IFPRI Discussion Paper 01154 January 2012 Resource-Rich Yet Malnourished Analysis of the Demand for Food Nutrients in the Democratic Republic of Congo John Ulimwengu Cleo Roberts Josee Randriamamonjy Development Strategy and Governance Division INTERNATIONAL FOOD POLICY RESEARCH INSTITUTE The International Food Policy Research Institute (IFPRI) was established in 1975. IFPRI is one of 15 agricultural research centers that receive principal funding from governments, private foundations, and international and regional organizations, most of which are members of the Consultative Group on International Agricultural Research (CGIAR). PARTNERS AND CONTRIBUTORS IFPRI gratefully acknowledges the generous unrestricted funding from Australia, Canada, China, Denmark, Finland, France, Germany, India, Ireland, Italy, Japan, the Netherlands, Norway, the Philippines, South Africa, Sweden, Switzerland, the United Kingdom, the United States, and the World Bank. AUTHORS John Ulimwengu, International Food Policy Research Institute Research Fellow, Development Strategy and Governance Division, & West and Central Affrica Office j.ulimwengu@cgiar.org Cleo Roberts, International Food Policy Research Institute Intern, Development Strategy and Governance Division Josee Randriamamonjy, International Food Policy Research Institute Research Analyst, Development Strategy and Governance Division Notices 1. IFPRI Discussion Papers contain preliminary material and research results. They have been peer reviewed, but have not been subject to a formal external review via IFPRI’s Publications Review Committee. They are circulated in order to stimulate discussion and critical comment; any opinions expressed are those of the author(s) and do not necessarily reflect the policies or opinions of IFPRI. 2. The boundaries and names shown and the designations used on the map(s) herein do not imply official endorsement or acceptance by the International Food Policy Research Institute (IFPRI) or its partners and contributors. Copyright 2012 International Food Policy Research Institute. All rights reserved. Sections of this material may be reproduced for personal and not-for-profit use without the express written permission of but with acknowledgment to IFPRI. To reproduce the material contained herein for profit or commercial use requires express written permission. To obtain permission, contact the Communications Division at ifpri-copyright@cgiar.org. mailto:j.ulimwengu@cgiar.org iii Contents Abstract v Acknowledgments vi 1. Introduction 1 2. Modeling the Demand for Food Nutrients 4 3. Descriptive Analysis 7 4. Expenditure and Own-Price Elasticities 22 5. Nutrient Elasticities 25 6. Concluding Remarks 29 References 30 iv Tables 3.1—Rural food budget shares, per capita expenditures, and prices 12 3.2—Urban food budget shares, per capita expenditures, and prices 12 3.3—Average nutrient consumption 13 3.4—Three main food contributors by nutrients 13 3.5—Sources of average nutrient consumption (%) 15 3.6—Correlations between nutrients 15 4.1—Expenditure elasticities of food demand among rural and urban households at population means 23 4.2—Hicksian price elasticities of food demand among rural and urban population 23 5.1—Nutrient elasticities with respect to household expenditure and food prices in rural areas 26 5.2—Nutrient elasticities with respect to household expenditure and food prices in urban areas 26 Figures 3.1—Food expenditures 8 3.2—Health expenditures 8 3.3—Housing expenditures 9 3.4—Education expenditures 9 3.5—Leisure expenditures 9 3.6—Cereal expenditures 10 3.7—Dairy expenditures 10 3.8—Tuber expenditures 11 3.9—Fruit and vegetable expenditures 11 3.10—Average nutrient deficiencies (%) 16 3.11—Mean nutrient intakes in deficient households as a percentage of mean nutrient intakes of sufficient households (%) 16 3.12—Deficiency by gender of household head (%) 17 3.13—Vitamin A deficiency by province (%) 17 3.14—Vitamin C deficiency by province (%) 18 3.15—Vitamin B6 deficiency by province (%) 18 3.16—Protein deficiency by province (%) 18 3.17—Calorie deficiency byprovince (%) 19 3.18—Vitamin E deficiency by province (%) 19 3.19—Folate deficiency by province (%) 19 3.20—Riboflavin deficiency by province (%) 20 3.21—Iron deficiency by province (%) 20 3.22—Zinc deficiency by province (%) 20 3.23—Vitamin B12 deficiency by province 21 v ABSTRACT Endowed with 80 million hectares of arable land (of which only 10 percent are used), diverse climatic conditions, and abundant water resources, the Democratic Republic of Congo (DRC) has the potential to become the breadbasket of the entire African continent. Instead, the country is one of the most affected by malnutrition. The DRC has the highest number of undernourished persons in Africa and the highest prevalence of malnutrition in the world. As a result, child stunting and infant mortality rates in the DRC are also among the highest in the world. Overall, at least 50 percent of the population is deficient in vitamin B12, calories, riboflavin, iron, vitamin E, folate, and zinc; vitamins A, C, and B6, for which palm oil and cassava are the main sources, are generally consumed in sufficient quantities. Across provinces, there is significant heterogeneity. All nutrients exhibit positive expenditure elasticities in both rural and urban areas; however, as expected, the expenditure elasticities of all nutrients are higher in urban areas than in rural areas. In rural areas, strategies to improve nutrition will need to use instruments that attack malnutrition directly rather than relying simply on rising incomes. With respect to prices, an increase in own price is expected to have a nonpositive effect on all nutrients. Our results also suggest significant substitution effects. Overall, our results highlight the paradox of the DRC, a country with huge potential for agricultural development but incapable of feeding itself in terms of both quantity and quality of nutrients. Keywords: nutrients, elasticity, poverty, demand, expenditure, price vi ACKNOWLEDGMENTS The authors acknowledge data collection support provided by the Congolese National Institute of Statistics. This paper benefited from helpful discussions and invaluable comments from Olivier Decker. 1 1. INTRODUCTION Context The Democratic Republic of Congo (DRC) is one of the countries most affected by malnutrition in the world. It received the worst Global Hunger Index (GHI) scores for 2009, 2010, and 2011 and recorded the greatest increase in GHI scores from 1990 to 2010. The DRC has the highest number of undernourished persons in Africa and the highest prevalence of malnutrition in the world. As a result, child stunting and infant mortality rates in the DRC are also among the highest in the world. Malnutrition is a particularly important development problem because its health and economic implications are far-reaching. Indeed, nutrient deficiencies impair immune systems, making it difficult or impossible for the physically vulnerable, such as pregnant women, children, disabled persons, and the elderly, to ward off common infections (Skoufias et al. 2009). The malnutrition of young children and mothers, in particular, poses a great risk to future social and economic development in poor countries (Abdulai and Aubert 2004). For example, if pregnant women and women of childbearing age obtain proper nutrition, they can avoid pregnancy complications, improve birth weights, and reduce infant mortality, as well as ward off stunting and wasting. Furthermore, proper nutrition can enhance children’s cognitive development, preparing them for the future (Sachs et al. 2004; Smith, Ruel, and Ndiaye 2004). The nutrition situation in the DRC is extremely alarming. According to the GHI published by the International Food Policy Research Institute (IFPRI), the DRC is the hungriest country in the world (IFPRI 2011; Pfingu 2011). Close to 75 percent of the total population is undernourished (UNDP 2010). Life expectancy in the DRC is around 47 years for men and 51 years for women; one in five children die before reaching age 5 (Pfingu 2011; UNDP 2010). In 2005 the Congolese Ministry of Health cited malnutrition as the underlying cause in 48 percent of cases of infant mortality (WHO 2005). Although chronic malnutrition, especially among women and children, declined