Search

• Community-centered development in Myanmar favors areas with less vulnerable populations and more luminosity. • Among villages with similar luminosity, aid goes to villages in greater need. • Aerial luminosity, consistently available and not constrained to a specific scale, is a robust proxy for regional needs at a granular level. • High-resolution spatial measures have stronger predictive power for aid location than surveyed wealth measures. • Donors adopting developmental state theories reach out to conflict areas while donors adopting neoliberal theories focus on poverty reduction. Development aid has greater potential to alleviate poverty when targeted towards areas of concentrated need. However, the body of research examining subnational aid targeting is often constrained to state- or district-level analysis due to the lack of aid or wealth data in small regions. This study fills this gap by examining the poverty orientation of aid at the granular spatial units where the pro-poor interventions are implemented. Specifically, it explores the extent to which community-centered development (CCD) in Myanmar takes place in poor villages. This paper estimates aid presence and density, given regional poverty, by linking detailed aid location with nightlight luminosity and wealth-related data. Overall, densely aided areas tend to shine brighter and have a lower share of vulnerable populations. However, for villages with similar luminosity, aid goes to areas with fewer resources. This study also finds heterogeneity in the targeting practice among donors who adopt different ideologies. The market-oriented World Bank project supports poorer villages whereas the Korean International Cooperation Agency's project, drawing from the developmental state, includes villages close to conflict zones. Some evidence of needs-based allocation in this study runs counter to previous findings that aid favors richer states and districts. The result of this study suggests that analyzing aid at a fine-grained level with high resolution spatial data can provide a more nuanced view of aid targeting. [ABSTRACT FROM AUTHOR]

• We introduce Rasch modeling to the quantification of a country's readiness to tackle agricultural policy issues. • Rasch modeling provides a fundamental scale that solves the 'mash-up' index problem. • Rwanda has a high degree of policy readiness, Kenya ranks low. • Markets and trade policy, and mutual accountability, are difficult. • Risk and resilience policy is least difficult. Positive agricultural and food policy environments are critically important to the success and inclusiveness of agricultural growth and transformation. Despite the catalytic potential of enabling policies, countries vary in their willingness, capacity, and ability to improve policy: in other words they have varying degrees of policy readiness. The profession has spawned a plethora of 'policy readiness' indices over the past decade. These indices are welcome first steps, but they are non-statistical compilations of a collection of inherently different measures with unclear relationships: to borrow Ravallion's term, they are mash-up indices. In this paper we propose a novel, empirically-based policy readiness index. In contrast to prior literature, we approach the problem from a measurement perspective. This perspective enables us to aggregate items into a multi-component index such that their combination achieves superior statistical properties and assures cross-country comparability, through the application of Rasch modeling. With these empirical qualities, the meaning and robustness of the scores then become clear. We apply the model to a unique data set of policy actions prioritized in 2012/13, and the extent to which these actions were executed by Fall of 2015. The results are statistically- and measurement-valid measures of both country policy readiness and the degree of difficulty of various dimensions of policy actions. The resulting policy readiness scores are a novel index that significantly advances the dialogue on countries' abilities to execute those agricultural and food policy changes that are priorities for accelerating agricultural transformation. Policy implications include continued support for capacity building in low-readiness countries and cross-country support through continental or global processes for lowering the burden across all countries of making progress in the most difficult policy areas. [ABSTRACT FROM AUTHOR]

• Managing risks to production and assets is central to achieving sustained rather than temporary escapes from extreme poverty. • The enabling environment is critical in determining whether households can ultimately sustain escapes from poverty. • Factors constitutive of this enabling environment may be social, economic, political, infrastructural, or climatic. • Policy-makers should acknowledge risks and develop multi-level collective risk management policy portfolios to enhance poverty eradication. The objectives of this research are to identify the difference between sustained and temporary poverty escapes for policy and program design, based on research in Bangladesh, Cambodia, Ethiopia, Kenya, Malawi, Nepal, Niger, the Philippines, Rwanda, Tanzania, and Uganda. The method adopted is the q-squared method used in studies on the dynamics of poverty, embedded in a conceptual extension of the capability approach through a risk chain analysis. The results show that key livelihood factors conducive to sustained escapes include diversification of economic activities typically spanning agricultural and off-farm sectors, and sometimes involving rural-urban migration. Important tangible assets include general asset holdings of consumer durables, farm or business equipment, livestock, and electricity. Intangible assets include the completion of at least lower secondary education of the household head, good health, and different forms of social relationships and networks. However, while livelihood strategies help improve income and build assets necessary for resilience, various risks permeate. In these risky contexts, the study finds that the enabling environment is critical in determining whether households can ultimately sustain escapes from poverty. Interactive features of this environment that are conducive to sustained escapes include context-specific pro-poor policy and infrastructure, for example around livestock insurance, climate-smart agriculture alongside predictable disaster risk management, universal health coverage, and supporting a negotiated approach to norm change. Together, these features place an emphasis on collective risk management, beyond a focus only or even primarily on inclusive growth policies. The paper argues that it is essential to understand the circumstances in which people live, from the local (micro) up to the societal (macro) contexts that can influence their ability to sustain a poverty escape, and respond in ways that together address the multifaceted sources of risk and vulnerability, which in turn allows for the less interrupted development of people's capabilities. [ABSTRACT FROM AUTHOR]

• The share of mixed households (with farm and non-farm jobs) in rural Bangladesh rose from 23 to 39% between 2000 and 2013. • The share of mixed households of the youngest cohort (those with heads age 15–30 years in 2000) increased from 17% to 30%. • In areas near cities, the share of pure farm households fell; the share of mixed households rose from 15 to 34%. This paper explores patterns of exit from agriculture in rural Bangladesh by utilizing nationally representative repeat cross-section and pseudo-panel survey data. Our analysis focuses at the rural household level where we focus on three types of households: (a) "pure" agriculture households in which all workers are employed only in agriculture; (b) "mixed" households in which some members remain in the farm sector and others pursue nonfarm activities, and (c) rural "nonfarm" households who are exclusively dependent on non-agricultural employment. We find that non-farm orientation has increased over the 2000 to 2013 period, and that nonfarm households rely more on salaried employment and less on unpaid work. Pseudo-panel data based on age-cohort of household heads from the Labor Force Survey (LFS) of 2000 and 2013 also shows a notable increase in mixed households formed by diversification of activities of formerly farm-only households. Employment patterns of younger households are changing especially rapidly: the share of mixed households among households with heads age 15–30 years increased from 17% to 30% in this period. Proximity to urban areas also is associated with a rapid shift in household employment patterns over time. In areas less than 2.5 kms from cities, the share of pure farm households fell from 46.5 to 30.3 percent of households, while the share of mixed households rose from 14.8 to 33.7 percent. Overall, our findings confirm a process of transformation involving a shift from predominantly agriculture employment to increased non-farm employment. We find that the structural transformation considered does not necessarily involve large-scale permanent migration to cities. Rather, much of the shift out of agriculture occurs within rural areas with especially rapid change happening in areas of close proximity to cities. [ABSTRACT FROM AUTHOR]