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We develop a model to estimate hunger as a function of climate indicators and nightlight intensity, measures of which are publicly available and updated monthly. Wethen compare this model to estimates of hunger based solely on time of year and past hunger rates at that time of year, and a model based on crop yields from the LSMS-ISA survey. These comparisons demonstrate how a model based on monthly, publicly available data and trained on past hunger data compares to a model based on no data other than the past hunger data, and a model based on agricultural survey data. The model based on monthly, publicly available data performs almost as well as the model based on survey data, illustrating how new frequently available data sources can aid development stakeholders in combatting food insecurity.

Digital credit products are characterized by a lending process that is instantaneous, automated, and remote. While digital credit has the potential to reach less collateralized, less mobile, and more remote cohorts of borrowers, there are also risks in relying on digital credit for financial inclusion. This paper investigates the digital credit policy environment and the extent to which it may support pro-poor digital credit market development using two types of documents: a set of 23 regulatory documents specifically mentioning either digital or online credit or lending, and another set of 298 informal documents relevant to digital credit based on a systematic web search. After reviewing the literature on the effects of credit expansion and automated credit scoring, we summarize the characteristics of the current digital credit regulatory environment in low- and middle-income countries. Our findings suggest that few regulations specifically target digital credit markets, and that the current regulatory environment may not support the full potential of digital credit to reach historically underserved credit consumers. Most countries do not explicitly target financial inclusion as part of their digital credit policies. However, we do find evidence that informal web documents consider financial inclusion to a greater extent than formal regulatory documents.

National identity programs are increasingly being introduced into low and middle income countries, and may be used by governments for surveillance, supporting fair and democratic elections, fostering national unity, and facilitating health, financial and agricultural transactions. We review evidence on 42 national ID programs in low- and middle-income countries, with particular attention to evidence of the implementation challenges the programs have faced. We identify seven general categories of implementation challenges, with evidence that 39 of 42 countries have faced at least one category of challenge while 8 countries have faced challenges in three or more areas. Further, we observe some associations between country and national ID program characteristics and evidence of particular implementation challenges. Better understanding of the challenges that countries have faced in implementing national ID programs may support the development of more sustainable and usable ID programs.

Despite the longstanding centrality of agricultural productivity growth as a development goal, reliable productivity measures remain elusive and costly. Crop yield is widely used as the primary productivity indicator, though it ignores inputs other than land and can be a poor proxy, particularly for smallholder and women farmers who are more likely to farm marginal lands and to grow multiple crops on the same plot, practices which complicate per hectare yield measurement. Crop yield is commonly calculated as a measure of production per harvested area, rather than production on the full area planted to the crop. Yet small-scale farmers are more likely to experience a loss in crop area between planting and harvesting, leading to systematic overestimation of mean crop yields so long as the null production on abandoned cropland goes unaccounted for. As a result, common yield measures may not be reliable indicators of aggregate agricultural productivity among smallholder farmers. We use plot-level data from the Tanzania National Panel Survey to investigate the conditions and crops for which the choice of yield measure might introduce significant error into crop yield estimates, and thereby bias research findings. We focus on three crops: maize, rice, and sorghum. We find that the choice of yield measure may lead to consistent under- or over-estimates of yield for sub-populations and crops that experience frequent and substantial losses in plot area between planting and harvest, with implications for the design of policy interventions to increase agricultural productivity and to target the least productive and poorest farmers.