Food Security
https://doi.org/10.1007/s12571-020-01099-8
ORIGINAL PAPER
Living income benchmarking of rural households in low-income
countries
Gerrie W. J. van de Ven1 & Anne de Valença2 & Wytze Marinus1 & Ilse de Jager3 & Katrien K. E. Descheemaeker1 &
Willem Hekman1 & Beyene Teklu Mellisse4 & Frederick Baijukya5 & Mwantumu Omari5 & Ken E. Giller1
Received: 16 March 2020 /Accepted: 17 August 2020
# The Author(s) 2020
Abstract
The extreme poverty line is the most commonly used benchmark for poverty, set at US$ 1.90 by the World Bank. Another
benchmark, based on the Anker living wage methodology, is the remuneration received for a standard work week necessary for a
worker to meet his/her family’s basic needs in a particular place. The living wage concept has been used extensively to address
incomes of plantation workers producing agricultural commodities for international markets. More recently intense discussion
has emerged concerning the ‘living income’ of smallholder farmers who produce commodities for international supply chains on
their own land. In this article we propose a simple method that can be used in all types of development projects to benchmark a
rural ‘living income’. We launch the Living Income Methodology, as adapted from the Living Wage Methodology, to estimate
the living income for rural households. In any given location this requires about one week of fieldwork. We express it per adult
equivalent per day (AE/day) and data collection is focused on rural households and their immediate surroundings. Our three case
studies showed that in 2017 in Lushoto District, rural Tanzania, the living incomewas US$ PPP 4.04/AE/day, in Isingiro District,
rural Uganda, 3.82 and in Sidama Zone, rural Ethiopia, 3.60. In all cases, the extreme poverty line of US$ PPP 1.90 per capita per
day is insufficient to meet the basic human rights for a decent living in low-income countries. The Living Income Methodology
provides a transparent local benchmark that can be used to assess development opportunities of rural households, by employers in
rural areas, including farmers hiring in labour, while respecting basic human rights on a decent living. It can be used to reflect on
progress of rural households in low-income countries on their aspired path out of poverty. It further provides a meaningful
benchmark to measure progress on Sustainable Development Goal 1, eliminating poverty, and 2, zero hunger and sustainable
food systems, allowing for consideration of the local context.
Electronic supplementary material The online version of this article
(https://doi.org/10.1007/s12571-020-01099-8) contains supplementary
material, which is available to authorized users.
* Gerrie W. J. van de Ven Frederick Baijukya
gerrie.vandeven@wur.nl f.baijukya@cgiar.org
Anne de Valença Mwantumu Omari
annedevalenca@gmail.com m.omari@cgiar.org
Ken E. Giller
Wytze Marinus
ken.giller@wur.nl
wytze.marinus@wur.nl
Ilse de Jager 1 Plant Production Systems, Wageningen University, PO Box 430,
dejager.ilse@gmail.com Wageningen, The Netherlands
2
Katrien K. E. Descheemaeker World Wildlife Fund, PO Box 7, Zeist, The Netherlands
katrien.descheemaeker@wur.nl 3 Human Nutrition and Health, Wageningen University, PO Box 17,
Willem Hekman Wageningen, The Netherlands
hekmanwillem@gmail.com 4 College of Agriculture, Hawassa University, Hawassa, Ethiopia
Beyene Teklu Mellisse 5 International Institute of Tropical Agriculture (IITA), PO Box 10,
beyteklu@gmail.com Duluti, Arusha, Tanzania
van de Ven G.W.J. et al.
Keywords Poverty line . Smallholder farms . Ethiopia . Tanzania . Uganda
1 Introduction the to be the absolute minimum, also referred to as the extreme
poverty line. For lower-middle income countries the poverty
Sustainable Development Goal (SDG) 1 aims to eliminate line is set at US$ 3.20 (experessed in PPP 2011). This also
poverty by 2030. SDG 2 addresses zero hunger and sustain- serves as a moderate poverty line for low-income countries
able food systems (United Nations General Assembly 2015). (Jolliffe and Prydz 2016).
While the elimination of poverty is a noble goal, the aspira- Other well-known benchmarks are the minimum wage and
tions of poor people are to rise above the poverty line and to the living wage. Several International Labour Organisation
educate their children, receive fair payments for their labour (ILO) conventions and other statements point to the interna-
and products. Agriculture is closely linked to both SDGs. It tional agreement on the right to a decent minimum wage or
contributes to food security and income to escape poverty. living wage (ILO 2008; United Nations 2007; United Nations
However, the margins in supply chains from farmers to re- General Assembly 1948, art 23). A living wage is the wage
tailers are unevenly distributed, with farmers least represented. keeping a worker and his family out of poverty. A minimum
The need to address social injustice in international supply wage is more of a political instrument, based on the prevailing
chains of key agricultural commodities such as tea and coffee economic situation in a country at national scale. Ideally it
has led to an increasing focus on the ‘living wage’ for planta- should not be below the living wage, but most low-income
tion workers. An extension of this approach to include small- countries do not have the political power and financial means
holder farmers who produce such commodities on their own for this (Croes and Vermeulen 2016). Based on their review,
farms, rather than through paid employment on plantations is Croes and Vermeulen (2016) conclude that no agreed interna-
captured in the concept of the ‘living income’. How to bench- tional system or standard is available for determining a fair
mark such a ‘living income’ is the focus of our approach minimum wage.
presented here. In rural areas in low-income countries often complex socio-
The most commonly used benchmark for poverty is economic relations prevail. Self-consumption of home pro-
the international poverty line. The international or extreme duced foods is often around 50% of the households’ food
poverty line for low-income countries was set at US$ requirements (FAO 2020). This could argue for a living in-
Purchasing Power Parity (PPP) 1.90 per capita per day in the come lowered with the value of home-produced food.
year 2011 (World Bank 2015b). It is based on the national However, food produced on-farm and consumed by the
poverty lines of the 15 poorest economies in the world in 2005 household represents a monetary value, a remuneration for
(Chen and Ravallion 2010) and adapted to increasing price production costs, including labour. For home consumption,
levels in 2011 (World Bank 2015b). The ‘costs of basic needs although costs per unit are lower, farmers have to produce
approach’ is widely used to establish the national poverty lines more volume compared with what they would buy, as losses
(World Bank 2015a). It complies with the definition of a de- in the phase of production and storage are much higher than
cent living (Jolly 1976; World Bank 2015a), although for the for purchased food; 19% for sub-Saharan Africa (Gustavsson
non-food part, an objective measure is difficult to find. A et al. 2011). Poor farmers often have to sell their crop just after
decent living is a basic human right, defined in Article 25.1 harvest, as they need cash for instance for school fees. Due to
of The Universal Declaration of Human Rights as “all people the oversupply at harvest time, prices in that period are low. In
have the right to a standard of living adequate for the health the hunger season, these poor households have to buy their
and well-being of himself and of his family, including food, food at higher prices (Leonardo et al. 2015). Often labour is
clothing, housing and medical care and necessary social ser- not paid in cash but in kind, as food. Labour is also shared or
vices, and the right to security in the events […] beyond his hired out, sometimes at the expense of their own production
control” (United Nations General Assembly 1948). In addi- (Leonardo et al. 2015). Other sources often contribute to
tion, everyone has the right to at least elementary education household income, such as petty trade, off-farm wages, either
(Article 26.1). However, methods and type of data collected - in- or outside agriculture, and remittances from family mem-
often partial - differ in national statistics among countries, bers working in cities or abroad. Infrastructure and remoteness
complicating comparison (Ferreira et al. 2016; World Bank of rural areas are important factors for market access and as
2015a). Aside from this and other methodological drawbacks such for local food prices (Mellisse et al. 2017).
(World Bank 2018b; Ferreira et al. 201; World Bank 2015a) In the real world all these factors vary widely and numerous
the poverty line of US$ 1.90 (PPP 2011) seems to be a robust combinations exist. In our search for a simple and rapid
benchmark for low-income countries (Jolliffe and Prydz benchmark for a living income in rural areas we therefore
2016). Despite the fact that eliminating poverty would be a focus on the local monetary value a household would need
major achievement for humanity, US$ 1.90 per day is deemed for a ‘decent living’ to meet the basic human rights,
Living income benchmarking of rural households in low-income countries
irrespective of its physical form (food, labour or money). As within a country can be very large, e.g. related to ac-
such, it indicates the minimum amount of money required if a cess to markets, prices of food and other commodities
household has to buy all of their food, finance housing, edu- and family size.
cation and health care at the local market. The actual pattern of The objectives of our study were to: (1) provide a rapid and
expenditures of households is not addressed here, as it is not incisive method to calculate the living income at local level;
part of the benchmark, but the benchmark serves as a basis for (2) develop a set of simple tools for rapid benchmarking of
comparison of households or regions. It can be used in region- living income; (3) test the method in three case studies in the
al development schemes both by policy makers and re- East African highlands; and (4) compare these three living
searchers to provide context for their plans. income estimates with other estimates based on the Anker
Despi te the lack of agreement and the many Methodology.
methodological issues, and given the urgency to address
poverty and injustice amongst the poor, Anker and Anker
(2017b) developed a methodology to calculate a living wage, 2 Methods
which is supported by several international NGOs (ISEAL
Alliance 2020; Living Income Community of Practice
The main differences between living wage and living
2020). It is defined as the remuneration for a ‘standard work
income is the expression per full time worker (fte) or
week’, necessary for a worker to meet ‘his family’s basic
per nuclear family (Grillo 2018; Anker and Anker
needs’ in a particular place (Anker and Anker 2013c). The
2017b), so the family size is a crucial determinant.
