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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.

Alternative forms of linearization variance estimators for generalized raking estimators are defined via different choices of the weights applied (a) to residuals and (b) to the estimated regression coefficients used in calculating the residuals. Some theory is presented for three forms of generalized raking estimator, the classical raking ratio estimator, the 'maximum likelihood' raking estimator and the generalized regression estimator, and for associated linearization variance estimators. A simulation study is undertaken, based upon a labour force survey and an income and expenditure survey. Properties of the estimators are assessed with respect to both sampling and nonresponse. The study displays little difference between the properties of the alternative raking estimators for a given sampling scheme and nonresponse model. Amongst the variance estimators, the approach which weights residuals by the design weight can be severely biased in the presence of nonresponse. The approach which weights residuals by the calibrated weight tends to display much less bias. Varying the choice of the weights used to construct the regression coefficients has little impact.

This publication presents statistical highlights and key tables from the Survey of Household Spending (SHS). This annual survey collects information about expenditures by households and families in Canada on a wide variety of goods and services, as well as their dwelling characteristics and possession of household equipment such as appliances, audio and video equipment, and vehicles. The publication also includes analytical text, summary-level tables, a detailed table, notes and definitions, and information about survey methodology and data quality.

This publication presents estimates of government revenues attributable to tourism for the years 2003 to 2009. Estimates of the revenue attributable to tourism spending by non-residents (i.e. tourism exports) and by residents (i.e. tourism domestic demand) are also included. The main data sources are the Canadian Tourism Satellite Account, National Tourism Indicators, the Income and Expenditure Accounts, the Input-Output tables and T-4 tax remittance files.

Government revenue covers receipts from taxes on incomes (i.e., on employment earnings, corporate profits, net income of unincorporated business and government business enterprises), contributions to social insurance plans (i.e., premiums for Canada/Quebec Pension Plan, Employment Insurance and workers compensation), taxes on production and products (such as sales and property taxes), and from sales of government goods and services. These revenues are broken down into parts that can be attributed to tourism spending, tourism domestic demand and tourism exports for government as a whole and for the three levels of government (federal, provincial/territorial and municipal) separately. Estimates of the government revenue generated per $100 of tourism spending overall and by residents and non-residents are reported as well. The publication contains several charts and summary tables showing revenues attributable to tourism by level of government and by source of revenue. It also contains a discussion of the concepts, definitions, data sources and methods used in the study.

The higher education sector is composed of all universities, colleges of technology and other institutes of postsecondary education, whatever their source of finance or legal status. It also includes all research institutes, experimental stations and clinics operating under the direct control of, or administered by, or associated with higher education establishments.

Revised estimates of the Income and Expenditure Accounts covering the period 2006 to 2009 have been released along with those for the first quarter of 2010. The current revisions to GDP resulted from the inclusion of the most current estimates from data sources, including survey results, administrative data and public accounts.

The Elementary-Secondary Education Survey (ESES) is administered by Statistics Canada and surveys, on an annual basis, every Ministry/Department of Education across Canada. The survey collects publicly-funded school data at the elementary and secondary level that includes enrolment, graduate, educator and expenditure statistics. The main objectives are to collect, analyze and publish relevant, comparable and timely statistics and to reduce the response burden on educational organizations that supply data.

The objective of this paper is to present a profile of registered charities across the rural to urban gradient.

This release contains estimates of total spending on research and development (R&D) in the health field in Canada. Tables demonstrate expenditures on health R&D by both performer and funder from 2005 to 2009 preliminary estimates.

Summary of the technical workshop on Estimates of Research and Development in the Higher Education Sector (HERD), held in Ottawa on October 16, 2009. Data users and experts from universities and colleges, granting councils and provincial and federal government departments proposed general and detailed recommendations for the methodology applied in estimating the HERD.

This publication presents statistical highlights and key tables from the Survey of Household Spending (SHS). This annual survey collects information about expenditures by households and families in Canada on a wide variety of goods and services, as well as their dwelling characteristics and possession of household equipment such as appliances, audio and video equipment, and vehicles. The publication also includes analytical text, summary-level tables, a detailed table, notes and definitions, and information about survey methodology and data quality.

The Elementary-Secondary Education Survey (ESES) is administered by Statistics Canada and surveys, on an annual basis, every Ministry/Department of Education across Canada. The survey collects publicly-funded school data at the elementary and secondary level that includes enrolment, graduate, educator and expenditure statistics. The main objectives are to collect, analyze and publish relevant, comparable and timely statistics and to reduce the response burden on educational organizations that supply data.

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.

Alternative forms of linearization variance estimators for generalized raking estimators are defined via different choices of the weights applied (a) to residuals and (b) to the estimated regression coefficients used in calculating the residuals. Some theory is presented for three forms of generalized raking estimator, the classical raking ratio estimator, the 'maximum likelihood' raking estimator and the generalized regression estimator, and for associated linearization variance estimators. A simulation study is undertaken, based upon a labour force survey and an income and expenditure survey. Properties of the estimators are assessed with respect to both sampling and nonresponse. The study displays little difference between the properties of the alternative raking estimators for a given sampling scheme and nonresponse model. Amongst the variance estimators, the approach which weights residuals by the design weight can be severely biased in the presence of nonresponse. The approach which weights residuals by the calibrated weight tends to display much less bias. Varying the choice of the weights used to construct the regression coefficients has little impact.

This publication presents estimates of government revenues attributable to tourism for the years 2003 to 2009. Estimates of the revenue attributable to tourism spending by non-residents (i.e. tourism exports) and by residents (i.e. tourism domestic demand) are also included. The main data sources are the Canadian Tourism Satellite Account, National Tourism Indicators, the Income and Expenditure Accounts, the Input-Output tables and T-4 tax remittance files.

Government revenue covers receipts from taxes on incomes (i.e., on employment earnings, corporate profits, net income of unincorporated business and government business enterprises), contributions to social insurance plans (i.e., premiums for Canada/Quebec Pension Plan, Employment Insurance and workers compensation), taxes on production and products (such as sales and property taxes), and from sales of government goods and services. These revenues are broken down into parts that can be attributed to tourism spending, tourism domestic demand and tourism exports for government as a whole and for the three levels of government (federal, provincial/territorial and municipal) separately. Estimates of the government revenue generated per $100 of tourism spending overall and by residents and non-residents are reported as well. The publication contains several charts and summary tables showing revenues attributable to tourism by level of government and by source of revenue. It also contains a discussion of the concepts, definitions, data sources and methods used in the study.

The higher education sector is composed of all universities, colleges of technology and other institutes of postsecondary education, whatever their source of finance or legal status. It also includes all research institutes, experimental stations and clinics operating under the direct control of, or administered by, or associated with higher education establishments.

Revised estimates of the Income and Expenditure Accounts covering the period 2006 to 2009 have been released along with those for the first quarter of 2010. The current revisions to GDP resulted from the inclusion of the most current estimates from data sources, including survey results, administrative data and public accounts.

The objective of this paper is to present a profile of registered charities across the rural to urban gradient.

This release contains estimates of total spending on research and development (R&D) in the health field in Canada. Tables demonstrate expenditures on health R&D by both performer and funder from 2005 to 2009 preliminary estimates.

Summary of the technical workshop on Estimates of Research and Development in the Higher Education Sector (HERD), held in Ottawa on October 16, 2009. Data users and experts from universities and colleges, granting councils and provincial and federal government departments proposed general and detailed recommendations for the methodology applied in estimating the HERD.