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Introduction
Traditionally participatory methods of analysis like wealth ranking exercises were favoured mainly by sociologists and development practitioners. But the use of participatory methods in conjunction with other more formal methods has increased recently. This is particularly true in poverty studies that focus on understanding
of rural livelihoods in developing countries. For example wealth ranking was used to divide the population into non-poor, poor and ultra-poor for the purpose of constructing a poverty index that used both qualitative and quantitative information in Iran (Dariush Hayati et al., 2006). Rosemary McGee used participatory method to understand the dynamics of poverty in Uganda in the recent past to contribute to the debate whether poverty has increased or decreased (Rosemary McGee, 2004). Wealth rankings were also used to understand destitution and poverty in Ethiopia (Stephen Devereux and Kay Sharp, 2006, Kay Sharp et al., 2003), to examine villagers’ perception of poverty in Zimbabwe (Trudy Owens, 2004), to develop an asset status tracking method in India (Richard Bond and Neela Mukherjee, 2002), to assess child poverty in rural Vietnam (Trudy Harpman et al., 2005) and analyse poverty among tribals in India (Amita Shah and D. C. Sah, 2004).
In addition to poverty analysis, wealth ranking has also been used in very different research and assessment exercises usually in combination with other research methods. It has been used to study biodiversity and recent changes in enset (false banana) production in Ethiopia (A. Tsegaye and P. C. Struick, 2002); to identify different approaches used by research and service providers in technology dissemination for different wealth groups in Uganda (G. Agwaru et al., 2004); to choose appropriate response by public health sector to reduce acute malnutrition among children in Cambodia (Bart Jacobs and Emma Robers, 2004); to understand the direct use-value of bioresources in rural households in South Africa (W. Twine et al., 2003); to analyse the diversity in livelihoods and farmers strategies in eastern
Ethiopia (Tesfaye Lemma Tefera et al., 2004); for the economic analysis of animal genetic resources (Adam G. Drucker and Simon Anderson, 2004); for mapping and understanding indigenous farmers agricultural knowledge and information system and implication to extension services (H. Bagnall-Oakeley et al., 2004); to analyse the
sustainability of participatory watershed development in India (V. Ratna Reddy et al., 2004); to indentify smallholders soil fertility management in Ethiopia (Amare Haileslassie et al., 2006); to assess the effect of abolishment of user fees in health services in Uganda (Jenny Yates et al., 2006); to ascertain whether microfinance reaches the poor in South Africa (Anton Simanowitz, 2000); to examine if the quality of science is affected by participatory research (Christina H.
Gladwin et al., 2002); to monitory the impacts of community forestry on livelihoods in Nepal (Om Prakash Dev et al., 2003); to trace the effect of community heterogeneity on community based forest projects in Nepal (Bhim Adhikari and Jon C. Lovett, 2006); to examine the inequity in the distribution of responsibilities in Forest User Groups in Nepal (Michael Richards et al., 2003). In most cases, wealth ranking is used as part of a broader participatory method and complemented with other quantitative-oriented research methods.
Apart from the use of participatory research methods in many contexts, a lively debate on the methodological validity of participatory methods including wealth ranking has developed (John Campbell, 2002; Caterina Ruggeri Laderchi et al., 2003; Linda Mayoux and Robert Chambers, 2005; Alayne M. Adams et al., 1997; Trevor Parfitt, 2004).
The main focus of this paper is on the use of information from wealth ranking exercises in conjunction with data collected from household surveys. The second section outlines a simple conceptual framework for a more systematic analysis of wealth ranks with information from household surveys. Section 3 provides a brief
description of the empirical data used. Section 4 presents the main empirical results while Section 5 provides conclusions.
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