Using Multiple Correspondence Analysis to Measure Multidimensional Poverty in Congo

The following analysis is based on a multidimensional understanding of poverty using a nonmonetary basic needs approach. It is ground on data from the first survey on household living conditions for poverty assessment, con-ducted by the National Institute of Statistics of Congo in 2005. Multiple Correspondence Analysis is applied to construct a composite indicator by aggregating several attributes likely to reflect the poverty of individuals or households. The application shows that Congolese households are not affected by the same type of poverty. Three types of non-monetary poverty are identified: infrastructure poverty, vulnerability of human existence and poverty of comfort. These households were then classified according to the composite indicator of Poverty. The results show that the incidence of poverty corresponds to the weight of poor class of about 70.67%.

For Sen [2], measuring poverty involves two distinct operations: 1) the identification of the poor in the total population; and 2) the aggregation of statistics on the identified poor to construct a global poverty index. However, it must be noted that specifying such an index raises several conceptual and methodological questions such as: 1) which indicators of well-being should be used; 2) how can all the information concerning the poor population be summarized into a synthetic measure of poverty? The literature on the subject shows that there is diversity in the answers to these questions, and that poverty is not an objective concept. Indeed, the study of poverty in a multidimensional perspective shows the existence of several approaches.
In Congo, the National Institute of Statistics produced an income poverty profile in 2005 using data from the survey on household living conditions to assess poverty [3]. With the same data from this survey, we will measure multidimensional poverty in Congo, based on a nonmonetary basic needs approach.
In concrete terms, the aim is to make a multidimensional exploration of poverty using multiple correspondence analysis from nonmonetary indicators of well-being. With this method, one possibility is to define a composite indicator of poverty by aggregating several attributes or variables likely to reflect the poverty of individuals or households; it is then on the basis of this aggregate that the various poverty measures can be made. In view of the importance that the Congo has recently been giving to the fight against poverty, the interest of this study is clear: to contribute to an in-depth knowledge of poverty in the country that can help the government authorities to make good decisions.
The rest of the text is organized as follows. Section 2 introduces the conceptual framework of poverty. Three main schools in conceptual approaches to poverty are identified. The first approach is monetary and places the conceptualization of well-being in the utility space. The second is the basic needs approach, which considers that an individual should be able to satisfy certain basic needs that are necessary to achieve a certain quality of life. The third is the capability approach, which reflects well-being through the positive rights of individuals. Section 3 describes the methodological framework by introducing some measures of multidimensional poverty that have been proposed in the literature. Section 4 is devoted to the presentation of the data, firstly by indicating the source which recalls the objectives of the survey and the collection procedures used, and secondly by specifying the non-monetary dimensions of poverty selected. In Section 5, Multiple Correspondence Analysis is applied to construct a composite indicator of poverty in Congo. Concluding remarks are provided in Section 6. the ranking by utilities through an identical pre-order relationship [5]. The Welfarist school emphasizes the importance of increasing income, through increased productivity and employment as a strategy to fight poverty.

The Basic Needs School
For the basic needs approach, the "something" that is lacking in the lives of the poor is a subset of goods and services specifically identified and perceived as universal, common to people of different cultures and civilizations. This poverty of "living conditions" or "poverty of existence", reflects a situation of lack in the areas of food, health, education, housing, etc. This approach to poverty is based on a humanist vision that goes beyond economics to appeal to morality and to human development in all its dimensions [6]. One of the main problems facing this approach is the determination of these basic needs, which can vary from one individual to another depending on age and gender. These basic needs consist of two elements [6] 1) the minimum necessary for a family for individual consumption; food, adequate clothing and housing, certain household items or furniture (so-called biological needs); 2) basic services that are both used and provided by the community as a whole, such as drinking water, garbage collection and sewerage, public transport, sanitation and education (access to public goods and services). This approach favours policies oriented towards the satisfaction of basic needs in the fight against poverty. Finally, it should be noted that this approach met with considerable success during the 1970s thanks to the work of the International Labour Office (ILO) during the World Conference on Employment, income distribution, social progress and the international division of la-Journal of Data Analysis and Information Processing bour in 1976. And, following the failures of structural adjustment programmes in developing countries in the 1980s, it helped to give a social orientation to the World Bank's development policies.

