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@@ -58,3 +58,63 @@ saved_model/**/* filter=lfs diff=lfs merge=lfs -text
 # Video files - compressed
 *.mp4 filter=lfs diff=lfs merge=lfs -text
 *.webm filter=lfs diff=lfs merge=lfs -text
+1/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+1/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+1/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+1/input/replication_data/transportation.csv filter=lfs diff=lfs merge=lfs -text
+10/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+10/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+10/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+11/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+11/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+11/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+11/input/replication_data/Final[[:space:]]replication[[:space:]]dataset.rds filter=lfs diff=lfs merge=lfs -text
+12/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+12/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+13/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+13/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+13/input/replication_data/data_clean.rds filter=lfs diff=lfs merge=lfs -text
+14/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+14/input/replication_data/Estimation[[:space:]]Data[[:space:]]-[[:space:]]Pitts[[:space:]](126zz).csv filter=lfs diff=lfs merge=lfs -text
+15/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+15/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+15/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+15/input/replication_data/Afghanistan_Election_Violence_2014.dta filter=lfs diff=lfs merge=lfs -text
+16/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+17/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+17/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+18/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+18/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+18/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+19/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+19/gt/human_report.docx filter=lfs diff=lfs merge=lfs -text
+19/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+2/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+2/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+2/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+2/input/replication_data/PISA2012.replication.RDS filter=lfs diff=lfs merge=lfs -text
+3/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+3/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+3/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+4/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+4/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+4/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+5/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+5/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+5/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+5/input/replication_data/replication_data.dta filter=lfs diff=lfs merge=lfs -text
+6/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+6/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+6/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+6/input/replication_data/GSSreplication.dta filter=lfs diff=lfs merge=lfs -text
+7/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+7/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+7/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+8/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+8/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+8/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+8/input/replication_data/ReplicationData_Cohen_AmEcoRev_2015_2lb5.dta filter=lfs diff=lfs merge=lfs -text
+9/gt/human_preregistration.pdf filter=lfs diff=lfs merge=lfs -text
+9/gt/human_report.pdf filter=lfs diff=lfs merge=lfs -text
+9/input/original_paper.pdf filter=lfs diff=lfs merge=lfs -text
+9/input/replication_data/CMP_final.dta filter=lfs diff=lfs merge=lfs -text

1/gt/expected_post_registration.json

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+{
+    "original_study": {
+        "claim": {
+            "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.",
+            "hypothesis_location": "The hypothesis is not stated explicitly in the text but the relationship is mentioned in the abstract, introduction and results sections.",
+            "statement": "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].",
+            "statement_location": "Page 4, Results section; page 7, Table 1.",
+            "study_type": "Observational" 
+        },
+
+        "data": { 
+            "source": "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).",
+            "wave_or_subset": "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.",
+            "sample_size": "3037",
+            "unit_of_analysis": "US counties.",
+            "access_details": "not stated; the authors thank Unacast for providing their social distancing dataset for research use, but no access details are provided.",
+            "notes": "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."
+        },
+
+        "method": {
+            "description": "The authors used multivariable OLS regression to assess how socioeconomic conditions and political orientation were associated with COVID social distancing.",
+            "steps": "1. Presumably the first step was to obtain the data from Unacast, American Community Survey, MIT Election Data and Science Lab, and Census. \n2. After cleaning and merging datasets, the authors had to calculate percentage point changes in average mobility.\n3. Then, they conducted bivariate analyses of per capita income and the share of voters supporting President Trump with social distancing (p. 4, Results section).\n4. Finally, they estimated associations between the degree of social distancing and socioeconomic\nand political factors using cross-sectional OLS regressions.",
+            "models": "cross-sectional ordinary least squares regressions",
+            "outcome_variable": "social distancing (measured as change in average mobility from March 19–28, relative to matched days of pre-COVID-19 reference week).",
+            "independent_variables": "income (county level per capita); vote share for Donald Trump (2016 elections, county level)",
+            "control_variables": "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).",
+            "tools_software": "not stated"
+        },
+        "results": {
+            "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).",
+            "numerical_results": [ 
+                {
+                    "outcome_name": "social distancing",
+                    "value": "4.12",
+                    "unit": "percentage point",
+                    "effect_size": "not stated",
+                    "confidence_interval": {
+                        "lower": "3.05",
+                        "upper": "5.19",
+                        "level": "0.95"
+                    },
+                    "p_value": "<0.001",
+                    "statistical_significance": "true",
+                    "direction": "positive"
+                }
+            ]
+        },
+       
+        "metadata": {
+            "original_paper_id": "https://doi.org/10.1101/2020.04.06.20055632",
+            "original_paper_title": "Association of County-Level Socioeconomic and Political Characteristics with Engagement in Social Distancing for COVID-19.",
+            "original_paper_code": "not stated",
+            "original_paper_data": "not stated"
+        }
+    }
+}

1/gt/expected_post_registration_2.json

@@ -0,0 +1,64 @@
+{
+  "original_study": {
+    "claim": {
+      "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.",
+      "hypothesis_location": "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').",
+      "statement": "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.",
+      "statement_location": "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').",
+      "study_type": "Observational (cross-sectional ecological analysis using ordinary least squares regression on county-level data)."
+    },
+
+    "data": {
+      "source": "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.",
+      "wave_or_subset": "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.",
+      "sample_size": "3,037 U.S. counties with available mobility, socioeconomic, and political data.",
+      "unit_of_analysis": "County (county-level percentage-point change in average mobility).",
+      "access_details": "not stated (the paper notes that Unacast provided the social distancing dataset for research use but does not describe public access procedures).",
+      "notes": "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."
+    },
+
+    "method": {
+      "description": "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.",
+      "steps": [
+        "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."
+      ],
+      "models": "Cross-sectional ordinary least squares regression with state fixed effects for percentage-point change in average county mobility.",
+      "outcome_variable": "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).",
+      "independent_variables": "County-level share of voters supporting President Trump in the 2016 election (continuous, modeled per interquartile-range increase).",
+      "control_variables": "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.",
+      "tools_software": "not stated"
+    },
+
+    "results": {
+      "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).",
+      "numerical_results": [
+        {
+          "outcome_name": "Percentage-point change in average county mobility (social distancing) associated with Trump vote share",
+          "value": 4.12,
+          "unit": "percentage-point change in average mobility per interquartile increase in county Trump vote share",
+          "effect_size": "OLS regression coefficient = 4.12",
+          "confidence_interval": {
+            "lower": 3.05,
+            "upper": 5.19,
+            "level": 95
+          },
+          "p_value": "<0.001",
+          "statistical_significance": 1,
+          "direction": "positive"
+        }
+      ]
+    },
+
+    "metadata": {
+      "original_paper_id": "10.1101/2020.04.06.20055632",
+      "original_paper_title": "Association of County-Level Socioeconomic and Political Characteristics with Engagement in Social Distancing for COVID-19",
+      "original_paper_code": "not stated",
+      "original_paper_data": "not stated"
+    }
+  }
+}

1/gt/human_preregistration.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:99901bbc93a89c8c658169241f6e10bafd59d285f4117f23e654faec63d58311
+size 538962

1/gt/human_report.pdf

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+version https://git-lfs.github.com/spec/v1
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+size 288106

1/input/initial_details.txt

@@ -0,0 +1,5 @@
+[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.

1/input/original_paper.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:663f4f56cba4ead4f6a64648d055ffaaa138d43444fa91fbbf30dc60891a812f
+size 954975

1/input/replication_data/kavanagh_analysis.R

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+{
+  library(tidyverse)
+  library(haven)
+  library(glue)
+  library(jtools)
+  library(lubridate)
+  library(huxtable)
+  library(multcomp)
+  library(lfe)
+}
+
+# Data merging code copied in from kavanagh_g66z_data_merge.R
+
+# load input files
+
+# 5% Sample
+set.seed(2982)
+county_variables <- read_csv('replication_data/county_variables.csv') %>%
+  sample_frac(.05)
+transportation <- read_csv('replication_data/transportation.csv')
+
+# changes in distancing
+flat_data <- transportation %>%
+  mutate(prop_home = pop_home/(pop_home + pop_not_home),
+         # Define the three time periods
+         time_period = case_when(
+    between(date, ymd('2020-02-16'),ymd('2020-02-29')) ~ 'AAA Reference',
+    between(date, ymd('2020-03-19'),ymd('2020-04-01')) ~ 'March',
+    between(date, ymd('2020-08-16'),ymd('2020-08-29')) ~ 'August')
+  ) %>%
+  filter(!is.na(time_period), !is.na(pop_home)) %>%
+  group_by(time_period, fips, state) %>%
+  # Average over county, time period
+  summarize(prop_home = mean(prop_home, na.rm = TRUE)) %>%
+  arrange(state, fips, time_period) %>%
+  group_by(fips, state) %>%
+  # Scale to 100
+  mutate(prop_home_change = 100*(prop_home/first(prop_home) - 1)) %>%
+  filter(time_period != 'AAA Reference') %>%
+  # Reshape to get one variable for March and one for August
+  pivot_wider(id_cols = c('fips','state'),
+              names_from = 'time_period',
+              values_from = c('prop_home','prop_home_change')) %>%
+  # Bring in county-level data
+  right_join(county_variables, by = 'fips')
+
+
+
+
+# IQR of Trump support
+trumpIQR <- county_variables %>%
+  dplyr::select(fips, trump_share) %>%
+  unique() %>%
+  pull(trump_share) %>%
+  quantile(c(.25, .75), na.rm = TRUE) %>%
+  {.[2] - .[1]} %>%
+  unname()
+
+# Variable construction
+flat_data <- flat_data %>%
+  mutate(state = factor(state)) %>%
+  dplyr::select(prop_home_change_March,
+         prop_home_change_August,
+         income_per_capita,
+         trump_share,
+         male_percent,
+         percent_black,
+         percent_hispanic,
+         percent_college,
+         percent_retail,
+         percent_transportation,
+         percent_hes,
+         prop_rural,
+         ten_nineteen,
+         twenty_twentynine,
+         thirty_thirtynine,
+         forty_fortynine,
+         fifty_fiftynine,
+         sixty_sixtynine,
+         seventy_seventynine,
+         over_eighty,
+         state,
+         fips) %>%
+  ungroup() %>%
+  # These are stored as 0-1 but everything else is 0-100
+  mutate(across(starts_with('percent_'),function(x) x*100)) %>%
+  mutate(male_percent = male_percent*100,
+         percent_college = percent_college/100) %>%
+  mutate(income_per_capita = income_per_capita/1000)
+
+
+# Create regression formulae
+formula_maker <- function(depvar, data) {
+  vnames <- data %>%
+    dplyr::select(-fips, -prop_home_change_March, -prop_home_change_August, -state) %>%
+    names()
+  
+  form <- paste0(depvar,'~',
+                 paste(vnames, collapse ='+'),
+                 ' | state')
+
+  return(as.formula(form))  
+}
+
+# Run fixed effect regressions
+m1 <- felm(formula_maker('prop_home_change_March',flat_data), data = flat_data)
+m2 <- felm(formula_maker('prop_home_change_August',flat_data), data = flat_data)
+
+# Regression table
+results_tab <- export_summs(m1, m2,
+             digits = 3,
+             model.names = c('March 19-April 1','August 16-29'),
+             coefs = c('Income per Capita (Thousands)' = 'income_per_capita',
+                       'Share of Trump Voters' = 'trump_share',
+                       'Percent Male' = 'male_percent',
+                       'Percent Black' = 'percent_black',
+                       'Percent Hispanic' = 'percent_hispanic',
+                       'Percent with College Degree' = 'percent_college',
+                       'Percent in Retail' = 'percent_retail',
+                       'Percent in Transportation' = 'percent_transportation',
+                       'Percent in Health / Ed / Soc. Svcs' = 'percent_hes',
+                       'Percent Rural' = 'prop_rural',
+                       'Percent Age 10-19' = 'ten_nineteen',
+                       'Percent Age 20-29' = 'twenty_twentynine',
+                       'Percent Age 30-39' = 'thirty_thirtynine',
+                       'Percent Age 40-49' = 'forty_fortynine',
+                       'Percent Age 50-59' = 'fifty_fiftynine',
+                       'Percent Age 60-69' = 'sixty_sixtynine',
+                       'Percent Age 70-79' = 'seventy_seventynine',
+                       'Percent Age 80+' = 'over_eighty'),
+             statistics = c(N = 'nobs',
+                            R2 = 'r.squared')) %>%
+  add_footnote('More-positive numbers indicate more stay-at-home activity. State fixed effects included.')
+
+quick_html(results_tab, file = 'regression_table.html')
+
+# Effect of a one-IQR change in Trump share
+summary(glht(m1, paste0(trumpIQR,'*trump_share = 0')))
+summary(glht(m2, paste0(trumpIQR,'*trump_share = 0')))
+
+## Additional analysis: spatial autocorrelation
+{
+  library(tigris)
+  library(spdep)
+  library(sphet)
+  library(spatialreg)
+}
+
+# Get information on central county latitude/longitude
+counties <- counties()
+counties <- as_tibble(counties[,c('STATEFP','COUNTYFP','INTPTLAT','INTPTLON')]) %>%
+  mutate(fips = as.numeric(STATEFP)*1000 + as.numeric(COUNTYFP)) %>%
+  dplyr::select(-geometry, -STATEFP, -COUNTYFP) %>%
+  rename(lat = INTPTLAT, lon = INTPTLON) %>%
+  mutate(lat = as.numeric(lat),
+         lon = as.numeric(lon))
+
+# Bring in to data
+flat_data <- left_join(flat_data, counties)
+
+# K nearest neighbors for spatial spillovers
+kn <- knearneigh(as.matrix(flat_data[,c('lon','lat'), with = FALSE]), 5)
+nb <- knn2nb(kn)
+listw <- nb2listw(nb)
+
+# Create regression formulae
+formula_maker <- function(depvar, data) {
+  vnames <- data %>%
+    dplyr::select(-fips, -prop_home_change_March, -prop_home_change_August) %>%
+    names()
+  
+  form <- paste0(depvar,'~',
+                 paste(vnames, collapse ='+'))
+  
+  return(as.formula(form))  
+}
+
+# Run models with spatial autocorrelation term
+m3 <- lagsarlm(formula_maker('prop_home_change_March',flat_data), data = flat_data, listw = listw)
+m4 <- lagsarlm(formula_maker('prop_home_change_August',flat_data), data = flat_data, listw = listw)
+
+# Regression table
+results_tab <- export_summs(m3, m4,
+                            digits = 3,
+                            model.names = c('March 19-April 1','August 16-29'),
+                            coefs = c('Income per Capita (Thousands)' = 'income_per_capita',
+                                      'Share of Trump Voters' = 'trump_share',
+                                      'Percent Male' = 'male_percent',
+                                      'Percent Black' = 'percent_black',
+                                      'Percent Hispanic' = 'percent_hispanic',
+                                      'Percent with College Degree' = 'percent_college',
+                                      'Percent in Retail' = 'percent_retail',
+                                      'Percent in Transportation' = 'percent_transportation',
+                                      'Percent in Health / Ed / Soc. Svcs' = 'percent_hes',
+                                      'Percent Rural' = 'prop_rural',
+                                      'Percent Age 10-19' = 'ten_nineteen',
+                                      'Percent Age 20-29' = 'twenty_twentynine',
+                                      'Percent Age 30-39' = 'thirty_thirtynine',
+                                      'Percent Age 40-49' = 'forty_fortynine',
+                                      'Percent Age 50-59' = 'fifty_fiftynine',
+                                      'Percent Age 60-69' = 'sixty_sixtynine',
+                                      'Percent Age 70-79' = 'seventy_seventynine',
+                                      'Percent Age 80+' = 'over_eighty',
+                                      'rho' = 'rho'),
+                            statistics = c(N = 'nobs',
+                                           R2 = 'r.squared')) %>%
+  add_footnote('More-positive numbers indicate more stay-at-home activity.\nState fixed effects included.\nSpatial autocorrelation included with 5-nearest-neighbor neighbors.')
+
+quick_html(results_tab, file = 'spatial_regression_table.html')

1/input/replication_data/transportation.csv

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+version https://git-lfs.github.com/spec/v1
+oid sha256:261bd27abe0293fa7715c0d63571d4bc8bb96591f2ea829cbd37d2ff722327cd
+size 200503842

10/.gitattributes

@@ -0,0 +1,2 @@
+*.csv filter=lfs diff=lfs merge=lfs -text
+*.dta filter=lfs diff=lfs merge=lfs -text

10/gt/expected_post_registration.json

@@ -0,0 +1,60 @@
+{
+    "original_study": {
+        "claim": {
+            "hypothesis": "Imports from the South will be positively associated with national affluence.",
+            "hypothesis_location": "it is discussed in the introduction and literature review sections",
+            "statement": "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).",
+            "statement_location": "Table 2 Model 4",
+            "study_type": "Observational" 
+        },
+
+
+        "data": {
+            "source": "Organization for Economic Cooperation and Development (OECD), specifically:\nnational affluence: OECD’s Annual National Accounts, volume 1: Comparative Tables;\nimport/export data: International Trade by Commodities Database;\nunemployment: Labour Force Statistics—Summary Tables;",
+            "wave_or_subset": "1970-2003",
+            "sample_size": "566",
+            "unit_of_analysis": "country-year",
+            "access_details": "not stated; there are only mentions which OECD database was used for which variable. they are referenced in the reference section",
+            "notes": "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;\nThe 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. \nnational 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."
+        },
+
+        "method": {
+            "description": "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.",
+            "steps": "1. Combine all datasets including 18 OECD countries (see notes) for the years 1970–2003 using databases described in the source field.\n2. Construct national affluence as GDP per capita (in 2000 PPP U.S. dollars). \n3. 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.\n4. Normalize all trade variables express each trade measure as a percentage of GDP.\n5. 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.\n6. Estimate Model 4 with national affluence as the DV and import/export data as the IV. Include unemployment and period dummies as controls.",
+            "models": "two-way fixed-effects regression",
+            "outcome_variable": "national affluence",
+            "independent_variables": "imports from the South, exports to the South",
+            "control_variables": "unemployment, period indicators (dummies)",
+            "tools_software": "not stated"
+        },
+        "results": {
+            "summary": "Model 4 shows a significant positive effect of imports from the South on national affluence (b = .910, SE = .104, p < .001).",
+            "numerical_results": [ 
+                {
+                    "outcome_name": "national affluence",
+                    "value": "0.910",
+                    "unit": "NA",
+                    "effect_size": "not stated",
+                    "confidence_interval": {
+                        "lower": "not stated",
+                        "upper": "not stated",
+                        "level": "not stated"
+                    },
+                    "p_value": "<0.001",
+                    "statistical_significance": "true",
+                    "direction": "positive"
+                }
+            ]
+        },
+       
+        "metadata": {
+            "original_paper_id": "0002-9602/2009/11406-0002",
+            "original_paper_title": "Explaining Deindustrialization: How Affluence, Productivity Growth, and Globalization Diminish Manufacturing Employment.",
+            "original_paper_code": "not stated",
+            "original_paper_data": "not stated"
+        }
+    }
+}
+
+
+

10/gt/expected_post_registration_2.json

@@ -0,0 +1,64 @@
+{
+  "original_study": {
+    "claim": {
+      "hypothesis": "Greater levels of imports originating from South correspond with higher levels of national affluence in advanced economies.",
+      "hypothesis_location": "Discussion of indirect effects of global South trade in the conceptual framework.",
+      "statement": "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.",
+      "statement_location": "Table 2, column with national affluence as the dependent variable, row labeled “Imports from the South,” showing coefficient = .910, SE = .104, significance p < .001.",
+      "study_type": "Observational (cross-national panel analysis)."
+    },
+
+    "data": {
+       "source": "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.",
+      "wave_or_subset": "Annual observations for 18 OECD countries from 1970 to 2003.",
+      "sample_size": "612 country–year observations.",
+      "unit_of_analysis": "Country–year.",
+      "access_details": "not stated",
+      "notes": "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."
+    },
+
+    "method": {
+      "description": "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.",
+      "steps": [
+        "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."
+      ],
+      "models": "Country fixed-effects panel regression with year dummies predicting logged national affluence.",
+      "outcome_variable": "Logged national affluence (GDP per capita).",
+      "independent_variables": "Logged imports from the South.",
+      "control_variables": "Exports to the South, unemployment, Period Indicators .",
+      "tools_software": "Stata"
+    },
+
+    "results": {
+      "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.",
+      "numerical_results": [
+        {
+          "outcome_name": "Logged national affluence",
+          "value": 0.910,
+          "unit": "unstandardized regression coefficient (change in logged affluence per unit change in logged imports from the South)",
+          "effect_size": "OLS fixed-effects coefficient = 0.910",
+          "confidence_interval": {
+            "lower": "not stated",
+            "upper": "not stated",
+            "level": "not stated"
+          },
+          "p_value": "< .001",
+          "statistical_significance": 1,
+          "direction": "positive"
+        }
+      ]
+    },
+
+    "metadata": {
+      "original_paper_id": "not stated",
+      "original_paper_title": "Explaining Deindustrialization: How Affluence, Productivity Growth, and Globalization Diminish Manufacturing Employment",
+      "original_paper_code": "not stated",
+      "original_paper_data": "not stated"
+    }
+  }
+}

10/gt/human_preregistration.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:d6890a47e87ee33461c2d885c069ae42576df29cf78985b875bf1417043ab7f8
+size 273922

10/gt/human_report.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:76312f041da603f9f65d3401c9908aa3069b3370b1793335ff0573f4f2ef3296
+size 124290

10/input/initial_details.txt

@@ -0,0 +1,5 @@
+[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.

