Tuesday, Aug 4: 8:30 AM - 10:20 AM
6449
Contributed Papers
Thomas M. Menino Convention & Exhibition Center
Room: CC-107A
Main Sponsor
Survey Research Methods Section
Presentations
The in-work at-risk-of-poverty rate has become an increasingly relevant issue in Europe, raising concerns about the effectiveness of labour taxation and labour market institutions in ensuring adequate living standards for employed individuals. This paper examines the relationship between labour taxation and in-work poverty in Europe using a macro-level panel dataset covering 25 EU countries over the period 2013–2024. In-work poverty is measured for single individuals without children earning 50%, 67%, and 80% of the average earnings, standard thresholds used to identify low-wage workers across the wage distribution and consistent with Eurostat's framework. To account for unobserved common factors and cross-sectional dependence, the analysis employs the Common Correlated Effects pooled (CCE pooled) estimator (Pesaran, 2006). The empirical framework controls for key labour market and economic characteristics, including part-time employment, temporary and involuntary employment, and labour productivity. The study highlights the importance of considering labour taxation within a macroeconomic and institutional context when designing policies reduce in-work poverty in Europe.
Keywords
Common Correlated Effects pooled
In-work at-risk-of-poverty rate
Labour market
Speaker
Matilde Bini, European University of Rome
Co-Author
Lucio Masserini, Department of Economics and Management, University of Pisa
Accurately assessing economic well-being in developing countries is challenging due to rapid socioeconomic change and the lack of recent census or local-level survey data. Conventional small area estimation methods often rely on outdated census information, while geospatial data, though timely and granular, provide limited predictors and may introduce bias through preprocessing. This motivates the need for modelling strategies that integrate multiple data sources to improve wealth measurement when census data are unavailable or outdated.
We propose an integrated modelling framework that jointly models two contemporaneous sample surveys estimating similar asset wealth indices, while incorporating geospatial covariates as alternative auxiliary information. Challenges arising from differences in data granularity and quality are addressed through a spatial matching procedure. Our results show that the proposed joint model yields more reliable estimates than conventional univariate approaches, reducing errors from reliance on geospatial data. We conclude by discussing implications for small area estimation and extensions to modelling and variance estimation in data-limited settings.
Keywords
Small area estimation
Asset-based wealth index
Geospatial covariates
Survey data integration
Spatial matching
Joint modelling
Model-based estimators are widely used in small area estimation problems to provide reliable estimates for domains with small sample sizes. When auxiliary information is available only at the area level, area-level models are typically used. We propose a new estimator based on an area-level model with clustered effects. This model allows for heterogeneity in regression coefficients across areas by incorporating clustered coefficients. Pairwise penalties are used to simultaneously identify clusters and estimate parameters. In simulation studies, we compare the performance of the proposed estimator with existing estimators. Additionally, we apply the new estimator to the Forest Inventory and Analysis (FIA) data.
Keywords
Area-level models
Clustered effects
Penalty functions
Small area estimation
Multiple outcomes from the same sample are often analyzed separately, ignoring correlations between outcomes. Multivariate multilevel methods (MM) that explicitly model multiple outcomes simultaneously may be more effective, but are rarely used, particularly in complex surveys. We applied MM to evaluate an intervention using an 8-domain survey assessing population health management capabilities of health centers (HCs). Data were nested at three levels: 310 staff across 32 HCs, surveyed at two time points. To address unequal selection probabilities, we used weighted MM (WMM). Four WMMs were specified under varying assumptions about correlation structures across domains and data levels with robust standard errors estimated. Model performance was assessed using information criteria, absolute fit, and prediction measures. All WMMs outperformed combined univariate models. Models accounting for HC- or staff-level correlations demonstrated superior fit and predictive performance, while the most complex model showed evidence of overfitting and high computational burden. The findings highlight the advantages of WMM and demonstrate the first application of a three-level WMM for 8 outcomes.
Keywords
Multi-domain survey
Weighted multilevel model
Multivariate weighted multilevel model
Multivariate longitudinal data
Estimating subarea-level means is difficult when only aggregated area-level totals are observed. We propose a two-fold Bayesian framework that uses hierarchical modeling and covariate driven borrowing of strength to recover latent subarea signals. To incorporate known area-level totals, we introduce a Soft Constraint Theorem that modifies the posterior distribution through a Kullback–Leibler divergence projection. This approach enforces the aggregate constraint while retaining the flexibility of the posterior and avoiding the rigidity of hard constraints. We further study the uncertainty of the Bayesian estimators from a frequentist perspective by estimating bias and variance based on corrected Markov chain Monte Carlo samples. Through simulation studies, we show that the proposed method provides a good approximation to the empirical mean squared error
Keywords
Small area estimation
Hierarchical Bayesian modeling
Speaker
Chen Zhao, Division of Biostatistics & Health Data Science, University of Minnesota
Co-Author
J. Sunil Rao
Fields like public health and labor economics utilize ordered categorical outcomes (e.g., self-rated health) to monitor group disparities. However, survey methodology often overlooks inference for ordinal disparity measures under complex sampling. Unlike linear statistics such as means and proportions, ordinal indices based on stochastic dominance are nonlinear and biased, even in simple designs. Multistage, unequal-probability sampling further complicates point and variance estimation, especially when using software intended for linear statistics or assuming independent sampling.
We propose the Design-Aware Ordinal Weighted Likelihood Bootstrap (D–OWLB) for model-based inference on these measures. D–OWLB embeds a primary sampling unit-level rescaled bootstrap within a weighted likelihood estimation framework using Gamma-perturbed replicate weights. This approach accounts for stratification, clustering, and unequal selection probabilities to target the sampling distribution of nonlinear disparity functionals and produce population-level inference based on these functionals. While this article focuses primarily on health applications, the framework can be implemented for a broad range of variables where inference needs to be made from ordinal data.
Simulations show that while design-agnostic bootstraps underestimate variance as complexity increases, D–OWLB aligns closely with design-based benchmarks. Applied to 2023 BRFSS data, D–OWLB provides better-calibrated intervals for self-rated health disparities compared to standard weighted likelihood methods.
Keywords
Ordinal health outcomes
Model-based inference
Complex survey design
Bootstrap
Weighted likelihood
Multilevel models
Multilevel normal hierarchical models play an important role in developing statistical theory in multiparameter estimation for a wide range of applications. In this article, we propose a new reconciliation framework of the empirical Bayes and hierarchical Bayes approaches for interval estimation of random effects under a two-level normal model. Our framework shows that a second-order efficient empirical Bayes confidence interval, with empirical Bayes coverage error of order O(m^{-3/2}), can also be viewed as a credible interval whose posterior coverage is close to the nominal level, provided a carefully chosen prior-referred to as a 'matching prior'-is placed on the hyperparameters. While existing literature has examined matching priors that reconcile frequentist and Bayesian inference in various settings, this paper is the first to study matching priors with the goal of interval estimation of random effects in a two-level model. We obtain an area-dependent matching prior on the variance component that achieves a proper posterior under mild regularity conditions. The theoretical results in the paper are corroborated through a Monte Carlo simulation study and a real data analysis.
Keywords
Credible interval
Empirical best linear unbiased prediction
Linear mixed model
Matching prior