WDI

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Index

1. Gender Parity Index, GPI

• 1.1 Adolescent fertility rate (births per 1,000 women ages 15-19)
• 1.2 Age at first marriage
• 1.3 Contributing family workers by genger
• 1.4 Unemployment of labor force with basic education (%)
• 1.5 Unemployment of labor force with advanced education (%)
• 1.6 Proportion of seats held by women in national parliaments (%)
• 1.7 Proportion of women in ministerial level positions (%)
• 1.8 Women Business and the Law Index Score (scale 1-100)

2. Education Index

• 2.1 Gross enrolment ratio of educational level, GPI
• 2.2 Educational attainment at least Bachelor’s or equivalent, population 25+ (%, cumulative)
• 2.3 Educational attainment at least Master’s or equivalent, population 25+ (%, cumulative)
• 2.4 Educational attainment, Doctoral or equivalent, population 25+ (%, cumulative)
• 2.5 Percentage of students in tertiary ISCED 6 or 8 programmes who are female (%)
• 2.6 Percentage of graduates from tertiary ISCED 6 or 8 programmes who are female (%)
• 2.7 Net flow ratio of internationally mobile students (inbound - outbound), both sexes (%)

3. Other Related Index

• 3.1 Internet users (per 100 people)
• 3.2 Suicide mortality rate
• 3.3 Press Freedom Rank

4. Appendix

• 4.1 URL link & Data download

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1. GPI, Gender Parity Index

1.1 Adolescent fertility rate (births per 1,000 women ages 15-19)

SP.ADO.TFRT <- WDI %>% filter(Series.Name %in% c("Adolescent fertility rate (births per 1,000 women ages 15-19)"))
ggplot(data=SP.ADO.TFRT, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
ylab("Adolescent fertility rate, births per 1,000 women ages 15-19")

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1.2 Age at first marriage

Female

SP.DYN.SMAM.FE <- WDI %>% filter(Series.Name %in% c("Age at first marriage, female"))
ggplot(data=SP.DYN.SMAM.FE, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Age at first marriage, female")

Male

SP.DYN.SMAM.MA <- WDI %>% filter(Series.Name %in% c("Age at first marriage, male"))
ggplot(data=SP.DYN.SMAM.MA, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Age at first marriage, male")

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1.3 Contributing family workers by genger

Female

SL.FAM.WORK.FE.ZS <- WDI %>% filter(Series.Name %in% c("Contributing family workers, female (% of female employment) (modeled ILO estimate)"))
ggplot(data=SL.FAM.WORK.FE.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Contributing family workers, female (%)")

Male

SL.FAM.WORK.MA.ZS <- WDI %>% filter(Series.Name %in% c("Contributing family workers, male (% of male employment) (modeled ILO estimate)"))
ggplot(data=SL.FAM.WORK.MA.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Contributing family workers, male (%)")

Definition: Contributing family workers are those workers who hold “self-employment jobs” as own-account workers in a market-oriented establishment operated by a related person living in the same household.

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1.4 Unemployment of labor force with basic education (%)

Female

SL.UEM.BASC.FE.ZS <- WDI %>% filter(Series.Name %in% c("Unemployment with basic education, female (% of female labor force with basic education)"))
ggplot(data=SL.UEM.BASC.FE.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Unemployment with basic education, female (% of female labor force with basic education)")

Male

SL.UEM.BASC.MA.ZS <- WDI %>% filter(Series.Name %in% c("Unemployment with basic education, male (% of male labor force with basic education)"))
ggplot(data=SL.UEM.BASC.MA.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Unemployment with basic education, male (% of male labor force with basic education)")

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1.5 Unemployment of labor force with advanced education (%)

Female

SL.UEM.ADVN.FE.ZS <- WDI %>% filter(Series.Name %in% c("Unemployment with advanced education, female (% of female labor force with advanced education)"))
ggplot(data=SL.UEM.ADVN.FE.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Unemployment with advanced education, female (% of female labor force with advanced education)")

