[CLAIM]
An interquartile increase in support for Trump (I.Q.R. = 20.3%) resulted in a 4.1 percentage point decrease in social distancing (95% C.I. = 3.0–5.2) [ p < 0.001].

[HYPOTHESES]
At the level of U.S. counties, support for Donald Trump in the 2016 presidential election will be negatively associated with social distancing behavior.

Hypothesis: At the level of U.S.counties,support for Donald Trump in the 2016 presidential election will be negatively associated with social distancing behavior.

The hypothesis is not stated explicitly in the text but the relationship is mentioned in the abstract, introduction and results sections.

Page 4, Results section; page 7, Table 1.

Unacast (for social distancing. Unacast measures county-level averages of distance traveled per person); American Community Survey (for socioeconomic status, operationalized by income per capita); MIT Election Data and Science Lab (political preferences, operationalized by the 2016 county-level vote share for President Trump); Census (for rurality).

Unacast: March 19–28, 2020. American Community Survey: 5-year averages from 2014–2018. MIT Election Data and Science Lab: 2016 county-level vote share. Census: 2010.

not stated; the authors thank Unacast for providing their social distancing dataset for research use, but no access details are provided.

There is a potential for omitted-variable and ecological biases due to aggregate, cross-sectional data. The data do not sample all cell-phone users and do not reflect non-users.

The authors used multivariable OLS regression to assess how socioeconomic conditions and political orientation were associated with COVID social distancing.

1. Presumably the first step was to obtain the data from Unacast, American Community Survey, MIT Election Data and Science Lab, and Census.
2. After cleaning and merging datasets, the authors had to calculate percentage point changes in average mobility.
3. Then, they conducted bivariate analyses of per capita income and the share of voters supporting President Trump with social distancing (p. 4, Results section).
4. Finally, they estimated associations between the degree of social distancing and socioeconomic
and political factors using cross-sectional OLS regressions.

social distancing (measured as change in average mobility from March 19–28, relative to matched days of pre-COVID-19 reference week).

income (county level per capita); vote share for Donald Trump (2016 elections, county level)

percentage male; percentage Black; percentage Hispanic; percentage with college degree; employment in retail; employment in transportation; employment in health, education, social services; percentage rural; age distribution (p. 3, Methods section).

Summary: An interquartile increase in support for Trump (20.3%) was associated with a 4.1-percentage-point decrease in social distancing (95% CI = 3.0–5.2, p < 0.001).

Association of County-Level Socioeconomic and Political Characteristics with Engagement in Social Distancing for COVID-19.

Hypothesis: Higher county-level support for President Trump in the 2016 election is associated with reduced engagement in social distancing during March 19–28, 2020.

Abstract (page 2: 'greater Republican orientation were associated with significantly reduced social distancing among U.S. counties') and Introduction (page 3: 'socioeconomic and political determinants of engagement in social distancing among U.S. counties').

The study finds that an interquartile increase in county-level support for President Trump in 2016 (20.3 percentage points) is associated with a 4.1 percentage point decrease in social distancing, indicating less reduction in mobility in those counties.

Results section, page 4 ('An interquartile increase in support for Trump ... resulted in a 4.1 percentage point decrease in social distancing (95% C.I. = 3.0–5.2).') and Table 1 (page 7, row 'Share of Trump voters').

Observational (cross-sectional ecological analysis using ordinary least squares regression on county-level data).

County-level social distancing data from Unacast based on anonymized cell phone location records, linked with county-level socioeconomic and political data from the American Community Survey (ACS, 2014–2018) and the MIT Election Data and Science Lab.

Cross-sectional measurement of social distancing for March 19–28, 2020, expressed as change relative to matched days in a pre-COVID-19 reference week, across U.S. counties.

3,037 U.S. counties with available mobility, socioeconomic, and political data.

County (county-level percentage-point change in average mobility).

not stated (the paper notes that Unacast provided the social distancing dataset for research use but does not describe public access procedures).

Social distancing is measured as percentage change in average distance traveled per person during March 19–28, 2020 relative to a pre-COVID-19 reference week, using data from 15–17 million anonymous cell phone users per day. Negative values indicate greater social distancing (larger mobility reductions). Analyses adjust for county sociodemographic and labor market characteristics, rurality, and state fixed effects.

The authors used cross-sectional ordinary least squares regressions to estimate how county-level socioeconomic status and political preferences, including Trump 2016 vote share, are associated with the degree of social distancing, measured as changes in average mobility during March 19–28, 2020 relative to a pre-COVID-19 reference week.

Subset data to U.S. counties with valid Unacast mobility metrics and linked ACS and MIT Election Data and Science Lab measures.

Compute county-level change in average mobility for March 19–28, 2020 relative to matched days of a pre-COVID-19 reference week as the outcome measure of social distancing.

Specify main exposure variables: county per capita income and county-level share of voters supporting President Trump in the 2016 election.

Assemble covariates: county percentages of male, Black, and Hispanic residents; age distribution; share of adults with college degrees; shares of workers in retail, transportation, and health/education/social services; and rurality, plus indicators for each state.

Estimate multivariable ordinary least squares regressions of percentage-point change in average mobility on the main exposures and covariates, including state fixed effects and age-decade controls.

Interpret the regression coefficient for county Trump vote share as the change in social distancing (percentage-point change in mobility) associated with an interquartile increase in support for Trump.

Cross-sectional ordinary least squares regression with state fixed effects for percentage-point change in average county mobility.

Percentage-point change in average county mobility during March 19–28, 2020 relative to matched days in a pre-COVID-19 reference week (negative values indicate greater social distancing).

County-level share of voters supporting President Trump in the 2016 election (continuous, modeled per interquartile-range increase).

Per capita income; percentages male, Black, and Hispanic; age distribution (percentage in each decade of life); percentage of adults with a college degree; county shares of employment in retail, transportation, and health/education/social services; percentage rural; and state fixed effects.

Summary: In multivariable models adjusting for sociodemographic characteristics, labor market composition, rurality, and state fixed effects, higher county-level support for President Trump in 2016 is significantly associated with reduced engagement in social distancing: an interquartile increase in Trump vote share corresponds to a 4.1 percentage point decrease in social distancing (less reduction in mobility).

Percentage-point change in average county mobility (social distancing) associated with Trump vote share

percentage-point change in average mobility per interquartile increase in county Trump vote share


[CLAIM]
Use of memorization moderated the effect of school-average ability on mathematical self-concept (-.089,p<0.001; ), with an effect size of -0.157

[HYPOTHESES]
The interaction of the use of memorization and school-average ability will be negative in its association with mathematical self-concept

Hypothesis: The interaction of the use of memorization and school-average ability will be negative in its association with mathematical self-concept.

p. 401, Hypotheses and Research Questions Addressed by the Present Investigation section, point 6.

Use of memorization moderated the effect of school-average ability on mathematical self-concept (-.089,p<0.001; ), with an effect size of -0.157.

p. 409, Study Methods: Cognitive and Metacognitive Learning Strategies section; also Table 3 and Table 4 (for effect size).

Program for International Student Assessment (PISA) database - Organisation for Economic Cooperation and Development (OECD). (2005b).

Students are nested within schools, and schools are nested within countries. N = 265,180 students who attended 10,221 schools in 41 countries. To enable multilevel modeling schools with 10 or fewer students were excluded from further analysis. Only students who completed math self-concept items were included. other confounding variables not considered here (e.g., school expenditure levels, teacher characteristics, and other individual student  characteristics) are likely to have an impact upon academic performance and school climate, and these likely vary greatly across the many schools included in this sample. Also, no school policy or school practice variables were included in the present investigation.

The authors used three-level multilevel regression analyses to examine how school-average mathematics ability influenced students’ mathematics self-concept and whether this relationship was moderated by 16 socioeconomic and academic self-regulation constructs.

1. The authors presumably obtained the data from the OECD technical report, cleaned it, and removed students who did not complete math self-concept items, and schools with 10 or fewer students.
2. Then they standardised the values for mathematics ability, mathematics self-concept, and all the potential moderators (including memorisation) across the entire sample.
3. A school-average mathematics ability variable was calculated by averaging each plausible value separately within each school.
4. This school-average mathematics ability variable was not restandardized, thus keeping all variables in the same metric as the individual test scores.
5. Cross-products with school-average ability were created for each potential moderator (again, including memorisation) but were not restandardized.
6. Sample weights were used to prevent biased estimates of population parameters.
7. The authors then ran three-level multilevel regression analyses (students within schools within countries) for each of the PISA plausible value of mathematics ability.
8. Then the authors averaged the regression results to get final parameter estimates that represented the overall pattern across those regressions.
9. Finally, the effect sizes comparable with Cohen’s d were calculated using Tymms’s (2004) formula.

Individual mathematics ability (linear and quadratic), school-average mathematics ability, use of memorization (moderator), and the interaction between school-average mathematics ability and use of memorization

Summary: Memorization use significantly moderated the relationship between school-average ability and mathematical self-concept, weakening this association (–0.089, p < 0.001; effect size = –0.157).

standard deviation (the DV was standardised)

Big-Fish-Little-Pond Effect: Generalizability and Moderation—Two Sides of the Same Coin.

Hypothesis: The interaction of the use of memorization and school-average ability will be negatively related to mathematics self-concept

Page 12 (Hypotheses and Research Questions Addressed
by the Present Investigation) Hypothesis 6

The study finds that the Memorization × School-Average Mathematics Ability interaction is statistically significant and negative (b = -0.089), indicating that students who use memorization more experience a stronger negative effect of high school-average ability on mathematical self-concept.

