Literacy in India has been increasing as more and more people receive a better education, but it is still far from all-encompassing. In 2023, the degree of literacy in India was about 77 percent, with the majority of literate Indians being men. It is estimated that the global literacy rate for people aged 15 and above is about 86 percent. How to read a literacy rateIn order to identify potential for intellectual and educational progress, the literacy rate of a country covers the level of education and skills acquired by a country’s inhabitants. Literacy is an important indicator of a country’s economic progress and the standard of living – it shows how many people have access to education. However, the standards to measure literacy cannot be universally applied. Measures to identify and define illiterate and literate inhabitants vary from country to country: In some, illiteracy is equated with no schooling at all, for example. Writings on the wallGlobally speaking, more men are able to read and write than women, and this disparity is also reflected in the literacy rate in India – with scarcity of schools and education in rural areas being one factor, and poverty another. Especially in rural areas, women and girls are often not given proper access to formal education, and even if they are, many drop out. Today, India is already being surpassed in this area by other emerging economies, like Brazil, China, and even by most other countries in the Asia-Pacific region. To catch up, India now has to offer more educational programs to its rural population, not only on how to read and write, but also on traditional gender roles and rights.

Context

The literacy rate is defined by the percentage of the population of a given age group that can read and write. The adult literacy rate corresponds to ages 15 and above, the youth literacy rate to ages 15 to 24. It is typically measured according to the ability to comprehend a short simple statement on everyday life.

Content

The dataset contains information about the literacy rates across the globe for various countries. It is grouped into 3 major categories, Total %, Male% and Female %. The dataset has 2 csv files. 1) Adults_15YrsAndUp.csv - Contains literacy rate information for Adults

2) Youth_15to24Yrs.csv - Contains literacy rate information for Youth

Acknowledgements

The dataset is sourced from Unicef.

Inspiration

Exploratory Data Analysis to find greater insights into the data.

Female Literacy rate in District Khyber Year 2021

In the past five decades, the global literacy rate among adults has grown from 67 percent in 1976 to 87.36 percent in 2023. In 1976, males had a literacy rate of 76 percent, compared to a rate of 58 percent among females. This difference of over 17 percent in 1976 has fallen to just seven percent in 2020. Although gaps in literacy rates have fallen across all regions in recent decades, significant disparities remain across much of South Asia and Africa, while the difference is below one percent in Europe and the Americas. Reasons for these differences are rooted in economic and cultural differences across the globe. In poorer societies, families with limited means are often more likely to invest in their sons' education, while their daughters take up a more domestic role. Varieties do exist on national levels, however, and female literacy levels can sometimes exceed the male rate even in impoverished nations, such as Lesotho (where the difference was over 17 percent in 2014); nonetheless, these are exceptions to the norm.

The dataset provides information on the literacy rate in Himachal Pradesh, India, from 1951 to 2021. The dataset includes the census year, the percentage of female, male, and total population who are literate.

Kenya KE: Literacy Rate: Adult Female: % of Females Aged 15 and Above data was reported at 74.006 % in 2014. This records an increase from the previous number of 66.863 % for 2007. Kenya KE: Literacy Rate: Adult Female: % of Females Aged 15 and Above data is updated yearly, averaging 74.006 % from Dec 2000 (Median) to 2014, with 3 observations. The data reached an all-time high of 77.893 % in 2000 and a record low of 66.863 % in 2007. Kenya KE: Literacy Rate: Adult Female: % of Females Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Kenya – Table KE.World Bank.WDI: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Feed the Future activities in Nepal include the Business Literacy (BL) Program, which operates in conjunction with the Knowledge-Based Integrated Sustainable Agriculture and Nutrition (KISAN) Project. Nepal Business Literacy (BL) Impact Evaluation (IE) is designed to collect and analyze three rounds of qualitative and quantitative data collected in order to learn from evidence of the extent to which initial and persistent results of the BL training course occur. The Nepal BL IE is designed to investigate initial and longer-term or persistent impacts of the training experience on targeted aspects of beneficiaries’ knowledge, skills, attitudes (KSA), and behaviors. Investigators also are interested in whether and to what extent the BL training experience leads to adoption of targeted behaviors, such as starting new micro enterprises, that persist over time. This dataset (n=1,434, vars=414) contains women’s records for Modules D (Self-Efficacy in Business Literacy Topics), E (Program Participation and Business Literacy Learning), and F (Household Resources and Production). Records can be uniquely identified by pbs_id + hm_id (although pbs_id can also be used alone because only one woman per household was interviewed).

