2. A Material Cost to Business
Productivity losses resulting from adult underweight and obesity alone are estimated to cost businesses up to $65 billion a year in the 19 countries modelled for this study.
For many companies active in low- and middle-income countries where malnutrition is prevalent among the population, malnutrition can impose a material cost to business operations by compromising the quality of human capital. The potential costs are also high for MNCs with vertically integrated supply chains extending into those countries. A worker’s experience of malnutrition can result in reduced physical and cognitive capacity and ill-health. As such, malnutrition can lead to both presenteeism (reduced productivity at work) and absenteeism (physical absence from the workplace), as indicated by certain among our semi-structured interview participants:
We do have some employees who show symptoms of anaemia – they tend to take leave from work.
Overweight and obesity often show up as key morbidity and mortality and health risk factors.
We believe the quality of nutrition is directly related to the health, performance and productivity of our workforce.
Presenteeism and absenteeism in turn reduce the capacity of the workforce and so contribute both to reduced economic output and to increased firm costs in the form of increased sickness absence, healthcare expenditure, staff turnover, early retirement and staff training (Figure 3). Aggregated up to sector level, the costs of malnutrition can be significant.
Figure 3: The impact channel framework summarizes the mechanisms by which malnutrition imposes costs on business
— Source: Vivid Economics.
Figure 3: The impact channel framework summarizes the mechanisms by which malnutrition imposes costs on business
Table 1: The scale and cost of malnutrition-related productivity losses
|
Form of malnutrition |
Impact channel |
Estimated cost to business in modelled countries, US$ |
|---|---|---|
|
Adult underweight |
Underweight reduces the physical and cognitive capacity of workers, which impairs labour productivity. Impacts may be more severe among manual workers and among individuals in physically demanding roles. Extensive literature shows that in low-income or middle-income countries, worker output and wages increase as body mass index (BMI) increases towards a healthy weight, meaning that underweight adults are less productive and earn less than those of a healthy weight.23 |
$8–38 billion per annum |
|
Obesity |
Obesity can lead to difficulties in performing physical tasks or completing tasks on time,24 and is associated with both reductions in productivity and increases in ill-health-related absence from work.25 While the vast majority of studies into the impact of obesity on the workforce have centred on developed-country settings, two studies were undertaken in China during the period of that country’s nutrition transition towards more ‘Western’ diets, when there was a rapid increase in the prevalence of overweight and obesity while the population was still experiencing food insecurity. These studies found obesity to have negative labour market impacts, consistent with the developed-country literature.26 |
$4–27 billion per annum |
|
Anaemia |
Micronutrient deficiencies at any stage of life can have implications for productive capacity and cognition, and are particularly impactful during the early formative stages of growth. Our model focuses on anaemia (iron or vitamin B12 deficiency). Anaemia reduces worker productivity, even when workers are consuming sufficient calories.27 It causes fatigue and lethargy, impairs physical capacity and work performance,28 and has been associated with a 17 per cent reduction in productivity among those performing heavy manual labour.29 Female workers are significantly more likely than their male counterparts to suffer from micronutrient deficiencies, even within the same occupation. This is due both to biological differences30 (the risk of iron deficiency is heightened by menstrual blood loss and pregnancy) and to the status of women within households (and the implications for the division of available food).31 |
$21.8 billion per annum32 |
|
Adult short stature, resulting from stunting in childhood |
Stunting in childhood contributes to lifelong disability, impairs physical and cognitive development, and reduces a child’s ability to access and progress within education.33 Our model looks at short adult stature, of which childhood stunting is a direct cause.34 Individuals who were stunted in childhood are likely to suffer from lower cognitive and physical capacity in adulthood, and to earn less.35 |
$3.9 billion per annum36 |
The Vivid Economics model, developed for the purposes of this study, estimates the direct costs of malnutrition – adult underweight, obesity and anaemia, together with the lasting physical impacts of stunting experienced in childhood – for 13 sectors across a maximum of 19 low-, lower-middle- and upper-middle-income countries. Collectively across the 19 countries, adult underweight alone is estimated to cost businesses between $8 billion and $38 billion a year, while obesity costs an additional $4 billion to $27 billion (Table 1). Not accounted for in this estimate are the indirect costs of such losses, for example of additional staff or replacement workers, paid sick leave for malnutrition-related illness, and associated healthcare costs for companies that offer private insurance for employees.
