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Does monetary poverty reflect caloric intake?

Abstract

The use of expenditure surveys to measure food insecurity is widely discussed. In this study, we investigate food insecurity in terms of monetary poverty. Using a Malian survey that incorporates exceptionally detailed information on food consumption, we estimate that 35 % of the households are in a paradoxical situation, some poor households managing to cover their caloric requirements by eating cheap calories and some non-poor households not doing so because they consume expensive calories and/or face constraints such as the obligation to share meals with visitors and high expenditure on health care or transportation. These findings highlight precautions that need to be taken when measuring food insecurity through monetary income or expenditure indicators.

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Fig. 1

Notes

  1. The workshop has led to a special issue of Food and Nutrition Bulletin: Food and Nutrition Bulletin, vol. 33, no. 3, 2012.

  2. Engel, a nineteenth-century statistician, was interested in the evolution of budget proportions according to income. We are interested in caloric intake, but we simplify it by saying “Engel curve.”

  3. These surveys were conducted with 610 farms in 24 villages in the different production areas of Mali.

  4. The FAO website assessed on 25/03/2012. http://www.fao.org/economic/ess/ess-fs/fs-data/ess-fadata/fr/

  5. Results available upon request.

  6. The highest correlation coefficients were about 0.3.

  7. The poor consume cheaper calories in general. But, the table of descriptive statistics shows that the poor with sufficient calories consume even cheaper calories than the poor with insufficient calories.

  8. Actually, farming in Mali mainly relies on extensive agricultural systems with very few modern inputs. Even if it were possible for farmers to diversify their crops, it would be difficult to do so because of the bad roads and difficulties of accessing inputs. Moreover, as in many other countries, the agricultural policies of the last decades have not encouraged diversification since they have focused on cotton/maize systems and mono-cropping rice. As a result of their isolation (both for accessing inputs and selling outputs), unevenly distributed rainfall, and highly risky natural and economic environment (very low prices of most commodities and production highly unstable) most farmers adopt risk avoidance strategies to insure minimum production of staple cereals in order to be able to feed their household.

References

  • Abdulai, A., & Aubert, D. (2004a). Nonparametric and parametric analysis of calorie consumption in Tanzania. Food Policy, 29(2), 113–129.

    Article  Google Scholar 

  • Abdulai, A., & Aubert, D. (2004b). A cross-section analysis of household demand for food and nutrients in Tanzania. Agricultural Economics, 31(1), 67–79.

    Article  Google Scholar 

  • Banerjee A. V. & Duflo E. (2007). The economic lives of the poor. The journal of economic perspectives, 21(1), p. 141.

    Google Scholar 

  • Bartus, T. (2005). Estimation of marginal effects using margeff. Stata Journal, 5(3), 309–329.

    Google Scholar 

  • Baulch, B., & Masset, E. (2003). Do monetary and nonmonetary indicators tell the same story about chronic poverty? A study of Vietnam in the 1990s. World Development, 31(3), 441–453.

    Article  Google Scholar 

  • Behrman J. R. & Deolalikar A. B. (1987). Will Developing Country Nutrition Improve with Income? A Case Study for Rural South India. The Journal of Political Economy, 95(3), p. 492.

    Google Scholar 

  • Behrman, J. R., & Wolfe, B. L. (1984). More Evidence on Nutrition Demand: Income Seems Overrated and Women’s Schooling Underemphasized. Journal of Development Economics, 14(1–2), 105–128.

    Article  Google Scholar 

  • Bocoum I. (2011). Sécurité alimentaire et pauvreté. Analyse économique des déterminants de la consommation des ménages. Application au Mali. Thèse de doctorat d’Economie, Université Montpellier 1, Montpellier. 245 pages plus annexes.

  • Bouis, H. E., & Haddad, L. J. (1992). Are estimates of calorie-income elasticities too high?: A recalibration of the plausible range. Journal of Development Economics, 39, 333–364.

    Article  Google Scholar 

  • Cahuzac E. & Bontemps C. (2008). Stata par la pratique: statistiques, graphiques et éléments de programmation. Stata Press Publ.

  • Chamberlain, G. (1982). Multivariate regression models for panel data. Journal of Econometrics, 18(1), 5–46.

    Article  Google Scholar 

  • Darmon N., Bocquier A., Vieux F. & Caillavet F. (2010). L’insécurité alimentaire pour raisons financières en France. Lettre de l’ONPES (4).

  • Deaton A.S. (1997). The Analysis of Household Surveys: A Microeconometric Approach to Development Policy. Johns Hopkins Univ Pr.

  • Deaton, A., & Drèze, J. (2009). Food and Nutrition in india: Facts and interpretations. Economic and political weekly, 44(7).

  • DNSI (2004). Enquête malienne sur l’évaluation de la pauvreté (EMEP) 2001. Principaux résultats.

  • Dop, C., Pereira, C., Mistura, L., Martinez, C., & Cardoso, E. (2012). Using Household Consumption and Expenditures Survey (HCES) data to assess dietary intake in relation to the nutrition transition: A case study from Cape Verde. Food & Nutrition Bulletin, 33(Supplement 2), 221S–227S.

    Google Scholar 

  • Eozenou P., Madani D. & Swinkels R. (2013). Poverty, Malnutrition and Vulnerability in Mali. Policy Research Working Paper 6561. The World Bank.

