Since 2005 in Senegal, progress in reducing poverty has stagnated. Successive exogenous shocks and a slowdown in reforms have, in part, reduced average growth to 3.3%, a rate barely above that of population growth (2.5%). Exogenous shocks include floods, which have become recurrent in the country ’ s major cities, but especially in the capital Dakar, where they remain an omnipresent reality. These floods, with heavy consequences sometimes even disastrous (damage and loss of human life), are one of the main reasons that push and/or keep poor households in a vicious circle of poverty. Indeed, thanks to the geographic information system implemented in this paper, the analysis of the spatial relationship between floods and poverty has made it possible to oppose the multidimensional and dimensional poverty rates of the department of Dakar, the least affected by floods, to those in Pikine, the most affected by the floods. It has also shown that the poorest populations in the region of Dakar are those affected by floods and are mainly located in the departments of Pikine, Guédiawaye and Rufisque. In Pikine, where floods were experienced in 2005, 2009 and 2011, multidimensional poverty rates crossed the 50% line while they are around 30% and 40% for Guédiawaye and Rufisque respectively. Multidimensional poverty rates derive primarily from indicators such as nutrition, level of education, cooking fuel and holding of assets.
The main challenge of the millennium for development is the reduction of poverty in the world. For the developing countries in particular, this reduction of poverty remains a means of fighting hunger, mortality, disease and disasters. Senegal, like many sub-Saharan African countries, had therefore developed and implemented an economic and social policy document for growth and poverty reduction.
The latter, as a poverty reduction strategy document, achieved satisfactory results during the period 2003-2005 with economic growth which stood on a positive slope and an annual average of around 5%: 6.7% in 2003 after the slowdown in 2002, 5.6% in 2004 and 5.5% in 2005 [
Among the exogenous shocks suffered by Senegal, the floods have become a regular feature of the country’s major cities, but especially in the capital Dakar, where they have remained a pervasive reality since 2005. Floods have the effect of keeping poor households in a vicious circle of poverty. The findings of the studies on the intergenerational impacts of shocks on household poverty in Senegal show that floods are among the main disasters that retain poor households in endemic poverty [
The region of Dakar, which is the most affected by the floods (in terms of the recurrence, the extent of the damage and losses), accounts for the largest number of poor people in absolute terms. The national agency for statistics and demography estimates its poor household rate at 26.1 percent for a population of 3 137 196 inhabitants in 2013, nearly a quarter (23.2 percent) of the national population [
Over the past 30 years, floods have killed more than 500,000 people worldwide and resulted in economic losses of more than $500 billion [
Reference [
Based on the findings of previous work, [
Reference [
Reference [
According to the same findings, studies conducted in Senegal between 2008 and 2009 show that floods are among the shocks that push and/or keep poor households in a vicious circle of poverty. Already, less resilient than others to shocks such as floods, poor households adopt survival strategies involving debt, the sale of goods or the elimination of the education of children and young people, which further aggravates their vulnerability to future shocks [
Reference [
The main objective of this paper is to determine if the poor populations are more victims (if they are not the only victims) floods that have become recurrent in Dakar. To do this, a complete mapping of the various flooded areas of Dakar (in 2009 and 2011) was first carried out. The 2011 household survey was then used to calculate the dimensional and multidimensional poverty rates for the 100 census districts (CDs) in the region. Multidimensional poverty rates are then projected onto so-called flood maps to detect a possible correlation between poverty levels and floods.
It is the mapping of the main flooded areas of Dakar in October 2009 and 2011. The choice of October is justified by the fact that this month marks the end of the winter period in Dakar, and therefore the flooded areas considered are those that remained under water despite the efforts of evacuation of rainwater (pumping, runoff, evaporation, etc.). For the years, 2005 and 2009 are certainly the years of greatest floods if one refers to the damage and losses caused, but because of the ESPS-22 which was carried out in 2011, the years 2009 (the closest to 2011) and 2011 were selected.
The flood maps are made from satellite images taken under Google Earth at an altitude of 600m above the ground, and this for the entire region of Dakar. These images are then geo-referenced and the layers of flooded areas digitized using ArcGIS version 10.2. It should be noted that we have neither estimated the risk nor the depth of the floods. We limited ourselves to delimit the areas that, in 2009 and 2011, were flood victims in Dakar (
In addition, the administrative division of the region of Dakar into district municipalities (2009 administrative division) and the map of the geographical distribution of the 100 census districts in which the ESPS-2 was carried out, allowed the identification and the allocation to each district of its multidimensional poverty rate. A rate calculated from the characteristics of individuals living in these districts.
The multidimensional poverty measurement methodology used in this paper is that proposed by [
The M0 index belongs to the family of multidimensional poverty proposed by [
Consider poverty in d dimensions on a population of n individuals. Let [yij] the matrix (n ´ d) of the realizations of the individuals i on j dimensions, yij ≥ 0
represents the realization of the i individual on all j dimensions. The line vector y i = ( y i 1 , y i 2 , ⋯ , y i d ) gives the realizations of the individual i in the different dimensions and the column vector y . j = ( y 1 j , y 2 j , ⋯ , y n j ) , the distribution of the realizations of individuals in the dimension j.
