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cee econ data.R
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cee econ data.R
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#Get data
library(wbstats)
countries = c("AUT","BEL","BGR","CHE","CYP","CZE","DEU","DNK","ESP","EST",
"FIN","FRA","GBR","GRC","HRV","HUN","IRL","ISL","ITA","LUX",
"LTU","LVA","MLT","NLD","NOR","POL","PRT","ROU","SVK","SVN","SWE", 'RUS', 'SRB')
countries.e = c("CZE", "SVN", "SVK", "LTU", "EST", "POL", "HUN","LVA","ROU", "HRV", "BGR","SRB")
countries.e2 = c("CZE", "SVK", "LTU", "EST", "POL", "HUN","LVA")
countries.e3 = c("SVN", "HRV", "BGR","ROU")
countries.w = c("AUT","BEL","DEU","FRA","GBR","IRL", "LUX","NLD")
countries.n = c("DNK", "FIN", "NOR", "SWE")
countries.s = c("ESP", "GRC","ITA","PRT")
#GINI
#poverty
#life expectancy SP.DYN.LE00.MA.IN SP.DYN.LE00.FE.IN
#mortality rate under 5 SH.DYN.MORT
#secondary school enrolment SE.SEC.ENRR
#firms with female top managers IC.FRM.FEMM.ZS
#labour force participation rate SL.TLF.CACT.ZS
#employment to population rate SL.EMP.TOTL.SP.ZS
#exports % of GDP NE.EXP.GNFS.ZS
#productivity GDP per person employed SL.GDP.PCAP.EM.KD
#ICT goods exports % of goods exports TX.VAL.ICTG.ZS.UN
d<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries, startdate = 1990, enddate = 2019, return_wide = TRUE)
de<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries.e, startdate = 1990, enddate = 2019, return_wide = TRUE)
de2<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries.e2, startdate = 1990, enddate = 2019, return_wide = TRUE)
de3<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries.e3, startdate = 1990, enddate = 2019, return_wide = TRUE)
dw<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries.w, startdate = 1990, enddate = 2019, return_wide = TRUE)
ds<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries.s, startdate = 1990, enddate = 2019, return_wide = TRUE)
dn<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries.n, startdate = 1990, enddate = 2019, return_wide = TRUE)
de<-wb(indicator = c("SP.POP.TOTL","NY.GDP.PCAP.PP.KD","SL.UEM.TOTL.ZS",'SI.POV.GINI', 'SI.POV.DDAY', 'SI.POV.UMIC', 'SP.DYN.LE00.MA.IN', 'SP.DYN.LE00.FE.IN',
'SE.SEC.ENRR', 'SL.TLF.CACT.ZS', 'SL.EMP.TOTL.SP.ZS', 'SH.DYN.MORT', 'NE.EXP.GNFS.ZS', 'SL.GDP.PCAP.EM.KD', 'IC.FRM.FEMM.ZS', 'TX.VAL.ICTG.ZS.UN'),
country=countries.e, startdate = 1990, enddate = 2019, return_wide = TRUE)
head(dn)
library(dplyr)
de<-de %>%
group_by(date) %>%
mutate(gdppc.wm = weighted.mean(NY.GDP.PCAP.PP.KD, SP.POP.TOTL, na.rm=TRUE),
unempl.wm = weighted.mean(SL.UEM.TOTL.ZS, SP.POP.TOTL, na.rm=TRUE),
empl.wm = weighted.mean(SL.EMP.TOTL.SP.ZS, SP.POP.TOTL, na.rm=TRUE),
labour.wm = weighted.mean(SL.TLF.CACT.ZS, SP.POP.TOTL, na.rm=TRUE),
product.wm = weighted.mean(SL.GDP.PCAP.EM.KD, SP.POP.TOTL, na.rm=TRUE),
exports.wm = weighted.mean(NE.EXP.GNFS.ZS, SP.POP.TOTL, na.rm=TRUE),
ict.wm = weighted.mean(TX.VAL.ICTG.ZS.UN, SP.POP.TOTL, na.rm=TRUE),