Table 3 (page 21), row 'School-Average Ability × Moderator' under Memorization (value = -0.089), and text on p. 20 (Study Methods: Cognitive and Metacognitive Learning Strategies) stating it had a 'statistically significant negative association.'

Programme for International Student Assessment (PISA) 2003 dataset.

PISA 2003 mathematics assessment sample.

Individual student (nested within schools and countries in a multilevel model).

Analyses include students with non-missing data for mathematical self-concept, memorization strategy use, individual mathematics ability, and school-average mathematics ability.

The authors used multilevel modeling to test whether memorization strategy use moderates the big-fish–little-pond effect (the negative association between school-average mathematics ability and mathematical self-concept).

Identify students with complete data on mathematical self-concept, memorization strategy use, individual ability, and school-average mathematics ability.

Compute school-average mathematics ability for each school based on aggregated achievement scores.

Specify a multilevel model with students nested within schools and countries.

Include memorization as a student-level predictor and school-average ability as a school-level predictor.

Add the Memorization × School-Average Ability interaction term to test moderation.

Estimate the model and interpret the interaction coefficient from Table 3.

Multilevel linear regression (three-level hierarchical model with students nested within schools nested within countries).

Memorization strategy use, School-average mathematics ability, Memorization × School-Average Ability interaction.

Individual mathematics ability; intrinsic motivation; instrumental motivation; self-efficacy; elaboration; control strategies; anxiety; competitiveness; cooperativeness; sense of belonging; teacher–student relations; gender; family SES; school-average SES; and country-level effects (three-level model structure).

Summary: The Memorization × School-Average Ability interaction is statistically significant and negative, indicating that memorization strengthens the negative association between attending a high-ability school and mathematical self-concept.

Mathematical self-concept (interaction effect of Memorization × School-Average Mathematics Ability)

Big-Fish–Little-Pond Effect: Generalizability and Moderation—Two Sides of the Same Coin


[CLAIM]
The positive sign of democracy index indicates that more democratic countries are affected more by the disease. coefficient = 86.76467, p = 0.0001.

[HYPOTHESES]
At the country level, the democracy index will be positively associated with the total number of confirmed infections per one million people.

3/input/replication_data/Hossain 2020 - Replication Analysis.do

Hypothesis: At  thecountry level,the democracy index will be positively associated with the total number of confirmed infections per one million people.

p. 8, Determinants of Infectious Disease Spread section; it also mentioned in the abstract.

European Centre for Disease Prevention and Control (ECDC) for data on coronavirus and population; meteoblue.com for the average yearly temperature; indexmundi.com for the average precipitation; Databank of the World Bank for the openness data; Wikipedia for the scores of democracy index.

coronavirus total infection cases: from 31 December 2019 to 03 April 2020; population data: year 2018; Wikipedia data: year 2019.

not stated; all sources seem to be open access.

Some countries were excluded (it is not mentioned which ones) due to unavailability of all data. Total cases of infection were converted to cases per one million population to capture the population effect. The economic and social variables, viz. openness, democracy index and population density were measured on yearly basis. The explanatory variables are static in nature.The estimated models suffer from heteroscedasticity and violate the normality assumption. Also, the model of interest (Model 1) is not free from autocorrelation.

The authors check linkages between the severity of COVID-19 infections and environmental, economic, and social factors across countries.

1. The process is not described explicitly but it can be infered that: the authors collected the data from relevant sources.
2. After cleaning they converted total infection cases to cases per one million population.
3. Then the data was merged and the model estimated with least squares method.

Y = cases of infection per one million people on 03 April 2020 by countries

yearly average temperature, yearly average precipitation, openness (ratio of international trade to GDP), democracy index (proxy for social cohesion) , population density

Excel and STATA for manipulation and transformation of data; EVIEWS for estimation.

Summary: The results show that higher democracy scores are associated with greater COVID-19 impact (coefficient = 86.76, p = 0.0001).

Is the spread of COVID-19 across countries influenced by environmental, economic and social factors?

Hypothesis: Higher yearly average temperature is associated with fewer confirmed cases of COVID-19 infection per million population across countries.

Data sources and characteristics (p. 9) / Modeling Approach, p. 11)

The study finds that yearly average temperature has a statistically significant negative association with cases of COVID-19 infection per million people, such that countries with higher average temperatures tend to have fewer confirmed cases per million.

Abstract (p. 1) and Estimation Results and Discussion / Table 1 (p. 14, Temperature row).

Country-level data on confirmed COVID-19 cases and population from the European Centre for Disease Prevention and Control (ECDC); yearly average temperature from meteoblue.com; average precipitation from indexmundi.com; economic openness (trade as a percentage of GDP) from the World Bank Databank; and democracy index scores from the Economist Intelligence Unit’s Democracy Index 2019, as compiled on Wikipedia.

Cross-sectional snapshot of total confirmed COVID-19 cases by country on 03 April 2020, converted to cases per one million population, for 163 countries.

not stated (the paper lists data sources but does not describe specific access procedures or restrictions for these datasets).

Total confirmed cases of COVID-19 by country are taken from 31 December 2019 to 03 April 2020, but only cases per one million people on 03 April 2020 are used as the dependent variable. Environmental, economic, and social explanatory variables (temperature, precipitation, openness, democracy index, population density) are measured on a yearly basis. Some countries are excluded due to missing data on one or more of these variables.

The study uses cross-sectional ordinary least squares regressions to examine whether country-level environmental, economic, and social characteristics—including yearly average temperature—are associated with confirmed COVID-19 cases per million population across countries.

Collect total confirmed COVID-19 cases and 2018 population by country from the ECDC and compute cases per one million population for 03 April 2020 as the dependent variable Y.

Merge country-level yearly average temperature, average precipitation, economic openness (international trade as a percentage of GDP), democracy index scores, and population density into the dataset for the set of countries with complete data.

Specify an initial linear regression model regressing cases of infection per million (Y) on temperature, precipitation, openness, democracy index, and population density.

Estimate this model with least squares and identify that precipitation and population density are not statistically significant predictors.

Re-estimate the model excluding precipitation and population density, retaining yearly average temperature, openness, and democracy index as predictors (Model 1, no lag).

Conduct further models including a transformed lagged dependent variable via residuals to account for past infection levels (Models 2 and 3), while keeping temperature, openness, and democracy as predictors.

Use Excel and STATA for data manipulation and EVIEWS software to estimate the regression models.

Ordinary least squares linear regression of cases of infection per million population on yearly average temperature, economic openness, and democracy index (with additional specifications including a residual-based proxy for lagged infections).

Total confirmed cases of COVID-19 infection per one million people in each country on 03 April 2020.

Yearly average temperature of countries (X1, measured as country-level average temperature).

Economic openness (international trade as a percentage of GDP); democracy index score; and, in extended lag models, the residual term v_i derived from regressing lagged cases on the explanatory variables.

Excel and STATA for data manipulation and transformation; EVIEWS for estimation of the regression models.

Summary: Across the reported regression models, yearly average temperature is negatively and significantly associated with COVID-19 cases per million.

Cases of COVID-19 infection per one million people (effect of yearly average temperature)

unstandardized OLS regression coefficient (change in cases per million per one-unit increase in yearly average temperature)


Hypothesis: Women will express more concern about COVID-19 than men.

the abstract, also mentioned in the Summary section, p. 10.

In detail, 62.1% agree (answering either with“strongly agree” or“agree” onthe5-pointLikert Scale) that they are worriedabout COVID-19 in general (women=68.2%, men=55.7%, p<.01).

There is no specific subset but the data were collected from the 19th to the 23rd of March 2020.

no explicit mention in the text but the panel provider was responsible for data collection, so the data was probably sent to the authors

The authors analysed a representative online survey of German adults to examine gender, age, and regional differences in concern, fear, and coping strategies related to COVID-19.

1. The data was collected by an external panel provider, then cleaned.
2. All respondents who took less than seven minutes to complete the questionnaire were removed.
3. Then descriptives were calculated and reported.

the text doesn’t explicitly mention the model, but probably it was chi-squared

Summary: About 62% of the respondents reported being worried about COVID-19, with women expressing more concern (68.2%) than men (55.7%) (p < .01).

COVID-19: Risk perception and Coping strategies. Results from a survey in Germany.

Hypothesis: Women are more concerned about COVID-19 than men.

Gender differences in concern are reported in the Results section under 'Fear', page 5, where women show higher agreement that they are worried about COVID-19.

The study finds that women report significantly higher concern about COVID-19 than men, with 68.2% of women agreeing that they are worried about COVID-19 compared to 55.7% of men (p < .01).

Online access panel survey of adults in Germany conducted by an ISO-certified panel provider.

Respondents were quota-sampled to be online-representative by gender, age, and federal state. All survey questions were mandatory. Respondents who completed the survey in under seven minutes were removed.

The study measured concern about COVID-19 using Likert-scale items and compared responses between women and men to assess gender differences in expressed concern.

Administer a mandatory-question online survey to adult respondents in Germany.

Measure concern using the item: 'The COVID-19 worries me' on a 5-point Likert scale.

Calculate the percentage of respondents agreeing ('strongly agree' or 'agree') by gender.

Compare percentages between women and men and report statistical significance.

not stated (results reported as descriptive percentages with significance testing).

