ASSET, ACTIVITY, AND INCOME DIVERSIFICATION AMONG AFRICAN AGRICULTURALISTS: SOME PRACTICAL ISSUES

This paper starts from the premise that diversification of assets, activities, and incomes is important to African rural households, in that diversification into nonfarm income constitutes on average about 45 percent of incomes, and the push and pull factors driving that diversification are bound to persist. From that premise, we noted that the empirical study of diversification has been beset by practical problems and issues relating to (1) definitions and concepts, (2) data collection, and to (3) measurement of the nature and extent of diversification. The paper addressed each of those problems. Two points are of special interest to the overall conceptualization of diversification research. The first is that empirical studies have exhibited a wide variety - bordering on confusion - of systems of classification of assets, activities, and incomes as pertains to diversification behavior. We argued that the classification should conform to that used in standard practice of national accounts and macro input-output table construction, classifying activities into economic sectors that have standard definitions, and the classification of which does not depend on the location or functional type (wage- or self-employment) of the activity. We further argued that given a sectoral classification, it is useful to make a functional and locational categorization of the activity, and keep each of these three dimensions of the activity - sectoral, functional, and locational - separate and distinct so as to avoid confusion. The second is that it is useful to have an image of a production function in mind when analyzing the components of diversification behavior: (1) assets are the factors of production, representing the capacity of the household to diversify; (2) activities are the ex ante production flows of asset services; (3) incomes are the ex post flows of incomes, and it is crucial to note that the goods and services produced by activities need to be valued by prices, formed by markets at meso and macro levels, in order to be the measured outcomes called incomes. "Livelihoods" is a term used frequently in recent diversification research, and while its meaning differs somewhat over studies, it generally means household and community behavior, with respect to holdings and use of assets and the productive activities to which the assets are applied. The link between livelihoods and incomes needs to be made by valuing the output of livelihood activities at market (and/or virtual) prices. That valuation permits an analytical link between household/community behavior (thus a micro view of diversification) and the aggregate functioning of markets (thus a link with the meso and macro levels and the policies pertaining thereto).Agribusiness, O, Q12,

In the Driver's Seat

In the mid-1990s, P/PV launched the Bridges to Work demonstration to test the idea that improved access to suburban jobs might benefit low-income urban residents. The project sought to measure the impact of reverse-commuting initiatives in five major cities: Baltimore, Chicago, Denver, Milwaukee and St. Louis. While the project was carefully planned, program staff still faced numerous unforeseen events that required program directors to adapt the design to meet local needs, impediments, and opportunities, while maintaining the quality of the original design. In the Drivers Seat examines the experiences of five project directors and their ability to address the challenges that arose, including discrimination in the workplace, ethical issues with random assignment, and difficulties in recruitment and placement

Using Automated Task Solution Synthesis to Generate Critical Junctures for Management of Planned and Reactive Cooperation between a Human-Controlled Blimp and an Autonomous Ground Robot

This thesis documents the use of an approach for automated task solution synthesis that algorithmically and automatically identifies periods during which a team of less-than-fully capable robots benefit from tightly-coupled, coordinated, cooperative behavior. I test two hypotheses: 1) That a team’s performance can be increased by cooperating during certain specific periods of a mission and 2) That these periods can be identified automatically and algorithmically. I also demonstrate how identification of cooperative periods can be performed both off-line prior to the application and reactively during mission execution. I validate these premises in a real-world experiment using a human-piloted Unmanned Aerial Vehicle (UAV) and an autonomous mobile robot. For this experiment I construct a UAV and use an off-the-shelf robot. To identify the cooperative periods I use the ASyMTRe task solution synthesis system, and I use the Player robot server for control tasks such as navigation and path planning. My results show that teams employing cooperative behaviors during algorithmically identified cooperative periods exhibit better performance than non-cooperative teams in a target localization task. I also present results showing an increased time cost for cooperative behaviors and compare the increased time cost of two cooperative approaches that generate cooperative periods prior to and during mission execution

An Intelligent Robot and Augmented Reality Instruction System

Human-Centered Robotics (HCR) is a research area that focuses on how robots can empower people to live safer, simpler, and more independent lives. In this dissertation, I present a combination of two technologies to deliver human-centric solutions to an important population. The first nascent area that I investigate is the creation of an Intelligent Robot Instructor (IRI) as a learning and instruction tool for human pupils. The second technology is the use of augmented reality (AR) to create an Augmented Reality Instruction (ARI) system to provide instruction via a wearable interface. To function in an intelligent and context-aware manner, both systems require the ability to reason about their perception of the environment and make appropriate decisions. In this work, I construct a novel formulation of several education methodologies, particularly those known as response prompting, as part of a cognitive framework to create a system for intelligent instruction, and compare these methodologies in the context of intelligent decision making using both technologies. The IRI system is demonstrated through experiments with a humanoid robot that uses object recognition and localization for perception and interacts with students through speech, gestures, and object interaction. The ARI system uses augmented reality, computer vision, and machine learning methods to create an intelligent, contextually aware instructional system. By using AR to teach prerequisite skills that lend themselves well to visual, augmented reality instruction prior to a robot instructor teaching skills that lend themselves to embodied interaction, I am able to demonstrate the potential of each system independently as well as in combination to facilitate students\u27 learning. I identify people with intellectual and developmental disabilities (I/DD) as a particularly significant use case and show that IRI and ARI systems can help fulfill the compelling need to develop tools and strategies for people with I/DD. I present results that demonstrate both systems can be used independently by students with I/DD to quickly and easily acquire the skills required for performance of relevant vocational tasks. This is the first successful real-world application of response-prompting for decision making in a robotic and augmented reality intelligent instruction system