Anker Methodology has been used to assess living wages
Expressing the income in either unit is inadequate when
particularly with workers in agricultural commodity supply
applied to smallholder farming. Both are based on the
chains, such as tea plantations in rural Malawi (Anker and
size of a typical nuclear family for a region. Neither of
Anker 2014b) and flower farms in urban and rural Kenya
these allow for the fact that the number of persons in a
(Anker and Anker 2014a). Given the debates on living wage
family can vary widely within a region and therefore the
and living income and the lack of scientific literature
benchmark is not applicable to individual households.
concerning these concepts, we chose to build on the Anker
The approach is difficult to apply where extended
Methodology, which is the most concrete and practical ap-
families are predominant. Although Grillo (2018) sug-
proach currently available. In this paper we focus on our ad-
gested the benchmark should be adjusted to (extended)
aptations from the Anker Methodology.
household size if contextually necessary, this still does
The living wage or living income differs from the pov-
not make it applicable to individual households. For this
erty lines both conceptually and methodologically. The
reason, we propose a way of combining both approaches
international poverty line for low-income countries is
by expressing the living income on the basis of an adult
based on national poverty lines for the 15 poorest econo-
equivalent (AE). The living income per AE facilitates
mies in the world. Non-food expenditures are generally
calculation of the income for individual households in
based on the spending of people who live close to the
rural areas, explicitly considering household size.
poverty line and therefore it is questionable if the basic
Household composition is often a defining characteristic
human needs, such as education, housing and health care
of different socio-economic groups within a region,
are really covered. The resulting value of US$ 1.90 per
which may require different types of support or develop-
capita per day is subsequently applied to all other low-
ment interventions.
income countries in the world. By contrast, the living in-
come benchmark is based on local surveys, so more re-
gionally focused and mainly on rural households, assessing 2.1 Living income methodology – General framework
explicitly the non-food costs.
The Anker Methodology (Anker and Anker 2017b) is de- The Living Income Methodology presented here relies on a
tailed and, if the guidelines are followed, takes on average mixture of methods, triangulating information from household
about 60 person days for a full study including broad stake- surveys, key informant interviews and secondary reports to
holder engagement (Bhattacharyya 2018). This makes it assess the annual income required per adult equivalent to af-
difficult to deploy quickly as a benchmark for rural ford a decent standard of living. It builds on the Anker
developments projects and industries. To explore agri- Methodology while attempting to improve standardization
cultural development opportunities for smallholder and simplify the assessment procedures (Online Resource 1).
farmers in low-income countries, rapid locally-specific For each issue in which we differ from the Anker
assessments are crucial. Further, regional differences Methodology we indicate why and how.
van de Ven G.W.J. et al.
Low-cost Value of owner- Health care costs
nutritious diet occupied house
costs
Education costs
Utilities, 
Miscellaneous maintenance Other NFNH goods 
costs a and tax costs and service costsb
Food costs Non-food Housing costs Unforeseen non-housing costs c
(NFNH) costs
Living income
Fig. 1 Overview of cost items included in the Living Income total Food, Housing, and NFNH costs (Section 2.4.3); c Unforeseen costs
Methodology. a Miscellaneous costs is 16% of Low-cost nutritious diet is 10% of the Living income (Section 2.5)
costs (Section 2.4.1); b Other NFNH goods and services costs is 20% of
The Living Income Methodology covers four major the Living Income is available at a model portal (https://
expenditure groups: food, housing, non-food non-housing models.pps.wur.nl).
(NFNH) and unforeseen costs (Fig. 1). The food costs are
the sum of costs for a low-cost nutritious diet plus mis- Box 1. Ten guiding questions to estimate the living income in a
cellaneous food costs. The housing costs cover the value particular time and place
of an owner-occupied house plus expenses for utilities,
Reference household size and composition (local survey)
maintenance and taxes. The NFNH costs cover basic 1. What is the average number of adult males, adult females and children
health care and education, plus a margin for other (<18 years old) per rural household?
NFNH goods and services, such as clothing and footwear, Food costs (cover a range of markets)
household equipment, transportation and communication. 2. What are the two cheapest foods and the one most consumed food for
each of the 13 food (sub)groups, available at vendor locations where
The living income is determined for rural households in a rural households commonly shop for cheap foods?
specific area and moment in time, allowing for adjust- 3. For these foods: what is the current price (per kg or L)?
ments for inflation over time (Anker and Anker 2017b). 4. For foods with strongly fluctuating prices within a year (>25%), what is
In the next section we explain the components in more the most common price throughout the year (per kg or L)?
Housing costs (key informants, focus group discussion, secondary
detail. reports)
5. What are the local minimum standards for decent housing for a rural
household in relation to the international housing standards?
2.2 Assessing the living income 6. What are the annual housing costs for a house that complies with these
local minimum standards for decent housing?
Health care costs (key informant interviews, secondary reports)
A short list of guiding questions to assess living income 7. What are the costs of basic health care insurance; which health care
is presented in Box 1. These questions link to the service types are (not) covered?
Living Income Survey, which includes specific local 8. What are the costs of health care services not covered by the basic
insurance?
data collection guidelines, and the Living Income Diet Education costs (key informants, focus group discussion, secondary
Tool, which calculates a low-cost nutritious diet using reports)
linear programming. Both are tools to rapidly bench- 9. How many years of education are officially counted for completion of
mark the living income in a rural area in a transparent primary and lower secondary school?
10. What are the household out-of-pocket expenses per child for one year
and consistent way. A detailed manual for using the of public education at each these education levels?
Living Income Diet Tool is available in the supplemen- For more information, see Online Resource 1, and the full Living Income
tary material. A graphical user interface for calculating Survey in Online Resource 2
Living income benchmarking of rural households in low-income countries
2.3 Reference household size and composition that in low-income countries the equivalence elasticity is less
than in high-income countries. This approach allows to easily
We build on the living income concept as smallholder agricul- calculate the living income related to food security for house-
ture is mostly a family business. The local average household holds of different size based on their composition, including
size and composition of male and female adults, and children extended families. From the survey the average household
(<18 years old) is referred to as the reference household. This composition is calculated and converted to AME and AE by
size and composition can be determined using own survey data using the equivalence factors. Subsequently, all calculated
or secondary data, for example from local household surveys or variables are expressed per AE. This value can then be used
a recent national demographic survey. Preferably, region- to calculated the living income for an individual household,
specific data are used, as household composition can vary be- based on household composition.
tween urban and rural areas and between regions. The Anker
Methodology refers to the nuclear family if possible (Anker 2.4 Methods for cost calculation
andAnker 2017b).We follow the approach to include extended
family members for societies where this is relevant. In regions 2.4.1 Food costs
such as north Ghana with 10–14 people per household (De
Jager et al. 2017) and southern Mali with 8–45 household The Anker Methodology starts from a preliminary model diet,
members (Falconnier et al. 2015) the nuclear family does not based on current diets, a poverty line-related diet or a diet
reflect reality sufficiently. Depending on the variation the av- proposed by a nutritionist and is adapted stepwise to meet
erage or median household size is determined. For the living WHO standards for a nutritious diet. Two excel sheets were
wage also the number of full time labourers is required, which developed to calculate i) the household energy needs based on
is generally based on statistics on labour force participation age, sex, body size and physical activity and ii) a low cost diet
rates, unemployment rates and age. It is set to a value between in an iterative procedure, going from current or expensive
one and two persons per family (Anker and Anker 2017a). We (nutritionist diet) to low cost, covering 20 foods and 11 food
do not follow the limit of the Anker Methodology to a mini- groups (Anker and Anker 2017b).
mum of four and a maximum of six members per reference We take the position that the living income covers the lowest
household, nor for the number of full time labourers between cost nutritious diet, irrespective of the current diet. Based on the
one and two per reference household. WomanDietary Diversity Score (WDDS; Kennedy et al. 2010)
To assess the role of agriculture in rural development and which ensures sufficient food options for a nutritious and
food security in low-income countries the living income per micronutrient-sensitive diet, we distinguish nine food groups:
adult equivalent (AE) seems the most appropriate benchmark starchy staples, vegetables, fruits, meat, fish and seafood, eggs,
to reflect the potential aspirations of rural families. Here we milk and milk products, legumes nuts and seeds, and fats and
deviate from the Anker Methodology, based on national sta- oils, some of which are subdivided. Online Resource 1 gives
tistics on labour participation (Anker and Anker 2017b), as we the details on the food subgroups and the foods covered.
prefer a flexible benchmark that can be used for individual First, a selection of cheap and commonly-consumed foods
households, irrespective of their size. is identified within each food group from the available foods
Our assessment is split in two parts, i) food costs expressed at multiple local vendors, such as open-air markets and small
per Adult Male Equivalent (AME) and subsequently convert- village shops, specifically the places where the poor buy their
ed to Adult Equivalent (AE) per household, and ii) other costs foods. To ensure sufficient options for a low-cost nutritious
expressed directly per AE. In an AME, men, women and diet, prices are collected for at least three foods of acceptable
children are included, according to their energy needs. One quality per food (sub)group, free of mycotoxins. Two of them
AME requires 2500 kcal per person per day, which is the are the cheapest (per kg or L) and one is the most commonly
accepted international standard for manual work in agriculture consumed food (Online Resource 2). For all selected foods,
(FAO/WHO/UNU 2001), the common situation in rural areas current prices (at the moment of collection) are collected from
in low-income countries. Females are equivalent to 0.82 AME 5 to 10 different vendor locations, depending on the price
and children (0–18 years) to 0.75 AME (FAO/WHO/UNU variability. In case of strong fluctuations throughout the year
2001). Old people are included as adults according to their (>25%) themost common prices (throughout the year) are also
sex, as not all surveys include separate age classes for elderly. collected from the same vendor locations and used in the
In the AE, the first adult in a household is assigned value of calculations. Starchy staples, vegetables and fruits are most
1.0, all additional adults a value of 0.7 and children a value of likely to require such a price correction, due to their seasonal
0.5, accounting for economies of scale in the needs of house- availability. For both the current and the most common price,
hold members, e.g. for housing (OECD 2011; Atkinson et al. the median price is used in our calculations.
