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

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

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

  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. 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. 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. 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. 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. 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. 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. 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. 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.

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Authors and Affiliations

  1. International Food Policy Research Institute, Washington, DC, USA

    Athur Mabiso

  2. Department of Agricultural Food and Resource Economics, Michigan State University, East Lansing, MI, 48824, USA

    Benedito Cunguara & Rui Benfica

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  1. Athur Mabiso
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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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