I checked 7 public opinion journals on Wednesday, November 11, 2026 using the Crossref API. For the period November 04 to November 10, I found 8 new paper(s) in 5 journal(s).

International Journal of Public Opinion Research

Political trust in crisis? An experimental panel study into the origins of subjective perceptions of structurally declining political trust
H Bey, T W G van der Meer
Full text
The perception that political trust is in a deep crisis, driven by structurally declining trends, seems intrinsically tied to democracy. For decades, scholars responded by studying actual trends. Yet, actual trends can only partially explain the prevalence of the trust crisis narrative. Therefore, this study shifts the focus to perceived trends by comparing the actual longitudinal changes in political trust with citizens’ retrospective perceptions of these changes. We rely on observational and novel experimental data (n = 1882) from the Dutch LISS panel to investigate these incongruencies between actual trends and perceptions over a 16-year period (2008–2024). We find evidence for a bias in public opinion as past levels of trust are generally overestimated. By probing explanations from political science, sociology, and psychology, we find that this bias is primarily political rather than personal: how people remember their trust depends on whether their party governed, and reminding them who did corrects that memory.

Journal of Elections, Public Opinion and Parties

Citizen preferences for their legislative body: evidence from the United States
Neil S. Williams, Aaron A. Hitefield, Karlee L. Kimbrell
Full text
Candidate positioning or limited choice? Voter–candidate fit in the 2024 US election
Samir Varma
Full text

Journal of Official Statistics

A Robust Measure of Wealth Inequality in Small Areas
Silvia Scarpa, Stefan Sperlich
Full text
This work provides a guide to small area estimation of the quantile ratio index (QRI) to measure wealth inequality across socio-economic or geographical groups. Wealth exhibits extreme positive skewness, rendering traditional inequality measures unreliable, as they are highly sensitive to extreme values. The QRI’s robustness to this phenomenon addresses this problem, making it particularly interesting for small area estimation problems. Relying on well-established methods, we employ a logit-transformed area-level model with bias-corrected back-transformation, validate it through model-based simulations, and apply it to Italian Survey on Household Income and Wealth data. The results confirm the expected strong heterogeneity in wealth inequality across municipalities and household groups, now based on an indicator that is much more robust than the traditional ones. This work thereby highlights the practical value of combining robust inequality measurement with small area estimation techniques for the production of reliable wealth inequality estimates in policy-relevant domains.
Testing a Targeted Refusal Conversion Strategy for an Establishment Web Survey
Ellen Laupper, Ulf-Dietrich Reips
Full text
The paper proposes a strategy for implementing an automated and interactive feature of persuasion for late and nonrespondents in establishment web surveys. The response rate for establishment web surveys is under constant pressure, and strategies to motivate small establishments are particularly costly. Consequently, the tested survey design feature, which emulates the approach of telephone follow-ups in interviewer-administered surveys, offers a potentially new, practical, and cost-effective strategy to counteract these shortcomings. A randomly selected sample of late and nonresponding establishments ( n = 3,000) of a well-established Swiss web survey of establishments was asked to indicate their primary nonresponse reason and was then randomly assigned to one of three appeal types: (1) a targeted appeal addressing their previously indicated nonresponse reason, (2) a general appeal addressing the same reasons as the study’s standard reminder letter, or (3) a main appeal addressing the main reason for nonresponse, “lack of time.” The findings suggest that the type of appeal has no significant impact on response or data quality, whereas the reason given for nonresponse and the timing within the survey period do have an impact. The implications of these findings for survey design research are discussed.
Automatically Coding Economic Activities via Semantic Search
Francesco Ortame, Mauro Bruno
Full text
Automated coding of free-text descriptions is a core challenge for National Statistical Institutes (NSIs), which must map colloquial respondent inputs to official taxonomies under strict reliability and transparency requirements. Rule-based systems, the current standard, demand extensive, ongoing human effort to author and maintain the large manual rule sets needed to cover the diversity of real world inputs. This paper evaluates embedding-based semantic retrieval as a lower-maintenance alternative, using the Italian ATECO classification as the empirical setting. We compare several knowledge base construction strategies across seven sentence transformer models (ranging from 118 million to 8 billion parameters), evaluated against 33,544 real-world queries labelled by CIRCE, Istat’s operational rule-based coder, at the 5-digit and 2-digit levels. Moderate-size models (300–700 million parameters) are competitive with much larger ones, achieving Hit@5 above 0.82 and 0.94 at the respective levels. Query length is a significant performance driver, and manual mismatch analysis reveals that roughly one in five disagreements with CIRCE reflect label noise rather than retrieval errors. Embedding-based retrieval thus offers competitive performance with no task-specific training data and a fraction of the human labour required by rule-based approaches.

Journal of Survey Statistics and Methodology

Bayesian Benchmarking Small Area Estimation via Entropic Tilting†
Shonosuke Sugasawa, Genya Kobayashi, Yuki Kawakubo
Full text
The problem of benchmarking estimation and its risk evaluation is practically important in small area estimation. While Bayesian methods have been widely adopted in small area estimation, existing benchmarking approaches are often ad hoc, such as projecting each Markov chain Monte Carlo draw to satisfy the constraint. In contrast, our work provides a unified Bayesian formulation based on entropic tilting, which offers a more principled way to define the benchmarked posterior distribution. This approach yields benchmarked point estimates together with coherent uncertainty quantification. We first introduce general Monte Carlo methods for obtaining a benchmarked posterior under hierarchical Bayesian approaches and then show that the benchmarked posterior under empirical Bayesian frameworks can be obtained in an analytical form for some small-area models. We demonstrate the usefulness of the proposed method through simulation and empirical studies.

Social Science Computer Review

Who Goes Toxic? Understanding Political Toxicity on Social Media Across Individuals and Contexts
Alon Zoizner, Nir Lotan, Sagi Pendzel, Einat Minkov
Full text
As political toxicity has become widespread on social media, scholars have sought to understand what drives it. While studies often emphasize platform-level explanations, such as algorithmic amplification, they offer limited insight into which users are more likely to produce toxic content. Even less is known about how the relationship between individual-level factors and toxicity varies across political contexts. Using data from 184,354 randomly sampled U.S. Twitter (now X) users, their original tweets (25M), and the accounts they follow, we link users’ inferred characteristics and political context to their real-life toxic behavior. Results show that Republican, male, and older users are consistently more likely to post both impolite and intolerant content, whereas education shows no clear association. We also find that state-level electoral competitiveness increases overall toxicity and, in some cases, conditions individual-level relationships. The age gap in impolite behavior significantly narrows in more competitive states, while the corresponding moderation for intolerance is only marginally significant. There is also some indication that partisan differences in impoliteness become larger in more competitive states, but this moderation is only marginally significant. Together, these results demonstrate that political toxicity is shaped jointly by users’ individual characteristics and the political contexts in which they operate.