1995). We took the “Oxford” scale, as the modified OECD Next, the Living Income Diet Tool is used to calculate the
scale is based on the situation in OECD countries. We assume lowest-cost diet per AME, The Living Income Diet Tool is an
van de Ven G.W.J. et al.
Table 1 Nutritive requirements in the Living Income Diet Tool
Dietary components Required intake per AME Unit Source
Energy a 2500 (2400 – 2600) b kcal/day (FAO/WHO/UNU 2001)
Carbohydrate a ≥ 344 g/day (WHO/FAO 2003)
Proteina ≥ 63 g/day (WHO/FAO 2003)
Total lipid (fat) a ≥ 42 g/day (WHO/FAO 2003)
Calcium, Ca ≥ 833 mg/day (WHO/FAO 2004)
Iron, Fe ≥ 36 mg/day (IOM 2001)
Zinc, Zn ≥ 15 mg/day (Hotz and Brown 2004)
Vitamin Ac ≥ 99 IU/day (WHO/FAO 2004)
Vitamin C, total ascorbic acid ≥ 43 mg/day (WHO/FAO 2004)
Folate, DFE ≥ 320 μg/day (WHO/FAO 2004)
Thiamine ≥ 1.0 mg/day (WHO/FAO 2004)
Riboflavin ≥ 1.1 mg/day (WHO/FAO 2004)
Vitamin B12 ≥ 2.0 μg/day (WHO/FAO 2004)
The required daily intake of the dietary components is the minimum intake that poses no risk or adverse health effects to people between 19 and 50 years
of age
a Energy originates for at least 55% from carbohydrates (4 kcal/g; 2500 × 0.55/4 = 344 g/day carbohydrates), 10% from protein (4 kcal/g; 2500 × 0.10)/
4 = 63 g/day protein), 15% from fat (9 kcal/g; 2500 × 0.15)/9 = 42 g/day fat) (WHO/FAO 2003)
b Constraint in Living income Diet Tool to ensure sufficient flexibility to compose diets
c IU: International unit, depending on the type of vitamin A
optimizationmodel and described in detail in Online Resource 3. as calculated with the Living Income Diet Tool, contains a lim-
It ensures that meeting all dietary requirements for energy, car- ited set of foods, does not consider portion size as a limit to the
bohydrates, protein, fat, and a selection of micronutrients, based consumption a single food, and it assumes people eat this same
on the most common deficiencies in low-income countries (Beal diet every day. In reality people buy and consumemore different
et al. 2017; Table 1) are met. It covers food waste, common foods. To allow some variation in the diet for matters of palat-
cooking practices and retention factors. The model behaves as ability and portion size, another 10%was added to the food costs.
expected and intended (Online Resource 3). We assume that by This is similar to the value used by Anker and Anker (2017b).
covering these nutrients other essential nutrients are also cov-
ered. Dietary requirements are such that both females and males
cover their minimum requirements, e.g. the threshold for iron is 2.4.2 Housing cost
adequate for women and consequently slightly higher than strict-
ly needed formales.We assume that foods are distributed among Whilst the cost of ‘shelter’ is included in the international poverty
householdmembers according to their needs. The calculated diet line, what constitutes acceptable shelter is unclear. To estimate a
is an intermediate product, solely for the purpose of cost assess- cost for the Living Income Methodology we follow the local
ment. We do not evaluate the diet itself. minimum standard for basic healthy housing which is based on
Nutrient contents of foods are based on the nutrient com- international standards for healthy housing adjusted to local con-
position of the raw purchased product, the average waste fac- ditions, such as material availability, climate, and the reference
tor of its food group (USDA 2018; Gustavsson et al. 2011; household size and composition. The housing construction
FAO 2018) and the average retention factor per nutrient after should be able to sustain about 50 years without major repairs
preparation (e.g. boiling, frying) specified for the food group – also called the ‘expected service life’ of the house (Anker and
(Online Resource 3) based on USDA (2007). Anker 2017b). Estimation of housing costs involves: 1) defining
In addition to the calculated lowest cost, 4% is added to cover a local standard for basic healthy housing for a reference house-
food waste, based on 5% waste of fresh foods and 1% waste of hold, and 2) estimating the annual costs of a house that meets
processed foods in the consumption phase for Sub Saharan these standards. While the Anker Methodology is based on sec-
Africa (Gustavsson et al. 2011). A small amount of additions like ondary data, we use a combination of secondary reports, local
salt, spices and condiments are required to make a meal observations, key informant interviews and/or focus group dis-
palatable. Similar to Anker and Anker (2017b) we set this at cussions. The expected service life of a house needs to be locally
2%. Home-produced foods are valued against market prices. checked, as from our experience 50 years is unrealistically long
The minimum cost diet covering the nutritional requirements, for some rural areas in East Africa, depending on the building
materials used. This is then included in the calculations.
Living income benchmarking of rural households in low-income countries
The required number of interviews depends on the variation Education costs Education costs include all annual household-
in costs estimates. Based on our experience in the field in East out-of-pocket expenses to cover decent public primary and
Africa we recommend to include ten if possible, but at least five lower secondary education for all children in the reference
informants. Information should be collected on 1) total costs of household. In most countries education is compulsory starting
constructing the house including all materials and labour and the at the age of 5 to 7 until the age of 11 to 18 years, roughly
expected service life, 2) annual utility costs, including water, covering primary and lower secondary school (UNESCO
electricity, cooking fuel, heat, and lighting, 3) annual costs for 2000). Primary education is not always free, despite Article
routine maintenance and repairs, and 4) annual costs for taxes, 26 of the International Declaration of Human Rights. We as-
levies, fees and house insurance.When unclear, costs for routine sumed that public schools provide education of sufficient qual-
maintenance and repairs can be set at 0.3% of total construction ity. Information on costs is ideally gathered from secondary
costs, which is the average in our case studies (see Results). data and key informant interviews and/or focus group discus-
Construction costs are divided over the expected service life. sions with e.g. local education experts and parents/caretakers of
school children. Such informants and secondary data may be
more reliable than teachers or government officials.We suggest
2.4.3 Non-food non-housing costs to include at least ten informants, but more can be interviewed
depending on the variation among sources. Household out-of-
Non-food non-housing costs (NFNH) cover costs for educa- pocket expenses cover only parental responsibilities for essen-
tion, health and other basic needs, such as clothing and foot- tial needs for a child to go to school (school fees, clothing/
wear, transport, communication. The AnkerMethodology cal- uniform, materials such as books). The list of ‘essential needs’
culates the NFNH from secondary data on the ratio of can be deduced from cost items reported by the majority of
NFNH:food costs multiplied by the costs of the living wage informants, or consensus within a focus group. Costs for school
model diet. Subsequently, it checks if the calculated NFNH lunches are included in the food costs. The total education costs
cost cover the health care and education costs based on a rapid per child are assessed and divided over 18 years, giving the
assessment using local primary and secondary data. Food, average annual education costs per child.
housing, health care and education costs should typically be
below 60–70% of all spendings (Anker and Anker 2017b). In 2.5 Total budget for living income
our methodology we assess health care and education costs
similarly to the rapid assessment in the Anker Methodology. The total living income includes the sum of estimated costs for
However, we calculate ‘Other NFNH goods and services’ as a food, housing and NFNH per reference household, plus a mar-
margin of 20% of total Food, Housing and NFNH budget gin for Unforeseen Events estimated at 10% of the total living
(Online Resource 1). Although our costs for health and edu- income budget. An excel file is available to combine the survey
cation also rely partly on current spending, we do not include and diet data into the living income (Online Resource 4).
current spending ratios in our search for a living income. To be able to compare the living income and its compo-
nents across countries the local units are converted to
Health care costs Costs of health care include all annual Purchasing Power Parity to the international (US) dollar
household out-of-pocket expenses to cover basic health care (US$ PPP). The PPP conversion factor is the equivalent local
based on the locally available services. If basic health insur- currency required to buy goods and services in the domestic
ance is available, this may be the best option, but coverage market equivalent to what a US dollar would buy in the United
should be checked. If insurance is not available, or only partly, States. The PPP covers a correction for relative price develop-
an extra assessment is needed to estimate expenses for health ments and exchange rates (World Bank 2015b). We use the
care not covered by insurance. We assess each identified local US$ PPP for individual household consumption expenditure
health care service. Costs are estimated per household per year (US$ PPP-IHC), which is also the basis for theWorld Banks’s
based on the average usage and the average costs per treat- international poverty threshold of US$ 1.90 per capita per day
ment for the three most common diseases in the study area, as (World Bank 2015b). It covers the same items that we distin-
the most common diseases have the largest impact on health in guished in our framework (Fig. 1). The only exception is for
terms of people affected and are relatively easy to cure NFNH costs, where we do not consider recreation, restaurants,
(Online Resource 1). A combination of secondary reports alcoholic beverages, tobacco and narcotics as belonging to
(e.g. demographic health surveys, local health care facility, basic needs, and the PPP-IHC does (World Bank 2015b).
patient records, etc.) and key informant interviews (e.g. staff However, Dikhanov et al. (2017) reported that the influence
in local clinics, health care extension officers) can be used as of leaving out non-poverty items and the selection of poverty
source of information. The required number of informants specific goods, as we do in our procedure to select the
depends on local circumstances, but a minimum of three cheapest food items from each food group, has a negligible
cross-checks per cost item is recommended. effect on the PPP value.
van de Ven G.W.J. et al.
