Multivariate Typology of Farm Households Based on Socio-Economic Characteristics Explaining Adoption of New Technology in Rwanda

The challenge for agricultural policymakers and planners, particularly in the context of Rwanda with high population density and consequently food insecurity, is how to enable farmers to adopt new technology. It is known that adoption of new technology may vary among farm households because of socio-economic characteristics. This paper intends to typify farm households in Rwanda based on the exploration of factors explaining adoption of new technology. Ultimately, typical farms obtained from the typology will be used, later as basis to develop representative mathematical programming models. Multivariate statistical techniques offer the means of creating such typologies, particularly when an in-depth database is available. This multivariate analysis approach, combining principal component analysis (PCA) and cluster analysis (CA), has allowed us to identify clearly five typical farm households and their socio-economic characteristics explaining adoption of new technology.. Multivariate statistical techniques, such as PCA and CA, are great tools to envisage building mathematical programming models on the basis of typical farm households.Agricultural and Food Policy, Community/Rural/Urban Development, Consumer/Household Economics, Food Consumption/Nutrition/Food Safety, Food Security and Poverty, Land Economics/Use, Marketing, Production Economics, Research and Development/Tech Change/Emerging Technologies,

Variables Determining Total and Electrical Expenditure in Spanish Households

This version of the article has been accepted for publication, after peer review, but is not the Version of Record and does not reflect post-acceptance improvements, or any corrections. The Version of Record is available online at: https://doi.org/10.1016/j.scs.2019.101535[Abstract] Our aim is to discover the variables influencing total and electrical expenditure in Spanish households in the Survey of Family Budgets. Using a principal component analysis, a cluster analysis, and a stepwise regression analysis, we find that income-related variables are the most influential factor in determining total expenditure; however, dwelling size is the most influential factor in determining electricity expenditure. Regional location is the second most important factor for total household expenditure but not electricity expenditure. We find that electricity expenditure depends on the surface area of the house and the number of people in the household, as well as heating and hot water systems and building type. Energy savings will not only reduce household electricity costs, but will benefit the environment. In general, in a developed consumer society, the determining factors go beyond income and include other lifestyle aspects.This research was supported by project ECO2016-77843-P (AEI/FEDER, UE) and S52_17R: Compete Research Group (Government of Aragón/FEDER, UE)Gobierno de Aragón; S52_17

Sub-Saharan Africa at a crossroads: a quantitative analysis of regional development

This repository item contains a single issue of The Pardee Papers, a series papers that began publishing in 2008 by the Boston University Frederick S. Pardee Center for the Study of the Longer-Range Future. The Pardee Papers series features working papers by Pardee Center Fellows and other invited authors. Papers in this series explore current and future challenges by anticipating the pathways to human progress, human development, and human well-being. This series includes papers on a wide range of topics, with a special emphasis on interdisciplinary perspectives and a development orientation.Sub-Saharan Africa is at a crossroads of development. Despite a quarter of a century of economic reforms propagated by national policies and international financial agencies and institutions, sub-Saharan Africa is still lagging in development. In this paper, the authors adopt two techniques using both qualitative (e.g. governance) and quantitative factors (e.g., GDP) to examine regional patterns of development in sub-Saharan Africa. More specifically, they examine and analyze similarities and differences among the countries in this region using a multivariate statistical technique, Principal Component Analysis (PCA), and a unsupervised neural network called Kohonen’s Self-Organizing Map (SOM) to cluster levels of development. PCA serves as a tool for determining regional patterns while SOM is more useful for determining continental patterns in development. Both PCA and SOM results show a “developed” cluster in Southern Africa (South Africa, Namibia, Botswana, and Gabon). SOM exhibits a cluster of least developed countries in southern Western Africa and western Central Africa. The results demonstrate that the applied techniques are highly effective to compress multidimensional qualitative and quantitative data sets to extract relevant information about development from a policy perspective. Our analysis indicates the significance of governance variables in some clusters while a combination of variables explains other regional clusters. Zachary Tyler works for a consulting firm in Massachusetts that conducts program evaluations for energy efficiency programs, and he continues to work on statistical and geospatial analyses of human development issues. In 2010, he will receive a master’s degree in energy and environmental analysis from Boston University. Sucharita Gopal is Professor and Director of Graduate Studies in the Department of Geography and Environment and a member of the Cognitive & Neural Systems (CNS) Technology Lab at Boston University. She teaches and conducts research in geographical information systems (GIS), spatial analysis and modeling, and remote sensing for environmental and public health applications. Her recent research includes the development of a marin integrated decision analysis system (MIDAS) for Belize, Panama, and Massachusetts, and a post-disaster geospatial risk model for Haiti. This paper is part of the Africa 2060 Project, a Pardee Center program of research, publications, and symposia exploring African futures in various aspects related to development on continental and regional scales. For more information, visit www-staging.bu.edu/pardee/research/

