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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.

Presentation18820

Presentation ID 16879

Background: We examined recent trends in mobile money and branchless banking regulations related to cash-in, cash-out (CICO) networks (physical access points allowing users to exchange physical cash and electronic money) in low- and middle-income countries, and reviewed evidence on the impacts of CICO regulations on markets and financial inclusion. Methods: Regulation and literature searches began in August 2017 and concluded in June 2018. For the regulatory search we compiled an original database of regulations targeting CICO networks in Bangladesh, India, Indonesia, Kenya, Nigeria, Pakistan, Tanzania, and Uganda. To review evidence of impacts of regulations we conducted additional global searches on Scopus, Google Scholar, and Google using keywords for specific regulatory approaches (e.g., regulation of CICO agents) or hypothesized impacts (e.g., financial inclusion). Results: The resulting database of CICO regulations in the eight focus countries includes 127 regulatory documents, which we coded for four groups of regulations, namely: Business Channel Requirements; Agent Requirements; Regulations on Caps, Fees and Charges; and Customer Identification Requirements. Early CICO regulations focused on agent selection rules, limits on fees, and know-your-customer requirements. More recent waves of regulation have expanded or restricted services CICO agents provide, and also imposed reporting requirements on service providers in an effort to prevent fraud or enhance financial inclusion. Our search for evidence of impacts of CICO regulations resulted in a sample of 90 documents published since 2005, of which only 31 provided evidence on CICO regulation impacts, with most limited in scope—suggesting rigorous policy analysis remains lacking in this quickly expanding sector. Conclusions: Many low- and middle-income countries have introduced regulations that may affect CICO networks, with regulatory approaches differing across geographies and over time. While anecdotal reports of regulatory impacts exist, we found limited evidence of impacts of regulations on CICO networks or on CICO-related financial inclusion.

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.

Common estimates of agricultural productivity rely upon crude measures of crop yield, typically defined as the weight of a crop harvested divided by the area harvested. But this common yield measure poorly reflects performance among farm systems combining multiple crops in one area (e.g., intercropping), and also ignores the possibility that farmers might lose crop area between planting and harvest (e.g., partial crop failure). Drawing on detailed plot-level data from Tanzania’s National Panel Survey, this paper contrasts measures of smallholder productivity using production per hectare harvested and production per hectare planted. Yield by area planted differs significantly from yield by area harvested, particularly for smaller farms and female-headed households. OLS regression further reveals different demographic and management-related drivers of variability in yield gains – and thus different implications for policy and development interventions – depending on the yield measurement used. Findings suggest a need to better specify “yield” to more effectively guide agricultural development efforts.

This paper examines husband and wife perspectives on the division of authority over agriculture-related decision-making within households in Tanzania and Mali. We develop a theoretical model of intrahousehold “accord,” defined as the level of agreement between husbands and wives over who holds authority for different decisions. We then empirically analyze husband and wife claims to authority over thirteen household farming decisions, explaining accord as a function of household characteristics and decision characteristics. We posit that lower transaction costs (in terms of negotiation and enforcement costs) make property rights over some decisions relatively more secure, resulting in greater accord over household authority for those decisions. We test our theoretical model using survey data from a stratified random sample of 3,763 households in Mali (n = 1,766) and Tanzania (n = 1,997). Cluster analysis and binary logistic regression suggest that variation in intra-household accord can be explained by both household characteristics (including individual spousal attitudes, relative spousal assets, and overall household resources) and by decision characteristics (such as whether the benefits of a given decision accrue to the individual spouse or to the household as a whole). Furthermore patterns of intra-household accord and predictors of intra-household accord both vary significantly by country (Mali versus Tanzania), but are consistent with the interpretation that cultural norms might lower decision-related transaction costs leading to efficient, if not necessarily equitable, household decision-making processes.

Many development policies and programmes are premised on a traditional economic model of rationality to predict how individuals will respond to changes in incentives. Despite the emphasis of these programmes on poverty reduction, economists and the development community in general are still unable to fully understand how the poor make decisions, especially under uncertainty and over time. Individuals avail themselves less than predicted in health programmes, participate less than expected in market opportunities, under or over insure themselves, and make short-run decisions that are inconsistent with their long-run welfare. The rise and fall of different descriptive models and paradigms of poor household behaviour can partly be attributed to this limited understanding. More helpful answers may lay within behavioural economics, that these insights are particularly important for poor populations, and that they can improve the future design, implementation and subsequent effectiveness of development programmes. Behavioural economics is an approach that rigorously combines the insights of psychology and economics to try to better understand and predict human decision making. Empirical evidence is helping us learn, for example, how cognitive limitations, fairness, loss aversion, framing of choices, variable discount rates, and the qualitative dimensions of risk—such as proximity and control—affect decision making. The regularity of many of these anomalies suggests that these behaviours are anomalous only to traditional models, but that they may otherwise be the norm.