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Replication and further analysis of Fiscal Governance in the Eurozone: How Effectively Does the Stability and Growth Pact Limit Governmental Debt in the Euro Countries? by Sebastian Koehler and Thomas König
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| #This code is the replication and further analysis of paper "Fiscal Governance in the Eurozone: | |
| #How Effectively Does the Stability and Growth Pact Limit Governmental Debt in | |
| #the Euro Countries? by Sebastian Koehler and Thomas König | |
| # Replication of Euro 11 by using the author's code with minimal editions | |
| #### Let's begin with defining some predictors: | |
| pred <- names(scdata)[c(# | |
| 7, # pop65+ | |
| 8, # pop15- | |
| #9,# unemployment (1) | |
| #10,# system | |
| 11,# yrcurnt | |
| 12,# allhouse | |
| 13,# legelec | |
| 14, # pluralty | |
| 15, # pr | |
| #16, # checks | |
| #17, # fri | |
| #18,# GDP growth | |
| #19,# growth in multi factor productivity | |
| #20,# labor productivity annual growth | |
| #21, # health expenditure/GDP | |
| 22,# GDP expenditure approach | |
| #23,# tax revenue %GDP (general) | |
| 24,# taxrev %GDP, no Social Security | |
| #25, # CO2 emissions | |
| #26, # FDI | |
| #27, # GDP growth | |
| #28,# Gini index | |
| #29,#,# Inflation (Consumer Prices) | |
| #30, # Poverty | |
| #31,#, # unemployment (World Bank) | |
| ##32, #Population | |
| #33,#, #openness (PWT) | |
| 34,#,# openness (expenditure) | |
| #35, # Expenditure on Families %GDP | |
| #36, # PolconIII | |
| #37, # PolconV | |
| 38, # Potrafke ideology | |
| 39, # Majority margin | |
| #40, # Herfindahl Index Government | |
| 41, #lag debt/gdp (RR) | |
| 42#,# Rae Fractionalisation index (government) | |
| #43 # Rae Fractionalisation Index (total) | |
| )] | |
| #### define countries used for synthetic control group | |
| contr <- sort(unique(scdata$ccode[is.element(scdata$country,setdiff(scdata$country,c(Euro12,"Euro 11","Slovenia")))])) | |
| #### The following countries have to be excluded due to data constraints (missing values) | |
| contr <- setdiff(contr, c(1111,2222,70,155,225,269,290,310,316,317,349,355,360,366,666,732,sort(unique(scdata$ccode[scdata$eu==0 & scdata$oecd==0])))) | |
| #### Show countries that are available for Synthetic Greece | |
| country <- sort(unique(scdata$country[scdata$ccode %in% contr])) | |
| #### Generate Synth object (debt/gdp ratio) to be used to run the analysis | |
| sdata <- dataprep(foo = scdata[scdata$ccode %in% contr | scdata$country == "Euro 11",], | |
| predictors = pred, | |
| dependent = names(scdata[6]), | |
| unit.variable = "ccode", | |
| time.variable = "Year", | |
| treatment.identifier = 0, | |
| controls.identifier = contr, | |
| time.predictors.prior = c(1983:1998), | |
| time.optimize.ssr = c(1983:1999), | |
| unit.names.variable = "country", | |
| time.plot = 1983:2010 | |
| ) | |
| #### Run the synthetic control analysis: | |
| synth.out <- synth(data.prep.obj = sdata, method = "BFGS") | |
| #### calculate output gaps from the results | |
| gaps <- sdata$Y1plot - (sdata$Y0plot %*% synth.out$solution.w) | |
| #### | |
| synth.tables <- synth.tab(dataprep.res = sdata, synth.res=synth.out) | |
| #### Plot the Path of the Debt to GDP ratio for the Euro11 and the Synthetic control | |
| path.plot(synth.res = synth.out, | |
| dataprep.res = sdata, | |
| Ylab="Debt/GDP (Nominal)", | |
| Xlab="Year", | |
| Legend=c("Euro 11","Synthetic Euro 11"), | |
| Legend.position="bottomright", abline(v=1999,lty="dashed") | |
| ) | |
| #### Plot the gap of the Debt to GDP ratio for the Euro11 and the Synthetic control | |