Agreement with the statement 'The COVID-19 worries me' (5-point Likert scale, summarized as percentage agreeing).

not stated (no regression or adjusted model is reported).

Summary: Women express significantly more concern about COVID-19 than men, as measured by agreement with the statement 'The COVID-19 worries me.'

Percentage agreeing they are worried about COVID-19

difference in proportions (68.2% vs 55.7%)

COVID-19: Risk perception and coping strategies. Results from a survey in Germany.


[CLAIM]
In  terms  of  disease  detection,  more  educated  respondents  have  a  higher probability of being diagnosed, but only conditional on being in poor general health (marginal effect for Years of Education=-0.00867, SE=0.00420, significantat 5% level).

[HYPOTHESIS]
Among the sample of respondents in poor general health who were found to be hypertensive during a screening, the probability of being undiagnosed decreases with education

5/input/replication_data/Kim & Radoias 2016 - Replication Analysis.do

Hypothesis: Among the sample of respondents in poor general health who were found to be hypertensive during a screening, the probability of being undiagnosed decreases with education.

the abstract, it is also discussed in sections: 1. Introduction, 2. Theoretical framework.

In  terms  of  disease  detection,  more  educated  respondents  have  a  higher probability of being diagnosed, but only conditional on being in poor general health (marginal effect for Years of Education=-0.00867, SE=0.00420, significant at 5% level).

Table 2, row: Years of Education, column: Respondents in poor health.

Indonesian Family Life Survey (IFLS) fielded by the RAND corporation in collaboration with the Center for Population and Policy Studies of the University of Gadjah Mada and Survey METER.

fourth wave, the data were collected between November 2007 and April 2008.

hypertensive adults (over 45 years of age in 2007)

There are no details provided except for the information that the IFLS is a publicly available data set.

Blood pressure was measured three times by trained nurses. The first reading was dropped and average of last two used to construct the hypertension variable. Under-diagnosed individuals are those hypertensives at survey but never diagnosed by a doctor. Respondents’ health variablw was constructed using self-reported health status. It was on a 1–4 scale and later dichotomized into ‘good’ vs. ‘poor’ groups. Per capita expenditures (PCE) was used as proxy for household income.

The authors analyzed data from the fourth wave of the IFLS, focusing on adults over 45 who were found hypertensive during clinical screenings. They examined how education, time preferences, and other socio-economic factors influence the likelihood of being hypertensive but undiagnosed.

1. The authors got access to the publicly available Indonesian Family Life Survey dataset.
2. Then they restricted the sample to hypertensive adults aged 45 and older.
3. Then, individuals who reported never having been diagnosed with hypertension by a doctor were coded these as under-diagnosed.
4. The sample was split into those in good health (very healthy or somewhat healthy) and those in poor health (unhealthy or somewhat unhealthy).
5. Adding explanatory variables including years of education, time and risk preference parameters, age, age squared, per capita household expenditures, distance to nearest health center, and sex, the authors estimated separate probit models for the two subsamples (good vs. poor general health), using the binary under-diagnosis variable as the dependent variable.
6. Finally, marginal effects for all explanatory variables were computed and reported.

being hypertensive but not previously diagnosed (binary yes = 1, no = 0)

education (measured in years of formal education), age, age squared (to allow for possible non-linear effects), individual risk and time preferences, the distance from the closest health center (to proxy for the ease of access to medical care), household per capita expenditures (PCE), and a sex dummy

Summary: More educated respondents were more likely to be diagnosed with the disease, but only among those in poor general health (marginal effect for Years of Education=-0.00867, SE=0.00420, significantat 5% level).

Education, individual time preferences, and asymptomatic disease
detection.

Hypothesis: For hypertensive adults in good general health, education will not significantly reduce the probability of being under-diagnosed with hypertension.

Section 2. Theoretical framework (page 3), where the authors state that they expect 'significantly smaller (possibly zero or even negative) effects of education for generally healthy people than for unhealthy people'.

The study finds that among hypertensive adults who report good general health, years of education have a small and statistically nonsignificant association with the probability of being under-diagnosed, indicating that education does not meaningfully reduce under-diagnosis in this group.

Section 4. Disease detection results (page 5), where the authors note that for individuals in good health 'the education level does not matter at all,' and Table 2 (page 5), column 'Respondents in good health', row 'Years of Education' (marginal effect = 0.00295, standard error = 0.00206, no significance stars).

IFLS Wave 4 (2007-2008), restricted to adults over age 45 who were hypertensive based on survey blood pressure measurements.

4,209 hypertensive adults (1,793 men and 2,416 women); disease-detection probit subsamples of 3,145 respondents in good general health and 1,064 respondents in poor general health.

The IFLS is described as a publicly available dataset that has received IRB approval at RAND and in Indonesia.

Hypertension status is based on nurse-measured blood pressure (average of the second and third readings), using WHO cutoffs (systolic ≥140 or diastolic ≥90). Under-diagnosed respondents are those found hypertensive in the IFLS measurements who reported never having been diagnosed with hypertension by a doctor. General health status (GHS) is self-rated on a 1-4 scale and used to split the sample into 'good' (very healthy or somewhat healthy) and 'poor' (unhealthy or somewhat unhealthy) general health groups.

The authors estimate probit models of hypertension under-diagnosis using IFLS data, examining how education and individual time preferences relate to the probability of being under-diagnosed, separately for respondents in good versus poor general health.

Identify adults (over age 45) in IFLS Wave 4 who are hypertensive based on nurse-measured blood pressure following WHO cutoffs.

Classify respondents as 'under-diagnosed' if they are hypertensive in the survey measurements but report never having been diagnosed with hypertension by a doctor.

Obtain self-rated general health status (GHS) and split the hypertensive sample into two groups: respondents in good general health (very healthy or somewhat healthy) and respondents in poor general health (unhealthy or somewhat unhealthy).

Construct explanatory variables including years of education, household per capita expenditures, time preference, risk preference, distance to the nearest health center, age, age squared, and sex.

Estimate probit models of the probability of being under-diagnosed for the full sample and separately for the good-health and poor-health subsamples, and report marginal effects.

Interpret the marginal effect of years of education on under-diagnosis within the good-health subsample to assess whether education significantly affects disease detection for generally healthy hypertensive adults.

Probit regression models of the probability of being under-diagnosed with hypertension, reporting marginal effects for the full sample and for subsamples defined by general health status.

Indicator variable equal to 1 if a respondent is hypertensive based on IFLS blood pressure measurements but has not previously been diagnosed with hypertension by a doctor (i.e., under-diagnosed), conditional on being in good general health.

Years of formal education completed by the respondent.

Household per capita expenditures (log PCE); time preference category; risk preference category; distance to the closest health center; age; age squared; and a female dummy indicator.

Summary: In the subsample of hypertensive adults who report good general health, years of education have statistically nonsignificant marginal effect on the probability of being under-diagnosed.

Probability of being under-diagnosed with hypertension (good general health subsample, effect of years of education)

marginal effect on probability of being under-diagnosed per additional year of education

not stated (no significance stars are reported for this coefficient in Table 2, indicating nonsignificance at conventional levels).


[CLAIM]
Overall, most individuals favor either scientific or religious ways of understanding, but many scientifically inclined individuals prefer certain religious accounts. Members of the post-secular category were significantly less likely than members of the traditional group to respond that humans evolved from other animals (3 percent, significant at p < 0.05 on a two-tailed test).

[HYPOTHESIS]
Respondents with a post-secular perspective on science and religion will be less likely than respondents with a traditional perspective on science and religion to respond that humans evolved from other animals.

it is discussed in Theoretical Perspectives: Conflict between Science and Religion.

Table 2, Conditional Means by Latent Class, Post-Secular; also discussed in Results: Perspectives on Science and Religion.

the survey is referenced in the article but no other access details are provided: Smith, Tom W., Peter Marsden, Michael Hout, and Jibum Kim. 2011. General Social Surveys, 1972–2010 [machine-readable data file]. Principal Investigator, Tom W. Smith; Co-Principal Investigator, Peter V. Marsden; Co-Principal Investigator,  Michael Hout; Sponsored by National Science Foundation, NORC. Chicago: National Opinion Research Center [producer]; Storrs, CT: The Roper Center for Public Opinion Research, University of Connecticut [distributor].

Accounting for the survey’s split-ballot design and missing cases, the full sample comprises 2,901 respondents (1,563 from 2006; 988 from 2008; and 350 from 2010). The Post-Secular latent class, used for the evolution item result, includes 622 respondents.

Post-Secular respondents were identified via latent class analysis of GSS science and religion items; their responses to human evolution were compared to Traditional respondents using two-tailed tests.

1.Combine GSS waves to get the whole dataset.
2.Select survey items measuring science knowledge, attitudes, and religious beliefs.
3.Conduct latent class analysis to identify groups within respondents.
4.Assign respondents to latent classes based on based on their greatest posterior probability of class membership.
5.Define the outcome variable as the binary response to the question: Humans evolved from earlier species of animals (1 = correct, 0 = incorrect).
6.Compare the Post-Secular latent class to the Traditional class on the evolution item using two-tailed t-tests.

Binary response to Humans evolved from earlier species of animals

post-secular (latent class assigned via LCA)

LCA: Mplus; descriptive and regression analyses: Stata

Human beings developed from earlier species of animals

proportion (in the table, 3% in the text)

Traditional, Modern, and Post-Secular Perspectives on Science and Religion in the United States.

Hypothesis: Post-secular individuals are less likely than modern individuals to believe humans evolved from earlier species.

