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While the technological revolution is accelerating, digital poverty is undermining the Sustainable Development Goals. This article introduces a justice-oriented digital framework which considers how fair access to digital capabilities, commodities, infrastructure, and governance can reduce global inequality and advance the SDGs.

The data described in this paper were collected from four jurisdictions in south Asia, Assam and Bihar in India and Punjab and Sindh in Pakistan. The data were collected from farmer households involved in surface water irrigation with the aim of understanding the merits of participatory irrigation management (PIM) in different settings in south Asia. The data were collected using four structured survey instruments, which comprised three paper-based surveys and one online survey collected via tablets. This data can be used by researchers to empirically analyze: overall institutional performance and its relationship to agro-economic variables; drivers of compliance; gender differences and their impact on participation in water groups and perceptions of performance; preferred charging regimes and broader institutional arrangements for managing water at the local level. These data are unique, having been collected simultaneously across the four jurisdictions.

Exclusive breastfeeding (EBF)—giving infants only breast-milk for the first 6 months of life—is a component of optimal breastfeeding practices effective in preventing child morbidity and mortality. EBF practices are known to vary by population and comparable subnational estimates of prevalence and progress across low- and middle-income countries (LMICs) are required for planning policy and interventions. Here we present a geospatial analysis of EBF prevalence estimates from 2000 to 2018 across 94 LMICs mapped to policy-relevant administrative units (for example, districts), quantify subnational inequalities and their changes over time, and estimate probabilities of meeting the World Health Organization’s Global Nutrition Target (WHO GNT) of ≥70% EBF prevalence by 2030. While six LMICs are projected to meet the WHO GNT of ≥70% EBF prevalence at a national scale, only three are predicted to meet the target in all their district-level units by 2030., Bhattacharjee and Schaeffer et al. map exclusive breastfeeding (EBF) in 94 low- and middle-income countries (LMICs), finding increased EBF practice and reduced subnational variation across the majority of LMICs from 2000 to 2018. However, only six LMICs will meet WHO’s target of ≥70% EBF by 2030 nationally, and only three will achieve this in all districts.

The savannas of the Kenya-Tanzania borderland cover >100,000 km2 and is one of the most important regions globally for biodiversity conservation, particularly large mammals. The region also supports >1 million pastoralists and their livestock. In these systems, resources for both large mammals and pastoralists are highly variable in space and time and thus require connected landscapes. However, ongoing fragmentation of (semi-)natural vegetation by smallholder fencing and expansion of agriculture threatens this social-ecological system. Spatial data on fences and agricultural expansion are localized and dispersed among data owners and databases. Here, we synthesized data from several research groups and conservation NGOs and present the first release of the Landscape Dynamics (landDX) spatial-temporal database, covering ~30,000 km2 of southern Kenya. The data includes 31,000 livestock enclosures, nearly 40,000 kilometres of fencing, and 1,500 km2 of agricultural land. We provide caveats and interpretation of the different methodologies used. These data are useful to answer fundamental ecological questions, to quantify the rate of change of ecosystem function and wildlife populations, for conservation and livestock management, and for local and governmental spatial planning., Measurement(s)livestock enclosures • agriculture • fenceTechnology Type(s)digital curationSample Characteristic - EnvironmentsavannaSample Characteristic - LocationEast Africa Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.16828204

The El Niño Southern Oscillation (ENSO) is a principal component of global climate variability known to influence a host of social and economic outcomes, but its systematic effects on human health remain poorly understood. We estimate ENSO’s association with child nutrition at global scale by combining variation in ENSO intensity from 1986-2018 with children’s height and weight from 186 surveys conducted in 51 teleconnected countries, containing 48% of the world’s under-5 population. Warmer El Niño conditions predict worse child undernutrition in most of the developing world, but better outcomes in the small number of areas where precipitation is positively affected by warmer ENSO. ENSO’s contemporaneous effects on child weight loss are detectable years later as decreases in height. This relationship looks similar at both global and regional scale, and has not appreciably weakened over the last four decades. Results imply that almost 6 million additional children were underweight during the 2015 El Niño compared to a counterfactual of neutral ENSO conditions in 2015. This demonstrates a pathway through which human well-being remains subject to predictable climatic processes., The El Nino Southern Oscillation (ENSO) influences the weather around the world and, therefore, has strong impacts on society. Here, the authors show that ENSO is associated with child nutrition in many countries, with warmer El Niño conditions leading to more child undernutrition in large parts of the developing world.