The extent of losses and costs at a company level depends on multiple factors, including the prevalence of malnutrition among the local population (Box 3), the nature of employment (manual labour or desk-based labour, for example), and the extent to which the company’s output depends on human labour as opposed to capital assets (e.g. equipment and machinery). The demography of the workforce will also have a bearing, as certain demographic groups are more vulnerable than others to malnutrition and its health impacts – so, too, will workers’ socioeconomic and ethnic backgrounds.37 But all companies, regardless of sector, are likely to suffer the economic effects of at least one form of malnutrition.
Box 3: The new nutrition reality
Low- and middle-income countries now face a ‘new nutrition reality’ in which undernutrition and obesity are co-occurring, not only within populations but within households and even individuals – a phenomenon known as the ‘double burden’ of malnutrition.38 The prevalence of overweight/obesity among adults is rising in low-, lower-middle and upper-middle-income countries, while the prevalence of underweight has, until recently, been falling (Figure 4). Since 2015, however, the share of the global population suffering from undernutrition has remained largely unchanged and, with population growth, the absolute number of individuals suffering from undernutrition has been increasing.39
Today, just under 16 million children are both stunted and wasted, while over 8 million children are stunted and overweight.40 Among 126 low- and middle-income countries recently studied, 48 have high rates of undernutrition (with over 30 per cent of children under the age of five stunted and over 15 per cent wasted, and over 20 per cent of women underweight) and of overweight/obesity (with more than 20 per cent of children or adults overweight or obese).41 At a regional level, South Asia and sub-Saharan Africa are particularly affected by childhood stunting and wasting but also have significant, and growing, numbers of obese children and adults (Figure 5).
Figure 4: Evidence of the global nutrition transition
Figure 4: Evidence of the global nutrition transition
Figure 5: Childhood malnutrition by region (stunting, wasting and overweight, by prevalence and number of children affected)
Figure 5: Childhood malnutrition by region (stunting, wasting and overweight, by prevalence and number of children affected)
Figure 6: Adult obesity prevalence by country
Figure 6: Adult obesity prevalence by country
Source: Global Obesity Observatory, ‘Obesity prevalence worldwide – Adults’, https://www.worldobesitydata.org/map/overview-adults (accessed 24 Apr. 2020).
2.1 The costs of workforce underweight and obesity – perceived and real
While various studies have sought to estimate the social and economic costs of malnutrition (see Table 3 in Annex I), little research has been undertaken to date into the scale of costs to business associated with malnutrition.
For the purposes of this report, Vivid Economics developed an innovative model to estimate the economic costs, directly to businesses, based on an analysis of the distribution of the workforce across economic sectors, and the degree to which, within sectors, the workforce suffers from a measurable indicator of malnourishment (Box 4). In parallel, our interviews with representatives from MNCs sought to gauge perceptions of malnutrition as a material risk – or otherwise – to their operations.
What emerges from both analyses is a marked disconnect between the perceived and real scale of the malnutrition burden at company level. Below we discuss some of the key findings; further findings from the Vivid Economics model can be found in Annex II.
Box 4: The Vivid Economics model
The Vivid Economics model, developed for this report, combines household survey data on malnourishment and occupation with economic data from the International Labour Organization to calculate the potential economic output lost as a result of malnutrition. The model estimates the losses due to underweight and obesity for 13 sectors across 19 low-, lower-middle- and upper-middle-income countries.42 The lost output associated with two other conditions – anaemia and adult short stature – is also calculated in 13 sectors, but for five countries and 17 countries respectively owing to data limitations (see below). The model is a static model of a country’s economy and estimates the additional benefit that would arise if the workforce changed from its current state of malnourishment to being well-nourished, without any subsequent change in the way the economy is structured. It is not a dynamic model, and cannot estimate how changes in childhood nutrition would manifest in labour availability across skilled and unskilled occupations and economic output in subsequent decades.
The model uses physical indicators to identify the outcomes of malnourishment, rather than using indicators of an individual’s intake of energy or nutrients. Physical indicators can reflect many factors beyond food intake alone – poor water, sanitation and hygiene conditions, for example, or lack of access to health services – that can exacerbate the health issues caused by inadequate nutrition.