10/input/original_paper.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:7f9e85cc7e6c5b2df8c806420cc971f51ca6fdf47627c3442dbda4a148a31290
+size 318438

10/input/replication_data/KMYR.do

@@ -0,0 +1,67 @@
+* Import the data file from the folder (alter file path to your specific machine)
+import delimited "C:\Users\Christopher\Dropbox\Replications\Kollmeyer\data\finaldata_noNA.csv"
+* Create a STATA-required non-string value for countries
+encode country, gen(countrynum)
+
+* Setup the panel ID
+xtset countrynum
+
+* Set the time variable for the panel
+xtset countrynum year, yearly
+
+* Define National Affluence as in the paper
+gen NAff = gdp/pop
+
+* Define Imports from South as in the paper
+gen IMS = totalimport/(gdp*10000)
+
+* Define Exports to South as in the paper
+gen EXS = totalexport/(gdp*10000)
+
+* Detect outliers using Hadi outlier detection as in the paper
+hadimvo NAff IMS EXS unemp, gen(bad)
+
+* Command drops observations tagged as outliers 
+drop if bad == 1
+
+* Retain only the columns necessary for estimation 
+drop country countryyear gdp pop totalimport totalexport bad
+
+* Include new 5-year time dummies to account for new observations added before 1970
+gen DUM70to74 = 0
+replace DUM70to74 = 1 if year >= 1970 & year <= 1974
+
+* Generate 5-year time dummies as in the paper
+gen DUM75to79 = 0
+replace DUM75to79 = 1 if year >= 1975 & year <= 1979
+gen DUM80to84 = 0
+replace DUM80to84 = 1 if year >= 1980 & year <= 1984
+gen DUM85to89 = 0
+replace DUM85to89 = 1 if year >= 1985 & year <= 1989
+gen DUM90to94 = 0
+replace DUM90to94 = 1 if year >= 1990 & year <= 1994
+gen DUM95to99 = 0
+replace DUM95to99 = 1 if year >= 1995 & year <= 1999
+
+* Include new 5-year time dummies to account for new observations added after 2003
+gen DUM00to04 = 0
+replace DUM00to04 = 1 if year >= 2000 & year <= 2004
+gen DUM05to09 = 0
+replace DUM05to09 = 1 if year >= 2005 & year <= 2009
+gen DUM10to14 = 0
+replace DUM10to14 = 1 if year >= 2010 & year <= 2014
+gen DUM15to18 = 0
+replace DUM15to18 = 1 if year >= 2015 & year <= 2018
+
+* Re-order panel according to year - required to enable the lag operator "L.x"
+sort countrynum year
+
+** Uncomment the following set of commands to estimate the following FGLS model (without controls) and then verify the presence of serial autocorrelation in the residuals, spatial correlation, and groupwise heteroskedasticity:
+*xtgls NAff L.IMS L.EXS L.unemp i.countrynum DUM70to74 DUM75to79 DUM80to84 DUM85to89 DUM90to94 DUM95to99 DUM00to04 DUM05to09 DUM10to14 DUM15to18
+*xtserial
+*xttest2
+*xttest3
+
+* Re-estimate the model controlling for autocorrelation w/in panels, cross-sectional correlation, and heteroskedasticity across panels
+xtgls NAff L.IMS L.EXS L.unemp i.countrynum DUM70to74 DUM75to79 DUM80to84 DUM85to89 DUM90to94 DUM95to99 DUM00to04 DUM05to09 DUM10to14 DUM15to18, panels(hetero) corr(psar1) force
+ 

