| { | |
| "original_study": { | |
| "claim": { | |
| "hypothesis": "Countries with stronger social norms (greater cultural tightness) and more capable public institutions (greater government efficiency) will experience slower COVID-19 infection growth, with the slowest growth occurring where both tightness and efficiency are high.", | |
| "hypothesis_location": "Early theory and prediction statement (p. 1-2) where the authors propose that cultural tightness and government efficiency should combine to predict slower COVID-19 growth rates.", | |
| "statement": "The regression results (p. 7) support the interactive prediction for infection growth: the tightness-by-efficiency interaction is negative and statistically significant, indicating that tightness is linked to slower infection growth particularly in countries with more efficient governments (and efficiency is likewise linked to slower growth particularly in tighter cultures).", | |
| "statement_location": "Results paragraph reporting the interaction model for infection rate and the coefficient for the tightness × government efficiency interaction (reported with b, SE, t, and p).", | |
| "study_type": "Observational" | |
| }, | |
| "data": { | |
| "source": "European Centre for Disease Prevention and Control (ECDC) daily country-level COVID-19 cases and deaths; World Bank Government Efficiency Index (2017); cultural tightness index (Gelfand et al. 2011) expanded to additional nations (Eriksson et al. 2020); covariates from IMF (GDP per capita, 2019 release), World Bank (Gini coefficient; most recent estimate per nation), and CIA World Factbook (median age, 2018 release).", | |
| "wave_or_subset": "COVID-19 case/death data downloaded for 161 nations and updated between March 21 and March 30, 2020. Infection-growth modeling begins after each country exceeds 1 case per million people. Government efficiency data available for 126 nations (2017). Cultural tightness data available for 57 nations.", | |
| "sample_size": "The paper reports 528,019 confirmed cases (including 23,672 deaths) across 141 nations as the global case/death totals used in the study framing.", | |
| "unit_of_analysis": "Country (nation).", | |
| "access_details": "The paper states that all data and code associated with the analyses are available on OSF.", | |
| "notes": "Infection rate is operationalized as a country-specific growth-rate estimate obtained by fitting a log–log model of cases-per-million over days (after exceeding 1 case per million). Mortality likelihood is measured as deaths divided by total cases. In the second-stage country-level regressions predicting infection rate, cases are weighted by the number of observations (days) used to estimate each country’s growth curve." | |
| }, | |
| "method": { | |
| "description": "The study first estimates country-specific COVID-19 infection growth rates from daily case-per-million time series and then uses country-level regressions to test whether cultural tightness and government efficiency—individually and interactively—predict cross-national variation in infection growth (and separately mortality likelihood), controlling for economic development, inequality, and median age.", | |
| "steps": [ | |
| "Download daily COVID-19 cases and deaths by country and convert cases to cases per million people.", | |
| "For each country, start the growth-rate series once the country exceeds 1 case per million people.", | |
| "Estimate each country’s infection growth rate by fitting a regression with log(cases per million) as the outcome and log(days) as the predictor.", | |
| "Compute mortality likelihood as deaths divided by total cases for each country.", | |
| "Assemble country-level predictors: government efficiency and cultural tightness, plus GDP per capita, Gini, and median age.", | |
| "Standardize the covariates (GDP per capita, Gini, median age) prior to estimation.", | |
| "Estimate an OLS regression (Gaussian) predicting infection rate and include the tightness × government efficiency interaction; weight the country-level regression by the number of observations contributing to each country’s growth estimate." | |
| ], | |
| "models": "Country-level OLS regression predicting infection rate (Gaussian) with an interaction term between cultural tightness and government efficiency; weighted by the number of daily observations used to estimate each nation’s growth curve.", | |
| "outcome_variable": "Infection rate (country-specific log-transformed growth-rate estimate of COVID-19 cases per million).", | |
| "independent_variables": "Cultural tightness; government efficiency; cultural tightness × government efficiency interaction.", | |
| "control_variables": "GDP per capita; Gini coefficient; median age (all included in the regressions; covariates standardized prior to estimation).", | |
| "tools_software": "not stated" | |
| }, | |
| "results": { | |
| "summary": "The interaction between cultural tightness and government efficiency is negative and statistically significant for infection growth, consistent with the idea that countries that are both tighter and more institutionally efficient show especially slow growth in COVID-19 cases per million during the early phase of the pandemic.", | |
| "numerical_results": [ | |
| { | |
| "outcome_name": "Infection rate (log-transformed growth rate of cases per million)", | |
| "value": "-0.17", | |
| "unit": "regression coefficient units (on the modeled infection-rate scale)", | |
| "effect_size": "interaction-term coefficient (cultural tightness × government efficiency)", | |
| "confidence_interval": { | |
| "lower": "not stated", | |
| "upper": "not stated", | |
| "level": "not stated" | |
| }, | |
| "p_value": "0.031", | |
| "statistical_significance": 1, | |
| "direction": "negative" | |
| } | |
| ] | |
| }, | |
| "metadata": { | |
| "original_paper_id": "not stated", | |
| "original_paper_title": "Cultural and Institutional Factors Predicting the Infection Rate and Mortality Likelihood of the COVID-19 Pandemic", | |
| "original_paper_code": "https://osf.io/pc4ef/", | |
| "original_paper_data": "https://osf.io/pc4ef/" | |
| } | |
| } | |
| } |