The descriptive comparison appears in the Results section (Table 2) where evolution beliefs are summarized for each latent perspective group.

The study reports that only 3% of post-secular individuals agree that humans evolved from earlier species, indicating substantially lower acceptance of human evolution among post-secular respondents.

Belief in human evolution is measured by agreement with the statement that humans evolved from earlier species. Respondents are classified into three latent perspectives—traditional, modern, and post-secular—based on latent class analysis using religion and science belief indicators.

The authors used latent class analysis to identify three worldview groups—traditional, modern, and post-secular—and then descriptively compared the percentage in each group agreeing that humans evolved from earlier species.

Identify indicators of scientific and religious beliefs in the GSS dataset.

Estimate a latent class model to classify respondents into traditional, modern, and post-secular perspectives.

Extract predicted class membership for respondents.

Calculate descriptive percentages of agreement with the statement that humans evolved from earlier species for each latent class.

Compare evolution-belief percentages between post-secular and modern perspectives.

Latent class analysis used for group classification; the evolution comparison itself is descriptive and not based on regression modeling.

Agreement that humans evolved from earlier species (percentage agreeing).

Latent perspective membership (post-secular vs. modern).

not stated (no regression model is used for this descriptive comparison).

Summary: Post-secular individuals show substantially lower acceptance of human evolution than modern individuals, with only 3% agreeing that humans evolved from earlier species.


[CLAIM]
State-level carbon emissions and average working hours have a strong, positive relationship, which holds across a variety of model estimation techniques and net of various political, economic, and demographic drivers of emissions. Specifically, they find that, over time, a 1 percent increase in average working hours per worker is associated with a 0.668 percent increase in emissions, holding all else constant

[HYPOTHESIS]
Average working hours per worker in a state will be positively associated with carbon emissions.

7/input/replication_data/Fitzgerald 2018 Script_clean v2.R

it is discussed in the abtsract and the introduction

State-level carbon emissions and average working hours have a strong, positive relationship, which holds across a variety of model estimation techniques and net of various political, economic, and demographic drivers of emissions. Specifically, they find that, over time, a 1 percent increase in average working hours per worker is associated with a 0.668 percent increase in emissions, holding all else constant.

Results section, p. 1862; also Table 4 model 1.

carbon emissions: US Environmental Protection Agency (2016);
working hours, labor productivity, and employed population: US Bureau of Labor Statistics (BLS) Current Employment Statistics (CES) database (2016);
GDP, and manufacturing as a percentage: US Department of Commerce Bureau of Economic Analysis (2016);
total population size: US Census Bureau 2016;
state’s energy production: EIA’s State Energy Database System (2016);
working-age population, and average household size: US Census Bureau’s American Community Survey (2017);

50 states (350 total state-year observations)

carbon emissions: Currently, the EPA website has been updated and no longer includes access to these data. However, it is possible to access the files through the January 19, 2017, snapshot version of the webpage (https://19january2017snapshot.epa.gov/statelocalclimate/state-energy-co2-emissions_.html). The lead author of this study will share these data upon request;
working hours, labor productivity, and employed population: there's a link in references (https://www.bls.gov/ces/) but no other access details are mentioned;
GDP, and manufacturing as a percentage: there's a link in references (https://www.bea.gov/itable/index.cfm) but no other access details are mentioned;
total population size: there's a link in references (https://www.census.gov/en.html) but no other access details are mentioned;
state’s energy production: there's a link in references (https://www.eia.gov/state/seds/) but no other access details are mentioned;
working-age population, and average household size: there's a link in references (https://www.census.gov/programs-surveys/acs/data.html) but no other access details are mentioned;

All non-binary variables are transformed into logarithmic form. For such variables, the regression models estimate elasticity coefficients where the coefficient for the independent variable is the estimated net percent change in the dependent variable associated with a 1 percent increase in the independent variable. The working hours data cover all nonfarm private employees, but exclude public employees. The effect of working time on environmental outcomes is considered as a scale effect. The scale effect is measured as working time’s contribution to GDP. The authors test for the scale effect by disaggregating GDP into three parts: working hours, labor productivity, and employment to population ratio. In Model 1 they include both labor productivity and employed population percentage as the other components of GDP. Labor productivity is measured as GDP per hour of work. Employed population percentage is measured as the employed population of a state divided by its total population. The composition effect is measured net of GDP. All continuous variables are logged (ln). All models are calculated with AR(1) correction. All models contain unreported year-specific intercepts and unreported unit-specific intercept.

The study evaluates the relationship between average working hours per worker and state-level carbon dioxide emissions across U.S. states, using panel data to test a positive association over time.

1. Collect the data.
2. Transform all non-binary variables into logarithmic form.
3. Specify a model to examine the scale effect of average weekly working hours per worker on state-level carbon dioxide emissions.
4. Include control variables: GDP per hour, employed population percentage, total population, energy production, manufacturing as a percentage of GDP, average household size, and working-age population percentage (ages 15–64).
5. Estimate two-way fixed effects models with both state-specific and year-specific intercepts
6. Apply Prais–Winsten estimation with panel-corrected standard errors (PCSEs) to account for heteroskedasticity and contemporaneous correlation
7. Correct for first-order autocorrelation (AR(1)) within panels.

fixed effects regression (Prais-Winsten model with panel corrected standard errors)

working hours, GDP per hour, employed population percentage

total population, manufacturing, state's energy production, working-age, average household size

Summary: Results show a strong positive association between average working hours and state-level carbon emissions; a 1% increase in working hours corresponds to a 0.668% increase in emissions, holding other factors constant (SE = 0.179).

% (increase in emissions per 1 percent increase in average working hours)

Working Hours and Carbon Dioxide Emissions in the United States, 2007–2013.

Hypothesis: Higher average weekly working hours at the U.S. state level will be associated with higher carbon dioxide emissions from fossil fuel combustion.

Introduction and Literature review, which motivate examining the relationship between working hours and emissions (pages 1–3).

The study finds that, over time, a 1 percent increase in average working hours per worker in a state is associated with a 0.668 percent increase in state-level CO2 emissions from fossil fuel combustion, net of other political, economic, and demographic drivers of emissions.

State-level carbon dioxide emissions from fossil fuel combustion from the U.S. Environmental Protection Agency (EPA), working hours and labor statistics from the U.S. Bureau of Labor Statistics Current Employment Statistics (CES), GDP and manufacturing share from the U.S. Bureau of Economic Analysis, energy production from the U.S. Energy Information Administration’s State Energy Data System, and demographic variables (population, household size, working-age share) from the U.S. Census Bureau and American Community Survey. State environmentalism scores come from Dietz et al. (2015).

Annual state-level data for all 50 U.S. states from 2007 to 2013, forming a balanced panel.

350 state-year observations (50 states observed annually over 7 years, 2007–2013).

The authors note that the EPA state CO2 emissions files were obtained from an earlier snapshot of the EPA website and that the lead author will share these data upon request; other data sources (BLS, BEA, EIA, Census, ACS) are described as coming from publicly available databases.

All non-binary variables are log-transformed. The dependent variable is total state-level CO2 emissions from fossil fuel combustion in million metric tons. Average weekly working hours are for nonfarm private employees only.

The authors construct a balanced panel of the 50 U.S. states from 2007 to 2013 and estimate fixed-effects, random-effects, and hybrid panel regression models to assess whether average weekly working hours per worker are positively associated with state-level carbon dioxide emissions from fossil fuel combustion, net of economic, demographic, political, and energy-related controls.

Assemble annual state-level data for all 50 U.S. states from 2007 to 2013, including CO2 emissions from fossil fuel combustion, average weekly working hours per worker, GDP per capita, GDP per hour, employment-to-population ratio, population, manufacturing share of GDP, state energy production, average household size, working-age population percentage, state environmentalism scores, and census region.

Log-transform all non-binary variables so that coefficients can be interpreted as elasticities (percent changes).

Specify panel regression models with total CO2 emissions (logged) as the dependent variable and average weekly working hours (logged) as the main independent variable.

Estimate two-way fixed-effects Prais–Winsten models with AR(1) corrections, including state-specific and year-specific intercepts, to assess the scale effect of working hours (models that also include GDP per hour and employed population percentage) and the composition effect (models that include GDP per capita instead of its components).

Estimate random-effects panel models with year-specific intercepts, AR(1) correction, state environmentalism, and census-region dummy variables as additional controls to assess robustness.

Conduct robustness checks using hybrid models that include both unit-specific means and deviations for time-varying covariates, and sensitivity analyses excluding one state at a time and excluding states experiencing recent fracking booms.

Interpret the elasticity coefficient for logged average working hours as the percent change in CO2 emissions associated with a 1 percent change in working hours, net of controls.

Two-way fixed-effects panel regression models with Prais–Winsten estimation and AR(1) correction; random-effects panel regression models with AR(1) correction; and hybrid models that decompose time-varying covariates into unit-specific means and deviations.

Log of total state-level carbon dioxide emissions from fossil fuel combustion (million metric tons CO2).

Log of average weekly working hours per worker in each state.

Log GDP per hour; log employed population percentage; log GDP per capita (in composition-effect models); log total population; log state energy production; log manufacturing as a percentage of GDP; log average household size; log working-age population percentage (15–64); state environmentalism index; and census region dummy variables (Midwest, South, West, with Northeast as the reference category), along with state and year fixed effects in the fixed-effects models.