Egypt EG: Literacy Rate: Adult Female: % of Females Aged 15 and Above data was reported at 67.180 % in 2013. This records an increase from the previous number of 65.757 % for 2012. Egypt EG: Literacy Rate: Adult Female: % of Females Aged 15 and Above data is updated yearly, averaging 50.704 % from Dec 1976 (Median) to 2013, with 8 observations. The data reached an all-time high of 67.180 % in 2013 and a record low of 22.438 % in 1976. Egypt EG: Literacy Rate: Adult Female: % of Females Aged 15 and Above data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Egypt – Table EG.World Bank.WDI: Education Statistics. Adult literacy rate is the percentage of people ages 15 and above who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Afghanistan Literacy Rate: Youth Female: % of Females Aged 15-24 data was reported at 44.172 % in 2022. This records an increase from the previous number of 42.000 % for 2021. Afghanistan Literacy Rate: Youth Female: % of Females Aged 15-24 data is updated yearly, averaging 32.000 % from Dec 1979 (Median) to 2022, with 5 observations. The data reached an all-time high of 44.172 % in 2022 and a record low of 11.000 % in 1979. Afghanistan Literacy Rate: Youth Female: % of Females Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Afghanistan – Table AF.World Bank.WDI: Social: Education Statistics. Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.;UNESCO Institute for Statistics (UIS). UIS.Stat Bulk Data Download Service. Accessed April 5, 2025. https://apiportal.uis.unesco.org/bdds.;Weighted average;

The data shows the year-wise and state or union territory-wise literacy and rural and urban literacy, for male, female, and total literacy, in India according to Census.

Note: 1. Literacy rate is defined as the population of literates in the population aged 7 year and above. 2. The 1991 data (Excluding Jammu & Kashmir)and 2001 data (Excludes figures of Paomata, Mao Maran and Pura sub-divisions of Senapati district of Manipur for 2001) refer to Census of India.

Documentation

Description and codebook for subset of harmonized variables:

Section 2

Guide to datasets:

Full Project Name: The Impact of Mother Literacy and Participation Programs on Child Learning in India

Unique ID: 458

PIs: Rukmini Banerji, James Berry, Marc Shotland

Location: Indian states of Bihar and Rajasthan

Sample: Around 9,000 households in 480 villages

Timeline: 2010 to 2012

Target Group: Children Parents Rural population Women and girls

Outcome of Interest: Employment, Student learning ,Women’s/girls’ decision-making, Gender attitudes and norms

Intervention Type: Early childhood development, Tracking and remedial education, Empowerment training

Associated publications: https://www.aeaweb.org/articles?id=10.1257/app.20150390

More information: https://www.povertyactionlab.org/evaluation/impact-mother-literacy-and-participation-programs-child-learning-india

Dataverse: Banerji, Rukmini; Berry, James; Shotland, Marc, 2017, “The Impact of Maternal Literacy and Participation Programs: Evidence from a Randomized Evaluation in India”, https://doi.org/10.7910/DVN/19PPE7, Harvard Dataverse, V1

Section 3

Survey instrument:

Testing tools:

Section 4

Survey instrument:

Testing tools:

Section 5

No associated survey instrument

Section 6

This dataset was created on 2021-10-06 20:35:41.921 by merging multiple datasets together. The source datasets for this version were:

Maternal Literacy in India Baseline: Modified from ml_merged : contains data with variables only from baseline surveys

Maternal Literacy in India Endline: Modified from ml_merged : contains data with variables only from endline surveys

Maternal Literacy in India Raw Administrative Statistics: ml_admin_stats_raw: Contains administrative statistics from the 2011 census and aser surveys used in online Appendix Table 1 in the paper; this is merged with some of the survey data to create ml_admin_stats

Analysis of ‘Education in India’ provided by Analyst-2 (analyst-2.ai), based on source dataset retrieved from https://www.kaggle.com/rajanand/education-in-india on 12 November 2021.

