More for Less? Comparing small area estimation, spatial microsimulation, and mass imputation

Combining data sources is often seen as a panacea, having the potential to produce more to produce cost-effective, accurate, fine-level statistics for a lower cost. This paper clarifies conditions under which Official Statistics data sources, particularly surveys and censuses or surveys and administrative sources, should and should not be combined using statistical models based on mass imputation, spatial microsimulation, and small area and domain estimation. The theoretical links between these three techniques are explored. The wider research from which this paper is a report considers the relevant literature in depth, further develops existing statistical methods, considers their application in principle to set of case studies in sociology, economics, and business, and provides guidelines for use of the three techniques based on this research.

[1]  Robert Chambers,et al.  Analysis of survey data , 2003 .

[2]  G. Bramley,et al.  Modelling Local Income Distributions in Britain , 1996 .

[3]  Wayne A. Fuller,et al.  Fractional hot deck imputation , 2004 .

[4]  Mass Imputation of Agricultural Economic Data Missing by Design A Simulation Study of Two Regression Based Techniques , 2001 .

[5]  Marissa Cinco Isidro,et al.  Intercensal updating of small area estimates : a thesis presented in partial fulfillment of the requirements for the degree of Doctor of Philosophy in Statistics at Massey University, Palmerston North, New Zealand , 2010 .

[6]  G. Arnold,et al.  Small Area Estimation via Generalized Linear Models , 2002 .

[7]  Chris J. Skinner,et al.  Analysis of complex surveys , 1991 .

[8]  Malay Ghosh,et al.  Small Area Estimation: An Appraisal , 1994 .

[9]  Graham Clarke,et al.  Modelling Regional Changes in US Household Income and Wealth: A Research Agenda , 1998 .

[10]  Holly Sutherland,et al.  Microsimulation Modelling for Policy Analysis: Challenges and Innovations , 2000 .

[11]  N. Tzavidis,et al.  M-quantile models for small area estimation , 2006 .

[12]  J. H. Johnson,et al.  LARGE SCALE IMPUTATION OF SURVEY DATA , 2002 .

[13]  G. Bramley,et al.  Modelling Local and Small-Area Income Distributions in Scotland , 1998 .

[14]  Robert Kozak.  THE BANFF SYSTEM FOR AUTOMATED EDITING AND IMPUTATION , 2005 .

[15]  John G. Kovar,et al.  Imputation of Business Survey Data , 2011 .

[16]  M. Tanner,et al.  Ecological Inference: New Methodological Strategies , 2004 .

[17]  Risto Lehtonen,et al.  Practical Methods for Design and Analysis of Complex Surveys , 1995 .

[18]  Robert Chambers,et al.  Small area estimates for cross‐classifications , 2004 .

[19]  Local estimation of poverty and malnutrition in Bangladesh: some practical and statistical issues. 1 , 2004 .

[20]  Marianne Houbiers,et al.  Towards a Social Statistical Database and Unified Estimates at Statistics Netherlands , 2004 .

[21]  Cathal O'Donoghue,et al.  Dynamic Microsimulation: A Methodological Survey , 2001 .

[22]  Martin Greenberger,et al.  Microanalysis of Socioeconomic Systems: A Simulation Study , 1962 .

[23]  Nicholas T. Longford,et al.  Missing Data and Small-Area Estimation , 2005 .

[24]  G. Orcutt,et al.  A new type of socio-economic system , 1957 .

[25]  G. Bramley.  Homeownership affordability in England , 1992 .

[26]  J. Rao.  Small Area Estimation , 2003 .

[27]  Richard Kingston,et al.  Building a Spatial Microsimulation-Based Planning Support System for Local Policy Making , 2007 .

[28]  Joachim Merz,et al.  Microanalytic simulation models to support social and financial policy , 1986 .

[29]  Martin Greenberger,et al.  Microanalysis of Socioeconomic Systems: A Simulation Study , 1962 .

[30]  Eric J. Beh,et al.  The Information in Aggregate Data , 2004 .

[31]  J. Lanjouw,et al.  Micro-Level Estimation of Poverty and Inequality , 2003 .