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Rural Youth Welfare along the Rural-urban Gradient: An Empirical Analysis across the Developing World
Aslihan Arslan , David E. Tschirley & Eva-Maria Egger
To cite this article: Aslihan Arslan , David E. Tschirley & Eva-Maria Egger (2020): Rural Youth Welfare along the Rural-urban Gradient: An Empirical Analysis across the Developing World, The Journal of Development Studies, DOI: 10.1080/00220388.2020.1808197
To link to this article: https://doi.org/10.1080/00220388.2020.1808197
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Rural Youth Welfare along the Rural-urban Gradient: An Empirical Analysis across the Developing World
ASLIHAN ARSLAN *, DAVID E. TSCHIRLEY ** & EVA-MARIA EGGER†
*Research and Impact Assessment Division, International Fund for Agricultural Development (IFAD), Rome, Italy,
**Department of Agricultural, Food, and Resource Economics, Michigan State University, East Lansing, MI, USA, †United Nations University World Institute for Development Economics Research (UNU-WIDER), Maputo, Mozambique
ABSTRACT We use survey data on 170,000 households from Asia, Latin America and Africa, global geo-spatial data, and an economic geography framework to highlight five findings about rural youth in developing countries. First, the youth share in population is falling rapidly, and youth numbers are stable or falling slowly everywhere, except in Africa. In Africa, youth share is rising very slowly, but numbers are set to double in 40 years. Second, large majorities of rural youth live in spaces that are not inherently limiting: two-thirds live in zones with highest agricultural potential, and one-quarter combine this with highest commercialisation poten- tial. The 4% that do live in inherently challenging spaces are concentrated in pockets of persistent poverty in middle-income countries. Third, rural spaces’ commercial potential has large impacts on welfare outcomes, but their agricultural potential has no detectable impact. Fourth, households with young members face income- and poverty ‘penalties’ in all regions and spaces within them, compared to households without young members. The poverty penalty declines sharply over space as commercial potential rises, but the income penalty shows ambiguous patterns. Fifth, households with young members earn lower relative returns to education, with varying patterns over space.
1. Introduction
Youth have moved to the centre stage in the development debate over the past decade. Rising numbers of youth, especially in the poorest countries, combined with broad concerns about employ- ment, have led to a plethora of studies and to multiple programmatic initiatives among donors and developing country governments. Much of this discourse and much of the programmatic design, however, lack robust micro evidence on youth welfare in the developing world, its drivers, and how the effects of these drivers vary depending on the circumstances in which youth live. This paper aims to fill part of this gap, focusing on five questions about youth, defined as individuals between 15 and 24 years of age.1
First, we ask whether trends in youth numbers, shares of the population, and rural/urban distribu- tion are largely comparable across the developing world, or whether instead there exist major differences that need to be taken into account in establishing priorities, formulating policies and designing youth-oriented programmes. Second, do characteristics of the physical spaces in which rural youth live pose inherent challenges for their social and economic advance, or do other factors, more subject to influence by policy and programme design, loom more important? Third, what impact does the physical space have, and what attributes of this space most affect rural youth welfare? Fourth, do youth in the developing world face persistent income- or poverty ‘penalties,’
Correspondence Address: Aslihan Arslan, International Fund for Agricultural Development (IFAD), 00142 Rome, Italy. Email:
Supplementary Materials are available for this article which can be accessed via the online version of this journal available at https://doi.org/10.1080/00220388.2020.1808197
https://doi.org/10.1080/00220388.2020.1808197
© 2020 Informa UK Limited, trading as Taylor & Francis Group
relative to non-youth and if so, how does this relationship vary across regions of the world, across countries, and over space within a country? Finally, do spatial patterns in returns to education shape this potential income penalty for youth households?
To explore these questions, we develop a conceptual framework for rural youth opportunity that accounts for the extent of economic transformation at national level, attributes of the physical spaces in which youth live, and characteristics of their households. We use global spatially explicit data to define a Rural Opportunity Space (ROS) over the entire developing world.2 We then use this ROS first to examine the distribution of all developing country youth across categories of ROS and to draw unique insights about how spatial characteristics condition rural youth opportunities. Next we link 12 nationally representative geo-referenced data sets across Africa, Latin America, and Asia to the ROS, to econometrically explore whether youth face a consistent income- or poverty penalty relative to non-youth and how spatial characteristics of the ROS, and household- and national-level character- istics, drive any such penalty.
The paper proceeds as follows. We lay out the conceptual framework in section two. Section three presents data, definitions, and methods. Section four presents our empirical results organised around the five questions above and section five concludes.
2. Conceptual framework
Rural youth opportunity is conditioned by the characteristics of their national economy, the local physical spaces in which they reside, and the households of which they are a part.3 We discuss these three nested components of our conceptual framework below.
2.1. National setting
The structure and productivity of the national economy shape the opportunities rural youth face and the welfare they can attain. We focus on structural and rural transformation to characterise national economies in a tractable and relevant way. Structural transformation involves the progressive move, in a relative sense, of economic output and employment out of the farm sector into manufacturing and services, whether in rural or urban areas. It has its roots in the seminal dual economy model of Lewis (1954), which accounted for the productivity differences between rural (‘capitalist’) and urban (‘subsistence’) sectors within countries, and remains still relevant for understanding the growth process (Gollin, 2014). The process is strongly associated with higher per capita incomes, lower poverty, improved public sector capacities, and increased investment in public goods that provide more opportunities to all residents (Timmer, 2014; World Bank, 2018).