It is important to note that the modelling exercise offers only an initial exploration into the impacts of childhood stunting on adult workers. Our modelling is limited to the physical impacts of childhood stunting on adult height, using adult short stature as a crude proxy for the experience of stunting in childhood. Further research is required to fully understand – and quantify – the static and dynamic impacts of childhood stunting, and childhood malnutrition more broadly, on labour productivity, human capital development and economic growth.
The indicators of malnutrition included in the model are:
- Underweight arising from chronic hunger: as proxied by low body mass index (BMI);43
- Obesity: as measured by high BMI;
- Anaemia: as measured by altitude-adjusted blood haemoglobin levels;44
- Physical impacts of childhood stunting: as proxied by short adult stature.45, 46
The estimated proportion of current adult workers suffering from malnutrition is combined with the proportion of current adult workers estimated to have experienced the physical effects of childhood malnutrition, along with productivity coefficients sourced from academic studies, to estimate the loss in workforce productivity by sector as a result of malnutrition. Productivity coefficients reflect reduced workforce productivity (i.e. reflecting lack of stamina, strength, concentration) while people are at work, a phenomenon also known as ‘presenteeism’. Presenteeism increases direct costs to employers as a result of this lower productivity, and affects the private sector more generally by reducing sector-wide output potential. The reduction in output has consequences for other sectors which supply into the focal sector or take outputs from the focal sector.
Given the inputs (estimates of the prevalence of malnutrition, the labour productivity coefficients, and the distribution of labour across the workforce), the model estimates the increase in output that would occur, without changing the structure of the economy, were each sector’s workers to have full productive capacity without malnutrition.
The Vivid Economics model covers 19 low-, lower-middle- and upper-middle-income countries and 13 sectors, as follows:
Countries
- Europe: Albania*
- Asia: Bangladesh, Cambodia, India*, Nepal, Pakistan
- North Africa: Egypt
- Sub-Saharan Africa: Côte d’Ivoire, Ethiopia*, Ghana, Mozambique, Namibia*, Rwanda, Tanzania, Zambia, Zimbabwe*
- Central America: Dominican Republic, Guatemala, Honduras
Sectors
- Agriculture
- Construction
- Education/health
- Electricity
- Financial/insurance
- Household services
- Information and communications
- Manufacturing
- Mining
- Professional services (including real estate activities, and scientific and technical activities)
- Retail/trade
- Transportation
- Water/sewerage
Note: Countries marked with an asterisk are those for which we modelled the cost of workforce anaemia. The two countries in italics – India and Côte d’Ivoire – are countries for which the cost of childhood stunting experienced by today’s workforce is not modelled. The non-inclusion of these countries is due to insufficient data.
2.1.1 Underweight
As noted in Table 1, underweight (proxied by low BMI) reduces the physical and cognitive capacity of workers, particularly those involved in physically demanding roles. Undernutrition (defined for the purposes of the interviews as comprising underweight and micronutrient deficiencies) was not considered by interview participants to be a material issue for their business. For the most part, participants were confident that their company would not come into contact with segments of the population among whom undernutrition is common and who suffer from its cognitive and physical impacts, since the educational and skills barrier for employment in the company would be prohibitively high:
The company’s own employees are white-collar workers, they have more education, and are better-off.
Our employees are highly skilled workers, so it’s not an issue.
Because we recruit the most highly educated, we don’t see nutritional issues as much.
Participants consistently indicated their belief that undernutrition affected only low-skilled, low-earning workers who, for the majority of companies interviewed, either form a small share of the salaried workforce, are employed as contractors, or are employed by upstream suppliers:
Income-related malnutrition should not be an issue in our workforce, except there may be other compounding variables.
It is unlikely to be a big issue within the four walls of the company, since we pay well above our peers and well above the minimum wage.
Only one interviewee noted any prevalence among employees of underweight, and this was attributed to an aesthetic desire to be ‘skinny’.
Contrary to these perceptions, a significant share of the workforce in the 19 modelled countries is estimated to be suffering from underweight, across all sectors, including those represented by the interview participants. While MNCs may employ individuals less exposed to malnutrition, their operations rely on supply chains along which exposure is likely. On average, 15 per cent of workers in the mining sector in the 19 countries are likely to be underweight, together with 12 per cent in the manufacturing sector, 10 per cent in the retail and trade sector, and 8 per cent in the professional services sector (Annex II – Figure 17).
