Patience and Vaccination

Vaccination against COVID-19 and other diseases is a pressing public health issue. We hypothesize that a short-term orientation (impatience) – as it heavily discounts the future benefits of actions taken today – leads to lower rates of vaccination. Using a recently constructed, experimentally validated measure of patience, we document four results consistent with our hypothesis. First, patience alone explains a large share (21%) of the global variation in COVID-19 vaccinations across countries as of November 2021 (Study 1a; N = 76). An increase in patience of one S.D. is associated with 12 p.p. larger vaccination rates. Second, using duration models (Study 1b; 4,180 ≤ N ≤ 9,973), we demonstrate that more patient countries are quicker to reach high COVID-19 vaccination thresholds. Third, our results are not specific to the COVID-19 pandemic: in Study 2a, we show that beliefs regarding the safety and effectiveness of vaccination against swine influenza (H1N1) in 2009 are also well-explained by patience in a sample of sub-national regions of Europe (N regions = 138; N countries = 17). Fourth, in Study 2b, we show that our results are not specific to pandemics: patience also explains the global variation in infant vaccinations against 12 common diseases (N = 75).

Stage-I Shariah compliant Macaulay’s duration model testing

Purpose The purpose of this study is to develop, test and examine econometric methodology for Sharīʿah-compliant duration models of Islamic banks. Design/methodology/approach The research evaluates all existing duration models from Sharīʿah’s perspective and develops a four-stage framework for testing Sharīʿah-compliant duration models. The econometric methodology consists of multiple regression, Johansen co-integration, error correction model, vector error correction model (VECM) and threshold vector error models (TVECM). Findings Regressions analysis suggests that returns on earning assets and interbank offered rates are significant factors for calculating the duration of earning assets, whereas returns paid on return bearing liabilities and average interbank rates of deposits are significant factors for duration of return bearing liabilities. VECM suggests that short run duration converges into long run duration and TVECM suggests that management of assets and liabilities also plays a significant role that can bring about a change of about 15% in respective durations. Practical implications Sharīʿah-compliant duration models will improve risk and Sharīʿah efficiency, which will ultimately improve market capitalization and returns stability of Islamic banks in the long run. Originality/value Sharīʿah-compliant duration models testing provides insight into how various factors, namely, rates of return, benchmark rates and managerial skills of Islamic bank risk managers impact durations of assets and liabilities. It also explains the future course of action for Sharīʿah-compliant duration model testing.

Do Larger Sample Sizes Increase the Reliability of Traffic Incident Duration Models? A Case Study of East Tennessee Incidents

Incident duration models are often developed to assist incident management and traveler information dissemination. With recent advances in data collection and management, enormous achieved incident data are now available for incident model development. However, a large volume of data may present challenges to practitioners, such as data processing and computation. Besides, data that span multiple years may have inconsistency issues because of the data collection environments and procedures. A practical question may arise in the incident modeling community—Is that much data really necessary (“all-in”) to build models? If not, then how many data are necessary? To answer these questions, this study aims to investigate the relationship between the data sample sizes and the reliability of incident duration analysis models. This study proposed and demonstrated a sample size determination framework through a case study using data of over 47,000 incidents. This study estimated handfuls of hazard-based duration models with varying sample sizes. The relationships between sample size and model performance, along with estimate outcomes (i.e., coefficients and significance levels), were examined and visualized. The results showed that the variation of estimated coefficients decreases as the sample size increases, and becomes stabilized when the sample size reaches a critical threshold value. This critical threshold value may be the recommended sample size. The case study suggested a sample size of 6,500 to be enough for a reliable incident duration model. The critical value may vary significantly with different data and model specifications. More implications are discussed in the paper.

Ground-Motion Duration Prediction Model from Recorded Mexican Interplate and Intermediate-Depth Intraslab Earthquakes

ABSTRACT Predictive models for ground-motion duration of Mexican subduction interplate and intermediate-depth intraslab earthquakes are presented. The considered sites are rock sites. For the ground-motion duration models, the significant durations for ranges between 5%–75%, 5%–95%, and 2.5%–97.5% of Arias intensity are considered for the analyses. The significant duration predictive models are expressed in terms of magnitude, distance, and focal depth; this last variable is considered only for intraslab earthquakes. A total of 418 and 366 accelerograms obtained from 40 Mexican interplate and 23 intraslab earthquakes, respectively, are used. The applicability of the duration equation for subduction interplate events is restricted to moment magnitudes 5<Mw<8 and distances to the fault surface 17<R<400 km; for intraslab events, it is restricted to 5.2<Mw<8.2, 22<R<400 km, and focal depths 35<HD<75 km. The models are compared against existent models for Mexico and other regions. The analyses and comparisons indicate that using ground-motion duration models accounting for the two types of earthquakes is required and that such models should be developed for specific regions.

The implementation of the random survival forests in conflict management data: An examination of power sharing and third party mediation in post-conflict countries

Time-to-event analysis is a common occurrence in political science. In recent years, there has been an increased usage of machine learning methods in quantitative political science research. This article advocates for the implementation of machine learning duration models to assist in a sound model selection process. We provide a brief introduction to the random survival forest (RSF) algorithm and contrast it to a popular predecessor, the Cox proportional hazards model. We implement both methods for simulated time-to-event data and the Power-Sharing Event Dataset (PSED) to assist researchers in evaluating the merits of machine learning duration models. We provide evidence of significantly higher survival probabilities for peace agreements with 3rd party mediated design and implementation. We also detect increased survival probabilities for peace agreements that incorporate territorial power-sharing and avoid multiple rebel party signatories. Further, the RSF provides a novel approach for ranking of peace agreement criteria importance in predicting peace agreement duration. Our findings justify the robust interpretability and competitive performance of the random survival forest algorithm in numerous circumstances, in addition to promoting a diverse, holistic model-selection process for time-to-event political science data.