Provincial level education indicators in China from 2003 to 2020

This dataset explores education inequality across 1,000 schools in the United States. It includes key indicators such as funding per student, average test scores, student-teacher ratios, low-income and minority student percentages, internet access levels, and dropout rates.

The goal is to help researchers and machine learning engineers:

• Identify underserved or at-risk school districts
• Predict dropout risks using demographic and resource indicators
• Explore the relationship between funding and academic outcomes
• Build models for education policy simulations and social impact research

The data is synthetically generated to reflect realistic distributions and variations seen in U.S. education systems, ensuring it's safe for public sharing and experimentation.

Context

In today's interconnected world, the issue of inequality in education stands as a stark reminder of the disparities that persist across countries and communities. While strides have been made to improve access to education, a significant proportion of children still lack the opportunity to learn, particularly in low-income and conflict-affected regions. Quality of education also diverges, with well-equipped schools in affluent areas contrasting with under-resourced institutions in marginalized settings. Gender inequality further compounds the problem, as cultural norms and economic factors often impede girls' education in certain societies. Tackling inequality in education isn't just a matter of fairness; it's a critical step towards building equitable societies and empowering individuals to contribute meaningfully to their own development and that of their nations.

Content

This dataset contains historical data covering a range of indicators pertaining to educational inequality on a global scale. The dataset's prominent components include: ISO3, Country, Human Development Groups, UNDP Developing Regions, HDI Rank (2021), and Inequality in Education spanning the years 2010 to 2021.

Dataset Glossary (Column-wise)

• ISO3 - ISO3 for the Country/Territory
• Country - Name of the Country/Territory
• Human Development Groups - Human Development Groups
• UNDP Developing Regions - UNDP Developing Regions
• HDI Rank (2021) - Human Development Index Rank for 2021
• Inequality in Education (2010) - Inequality in Education for 2010
• Inequality in Education (2011) - Inequality in Education for 2011
• Inequality in Education (2012) - Inequality in Education for 2012
• Inequality in Education (2013) - Inequality in Education for 2013
• Inequality in Education (2014) - Inequality in Education for 2014
• Inequality in Education (2015) - Inequality in Education for 2015
• Inequality in Education (2016) - Inequality in Education for 2016
• Inequality in Education (2017) - Inequality in Education for 2017
• Inequality in Education (2018) - Inequality in Education for 2018
• Inequality in Education (2019) - Inequality in Education for 2019
• Inequality in Education (2020) - Inequality in Education for 2020
• Inequality in Education (2021) - Inequality in Education for 2021

Data Dictionary

• UNDP Developing Regions:
  • SSA - Sub-Saharan Africa
  • LAC - Latin America and the Caribbean
  • EAP - East Asia and the Pacific
  • AS - Arab States
  • ECA - Europe and Central Asia
  • SA - South Asia

Structure of the Dataset

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Acknowledgement

This Dataset is created from Human Development Reports. This Dataset falls under the Creative Commons Attribution 3.0 IGO License. You can check the Terms of Use of this Data. If you want to learn more, visit the Website.

Cover Photo by: Image by storyset on Freepik

Thumbnail by: Educational Vectors by Vecteezy

Educational resources and lesson plans based on Income Inequality (Gini Coefficients) for Australian regions data collection Lineage: Fleming, David; Measham, Tom (2015): Income Inequality (Gini Coefficients) for Australian regions. v1. CSIRO. Data Collection. https://doi.org/10.4225/08/55093772960E4

According to a survey conducted in December 2020, more than ** percent of female Japanese aged between 17 and 19 years old stated that they noticed educational inequality. In the same survey, more than half of all respondents believed that the education gap would continue to widen from now on.

ObjectiveThe aim of this paper is to empirically evaluate whether widening educational inequalities in mortality are related to the substantive shifts that have occurred in the educational distribution.Materials and methodsData on education and mortality from 18 European populations across several decades were collected and harmonized as part of the Demetriq project. Using a fixed-effects approach to account for time trends and national variation in mortality, we formally test whether the magnitude of relative inequalities in mortality by education is associated with the gender and age-group specific proportion of high and low educated respectively.ResultsThe results suggest that in populations with larger proportions of high educated and smaller proportions of low educated, the excess mortality among intermediate and low educated is larger, all other things being equal.ConclusionWe conclude that the widening educational inequalities in mortality being observed in recent decades may in part be attributed to educational expansion.