Underweight is particularly prevalent among sectors where a significant share of the workforce is engaged in low-skilled, low-earning and heavy manual labour, such as agriculture, construction and mining. At country level, the rates of underweight among the workforce reflect population-level rates: countries in South and Southeast Asia are particularly affected, as is Ethiopia, where 28 per cent of the overall workforce is estimated to be underweight (as compared with just 2 per cent in Guatemala) (Annex II – Figure 17). Workforce underweight results in particularly high costs to business (in terms of losses to the gross value added (GVA) of the sector) where overall output is dependent to a large degree on the productive capacity of manual labourers: agricultural businesses in Ethiopia, for example, where the sector is minimally mechanized, or the mining sector in India (Figure 7).
Figure 7: The cost of underweight in the workforce (as a proportion of GVA, %)
— Note: The absolute cost to business is estimated by modelling potential sector GVA in the hypothetical instance of no malnutrition and comparing it to current sector GVA. The relative cost is calculated by dividing the absolute cost to the sector by the potential sector GVA in the no-malnutrition instance. Grey cells indicate sectors with too few data points to estimate sectoral prevalence, and hence calculate loss.
Source: Vivid Economics.
Source: Vivid Economics.
Figure 7: The cost of underweight in the workforce (as a proportion of GVA, %)
Source: Vivid Economics.
2.1.2 Obesity
In contrast to undernutrition, obesity was recognized by over half of participants (11 of the 19) as a significant issue for their company, and was noted as prevalent in their workforces in countries across low- and middle-income regions. In fact, obesity was assumed to be more prevalent – and more of a material concern – than undernutrition:
The issues are mainly in overnutrition.
Within our company, there is a tendency more towards overweight, rather than underweight.
Obesity and overweight are certainly an issue across the board.
We have more problems with overnutrition rather than malnutrition.
According to the model estimates, obesity is less prevalent overall than underweight across all 13 sectors (see Annex II – Figures 17 and 18). At country level, obesity is significantly more prevalent than underweight among the workforce in each of the 13 modelled sectors in Albania, Egypt, the Dominican Republic, Guatemala and Honduras, as well as slightly more prevalent overall in Ghana (8 per cent for obesity compared with 6 per cent for underweight), Namibia (12 per cent versus 10 per cent) and Zimbabwe (8 per cent versus 7 per cent) (Annex II – Figures 17 and 18).
A number of participants referred implicitly to the ‘nutrition transition’ (as diets become richer in meat and dairy, processed foods, and high-salt and -sugar foods, for example), with observations of increasing prevalence of obesity, and its co-occurrence with undernutrition, in countries in sub-Saharan Africa, South and Central America, and South and Southeast Asia.
We see the double burden of overnutrition and undernutrition – and the lack of micronutrients for those who are overweight. Central America, India and the US are places where overnutrition and the associated non-communicable diseases are becoming big issues.
Africa has a huge problem of obesity and overweight, and we try to reflect this.
Figure 8: The relative cost of workforce underweight and obesity
— Note: The darker the shade of ochre, the more costly underweight is to the sector relative to obesity. The darker the shade of aqua, the more costly obesity is to the sector relative to underweight. White indicates that underweight and obesity are equally costly to the sector. Grey cells indicate sectors with too few data points to estimate sectoral prevalence, and hence calculate loss.
Source: Vivid Economics.
Source: Vivid Economics.
Figure 8: The relative cost of workforce underweight and obesity
Source: Vivid Economics.
This was borne out to an extent by the model estimates: Ghana, Namibia, Tanzania and Zimbabwe are all countries in which businesses are suffering relatively high costs from both workforce underweight and workforce obesity (Figure 8).
Obesity was not framed by participants as a form of malnutrition, however, but rather as a lifestyle condition. Participants broadly associated obesity with high-earning and/or skilled workers in sedentary or non-physically demanding occupations:
Overweight is an issue among our manufacturers and salesforce – they use cars for transportation and so have less physical activity.
The driving division generally has poorer health because they are in sedentary work. We do have a number who would be in the obese category.