As an economy undergoes structural transformation, productivity in farming must increase, in response to falling shares of labour on the farm that need to feed rapidly rising populations working off the farm (Timmer, 2014). Yet farm productivity can vary widely for a given level of structural transformation, driven by agro-ecological endowments, structural characteristics of the farm sector (for example large or small farms, capital intensive or more labour intensive), rates of rural-to-urban migration, and government policy. We thus include a measure of rural transformation in our frame- work, resulting in a two-dimensional space of economic transformation with axes of structural transformation (ST) and rural transformation (RT).
We measure structural transformation by the share of agriculture in total GDP and rural transfor- mation by per capita value added in farming. We classify 85 developing world countries into four categories: high ST and high RT (high-high), high ST and low RT (high-low), low ST and high RT (low-high), and low ST and low RT (low-low).4 These classifications are used to shed light into the first question we posed above; whether or not trends in youth numbers, shares of the population, and rural/urban distribution are largely comparable across the developing world.
2.2. Local setting
Within a country, characteristics of the rural space affect the types and attractiveness of opportunities available to rural youth. In rural areas, opportunities are determined to a large extent by access to markets (for agricultural output, inputs, labour, finance, services, information, and others) that determines the area’s commercialisation potential, and by the natural resource base that determines agricultural potential. Both factors have strong spatial dimensions (Ripoll et al., 2017; Wiggins &
Proctor, 2001). Together, they form the ROS (Figure 1) that affects the kinds of opportunities available to rural youth and their attractiveness, mediated by the broader national economy and individual and household constraints.5 This economic geography framework structures what is possible at the highest level within a country, independent of specific social norms, government policies and programmes, or any individual preferences (Abay, Asnake, Ayalew, Chamberlin, &
Sumberg, 2020).
Commercialisation potential increases with connectivity to cities and towns, their markets and potential for private sector investment, all of which are crucial for extending opportunities to rural youth. Yet physical and virtual connections of these urban centres with rural areas are often poor.
Many needed connections depend on public goods, such as improved roads and communications infrastructure, and on private investment such as in mobile connectivity, post-harvest facilities and processing, and agricultural inputs in rural areas. The two are complementary, with high quality public goods spurring private investment, whose use generates fiscal revenues that can be used to further improve the public goods. This dynamic is expected to be positively associated with the level of transformation of a country, especially its structural transformation, meaning that the productivity of the ROS is heavily influenced by that transformation. We use the ROS to answer the second question posed above, regarding the relative importance of inherent characteristics of youths’
physical spaces compared to factors more subject to influence by policy and program design.
2.3. The household setting
A large majority of rural youth in developing countries live as dependents within families (Doss, Heckert, Myers, Pereira, & Quisumbing, 2019). Thus, in addition to the national setting and the ROS,
Figure 1. Rural opportunity space.
Note: Authors’ conceptualisation.
the characteristics of youths’ households influence their opportunities and challenges. We therefore analyse households with young members and compare their outcomes to those without young members, controlling for multiple household variables in our econometric analysis. The household level analysis helps us answer the third, fourth and fifth questions that motivate our study: what are the impacts of physical space on rural youth welfare; does a youth welfare penalty exist and if so how does it vary over space and across regions; and do spatial patterns in returns to schooling shape the potential income penalty for youth households?
3. Data and methods
3.1. Data for the national and local level analyses
We use population data for 85 low-and-middle income countries from the United Nations Department of Economic and Social Affairs (2017) to document historical, current, and projected rural youth population across countries. For our classifications of transformation at country level, we use 2016 data from the World Development Indicators on structural transformation, measured by the share of non-agricultural activity in GDP, and rural transformation, measured by agricultural value added per worker (IFAD, 2016).
We create the ROS, used to assess the local setting, using global data from WorldPop on spatially explicit age- and gender-differentiated population distributions.6 We generate high-resolution maps of these distributions, then spatially link them to the Enhanced Vegetation Index (EVI) as our indicator of agricultural potential and household survey data, which allows placement of each spatial unit and each surveyed household into the ROS. The next sections explain this process in more detail.
3.1.1. Commercialisation potential. Administrative definitions of ‘rural’ and ‘urban’ suffer from two analytical problems. First, they differ across countries, which compromises cross-country comparisons. Second, the definitions are based on a simple dichotomy increasingly at odds with how people live. Urban and rural qualities have become blurred due to urbanisation, increased rural population densities (with settlement along roads adding to the long historical pattern of settling in areas of higher agro-ecological potential), and economic transformation in rural areas that has driven an increase in ‘urban’ characteristics such as reliance on markets for food consumption (Dolislager et al., 2020; Reardon et al., 2019; Tschirley, Reardon, Dolislager, & Snyder, 2015). It thus becomes more important than ever to generate a spatial classification that captures this continuum.
The concept of ‘peri-urban areas’ – rural locations that have ‘become more urban in character’
(Webster, 2002) – is one way of doing this (Simon, 2008; Simon, McGregor, & Thompson, 2006).
Households in such areas tend to have more diversified income-generating activities, while still residing in what appear to be ‘largely rural landscapes’ (Lerner & Eakin, 2010).
We apply this idea with more detail by creating a rural-urban gradient with four categories based on population density, using data from WorldPop. We use these four categories as a proxy for commercialisation potential (see Jones, Acharya, and Galway (2016) for a recent application).7 Supporting this approach, we note that, in every region of our 12-country household analysis, the share of cash income in total income – a measure of the weight of commercial transactions, whether sales of farm produce or earning of cash income off the farm, in a household’s livelihood portfolio – rises monotonically across this space, from 34 to 95% in Asia, 75 to 100% in LAC, and 40 to 97% in Africa. Bilsborrow (1987) and Wood (1974) also document the correlation between population density and agricultural commercialisation, off-farm diversification and market density.
