── Attaching core tidyverse packages ──────────────────────── tidyverse 2.0.0 ──
✔ dplyr 1.1.2 ✔ readr 2.1.4
✔ forcats 1.0.0 ✔ stringr 1.5.0
✔ ggplot2 3.4.2 ✔ tibble 3.2.1
✔ lubridate 1.9.2 ✔ tidyr 1.3.0
✔ purrr 1.0.1
── Conflicts ────────────────────────────────────────── tidyverse_conflicts() ──
✖ dplyr::filter() masks stats::filter()
✖ dplyr::lag() masks stats::lag()
ℹ Use the conflicted package (<http://conflicted.r-lib.org/>) to force all conflicts to become errors
library(readxl)
Introduction
Using data compiled by the World Bank over the last half-century, this report compares changes in participation and outcomes across males and females in the Bhutanese education system in the context of changes in the teacher population and national education reforms. This report takes a close look at trends in girls’ and women’s educational participation and attainment in the 15 years since Bhutan’s political transition to democracy (in 2008), and
World Development Indicators
The Development Data Group at the World Bank collects, compiles, and disseminates statistical and data work and maintains macro, financial and sector databases for countries and regions worldwide. According to the World Bank Open Data website, the majority of data comes from the statistical systems of member countries; therefore, while the Development Data Group upholds professional standards for quality and integrity, the quality of these data ultimately depend on the practices of the member countries. To support the larger goal of generating and making available good-quality statistical data on all aspects of development, the Development Data group coordinates with the Bank’s Global Practices and regional groups to “help developing countries improve the capacity, efficiency, and effectiveness of national statistical systems.” (https://data.worldbank.org/about) Not only do timely and reliable statistics support the critical management decisions of the countries themselves, they inform the World Bank’s broad development strategy.
More information about the World Bank’s databases and global statistical strategy can be found here.
The World Development Indicators (WDI) is a centralized compilation of development indicators from international sources that are officially recognized by the World Bank. WDI presents the most accurate global development data that is currently available, and its database includes national, regional, and global estimates. The data used in this report are all sourced from the WDI collection on the Kingdom of Bhutan, which is organized by series (Economic Policy & Debt, Education, Environment, Financial Sector, Gender, Health, Infrastructure, Poverty, Private Sector & Trade, Public Sector, Social Protection & Labor, and Social: health) which each have many sub-categories. Each indicator (for example, “Adjusted net enrollment rate, primary, female (% of primary school age children)”) is published with a definition, source, (eg. UNESCO Institute for Statistics. Data as of February 2020.), and details such as periodicity and aggregation method.
For each indicator’s specific dataset, the Development Data group has published short descriptions of the statistical concept and methodology, development relevance, and limitations and exceptions to the indicator. This documentation is crucial for interpretation of Bhutan’s data, as the Kingdom’s processes and standards for data collection and its participation in international surveys and data-sharing have all changed dramatically since 1960, when the WDI was established. Indeed, the first data for any indicator appears in 1970. This corresponds with the establishment of a statistical cell within Bhutan’s Ministry of Development in 1971, the same year that Bhutan became a member of the United Nations. These events marked a major shift in Bhutan’s development paradigm, away from isolationist policies toward the development of international relations and diplomacy. It also marked the beginning of rapid modernization of the whole nation, including development of secular education. This development - among other other social, political, economic and environmental changes - has brought profound societal transformation.
Development Indicators for Bhutan: Education and Gender
The complete tabular dataset of nearly 1500 time series WDI for Bhutan is available for download on the World Bank’s Data Development country page for Bhutan. The country page presents some basic plots showing trends for particular indicators over time. The World Bank databank allows users to query and download a tabular dataframe of specific indicators for a set of dates. This report thereby pulls from two series of World Development Indicators for Bhutan, “Education” and “Gender”, across the years 1970-2022 into a combined dataframe.
#read in dataBhutanWDI<-read_csv("_data/Bhutan_WDI_Edu.csv")%>%select(-"Country Name", -"Country Code", -"Series Name")
Rows: 152 Columns: 57
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (57): Country Name, Country Code, Series Name, Series Code, 1970 [YR1970...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
In the resulting dataframe, columns represent the years between 1970 and 2022, with each row being an indicator represented by a code. Each value in the dataset is a unique observation of an indicator in a particular year. All values are numeric, with a mixture of discrete and continuous data depending on the indicator. Most values represent percentages of a population or expenditure, ratios (eg. pupil-teacher ratio), points on a scale (eg. scale 1-100 on the Women Business and the Law Index Score, or the Gender Parity Index), or years (eg. of compulsory education).