in the DRC from 1995 to 2000, acute malnutrition increased over the same period (Tollens 2003). As of 2007, 45.8 percent of children suffered from stunting (low height for their age), 28.2 percent from being underweight for their age, and 14 percent from wasting (low weight for height). In addition, 61.1 percent of preschool-age children suffered from a subclinical deficiency in vitamin A, and 70.6 percent of children under age 5 and 67.3 percent of pregnant women suffered from anemia (WHO 2011). For many nutrients, there is little information about deficiencies (WHO 2011). Currently, the provinces most affected by child malnutrition are Nord-Kivu, Sud-Kivu, Katanga, and Bandundu (Pfingu 2011). The geographic distribution of malnutrition has likely changed over the years, as food supply routes have shifted from Equateur, Orientale, and the two Kivu provinces to Bandundu and the two Kasai provinces (Tollens 2003). DRC households’ budget shares allotted to food declined between 1986 and 2004, which suggests, according to Engel’s law, that their well-being improved (De Herdt, Marivoet, and Marysse 2008). However, their consumption of protein and overall calories has been falling since the 1960s as households have switched to less-expensive foods. In recent years the DRC’s armed conflict disrupted food markets further and worsened nutrient deficiencies among the poor (De Herdt, Marivoet, and Marysse 2008; Tollens 2003; Pfingu 2011). Households’ consumption of milk, dried or smoked fish, chicken, groundnuts, beans, and alcohol has declined because of price increases and income decreases. However, rice and fresh or frozen fish consumption has increased because of low international prices. Vegetable consumption has changed little, probably due to international programs supporting their production (Tollens 2003). The major sources of DRC households’ calories are, in order, cassava, bread, rice, and palm oil. Average protein intake in the DRC is just 39.5 grams per day. Major sources of protein include wheat (bread), rice, fresh and frozen fish, cassava, vegetables, and chicken (Tollens 2003). Interventions to fight malnutrition in the DRC are more than urgent. However, for such interventions to yield the desired outcomes, research-based knowledge is necessary to guide their elaboration, implementation, and monitoring. The purpose of this study is precisely to generate knowledge that can improve policymaking in the area of food and nutrition security. As pointed out by 2 Ecker and Qaim (2010), broader nutritional policies require better knowledge about the population’s food and nutrient consumption patterns and responses to changes in household income and food prices. The recent review by Ecker and Qaim (2010) shows that knowledge about the effects of income and price changes on micronutrient malnutrition is scarce, especially for Sub-Saharan Africa. Abdulai and Aubert (2004) for Tanzania and Ecker and Qaim (2010) for Malawi are the only studies that provide income and price elasticities for several micronutrients. Income and selected price elasticities for key micronutrients such as iron and vitamin A are also available from Behrman and Deolalikar (1987) for rural South India, Pitt (1983) for rural Bangladesh, Behrman and Wolfe (1984) for Nicaragua, and Skoufias et al. (2009) for Mexico. However, as Skoufias et al. note, elasticity estimates differ substantially between countries and nutrients, so the transferability of empirical results is not assured. In this paper, we use the 2005 1-2-3 DRC household survey to analyze nutrient deficiencies and estimate income and price elasticities for a variety of nutrients using the Quadratic Almost Ideal Demand System (QUAIDS). Literature Review Iron deficiency, which is associated with anemia, is the most widespread nutritional deficiency in the world; it affects mainly women and children (Diaz, de las Cagigas, and Rodriguez 2003; WHO 2006). Deficiencies in vitamins C, B6, and B12, as well as folate, also contribute to anemia and generalized fatigue (Levine et al. 1999). Vitamin B6 deficiency additionally suppresses the immune system and invites dermatitis. In order for humans to digest vitamin B6 properly, they must have a sufficient supply of riboflavin (Powers 2003; MIC 2011). Iron and vitamin E deficiencies increase individuals’ vulnerability to bacterial infections, and zinc deficiencies lead to a plethora of maladies, including stunting, weight loss, torpor, and delayed sexual development (FAO 1997; Victoria et al. 2008; NIH ODS 2011; UNICEF 2011; Rodriguez, Cervantes, and Ortíz 2011; CDC 2011; WHO 2011). Alderman and Higgins (1992) and Abdulai and Aubert (2004) argue that malnutrition is the result of a lack of purchasing power and resource control. Evidence suggests that countries with higher per capita incomes tend to have lower rates of malnutrition (Haddad et al. 2003). Moreover, Subramanian and Deaton (1996) find that as households’ incomes (expenditures) rise, the number and cost of the calories they consume also rise. However, increases in total food consumption are likely to be smaller than those in total income, and there is likely to be a lag between changes in households’ consumption possibilities and changes in their actual consumption (Alderman and Higgins 1992; Engel 1857; De Herdt, Marivoet, and Marysse 2008; Bhargava 1991). As many households in developing countries are largely dependent on agriculture, rates of malnutrition are likely seasonal. Furthermore, Subramanian and Deaton (1996) observe that some foods that are inexpensive are rich in calories, whereas some expensive foods are poor in calories. Thus, while increased income expands households’ consumption options, it alone does not necessarily bring improved nutrition. In addition to income, many other factors affect malnutrition rates. Countries with high female secondary school enrollment rates, high percentages of households with access to safe water, and low Gini coefficients tend to have lower rates of malnutrition than others (Haddad et al. 2003). Smith, Ruel, and Ndiaye (2004), for instance, observe that low food intake, poor health, poor caregiving, low maternal education, little access to safe water and sanitation, low economic status, and low women’s status all contribute to malnutrition. Furthermore, malnourished women often give birth to low-weight babies who have difficulty thriving. Severely malnourished women may have trouble breastfeeding, which can only exacerbate their children’s nutritional deficiencies. One major form of malnutrition is undernutrition. A lack of calories can disrupt individuals’ physical and mental growth and lead to repeated infections (FAO 1997; UNICEF 2011; Victoria et al. 2008). However, as pointed out by Skoufias et al. (2009), an increase in calorie intake does not in itself defeat malnutrition. Beyond examining income and food availability, analyzing the nutrient composition in various foods is essential. Although the amount of food available to each household is an important indicator of well-being (Alderman and Higgins 1992), it does not necessarily indicate the quality of 3 nutrient intake. For example, De Herdt, Marivoet, and Marysse (2008) found that households consumed more calories per capita in absolute terms in 2004 than in 1986, but the quality of households’ consumption declined between the two periods. Indeed, while households consumed more grains and fats in 2004, they consumed much less protein compared to 1986. It follows that malnutrition may be due to a lack of access to required nutrients, or it may be due to a lack of knowledge about nutrition (Abdulai and Aubert 2004). Therefore, it is important to devise policies with high potential to reduce malnutrition resulting from nutrient deficiencies (Alderman and Higgins 1992). If an increase in income precipitates households’ consumption of higher-nutrient foods, then income transfers may be a good way to decrease deficiencies (Skoufias et al. 2009). If not, then further measures may be required to improve nutrition. In any case, nutrient deficiencies can lead to serious and chronic health problems. 