BackgroundPrevious studies on socio-economic inequalities in mortality have documented a substantial contribution of alcohol-attributable mortality (AAM) to these inequalities. However, little is known about the extent to which AAM has contributed to time trends in socio-economic inequalities in mortality.ObjectiveTo study long-term trends in educational inequalities in AAM and assessed their impact on trends in educational inequalities in life expectancy in three European countries.MethodsWe analyzed cause-specific mortality data by educational group (low, middle, high) for individuals aged 30 and older in England and Wales, Finland, and Turin (Italy) over the 1972–2017 period. To estimate AAM, we used the multiple causes of death approach for England and Wales and Finland (1987–2017), and a recently introduced method for Turin (Italy). We used segmented regression analysis to study changes in absolute educational inequalities in AAM, measured by the Slope Index of Inequality (SII). We assessed the contribution of AAM to trends in educational differences in remaining life expectancy at age 30 (e30) using cause-deleted life tables.ResultsAAM increased more among the low-educated than the high-educated in England and Wales (1972–2017) and Finland (1987–2007). In contrast, in Finland (2007 onwards) and Turin (1972–2017), AAM decreased more among the low-educated than the high-educated. In England and Wales, AAM contributed 37% (males) and 24% (females) of the increase in educational inequalities in e30. In Finland in 1987–2007, AAM contributed 50% (males) and 34% (females) of the increase in educational inequalities in e30. AAM also contributed to recent trend breaks, such as to the onset of an increase in educational inequalities in e30 in England and Wales, and to the onset of a decline in educational inequalities in e30 in Finland after 2007.DiscussionAAM mortality contributed substantially not only to levels of, but also to changes in educational inequalities in e30 in the studied populations. Reducing the impact of alcohol on mortality among low-educated groups may positively affect trends in educational inequalities in life expectancy.

The replication package for Identifying the Role of High School in Educational Inequality: A Causal Mediation Approach.

Observed average proportion and standard deviation of low and high educated in pooled data, definition of scenarios used to estimate educational inequalities mortality in different educational distributions, men and women.

These files contain the data files and programs used to generate the estimates found in “States as Sites of Educational (In)Equality: State Contexts and the Socioeconomic Achievement Gradient” published in AERA Open, 2019. The abstract for the paper is found below: Socioeconomic achievement gaps have long been a central focus of educational research. However, not much is known about how (and why) between-district gaps vary among states, even though states are a primary organizational level in the decentralized education system in the United States. Using data from the Stanford Education Data Archive (SEDA), this study describes state-level socioeconomic achievement gradients and the growth of these gradients from Grades 3 to 8. We also examine state-level correlates of the gradients and their growth, including school system funding equity, preschool enrollment patterns, the distribution of teachers, income inequality, and segregation. We find that socioeconomic gradients and their growth rates vary considerably among states, and that between-district income segregation is positively associated with the socioeconomic achievement gradient.

Iceland had the highest inequality-adjusted education index score worldwide, amounting to **** out of one on the index. Germany followed with an index score of ****. The inequality-adjusted education index is the education index in the Human Development Index adjusted for inequality.

OBJECTIVE To estimate the degree of educational inequality in the occurrence of abdominal obesity in a population of non-faculty civil servants at university campi.METHODS In this cross-sectional study, we used data from 3,117 subjects of both genders aged 24 to 65-years old, regarding the baseline ofPró-Saúde Study, 1999-2001. Abdominal obesity was defined according to abdominal circumference thresholds of 88 cm for women and 102 cm for men. A multi-dimensional, self-administered questionnaire was used to evaluate education levels and demographic variables. Slope and relative indices of inequality, and Chi-squared test for linear trend were used in the data analysis. All analyses were stratified by genders, and the indices of inequality were standardized by age.RESULTS Abdominal obesity was the most prevalent among women (43.5%; 95%CI 41.2;45.9), as compared to men (24.3%; 95%CI 22.1;26.7), in all educational strata and age ranges. The association between education levels and abdominal obesity was an inverse one among women (p < 0.001); it was not statistically significant among men (p = 0.436). The educational inequality regarding abdominal obesity in the female population, in absolute terms (slope index of inequality), was 24.0% (95%CI 15.5;32.6). In relative terms (relative index of inequality), it was 2.8 (95%CI 1.9;4.1), after the age adjustment.CONCLUSIONS Gender inequality in the prevalence of abdominal obesity increases with older age and lower education. The slope and relative indices of inequality summarize the strictly monotonous trend between education levels and abdominal obesity, and it described educational inequality regarding abdominal obesity among women. Such indices provide relevant quantitative estimates for monitoring abdominal obesity and dealing with health inequalities.

ABSTRACT Brazil is characterized by high inequality where initial conditions have a decisive impact on the trajectory of individuals. In this context, black people are doubly disadvantaged by their lower income levels and low expectations for development of their potential. This article used the method of Oaxaca-Blinder (1973) to decompose the performance difference between white and black students in SAEB 2017, identifying the part that can not be attributed to its characteristics. The results showed that this component represents about 43% of this difference, and that there is less response from black students to improvements in educational conditions.

Relative index of inequality (RII) and Slope index of inequality (SII) and 95-percent confidence intervals (95% CI).

In most countries basic education is nowadays perceived not only as a right, but also as a duty – governments are typically expected to ensure access to basic education, while citizens are often required by law to attain education up to a certain basic level.1

This was not always the case: the advancement of these ideas began in the mid-19th century, when most of today’s industrialized countries started expanding primary education, mainly through public finances and government intervention. Data from this early period shows that government funds to finance the expansion of education came from a number of different sources, but taxes at the local level played a crucial role. The historical role of local funding for public schools is important to help us understand changes – or persistence – in regional inequalities.