PPP values change over time and for each country we used region, including the capital Kampala, is relatively better off
the PPP values for the year the prices were reported and con- world (World Bank 2016). The national poverty line was US$
verted them to 2017, based on inflation rates and the consumer 1.46 (2011 PPP) in 2012 (Jolliffe and Prydz 2016).
price indices as reported by the World Bank following their The living income for rural households in Isingiro District, in
calculation procedure (World Bank 2018a). the Western region of Uganda, was estimated based on second-
ary data and primary data collected through key informant in-
2.6 Case studies: Lushoto District, Tanzania; Isingiro, terviews in Birere and Kaberebere sub-counties. Food price data
Uganda and Sidama, Ethiopia was collected in village shops and at the local market in the rural
town of Kaberebere. All data was collected in August 2018.
All three countries of our cases, i.e. Ethiopia, Tanzania and
Uganda, are classified as low-income countries and were part 2.6.3 Ethiopia
of the 15 countries included in the assessment of the extreme
poverty line of US $ 1.90 in 2011 (Chen and Ravallion 2010; Ethiopia had a population of 105 million people in 2017
World Bank 2015b). (World Bank 2018b). Ethiopia is characterized by an average
GDP growth rate of 11% over the last decade, which is about
2.6.1 Tanzania double of the average growth for Sub Saharan Africa (UNDP
2014). Despite this two-digit GDP growth, 27% of the popu-
The United Republic of Tanzania has over 57 million inhabi- lation lives below the international poverty line of US$ 1.90
tants, 67% of which live in rural areas (World Bank 2018a). per day. The national poverty line was US$ 1.80 (2011 PPP;
General development trends in Tanzania include population Jolliffe and Prydz 2016). Although agriculture has been the
growth, urbanization, and economic growth of 7% annual back bone of Ethiopian economy, the increasing rural popu-
GDP gains since 2010, and decreases in extreme poverty. lation and resulting farmland fragmentation put huge pressure
Despite these positive trends, Tanzania remains a low income on meeting food requirements of both urban and rural popu-
country and poverty is widespread. Half of the population lives lation, which has huge implications on the affordability of
of less thanUS$ 1.90 a day and 93% lives on less thanUS$5.50 foods for the poor (Mellisse et al. 2017).
a day, both in US$ 2011 PPP (World Bank 2014). Themajority The population density in rural Wondo Genet and Melga dis-
of the households facing poverty lives in rural areas. The na- tricts in Sidama zone in southern Ethiopia exceeds 1000 person
2
tional poverty line was US$ PPP 1.47 per adult equivalent per per km and the average farm size per household is less than 1 ha
day1 in 2011, excluding budget for housing and clothing na- (Mellisse et al. 2017). The data for assessing the living income for
tional (National Bureau of Statistics Tanzania 2015). rural households were based on secondary and primary sources
The living income for rural households in Lushoto District, collected through key informant interviews in Wondo Genet and
in the West Usambara Mountains of Tanga region, northern Melga districts. Food price data were collected from several ven-
Tanzania, was estimated based on secondary data and primary dor locations in July 2018 in Tula and Wugigra, capital towns of
data collected through key informant interviews in Lushoto Wondo Genet and Melga districts, respectively.
(district capital) and the villages Kongei, Migambo and
Mshizii. Food price data was collected in May 2018, and all
other data was collected in October 2017. 3 Results
2.6.2 Uganda 3.1 Reference household size and composition
The republic of Uganda has over 43million inhabitants (United Reference household (RH) size and composition in Lushoto
Nations et al. 2017), 80% of whom live in rural areas (Uganda District were obtained from the Rural Household Multiple
Bureau of Statistics 2014). Uganda is among the countries with Indicator Survey database (RHoMIS) for 2015 (Hammond
the highest growth in GDP among countries in SSA world et al. 2017). For Isingiro District, Uganda, they were obtained
(World Bank 2016). The share of the population living below from the Banana Agronomy Baseline Survey among 92 farm
the poverty line decreased from 31% in 2006 to 20% in 2013. households in the region (Banana Agronomy Baseline
Absolute numbers living in poverty, however, increased due to Survey, NARO-IITA Uganda, unpubl.) and for Sidama they
the rapid population growth (Uganda Bureau of Statistics were obtained from a survey of 120 farm households
2016). Poverty in Uganda differs among regions and is more (Mellisse et al. 2017; Table 2). All of these surveys were based
severe in rural than in urban areas. Most of the poor live in the on random sampling of rural households in their study areas
Northern, Eastern and Western regions, whereas the Central and were taken from project baseline studies. The living in-
come calculated from this data refers to the sampled districts
1 TZS 23,933 per 28 days, with prices from Oct. 2010 to Sept. 2011; only and are hence local/regional living income. Reference
Living income benchmarking of rural households in low-income countries
Table 2 Reference household (RH) composition for Lushoto District, onion, chili pepper, papaya, mango, orange, sheep meat, har-
Tanzania, Isingiro District, Uganda, and north east Sidama Zone, icot bean and faba bean. Kocho is the processed corm of enset
Ethiopia
(Ensete ventricosum) and is a typical Ethiopian staple food.
Age & gender group number/RH Nutrient composition of kocho was obtained from Ethiopian
Food Composition Table part IV (FAO and Ethiopian Health
Lushoto Isingiro Sidama
and Nutrition Research Institute 1995). The Living Income
Adults, male 1 1.5 2.2 Diet Tool showed that the minimum costs for a nutritious diet
Adults, female 1 1.5 1.7 were US$ 1.19 PPP/AME/day or US$ 2985 PPP/RH/year
Children (<18) 3 2.6 4.3 (Table 3).
AME 4.1 4.7 6.8
AE 3.2 3.7 5.2 3.2.2 Housing costs
AME adult male equivalent, AE adult equivalent Local minimum standards for decent housing in Lushoto
District, Tanzania, were compiled based on discussions with
household sizes were 3.2 AE in Lushoto, 3.7 AE in Isingiro four local agricultural extension officers, six resource-poor
and 5.2 AE in Sidama. household heads, and observations of the research team. A
Lushoto reference family requires a living space of at least
3.2 Costs per item of the living income 30 m2 with one living room and at least two bedrooms
(Table 4). The toilet/bathroom and kitchen may be outside the
3.2.1 Diet composition and food costs housing unit. Housing costs were based on information from five
owners of houses slightly above the local minimum standards for
Food prices for Lushoto District were collected in decent housing (three resource-rich farmers, one village school
May 2018 at multiple vendor locations, covering central open teacher, and one village doctor) and from ten owners of houses
air markets and small shops in Lushoto (district capital), below the local minimum standard. Estimated annual housing
Migambo and Mshizii (villages; Table 3). Both the current costs for a reference family in Lushoto are US$ PPP 1170, in-
costs and the most common prices are collected (for details cluding construction costs (US$ PPP 18,000 over 50 years), plus
see Online resources 2). The most common prices throughout routine maintenance and repair costs and utility costs (Table 5).
the year were used for tomato, carrot, cabbage, avocado, ba- Taxes, levies and house insurance costs were not common in
nana, chicken eggs, duck eggs, cocoyam, because their current Lushoto District. Local minimum standards and costs for decent
price differed >25% (−50% to +67%) from the most common housing in Isingiro District, Uganda, are assessed in a compara-
price throughout the year. Nutrient contents of Gallant soldier ble manner and resulted in similar estimated annual costs of US$
(Galinsoga parviflora), which was lacking in the USDA Food PPP 1147 (Table 4). In Sidama zone, Ethiopia, two focus group
Composition database, were taken from Wehmeyer and Rose discussions were organized with six people inWondo Genet and
(1983). Results of the Living Income Diet Tool show that the five inMelga. The expected service life of housingwithoutmajor
minimum costs for a nutritious diet were 1.29 US$ PPP/AME/ investments in repairs was about 30 years instead of 50. Hence,
day or 1920 US$ PPP/RH/year. in our calculations we increased the reported construction costs
Food prices for Isingiro District were collected in by 25% to cover the extra maintenance. Total annual housing
June 2018 at multiple vendor locations, covering stands at costs were estimated at US$ PPP 1541 (Table 5).
the central market and small shops in Kaberebere town, and
small shops in Birere. A price correction was made for carrots, 3.2.3 Health care
cabbage, papaya, sweet banana, dried tilapia, groundnut
(flour), peas, and ghee. Nutrient composition of ‘small dried The health care system in Lushoto District, Tanzania includes
fish’ called Silver cyprinid (Rastrineobola argentea; also public facilities (dispensaries) in most villages and private
Lake Victoria sardine or mukene,) was lacking for Uganda facilities (missionary) in some villages. Key informant inter-
and was obtained from the Tanzania food composition tables views were held with six health care workers from two dis-
(item 313, Lukmanji et al. 2008). Results of the Living pensaries and one missionary health care facility, and with one
Income Diet Tool show that minimum costs for a nutritious pharmacist. Basic health care insurance is available for all
diet were US$ 1.11 PPP/AME/day or US$ 1900 PPP/RH/year villagers through the Community Health Fund (CHF). The
(Table 3). membership covers all public health care costs for six house-
Food prices for Sidama were collected in July 2018 at mul- hold members, including doctor consultation and complete
tiple vendor locations, covering markets and small shops in treatment with medicine and laboratory tests for all common
the villages Tula, Yirgalem and Wugigra. Price corrections diseases. The interviewees reported that this does not cover all
were made for kocho, maize, kale, lettuce, tomato, carrot, household expenses on health care, as medicines are regularly
van de Ven G.W.J. et al.
