finalized R code

assumptions.R

@@ -2,9 +2,9 @@
 # Changeable Assumptions
 #----------------------------------------------------------------------------------------------------------------------#
 
-# Economic Assumptions:
+# Basic Assumptions:
 discount_rate <- 0.03
-retirement_age <- 64 # This is more or less set in stone by the IAB data. We cannot go higher only lower.
+retirement_age <- 64 # This is more or less set in stone by the IAB data. We cannot go higher than 64 only lower.
 wage_growth_rate <- 0.005
 
 # Tax System Assumptions:
@@ -22,7 +22,7 @@ global_income_fraction_of_long_term_care_contribution <- 0 # The fraction of une
 global_welfare_benefit_monthly <- 700
 value_added_tax <- 0.19
 
-# Behaviroal reponses to taxation
+# Behavioural responses to taxation
 cost_of_raising_public_funds <- 0.3 # Relevant for the more traditional benefit cost ratio
 # Elasticity of Taxable Income
 overwrite_eti <- FALSE # If set to true the ETI specified below is used for all tax reforms
@@ -31,7 +31,7 @@ global_eti <- 0.3 # Irrelevant unless overwrite_eti == TRUE
 # Preferences
 global_relative_risk_aversion <- 2
 
-# Value of statistical Life and Injuries:
+# Value of statistical life and injuries:
 use_single_statistical_life_value <- TRUE # If set to true only one statistical life value has to be specified. All of the
 # other values for injuries and the differentiation between resource cost and risk value are derived from the value set
 # in value_of_statistical_life
@@ -90,15 +90,16 @@ excluded_from_deflating <- c("taxReform2005",
 order_of_categories <- c("Top Tax Reform",
                          "Education",
                          "Job Training",
-                         "Start up Subsidy",
+                         "Start-Up Subsidy",
                          "Subsidized Employment",
                          "Other Labor Market Policies",
                          "Unemployment Insurance",
                          "Parental Leave Reform",
                          "Climate Policy",
                          "Health Program",
                          "Other") #This determines the order in which the reforms categories are displayed in the graphs
-# and tables.
+# and tables. If a new category is added (or a existing is renamed this has to be updated). Otherwise the program crashes
+# when trying to calculate category averages.
 
 # Exclude Programs from category average:
 excluded_from_category_average <- c("bicycleHelmet", "coronavirusLockdownR1")

functions.R

@@ -74,8 +74,10 @@ getPointEstimates <- function(programs) {
   mvpf_results <- data.frame(program = programs)
   for (i in 1:length(programs)) {
     message(paste("Running", programs[i], "once to get the point estimate."))
+    # Call program "i" and store the return values
     return_values <- do.call(programs[i], list())
 
+    # Deflate unless deflating is explicitely disabled.
     if (!programs[i] %in% excluded_from_deflating) {
       return_values <- deflateReturnValues(return_values, results_prices)
     }
@@ -86,13 +88,15 @@ getPointEstimates <- function(programs) {
       mvpf_results[names(return_values), ] <- unlist(return_values)
       next
     }
+    # Store the return values in the results data.frame
     mvpf_results[i, names(return_values)] <- unlist(return_values)
+    # Calculate the MVPF and also store it in the results data.frame
     mvpf_results[i, "mvpf"] <- calculateMVPF(mvpf_results[i, "willingness_to_pay"], mvpf_results[i, "government_net_costs"])
   }
   return(mvpf_results)
 }
 
-# Run Programs without priting messages
+# Run Programs without printing messages
 quietelyRunPrograms <- function(programs, bootstrap = FALSE) {
   results <- suppressMessages(getPointEstimates(programs))
   if (bootstrap) {
@@ -712,7 +716,7 @@ getCategoryPlotData <- function(plot_data, bootstrap_results, include_additional
       return(1 - bootstrap_results$government_net_costs / bootstrap_results$program_cost)
     })
 
-    # Calculate numerator and denominator as before expcet that the result now is a vector with number of bootstrap replications rows:
+    # Calculate numerator and denominator as before except that the result now is a vector with 'number of bootstrap replications' rows:
     numerator_bootstrap <- 1 / ncol(willingness_to_pay_per_euro) * rowSums(willingness_to_pay_per_euro)
     denominator_bootstrap <- 1 / ncol(willingness_to_pay_per_euro) * (-rowSums(fiscal_externality_per_euro) + ncol(fiscal_externality_per_euro))
 
@@ -1063,7 +1067,7 @@ getTaxSystemEffects <- function(gross_income,
   # Higher pension contributions result in higher pension payments but due to demographics the return is probably quite low.
 