To weight the dimensions, we define a weighting vector p whose jth element pj
represents the weighting of the dimension j. Note that ∑ j = 1 d p j , i.e. the sum of
the dimensional weights is equal to the total number of dimensions. In the case of the multidimensional poverty index, d = 10.
To identify the poor in the population, a two-step procedure is applied using two types of cutoffs: first, a dimensional poverty line is defined to determine whether or not a person is private in each dimension; secondly, a second multidimensional poverty line is chosen to identify those who should be considered poor.
Let zj the poverty cutoff (or deprivation) for the j dimension, and z the vector of poverty cutoffs. Let g 0 = [ g i 0 ] a deprivation matrix defined by:
g i j 0 = { p j si y i j < z j 0 si y i j > z j .
In other words, the ijth entry of the matrix corresponds to the weight pj of the dimension j if the individual i is considered poor in this dimension, and 0 otherwise.
From g0, we build a vector of {intensity of privations} c, where the ith entry
c i = ∑ j = 1 d g i 0
is the sum of weighted deprivations suffered by an individual i.
The next step is to identify individuals who are poor in the multidimensional sense. To do this, we define a second multidimensional poverty cutoff k > 0 that we apply to the column vector c.
Let ρ : R + d ∗ R + + d → { 0 , 1 } , ρk an identifying function that associates y i ∈ R + d the vector of the realizations of the individual i and z ∈ R + + d the cutoff vector to an indicator variable.
ρ k ( y i , z ) = { 1 si c i ≥ k 0 si c i < k .
An individual is considered poor in the multidimensional sense if the sum of his weighted privations ci is greater than k.
To aggregate the information of poor individuals in the population, we construct a second matrix g0(k), obtained from g0 by replacing its ith line g i 0 by a vector of zeros when ci ≤ k. This matrix contains the weighted deprivations of the only individuals identified as multidimensionally poor and excludes the deprivations of the non-poor.
Thus, M0 is the arithmetic mean of the g0 matrix: M 0 = μ ( g 0 ( k ) ) where μ is the operator of the arithmetic mean.
An important feature of M0 is that it can be directly broken down into several indices: the incidence of multidimensional poverty (H) and the intensity of poverty (A). H is the percentage of individuals who are identified as multidimensionally poor; H = q/n where q is the number of poor individuals. A represents
the intensity of multidimensional poverty; A = ∑ i = 1 n c i ( k ) / d q . The M0 measure
therefore summarizes information on the incidence and intensity of poverty, hence the name of adjusted staffing ratio.
However, [
Reference [
The MPI has ten indicators divided into three dimensions3: nutrition and infant mortality for the health dimension; number of years of schooling and leaving school for the education dimension; electricity, drinking water, sanitary, cooking fuel, flooring and assets for the standard of living. Each dimension has a weighting of one third (33.33%) and each indicator in one dimension has a weight equal to that of the others: 16.67% for the health and education indicators and 5.56% for those of standard of living. The
Poverty cutoff k
The poverty cutoff k reflects the sum of the weighted indicators in which an individual must be deprived in order to be considered multidimensionally poor. In the case of the MPI, an individual is identified as multidimensionally poor, if and only if, he suffers from deprivations in at least a third of the weighted indicators (k = 33.33%). In other words, an individual is poor if he suffers from deprivations in a health indicator and an education indicator, in all six indicators of standard of living, or in three indicators of standard of living and in a health or education indicator.
Unit of analysis
To calculate the MPI, [
Dimension | Indicator | Deprived if... | Weight |
---|---|---|---|
Health | Mortality | Any child has died in the family. | 16.67% |
Nutrition | Any adult or child for whom there is nutritional information is malnourished. | 16.67% | |
Education | Years of schooling | No household member has completed five years of schooling. | 16.67% |
Child school attendance | Any school-aged child is not attending school in years 1 to 8. | 16.67% | |
Standard of living | Electricity | The household has no electricity. | 5.56% |
Water | The household does not have access to clean drinking water (according to the MDG guidelines) or clean water is more than 30 minutes walking from home. | 5.56% | |
Sanitation | The household’s sanitation facility is not improved (according to the MDG guidelines) or it is improved but shared with other households. | 5.56% | |
Cooking fuel | The household cooks with dung, wood or charcoal. | 5.56% | |
Floor | The household has dirt, sand or dung floor. | 5.56% | |
Assets | The household does not own more than one of: radio, TV, telephone, bike, motorbike or refrigerator and does not own a car or truck. | 5.56% |
Adapted by Alkire and Santos (2014).
of analysis, which is possible to do with the methodology of Alkire and Foster; 2) a comparison between age and sex groups, for example, is now possible. In our methodology, it is therefore important to note that a person will be considered private in the indicators of standard of living, if the household to which he belongs suffers deprivation in these indicators.
Considered indicators
It is well known that health is often the most difficult dimension to measure. Reference [
Due to the lack of consensus on health indicators and because data is a mandatory constraint, our two health indicators differ from those of [
In education, the number of years of schooling, used as a proxy variable for the level of education, is replaced by the latter since it is captured by the survey. Regarding the age of schooling of children, we consider ages 7 to 12 because, in Senegal, the school officially starts at 7 years old. For the standard of living, the indicators remain unchanged and each person is assigned the characteristics of the household in which he lives.