Summary: Across fixed-effects, random-effects, and hybrid panel models, average weekly working hours are positively and significantly associated with state-level CO2 emissions from fossil fuel combustion. In the preferred fixed-effects scale model, a 1 percent increase in average working hours per worker is associated with a 0.668 percent increase in emissions, net of productivity, employment, population, energy production, manufacturing share, household size, and working-age population. Similar positive and significant elasticities are found in other model specifications, supporting the conclusion that longer working hours contribute to higher emissions at the state level.

Log total CO2 emissions from fossil fuel combustion (elasticity with respect to average weekly working hours, fixed-effects scale model)

elasticity coefficient (percent change in CO2 emissions associated with a 1 percent increase in average weekly working hours)

panel regression elasticity coefficient = 0.668 (model 1)


[CLAIM]
a very high subsidy (such as the one under consideration by the international community) dramatically increases access, but nearly one-half of subsidized pills go to patients with-out malaria. The coefficient for “Any ACT subsidy” is 0.187 with robust standard errors clustered at the household level of 0.038, significant at the 1 percent level

[HYPOTHESIS]
ACT [artemisinin combination therapies] subsidies induce take-up of ACT

8/input/replication_data/Cohen et al 2015 - Replication Analysis.do

discussed in the abstract and introduction

a very high subsidy (such as the one under consideration by the international community) dramatically increases access, but nearly one-half of subsidized pills go to patients with-out malaria. The coefficient for “Any ACT subsidy” is 0.187 with robust standard errors clustered at the household level of 0.038, significant at the 1 percent level.

The experimental data are presumably stored by IPAKenya field officers who collected the data or one of the authors. There is a note on the first page that there are additional materials at: http://dx.doi.org/10.1257/aer.20130267, but no mention if the repository contains the actual data or the code.

The study was conceived and implemented in 2008–2009. The study to included three subsidy levels 80, 88, and 92 percent. The experiment was conducted in the districts of Busia, Mumias, and Samia in Western Kenya between May and December of 2009. If more than one household member got sick simultaneously, the authors included all concurrent first episodes, and therefore clustered the standard errors in all illness episode regressions at the household level.

The study evaluates the impact of randomized subsidies for artemisinin combination therapies (ACTs) on ttake-up of ACTs, using experimental data and regression analysis to estimate effects on ACT access and targeting.

1. The authors selected four rural market centers in Western Kenya and partnered with one drug shop in each; they sampled all households within a 4-kilometer catchment radius, excluding areas near health facilities.
2. They administered a baseline household survey and distributed two vouchers for artemisinin-based combination therapies (ACTs).
3. They randomly assigned households using a computerized algorithm (stratified by drug shop, distance quartile, and presence of children) to one of two groups: (a) No Subsidy: Unsubsidized ACTs at the market price. (b) ACT Subsidy: ACTs discounted at 80%, 88%, or 92% subsidy levels.
4. Then, they recorded voucher redemptions and treatment details for illness episodes at participating drug shops over a four-month period.
5. Finally, they conducted an endline household survey approximately four months after baseline to document illness episodes, treatment-seeking behavior, and medication use.
6. Having all the data, they estimated the causal effect of ACT subsidies on ACT access using OLS regressions with household-clustered robust standard errors and strata fixed effects.

pooled dummies for ACT subsidy levels (80%, 88%, 92%)

household head age and a full set of strata dummies (drug shop, household’s distance to the drug shop (in quartiles), presence of children in the household)

Price Subsidies, Diagnostic Tests, and Targeting of Malaria Treatment: Evidence from a Randomized Controlled Trial.

Hypothesis: Providing ACT at a subsidized price increases the likelihood of taking ACT.

The expectation is developed in the discussion of the price-intervention design and the motivation for assessing whether lowering the ACT price increases medication uptake in Section II: Theoretical Framework.

The regression results indicate that the presence of an ACT subsidy has a positive and statistically significant effect on obtaining ACTs; the estimated coefficient is 0.187.

Regression results table 2: coefficient on the indicator for any ACT subsidy reported as 0.187 with a standard error of 0.038, significant at the one-percent level.

Experimental (Randomized Controlled Trial).

Data collected by the research team during the ACT price-subsidy randomized controlled trial implemented in Western Kenya.

Individual household illness episode or voucher recipient.

The subsidy level was randomized, creating exogenous variation in the cost of ACT to households. Standard errors are clustered at the household level.

The study estimates the causal influence of subsidizing ACTs on household take-up by randomly varying ACT prices and regressing ACT acquisition on the subsidy indicator.

Randomly assign different ACT price levels to households through voucher offers.

Observe whether each household obtained ACT following an illness episode.

Construct an indicator variable for whether the ACT was subsidized in that household’s voucher assignment.

Estimate a linear probability model of ACT take-up on the subsidy indicator.

Cluster standard errors at the household level to account for repeated observations within households.

Linear probability regression of ACT acquisition on the indicator for receiving an ACT subsidy.

Indicator equal to 1 if the household acquired ACT for an illness episode.

Indicator for assignment to any ACT subsidy.

Summary: Assignment to an ACT subsidy significantly increases the probability that households obtain ACTs. The estimated effect size is positive and statistically significant at the one-percent level.

change in probability of obtaining ACT associated with subsidy assignment


[CLAIM]
The authors find that as the number of parties in the system increases, party dispersion increases and the effect is statistically significant for both policy dimensions (coefficient on log count of parties in system term = 0.39, robust SE clustered by country = 0.14, coefficient falls within a 95% confidence interval)

[HYPOTHESIS]
On the economic policy dimension, the number of parties in the party system is positively associated with policy dispersion.

section Predictions of spatial theory, it is also discussed in the abstract and introduction

The authors find that as the number of parties in the system increases, party dispersion increases and the effect is statistically significant for both policy dimensions (coefficient on log count of parties in system term = 0.39, robust SE clustered by country = 0.14, coefficient falls within a 95% confidence interval).

Table 4, Economic Policy Model 1a, Log count of parties in system.

Comparative manifesto project (CMP) - the data was published in Ian Budge, Hans-Dieter Klingemann, Andrea Volkens, Judith Bara and Eric Tanenbaum, Mapping Policy Preferences: Estimates for Parties, Electors, and Governments 1945–1998 (Oxford: Oxford University Press, 2001).

country-election (party system per one election in established parliamentary democracies)

the CMP data was published so, most likely, it is open access

Parties were included if they gained at least 1% of parliamentary seats in two consecutive elections. Parties were dropped if below 1% in three consecutive elections. Data was pooled across countries to create a shared two-dimensional policy space. The number of parties is measured both by a simple count of parties meeting the 1% seat threshold and by the effective number of parties based on seat shares. Party dispersion is measured as the distance between the two most extreme parties along each policy dimension (how far apart political parties are from each other in terms of their policy positions).

The study examines how the number of parties in a political system affects party dispersion in a two-dimensional policy space (economic and social dimensions).

1. Select twenty established parliamentary democracies (Australia, Austria, Belgium, Canada, Denmark, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, New Zealand, Portugal, Spain, Sweden, Switzerland, United Kingdom).
2. Include parties with at least 1% of seats in parliament in two consecutive elections.
3. Exclude parties below 1% in three consecutive elections.
4.Compute number of parties.
4. Define two policy dimensions (economic and social) using specific CMP categories (p. 814., note 37).
5. Conduct principal components analysis (PCA) to estimate party positions along each dimension.
6. Calculate party dispersion as the distance between the two most extreme parties on each dimension within each country–election.
7. Regress dispersion on the log of number of parties, with robust standard errors clustered by country.

ordinary least squares regression of log party dispersion on log count of parties and electoral rules

log of the count of parties in the system, electoral rules (dummy: 1 = single-member district, 0 = proportional)

Summary: As the number of parties in the system increases, party dispersion increases and the effect is statistically significant (coefficient on log count of parties in system term = 0.39, robust SE clustered by country = 0.14, coefficient falls within a 95% confidence interval).

not stated explicitly; a 95% confidence interval indicates statistical significance.

The Spatial Structure of Party Competition: Party Dispersion within a Finite Policy Space.

Hypothesis: As the number of parties in a party system increases, the dispersion of parties along the economic policy dimension increases.

Introduction and Predictions of Spatial Theory sections (pages 1–4).

The empirical analysis shows that systems with more parties exhibit greater dispersion: in regressions of economic-policy dispersion on the number of parties, the log count of parties has a positive and statistically significant association with the log distance between the most extreme parties along the economic dimension.

Results of Empirical Analysis and Regression Analysis sections, pages 13–17, and Table 4, where the coefficient on the log count of parties predicting log economic dispersion is reported as 0.39 with a robust standard error of 0.14 and marked as statistically significant within a 95% confidence interval.

Comparative Manifesto Project (CMP) data on party manifestos for parliamentary parties, combined with information on parliamentary seat shares and electoral systems in twenty established parliamentary democracies.

All national parliamentary elections from the first election after the Second World War through the last election in the 1990s for twenty parliamentary democracies (Australia, Austria, Belgium, Canada, Denmark, France, Germany, Greece, Iceland, Ireland, Italy, Luxembourg, Netherlands, Norway, New Zealand, Portugal, Spain, Sweden, Switzerland, United Kingdom).

295 country–election–year party systems (Table 1).

Party system in a given country and election year (country–election–year).