fmanager.wm = weighted.mean(IC.FRM.FEMM.ZS, SP.POP.TOTL, na.rm=TRUE),
secondary.wm = weighted.mean(SE.SEC.ENRR, SP.POP.TOTL, na.rm=TRUE),
mortality.wm = weighted.mean(SH.DYN.MORT, SP.POP.TOTL, na.rm=TRUE),
lifeexpm.wm = weighted.mean(SP.DYN.LE00.MA.IN, SP.POP.TOTL, na.rm=TRUE),
lifeexpf.wm = weighted.mean(SP.DYN.LE00.FE.IN, SP.POP.TOTL, na.rm=TRUE),
gini.wm = weighted.mean(SI.POV.GINI, SP.POP.TOTL, na.rm=TRUE),
poverty.wm = weighted.mean(SI.POV.DDAY, SP.POP.TOTL, na.rm=TRUE),
poverty2.wm = weighted.mean(SI.POV.UMIC, SP.POP.TOTL, na.rm=TRUE))
de2<-de2 %>%
group_by(date) %>%
mutate(gdppc.wm = weighted.mean(NY.GDP.PCAP.PP.KD, SP.POP.TOTL, na.rm=TRUE),
unempl.wm = weighted.mean(SL.UEM.TOTL.ZS, SP.POP.TOTL, na.rm=TRUE),
empl.wm = weighted.mean(SL.EMP.TOTL.SP.ZS, SP.POP.TOTL, na.rm=TRUE),
labour.wm = weighted.mean(SL.TLF.CACT.ZS, SP.POP.TOTL, na.rm=TRUE),
product.wm = weighted.mean(SL.GDP.PCAP.EM.KD, SP.POP.TOTL, na.rm=TRUE),
exports.wm = weighted.mean(NE.EXP.GNFS.ZS, SP.POP.TOTL, na.rm=TRUE),
ict.wm = weighted.mean(TX.VAL.ICTG.ZS.UN, SP.POP.TOTL, na.rm=TRUE),
fmanager.wm = weighted.mean(IC.FRM.FEMM.ZS, SP.POP.TOTL, na.rm=TRUE),
secondary.wm = weighted.mean(SE.SEC.ENRR, SP.POP.TOTL, na.rm=TRUE),
mortality.wm = weighted.mean(SH.DYN.MORT, SP.POP.TOTL, na.rm=TRUE),
lifeexpm.wm = weighted.mean(SP.DYN.LE00.MA.IN, SP.POP.TOTL, na.rm=TRUE),
lifeexpf.wm = weighted.mean(SP.DYN.LE00.FE.IN, SP.POP.TOTL, na.rm=TRUE),
gini.wm = weighted.mean(SI.POV.GINI, SP.POP.TOTL, na.rm=TRUE),
poverty.wm = weighted.mean(SI.POV.DDAY, SP.POP.TOTL, na.rm=TRUE),
poverty2.wm = weighted.mean(SI.POV.UMIC, SP.POP.TOTL, na.rm=TRUE))
de3<-de3 %>%
group_by(date) %>%
mutate(gdppc.wm = weighted.mean(NY.GDP.PCAP.PP.KD, SP.POP.TOTL, na.rm=TRUE),
unempl.wm = weighted.mean(SL.UEM.TOTL.ZS, SP.POP.TOTL, na.rm=TRUE),
empl.wm = weighted.mean(SL.EMP.TOTL.SP.ZS, SP.POP.TOTL, na.rm=TRUE),
labour.wm = weighted.mean(SL.TLF.CACT.ZS, SP.POP.TOTL, na.rm=TRUE),
product.wm = weighted.mean(SL.GDP.PCAP.EM.KD, SP.POP.TOTL, na.rm=TRUE),
exports.wm = weighted.mean(NE.EXP.GNFS.ZS, SP.POP.TOTL, na.rm=TRUE),
ict.wm = weighted.mean(TX.VAL.ICTG.ZS.UN, SP.POP.TOTL, na.rm=TRUE),
fmanager.wm = weighted.mean(IC.FRM.FEMM.ZS, SP.POP.TOTL, na.rm=TRUE),
secondary.wm = weighted.mean(SE.SEC.ENRR, SP.POP.TOTL, na.rm=TRUE),
mortality.wm = weighted.mean(SH.DYN.MORT, SP.POP.TOTL, na.rm=TRUE),
lifeexpm.wm = weighted.mean(SP.DYN.LE00.MA.IN, SP.POP.TOTL, na.rm=TRUE),
lifeexpf.wm = weighted.mean(SP.DYN.LE00.FE.IN, SP.POP.TOTL, na.rm=TRUE),
gini.wm = weighted.mean(SI.POV.GINI, SP.POP.TOTL, na.rm=TRUE),
poverty.wm = weighted.mean(SI.POV.DDAY, SP.POP.TOTL, na.rm=TRUE),
poverty2.wm = weighted.mean(SI.POV.UMIC, SP.POP.TOTL, na.rm=TRUE))
dw<-dw %>%
group_by(date) %>%
mutate(gdppc.wm = weighted.mean(NY.GDP.PCAP.PP.KD, SP.POP.TOTL, na.rm=TRUE),
unempl.wm = weighted.mean(SL.UEM.TOTL.ZS, SP.POP.TOTL, na.rm=TRUE),
empl.wm = weighted.mean(SL.EMP.TOTL.SP.ZS, SP.POP.TOTL, na.rm=TRUE),
