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A survey of poverty indicators surfaced 139 candidates, of which 36 were ultimately selected for inclusion in the study based on indicator construction, use, and timeliness.
The selected 36 poverty indicators relied primarily on 26 data sources, mainly household surveys and administrative government data.
Most indicators relied on household survey data and used multidimensional indices to comprehensively measure poverty, aside from poverty line and poverty gap measures which relied exclusively on income and consumption.
Indicators or indicator components were typically based on quantitative estimates of income or consumption, although an increasing number of measurements are instead classifying households according to deprivation of assets, food, or access to services and basic infrastructure.
Overall, critics find that an emphasis on poverty line measurements has led to an incomplete understanding of poverty’s prevalence and trends over the last several decades (UN Special Rapporteur, 2020).
No single indicator dominates on considerations of reliability, dimensions, depth or intensity, comparability, etc., but rather each measure involves tradeoffs.
If the goal is to increase the utility of commonly used indicators, including those considering multiple dimensions of poverty, then investments focused on expanding the coverage, frequency, or scope of nationally representative household surveys is a necessary first step.
Making cross-country comparisons using any poverty indicator runs the risk of using a common metric based on different data sources and collected in different years that may not fully reflect a household’s welfare. Indices which include multiple subcomponents may be more holistic, but even less reliable as the number of components requiring data increases.
Landscape Review of Poverty Measures. EPAR Technical Report #424 (2022). Evans School of Public Policy & Governance, University of Washington. Retrieved <Day Month Year> from https://epar.evans.uw.edu/research
While literature on achieving Inclusive Agricultural Transformation (IAT) through input market policies is relatively robust, literature on the effect of output market policies on IAT is rarer. We conduct a selective literature review of output market policies in low- and middle-income countries to assess their influence on IAT and find that outcomes are mixed across all policy areas. We also review indicators used to measure successful IAT, typologies of market institutions involved in IAT, and agricultural policies and maize yield trends in East Africa. This report details our findings on these connected, yet somewhat disparate elements of IAT to shed more light on a topic that has not been the primary focus of the literature thus far.
The private sector is the primary investor in health research and development (R&D) worldwide, with investment annual investment exceeding $150 billion, although only an estimated $5.9 billion is focused on diseases that primarily affect low and middle-income countries (LMICs) (West et al., 2017b). Pharmaceutical companies are the largest source of private spending on global health R&D focused on LMICs, providing $5.6 billion of the $5.9 billion in total private global health R&D per year. This report draws on 10-K forms filed by Pharmaceutical companies with the U.S. Securities and Exchange Commission (SEC) in the year 2016 to examine the evidence for five specific disincentives to private sector investment in drugs, vaccines and therapeutics for global health R&D: scientific uncertainty, weak policy environments, limited revenues and market uncertainty, high fixed costs for research and manufacturing, and imperfect markets. 10-K reports follow a standard format, including a business section and a risk section which include information on financial performance, investment options, lines of research, promising acquisitions and risk factors (scientific, market, and regulatory). As a result, these filings provide a valuable source of information for analyzing how private companies discuss risks and challenges as well as opportunities associated with global health R&D targeting LMICs.
The share of private sector funding, relative to public sector funding, for drug, vaccine, and diagnostic research & development (R&D) differs considerably across diseases. Private sector investment in overall health R&D exceeds $150 billion annually, but is largely concentrated on non-communicable chronic diseases with only an estimated $5.9 billion focused on "global health", targeting diseases that primarily affect low and middle-income countries (LMICs). We examine the evidence for five specific disincentives to private sector global health R&D investment: scientific uncertainty, weak policy environments, limited revenues and market uncertainty, high fixed and sunk costs, and downstream rents from imperfect markets. Though all five may affect estimates of net returns from an investment decision, they are worth examining separately as each calls for a different intervention or remediation to change behavior.