Figure 9: The cost of workforce obesity (as a proportion of GVA, %)
— Note: The absolute cost to business is estimated by modelling potential sector GVA in the hypothetical instance of no malnutrition and comparing it to current sector GVA. The relative cost is calculated by dividing the absolute cost to the sector by the potential sector GVA in the no-malnutrition instance. Grey cells indicate sectors with too few data points to estimate sectoral prevalence, and hence calculate loss.
Source: Vivid Economics.
Source: Vivid Economics.
Figure 9: The cost of workforce obesity (as a proportion of GVA, %)
Source: Vivid Economics.
In fact, our model shows that sectors characterized by physically demanding roles suffer the greatest costs associated with obesity among the workforce: across the 19 countries modelled, the cost of obesity is most concentrated in mining, education and health, and household services. In Egypt and Albania, where the prevalence of workforce obesity is particularly high, it is the agricultural sector that experiences the greatest costs. The economic burden of obesity on businesses is highest in Egypt, Albania and Honduras (Figure 9), countries where obesity is highly prevalent across the workforce (Annex II – Figure 18) and across the population as a whole (Figure 6). In these countries, and also in the Dominican Republic, Guatemala, Ghana, Namibia and Zimbabwe, the costs of workforce obesity exceed those of workforce underweight (Figures 7 and 9).
2.2 The additional costs of anaemia and childhood stunting
2.2.1 Anaemia among the workforce
Anaemia, as one example among many types of micronutrient deficiency, reduces the physical capacity of workers, particularly those engaged in manual labour (Annex I – Table 7). As with workforce underweight, workforce anaemia is particularly costly to the agriculture and mining sectors, costing them 3 per cent of GVA and 1.1 per cent of GVA respectively across the five countries modelled (Albania, Ethiopia, India, Namibia and Zimbabwe). Generally speaking, a lower prevalence of manual workers with anaemia corresponds with a lower relative cost per sector, but two sectors buck this trend: mining, and education and health. Despite a relatively low prevalence of manual workers with anaemia, both sectors have a high proportional GVA loss. This is likely a reflection of the fact that output in both sectors is much more reliant on human capital – worker productivity – than on technology or equipment, meaning that losses to productive capacity among the workforce have a more direct impact on economic performance.
Women workers are 1.4 to 2.6 times more likely to be anaemic than their male counterparts
Women in the workforce are much more likely than men to be anaemic (though prevalence among women in the workforce is slightly lower than among the female population as a whole). Women workers are 1.4 to 2.6 times more likely to be anaemic than their male counterparts, and this holds true even within occupation categories: 30 per cent of women in skilled agriculture and elementary occupations are anaemic, compared with 17 and 23 per cent of their male counterparts respectively (Annex II – Figure 22). Anaemia reduces economic output by an additional 0.8 per cent of GDP on average. Anaemia costs India alone $20 billion (0.7 per cent of GDP) in lost worker productivity. Given that anaemia arises from a single mineral deficiency, the actual impacts and costs of micronutrient deficiencies on the economics of business are likely to be significantly under-represented by our analysis.
2.2.2 Experience of childhood stunting among the workforce
We undertook a partial exploration of the effects of stunting experienced in childhood on adult workers and their labour productivity, using adult short stature as a crude proxy for the physical impacts of childhood stunting. Data on adult short stature were available for only 17 of the 19 countries (Côte d’Ivoire and India are not included). Across the 17 countries, just under a sixth of the workforce (16 per cent) is estimated to be suffering the physical effects of childhood stunting. The prevalence of adult short stature among the current workforce is particularly high in Central America (with rates of 60 per cent in Guatemala and 30 per cent in Honduras) and Southeast and South Asia (with rates of 27 per cent in Cambodia and 26 per cent in Pakistan).47
Across the 17 countries for which data on prevalence were available, the physical impacts of childhood stunting are estimated to impose a cost to business of $3.9 billion annually, equivalent to 0.4 per cent of GDP. This is likely to be a significant underestimate of the true total, as stunting also reduces cognitive development and educational attainment. Were the indirect impacts of childhood stunting on educational attainment to be included, a first-pass calculation suggests the costs could increase by 4.5 times, up to a total of 1.8 per cent of GDP (Annex I – Box 11). Moreover, it is likely that the impacts of childhood stunting are even more significant for economic development. The cognitive and educational impacts of childhood malnutrition are likely to have a dynamic effect on the economy, such as the value of the labour force, the education status of the labour force and the size of economic sectors. Further research is needed to examine this effect and quantify the likely impact on business.