4 2. MODELING THE DEMAND FOR FOOD NUTRIENTS Ecker and Qaim (2010) argue that a demand model based on neoclassical theory is inadequate for calculating the elasticities of micronutrients because such a model would assume that consumers are very informed about consumption possibilities and how they can maximize their utility. In the absence of adequate education about nutrition, such assumptions are unfounded. Furthermore, single-equation models do not account for interdependencies among foods. Standard demand models are inadequate to predict observed consumption behavior, especially across multiple income levels, because as income levels change, the income elasticities of various goods are also likely to change (Banks, Blundell, and Lewbel 1997; Skoufias et al. 2009). In fact, other studies have found that linear models misspecify demand curves for particular nutrients (Skoufias et al. 2009; Abdulai and Aubert 2004). Also, according to Banks, Blundell, and Lewbel (1997), although a linear model is adequate to demonstrate overall food demand, it is inappropriate for other goods, especially luxuries. As some foods are considered luxuries and others necessities, regardless of their nutritional value, linear models are unlikely to capture nutrient demand (Subramanian and Deaton 1996; Abdulai and Aubert 2004). Using nonsaturating Engel curves based on a log-linear demand model reflects constrained utility maximization. Under such an assumption, the sum of the expenditure elasticities of all expenditure categories must equal unity. This means that households always consume at the edge of their opportunity sets and that there is no point inside that boundary at which they will stop consuming (at which they reach saturation) (Moneta and Chai 2010). Liviatan (1961), however, suggests that ordinary least squares estimates of Engel curves biases necessities’ elasticities upward and luxuries’ downward, because some households might not use their entire budgets once they attain a certain income level (Deaton and Muellbauer 1980; Moneta and Chai 2010). Because modeling some individuals’ demand requires extra income terms, parsimony, according to Banks, Blundell, and Lewbel (1997), allows for a quadratic model, using a quadratic of the natural logarithm of income. As Engel suggested that the relationship between luxuries and income is likely geometric, this type of estimation allows food expenditures’ status to vary from necessities to luxuries (Zimmerman 1932; Engel 1857; Banks, Blundell, and Lewbel 1997). Their Quadratic Almost Ideal Demand System (QUAIDS) allows goods to be both luxuries and necessities, depending on household income levels (Banks, Blundell, and Lewbel 1997; Abdulai and Aubert 2004; Ecker and Qaim 2010). As previously mentioned, some foods, like other items, may be luxuries for some but necessities for others, while items in both categories might have bioessential nutrients. Therefore, QUAIDS may be the best way to illustrate their individual elasticities. In addition to finding the model specification, errors in variables pose challenges in estimating Engel curves. Rather than being based on total income or expenditures, Liviatan (1961) argues, purchase decisions are based on “permanent income.” Current income may not be an appropriate proxy for permanent income. To correct for possible endogeneity, measurement error, and “nonnormality of errors,” it is possible to employ a generalized method-of-moments approach to estimate the food expenditures share (Hausman, Neweya, and Powell 1995; Banks, Blundell, and Lewbel 1997; Abdulai and Aubert 2004). In this paper, nutrient expenditures and price elasticities are derived from the QUAIDS model, an extension of the Almost Ideal Demand System (AIDS) developed by Deaton and Muellbauer (1980). The AIDS model is part of the price-independent generalized logarithmic (PIGLOG) class of demand models and has budget shares that are linear functions of log total expenditure (Muellbauer 1976). Banks, Blundell, and Lewbel (1997), however, show that using AIDS can be misleading if there is nonlinearity in the budget share equations and thus developed QUAIDS, which has quadratic budget shares that are in log of total expenditure. Following Banks, Blundell, and Lewbel (1997), QUAIDS has indirect utility functions (V) of the form 5 𝑙𝑛 𝑉 = ��𝑙𝑛𝑚−𝑙𝑛𝑎(𝑝) 𝑏 (𝑝) � −1 + 𝜆 (𝑝)� −1 , (1) where m represents total food expenditure and p a vector of food prices. The term in squared brackets is the indirect utility function of a demand system of the PIGLOG preference class. The functions ln a(p) and b(p) are, respectively, the translog and the Cobb-Douglas price aggregator functions defined by ln a(p) = α0 + ∑ αi ln pi n i=1 + 1 2 ∑ ∑ γij ln pi n j=1 n i=1 ln pj, (2) 𝑏(𝑝) = ∏ 𝑝𝑖𝛽𝑖𝑛 𝑖=1 (3) The price aggregator function λ(p) is given by 𝜆(𝑝) = ∑ 𝜆𝑖 𝑛 𝑖=1 ln𝑝𝑖 (4) where ∑ 𝜆𝑖𝑖 = 0. Applying Roy’s identity to equation (1), food budget shares for each food group can be expressed as 𝑤𝑖 = 𝛼𝑖 + ∑ 𝛾𝑖𝑗 𝑙𝑛 𝑝𝑗 𝑛 𝑗=1 + 𝛽𝑖 𝑙𝑛 � 𝑚 𝑎(𝑝) � + 𝜆𝑖 𝑏(𝑝) �𝑙𝑛 � 𝑚 𝑎(𝑝) �� 2 . (5) Differentiating the budget share equations with respect to expenditure (m) and price (p) yield the following expenditure and price elasticities, respectively: µ𝑖 ≡ 𝜕𝑤𝑖 𝜕 𝑙𝑛𝑚 = 𝛽𝑖 + 2𝜆𝑖 𝑏(𝑝) �𝑙𝑛 � 𝑚 𝑎(𝑝) ��. (6) µ𝑖𝑗 ≡ 𝜕𝑤𝑖 𝜕 𝑙𝑛𝑝𝑗 = 𝛾𝑖𝑗 − µ𝑖 �𝛼𝑖 +∑ 𝛾𝑗𝑘𝑘 𝑙𝑛𝑃𝑘� − 𝜆𝑖𝛽𝑗 𝑏(𝑝) �𝑙𝑛 � 𝑚 𝑎(𝑝) �� 2 . (7) In terms of µ𝑖, expenditure elasticities are given by 𝑒𝑖 = 1 + 𝜇𝑖 𝑤𝑖 . (8) Similarly, the Marshallian or uncompensated price elasticities of demand can be expressed as 𝑒𝑖𝑗𝑢 = 𝜇𝑖𝑗 𝑤𝑖 − 𝛿𝑖𝑗, (9) where 𝛿𝑖𝑗 is the Kronecker delta taking the value of 1 if i=j and 0 otherwise. Using the Slutsky equation, the Hicksian or compensated price elasticities are given by 𝑒𝑖𝑗𝑐 = 𝑒𝑖𝑗𝑢 − 𝑤𝑗𝑒𝑖, (10) Theoretical restrictions of adding up, homogeneity, and Slutsky symmetry are imposed by setting ∑ 𝛼𝑖𝑖 = 0, ∑ 𝛽𝑖𝑖 = 0, ∑ 𝛾𝑖𝑗𝑖 = 0, ∑ 𝛾𝑖𝑗𝑗 = 0, ∑ 𝜆𝑖𝑖 = 0, 𝛾𝑖𝑗 = 𝛾𝑗𝑖. (11) 6 The system of nonlinear budget share equations specified in equation (5) can be estimated using either maximum likelihood (Poi 2002) or nonlinear seemingly unrelated regressions (Poi 2008). Following Ecker and Qaim (2010), nutrient elasticities with respect to expenditure (𝐸𝑁) and food prices (𝑒𝑖𝑁) are estimated as follows: 𝐸𝑁 = ∑ ∑ 𝑐𝑗𝑓𝑁𝑠𝑗𝑓𝑞𝑗𝐸𝑗𝑓𝑗 ∑ ∑ 𝑐𝑖𝑗𝑁𝑠𝑖𝑓𝑞𝑗𝑓𝑗 (12) 𝑒𝑖𝑁 = ∑ ∑ 𝑐𝑗𝑓𝑁𝑠𝑗𝑓𝑞𝑗𝑒𝑖𝑗𝑓𝑗 ∑ ∑ 𝑐𝑖𝑗𝑁𝑠𝑖𝑓𝑞𝑗𝑓𝑗 (13) 7 3. DESCRIPTIVE ANALYSIS The 1-2-3 survey, a nationally representative survey conducted in the DRC in 2004–05 from which the data for this paper originate, includes three major components: (1) employment, (2) informal sector, and (3) household consumption. Data for all three components were collected simultaneously by the same interviewer in each household. The consumption component, used in this study, is composed of 21 modules of questions. Prior to the 1-2-3 survey, only two other national surveys with household-level data had been undertaken since the late 1980s, the MICS1 in 1995 and the MICS2 in 2001. Both investigated women and children’s issues and were directed by international organizations rather than the National Institute of Statistics. Due to the lack of household data in the DRC, the 1-2-3 survey is a vital source of information. In Kinshasa, the capital city, neighborhoods were selected randomly, and within those neighborhoods, households were selected randomly. Outside Kinshasa, the country was divided into three strata: (1) all major cities outside Kinshasa, (2) all 179 towns of the DRC, and (3) all the districts of the DRC. This last category was altered slightly because of the varying population sizes of the districts. All districts with populations of more than one million people were divided into subdistricts of approximately equal size and composition. In total, there were 38 districts for the purposes of the survey. In rural areas, villages were selected randomly and households within those villages were selected randomly. Overall, 13,688 households were interviewed with a response rate of 95 percent (INS 2008). Engel Curves The relationship between food expenditures and income is called Engel’s Law (Zimmerman 1932; Lewbel 2008; Moneta and Chai 2010). The graphical representation of Engel’s Law, with total income on the x-axis and food expenditure shares on the y-axis, is called an Engel curve (Banks, Blundell, and Lewbel 1997; Lewbel 2008; Moneta and Chai 2010). The Engel curve represents income, not price, elasticity of demand (Lewbel 2008). Whereas Engel did not propose linear relationships between budget shares for items other than