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Food (In)security and its drivers: insights from trends and opportunities in rural Mozambique

Abstract

We used multiple rounds of nationally representative agricultural survey data to analyze the trends and drivers of food insecurity in rural Mozambique. Reduced-form Probit models were estimated to explain the observed trends as a function of underlying drivers and factors related to agricultural policy interventions. Despite rapid macroeconomic growth, food insecurity in the rural areas had increased from 42.9 % in 2002 to 47.8 % in 2008. Significant inequalities were also observed in the distribution of food insecurity with a substantial disadvantage to the bottom quintile households and rural households located in the Northern provinces. Limited progress on several drivers of agricultural production and food access as well as geographic disparities appear to explain a significant part of the food insecurity trends and distribution. Whether the indicator was use of improved farm inputs and technology, receipt of agricultural extension services, farm production, or cash income, progress did not occur. This implies that to achieve broad-based food security in rural Mozambique, interventions may need to focus on addressing these drivers to increase agricultural productivity while enhancing resilience to price and weather shocks. Interventions must also be spatially targeted and tailored to each segment of the population.

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Notes

  1. 1.

    That increase was dominated by trends in rural areas where the headcount ratio increased from 55.3 % to nearly 56.9 %. Urban poverty dropped from 51.5 to 49.6 %.

  2. 2.

    This commitment is also built into the CAADP process that commits to higher and sustained spending in the agricultural sector through productivity and marketing enhancing investments.

  3. 3.

    For recent reviews and perspectives, see Headey and Ecker (2013), FAO and IFAD (2012), Cafiero (2012) and Carletto et al. (2012).

  4. 4.

    These include the food variety score, the dietary diversity score and WFP’s food consumption score.

  5. 5.

    The recommendation however is that dietary diversity data, which have been shown to be strongly correlated with micronutrient and macronutrient intake, be collected on a regular basis to enable better analysis of trends in food and nutrition security (Ruel 2003; Headey and Ecker 2013).

  6. 6.

    Cafiero (2012) and Smith and Subandoro (2007) adeptly argue that data on body sizes (height and mass) as well as physical activity levels of household members would be essential to more accurately estimate the household-specific minimum calorie requirement. In addition, one would have accordingly to adjust the estimate for pregnant and lactating women, if present in the household. Unfortunately, the household surveys used in this study do not contain all the necessary data. Therefore, our estimates of food insecurity are computed assuming a median mass and height (body mass index) for each age and sex and corresponding moderate physical activity levels as defined by the FAO (Food and Agriculture Organization of the United Nations) (2008).

  7. 7.

    While Engel’s law would predict that food shares decrease with increases in income, recent studies have found that the poor and wealthy may spend similar shares of their income on food. In the Mozambican context the data suggest that poorer households in fact spend higher shares of their income on food, primarily because of differences in the composition of their food basket (Mather et al. 2008), and poorer households consume more calorie-dense foods that are cheaper. This implies that our conversion of food shares into calories using maize prices is less likely to be problematic among the poorer households, but may underestimate food insecurity among the wealthier households whose food baskets may include more expensive sources of calories.

  8. 8.

    Poor sanitation facilities can result in diseases and co-morbidities that lower food utilization e.g. intestinal parasites and diarrhea that reduce calorie intake and nutrient absorption. Similarly, poor food storage can lead to aflatoxin contamination and other food safety challenges that lower food utilization and stability of food supply.

  9. 9.

    This means that households that have cash flow constraints and credit constraints and do not have the ability to store food may sell farm output when prices are low, during the harvest season, only returning to the market to purchase food when prices are high.

  10. 10.

    For instance when floods occurred in 2000, households in remote areas of Mozambique did not have access to the rest of the country and had limited access to food from regional and international markets.

  11. 11.

    A brief description of the TIA surveys can be found at the following website address: http://www.aec.msu.edu/fs2/mozambique/survey/index.htm. Mather (2009; 2012) and MPF/IFPRI/PU (Mozambique Ministry of Planning and Finance/International Food Policy Research Institute/Purdue University) (2004) provide additional descriptions of the TIA data.

  12. 12.

    Depending on the region, other crops can dominate as cash crops, such as cashew in Inhambane. For the most part the spatial distribution of crop sales is a result of the distribution in climate and agro-ecological conditions that suit different crops’ production.