Dimension | Indicator | Deprived if... | Weight |
---|---|---|---|
Health | Nutrition | The person has difficulty to meet its nutritional needs, often expressed in number of meals, excluding micronutrient intakes. | 16.67% |
Disability | The person lives with a disability that does not allow him to have a sustained activity or normal schooling or that his state of health prevents him from working 40 hours a week. | 16.67% | |
Education | Level of education | The person has a level of education lower than primary education. | 16.67% |
Child school attendance | The child of school-aged has left school before 12 (for ages 7 to 12). | 16.67% | |
Standard of living | Electricity | The household in which the person lives has no electricity. | 5.56% |
Water | The household in which the person lives does not have access to clean drinking water (according to the MDG guidelines) or clean water is more than 30 minutes walking from home. | 5.56% | |
Sanitation | The household in which the person lives does not have improved sanitation (according to the MDG guidelines) or it is improved but shared with other households. | 5.56% | |
Cooking fuel | The household in which the person lives cooks with dung, wood or charcoal. | 5.56% | |
Floor | The household in which the person lives has dirt, sand or dung floor. | 5.56% | |
Assets | The household in which the person lives does not own more than one of: radio, TV, telephone, bike, motorbike or refrigerator and does not own a car or truck. | 5.56% |
Adapted by the authors.
The database used in this paper comes from the poverty monitoring survey in Senegal second phase (ESPS-2) carried out in 2011 by the national agency for statistics and demography. The objectives of this survey were essentially based on information on indicators for monitoring living conditions, poverty and the Millennium Development Goals (MDGs). ESPS-2 collected information on the education, health, heritage, comfort ... of 17,891 sampled households in the country. The details and sampling methods used are to be found in [
The region of Dakar alone has a total of 1,638 households (about one-tenth of the total number of households) divided into two types: urban households (90%) and rural households (10%) located only in the department of Rufisque. In total of 13,398 individuals were surveyed, of whom 12,683 lived in urban-type households and 715 in rural-type households. Regarding the distribution among the four (4) departments in the region, we counted 3340 individuals in Dakar; 3,308 in Guédiawaye; 3710 in Pikine and 3040 in Rufisque [
On these individuals, ESPS collected information on individual characteristics such as age, sex, ethnicity, religion, marital status and so on. It also found information on the level of education, the current attendance of the school (especially for children), cohabitation with a disability preventing the interviewee from having a sustained activity or attending normal schooling or even work 40 hours a week. At the level of the households in which the interviewees live we collected, among other variables, the type of toilet, the main source of drinking water, the main source of fuel for cooking, access to electricity, the main soil material and the holding of assets.
Given the dimensions, indicators, weights and cutoffs retained (
At the same time, 46.1% of people over the age of 12 have lower levels of education than primary school and, as a result, are deprived according to this indicator. However, there is hope for a significant drop in this rate for the years to come, since in 2011 the percentage of children of school age who did not attend school was estimated at 0.2%. And more, we note that the percentage of children
Department | Health | Education | Standard of living | MPI | ||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|
Disability | Nutrition | Child school attendance | Level of education | Electricity | Water | Sanitation | Cooking fuel | Floor | Assets | Rate | A | |
Dakar | 1.7% | 19.6% | 0.2% | 42.3% | 2.2% | 0.7% | 2.2% | 20.5% | 3.8% | 49.6% | 21.7% | 1.4 |
Pikine | 2.4% | 25.4% | 0.2% | 48.3% | 10.1% | 3.4% | 1.0% | 37.5% | 14.9% | 78.6% | 32.6% | 1.6 |
Guédiawaye | 1.6% | 20.4% | 0.2% | 45.2% | 5.6% | 1.1% | 0.7% | 29.9% | 3.3% | 60.8% | 22.7% | 1.5 |
Rufisque | 3.2% | 15.8% | 0.4% | 48.6% | 7.4% | 1.6% | 4.3% | 42.9% | 10.1% | 61.7% | 23.2% | 1.3 |
Region | 2.2% | 20.6% | 0.2% | 46.1% | 6.4% | 1.7% | 2.0% | 32.6% | 8.2% | 63.1% | 25.3% | 5.7 |
Calculation of authors.
absent from school remains low overall in all departments even if Rufisque is above average (0.4%).