The Capability School
For this school, the "thing" that is lacking refers neither to utility nor to the satisfaction of basic needs, but to human abilities, or capacities. This approach, which stems from the work of Sen [7] [8], winner of the 1998 Nobel Prize in Economics (Nobel Prize for the Poor according to the British press), differs from the basic needs approach and is part of a reflection on social justice, equality and inequality. The three main components of this approach are "commodities", "functionings" and "capabilities". Commodities correspond to all goods and services and have the characteristic of making "functionings" possible. The functionings take into account the achievements of individuals, i.e. what they "are" and what they "do" with their resources. Capabilities are the set of opportunities that a person can choose from: they are the various combinations of functionings that a person can perform. Thus, the capability school makes it possible to approach poverty by considering it as the result of an inability to seize the opportunities that arise due to a lack of capacity resulting from poor health, insufficient education, nutritional imbalances, etc. Poverty reduction strategies would focus here on human capacity building.
It should be specified that the capability approach has had considerable success internationally. In 1990, the UNDP drew inspiration from this approach in order to lay the foundations for the now widely used concept of "human development" and the indicator derived from it, known as the Human Development Index (HDI). The World Bank too has not remained indifferent to this approach; indeed, it has moved from a policy of liberal orthodoxy to a strategy that includes good governance and the fight against poverty and inequality. Since 1997, in association with the IMF, it has placed the fight against poverty at the centre of social policies.

Methodological Framework
This section provides a brief review of the literature on key measures of multidimensional poverty. Two families of methods are distinguished in the measurement of multidimensional poverty, namely, axiomatic and nonaxiomatic methods. Axiomatic methods are rather an extension to two or more dimensions of certain classes of poverty indices proposed in the unidimensional framework.
In nonaxiomatic methods, a distinction is made between methods based on aggregated data and methods concerning individual data. In what follows, we draw heavily on the general statements made earlier [9] [10] [11] [12].

Axiomatic Methods
Let us then consider a population where each individual or household Then we get a multidimensional extension of the poverty index. The properties of ( )

Nonaxiomatic Methods
Aggregated indicators provided either by the World Bank such as the Quality of Life, Human Development Index (QLHDI) or by the UNDP such as the Human Development Index (HDI) or the Human Poverty Indicators (HPI) will not be discussed here. We will therefore limit ourselves to methods based on individual data. However, the framework that we have been given obliges us to confine Journal of Data Analysis and Information Processing ourselves to a narrow field of measurements by concentrating on measurements using: fuzzy set theory, information theory, distance function and statistical methods, one of which, multiple correspondence analysis, will be used in the following.

Fuzzy Set Theory Approach
If N is the whole population of n individuals and P is the subset of the poor in that population, then from the above it can still be written that: This boolean logic does not seem to us very suitable for the study of poverty as a vague concept with ill-defined boundaries. We shall therefore see how to soften this abrupt division of the population into two classes and subsequently admit that the transition from a state of deprivation to a situation of nonprivation is gradual. To do this, we adopt the new concept of gradual belonging of an element of a whole introduced by Zadeh [13] and developed by Dubois and Prade [14]. In this so-called fuzzy logic, we consider a more general function of belonging, varying uniformly between zero and one. Three cases are observed: In this fuzzy logic applied to poverty, we will say that: This method was applied by the author to measure multidimensional poverty in Congo with the same data used in this article [17].

Information Theory Approach
It is also sometimes called the entropy approach. This information theory was originally developed by engineers. Its adaptation to measures of inequality and poverty was made by E. Maasouami [18]. The basic principles of this approach can be explained in a simplified way by following D. Miceli [19]. Reconsider the previous notations where each individual or household ( ) where one can read in the i-th row the well-being situation faced by the individual or household i according to the m indicators, and in the j-th column, the distribution of the j-th indicator for the individuals.
The proposal made [18] is to replace the m information on all indicators of well-being with a single composite indicator c x synthesizing all the aspects of well-being selected and represented by a vector with n components, one for each individual. In fact, it suggests that for each individual or household i to replace the vector ( ) 1 2 , , , by the scalar ci x and thus find a function that aggregates or summarizes the information provided by the m indicators selected at the outset.
The question is then that of the choice of the aggregation function used to construct the composite indicator of well-being ci x . The proposed solution [18] is to find a vector c x whose distribution is as close as possible to those of all the indicators of well-being by using criteria of divergence between distributions borrowed from information theory. In this case, the proximity criterion is to minimize the following generalized entropy function: Family Life Survey [20].