--- Dataset description provided by original source is as follows ---

Connect/Follow me on LinkedIn for more updates on interesting dataset like this. Thanks.

Context

When India got independence from British in 1947 the literacy rate was 12.2% and as per the recent census 2011 it is 74.0%. Although it looks an accomplishment, still many people are there without access to education.

It would be interesting to know the current status of the Indian education system.

Content

This dataset contains district and state wise Indian primary and secondary school education data for 2015-16.

Granularity: Annual

List of files:

1. 2015_16_Districtwise.csv ( 680 observations and 819 variables )
2. 2015_16_Statewise_Elementary.csv ( 36 observations and 816 variables )
3. 2015_16_Statewise_Secondary.csv ( 36 observations and 630 variables )

Acknowledgements

Ministry of Human Resource Development (DISE) has shared the dataset here and also published some reports.

Source of Banner image.

Inspiration

This dataset provides the complete information about primary and secondary education. There are many inferences can be made from this dataset. There are few things I would like to understand from this dataset.

1. Drop out ratio in primary and secondary education. (Govt. has made law that every child under age 14 should get free compulsary education.)
2. Various factors affecting examination results of the students.
3. What are all the factors that makes the difference (in literacy rate) between Kerala and Bihar?
4. What could be done to improve the female literacy rate and literacy rate in rural area?

--- Original source retains full ownership of the source dataset ---

TIGER, TIGER/Line, and Census TIGER are registered trademarks of the Bureau of the Census. The Redistricting Census 2000 TIGER/Line files are an extract of selected geographic and cartographic information from the Census TIGER data base. The geographic coverage for a single TIGER/Line file is a county or statistical equivalent entity, with the coverage area based on January 1, 2000 legal boundaries. A complete set of Redistricting Census 2000 TIGER/Line files includes all counties and statistically equivalent entities in the United States and Puerto Rico. The Redistricting Census 2000 TIGER/Line files will not include files for the Island Areas. The Census TIGER data base represents a seamless national file with no overlaps or gaps between parts. However, each county-based TIGER/Line file is designed to stand alone as an independent data set or the files can be combined to cover the whole Nation. The Redistricting Census 2000 TIGER/Line files consist of line segments representing physical features and governmental and statistical boundaries. The Redistricting Census 2000 TIGER/Line files do NOT contain the ZIP Code Tabulation Areas (ZCTAs) and the address ranges are of approximately the same vintage as those appearing in the 1999 TIGER/Line files. That is, the Census Bureau is producing the Redistricting Census 2000 TIGER/Line files in advance of the computer processing that will ensure that the address ranges in the TIGER/Line files agree with the final Master Address File (MAF) used for tabulating Census 2000. The files contain information distributed over a series of record types for the spatial objects of a county. There are 17 record types, including the basic data record, the shape coordinate points, and geographic codes that can be used with appropriate software to prepare maps. Other geographic information contained in the files includes attributes such as feature identifiers/census feature class codes (CFCC) used to differentiate feature types, address ranges and ZIP Codes, codes for legal and statistical entities, latitude/longitude coordinates of linear and point features, landmark point features, area landmarks, key geographic features, and area boundaries. The Redistricting Census 2000 TIGER/Line data dictionary contains a complete list of all the fields in the 17 record types.

Dataset name: asppl_dataset_v2.csv

Version: 2.0

Dataset period: 06/07/2018 - 01/14/2022

Dataset Characteristics: Multivalued

Number of Instances: 8118

Number of Attributes: 9

Missing Values: Yes

Area(s): Health and education

Sources:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Occupational Classification (CBO) (Brasil, 2022b);

• National Registry of Health Establishments (CNES) (Brasil, 2022c);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the asppl_dataset_v2.csv dataset (see Table 1) originates from participants of the technology-based educational course “Health Care for People Deprived of Freedom.” The course is available on the AVASUS (Brasil, 2022a). This dataset provides elementary data for analyzing the course’s impact and reach and the profile of its participants. In addition, it brings an update of the data presented in work by Valentim et al. (2021).

Table 1: Description of AVASUS dataset features.