Cash transfer programs are interventions that directly provide cash to target specific populations with the aim of reducing poverty and supporting a variety of development outcomes. Low- and middle-income countries have increasingly adopted cash transfer programs as central elements of their poverty reduction and social protection strategies. Bastagli et al. (2016) report that around 130 low- and middle-income countries have at least one UCT program, and 63 countries have at least one CCT program (up from 27 countries in 2008). Through a comprehensive review of literature, this report primarily considers the evidence of the long-term impacts of cash transfer programs in low- and lower middle-income countries. A review of 54 reviews that aggregate and summarize findings from multiple studies of cash transfer programs reveals largely positive evidence on long-term outcomes related to general health, reproductive health, nutrition, labor markets, poverty, and gender and intra-household dynamics, though findings vary by context and in many cases overall conclusions on the long-term impacts of cash transfers are mixed. In addition, evidence on long-term impacts for many outcome measures is limited, and few studies explicitly aim to measure long-term impacts distinctly from immediate or short-term impacts of cash transfers.
An ongoing stream of EPAR research considers how public good characteristics of different types of research and development (R&D) and the motivations of different providers of R&D funding affect the relative advantages of alternative funding sources. For this project, we seek to summarize the key public good characteristics of R&D investment for agriculture in general and for different subsets of crops, and hypothesize how these characteristics might be expected to affect public, private, or philanthropic funders’ investment decisions.
Land tenure refers to a set of land rights and land governance institutions which can be informal (customary, traditional) or formal (legally recognized), that define relationships between people and land and natural resources (FAO, 2002). These land relationships may include, but are not limited to, rights to use land for cultivation and production, rights to control how land should be used including for cultivation, resource extraction, conservation, or construction, and rights to transfer – through sale, gift, or inheritance – those land use and control rights (FAO, 2002). In this project, we review 38 land tenure technologies currently being applied to support land tenure security across the globe, and calculate summary statistics for indicators of land tenure in Tanzania and Ethiopia.
This research considers how public good characteristics of different types of research and development (R&D) and the motivations of different providers of R&D funding affect the relative advantages of alternative funding sources. We summarize the public good characteristics of R&D for agriculture in general and for commodity and subsistence crops in particular, as well as R&D for health in general and for neglected diseases in particular, with a focus on Sub-Saharan Africa and South Asia. Finally, we present rationales for which funders are predicted to fund which R&D types based on these funder and R&D characteristics. We then compile available statistics on funding for agricultural and health R&D from private, public and philanthropic sources, and compare trends in funding from these sources against expectations. We find private agricultural R&D spending focuses on commodity crops (as expected). However contrary to expectations we find public and philanthropic spending also goes largely towards these same crops rather than staples not targeted by private funds. For health R&D private funders similarly concentrate on diseases with higher potential financial returns. However unlike in agricultural R&D, in health R&D we observe some specialization across funders – especially for neglected diseases R&D - consistent with funders’ expected relative advantages.
The concept of global public goods represents a framework for organizing and financing international cooperation in global health research and development (R&D). Advances in scientific and clinical knowledge produced by biomedical R&D can be considered public goods insofar as they can be used repeatedly (non-rival consumption) and it is difficult or costly to exclude non-payers from gaining access (non-excludable). This paper considers the public good characteristics of biomedical R&D in global health and describes the theoretical and observed factors in the allocation R&D funding by public, private, and philanthropic sources.
A “new wave” of digital credit products has entered the digital financial services (DFS) market in recent years. These products differ from traditional credit by offering loans to borrowers that can be applied for, approved, and disbursed remotely (often without any brick-and-mortar infrastructure), automatically (generally minimizing or eliminating person-to-person interaction), and instantly (often in less than 72 hours). Digital credit also increasingly considers creditworthiness by using alternative (nontraditional) data—ranging from mobile phone activity to utility payments and social media data—potentially allowing for loans to populations previously unable to access bank credit. Two EPAR reports review the characteristics of digital credit offerings in India, Kenya, Nigeria, Tanzania, and Uganda, and regulations specific to digital credit in Africa and Asia.