10/input/replication_data/finaldata_noNA.csv

@@ -0,0 +1,785 @@
+"country","year","countryyear","gdp","pop","unemp","totalimport","totalexport"
+"australia",1967,"australia 1967",237976.9627,11.799078,1.875,74042292,193616469
+"australia",1968,"australia 1968",254740.6131,12.008635,1.85,82367173,212671914
+"australia",1969,"australia 1969",273011.1372,12.263014,1.8,101922052,293961915
+"australia",1970,"australia 1970",283941.7484,12.507349,1.625,111325786,339301459
+"australia",1971,"australia 1971",295051.413,13.067265,1.925,131122026,398416993
+"australia",1972,"australia 1972",302762.2686,13.303664,2.625,170070583,460712900
+"australia",1973,"australia 1973",315186.2764,13.504538,2.325,318746117,788233241
+"australia",1974,"australia 1974",319441.2055,13.722571,2.7,573215891,932393780
+"australia",1975,"australia 1975",327707.1913,13.892995,4.925,483065418,1101614321
+"australia",1976,"australia 1976",339496.4895,14.033083,4.75,622750057,867139707
+"australia",1977,"australia 1977",342541.042,14.192234,5.65,707275533,964571198
+"australia",1978,"australia 1978",356391.7025,14.359255,6.442533,832536243,1299718316
+"australia",1979,"australia 1979",367204.8699,14.515729,6.265499,1007372085,1707893692
+"australia",1980,"australia 1980",379462.0177,14.695356,6.106246,1196755044,2024887758
+"australia",1981,"australia 1981",392092.0333,14.92326,5.783571,1452361743,1862224936
+"australia",1982,"australia 1982",383385.7939,15.184247,7.156132,1464575588,1432832957
+"australia",1983,"australia 1983",400949.7303,15.393472,9.961594,1139680826,1402680949
+"australia",1984,"australia 1984",421996.5475,15.579391,8.98968,1540770812,1329997988
+"australia",1985,"australia 1985",439033.5915,15.788312,8.262986,1471190735,1316131366
+"australia",1986,"australia 1986",450243.3722,16.01835,8.081038,1608922142,1365369605
+"australia",1987,"australia 1987",476090.6222,16.263874,8.10804,2083403956,1608579910
+"australia",1988,"australia 1988",494490.142,16.532164,7.227954,2826713972,2164607646
+"australia",1989,"australia 1989",512146.4932,16.814416,6.179825,3551088648,2490194576
+"australia",1990,"australia 1990",510112.0851,17.065128,6.923,3351242985,2859309011
+"australia",1991,"australia 1991",512216.446,17.284036,9.576522,3965260216,3385942128
+"australia",1992,"australia 1992",532855.7399,17.478635,10.72814,4645841594,4059361431
+"australia",1993,"australia 1993",554077.7601,17.634808,10.87467,5218613562,4611700060
+"australia",1994,"australia 1994",575332.5105,17.805468,9.72219,6642965672,5335599488
+"australia",1995,"australia 1995",597647.4635,18.004882,8.472281,7975308436,6626687571
+"australia",1996,"australia 1996",621353.1301,18.224767,8.506114,8882724696,7381521579
+"australia",1997,"australia 1997",649790.5601,18.423037,8.362488,9687150670,7531221599
+"australia",1998,"australia 1998",682758.2382,18.607584,7.677429,10082414110,5903007190
+"australia",1999,"australia 1999",709611.7822,18.812264,6.873791,12120878377,5816173768
+"australia",2000,"australia 2000",723313.5522,19.028802,6.285546,13985920958,6636816103
+"australia",2001,"australia 2001",752256.0545,19.274701,6.742173,13001278181,6825615381
+"australia",2002,"australia 2002",774717.9726,19.49521,6.368911,15486238440,7132837548
+"australia",2003,"australia 2003",806137.0619,19.720737,5.92842,19545706008,8217820711
+"australia",2004,"australia 2004",831966.9842,19.932722,5.396734,26291155712,9538967373
+"australia",2005,"australia 2005",855216.9947,20.176844,5.033881,31542430128,11328963045
+"australia",2006,"australia 2006",888094.1833,20.450966,4.78524,36018860428,13308159065
+"australia",2007,"australia 2007",920579.8928,20.827622,4.379151,45051839193,16500930420
+"australia",2008,"australia 2008",938410.8051,21.249199,4.23433,54043023401,17069331690
+"australia",2009,"australia 2009",957812.1264,21.691653,5.560385,48638027484,14539760802
+"australia",2010,"australia 2010",981400.9911,22.03175,5.21334,61595911513,17238250009
+"australia",2011,"australia 2011",1019853.8695,22.340024,5.081195,69027256025,20662775491
+"australia",2012,"australia 2012",1046215.8184,22.733465,5.223376,74453007620,20858325079
+"australia",2013,"australia 2013",1072717.6729,23.128129,5.66194,74781171769,19350500192
+"australia",2014,"australia 2014",1096238.5099,23.475686,6.077244,75663818700,17952731487
+"australia",2015,"australia 2015",1126611.5156,23.815995,6.056423,73392219040,16201069982
+"australia",2016,"australia 2016",1153285.3642,24.190907,5.710301,69744856214,16203247904
+"australia",2017,"australia 2017",1187195.8011,24.60186,5.59376,78301806879,17488615015
+"australia",2018,"australia 2018",1209714.8366,24.99286,5.299829,86725495540,18408035955
+"austria",1970,"austria 1970",152556.2727,7.467086,1.412589,37662865,148198546
+"austria",1971,"austria 1971",160356.6722,7.500482,1.238531,38734174,179269018
+"austria",1972,"austria 1972",170311.3915,7.544201,1.164418,47957352,235248951
+"austria",1973,"austria 1973",178635.021,7.586115,1.084896,71277678,295044979
+"austria",1974,"austria 1974",185679.9403,7.599038,1.345436,100110473,516870858
+"austria",1975,"austria 1975",185007.2159,7.578903,1.752424,107252032,723561935
+"austria",1976,"austria 1976",193475.0007,7.565525,1.809018,156105216,916079797
+"austria",1977,"austria 1977",203303.4936,7.56843,1.638162,189133732,917240742
+"austria",1978,"austria 1978",202875.2277,7.562305,2.062655,196018264,1114030229
+"austria",1979,"austria 1979",213742.6447,7.549425,2.104195,256211405,1409741283
+"austria",1980,"austria 1980",217443.5675,7.549433,1.854277,349640669,1705424236
+"austria",1981,"austria 1981",217129.8083,7.56871,2.515609,285381848,2022977291
+"austria",1982,"austria 1982",221496.8411,7.57414,3.464959,286444422,2045986582
+"austria",1983,"austria 1983",228082.1371,7.56191,4.107561,296911601,1771682774
+"austria",1984,"austria 1984",228199.0868,7.561434,3.800045,330628943,1669542860
+"austria",1985,"austria 1985",233901.0483,7.564984,3.599384,332472204,1800181684
+"austria",1986,"austria 1986",239284.0301,7.569794,3.134631,449280638,1751736737
+"austria",1987,"austria 1987",242531.5165,7.574586,3.794924,666712045,1770066614
+"austria",1988,"austria 1988",250525.0648,7.585317,3.563734,885134812,2015678147
+"austria",1989,"austria 1989",260263.1614,7.619566,3.132628,1042006134,1957372588
+"austria",1990,"austria 1990",271573.2654,7.67785,3.240462,1450050921,2673493650
+"austria",1991,"austria 1991",280919.8054,7.754891,3.475676,1583350906,2770799149
+"austria",1992,"austria 1992",286800.931,7.840709,3.593146,1857035453,3558920941
+"austria",1993,"austria 1993",288311.8254,7.905632,4.217466,2032607044,3368246334
+"austria",1994,"austria 1994",295237.4179,7.936118,3.577942,2657349653,3473690492
+"austria",1995,"austria 1995",303114.3039,7.948278,3.699785,2452511865,3987745858
+"austria",1996,"austria 1996",310236.0771,7.959016,4.139831,2582108531,4051169867
+"austria",1997,"austria 1997",316731.1777,7.968041,4.242406,2609978228,4228719139
+"austria",1998,"austria 1998",328074.6699,7.976789,4.235766,2733274823,3932909745
+"austria",1999,"austria 1999",339742.0919,7.992323,3.727968,2737111817,3703282939
+"austria",2000,"austria 2000",351210.8409,8.011566,3.533025,3138534313,3850045282
+"austria",2001,"austria 2001",355661.273,8.042293,3.59086,3977272835,4620781628
+"austria",2002,"austria 2002",361535.2107,8.082121,3.951978,3871096764,5225704057
+"austria",2003,"austria 2003",364938.9596,8.118245,4.286487,4890815504,6434375520
+"austria",2004,"austria 2004",374920.4789,8.169441,5.49025,6195065840,8417482175
+"austria",2005,"austria 2005",383333.9393,8.225278,5.628417,7100531973,9381217444
+"austria",2006,"austria 2006",396574.4539,8.267948,5.246718,8792969717,11422231740
+"austria",2007,"austria 2007",411356.4308,8.295189,4.858952,10191648967,14760322570
+"austria",2008,"austria 2008",417363.9775,8.321541,4.129104,12534157895,17554504913
+"austria",2009,"austria 2009",401651.9843,8.341483,5.298988,10748390277,14195050846
+"austria",2010,"austria 2010",409030.7075,8.361069,4.82168,12196055462,15870540367
+"austria",2011,"austria 2011",420985.8459,8.388534,4.566622,14705318737,18668966279
+"austria",2012,"austria 2012",423850.4255,8.426311,4.863267,14280717126,18900905958
+"austria",2013,"austria 2013",423958.5273,8.47723,5.336031,15212141039,19958853709
+"austria",2014,"austria 2014",426762.05,8.543932,5.621219,16837601308,20094891854
+"austria",2015,"austria 2015",431091.5577,8.629519,5.723468,15430066154,15955075412
+"austria",2016,"austria 2016",440069.5068,8.739806,6.014071,16112141896,15158763682
+"austria",2017,"austria 2017",450967.5814,8.795073,5.500528,17477330079,16482114429
+"austria",2018,"austria 2018",461879.9309,8.837707,4.848801,19622875158,17967484084
+"belgium",1999,"belgium 1999",397394.4018,10.226419,8.561709,13679010027,13629830432
+"belgium",2000,"belgium 2000",412164.2686,10.251247,7.00965,16295964851,15408994076
+"belgium",2001,"belgium 2001",416696.4935,10.286571,6.607931,16931475943,15246198685
+"belgium",2002,"belgium 2002",423809.0408,10.332783,7.511277,18338305250,18149888333
+"belgium",2003,"belgium 2003",428207.9796,10.37613,8.172144,21082950975,21057576532
+"belgium",2004,"belgium 2004",443500.1126,10.421133,8.387378,27353580799,26220466828
+"belgium",2005,"belgium 2005",453797.0205,10.478617,8.438975,32673896992,29933481043
+"belgium",2006,"belgium 2006",465379.5258,10.547956,8.249064,36733107611,32700659686
+"belgium",2007,"belgium 2007",482491.1505,10.625701,7.459264,47227324828,39907794447
+"belgium",2008,"belgium 2008",484647.4833,10.709976,6.970808,51706490097,45380615885
+"belgium",2009,"belgium 2009",474854.0864,10.796498,7.906314,39471991353,38508976883
+"belgium",2010,"belgium 2010",488455.2624,10.895589,8.293048,49213672570,47327069990
+"belgium",2011,"belgium 2011",496732.1243,10.993616,7.139795,61295388789,59156388195
+"belgium",2012,"belgium 2012",500404.1167,11.067748,7.537931,56500866131,58600717888
+"belgium",2013,"belgium 2013",502702.1128,11.125033,8.425185,63494938382,67206842930
+"belgium",2014,"belgium 2014",510637.3824,11.179778,8.52289,63745741021,61440103732
+"belgium",2015,"belgium 2015",521017.5628,11.238474,8.481071,57049811280,48908046505
+"belgium",2016,"belgium 2016",528719.7694,11.295003,7.833329,59999954354,49946237470
+"belgium",2017,"belgium 2017",538785.865,11.349081,7.088736,61779074603,54144821852
+"belgium",2018,"belgium 2018",546801.0312,11.40374,5.949855,68403334160,55075887310
+"canada",1965,"canada 1965",375862.6954,20.084314,3.95,62610597,241871618
+"canada",1966,"canada 1966",401064.2582,20.420867,3.375,69232496,300262573
+"canada",1967,"canada 1967",413461.9805,20.795138,3.833333,88668021,304459990
+"canada",1968,"canada 1968",434117.1626,21.129417,4.525,96556377,372148870
+"canada",1969,"canada 1969",456020.3546,21.43983,4.425,118669714,417997638
+"canada",1970,"canada 1970",469518.6322,21.746497,5.675,124353809,532139070
+"canada",1971,"canada 1971",488159.6992,21.962032,6.2,151877752,525499688
+"canada",1972,"canada 1972",515055.7547,22.218463,6.233333,238452539,654766597
+"canada",1973,"canada 1973",550263.6386,22.491777,5.566667,298380906,727551274
+"canada",1974,"canada 1974",568314.0633,22.807969,5.333333,440951614,1154951703
+"canada",1975,"canada 1975",576618.4778,23.143275,6.916667,466942563,1542578779
+"canada",1976,"canada 1976",610515.7454,23.449808,7.083333,678843450,1763215700
+"canada",1977,"canada 1977",631506.2014,23.725843,8.058333,635745903,1880845571
+"canada",1978,"canada 1978",654767.7883,23.963203,8.383333,665626527,2246807833
+"canada",1979,"canada 1979",679067.3241,24.201544,7.55,986842776,2519287502
+"canada",1980,"canada 1980",693704.8741,24.515667,7.55,1047390900,3412190641
+"canada",1981,"canada 1981",717805.0578,24.819915,7.633333,1300196963,3746262037
+"canada",1982,"canada 1982",694926.7294,25.116942,11.03333,1286752010,3674512898
+"canada",1983,"canada 1983",713004.1325,25.366451,12.03333,1665416069,2446725578
+"canada",1984,"canada 1984",755131.6754,25.607053,11.35,2324130776,2685433776
+"canada",1985,"canada 1985",790905.2826,25.842116,10.66667,2208569579,2372284261
+"canada",1986,"canada 1986",807869.9799,26.100278,9.675,2676806509,2563018559
+"canada",1987,"canada 1987",840774.194,26.446601,8.833333,3125434374,2480633549
+"canada",1988,"canada 1988",877853.269,26.791747,7.766667,4551758155,3182764785
+"canada",1989,"canada 1989",898192.9476,27.276781,7.566667,5242721963,3202730768
+"canada",1990,"canada 1990",899672.0247,27.691138,8.15,5421673983,3674055145
+"canada",1991,"canada 1991",880904.0972,28.03742,10.325,6673598731,3856429544
+"canada",1992,"canada 1992",888834.5207,28.371264,11.19167,7554928312,3925725329
+"canada",1993,"canada 1993",912485.1325,28.684764,11.4,8915773420,4509969618
+"canada",1994,"canada 1994",953496.4685,29.000663,10.41667,10606281507,4911832701
+"canada",1995,"canada 1995",979183.4371,29.302311,9.508333,12476947919,6710064274
+"canada",1996,"canada 1996",995038.1057,29.610218,9.608334,13078253416,6303516335
+"canada",1997,"canada 1997",1037626.1354,29.905948,9.116667,15776088989,6768800867
+"canada",1998,"canada 1998",1078053.6252,30.155173,8.283334,16568209081,5185234958
+"canada",1999,"canada 1999",1133712.6024,30.401286,7.591667,18800003909,4776293313
+"canada",2000,"canada 2000",1192412.1107,30.68573,6.816667,23904995826,6066079413
+"canada",2001,"canada 2001",1213755.9636,31.020902,7.216667,22585682683,6312494059
+"canada",2002,"canada 2002",1250387.732,31.360079,7.675,25255384752,6262431223
+"canada",2003,"canada 2003",1272913.6933,31.644028,7.575,30090503577,7290560453
+"canada",2004,"canada 2004",1312208.2949,31.940655,7.191667,38936180139,9844234080
+"canada",2005,"canada 2005",1354254.0787,32.243753,6.758333,47531324338,12621204399
+"canada",2006,"canada 2006",1389929.2455,32.571174,6.333333,57085320361,15611578910
+"canada",2007,"canada 2007",1418726.1477,32.889025,6.05,65174832451,19319372553
+"canada",2008,"canada 2008",1432974.261,33.247118,6.141667,70141843650,22385780541
+"canada",2009,"canada 2009",1391046.0702,33.628895,8.35,60031614799,17116712983
+"canada",2010,"canada 2010",1434028.2734,34.004889,8.058333,76588534504,19932673891
+"canada",2011,"canada 2011",1479144.244,34.339328,7.525,87200763089,23068140234
+"canada",2012,"canada 2012",1505191.4055,34.714222,7.291667,90477771059,22859817657
+"canada",2013,"canada 2013",1540249.1575,35.082954,7.083333,91113169514,22418017236
+"canada",2014,"canada 2014",1584454.8635,35.437435,6.908333,94190759964,22372010359
+"canada",2015,"canada 2015",1594899.2238,35.702908,6.908333,91705251021,21460855285
+"canada",2016,"canada 2016",1610870.4557,36.109487,6.991667,88418845961,19912031146
+"canada",2017,"canada 2017",1661946.8064,36.540268,6.341667,99526073415,20418854609
+"canada",2018,"canada 2018",1695412.419,37.058856,5.833333,106739748659,22350454689
+"czech republic",1998,"czech republic 1998",226227.7471,10.294943,6.476871,1363535792,1589927859
+"czech republic",1999,"czech republic 1999",229469.2275,10.282784,8.755593,1449930215,1251379895
+"czech republic",2000,"czech republic 2000",239260.086,10.272503,8.823602,1780486999,1558838702
+"czech republic",2001,"czech republic 2001",246219.5993,10.224192,8.165375,2555988371,1647944904
+"czech republic",2002,"czech republic 2002",250288.3636,10.200774,7.313614,4520105719,2403553547
+"czech republic",2003,"czech republic 2003",259306.2269,10.201651,7.812037,5593615133,2404914923
+"czech republic",2004,"czech republic 2004",272029.2523,10.206923,8.322025,6769771792,3708703502
+"czech republic",2005,"czech republic 2005",289802.1986,10.234092,7.928005,7128571243,4720268678
+"czech republic",2006,"czech republic 2006",309663.8571,10.266646,7.148034,9660328899,5737373448
+"czech republic",2007,"czech republic 2007",327013.2196,10.322689,5.321516,14582342204,8326906458
+"czech republic",2008,"czech republic 2008",335784.6387,10.429692,4.392356,19443363276,10206303557
+"czech republic",2009,"czech republic 2009",319658.3393,10.491492,6.658732,16008185401,8925463131
+"czech republic",2010,"czech republic 2010",326925.5161,10.517247,7.279707,22685658399,10882942208
+"czech republic",2011,"czech republic 2011",332737.7064,10.496672,6.711369,27757504145,13992412235
+"czech republic",2012,"czech republic 2012",330076.3229,10.509286,6.977849,23451179505,15195067788
+"czech republic",2013,"czech republic 2013",328479.8393,10.510719,6.953799,23549661498,16158714280
+"czech republic",2014,"czech republic 2014",337398.4484,10.524783,6.109115,26287433178,16679685086
+"czech republic",2015,"czech republic 2015",355311.7368,10.542942,5.046221,27777284783,13385292021
+"czech republic",2016,"czech republic 2016",364018.8016,10.565284,3.952049,26841780539,13470839859
+"czech republic",2017,"czech republic 2017",379863.0967,10.589526,2.890817,31297174125,15184150906
+"czech republic",2018,"czech republic 2018",390674.2195,10.62643,2.243518,38339208089,16269266622
+"denmark",1995,"denmark 1995",208798.5294,5.227861,7.09748,2157831648,3017021986
+"denmark",1996,"denmark 1996",214853.8949,5.261503,6.903341,2212972166,2723228748
+"denmark",1997,"denmark 1997",221860.0446,5.28422,6.157914,2345917602,2582719983
+"denmark",1998,"denmark 1998",226781.2524,5.301304,5.536391,2427561671,2490011361
+"denmark",1999,"denmark 1999",233466.814,5.319111,5.586067,2607856042,2283353750
+"denmark",2000,"denmark 2000",242214.4948,5.337344,4.625956,2877975906,2585424721
+"denmark",2001,"denmark 2001",244208.2907,5.355082,4.606088,2879455763,2762333047
+"denmark",2002,"denmark 2002",245347.1452,5.374255,4.586689,3097835495,2788169716
+"denmark",2003,"denmark 2003",246304.1427,5.387174,5.406289,4667545719,3532909945
+"denmark",2004,"denmark 2004",252876.0773,5.401177,5.507644,6084676353,4267512974
+"denmark",2005,"denmark 2005",258784.8843,5.415978,4.830782,6979183998,4654498990
+"denmark",2006,"denmark 2006",268911.1574,5.434567,3.898582,8134024126,5025799888
+"denmark",2007,"denmark 2007",271356.2024,5.457415,3.79897,9584154696,6279572138
+"denmark",2008,"denmark 2008",269966.8132,5.489022,3.683691,11170637463,7985884164
+"denmark",2009,"denmark 2009",256720.7626,5.519441,6.411551,10076546336,6924674796
+"denmark",2010,"denmark 2010",261523.9853,5.543819,7.747934,10943014965,7675721048
+"denmark",2011,"denmark 2011",265019.9798,5.566856,7.769598,11296513077,9361263034
+"denmark",2012,"denmark 2012",265620.2495,5.587085,7.797653,10454899509,8229873068
+"denmark",2013,"denmark 2013",268099.3921,5.608784,7.382339,10603160775,9217817682
+"denmark",2014,"denmark 2014",272440.9771,5.639719,6.927884,11567821151,9464826541
+"denmark",2015,"denmark 2015",278823.1533,5.678348,6.278275,10452989967,8307280595
+"denmark",2016,"denmark 2016",287873.6349,5.724456,5.988536,11099859020,8004922097
+"denmark",2017,"denmark 2017",293735.2177,5.760694,5.833254,11843134713,7859159010
+"denmark",2018,"denmark 2018",300759.3951,5.789957,5.132677,12724402715,8119043514
+"estonia",2000,"estonia 2000",23434.7018,1.396985,14.62371,634352795,186432350
+"estonia",2001,"estonia 2001",24836.004,1.388115,13.01181,882832515,272750770
+"estonia",2002,"estonia 2002",26525.446,1.37935,11.21852,849705202,313826248
+"estonia",2003,"estonia 2003",28532.5448,1.37072,10.35088,1029585802,507115721
+"estonia",2004,"estonia 2004",30463.4725,1.36255,10.13928,1355586073,651154957
+"estonia",2005,"estonia 2005",33354.5465,1.354775,8.033425,1428108384,773726377
+"estonia",2006,"estonia 2006",36596.1944,1.34681,5.914059,1765443684,1102325086
+"estonia",2007,"estonia 2007",39367.8367,1.34068,4.592134,2002358381,1269124612
+"estonia",2008,"estonia 2008",37364.4303,1.33709,5.434531,2249932331,2001693892
+"estonia",2009,"estonia 2009",31971.2976,1.334515,13.54691,1362364543,1726479412
+"estonia",2010,"estonia 2010",32831.4317,1.331475,16.70262,1702503669,1945247774
+"estonia",2011,"estonia 2011",35275.382,1.327439,12.33337,2523574567,2908723118
+"estonia",2012,"estonia 2012",36377.6824,1.322696,10.02366,2721447226,3449371847
+"estonia",2013,"estonia 2013",36867.3943,1.317997,8.631536,2676806644,3454179425
+"estonia",2014,"estonia 2014",37968.7435,1.314545,7.355543,2799670218,2710395175
+"estonia",2015,"estonia 2015",38669.2233,1.314608,6.191967,2247031942,1608497253
+"estonia",2016,"estonia 2016",39686.8301,1.31579,6.753039,2300189670,1748089268
+"estonia",2017,"estonia 2017",41968.3474,1.317384,5.762793,2715789865,1967860420
+"estonia",2018,"estonia 2018",43966.527,1.321977,5.374337,3356786073,2235173274
+"finland",1998,"finland 1998",176402.3837,5.153499,11.41814,2006702162,6668024117
+"finland",1999,"finland 1999",184128.1976,5.16547,10.21153,2147579637,5622619737
+"finland",2000,"finland 2000",194758.5919,5.176203,9.800185,2754187022,7733294324
+"finland",2001,"finland 2001",199841.2993,5.188005,9.121559,2652919619,7674202761
+"finland",2002,"finland 2002",203252.7937,5.200598,9.081173,2906807233,7783813633
+"finland",2003,"finland 2003",207325.3731,5.21301,9.016909,4148874241,10097456417
+"finland",2004,"finland 2004",215602.5023,5.228173,8.816031,5688899579,13727759399
+"finland",2005,"finland 2005",221595.4349,5.2461,8.37867,7112427730,16350679259
+"finland",2006,"finland 2006",230520.0819,5.266266,7.714881,9699337976,18034620284
+"finland",2007,"finland 2007",242735.3535,5.288719,6.853993,11416552124,20812946394
+"finland",2008,"finland 2008",244638.4395,5.313398,6.362757,12186668525,23049399563
+"finland",2009,"finland 2009",224884.8259,5.338867,8.237782,8400554097,13069198502
+"finland",2010,"finland 2010",232049.4576,5.363341,8.386981,9143403294,15177862698
+"finland",2011,"finland 2011",237960.9879,5.388272,7.774969,7211053038,16267933593
+"finland",2012,"finland 2012",234635.8292,5.413967,7.68181,10180239972,15525610794
+"finland",2013,"finland 2013",232519.3706,5.438975,8.186392,9213846194,14776965936
+"finland",2014,"finland 2014",231670.8138,5.461507,8.657802,9224853052,13391941156
+"finland",2015,"finland 2015",232930.082,5.479528,9.367311,7671574964,10024347112
+"finland",2016,"finland 2016",239359.6267,5.495297,8.811286,7931201595,9305534406
+"finland",2017,"finland 2017",247186.5767,5.508209,8.63445,10108768762,10787837608
+"finland",2018,"finland 2018",250934.7805,5.515525,7.357432,10542927474,11569194340
+"france",2005,"france 2005",2480212.6301,62.958328,8.493855,112760463556,133869818936
+"france",2006,"france 2006",2540961.0634,63.393406,8.449007,128459367260,154325151228