Regarding the indicators of standard of living, we estimate that 6.4% of the population of Dakar lives in households without access to electricity. The department of Dakar has the lowest deprivation rate while Pikine has one-tenth of its population without electricity. 3.4% of interviewees in Pikine also live in households without access to clean drinking water7 or that water is more than 30 minutes walk from their household, when the regional rate is 1.7%. Although the main cooking fuel used in Dakar is gas, the fact remains that 32.6% of households use wood (6.3%), charcoal (23.5%) or combine the two fuels indifferently (2.8%); manure (or animal waste) is not used by households in Dakar at all. The proportion of households without adequate sanitation8 is 2.0% and all indicators, the latter is the only one for which the department of Dakar has a higher deprivation rate than Pikine; Rufisque totaling the largest number of private persons with a rate of 4.3%. In terms of the main soil material, 8.2% of households are covered with sand or banco, and once again, Pikine has a much higher rate of deprivation than other departments. When we look at the assets held by households in Dakar, we note a deprivation rate of 63% due in part to that of Pikine which reaches 78%. In other words, more than 78% of the population of Pikine live in households with no more than one of the equipment listed in
At the end, we estimate that 25.3% of the population of Dakar is multidimensionally poor with a poverty intensity of 5.7 and a calculated MPI of 0.011. At the departmental level, Dakar has the lowest multidimensional poverty rate (21.7%) and the lowest rates of deprivation. In contrast, the department of Pikine has the highest multidimensional poverty rate (32.6%) and the largest number of poor people in the dimensional sense. Indeed, on all indicators (with the exception of sanitation only), deprivation rates of Pikine exceed the regional average and often correspond to maxima.
In addition, the flood maps (Figures 2-5) reveal that at the departmental level, the nearest CDs flooded areas or even those most affected by floods note the highest poverty rates. This is the case, for example, of Malika, located in the area of lowlands and natural water reserves, which has the highest poverty rate (53.3%, maximum of maxima), Djiddah Kao (51.6% and 45.8%), Diamaguene/ Sicap Mbao (49.3%), Guinao Rail Nord and Sud with respectively 43.2% and
43.7% and Dalifort (43.1%), all located in the department of Pikine (
education and fail to meet their food needs properly. In addition, they live in households whose main cooking fuel is considered inadequate, do not have assets and they do not always have access to electricity.
In Guédiawaye, such levels of poverty are never equaled, but districts like those of Gold Sud (44.4%), Nimzatt (32.4% and 31.0%), Sam Notary (31.5%) and Medina Gounass (30.4%) (straddling Pikine and Guédiawaye) have more than 3 poor people out of 10 inhabitants (
In the department of Dakar where the flooded areas mainly correspond to unoccupied empty spaces (land, parking, etc.) and there is a clear decrease in these areas between 2009 and 2011 due to real estate development, poverty rates remain globally mixed. At Yoff, we would attempt to explain the poverty rate (32.9%) by the fact that the population is essentially Lebou and lives in traditional-type homes (often private in all three (3) indicators of standard of living (floor, assets and fuel)). However, this reasoning contrasts with the results of Ngor (23.2%) and Ouakam (15.7%) who concentrate the same Lebou population and who, nevertheless, note relatively low poverty rates. In Fann/Point-E (36.8%), Parcelles Assainies (32.3%) and HLM (30.8%), almost a third of the population is considered multidimensionally poor, although they have not been flooded in any way. However, it is also important to note that the districts with the lowest poverty rates (7 to 15%) have never recorded floods (
With regard to the department of Rufisque and depending on whether we are in urban or rural areas, we observed fairly contradictory levels of poverty (1.3% for the less poor census district compared to 44.4% for the poorest in the multidimensional sense). Indeed, in Sangalkam (1.3% and 3.3%), Jaxaay (2.1% and 2.3%) and Yene (3.8% and 4.2%), all non-flooded and rural-type, population have no great difficulty to meet their food needs (often expressed in number of meals). And, all things being equal elsewhere, the very low levels of poverty in these areas can be explained by the null scores of the nutrition indicator (strongly correlated with the multidimensional poverty rate (87.4%)). For urban-type districts, floods are noted in both 2009 and 2011, and as a result poverty rates remain close to those of Pikine. This is the case, for example Nord Rufisque (44.4% and 43.4%), Est Rufisque (44.3%) and Bargny (43% and 38.8%) (
Thanks to the new multidimensional poverty measure introduced by the UNDP in its 20th human development report, this work made it possible to estimate the multidimensional poverty index (MPI) of the region of Dakar. This index of 0.011 corresponding to a poverty rate of 25.3% (i.e. a quarter of the population of Dakar) is relatively lower than the monetary poverty index given the ANSD (26.1%) and therefore differs from the MPI calculated by [
This work has also made it possible to oppose the multidimensional and dimensional poverty rates of the department of Dakar, considered the least affected by the floods, to those of Pikine, the most affected by the floods. The proportion of multidimensionally poor individuals in Dakar (department) is estimated at 21.7%, compared to 32.6% in Pikine, 22.7% in Guédiawaye and 23.2% in Rufisque (
This work has, and above all, shown that the poorest populations in the region of Dakar are those who are victims of floods and are mainly located in the departments of Pikine, Guédiawaye and Rufisque. In Pikine, where floods were experienced in 2005, 2009 and 2011, poverty rates crossed the 50% line while they are located in Guédiawaye and Rufisque districts at around 30% and 40%. One particularity is, however, noted in Rufisque (the only department with 10% of rural households): the very low levels of poverty can be explained by the nullity of the nutrition indicator scores, strongly and positively correlated to the multidimensional poverty rate. These floods highlight the failure of spatial planning policies taken by uncontrolled soil occupation because the flooded areas in Pikine and Guédiawaye often correspond to lowlands and natural water reserves.