Male

SL.UEM.ADVN.MA.ZS <- WDI %>% filter(Series.Name %in% c("Unemployment with advanced education, male (% of male labor force with advanced education)"))
ggplot(data=SL.UEM.ADVN.MA.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Unemployment with advanced education, male (% of male labor force with advanced education)")

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1.6 Proportion of seats held by women in national parliaments (%)

SG.GEN.PARL.ZS <- WDI %>% filter(Series.Name %in% c("Proportion of seats held by women in national parliaments (%)"))
ggplot(data=SG.GEN.PARL.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
ylab("Proportion of seats held by women in national parliaments (%)")

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1.7 Proportion of women in ministerial level positions (%)

SG.GEN.MNST.ZS <- WDI %>% filter(Series.Name %in% c("Proportion of women in ministerial level positions (%)"))
ggplot(data=SG.GEN.MNST.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
ylab("Proportion of women in ministerial level positions (%)")

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1.8 Women Business and the Law Index Score (scale 1-100)

SG.LAW.INDX <- WDI %>% filter(Series.Name %in% c("Women Business and the Law Index Score (scale 1-100)"))
ggplot(data=SG.LAW.INDX, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Women Business and the Law Index Score (scale 1-100)")

Definition: The index measures how laws and regulations affect women’s economic opportunity. Overall scores are calculated by taking the average score of each of the eight areas (Going Places, Starting a Job, Getting Paid, Getting Married, Having Children, Running a Business, Managing Assets and Getting a Pension), with 100 representing the highest possible score.

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2. Education Index

2.1 Gross enrolment ratio of educational level, GPI

Primary EDU level

SE.ENR.PRIM.FM.ZS <- WDI %>% filter(Series.Name %in% c("Gross enrolment ratio, primary, gender parity index (GPI)"))
ggplot(data=SE.ENR.PRIM.FM.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
ylab("Gross enrolment ratio, primary, gender parity index (GPI)")

Secondary EDU level

SE.ENR.SECO.FM.ZS <- WDI %>% filter(Series.Name %in% c("Gross enrolment ratio, secondary, gender parity index (GPI)"))
ggplot(data=SE.ENR.SECO.FM.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
ylab("Gross enrolment ratio, secondary, gender parity index (GPI)")

Tertiary EDU level

SE.ENR.TERT.FM.ZS <- WDI %>% filter(Series.Name %in% c("Gross enrolment ratio, tertiary, gender parity index (GPI)"))
ggplot(data=SE.ENR.TERT.FM.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
ylab("Gross enrolment ratio, tertiary, gender parity index (GPI)")

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2.2 Educational attainment at least Bachelor’s or equivalent, population 25+ (%, cumulative)

Female

SE.TER.CUAT.BA.FE.ZS <- WDI %>% filter(Series.Name %in% c("Educational attainment, at least Bachelor's or equivalent, population 25+, female (%) (cumulative)"))
ggplot(data=SE.TER.CUAT.BA.FE.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Educational attainment, at least Bachelor's or equivalent, population 25+, female (%, cumulative)")

Male

SE.TER.CUAT.BA.MA.ZS <- WDI %>% filter(Series.Name %in% c("Educational attainment, at least Bachelor's or equivalent, population 25+, male (%) (cumulative)"))
ggplot(data=SE.TER.CUAT.BA.MA.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Educational attainment, at least Bachelor's or equivalent, population 25+, male (%, cumulative)")

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2.3 Educational attainment at least Master’s or equivalent, population 25+ (%, cumulative)

Female

SE.TER.CUAT.MS.FE.ZS <- WDI %>% filter(Series.Name %in% c("Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)"))
ggplot(data=SE.TER.CUAT.MS.FE.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Educational attainment, at least Master's or equivalent, population 25+, female (%) (cumulative)")

Male

SE.TER.CUAT.MS.MA.ZS <- WDI %>% filter(Series.Name %in% c("Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)"))
ggplot(data=SE.TER.CUAT.MS.MA.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Educational attainment, at least Master's or equivalent, population 25+, male (%) (cumulative)")

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2.4 Educational attainment, Doctoral or equivalent, population 25+ (%, cumulative)