The number of parties in a party system is measured in two ways: (1) a count of parliamentary parties that win at least 1% of seats in at least two consecutive elections (with parties dropped if they fall below 1% for three consecutive elections), and (2) the effective number of parties in parliament based on the Laakso–Taagepera formula. Party positions are estimated from CMP manifesto categories via principal components analysis to construct an economic and a social policy dimension. Party dispersion is measured as the distance between the two most extreme parties on each dimension within each party system.

The authors pool party-position data from twenty parliamentary democracies over time, estimate parties’ locations on economic and social policy dimensions using CMP manifesto data, compute dispersion as the distance between the most extreme parties in each system, and regress dispersion on the number of parties (and electoral rules) to test whether systems with more parties are more dispersed.

Use Comparative Manifesto Project data to code party policy positions for parliamentary parties in twenty established parliamentary democracies from 1945 to 1999.

Construct two policy dimensions (economic and social) by performing principal components analysis on selected CMP issue categories, obtaining a score on each dimension for every party in every election year.

Define membership in the party system using a 1% parliamentary seat-share threshold over at least two consecutive elections, dropping parties that fall below 1% for three consecutive elections; for each country–election–year, count how many parties belong to the system.

For each country–election–year, identify the two most extreme parties along the economic policy dimension and compute the distance between them to obtain a measure of economic dispersion (and similarly for social dispersion).

Assemble a dataset of 295 party systems (country–election–years) including dispersion measures, the count of parties, the effective number of parties, and an indicator for single-member-district versus proportional electoral rules.

Log-transform dispersion and the number-of-parties measures, and estimate ordinary least squares regressions of logged dispersion on logged number of parties, electoral-system dummy, and the lagged dependent variable, clustering robust standard errors by country.

Interpret the coefficient on the logged count of parties as the elasticity of party dispersion with respect to the number of parties in the system.

Ordinary least squares regression models of the natural log of party dispersion on the natural log of the number of parties in the party system and electoral rules, including a lagged dependent variable and robust standard errors clustered by country.

Natural log of the distance between the two most extreme parties along the economic policy dimension in each country–election–year party system.

Natural log of the count of parties in the party system (based on parliamentary parties with at least 1% of seats in at least two consecutive elections).

Indicator for single-member-district electoral systems versus proportional representation; lagged value of logged economic dispersion.

Summary: Both descriptive comparisons and regression models show that party systems with more parties are more dispersed in policy space: mean distances between extreme parties increase as the number of parties rises up to about five parties, and in regression models the log count of parties has a positive and statistically significant effect on the log distance between the most extreme parties along the economic policy dimension, while electoral rules have no independent effect once the number of parties is controlled for.

Logged economic-policy dispersion (distance between most extreme parties) as a function of logged number of parties in the system

OLS elasticity coefficient (percent change in economic-policy dispersion associated with a 1 percent increase in the count of parties)

OLS regression coefficient = 0.39 (Model 1a)


[CLAIM]
The focal test result concerns the location of the estimated coefficient “Imports from the South”. The dependent variable is national affluence. The result was a statistically significant estimated coefficient for “Imports from the South” (b=.910,SE=.104,p<.001)

[HYPOTHESIS]
Imports from the South will be positively associated with national affluence.

it is discussed in the introduction and literature review sections

The focal test result concerns the location of the estimated coefficient “Imports from the South”. The dependent variable is national affluence. The result was a statistically significant estimated coefficient for “Imports from the South” (b=.910,SE=.104,p<.001).

Organization for Economic Cooperation and Development (OECD), specifically:
national affluence: OECD’s Annual National Accounts, volume 1: Comparative Tables;
import/export data: International Trade by Commodities Database;
unemployment: Labour Force Statistics—Summary Tables;

not stated; there are only mentions which OECD database was used for which variable. they are referenced in the reference section

countries used in this study are Australia, Austria, Belgium, Canada, Denmark, Finland, France, Germany, Ireland, Italy, Japan, the Netherlands, New Zealand, Norway, Sweden, Switzerland, the United Kingdom, and the United States;
The study defines the South as Africa, Asia, Central and South America, and Oceania; the study defines the North as Europe and North America. Adjustments were made by moving Mexico and Turkey (from North America and Europe, respectively) to the South, and by moving Australia and New Zealand (from Oceania) and Israel, Japan, and South Korea (from Asia) to the North. To facilitate international comparison, values for imports and exports are expressed as a percentage of GDP for all countries.
national affluence is measured as a country’s gross domestic product (GDP) divided by its total population, with GDP expressed in U.S. dollars at prices and purchasing parities (PPP) from the year 2000.

The study analyzes why the world’s most economically advanced countries have deindustrialized over the last few decades. Model 4 estimates the relationship between imports from the South and national affluence.

1. Combine all datasets including 18 OECD countries (see notes) for the years 1970–2003 using databases described in the source field.
2. Construct national affluence as GDP per capita (in 2000 PPP U.S. dollars).
3. Define imports from the South as the total value of manufactured goods that each OECD country imports from Southern countries, and exports to the South as the total value of manufactured goods that each OECD country exports to Southern countries.
4. Normalize all trade variables express each trade measure as a percentage of GDP.
5. Create control variables: the unemployment rate (from OECD data) and a set of period dummies (1975–79, 1980–84, 1985–89, 1990–94, 1995–99, and 2000–2003) to capture temporal and macroeconomic effects.
6. Estimate Model 4 with national affluence as the DV and import/export data as the IV. Include unemployment and period dummies as controls.

imports from the South, exports to the South

unemployment, period indicators (dummies)

Summary: Model 4 shows a significant positive effect of imports from the South on national affluence (b = .910, SE = .104, p < .001).

Explaining Deindustrialization: How Affluence, Productivity Growth, and Globalization Diminish Manufacturing Employment.

Hypothesis: Greater levels of imports originating from South correspond with higher levels of national affluence in advanced economies.

Discussion of indirect effects of global South trade in the conceptual framework.

The analysis demonstrates that inflows of manufactured goods from developing countries have a strong positive association with national affluence. The regression coefficient for imports from the South in the affluence equation is reported as 0.910 with a standard error of 0.104, and is statistically significant at the 0.001 level.

Table 2, column with national affluence as the dependent variable, row labeled “Imports from the South,” showing coefficient = .910, SE = .104, significance p < .001.

Observational (cross-national panel analysis).

OECD STAN (Structural Analysis) Database for manufacturing employment and workforce data; OECD Annual National Accounts for GDP and population used to construct national affluence; OECD International Trade by Commodities Database for imports and exports disaggregated by region and SITC codes; UN National Accounts Main Aggregates Database for real value added in manufacturing and service sectors; OECD Labour Force Statistics for unemployment data.

Annual observations for 18 OECD countries from 1970 to 2003.

National affluence is measured using logged GDP per capita. Imports from the South are calculated as the logged value of manufactured imports originating from non-OECD countries. All continuous variables are logged. Country fixed effects and year dummies appear in the models.

The study estimates panel regression models (OLS) to assess how trade with developing countries relates to changes in national affluence, net of other macroeconomic conditions.

Assemble annual country-level data on GDP per capita, trade flows with developing countries, productivity measures, and labor market indicators for OECD nations.

Construct measures of imports from the South using COMTRADE data and log-transform GDP per capita to represent national affluence.

Specify a fixed-effects regression model of national affluence on imports from the South and control variables.

Include year indicators to adjust for common temporal shocks.

Cluster standard errors by country and estimate the regression.

Interpret the coefficient on imports from the South as the estimated association with national affluence.

Country fixed-effects panel regression with year dummies predicting logged national affluence.

Logged national affluence (GDP per capita).

Exports to the South, unemployment, Period Indicators .

Summary: Imports from developing countries exhibit a strong and statistically significant positive association with national affluence across OECD nations. The magnitude of the coefficient indicates that increases in such imports correspond with higher levels of GDP per capita.

unstandardized regression coefficient (change in logged affluence per unit change in logged imports from the South)


[CLAIM]
Controlling for cognitive abilities, age, gender, socio-economic status, parental education, and indicators of cultural capital, the analysis revealed a general positive trend between bilingualism and English foreign language achievement (estimate = 2.68; p < .01)

[HYPOTHESIS]
Bilingual group membership will be positively associated with foreign language achievement when controlling for background variables

11/input/replication_data/Replication attempt code (FINAL).R

Hypothesis: Bilingual group membership will be positively associated with foreign language achievement when controlling for background variables.

the hypothesis is stated in the 1.4. Research questions section; it is also discussed in the abstract, introduction, section 1.2. Factors affecting language learning.

Controlling for cognitive abilities, age, gender, socio-economic status, parental education, and indicators of cultural capital, the analysis revealed a general positive trend between bilingualism and English foreign language achievement (estimate = 2.68; p < .01).

the main source: Assessment of Reading and Mathematics Development Study (ELEMENT); for socioeconomic status data: International Socio-Economic Index.

the sixth grade elementary school cohort

individual - German 6th graders nested in 134 elementary school classes

access details are not specified; no restrictions are mentioned, perhaps the datasets are open access.

Data were collected from a sample of students from a major European city, with about 15% of students speaking a language other than German at home. The sample is representative of public elementary school students. English achievement was assessed with a Cloze test, scaled using one-parameter item response theory in ConQuest with weighted likelihood estimates (WLEs; mean = 100, SD = 20). General cognitive abilities were measured using a composite score from two subtests of the CFT4-12R (verbal and figural analogies). All analyses used five imputed datasets to replace missing values, based on a background model including individual-level factors (grades, self-concept, interest, motivation) and classroom-level factors (achievement, socio-economic status, percentage of students with immigration background). Results were combined using MPlus 5.21 with type = imputation.