food, the application of the Engel curve has spread beyond just food expenditures (Zimmerman 1932; Lewbel 2008; Moneta and Chai 2010). Zimmerman (1932) postulates that Engel’s Law breaks down in cases where the income elasticity of demand for food is unusually high or where the elasticities of other goods are unusually low. He says that as households move out of the lowest income ranks, their first major increase in food purchases comes from a change in the type of food purchased rather than a simple increase in food expenditures. Among German peasants in the 1880s, von der Goltz found that the proportion of food expenditures actually rose with income, because food, housing, and other expenditures are determined by custom (Zimmerman 1932). This observation supports the idea that the household elasticities of particular foods may vary outside the bounds of the elasticity of food in general. The proportion of food expenditures among the poorest likely rises slowly as incomes rise because these households likely switch from cheap foods to more expensive ones (Zimmerman 1932). Therefore, the share of income spent on food may not decline immediately. Moreover, the relationship between the proportion of food expenditures and income is likely not totally arithmetic. Zimmerman concludes that Engel’s Law holds only at particular levels of income. Outside those bounds, where foods can switch from bare necessities to luxuries or in contexts where the proportion of food is fixed culturally, Engel’s Law fails. Similarly, Banks, Blundell, and Lewbel (1997) find that elasticity levels vary with income. For U.K. households, they find that the linear logarithmic expenditure share gives a robust demonstration of behavior for food. For other goods, however, more terms must be included. DRC Engel curves are represented in Figures 3.1–3.9.The Engel curves in Figures 3.1 through 3.5 for household expenditures represent expected behaviors as households’ total expenditure levels rise. As Engel predicted, food expenditure shares rise, then fall dramatically as total expenditures rise, as Figure 3.1 shows. In Figure 3.2, health expenditures rise sharply at first, then rise more slowly between household expenditures of CDF 36,300 and 1,202,600, and then rise sharply for households with log 8 expenditures above CDF 8,886,100. Housing expenditures comprise 30 percent of the poorest households’ budgets. The corresponding Engel curve in Figure 3.3 suggests a minimum level of housing spending that increases with total expenditures. Education expenditure shares, represented in Figure 3.4, exhibit a similar trend, falling sharply and then rising. The inflection point may reflect households’ switch from public to private schools as their total expenditures increase, or sharp differences in education costs between provinces (De Herdt, Titeca, and Wagemakers 2010). For health, housing, and education, in Figures 3.2 through 3.4, the quadratic fit predicts spending behavior for households with log expenditures between CDF 59,900 and 8,886,100, but not for the richest households. Leisure expenditure, as expected, rises as total expenditures rise in Figure 3.5. The trend indicated by the quadratic fit is perfectly linear. Although the quadratic fit does not match the nonparametric line exactly, the trend is clearly increasing. Figure 3.1—Food expenditures Source: Authors’ calculations from the 1-2-3 survey data. Figure 3.2—Health expenditures Source: Authors’ calculations from the 1-2-3 survey data. 9 Figure 3.3—Housing expenditures Source: Authors’ calculations from the 1-2-3 survey data. Figure 3.4—Education expenditures Source: Authors’ calculations from the 1-2-3 survey data. Figure 3.5—Leisure expenditures Source: Authors’ calculations from the 1-2-3 survey data. 10 Although many studies suggest that food budget shares of cereals should decline as food expenditures increase (Regmi 2001; Seale, Regmi, and Bernstein 2003; Abdulai and Aubert 2004; De Herdt, Marivoet, and Marysse 2008), in this case richer households spend a greater share of their food budgets on cereals than poor households do. The quadratic fit closely predicts spending behavior at different income levels. It is possible that this pattern reflects a decrease in self-production as household food expenditures rise. Fruit and vegetable shares are predicted closely by the quadratic fit in Figure 3.9. They decline as food expenditures rise, sharply at first, then more gradually. For the richest households in the sample, fruit and vegetable expenditure shares approach zero. The quadratic fit is almost linear. Tuber expenditure shares fluctuate greatly among different levels of food expenditures. They show a general downward trend, which the quadratic fit captures imperfectly. Figure 3.6—Cereal expenditures Source: Authors’ calculations from the 1-2-3 survey data. Figure 3.7—Dairy expenditures Source: Authors’ calculations from 1-2-3 survey data. 11 Figure 3.8—Tuber expenditures Source: Authors’ calculations from the 1-2-3 survey data. Figure 3.9—Fruit and vegetable expenditures Source: Authors’ calculations from the 1-2-3 survey data. Food Consumption Patterns In both rural and urban areas, meat and fish are the most expensive food group and tubers the least expensive (Tables 3.1 and 3.2). Therefore, a 1 percent change in the price of meat and fish should be expected to have a bigger impact on households’ budget compositions than a change in the price of tubers, through substitution and income effects. Despite the extreme difference in prices for tubers and meat and fish, tubers and meat and fish comprise the greatest and second-greatest shares, respectively, of households’ food expenditures in rural areas. In urban areas, meat and fish contribute the most to household food expenditures, followed closely by cereals, the second-least-expensive food group. The difference in the expenditure shares of cereals and tubers between rural and urban households may be due to staple self-production in rural areas. It is possible that many households grow their own staples and fruits and vegetables, in which case the items they buy are luxuries or complements to self-produced foods. 12 Table 3.1—Rural food budget shares, per capita expenditures, and prices Budget Share Expenditure per Capita per Day (CDF) Price in CDF/kg Mean St. Dev. Mean St. Dev. Mean St. Dev. Food 1.00 1.00 160.03 161.99 Cereals 0.12 0.14 29.30 50.55 400.54 203.18 Tubers 0.30 0.17 42.69 42.15 157.66 112.68 Legumes and nuts 0.09 0.09 14.53 22.51 370.13 232.92 Meat and fish 0.26 0.15 39.82 54.98 1,209.16 1,351.07 Fruits and vegetables 0.14 0.09 18.85 21.40 301.86 209.44 Fat and oil 0.09 0.07 13.64 19.07 390.32 276.16 Other 0.00 0.02 11.58 15.21 899.95 515.99 Source: Authors’ calculations from the 1-2-3 survey data. Table 3.2—Urban food budget shares, per capita expenditures, and prices Budget Share Expenditure per Capita per Day (CDF) Price in CDF/kg Mean St. Dev. Mean St. Dev. Mean St. Dev. Food 1.00 1.00 218.78 205.70 Cereals 0.24 0.14 51.24 56.18 432.39 245.73 Tubers 0.19 0.13 35.39 34.85 328.04 559.14 Legumes and nuts 0.08 0.07 15.65 21.33 509.03 219.97 Meat and fish 0.25 0.12 55.79 69.97 1,322.75 597.68 Fruits and vegetables 0.14 0.07 26.79 28.66 472.88 210.30 Fat and oil 0.09 0.05 18.87 23.68 554.23 377.50 Other 0.02 0.03 15.92 22.49 1356.46 636.14 Source: Authors’ calculations from the 1-2-3 survey data. In Malawi and Mexico, places where poor households depend on just one staple for most of their calories, nutrient deficiencies are likely to be both acute and chronic (Skoufias et al. 2009; Ecker and Qaim 2010). All households that Ecker and Qaim (2010) and Skoufias et al. (2009) study suffer from deficiencies in iron and vitamin A because they rely mainly on maize for their calories. Ecker and Qaim’s sample suffers additionally from zinc and vitamin B12 deficiencies and Skoufias et al.’s from calcium deficiencies. The poorest members of Skoufias et al.’s sample suffer from zinc and vitamin C deficiencies. Whereas in Skoufias et al.’s Mexican sample the nutrients in which households face deficiencies are also those to which they are most responsive with changing income, except zinc, households in Ecker and Qaim’s Malawi example exhibit low expenditure elasticities of demand for the nutrients in which they are