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  • 1. Comparison of survey regression techniques in the context of small area estimation of poverty Archived
    Articles and reports: 12-001-X201000211378
    Description:

    One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.

    Release date: 2010-12-21
  • 2. Comparing Income Statistics from Different Sources: Aggregate Income, 2005 Archived
    Articles and reports: 75F0002M2010002
    Description:

    This report compares the aggregate income estimates as published by four different statistical programs. The System of National Accounts provides a portrait of economic activity at the macro economic level. The three other programs considered generate data from a micro-economic perspective: two are survey based (Census of Population and Survey of Labour and Income Dynamics) and the third derives all its results from administrative data (Annual Estimates for Census Families and Individuals). A review of the conceptual differences across the sources is followed by a discussion of coverage issues and processing discrepancies that might influence estimates. Aggregate income estimates with adjustments where possible to account for known conceptual differences are compared. Even allowing for statistical variability, some reconciliation issues remain. These are sometimes are explained by the use of different methodologies or data gathering instruments but they sometimes also remain unexplained.

    Release date: 2010-04-06
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  • 1. Comparison of survey regression techniques in the context of small area estimation of poverty Archived
    Articles and reports: 12-001-X201000211378
    Description:

    One key to poverty alleviation or eradication in the third world is reliable information on the poor and their location, so that interventions and assistance can be effectively targeted to the neediest people. Small area estimation is one statistical technique that is used to monitor poverty and to decide on aid allocation in pursuit of the Millennium Development Goals. Elbers, Lanjouw and Lanjouw (ELL) (2003) proposed a small area estimation methodology for income-based or expenditure-based poverty measures, which is implemented by the World Bank in its poverty mapping projects via the involvement of the central statistical agencies in many third world countries, including Cambodia, Lao PDR, the Philippines, Thailand and Vietnam, and is incorporated into the World Bank software program PovMap. In this paper, the ELL methodology which consists of first modeling survey data and then applying that model to census information is presented and discussed with strong emphasis on the first phase, i.e., the fitting of regression models and on the estimated standard errors at the second phase. Other regression model fitting procedures such as the General Survey Regression (GSR) (as described in Lohr (1999) Chapter 11) and those used in existing small area estimation techniques: Pseudo-Empirical Best Linear Unbiased Prediction (Pseudo-EBLUP) approach (You and Rao 2002) and Iterative Weighted Estimating Equation (IWEE) method (You, Rao and Kovacevic 2003) are presented and compared with the ELL modeling strategy. The most significant difference between the ELL method and the other techniques is in the theoretical underpinning of the ELL model fitting procedure. An example based on the Philippines Family Income and Expenditure Survey is presented to show the differences in both the parameter estimates and their corresponding standard errors, and in the variance components generated from the different methods and the discussion is extended to the effect of these on the estimated accuracy of the final small area estimates themselves. The need for sound estimation of variance components, as well as regression estimates and estimates of their standard errors for small area estimation of poverty is emphasized.

    Release date: 2010-12-21
  • 2. Comparing Income Statistics from Different Sources: Aggregate Income, 2005 Archived
    Articles and reports: 75F0002M2010002
    Description:

    This report compares the aggregate income estimates as published by four different statistical programs. The System of National Accounts provides a portrait of economic activity at the macro economic level. The three other programs considered generate data from a micro-economic perspective: two are survey based (Census of Population and Survey of Labour and Income Dynamics) and the third derives all its results from administrative data (Annual Estimates for Census Families and Individuals). A review of the conceptual differences across the sources is followed by a discussion of coverage issues and processing discrepancies that might influence estimates. Aggregate income estimates with adjustments where possible to account for known conceptual differences are compared. Even allowing for statistical variability, some reconciliation issues remain. These are sometimes are explained by the use of different methodologies or data gathering instruments but they sometimes also remain unexplained.

    Release date: 2010-04-06
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