We present an overview of the literature on agri-food value chains in low- and middle-income countries. Starting from farmers’ decision of whether to move away from subsistence agriculture to participate in agri-food value chains, we study the process whereby agricultural commodities make their way from the farm gate to the final consumer, documenting the procurement relationships that arise and the organization of markets at every step of the way. In each step, we take stock of the empirical evidence, critically assess the research so far, and offer a number of directions for future research. We further discuss the challenges and opportunities for global agri-food value chains.
This chapter provides an overview of the neuroeconomics field, with particular emphasis and applications to the agricultural and applied economics profession. First, I provide a brief overview of the brain. Next, I highlight the priority areas in the applied economics agenda, including developing, testing, and refining theory; the value of using neurophysiological data to enrich the underlying motivations of the choice process preference formation; evaluating treatment compliance and effort (internal validity); generalizability of behavior from the lab to the real world (external validity); and recent neuroeconomic advances in the prediction power of choice models. Next, I provide an overview of a wide range of available neuro-physiological tools varying in cost, obtrusiveness, and complexity. I highlight some basic, low-cost measures that can be incorporated using existing resources for most researchers. Throughout the chapter, I discuss opportunities for the neuroeconomics agenda to address relevant questions in the food and agriculture domain. Finally, I raise potential ethical concerns about the use of the neuroeconomics paradigm to induce changes that could harm individuals and result in suboptimal and costly behavior.
Rapid increases in food supplies have reduced global hunger, while rising burdens of diet-related disease have made poor diet quality the leading cause of death and disability around the world. Today's "double burden" of undernourishment in utero and early childhood then undesired weight gain and obesity later in life is accompanied by a third less visible burden of micronutrient imbalances. The triple burden of undernutrition, obesity, and unbalanced micronutrients that underlies many diet-related diseases such as diabetes, hypertension and other cardiometabolic disorders often coexist in the same person, household and community. All kinds of deprivation are closely linked to food insecurity and poverty, but income growth does not always improve diet quality in part because consumers cannot directly or immediately observe the health consequences of their food options, especially for newly introduced or reformulated items. Even after direct experience and epidemiological evidence reveals relative risks of dietary patterns and nutritional exposures, many consumers may not consume a healthy diet because food choice is driven by other factors. This chapter reviews the evidence on dietary transition and food system transformation during economic development, drawing implications for how research and practice in agricultural economics can improve nutritional outcomes.
Livestock health and disease constrain animal protein production and trade, contributing directly to the health outcomes, livelihoods, and food security of almost a billion people worldwide. They also indirectly affect the diet, nutrition and health security of all people. Strong public interest in the matter arises from myriad external effects, including contagion and information failures. So as to ensure that resource allocations to the sector are efficient and sustainable, relevant economic theory and empirical evidence should guide individual and social investments, programs, and interventions for animal health and livestock diseases. We discuss recent conceptual advances and empirical findings in the economics of animal health and livestock disease, including benefits and costs measurement, and designing and implementing prevention and management programs. We close with a discussion of implications for policy and suggestions for future economic research directions in the area.
There is a set of social connections linking individuals, households, and villages which structure the flow of economic goods and information. The connections form broad networks that serve as conduits of information and goods, substituting for state institutions in agricultural economies where formal government services may be lacking or entirely absent. The close familial and social ties between individuals and households that underpin economic and social relations leads to instances where the choices made by a single person or household can spill over into the choices and outcomes of others. Understanding the way in which people form social ties, and then how these networks behave on larger scales can lend insight into the determinants and outcomes of a wide range of social and economic behavior. This chapter details the basis of network theory and its appropriate applications. It comprises models of network formation, directed and undirected networks, and diffusion and aggregation models. It describes the data necessary to estimate models of network formation and network statistics, data typically generated through the use of census surveys or estimates derived using aggregated relational data. Prominent examples of network analysis include the study of the transmission of information within agrarian societies, the impact of microfinance institutions on network structure, risk-sharing networks, migration, and technology adoption. The chapter concludes with caveats on the application of network theory, including questions as feasibility of network-based targeting, and the limited amount of data available to researchers.