deficient. As Table 3.3 shows, average households in the sample consume, per capita, 1959.2 calories per day, more than the required 1,706.4 but less than the 2,049.8 recommended for optimal health. They face deficiencies in vitamin E, riboflavin, folate, vitamin B12, iron, and zinc. Households’ intakes of protein and vitamin B6 are higher than both the required and recommended values, at 40.4 g and 2.0 μg, respectively. Their intakes of vitamins C and especially A far exceed the recommended values, due to high consumption of palm oil and cassava, which are abundantly available in the DRC. Table 3.4 shows that the largest single contributors of calories are maize flour, palm oil, and cassava flour, all from the three food groups from which households obtain most of their calories: cereals, fats, and tubers. Likewise, the greatest contributors of folate, iron, zinc, and vitamin B12 come from the food groups that provide most of those nutrients. For nutrients in which households are sufficient the same is true. Households’ intake of vitamin C comes mainly from cassava in various forms: cassava flour, chikwangues (cassava prepared in banana leaves), and whole cassava. The extremely large average intake of vitamin A comes from palm oil, which is an important part of West and Central African diets (Atinmo and Bakre 2003). 13 Table 3.3—Average nutrient consumption Food Group Calories (kcal) Protein (g) Vitamin A (μg RE) Vitamin E (mg) Vitamin C (mg) Riboflavin (mg) Vitamin B6 (μg ) Folate (μg DFE) Vitamin B12 (μg ) Iron (mg) Zinc (mg) Cereals 566.0 12.2 0.2 0.9 0.0 0.2 0.4 29.9 0.0 3.7 2.4 Tubers 541.3 4.8 241.1 1.6 118.1 0.1 1.2 67.9 0.0 3.3 1.2 Legumes and nuts 169.6 8.9 124.3 0.9 1.4 0.1 0.1 113.0 0.0 2.7 1.1 Meat and fish 80.6 12.0 2.1 0.2 0.4 0.1 0.1 5.5 1.0 0.5 1.1 Eggs 6.6 0.5 8.1 0.1 0.0 0.0 0.0 1.9 0.0 0.1 0.0 Fruits and vegetables 43.8 1.7 29.5 0.1 24.7 0.1 0.2 30.7 0.0 0.5 0.2 Fat and oil 511.7 0.0 2,950.6 2.4 0.0 0.0 0.0 0.0 0.0 0.0 0.0 Other 39.6 0.3 11.7 0.0 1.8 0.0 0.0 1.6 0.0 0.2 0.0 Total 1,959.2 40.4 3,367.7 6.2 146.3 0.6 2.0 250.5 1.0 10.9 6.2 Required 1,706.4 31.5 374.0 6.1 32.1 0.8 0.9 265.7 1.6 17.1 9.8 Recommended 2,049.8 33.2 523.4 7.6 39.2 1.0 1.1 332.1 1.9 30.3 11.8 Source: Authors’ calculations from the 1-2-3 survey data. Table 3.4—Three main food contributors by nutrients First largest contributors Second largest contributors Third largest contributors Calories (kcal) Maize flour Palm oil Cassava flour Protein (g) Maize flour Multicolored beans Dried, husked maize Vitamin A (μg RE) Palm oil Vegetable oil (a) Other plants rich in oil Vitamin E (mg) Taro Palm oil Sweet potato Vitamin C (mg) Cassava flour Large chikwangues (processed cassava root) Cassava leaves Riboflavin (mg) Multicolored beans Cassava flour Sweet potato Vitamin B6 (μg ) Cassava flour Large chikwangues Maize flour Folate (μg DFE) Multicolored beans White beans Other plants rich in oil Vitamin B12 (μg ) Fresh caterpillars Fresh caterpillars Dried caterpillars Iron (mg) Maize flour Dried, husked maize Multicolored beans Zinc (mg) Maize flour Dried, husked maize Multicolored beans Source: Authors’ calculations from the 1-2-3 survey data. . 14 For vitamin E, the most important food source is fat and oil. In terms of individual food items, the largest single contributors of vitamin E are taro, palm oil, and sweet potato. Similarly, while beans, cassava, and sweet potato are the largest single contributors of riboflavin, most of households’ riboflavin comes from cereals. As Regmi (2001); Seale, Regmi, and Bernstein (2003); and Abdulai and Aubert (2004) suggest, cereals make up the greatest share of per capita calorie consumption, at 28.9 percent, followed closely by tubers and fats and oils, at 27.6 and 26.1 percent, respectively, as shown in Table 3.5. Meat and fish, eggs, dairy, and fruits and vegetables together make up less than 10 percent of consumed calories, possibly because of their high cost, as demonstrated in Table 3.5. Cereals are the greatest source of per capita intakes of riboflavin, iron, and zinc, though not enough to combat deficiencies in those nutrients. Meat and fish provide all the vitamin B12 that households consume, though only 80.6 of their daily calories come from meat or fish, suggesting that an increase in meat and fish intake could erase those deficiencies. High consumption of tubers is largely responsible for average sufficiencies in vitamins C and B6. Households obtain their protein from a combination of cereals, meat and fish, and legumes and nuts. Their vitamin A intake would be more than sufficient even without any contribution from fats and oils, but with their contribution it represents 644 percent of the recommended value. All the micronutrients listed in Table 3.6 are positively correlated with each other. Calories are highly1 correlated with all nutrients except vitamin C and vitamin B12. Protein is highly correlated with fewer micronutrients—vitamins B6 and E, folate, and especially riboflavin, iron, and zinc, the last three of which are extremely highly correlated with each other. The fact that vitamin B12 and protein are only weakly correlated supports the findings in Tables 3.4 and 3.5 suggesting that households do not need meat to obtain enough protein. The average household obtains more than enough protein per capita (Table 3.5), but not nearly enough vitamin B12, which is only found in animal products. In fact, vitamin B12 is weakly correlated with every other nutrient presented, and vitamin A is weakly correlated with every micronutrient except vitamin E. Whereas vitamin B12’s weak correlation with other nutrients is likely due to households’ low consumption of meat, vitamin A’s low correlation with other nutrients is probably linked to the fact that most households obtain their vitamin A from one source: palm oil, which is also a major contributor of vitamin E (Table 3.4). Iron is highly correlated with all the other nutrients except vitamins A, C, and B12 1 This paper defines a high correlation as a correlation exceeding 0.6. 15 Table 3.5—Sources of average nutrient consumption (%) Food Group Calories Protein Vitamin A Vitamin E Vitamin C Riboflavin Vitamin B6 Folate Vitamin B12 Iron Zinc Cereals 28.9 30.2 0.0 15.3 0.0 38.3 18.0 11.9 0.1 33.7 38.4 Tubers 27.6 11.9 7.2 26.1 80.7 18.8 59.8 27.1 0.0 30.0 19.9 Legumes and nuts 8.7 22.1 3.7 13.8 0.9 9.9 6.0 45.1 0.0 24.5 18.3 Meat and fish 4.1 29.6 0.1 3.5 0.3 8.8 7.4 2.2 94.7 5.0 18.1 Eggs 0.3 1.3 0.2 1.4 0.0 3.7 0.3 0.7 4.6 0.5 0.8 Fruits and vegetables 2.2 4.1 0.9 1.0 16.9 16.7 8.0 12.2 0.0 4.6 3.9 Fat and oil 26.1 0.0 87.6 38.6 0.0 2.0 0.0 0.0 0.0 0.0 0.0 Other 2.0 0.7 0.3 0.3 1.2 1.9 0.5 0.6 0.6 1.8 0.6 Total 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 100.0 Source: Authors’ calculations from the 1-2-3 survey data Table 3.6—Correlations between nutrients Calories Protein Vitamin A Vitamin C Vitamin E Riboflavin Vitamin B6 Folate Vitamin B12 Iron Zinc Calories 1 Protein 0.8293 1 Vitamin A 0.8261 0.4873 1 Vitamin C 0.5576 0.4052 0.3165 1 Vitamin E 0.7904 0.6500 0.7695 0.3326 1 Riboflavin 0.8732 0.9057 0.5511 0.4172 0.7080 1 Vitamin B6 0.8069 0.7426 0.465 0.8916 0.5704 0.7559 1 Folate 0.6246 0.7925 0.336 0.4811 0.5062 0.6804 0.6886 1 Vitamin B12 0.2901 0.355 0.2134 0.2417 0.2217 0.3359 0.2895 0.182 1 Iron 0.8234 0.9018 0.4639 0.4896 0.6419 0.9299 0.8022 0.8468 0.2757 1 Zinc 0.8031 0.8655 0.4817 0.4367 0.6325 0.9033 0.7241 0.7009 0.6327 0.8961 1 Source: Authors’ calculations from the 1-2-3 survey data. 16 The nutrients for which the fewest households face deficiencies, vitamins A, C, and B6 are found mostly in tubers and fats, of which households consume high amounts. Vitamin B12, in which almost 90 percent of households are deficient, is found only in meat, a food group from which households obtain few calories. Riboflavin, iron, and zinc, whose intakes are highly correlated, are also deficient in a large percentage of households (Figures 3.10 and 3.11). Figure 3.10—Average nutrient deficiencies (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). Figure 3.11—Mean nutrient intakes in deficient households as a percentage of mean nutrient intakes of sufficient households (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). Female-headed households are less deficient in all nutrients than male-headed