Commercialisation potential can be proxied by other variables such as road density or average time to nearest city (Abay et al., 2020; Sebastian, 2007). We prefer population density for two reasons.
First, it is a more direct measure of opportunities for exchange than is road density, which is undoubtedly correlated with population density but can be affected by other factors, as well. The existence of more people in a given area, controlling for disposable income, directly raises
opportunities for exchange. Second, population density avoids arbitrary decisions, such as the choice of minimum size of urban area that qualifies as a ‘city’ in creating variables on distance to nearest city.
The WorldPop project provides age- and gender-differentiated population density maps modelled at 1 km spatial resolution for each country globally.8 We use data from 85 low-and middle-income countries to spatially locate rural youth of the developing world.9 The globally comparable scale of the rural-urban gradient was created by ordering all grids from these 85 countries from least to most dense, and summing population starting at the least dense grid to create four quartiles of equal population. The least dense quartile represents rural hinterland areas, while the most dense represents urban areas. In between are semi-rural (2nd quartile) and peri-urban (3rd quartile) areas.10 We use the three non-urban categories to define the y-axis of the ROS.
Table 1 displays how our rural-urban gradient compares to administratively defined urban areas by region. In every region, the share of administratively urban population is much higher than the top global quartile that we consider urban. In Asia and Africa, average administrative urban shares are roughly equal to the sum of our urban and peri-urban shares. The difference is greater in LAC, where administratively urban share (67%) exceeds the sum of our semi-rural (16%), peri-urban (20%), and urban (22%), thus including some of the population that we classify as rural hinterland.
Yet livelihood portfolios along our gradient tell a story that belies these high shares of adminis- tratively urban households. Table 2 shows the share of labour dedicated to working on the farm (both for own account and wages) in the total labour days measured by Full Time Equivalents (FTE).11 For example, in Asia, the share of farm labour in total labour falls only from 75% in the rural hinterland to 46% in peri-urban areas, before dropping sharply to 10% in urban areas. A similar pattern prevails in Africa, where shares fall from 70% rural hinterland, 37% peri-urban, and 10% urban areas. The farm economy remains very important in peri-urban areas of these regions, meaning it remains very important in many areas administratively classified as urban.
More transformed economies in Latin America mean that non-farm labour and income are important even in rural areas. The equation of ‘rural’ with ‘farm’, never fully valid anywhere in the world, is especially untenable in these more transformed economies. In that region, the share of time worked on the farm falls from 56% in rural hinterland to 11% in peri-urban, to 1% in urban areas. While any classification scheme necessarily misses many nuances of household behaviour, we argue that these patterns justify our categorisation of the bottom three density quartiles as mean- ingfully ‘rural’ for our analysis.
3.1.2. Agricultural potential. Analysts increasingly proxy agricultural potential with vegetation indices based on remote sensing data, in part to facilitate global comparisons (Chivasa, Mutanga,
& Biradar, 2017; Jaafar & Ahmad, 2015). We use the MODIS Enhanced Vegetation Index (EVI),
Table 1.Comparing the population shares within the categories of the population density based rural-urban gradient to administrative urbanisation rates by region
Population density based rural-urban gradient Administrative
Regions Rural Hinterland Semi-Rural Peri-Urban Urban urbanisation rate
Percent of population
LAC 42 16 20 22 67
APR 33 23 24 20 38
SSA 47 14 14 25 38
Global Average 42.6 15.8 18.0 23.6 46.5
Notes: Population density based rural-urban gradient categories are the quartiles of the population density distribution from the WorldPop project. Data for the administrative urbanisation rates are from United Nations Population Division. Both data sources cover 85 low and middle income countries. LAC is Latin America, APR is Asia and SSA Sub-Saharan Africa.
excluding built- and forested areas, to measure the influence of geography on the potential for productivity in farming.12 EVI data covering all developing countries at 250 m x 250 m resolution were aggregated to 1 km level to match the resolution of population data. Three-year average EVI values (2013–2015) are used to minimise the impacts of seasonality and annual agro-climatic variation. EVI grids for all non-built and non-forested land in our bottom three density quartiles are ordered from lowest to highest EVI values, and area is summed to create three groups (terciles) of equal total land area representing low, medium, and high agricultural potential categories on the x- axis of the ROS.
3.2. Data for household level analysis
We analyse the welfare outcomes of rural households with young members using 12 nationally representative household surveys from countries in Latin America (LAC; Mexico, Nicaragua and Peru), Sub-Saharan Africa (SSA; Ethiopia, Malawi, Niger, Nigeria, Tanzania, and Uganda), and Asia (APR; Bangladesh, Cambodia and Nepal).13 All data sets are geo-referenced, allowing us to locate each household in the ROS.14
Because most rural youth live in households as dependents, we analyse youth welfare by differ- entiating households with and without young members, while controlling for whether the household head is young, whether household members are predominantly young (compared to the country average), and other demographic variables.
We use per capita expenditure (constant 2011 international USD) and poverty status at the international poverty line of 1.90 USD per person per day to measure welfare.15 Education is an important determinant of employment and welfare at the individual and household levels. Given that our analysis is at the household level, we use the share of working-age (18–65 years of age) household members who have completed secondary school as an indicator of a household’s education level.
Table 3 shows the prevalence of households with and without youth and Table 4 shows selected characteristics.16 Fifty two% of households have at least one young member, with the lowest share in LAC and highest in SSA – both as expected given where they lie in their demographic transition.
Semi-rural areas have the highest prevalence of households with young members at 55%, and urban areas have the lowest at 48%.