Table 3 Composition and costs for a low-cost nutritious diet calculated with the Living Income Diet Tool for the three case study regions based on
local market prices per adult male equivalent (AME) per day
Food (sub)group Food Market price Calculated amount purchased Calculated food costs 2017
g/AME/day US$ PPP/AME/day
Lushoto District, Tanzania April ‘18 in TZS/kg
Starchy staple Maize, whole grain flour 800 445 0.43
Dark green leafy vegetables Gallant soldier a 833 420 0.42
Dark green leafy vegetables Cabbage 313 71 0.03
Legumes, nuts and seeds Beans, common 2400 30 0.08
Legumes, nuts and seeds Groundnut 2500 27 0.08
Fats and oils Palm oil b 3804 7 0.03
Organ meat Liver, cow 7750 5 0.04
Low-cost nutritious diet 1.11
Miscellaneous food costs (16% of low-cost nutritious diet costs) 0.18
Total food costs (US$ PPP/AME/day) 1.29
Isingiro District, Uganda June ‘18 in UGX/kg
Legumes, nuts and seeds Beans, common 1550 431 0.51
Starchy staples Maize, white flour 1500 168 0.19
Dark green leafy vegetables Amaranth leaves 1500 32 0.04
Other fruits Avocado 1200 196 0.18
Fish and seafood Small silver fish, dried c 4000 10 0.03
Organ meat Liver, cow 6000 2 0.01
Low-cost nutritious diet 0.96
Miscellaneous food costs (16% of low-cost nutritious diet costs) 0.16
Total food costs (US$ PPP/AME/day) 1.11
Sidama zone, Ethiopia July ‘18 in birr/kg
Starchy staple Maize grain 7 219 0.13
Starchy staple Kocho d 7 257 0.14
Legumes nuts and seeds Haricot beans 10 296 0.26
Organ meat Liver and kidney 58 21 0.11
Dark green leafy vegetable Kale 16 104 0.14
Fats and oils Soyabean oil 72 25 0.16
Low-cost nutritious diet 0.93
Miscellaneous food costs (16% of low-cost nutritious diet costs) 0.15
Total food costs (US$ PPP/AME/day) 1.08
Lushoto District, Tanzania US$ 1 PPP = 835 TZS for 2017; Isingiro District, Uganda US$ 1 PPP = 1243 UGX for 2017; Sidama Zone, Ethiopia US$ 1
PPP = 9.33 birr in 2017 (World Bank 2018a)
aGalinsoga parviflora; b Fortified with 2 g/100 g vitamin A; c Silver cyprinid (Rastrineobola argentea); d the corm of enset
out of stock and have to be purchased privately. Total health area as reported in patient records of the local health care centre
care costs added up to US$ 48 PPP/RH/year (Table 6). were: malaria, urinary tract infection (UTI), and cough/flu.
Basic health care insurance was not available in rural Altogether, health care costs in Isingiro District were estimated
Uganda. Key informant interviews were performedwith health at US$ 131 PPP/RH/year (Table 6).
care workers at three public health care centres at sub-county Basic health care in Ethiopia was obtained from two public
level, and at pharmacies. The three most important health care clinics. The main health costs of a household related to med-
service types were: (i) doctor consultation at a public health icines. The three most common diseases treated were typhoid
care centre, (ii) medicine from a pharmacy, and (iii) laboratory fever, diarrheal diseases and malaria. Medicines from a public
self-test for malaria. The three most common diseases in this pharmacy cost US$ 89 PPP/RH/year (Table 6).
Living income benchmarking of rural households in low-income countries
Table 4 Overview of local minimum standards for decent housing for the reference household in rural areas of Lushoto District, Tanzania, Isingiro
District, Uganda, and Sidama zone, Ethiopia
International minimum standard for decent housing Local minimum standard
Principle Specification Lushoto District, Isingiro District, Sidama Zone,
Tanzania Uganda Ethiopia
Acceptable construction materials for walls, roof and floor
Walls from durable Well-joined bricks or cement ... or baked bricks + mud ... or stones + … or timber
material without cement (Juniperus) + mud
leaks + cement
Roof from durable Cement, tile, or zinc/iron sheets ... or cured timber + ... or corrugated iron
material without corrugated iron
leaks
Floor from durable Cement, stone, tile or wood; can’t bemud or dung cement
material without
leaks
Acceptable amenities such as toilet and, water
Safe sanitation (toilet Flush toilet, pit latrine with slab, or VIP toilet; in
and sewage disposal) or near the house; shared by <15 people
Safe drinking water Piped into house/yard, pump, public tap,
not far from home protected well, or bore hole
Acceptable ventilation, lighting and temperature
Good ventilation ≥1 window per room; extra ventilation when Chimney or extra window
quality cooking indoors when cooking indoors
Adequate lighting >1 window per room or another light source Light source: electricity Light source:
solar, or kerosene kerosene or solar
Comfortable ambient Indoor heating or air conditioning in areas with Not necessary for climate Ventilator in each
temperature extreme temperatures conditions room
Acceptable living space
Sufficient living space 30–36 m2 in low income country; 36–60 m2 in 30 m2 excl. toilet and 16 m2 30 m2 excl. toilet
middle income country; Ceiling ≥2 m kitchen (may be outside)
Sufficient bedrooms Max. 2–3 persons per bedroom 2 bedrooms/RH 3 bedrooms/RH 4 bedrooms/RH
Acceptable house condition and environment
Proper house House in good state of repair and good foundation
condition
Safe outside No risk of landslides, floods, pollution, etc.
environment
Separation from Animal housing outside the house
production
The local minimum standard complies with international minimum standards, and is adjusted where needed to local conditions
3.2.4 Education The total education costs were US$ 1580 PPP per child or
US$ 260 PPP/RH/year (Table 7).
The Tanzanian education system includes seven years of pri- The Ugandan education system also includes seven years
mary education (ages 7–13) and 4 years of secondary educa- of primary and four years of secondary education. Cost items
tion (ages 14–17). Key informant interviews were performed were estimated through focus group discussion with parents/
with parents of children in public primary school (n = 13), and caretakers of school-going children (n = 10). Estimated annual
lower secondary school (n = 10) The household out-of-pocket education costs in Isingiro District are US$ 510 PPP/RH/year
expenses per child per year were reported. Essential items (Table 7).
reported most frequently by informants (n ≥ 5) as parental In Ethiopia primary school takes six years and secondary
responsibility were: uniform, shoes and a schoolbag (category school four years and no school fees are paid. The costs were
Clothing), books and supplies (category Materials), school estimated in a focus group discussion with parents and
maintenance fee, exam fee and security fee (category Fees). amounted to US$ 310 PPP/RH/year (Table 7).
van de Ven G.W.J. et al.
Table 5 Overview of estimated housing costs for a house complying with local minimum standards for decent housing for a reference household in
rural areas of Lushoto District, Tanzania, Isingiro, Uganda and Sidama Zone, Ethiopia, for 2017
Cost item Lushoto District, Tanzania Isingiro District, Uganda Sidama zone, Ethiopia
US$ PPP/RH/year
Construction costs 359 387 1387
Routine maintenance and repairs 90 63 62
Taxes, levies, fees and house insurance 0 0 0
Utilities (water, electricity, cooking fuel) 719 714 93
Total housing costs 1168 1174 1542
US$ PPP/AE/day
Total housing costs 1.00 0.85 0.81
RH Reference household, AE Adult equivalent
3.3 Living income in Tanzania and Uganda by about 25% and in Ethiopia by
10%.
Based on the previously presented data the living income
expressed in US$ PPP per adult equivalent per day is estimat-
ed at 4.04 in rural Lushoto District, Tanzania 3.82 in Isingiro 4 Discussion
district, Uganda and 3.60 in Sidama Zone, Ethiopia (Table 8).
