Anchoring Bias in Runoff Polls: Evidence from the 2025 Ecuadorian Election

The study examines temporal anchoring bias in recall-weighted runoff polls using three empirical strategies: (1) a meta-analysis of 12 publicly available polls, (2) a provincial-level counterfactual simulation model, and (3) microdata analysis of a face-to-face survey (CIEES). Repository Contents Meta-analysis dataset (publicly compiled data) Structured database of 12 pre-election runoff polls (February–April 2025). Variables: polling firm, fieldwork dates, mode, sample size, vote intention (valid votes), disclosed weighting procedures. Python scripts implementing fixed- and random-effects meta-analysis, heterogeneity tests (Q, I²), and pooled estimates. Counterfactual simulation materials Official provincial-level first- and second-round vote shares from Ecuador’s National Electoral Council (CNE). Synthetic provincial microdata (27 districts × 1,000 simulated cases). Python scripts implementing geographic weighting, first-round recall calibration, and migration sensitivity scenarios. Python replication code Fully documented scripts for reproducing all tables and figures in the article. Meta-analysis aggregation procedures. Counterfactual estimations and sensitivity analysis. Weighting comparisons and vote loyalty matrix generation. Effective sample size and design effect calculations. Data Availability Statement The CIEES data used in this study derive from a face-to-face survey conducted in Ecuador in 2025 and were shared with the author for academic research purposes. The author does not hold ownership rights over the dataset. Due to third-party data ownership and the protection of human subjects, the individual-level data cannot be made publicly available. Replication materials, including full Python code, documentation, and all publicly available datasets used in the analysis, are available from the author upon reasonable request.本研究采用三种实证策略，考察召回加权决胜投票中的时间锚定偏差：(1) 针对12项公开可用民调的元分析；(2) 省级层面反事实模拟模型；(3) 针对面对面调查（CIEES）的微观数据分析。 仓库所含内容 元分析数据集（公开汇编数据） 2025年2月至4月间12场选举前决胜投票的结构化数据库 变量涵盖：民调机构、实地调查时段、调查模式、样本规模、投票意向（有效票数）及公开披露的加权实施流程 用于实现固定效应与随机效应元分析、异质性检验（Q检验、I²统计量）及合并估计量计算的Python脚本 反事实模拟材料 厄瓜多尔全国选举委员会（National Electoral Council, CNE）公布的省级首轮、第二轮得票率数据 合成省级微观数据集（27个选区 × 1000个模拟样本） 用于实现地理加权、首轮召回校准及移民敏感性情景分析的Python脚本 Python复现代码 附有完整文档说明的脚本，可复现论文中的全部表格与图表 元分析聚合流程 反事实估计与敏感性分析 加权方式对比与投票忠诚度矩阵生成 有效样本量与设计效应计算 数据可用性声明 本研究使用的CIEES数据来源于2025年在厄瓜多尔开展的面对面调查，仅为学术研究目的向作者共享。作者不拥有该数据集的所有权。鉴于第三方数据权属要求及人类受试者保护规范，该个体层面数据集无法公开获取。 本研究的复现材料（含完整Python代码、文档说明及分析所用的全部公开数据集）可在作者提出合理请求后获取。