Examining income composition and demographic indicators (Table 4), the similarity between the two types of households is striking. Compared to households without youth, those with youth are only slightly more oriented towards farming, sell an identical share of their farm output, have heads of nearly identical age, are slightly less likely to be headed by a female, and have nearly identical levels of education.17
Where the two household categories differ most is in welfare outcomes: households with youth have lower expenditure per capita, lower incomes per active member, and are significantly more Table 2. Share of labour dedicated to work on the farm (own and wage) in total labour FTE, by region and rural- urban gradient
Region Rural Hinterland (%) Semi-Rural (%) Peri-Urban (%) Urban (%)
LAC 55.8 26.6 11.4 1.2
APR 75.2 59.0 45.5 10.0
SSA 69.9 55.7 37.0 10.2
Total 66.9 52.0 37.4 4.9
Notes: Total labour FTE are full-time equivalents of all economic activities of each household member. LAC is Latin America, APR is Asia and SSA Sub-Saharan Africa.
Source: Authors’ calculations using survey data that cover 170,000 households in 12 countries in Latin America, Asia and sub-Saharan Africa.
likely to be poor (based on unconditional averages). They have larger families, more economically active members, and a significantly lower dependency ratio. Only 6% of households with young members are headed by a young person, and almost all (83%) are ‘young households’, which we define as households with a share of young members exceeding the country average.18
3.3. Methodology to assess welfare of youth households
We use regression analysis to describe how welfare outcomes of households with youth vary over the rural-urban gradient and how they compare to households without youth, while controlling for other characteristics.
We model welfare outcomes as follows:
Table 3. Prevalence of households with and without youth by region and rural opportunity space (ROS)
HH with youth HH without youth
Overall 0.52 0.48
APR 0.53 0.47
LAC 0.43 0.57
SSA 0.56 0.44
Rural 0.52 0.48
Semi-rural 0.55 0.45
Peri-urban 0.52 0.48
Urban 0.48 0.52
Notes: Youth are individuals between 15 and 24 years old. Statistics are population weighted.
Source: Authors’ calculations using survey data that cover 170,000 households in 12 countries in Latin America, Asia and sub-Saharan Africa.
Table 4.Descriptive statistics and t-tests for differences between households with and without youth (weighted)
Variable
HHs with young members
HHs without young
members Diff.
Mean values Income and welfare indicators
Poor, intl. poverty line $1.90 daily p.c. 0.26 0.20 0.07***
Per capita expenditure, international PPP$ 4.24 5.92 −1.69***
Returns to labour (total income/active member) 3.25 5.39 −2.15***
Farming share of total income 0.30 0.27 0.04***
Share of sales in own farm income 0.32 0.32 −0.01
Demographic indicators
Number of economically active members 2.58 1.58 0.99***
Total household size 5.50 3.68 1.82***
Young household head 0.06 0.00 0.06***
Age of household head 47.9 48.7 −0.84***
Young household (youth share>country average) 0.83 0.00 0.83
Dependency ratio 0.64 1.12 −0.49***
Female household head 0.22 0.25 −0.03***
Share of working age household members with secondary education
32.3 33.7 −1.36***
No. of observations 79 989 89 714 .
Note: .01 – ***;.05 – **;.1 – *; Point estimates are population means. Asterisks represent level of statistical significance of Wald-test of difference in means.
Source: Authors’ calculations using data that cover 170,000 households in 12 countries in Latin America, Asia and sub-Saharan Africa.
Whi¼αiþβ1iAPhþβ2iYHhþβ3iEduchþβ4iFemalehþβ5iYoungerhþβ6iDepRatioh
þβ7iEduc�YHhþβ8iFemale�YHhþγiCountrycþehi (1) The welfare outcome of household h in rural-urban gradient category i, Whi, is measured by two indicators in separate models: per capita total expenditure and poverty status. Given the anticipated strong influence of commercialisation potential in the ROS on welfare (IFAD, 2019), we model welfare separately for each category i to account for structural differences among rural hinterlands, semi-rural, peri-urban and urban areas. APh, is the agricultural potential of location h, where the household lives, which defines the second component of the ROS. YHh is a dummy variable indicating households with youth. The share of working age adults with secondary education (Educh) is expected to be positively correlated with welfare outcomes, with an expected spatial pattern to reflect the differences in returns to education in places with different commercialisation potentials. The literature on returns to schooling mostly focuses on waged work primarily in urban areas, nevertheless establishes a rural disadvantage that decreases as countries develop (Montenegro
& Patrinos, 2017). Households with female heads (Femaleh) are generally expected to have worse welfare outcomes than male-headed households, though empirical literature on this issue is more ambiguous than is commonly realised (Brown & Van de Walle, 2020; Chant, 1997, 2003, 2008;
Kabeer, 1996; Lipton & Ravallion, 1995; Marcoux, 1998; Quisumbing, Haddad, & Pena, 2001).
We control for the effects of household demographic structure with an indicator of mostly young households (following Abay et al., 2020) and the dependency ratio. To assess whether and how education and female head differentially influence welfare outcomes, we interact these variables with the indicator of households with youth. Countryc is a vector of country dummies controlling for policy, institutional and economic settings in each country that affect welfare outcomes, and ehi is a normally distributed error term. Expenditure equations are estimated using OLS, while poverty equations use probit models. Standard errors are clustered at the country level.
Given the difficulty in interpreting estimated coefficients of nonlinear models with interaction terms, we follow Ai and Norton (2003) and calculate marginal effects by taking the cross derivative of the estimated equation with respect to our indicator of households with young members. The marginal effects are calculated at average values of all other variables within each category. As the 12 countries in our sample have economies at very different stages of structural and rural transformation spread over three regions with different histories, we run our main analyses by region.
4. Findings
We present the findings of our analyses organised around the five key questions we set out to answer in this section.