Distance Function Approach
The distance function is a concept used in production theory and particularly in efficiency analysis [21]. Lovell et al. [22] were probably the first to adopt this concept in the context of the analysis of household behaviour. Subsequent research followed: multidimensional poverty [9], well-being [23], human development dimensions [24] and human development index [25]. The distance function can be defined as an approach that seeks to measure how much to adjust the set of attributes of an individual to achieve the reference level of well-being [25]. This is done by using a measure of the distance between a vector of goods (resources and capabilities) of that individual and a reference vector. The analytical expression of the distance function is as follows: where i x is a vector of characteristics of the individual's condition i, W the weight function (welfare) chosen, * W the weight function of the reference level and d represents the measure of the distance that should correspond to the amount of the minimum adjustment to be made to bring the individual's condition back to the reference level. In most of the applications cited above for the analysis of this problem, a translog function that aggregates all of the welfare characteristics of each agent and normalizes to one of them is used as a measure of welfare. In their approach called the lower convex hull approach, Anderson, Crawford and Leicester [25] have proposed a more general formulation that does not specifically require the choice of an aggregation function. It relies on some general properties (monotonicity and quasi-concavity) of the function W to derive the lower bound of the distance measure of relative well-being as follows: Taking into account the properties of W, the sets of well-being levels are then convex with respect to the origin. Let X be a finite dataset, it is possible to derive a convex set whose lower bound which is convex curve ( ) inf X corresponds to the level of well-being of the reference individual. This curve has a particularly useful interpretation in the case of analyzing poverty states: "it is what we will term the Rawlsian Frontier, the set of potentially poorest agents in the sample, and consequently represents the frontier of poorest individuals in the population" [25].

Statistical Methods
As before, let us now note by i x the indicator of wealth for the household or the individual i, ij x its endowment in basic needs j and by j α the weight of each basic need, the analytical expression of i x is defined by: Two groups of statistical methods of multivariate analysis allow us to study this S. Ambapour Journal of Data Analysis and Information Processing relationship [12]: descriptive methods and latent variable models.

1) Descriptive methods
Here we adopt the inertia approach to present these methods. To do so, let us reconsider the matrix , n m X . Each of the n rows of matrix can be treated either as a vector or a point in m R called the individuals' space. The entire matrix constitutes a set of n points in m R . The principle of this approach is the following: find in m R a sub-space of dimension l ( l m < ) that is the best for the set of points in the least square sense. The result is well known: it is the l dimensional subspace generated by the l first eigenvectors of symmetric matrix X X ′ corresponding to the l first eigenvalues. Thus, the inertia approach has situated lines on the Euclidean space where the set of points is located; this system of lines which are mutually perpendicular is taken as a system of coordinate axes and the coordinates are called factors. In the poverty analysis the first factor, (the principal axis of inertia or line in the direction of which the inertia of the set of points attains its maximum) is an interesting candidate for the composite indicator of poverty.
Depending on the nature of the data in the table X two methods are often used: 1) principal component analysis [26] in the case of quantitative variables; the Euclidean metric is then required to measure the proximity between observations; 2) multiple correspondence analysis [26] [27] which is an extension of correspondence analysis [28], in the case of categorical variables and using a particular one, the 2 χ metric.
A third method, namely 3) cluster analysis [26] [29] is often used. Let us note by Q a partition of all individuals I (all households) into q clusters (here 2 q = ; poor and non-poor); Q is a finite set of non-empty q parts of I two to two of empty intersection and whose combine is I. This is written: Let us note by q g the center of gravity of the cluster q. And one supposes that the q g are provided with mass q n and each point i x with mass i n . The inertia of the cluster q in relation to its center of gravity is: this quantity is called within cluster inertia ( 2 d distance squared). We can also define the inertia of q g in relation to the center of gravity g of the set of points: This quantity is called between cluster inertia. Then, Huyghen's equation provides a decomposition of total inertia ( ) n I t into within cluster and between cluster inertia: The global quantity of a partition is related to the homogeneity within the clusters.
( ) n I t being a constant quantity, it is thus a question of minimizing the quantity relative to the within cluster, or to maximize the quantity relative to the Journal of Data Analysis and Information Processing between cluster inertia. Cluster analysis is often used in relationship with the two previous methods. In this case, the objects to classify are located by their coordinates on the first axes of the analysis.
As far as applications are concerned, an interesting illustration of principal component analysis has been made by Klasen [30]. Recently, a lot of data have been collected in developing countries, particularly in Africa, as part of poverty reduction strategies. And, multiple correspondence analysis has often been used to study these data [31]. Ferro-Luzzi et al. [32] have thus combined factor and cluster analysis to identify the subpopulation of poor in Switzerland.

