Small Area Estimation Using a Reweighting Algorithm

Tanton, Robert; Vidyattama, Yogi; Nepal, Binod; McNamara, Justine

doi:10.1111/j.1467-985x.2011.00690.x

Cited by 89 publications

(73 citation statements)

References 19 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…This relies on spatial microsimulation techniques (Tanton and Edwards, 2013) or, more commonly, re-weighting national or regional micro-data so that key characteristics match those from census data for the small area (Tanton et al, 2011). The primary policy use of these models in the developed world generally is to predict the demand for services such as care facilities (for example, Lymer et al (2009) for Australia and Wu and Birkin (2013) for the UK).…”

Section: Crossing Boundaries: Sub-and Supra-national Modellingmentioning

confidence: 99%

Microsimulation and Policy Analysis

Figari

Paulus

Sutherland

2015

Handbook of Income Distribution

View full text Add to dashboard Cite

ISER Working Paper Series www.iser.essex.ac.uk Microsimulation and Policy AnalysisNon-technical summary This paper is prepared as a chapter forthcoming in the Handbook of Income Distribution, Volume 2 (edited by A. B. Atkinson and F. Bourguignon, Elsevier-North Holland). Microsimulation methods are increasingly used to study the effects of policies on the income distribution contributing both to policy debates and academic literature. In general terms, microsimulation refers to modelling changes in the state or behaviour of micro-level units. We provide an overview of approaches that address questions related to the impact of taxbenefit policies on household income distribution. We first discuss how microsimulation modelling contributes to the analysis of the income distribution more broadly, pointing out that it can enrich survey and administrative data by checking its consistency, adding further details and enhancing analytical flexibility. In addition, it provides the basis for calculating indicators that cannot otherwise be estimated directly from micro-data such as measures of work incentives, net support for children and automatic stabilisers embedded in tax-benefit systems. We then focus more specifically on the role of microsimulation in policy evaluation. Taxbenefit models can provide information on household disposable income and its components under various policy scenarios and as such constitute a unique tool for ex ante policy analysis, though equally can be used for ex post analysis. We review the formal framework underlying such microsimulation-based decomposition analysis of income distribution and suggest a broader perspective to accommodate a wider range of applications. Our main emphasis is on static modelling methods, though we also consider extensions accounting for behavioural reactions and links with macroeconomic models. As microsimulation models are often concerned with current and future policies, we discuss various approaches to predict the income distribution given that data providing information on the current situation becomes available with a time lag, distinguishing between nowcasting and forecasting. We emphasise the role of comparative analysis as cross-country differences in tax-benefit systems and population structures provide a basis to assess the robustness of results and generalise conclusions. We provide empirical illustrations drawing mainly on analysis using the EU-wide tax-benefit model EUROMOD. Covering 27 countries and made generally accessible this is now one of the most widely used models. There are many remaining challenges to providing estimates of the effects of policy and policy changes that can be used with confidence within policy analysis and we consider three major issues: reconciling the simulated income distribution and that measured using the original micro-data; accounting for benefit non take-up and tax non-compliance, as well as validating and assessing the statistical reliability of microsimulation estimates. While our main focus is on the distributio...

show abstract

Section: Crossing Boundaries: Sub-and Supra-national Modellingmentioning

confidence: 99%

Microsimulation and Policy Analysis

Figari

Paulus

Sutherland

2015

Handbook of Income Distribution

View full text Add to dashboard Cite

show abstract

“…Therefore, some of the indicators were estimated using a spatial microsimulation model based on the corresponding census (Tanton et al 2011).…”

Section: Data Sourcesmentioning

confidence: 99%

Assessing adaptive capacity during the drought period in the Murray–Darling Basin

et al. 2017

Self Cite

View full text Add to dashboard Cite

The severity and intensity of drought are predicted to worsen under expected changing climatic conditions. The ability of communities to adapt and be resilient during a severe event such as drought depends on the capacity of the area. This study looks at the capacity of regional communities to adapt during a period of severe condition, using a case study of the Murray-Darling Basin in Southeast Australia during the Millennium drought. Using principal component analysis methodology, this study creates several indexes to summarise important indicators for socioeconomic adaptive capacity. Results indicate that that the adaptive capacity measure works well in the areas near cities. On the other hand, although areas away from cities were more likely to suffer lower adaptive capacity after the drought, the areas with higher capacity index seem to be able to maintain their position in the quintile or at least not drop further down into lower quintile even when compared to the area near the city.

show abstract

“…In fact, BC may have no exact solution (zero error), either considered solely or in combination with RR on weights (Singh and Mohl 1996). This can be due to: the sample not being representative (enough) of every non-void class in the cross-classified BC; the RR being too tight; the BC forming an inconsistent system of equations, due to their derivation from differing data sources or from data with added noise as a result of statistical disclosure control procedures (Tanton et al 2011). In addition, calibration algorithms may be unstable for too many BC or when multi-collinear survey variables are being benchmarked (Sautory 1991;Rao and Singh 1997).…”

Section: Introductionmentioning

confidence: 99%

“…Failure of survey calibration methods may occur with real data (Sautory 1991;Tanton et al 2011). In fact, BC may have no exact solution (zero error), either considered solely or in combination with RR on weights (Singh and Mohl 1996).…”

Section: Introductionmentioning

confidence: 99%

“…Heuristics typically used include: broadening the categorisation of benchmark variables, modifying the benchmarking values, loosening the RR on weights, or even deleting some BC (Sautory 1991;Bankier et al 1992). In Tanton et al (2011), an upper threshold on the total absolute error (TAE) in BC is used to accept non-convergent solutions satisfying RR, with no further control for error in BC. Penalised calibration allows a certain degree of relaxation in each BC, being controlled by costs (or tolerances), while still providing approximately unbiased and asymptotically design-consistent estimators.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A global optimisation approach to range-restricted survey calibration

2017

View full text Add to dashboard Cite

Survey calibration methods modify minimally sample weights to satisfy domain-level benchmark constraints (BC), e.g. census totals. This allows exploitation of auxiliary information to improve the representativeness of sample data (addressing coverage limitations, non-response) and the quality of sample-based estimates of population parameters. Calibration methods may fail with samples presenting small/zero counts for some benchmark groups or when range restrictions (RR), such as positivity, are imposed to avoid unrealistic or extreme weights. Userdefined modifications of BC/RR performed after encountering non-convergence allow little control on the solution, and penalisation approaches modelling infeasibility may not guarantee convergence. Paradoxically, this has led to underuse in calibration of highly disaggregated information, when available. We present an always-convergent flexible twostep global optimisation (GO) survey calibration approach. The feasibility of the calibration problem is assessed, and automatically controlled minimum errors in BC or changes in RR are allowed to guarantee convergence in advance, while preserving the good properties of calibration estimators. Modelling alternatives under different scenarios using various error/change and distance measures are formulated and discussed. The GO approach is validated by calibrating the weights of the 2012 Health Survey for England to a fine age-gender-region cross-tabulation (378 counts) from the 2011 Census in England and Wales.

show abstract

Small Area Estimation Using a Reweighting Algorithm

Cited by 89 publications

References 19 publications

Microsimulation and Policy Analysis

Microsimulation and Policy Analysis

Assessing adaptive capacity during the drought period in the Murray–Darling Basin

A global optimisation approach to range-restricted survey calibration

Contact Info

Product

Resources

About