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Exploring agriculture-child nutrition pathways: Evidence from Malawi’s Farm Input Subsidy Program

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Abstract

Child undernutrition is highly prevalent around the world, particularly in low-income countries where economies are largely driven by the agricultural sector. Agricultural policies have the potential to impact total food production as well as food quality and diversity, thereby shaping nutritional status. In this study, we first corroborate evidence that Malawi’s Farm Input Subsidy Program (FISP), which provides subsidized vouchers for farm inputs to targeted rural households, boosts child nutritional status. Our analysis includes recent years during which the program’s nutrition impacts have not been previously examined. We then investigate three broad categories of agriculture-child nutrition linkages in the context of this program: (1) farm production and diversity, (2) crop sales, non-farm enterprises, and food consumption from different sources (purchases and own production), and (3) women’s empowerment and the health environment. In order to identify plausibly causal estimates, we employ a fixed effects-instrumental variable (FE-IV) approach. Our results demonstrate that FISP is associated with an increase in use of agricultural inputs (fertilizer) and boosts crop production. In addition, there are positive impacts on the likelihood that households sell maize, the crop targeted specifically by the program, and operate non-farm enterprises. Recipient households also purchase more vegetables on the market and consume more cereals from the crops they produce themselves. The evidence from this study highlights the main pathways through which an agricultural policy shapes short-term hunger and child nutritional outcomes.

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Notes

  1. The study also shows that FISP households, both those with and without children, consume cereals, nuts, vegetables, meat and fruits on more days of the week than non-FISP households.

  2. The program is implemented by Malawi’s National Statistical Office with assistance from the Living Standards Measurement Study–Integrated Surveys on Agriculture, a World Bank household survey program.

  3. These follow-up samples were selected such that reliable estimates could be obtained for rural and urban Malawi (Republic of Malawi National Statistical Office, 2017).

  4. Data is available from https://microdata.worldbank.org/index.php/catalog/3819.

  5. We drop 214 household-wave level observations because there are missing data for the control variables that we use (described later in the Section 5).

  6. A fourth questionnaire, a community survey, was administered to community representatives. The 2016–17 survey round also administered an individual questionnaire to collect information on assets and food security.

  7. Among fertilizer inputs, we restrict our attention to inorganic fertilizer which is the most commonly used fertilizer—78 percent of farming household used this input. In contrast, 28 percent used organic fertilizer and six percent used pesticides/herbicides.

  8. Fertilizer and seed quantities are in kilogram (kg) and are transformed using the inverse hyperbolic sine transformation since these variables are positively skewed. Crop production is measured in kilogram (kg) and we log the positive production values to normalize the distribution (production estimates are positively skewed).

  9. It would have been ideal to use the Women’s Empowerment in Agriculture Index (Malapit et al., 2019) to investigate the impact of FISP on women’s role in the household. However, we do not have the data for all the indicators used to construct this composite measure. Note, though that the WEAI does incorporate the information we examine—women’s input in productive decisions, and their ownership of land and assets.

  10. Note that all households stemming or splitting off from a household at baseline are still compared to outcomes for the original household in previous rounds.

  11. An IV approach requires a variable, known as the instrument that shapes the potentially endogenous covariate of interest and that influences the outcome(s) only through this variable (Wooldridge, 2010). The instrument carves out the plausibly exogenous variation in the covariate of interest and allows for the identification of plausibly causal effects.

  12. Results are essentially the same when we conduct the estimation using the leave-one-out instrument - that is, when we instrument for household receipt of FISP with the average proportion of households benefiting from FISP after excluding that household from this average. These results are available upon request.

  13. One way in which the LATE might be different from the average treatment effect (ATE) is if the instrument pushes a select group into the treatment. As discussed above, FISP might largely benefit wealthier households, but when the scope of the program in a region expands, less wealthy households might come to be enrolled. If this is the case, the marginal household whose treatment status would be affected by the instrument might have a high potential to benefit from the subsidy and therefore experience higher impacts (the LATE would then be larger than the ATE). Note though that we are unable to conclusively determine the characteristics of those whose treatment status is determined by the instrument and indicate how the LATE might compare to the ATE.

  14. Since farm input subsidies can plausibly shape a wide range of household characteristics (for example, by boosting income), we use only a sparse list of household-level controls so as to not introduce bias into the estimates we identify. Note, however, that the household fixed effects account for all time-invariant household characteristics. Dependency ratio is the ratio of number of household members 0–14 years and 65 years and above to the number of household members aged 15–64 years. When examining child nutritional outcomes, we also include the following controls: child gender and age indicators, as well as mother’s characteristics—age, marital status, literacy and school attendance. Note, we use a linear model to reduce potential bias from having a low number of time periods and high number of fixed effects (what is known as the “incidental parameters problem”) (Neyman & Scott, 1948; Lancaster, 2000).

  15. We implement the Simes multiple hypothesis test adjustment procedure using the STATA command qqvalue (Newson, 2010).

  16. The only exception is the F-statistic in Panel A, column 4 of Table 5.

  17. As we show in Table 11 in the Appendix, estimates obtained for the quantity of fertilizers used and quantity of harvests are similar when we measure these quantities in kg per acre.

  18. We use the World Food Programme’s (WFP) methodology to construct this variable, one that summarizes the frequency of consumption of different food groups and weights the report for each group with the nutritional value of that group (WFP, 2008).

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Author notes

  1. Averi Chakrabarti and Aurélie P. Harou share first authorship.

Authors and Affiliations

  1. American Institutes for Research, Waltham, MA, USA

    Averi Chakrabarti

  2. McGill University, Department of Natural Resource Sciences, Agricultural Economics, Sainte-Anne-de-Bellevue, Canada

    Aurélie P. Harou

  3. John Hopkins University, Berman Institute of Bioethics, Baltimore, MD, USA

    Jessica Fanzo

  4. University of Florida, Agricultural and Biological Engineering, Gainesville, FL, USA

    Cheryl A. Palm

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  1. Averi Chakrabarti
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Correspondence to Aurélie P. Harou.

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Appendix

Appendix

Table 9 Description of outcome variables
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Table 10 First stage results
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Table 11 Impacts of Malawi's Farm Input Subsidy Program (FISP) on fertilizers and crop yields (in kilograms (kg) per acre)
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Table 12 Impacts of Malawi's Farm Input Subsidy Program (FISP) on diet diversity
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Chakrabarti, A., Harou, A.P., Fanzo, J. et al. Exploring agriculture-child nutrition pathways: Evidence from Malawi’s Farm Input Subsidy Program. Food Sec. 16, 201–221 (2024). https://doi.org/10.1007/s12571-023-01416-x

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