BUILDING SYNTHETIC INDICATORS FOR ASPECTS OF TERRITORIAL CAPITAL TOWARDS THEIR IMPACT ON REGIONAL PERFORMANCE

Empirical analyses highlight local structural features (territorial capital) as constraints on regional growth and interregional convergence processes, but scant attention is devoted to traditional localised resources and specifically the natural and cultural heritage. However, only the application of know-how embodied in human capital to resources provides value. Specifically, heritage becomes economically relevant through human capital acting via tourist, recreational and cultural activities. Although, because of its service exporting nature, tourism contributes to economic growth and job creation similarly to manufacturing, the literature concerned manufacturing and rarely studied tourism or extended results to it. Besides, in Europe tourism is the market activity most favouring policentricity, territorial cohesion and equity. On the other hand, heritage valorisation responding to tourist service demand has adverse effects on development (congestion) and environmental quality / resource consumption (heritage dissipation); these partly offset strictly economic benefits and over time weaken the destination’s pull, hence its value and its population’s welfare. Our goal is to analyse the role of territorial capital, and specifically of intangibles such as natural and cultural capital, in regional growth processes and in local response processes to exogenous crises, by building a national database of territorial capital in Italian provinces, containing synthetic endowment indicators for natural and cultural heritage, human capital, and structure and distribution of tourism and leisure industries. Our methodology includes the application of multivariate analyses, with state-of-the-art techniques. We use available European and national databases, augmented by ad hoc integrations if and when needed. The study area is Italy; the optimal tier is NUTS3, i.e. provinces, in Italy. The time reference is 1990-2010, to ensure a structural long-term approach. The paper is organised in the following way: - an initial section outlines the original data on 103 provinces, providing 33 proxy indicators of which major univariate statistics and correlations are explored; - a first main section reduces indicators into 5 synthetic indicators, by means of factor analysis; - a second main section reduces provinces into 11 ideal types, by means of cluster analysis; - a final section compares and interprets results, also with reference to 2007-2009 economic dynamics.

Analysis of customer profiles on an electrical distribution network.

It has become increasingly important for electrical distribution companies to understand the drivers of demand. The maximum demand at any given substation can vary materially on an annual basis which means it is difficult to create a load related investment plan that is robust and stable. Currently, forecasts are based only on historical demand with little understanding about contributions to load profiles. In particular, the unique diversity of customers on any particular substation can affect load profile shape and future forecasts. Domestic and commercial customers can have very different behaviours generally and within these groups there is room for variation due to economic conditions and building types. This paper analyses customer types associated to substations on a distribution network by way of principal component analysis and identification of substations which deviate from the national demand trend. By examining the variance spread of this deviation, data points can be labelled in the principal component space. Groups of substations can then be categorised as having typical or atypical load profiles. This will support the need for further investigation into particular customer types and highlight the key factors of customer categorisation

An exploratory classification of ecological incubator environments in Wales

School of Managemen