxation without a commensurate increase in services. In addition, the impact on cities in poorer regions needs to be closely examined. - =t - ) = Reor o 121 SE -a ~~~~~~~~~~ye: ERZ-