The living income and the share of the different cost items for We adapted the living wage methodology of (Anker and
the districts in Tanzania and Uganda are quite similar. In Anker 2017b) and present a Living Income Methodology for
Sidama Zone, Ethiopia both the absolute and the relative ex- rural households. We further developed a set of simple tools
penses for a nutritious diet are higher and those for housing are for rapid, transparent and consistent benchmarking. Along
lower than in both other locations. with this methods paper, we provide the survey tool (Living
The international or extreme poverty line is US$ PPP 1.90 Income Survey: Online Resource 2) and the Living Income
and the poverty line is US$ PPP 3.20 per capita per day Diet Tool for calculating a nutritious diet at minimum cost
(World Bank 2015b). We converted this to US$/AE/day and (Online Resource 3 and 4). Both are accessible via a graphical
to the values for 2017 (World Bank 2018a). This means that in user interface. Themethodology was tested in three rural areas
Lushoto US$ 1.21/AE is equivalent to the extreme poverty in the East African highlands: Lushoto District of Tanzania,
line (Table 8). For Isingiro and Sidama zone those values and Isingiro District of Uganda, and Sidama Zone in Ethiopia.
are US$ 1.08 and US$ 1.41, respectively. If we express the Below we first discuss the results obtained, second
living income in the local US$, the living income per AE is we compare our findings with other estimates of living income
US$ 1.51 in Lushoto, Tanzania, US$ 1.31 in Isingiro, Uganda from developing countries, and third we reflect on the adap-
and US$ 1.54 in Sidama, Ethiopia. Table 8 shows that in all 3 tations we have made to the method for estimating living
countries the living income is above the extreme poverty line, income.
Table 6 Overview of the estimated health care costs (US$ PPP) per reference household (RH) per year for rural areas in Lushoto District, Tanzania,
Isingiro District, Uganda and Sidama Zone, Ethiopia for 2017
Cost item Lushoto District, Isingiro District, Sidama Zone,
Tanzania Uganda Ethiopia
US$ PPP/RH/year
Basic health care insurance 12 n.a. n.a.
Doctor consultation, public Covered by insurance 64 –
Medicine from pharmacy 36 63 89
Laboratory (self-)test – 4 –
Total health care costs 48 131 89
US$ PPP/AE/day
Total health care cost 0.04 0.10 0.05
Living income benchmarking of rural households in low-income countries
Table 7 Overview of the estimated education costs (US$ PPP) for a reference household (RH) per year, in rural areas of Lushoto District, Tanzania,
Isingiro District, Uganda and Sidama Zone, Ethiopia for 2017
Cost item Lushoto District, Tanzania Isingiro District, Uganda Sidama Zone, Ethiopia Unit
Primary education Clothing 54 28 71 US$ PPP/child/year
Materials 62 1303 40 US$ PPP/child/year
Fees 14 115 – US$ PPP/child/year
Duration 7 7 6 Year
Lower secondary education Clothing 62 95 96 US$ PPP/child/year
Materials 77 182 66 US$ PPP/child/year
Fees 27 147 – US$ PPP/child/year
Duration 4 4 4 Year
Full costs per child 1576 3630 1316 US$ PPP/child
Average costs per child per yeara 88 202 73 US$ PPP/child/year
Children per RH 3 2.6 4.3 Children/RH
Total education costs 263 512 312 US$ PPP/RH/year
Total education costs 0.22 0.38 0.16 US$ PPP/AE/day
a Full costs per child for 10 or 11 years of education, divided by 18 years of parental financial responsibility
4.1 Living income in the case study areas This analysis also shows that one general poverty line (US$
3.20 per capita per day) or international poverty line (US$
Both for the Lushoto, Tanzania and Isingiro, Uganda, the 1.90 per capita per day) does not adequately reflect the basic
living income was estimated to be 1.25 times the international rural human needs in different regions. Having said that, if a
poverty line of 1.90 US$ PPP/person/day, the generally ac- generally applicable benchmark is required, the poverty line is
cepted benchmark for low-income countries, while in Sidama, more realistic than the international or extreme poverty line,
Ethiopia it was 10%. This suggests that rural households in also for rural areas in low-income countries.
low-income countries require an income above the interna- In rural Tanzania the living income in 2017 of US$ 4.04
tional poverty line in order to comply with all their basic was more than double the national poverty line of US$ 1.60
human rights for nutritious food, healthy housing, decent PPP/AE/day (National Bureau of Statistics Tanzania 2015).
health care, sufficient education and other essential needs. This difference between the living income and national
Table 8 The estimated living income in US$ PPP/AE/day for a reference household in Lushoto District, Tanzania, Isingiro District, Uganda, and
Sidama Zone, Ethiopia for 2017
Lushoto District, Tanzania Isingiro District, Uganda Sidama Zone, Ethiopia
Cost item US$ PPP/AE/day
Food 1.64 (41%) 1.43 (37%) 1.57 (44%)
Housing 1.00 (25%) 0.85 (22%) 0.81 (23%)
Health care 0.04 (1%) 0.10 (3%) 0.05 (1%)
Education 0.22 (6%) 0.38 (10%) 0.16 (5%)
Other NFNH 0.73 (18%) 0.69 (18%) 1.19 (18%)
NFNH 0.99 (25%) 1.16 (30%) 0.86 (24%)
Unforeseen 0.40 (10%) 0.38 (10%) 0.36 (10%)
Total living income 4.04 (100%) 3.82(100%) 3.60 (100%)
Local value US$/AE/day
Extreme poverty linea 1.21 1.08 1.41
Poverty lineb 1.98 1.76 2.30
Living income 1.51 1.32 1.54
The relative share of each item in the total living income is given between brackets. Poverty lines and living income in local US$ in 2017
a based on US$ 1.90 PPP per capita per day in 2011 (World Bank 2015b), converted to 2017 (World Bank 2018a)
b based on US$ 3.20 PPP per capita per day in 2011 (World Bank 2015b), converted to 2017 (World Bank 2018a)
van de Ven G.W.J. et al.
poverty line is likely due to differences in the data used, the All living income estimates were converted from national cur-
bundle of goods and the year of assessment. The food basket rency to US$ PPP, from per full time worker to per adult
at local or regional scale may differ from the national food equivalent and from the year of study to 2017 to make them
basket. As food comprises some 50% of the living income in comparable across countries. The living income estimates for
low-income countries, the national and regional living income Lushoto District, Tanzania and Isingiro District, Uganda, are
may very well differ (Appleton 2003). The national poverty in the same order of magnitude as benchmarks in other rural
line includes a daily energy intake of 2200 kcal per adult areas in East African countries, such as Mount Kenya in
equivalent, against 2500 kcal/AME in the Living Income Kenya and Mulanje District and Thyolo District in Malawi
Methodology to account for heavy labour activities. The costs (Fig. 2a). Tanzania, Uganda and Malawi are low-income
for non-food items in the national poverty line are based on the countries; Kenya used to be a low-income country before it
current food cost of the poorest 25% of the population and no officially graduated to be a lower-middle income country in
allowance for clothing and housing rent are included. The 2014 – just before the study in 2015 (World Bank 2018c) for
national poverty benchmark is primarily based on actual which an international poverty line of US$ 3.20 was set
household expenditure data, whereas the living income is (Jolliffe and Prydz 2016). The living income in Ethiopia, also
based on expenditures required to supply all required goods a low-income country, is higher, but still at the lower end of
and services and provide a nutritious diet, based on basic the range. Ghana, Malawi, Kenya, Tanzania, Uganda and
human rights. This can result in essential differences, as illus- Ethiopia are among the 15 poorest countries on which the
trated by the education costs. The living income budget for World Bank based the international poverty line in 2011
education was US$ 262 PPP/RH/year. This is six times the (Chen and Ravallion 2010). All other included studies were
actual household expenditure of US$ 43 PPP/HH/year from rural areas in lower-middle income and upper-middle
(National Bureau of Statistics Tanzania 2014). Decent educa- income countries, and these higher development levels are
tion makes up 5% of the total living income budget, as com- reflected in higher living income benchmarks ranging be-
pared with 1% of the current household expenditure. This is in tween 7.60 US$ PPP/AE/day (Bhadohi, Uttar Pradesh,
line with our findings that interviewees rarely incurred costs India) and 15.40 US$ PPP/AE/day (Minas Gerais South/
for all of the items listed. Most households reported expendi- Southwestern Mesa Region, Brazil).
tures only on strictly necessary items (e.g. uniform, registra- As shown in Fig. 2b the share of different cost categories
tion and security fee) and saved on optional items such as (food, housing, NFNH, and unforeseen) in the total living
books. Early drop-out rates from school are high in Tanzania income following our methodology was within the range
- which could potentially be a consequence of the expenditure found for the countries assessed by the Anker Methodology,
saving strategy of resource-poor households. This example but the share of the food costs was at the lower end. This is
underlines the importance of a decent living as a basis for probably due our procedure of minimizing the cost of a nutri-
assessing living income and to avoid current expenditures tious diet, instead of considering only commonly purchased
wherever possible. foods. Unfortunately, we do not have data in some of the
These same considerations hold for other national poverty countries assessed by the Anker Methodology, so we are not
lines. Comparing data across years and regions or countries able to give a final direct comparison.
requires conversion into comparable units, such as US$ PPP
for a specific year. The difference between US$ 1.47 PPP in 4.3 Reflection and limitations of the living income
2011 and 1.60 in 2017 is only 9% over 6 years. However, if methodology
not corrected for inflation and exchange rates large fluctua-
tions over time may occur especially in economically unsta- The Living Income Methodology described in this paper al-
ble, low-income countries. This is also true for the internation- lows a rapid benchmarking of the living income in a rural area
al poverty line of US$ PPP 1.90/capita/day, last reviewed in in Africa or in other low-income countries. It took about one
2011 and increased from US$ PPP 1.25/capita/day in 2005. day to prepare for the data collection, provided the enumerator
The international poverty line is only updated intermittently, is familiar with the study area, three days for data collection,
but national poverty lines are updated when new national sur- one - two days for data analysis and reporting. So overall we
vey data become available (World Bank 2015b). found that an assessment of Living Income can be completed
within 5–7 working days, if survey data are available. If not,
4.2 Comparison of living income benchmarks additional time is required to review existing databases. In
developing the Living Income Methodology we sought to
The living income for all three cases was compared with ten balance detail and rapidity for each issue. Thus some simpli-
other living income benchmarks for rural areas assessed by the fications and short cuts were made compared with the Anker
Global Living Wage Coalition (GLWC) using the Anker Methodology (Anker 2006; Anker and Anker 2017b), but
Methodology (Fig. 2; data overview in Online Resource 5). other items were included more explicitly, such as costs for
Living income benchmarking of rural households in low-income countries
Fig. 2 Estimated living income (a) and the relative contribution of cost 2013b), Ghana (Smith et al. 2017), South Africa (Anker and Anker
items to the total living income (b) of rural areas in Tanzania (Lushoto 2013a), Vietnam (Trang and Binh 2017), Guatemala (Voorend et al.