The metadata for each indicator (the name and description for each code) can be seen in a corresponding Metadata dataframe.
# read in metadataWDImetadata<-read_csv("_data/Bhutan_WDI_Edu_Metadata.csv",skip =153)
Rows: 147 Columns: 12
── Column specification ────────────────────────────────────────────────────────
Delimiter: ","
chr (12): Code, License Type, Indicator Name, Long definition, Source, Topic...
ℹ Use `spec()` to retrieve the full column specification for this data.
ℹ Specify the column types or set `show_col_types = FALSE` to quiet this message.
The Education indicators can be classed into the following “series”: Efficiency, Inputs, Outcomes, and Participation. Here is a sample group of indicators in the Participation series, all relating to school enrollment:
School enrollment can be understood as an indicator group, wherein school enrollment is observed for each unique combination of the following variables: 3 levels of schooling, genders, and by % of gross or net enrollment. It also includes a gender parity index for each level of schooling, which is calculated by dividing the female rate by the male rate. One can see each combination of variables within this group represented in the corresponding code for each indicator.
Other indicator groups in the Participation series include: - number of pupils (across primary and secondary school, and genders); - children/adolescents out of school (across primary and secondary school, and genders); - adjusted net enrollment rate for primary school (across genders).
In the Outcomes series, indicator groups include: - duration education in years (for pre-primary, primary, secondary, and total compulsory education) - educational attainment of adults >25 (cumulative completion of each level of education, total and across genders) - completion rate (for primary and lower secondary school, total and across genders) - literacy rate (for youth and adult, total and across genders)
In the Inputs series, indicator groups include: - number of teachers (primary, secondary, and tertiary levels, total and across genders) - % trained teachers (pre-primary, primary, lower secondary, secondary, and upper secondary levels, total and across genders)
In the Efficiency series, indicator groups include: - gross and net intake ratios in grade 1 (total and across genders) - over-age students in primary school (total and across genders) - persistence to grade 5 (total and across genders) - progression to secondary school (total and across genders) - repeaters (total and across genders)
For any given year with data, the WDI series for education can be used to compare male and female enrollment, persistence, and completion of different levels of schooling, and where they deviate from the standard progression for their age group. These series also allow us to compare adults’ educational attainment and literacy. Furthermore, these WDI can be used to compare the gender ratios of teachers, and their respective levels of training, in each level of schooling – allowing us to see any relationships between changing gender rations among teachers and student trends.
Rationale for this report
Bhutan is praised for its relative gender equity in society with respect to its country neighbors in Asia, but inequities persist and there seems to be some denial amongst the national and school leadership about the persistence and extent of gender inequity in schools. When I was working on the development of the Bhutan Baccalaureate for the Royal Secretariat, I experienced resistance to my suggestions that institutional policies must be sensitive to gender differences, and more data must be collected on the experiences of girls and women within schools. Furthermore, I was concerned that girls’ and women’s enrollment seemed low or inconsistent, and this was explained as a consequence of demands for household, family, or agricultural labor. Since the establishment of Bhutan’s first democratic government in 2008, some national policies have highlighted the need for improving inclusion and outcomes for female youth, by and large these stated values and priorities have not been translated into programs and practices within schools.
Focus of analysis
This exploration of the World Bank’s Global Development Indicators related to education and gender is intended to explore the relationships among educational access, participation, and attainment across genders, in order to:
Describe recent patterns in female student engagement and outcomes (2008-2022, since the establishment of the first democratic government), and
Take a historical view (1970-2022) of gendered trends in female student access, participation, and attainment and look for: – variation with respect to major educational reforms and policy changes; – a possible relationship between trends in female student persistence and attainment with changes in the gender ratio and training of teachers.
These data can give us a view on girls’ and women’s access to and persistence in education - their bare participation - but the data can not give insight into the dynamics of inclusion within schools, nor the external conditions (eg. personal, familial, cultural, social, economic) which interfere with girls’ or women’s access to education. I will draw upon my research and personal experience working to develop Bhutan’s education system to contextualize the observed trends and suggest possible hypotheses for further investigation.