| gaps.plot(synth.res = synth.out, dataprep.res = sdata, | |
| Ylab= "Gap in Debt/GDP (percentage points, 1983-2010)", Xlab="Year", | |
| Main=NA, abline(v=1999,lty="dashed")) | |
| #### extract Country weights from Synthetic control | |
| a <- cbind(synth.tables$tab.w[,1],synth.tables$tab.w[,2]) | |
| row.names(a) <- synth.tables$tab.w[,2] | |
| #### Plot Country weights | |
| dotchart(a[,1],pch=16) | |
| ### Time placebo test for Euro 11 (it is mentioned in paper but there were no figures thus we decided to test for robustness) | |
| pred <- names(scdata)[c(# | |
| 7, # pop65+ | |
| 8, # pop15- | |
| #9,# unemployment (1) | |
| #10,# system | |
| 11,# yrcurnt | |
| 12,# allhouse | |
| 13,# legelec | |
| 14, # pluralty | |
| 15, # pr | |
| #16, # checks | |
| #17, # fri | |
| #18,# GDP growth | |
| #19,# growth in multi factor productivity | |
| #20,# labor productivity annual growth | |
| 21, # health expenditure/GDP | |
| 22,# GDP expenditure approach | |
| #23,# tax revenue %GDP (general) | |
| 24,# taxrev %GDP, no Social Security | |
| 25, # CO2 emissions | |
| #26, # FDI | |
| #27, # GDP growth | |
| #28,# Gini index | |
| #29,#,# Inflation (Consumer Prices) | |
| #30, # Poverty | |
| 31,#, # unemployment (World Bank) | |
| ##32, #Population | |
| #33,#, #openness (PWT) | |
| 34,#,# openness (expenditure) | |
| #35, # Expenditure on Families %GDP | |
| 36, # PolconIII | |
| #37, # PolconV | |
| 38, # Potrafke ideology | |
| 39, # Majority margin | |
| #40, # Herfindahl Index Government | |
| 41, #lag debt/gdp (RR) | |
| 42#,# Rae Fractionalisation index (government) | |
| #43 # Rae Fractionalisation Index (total) | |
| )] | |
| #### define countries used for synthetic control group | |
| contr <- sort(unique(scdata$ccode[is.element(scdata$country,setdiff(scdata$country,c(Euro12,"Euro 11","Slovenia")))])) | |
| #### The following countries have to be excluded due to data constraints (missing values) | |
| contr <- setdiff(contr, c(1111,2222,70,155,225,269,290,310,316,317,349,355,360,366,666,732,sort(unique(scdata$ccode[scdata$eu==0 & scdata$oecd==0])))) | |
| #### Show countries that are available for Synthetic Greece | |
| country <- sort(unique(scdata$country[scdata$ccode %in% contr])) | |
| #### Generate Synth object (debt/gdp ratio) to be used to run the analysis | |
| sdata <- dataprep(foo = scdata[scdata$ccode %in% contr | scdata$country == "Euro 11",], | |
| predictors = pred, | |
| dependent = names(scdata[6]), | |
| unit.variable = "ccode", | |
| time.variable = "Year", | |
| treatment.identifier = 0, | |
| controls.identifier = contr, | |
| time.predictors.prior = c(1983:1991), #changing the treatment time to 1991 (cannot do earlier due to missing data) | |
| time.optimize.ssr = c(1983:1991), #changing the treatment time to 1991 (cannot do earlier due to missing data) | |
| unit.names.variable = "country", | |
| time.plot = 1983:2010 | |
| ) | |
| #### Run the synthetic control analysis: | |
| synth.out <- synth(data.prep.obj = sdata, method = "BFGS") | |
| #### calculate output gaps from the results | |
| gaps <- sdata$Y1plot - (sdata$Y0plot %*% synth.out$solution.w) | |
| #### | |
| synth.tables <- synth.tab(dataprep.res = sdata, synth.res=synth.out) | |
| #### Plot the Path of the Debt to GDP ratio for the Euro11 and the Synthetic control | |
| path.plot(synth.res = synth.out, | |
| dataprep.res = sdata, | |
| Ylab="Debt/GDP (Nominal)", | |
| Xlab="Year", | |
| Legend=c("Euro 11","Synthetic Euro 11"), | |
| Legend.position="bottomright", abline(v=1999,lty="dashed") | |
| ) | |
| # Replication of donor countries by using the author's code for Euro 11 | |