This paper presents a dataset of yearly land use and land cover classification maps for Mato Grosso State, Brazil, from 2001 to 2017. Mato Grosso is one of the world’s fast moving agricultural frontiers. To ensure multi-year compatibility, the work uses MODIS sensor analysis-ready products and an innovative method that applies machine learning techniques to classify satellite image time series. The maps provide information about crop and pasture expansion over natural vegetation, as well as spatially explicit estimates of increases in agricultural productivity and trade-offs between crop and pasture expansion. Therefore, the dataset provides new and relevant information to understand the impact of environmental policies on the expansion of tropical agriculture in Brazil. Using such results, researchers can make informed assessments of the interplay between production and protection within Amazon, Cerrado, and Pantanal biomes., Measurement(s)land • land useTechnology Type(s)computational modeling techniqueFactor Type(s)year • geographic location • land use and cover classSample Characteristic - EnvironmentlandSample Characteristic - LocationMato Grosso State Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11440461

Funder: Uganda Government Association for Strengthening Agricultural Research in Eastern and Central Africa, Funder: Bill and Melinda Gates Foundation (Bill & Melinda Gates Foundation), Cassava brown streak disease (CBSD) is currently the most devastating cassava disease in eastern, central and southern Africa affecting a staple crop for over 700 million people on the continent. A major outbreak of CBSD in 2004 near Kampala rapidly spread across Uganda. In the following years, similar CBSD outbreaks were noted in countries across eastern and central Africa, and now the disease poses a threat to West Africa including Nigeria - the biggest cassava producer in the world. A comprehensive dataset with 7,627 locations, annually and consistently sampled between 2004 and 2017 was collated from historic paper and electronic records stored in Uganda. The survey comprises multiple variables including data for incidence and symptom severity of CBSD and abundance of the whitefly vector (Bemisia tabaci). This dataset provides a unique basis to characterize the epidemiology and dynamics of CBSD spread in order to inform disease surveillance and management. We also describe methods used to integrate and verify extensive field records for surveys typical of emerging epidemics in subsistence crops.

Spatially explicit data on electricity access and use are essential for effective policy-making and infrastructure planning in low-income, data-scarce regions. We present and validate a 1-km resolution electricity access dataset covering sub-Saharan Africa built on gridded nighttime light, population, and land cover data. Using light radiance probability distributions, we define electricity consumption tiers for urban and rural areas and estimate the by-tier split of consumers living in electrified areas. The approach provides new insight into the spatial distribution and temporal evolution of electricity access, and a measure of its quality beyond binary access. We find our estimates to be broadly consistent with recently published province- and national-level statistics. Moreover, we demonstrate consistency between the estimated electricity access quality indicators and survey-based consumption levels defined in accordance with the World Bank Multi-Tier Framework. The dataset is readily reproduced and updated using an open-access scientific computing framework. The data and approach can be applied for improving the assessment of least-cost electrification options, and examining links between electricity access and other sustainable development objectives., Design Type(s)modeling and simulation objective • observational design • data integration objectiveMeasurement Type(s)ElectricityTechnology Type(s)digital curationFactor Type(s)temporal_interval • geographic locationSample Characteristic(s)Sub-Saharan Africa • anthropogenic habitat Machine-accessible metadata file describing the reported data (ISA-Tab format)