However, it should be noted that this work was not done without major constraints. The administrative divisions, constantly changing, have made it often difficult to correctly identify district municipalities and therefore to locate certain districts between Pikine and Guédiawaye. In Rufisque, only three district municipalities have been demarcated and some districts, supposed to be in the department, even come out of the administrative boundaries of the region. In addition, the satellite images with which we digitized the flooded areas for the year 2011, presented several cloudy areas. For these areas, we made cross-checks with August 2011 or simply leave without any treatment because the intersected images also presented clouds. The flooded areas of 2011 presented on the maps can, therefore, be victims of under/over-estimation.
We gratefully acknowledge the financial support for this project by Unit for Mathematical and Computer Modeling of Complex Systems (UMMISCO).
Cissé, A. and Mendy, P. (2018) Spatial Relationship between Floods and Poverty: The Case of Region of Dakar. Theoretical Economics Letters, 8, 256-281. https://doi.org/10.4236/tel.2018.83019
Department | Health | Education | Standard of living | MPI | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Disability | Nutrition | Child school attendance | Level of education | Electricity | Water | Sanitation | Cooking fuel | Floor | Assets | Rate | |
Plateau | 2.7% | 21.8% | 0.9% | 57.3% | 1.8% | 0.0% | 0.0% | 44.5% | 7.3% | 86.4% | 30.0% |
Medina | 1.2% | 18.8% | 0.0% | 42.4% | 2.4% | 8.2% | 2.4% | 15.3% | 8.2% | 56.5% | 22.4% |
Medina | 3.6% | 12.4% | 0.0% | 45.1% | 7.1% | 0.0% | 0.0% | 31.0% | 0.0% | 66.4% | 15.9% |
Colobane | 0.0% | 26.5% | 0.0% | 40.2% | 3.9% | 0.0% | 0.0% | 43.1% | 3.9% | 68.6% | 30.4% |
Colobane | 2.0% | 14.1% | 1.3% | 52.3% | 0.0% | 0.0% | 6.7% | 54.4% | 6.7% | 42.3% | 20.1% |
Fann/Point E | 3.2% | 36.8% | 0.0% | 55.8% | 6.3% | 0.0% | 0.0% | 18.9% | 0.0% | 80.0% | 36.8% |
Grand Dakar | 0.8% | 26.2% | 0.0% | 47.7% | 1.5% | 0.0% | 0.0% | 2.3% | 0.0% | 73.1% | 29.2% |
Biscuiterie | 4.6% | 27.8% | 0.0% | 47.4% | 3.8% | 0.0% | 0.8% | 35.3% | 12.0% | 62.4% | 30.8% |
HLM | 1.2% | 16.0% | 0.0% | 29.0% | 0.6% | 0.0% | 0.0% | 13.0% | 0.0% | 27.2% | 17.9% |
Hann/Bel-Air | 2.7% | 9.5% | 0.0% | 21.6% | 0.0% | 0.0% | 0.0% | 1.4% | 0.0% | 9.5% | 9.5% |
Sicap/Liberté | 2.4% | 22.0% | 0.0% | 28.5% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 22.8% | 23.6% |
Dieuppeul | 1.1% | 6.8% | 0.5% | 38.9% | 0.0% | 0.0% | 0.0% | 7.4% | 0.0% | 28.4% | 7.9% |
Ouakam | 2.4% | 12.0% | 0.0% | 53.6% | 12.7% | 0.0% | 1.2% | 9.0% | 7.8% | 70.5% | 15.7% |
Ngor | 1.1% | 20.5% | 0.0% | 56.8% | 4.9% | 0.0% | 8.6% | 23.8% | 2.2% | 38.9% | 23.2% |
Yoff | 0.8% | 32.5% | 0.9% | 49.1% | 0.0% | 2.2% | 0.0% | 49.1% | 14.5% | 65.8% | 32.9% |
Mermoz | 0.0% | 13.9% | 0.0% | 22.2% | 1.9% | 0.0% | 0.0% | 0.0% | 0.0% | 12.0% | 13.9% |
Grand-Yoff | 2.7% | 21.3% | 0.0% | 44.7% | 0.0% | 0.0% | 0.0% | 15.3% | 0.0% | 62.0% | 22.7% |
Grand-Yofff | 1.1% | 23.2% | 0.0% | 36.8% | 1.1% | 0.0% | 0.0% | 6.3% | 0.0% | 46.3% | 23.2% |
Grand-Yoff | 1.8% | 14.5% | 0.0% | 23.1% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 37.6% | 14.5% |
Patte d’Oie | 2.0% | 13.4% | 0.0% | 30.9% | 6.7% | 0.0% | 0.0% | 8.1% | 0.0% | 20.1% | 1.8% |
P. Assainies | 3.6% | 13.8% | 0.9% | 41.3% | 0.0% | 10.1% | 34.9% | 7.3% | 0.0% | 56.0% | 15.6% |
P. Assainies | 0.8% | 13.9% | 0.0% | 46.7% | 0.0% | 0.0% | 0.0% | 5.7% | 0.0% | 47.5% | 14.8% |
P. Assainies | 0.0% | 32.3% | 0.0% | 30.0% | 0.0% | 0.0% | 0.0% | 10.0% | 0.0% | 42.3% | 32.3% |
P. Assainies | 0.8% | 21.4% | 0.0% | 36.6% | 0.0% | 0.0% | 3.1% | 24.4% | 11.5% | 80.9% | 29.8% |
Camberene | 1.1% | 16.8% | 0.0% | 54.3% | 0.0% | 0.0% | 0.0% | 46.7% | 9.8% | 40.8% | 16.8% |
Dakar | 1.7% | 19.6% | 0.2% | 42.3% | 2.2% | 0.7% | 2.2% | 20.5% | 3.8% | 49.6% | 21.7% |
Calculation of authors.
