Gendered food security in rural Malawi: why is women’s food security status lower?

Abstract

Gendered food security gaps between female- and male-headed households (FHHs and MHHs) can be decomposed into two sets of components: those explained by observable differences in levels of resource use, and those due to unobserved differences affecting the returns to the resources used. Employing exogenous switching ordered probit and binary probit regression models, this paper examines the gendered food security gap and its causes in rural Malawi. We conducted a counterfactual analysis and found that the food security of FHHs would improve significantly if they had the same levels of resource use as MHHs. However, even if FHHs had the same levels of resource use as MHHs, the gendered food security gap would not be closed because of the differences in the returns to those resources. Such differences in returns to resources explain 40 % (45 %) of the observed gendered chronic (transitory) food insecurity gap and 54 % (19 %) of the food break-even (surplus) gap. Further analysis suggests that the intensity with which sustainable agricultural practices have been adopted has a greater impact on the food security of FHHs than on MHHs.

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Notes

  1. 1.

    Numerous authors, e.g., Eswaran (2014), have pointed out that the unitary household model is frequently a simplification of the decision-making process in a household. We acknowledge that the concept of headship and the assumption of a unitary household model may ignore women’s role in decision-making affecting households’ food-security status within male-headed households and, hence, may bias results. In addition, the use of female headship as an indicator of the gender of the farmer in the African setting is complicated because of the complex familial structure of households, which includes monogamous, polygamous and skipped-generation households (Peterman et al. 2011). Future work should consider these issues in food-security analysis and test the unitary household model by collecting disaggregated data within households.

  2. 2.

    Kahneman and Krueger (2006) and Van Praag et al. (2003) discuss the reliability of subjective measures but in different contexts.

  3. 3.

    In this study, we define a household as FHH if it belongs to either of the following categories: de jure FHH (if it is run by a single, widowed, divorced or separated woman) and de facto FHH (where there still is a virtual husband who is not physically present on the farm, because he is working elsewhere, for example). This is in line with previous literature in this field (see e.g., Dolan 2004; Kennedy and Peters 1992; Peters 1995, 1997; Takane 2008, 2009).

  4. 4.

    Subjective questions may depend on a number of distorting factors related to individual differences, including cognitive ability, personality traits, circumstances, aspirations and comparisons with others. If these factors are important, subjective evaluations may contain a great deal of noise relative to the signal value, and the resulting estimations should be interpreted sceptically. However, studies in both psychology and economics have demonstrated that the influence of these factors is limited, and that self-reported data pass a number of validation tests (Santiago and Pedro 2009). An additional point (see e.g., Onjala et al. 2014, who studied subjective risk perceptions) is that behaviour will be determined by subjective perceptions rather than by objective indicators; thus, studies that purport to explain people’s behaviour can benefit from using these subjective perceptions.

  5. 5.

    The correlation coefficients were 14 % for FHHs and 19 % for MHHs, both of which are statistically significant at the 1 % level.

  6. 6.

    These equations can be derived by taking the expected difference of Eqs. (1) and (2) and by subtracting and adding the term \( {\beta}_m{X}_f \) to the difference to derive Eq. (6a) and \( {\beta}_f{X}_m \) to derive Eq. (6b).

  7. 7.

    Teklewold et al. (2013a) and Wollni et al. (2010) used the number of SAPs as a proxy measure of intensity of adoption.

  8. 8.

    It is net of fertiliser, seeds, pesticides and hired labour – (these results are not reported).

  9. 9.

    Results for the entire FHH group are available from the authors upon request.

  10. 10.

    The number of SAPs and participation in subsidy and off-farm activities could be potentially endogenous variables, although comprehensive covariates including 15 location variables (district dummies) were included to capture geographic spatial differences and landholding systems in the country and, notably, regional differences in inheritance traditions. Furthermore, as the decision for some of the variables (e.g., SAPs and subsidies) is made at planting time, they are less likely to suffer from an endogeneity problem. In most cases, the results (not reported here) are stable after rerunning regression models excluding these variables.

  11. 11.

    The results from including individual practice in the regression showed that, compared with other practices, the use of chemical fertiliser and improved seeds played a large role in improving food security (results are not reported here).

  12. 12.

    As mentioned in footnote 8, the food-security gaps were estimated with and without potential endogenous variables. However, because the qualitative results (not reported here) excluding these variables were similar to those where the food-security gap was estimated including these variables, the results discussion is based on results that include potentially endogenous variables.

  13. 13.

    The generalised ordered probit model provides qualitatively similar results. Results are available from the authors.

  14. 14.

    A nonlinear decomposition method ( Sinning et al. 2008) was used to test the robustness of our results. We found the estimates to be close to the probit switching regression results. The gendered food-security gap due to differences in resource use and returns to resource use, respectively, was 66 and 34 %. The nonlinear decomposition method can also be used for ordinal outcome variables, but it does not produce results for each category; thus, we did not compare the results of the ordered probit model.

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Acknowledgment

This study was supported by the Australian Centre for International Agricultural Research (ACIAR) and the Australian International Food Security Research Centre (AIFSRC) through the Adoption Pathways Project and the Sustainable Intensification of Maize-Legume Cropping Systems for Food Security in Eastern and Southern Africa (SIMLESA) Programme led by the International Maize and Wheat Improvement Center (CIMMYT). We would also like to acknowledge the CGIAR Research Program on Maize for its financial support for the collection of national maize survey data. Jesper Stage acknowledges support from the Jan Wallander and Tom Hedelius Foundation, as well as from the Swedish Research Council Formas through its Commons Programme. We would like to thank Chalmers Mulwa and Geoffrey Muricho for cleaning and organising the data. The views expressed here are those of the authors and do not necessarily reflect the views of the donors or the authors’ institution. The usual disclaimers apply.

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Correspondence to Menale Kassie.

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Kassie, M., Stage, J., Teklewold, H. et al. Gendered food security in rural Malawi: why is women’s food security status lower?. Food Sec. 7, 1299–1320 (2015). https://doi.org/10.1007/s12571-015-0517-y

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Keywords

  • Food security
  • Gender gap
  • Switching regression
  • Malawi