IMPOVERISHMENT RISKS ANALYSIS OF POPULATION DISPLACEMENT USING STRUCTURAL EQUATION MODELING: A Case Study of Involuntary Resettlement Project in Phnom Penh

Abstract

counteraction of the anticipated and predictable risk factors to work towards double sustainable development (for both the development projects and the socio-economic aspects of the affected people); but effective assessments of the model require individual observations of risk factors and construction of them into a comprehensive model. By using Structural Equation Modeling (SEM), researchers are possible to evaluate causal relationships in this theoretical model with complex variables structure. Use of this model will provide insight into the effect of changing living environmental factors when assessing risks. To improve the realm of impact evaluation of population displacement and to evaluate causal relationships of complex instruments, SEM and Geographic Information System (GIS) are used in a case study in Phnom Penh. The three objectives of the impoverishment risks analysis in this study are: (1) to ascertain the scales and causations effect of population displacement by zone, (2) to analyze risks relationship by using SEM, and (3) to identify recurrent problems affecting performance, initiate midstream remedial actions, and propose reconstruction strategy for addressing resettlement more effective. This paper outlines an impact assessment model to improve theory-led fieldwork on the socio-economic and environmental impacts of involuntary resettlement. 2. Review on Related Papers During the last several years, the IR model has been increasingly discussed by researchers and practitioners and is currently “at work” in numerous development and research projects. A large study carried out by the Institute for Socioeconomic Development (ISED) in Orissa, India, took the IR model as its conceptual and methodological basis in exploring resettlement processes caused by seven major projects (in dam construction, thermal plants, mining, and industry). The sample included 31 villages and 441 households with 2,274 people, selected from among 95 affected villages with 1,977 households. That study 3) produced one of the most comprehensive and integrated surveys of displacement impacts published to date in India, practically confirming the framework under the demands of a large-scale field investigation. Its key findings are structured along the model’s impoverishment risks. Another study 4),5) focused on “countering the impoverishment risks,” reported from India’s Rengali dam; the study measured actual impacts of each risk variable, analyzing counter-risk measures and formulating recommendations about what needs to be done on the ground. Research 6) on impoverishment risk and impacts was started in Lesotho at the request of the international panel monitoring the Water Engineering Project. In Nepal (Kali Gandaki Project) the application of the model in several ongoing impact evaluation resettlement studies 7) has revealed positive experiences and produced operational recommendations.