cross-country harmonization

This study addresses the lack of cross-national comparability in global Gini coefficient estimates, which arises from discrepancies in data sources, welfare metrics, and methodological choices. By harmonizing 12 international databases, the authors construct a unified dataset encompassing 222 countries and over 122,000 observations. They provide the first systematic quantification of pairwise inconsistencies among alternative Gini estimates for the same country-year and rigorously assess the influence of key factors—including welfare indicators, reference units, equivalence scales, and survey design. The analysis reveals that Gini coefficients for identical country-years can differ by as much as 50 percentage points, with the choice of welfare metric identified as the primary driver of cross-country incomparability. The paper proposes a methodological framework to enhance temporal and spatial comparability, establishing a standardized foundation for measuring economic inequality.

Existing data integration methods struggle to accommodate complex survey designs and typically assume that multiple data sources originate from the same population, rendering them unsuitable for non-probability samples. This work proposes a model-assisted calibration framework that extends such integration to multiple probability survey samples—a first in the literature—accepting either individual-level data or aggregated summary statistics as input. The approach guarantees design-consistent estimation without requiring correct specification of the outcome model and naturally accommodates complex sampling designs. Coupled with Taylor linearization for variance estimation, the method substantially enhances the efficiency of regression analysis while preserving validity for finite-population inference. Simulation studies and empirical analyses using NHANES and NHIS data demonstrate consistent efficiency gains across diverse scenarios.

This study systematically characterizes measurement error in individual labor income using exact probabilistic linkage between the German Socio-Economic Panel (SOEP) and administrative Integrated Employment Biography (IEB) data. Methodologically, it innovatively constructs a causal identification framework grounded in real-data linkage, combining reliability ratio estimation, first-difference error amplification analysis, and selection bias diagnostics. Key contributions: SOEP income measurement error is nonclassical—exhibiting systematic underreporting, temporal autocorrelation, and dependence on both true income and observable covariates; consent to data linkage introduces observable selection bias, undermining the assumption of random sampling. Results show that the reliability ratio for level income exceeds 0.94, implying minimal attenuation bias in single-period linear regressions; however, measurement error in income changes (first differences) is substantially amplified, necessitating careful modeling in dynamic analyses.

In low- and middle-income countries (LMICs), high-resolution subnational mapping of health indicators—such as vaccination coverage—is hindered by sparse household survey data, complex sampling designs, and the absence of reliable population denominators. To address this, we propose a novel integrated framework combining small-area estimation (SAE) with model-based geostatistics (MBG). Our approach jointly incorporates survey weights, spatial random effects, and geographic covariates at the unit level and calibrates estimates to administrative-level totals using Demographic and Health Surveys (DHS) data. This is the first method to systematically bridge design-based and model-based paradigms, ensuring both theoretical consistency and spatial smoothness. Applied to Nigeria’s 2018 DHS data, our pixel-level maps demonstrate substantially improved estimation accuracy and more realistic uncertainty quantification. The framework provides WHO, UNICEF, and other stakeholders with a verifiable, scalable alternative for subnational health mapping.

This study addresses selection bias in estimating long-term causal effects—such as graduation rates—from observational studies. We propose a novel control function approach that leverages experimental estimates of treatment effects on short-term outcomes (e.g., eighth-grade test scores) to correct for unobserved confounding in large-scale administrative observational data. Our method integrates insights from difference-in-differences estimation, covariate balancing, and cross-sample effect calibration, enabling the first systematic correction based on heterogeneity in short-term treatment effects. By bridging randomized experiments and observational datasets, the framework jointly preserves internal validity from experiments and external representativeness from administrative records, overcoming inferential limitations inherent to single-data-source designs. Empirical validation using the STAR randomized experiment and New York State school administrative data demonstrates substantial improvements in both accuracy and external validity of estimated causal effects of class size on academic performance.

This study addresses significant discrepancies in global Gini coefficient estimates across data sources, which undermine the accuracy of policy formulation. By systematically integrating over 120,000 Gini observations spanning 158 years and covering 222 countries and territories, the authors construct the first unified and comparable database. Through a comprehensive literature review and statistical modeling, they quantify how measurement dimensions—such as income versus consumption and pre-tax versus post-tax—systematically affect Gini estimates. The analysis reveals that income-based Gini coefficients are on average 4.7 points higher than consumption-based ones, with this gap widening over time. To correct for welfare concept–induced biases, the study proposes adjustment factors and underscores data transparency as essential for achieving cross-source comparability.

This study addresses the challenge of comparing multidimensional socioeconomic outcomes across groups when data exhibit clustered structures and within-cluster correlations. The authors propose a distribution-free, robust cross-group comparison method that treats clusters as independent units and integrates longitudinal rank-sum tests (LRST) with order statistics to construct a rank-based multivariate aggregation framework. This approach effectively synthesizes high-dimensional, correlated indicators into an interpretable composite ranking while avoiding reliance on parametric modeling assumptions. Innovatively combining rank fusion with the inherent clustering structure of the data, the method is applied to evaluate the refundable Earned Income Tax Credit (EITC) policy, revealing systematic differences in county-level multidimensional outcomes between states that implemented the policy and those that did not. These findings remain robust across varying cluster sizes and resampling schemes.

Official statistics often exhibit complex structures across geographic and subpopulation dimensions that traditional tabular formats struggle to convey effectively, thereby hindering policymakers’ comprehension and application. This study introduces linked micromaps as a visualization framework that systematically integrates descriptive statistics, multivariate relationships, ranking structures, and spatiotemporal heterogeneity to enable intuitive exploration of high-dimensional official data. The approach substantially enhances the interpretability and readability of statistical information, uncovering latent patterns while also offering new avenues for subsequent modeling and uncertainty quantification. By doing so, it expands the potential of linked micromaps in public policy analysis and social science research.

Existing evaluation metrics for survey simulation are fragmented and lack standardization, often overlooking the critical dimension of response option alignment, which hinders meaningful comparison of model performance. To address this gap, this work proposes RADIUS—the first two-dimensional evaluation framework that jointly incorporates rank alignment and distribution alignment. RADIUS systematically assesses the quality of large language models in survey simulation by integrating rank consistency measures, distributional similarity metrics (e.g., KL divergence), and statistical significance testing. The framework not only exposes the limitations of conventional metrics but also establishes a more reliable, comparable, and decision-relevant benchmark. To foster standardized evaluation practices in the research community, the authors open-source the RADIUS implementation.

This study addresses long-standing limitations in India’s inter-state migration census data, which have been plagued by uneven state-level coverage and inconsistent measurement practices, leading to systematic biases that undermine analytical reliability. For the first time, the paper systematically disentangles measurement bias from representativeness bias and introduces a data-driven Harmonized Inter-State Migration (HICM) framework. Integrating statistical diagnostics, imputation, smoothing, and bias correction techniques, HICM standardizes and reconciles migration data across states and time. The proposed approach delivers a reproducible, bias-aware preprocessing and validation pipeline that substantially enhances structural consistency and temporal stability. Empirical results demonstrate that the corrected data significantly improve the credibility of migration network analyses, offering policymakers more accurate evidence for informed decision-making.