Declining response rates and growing concerns about nonresponse bias threaten the quality of official statistics. Although prior studies have identified various factors associated with survey cooperation, such as privacy concerns, response burden, and political trust, most have examined these determinants independently without modeling the dependency structure among them. This study applied Bayesian networks to data from a web-based survey conducted in Japan to explore the probabilistic dependency structure linking broader social orientations, survey attitudes and perceptions, and response intention in official statistical surveys. The analysis identified relationships in which perceived social fairness was associated with trust in official statistics, which, in turn, was associated with survey response intention. Perceptions of others’ honesty in answering sensitive income questions were linked to individuals’ willingness to respond to such items, which was in turn associated with overall participation intention. Community participation was associated with opinions on social issues, which were in turn linked to response intention toward official statistical surveys. These findings suggest that intention to cooperate with official statistical surveys is embedded in broader social orientations. Understanding these dependency structures may help generate hypotheses about variables that could be examined in future studies of actual nonresponse and nonresponse adjustment.
In survey sampling, efficient estimation of population parameters under uncertainty remains an essential challenge, especially when auxiliary information is imprecise or incomplete. This article proposes an optimized strategy for estimating the population mean by incorporating auxiliary attributes, which are qualitative in nature, easily accessible, and strongly associated with the study variable. A novel class of neutrosophic estimators is constructed to improve estimation efficiency under uncertainty using simple random sampling (SRS). The statistical properties of the proposed estimators such as bias and mean square error (MSE) are determined using first-order approximations. The efficiency of the proposed estimators is theoretically examined and compared with the adapted estimators. The results demonstrate that the proposed neutrosophic estimators provide substantial efficiency gains over adapted estimators. An empirical study and a simulation study based on real datasets support the theoretical findings. The study emphasizes the practical usefulness of auxiliary attributes in enhancing the accuracy and reliability of population mean estimation under uncertainty.
National statistical offices (NSOs) increasingly operate within data ecosystems in which administrative data remain under the authority of sectoral institutions. This creates pressure for NSOs to contribute to government-wide data governance without assuming responsibility for all government data. This conceptual and practitioner-oriented paper proposes bounded data stewardship as a model for allocating horizontal stewardship functions to an NSO while preserving sectoral authority and operational accountability. It develops four criteria for allocation: cross-government value, dependence on semantic or methodological coordination, alignment with NSO competence, and the ability to exercise the function without transferring substantive sectoral responsibility. Comparative arrangements in Lithuania, New Zealand, Canada, Switzerland and Germany illustrate alternative distributions of stewardship and statistical authority. The paper relates the model to public-sector interoperability frameworks and to SDMX and GSIM, identifying metadata, classifications, reference data and quality methodology as candidate functions rather than universal NSO responsibilities. Practitioner evidence from statistical modernization in the Kyrgyz Republic shows that an expert-reviewed specification for a statistical metadata repository did not proceed to development because operational responsibility, maintenance processes and the readiness of the initial metadata content remained insufficiently defined. Bounded stewardship combines distributed responsibility with horizontal coordination and requires sufficient institutional definition before technological formalization.
The increasing availability of mobile network operator (MNO) data has opened up new opportunities for the production of official statistics, particularly in domains such as mobility and tourism. MNO data provide continuous, large-scale and fine-grained information that can complement, and in some cases partially replace, traditional survey and administrative data sources. However, their use also introduces new methodological challenges. This paper focuses on one of these challenges: the international cross-roaming and its impact on the measurement of inbound tourism indicators. Cross-roaming occurs when a foreign mobile device connects to multiple local MNOs within the same visited country during a single stay. This behavior can fragment the observation of tourists across different operators, potentially leading to an underestimation of the total number of overnight stays and, conversely, an overestimation of the tourist arrivals. Using data from multiple MNOs across several European countries within the framework of the Eurostat-funded Multi-MNO project, this study investigates the prevalence and magnitude of cross-roaming, its variability across origin and destination countries, and its impact on inbound tourism statistics. The results show that cross-roaming is widespread and can affect tourism indicators derived from data provided by a single MNO or from the naïve aggregation of data from multiple MNOs. To the best of our knowledge, this is the first study to provide an empirical assessment of the implication od cross-roaming for inbound tourism statistics. The analysis contributes to a more informed and careful re-use of MNO data for official statistics, particularly in the field of tourism statistics.
Reliable estimation of categorical outcomes at detailed geographical levels is challenging when local survey samples are small or outcome categories are sparse. This study develops a Bayesian unit-level small area estimation framework for multinomial outcomes, integrating hierarchical multinomial logistic regression, multilevel regression and post-stratification (MRP), Iterative Proportional Fitting (IPF), survey-design-based bootstrap uncertainty propagation, and stochastic benchmarking to uncertain aggregate estimates. The framework accounts for uncertainty arising from model estimation, reconstruction of joint auxiliary-variable distributions, and benchmarking. The methodology is applied to depression severity across 54 Greek prefectures using the 2019 Greek Health Survey and auxiliary population information. The resulting area-level estimates were stable and internally coherent and generally more precise than direct survey estimates, particularly for less prevalent categories and areas with limited sample information. A design-aware Monte Carlo simulation with 200 replications showed generally small parameter bias, satisfactory recovery of area-level heterogeneity, and empirical coverage reasonably close to nominal levels. Benchmarking reduced mean squared error relative to unbenchmarked MRP, while deterministic and stochastic benchmarking yielded virtually identical point-estimation performance. Stochastic benchmarking additionally propagated uncertainty in the estimated aggregate benchmark, with slightly greater posterior uncertainty and modestly improved coverage. Overall, the empirical and simulation results support the finite-sample performance and practical applicability of the proposed multinomial small area estimation (SAE) framework under the conditions examined.