District), Uganda (Isingiro District) and Ethiopia (Sidama zone) using the 2018), Pakistan (Sayeed and Dawani 2017), India (Mamkoottam and
Living Income Methodology explained in this paper, and of 10 living Kaicker 2016), Malawi (Anker and Anker 2014b), and Kenya (Anker
income studies prepared for the Global Living Wage Coalition and Anker 2017a). Between brackets the extreme poverty line US$
(GLWC) using the Anker Methodology in rural areas of Brazil (De 1.90 PPP expressed in the local US$ value and converted to 2017. See
Freitas Barbosa et al. 2016), Dominican Republic (Anker and Anker Online Resource 5 for data overview
health care and education (Online resources 1–4). Given the national governments. Without functional institutions for both
importance of good health care and education for achieving no level of income is sufficient to meet the basic human rights
a decent living, we explicitly address the local situation in- of the local people.
stead of using a relative share. Some data are difficult to as- We set the margin for Other NFNH goods and services at
sess, such as the quality of health care and education. We 20%. This is an average of current household expenditure of
assumed that local services for both education and health care some SSA countries. This is questionable as it includes current
meet the human needs. This is examined in the interviews and expenditures which we wanted to avoid. We are unsure how
focus group discussions and costs can be adapted based on this compares with minimum costs, and with other regions, so
real data if needed. In reality, the quality of local education this may need further investigation. We set the Unforeseen
and health care in low-income countries is sadly lacking. As costs at 10% allowing for expenses that we might have cut
one example, many families in Lushoto, Tanzania indicated too short. In general, collection of reliable data is the
they would prefer to send their children to private schools and Achilles’ heel of any survey-based method which means that
to use private hospitals if they could afford to – which would careful triangulation with different sources is needed. Our
more than double the living income. However, we felt we had methodology is no exception to this, and being a rapid assess-
no choice other than to accept that the education and medical ment, this requires careful attention.
care provided was designed to meet basic human needs. Local The unit in which income and poverty standards are
education and health care are the responsibility of local/ expressed differs. We expressed the living income per adult
van de Ven G.W.J. et al.
equivalent, in contrast to the international poverty line (which a rural household in a given region of a developing country,
is expressed as income per capita), to the Anker Methodology we consider the Living Income Diet Tool to be sufficient,
(living wage per worker) and to the Living Income based on our results, analysis and sensitivity analysis
Community of practice (living income per reference house- (Online resource 3).
hold). Calculating the indicator on a per capita basis does not The Anker Methodology is often applied to households
cover the differences between children and adults. The expres- who produce internationally-traded commodities such as in
sion per worker needs the number of people in the household cocoa, coffee and tea in order to derive wages which business
actually working, which is very difficult to assess and fluid in would have to pay farmers, so that they can earn a decent
a rural setting with seasonal work on- and off-farm in contrast living (Rusman et al. 2018; Tyszler et al. 2018). Although
to a commodity production setting which was the basis for the the Anker Methodology was used in specific rural-urban and
living wage definition. The expression per reference rural settings such as in Kenya (Anker and Anker 2014a,
household represents a single average value, whereas in rural 2017a), the results were used to compare price levels between
areas the composition of households is highly diverse, or more regions to show that the living income assessed is representa-
reference households need to be identified (Rusman et al. tive for most of rural Kenya. This facilitates the use of the
2018). We chose to express the indicator as an adult living income by commercial companies in commodity
equivalent (AE), based on the presence of the number of chains, which is one of the goals of the Living Income
adults and children below 18 years, for two reasons. First Community of Practice (S. Daniels and K. Komives,
because it is easy to assess. Second, and more importantly, personal communication, 2019). The data collection in the
because it facilitates comparison among households of differ- Anker Methodology reverts more often to national statistics
ent composition within a region. The component food costs is and surveys, e.g. for labour participation. Our Living Income
calculated per adult male equivalent, as food requirements Methodology is intentionally focused on local data and sur-
are age and gender specific and subsequently converted into veys to benchmark a minimum income that rural households
adult equivalent. Specifically for low-income countries, where would need to earn in a given locality. This income could be
food constitutes a relatively large part of the living income, derived from their own farming activities, from selling their
this is appropriate (World Bank 2018b). labour locally, or from other types of employment.
The Living Income Survey Tool provided good guidance We do not explicitly address labour input, as the living
on the questions, adequate registration of the data and rapid income benchmark refers to a daily monetary value required
analysis once collected. Our assessment of the living income per AE, irrespective of the length of a working day. In the
is based on essential human rights, so it is of major importance Anker Methodology the number of hours in a working day
to stick to those issues and not to be misled by current habits, has to be mentioned, but it is not limited (Anker and Anker
which easily occurs when collecting data. For instance, 2017b). For wages in commodity production this is a serious
selecting the most commonly bought foods as in the Anker shortcoming. However, our local benchmark serves a different
Methodology, or using current education expenditure does not purpose than a (national) living income benchmark for com-
necessarily allow calculation of the cost of a nutritious diet, or modities. For instance, our methodology can be used to assess
of the real cost of education to secondary level. The Living the potential impact of a wide variety of rural development
Income Tool assists in collecting the required data by using interventions on the households’ incomes, such as subsidies
clear and explicit guidelines, questions and formats. on inputs, technology development, farmer organization to
The Living Income Diet Tool behaves as expected and improve market access, etc. or to assess the minimum land
intended: it meets nutritional demands at the lowest cost based area that would be required to achieve a living income from
on the data collected on the cost of foods available in the farming. If projects propose investment in farming activities
location (Online Resource 3). A limitation is that focusing the required labour input per day needs explicit attention, to
on the cheapest foods per food group does not take account avoid falling into the trap of implicitly assuming 12 or 16 h
of the nutrition density of a food. It might be cheaper to buy a work days.
more expensive food if a smaller amount would be required. More case studies in the same regions as where the Anker
This would require a price per ingredient of a food (e.g. per Methodology was applied will clarify robustness of our
kcal or per g protein) for each food. In addition, the composi- Living Income Methodology, but given the results and com-
tion of diets is sometimes questionable from a consumption parisons obtained to date we are confident that it can be ap-
perspective, e.g. the large number of eggs per day in Ethiopia plied to rural areas in low-income countries.
(Table 3). We set a 10% margin above the basic costs of a
nutritious diet to allow modification of the diet to respect 4.4 Future use and developments
individual preferences. For a study more focused on nutrition
of rural households, this tool would not be sufficiently de- The Living Income is gaining importance for NGOs, govern-
tailed. For our purpose, that is assessing the living income of ments and companies to support development of rural areas
Living income benchmarking of rural households in low-income countries
(Gneiting 2018; Huetz-Adams et al. 2017). For a good over- their aspired path out of poverty. It further provides a
view in variation of the living income across rural areas, coun- meaningful benchmark to measure progress on SDG1,
tries and regions, it would be worthwhile to record all studies to eliminate poverty and SDG2, zero hunger and sus-
in a central (web) database, where case studies can be stored tainable food systems, allowing for explicit consider-
and viewed publicly. We propose that the regular inventories ation of the local context.
and surveys in agricultural research and development, such as
the RhoMIS survey (Hammond et al. 2017) are extended to Acknowledgements We thank six referees for their critical reading and
include data that enable i) calculation of the living income at comments on earlier versions of the manuscript that helped us improve
both the methods of calculation and the explanation. All errors and omis-
the local level, better reflecting a poverty threshold than a sions remain the responsibility of the authors. This research was funded
worldwide international benchmark and ii) assessment of through various sources: strategic investment of the Plant Production
households against the living income. Our living income Systems group, University of Wageningen, the Bill & Melinda Gates
methodology can provide the basis for such an extension. Foundation through the project N2Africa: Putting Nitrogen Fixation to
Work for Smallholder Farmers in Africa (www.N2Africa.org) and a grant
Subsequently, assessment of development options for small- from the NWO-WOTRO Strategic Partnership NL-CGIAR. We are
holder farmers can get a consistent and consolidated basis. grateful to IITA Dar es Salaam data collection team: Abby Gamba,
The tools of the Living Income Methodology described in Bakari and Augustino, NARO and IITA Uganda (Godfrey Taulya) for
this paper offer a consistent harmonized approach. They are using Banana Agronomy Socio-Economic survey data for the reference
household, David Verhoog, Wageningen Economic Research for advice
easy to use and results are immediately calculated. It can be and checking the conversion calculations of the economic data to one year
expanded from rural to urban areas and from less-developed to for all countries.