households, as shown in Figure 3.12. The only nutrient for which a similar number are deficient is vitamin B12. It must be noted, however, that required nutrient intakes are averages. Therefore, although the amount of each nutrient that each person consumes is greater in female-headed households, the actual required values may vary between male- and female-headed households, depending on their composition 17 Figure 3.12—Deficiency by gender of household head (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). The prevalence of deficiencies across provinces varies greatly, as Figures 3.13 through 3.23 show. Maniema, Bas-Congo, Kinshasa, Equateur, and the two Kasai provinces experience nutrient deficiencies that exceed the national levels for some nutrients and remain below national levels for others. Rates of nutrient deficiencies in Katanga and Orientale are all at or below the national average except the rate for vitamin C in the former and riboflavin in the latter. In contrast, Bandundu and the two Kivu provinces have rates of deficiencies that exceed the national averages. In Bandundu, all rates of deficiencies are above the national average. The two Kivus’ nutrient deficiency rates are above the national average, except for folate, protein, and iron in the north and vitamin C and folate in the south. Figure 3.13—Vitamin A deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001) 18 Figure 3.14—Vitamin C deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001) Figure 3.15—Vitamin B6 deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001) Figure 3.16—Protein deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001) 19 Figure 3.17—Calorie deficiency byprovince (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001) Figure 3.18—Vitamin E deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001) Figure 3.19—Folate deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). 20 Figure 3.20—Riboflavin deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). Figure 3.21—Iron deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). Figure 3.22—Zinc deficiency by province (%) Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). 21 Figure 3.23—Vitamin B12 deficiency by province Sources: Authors’ calculations from the 1-2-3 survey data; nutrient requirements from WHO and FAO (2004, 2006) and FAO, WHO, and UNU (1985, 2001). 22 4. EXPENDITURE AND OWN-PRICE ELASTICITIES2 Across studies, calories, protein, and iron exhibit low expenditure elasticities of demand (Bhargava 1991; Abdulai and Aubert 2004; Skoufias et al. 2009; Ecker and Qaim 2010). The elasticities of calcium, vitamin A, and zinc vary with household expenditure levels as well as household geographic and cultural contexts. While iron generally has a low expenditure elasticity of demand, Skoufias et al. (2009) show that its elasticity varies when it is disaggregated into heme iron, which humans absorb readily, and its less digestible counterparts. Almost all B vitamins’ expenditure elasticities of demand are generally low, especially for the poorest members of society. However, Abdulai and Aubert (2004) find high expenditure elasticity for vitamin B12 in Tanzania. In Malawi, Ecker and Qaim (2010) find high expenditure elasticity for vitamin B12 only for the urban population. Cereals often have low expenditure and price elasticities because developing-country households consume them even when times are tough (Regmi 2001; Abdulai and Aubert 2004; Ecker and Qaim 2010). However, these households are expected to make great changes in their spending on other commodities with income and price changes (Regmi 2001). In the short term, Bhargava (1991) finds a low elasticity of demand for cereals, but in the long term that elasticity hovers around unity. Behrman and Deolalikar (1987) estimate the expenditure elasticity of cereals to be 1.52. Meat’s and fruits and vegetables’ expenditure elasticities depend largely on the geographic and cultural context. Dairy products’ elasticities vary as well, though they tend to be high (Abdulai and Aubert 2004; Bhargava 1991; Ecker and Qaim 2010). Skoufias et al. (2009) give the most detailed curves for micronutrients. Although what they construct are not exactly Engel curves, they demonstrate the relationship between the increase in the intake of nutrients and increasing expenditures. They plot the slope of log per capita intakes of various nutrients against log per capita household expenditures. These curves could be adapted to represent the share of the nutrients in per capita calories against the change in per capita food expenditures. They find that for all nutrients, the increase in intake falls eventually as log per capita expenditures rise. The slopes for calories, fiber, and protein give S-curves, suggesting that they remain high over large income increases. While the slope of calcium remains above the mean and drops toward zero at the upper bound of the log per capita scale, the slope of heme iron drops initially and then rises and stays above the mean for the rest of the log per capita scale. Thus, empirical Engel and non-Engel curves illustrate that although food shares in budgets may fall as incomes rise, the budget shares of particular macro- and micronutrients may vary considerably. If policymakers hope to affect the consumption of particular nutrients, these relationships can give guidance about how effective income transfers may be. Contrary to the findings of Regmi (2001); Seale, Regmi, and Bernstein (2003); Abdulai and Aubert (2004); and Ecker and Qaim (2010), cereals are the food group most responsive to rural households’ changes in expenditures (Table 4.1). For every 1 percent increase in food expenditures, rural households increase their expenditures on cereals by 2.389 percent. Rural expenditure elasticities for meat and fish and fat and oil are also superior to unity at 1.367 and 1.081, respectively. The rural expenditure elasticity for fruits and vegetables is just shy of unity, at 0.918. Only tubers and legumes and nuts have low rural elasticities (0.275 and 0.232, respectively). Expenditure elasticities for the richest 20 percent and poorest 20 percent of rural households follow similar patterns. Only for tubers and legumes and nuts is there a significant divergence. The poorest households’ purchases of those two food groups remain positively responsive to changes in household expenditures, with elasticities of 0.552 and 0.515 for tubers and legumes and nuts, respectively. However, the richest households exhibit negative expenditure elasticities for both: -0.017 for tubers and -0.032 for legumes and nuts. 2 To deal with outliers, elasticities less than -5.0 or higher than 5.0 were dropped from the sample. 23 Table 4.1—Expenditure elasticities of food demand among rural and urban households at population means Rural Urban Food Group Mean Poorest Richest Mean Poorest Richest Cereals 2.389 2.062 2.308 1.377 1.468 1.295 Tubers 0.275 0.552 -0.017 0.063 0.468 -0.260 Legumes and nuts 0.232 0.515 -0.032 0.210 0.475 -0.588 Meat and fish 1.367 1.329 1.350 1.310 1.362 1.247 Fruits and vegetables 0.918 0.876 0.949 0.908 0.896 0.919 Fat and oil 1.081 0.996 1.156 1.023 0.992 1.039 Source: Authors’ calculations from the 1-2-3 survey data. Urban households’ cereal purchases are very responsive to food expenditure changes, but less so than those of rural households. The overall cereal expenditure elasticity is 1.377, and there is little difference between the poorest (1.468) and richest (1.295) urban households. As in rural households, urban expenditure elasticities for fruits and vegetables and fat and oil remain close to unity, at 0.908 and 1.023, respectively. With an expenditure elasticity of 1.367, urban households’ meat purchases’ responsiveness to expenditure changes differs from that of rural households by just 0.005. Similarly to rural households, there are great differences between the expenditure elasticities for the poorest and richest households’ purchases of tubers and legumes and nuts. While the poorest urban households have expenditure elasticities for tubers and legumes and nuts of 0.468 and 0.475, respectively, the richest households’ expenditure elasticities for the same food groups are negative. The richest households’ expenditure elasticity for tubers is -0.260 and for