Several intervention strategies are available to reduce micronutrient deficiencies, but uncoordinated implementation of multiple interventions may result in excessive intakes. We reviewed relevant data collection instruments and available information on excessive intakes for selected micronutrients and considered possible approaches for weighing competing risks of intake above tolerable upper intake levels (ULs) versus insufficient intakes at the population level. In general, population‐based surveys in low‐ and middle‐income countries suggest that dietary intakes greater than the UL are uncommon, but simulations indicate that fortification and supplementation programs could lead to high intakes under certain scenarios. The risk of excessive intakes can be reduced by considering baseline information on dietary intakes and voluntary supplement use and continuously monitoring program coverage. We describe a framework for comparing risks of micronutrient deficiency and excess, recognizing that critical information for judging these risks is often unavailable. We recommend (1) assessing total dietary intakes and nutritional status; (2) incorporating rapid screening tools for routine monitoring and surveillance; (3) addressing critical research needs, including evaluations of the current ULs, improving biomarkers of excess, and developing methods for predicting and comparing risks and benefits; and (4) ensuring that relevant information is used in decision‐making processes.

India has the world’s highest number of undernourished children and the largest school feeding program, the Mid-Day Meal (MDM) scheme. As school feeding programs target children outside the highest-return “first 1000-days” window, they have not been included in the global agenda to address stunting. School meals benefit education and nutrition in participants, but no studies have examined whether benefits carry over to their children. Using nationally representative data on mothers and their children spanning 1993 to 2016, we assess whether MDM supports intergenerational improvements in child linear growth. Here we report that height-for-age z-score (HAZ) among children born to mothers with full MDM exposure was greater (+0.40 SD) than that in children born to non-exposed mothers. Associations were stronger in low socioeconomic strata and likely work through women’s education, fertility, and health service utilization. MDM was associated with 13–32% of the HAZ improvement in India from 2006 to 2016., India’s national school feeding program is the largest of its kind in the world, but the long-term program benefits on nutrition are unknown. Here, the authors show intergenerational program benefits, in that women who received free meals in primary school have children with improved linear growth.

Global value chain (GVC) participation is generally seen as an important avenue for developing countries to access new markets and diversify exports, to add value to local industries and to increase employment. For developing country firms it provides opportunities to access knowledge to enhance learning and capability building. However, many firms in developing countries do not directly integrate into GVCs but rather into regional value chains (RVCs) or local value chains (LVCs), as these have become increasingly important due to the emergence of “Southern”-end markets and increased South-South trade. In this paper we examine and compare the role of involvement in these different types of value chains in stimulating supplier learning in the context of the IT-enabled service (ITES) industry. We distinguish between different types of learning and argue that learning outcomes depend crucially on value chain governance: the power balance and interactions between the lead firms and suppliers. We draw on a survey among the population of ITES providers in South Africa. These novel firm level data allow for a study of learning mechanisms in value chains at the firm level, going beyond prior studies of aggregate level relationships. In a multivariate analysis, we find that service providers in GVCs learn via interactions with their client firms. Yet, GVC participation is not the only avenue for client learning and capability building; we similarly observe learning in LVCs and RVCs. Learning is generally and strongly enhanced by trust-based governance of the client-supplier relationship, while in GVCs, control-based governance additionally promotes learning specifically in the IT domain. These findings provide a better understanding of how firms in developing countries can build capabilities in local, regional and global value chains of services, a topic that has gained interest among development practitioners. ispartof: World Development vol:141 issue:2021 pages:1-10 status: Published online

The COVID-19 pandemic has resulted in substantial mortality worldwide. However, to date, countries in the Middle East and Africa have reported considerably lower mortality rates than in Europe and the Americas. Motivated by reports of an overwhelmed health system, we estimate the likely under-ascertainment of COVID-19 mortality in Damascus, Syria. Using all-cause mortality data, we fit a mathematical model of COVID-19 transmission to reported mortality, estimating that 1.25% of COVID-19 deaths (sensitivity range 1.00% – 3.00%) have been reported as of 2 September 2020. By 2 September, we estimate that 4,380 (95% CI: 3,250 – 5,550) COVID-19 deaths in Damascus may have been missed, with 39.0% (95% CI: 32.5% – 45.0%) of the population in Damascus estimated to have been infected. Accounting for under-ascertainment corroborates reports of exceeded hospital bed capacity and is validated by community-uploaded obituary notifications, which confirm extensive unreported mortality in Damascus., Reported COVID-19 mortality rates have been relatively low in Syria, but there has been concern about overwhelmed health systems. Here, the authors use community mortality indicators and estimate that