4.1. Are youth rising as a share of the population in the developing world?
With the exception of SSA, the youth share in total population is falling rapidly, not rising, every- where in the developing world. In SSA it is rising slowly and is projected to begin falling in 10–
15 years (Figure 2). More generally, the youth share is continuing to rise only in the least transformed countries (Low-Low group in the right panel of Figure 2), most but not all of which are in Africa.
Moreover, the share of rural youth in population is falling even in Africa, and has been falling for more than 30 years, driven by the most rapid urbanisation in the world. From 1985 to 2015, rural youths’ share in overall population declined to about 6% in Latin America and the Caribbean, 9% in the Near East and North Africa, 11% in Asia, and about 12% in SSA (UNDESA, 2017).
However, the absolute numbers of youth are rising dramatically in SSA even as they have plateaued or are falling slowly in the rest of the developing world (Figure 3).19 Population projections indicate that the absolute youth population will approximately double by 2050 and the continent will hold the second largest share of all youth globally (UNDESA, 2017). Looking again more broadly
based on country transformation categories, the number of youth is rising rapidly in less structurally transformed countries, and falling in the rest of the developing world.
These patterns suggest that, if understood as a surge in the sheer number of youth, the ‘youth bulge’ is a very real problem in Africa, but much less so in the rest of the developing world. This youth bulge in Africa is primarily a problem of rapid overall population growth linked to a very slow demographic transition (Stecklov & Menashe-Oren, 2019): populations in all age groups are rising rapidly on the continent, and youth are growing only slightly more rapidly than other groups. The slow demographic transition, which is a transitory period of low dependency ratios, can offer a demographic dividend with the right investments, however most countries in Africa are struggling to reap the dividend due to high youth unemployment rates (Filmer & Fox, 2014; Stecklov & Menashe- Oren, 2019). Thus, attempts in Africa to expand opportunities for youth are likely to bring little success until birth rates and overall population growth are brought down.
Figure 2. The share of youth in populations everywhere has been decreasing, and is projected to continue to do so – except in SSA and countries with lowest levels of transformation.
Source: Authors’ calculations based on United Nations World Population Prospects: The 2017 Revision. The dataset covers 85 low- and middle-income countries (based on the World Bank definitions of these categories
and data for 2018).
Figure 3. The number of young people is growing rapidly in SSA and in countries with low levels of structural transformation.
Note: ST: structural transformation; RT: rural transformation; APR: Asia and the Pacific; LAC: Latin America and the Caribbean; NEN: Near East, North Africa, Europe and Central Asia; SSA: sub-Saharan Africa. The dataset covers 85 low- and middle-income countries (based on the World Bank definitions of these categories
and data for 2018).
4.2. Do most rural youth in the developing world live in physical spaces that pose inherent challenges for economic and social advance?
We argue that this is not the case, based on five important patterns. First, most developing country youth overall, and also most rural youth in developing countries (that is 375 million out of 778 million that live in rural spaces), live in China and India, an upper middle income country and lower middle income country, respectively. Though these countries (especially India) still face big chal- lenges of poverty, they can mobilise far more resources to address their youth challenge than can most countries in Africa and the least transformed set of countries.
Second, mapping all developing country rural youth into the ROS shows that two-thirds of them live in areas of highest agricultural potential (Figure 4, right-hand side column).20 Third, rural youth live predominantly in relatively densely populated rural areas. To see this, note that the one-third of the rural youth population living in the areas of least commercial potential (the bottom row in the figure) occupy 92% of all rural land in the developing world.21 The remaining two-thirds of the rural youth population lives on only 8% of the land area, meaning that this two-thirds lives in areas with average population densities of more than 23 times those of the least connected one-third.22 The top one-third lives in areas with even more possibility for connectivity, based on the population density of the areas they live. The inherent potential for connection to markets, information, ideas, and commercial possibilities is thus relatively high for most of the developing world’s rural youth.
Figure 4. Two out of every three rural youth in the developing world live in spaces with high agricultural potential (distribution of all developing country rural youth across the ROS).
Note: Commercialisation potential is defined using 2015 population density data for 85 low and middle income countries from the WorldPop Project. All grids are ordered from least- to most dense, and cut-offs are set to place 25% of population in each of four groups. The highest density quartile is called urban. The remaining three non-urban quartiles each hold one-third of non-urban population and define the three groups of the rural-urban gradient: rural, semi-rural and peri-urban. These respectively represent the low, medium, and high commercial potential categories on the vertical axis. Agricultural potential is defined using the Enhanced Vegetation Index (EVI) of MODIS-NASA for the same grids ordered from lowest to highest. Each of the three groups (terciles) hold one-third of all non-urban space and represent the low, medium, and high agricultural potential categories on the horizontal axis. The numbers in each quadrant show the share of all rural youth living in that category
spaces, and the numbers at the outside the ROS show the column and row totals for each axis.
The fourth and fifth patterns are based on the grouping of the ROS into five categories – shown in Figure 4 – that capture the broad challenges and opportunities faced by rural youth. Pattern four is that one-quarter of all rural youth live in spaces that combine the best agricultural potential with the highest commercial potential. This is the top-right cell in Figure 4, which we call ‘diverse and potentially remunerative opportunities.’ These youth live in peri-urban areas with high rural popula- tion densities, close to cities, and thus face the potential for strong connections to markets and information. Should they choose to engage in farming – for example horticultural production for growing urban markets – they also enjoy the highest agricultural potential, suggesting that yields could be high in the presence of working markets for inputs, outputs, and services.