Department | Health | Education | Standard of living | MPI | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Disability | Nutrition | Child school attendance | Level of education | Electricity | Water | Sanitation | Cooking fuel | Floor | Assets | Rate | |
Yeumbeul Nord | 0.0% | 29.6% | 0.6% | 47.8% | 25.8% | 2.5% | 0.0% | 48.4% | 6.3% | 76.7% | 31.4% |
Yeumbeul Nord | 3.6% | 18.5% | 0.0% | 54.8% | 19.6% | 7.1% | 7.1% | 33.9% | 7.1% | 79.2% | 31.0% |
Yeumbeul Nord | 2.6% | 17.7% | 0.0% | 9.4% | 1.3% | 6.3% | 0.0% | 67.1% | 1.3% | 79.7% | 20.3% |
Yeumbeul Nord | 3.3% | 31.0% | 0.5% | 50.0% | 6.5% | 5.6% | 0.0% | 15.7% | 2.3% | 69.4% | 33.3% |
Diack Sao | 2.2% | 24.7% | 0.9% | 44.5% | 22.0% | 0.0% | 0.0% | 19.8% | 7.9% | 68.3% | 26.0% |
Diack Sao | 1.4% | 27.0% | 0.7% | 38.7% | 8.0% | 16.8% | 2.9% | 9.5% | 3.6% | 67.9% | 32.1% |
Yeumbeul Sud | 3.5% | 25.7% | 0.0% | 50.0% | 12.9% | 0.0% | 0.0% | 60.0% | 11.4% | 72.9% | 30.0% |
Malika | 1.2% | 37.9% | 0.6% | 52.7% | 27.8% | 26.0% | 0.0% | 56.2% | 36.1% | 90.5% | 53.3% |
Keur Massar | 0.6% | 26.3% | 0.0% | 48.6% | 5.6% | 9.5% | 0.0% | 32.4% | 40.8% | 95.0% | 39.7% |
Keur Massar | 1.2% | 32.1% | 0.0% | 39.4% | 5.5% | 0.0% | 0.0% | 21.8% | 0.0% | 93.9% | 36.4% |
Pikine Ouest | 4.5% | 12.4% | 0.0% | 57.5% | 1.8% | 0.0% | 0.0% | 32.7% | 4.4% | 61.1% | 15.9% |
Pikine Est | 4.2% | 22.4% | 0.0% | 53.9% | 2.4% | 0.0% | 0.0% | 45.5% | 10.3% | 90.3% | 28.5% |
Pikine Nord | 7.0% | 18.8% | 1.0% | 45.5% | 5.9% | 0.0% | 0.0% | 22.8% | 3.0% | 80.2% | 21.8% |
Dalifort | 3.0% | 17.6% | 0.0% | 48.8% | 12.7% | 5.9% | 10.8% | 44.1% | 37.3% | 70.6% | 43.1% |
Djiddah Kao | 2.3% | 7.0% | 0.0% | 58.7% | 5.2% | 0.0% | 0.0% | 72.1% | 29.7% | 67.4% | 18.6% |
Djiddah Kao | 3.9% | 34.0% | 0.0% | 57.5% | 0.0% | 0.0% | 0.0% | 49.7% | 23.5% | 77.8% | 45.8% |
Djiaddah Kao | 3.2% | 45.1% | 0.0% | 51.6% | 23.0% | 0.0% | 0.0% | 47.5% | 9.0% | 94.3% | 51.6% |
Guinao Rail Nord | 0.0% | 43.2% | 0.0% | 40.5% | 0.0% | 0.0% | 0.0% | 43.2% | 0.0% | 54.1% | 43.2% |
Guinao Rail Sud | 2.5% | 37.8% | 0.0% | 49.6% | 3.4% | 0.0% | 0.0% | 36.1% | 19.3% | 72.3% | 43.7% |
Thiaroye sur Mer | 2.3% | 18.9% | 0.0% | 39.4% | 6.1% | 0.0% | 2.3% | 18.9% | 14.4% | 57.6% | 21.1% |
Diamaguene | 1.9% | 32.3% | 0.5% | 53.5% | 1.4% | 0.0% | 0.0% | 72.4% | 32.3% | 97.2% | 49.3% |
Diamaguene | 2.9% | 9.8% | 0.0% | 32.4% | 9.2% | 1.2% | 0.0% | 22.5% | 15.6% | 68.8% | 11.0% |
Diamaguene | 1.4% | 26.1% | 0.0% | 44.4% | 15.7% | 0.0% | 0.0% | 25.5% | 7.8% | 95.5% | 34.0% |
Mbao | 3.3% | 16.8% | 0.0% | 49.7% | 10.1% | 0.0% | 0.0% | 24.8% | 24.2% | 61.7% | 25.5% |
Mbao | 0.8% | 23.8% | 0.0% | 45.2% | 7.1% | 0.0% | 4.8% | 22.2% | 3.2% | 89.7% | 24.6% |
Pikine | 2.4% | 25.4% | 0.2% | 48.3% | 10.1% | 3.4% | 1.0% | 37.5% | 14.9% | 78.6% | 32.6% |
Calculation of authors.
