Wye City Group Meeting on Rural Development and Agricultural Household Income Measuring under-nourishment : comparative analysis between parametric and non-parametric methods based on Burkina Faso agricultural survey FAO Head-Quarters, Rome, Italy June 2009 Moussa Kabore, Statistician

T ARGET Measuring undernourishment is necessary for the monitoring Millennium Development Goals, in particular the objective n°1: “reduce by half the proportion of people suffering from hunger”. At sub- national level, it enables: To carry out a cartography of food insecurity, To monitor food insecurity in time, To identify the most vulnerable groups within a country, To identify the causes of food insecurity, To evaluate the impact of policies and projects, in order to improve the decisions taken on the matter.

Several methods are used to determine the phenomenon. They are based on data sources of different nature. The idea of this paper is not to make an exhaustive inventory on the methods for estimating food insecurity but to do a comparative analysis on the two main methods used: the parametric method based on food balance sheets and the distribution of food consumption in the population, used by FAO at national level, the non-parametric method based on the FGT index (FOSTER, GREER and THORBECKE) and household survey micro-data, which was developed for the measurement of the incidence of monetary poverty and that we adapted for the measurement of the incidence of undernourishment.

The aim is to seek a possible convergence between the two methods in order to enable national statistical systems to regularly calculate the incidence of food insecurity at sub-national level depending on the nature of available data. We will initially describe the two methods before estimating the incidence of food insecurity with each method. Data used is related to rural area households survey (National annual crop sample survey data). We will then propose an estimation method taking urban areas into account.

DESCRIPTION OF PARAMETRIC METHOD The method is based on the distribution of per caput energy food consumption. This distribution is assumed to be log-normal. Practically, the incidence of undernourishment is estimated in the following way: Where: P (U) represents the proportion underfed population on total population, X is food energy consumption per individual, rl is the minimum energy requirement and F (X) is the food density function within the population. In the literature, we assume that F follows the lognormal law.

Its implementation requires the knowledge of two distribution statistics : the average per capita energy food consumption and the standard deviation of its distribution. Per capita food consumption is derived from national food balance sheets or from data from household surveys. Standard deviation estimates require necessarily households expenditures or food consumption survey data. However, its use at sub-national level requires the knowledge of these parameters for each regional entity, which can be difficult to obtain. DESCRIPTION OF PARAMETRIC METHOD

DESCRIPTION OF THE NON- PARAMETRIC METHOD It is based on the FGT index function which is described in the following way: Z : food consumption poverty line (in Kcal), Yi : energy consumed by the i th individual, q : number of individuals in the population considered as undernourished, N : total number of the population, and α that is a parameter (α = 0 in our case).

This FGT method is usually applied to calculate the incidence of monetary poverty (see paper content for mathematical purpose) ; The implementation of this method necessarily requires data from Household Expenditure or Food Consumption surveys. Food consumption poverty line ( Z) is derived from FAO food consumption table that is applied on each sample household member taking account its demographic characteristics; Food energy consumption per individual ( Yi ) is obtained in each household by dividing total food intake with the household population size; Each household member is assumed to be undernourished if Yi < Z

DATA PRESENTATION In the Burkina Faso context, farmer unit and rural household unit are the same (and 97% are crop producer). Data used for this paper come from national annual crop survey (Enquête permanente agricole) for 2006 and This survey is a PPS sample design into two degrees (706 Village unit and 4000 farmer unit). The survey questionnaire enables to set a balance between supplies and utilizations of each product used by each household member between October of the year n-1 to September of year N. In each household, quantity data are collected directly per product and per member who manipulated the food product (available from own production, sale, purchase, gift..) during the 12 last month. More specifically: crop production, opening and closing stock data come from direct observation. On supply, the following data are collected for each product, each member : Production, Purchase, Gifts, Initial Stock. In Utilizations the following are collected: Sale, Closing Stock, Gifts.

ESTIMATION OF ENERGY AVAILABILITY Food product balances at household level are used to deduct food consumption. Food energy conversion table (FAO, 1996) is used to convert physical quantities into food energy. Per capita food energy consumption is calculated by simple ratio between total energy consumed in the household with the household size. The consumption deduced from this data source cannot be used to measure food energy intake for each member (ex. Children who did not produce or buy products but are consumers). But in the absence of an investigation on individual consumption, we use average energy consumption in the household as proxy which in any case improves the analysis of undernourishment.

R ESULTS

F OOD CONSUMPTION N ORMALITY TEST IN RURAL AREA Le kernel density estimation in STATA is used to generate the distribution of food consumption The test concludes to the normality of data distribution. Distribution parameters Mean: 2704 kcal. (It can be also obtained from the food balance) deviation : 1320 kcal Where the Skewness value is egal to

After applying the procedure of Skewness value (K) annulations we obtain this new distribution of ln(contete-K) :

E STIMATION OF M INIMUM R EQUIRED E NERGY (MRE) The minimum required energy is estimated in the following way: rl = Σ ij (MRE ij * P ij ) MREij = minimum required food energy per person per day by age and sex (from FAO table) Pij = population structure by age and sex (from demographic questionnaire of agricultural annual survey) we obtain: rl =2102 Kcal/pers/day in rural Application of the parametric method assume the normality of data distribution

ESTIMATION OF NDERNOURISHMENT USING PARAMETRIC METHOD Food consumption (X) is distributed as log-normal law N ( 2704 ; 1320) P(X < rl) =F((rl-2704)/ 1320) = 0,324 F is the log-normal density function. Result: 32,4% of the rural population were under feed in 2006

ESTIMATE OF UNDERNOURISHMENT USING NON-PARAMETRIC METHOD We calculate average energy consumption per capita for each household from the data of the investigation. Each household (and each member) is classified as undernourished if the average per caput food consumption is less than the minimum required energy (2102 Kcal/pers/day). The characteristic of the household is attributed to each member. Result : 35,7% of rural population are classified undernourished in 2006.

CONCLUSION The non- parametric method can be applied on households data from national agricultural survey; The results obtained seem close to the parametric method but the tests statistics of convergence must continue; The non- parametric method based on data from national crop and food security surveys enhance estimation quality by avoiding price effect encountered in data from household expenditure survey ; Agricultural survey in Burkina Faso and some Sahelian countries is conducted every year, which allows the annual calculation of this indicator; The non- parametric method allows cross-analysis of household undernourishment status with other socio- economic variables and the ability to monitor MDG at sub national level.

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