  13. 13.

    Marginal probability effect for female household head status is about an 11 % increase in food insecurity while the marginal effect of an additional adult equivalent is 46 % for the bottom four quintile households in the North. These marginal effects are evaluated at the means for continuous explanatory variables, with the dummy variables set at 0. The formula for converting the Probit regression coefficients to marginal probability effects is \( m \arg inaleffect=\frac{1}{\sqrt{2\pi}} \exp \left(-\frac{1}{2}{\left({\beta}^{\prime } x\right)}^2\right)\times \beta \), where β is the vector of coefficients shown in the tables and x is the vector of independent variables, evaluated at the means or at 0 in the case of the dummy or categorical variables. For dummy variables the marginal effect is the difference between the predicted probability with and without that dummy variable set to 1.

References

  1. Abdulai, A., Barrett, C., & Hazell, P. (2004). Food aid for market development in Sub-Saharan Africa. IFPRI DSGD discussion paper no. 5. Washington: International Food Policy Research Institute.

    Google Scholar 

  2. Alfani, F., Azzarri, C., d’Errico, M., & Molini, V. (2012). Poverty in Mozambique: new evidence from recent household surveys. Washington: World Bank Policy Research Working Paper 6217.

    Google Scholar 

  3. Alfredo, A., Jonsson, N., Finch, T., Neves, L., Molloy, J., & Jorgensen, W. (2005). Serological survey of Babesia bovis and Anaplasma marginale in cattle in Tete Province, Mozambique. Tropical Animal Health and Production, 37(2), 121–131.

    CAS  PubMed  Article  Google Scholar 

  4. Arndt, C., James, R., & Simler, K. (2006). Has economic growth in Mozambique been pro-poor? Journal of African Economies, 15(4), 571–602.

    Article  Google Scholar 

  5. Arndt, C., Benfica, R., Maximiano, N., Nucifora, A., & Thurlow, J. (2008). Higher fuel and food prices: impacts and responses for Mozambique. Agricultural Economics, 39, 497–511.

    Article  Google Scholar 

  6. Arndt, C., Hussain, M. A., Jones, E. S., Nhate, V., Tarp, F., & Thurlow, J. (2012). Explaining the evolution of poverty: the case of Mozambique. American Journal of Agricultural Economics, 94(4), 854–872.

    Article  Google Scholar 

  7. Babatunde, R., Omotesho, O., & Sholotan, O. (2007). Factors influencing food security status of rural farming households in north central Nigeria. Agricultural Journal, 2(3), 351–357.

    Google Scholar 

  8. Barrett, C. (2002). Food security and food assistance programs. In B. L. Garner & G. C. Rausser (Eds.), Handbook of agricultural economics, vol. 2 (pp. 2103–2190). Amsterdam: Elsevier Science.

    Google Scholar 

  9. Barrett, C. (2010). Measuring food insecurity. Science, 327(5967), 825–828.

    CAS  PubMed  Article  Google Scholar 

  10. Barrett, C., Reardon, T., & Webb, P. (2001). Non-farm income diversification and household livelihood strategies in rural Africa: concepts, dynamics, and policy implications. Food Policy, 26(4), 315–331.

    Article  Google Scholar 

  11. Benfica, R. (1998). The Contribution of Micro and Small Enterprises to Rural Household Income in Mozambique. MS. Thesis. Michigan State University, Esat Lansing, Micigan.

  12. Benfica, R. (2012). An Analysis of Poverty in Cash Cropping Economies of Rural Mozambique: Blending Econometrics and Economy-wide Models. First Edition (Ed.) Saarbrucken: LAP - Lambert Academic Publishing.

  13. Benfica, R., Boughton, D., Mouzinho, B., & Uaiene, R. (2014). Food crop marketing and agricultural productivity in a high price environment: Evidence and implications for Mozambique. Maputo: Michigan State University.

    Google Scholar 

  14. Boughton, D., Mather, D., Tschirley, D., Walker, T., Cunguara, B., & Payongayong, E. (2006). Changes in rural household income patterns in Mozambique 1996–2002 and implications for Agriculture’s contribution to poverty reduction. Maputo: MINAG Working Paper.