+"france",2007,"france 2007",2602572.6671,63.781275,7.658579,159127031758,180348666196
+"france",2008,"france 2008",2609207.8206,64.133174,7.062972,176577115410,210804908562
+"france",2009,"france 2009",2534237.0909,64.458715,8.735972,154454841810,178487170140
+"france",2010,"france 2010",2583640.4624,64.773169,8.871792,90208772624,100957630700
+"france",2011,"france 2011",2640291.9636,65.087317,8.811044,107365312476,110916848866
+"france",2012,"france 2012",2648559.6351,65.402998,9.399351,100009710258,114747581713
+"france",2013,"france 2013",2663823.9909,65.735961,9.921325,101407203590,114779841059
+"france",2014,"france 2014",2689295.0243,66.276671,10.29171,105417155257,111475068393
+"france",2015,"france 2015",2719224.5205,66.512558,10.35453,97278096417,99305167579
+"france",2016,"france 2016",2749012.6569,66.68553,10.03868,98340495448,96480645306
+"france",2017,"france 2017",2812004.0824,66.829563,9.406466,106918759820,106005063538
+"france",2018,"france 2018",2862419.49,66.941698,9.016774,115706543058,112258017000
+"germany",1991,"germany 1991",2848505.5901,80.013901,5.569215,26788622272,37585193944
+"germany",1992,"germany 1992",2903426.8696,80.624601,6.621125,30102815752,50465849080
+"germany",1993,"germany 1993",2874992.4516,81.156368,7.869413,29972006704,48001147048
+"germany",1994,"germany 1994",2943936.1818,81.438346,8.401136,33667286200,51772241672
+"germany",1995,"germany 1995",2989119.64,81.678046,8.126454,39616550736,61878033144
+"germany",1996,"germany 1996",3013658.9321,81.914834,8.86304,39422833282,64043591001
+"germany",1997,"germany 1997",3067411.675,82.034775,9.817107,39974526282,66166032225
+"germany",1998,"germany 1998",3129344.1734,82.047197,9.201705,42123411408,62758921112
+"germany",1999,"germany 1999",3188550.0824,82.100243,8.414573,43325507670,56034614423
+"germany",2005,"germany 2005",3369673.4445,82.469421,11.16768,203842046000,277305872000
+"germany",2006,"germany 2006",3498212.6001,82.376447,10.25272,252940746000,340102234000
+"germany",2007,"germany 2007",3602601.9673,82.266373,8.66153,294489706000,405141950000
+"germany",2008,"germany 2008",3637268.5971,82.110097,7.527764,342224781600,481307412142
+"germany",2009,"germany 2009",3430047.89,81.902308,7.743155,282474302166,393310067942
+"germany",2010,"germany 2010",3573388.5293,81.776936,6.967637,179109795487,245649095466
+"germany",2011,"germany 2011",3713613.0627,80.274981,5.827424,203224769040,298238613291
+"germany",2012,"germany 2012",3729193.5664,80.425826,5.380757,185125407371,295189411141
+"germany",2013,"germany 2013",3745163.5866,80.645605,5.231962,184903423816,301798621916
+"germany",2014,"germany 2014",3828519.2712,80.982495,4.981592,197689089326,301174533334
+"germany",2015,"germany 2015",3895125.9284,81.686608,4.624955,188692927829,251616341728
+"germany",2016,"germany 2016",3981987.2353,82.348669,4.122733,194031423037,252020923052
+"germany",2017,"germany 2017",4080144.4164,82.656997,3.746631,218280857375,276695526718
+"germany",2018,"germany 2018",4142466.4313,82.914191,3.38409,238360106274,297494107127
+"greece",1998,"greece 1998",279319.0554,10.720507,11.22358,1962487270,972105765
+"greece",1999,"greece 1999",287901.4034,10.761698,12.09605,1940682806,857710049
+"greece",2005,"greece 2005",362022.0686,10.987316,9.994969,11109107402,3563180044
+"greece",2006,"greece 2006",382485.126,11.020366,9.008785,13182107774,3685177518
+"greece",2007,"greece 2007",395006.7209,11.048466,8.397298,18301075482,4024241630
+"greece",2008,"greece 2008",393682.7667,11.077839,7.760761,21945846668,5362910692
+"greece",2009,"greece 2009",376751.5194,11.107017,9.615903,16983546912,4501871476
+"greece",2010,"greece 2010",356110.6832,11.121344,12.71716,8131529237,2591965425
+"greece",2011,"greece 2011",323588.8947,11.1049,17.87071,7076569883,2947228021
+"greece",2012,"greece 2012",299965.3035,11.04501,24.44174,5806346927,3038001937
+"greece",2013,"greece 2013",290242.1531,10.965209,27.46715,5956645039,3011864924
+"greece",2014,"greece 2014",292389.2982,10.892415,26.49027,6643041376,3019553115
+"greece",2015,"greece 2015",291109.1186,10.820883,24.90084,5793364614,2444288341
+"greece",2016,"greece 2016",290553.2391,10.775971,23.54104,6253937570,2182674154
+"greece",2017,"greece 2017",294926.354,10.754679,21.48901,6879342818,2434132216
+"greece",2018,"greece 2018",300631.3281,10.725886,19.29447,8550370528,2574914247
+"hungary",1999,"hungary 1999",184980.5165,10.237527,6.995957,2363299000,921252000
+"hungary",2005,"hungary 2005",239611.6611,10.087064,7.188301,13910096000,9430186000
+"hungary",2006,"hungary 2006",249270.4391,10.071374,7.494521,15028112000,13401650000
+"hungary",2007,"hungary 2007",249874.1167,10.055778,7.406472,19871884000,17796404000
+"hungary",2008,"hungary 2008",252518.5136,10.038186,7.817173,22520278000,20603910000
+"hungary",2009,"hungary 2009",235600.8976,10.022647,10.02932,17936024000,15881288000
+"hungary",2010,"hungary 2010",237165.9751,10.00002,11.17377,10809130000,10473007000
+"hungary",2011,"hungary 2011",241480.2138,9.958824,11.03269,10774665000,13438325000
+"hungary",2012,"hungary 2012",237926.135,9.920364,11.00812,9675653414,11825259975
+"hungary",2013,"hungary 2013",242596.5871,9.893083,10.18524,10166244736,12015681012
+"hungary",2014,"hungary 2014",252773.7138,9.866466,7.728486,9249007736,10923914837
+"hungary",2016,"hungary 2016",268269.1436,9.814026,5.119285,8926816950,9046722928
+"hungary",2017,"hungary 2017",279865.9063,9.787969,4.157162,10042889356,10397102044
+"hungary",2018,"hungary 2018",294122.2827,9.7676,3.707886,12070373039,9965406770
+"iceland",2005,"iceland 2005",13307.7316,0.296734,2.547312,918033880,50006362
+"iceland",2006,"iceland 2006",14006.2306,0.303784,2.825685,1089484612,157778124
+"iceland",2007,"iceland 2007",15317.9588,0.311567,2.235352,1099712558,223869678
+"iceland",2008,"iceland 2008",15623.0896,0.317404,2.944448,1259172114,287847048
+"iceland",2009,"iceland 2009",14564.2792,0.318501,7.218621,946296630,205261656
+"iceland",2010,"iceland 2010",14063.8687,0.318044,7.548411,752259365,45416969
+"iceland",2011,"iceland 2011",14328.4973,0.319011,7.033196,813471861,41423359
+"iceland",2012,"iceland 2012",14514.2573,0.320723,5.983469,918604180,95071819
+"iceland",2013,"iceland 2013",15114.2551,0.323763,5.381035,899841398,39632808
+"iceland",2014,"iceland 2014",15429.0281,0.327379,4.890404,900330804,79675589
+"iceland",2015,"iceland 2015",16161.7503,0.330818,3.966664,975810230,52888444
+"iceland",2016,"iceland 2016",17232.7955,0.335435,2.968629,798824384,57367369
+"iceland",2017,"iceland 2017",18016.1954,0.343399,2.743788,1221077827,116725758
+"iceland",2018,"iceland 2018",18703.4093,0.352722,2.711352,1280355095,112492129
+"ireland",1998,"ireland 1998",146438.0664,3.703082,7.573754,4658491856,3094594400
+"ireland",1999,"ireland 1999",161819.256,3.741647,5.616338,4629116752,3851018210
+"ireland",2000,"ireland 2000",177104.3183,3.789536,4.332074,5155350458,4558307397
+"ireland",2001,"ireland 2001",186452.6487,3.847198,3.914628,4754687495,5634775785
+"ireland",2002,"ireland 2002",197490.3026,3.917203,4.443757,5168678512,4908803892
+"ireland",2003,"ireland 2003",203454.949,3.979853,4.737063,6416020915,5472274473
+"ireland",2004,"ireland 2004",217130.5015,4.045188,4.538512,7681784104,6363830260
+"ireland",2005,"ireland 2005",229506.6341,4.133839,4.338238,9114842390,6773214257
+"ireland",2006,"ireland 2006",241144.6883,4.232929,4.411824,10542933509,7759018666
+"ireland",2007,"ireland 2007",253983.876,4.375842,4.979443,11590204289,9074984049
+"ireland",2008,"ireland 2008",242604.4248,4.48507,6.773265,10610489454,10537796386
+"ireland",2009,"ireland 2009",230282.03,4.533395,12.60812,7011489431,8970621696
+"ireland",2010,"ireland 2010",234450.0881,4.554763,14.53227,7178710081,9363101895
+"ireland",2011,"ireland 2011",235255.2787,4.574888,15.35064,8146429660,9727349107
+"ireland",2012,"ireland 2012",235786.2891,4.593697,15.44939,7879579650,9205379119
+"ireland",2013,"ireland 2013",238973.2597,4.614669,13.73561,7921953979,9263893888
+"ireland",2014,"ireland 2014",259421.4601,4.64544,11.85948,8868652084,10608836858
+"ireland",2015,"ireland 2015",324698.4755,4.687787,9.909575,9051554937,10117662685
+"ireland",2016,"ireland 2016",336640.2849,4.739597,8.37511,8692598881,10585553074
+"ireland",2017,"ireland 2017",364060.627,4.79249,6.71373,9302952384,12046967367
+"ireland",2018,"ireland 2018",393803.91,4.857015,5.738596,10893608393,13442149239
+"israel",1995,"israel 1995",142432.2665,5.5449,6.869637,1512205008,2967929008
+"israel",1996,"israel 1996",149574.5895,5.6851,6.677445,1545470000,3376681000
+"israel",1997,"israel 1997",155645.0592,5.8289,7.673759,1779838000,3783848000
+"israel",1998,"israel 1998",162219.9735,5.9707,8.535764,2203056000,3281943968
+"israel",1999,"israel 1999",168095.6944,6.1253,8.884659,2835664000,3787725000
+"israel",2000,"israel 2000",182963.7082,6.2892,8.770606,3209117000,4367130000
+"israel",2001,"israel 2001",183252.8823,6.439,9.348134,3730079000,4023552000
+"israel",2002,"israel 2002",182929.9724,6.57,10.29844,4494996000,4341068000
+"israel",2003,"israel 2003",184950.3242,6.6897,10.71266,4866936000,5471681000
+"israel",2004,"israel 2004",194007.767,6.809,10.36771,6250764000,6713506000
+"israel",2005,"israel 2005",202046.828,6.930128,8.989903,7545333000,7232920000
+"israel",2006,"israel 2006",213722.1646,7.053707,8.403837,8774480000,8080730000
+"israel",2007,"israel 2007",226680.3531,7.180115,7.318558,10975445000,8785934000
+"israel",2008,"israel 2008",234238.8962,7.308795,6.099141,11556119000,14759001000
+"israel",2009,"israel 2009",236640.571,7.485565,7.544086,8597500000,10969689000
+"israel",2010,"israel 2010",249835.8385,7.623561,6.636595,11844995000,16266152000
+"israel",2011,"israel 2011",262024.6046,7.765832,5.602376,13997317000,19640469000
+"israel",2012,"israel 2012",268234.4567,7.910525,6.85,13091191000,18507298000
+"israel",2013,"israel 2013",279686.1367,8.059456,6.208333,14098183000,20963832000
+"israel",2014,"israel 2014",290252.8188,8.215668,5.908333,15755203000,21544740000
+"israel",2015,"israel 2015",296964.0155,8.380149,5.241667,14389580000,20145320000
+"israel",2016,"israel 2016",308788.9337,8.546009,4.808333,14772814000,16907988000
+"israel",2017,"israel 2017",319827.0573,8.7133,4.216667,15654286000,14886979000
+"israel",2018,"israel 2018",330832.9141,8.872943,4,21300403000,16930052000
+"italy",1998,"italy 1998",2131539.4441,56.906744,11.84062,16104743194,38913359749
+"italy",1999,"italy 1999",2166192.4692,56.916316,11.42852,16590676655,32565296283
+"italy",2000,"italy 2000",2248225.2063,56.942108,10.58827,20224008051,35967360267
+"italy",2001,"italy 2001",2292096.4335,56.974097,9.524396,20346471637,38518232826
+"italy",2002,"italy 2002",2297917.0519,57.059013,9.007127,21859368628,40316164207
+"italy",2003,"italy 2003",2301102.5829,57.3132,8.672171,28266816463,46490916561
+"italy",2004,"italy 2004",2333860.9448,57.685327,7.998679,37201726978,57551670157
+"italy",2005,"italy 2005",2352948.4026,57.969482,7.728764,41484235961,61479210143
+"italy",2006,"italy 2006",2395081.2304,58.14398,6.775816,54089454414,71854742557
+"italy",2007,"italy 2007",2430697.8362,58.438309,6.075274,67586092203,92085819283
+"italy",2008,"italy 2008",2407314.2109,58.826733,6.723351,72820096121,105754104084
+"italy",2009,"italy 2009",2280185.459,59.095367,7.747658,52499881024,84983608396
+"italy",2010,"italy 2010",2319251.7816,59.277414,8.359172,70849803217,92690741063
+"italy",2011,"italy 2011",2335656.6229,59.379446,8.353756,80887047453,107004571618
+"italy",2012,"italy 2012",2266032.8999,59.539725,10.65126,64385225548,103773145053
+"italy",2013,"italy 2013",2224313.7511,60.233944,12.14521,64605639131,111341227025
+"italy",2014,"italy 2014",2224212.5994,60.789144,12.6798,68354592055,111414145513
+"italy",2015,"italy 2015",2241523.7429,60.730585,11.89388,63016413852,92302475973
+"italy",2016,"italy 2016",2270517.0171,60.627494,11.68803,62344462001,90728459911
+"italy",2017,"italy 2017",2308386.0405,60.536713,11.21117,67753284394,98254397037
+"italy",2018,"italy 2018",2326809.7162,60.421797,10.60792,76222819883,100467600866
+"japan",1970,"japan 1970",1673414.6096,104.665171,1.15,778857860,5347725672
+"japan",1971,"japan 1971",1752048.2289,106.1,1.225,654376645,6658536096
+"japan",1972,"japan 1972",1899457.6346,107.595,1.416667,891221975,7959107392
+"japan",1973,"japan 1973",2052033.4681,109.104,1.266667,1914212899,11436249875
+"japan",1974,"japan 1974",2026891.1368,110.573,1.375,2180741378,19269959591
+"japan",1975,"japan 1975",2089554.015,111.939643,1.891667,1606704154,22958068133
+"japan",1976,"japan 1976",2172613.4547,113.094,2.008333,2048231882,24567371347
+"japan",1977,"japan 1977",2267998.5277,114.165,2.025,2206170394,29261152423
+"japan",1978,"japan 1978",2387566.0833,115.19,2.241667,3034812174,36287242468
+"japan",1979,"japan 1979",2518501.2061,116.155,2.083333,4507123040,37206842220
+"japan",1980,"japan 1980",2589462.2747,117.060396,2.008333,5116135019,50258995330
+"japan",1981,"japan 1981",2698461.4544,117.902,2.208333,5365682034,59102436249
+"japan",1982,"japan 1982",2787846.8227,118.728,2.35,5183671993,53260148382
+"japan",1983,"japan 1983",2886063.9204,119.536,2.658333,5274262724,51647872524
+"japan",1984,"japan 1984",3015994.3685,120.305,2.708333,6755426689,53245593673
+"japan",1985,"japan 1985",3173832.8431,121.048923,2.616667,6342188868,53611203571
+"japan",1986,"japan 1986",3279411.2056,121.66,2.758333,7326978216,51334098304
+"japan",1987,"japan 1987",3434549.192,122.239,2.85,10674120696,54134966179
+"japan",1988,"japan 1988",3667584.0453,122.745,2.533333,16425758762,64175710953
+"japan",1989,"japan 1989",3845756.6604,123.205,2.266667,20530452035,66611331183
+"japan",1990,"japan 1990",4033918.499,123.611167,2.108333,22008147622,75134368731
+"japan",1991,"japan 1991",4171777.5331,124.101,2.1,25820401416,89841788796
+"japan",1992,"japan 1992",4207157.1093,124.567,2.15,28876292825,107296418312
+"japan",1993,"japan 1993",4185367.4077,124.938,2.5,35162317249,123261784636
+"japan",1994,"japan 1994",4226930.8836,125.265,2.891667,45436248598,136406093157
+"japan",1995,"japan 1995",4342839.2597,125.570246,3.15,63548647903,158412133382
+"japan",1996,"japan 1996",4477467.246,125.864,3.35,69659430672,148259400866
+"japan",1997,"japan 1997",4525646.8189,126.166,3.4,70069706692,149648425999
+"japan",1998,"japan 1998",4474578.9754,126.486,4.108333,59436537071,123347219784
+"japan",1999,"japan 1999",4463305.0825,126.686,4.683333,70134919047,127146284848
+"japan",2000,"japan 2000",4587368.5756,126.925843,4.716667,92067283465,154896282519
+"japan",2001,"japan 2001",4606008.7012,127.291,5.033333,90244388419,133482777215
+"japan",2002,"japan 2002",4611443.4587,127.435,5.375,91749727111,144938739797
+"japan",2003,"japan 2003",4681916.4668,127.619,5.258333,109289891899,174736937905
+"japan",2004,"japan 2004",4785138.1118,127.687,4.716667,134530840366,214353582815
+"japan",2005,"japan 2005",4864699.187,127.767994,4.425,149845160181,229055623615
+"japan",2006,"japan 2006",4933778.2344,127.900515,4.141667,164858135123,252865635507
+"japan",2007,"japan 2007",5015391.9987,128.032743,3.841667,178612444600,293688087475
+"japan",2008,"japan 2008",4960546.6509,128.08396,3.991667,199488841039,339533004829
+"japan",2009,"japan 2009",4691862.9673,128.031514,5.066667,163998846071,262427762804
+"japan",2010,"japan 2010",4888533.6293,128.057352,5.05,209626725335,364063253368
+"japan",2011,"japan 2011",4882891.2183,127.834233,4.583333,249276669245,391154249889
+"japan",2012,"japan 2012",4955894.8164,127.592657,4.35,253581450165,382819320089
+"japan",2013,"japan 2013",5055025.9866,127.413888,4.025,246110080083,331918182990
+"japan",2014,"japan 2014",5073968.1536,127.23715,3.591667,249680894095,320081358677
+"japan",2015,"japan 2015",5136018.7777,127.094745,3.375,225293502738,280463140727
+"japan",2016,"japan 2016",5162825.943,126.932772,3.116667,223614475192,281225621272
+"japan",2017,"japan 2017",5274771.0197,126.70621,2.808333,241212325320,307651013311
+"japan",2018,"japan 2018",5291819.4667,126.44318,2.441667,259414544908,333755752265
+"korea",1989,"korea 1989",490488.4287,42.449038,2.583333,4609973242,13780838035
+"korea",1990,"korea 1990",538611.375,42.869283,2.458333,5271757468,15537922264
+"korea",1991,"korea 1991",594378.9347,43.295704,2.45,6769245931,21563077236
+"korea",1992,"korea 1992",631084.8396,43.747962,2.525,7996581930,28027821286
+"korea",1993,"korea 1993",674293.6025,44.194628,2.9,8417566063,32980096100
+"korea",1994,"korea 1994",736370.0258,44.64154,2.475,11252457673,39657128972
+"korea",1995,"korea 1995",806845.0859,45.092991,2.066667,16250950567,50641903942
+"korea",1996,"korea 1996",868121.0093,45.524681,2.058333,17641228422,56100004414
+"korea",1997,"korea 1997",919532.7455,45.95358,2.616667,18159884808,58318075292
+"korea",1998,"korea 1998",869223.0928,46.286503,6.95,11490607151,53322596118
+"korea",1999,"korea 1999",967520.2423,46.616677,6.583333,17757152384,55286783627
+"korea",2000,"korea 2000",1053865.8707,47.008111,4.425,25592415555,65354386771
+"korea",2001,"korea 2001",1105003.6537,47.370164,4,24510507091,61427008040
+"korea",2002,"korea 2002",1190366.7626,47.644736,3.258333,29462577250,69386533581
+"korea",2003,"korea 2003",1227831.0708,47.89233,3.55,35608266408,89132873129
+"korea",2004,"korea 2004",1291646.2569,48.082519,3.658333,46559963531,116846927151
+"korea",2005,"korea 2005",1347297.3876,48.184561,3.75,56840246219,136982271948
+"korea",2006,"korea 2006",1418223.5223,48.438292,3.475,70302580826,160452566761
+"korea",2007,"korea 2007",1500474.0821,48.683638,3.258333,87218357599,191191137525
+"korea",2008,"korea 2008",1545683.1392,49.054708,3.175,105213488146,219257654001
+"korea",2009,"korea 2009",1557935.7538,49.307835,3.633333,79231788934,199356723704
+"korea",2010,"korea 2010",1663950.5542,49.554112,3.708333,103050331611,261687762973
+"korea",2011,"korea 2011",1725278.2437,49.936638,3.408333,122167988403,307975526705
+"korea",2012,"korea 2012",1766728.5882,50.199853,3.225,115186104658,310348256824
+"korea",2013,"korea 2013",1822640.4004,50.428893,3.1,119702360162,325348503656
+"korea",2014,"korea 2014",1881009.6171,50.746659,3.491667,130654221532,328675106348
+"korea",2015,"korea 2015",1933849.1197,51.014947,3.591667,129645252444,307407995426
+"korea",2016,"korea 2016",1990837.3658,51.245707,3.675,127217796662,290085590686
+"korea",2017,"korea 2017",2053740.5747,51.446201,3.683333,147233336437,334912029236
+"korea",2018,"korea 2018",2108471.3305,51.635256,3.833333,160028460272,358602963952
+"latvia",2002,"latvia 2002",32489.1638,2.310176,12.4844,225496680,154276082
+"latvia",2003,"latvia 2003",35230.0749,2.287955,11.6386,323920620,165301391
+"latvia",2004,"latvia 2004",38168.7445,2.263123,11.74615,465330670,317502203
+"latvia",2005,"latvia 2005",42269.3569,2.238799,10.03827,530310213,438855010
+"latvia",2006,"latvia 2006",47294.3756,2.218351,7.043975,705480436,517798150
+"latvia",2007,"latvia 2007",52028.9484,2.200325,6.063879,999649015,719955330
+"latvia",2008,"latvia 2008",50287.8685,2.177324,7.739463,1147530855,947765702
+"latvia",2009,"latvia 2009",43127.8089,2.141668,17.55346,560302595,758095900
+"latvia",2010,"latvia 2010",41198.5114,2.097553,19.48375,817089136,934243893
+"latvia",2011,"latvia 2011",43787.8931,2.05971,16.21216,1109560197,1104942269
+"latvia",2012,"latvia 2012",45598.1841,2.034324,15.0552,1220926508,1336595555
+"latvia",2013,"latvia 2013",46659.7138,2.012647,11.8683,1275650634,1291557019
+"latvia",2014,"latvia 2014",47553.3341,1.993785,10.84309,1337461047,1127606222
+"latvia",2015,"latvia 2015",49103.8243,1.977523,9.875636,1276189524,1115629403
+"latvia",2016,"latvia 2016",49974.8326,1.959535,9.641258,1221420701,1045308043
+"latvia",2017,"latvia 2017",51867.5363,1.942247,8.716354,1420737687,1229626546
+"latvia",2018,"latvia 2018",54089.9695,1.92717,7.415344,1699185735,1284199753
+"lithuania",2005,"lithuania 2005",65692.9733,3.322525,8.32191,1090635663,1489124189
+"lithuania",2006,"lithuania 2006",70558.4868,3.269903,5.778923,1199596839,1799594636
+"lithuania",2007,"lithuania 2007",78381.274,3.231297,4.254303,1622028557,2516975425
+"lithuania",2008,"lithuania 2008",80441.1949,3.198234,5.812195,1893497756,3694459267
+"lithuania",2009,"lithuania 2009",68524.5051,3.162911,13.78231,1126992120,2047888639
+"lithuania",2010,"lithuania 2010",69540.1976,3.097292,17.81879,1328093200,2869664210
+"lithuania",2011,"lithuania 2011",73730.4381,3.028119,15.38251,1528571716,4197359380
+"lithuania",2012,"lithuania 2012",76556.749,2.987773,13.36761,1579869122,4870725341
+"lithuania",2013,"lithuania 2013",79280.9632,2.957689,11.77156,1676611119,5727241665
+"lithuania",2014,"lithuania 2014",82062.1165,2.932366,10.69969,1805759114,6453276663
+"lithuania",2015,"lithuania 2015",83730.5611,2.904908,9.119895,1631902844,3803225110
+"lithuania",2016,"lithuania 2016",85870.6636,2.868234,7.860914,1553256844,3739957685
+"lithuania",2017,"lithuania 2017",89518.0859,2.828398,7.07182,1819440198,5052211340
+"lithuania",2018,"lithuania 2018",92780.8022,2.801541,6.152077,2153696824,5298658504
+"luxembourg",2003,"luxembourg 2003",42521.3019,0.451626,3.656025,204385843,571316815
+"luxembourg",2004,"luxembourg 2004",44057.2465,0.458098,5.138539,289441496,651719029
+"luxembourg",2005,"luxembourg 2005",45454.9374,0.465152,4.489393,392334027,833608247
+"luxembourg",2006,"luxembourg 2006",47808.815,0.472641,4.731707,583150508,1032657754
+"luxembourg",2007,"luxembourg 2007",51803.0278,0.479992,4.066088,573319695,1189936188
+"luxembourg",2008,"luxembourg 2008",51140.1638,0.488647,5.050495,825339584,1178681673
+"luxembourg",2009,"luxembourg 2009",48911.165,0.497783,5.111732,683047572,1047530693
+"luxembourg",2010,"luxembourg 2010",51290.6778,0.506953,4.351644,747981917,1290997705
+"luxembourg",2011,"luxembourg 2011",52593.0686,0.518351,4.896927,834858183,1366693270
+"luxembourg",2012,"luxembourg 2012",52407.6678,0.530952,5.138287,823311857,1410613118
+"luxembourg",2013,"luxembourg 2013",54322.8381,0.543358,5.836521,950560308,1276489160
+"luxembourg",2014,"luxembourg 2014",56656.9744,0.556322,5.854882,1018488785,1345348493
+"luxembourg",2015,"luxembourg 2015",59096.8688,0.569605,6.658826,874168146,1243433326
+"luxembourg",2016,"luxembourg 2016",61800.3143,0.583459,6.285928,939885818,1128905323
+"luxembourg",2017,"luxembourg 2017",62913.4503,0.596337,5.516146,990676963,1184614998