Human capital—that is, resources associated with the knowledge and skills of individuals—is a critical component of economic development1,2. Learning metrics that are comparable for countries globally are necessary to understand and track the formation of human capital. The increasing use of international achievement tests is an important step in this direction3. However, such tests are administered primarily in developed countries4, limiting our ability to analyse learning patterns in developing countries that may have the most to gain from the formation of human capital. Here we bridge this gap by constructing a globally comparable database of 164 countries from 2000 to 2017. The data represent 98% of the global population and developing economies comprise two-thirds of the included countries. Using this dataset, we show that global progress in learning—a priority Sustainable Development Goal—has been limited, despite increasing enrolment in primary and secondary education. Using an accounting exercise that includes a direct measure of schooling quality, we estimate that the role of human capital in explaining income differences across countries ranges from a fifth to half; this result has an intermediate position in the wide range of estimates provided in earlier papers in the literature5–13. Moreover, we show that average estimates mask considerable heterogeneity associated with income grouping across countries and regions. This heterogeneity highlights the importance of including countries at various stages of economic development when analysing the role of human capital in economic development. Finally, we show that our database provides a measure of human capital that is more closely associated with economic growth than current measures that are included in the Penn world tables version 9.014 and the human development index of the United Nations15., Analyses of a global database reveal that in many developing countries progress in learning remains limited despite increasing enrolment in primary and secondary education, and uncover links between human capital and economic development.

Digital contact tracing is a relevant tool to control infectious disease outbreaks, including the COVID-19 epidemic. Early work evaluating digital contact tracing omitted important features and heterogeneities of real-world contact patterns influencing contagion dynamics. We fill this gap with a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing in the COVID-19 pandemic. We investigate how well contact tracing apps, coupled with the quarantine of identified contacts, can mitigate the spread in real environments. We find that restrictive policies are more effective in containing the epidemic but come at the cost of unnecessary large-scale quarantines. Policy evaluation through their efficiency and cost results in optimized solutions which only consider contacts longer than 15–20 minutes and closer than 2–3 meters to be at risk. Our results show that isolation and tracing can help control re-emerging outbreaks when some conditions are met: (i) a reduction of the reproductive number through masks and physical distance; (ii) a low-delay isolation of infected individuals; (iii) a high compliance. Finally, we observe the inefficacy of a less privacy-preserving tracing involving second order contacts. Our results may inform digital contact tracing efforts currently being implemented across several countries worldwide., Digital contact tracing is increasingly considered as one of the tools to control infectious disease outbreaks, in particular the COVID-19 epidemic. Here, the authors present a modeling framework informed by empirical high-resolution contact data to analyze the impact of digital contact tracing apps.

This article showcases digital inequalities that came to the forefront for online learning during the COVID-19 lockdown across five developing countries, India, Pakistan, Bangladesh, Nepal and Afghanistan. Large sections of population in developing economies have limited access to basic digital services; this, in turn, restricts how digital media are being used in everyday lives. A digital divide framework encompassing three analytical perspectives, structure, cultural practices and agency, has been developed. Each perspective is influenced by five constructs, communities, time, location, social context and sites of practice. Community relates to gendered expectations, time refers to the lockdown period while locations are interleaved online classrooms and home spaces. Societal contexts influence aspects of online learning and how students engage within practice sites. We find structural issues are due to lack of digital media access and supporting services; further that female students are more often placed lower in the digital divide access scale. Cultural practices indicate gendered discriminatory rules, with female students reporting more stress due to added household responsibilities. This impacts learner agency and poses challenges for students in meaningfully maximising their learning outcomes. Our framework can inform policy-makers to plan initiatives for bridging digital divide and set up equitable gendered learning policies.

Choosing to do an elective in a developing country can be a gamble. Everyone has heard stories of unsuspecting students being deposited in a remote hospital and told that actually they are the doctor, and here are the hordes of patients they must look after for the next eight weeks. As if this isn't enough, the students often find themselves suffering severe culture shock, working in a health care service that bears no resemblance to the one they are accustomed to and encountering patients who frequently present with either unfamiliar diseases or unfamiliar presentations of familiar diseases.