    Google Scholar 

  15. Boughton, D., Mather, D., Barrett, C., Benfica, R., Abdula, D., Tschirley, D., & Cunguara, B. (2007). Market participation by rural households in a low-income country: an asset-based approach applied to Mozambique. Faith and Economics, 50, 64–101.

    Google Scholar 

  16. Cafiero, C. 2012. Advances in hunger measurement: traditional FAO methods and recent innovations. Rome: FAO. Accessed December 8, 2012 http://www.fao.org/fileadmin/templates/ess/ess_test_folder/Food_security/Cafiero_Global_Food_Security.pdf

  17. Carletto, C., Zezza, A. and Banerjee, R. (2012). Toward better measurement of household food security: harmonizing indicators and the role of household surveys. Global Food Security: http://dx.doi.org/10.1016/j.gfs.2012.11.006

  18. Cunguara, B., & Darnhofer, I. (2011). Assessing the impact of improved agricultural technologies on household income in rural Mozambique. Food Policy, 36(3), 378–390.

    Article  Google Scholar 

  19. Cunguara, B., & Hanlon, J. (2012). Whose wealth is it anyway? Mozambique’s outstanding economic growth with worsening rural poverty. Development and Change, 43(3), 623–647.

    Article  Google Scholar 

  20. Cunguara, B., & Moder, K. (2011). Is agricultural extension helping the poor? Evidence from rural Mozambique. Journal of African Economies, 20(4), 562–595.

    Article  Google Scholar 

  21. Cunguara, B., Langyintuo, A., & Darnhofer, I. (2011). The role of nonfarm income in coping with the effects of drought in southern Mozambique. Agricultural Economics, 42(6), 701–713.

    Article  Google Scholar 

  22. Deaton, A. (1997). The analysis of household surveys. Washington: World Bank.

    Book  Google Scholar 

  23. Deolalikar, A., & Vijverberg, W. (1987). A test of heterogeneity of family and hired labour in Asian agriculture. Oxford Bulletin of Economics and Statistics, 49(3), 291–305.

    Article  Google Scholar 

  24. Dercon, S. (1998). Wealth, risk, and activity choice: cattle in western Tanzania. Journal of Development Economics, 55, 1–42.

    Article  Google Scholar 

  25. Diogo, D., Amade, C., Paulo, A., & Sibrian, R. (2008). Deriving food security information from national household budget surveys: Experiences, achievements, challenges (pp. 35–44). Rome: FAO.

    Google Scholar 

  26. Donovan, C., & Tostão, E. (2010). Staple food prices in Mozambique. Prepared for the Comesa policy seminar on “Variation in staple food prices: Causes, consequence, and policy options”, held in Maputo, Mozambique, 25–26 January 2010.

  27. Ecker, O., & Breisinger, C. (2012). The food security system: A new conceptual framework. IFPRI discussion paper 01166. Washington: International Food Policy Research Institute.

    Google Scholar 

  28. EIU (Economist Intelligence Unit). (2012). Global food security index. Accessed August 17, 2012. http://foodsecurityindex.eiu.com/

  29. FAO (Food and Agriculture Organization of the United Nations). (1996). Rome Declaration on World Food Security and World Food Summit Plan of Action. Accessed July 18, 2012. http://www.fao.org/DOCREP/003/W3613E/W3613E00.HTM

  30. FAO (Food and Agriculture Organization of the United Nations). (2008). FAO Methodology for the measurement of food deprivation: updating the minimum dietary energy requirements. Rome: FAO Statistics Division.

    Google Scholar 

  31. FAO (Food and Agriculture Organization of the United Nations). (2009). Declaration of the World Summit on Food Security. WSFS 2009/2. Rome, FAO.

  32. FAO, W. F. P., & 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.

    Google Scholar 

  33. Garrett, J., & Ruel, M. (1999). Are determinants of rural and urban food security and nutritional status different? Some insights from Mozambique. World Development, 27(11), 1955–1975.

    Article  Google Scholar 

  34. Government of Mozambique. (2006). Plano de Acção de Redução de Pobreza Absoluta 2006–2009. Maputo: Conselho de Ministros.

    Google Scholar 

  35. Grupo de Estudo (Grupo de Estudo de Aprofundamento na área de Nutrição), (2009). Relatório de avaliação de impacto do PARPA II 2006-2009. Study as input to Impact Evaluation Report (RAI) of PARPA II, Maputo, at http://www.open.ac.uk/technology/mozambique/sites/www.open.ac.uk.technology.mozambique/files/pics/d119370.pdf; Accessed July 25, 2010.