+"luxembourg",2018,"luxembourg 2018",64870.5847,0.60795,5.582682,1148652430,1351745135
+"netherlands",2000,"netherlands 2000",717129.0799,15.925505,2.947122,19046243459,10145889047
+"netherlands",2001,"netherlands 2001",733816.3517,16.046182,2.251434,19852986654,10942695764
+"netherlands",2002,"netherlands 2002",735410.7406,16.148921,2.755692,19117911180,12361463996
+"netherlands",2003,"netherlands 2003",736555.3785,16.225303,3.682191,29437839823,16659010175
+"netherlands",2004,"netherlands 2004",751175.6027,16.281777,4.557948,41433792549,21979814418
+"netherlands",2005,"netherlands 2005",766581.2835,16.319871,5.873712,48847175242,26348332643
+"netherlands",2006,"netherlands 2006",793112.5774,16.346096,5.004153,57201095553,30563089459
+"netherlands",2007,"netherlands 2007",823035.4659,16.381696,4.154393,66631698215,38352441266
+"netherlands",2008,"netherlands 2008",840898.0086,16.44559,3.655021,69287709971,40265266173
+"netherlands",2009,"netherlands 2009",810063.254,16.530387,4.347569,56712393756,35049123999
+"netherlands",2010,"netherlands 2010",820940.2928,16.61539,4.989325,72117288490,40265451835
+"netherlands",2011,"netherlands 2011",833674.6303,16.693074,4.976648,78227193607,46053652174
+"netherlands",2012,"netherlands 2012",825084.8304,16.754963,5.820699,75634755128,47322631726
+"netherlands",2013,"netherlands 2013",824010.7743,16.80443,7.242696,77203050853,48983202216
+"netherlands",2014,"netherlands 2014",835739.7058,16.865008,7.416319,82453349526,52092300614
+"netherlands",2015,"netherlands 2015",852113.2649,16.939925,6.87228,67040763091,42438564525
+"netherlands",2016,"netherlands 2016",870789.1482,17.030314,6.00791,69839907358,43395336215
+"netherlands",2017,"netherlands 2017",896136.972,17.131295,4.840418,80259102265,51875782037
+"netherlands",2018,"netherlands 2018",919413.7142,17.231622,3.832352,90750534582,54860598624
+"new zealand",1970,"new zealand 1970",57569.5925,2.82802,0.09267841,27662094,25800235
+"new zealand",1971,"new zealand 1971",59748.8654,2.875305,0.1829826,56189121,27373825
+"new zealand",1972,"new zealand 1972",62807.4743,2.929135,0.4508566,38253964,42172243
+"new zealand",1973,"new zealand 1973",67706.6322,2.992335,0.1746725,70287781,50177308
+"new zealand",1974,"new zealand 1974",71764.5886,3.058435,0.0838223,171506095,64831019
+"new zealand",1975,"new zealand 1975",70521.8535,3.117765,0.2465078,89229250,84943643
+"new zealand",1976,"new zealand 1976",71176.8691,3.153535,0.322841,152504618,132328734
+"new zealand",1977,"new zealand 1977",68375.1071,3.16483,0.317965,107355512,156040694
+"new zealand",1978,"new zealand 1978",68592.5871,3.165705,1.657459,126626674,253068166
+"new zealand",1979,"new zealand 1979",70096.0029,3.16462,1.942502,170541803,249674708
+"new zealand",1980,"new zealand 1980",70996.2337,3.170245,2.242846,179934460,359780882
+"new zealand",1981,"new zealand 1981",74300.8689,3.18546,3.601532,183186868,327844883
+"new zealand",1982,"new zealand 1982",74991.9552,3.210645,3.536494,208860034,348123923
+"new zealand",1983,"new zealand 1983",77610.8085,3.24577,5.663189,176648972,256826537
+"new zealand",1984,"new zealand 1984",81330.7016,3.27885,5.743741,241337757,253843299
+"new zealand",1985,"new zealand 1985",82643.9171,3.29805,4.181687,221908978,248832832
+"new zealand",1986,"new zealand 1986",79846.9879,3.30831,4.175,238386539,260353412
+"new zealand",1987,"new zealand 1987",81172.3277,3.327785,4.225,352997715,253787883
+"new zealand",1988,"new zealand 1988",82800.7755,3.34368,5.8,440197448,291025517
+"new zealand",1989,"new zealand 1989",83231.1889,3.357535,7.275,524378068,356521137
+"new zealand",1990,"new zealand 1990",83768.4479,3.390085,7.975,537712800,432858022
+"new zealand",1991,"new zealand 1991",82415.0706,3.4951,10.6,590080219,540490902
+"new zealand",1992,"new zealand 1992",83456.2466,3.5317,10.65,812787314,619239341
+"new zealand",1993,"new zealand 1993",88907.6444,3.5722,9.8,844780031,678844164
+"new zealand",1994,"new zealand 1994",93568.6879,3.6201,8.35,1106351661,645307515
+"new zealand",1995,"new zealand 1995",97839.4799,3.6734,6.45,1383794779,824607947
+"new zealand",1996,"new zealand 1996",101119.1087,3.732,6.3,1457953207,847118145
+"new zealand",1997,"new zealand 1997",104148.6734,3.7813,6.825,1644005604,871341631
+"new zealand",1998,"new zealand 1998",105261.8376,3.815,7.725,1472047021,654003038
+"new zealand",1999,"new zealand 1999",110735.2106,3.8351,7.025,1931036757,677938623
+"new zealand",2000,"new zealand 2000",113254.0383,3.8577,6.15,2071604179,835218614
+"new zealand",2001,"new zealand 2001",117591.514,3.8805,5.45,2099176478,822340115
+"new zealand",2002,"new zealand 2002",123539.2512,3.9485,5.3,2472464588,904355843
+"new zealand",2003,"new zealand 2003",129167.2093,4.0272,4.775,3200167564,982162551
+"new zealand",2004,"new zealand 2004",133381.9263,4.0875,4.025,4268343812,1283076236
+"new zealand",2005,"new zealand 2005",137842.1608,4.1339,3.825,5266723895,1209316250
+"new zealand",2006,"new zealand 2006",141386.2514,4.1846,3.85,5627085398,1406857589
+"new zealand",2007,"new zealand 2007",146845.9846,4.2238,3.575,7176375484,1465874066
+"new zealand",2008,"new zealand 2008",144421.8782,4.2598,4.025,8017377936,1820279005
+"new zealand",2009,"new zealand 2009",147221.0807,4.3026,5.825,6104956184,1527900201
+"new zealand",2010,"new zealand 2010",148697.9746,4.3507,6.15,7850900701,1753501018
+"new zealand",2011,"new zealand 2011",152731.5845,4.384,5.95,11733032985,2521416951
+"new zealand",2012,"new zealand 2012",156512.8571,4.4081,6.4,9855677522,2166390190
+"new zealand",2013,"new zealand 2013",159715.9511,4.4421,5.75,10844057089,2288653859
+"new zealand",2014,"new zealand 2014",165330.2694,4.5097,5.375,11456835104,2160641708
+"new zealand",2015,"new zealand 2015",172476.7987,4.5957,5.35,11128881729,2217472591
+"new zealand",2016,"new zealand 2016",178978.4657,4.6932,5.1,11136133933,2059604258
+"new zealand",2017,"new zealand 2017",186155.3059,4.7939,4.7,12351081929,1956074946
+"new zealand",2018,"new zealand 2018",191416.5033,4.8855,4.3,13453464225,1990574536
+"norway",2000,"norway 2000",245957.2437,4.490973,3.331001,2926649587,1352366380
+"norway",2001,"norway 2001",251060.3409,4.513747,3.509895,2811296912,1563104385
+"norway",2002,"norway 2002",254691.3115,4.538157,3.760192,3672631133,1655288491
+"norway",2003,"norway 2003",257009.2715,4.564856,4.043806,3928332758,2308244070
+"norway",2004,"norway 2004",267211.035,4.591909,4.184015,5352782886,2609936732
+"norway",2005,"norway 2005",274226.5475,4.623293,4.380952,6566926858,3190732784
+"norway",2006,"norway 2006",280807.411,4.660673,3.399901,7801392904,3838582315
+"norway",2007,"norway 2007",289215.4921,4.709156,2.495641,10033982276,5104629523
+"norway",2008,"norway 2008",290593.2569,4.768215,2.549649,11335618835,6649159845
+"norway",2009,"norway 2009",285574.6523,4.828716,3.1025,9239786708,6217249358
+"norway",2010,"norway 2010",287578.9118,4.889253,3.521047,11423646888,6106392473
+"norway",2011,"norway 2011",290401.4111,4.953089,3.214387,13569281287,6147463064
+"norway",2012,"norway 2012",298251.1757,5.018574,3.123188,13249953488,6410857155
+"norway",2013,"norway 2013",301335.3103,5.080171,3.422888,13357918561,6219774365
+"norway",2014,"norway 2014",307270.2428,5.137427,3.484511,14256421389,6988369669
+"norway",2015,"norway 2015",313314.6431,5.189898,4.295396,12647278976,6246505389
+"norway",2016,"norway 2016",316671.9943,5.236152,4.677024,12629996113,5376996649
+"norway",2017,"norway 2017",324029.103,5.276965,4.162159,14373207815,4321375625
+"norway",2018,"norway 2018",328207.6145,5.311916,3.799347,14810066966,4321423825
+"poland",2000,"poland 2000",598144.4262,38.258478,16.10612,2948690000,1738783000
+"poland",2001,"poland 2001",605607.8378,38.248076,18.24322,3811300000,2089612000
+"poland",2002,"poland 2002",617972.6668,38.232301,19.93272,4632639000,2293337000
+"poland",2003,"poland 2003",639987.0857,38.195177,19.61964,6108954000,2735453000
+"poland",2004,"poland 2004",672854.5564,38.180249,18.97837,8525989111,5319831755
+"poland",2005,"poland 2005",696358.8602,38.161313,17.75269,10211043086,7064204844
+"poland",2006,"poland 2006",739389.1708,38.132277,13.84889,13816790695,8756336430
+"poland",2007,"poland 2007",791406.8325,38.115967,9.607605,19836561937,11497886507
+"poland",2008,"poland 2008",825039.9217,38.115909,7.120995,26782854492,16103146139
+"poland",2009,"poland 2009",848307.0601,38.153389,8.166371,21322129081,11013886375
+"poland",2010,"poland 2010",878909.2976,38.516689,9.640347,26150646027,13800640909
+"poland",2011,"poland 2011",923006.8523,38.52567,9.632815,29530226174,17251697322
+"poland",2012,"poland 2012",937847.9311,38.533789,10.08882,28393480223,18712054156
+"poland",2013,"poland 2013",950901.7559,38.502396,10.32862,32089444522,21292372652
+"poland",2014,"poland 2014",982456.9116,38.483957,8.990872,37057703634,21186190168
+"poland",2015,"poland 2015",1020174.11,38.454576,7.50308,36779782482,17182833355
+"poland",2016,"poland 2016",1051431.2887,38.426809,6.161703,38070685502,16376770213
+"poland",2017,"poland 2017",1103348.0031,38.422346,4.887239,43417493009,18132845416
+"poland",2018,"poland 2018",1162366.2999,38.413139,3.846072,52761618704,19698277946
+"portugal",1998,"portugal 1998",276321.0403,10.160196,5.072283,1456172024,1283015037
+"portugal",1999,"portugal 1999",287115.7332,10.217828,4.515511,1395423055,1040665660
+"portugal",2000,"portugal 2000",298072.5803,10.289898,4.029012,1559495565,1321518575
+"portugal",2001,"portugal 2001",303866.1349,10.362722,4.008832,1537226320,1465224918
+"portugal",2002,"portugal 2002",306208.7123,10.419631,4.993313,1590640778,1595445721
+"portugal",2003,"portugal 2003",303359.3772,10.458821,6.264945,1943505256,2142686841
+"portugal",2004,"portugal 2004",308785.6731,10.483861,6.622547,2671781663,2658375740
+"portugal",2005,"portugal 2005",311199.9081,10.50333,7.580669,2736930192,3069336579
+"portugal",2006,"portugal 2006",316257.0119,10.522288,7.647437,3322776994,4049519777
+"portugal",2007,"portugal 2007",324184.2468,10.542964,7.963667,4256484463,5563336534
+"portugal",2008,"portugal 2008",325219.2004,10.558177,7.551991,4742792612,7405549436
+"portugal",2009,"portugal 2009",315065.5965,10.568247,9.432886,3518118893,5460387616
+"portugal",2010,"portugal 2010",320540.2576,10.5731,10.76982,4432785017,5610102901
+"portugal",2011,"portugal 2011",315103.3636,10.55756,12.68106,4841363677,7066205804
+"portugal",2012,"portugal 2012",302318.6925,10.514844,15.53028,4094798675,8496255196
+"portugal",2013,"portugal 2013",299529.368,10.457295,16.1838,4470971293,9183021343
+"portugal",2014,"portugal 2014",301902.2112,10.401062,13.89509,4967455237,9397584762
+"portugal",2015,"portugal 2015",307312.4403,10.358076,12.44469,4591120800,6882500974
+"portugal",2016,"portugal 2016",313518.5727,10.325452,11.06846,5256596805,5734159316
+"portugal",2017,"portugal 2017",324511.61,10.3003,8.870407,6050212931,6606975728
+"portugal",2018,"portugal 2018",333069.7779,10.283822,6.994051,7317323810,7060713848
+"slovenia",1999,"slovenia 1999",47185.5452,1.985557,7.417529,368675581,406181133
+"slovenia",2000,"slovenia 2000",48918.3726,1.990272,6.731878,430842758,482662539
+"slovenia",2001,"slovenia 2001",50491.8618,1.992035,6.175611,538244887,570339347
+"slovenia",2002,"slovenia 2002",52261.9042,1.995718,6.310935,581007253,652536459
+"slovenia",2003,"slovenia 2003",53808.9926,1.996773,6.675458,850755096,835433118
+"slovenia",2004,"slovenia 2004",56154.6305,1.997004,6.294299,1136161558,1193754526
+"slovenia",2005,"slovenia 2005",58287.3667,2.001114,6.503201,1347272963,1287823144
+"slovenia",2006,"slovenia 2006",61636.7903,2.008516,5.952952,1644544077,1548566328
+"slovenia",2007,"slovenia 2007",65939.1383,2.019406,4.824745,2329626390,1897356645
+"slovenia",2008,"slovenia 2008",68253.4974,2.022629,4.375046,2806681153,2313127809
+"slovenia",2009,"slovenia 2009",63101.424,2.042335,5.847313,2147771060,1746043501
+"slovenia",2010,"slovenia 2010",63949.3484,2.049261,7.237447,2681199297,1944430585
+"slovenia",2011,"slovenia 2011",64500.1627,2.052496,8.169436,2971619976,2484532028
+"slovenia",2012,"slovenia 2012",62797.7205,2.056262,8.838806,2859291894,2799898179
+"slovenia",2013,"slovenia 2013",62151.3536,2.059114,10.10859,2752286953,2923661031
+"slovenia",2014,"slovenia 2014",63871.8013,2.061623,9.673278,3286951009,2912458716
+"slovenia",2015,"slovenia 2015",65283.4203,2.063077,8.962607,3093283322,2322678939
+"slovenia",2016,"slovenia 2016",67321.3726,2.064241,8.006598,3285091335,2398052931
+"slovenia",2017,"slovenia 2017",70575.2724,2.066161,6.569075,3818812423,2724412098
+"slovenia",2018,"slovenia 2018",73481.7157,2.07005,5.110229,4482620216,2907503099
+"spain",1999,"spain 1999",1251417.2499,40.369667,15.70546,10302897061,12608015065
+"spain",2000,"spain 2000",1317066.5322,40.554387,13.91963,11587564959,13221007223
+"spain",2001,"spain 2001",1368866.0852,40.766049,10.54965,11967134107,13061848135
+"spain",2002,"spain 2002",1406250.1223,41.42352,11.44908,14132575456,14436705224
+"spain",2003,"spain 2003",1448183.022,42.196231,11.48588,19394871152,17504844186
+"spain",2004,"spain 2004",1493406.8125,42.859172,10.96565,26335828884,21546242082
+"spain",2005,"spain 2005",1547946.5173,43.662613,9.151379,32539508183,23636936738
+"spain",2006,"spain 2006",1611454.5388,44.360521,8.454682,40079751191,26481374475
+"spain",2007,"spain 2007",1669542.4467,45.236004,8.231978,50780092861,33219902809
+"spain",2008,"spain 2008",1684353.7121,45.983169,11.2443,56955827657,39949649700
+"spain",2009,"spain 2009",1620967.5754,46.36755,17.85731,38196353154,33135653227
+"spain",2010,"spain 2010",1623609.9184,46.562483,19.8598,47083753775,38332175267
+"spain",2011,"spain 2011",1610387.6702,46.736257,21.39048,51493319250,45879935494
+"spain",2012,"spain 2012",1562729.1924,46.766403,24.78815,45507989489,47835058724
+"spain",2013,"spain 2013",1540297.8673,46.593236,26.0919,46963483967,55455139827
+"spain",2014,"spain 2014",1561614.1718,46.455123,24.44284,52471155258,54290646714
+"spain",2015,"spain 2015",1621504.7716,46.410149,22.05655,51981693731,47732193979
+"spain",2016,"spain 2016",1670657.4673,46.449874,19.63388,54297203510,46142818356
+"spain",2017,"spain 2017",1718969.0071,46.532869,17.22489,61401243930,50925515169
+"spain",2018,"spain 2018",1759382.1083,46.733038,15.25788,68939376011,53388369659
+"sweden",2001,"sweden 2001",352642.6918,8.895963,4.850808,3554927210,8894225195
+"sweden",2002,"sweden 2002",360389.9734,8.924959,5.072618,3702305911,9588881109
+"sweden",2003,"sweden 2003",368714.2992,8.958233,5.677462,4936368579,11906328705
+"sweden",2004,"sweden 2004",384704.9079,8.993533,6.525636,6324115633,14913725922
+"sweden",2005,"sweden 2005",395702.8643,9.029567,7.475815,7757657223,15782243065
+"sweden",2006,"sweden 2006",414153.5928,9.080506,7.068759,9363191128,17548596746
+"sweden",2007,"sweden 2007",428397.2534,9.148093,6.163466,11770038176,21079382574
+"sweden",2008,"sweden 2008",426467.0699,9.219639,6.234297,12490346101,25509384417
+"sweden",2009,"sweden 2009",407959.2922,9.29851,8.346865,9500895130,18987671912
+"sweden",2010,"sweden 2010",432241.4658,9.37813,8.610753,13147926444,23394332495
+"sweden",2011,"sweden 2011",446053.021,9.449216,7.80387,14572914654,30645756640
+"sweden",2012,"sweden 2012",443428.8707,9.519378,7.975423,13724968790,25242943818
+"sweden",2013,"sweden 2013",448695.8111,9.600375,8.052903,13503680133,25045175169
+"sweden",2014,"sweden 2014",460621.2407,9.696105,7.955972,14705039532,23976195721
+"sweden",2015,"sweden 2015",481299.8252,9.799183,7.432082,13686569570,19674608945
+"sweden",2016,"sweden 2016",491265.5864,9.923085,6.991096,13168469695,18345314197
+"sweden",2017,"sweden 2017",503880.9159,10.057695,6.718704,14063472655,20667855591
+"sweden",2018,"sweden 2018",513706.7089,10.175214,6.364352,15344874311,21468071824
+"switzerland",2010,"switzerland 2010",487588.1791,7.82491,4.803483,17671841149,39332917389
+"switzerland",2011,"switzerland 2011",495842.1133,7.912396,4.401403,19859045147,50698621175
+"switzerland",2012,"switzerland 2012",500830.4044,7.996861,4.48394,25801901045,49040514757
+"switzerland",2013,"switzerland 2013",510105.9826,8.089346,4.746688,27596182311,51192513867
+"switzerland",2014,"switzerland 2014",522599.5906,8.188646,4.829303,28833072194,51573663255
+"switzerland",2015,"switzerland 2015",529567.772,8.282398,4.80018,26794974089,47772140565
+"switzerland",2016,"switzerland 2016",538690.8513,8.373334,4.918711,26620332277,45037147919
+"switzerland",2017,"switzerland 2017",548384.1028,8.451834,4.797114,30658146998,47825982572
+"switzerland",2018,"switzerland 2018",563467.7021,8.513227,4.714227,37244766101,51143630024
+"united kingdom",2000,"united kingdom 2000",2125916.6737,58.886065,5.57515,53274656681,31135349465
+"united kingdom",2001,"united kingdom 2001",2189149.1671,59.113016,5.01319,48766280191,29142126517
+"united kingdom",2002,"united kingdom 2002",2240032.6799,59.365677,5.132106,56070517270,30773511590
+"united kingdom",2003,"united kingdom 2003",2313648.6114,59.636662,4.966743,65944944303,38682580993
+"united kingdom",2004,"united kingdom 2004",2368573.506,59.950364,4.690948,82797414656,45457176819
+"united kingdom",2005,"united kingdom 2005",2443887.7228,60.413276,4.74926,90048329444,55633435845
+"united kingdom",2006,"united kingdom 2006",2512031.2445,60.827067,5.348789,100849161476,54660060295
+"united kingdom",2007,"united kingdom 2007",2573087.5005,61.319075,5.262478,120031262937,62285159693
+"united kingdom",2008,"united kingdom 2008",2565853.1727,61.823772,5.61258,118394204676,71616721915
+"united kingdom",2009,"united kingdom 2009",2456860.3036,62.260486,7.536723,82712667028,52412489756
+"united kingdom",2010,"united kingdom 2010",2504757.323,62.759456,7.786648,114363471825,68486504150
+"united kingdom",2011,"united kingdom 2011",2543334.2771,63.285145,8.036686,124826452197,82827228182
+"united kingdom",2012,"united kingdom 2012",2580948.2567,63.70503,7.885344,108786938979,88334554856
+"united kingdom",2013,"united kingdom 2013",2636167.2756,64.105654,7.525075,108167511301,93416603381
+"united kingdom",2014,"united kingdom 2014",2704904.7022,64.596752,6.110472,114035894314,95522348451
+"united kingdom",2015,"united kingdom 2015",2768619.3899,65.110034,5.301053,111844983266,84977885347
+"united kingdom",2016,"united kingdom 2016",2821725.8041,65.648054,4.809951,105937617633,76014459091
+"united kingdom",2017,"united kingdom 2017",2875115.0302,66.040229,4.330292,108389918108,82442210601
+"united kingdom",2018,"united kingdom 2018",2913662.2889,66.43555,3.996093,114862668934,81867727596
+"united states",1981,"united states 1981",7257630.685,229.465714,7.6,23166926826,56272434841
+"united states",1982,"united states 1982",7126784.7155,231.664458,9.708333,23370962991,49642278371
+"united states",1983,"united states 1983",7453471.3468,233.791994,9.616667,28514956727,39287869251
+"united states",1984,"united states 1984",7992850.7446,235.824902,7.525,38283366189,41450579615
+"united states",1985,"united states 1985",8326125.1215,237.923795,7.191667,39234523331,41915761593
+"united states",1986,"united states 1986",8614429.8356,240.132887,6.991667,44315703976,41784587233
+"united states",1987,"united states 1987",8912452.2861,242.288918,6.191667,55538607547,46421888792
+"united states",1988,"united states 1988",9284729.552,244.498982,5.491667,67874853334,59710715561
+"united states",1989,"united states 1989",9625725.7596,246.81923,5.266667,76629461011,67582641061
+"united states",1990,"united states 1990",9807263.1281,249.622814,5.616667,84057877939,75402090795
+"united states",1991,"united states 1991",9796645.8728,252.980941,6.816667,91346719804,90896974102
+"united states",1992,"united states 1992",10141727.09,256.514224,7.508333,113329832821,109190722315
+"united states",1993,"united states 1993",10420913.0489,259.918588,6.9,133748240366,119639032414
+"united states",1994,"united states 1994",10840754.8646,263.125821,6.083333,165064252849,132065338768
+"united states",1995,"united states 1995",11131751.8524,266.278393,5.608333,195111228925,143979207913
+"united states",1996,"united states 1996",11551697.3379,269.394284,5.416667,215098304649,162206691654
+"united states",1997,"united states 1997",12065426.3098,272.646925,4.95,246978593107,195642989599
+"united states",1998,"united states 1998",12606127.236,275.854104,4.508333,274797678145,196379477994
+"united states",1999,"united states 1999",13205326.2125,279.040168,4.216667,308177079361,192170302941
+"united states",2000,"united states 2000",13750373.9409,282.162411,3.991667,355934019709,225543730742
+"united states",2001,"united states 2001",13887649.5334,284.968955,4.733333,342294830378,216249029996
+"united states",2002,"united states 2002",14129530.0656,287.625193,5.775,385242935097,204257835300
+"united states",2003,"united states 2003",14533805.7012,290.107933,5.991667,420160151543,210100752206
+"united states",2004,"united states 2004",15085929.1563,292.805298,5.533333,498232791511,230562277169
+"united states",2005,"united states 2005",15615930.1008,295.516599,5.066667,569441079274,256299183232
+"united states",2006,"united states 2006",16061760.5783,298.379912,4.616667,652749758134,327652579426
+"united states",2007,"united states 2007",16363106.7459,301.231207,4.616667,693338824523,362408766686
+"united states",2008,"united states 2008",16340758.0466,304.093966,5.775,704444649545,407260268085
+"united states",2009,"united states 2009",15926232.7123,306.771529,9.266666,586876705037,315884611076
+"united states",2010,"united states 2010",16334544.1406,309.326085,9.616667,734242629676,392572994220
+"united states",2011,"united states 2011",16587866.0508,311.580009,8.95,812258096636,446838223395
+"united states",2012,"united states 2012",16961017.7036,313.874218,8.066667,869465822167,471580188863
+"united states",2013,"united states 2013",17273453.4001,316.057727,7.375,896763733513,487976977036
+"united states",2014,"united states 2014",17709776.6224,318.386421,6.166667,954895496446,501713558646
+"united states",2015,"united states 2015",18224780,320.742673,5.291667,1000321246880,476906188323
+"united states",2016,"united states 2016",18523272.6297,323.071342,4.866667,984459724995,453093858423
+"united states",2017,"united states 2017",18962237.8616,325.147121,4.35,1061975110263,469660109145
+"united states",2018,"united states 2018",19517323.7603,327.167434,3.9,1145765096270,490921984331