Human settlements are usually nucleated around manmade central points or distinctive natural features, forming clusters that vary in shape and size. However, population distribution in geo-sciences is often represented in the form of pixelated rasters. Rasters indicate population density at predefined spatial resolutions, but are unable to capture the actual shape or size of settlements. Here we suggest a methodology that translates high-resolution raster population data into vector-based population clusters. We use open-source data and develop an open-access algorithm tailored for low and middle-income countries with data scarcity issues. Each cluster includes unique characteristics indicating population, electrification rate and urban-rural categorization. Results are validated against national electrification rates provided by the World Bank and data from selected Demographic and Health Surveys (DHS). We find that our modeled national electrification rates are consistent with the rates reported by the World Bank, while the modeled urban/rural classification has 88% accuracy. By delineating settlements, this dataset can complement existing raster population data in studies such as energy planning, urban planning and disease response., Measurement(s) electricity access • urban area • rural area Technology Type(s) digital curation Factor Type(s) Administrative boundaries • Population • Night-time lights Sample Characteristic - Environment populated place Sample Characteristic - Location Sub-Saharan Africa Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.13516283

Human settlements are the cause and consequence of most environmental and societal changes on Earth; however, their location and extent is still under debate. We provide here a new 10 m resolution (0.32 arc sec) global map of human settlements on Earth for the year 2015, namely the World Settlement Footprint 2015 (WSF2015). The raster dataset has been generated by means of an advanced classification system which, for the first time, jointly exploits open-and-free optical and radar satellite imagery. The WSF2015 has been validated against 900,000 samples labelled by crowdsourcing photointerpretation of very high resolution Google Earth imagery and outperforms all other similar existing layers; in particular, it considerably improves the detection of very small settlements in rural regions and better outlines scattered suburban areas. The dataset can be used at any scale of observation in support to all applications requiring detailed and accurate information on human presence (e.g., socioeconomic development, population distribution, risks assessment, etc.)., Measurement(s) global settlement extent Technology Type(s) satellite imaging • machine learning Factor Type(s) geographic location Sample Characteristic - Environment anthropogenic environment • populated place Sample Characteristic - Location Earth (planet) Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.12424970

With more than 820 million undernourished people living in rural areas of low- and middle-income countries (LMICs), ending hunger and ensuring access to food by all is a global priority. In the past few decades, the adoption of technological innovations in the agricultural sector and related crop yield improvements have not led to expected improvements in the nutritional status of rural households in many LMICs. The increased energy expenditure associated with the adoption of productivity-enhancing innovations may provide an important explanation of the disconnect between agricultural productivity enhancements and improved nutritional outcomes. We develop a methodology for generating reliable livelihood energy/calorie expenditure profiles for rural agricultural households using research-grade accelerometer devices. We integrate the data on physical activity and energy expenditure in rural households with data on time-use and food intakes to generate a data set that provides a unique window into rural livelihoods. This can be a valuable resource to analyse agriculture-nutrition impact pathways and improve the welfare of rural and agricultural households., Measurement(s)physical activityTechnology Type(s)Accelerometer • SurveyFactor Type(s)geographic location • community typeSample Characteristic - Environmentrural areaSample Characteristic - LocationGhana • Nepal • India Machine-accessible metadata file describing the reported data: 10.6084/m9.figshare.11871537

Postharvest loss exacerbates the food insecurity and welfare loss of farming households in developing countries. This paper analyzes the impact of improved storage technologies on food and nutrition security and welfare using nationally representative data from Ethiopia. Endogenous switching regression models are employed to control for unobserved heterogeneity. The study finds that the use of improved storage technologies increases dietary diversity and reduces child malnutrition and self-reported food insecurity. We also find that non-user households would have experienced these benefits had they used improved storage technologies. Overall, the study suggests that improved storage technologies can enhance food and nutrition security, and could play a key role in alleviating the challenges of feeding a growing population.