Throughout this report, I will highlight the perceptible lacks and weaknesses in data, and emphasize the need for more extensive research on the situation of female Bhutanese to better diagnose sources of educational discrimination and exclusion. Without continuous collection of high quality data, it will be impossible to detect and understand problems, design interventions, and know when they are successful.
Source Code
---title: "Homework 2 - Susannah"author: "Susannah Reed Poland"description: "Gender and Education in Bhutan"date: "6/27/2023"format: html: df-print: paged toc: true code-copy: true code-tools: truecategories: - homework_2 - Susannah Reed Poland---```{r}library(tidyverse)library(readxl)```## Introduction Using data compiled by the World Bank over the last half-century, this report compares changes in participation and outcomes across males and females in the Bhutanese education system in the context of changes in the teacher population and national education reforms. This report takes a close look at trends in girls’ and women’s educational participation and attainment in the 15 years since Bhutan’s political transition to democracy (in 2008), and ## World Development IndicatorsThe Development Data Group at the World Bank collects, compiles, and disseminates statistical and data work and maintains macro, financial and sector databases for countries and regions worldwide. According to the World Bank Open Data [website](https://data.worldbank.org/about), the majority of data comes from the statistical systems of member countries; therefore, while the Development Data Group upholds professional standards for quality and integrity, the quality of these data ultimately depend on the practices of the member countries. To support the larger goal of generating and making available good-quality statistical data on all aspects of development, the Development Data group coordinates with the Bank’s Global Practices and regional groups to “help developing countries improve the capacity, efficiency, and effectiveness of national statistical systems.” (https://data.worldbank.org/about) Not only do timely and reliable statistics support the critical management decisions of the countries themselves, they inform the World Bank’s broad development strategy. More information about the World Bank’s databases and global statistical strategy can be found [here](https://data.worldbank.org/about).The World Development Indicators (WDI) is a centralized compilation of development indicators from international sources that are officially recognized by the World Bank. WDI presents the most accurate global development data that is currently available, and its database includes national, regional, and global estimates. The data used in this report are all sourced from the WDI collection on the Kingdom of Bhutan, which is organized by series (Economic Policy & Debt, Education, Environment, Financial Sector, Gender, Health, Infrastructure, Poverty, Private Sector & Trade, Public Sector, Social Protection & Labor, and Social: health) which each have many sub-categories. Each indicator (for example, “Adjusted net enrollment rate, primary, female (% of primary school age children)”) is published with a definition, source, (eg. [UNESCO Institute for Statistics](http://uis.unesco.org/). Data as of February 2020.), and details such as periodicity and aggregation method. For each indicator’s specific dataset, the Development Data group has published short descriptions of the statistical concept and methodology, development relevance, and limitations and exceptions to the indicator. This documentation is crucial for interpretation of Bhutan’s data, as the Kingdom’s processes and standards for data collection and its participation in international surveys and data-sharing have all changed dramatically since 1960, when the WDI was established. Indeed, the first data for any indicator appears in 1970. This corresponds with the establishment of a statistical cell within [Bhutan’s Ministry of Development](https://www.nsb.gov.bt/about/background/) in 1971, the same year that Bhutan became a member of the United Nations. These events marked a major shift in Bhutan’s development paradigm, away from isolationist policies toward the development of international relations and diplomacy. It also marked the beginning of rapid modernization of the whole nation, including development of secular education. This development - among other other social, political, economic and environmental changes - has brought profound societal transformation. ## Development Indicators for Bhutan: Education and Gender The complete tabular dataset of nearly 1500 time series WDI for Bhutan is available for download on the World Bank’s Data Development [country page for Bhutan](https://data.worldbank.org/country/BT). The country page presents some basic plots showing trends for particular indicators over time. The [World Bank databank](https://databank.worldbank.org/source/world-development-indicators) allows users to query and download a tabular dataframe of specific indicators for a set of dates. This report thereby pulls from two series of World Development Indicators for Bhutan, “Education” and “Gender”, across the years 1970-2022 into a combined dataframe. ```{r}#read in dataBhutanWDI<-read_csv("_data/Bhutan_WDI_Edu.csv")%>%select(-"Country Name", -"Country Code", -"Series Name")```In the resulting dataframe, columns represent the years between 1970 and 2022, with each row being an indicator represented by a code. Each value in the dataset is a unique observation of an indicator in a particular year. All values are numeric, with a mixture of discrete and continuous data depending on the indicator. Most values represent percentages of a population or expenditure, ratios (eg. pupil-teacher ratio), points on a scale (eg. scale 1-100 on the Women Business and the Law Index Score, or the Gender Parity Index), or years (eg. of compulsory education). The metadata for each indicator (the name and description for each code) can be seen in a corresponding Metadata dataframe.