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Monitoring Policy Impacts (MPI): Setting-up and Organizing Monitoring Policy Impacts










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    Monitoring Policy Impacts (MPI): The Eight Methodo-"logical" Steps for MPI  2005
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    This Module presents the eight methodo-“logical” steps for monitoring policy impacts (MPI), comprising:

    • Step 1: Initiation and preparation of MPI
    • Step 2: Policy review and analysis
    • Step 3: Development of the impact model
    • Step 4: Selection of impact indicators
    • Step 5: Research design
    • Step 6: Information and data collection
    • Step 7: Data compilation, processing and analysis
    • Step 8: Feedback of results of MPI t o policy makers, clients, public.

    The activities to be performed on the various steps are described, and an overview of the methods to be applied in performing these activities is given. It is pointed out that the eight steps represent a logical sequence but that the steps are closely interlinked and that there are likely possible feed back cycles to previous steps. In a concluding section, conditions for a practical application of MPI to specific policy cases are set out.
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    Monitoring Policy Impacts (MPI): The Application of the LogFrame Method  2005
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    The LogFrame method is an instrument employed by analysts, planners and managers for:

    • problem analysis,
    • objective formulation, ,
    • planning, ,
    • implementation, ,
    • monitoring and evaluation,

    of selected, objective-oriented interventions that aim at a change of reality from a situation which is perceived as negative towards a positive situation. Because of its general logic, the LogFrame method can be applied to any type of objective orient ed tasks, irrespective of the nature, level of aggregation or complexity of the problem to be solved. Though originally developed as a method for project planning and management, the method and its methodological principles can analogously be applied to analysis, planning and management of programmes and policies.
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    Policy Impacts on Inequality. Weighting Samples: Introductory Issues 2006
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    This module illustrates the concept of weighting samples in survey analysis. In particular, it deals with: a) the concept of weights and inflation factors; b) why weights are important in calculating statistical measures; c) how disregarding weights may lead to misleading results in empirical analysis.

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