| # Replication does not exactly match to the paper (still proves te same tyor of causal relation) | |
| # This difference might be due to difference prediction variables between Euro 11 and donor countries | |
| # Without seeing the original code, it is hard to predict the exact cause of the small difference. | |
| pred <- names(scdata)[c(# | |
| 7, # pop65+ | |
| 8, # pop15- | |
| #9,# unemployment (1) | |
| #10,# system | |
| 11,# yrcurnt | |
| 12,# allhouse | |
| 13,# legelec | |
| 14, # pluralty | |
| 15, # pr | |
| #16, # checks | |
| #17, # fri | |
| #18,# GDP growth | |
| #19,# growth in multi factor productivity | |
| 20,# labor productivity annual growth | |
| 21, # health expenditure/GDP | |
| 22,# GDP expenditure approach | |
| #23,# tax revenue %GDP (general) | |
| 24,# taxrev %GDP, no Social Security | |
| 25, # CO2 emissions | |
| #26, # FDI | |
| #27, # GDP growth | |
| #28,# Gini index | |
| 29,#,# Inflation (Consumer Prices) | |
| #30, # Poverty | |
| 31,#, # unemployment (World Bank) | |
| ##32, #Population | |
| #33,#, #openness (PWT) | |
| 34,#,# openness (expenditure) | |
| #35, # Expenditure on Families %GDP | |
| 36, # PolconIII | |
| #37, # PolconV | |
| 38, # Potrafke ideology | |
| 39, # Majority margin | |
| #40, # Herfindahl Index Government | |
| 41, #lag debt/gdp (RR) | |
| 42#,# Rae Fractionalisation index (government) | |
| #43 # Rae Fractionalisation Index (total) | |
| )] | |
| #### define countries used for synthetic control group | |
| contr <- sort(unique(scdata$ccode[is.element(scdata$country,setdiff(scdata$country,c(Euro12,"Euro 11","Slovenia", "donor countries")))])) | |
| #### The following countries have to be excluded due to data constraints (missing values) | |
| contr <- setdiff(contr, c(1111,2222,70,155,225,269,290,310,316,317,349,355,360,366,666,732,sort(unique(scdata$ccode[scdata$eu==0 & scdata$oecd==0])))) | |
| #### Show countries that are available for Synthetic Greece | |
| country <- sort(unique(scdata$country[scdata$ccode %in% contr])) | |
| #### Generate Synth object (debt/gdp ratio) to be used to run the analysis | |
| sdata <- dataprep(foo = scdata[scdata$ccode %in% contr | scdata$country == "donor countries",], | |
| predictors = pred, | |
| dependent = names(scdata[6]), | |
| unit.variable = "ccode", | |
| time.variable = "Year", | |
| treatment.identifier = 1111, #(1111 is the donor countries code) | |
| controls.identifier = contr, | |
| time.predictors.prior = c(1983:1998), | |
| time.optimize.ssr = c(1983:1999), | |
| unit.names.variable = "country", | |
| time.plot = 1983:2010 | |
| ) | |
| #### Run the synthetic control analysis: | |
| synth.out <- synth(data.prep.obj = sdata, method = "BFGS") | |
| #### calculate output gaps from the results | |
| gaps <- sdata$Y1plot - (sdata$Y0plot %*% synth.out$solution.w) | |
| #### | |
| synth.tables <- synth.tab(dataprep.res = sdata, synth.res=synth.out) | |
| #### Plot the Path of the Debt to GDP ratio for the Euro11 and the Synthetic control | |
| path.plot(synth.res = synth.out, | |
| dataprep.res = sdata, | |
| Ylab="Debt/GDP (Nominal)", | |
| Xlab="Year", | |
| Legend=c("Donor countries","Synthetic donor countries"), | |
| Legend.position="bottomright", abline(v=1999,lty="dashed") | |
| ) | |
| #### Plot the gap of the Debt to GDP ratio for the Euro11 and the Synthetic control | |
| gaps.plot(synth.res = synth.out, dataprep.res = sdata, | |
| Ylab= "Gap in Debt/GDP (percentage points, 1983-2010)", Xlab="Year", | |
| Main=NA, abline(v=1999,lty="dashed")) | |
| #### extract Country weights from Synthetic control | |