   # adapted from:
-  # 'Identifying Laffer Bounds: A Sufficient-Statistics Approach with an Application to Germany' by Sachs and Lorenz (2015) Appendix A
+  # 'Identifying Laffer Bounds: A Sufficient-Statistics Approach with an Application to Germany' by Lorenz and Sachs (2015) Appendix A
   # and their supplementary excel file
 
   # Calculations are for a single household without children

main.R

@@ -1,5 +1,5 @@
 #----------------------------------------------------------------------------------------------------------------------#
-# Unified Welfare Analysis for Germany using MVPFs (Marginal Value of Public Funds)
+# Unified Welfare Analysis for Germany using the MVPF (Marginal Value of Public Funds)
 #----------------------------------------------------------------------------------------------------------------------#
 
 # Clear the environment:
@@ -37,7 +37,7 @@ if (length(not_installed_packages) > 0) {
       break
     }
     else if (confirmation == "n") {
-      stop("Need to have required packages installed. Exiting...")
+      stop("Need to have the required packages installed. Exiting...")
     }
   }
 }
@@ -129,7 +129,7 @@ plotResults(plot_data = plot_data,
 # Figure that plots the Net Costs of all policies against the year they were implemented (with text lables)
 plotResults(plot_data = plot_data,
             y_axis = "government_net_costs_per_program_cost",
-            y_label = "Net Costs per Euro Progammatic Expenditure",
+            y_label = "Net Costs per Euro Programmatic Expenditure",
             x_axis = "year", x_label = "Year",
             save = "cost_against_year.pdf",
             lower_cutoff = 0,
@@ -140,7 +140,7 @@ plotResults(plot_data = plot_data,
 # Figure that plots the WTP of all policies against the year they were implemented (with text lables)
 plotResults(plot_data = plot_data,
             y_axis = "willingness_to_pay_per_program_cost",
-            y_label = "Willingness to Pay per Euro Progammatic Expenditure",
+            y_label = "Willingness to Pay per Euro Programmatic Expenditure",
             x_axis = "year", x_label = "Year",
             save = "wtp_against_year.pdf",
             lower_cutoff = 0,
@@ -236,7 +236,7 @@ robustnessCheck(programs,
                     global_flat_tax <<- 0.5
                   }
                 },
-                headlines = c("τ = 0.1", "Only Income Tax", "Taxes and Transfers (Baseline)", "τ = 0.5"),
+                headlines = c("τ = 0.1", "Only Income Tax", "German Tax and Transfer System (Baseline)", "τ = 0.5"),
                 overwrite_bootstrap_replications = 100,
                 save = "robustness_check_tax_rate.pdf")
 
@@ -255,7 +255,7 @@ robustnessCheck(programs,
                     yearly_return_to_schooling <<- 0.11
                   }
                   else if (specification == 4) {
-                    # Baseline
+                    # nothing baseline
                   }
                 },
                 headlines = c("RTS = 5%", "RTS = 8%", " RTS = 11%", "IAB Data (Baseline)"),
@@ -270,7 +270,4 @@ cat("Robustness checks completed in ", difftime(Sys.time(), start_time, units='m
 #exportPlotCSV(programs, assumption_list = getListOfAllMetaAssumptions(), bootstrap  = FALSE, meta_assumptions = TRUE)
 
 # Export Tables:
-exportLatexTables(plot_data)
-
-# Copy Files:
-FolderCopy()
+exportLatexTables(plot_data)

web/csv/readme.txt

@@ -0,0 +1,3 @@
+the files generated by
+exportPlotCSV(programs, assumption_list = getListOfAllMetaAssumptions(), bootstrap  = FALSE, meta_assumptions = TRUE)
+need to be placed here.