2) Latent variable models
A more elegant mathematical presentation of the three methods described here can be found in Krishnakumar's paper [33].

a) Factor Analysis
Here, the observed values are postuled to be linear functions of a certain number of unobserved latent variables called factors: y f ε =Λ + (19) where y represents the observed variables, f the latent variables and Λ the coefficient matrix. In the study cited above [32], this method was used in conjunction here as x: As in Factor Analysis, y refers to indicators, f to latent variables while here x refers to "causes". For the application of this model to the study of poverty, see the text by Naga and Bolzani [34].

c) Structural Equations Model
This model can be summarized as follows by including the following different equations: where * y refers to latent endogenous variables, * x refers to latent exogenous variables, y and x are the observed indicators corresponding to * y and * x . In the reference already quoted above [33], an application of this model is illustrated.

Data Source
The process of drafting the Poverty Reduction Strategy Paper in Congo required a

Non-Monetary Dimensions of Poverty: Some Comments
This is based on the second module of the survey concerning the basic indicators of well-being used to construct the composite indicator of non-monetary poverty.
It is therefore the basic needs approach that is adopted here, the themes of which are the subject of a brief commentary.

Education
The influence that men's education can have on their productivity and economic Journal of Data Analysis and Information Processing growth had been emphasized by classical economists, notably Adam Smith. Today, the role of education is glorified by international institutions. Indeed, it is the second point of the Millennium Development Goals (MDGs). Education is therefore one of the most important dimensions of poverty analysis. It can, however, be captured by several indicators, each characterizing a particular aspect of the mobilization of this asset. For the purposes of this poverty study, the variables used to characterize education are: literacy, time to access a primary school and time to access a secondary school. It should be noted that the literacy rate is 80.4% in Congo. This is still a good performance, as the rate is generally below 50% for African countries south of the Sahara.

Health
Health is now seen as a component of human capital, along with education and nutrition. It is an essential element of well-being (only men have wealth). A healthy individual is economically more productive and thus contributes to economic growth. Investing in health should be a commitment by a state to reduce or alleviate poverty. This is one of the purposes pursued by the Poverty Reduction Strategy Paper and the National Health Development Plan of Congo. Health is the fourth, fifth and sixth point of the Millennium Development Goals. Information constraints mean that health is captured here by only two indicators: the time taken to access health infrastructures and morbidity. For the latter indicator, its rate in the Congo is estimated at 39% in 2005.

Nutrition
Eradicating extreme poverty and hunger is the first point of the Millennium Development Goals. The Economic Commission for Africa noted in 2005 that since the launch of these goals in Africa, the proportion of people without enough food has fallen by only 3 points. Hunger still prevails. This is particularly true in rural Africa. There are many indicators that capture this important dimension of poverty. Unfortunately in the case of this survey, the indicator selected only covers food problems.

Drinking Water
Water is considered to be man's first food. It fulfills two essential functions: physiological and biological. To deprive oneself of water is therefore to deprive oneself of life. It is a source of development and progress and has many applications. Access to water, its availability and quality is therefore a major challenge. In the case of our study, in order to assess this dimension, we refer to the partial indicators that are the water supply sources and the time taken to have access to a drinking water point. In Congo, 68.5% of households have access to a drinking water source.