Department | Health | Education | Standard of living | MPI | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Disability | Nutrition | Child school attendance | Level of education | Electricity | Water | Sanitation | Cooking fuel | Floor | Assets | Rate | |
Golf Sud | 4.0% | 20.0% | 0.0% | 40.0% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 64.0% | 24.0% |
Golf Sud | 3.2% | 43.7% | 0.0% | 38.9% | 18.3% | 0.0% | 0.0% | 41.3% | 12.7% | 59.5% | 44.4% |
Golf Sud | 2.5% | 16.9% | 0.6% | 50.0% | 1.9% | 0.0% | 0.0% | 29.9% | 0.0% | 84.4% | 18.8% |
Golf Sud | 1.4% | 14.% | 0.0% | 34.2% | 5.5% | 12.3% | 5.5% | 18.5% | 5.5% | 52.1% | 19.9% |
Golf Sud | 0.9% | 22.8% | 0.0% | 46.5% | 0.9% | 0.0% | 0.9% | 8.8% | 0.9% | 49.1% | 24.6% |
Golf Sud | 1.1% | 28.7% | 0.0% | 41.5% | 0.0% | 0.0% | 0.0% | 25.0% | 0.0% | 63.8% | 29.3% |
Golf Sud | 1.5% | 18.0% | 0.0% | 45.1% | 0.0% | 0.0% | 0.0% | 6.8% | 0.0% | 35.3% | 18.8% |
Golf Sud | 1.2% | 14.8% | 0.0% | 32.1% | 0.0% | 0.0% | 0.0% | 11.1% | 0.0% | 63.0% | 16.0% |
Sam Notaire | 1.6% | 29.8% | 0.0% | 51.6% | 2.4% | 0.0% | 0.8% | 30.6% | 7.3% | 69.4% | 31.5% |
Sam Notaire | 0.6% | 12.3% | 0.0% | 54.2% | 0.0% | 0.0% | 0.0% | 43.0% | 0.0% | 77.1% | 12.8% |
Sam Notaire | 3.3% | 17.8% | 0.0% | 49.4% | 1.1% | 0.0% | 0.0% | 43.9% | 5.0% | 60.0% | 18.9% |
Limamoulaye | 0.9% | 14.4% | 1.8% | 46.8% | 4.5% | 0.0% | 0.0% | 23.4% | 0.0% | 46.8% | 14.4% |
Limamoulaye | 0.6% | 22.2% | 0.0% | 48.0% | 2.9% | 0.0% | 0.0% | 45.0% | 5.8% | 46.2% | 25.1% |
Limamoulaye | 0.8% | 6.5% | 0.0% | 28.2% | 4.8% | 4.8% | 4.8% | 6.5% | 4.8% | 28.2% | 11.3% |
Limamoulaye | 0.9% | 8.0% | 0.0% | 24.1% | 0.0% | 0.0% | 0.0%6 | 3.6% | 0.0% | 24.1% | 31.0% |
Nimzatt | 1.6% | 31.0% | 0.0% | 49.2% | 7.9% | 0.0% | 0.0% | 18.3% | 0.0% | 72.2% | 31.0% |
Nimzatt | 0.6% | 18.8% | 1.3% | 43.5% | 13.0% | 0.0% | 0.0% | 66.9% | 7.1% | 67.5% | 21.4% |
Nimzatt | 1.9% | 18.6% | 0.0% | 50.0% | 22.4% | 0.0% | 5.1% | 30.8% | 0.0% | 70.5% | 24.4% |
Nimzatt | 1.2% | 27.6% | 0.0% | 57.1% | 10.0% | 6.5% | 0.0% | 33.5% | 9.4% | 81.2% | 32.4% |
Nimzatt | 2.6% | 16.2% | 0.0% | 33.3% | 0.0% | 0.0% | 0.0% | 51.3% | 0.0% | 61.5% | 17.1% |
Nimzatt | 0.8% | 14.8% | 0.0% | 32.0% | 0.0% | 0.0% | 0.0% | 14.8% | 0.0% | 28.7% | 14.8% |
Gounass | 2.0% | 18.0% | 0.0% | 54.6% | 6.2% | 0.0% | 0.0% | 34.5% | 2.6% | 62.9% | 21.1% |
Gounass | 0.8% | 27.3% | 0.0% | 54.7% | 3.1% | 0.0% | 0.0% | 36.7% | 14.8% | 85.9% | 28.1% |
Gounass | 3.4% | 22.3% | 0.0% | 52.0% | 20.3% | 0.0% | 0.0% | 37.8% | 0.0% | 79.1% | 30.4% |
Guédiawaye | 1.6% | 20.4% | 0.2% | 45.2% | 5.6% | 1.1% | 0.7% | 29.9% | 3.3% | 60.8% | 22.7% |
Calculation of authors.