Advances in agricultural data production provide ever-increasing opportunities for pushing the research frontier in agricultural economics and designing better agricultural policy. As new technologies present opportunities to create new and integrated data sources, researchers face tradeoffs in survey design that may reduce measurement error or increase coverage. In this chapter, we first review the econometric and survey methodology literatures that focus on the sources of measurement error and coverage bias in agricultural data collection. Second, we provide examples of how agricultural data structure affects testable empirical models. Finally, we review the challenges and opportunities offered by technological innovation to meet old and new data demands and address key empirical questions, focusing on the scalable data innovations of greatest potential impact for empirical methods and research.
Agricultural employment is critical to the lives of hundreds of millions of men and women across the globe as well as to the farms that employ them and the communities in which they live. However, as the agricultural transformation unfolds, workers move off the farm to jobs in an expanding food services sector, in urban areas, and abroad, with far-reaching ramifications for agricultural producers and labor markets. This chapter examines the changing role of agricultural employment in developing and developed economies. It draws from two decades of research using a wide diversity of analytical approaches to document how agricultural labor markets evolve and the impact this evolution has on workers, farmers, and rural economies. We highlight new empirical findings, emerging themes, and policy implications, including the growing concentration of off-farm agri-food employment, migration, changing gender roles, climate change and the legacy of the COVID-19 pandemic.
Agricultural and applied economists have begun routinely using behavioral and experimental economics tools to answer important questions about agri-environmental policies and programs. These tools offer valuable insights into decision-making that can advance our economic understanding of human behavior and inform evidence-based policies. However, conducting robust economic experiments on agri-environmental topics presents unique challenges that can make implementation of these studies difficult and limit the applicability of results. This chapter provides a practical guide for researchers regarding best practices for applying experimental and behavioral economics to agri-environmental research focused on producer decision-making. We begin with a brief overview of how insights from behavioral economics have contributed to related literatures over past decades and highlight how economic experiments have been used to answer important research questions in those domains. We describe the types of economic experiments used to answer policy-relevant questions and carefully consider the advantages and limitations of each method in various contexts. We also highlight important trade-offs between control, context, and representativeness to consider when determining the most appropriate type of experiment to conduct. The chapter emphasizes five contemporary issues related to conducting robust experimental economics studies: replicability, statistical power, publication bias, farmer and rural landowner recruitment, and detection of heterogeneous treatment effects. To assist researchers in addressing each issue, we outline best practices and we offer recommendations for researchers, editors, reviewers, and funders. We also discuss research ethics and community engagement. Finally, we present a framework for prioritizing future economics research that can inform agri-environmental programs and policies.
Both women and men are involved in agriculture globally, although their roles differ significantly by region and are changing rapidly. Gender shapes access to productive resources and opportunities, with women having less access to many assets, inputs, and services across a wide range of contexts. These gender differences in resources and opportunities shape the agricultural sector across different types of farming systems. This chapter critically reviews the rapidly growing empirical literature on gender and agriculture in low- and middle-income countries. We first deal with models and measurement, including household models of production and consumption, contrasting models that assume Pareto efficiency with those that do not. We discuss the implications of complex household structures, the neglect of jointness of household decisions, and incomplete risk-sharing within the household. We also discuss advances in measurement and data collection, focusing on measuring assets, decision-making, empowerment, and time use. We then review empirical studies applying gender analysis to production, markets, and well-being outcomes. We review studies on gender gaps in agricultural resources, agricultural productivity, and the gender dynamics of technology adoption. We then examine studies of gendered participation in markets, including impact evaluations of interventions to improve gender equity in marketing schemes. We review the literature on how women's empowerment and gender equality affect nutrition outcomes, and how gender dynamics affect the takeup and impact of nutrition-sensitive agricultural programs. We conclude and identify areas for future work.
This review presents machine learning (ML) approaches from an applied economist's perspective. We first introduce the key ML methods drawing connections to econometric practice. We then identify current limitations of the econometric and simulation model toolbox in applied economics and explore potential solutions afforded by ML. We dive into cases such as inflexible functional forms, unstructured data sources and large numbers of explanatory variables in both prediction and causal analysis, and highlight the challenges of complex simulation models. Finally, we argue that economists have a vital role in addressing the shortcomings of ML when used for quantitative economic analysis.