The authors examined the effect of immigrant bilingualism on English as a foreign language achievement. They controlled for cognitive, demographic, and socio-cultural factors.

1. Download the data and identify bilingual and monolingual groups based on language spoken at home.
2. Exclude 111 students with no language information.
3. Classify students into the monolingual group (n=1896) and the bilingual group (n=939).
4. Assess English language achievement using a Cloze test, scaled with one-parameter item response theory in ConQuest using weighted likelihood estimates (WLEs; mean=100, SD=20).
5. Measure general cognitive abilities (by the authors) using a composite score from two subtests of the CFT4-12R (verbal and figural analogies).
6. Collect control variables from the dataset: age, gender, socio-economic status (ISEI data), parental education, and cultural capital.
7. Handle missing data using multiple imputation.
8. Run multiple linear regression in MPlus 5.21 with type = complex.

general cognitive abilities, gender, age, socio-economic status, parental education, and cultural capital

For multiple regression: MPlus 5.21 taking into account the nested nature (students in classes) of the dataset (type = complex). For items scaling: ConQuest.

Summary: After controlling for cognitive abilities, age, gender, socio-economic status, parental education, and indicators of cultural capital, bilingual students showed a general positive trend between bilingualism and English foreign language achievement (estimate = 2.68; p < .01).

points (raw Cloze test scores scaled to mean = 100, SD = 20)

The effect of speaking a minority language at home on foreign language learning.

Hypothesis: Bilingual students, on average, achieve higher scores in English as a foreign language than their monolingual classmates, once individual and family background characteristics are taken into account.

Section 1.4 Research questions, Hypothesis 1a.

After adjusting for general cognitive abilities, age, gender, socio-economic status, parental education, and cultural capital, membership in the bilingual group is positively related to English foreign language achievement: bilingual students score about 2.68 points higher than monolinguals on the English scale, and this effect is statistically significant.

Results section 3.2, Table 3 ('Multiple regression models explaining English achievement of bilinguals'), Model B.

Secondary analysis of the ELEMENT study (Assessment of Reading and Mathematics Development Study), a large-scale assessment conducted in public elementary schools in a major German city.

2,835 students nested in 134 elementary school classes.

Students attend public elementary schools; the sample is representative for sixth-grade students in the city. Language-group classification is based on parent (and, if missing, student) reports of languages spoken regularly at home. The analytic sample includes a monolingual German group (n = 1,896) and several bilingual groups (Arabic–German, Chinese–German, Polish–German, Turkish–German, and a heterogeneous ‘other bilingual’ group). Missing data were handled via multiple imputation (five imputed datasets), and analyses accounted for the clustering of students within classes using complex survey procedures in Mplus 5.21.

The authors used multiple regression models on a large, imputed sixth-grade dataset to test whether being bilingual (speaking a minority language at home plus German) is associated with higher English foreign language achievement compared to monolingual German students, controlling for cognitive and socio-cultural background variables.

Use ELEMENT study data to identify students’ home language(s) from parent reports (and student reports when parent data are missing), and classify students as monolingual German or bilingual (speaking German and another language regularly at home).

Measure English foreign language achievement in grade 6 with a Cloze test consisting of four texts and 91 word-completion items, scaled using one-parameter item response theory; obtain individual weighted likelihood estimates (WLEs).

Collect background covariates: general cognitive abilities (from the CFT4-12R verbal and figural analogies subtests in grade 4), age, gender, socio-economic status (HISEI), parental education, and cultural capital (number of books at home).

Handle missing data using multiple imputation (five imputed datasets) based on a background model including individual and classroom-level factors; combine results across imputations.

Estimate an uncontrolled regression model of English achievement on a binary indicator for bilingual-group membership (Model A).

Estimate a controlled multiple regression model of English achievement on bilingual-group membership, adding general cognitive abilities, age, gender, socio-economic status, parental education, and cultural capital as covariates (Model B).

Account for the nested data structure (students within classes) by using complex survey options in Mplus 5.21 when fitting the regression models.

Multiple linear regression models predicting English foreign language achievement from bilingual-group membership and background covariates.

English language achievement in grade 6.

Binary indicator for group membership (1 = bilingual student who speaks a minority language at home and German; 0 = monolingual German student).

General cognitive abilities (CFT4-12R composite), age (centered), gender (girls = 1), socio-economic status (HISEI, z-score), parental education (highest parental qualification, z-score), and cultural capital (number of books at home, z-score).

Summary: In the uncontrolled model, bilingual students score lower in English than monolinguals. However, once general cognitive abilities, age, gender, socio-economic status, parental education, and cultural capital are held constant, the sign reverses: bilingual-group membership is positively and significantly associated with English foreign language achievement, indicating a small advantage for bilinguals over monolinguals.

English foreign language achievement (Cloze test WLE score)

score points on the English achievement scale (M = 100, SD = 20)

unstandardized multiple regression coefficient for bilingual-group membership in Model B of Table 3

Hypothesis: Previous research…suggests that, once controlling for individual and familial background factors, the bilingual students will, on average, have higher scores in English as a foreign language compared to the monolingual group. Therefore we hypothesize similar advantages for the bilingual students in the present sample (Hypothesis 1a).

Section 1: Introduction; Subsection 1.4: Research questions; p. 78-79

Given comparable individual and familiar background characteristics bilingual group membership is positively associated with English foreign language achievement.

Section 3: Results; Subsection 3.2: Bilingualism and English achievement; p. 80.

The final sample size (after cleaning) was 2835 students nested in 134 elementary
school classes.

The authors use regression analysis, controlling for background characteristics, to explore the effect of bilingualism on English language achievement in school.

(1) Clean the data by keeping only those with language information; (2) the bilingual group was divided into ﬁve proﬁciency groups from a standardized competency scale; (3) construct the English language achievement measure using weighted likelihood estimates (WLEs) for individual person parameters; (4) conducted all analyses using ﬁve imputed datasets, in which the missing values were replaced by plausible values.

uncontrolled regression model; regression model including controls

Summary: the bilingual group had a relatively slight advantage (b = 2.68), which can be interpreted as just over a quarter of a school year's achievement.


[CLAIM]
The income is substantially higher for low-caste households residing in villages dominated by a low caste. The estimation results show the robustness of the positive relationship between agricultural income and residing in a low-caste dominated village. [The coefficient for “Low-caste villages” is 566.5 with robust standard errors of 209, significant at the 1 percent level.]

[HYPOTHESIS]
Among low-caste households, residing in villages dominated by lower castes is associated with greater agricultural income compared to residing in villages dominated by upper castes

Hypothesis: Among low-caste households, residing in villages dominated by lower castes is associated with greater agricultural income compared to residing in villages dominated by upper castes.

The income is substantially higher for low-caste households residing in villages dominated by a low caste. The estimation results show the robustness of the positive relationship between agricultural income and residing in a low-caste dominated village. [The coefficient for “Low-caste villages” is 566.5 with robust standard errors of 209, significant at the 1 percent level.].

The primary data used in this paper were collected by a team of researchers based at the World Bank and in India.

not stated, the data source is not very clear either. However, there is information that additional materials are here: http://www.aeaweb.org/articles.php?doi=10.1257/app.3.1.239; there is no information if the website includes the data or code.

Dominant caste refers to the caste group owning the majority of land. The villages of study are located in south and southeastern Uttar Pradesh and north and central Bihar. The field survey was administered in villages drawn at random from 12 districts in Uttar Pradesh and 13 districts in Bihar. A total of 120 villages, with an overall sample size of 2,250 households, were sampled: 57 villages in Bihar and 63 in Uttar Pradesh. All of the study villages are rural and the economies in these areas are primarily dependent on agriculture. The author dropped Muslim households from the analysis (which comprises only 2 percent of the sample in Hindu dominated villages). Regression disturbance terms are clustered at the village level. There are district and state fixed effects

The study uses OLS regression to examine whether lower-caste households have higher agricultural income when residing in villages dominated by lower castes, relative to villages dominated by upper castes.

1. Get the data.
2. Restrict the sample to lower-caste households — BAC (Backward Agricultural Castes), OBC (Other Backward Castes), and SC (Scheduled Castes) — and exclude Muslim-dominated villages and Muslim households in Hindu-dominated villages.
3. Create the binary variable for low-caste dominated villages (1 = low-caste dominated, 0 = high-caste dominated).
4. Estimate an OLS regression of household crop income per acre on the low-caste village indicator, including household, district, and state-level controls.
5. Cluster standard errors at the village level.

literacy, land ownership, caste identity, district, state

Summary: Lower-caste households earn significantly more in low-caste dominated villages (“Low-caste villages” coefficient = 566.5, SE = 209, p < 0.01).

not stated explicitly (should be local currency though)

Hypothesis: Among lower-caste households, the association between being a groundwater buyer and farm performance will be more favorable in villages where landownership dominance is held by a lower caste.

The results indicate that the advantage of living in a low-caste dominated village is concentrated among groundwater buyers: the interaction between low-caste village status and water-buyer status is positive and statistically significant in the crop-income regressions.

Table 4 (OLS estimations with water-market interaction terms), “LCV × water buyer”.

UP-Bihar LSMS World Bank survey data, including a household questionnaire and a village questionnaire.

Field survey collected in 1997-1998 in rural villages in south and southeastern Uttar Pradesh and north and central Bihar; analysis focuses on Hindu-dominated villages and excludes Muslim-dominated villages and Muslim households.