Female

SE.TER.CUAT.DO.FE.ZS <- WDI %>% filter(Series.Name %in% c("Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)"))
ggplot(data=SE.TER.CUAT.DO.FE.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Educational attainment, Doctoral or equivalent, population 25+, female (%) (cumulative)")

Male

SE.TER.CUAT.DO.MA.ZS <- WDI %>% filter(Series.Name %in% c("Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)"))
ggplot(data=SE.TER.CUAT.DO.MA.ZS, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Educational attainment, Doctoral or equivalent, population 25+, male (%) (cumulative)")

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2.5 Percentage of students in tertiary ISCED 6 or 8 programmes who are female (%)

ISCED 6 (Bachelor level)

UIS.FEP.6 <- WDI %>% filter(Series.Name %in% c("Percentage of students in tertiary ISCED 6 programmes who are female (%)"))
ggplot(data=UIS.FEP.6, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Percentage of students in tertiary ISCED 6 programmes who are female (%)")

ISCED 8 (Doctoral level)

UIS.FEP.8 <- WDI %>% filter(Series.Name %in% c("Percentage of students in tertiary ISCED 8 programmes who are female (%)"))
ggplot(data=UIS.FEP.8, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Percentage of students in tertiary ISCED 8 programmes who are female (%)")

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2.6 Percentage of graduates from tertiary ISCED 6 or 8 programmes who are female (%)

ISCED 6 (Bachelor level)

UIS.FGP.6 <- WDI %>% filter(Series.Name %in% c("Percentage of graduates from tertiary ISCED 6 programmes who are female (%)"))
ggplot(data=UIS.FGP.6, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Percentage of graduates from tertiary ISCED 6 programmes who are female (%)")

ISCED 8 (Doctoral level)

UIS.FGP.8 <- WDI %>% filter(Series.Name %in% c("Percentage of graduates from tertiary ISCED 8 programmes who are female (%)"))
ggplot(data=UIS.FGP.8, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Percentage of graduates from tertiary ISCED 8 programmes who are female (%)")

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2.7 Net flow ratio of internationally mobile students (inbound - outbound), both sexes (%)

UIS.MENFR.56 <- WDI %>% filter(Series.Name %in% c("Net flow ratio of internationally mobile students (inbound - outbound), both sexes (%)"))
ggplot(data=UIS.MENFR.56, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Net flow ratio of internationally mobile students (inbound - outbound), both sexes (%)")

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3. Other Related Index

3.1 Internet users (per 100 people)

IT.NET.USER.P2 <- WDI %>% filter(Series.Name %in% c("Internet users (per 100 people)"))
ggplot(data=IT.NET.USER.P2, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Internet users (per 100 people)")

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3.2 Suicide mortality rate

Female

SH.STA.SUIC.FE.P5 <- WDI %>% filter(Series.Name %in% c("Suicide mortality rate, female (per 100,000 female population)"))
ggplot(data=SH.STA.SUIC.FE.P5, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Female (per 100,000 female population)")

Male

SH.STA.SUIC.MA.P5 <- WDI %>% filter(Series.Name %in% c("Suicide mortality rate, male (per 100,000 male population)"))
ggplot(data=SH.STA.SUIC.MA.P5, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Male (per 100,000 male population)")

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3.3 Press Freedom Rank

RF32417 <- WDI %>% filter(Series.Name %in% c("Press Freedom Rank"))
ggplot(data=RF32417, aes(x=Year, y=Value, group=Country.Code, color=Country.Code)) +
  geom_label(aes(label=Country.Code))+
  theme(axis.text.x = element_text(angle = 90))+
  theme(legend.position = "none")+
  geom_step()+
  ylab("Press Freedom Rank")

Reference: https://en.wikipedia.org/wiki/Press_Freedom_Index

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4. Appendix

4.1 URL link & Data download

• The World Bank Indicators: Link

• WDI Database Archives: Link

• The CSV file used in this page, csv.file: Download

• UNESCO GPI in selected Countries (2020/10/18): Link

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Last updated: 2020/10/25 11:19