Pattern five is that the 4% of rural youth that live in the ‘severe challenges’ spaces – those in the bottom left corner that combine the lowest agricultural potential and lowest commercial potential – are highly concentrated in only a few countries, all of them upper middle income: over 20% live in Iran, followed by Brazil and China each at around 10%. At 9%, the prevalence of these youth in these highly structurally transformed countries living in the most challenging physical spaces is double their overall prevalence and between three- and nine times higher than in less transformed and lower income countries, where their prevalence ranges from only 1% to 3%.23
We draw three important implications from this distribution of global rural youth across the ROS.
First and most fundamentally, agricultural potential per se is not a primary constraining factor for a large majority of rural youth. If their farming productivity is low, the reason relates primarily to lack of access to markets (inputs, outputs, and services) as well as to people and ideas to stimulate investments in increased productivity.
Second, the potential for connectivity – with markets, information, ideas and possibilities – is relatively high for many of the developing world’s rural youth. If some are poorly connected and lack opportunities, the reasons do not lie in the potential productivity and connectivity of the land and spaces that they occupy but rather in the level of transformation in the their broader economy, the characteristics of their households, and constraints specific to youth. Policy for stimulating private sector investment, and public investment in infrastructure and fundamental capabilities (McMillan, Rodrik and Sepulveda, 2017) are thus central to the youth challenge
Third, the most transformed and higher income countries that hold the largest numbers of youth living in the most challenging spaces face pockets of stubborn, persistent poverty. Ghani (2010) refers to this as the ‘lagging region’ problem. Among all low and middle income countries, these countries have the most fiscal resources, the strongest institutions (IFAD, 2019), and the most well-developed non-farm sectors, meaning that they should have the capacity to invest in these isolated rural youth.
Their biggest challenge may be to generate the political will to make these investments and ensure that their rural transformation is inclusive.
4.3. How does the physical space in which households live affect youth welfare?
We find that the commercial potential of the space a household resides in has major impacts on its economic welfare, while the agro-ecological potential of that space has no discernable impact. This can be seen both descriptively, based on Figure 5 that shows the average income and poverty for each ROS category, and in a multivariate analysis that controls for a rich set of conditioning variables.
Two features stand out in Figure 5. First, moving along the top row from ‘strong market access but low agricultural potential’ to ‘diverse opportunities’ space – that is, increasing agro-ecological potential while holding commercial potential constant – results in a small decrease in mean per capita expenditure and also a small decrease in the likelihood of poverty. In other words, agro- ecological potential within this highest strata of commercial potential has no meaningful impact on household welfare.
Second, moving vertically in the far right column from ‘High agricultural potential but limited markets’ to ‘Diverse opportunities’ – meaning an increase in commercial potential while holding
agro-ecological potential constant – has a major impact, with income rising nearly 30% (from 3.61 to 4.61) and the likelihood of poverty falling by nearly two-thirds, from 32% to 12%.
Multivariate analysis of our welfare indicators that controls for the two variables that make up the ROS as well as a rich set of conditioning variables robustly supports these results (Table 5).
Commercial potential, proxied in our analysis by population density, is largely and significantly correlated with higher household expenditure per capita (our income indicator) and negatively correlated with the likelihood of the household being poor, while agricultural potential (proxied by 3-year average EVI values) is not correlated with either. Meanwhile, households with young members face a substantial income penalty and a much higher likelihood of being poor compared to those with no young members (echoing the descriptive results in section 3). The dependency ratio and share of adults with secondary education have significant and expected results, respectively reducing (raising) incomes and raising (reducing) poverty.
Two potentially surprising results stand out. First, controlling for all these factors, households with female heads have higher incomes and a lower likelihood of poverty. These results robustly hold up separately in Africa, Asia, and LAC, when the models are run by region. While it is commonly believed that female-headed households face systematically lower incomes and higher poverty (the
‘feminisation of poverty’ thesis), the empirical evidence is ambiguous. Lipton and Ravallion (1995) find that such households are no more likely to be poor than are male-headed households. Kabeer (1996), Marcoux (1998), and Chant (1997, 2003, 2008) also challenge the ‘feminisation of poverty’
thesis. Quisumbing et al. (2001) find that only two out of 10 countries they analysed showed evidence of a systematic poverty and income penalty for female-headed households. Lastly, Brown and Van de Walle (2020) reassess these linkages across SSA, and find that female-headed households are in aggregate better-off than male- headed households in terms of per capita expenditure, with important sub-regional variations. They also find that once marital status is accounted for, female-headed households are overall better off.
Figure 5. Incomes rise and poverty falls consistently with commercial potential but not with agro-ecological potential.
Source: Author’s own elaborations overlaying the ROS in Figure 4, with average income and poverty values using survey data from 170,000 households in 12 countries in Latin America, Asia and sub-Saharan Africa.
The second potentially surprising result is that households with a young person as the head have higher incomes and lower likelihood of poverty, and households that we classify overall as ‘young’
are less likely to be poor. Recall, however, that the regression controls for whether a household includes any young members. Such households have large and significant penalties in income and poverty. Households with a young head, and households that overall are classified as young are subsets of this larger group of households with young members. Thus, the proper interpretation of the results is that, if a household has young members, then having a young household head eliminates roughly one-half of the ‘household with young members income penalty’, as shown by the coefficient on young household head (0.164) being about one-half in absolute value of the coefficient on households with young members (−0.344). Likewise, being a young household may help by reducing poverty, though this reduction eliminates only about 20% of the overall poverty penalty suffered by households with youth (coefficients of, respectively, −0.026 and +0.123).
4.4. How does the youth penalty vary across regions and space?
The exploratory econometric evidence presented in the previous section indicates income and poverty penalties for households that have young members, relative to households that have none, and that the commercialisation potential indicator of the ROS is significantly correlated with welfare improve- ments. To unpack this average result over a wide range of countries and ROS categories, we examine whether and how this penalty varies over regions and space using the empirical specification in Equation (1).