Electricity
The role that electricity plays or has played in economic growth is well established.
The use of electricity therefore contributes to the improvement of living conditions and quality of work. It is therefore a source of development, progress, S. Ambapour Journal of Data Analysis and Information Processing well-being, comfort and leisure, and its applications are constantly increasing.
The partial indicators used to assess the electricity dimension of non-monetary poverty are: lighting sources, means of lighting and fuel types. According to the results of the survey, it emerges that the majority of the population uses kerosene lamps (70.1% of households) for lighting, compared to 27% of households subscribing to the National Electricity Company. For cooking purposes, 55.1% of households use firewood, against 1.3% using electricity.

Housing
Housing is an important dimension of poverty. Indeed, it is a crucial problem especially in urban areas for the economically weak, given the high cost of land and materials. In this case, it is difficult to have access to quality housing. And in this context, it is the poorest who find themselves in precarious housing, in areas where there is generally no infrastructure and services. Housing thus becomes a factor of exclusion and can be considered as an indicator of poverty. The indicators defined to measure the housing dimension of non-monetary poverty in this study are: the nature of roofs, walls, soil and the occupancy status of the dwelling. Concerning the material used for the roof, the survey revealed that 83.3% are covered with sheet metal and 15.7% with straw. As for the material of the walls, one house out of two has walls made of materials considered durable: cement blocks (39.3%) or clay bricks (11.9%). Overall, 55.5% of Congo's dwellings have a cement floor, compared to 41% with clay bricks.

Sanitation
Sanitation is closely linked to housing. It is, in a way, the infrastructure part of the latter (roads, sewage disposal system …). A certain quality of life and the assurance of health security are conditioned by a healthy and pleasant living environment. The partial indicators selected are the type of toilets, the way in which household waste and waste water is disposed of. In Congo, more than half of the dwellings (51.8%) have a sanitation system deemed to be healthy. With regard to household waste treatment, the survey results show the predominance of the use of incineration, which consists of burning waste.

Communication
As an essential right, as a "fundamental freedom", communication (better known as the right to information) is part of the first generation of human rights. In the context of poverty reduction, the communication dimension can be seen in terms of people's access to the media. Indeed, a man who is well informed about the problems of society is better able to contribute to the development of his country than one who is under-informed. The communication dimension is captured by indicators such as household ownership of a television, radio or telephone. It should be noted that among these indicators, only the radio is owned by more than half of Congolese households (56.5%). Television sets and telephones are more common in urban households, 38% and 48.9% respectively, compared to 3.9% and 9.8% in rural households. Journal of Data Analysis and Information Processing

Goods of Comfort, Equipment and Other Assets
While poverty is characterized first and foremost by a lack of the necessities of life, it is also characterized by a lack of material comfort. The results of the survey show that, with regard to household appliances, 8.1% of households own a refrigerator, 16.8% an iron and 8.9% a sewing machine. They also indicate that, for furniture equipment, 94.5% of households have a bed or mattress and 17.3% have a modern armchair. As for means of transport, 1.9% of households have a car, 1.7% a motorcycle, 6.2% a bicycle and 4.1% a pirogue. With regard to assets, the survey shows that 27% of Congolese households own land.

Multiple Correspondence Analysis of Nonmonetary Poverty Dimensions
The statistical approach based on the inertia criterion is used to study multidimensional poverty in Congo. In order to do so, the Multiple Correspondence Analysis (MCA) is then adopted for all the analyses that will follow. We first show how this technique can be applied to aggregate the non-monetary dimensions of poverty selected to construct the composite poverty indicator. The two main steps in the construction of this indicator are then indicated, followed by the preliminary and final MCA of the non-monetary dimensions of poverty corresponding to these two steps. The scores of different indicators of the final MCA are then analysed. We conclude by the determination of the incidence of multidimensional poverty in Congo.