developed countries. A next step we are taking is to deploy the
living income bench mark to analyse the role of agriculture in Compliance with ethical standards
rural livelihoods and sustainable development. How large do
farms need to be to provide a living income from agriculture? Conflict of interest The authors declared that they have no conflict of
Currently we are addressing this question for six regions in interest
sub-Saharan Africa, including the three locations presented in Open Access This article is licensed under a Creative Commons
this study, based on household data collected in RHoMIS. Attribution 4.0 International License, which permits use, sharing,
This will support the identification of the configuration of adaptation, distribution and reproduction in any medium or format, as
long as you give appropriate credit to the original author(s) and the
technologies and management practices of farming systems source, provide a link to the Creative Commons licence, and indicate if
that are best adapted to the local conditions. changes weremade. The images or other third party material in this article
are included in the article's Creative Commons licence, unless indicated
otherwise in a credit line to the material. If material is not included in the
5 Conclusions article's Creative Commons licence and your intended use is notpermitted by statutory regulation or exceeds the permitted use, you will
need to obtain permission directly from the copyright holder. To view a
Through this paper, we present the Living Income copy of this licence, visit http://creativecommons.org/licenses/by/4.0/.
Methodology, as adapted from the Anker Living Wage
Methodology, to rapidly estimate the living income for
rural households in a specific area and time. Assessment
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employed at the Plant Production
United Nations General Assembly (1948). Universal declaration of hu-
Systems Group of that University.
man rights. Resolution 2017 A (III) of December 1948 (pp. 6). New
She combines research and teach-
York: United Nations.
ing with a focus on farming sys-
United Nations General Assembly. (2015). Transforming our world: The tems analysis and optimization of
2030 agensa for sustainable develpment. A/RES/70/1. (pp. 41). New land use systems related to all
York: United Nations. three dimensions of sustainability.
United Nations, Department of Economic and Social Affairs, & Nutrient cycling, environmental
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2017 revision, key findings and advance tables. Working paper action between crops and live-
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USDA. (2007). USDA table of nutrient retention factors - release 6 (p. quences, both in the western
18). Beltsville: Beltsville Human Nutrition Research Centre. world and in Africa, have her special attention. Her scientific work has
USDA (2018). USDA food composition databases. https://ndb.nal.usda. built on tools such as systems analysis and modelling approaches, mainly
gov/ndb/ Accessed 01-05-2018. at the farm and regional level.
van de Ven G.W.J. et al.
Anne de Valença holds a BSc. Katrien K.E. Descheemaeker is
B i o l o g y ( U n i v e r s i t y o f associate professor in the Plant
Amsterdam) and MSc. Organic Production Systems group of
agriculture with specialization in Wageningen University and
agroeco logy (Wageningen Research (Netherlands). Her re-
University). After these studies, search focuses on farming sys-
she worked as research & educa- tems analysis, resource use effi-
t ion ass is tan t a t the Plant ciency, natural resources manage-
Production Systems Group of ment, soil-plant-animal interac-
Wageningen Univers i ty & tions, and environmental sustain-
Research. Her work there includ- ability, with a special interest in
ed the crea t ion of a SDG the functioning and dynamics of
Academy MOOC on sustainable mixed crop-livestock systems.
agriculture and nutrition, and re- Current research combines exper-
search on living income method- imental trial work with simulation
ology for rural households. Currently, she works as Food & Agriculture modelling to identify and assess interventions for improved resource use
advisor at the World Wildlife Fund for Nature Netherlands (WWF-NL). efficiency and farm profitability, and reduced risks associated with cli-
mate andmarket variability. Across various projects on smallholder farm-
ing systems in sub- Saharan Africa, Dr. Descheemaeker and colleagues
develop effective methods for participatory research with farmers and
other stakeholders to increase the potential adoption and impact of tech-
nology and management options to increase farm productivity, food se-
Wytze Marinus i s a PhD- curity and natural resource integrity.
candidate at the Plant Production
Systems group of Wageningen
University. He holds and MSc in
Plant Science. The title of his PhD
project is “Towards more sustain-
able farming systems in the East Willem Hekman worked as a re-
African highlands”, which he search fel low in the Plant
tackles from two angles. In multi- Production Systems research
ple season co-learning cycles with group at Wageningen University.
groups of farmers in Kenya and He studied applied physics and
Uganda he explores current op- obtained his MSc degree with re-
portunities for sustainable intensi- search in quantum information
fication and the impacts of using theory. His interest in food pro-
these for participating farmers. In duction and food’s role in society
a more theoretical approach, based on survey data, he assesses income brought him to Wageningen
from farming in current and future farming systems. University where he mainly
worked on developing computer
models to estimate theoretical
crop production potentials.
Ilse de Jager has a PhD from the
Division of Human Nutrition and
the Plant Production Systems
group at Wageningen University, Beyene Teklu Mellisse works at
the Netherlands. She finished her Hawassa university, Ethiopia.
PhD research in 2019, after P r io r to jo in ing Hawassa
obtaining her MSc degree in Un i v e r s i t y, h e s e r v e d i n
Human Nutrition. Her research E t h i o p i a n I n s t i t u t e o f
focuses on impacts of agriculture Agricultural Research (EIAR) as
on improving diets and nutritional a researcher. Mellisse has con-
status of vulnerable groups from ducted research on forage agron-
rural households in sub-Saharan omy, agricultural sustainability,
Africa, in particular focusing on farming system dynamics and
the potential of grain legumes. food security and published in
She conducted her PhD research peer reviewed journals. His cur-
within the context of the agricultural development project N2Africa rent research focuses on farming
(www.N2Africa.org) that was one of the winning projects of the 2013 system dynamics and its implica-
Harvesting Nutrition Contest initiated by the World Bank. tion on socioeconomic and eco-
logical sustainability. He holds a PhD in System Agronomy/agricultural
sustainability from Wageningen University, the Netherlands.
Living income benchmarking of rural households in low-income countries
Frederick Baijukya is Farming
and International organizations in promoting agriculture technologies for
System Scientist at International
income, food and nutrition security among small holder farmers in East
Institute of Tropical Agriculture
Africa region.
(IITA) based in Dar es Salaam
Tanzania. Formerly, he was
Agronomist at Intentional Centre
of Tropical Agriculture (CIAT),
Nairobi office, and before that,
h e s e r v e d a s P r i n c i p a l Ken Giller is Professor of Plant
Agr icul tura l Off ice in the P r o d u c t i o n S y s t e m s a t
Department of Research and Wageningen University. He leads
Development in the Ministry of a group of scientists with pro-
Agriculture and Cooperative, found experience in applying sys-
Tanzania. He has worked exten- tems analysis to explore future
sively in different farming sys- scenarios for land use with a focus
tems in East Africa with special focus on intensification and diversifica- on food production. Ken’s re-
tion of smallholder- agricultural systems, participating in different pro- search has focused on smallholder
jects including ‘Exploring trade-offs around farming livelihoods and the farming systems in sub-Saharan
environment: the AfricaNUANCES framework and ‘Putting nitrogen Africa, and in particular problems
fixation to work for smallholder farmers in sub-Saharan Africa of soil fertility and the role of ni-
(N2Africa). trogen fixation in tropical le-
gumes, with emphasis on the tem-
poral and spatial dynamics of re-
sources within crop/livestock farming systems and their interactions. His
research interest is also in resource utilization efficiency and scaling in
systems analysis, particularly on the role of nitrogen fixing legumes in
Mwantumu Omar i i s a n
provision of food, feed, fuel, and soil fertility in tropical farming systems.
Agricul tural Economics at
He is author of the standard text “Nitrogen Fixation in Tropical Cropping
International Institute of Tropical Systems” published in second edition in 2001. He leads a number of
Agriculture (IITA), based in
initiatives such as N2Africa - Putting Nitrogen Fixation to Work for
Da r e s Sa l a am Tanzan i a .
Smallholder Farmers in Africa - http://www.n2africa.org/. N2Africa
Mwantumu has been working
works to scale promising technologies through >90 public-private part-
with IITA since 2012 in different
nerships in eleven countries of sub-Saharan Africa. Ken is member of the
capacities, first, as scaling special-
Unilever Sustainable Sourcing Advisory Board. He is co-chair of the
i s t o f Common Fund f o r
Thematic Network 7 on Sustainable Agriculture and Food Systems of
Commodities program on “Small
the Sustainable Development Solutions Network (SDSN) of the United
scale cassava processing and ver-
Nations. Ken joinedWageningen University as Chair of Plant Production
tical integration of the cassava
Systems in 2001 after holding professorships at Wye College, University
sub s ec t o r i n Ea s t e r n and
of London, and the University of Zimbabwe.
Southern Africa -Phase II, and
since July 2014, as business de-
velopment officer under N2Africa Project “Putting Nitrogen Fixation to
work for smallholders farmers in Africa” phase II in Tanzania.
Mwantumu has more than 10 years’ experience on working with national