Despite the significant and long-term impacts of childhood stunting on labour productivity, only one of the 19 interview participants noted that the company’s workers in low- and middle-income countries would likely have been affected by stunting in childhood.
23 Carrillo, B. and Charris, C. A. (2017), ‘New evidence of the effect of body weight on labor market outcomes in a developing country’, Economic Research and Planning, 47 (2), pp. 177–96. (accessed 2 Apr. 2020); Lafave, D. and Thomas, D. (2017), ‘Height and cognition at work: labour market productivity in a low-income setting’, Economic Human Biology, pp. 52–64, doi: 10.1016/j.ehb.2016.10.008 (accessed 2 Apr. 2020); and Kedir, A. M. (2013), ‘Schooling, BMI, Height and Wages: Panel Evidence on Men and Women’, Economic Issues, 18 (2): pp. 1–18, http://www.economicissues.org.uk/Files/2013/213Kedir.pdf?LMCL=W2m_DH (accessed 2 Apr. 2020).
24 OECD (2016), Health at a Glance: Europe 2016: State of Health in the EU Cycle, Paris: OECD Publishing, doi: 10.1787/9789264265592-en (accessed 2 Apr. 2020).
25 Goettler, A., Grosse, A. and Sonntag, D. (2017), ‘Productivity loss due to overweight and obesity: a systematic review of indirect costs’, BMJ Open, 7(10): e014632, doi: 10.1136/bmjopen-20156-014632 (accessed 4 Mar. 2020).
26 Luo, M. and Zhang, C. (2012), ‘Non-Linear relationship between Body Mass Index and labour market outcomes: new evidence from China’, CFPS Working Paper, pp. 13–103, https://mpra.ub.uni-muenchen.de/42683/ (accessed 2 Apr. 2020); and Shimokawa, S. (2011), ‘The labour market impact of body weight in China: a semiparametric analysis’, Applied Economics, 40(8): pp. 37–41, doi:10.1080/00036840600771239 (accessed 2 Apr. 2020).
27 Horton and Ross (2003), ‘The economics of iron deficiency’; and Weinberger, K. (2003), ‘The impact of micronutrients on labor productivity: evidence from rural India’, paper presented at the 25th International Conference of Agricultural Economists, 16 August 2003, Durban, South Africa, doi: 10.22004/ag.econ.25897 (accessed 2 Apr. 2020).
28 WHO (2012), Global Nutrition Targets 2025: Anaemia Policy Brief, Geneva: WHO, https://apps.who.int/iris/bitstream/handle/10665/148556/WHO_NMH_NHD_14.4_eng.pdf?ua=1 (accessed 6 Apr. 2020).
29 Horton and Ross (2003), ‘The economics of iron deficiency’.
30 Coad, J. and Conlon, C. (2011), ‘Iron deficiency in women: assessment, causes and consequences’, Current Opinion in Clinical Nutrition and Metabolic Care, 14(6): pp. 625–34, doi: 10.1097/MCO.0b013e32834be6fd (accessed 2 Apr. 2020).
31 Harris-Fry, H., Shrestha, N., Costello, A. and Saville, N. M. (2017), ‘Determinants of intra-household food allocation between adults in South Asia – a systematic review’, International Journal for Equity in Health, 16(1): p. 107, doi: 10.1186/s12939-017-0603-1 (accessed 2 Apr. 2020).
32 This is the aggregate cost across the five countries modelled for anaemia (Albania, Ethiopia, India, Namibia and Zimbabwe). India alone accounts for $20.5 billion of this total aggregated cost.
33 Galasso, E. and Wagstaff, A. (2016), The Economic Costs of Stunting and How to Reduce Them, World Bank Policy Research Note, pubdocs.worldbank.org/en/536661487971403516/PRN05-March2017-Economic-Costs-of-Stunting.pdf (accessed 23 May 2020); and McGovern, M. E., Krishna, A., Aguayo, V. M. and Subramanian, S. V. (2017), ‘A Review of the Evidence Linking Child Stunting to Economic Outcomes’, International Journal of Epidemiology, 46(4): pp. 1171–91, doi: 10.1093/ije.dyx017 (accessed 23 May 2020).