10/input/replication_data/processed_data.csv

@@ -0,0 +1,785 @@
+year,unemp,countrynum,NAff,IMS,EXS,DUM70to74,DUM75to79,DUM80to84,DUM85to89,DUM90to94,DUM95to99,DUM0to4,DUM5to9,DUM10to14,DUM15to18
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+2011,8.95,31,53237.902213424735,4.896700360061241,2.693765563494226,0,0,0,0,0,0,0,0,1,0
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+2018,3.9,31,59655.45996335319,5.870503099408484,2.5153140377246785,0,0,0,0,0,0,0,0,0,1

11/gt/expected_post_registration.json

@@ -0,0 +1,56 @@
+{
+    "original_study": {
+        "claim": {
+            "hypothesis": "Bilingual group membership will be positively associated with foreign language achievement when controlling for background variables.",
+            "hypothesis_location": "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.",
+            "statement": "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).",
+            "statement_location": "Table 3, Model B (Bilingual=1)",
+            "study_type": "Observational" 
+        },
+
+        "data": { 
+            "source": "the main source: Assessment of Reading and Mathematics Development Study (ELEMENT); for socioeconomic status data: International Socio-Economic Index.",
+            "wave_or_subset": "the sixth grade elementary school cohort",
+            "sample_size": "2835",
+            "unit_of_analysis": "individual - German 6th graders nested in 134 elementary school classes",
+            "access_details": "access details are not specified; no restrictions are mentioned, perhaps the datasets are open access.",
+            "notes": "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."
+        },
+
+        "method": {
+            "description": "The authors examined the effect of immigrant bilingualism on English as a foreign language achievement. They controlled for cognitive, demographic, and socio-cultural factors.",
+            "steps": "1. Download the data and identify bilingual and monolingual groups based on language spoken at home. \n2. Exclude 111 students with no language information.\n3. Classify students into the monolingual group (n=1896) and the bilingual group (n=939).\n4. 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).\n5. Measure general cognitive abilities (by the authors) using a composite score from two subtests of the CFT4-12R (verbal and figural analogies).\n6. Collect control variables from the dataset: age, gender, socio-economic status (ISEI data), parental education, and cultural capital. \n7. Handle missing data using multiple imputation.\n8. Run multiple linear regression in MPlus 5.21 with type = complex.",
+            "models": "linear regression",
+            "outcome_variable": "English achievement",
+            "independent_variables": "bilingual group membership",
+            "control_variables": "general cognitive abilities, gender, age, socio-economic status, parental education, and cultural capital",
+            "tools_software": "For multiple regression: MPlus 5.21 taking into account the nested nature (students in classes) of the dataset (type = complex). For items scaling: ConQuest."
+        },
+        "results": {
+            "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).",
+            "numerical_results": [ 
+                {
+                    "outcome_name": "English achievement",
+                    "value": "2.68",
+                    "unit": "points (raw Cloze test scores scaled to mean = 100, SD = 20)",
+                    "effect_size": "not stated",
+                    "confidence_interval": {
+                        "lower": "not stated",
+                        "upper": "not stated",
+                        "level": "not stated"
+                    },
+                    "p_value": "< .01",
+                    "statistical_significance": "true",
+                    "direction": "positive"
+                }
+            ]
+        },
+       
+        "metadata": {
+            "original_paper_id": "http://dx.doi.org/10.1016/j.learninstruc.2014.12.001",
+            "original_paper_title": "The effect of speaking a minority language at home on foreign language learning.",
+            "original_paper_code": "not stated",
+            "original_paper_data": "not stated"
+        }
+    }
+}

11/gt/expected_post_registration_2.json

@@ -0,0 +1,65 @@
+{
+  "original_study": {
+    "claim": {
+      "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.",
+      "hypothesis_location": "Section 1.4 Research questions, Hypothesis 1a.",
+      "statement": "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.",
+      "statement_location": "Results section 3.2, Table 3 ('Multiple regression models explaining English achievement of bilinguals'), Model B.",
+      "study_type": "Observational"
+    },
+
+    "data": {
+      "source": "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.",
+      "wave_or_subset": "Sixth-grade of the ELEMENT cohort.",
+      "sample_size": "2,835 students nested in 134 elementary school classes.",
+      "unit_of_analysis": "Individual student.",
+      "access_details": "not stated",
+      "notes": "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."
+    },
+
+    "method": {
+      "description": "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.",
+      "steps": [
+        "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."
+      ],
+      "models": "Multiple linear regression models predicting English foreign language achievement from bilingual-group membership and background covariates.",
+      "outcome_variable": "English language achievement in grade 6.",
+      "independent_variables": "Binary indicator for group membership (1 = bilingual student who speaks a minority language at home and German; 0 = monolingual German student).",
+      "control_variables": "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).",
+      "tools_software": "Mplus 5.21"
+    },
+
+    "results": {
+      "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.",
+      "numerical_results": [
+        {
+          "outcome_name": "English foreign language achievement (Cloze test WLE score)",
+          "value": 2.68,
+          "unit": "score points on the English achievement scale (M = 100, SD = 20)",
+          "effect_size": "unstandardized multiple regression coefficient for bilingual-group membership in Model B of Table 3",
+          "confidence_interval": {
+            "lower": "not stated",
+            "upper": "not stated",
+            "level": "not stated"
+          },
+          "p_value": "< .01",
+          "statistical_significance": 1,
+          "direction": "positive"
+        }
+      ]
+    },
+
+    "metadata": {
+      "original_paper_id": "10.1016/j.learninstruc.2014.12.001",
+      "original_paper_title": "The effect of speaking a minority language at home on foreign language learning",
+      "original_paper_code": "not stated",
+      "original_paper_data": "not stated"
+    }
+  }
+}

11/gt/expected_post_registration_3.json

@@ -0,0 +1,57 @@
+{
+    "original_study": {
+        "claim": {
+            "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).",
+            "hypothesis_location": "Section 1: Introduction; Subsection 1.4: Research questions; p. 78-79",
+            "statement": "Given comparable individual and familiar background characteristics bilingual group membership is positively associated with English foreign language achievement.",
+            "statement_location": "Section 3: Results; Subsection 3.2: Bilingualism and English achievement; p. 80.",
+            "study_type": "Observational" 
+        },
+
+
+        "data": {
+            "source": "Assessment of Reading and Mathematics Development Study",
+            "wave_or_subset": "NA",
+            "sample_size": "2946",
+            "unit_of_analysis": "Student",
+            "access_details": "not stated",
+            "notes": "The final sample size (after cleaning) was 2835 students nested in 134 elementary\nschool classes."
+        },
+
+        "method": {
+            "description": "The authors use regression analysis, controlling for background characteristics, to explore the effect of bilingualism on English language achievement in school.",
+            "steps": "(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.",
+            "models": "uncontrolled regression model; regression model including controls",
+            "outcome_variable": "English language achievement",
+            "independent_variables": "Bilingualism",
+            "control_variables": "general cognitive abilities, gender, age, socio-economic status, parental education, and cultural capital",
+            "tools_software": "MPlus 5.21"
+        },
+        "results": {
+            "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.",
+            "numerical_results": [
+                {
+                    "outcome_name": "English achievement",
+                    "value": "2.68",
+                    "unit": "not stated",
+                    "effect_size": "just over a quarter of a school year's achievement",
+                    "confidence_interval": {
+                        "lower": "not stated",
+                        "upper": "not stated",
+                        "level": "not stated"
+                    },
+                    "p_value": "<0.01",
+                    "statistical_significance": "1% level",
+                    "direction": "Positive"
+                }
+            ]
+        },
+       
+        "metadata": {
+            "original_paper_id": "10.1016/j.learninstruc.2014.12.001.",
+            "original_paper_title": "The effect of speaking a minority language at home on foreign language learning.",
+            "original_paper_code": "not stated",
+            "original_paper_data": "not sated"
+        }
+    }
+}

11/gt/human_preregistration.pdf

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+oid sha256:64b574e8be6aa8b3200be07abbc57c5e647b5ae5318338324b27218b7b4c8943
+size 578339

11/gt/human_report.pdf

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+oid sha256:f9621e2ffad4d1ebaedc1c664aed5a283f83a426cd5b667f5c63a4c7826a096e
+size 200788

11/input/initial_details.txt

@@ -0,0 +1,5 @@
+[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/original_paper.pdf

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+size 432603

11/input/replication_data/Final replication dataset.rds

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+version https://git-lfs.github.com/spec/v1
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+size 2230908