labour.wm = weighted.mean(SL.TLF.CACT.ZS, SP.POP.TOTL, na.rm=TRUE),
product.wm = weighted.mean(SL.GDP.PCAP.EM.KD, SP.POP.TOTL, na.rm=TRUE),
exports.wm = weighted.mean(NE.EXP.GNFS.ZS, SP.POP.TOTL, na.rm=TRUE),
ict.wm = weighted.mean(TX.VAL.ICTG.ZS.UN, SP.POP.TOTL, na.rm=TRUE),
#fmanager.wm = weighted.mean(IC.FRM.FEMM.ZS, SP.POP.TOTL, na.rm=TRUE),
secondary.wm = weighted.mean(SE.SEC.ENRR, SP.POP.TOTL, na.rm=TRUE),
mortality.wm = weighted.mean(SH.DYN.MORT, SP.POP.TOTL, na.rm=TRUE),
lifeexpm.wm = weighted.mean(SP.DYN.LE00.MA.IN, SP.POP.TOTL, na.rm=TRUE),
lifeexpf.wm = weighted.mean(SP.DYN.LE00.FE.IN, SP.POP.TOTL, na.rm=TRUE),
gini.wm = weighted.mean(SI.POV.GINI, SP.POP.TOTL, na.rm=TRUE),
poverty.wm = weighted.mean(SI.POV.DDAY, SP.POP.TOTL, na.rm=TRUE),
poverty2.wm = weighted.mean(SI.POV.UMIC, SP.POP.TOTL, na.rm=TRUE))
ds<-ds %>%
group_by(date) %>%
mutate(gdppc.wm = weighted.mean(NY.GDP.PCAP.PP.KD, SP.POP.TOTL, na.rm=TRUE),
unempl.wm = weighted.mean(SL.UEM.TOTL.ZS, SP.POP.TOTL, na.rm=TRUE),
empl.wm = weighted.mean(SL.EMP.TOTL.SP.ZS, SP.POP.TOTL, na.rm=TRUE),
labour.wm = weighted.mean(SL.TLF.CACT.ZS, SP.POP.TOTL, na.rm=TRUE),
product.wm = weighted.mean(SL.GDP.PCAP.EM.KD, SP.POP.TOTL, na.rm=TRUE),
exports.wm = weighted.mean(NE.EXP.GNFS.ZS, SP.POP.TOTL, na.rm=TRUE),
ict.wm = weighted.mean(TX.VAL.ICTG.ZS.UN, SP.POP.TOTL, na.rm=TRUE),
fmanager.wm = weighted.mean(IC.FRM.FEMM.ZS, SP.POP.TOTL, na.rm=TRUE),
secondary.wm = weighted.mean(SE.SEC.ENRR, SP.POP.TOTL, na.rm=TRUE),
mortality.wm = weighted.mean(SH.DYN.MORT, SP.POP.TOTL, na.rm=TRUE),
lifeexpm.wm = weighted.mean(SP.DYN.LE00.MA.IN, SP.POP.TOTL, na.rm=TRUE),
lifeexpf.wm = weighted.mean(SP.DYN.LE00.FE.IN, SP.POP.TOTL, na.rm=TRUE),
gini.wm = weighted.mean(SI.POV.GINI, SP.POP.TOTL, na.rm=TRUE),
poverty.wm = weighted.mean(SI.POV.DDAY, SP.POP.TOTL, na.rm=TRUE),
poverty2.wm = weighted.mean(SI.POV.UMIC, SP.POP.TOTL, na.rm=TRUE))
dn<-dn %>%
group_by(date) %>%
mutate(gdppc.wm = weighted.mean(NY.GDP.PCAP.PP.KD, SP.POP.TOTL, na.rm=TRUE),
unempl.wm = weighted.mean(SL.UEM.TOTL.ZS, SP.POP.TOTL, na.rm=TRUE),
empl.wm = weighted.mean(SL.EMP.TOTL.SP.ZS, SP.POP.TOTL, na.rm=TRUE),
labour.wm = weighted.mean(SL.TLF.CACT.ZS, SP.POP.TOTL, na.rm=TRUE),
product.wm = weighted.mean(SL.GDP.PCAP.EM.KD, SP.POP.TOTL, na.rm=TRUE),
exports.wm = weighted.mean(NE.EXP.GNFS.ZS, SP.POP.TOTL, na.rm=TRUE),
ict.wm = weighted.mean(TX.VAL.ICTG.ZS.UN, SP.POP.TOTL, na.rm=TRUE),
fmanager.wm = weighted.mean(IC.FRM.FEMM.ZS, SP.POP.TOTL, na.rm=TRUE),
secondary.wm = weighted.mean(SE.SEC.ENRR, SP.POP.TOTL, na.rm=TRUE),
mortality.wm = weighted.mean(SH.DYN.MORT, SP.POP.TOTL, na.rm=TRUE),
lifeexpm.wm = weighted.mean(SP.DYN.LE00.MA.IN, SP.POP.TOTL, na.rm=TRUE),
lifeexpf.wm = weighted.mean(SP.DYN.LE00.FE.IN, SP.POP.TOTL, na.rm=TRUE),
gini.wm = weighted.mean(SI.POV.GINI, SP.POP.TOTL, na.rm=TRUE),
poverty.wm = weighted.mean(SI.POV.DDAY, SP.POP.TOTL, na.rm=TRUE),
poverty2.wm = weighted.mean(SI.POV.UMIC, SP.POP.TOTL, na.rm=TRUE))