Descriptive results show that the penalty for households with youth, compared to those without youth, is extremely robust across regions, rural-urban gradient, transformation levels, and ROS categories (Table 6). With one exception, households with young members have on average lower incomes and higher poverty than households without youth in every category. The exception is Asia, where incomes are statistically equal and households with youth have a slightly lower probability of being poor.
We now turn to regression analysis to assess whether and how this penalty varies over region and rural-urban gradient while controlling for demographic factors at household level, agricultural potential and country fixed effects (see Methodology section for details on the regression). The Table 5. Regression results of welfare indicators as a function of rural opportunity space (ROS) axes and other controls
Per capita expenditure Poverty
coef p-val coef p-val
Population Density (1,000 persons/km2) 0.011*** 0.00 −0.008*** 0.00
EVI, 3-year average −0.379 0.16 −0.011 0.94
Household with young members −0.344*** 0.00 0.123*** 0.00
Female household head 0.121*** 0.00 −0.044*** 0.00
Household head is young 0.164*** 0.00 −0.037* 0.05
Young household 0.028 0.26 −0.026** 0.01
Dependency ratio −0.300*** 0.00 0.098*** 0.00
Share of working age household members with secondary education
0.006*** 0.00 −0.002*** 0.00
Number of observations 158,325 158,325
Note: All regressions control for country dummies, use household weights and SEs are clustered around country indicators. Expenditure is modelled using OLS and poverty using a probit specification, for which marginal effects are presented in the table.
Source: Authors’ calculations using survey data that cover 170,000 households in 12 countries in Latin America, Asia and sub-Saharan Africa.
model in Equation (1) is applied separately to each rural-urban gradient category in each region, resulting in 12 regressions each for expenditure and poverty.24 The interpretation of coefficients is not straightforward, due to the use of multiple interaction terms and the non-linear model for poverty regressions. We therefore summarise results in Figure 6 capturing the percentage difference in expenditure and percentage point difference in poverty probability between households with and without youth calculated using the Ai and Norton (2003) approach to marginal effects, by region and rural-urban gradient.
Two broad results stand out. First, the ‘youth penalty’ exists in all regions and across all spaces within them, both in income and poverty. This is shown by the persistently negative values on the left side of the figure (showing lower incomes for households with young members), and persistently positive figures on the right (showing higher poverty probabilities for these households). Second, the
‘poverty penalty’ for households with youth falls sharply with commercial potential over the rural- urban gradient, as proxied by population density. Third, the pattern of the income penalty over space is not as consistent, rising in some regions and falling in others.
Examining Figure 6 by region, we see sharply varying patterns in Asia and LAC. Considering the youth income penalty, Asia has the lowest (over all spaces) at an average of about 20% compared to LAC’s huge 60% penalty. Though the regression results show a penalty while the descriptive results did not, the results are consistent in that Asia shows the lowest income penalty in the regression.
Also, this income penalty declines in Asia as spaces become more urban, but rises sharply along this dimension in LAC. Africa lies between these two in the size of the income penalty, which increases from rural hinterland to peri-urban before falling slightly in urban areas.
Considering now the youth poverty penalty, this falls from most rural to most urban areas, in every region. In Asia, this pattern is consistent with the decline of that region’s youth income penalty over space. On the other hand, the pattern is inconsistent in LAC, where the youth poverty penalty declines slightly over the rural-to-urban gradient, in contrast to its income penalty, which rises sharply over space. A possible explanation for this pattern is that households without young members in LAC have incomes that are more skewed in a positive direction – showing a few households with very high incomes – than households without youth. This idea is supported by much higher variability of incomes among households without youth compared to those with youth in this region (differences are far lower in Asia and Africa), and the fact that mean differences between the households in LAC are much greater than median differences.25
All together, these results indicate that while the relatively higher poverty rates of youth house- holds are highest in rural hinterlands and decrease with population density, the gaps in expenditure per capita widen. This suggests that even if living in more densely populated areas makes households with youth less likely to be poor (relative to those without youth), youth’s households in these areas are less able than households without youth to seize the opportunities their ROS presents them. This pattern points to constraints specific to households with youth. The only exception in our sample is countries in Asia, where youth households are able to partially catch up with others as connectivity and commercialisation potential increase.
We note that there is a potential simultaneity between welfare outcomes and location in the rural- urban gradient if wealthier households move to less rural areas over time. This would apply equally to households both with and without youth, unless households with youth have special mobility constraints. Investigating such potentially youth-specific specific mobility constraints is beyond the scope of this paper. Nonetheless, we do not interpret our results as causal.
4.5. Does spatial variation in the return to education for households with- and without youth shape the observed income penalty?