Functional Form of the Composite Indicator of Poverty (CIP)
Asselin [35] is without a doubt the first to have used MCA to construct a composite poverty indicator. According to this author, let us adopt the following notations: K the total number of primary indicators; k I a primary indicator with k J categories; and by Subsequently the k J are binary variables 0/1 taking the value 1 when the population unit has the category k J . The statistical problem to be solved is: how to aggregate these primary indicators into a single simple composite indicator that has the property of being a good summary of the information provided by the initial indicators? The first step is then to perform MCA of the indicatrix matrix ( ) , X N J of N observations and K primary indicators decomposed into k J binary variables. This is done by applying the correspondence analysis program to the indicatrix matrix X. This computer program provides, as we know, a series of functions or factors defined on the set of observations noted by i F . Each factor corresponds to a positive real number, between 0 and 1, called the eigenvalue, which is noted by λ : 1 λ for the first factor (value of inertia on the first axis),

Steps in the Construction of the Composite Indicator of Poverty
The Other secondary criteria exist, such as measures of discrimination or sprawl on the first axis. Some automatic procedures implemented in some software such as the homogeneity analysis procedure in SPSS allow this selection to be carried out.
In principle, this second multiple correspondence analysis should improve the explanatory power of the first factorial axis.

Preliminary MCA
Preliminary MCA was performed with 5002 rows (households) and 81 columns (modalities). The list of 38 variables is presented in Table 1. The results of MCA are presented in Figure 1 that represent the configuration of points in projection plane formed by the first two axes. The first two eigenvalues are 1 0.2174 λ = the data. In other words, the data are arranged in a series that is not only on axis 1, but in the plane. This indicates that the factor 2 is a second-degree function of the factor 1: In the study of poverty, it is often classic to see on the first axis an opposition between rich and poor and to qualify this axis as "poverty-rich". This is indeed the case here. However, our analysis in the plane ( )  In the first quadrant ( ) , − − , that is 1 0 F < ; 2 0 F < , Poverty is mainly described in terms of accessibility to basic social services: health, education (primary and secondary), food market and transport. Indeed, the minimum time needed for each household to reach the nearest social service on foot is more than 30 minutes (considered as the poverty line). They only have access to an unsafe water source. In this framework, poverty is also described in terms of household living conditions, specifically those characterizing housing construction materials and sanitation. It can be seen that these poor illiterate or partially illiterate households have houses with straw roofs, walls and floors made of mud and the drainage of water and garbage is done in nature. They use wood for cooking. Although these households are poor, it should be noted that they own some assets: dugout canoe, bicycle, livestock, poultry and land.
In the quadrant ( ) , + − , i.e. 1 0 F < ; 2 0 F > , poverty is still found in its dimension of living conditions. These poor households, which have no access to electricity, live in houses without any comfort: no mattress, no modern chair, no refrigerator, no iron. The means of communication are also absent: no television, no radio, no telephone. As for sanitation, garbage is either buried or burned. Even worse, these households have food problems. The quadrant ( ) , + + , that is 1 0 F > ; 2 0 F > , characterizes an intermediate situation between poor and non-poor. Indeed, these literate households living in houses with solid roofs, solid walls, cement or tiled floors are either housed free of charge or are renters. Access to basic social services (schools, hospitals, food market, transportation) is within 30 minutes. The same is true for access to water, including a clean water source. However, these households do not own land or poultry. They use charcoal for cooking. The quadrant ( ) , + − , that is 1 0 F > ; 2 0 F < is the non-poor area. These wealthy households live in houses with sanitation toilets using an appropriate drainage system (sewerage and gutter systems) and a convenient system for the disposal of household waste, including public or NGO bins. They have access to modern lighting. As fuel for cooking, they use either gas or electricity.
In addition, these non-poor households have access to means of communication: radio, telephone and television. Not surprisingly, these households are equipped with modern conveniences and amenities such as modern chairs, refrigerators, irons, sewing machines, microcomputers, motorcycles and cars. Finally, it would not surprise anyone if we found that these households do not have food problems.