| a <- cbind(synth.tables$tab.w[,1],synth.tables$tab.w[,2]) | |
| row.names(a) <- synth.tables$tab.w[,2] | |
| #### Plot Country weights | |
| dotchart(a[,1],pch=16) | |
| # Further analysis on France | |
| pred <- names(scdata)[c(# | |
| 7, # pop65+ | |
| 8, # pop15- | |
| #9,# unemployment (1) | |
| #10,# system | |
| 11,# yrcurnt | |
| 12,# allhouse | |
| 13,# legelec | |
| 14, # pluralty | |
| 15, # pr | |
| #16, # checks | |
| #17, # fri | |
| #18,# GDP growth | |
| #19,# growth in multi factor productivity | |
| 20,# labor productivity annual growth | |
| 21, # health expenditure/GDP | |
| 22,# GDP expenditure approach | |
| #23,# tax revenue %GDP (general) | |
| 24,# taxrev %GDP, no Social Security | |
| 25, # CO2 emissions | |
| #26, # FDI | |
| #27, # GDP growth | |
| #28,# Gini index | |
| 29,#,# Inflation (Consumer Prices) | |
| #30, # Poverty | |
| 31,#, # unemployment (World Bank) | |
| ##32, #Population | |
| #33,#, #openness (PWT) | |
| 34,#,# openness (expenditure) | |
| #35, # Expenditure on Families %GDP | |
| 36, # PolconIII | |
| #37, # PolconV | |
| 38, # Potrafke ideology | |
| 39, # Majority margin | |
| #40, # Herfindahl Index Government | |
| 41, #lag debt/gdp (RR) | |
| 42#,# Rae Fractionalisation index (government) | |
| #43 # Rae Fractionalisation Index (total) | |
| )] | |
| #### define countries used for synthetic control group | |
| contr <- sort(unique(scdata$ccode[is.element(scdata$country,setdiff(scdata$country,c(Euro12,"Euro 11","Slovenia", "France")))])) | |
| #### The following countries have to be excluded due to data constraints (missing values) | |
| contr <- setdiff(contr, c(1111,2222,70,155,225,269,290,310,316,317,349,355,360,366,666,732,sort(unique(scdata$ccode[scdata$eu==0 & scdata$oecd==0])))) | |
| #### Show countries that are available for Synthetic Greece | |
| country <- sort(unique(scdata$country[scdata$ccode %in% contr])) | |
| #### Generate Synth object (debt/gdp ratio) to be used to run the analysis | |
| sdata <- dataprep(foo = scdata[scdata$ccode %in% contr | scdata$country == "France",], | |
| predictors = pred, | |
| dependent = names(scdata[6]), | |
| unit.variable = "ccode", | |
| time.variable = "Year", | |
| treatment.identifier = 220, #(220 is France country code) | |
| controls.identifier = contr, | |
| time.predictors.prior = c(1983:1998), | |
| time.optimize.ssr = c(1983:1999), | |
| unit.names.variable = "country", | |
| time.plot = 1983:2010 | |
| ) | |
| #### Run the synthetic control analysis: | |
| synth.out <- synth(data.prep.obj = sdata, method = "BFGS") | |
| #### calculate output gaps from the results | |
| gaps <- sdata$Y1plot - (sdata$Y0plot %*% synth.out$solution.w) | |
| #### | |
| synth.tables <- synth.tab(dataprep.res = sdata, synth.res=synth.out) | |
| #### Plot the Path of the Debt to GDP ratio for the Euro11 and the Synthetic control | |
| path.plot(synth.res = synth.out, | |
| dataprep.res = sdata, | |
| Ylab="Debt/GDP (Nominal)", | |
| Xlab="Year", | |
| Legend=c("France","Synthetic France"), | |
| Legend.position="bottomright", abline(v=1999,lty="dashed") | |
| ) | |
| #### Plot the gap of the Debt to GDP ratio for the Euro11 and the Synthetic control | |
| gaps.plot(synth.res = synth.out, dataprep.res = sdata, | |
| Ylab= "Gap in Debt/GDP (percentage points, 1983-2010)", Xlab="Year", | |
| Main=NA, abline(v=1999,lty="dashed")) | |
| #### extract Country weights from Synthetic control | |
| a <- cbind(synth.tables$tab.w[,1],synth.tables$tab.w[,2]) | |
| row.names(a) <- synth.tables$tab.w[,2] | |
| #### Plot Country weights | |
| dotchart(a[,1],pch=16) |
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