  36. Haggblade, S., Govereh, J., Nielson, H., Tschirley, D., & Dorosh, P. (2008). Regional trade in food staples: Prospects for simulating agricultural growth and moderating short-term food security crises in eastern and Southern Africa. Washington: A paper prepared for the World Bank.

    Google Scholar 

  37. Headey, D., & Ecker, O. (2013). Rethinking the measurement of food security: from first principles to best practice. Food Security, 5(3), 327–343.

    Article  Google Scholar 

  38. Heltberg, R., & Tarp, F. (2002). Agricultural supply response and poverty in Mozambique. Food Policy, 27, 103–124.

    Article  Google Scholar 

  39. Hoddinott, J. (1999). Choosing outcome indicators of household food security. Technical guide No. 7. Washington: International Food Policy Research Institute.

    Google Scholar 

  40. Howard, J., Crawford, E., Kelly, V., Demeke, M., & Jaime, J. J. (2003). Promoting high-input maize technologies in Africa: the Sasakawa-Global 2000 experience in Ethiopia and Mozambique. Food Policy, 28, 335–348.

    Article  Google Scholar 

  41. IMF. (2007). PARPA II (action plan for the reduction of absolute poverty 2006–2000). Republic of Mozambique: Poverty reduction strategy paper. Maputo: Government of the Republic of Mozambique.

    Google Scholar 

  42. Joubert, A., & Tyson, P. (1996). Equilibrium and fully coupled GCM simulations of future southern African climates. Southern African Journal of Science, 92, 471–484.

    Google Scholar 

  43. Korkalo, L., Hauta-aus, H., & Mutanen, M. (2011). Food composition tables for Mozambique, Version 2. Helsinki, Finland. Finland: Department of Food and environmental Sciences, University of Helsinki.

    Google Scholar 

  44. Latif, A. A., & Pegram, R. G. (1992). Naturally acquired host resistance in tick control in Africa. International Journal of Tropical Insect Science, 13, 505–513.

    Article  Google Scholar 

  45. Mather, D. (2009). Measuring the impact of public and private assets on household crop income in rural Mozambique, 2002–2005. MINAG Working Paper n. 67E, Maputo, Mozambique.

  46. Mather, D., Boughton, D., & Cunguara, B. (2008). Household income and assets in rural Mozambique 2002–2005: Can pro-poor growth be sustained? MINAG Working Paper no. 66E, Maputo, Mozambique.

  47. Maxwell, S., & Frankenberger, T. (1992). Household food security: Concepts, indicators, measurements. Rome: IFAD and UNICEF.

    Google Scholar 

  48. MPD (Mozambique Ministry of Planning and Development). (2010). Poverty and well-being in Mozambique: The third national assessment (2008–9). Maputo: Ministry of Planning and Development.

    Google Scholar 

  49. MPF/IFPRI/PU (Mozambique Ministry of Planning and Finance/International Food Policy Research Institute/Purdue University). (2004). Poverty and well-being in Mozambique: the second national assessment (2002–3). Maputo, Mozambique.

  50. Norval, R., Fivaz, B., Lawrence, J., & Dailecourt, T. (1983). Epidemiology of tick-borne diseases of cattle in Zimbabwe I Babesiosis. Tropical Animal Health and Production, 15, 87–94.

    CAS  PubMed  Article  Google Scholar 

  51. Pingali, P., Bigot, Y., & Binswanger, H. (1987). Agricultural mechanization and the evolution of farming systems in Sub-Saharan Africa. Baltimore: The John Hopkins University Press.

    Google Scholar 

  52. Pitoro, R, Walker, T., Tschirley, D., Swinton, S., Boughton, D., & de Marrule, H. (2009). Can Bt technology reduce poverty among African cotton growers? An ex ante analysis of the private and social profitability of Bt cotton seed in Mozambique. Contributed Paper prepared for presentation at the International Association of Agricultural Economists’ Conference, Beijing, China, August 16–22, 2009.