  • FAO, WFP & IFAD (2012). The State of Food Insecurity in the World 2012. Economic growth is necessary but not sufficient to accelerate reduction of hunger and malnutrition. Rome, FAO.

  • Fiedler J.L. (2012). Towards overcoming the food consumption information gap: Strengthening household consumption and expenditures surveys for food and nutrition policymaking. Global Food Security (2012), doi:10.1016/j.gfs.2012.09.002

  • Green, W. H. (2000). Econometric Analysis. Prentice-Hall International: Fourth Edition. 1004 p.

    Google Scholar 

  • Gujarati D. N. (2004). Econométrie. Traduction de la 4ième edition américaine. De Boeck. 1009 p.

  • Haddad, L. (2009). Lifting the Curse: Overcoming Persistent Undernutrition in India. IDS Bulletin, 40(4), 1–8.

    Article  Google Scholar 

  • Headey, D. D. (2013). The Impact of the Global Food Crisis on Self-Assessed Food Security. The World Bank Economic Review, 27(1), 1–27.

    Article  Google Scholar 

  • Murphy, S., Ruel, M., & Carriquiry, A. (2012). Should Household Consumption and Expenditures Surveys (HCES) be used for nutritional assessment and planning? Food & Nutrition Bulletin, 33(Supplement 2), 235S–241S.

    Google Scholar 

  • Nordeide M. B. (1997). Table de composition d’aliments du Mali. Institut de Nutrition. Oslo : Université d’Oslo.

  • Ohri-Vachaspati, P., Rogers, B. L., Kennedy, E., & Goldberg, J. P. (1998). The effects of data collection methods on calorie–expenditure elasticity estimates: a study from the Dominican Republic. Food Policy, 23(3–4), 295–304.

    Article  Google Scholar 

  • Pradhan, M., Suryahadi, A., Sumarto, S., & Pritchett, L. (2001). Eating Like Which“Joneses”? An Iterative Solution to the Choice of a Poverty Line“Reference Group”. Review of Income and Wealth, 47(4), 473–488.

    Article  Google Scholar 

  • Ravallion M. (1998). Poverty Lines in Theory and Practice. World Bank Publications.

  • Rogers, B. L., & Lowdermilk, M. (1991). Price policy and food consumption in urban Mali. Food Policy, 16(6), 461–473.

    Article  Google Scholar 

  • Samaké A., Bélières J-F., Corniaux C., Dembele N., Kelly V., Marzin J, Sanogo O. & Staatz J. (2008). Changements structurels des économes rurales dans la mondialisation. Programme RuralStruc Mali-Phase II: MSU IER Cirad. http://siteresources.worldbank.org/AFRICAEXT/Resources/RURALSTRUC-MALI_Phase2.pdf

  • Sen A. K. (1992). Repenser l’inégalité. Traduction française de Inequality Reexamined. Paris, Éditions du Seuil.

  • Smith, L. C., & Haddad, L. (2002). How Potent is Economic Growth in Reducing Undernutrition? What Are the Pathways of Impact? New Cross-Country Evidence. Economic Development and Cultural Change, 51(1), 55–76.

    Article  Google Scholar 

  • Smith, L. C., & Subandoro, A. (2007). Measuring Food Security Using Household Expenditure Surveys, Food Security in Practice. Washington DC: International Food and Policy Research Institute.

    Google Scholar 

  • Strauss, J., & Thomas, D. (1995). Human resources: Empirical modeling of household and family decisions. Handbook of development economics, 3(1), 1883–2023.

    Article  Google Scholar 

  • Subramanian S. & Deaton A. (1996). The Demand for Food and Calories. Journal of Political Economy, 104(1), p. 133.

    Google Scholar 

  • Svedberg, P. (1999). 841 Million Undernourished? World Development, 27(12), 2081–2098. doi:10.1016/s0305-750x(99)00102-3.

    Article  Google Scholar 

  • Svedberg, P. (2000). Poverty and undernutrition: Theory, measurement, and policy. USA: Oxford University Press.

    Book  Google Scholar 

  • Svedberg, P. (2002). Undernutrition Overestimated. Economic Development and Cultural Change, 51(1), 5–36.

    Article  Google Scholar 

  • Swindale A. & Ohri-Vachaspati P. (2005). Measuring household food consumption: a technical guide. 2005 ed.

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Acknowledgments

The authors warmly thank:

- Fellow nutritionists from UMR NUTRIPASS of IRD for their assistance with consumption data processing, especially Sabrina Eymard-Duvernay and Edwige Landais;

- Fellow statisticians in Mali, especially Ms. Assa Gakou Doumbia and Balla Keita from the National Institute of Statistics and Siriki Coulibaly from Afristat for their advice for the data recovery;

- The journal’s Editor and Assistant Editor as well as the three reviewers for their detailed comments on earlier versions of this text.

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Correspondence to Ibrahima Bocoum.

Appendix

Appendix

Table 4 Main characteristics of household food consumption by region in relation to the level of calorie consumption
Table 5 Monetary poverty lines calculated by region and type of area
Table 6 Matrix of correlation of the variables used in the regressions
Table 7 Average marginal effects

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Bocoum, I., Dury, S., Egg, J. et al. Does monetary poverty reflect caloric intake?. Food Sec. 6, 113–130 (2014). https://doi.org/10.1007/s12571-013-0318-0

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Keywords

  • Poverty
  • Food insecurity
  • Caloric intake
  • Household surveys
  • Mali