34 Dewey, K. G. and Begum, K. (2011), ‘Long-term consequences of stunting in early life’, Maternal and Child Nutrition, 7(s3), doi: 10.1111/j.1740-8709.2011.00349.x (accessed 24 May 2020).
35 Galasso and Wagstaff (2016), The Economic Costs of Stunting and How to Reduce Them; and McGovern, Krishna, Aguayo and Subramanian (2017), ‘A Review of the Evidence Linking Child Stunting to Economic Outcomes’.
36 Costs associated with adult short stature, the proxy used for the experience of childhood stunting in the model. Estimated for 17 of the 19 countries (Côte d’Ivoire and India are excluded owing to data limitations).
37 Traissac, P., El Ati, J., Gartner, A., Ben Gharbia, H. and Delpeuch, F. (2016), ‘Gender inequalities in excess adiposity and anaemia combine in a large double burden of malnutrition gap detrimental to women in an urban area in North Africa’, Public Health Nutrition, 19(8): pp. 1428–37, doi: 10.1017/S1368980016000689 (accessed 24 May 2020); and Mazariegos, M., Kroker-Lobos, M. F. and Ramírez-Zea, M. (2019), ‘Socio-economic and ethnic disparities of malnutrition in all its forms in Guatemala’, Public Health Nutrition, pp. 1–9, doi: 10.1016/S1368980019002738 (accessed 24 May 2020).
38 Popkin, B. M., Corvalan, C. and Grummer-Strawn, L. M. (2019), ‘Dynamics of the double burden of malnutrition and the changing nutrition reality’, The Lancet, doi: 10.1016/S0140-6736(19)32497-3 (accessed 2 Apr. 2020).
39 Food and Agriculture Organization of the United Nations (FAO), International Fund for Agricultural Development (IFAD), UNICEF, World Food Programme (WFP) and WHO (2019), The State of Food Security and Nutrition in the World 2019. Safeguarding against economic slowdowns and downturns, Rome: FAO, http://www.fao.org/3/ca5162en/ca5162en.pdf (18 May 2020).
40 Development Initiatives (2018), 2018 Global Nutrition Report.
41 Popkin, Corvalan and Grummer-Strawn (2019), ‘Dynamics of the double burden of malnutrition and the changing nutrition reality’.
42 The 19 countries included are: Albania, Bangladesh, Cambodia, Côte d’Ivoire, the Dominican Republic, Egypt, Ethiopia, Ghana, Guatemala, Honduras, India, Mozambique, Namibia, Nepal, Pakistan, Rwanda, Tanzania, Zambia and Zimbabwe.
43 Body mass index (BMI) is a measure of nutritional status in adults. It is defined as a person’s weight in kilogrammes divided by the square of the person’s height in metres (kg/m2). WHO (2019), ‘Body mass index – BMI’, http://www.euro.who.int/en/health-topics/disease-prevention/nutrition/a-healthy-lifestyle/body-mass-index-bmi (accessed 6 Apr. 2020).
44 Women with levels below 120 g/dL and men with levels below 130 g/dL are considered anaemic. WHO (2011), Haemoglobin concentrations for the diagnosis of anaemia and assessment of severity, Vitamin and Mineral Nutrition Information System, http://www.who.int/vmnis/indicators/haemoglobin.pdf (accessed 6 Apr. 2020).
45 Based on the expected healthy height of 19-year-olds, with women shorter than 150.1 cm and men shorter than 161.9 cm considered stunted. WHO (2007), ‘Height-for-age (5-19 years)’, https://www.who.int/growthref/who2007_height_for_age/en/ (accessed 6 Apr. 2020).
46 Estimates of childhood malnutrition rates in previous cohorts indicate higher rates of malnutrition than the estimates in this study that use the adult short stature proxy. However, it is impossible to determine if children who were malnourished enter the labour force at all, or in which sectors, using cohort prevalence alone. While no physical markers can perfectly indicate whether an adult experienced malnutrition as a child, there is research indicating that childhood nutrition is an important factor in adult height. While this indicator may not capture many adults who experienced childhood malnutrition and later entered the labour force, it is a useful partial proxy for the minimum cost of childhood malnutrition on today’s workers, indicating the magnitude of costs which childhood malnutrition imposes on individuals and society as a whole.
47 These results should be treated with caution, as they may to some extent reflect regional variation in adult height.