legumes and nuts -0.588. This last expenditure elasticity is not just of the opposite sign as the value for the poorest households; it is also of a greater magnitude. Among both rural and urban households, all food groups are negatively responsive to increases in their own prices (Table 4.2). The most responsive food group is cereals, whose own-price elasticities exceed unity in both rural and urban households. In fact, in rural households, cereals’ own-price elasticity approaches 3, at -2.805. As expected, cross-price elasticities with cereals are all positive, suggesting that households substitute other food groups for cereals when the prices of cereals rise. Of the other food groups, the most positively responsive to cereals’ price increases is the tuber food group. In rural areas, the magnitude of tubers’ responsiveness to cereals’ price (1.394) is much larger than in urban areas (0.505). Table 4.2—Hicksian price elasticities* of food demand among rural and urban population Cereals Tubers Legumes and Nuts Meat and Fish Fruits and Vegetables Fat and Oil Rural households Cereals -2.805 1.394 0.307 0.827 0.378 0.043 Tubers 0.316 -0.572 0.069 0.135 0.017 0.012 Legumes and nuts 0.336 0.160 -0.414 -0.433 0.121 0.331 Meat and fish 0.262 0.220 -0.027 -0.738 0.197 0.118 Fruits and vegetables 0.251 0.097 0.091 0.363 -0.866 0.039 Fat and oil 0.123 0.109 0.277 0.344 0.040 -0.983 Urban households Cereals -1.323 0.505 0.146 0.412 0.211 0.077 Tubers 0.469 -0.577 0.057 0.085 -0.046 -0.018 Legumes and nuts 0.420 0.108 -0.318 -0.572 0.116 0.367 Meat and fish 0.355 0.140 -0.038 -0.746 0.195 0.113 Fruits and vegetables 0.312 0.049 0.080 0.338 -0.861 0.051 Fat and oil 0.218 0.074 0.209 0.307 0.075 -0.958 * Compensated Source: Authors’ calculations from the 1-2-3 survey data. 24 Similarly to cereals, fruits and vegetables and fat and oil demonstrate negative own-price elasticities and positive cross-price elasticities, suggesting some substitution. Legumes and nuts and meat and fish, however, behave very differently. Although both food groups have negative own-price elasticities, their cross-price elasticities with each other are also negative. In both rural and urban areas, an increase in the prices of legumes and nuts is associated with a larger decrease in expenditures on meat and fish, while an increase in the prices of meat and fish is associated with a small decline in expenditures on legumes and nuts. In rural areas, tubers’ elasticities suggest some substitution. When tubers’ prices increase, households decrease their expenditures on tubers but increase their expenditures on every other food group. However, in rural areas, increases in tuber prices are associated with decreases in expenditures on not just tubers, but also fruits and vegetables and fat and oil. 25 5. NUTRIENT ELASTICITIES A recent review by Skoufias, Tiwari, and Zaman (2011) suggests substantial differences in micronutrient– income elasticities. In Indonesia, Pitt and Rosenzweig (1985) report very low income elasticities (below 0.03) for a set of nutrients that includes calories, protein, fat, carbohydrates, calcium, phosphorus, iron, vitamin A, and vitamin C. In Nicaragua, Behrman and Wolfe (1984) report significant income elasticity estimates in the range of 0.04 to 0.11 for calories, protein, iron, and vitamin A (with statistically significant but quantitatively small nonlinearities). The Philippine study by Bouis (1991) reports an iron– income elasticity of 0.44, a calorie–income elasticity of 0.16, and insignificant income elasticities for vitamin A and vitamin C. In Malawi, Ecker and Qaim (2010) find high average rural expenditures (above 0.7) elasticities for calories, protein, iron, zinc, riboflavin, folate, and vitamins A and B12. Only for vitamin C do they find a low expenditure elasticity (0.354). In rural areas, the expenditure elasticities of nutrients vary at most by 0.090 (vitamin A) between the richest and poorest quintiles. Among urban households, however, there is more variation. Average urban households exhibit a high expenditure elasticity for vitamin B12 (0.802) and low expenditure elasticities for both vitamin A (0.394) and vitamin C (0.352). All other average nutrient expenditure elasticities lie between 0.563 and 0.671. However, the poorest quintile of households demonstrate high expenditure elasticities for all micronutrients except vitamin A (0.307) and vitamin C (0.360). Vitamin B12’s expenditure elasticity even reaches 1.332. By contrast, for households in the richest expenditure quintile, all nutrients’ expenditure elasticities are below 0.500. The only three nutrients whose expenditure elasticities exceed 0.400 are calories, zinc, and riboflavin. In the DRC, all nutrients exhibit positive expenditure elasticities in rural and urban areas (Tables 5.1 and 5.2), suggesting that nutrient consumption increases with overall expenditures. The more households spend on food, the more of each nutrient they obtain. As pointed out by Huang and Lin (2000), food expenditure elasticities could be larger than income elasticities because the elasticities of food expenditure with respect to changes in income are in general less than 1. Behrman and Deolalikar (1987) argue that if nutrients’ income elasticity is close to zero the implication should be that increases in the incomes of the poor will have little impact on the extent of malnutrition. As a result, attempts to improve nutrition will need to use policy instruments that attack malnutrition directly rather than relying simply on rising incomes. 26 Table 5.1—Nutrient elasticities with respect to household expenditure and food prices in rural areas Calories Protein Vitamin A Vitamin E Vitamin C Riboflavin Vitamin B6 Folate Vitamin B12 Iron Zinc Expenditure elasticities Population mean 0.689 0.641 0.598 0.632 0.462 0.678 0.528 0.548 0.886 0.664 0.585 Poorest quintile 0.511 0.511 0.451 0.489 0.447 0.520 0.444 0.449 0.680 0.561 0.464 Richest quintile 0.845 0.816 0.744 0.805 0.452 0.856 0.633 0.666 1.068 0.811 0.737 Own-price elasticities Cereals -0.131 -0.187 0.000 -0.133 0.000 -0.173 -0.110 -0.093 -0.001 -0.155 -0.195 Tubers 0.181 -0.240 -0.147 -0.126 -0.453 -0.262 -0.406 -0.309 0.000 -0.333 -0.294 Legumes and nuts -0.043 -0.094 -0.030 -0.064 -0.012 -0.051 -0.028 -0.114 0.000 -0.081 -0.076 Meat and fish -0.011 -0.076 -0.001 -0.031 -0.001 -0.026 -0.017 -0.006 -0.674 -0.014 -0.039 Fruits and vegetables -0.121 0.000 -0.406 -0.268 0.000 -0.027 0.000 0.000 0.000 0.000 0.000 Fat and oil -0.017 -0.044 -0.020 -0.008 -0.089 -0.103 -0.044 -0.077 0.000 -0.033 -0.034 Cross-price elasticities Cereals 0.056 -0.031 0.123 -0.025 0.218 -0.016 0.101 0.068 0.100 0.030 -0.018 Tubers 0.056 -0.151 -0.159 -0.068 -0.450 -0.180 -0.335 -0.248 0.052 -0.251 -0.203 Legumes and nuts 0.024 -0.047 0.024 -0.015 0.048 0.032 0.036 -0.037 -0.249 -0.008 -0.010 Meat and fish -0.067 -0.134 -0.030 -0.063 -0.087 -0.078 -0.091 -0.081 -0.674 -0.085 -0.101 Fruits and vegetables -0.154 -0.006 -0.431 -0.274 -0.084 -0.050 -0.052 -0.040 0.084 -0.035 0.024 Fat and oil -0.078 -0.073 -0.075 -0.030 -0.176 -0.150 -0.117 -0.118 0.040 -0.084 -0.079 Source: Authors’ calculations from the 1-2-3 survey data. Table 5.2—Nutrient elasticities with respect to household expenditure and food prices in urban areas Calories Protein Vitamin A Vitamin E Vitamin C Riboflavin Vitamin B6 Folate Vitamin B12 Iron Zinc Expenditure elasticities Population mean 0.960 0.918 0.761 0.859 0.626 0.958 0.781 0.816 1.178 0.955 0.876 Poorest quintile 0.906 0.873 0.730 0.827 0.715 0.894 0.772 0.793 1.217 0.923 0.830 Richest quintile 0.925 0.939 0.787 0.879 0.501 0.958 0.778 0.809 1.110 0.920 0.891 Own-price elasticities Cereals -0.460 -0.547 -0.001 -0.333 0.000 -0.522 -0.377 -0.325 -0.003 -0.463 -0.573 Tubers 0.083 -0.166 -0.087 -0.089 -0.553 -0.200 -0.389 -0.270 0.000 -0.284 -0.220 Legumes and nuts -0.033 -0.084 -0.015 -0.041 -0.014 -0.048 -0.029 -0.153 0.000 -0.093 -0.069 Meat and fish -0.016 -0.106 -0.002 -0.039 -0.003 -0.034 -0.030 -0.011 -0.886 -0.022 -0.050 Fruits and vegetables -0.149 -0.001 -0.695 -0.376 0.000 -0.020 0.000 0.000 0.000 0.000 0.000 Fat and oil -0.012 -0.035 -0.018 -0.010 -0.180 -0.103 -0.052 -0.097 0.000 -0.034 -0.027 Cross-price elasticities Cereals -0.372 -0.471 0.053 -0.287 0.131 -0.446 -0.263 -0.238 0.048 -0.365 -0.486 Tubers -0.372 