Final MCA on CIP Variables
The objective of this second MCA is to select the relevant variables to be used in the construction of the composite indicator poverty according to the procedure mentioned above. In the end, this procedure allowed only 20 variables to be selected. These variables are presented in Table 2. Thus, a second MCA was carried out comprising 5002 individuals and 20 variables (with 42 modalities). . In fact, the explanatory power of the first factor increased from 18.72% to 26.67% and that of the second factor from 7.56% to 11.03%. The plane ( ) 1, 2 F now explains nearly 38% of the total inertia. In Figure 2, the configuration of points is more sparse than in Figure 1, but still looks parabolic. And as before, the more one tends towards the right of the figure, the less one becomes poor.
This plane highlights three types of poverty: 1) infrastructure poverty characterized by poor access to basic infrastructure (health, education, food markets).
This poverty is closely linked to the policy and capacity of the State to provide the country with the necessary basic infrastructure that can contribute to im-   Table 3 provides the scores of the different indicators used as weights for the composite poverty indicator. Recall that the weights correspond to the standardized scores on the first factor axis. Modalities with a positive score increase well-being, while those with a negative score decrease it. The modalities with the highest positive scores are those related to comfort. These include possession of a refrigerator, iron, television and use of modern lighting. Thus, access to these goods and services is limited to a certain number of affluent households. On the other hand, the modalities with the highest negative scores on the first axis concern the most accessible goods and services. In our case, it is the housing under a straw roof and the materials of the dirt floor, the lack of drinking water, the lack of access to the food market, primary school and transport and finally illiteracy.

Weights of the CIP
As can be seen, the weightings provided by MCA give the latter an interesting property: to separate at best, poor and rich. The logic of this method is therefore clear: the rarer the rarity of a modality, the greater its weight. Thus, MCA assigns important weights to rare or luxury goods in increasing well-being. This logic therefore aims at a better identification of poor populations. A household will be all the poorer if it does not have access to basic goods that are accessible to the majority of the population.  They describe the share of each modality in the total inertia of the axis. The most contributing modalities are those relating to modern energy and more specifically, access to electricity and lighting mode. Next come those relating to the possession of certain goods such as irons and televisions. The square cosines indicate the quality of representation of the modalities on axis 1. The higher the square cosine, the more the modality is correlated with the axis. Here again, the best represented modalities are those related to modern energy, having the highest square cosines. Finally, the distance to the center is simply the square of the distance from the origin.

Incidence of Multidimensional Poverty
Households were classified according to the CIP. The results of this classification of households into two classes are given in Table 4. As this table shows, the incidence of poverty with a hierarchical classification of households corresponds to the weight of the poor class, which is 70.67%. This figure seems to be in line with results found in other African countries: 83.5% in Burkina Faso and 61% in Senegal [31]. Indeed, the fact is that studies on non-monetary poverty of basic needs show high rates of poverty exceeding often more than 2/3 of households. From this CIP classification, the multidimensional poverty line can be determined as follows:  [36]. So a multidimensional non-monetary poverty profile of basic needs can be produced.

Conclusions
The conceptual framework of poverty adopted in this work, offers another al-Journal of Data Analysis and Information Processing ternative for defining and studying poverty in Congo. Although it differs from the monetary approach, it usefully complements it, giving a broader meaning to the concept of poverty. It is in fact a contribution to the search for solutions to the various issues related to the problems of poverty and thus gives indications for a better perception of poverty.
This multidimensional approach to poverty is based on the calculation of a composite indicator constructed from a data analysis technique and takes several aspects into account. The indicator thus obtained makes it possible to classify households or groups of households and to draw up a multidimensional non-monetary poverty profile of basic needs.
The study revealed that Congolese households are not affected by the same type of poverty. Three types of non-monetary poverty were identified: poverty from the point of view of infrastructure, poverty reflecting the vulnerability of human existence, and poverty from the point of view of household comfort. Moreover, the incidence of poverty with a hierarchical classification of households corresponds to the weight of the poor class of about 70.67%.
The method used, although very interesting, has some limitations: 1) it does not allow the contribution of each of the dimensions of poverty to overall poverty to be determined; in fact, the index can only be broken down into sub-groups but not into a single dimension (health, nutrition, education, housing, etc.) and it is therefore difficult to know the causes of poverty; 2) it is not possible to establish a hierarchy of the three types of non-monetary poverty detected and prioritize which to fight.