The second half of the 20th century marked the beginning of education expansion as a global phenomenon. Available data shows that by 1990 government spending on education as a share of national income in many developing countries was already close to the average observed in developed countries.2

This global education expansion in the 20th century resulted in a historical reduction in education inequality across the globe: in the period 1960-2010 education inequality went down every year, for all age groups and in all world regions. Recent estimates of education inequality across age groups suggest that further reductions in schooling inequality are still to be expected within developing countries.3

Recent cross-country data from UNESCO tells us that the world is expanding government funding for education today, and these additional public funds for education are not necessarily at the expense of other government sectors. Yet behind these broad global trends, there is substantial cross-country – and cross-regional – heterogeneity. In high-income countries, for instance, households shoulder a larger share of education expenditures at higher education levels than at lower levels – but in low-income countries, this is not the case.

Following the agreement of the Millennium Development Goals, the first decade of the 21st century saw an important increase in international financial flows under the umbrella of development assistance. Recent estimates show that development assistance for education has stopped growing since 2010, with notable aggregate reductions in flows going to primary education. These changes in the prioritization of development assistance for education across levels and regions can have potentially large distributional effects, particularly within low-income countries that depend substantially on this source of funding for basic education.4

When analyzing correlates, determinants and consequences of education consumption, the macro data indicates that national expenditure on education does not explain well cross-country differences in learning outcomes. This suggests that for any given level of expenditure, the output achieved depends crucially on the mix of many inputs.

Philippines Enrolment Data

Examining Private and Public Schools

By Humanitarian Data Exchange [source]

About this dataset

This dataset provides an interesting insight into the enrolment numbers in public and private schools across the Philippines. It covers all levels of enrolment – elementary, secondary, and post-secondary – as well as gender and urban/rural distinctions. This information is an invaluable asset for anyone looking to gain a comprehensive understanding of educational enrolment trends within the country in order to make informed decisions regarding resource allocation or policy implementations. However, keep in mind that due to differences in methodology and data collection techniques, caution should be taken when using this data as there may be inaccuracies or vague definitions applicable to specific age groups or subpopulations. Regardless, this dataset still serves as a valuable source of information for anyone wanting a proper picture of educational dynamics within the Philippines

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How to use the dataset

This dataset provides enrolment figures in public and private schools by level in the Philippines. With this data, users can explore disparities between public and private school enrolment and other potential inequalities associated with educational access.

In order to use this Kaggle dataset to analyze educational inequality in the Philippines, firstly one must understand which columns are included:

• Country: The name of the Philippine country
• School Level (Grouped): Groupings of school levels within primary/elementary and secondary level
• Enrolment Type: Public or Private
• Year: Time period of data collection

Now that you have an understanding about what this dataset contains, here are few ways you could use it for your analysis!

• Compare enrollment rates between genders - Use the 'School Level' column grouped into Primary/Elementary or Secondary fields along with 'Enrolment Type' (public vs. private) to sort out male/female enrollment differences from 2007 - 2018 at each grade level.
• Investigate discrepancies between urban vs rural areas - Look at where most students attend as reflected through the different divisions within provinces as defined by Commission on Elections (COMELEC). Depending if pupils mainly take up residence in urban or rural areas make sure to supplement this data with available measures towards educational disparities between these two settings such as infrastructure, resources etc.
• Analyze expansion trends over time - Using all columns within this dataset one could see how trends have changed over time since its inception year 2007 till recent year 2018 spanning different area types (such as mindanao through CAR etc.), school levels and regions across governance such provinces(NCR etc.).One could get additional insights such patterns around funding allocations too.

Using all these different analyses offered one can gain a better understanding about evolving disparities around education access in particular region or even countrywide!

Research Ideas

• Comparing enrolment statistics between public and private schools to identify more effective approaches in either sector.
• Identifying regions or areas which may benefit from additional investment in education infrastructure and resources.
• Visualizing enrolment rates at different levels of schooling to understand the relative level of educational attainment within a certain geographical area or region over time

Acknowledgements

If you use this dataset in your research, please credit the original authors. Data Source

License

See the dataset description for more information.

Columns

File: education-nscb-xls-1.csv

Acknowledgements

If you use this dataset in your research, please credit the original authors. If you use this dataset in your research, please credit Humanitarian Data Exchange.

Excel spreadsheet aggregating and analysing data, principally concerning the NAPLAN and senior secondary certificate performance of Tasmanian and schools in other states, extracted from publicly available data sets including My School, the Tasmanian Office of Assessment, Standards and Certification, and the Australian Bureau of Statistics.

The data and programs replicate tables and figures from "The Impact of Public School Choice: Evidence from Los Angeles's Zones of Choice", by Campos and Kearns. Please see the README file for additional details.

This paper analyzes a large-scale and long-running distance education program in Mexico. We estimate marginal treatment effects (MTEs) for learning in math and Spanish in telesecundarias relative to traditional Mexican secondary schools using an empirical framework that allows for unobserved sorting on gains. The estimated MTEs reveal that school choice is not random and that the average student experiences significant improvements in both math and Spanish after just one year of attendance in telesecundarias. We find that the existing policy reduces educational inequality, and our policy-relevant treatment effects show that expanding telesecundarias would yield significant improvements in academic performance.