Simultaneous Feature and Body-Part Learning for Real-Time Robot Awareness of Human Behaviors

Robot awareness of human actions is an essential research problem in robotics with many important real-world applications, including human-robot collaboration and teaming. Over the past few years, depth sensors have become a standard device widely used by intelligent robots for 3D perception, which can also offer human skeletal data in 3D space. Several methods based on skeletal data were designed to enable robot awareness of human actions with satisfactory accuracy. However, previous methods treated all body parts and features equally important, without the capability to identify discriminative body parts and features. In this paper, we propose a novel simultaneous Feature And Body-part Learning (FABL) approach that simultaneously identifies discriminative body parts and features, and efficiently integrates all available information together to enable real-time robot awareness of human behaviors. We formulate FABL as a regression-like optimization problem with structured sparsity-inducing norms to model interrelationships of body parts and features. We also develop an optimization algorithm to solve the formulated problem, which possesses a theoretical guarantee to find the optimal solution. To evaluate FABL, three experiments were performed using public benchmark datasets, including the MSR Action3D and CAD-60 datasets, as well as a Baxter robot in practical assistive living applications. Experimental results show that our FABL approach obtains a high recognition accuracy with a processing speed of the order-of-magnitude of 10e4 Hz, which makes FABL a promising method to enable real-time robot awareness of human behaviors in practical robotics applications.Comment: 8 pages, 6 figures, accepted by ICRA'1

HETEROGENEOUS CONSTRAINTS, INCENTIVES AND INCOME DIVERSIFICATION STRATEGIES IN RURAL AFRICA

A burgeoning recent literature emphasizes "livelihood" diversification among smallholder populations (Chambers and Conway 1992, Davies 1993, Ellis 1998, Bryceson 1999, Ellis 2000, Little et al. 2001). While definitions vary within this literature, the concept of livelihoods revolves around the opportunity set afforded an individual or household by their asset endowment and their chosen allocation of those assets across various activities to generate a stream of benefits, most commonly measured as income. This holistic perspective has the potential to enhance our understanding of the strategies that farm households pursue to ensure food and income security given the natural and economic environment in which they operate. Diversification patterns reflect individuals' voluntary exchange of assets and their allocation of assets across various activities so as to achieve an optimal balance between expected returns and risk exposure conditional on the constraints they face (e.g., due to missing or incomplete markets for credit, labor, or land). Because it offers a glimpse as to what people presently consider their most attractive options, given the incentives and constraints they face, the study of diversification behavior offers important insights as to what policy or project interventions might effectively improve either the poor's asset holdings or their access to higher return or lower risk uses of the assets they already possess. Since diversification is not an end unto itself, it is essential to connect observed livelihood strategies back to resulting income distributions and poverty. Not all diversification into off-farm or non-farm income earning activities offers the same benefits and not all households have equal access to the more lucrative diversification options. Yet the livelihoods literature offers little documentation or explanation of important differences between observed diversification strategies. This paper addresses that gap by offering a comparative analysis using data from three different countries, Cote d'Ivoire, Kenya and Rwanda. Like Dercon and Krishnan (1996) and Omamo (1999), we emphasize that interhousehold heterogeneity in constraints and incentives must factor prominently in any sensible explanation of observed diversification behaviors. Indeed, section 4 demonstrates that at a very fundamental level - the choice of basic livelihood strategy - households would prefer locally available livelihood strategies other than those they choose, were they not constrained from doing so. A simple appeal to the principle of revealed preference thus suggests that heterogeneous constraints and incentives play a fundamental role in determining livelihood diversification patterns manifest in income diversification data. The plan for the remainder of this paper is as follows. The next section presents the basic conceptual foundation from which we operate. Section 3 then introduces the data sets and definitions employed in the analysis. Section 4 presents findings relating to the observed variation in income sources across the income distribution, to distinct livelihood strategies pursued by rural African households, to the determinants of strategy choice, and to the effects of alternative livelihood strategies on income dynamics. These findings point especially to significant rural markets failures - especially with respect to finance and land - that force poorer subpopulations to select strategies offering demonstrably lower returns while wealthier subpopulations are able to enjoy higher return strategies to which entry is at least partly impeded by fixed costs and lower marginal costs of participation. Section 5 concludes.Labor and Human Capital, O & Q12,

AGROINDUSTRIALIZATION IN EMERGING MARKETS: OVERVIEW AND STRATEGIC CONTEXT

This article offers an overview for a special issue on agroindustrialization. It reviews eleven articles analyzing the agroindustrialization process in Latin America and Asia. It sets out a conceptual framework from the organizational economics and strategic management literature to enhance the understanding of the process of agroindustrialization from a competitive strategy point of view.Agribusiness, Industrial Organization,