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

@@ -0,0 +1,147 @@
+options(scipen=999)
+library(lmerTest)
+library(dplyr)
+library(tidyverse)
+library(MuMIn)
+library(reshape)
+dat <- readRDS(file="Final replication dataset.rds")
+
+#Data transformation
+
+#Creation of a new variable that classifies participants in a monolingual (0) and in a bilingual group (1)
+dat$bilingual = ifelse(dat$I03_ST_A_S26A == (2 | 3), 0, 1)
+dat <- dat[!(is.na(dat$bilingual)),]
+
+
+#Data exclusion: Excluding students who speak English (the target language) at home
+dat<-subset(dat, I03_ST_A_S27B==0)
+
+
+
+#Creation of an average score for writting, reading and listeting
+dat$ave_writing<- (dat$PV1_WRIT_C+dat$PV2_WRIT_C+dat$PV3_WRIT_C+dat$PV4_WRIT_C+dat$PV5_WRIT_C)/5
+dat$ave_reading<-(dat$PV1_READ+dat$PV2_READ+dat$PV3_READ+dat$PV4_READ+dat$PV5_READ)/5
+dat$ave_listening<-(dat$PV1_LIST+dat$PV2_LIST+dat$PV3_LIST+dat$PV4_LIST+dat$PV5_LIST)/5
+dat$average_english <- rowMeans(dat[ , c('ave_writing', 'ave_reading', 'ave_listening')], na.rm=TRUE)
+
+
+#Converting Cultural Capital into a continous variable
+dat$Cultural_capital = ifelse(dat$SQt21i01 == "0-10 books", 0, 
+                              ifelse(dat$SQt21i01 == "11-25 books", 1, 
+                                     ifelse(dat$SQt21i01 == "26-100 books", 2, 
+                                            ifelse(dat$SQt21i01== "101-200 books", 3,
+                                                   ifelse(dat$SQt21i01== "201-500 books", 4,   
+                                                          ifelse(dat$SQt21i01== "More than 500 books", 5,""))))))
+
+dat$Cultural_capital<-as.numeric(dat$Cultural_capital)
+
+
+#Exclusing observations with no oweights or weights == 0
+# Three datasets are created for each dimenion (Writing, Reading, Listening)
+dat<-dat %>% filter(FSW_WRIT_TR > 0 | FSW_READ_TR > 0 | FSW_LIST_TR > 0 )
+
+
+
+#Centering continous variables (for each of the three datasets)
+#Centering age
+dat$c_age<-scale(dat$I08_ST_A_S02A, center = TRUE, scale = FALSE)
+
+#Centering SES
+dat$c_HISEI<-scale(dat$HISEI, center = TRUE, scale = FALSE)
+
+#Converting to Z-scores the variable "parental education" and "cultural capital"
+dat$Z_Parental<-scale(dat$PARED, center = TRUE, scale = TRUE)
+dat$Z_Cultural<-scale(dat$Cultural_capital, center = TRUE, scale = TRUE)
+
+#Function to calculate standardized estimates
+stdCoef.merMod <- function(object) {
+  sdy <- sd(getME(object,"y"))
+  sdx <- apply(getME(object,"X"), 2, sd)
+  sc <- fixef(object)*sdx/sdy
+  se.fixef <- coef(summary(object))[,"Std. Error"]
+  se <- se.fixef*sdx/sdy
+  return(data.frame(stdcoef=sc, stdse=se))
+}
+
+
+#Three-level model
+
+results_m2<-lmer(average_english ~ 1+ bilingual + factor(SQt01i01) + c_age + c_HISEI + Z_Parental + Z_Cultural + (1|country_id/school_id), data=dat)
+summary(results_m2)
+r.squaredGLMM(results_m2)
+stdCoef.merMod(results_m2)
+
+# SECOND EXPLORATORY ANALYSIS
+
+#Three multilevel models are fitted separately on each English dimension, namely writing, reading and listening skills.
+
+data <- readRDS(file="Final replication dataset.rds")
+
+#Transformation
+data$bilingual = ifelse(data$I03_ST_A_S26A == (2 | 3), 0, 1)
+
+
+#Data exclusion: Exclusing students who speak English (the target language) at home
+data<-subset(data, I03_ST_A_S27B==0)
+
+
+#Creation of an average score for writting, reading and listeting
+data$ave_writing<- (data$PV1_WRIT_C+data$PV2_WRIT_C+data$PV3_WRIT_C+data$PV4_WRIT_C+data$PV5_WRIT_C)/5
+data$ave_reading<-(data$PV1_READ+data$PV2_READ+data$PV3_READ+data$PV4_READ+data$PV5_READ)/5
+data$ave_listening<-(data$PV1_LIST+data$PV2_LIST+data$PV3_LIST+data$PV4_LIST+data$PV5_LIST)/5
+
+
+#Converting Cultural Capital into a continous variable
+data$Cultural_capital = ifelse(data$SQt21i01 == "0-10 books", 0, 
+                               ifelse(data$SQt21i01 == "11-25 books", 1, 
+                                      ifelse(data$SQt21i01 == "26-100 books", 2, 
+                                             ifelse(data$SQt21i01== "101-200 books", 3,
+                                                    ifelse(data$SQt21i01== "201-500 books", 4,   
+                                                           ifelse(data$SQt21i01== "More than 500 books", 5,""))))))
+
+data$Cultural_capital<-as.numeric(data$Cultural_capital)
+
+
+dat_writing<-data %>% filter(FSW_WRIT_TR > 0)
+dat_reading<-data %>% filter(FSW_READ_TR > 0)
+dat_listening<- data %>% filter(FSW_LIST_TR > 0)
+
+#Centering continous variables (for each of the three datasets)
+#Centering age
+dat_writing$c_age<-scale(dat_writing$I08_ST_A_S02A, center = TRUE, scale = FALSE)
+dat_reading$c_age<-scale(dat_reading$I08_ST_A_S02A, center = TRUE, scale = FALSE)
+dat_listening$c_age<-scale(dat_listening$I08_ST_A_S02A, center = TRUE, scale = FALSE)
+
+#Centering SES
+dat_writing$c_HISEI<-scale(dat_writing$HISEI, center = TRUE, scale = FALSE)
+dat_reading$c_HISEI<-scale(dat_reading$HISEI, center = TRUE, scale = FALSE)
+dat_listening$c_HISEI<-scale(dat_listening$HISEI, center = TRUE, scale = FALSE)
+
+
+#Converting to Z-scores the variable "parental education" and "cultural capital"
+dat_writing$Z_Parental<-scale(dat_writing$PARED, center = TRUE, scale = TRUE)
+dat_reading$Z_Parental<-scale(dat_reading$PARED, center = TRUE, scale = TRUE)
+dat_listening$Z_Parental<-scale(dat_listening$PARED, center = TRUE, scale = TRUE)
+
+dat_writing$Z_Cultural<-scale(dat_writing$Cultural_capital, center = TRUE, scale = TRUE)
+dat_reading$Z_Cultural<-scale(dat_reading$Cultural_capital, center = TRUE, scale = TRUE)
+dat_listening$Z_Cultural<-scale(dat_listening$Cultural_capital, center = TRUE, scale = TRUE)
+
+
+#Three-leve models with control variables
+writing<-lmer(ave_writing ~ 1+ bilingual + factor(SQt01i01) + c_age + c_HISEI + Z_Parental + Z_Cultural + (1|country_id/school_id), data=dat_writing)
+summary(writing)
+r.squaredGLMM(writing)
+
+reading<-lmer(ave_reading ~ 1+ bilingual + factor(SQt01i01) + c_age + c_HISEI + Z_Parental + Z_Cultural + (1|country_id/school_id), data=dat_reading)
+summary(reading)
+r.squaredGLMM(reading)
+
+listening<-lmer(ave_listening ~ 1+ bilingual + factor(SQt01i01) + c_age + c_HISEI + Z_Parental + Z_Cultural +(1|country_id/school_id), data=dat_listening)
+summary(listening)
+r.squaredGLMM(listening)
+
+#Obtaining standardized estimates for all models
+stdCoef.merMod(writing)
+stdCoef.merMod(reading)
+stdCoef.merMod(listening)

12/gt/expected_post_registration.json

@@ -0,0 +1,56 @@
+{
+    "original_study": {
+        "claim": {
+            "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_location": "the abstract and introduction",
+            "statement": "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.].",
+            "statement_location": "Table 3 column 1",
+            "study_type": "Observational" 
+        },
+
+        "data": { 
+            "source": "The primary data used in this paper were collected by a team of researchers based at the World Bank and in India.",
+            "wave_or_subset": "1997–1998",
+            "sample_size": "1295",
+            "unit_of_analysis": "household",
+            "access_details": "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.",
+            "notes": "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"
+        },
+
+        "method": {
+            "description": "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.",
+            "steps": "1. Get the data.\n2. 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.\n3. Create the binary variable for low-caste dominated villages (1 = low-caste dominated, 0 = high-caste dominated).\n4. Estimate an OLS regression of household crop income per acre on the low-caste village indicator, including household, district, and state-level controls.\n5. Cluster standard errors at the village level.",
+            "models": "ordinary least squares regression",
+            "outcome_variable": "crop income per acre",
+            "independent_variables": "low-caste village",
+            "control_variables": "literacy, land ownership, caste identity, district, state",
+            "tools_software": "not stated"
+        },
+        "results": {
+            "summary": "Lower-caste households earn significantly more in low-caste dominated villages (“Low-caste villages” coefficient = 566.5, SE = 209, p < 0.01).",
+            "numerical_results": [ 
+                {
+                    "outcome_name": "Household Crop Income",
+                    "value": "566.5",
+                    "unit": "not stated explicitly (should be local currency though)",
+                    "effect_size": "not stated",
+                    "confidence_interval": {
+                        "lower": "not stated",
+                        "upper": "not stated",
+                        "level": "not stated"
+                    },
+                    "p_value": "< 0.01",
+                    "statistical_significance": "true",
+                    "direction": "positive"
+                }
+            ]
+        },
+       
+        "metadata": {
+            "original_paper_id": "http://www.aeaweb.org/articles.php?doi=10.1257/app.3.1.239",
+            "original_paper_title": "Caste as an Impediment to Trade.",
+            "original_paper_code": "not stated",
+            "original_paper_data": "not stated"
+        }
+    }
+}

12/gt/expected_post_registration_2.json

@@ -0,0 +1,61 @@
+{
+  "original_study": {
+    "claim": {
+      "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.",
+      "hypothesis_location": "Section III: Access to Irrigation.",
+      "statement": "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.",
+      "statement_location": "Table 4 (OLS estimations with water-market interaction terms), “LCV × water buyer”.",
+      "study_type": "Observational"
+    },
+    "data": {
+      "source": "UP-Bihar LSMS World Bank survey data, including a household questionnaire and a village questionnaire.",
+      "wave_or_subset": "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.",
+      "sample_size": "120 villages and 2,250 households in the full sample; the focal regressions use a lower-caste household sample with 1,295 observations.",
+      "unit_of_analysis": "Household (lower-caste household, BAC/OBC/SC) in a village.",
+      "access_details": "not stated",
+      "notes": "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."
+    },
+    "method": {
+      "description": "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.",
+      "steps": [
+        "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."
+      ],
+      "models": "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.",
+      "outcome_variable": "Household crop income per acre (crop income per acre of total land).",
+      "independent_variables": "Low-caste village indicator; water buyer indicator; interaction between low-caste village and water buyer (LCV × water buyer).",
+      "control_variables": "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.",
+      "tools_software": "not stated"
+    },
+    "results": {
+      "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.",
+      "numerical_results": [
+        {
+          "outcome_name": "Crop income per acre",
+          "value": "850.9",
+          "unit": "rupees per acre",
+          "effect_size": "OLS interaction coefficient (LCV × water buyer)",
+          "confidence_interval": {
+            "lower": "not stated",
+            "upper": "not stated",
+            "level": "not stated"
+          },
+          "p_value": "< 0.01",
+          "statistical_significance": 1,
+          "direction": "positive"
+        }
+      ]
+    },
+    "metadata": {
+      "original_paper_id": "10.1257/app.3.1.239",
+      "original_paper_title": "Caste as an Impediment to Trade",
+      "original_paper_code": "not stated",
+      "original_paper_data": "not stated"
+    }
+  }
+}

12/gt/expected_post_registration_3.json

@@ -0,0 +1,57 @@
+{
+    "original_study": {
+        "claim": {
+            "hypothesis": "Given these historical patterns, we may well expect lower castes to fair better in villages where no upper castes are present.",
+            "hypothesis_location": "Section: Introduction; p. 240",
+            "statement": "...lower castes fair significantly better, in terms of household income, if they reside in villages where a lower caste is dominant.",
+            "statement_location": "Section 2: Household Outcomes by Caste Dominance; p.247",
+            "study_type": "Observational" 
+        },
+
+        "data": {
+            "source": "World Bank survey data on Uttar Pradesh and Bihar in India",
+            "wave_or_subset": "NA",
+            "sample_size": "2,250",
+            "unit_of_analysis": "household",
+            "access_details": "Access details (e.g., restrictions, request process)",
+            "notes": "not stated"
+        },
+
+        "method": {
+            "description": "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.",
+            "steps": "(1) Clean the data; (2) construct the Caste Dominance measure by landownership percentage for the castes; (3) Estimate the effects using regression analysis.",
+            "models": "Fixed effects regression",
+            "outcome_variable": "crop income per acre of total land",
+            "independent_variables": "Caste Dominance",
+            "control_variables": "Exogenous household characteristics (such as education, land ownership, and caste identity); district fixed effects; state fixed effects.",
+            "tools_software": "not stated"
+        },
+        "results": {
+            "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.",
+        "numerical_results": [
+                {
+                    "outcome_name": "Household Crop Income",
+                    "value": "566.5",
+                    "unit": "",
+                    "effect_size": "",
+                    "confidence_interval": {
+                        "lower": "not stated",
+                        "upper": "not stated",
+                        "level": "not stated"
+                    },
+                    "p_value": "<0.01",
+                    "statistical_significance": "1% level",
+                    "direction": "Positive",
+"notes": "No confidence intervals, but robust standard errors are reported (209.0)."
+                }
+            ]
+        },
+       
+        "metadata": {
+            "original_paper_id": "10.1257/app.3.1.239",
+            "original_paper_title": "Caste as an Impediment to Trade",
+            "original_paper_code": "http://www.aeaweb.org/articles.php?doi=10.1257/app.3.1.239",
+            "original_paper_data": "http://www.aeaweb.org/articles.php?doi=10.1257/app.3.1.239"
+        }
+    }
+}

12/gt/human_preregistration.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:bd059a6ab51c26138582790dcd0afaf7c5adbc4e351642896d45b6d26cc9fb34
+size 252682

12/input/initial_details.txt

@@ -0,0 +1,5 @@
+[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

12/input/original_paper.pdf

@@ -0,0 +1,3 @@
+version https://git-lfs.github.com/spec/v1
+oid sha256:1a37a31bf8e27b6e33aa073d1bd97ce49241515e36e0ed73232bff6c63fc29fe
+size 937866

12/input/replication_data/anderson_2011_replication_data_analysis.do

@@ -0,0 +1,42 @@
+*Nathaniel Porter
+*2022-05-03
+*SCORE Anderson_AmEcoJourn_2011_bLe8_329k Data Analysis
+
+
+*change to local directory and load data
+cd "d:/aris_reds_data"
+
+log using "anderson_2011_replication_final_analysis", replace
+*log using "anderson_2011_replication_analysis"
+
+use "analysis_data.dta", clear
+
+/*
+*Preregistered analysis (5% sample)
+set seed 234
+sample 5
+
+*Regression (Table 3) with all (both types of locaste)
+*state code and caste interacted (because numbering differs by state)
+*alternative would be to recode state-specific castes into new castes
+qui regress raw_inc_per_acre literate_hh land_owned locaste_land_v stcode##caste, vce(cluster vill_id)
+*output (line 1 is b, line 2 is SE, line 3 is test stat, line 4 is p-value)
+etable, keep(literate_hh land_owned locaste_land_v) cstat(_r_b) cstat(_r_se) cstat(_r_z) cstat(_r_p) mstat(N) mstat(r2) mstat(r2_a) mstat(aic) mstat(bic) mstat(ll)
+*/
+
+*final analysis
+use "analysis_data.dta", clear
+qui regress raw_inc_per_acre literate_hh land_owned locaste_land_v stcode##caste, vce(cluster vill_id)
+*output (line 1 is b, line 2 is SE, line 3 is test stat, line 4 is p-value)
+etable, keep(literate_hh land_owned locaste_land_v) cstat(_r_b) cstat(_r_se) cstat(_r_z) cstat(_r_p, nformat(%6.4f)) mstat(N) mstat(r2) mstat(r2_a) mstat(aic) mstat(bic) mstat(ll)
+
+*Exploratory analysis using net income per acre
+qui regress net_inc_per_acre literate_hh land_owned locaste_land_v stcode##caste, vce(cluster vill_id)
+etable, keep(literate_hh land_owned locaste_land_v) cstat(_r_b) cstat(_r_se) cstat(_r_z) cstat(_r_p, nformat(%6.4f)) mstat(N) mstat(r2) mstat(r2_a) mstat(aic) mstat(bic) mstat(ll)
+
+*Alternative analysis using only subset of cases in UP/B (following original study)
+keep if inlist(stcode,2,15)
+qui regress raw_inc_per_acre literate_hh land_owned locaste_land_v stcode##caste, vce(cluster vill_id)
+etable, keep(literate_hh land_owned locaste_land_v) cstat(_r_b) cstat(_r_se) cstat(_r_z) cstat(_r_p, nformat(%6.4f)) mstat(N) mstat(r2) mstat(r2_a) mstat(aic) mstat(bic) mstat(ll)
+
+log close

13/gt/expected_post_registration.json

@@ -0,0 +1,56 @@
+{
+    "original_study": {
+        "claim": {
+            "hypothesis": "Individuals’ concerns about immigration is positively associated with distrust in their country’s parliament.",
+            "hypothesis_location": "p. 205 Proposition 1; also discussed in the introduction and section Concern about Immigration and Political Trust.",
+            "statement": "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.",
+            "statement_location": "Table 3, parliament column.",
+            "study_type": "Observational" 
+        },
+
+        "data": { 
+            "source": "European Social Survey (ESS)",
+            "wave_or_subset": "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.).",
+            "sample_size": "110732",
+            "unit_of_analysis": "individual",
+            "access_details": "the article states: Available at http://www.europeansocialsurvey.org/. no further access details are provided.",
+            "notes": "the analysis excludes the newer democracies of Central and Eastern Europe (CEE ); also, minorities and noncitizens have been omitted."
+        },
+
+        "method": {
+            "description": "The study used a three-level hierarchical linear model to test whether higher concern about immigration is associated with higher distrust in politics.",
+            "steps": "1.Combine ESS data into one dataset.\n2. Exclude newer democracies of Central and Eastern Europe (CEE), minorities, and noncitizens.\n3.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.\n4. Construct the key predictor “concern about immigration” as an index averaging three reversed-coded items (economic effect, cultural effect, overall effect on country).\n5.Add individual-level control variables: \n-Unhappiness: reversed 0–10 scale.\n-Dissatisfaction with life: reversed 0–10 scale.\n-Frequency of meeting friends: reversed 1–7 scale (such that high values represent rarely meeting with friends).\n-Interpersonal distrust: reversed 0–10 scale, index of three items (questions about general trust, fairness, and helpfulness of people).\n-Dissatisfied with country’s economy: reversed 0–10 scale.\n-Dissatisfied with personal income: reversed 0–10 scale.\n-Dissatisfied with health and education system: reversed 0–10 scales.\n-Winner effect: 1 if voted for governing party, 0 otherwise.\n-Vote for far-right party: 1 if respondent voted for anti-immigration party, 0 otherwise.\n-Left-right scale.\n-Household income (standardized across rounds).\n-Age.\n-Education.\n-Gender (0 = male, 1 = female).\n7.Estimate a three-level hierarchical linear model with individuals nested within country-rounds and countries using HLM software.",
+            "models": "multilevel modeling (three-level, linear)",
+            "outcome_variable": "political distrust",
+            "independent_variables": "Concern about immigration",
+            "control_variables": "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",
+            "tools_software": "HLM"
+        },
+        "results": {
+            "summary": "Higher concern about immigration is associated with higher distrust in politics (b=0.17 SE=0.00 p=0.00).",
+            "numerical_results": [
+                {
+                    "outcome_name": "distrust parliament",
+                    "value": "0.17",
+                    "unit": "NA",
+                    "effect_size": "not stated",
+                    "confidence_interval": {
+                        "lower": "not stated",
+                        "upper": "not stated",
+                        "level": "not stated"
+                    },
+                    "p_value": "0.000",
+                    "statistical_significance": "true",
+                    "direction": "positive"
+                }
+            ]
+        },
+       
+        "metadata": {
+            "original_paper_id": "S0043887112000032",
+            "original_paper_title": "The Cultural Divide in Europe Migration, Multiculturalism, and Political Trust.",
+            "original_paper_code": "not stated",
+            "original_paper_data": "not stated"
+        }
+    }
+}