120 villages and 2,250 households in the full sample; the focal regressions use a lower-caste household sample with 1,295 observations.

Household (lower-caste household, BAC/OBC/SC) in a village.

Village dominance is defined by which caste group owns the majority of land (upper-caste dominated vs BAC-dominated). Key irrigation measures include private tubewell ownership and groundwater buying. Regression disturbance terms are clustered at the village level in the household-level crop-income models.

The study compares economic outcomes of lower-caste households across villages where landownership is dominated by either an upper caste or a lower backward agricultural caste group, and then tests whether differences in agricultural outcomes are explained by access to irrigation through private groundwater markets by interacting village dominance with household water-market participation.

Draw villages at random from districts in Uttar Pradesh and Bihar and conduct village- and household-level surveys.

Classify villages by which caste group owns the majority of land (upper-caste dominated vs BAC-dominated).

Restrict the main household analysis to lower-caste households (BAC, OBC, SC) and exclude Muslim-dominated villages and Muslim households.

Construct agricultural outcome measures (e.g., crop income per acre) from reported crop sales and landholdings.

Measure groundwater market participation using indicators for being a water buyer and tubewell owner.

Estimate OLS regressions of crop income per acre on low-caste village status, household controls, and fixed effects, clustering standard errors at the village level.

Estimate interaction models where low-caste village status is interacted with water-buyer status to test whether the village-dominance effect operates through groundwater-market access.

OLS regression models of crop income per acre with interaction terms between village caste dominance and groundwater market participation (water buyer), with clustered standard errors at the village level and included controls/fixed effects as specified in the table.

Household crop income per acre (crop income per acre of total land).

Low-caste village indicator; water buyer indicator; interaction between low-caste village and water buyer (LCV × water buyer).

Literacy indicator and total land; caste controls; state controls; and (depending on specification) district controls and additional sets of controls such as crop, distance, groundwater, and public-goods controls as indicated in the regression table.

Summary: The interaction analysis shows that the positive association between living in a low-caste dominated village and agricultural performance is concentrated among households that buy groundwater: the interaction between low-caste village status and water-buyer status is positive and statistically significant in the crop-income regression.

OLS interaction coefficient (LCV × water buyer)

Hypothesis: Given these historical patterns, we may well expect lower castes to fair better in villages where no upper castes are present.

...lower castes fair significantly better, in terms of household income, if they reside in villages where a lower caste is dominant.

Section 2: Household Outcomes by Caste Dominance; p.247

World Bank survey data on Uttar Pradesh and Bihar in India

Access details (e.g., restrictions, request process)

The author uses fixed effects regression analysis to explore the effect of being a low caste member in a village dominated by the lower caste on crop income.

(1) Clean the data; (2) construct the Caste Dominance measure by landownership percentage for the castes; (3) Estimate the effects using regression analysis.

Exogenous household characteristics (such as education, land ownership, and caste identity); district fixed effects; state fixed effects.

Summary: ...lower castes fair significantly better, in terms of household income, if they reside in villages where a lower caste is dominant…The estimation results…confirm the robustness of the positive relationship between agricultural income and residing in a low-caste dominated village.

No confidence intervals, but robust standard errors are reported (209.0).


[CLAIM]
even after controlling for other predictors of trust in the political system, concerns about the effect of immigration on the national community have an impact on trust in politics, that higher concern about immigration is associated with higher distrust in politics.

[HYPOTHESES]
Individuals’ concerns about immigration is positively associated with distrust in their country’s parliament.

p. 205 Proposition 1; also discussed in the introduction and section Concern about Immigration and Political Trust.

rounds 1-4 (Fieldwork for round 1 was conducted in 2002–3, for round 2, in 2004–5 (except in Italy, where it was conducted in early 2006), for round 3, in 2006–7, and for round 4, in 2008–9.).

the article states: Available at http://www.europeansocialsurvey.org/. no further access details are provided.

the analysis excludes the newer democracies of Central and Eastern Europe (CEE ); also, minorities and noncitizens have been omitted.

The study used a three-level hierarchical linear model to test whether higher concern about immigration is associated with higher distrust in politics.

1.Combine ESS data into one dataset.
2. Exclude newer democracies of Central and Eastern Europe (CEE), minorities, and noncitizens.
3.Construct the DV measuring distrust in parliament by reversing coding of the 0–10 trust item (0 = no trust, 10 = complete trust) so that higher values indicate higher distrust.
4. Construct the key predictor “concern about immigration” as an index averaging three reversed-coded items (economic effect, cultural effect, overall effect on country).
5.Add individual-level control variables:
-Unhappiness: reversed 0–10 scale.
-Dissatisfaction with life: reversed 0–10 scale.
-Frequency of meeting friends: reversed 1–7 scale (such that high values represent rarely meeting with friends).
-Interpersonal distrust: reversed 0–10 scale, index of three items (questions about general trust, fairness, and helpfulness of people).
-Dissatisfied with country’s economy: reversed 0–10 scale.
-Dissatisfied with personal income: reversed 0–10 scale.
-Dissatisfied with health and education system: reversed 0–10 scales.
-Winner effect: 1 if voted for governing party, 0 otherwise.
-Vote for far-right party: 1 if respondent voted for anti-immigration party, 0 otherwise.
-Left-right scale.
-Household income (standardized across rounds).
-Age.
-Education.
-Gender (0 = male, 1 = female).
7.Estimate a three-level hierarchical linear model with individuals nested within country-rounds and countries using HLM software.

multilevel modeling (three-level, linear)

Unhappiness, Dissatisfaction with life, Frequency of meeting friends, Interpersonal distrust, Dissatisfied with country’s economy, Dissatisfied with personal income, Dissatisfied with health system, Dissatisfied with education system, Winner effect, Voted for far-right party in last general election, Left-right scale, HH income, Age, Education, Female

Summary: Higher concern about immigration is associated with higher distrust in politics (b=0.17 SE=0.00 p=0.00).

The Cultural Divide in Europe Migration, Multiculturalism, and Political Trust.

Hypothesis: Individuals who are more worried about immigration’s impact on the national community will report greater distrust of their country’s parliament.

Proposition 1(p.7) linking greater concern about immigration’s impact on the national community to greater distrust of politicians and political institutions.

The multilevel models show a positive and statistically significant association between concern about immigration and distrust in parliament, even when controlling for a wide set of alternative predictors of political distrust.

Table 3 (Three-Level Model of Distrust in Politics), row “Concern about immigration.”

Individual survey respondent (nested within country-round and country).

Available via the European Social Survey website.

The dependent variable is political distrust measured using 11-point (0–10) trust items for national institutions.

The study uses three-level multilevel models to estimate how individual concern about immigration relates to distrust in national political institutions (including parliament), while accounting for respondents nested within survey rounds and countries and controlling for alternative explanations of political distrust.

Select ESS respondents from rounds 1–4 and retain countries included in the analysis.

Construct the dependent variable for distrust in parliament from the 0–10 trust item (coded so that higher values indicate more distrust).

Construct the key independent variable measuring concern about immigration’s impact on the national community.

Include individual-level controls capturing pessimism/alienation, political attitudes, evaluations of government performance and the economy, and social capital.

Estimate a three-level multilevel model (individuals nested within country-round and country).

Compute coefficients and standard errors using HLM, as reported in the results tables.

Three-level multilevel model (HLM) with respondents nested within country-round (level 2) and country (level 3).

Distrust in parliament (0–10 scale, higher = more distrust).

Unhappiness; dissatisfaction with life; frequency of meeting friends; interpersonal distrust; dissatisfaction with the country’s economy; dissatisfaction with personal income; dissatisfaction with the health system; dissatisfaction with the education system; winner effect; voted for far-right party in last general election; left-right scale; household income (standardized); age; education; female; plus level-2 and level-3 covariates included in the Table 3 model.

Summary: Concern about immigration is positively and significantly related to distrust in parliament in the three-level model, and this association remains after controlling for a broad set of alternative predictors of political distrust.

The Cultural Divide in Europe: Migration, Multiculturalism, and Political Trust

Hypothesis:
If individuals perceive newcomers as a threat to that community, then the
governing institutions are likely to be called into question: those most worried about the effects of newcomers in the multicultural state may question the extent to which national political institutions will continue to represent a national citizenry.

Section: Concern about immigration and
political trust; p. 205

...the effect of concern about immigration on political trust remains—even after taking into account this potential automatic correspondence via voting for the far right and via left-right self-placement, as well as pessimism, attitudes to the economy, and attitudes to government provision of health and educational services.

It is aggregated to the country level for the analysis.

The authors use a multivariate analysis, which adjusts the standard errors for control variables at different levels of aggregation, to estimate the effect of immigration concerns on three measures of political distrust: politicians, parliament, and the legal system.

(1) Reverse the coding of the political trust variable; (2) clean the sample by removing the excluded countries; (3) Run bivariate correlations between variables; (3) Run multilevel, multivariate analysis

political distrust (politicians, parliament, legal system)

far-right popularity;level of spending on social protection; long-term country of migration; governance quality; gdp per capita; unemployment rate

Summary: These results indicate that after controlling for fairly powerful predictors of distrust in politics, concern about immigration has a statistically significant effect on distrust in politics, with maximum effects of 1.7 on the 11-point measure of distrust in parliament, 1.3 on distrust in politicians, and 1.4 on distrust of the legal system.

They also report standard errors = 0.000.