  53. Polgreen, L. (2012). As coal boosts Mozambique, the rural poor are left behind. New York Times, November 10, 2012. http://www.nytimes.com/2012/11/11/world/africa/as-coal-boosts-mozambique-the-rural-poor-are-left-behind.html?pagewanted = all

  54. Reardon, T. (1997). Using evidence of household income diversification to inform study of rural non-farm labor market in Africa. World Development, 25(5), 735–747.

    Article  Google Scholar 

  55. Reardon, T., & Taylor, J. (1996). Agroclimac shock, income inequality, and poverty: evidence from Burkina Faso. World Development, 24(5), 901–914.

    Article  Google Scholar 

  56. Riley, F., & Moock, N. (1995). Inventory of food security impact indicators. In food security indicators and framework: A handbook for monitoring and evaluation of food aid programs. Arlington: IMPACT.

    Google Scholar 

  57. Ruel, M. (2003). Operationalizing dietary diversity: a review of measurement issues and research priorities. Journal of Nutrition, 133(11), 3911S–3926S.

    CAS  PubMed  Google Scholar 

  58. Silva, J. A. (2008). A multilevel analysis of agricultural trade and socioeconomic inequality in rural Mozambique. The Professional Geographer, 60(2), 174–189.

    Article  Google Scholar 

  59. Smith, L. C., & Subandoro, A. (2007). Measuring food security using household expenditure surveys. Food security in practice technical guide series. Washington: IFPRI.

    Google Scholar 

  60. Stephens, E., & Barrett, C. (2008). Incomplete credit markets and commodity marketing behavior. Ithaca: Working Paper.

    Google Scholar 

  61. Thurlow, J. (2012). Mozambique. In X. Diao, J. Thurlow, S. Benin, & S. Fan (Eds.), Strategies and priorities for African agriculture: economywide perspectives from country studies (pp. 349–370). Washington: IFPRI.

    Google Scholar 

  62. Tostão, E., & Tschirley, D. (2010). On the role of government in food staples markets: Perspectives from recent research and implications for Mozambique. Flash series, volume 54e. East Lansing: Michigan State University.

    Google Scholar 

  63. Tschirley, D., & Abdula, D. (2007). Toward improved maize marketing and trade policies to promote household food security in central and southern Mozambique: 2007 update. Prepared for workshop on trade policy for food products conducive to development in eastern and southern Africa, March 2007. Rome, FAO.

  64. Tschirley, D., & Jayne, T. S. (2010). Exploring the logic behind Southern Africa’s food crises. World Development, 38(1), 76–87.

    Article  Google Scholar 

  65. Tschirley, D., & Weber, M. T. (1994). Food security strategies under extremely adverse conditions: the determinants of household income and consumption in rural Mozambique. World Development, 42(2), 159–173.

    Article  Google Scholar 

  66. Tschirley, D., Donovan, C., & Weber, M. T. (1996). Food aid and food markets: lessons from Mozambique. Food Policy, 21(2), 189–209.

    Article  Google Scholar 

  67. Usman, M., & Reason, C. (2004). Dry spell frequencies and their variability over southern Africa. Climate Research, 26, 199–211.

    Article  Google Scholar 

  68. Walker, T., D. Tschirley, J. Low, M. Tanque, D. Boughton, E. Payongayong, & Weber, M. T. (2004). Determinants of Rural Income, Poverty and Perceived Well-Being in Mozambique in 2001–2002. MINAG Research Report No. 57E, Maputo, Mozambique.

  69. Webb, P., Coates, J., Frongillo, E. A., Rogers, B. L., Swindale, A., & Bilinsky, P. (2006). Measuring household food insecurity: Why it’s so important and yet so difficult to do? Journal of Nutrition, 136, 1404–1408.

    Google Scholar 

  70. WFP (United Nations World Food Program). (2010). Mozambique Country Overview. Available at: http://www.wfp.org/countries/mozambique. Accessed on August 3, 2010

  71. World Bank. (2013). World Bank Databank. Available at: http://databank.worldbank.org/data/home.aspx. Accessed on November 3, 2013.

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Mabiso, A., Cunguara, B. & Benfica, R. Food (In)security and its drivers: insights from trends and opportunities in rural Mozambique. Food Sec. 6, 649–670 (2014). https://doi.org/10.1007/s12571-014-0381-1

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Keywords

  • Rural food security
  • Food policy
  • Calorie consumption
  • Rural Mozambique