13/gt/expected_post_registration_2.json

@@ -0,0 +1,60 @@
+{
+  "original_study": {
+    "claim": {
+      "hypothesis": "Individuals who are more worried about immigration’s impact on the national community will report greater distrust of their country’s parliament.",
+      "hypothesis_location": "Proposition 1(p.7) linking greater concern about immigration’s impact on the national community to greater distrust of politicians and political institutions.",
+      "statement": "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.",
+      "statement_location": "Table 3 (Three-Level Model of Distrust in Politics), row “Concern about immigration.”",
+      "study_type": "Observational"
+    },
+    "data": {
+      "source": "European Social Survey (ESS).",
+      "wave_or_subset": "ESS rounds 1–4 (fieldwork: 2002–2003; 2004–2005; 2006–2007; 2008–2009).",
+      "sample_size": "110,732",
+      "unit_of_analysis": "Individual survey respondent (nested within country-round and country).",
+      "access_details": "Available via the European Social Survey website.",
+      "notes": "The dependent variable is political distrust measured using 11-point (0–10) trust items for national institutions."
+    },
+    "method": {
+      "description": "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.",
+      "steps": [
+        "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."
+      ],
+      "models": "Three-level multilevel model (HLM) with respondents nested within country-round (level 2) and country (level 3).",
+      "outcome_variable": "Distrust in parliament (0–10 scale, higher = more distrust).",
+      "independent_variables": "Concern about immigration.",
+      "control_variables": "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.",
+      "tools_software": "HLM software"
+    },
+    "results": {
+      "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.",
+      "numerical_results": [
+        {
+          "outcome_name": "Distrust in parliament",
+          "value": "1.7",
+          "unit": "units on the 0–10 distrust scale",
+          "effect_size": "multilevel regression coefficient",
+          "confidence_interval": {
+            "lower": "not stated",
+            "upper": "not stated",
+            "level": "not stated"
+          },
+          "p_value": "0.000",
+          "statistical_significance": 1,
+          "direction": "positive"
+        }
+      ]
+    },
+    "metadata": {
+      "original_paper_id": "10.1017/S0043887112000032",
+      "original_paper_title": "The Cultural Divide in Europe: Migration, Multiculturalism, and Political Trust",
+      "original_paper_code": "not stated",
+      "original_paper_data": "not stated"
+    }
+  }
+}

13/gt/expected_post_registration_3.json

@@ -0,0 +1,84 @@
+{
+  "original_study": {
+    "claim": {
+      "hypothesis": "If individuals perceive newcomers as a threat to that community, then the \ngoverning 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.",
+      "hypothesis_location": "Section: Concern about immigration and  \npolitical trust; p. 205",
+      "statement": "...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.",
+      "statement_location": "Section:Multivariate Analyses ; p. 220",
+      "study_type": "Observational"
+    },
+    "data": {
+      "source": "European Social Survey",
+      "wave_or_subset": "1-4",
+      "sample_size": "not stated",
+      "unit_of_analysis": "respondent",
+      "access_details": "Available at http://www.europeansocialsurvey.org/",
+      "notes": "It is aggregated to the country level for the analysis."
+    },
+    "method": {
+      "description": "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.",
+      "steps": "(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",
+      "models": "Multivariate analysis",
+      "outcome_variable": "political distrust (politicians, parliament, legal system)",
+      "independent_variables": "concern about immigration",
+      "control_variables": "far-right popularity;level of spending on social protection; long-term country of migration; governance quality; gdp per capita; unemployment rate",
+      "tools_software": "not stated"
+    },
+    "results": {
+      "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.",
+      "numerical_results": [
+        {
+          "outcome_name": "distrust in parliament",
+          "value": "0.17",
+          "unit": "not stated",
+          "effect_size": "not stated",
+          "confidence_interval": {
+            "lower": "not stated",
+            "upper": "not stated",
+            "level": "not stated"
+          },
+          "p_value": "0.00",
+          "statistical_significance": "5% level",
+          "direction": "Positive",
+          "notes": "They also report standard errors = 0.000."
+        },
+        {
+          "outcome_name": "distrust in politicians",
+          "value": "0.13",
+          "unit": "not stated",
+          "effect_size": "not stated",
+          "confidence_interval": {
+            "lower": "not stated",
+            "upper": "not stated",
+            "level": "not stated"
+          },
+          "p_value": "0.00",
+          "statistical_significance": "5% level",
+          "direction": "Positive",
+          "notes": "They also report standard errors = 0.000."
+        },
+        {
+          "outcome_name": "distrust in legal system",
+          "value": "0.14",
+          "unit": "not stated",
+          "effect_size": "not stated",
+          "confidence_interval": {
+            "lower": "not stated",
+            "upper": "not stated",
+            "level": "not stated"
+          },
+          "p_value": "0.00",
+          "statistical_significance": "5% level",
+          "direction": "Positive",
+          "notes": "They also report standard errors = 0.000."
+        }
+      ]
+    },
+    "metadata": {
+      "original_paper_id": "S0043887112000032.",
+      "original_paper_title": "The Cultural Divide in Europe: Migration, Multiculturalism, and  Political Trust",
+      "original_paper_code": "not stated",
+      "original_paper_data": "http://www.europeansocialsurvey.org/; http://stats.oecd.org/index.aspx?DataSetCode=naG; http://titania.sourceoecd.org/vl =3262696/cl=11/nw=1/rpsv/factbook2009/06/02/01/index.htm"
+    }
+  }
+}

13/gt/human_preregistration.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:6cc41bdd5a24f67c463d1bdb276d9dd862c3f8e186680ad4170eb99b6e2c4be5
+size 258202

13/input/initial_details.txt

@@ -0,0 +1,5 @@
+[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.

13/input/original_paper.pdf

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+version https://git-lfs.github.com/spec/v1
+oid sha256:2e269b41dfb067d2312a591b15ff945dbca817c25dd873a5339a2f6d8d40ee77
+size 756190

13/input/replication_data/data_analysis_code.R

@@ -0,0 +1,254 @@
+# Replication of:
+# "The Cultural Divide in Europe: Migration, Multiculturalism, and Political Trust"
+# by Lauren M. McLaren
+# World Politics, Volume 64, Issue 2April 2012 , pp. 199-241
+# DOI: https://doi.org/10.1017/S0043887112000032
+# 
+# Data analysis code
+# June 26, 2020
+#
+# Marta Kolczynska, mkolczynska@gmail.com
+
+# 1. SETUP ----------
+
+sessionInfo()
+
+# R version 3.6.3 (2020-02-29)
+# Platform: x86_64-w64-mingw32/x64 (64-bit)
+# Running under: Windows 10 x64 (build 18362)
+# 
+# Matrix products: default
+# 
+# locale:
+#   [1] LC_COLLATE=English_United States.1252  LC_CTYPE=English_United States.1252    LC_MONETARY=English_United States.1252
+# [4] LC_NUMERIC=C                           LC_TIME=English_United States.1252    
+# 
+# attached base packages:
+#   [1] stats     graphics  grDevices utils     datasets  methods   base     
+# 
+# loaded via a namespace (and not attached):
+#   [1] compiler_3.6.3 tools_3.6.3    packrat_0.5.0 
+
+
+## 1.1. Packages ----------
+
+library(lme4) # for estimating multi-level models
+library(mice) # for imputation and analyzing imputed data
+
+
+# 2. Reading in the data ----------
+
+# complete-case survey data
+data_clean_5pct <- readRDS("data_clean_5pct.rds")
+
+# imputed data
+data_imp_5pct <- readRDS("data_imp_5pct.rds")
+
+
+# 3. Analyses -----------
+
+## 3.1 Main analysis (complete cases, weights) -----------
+
+m1 <- lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
+             stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
+             vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female + 
+             vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry),
+           weights = pspwght,
+           data = data_clean_5pct)
+
+summary(m1)
+
+
+# Linear mixed model fit by REML ['lmerMod']
+# Formula: trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev +  
+#   distrust_soc + stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +  
+#   vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ +  
+#   female + vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc +      unemp + (1 | cntry)
+# Data: data_clean_5pct
+# Weights: pspwght
+# 
+# REML criterion at convergence: 3694.5
+# 
+# Scaled residuals: 
+#   Min      1Q  Median      3Q     Max 
+# -3.5752 -0.6246 -0.0678  0.5711  3.5389 
+# 
+# Random effects:
+#   Groups   Name        Variance Std.Dev.
+# cntry    (Intercept) 0.0292   0.1709  
+# Residual             3.3620   1.8336  
+# Number of obs: 858, groups:  cntry, 13
+# 
+# Fixed effects:
+#   Estimate Std. Error t value
+# (Intercept)    3.246e+00  9.812e-01   3.308
+# imm_concern    1.797e-01  3.864e-02   4.651
+# happy_rev     -8.221e-02  5.449e-02  -1.509
+# stflife_rev    1.577e-01  5.239e-02   3.009
+# sclmeet_rev    8.809e-02  4.717e-02   1.868
+# distrust_soc   6.500e-02  4.605e-02   1.412
+# stfeco_rev     2.367e-01  3.805e-02   6.221
+# hincfel        3.787e-02  1.001e-01   0.378
+# stfhlth_rev    1.117e-02  3.419e-02   0.327
+# stfedu_rev     1.281e-01  3.599e-02   3.560
+# vote_gov1     -4.757e-01  1.401e-01  -3.394
+# vote_frparty1  2.782e-01  3.601e-01   0.773
+# lrscale       -3.453e-02  3.403e-02  -1.015
+# hhinc_std     -1.384e-01  8.175e-02  -1.693
+# agea          -4.726e-03  3.932e-03  -1.202
+# educ          -1.021e-01  5.105e-02  -2.000
+# female        -2.457e-02  1.321e-01  -0.186
+# vote_share_fr -6.641e-02  3.264e-02  -2.034
+# socexp        -9.360e-05  9.134e-05  -1.025
+# lt_imm_cntry  -9.512e-01  6.520e-01  -1.459
+# wgi           -4.637e-01  5.646e-01  -0.821
+# gdppc          7.341e-05  2.792e-05   2.630
+# unemp         -1.136e-01  5.120e-02  -2.219
+# 
+# Correlation matrix not shown by default, as p = 23 > 12.
+# Use print(x, correlation=TRUE)  or
+# vcov(x)        if you need it
+# 
+# fit warnings:
+#   Some predictor variables are on very different scales: consider rescaling
+
+
+## 3.2 Auxiliary analysis 1 (complete cases, no weights) -----------
+
+m2 <- lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
+             stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
+             vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female + 
+             vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry),
+           data = data_clean_5pct)
+
+summary(m2)
+
+# Linear mixed model fit by REML ['lmerMod']
+# Formula: trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev +  
+#   distrust_soc + stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +  
+#   vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ +  
+#   female + vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc +      unemp + (1 | cntry)
+# Data: data_clean_5pct
+# 
+# REML criterion at convergence: 3627.7
+# 
+# Scaled residuals: 
+#   Min      1Q  Median      3Q     Max 
+# -3.6483 -0.6477 -0.0737  0.6150  2.9536 
+# 
+# Random effects:
+#   Groups   Name        Variance Std.Dev.
+# cntry    (Intercept) 0.00     0.000   
+# Residual             3.58     1.892   
+# Number of obs: 858, groups:  cntry, 13
+# 
+# Fixed effects:
+#   Estimate Std. Error t value
+# (Intercept)    2.980e+00  8.623e-01   3.455
+# imm_concern    2.145e-01  3.925e-02   5.465
+# happy_rev     -1.045e-01  5.506e-02  -1.897
+# stflife_rev    1.505e-01  5.354e-02   2.811
+# sclmeet_rev    8.803e-02  4.746e-02   1.855
+# distrust_soc   8.275e-02  4.640e-02   1.784
+# stfeco_rev     2.582e-01  3.798e-02   6.796
+# hincfel        3.160e-02  1.001e-01   0.316
+# stfhlth_rev    5.227e-02  3.535e-02   1.479
+# stfedu_rev     9.396e-02  3.623e-02   2.593
+# vote_gov1     -3.669e-01  1.404e-01  -2.613
+# vote_frparty1  2.402e-01  3.846e-01   0.624
+# lrscale       -5.029e-02  3.483e-02  -1.444
+# hhinc_std     -1.134e-01  8.351e-02  -1.358
+# agea          -5.627e-03  4.071e-03  -1.382
+# educ          -1.100e-01  5.108e-02  -2.153
+# female        -2.732e-02  1.330e-01  -0.205
+# vote_share_fr -7.272e-02  2.740e-02  -2.654
+# socexp        -1.531e-04  7.600e-05  -2.015
+# lt_imm_cntry  -1.306e+00  5.447e-01  -2.397
+# wgi           -1.699e-02  4.757e-01  -0.036
+# gdppc          8.837e-05  2.309e-05   3.827
+# unemp         -1.448e-01  4.246e-02  -3.410
+# 
+# Correlation matrix not shown by default, as p = 23 > 12.
+# Use print(x, correlation=TRUE)  or
+# vcov(x)        if you need it
+# 
+# fit warnings:
+#   Some predictor variables are on very different scales: consider rescaling
+# convergence code: 0
+# boundary (singular) fit: see ?isSingular
+
+
+## 3.3 Auxiliary analysis 2 (imputed data, weights) -----------
+
+fitimp1 <- with(data_imp_5pct,
+               lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
+                      stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
+                      vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female + 
+                      vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry),
+                    weights = pspwght))
+
+summary(pool(fitimp1))
+
+# term      estimate    std.error  statistic         df      p.value
+# 1    (Intercept)  2.816689e+00 8.221700e-01  3.4259212  451.61567 6.687248e-04
+# 2    imm_concern  1.423741e-01 3.119772e-02  4.5636050 1208.19658 5.541157e-06
+# 3      happy_rev -4.983350e-02 4.560246e-02 -1.0927809  805.54346 2.748167e-01
+# 4    stflife_rev  7.047185e-02 4.111709e-02  1.7139311  818.63266 8.691993e-02
+# 5    sclmeet_rev  3.297687e-02 3.890915e-02  0.8475351  472.48439 3.971261e-01
+# 6   distrust_soc  1.626593e-01 3.786510e-02  4.2957577  629.47191 2.016327e-05
+# 7     stfeco_rev  2.771359e-01 3.316246e-02  8.3569146  239.35237 5.329071e-15
+# 8        hincfel -5.732207e-02 8.306497e-02 -0.6900872 1107.58966 4.902839e-01
+# 9    stfhlth_rev  5.563212e-02 2.951422e-02  1.8849260  372.91831 6.021716e-02
+# 10    stfedu_rev  1.259361e-01 3.149656e-02  3.9984089  291.20678 8.089455e-05
+# 11     vote_gov1 -2.859426e-01 1.236809e-01 -2.3119386  781.13063 2.104033e-02
+# 12 vote_frparty1  1.669440e-01 3.400873e-01  0.4908858 1172.40909 6.235991e-01
+# 13       lrscale -5.498991e-02 3.573257e-02 -1.5389295   34.40401 1.329694e-01
+# 14     hhinc_std -1.738076e-01 7.689969e-02 -2.2601861   64.11253 2.721389e-02
+# 15          agea -2.954197e-03 3.197596e-03 -0.9238808  500.03315 3.559940e-01
+# 16          educ -8.500129e-02 4.405474e-02 -1.9294472  419.87255 5.434886e-02
+# 17        female  6.804313e-02 1.115148e-01  0.6101711  653.26185 5.419607e-01
+# 18 vote_share_fr -7.867001e-02 3.095496e-02 -2.5414351 1235.93419 1.116120e-02
+# 19        socexp -3.232296e-05 8.388477e-05 -0.3853257 1203.17571 7.000642e-01
+# 20  lt_imm_cntry -9.573876e-01 4.922548e-01 -1.9449023 1237.44329 5.201302e-02
+# 21           wgi -4.402953e-01 4.968003e-01 -0.8862622 1246.29015 3.756472e-01
+# 22         gdppc  6.614430e-05 2.555400e-05  2.5884131 1231.41218 9.755476e-03
+# 23         unemp -1.224877e-01 4.030609e-02 -3.0389375 1159.40209 2.427313e-03
+
+
+## 3.4 Auxiliary analysis 3 (imputed data, no weights) -----------
+
+fitimp2 <- with(data_imp_5pct,
+                lmer(trstprl_rev ~ imm_concern + happy_rev + stflife_rev + sclmeet_rev + distrust_soc +
+                       stfeco_rev + hincfel + stfhlth_rev + stfedu_rev +
+                       vote_gov + vote_frparty + lrscale + hhinc_std + agea + educ + female + 
+                       vote_share_fr + socexp + lt_imm_cntry + wgi + gdppc + unemp + (1 | cntry)))
+
+summary(pool(fitimp2))
+
+
+# term      estimate    std.error    statistic         df      p.value
+# 1    (Intercept)  2.670425e+00 6.820727e-01  3.915162454  931.25755 9.693099e-05
+# 2    imm_concern  1.733889e-01 3.111443e-02  5.572619399 1244.66947 3.073233e-08
+# 3      happy_rev -3.984772e-02 4.427729e-02 -0.899958569 1176.99260 3.683264e-01
+# 4    stflife_rev  6.032247e-02 4.131462e-02  1.460075785 1138.43822 1.445451e-01
+# 5    sclmeet_rev  6.519698e-02 3.776982e-02  1.726165822  776.34841 8.471544e-02
+# 6   distrust_soc  1.442623e-01 3.720122e-02  3.877892535  988.38166 1.123306e-04
+# 7     stfeco_rev  2.832967e-01 3.073871e-02  9.216286016 1210.79162 0.000000e+00
+# 8        hincfel -1.648051e-02 8.131428e-02 -0.202676669 1070.52447 8.394263e-01
+# 9    stfhlth_rev  8.005235e-02 2.902248e-02  2.758287879  948.94275 5.922195e-03
+# 10    stfedu_rev  1.087282e-01 3.101416e-02  3.505760697  352.97420 5.140006e-04
+# 11     vote_gov1 -2.670967e-01 1.193788e-01 -2.237388784 1161.03361 2.545008e-02
+# 12 vote_frparty1  1.000650e-01 3.494422e-01  0.286356421 1237.24931 7.746531e-01
+# 13       lrscale -6.169859e-02 3.015253e-02 -2.046216038  501.96215 4.125558e-02
+# 14     hhinc_std -1.551995e-01 7.601284e-02 -2.041754293   89.44537 4.412153e-02
+# 15          agea -4.243024e-03 3.134274e-03 -1.353750000 1205.77576 1.760698e-01
+# 16          educ -9.205973e-02 4.230064e-02 -2.176320097  889.41254 2.979383e-02
+# 17        female  8.929983e-04 1.105279e-01  0.008079392  750.23635 9.935558e-01
+# 18 vote_share_fr -7.701945e-02 2.459950e-02 -3.130935119 1222.91623 1.783804e-03
+# 19        socexp -7.656541e-05 6.729134e-05 -1.137819684 1202.30676 2.554225e-01
+# 20  lt_imm_cntry -1.076957e+00 3.907036e-01 -2.756455645 1210.87103 5.930883e-03
+# 21           wgi -2.017787e-01 3.978470e-01 -0.507176717 1244.09435 6.121207e-01
+# 22         gdppc  7.336745e-05 1.999235e-05  3.669775968 1243.82138 2.530379e-04
+# 23         unemp -1.387186e-01 3.162545e-02 -4.386295851 1180.15849 1.255900e-05
+
+

13/input/replication_data/data_clean.rds

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+size 951875