Innovation in agriculture differs from innovation elsewhere in the economy in several important ways. In this chapter we highlight differences arising from (a) the atomistic nature of agricultural production, (b) the spatial specificity of agricultural technologies and the implications for spatial spillovers and the demand for adaptive research, and (c) the role of coevolving pests and diseases and changing weather and climate giving rise to demands for maintenance research, and other innovations that reduce the susceptibility of agricultural production to these uncontrolled factors. These features of agriculture mean that the nature and extent of market failures in the provision of agricultural research and innovation differ from their counterparts in other parts of the economy. Consequently, different government policies are implied, including different types of intellectual property protection and different roles of the government in funding and performing research. Informal innovation and technical discovery processes characterized agriculture from its beginnings some 10,000 years ago, providing a foundation for the organized science and innovation activities that have become increasingly important over the past century or two. This chapter reviews innovation and technical change in agriculture in this more-recent period, paying attention to research institutions, investments, and intellectual property. Special attention is given to issues of R&D attribution, the nature and length of the lags between research spending and its impacts on productivity, and various dimensions of innovation outcomes, including rates of return to agricultural research and the distribution of benefits.
The post-Green Revolution period has seen profound changes in the economic situation in South Asia and evolving challenges for the agricultural R&D system. The priorities have changed from a narrow focus on the productivity of food grains to a need for more work on natural resources management and sustainability issues; increasing the productivity and quality of high-value crops, trees, and livestock; agricultural intensification in many less favored areas; more precise targeting of the problems of the poor, including enhancing the micronutrient content of food staples; and analysis of policy and institutional options for achieving more sustainable and pro-poor outcomes in the rural sector. This study draws on the available literature to assess how successful the agricultural R&D system has been in achieving these new goals in South Asia. Overall, it finds that the R&D system has responded well to these changing needs in terms of both budgetary allocations and the kinds of research that has been undertaken. Moreover, market liberalization has enabled a more diverse set of agents to engage in agricultural R&D, and private firms and NGOs have helped ensure that important research and extension needs have not been overlooked.Findings on the impact of this evolving research agenda are mixed. The economic returns to crop improvement research have remained high and well in excess of national discount rates. Public investments in crop improvement research have also given higher returns than most other public investments in rural areas. There is little credible evidence to suggest that these rates of return are declining over time. Agricultural R&D has also made important contributions to reducing poverty in South Asia, but it has done less well in reducing interhousehold and interregional inequities. The greatest impact on poverty has been obtained by lowering food prices, but this pathway might be less important in the future now that food prices are aligned more with border prices and food accounts for a smaller share of consumers' budgets. Also, given that agriculture now plays a relatively small part in the livelihoods of many marginal farmers in South Asia, questions arise about the efficacy of continuing to target agricultural R&D to their problems. Agricultural R&D has also been successful in addressing many of the environmental problems associated with agriculture, with a demonstrated potential for favorable impacts in farmers' fields. Yet the uptake of improved technologies and management practices that reduce environmental damage has been disappointing, particularly in intensively farmed areas. Finally, a large amount of policy research has been undertaken in South Asia since the GR, and case studies show favorable returns to policy research, though the conditions under which it leads to policy change are not well understood.
This chapter is motivated by the question of whether development assistance directed at agriculture ("agricultural aid") is effective. It argues that development assistance is continually changing as the ascendant visions of strong global leaders interact with theories of economic growth and evidence on the impact of past aid, and that agricultural aid has reflected similar continual changes in its composition and mode of delivery.The chapter briefly summarizes evidence on the contribution of aid to overall economic growth, then reviews the evidence of whether agricultural aid accelerates agricultural or economic development. It reviews evaluations of projects and sets of projects related to agricultural credit, integrated rural development, irrigation, research, extension, and higher education. However, except for the World Bank's Operations Evaluation Department (OED) work, most studies of agricultural aid fail to estimate economic rates of return or contributions to incomes of farmers or national economies. It is impossible to conclude, from the evaluation literature, whether agricultural aid accelerates economic development or not.The chapter shows that donors change the object of their assistance with great frequency. Economic growth takes time, and donors have not stayed committed to key activities for a sufficiently long time to achieve results. Aid to agriculture from nearly all donors fell precipitously beginning in the mid-1980s despite clear evidence that there was a continuing need for broad, long-term support for agriculture in sub-Sahara Africa.This review suggests that those with responsibility for allocating assistance across sectors do not understand the crucial importance of agriculture at the early stages of development, because the urge to fund new approaches dominates decisions rather than judgment of what is needed and what is likely to be effective. However, one may also understand the unwillingness of aid decision makers to place too much credence in the results of impact evaluation studies because there are few consistent results, whether on national economic growth or agricultural growth, and most studies stress the difficulty of attributing agricultural production growth to development assistance.
