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The Business Case for Investment in Nutrition

Malnutrition acts as a brake on the development of individuals, communities and economies around the world. This report is the first of its kind to reveal the hidden costs of malnutrition for business, and the extent to which these costs are recognized and addressed by multinational companies (MNCs).

Chatham House report Updated 17 December 2020 ISBN: 978 1 78413 404 4

Workers at the textile production factory of JayJay Textiles in Addis Ababa, Ethiopia, April 2017. Photo: Michael Gottschalk/Photothek/Getty Images

— Workers at the textile production factory of JayJay Textiles in Addis Ababa, Ethiopia, April 2017. Photo: Michael Gottschalk/Photothek/Getty Images

Laura Wellesley

Former Senior Research Fellow, Environment and Society Centre

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Professor Tim Benton

Distinguished Fellow, Environment and Society Centre

Jason Eis

Executive Director, Vivid Economics

Cor Marijs

Principal, Vivid Economics

Caroline Vexler

Senior Economist, Vivid Economics

Florence Waites

Economist, Vivid Economics

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Annex II: Additional Results from the Vivid Economics Model

Below we include additional results on the prevalence and costs of malnutrition in its different forms.

Figure 17: The prevalence of underweight in the workforce (%)

— Notes: Grey cells indicate sectors with too few data points to estimate sectoral prevalence. Bangladesh’s agricultural sector is not included in our findings. The number of agricultural workers surveyed in Bangladesh only just surpassed our threshold for minimum sample size, and all surveyed workers were in the normal range of BMI. Given what we know about agricultural workers in other countries, and the prevalence of underweight in other sectors in Bangladesh (for which the sample size was significantly larger), this is likely not a representative sample. The overall economic cost to business in Bangladesh is therefore likely to be a significant underestimate, and sector-specific findings for Bangladesh will be more salient than the ‘all sector’ average.

Source: Vivid Economics.

Figure 17: The prevalence of underweight in the workforce (%) — — Notes: Grey cells indicate sectors with too few data points to estimate sectoral prevalence. Bangladesh’s agricultural sector is not included in our findings. The number of agricultural workers surveyed in Bangladesh only just surpassed our threshold for minimum sample size, and all surveyed workers were in the normal range of BMI. Given what we know about agricultural workers in other countries, and the prevalence of underweight in other sectors in Bangladesh (for which the sample size was significantly larger), this is likely not a representative sample. The overall economic cost to business in Bangladesh is therefore likely to be a significant underestimate, and sector-specific findings for Bangladesh will be more salient than the ‘all sector’ average.

Source: Vivid Economics.

Figure 18: The prevalence of obesity in the workforce (%)

— Note: Grey cells indicate sectors with too few data points to estimate sectoral prevalence.

Source: Vivid Economics.

Figure 18: The prevalence of obesity in the workforce (%) — — Note: Grey cells indicate sectors with too few data points to estimate sectoral prevalence.

Source: Vivid Economics.

Figure 19: The combined prevalence of underweight and obesity in the workforce, by country

— Source: Vivid Economics.

Figure 19: The combined prevalence of underweight and obesity in the workforce, by country — — Source: Vivid Economics.

Figure 20: The combined cost of underweight and obesity in the workforce (as a proportion of GVA, %)

— Note: Grey cells indicate sectors with too few data points to estimate sectoral prevalence.

Source: Vivid Economics.

Figure 20: The combined cost of underweight and obesity in the workforce (as a proportion of GVA, %) — — Note: Grey cells indicate sectors with too few data points to estimate sectoral prevalence.

Source: Vivid Economics.

Figure 21: The prevalence of anaemia by occupation

— Note: The prevalence by occupation is estimated as an average of the prevalence in the five countries modelled, where the occupation sample size is at least 20 survey respondents.

Source: Vivid Economics.

Figure 21: The prevalence of anaemia by occupation — — Note: The prevalence by occupation is estimated as an average of the prevalence in the five countries modelled, where the occupation sample size is at least 20 survey respondents.

Source: Vivid Economics.

Figure 22: Differences in the prevalence of anaemia between men and women, by occupation

— Note: The prevalence by occupation is estimated as an average of the prevalence in the five countries modelled, where the occupation sample size is at least 20 survey respondents.

Source: Vivid Economics.

Figure 22: Differences in the prevalence of anaemia between men and women, by occupation — — Note: The prevalence by occupation is estimated as an average of the prevalence in the five countries modelled, where the occupation sample size is at least 20 survey respondents.

Source: Vivid Economics.

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