0.083 -0.132 0.052 -0.556 0.041 -0.183 -0.094 0.087 -0.059 0.039 Legumes and nuts 0.111 0.040 0.042 0.084 0.034 0.125 0.088 -0.009 -0.358 0.057 0.088 Meat and fish -0.041 -0.132 -0.009 -0.048 -0.085 -0.058 -0.080 -0.070 -0.888 -0.068 -0.079 Fruits and vegetables -0.137 0.036 -0.711 -0.354 -0.087 0.001 -0.013 -0.005 0.095 0.007 0.052 Fat and oil -0.054 -0.048 -0.071 -0.038 -0.255 -0.131 -0.105 -0.115 0.043 -0.066 -0.054 Source: Authors’ calculations from the 1-2-3 survey data. 28 Our findings suggest that the expenditure elasticities of all nutrients are higher in urban areas than in rural areas. For the highest and lowest quintiles of expenditures in both rural and urban areas, vitamin B12 is the nutrient most responsive to changes in expenditure levels, exceeding unity for all expenditure levels in urban areas and for the richest quintile of households in rural areas. In rural areas, nutrient consumption by the richest quintile of households is more responsive to expenditure changes than nutrient consumption by the poorest quintile, possibly because of increased self-production by the poorest households relative to the richest. In urban areas, the consumption of iron and vitamins C and B12 varies more with expenditures for the poorest quintile of households than the richest. Food groups’ own-price elasticities in both rural and urban areas are largely negative or zero. In addition to own-price elasticities, we also estimated cross-price elasticities of nutrient consumption. We find that increases in the prices of fruits and vegetables affect only calories, vitamin A, and riboflavin in rural areas, and calories, protein, vitamin A, and riboflavin in urban areas. Only riboflavin is responsive to increases in the prices of all food groups in rural areas. Among urban households, riboflavin, protein, and vitamin A consumption are responsive to price increases in all food groups. The nutrient most responsive to any food group’s price is vitamin B12, to the price of meat and fish. In rural areas vitamin B12’s own- price elasticity is -0.674, and in urban areas it is -0.886. Calories, however, are positively responsive to increases in tuber prices, with elasticities of 0.181 in rural areas and 0.083 in urban areas. In both rural and urban areas, the nutrient own-price elasticities suggest that the level of responsiveness of nutrients to changes in the prices of food items depends mostly on the contribution of a food group to the total amount consumed of the respective nutrient. With 95 percent of vitamin B12 provided by the consumption of meat and fish, vitamin B12 is highly responsive to price changes in this food group. Similarly, an increase in the price of tubers, which represents 60 percent and 81 percent of daily consumption of vitamin B6 and vitamin C, respectively, is associated with significant decreases in the consumption of each of these two nutrients. The fat and oil group, which contributes 88 percent of vitamin A consumption, is an exception. The low vitamin A elasticities with respect to the price of fat and oil can be explained by the vast share of palm oil in the group; when the prices of fat and oil increase, generally, households may decrease their consumption of palm oil last. For example, it is possible that they consume less vegetable oil, butter, and animal fat immediately, but wait to decrease their consumption of palm oil, which supplies most of their vitamin A. In contrast, when the provision of a particular nutrient is shared between food items, the substitution of foods priced high per nutrient is common. A look at the cross-price elasticity of demand for calories from cereals and tubers in rural areas shows that households are able to adjust their consumption behavior by substituting calories from tubers for calories from cereals when the prices of cereals rise (and vice versa). The substitution of other food groups for cereals is also reflected in the change in the amount of vitamin C consumed; as a result of the substitution of fruits, vegetables, and tubers for cereals when the prices of cereals rise, households’ consumption of vitamin C from fruits, vegetables, and tubers increases. In urban areas, increasing maize prices result in decreases in the consumption of almost all nutrients except for vitamin C, where the same substitution effects as in rural area occur. 29 6. CONCLUDING REMARKS Nationally, at least 50 percent of the population is deficient in vitamin B12, calories, riboflavin, iron, vitamin E, folate, and zinc. Deficiencies in vitamins A, C, and B6, for which palm oil and cassava are the main providers, are less common. Across provinces, we find significant heterogeneity; overall, Maniema, Bas-Congo, Kinshasa, Equateur, and the two Kasai provinces experience nutrient deficiencies that exceed the national levels for some nutrients and remain below the national levels for others. In the mining-rich province of Katanga, deficiencies are all below the national average except for vitamin C. In contrast, the province of Bandundu in the west and the two Kivus in the east have rates of deficiency that often exceed the national averages. In the DRC, all nutrients exhibit positive expenditure elasticities in both rural and urban areas; however, as expected, the expenditure elasticities of all nutrients are higher in urban areas than in rural areas. In rural areas, strategies to improve nutrition will need to use instruments that attack malnutrition directly rather than relying simply on rising incomes. With respect to prices, an increase in own price is expected to have a nonpositive effect on all nutrients. Our results also suggest significant substitution effects; for example, in rural areas, an increase of 1 percent in the price of cereals is expected to reduce calorie intake by -0.131 percent. However, since a 1 percent increase in the price of cereals increases the demand for tubers by 1.394 percent, legumes and nuts by 0.307 percent, meat and fish by 0.827 percent, fruits and vegetables by 0.378 percent, and fat and oil by 0.043 percent, the overall is positive (0.056) when substitution effects are accounted for. Overall, our results highlight the paradox of the DRC, a country with huge potential for agricultural development but incapable of feeding itself. We do not believe that schemes such food aid fit the DRC case; in fact, if implemented, food aid will simply exacerbate the already dismal condition by discouraging local food producers. We agree that some level of food transfers in the form of a social safety net is needed to help some well-targeted social groups smooth their consumption and protect against nutrient deficiencies, and therefore prevent an increase in hunger and poverty. To preserve social equity we recommend that the government initiate social protection programs that include both protective actions to mitigate the short-term risks and actions to prevent negative long-term effects. Programs such as conditional cash transfers, school feeding, pension systems, and employment programs should be part of the protective actions. However, the most effective and sustainable response to the country’s alarming nutrient deficiency rates is to significantly increase the food supply by tapping into the huge agricultural potential. Endowed with 80 million hectares of arable land (of which only 10 percent are used), diverse climatic conditions, and abundant water resources, the DRC has the potential to become the breadbasket of the entire continent of Africa. Key investments are needed to transform this huge potential into an opportunity to curb hunger and malnutrition in the DRC as well as in Africa as a whole. Urgent investments include rural infrastructure, extension services, agricultural research, science, and technology. Rural infrastructure is completely dilapidated and hinders farmers’ access to both input and output markets. The government should also facilitate and accelerate small farmers’ access to improved seeds, fertilizers, and credit. World prices of seeds and fertilizers are out of reach for most small farmers in the DRC. The same is true regarding their access to agricultural credit. Since most farmers live in poverty, they do not have enough assets to use as collateral against bank loans. 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