Holding constant the characteristics of a household’s national economy and their location in the ROS, education is expected to improve welfare outcomes. We have two expectations in this regard. First, we expect returns to education to be higher in households without young members, since adults in
Table 6.Income and poverty penalties for households with youth are extremely robust: Averages of per capita expenditure & poverty headcount by region, rural-urban gradient, transformation level, and rural opportunity space (ROS) Expenditure per capitaPoverty headcount HHs w/o youthHHs with youthp-valHHs w/o youthHHs with youthp-val Region APR4.254.360.110.130.100.00 LAC9.296.490.000.060.090.00 SSA4.293.090.000.360.440.00 Rural-urban gradient Rural hinterland3.662.750.000.360.460.00 Semi-rural4.383.210.000.310.390.00 Peri-urban5.334.310.000.140.160.00 Urban9.856.770.000.040.070.00 Transformation level Most transformed (High-High)9.296.490.000.060.090.00 High-Low/Low-High5.013.930.000.160.230.00 Least transformed (Low-Low)3.413.100.000.400.420.01 Rural opportunity space Most challenged: Severe challenges3.302.550.000.420.500.00 Mixed challenges & opportunities4.173.030.000.340.440.00 High agricultural potential, limited market access4.133.150.000.280.360.00 High market access, limited agricultural potential5.244.350.000.140.150.20 Most opportunities: Diverse opportunities5.064.200.000.110.130.10 Notes: LAC is Latin America, APR is Asia and SSA sub-Saharan Africa. Transformation levels are defined at country level by the level of structural transformation (share of agriculture in GDP) and rural transformation (agricultural value added per worker). The rural opportunity space (ROS) is defined along the two axes of commercialisation potential (population density) and agricultural potential (enhanced vegetation index). For a detailed discussion see section 2 or Figure 4. Source: Authors’ calculations using data that cover 170,000 households in 12 countries in Latin America, Asia and sub-Saharan Africa.
such households should have on average more time to devote to work that rewards education. Also, to the extent that members with secondary education in these households are on average older than those in households with young members, they have had more time to gain experience applying their skills and increase their earnings. Second, we expect the return to education to be higher in more urban areas, since these offer more diverse and higher-earning opportunities for more educated job seekers to exploit.26 An interesting question, for which we do not have a priori expectations, is whether residing in a more densely settled area increases the return to secondary education more for households with youth compared to those without youth. In other words, does population density reduce the advantage that households without youth are expected to have in returns to education? The returns to education literature, which establishes a rural disadvantage overall, is mute on this question, which is a gap we address with our investigation (Montenegro & Patrinos, 2014;
Psacharopoulos & Patrinos, 2018).
Figure 7 shows the percent increase in income associated with one more person in the household having secondary education, holding household size constant. These changes are computed from the interaction effects between secondary education share and being a household with youth in the regressions as specified in section 3. They thus show the percent return to education relative to current incomes of households in the given region and position in the rural-urban gradient.27 The results fully confirm the first expectation – higher returns for households without young members – and confirm the second expectation (higher returns in more urban areas) for Asia and LAC, but not for Africa.
Three patterns stand out. First, the return to education is in all regions and across the entire rural- urban gradient, higher for households without youth than for households with youth; nowhere do households with youth earn higher returns to education than households without youth.
Second, returns increase over the rural-urban gradient for both types of households in Asia and LAC. Note that these are percent returns above current incomes, which are already higher in more urban/less rural areas, meaning that the increase in absolute return rises even more steeply along the gradient. This rise over progressively more urban/less rural space is nearly identical between the two types of households in Asia, but is much more modest for households with youth in LAC. In LAC, returns to education for households with youth increase only by about 4 percentage points from rural
Figure 6. Differences in marginal effects of households with youth on expenditure per capita and poverty probability over the rural-urban gradient, by region.
Source: Authors’ own calculations using household data from 170,000 in 12 countries in Latin America, Asia and sub-Saharan Africa.
Note: Values plotted in these graphs are the differences between marginal effects of households with youth and households without youth on expenditure and poverty, calculated using the coefficients of 12 regressions as specified in Equation (1) (one for each location in each region, with demographic and country controls). APR:
Asia and the Pacific; LAC: Latin America and the Caribbean; SSA: sub-Saharan Africa.
hinterland to urban areas, compared to about 14 percentage points for households without youth in LAC.
Third, returns in Africa rise for both types of households from rural to semi-rural areas, then decline through peri-urban and urban areas. This decline is very sharp for households without young members, and more modest for household with such members. However, because the gap in returns to households with- and without young members is so large in rural and semi-rural areas of Africa (far larger than in LAC and Asia), households without youth still earn a slightly higher return to education in urban areas than households with youth.
This pattern in Africa is difficult to explain. Per capita incomes in urban Africa are substantially higher than in rural areas of the continent, and opportunities for higher-paying salaried work are also higher. Both would be expected to increase the returns to education in urban areas of the continent.
The very high returns in semi-rural areas of Africa – the highest of the three regions in this part of the gradient for households without youth – seems also puzzling. Part of this finding may be explained by changing balances between the supply of and demand for skilled labour over the gradient: when the supply of labour with secondary education is very low, the returns to secondary education (controlling for other factors) could be higher than in places where a majority has secondary education. If the number of working age members with secondary education were to increase by one (holding household size constant), this would correspond to a 92 percentage point increase in the share of working age household members with secondary schooling in Africa (compared to 63% in Asia).28
The literature on returns to education establishes that in places with scarce human capital the returns tend to be higher (Montenegro & Patrinos, 2014; Psacharopoulos & Patrinos, 2018), though it mainly focuses on urban waged employment without a youth or spatial focus. Similar to our finding for rural and semi-rural areas of Africa, Duraisamy (2002) finds that the returns to primary and secondary education are ‘strikingly’ higher in rural areas of India.
Figure 7. Percentage increase in per capita expenditure in response to increasing the share of working age members with secondary schooling by the equivalent of one person (holding household size constant).
Source: Authors’ own calculations using survey data from 170,000 households in 12 countries in Latin America, Asia and sub-Saharan Africa.
Note: Values plotted in these graphs are the change in expenditure if average secondary schooling share increases by the equivalent of one person (holding household size constant) for households with and without youth. The values are calculated using the marginal effects of the average schooling share variable in 12 regressions as specified in Equation (1) (one for each location in each region, with demographic and country controls). APR:
Asia and the Pacific; LAC: Latin America and the Caribbean; SSA: sub-Saharan Africa.