```{r}# read in metadataWDImetadata<-read_csv("_data/Bhutan_WDI_Edu_Metadata.csv",skip =153)```The Education indicators can be classed into the following “series”: Efficiency, Inputs, Outcomes, and Participation. Here is a sample group of indicators in the Participation series, all relating to school enrollment: ```{r}WDImetadata%>%select("Code","Indicator Name")%>%slice(98:121)```School enrollment can be understood as an indicator group, wherein school enrollment is observed for each unique combination of the following variables: 3 levels of schooling, genders, and by % of gross or net enrollment. It also includes a gender parity index for each level of schooling, which is calculated by dividing the female rate by the male rate. One can see each combination of variables within this group represented in the corresponding code for each indicator. Other indicator groups in the Participation series include: - number of pupils (across primary and secondary school, and genders);- children/adolescents out of school (across primary and secondary school, and genders);- adjusted net enrollment rate for primary school (across genders).In the Outcomes series, indicator groups include: - duration education in years (for pre-primary, primary, secondary, and total compulsory education)- educational attainment of adults >25 (cumulative completion of each level of education, total and across genders)- completion rate (for primary and lower secondary school, total and across genders)- literacy rate (for youth and adult, total and across genders)In the Inputs series, indicator groups include: - number of teachers (primary, secondary, and tertiary levels, total and across genders)- % trained teachers (pre-primary, primary, lower secondary, secondary, and upper secondary levels, total and across genders)In the Efficiency series, indicator groups include: - gross and net intake ratios in grade 1 (total and across genders)- over-age students in primary school (total and across genders)- persistence to grade 5 (total and across genders)- progression to secondary school (total and across genders)- repeaters (total and across genders)For any given year with data, the WDI series for education can be used to compare male and female enrollment, persistence, and completion of different levels of schooling, and where they deviate from the standard progression for their age group. These series also allow us to compare adults' educational attainment and literacy. Furthermore, these WDI can be used to compare the gender ratios of teachers, and their respective levels of training, in each level of schooling -- allowing us to see any relationships between changing gender rations among teachers and student trends. ## Rationale for this report Bhutan is praised for its relative gender equity in society with respect to its country neighbors in Asia, but inequities persist and there seems to be some denial amongst the national and school leadership about the persistence and extent of gender inequity in schools. When I was working on the development of the Bhutan Baccalaureate for the Royal Secretariat, I experienced resistance to my suggestions that institutional policies must be sensitive to gender differences, and more data must be collected on the experiences of girls and women within schools. Furthermore, I was concerned that girls' and women's enrollment seemed low or inconsistent, and this was explained as a consequence of demands for household, family, or agricultural labor. Since the establishment of Bhutan's first democratic government in 2008, some national policies have highlighted the need for improving inclusion and outcomes for female youth, by and large these stated values and priorities have not been translated into programs and practices within schools. ## Focus of analysisThis exploration of the World Bank’s Global Development Indicators related to education and gender is intended to explore the relationships among educational access, participation, and attainment across genders, in order to:- Describe recent patterns in female student engagement and outcomes (2008-2022, since the establishment of the first democratic government), and- Take a historical view (1970-2022) of gendered trends in female student access, participation, and attainment and look for:-- variation with respect to major educational reforms and policy changes; -- a possible relationship between trends in female student persistence and attainment with changes in the gender ratio and training of teachers.These data can give us a view on girls' and women's access to and persistence in education - their bare participation - but the data can not give insight into the dynamics of inclusion within schools, nor the external conditions (eg. personal, familial, cultural, social, economic) which interfere with girls' or women's access to education. I will draw upon my research and personal experience working to develop Bhutan's education system to contextualize the observed trends and suggest possible hypotheses for further investigation. Throughout this report, I will highlight the perceptible lacks and weaknesses in data, and emphasize the need for more extensive research on the situation of female Bhutanese to better diagnose sources of educational discrimination and exclusion. Without continuous collection of high quality data, it will be impossible to detect and understand problems, design interventions, and know when they are successful.