Department | Health | Education | Standard of living | MPI | |||||||
---|---|---|---|---|---|---|---|---|---|---|---|
Disability | Nutrition | Child school attendance | Level of education | Electricity | Water | Sanitation | Cooking fuel | Floor | Assets | Rate | |
Rufisque Ouest | 0.0% | 23.6% | 0.0% | 38.5% | 3.4% | 3.4% | 3.4% | 22.4% | 3.4% | 59.8% | 25.3% |
Rufisque Ouest | 4.9% | 12.4% | 0.0% | 46.9% | 17.2% | 0.0% | 0.0% | 15.9% | 0.0% | 76.6% | 14.5% |
Rufisque Nord | 2.1% | 28.8% | 0.0% | 43.2% | 4.3% | 0.0% | 0.0% | 25.9% | 5.8% | 81.3% | 30.2% |
Rufisque Nord | 0.7% | 5.3% | 0.0% | 41.1% | 0.0% | 0.0% | 0.0% | 7.3% | 0.0% | 70.2% | 6.0% |
Rufisque Nord | 4.1% | 35.2% | 0.0% | 45.1% | 0.0% | 0.0% | 0.0% | 45.1% | 13.9% | 31.1% | 43.4% |
Rufisque Nord | 1.1% | 11.7% | 0.6% | 49.4% | 2.8% | 0.0% | 2.8% | 16.7% | 1.7% | 62.2% | 12.8% |
Rufisque Nord | 3.5% | 26.8% | 0.7% | 44.4% | 26.8% | 0.0% | 28.9% | 35.2% | 22.5% | 68.3% | 44.4% |
Rufisque Est | 2.3% | 43.7% | 1.9% | 60.1% | 0.0% | 0.0% | 8.2% | 45.6% | 0.0% | 86.7% | 44.3% |
Rufisque Est | 2.8% | 13.6% | 0.0% | 43.8% | 0.6% | 0.0% | 0.0% | 17.6% | 2.8% | 49.4% | 16.5% |
Rufisque Est | 3.5% | 7.5% | 1.2% | 53.2% | 4.0% | 0.0% | 4.6% | 57.2% | 12.7% | 43.9% | 11.6% |
Bargny | 3.2% | 28.1% | 1.6% | 53.9% | 13.3% | 0.0% | 8.6% | 56.2% | 32.0% | 69.5% | 43.0% |
Bargny | 1.7% | 31.0% | 0.0% | 39.7% | 11.2% | 0.0% | 6.0% | 31.0% | 9.5% | 52.6% | 38.8% |
Diamniadio | 6.0% | 19.9% | 0.6% | 58.4% | 3.6% | 3.6% | 6.6% | 92.2% | 17.5% | 68.7% | 31.9% |
Sebikotane | 3.8% | 17.0% | 0.0% | 55.5% | 2.2% | 0.0% | 0.0% | 44.0% | 21.4% | 79.7% | 27.5% |
Sebikotane | 5.7% | 17.3% | 0.0% | 49.7% | 26.0% | 0.0% | 16.8% | 75.7% | 25.4% | 65.9% | 41.6% |
Bambylor | 6.8% | 0.0% | 0.0% | 45.8% | 0.0% | 0.0% | 0.0% | 49.2% | 32.2% | 42.4% | 16.9% |
Sangalkam | 3.4% | 0.0% | 0.0% | 56.7% | 1.7% | 0.0% | 0.0% | 36.7% | 0.0% | 66.7% | 3.3% |
Sangalkam | 1.3% | 0.0% | 1.3% | 43.0% | 17.7% | 0.0% | 0.0% | 31.6% | 0.0% | 34.2% | 1.3% |
Peulh Niagha | 1.5% | 0.0% | 1.5% | 54.5% | 18.2% | 18.2% | 0.0% | 77.3% | 10.6% | 37.9% | 12.1% |
Peulh Niagha | 7.5% | 0.0% | 0.0% | 50.7% | 0.0% | 0.0% | 0.0% | 80.6% | 34.3% | 58.2% | 25.4% |
Jaxaay | 2.1% | 0.0% | 0.0% | 58.5% | 11.7% | 7.4% | 0.0% | 55.3% | 0.0% | 74.5% | 2.1% |
Jaxaay | 7.0% | 0.0% | 0.0% | 20.9% | 0.0% | 0.0% | 0.0% | 0.0% | 0.0% | 23.3% | 2.3% |
Jaxaay | 0.0% | 0.0% | 0.0% | 42.3% | 21.1% | 0.0% | 0.0% | 59.2% | 0.0% | 76.1% | 9.9% |
Yene | 3.8% | 0.0% | 0.0% | 56.2% | 0.0% | 0.0% | 0.0% | 85.0% | 0.0% | 40.0% | 3.8% |
Yene | 6.2% | 0.0% | 1.0% | 47.9% | 0.0% | 18.8% | 0.0% | 45.8% | 0.0% | 53.1% | 4.2% |
Rufisque | 3.2% | 15.8% | 0.4% | 48.6% | 7.4% | 1.6% | 4.3% | 42.9% | 10.1% | 61.7% | 23.2% |
Calculation of authors.