Attributes	Description	datatype	Value
gender	Gender of the course participant.	Categorical.	Feminino / Masculino / Não Informado. (In English, Female, Male or Uninformed)
course_progress	Percentage of completion of the course.	Numerical.	Range from 0 to 100.
course_evaluation	A score given to the course by the participant.	Numerical.	0, 1, 2, 3, 4, 5 or NaN.
evaluation_commentary	Comment made by the participant about the course.	Categorical.	Free text or NaN.
region	Brazilian region in which the participant resides.	Categorical.	Brazilian region according to IBGE: Norte, Nordeste, Centro-Oeste, Sudeste or Sul (In English North, Northeast, Midwest, Southeast or South).
CNES	The CNES code refers to the health establishment where the participant works.	Numerical.	CNES Code or NaN.
health_care_level	Identification of the health care network level for which the course participant works.	Categorical.	“ATENCAO PRIMARIA”, “MEDIA COMPLEXIDADE”, “ALTA COMPLEXIDADE”, and their possible combinations. (In English "PRIMARY HEALTH CARE", "SECONDARY HEALTH CARE" AND "TERTIARY HEALTH CARE")
year_enrollment	Year in which the course participant registered.	Numerical.	Year (YYYY).
CBO	Participant occupation.	Categorical.	Text coded according to the Brazilian Classification of Occupations or “Indivíduo sem afiliação formal.” (In English “Individual without formal affiliation.”)

Dataset name: prison_syphilis_and_population_brazil.csv

Dataset period: 2017 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 13

Missing Values: No

Source:

• National Penitentiary Department (DEPEN) (Brasil, 2022d);

Description: The data contained in the prison_syphilis_and_population_brazil.csv dataset (see Table 2) originate from the National Penitentiary Department Information System (SISDEPEN) (Brasil, 2022d). This dataset provides data on the population and prevalence of syphilis in the Brazilian prison system. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil.

Table 2: Description of DEPEN dataset Features.

Attributes	Description	datatype	Value
Region	Brazilian region in which the participant resides. In addition, the sum of the regions, which refers to Brazil.	Categorical.	Brazil and Brazilian region according to IBGE: North, Northeast, Midwest, Southeast or South.
syphilis_2017	Number of syphilis cases in the prison system in 2017.	Numerical.	Number of syphilis cases.
syphilis_rate_2017	Normalized rate of syphilis cases in 2017.	Numerical.	Syphilis case rate.
syphilis_2018	Number of syphilis cases in the prison system in 2018.	Numerical.	Number of syphilis cases.
syphilis_rate_2018	Normalized rate of syphilis cases in 2018.	Numerical.	Syphilis case rate.
syphilis_2019	Number of syphilis cases in the prison system in 2019.	Numerical.	Number of syphilis cases.
syphilis_rate_2019	Normalized rate of syphilis cases in 2019.	Numerical.	Syphilis case rate.
syphilis_2020	Number of syphilis cases in the prison system in 2020.	Numerical.	Number of syphilis cases.
syphilis_rate_2020	Normalized rate of syphilis cases in 2020.	Numerical.	Syphilis case rate.
pop_2017	Prison population in 2017.	Numerical.	Population number.
pop_2018	Prison population in 2018.	Numerical.	Population number.
pop_2019	Prison population in 2019.	Numerical.	Population number.
pop_2020	Prison population in 2020.	Numerical.	Population number.

Dataset name: students_cumulative_sum.csv

Dataset period: 2018 - 2020

Dataset Characteristics: Multivalued

Number of Instances: 6

Number of Attributes: 7

Missing Values: No

Source:

• Virtual Learning Environment of the Brazilian Health System (AVASUS) (Brasil, 2022a);

• Brazilian Institute of Geography and Statistics (IBGE) (Brasil, 2022e).

Description: The data contained in the students_cumulative_sum.csv dataset (see Table 3) originate mainly from AVASUS (Brasil, 2022a). This dataset provides data on the number of students by region and year. In addition, it brings a rate that represents the normalized data for purposes of comparison between the populations of each region and Brazil. We used population data estimated by the IBGE (Brasil, 2022e) to calculate the rate.

Table 3: Description of Students dataset Features.

Turkey TR: Literacy Rate: Youth Female: % of Females Aged 15-24 data was reported at 99.167 % in 2015. This records an increase from the previous number of 98.951 % for 2014. Turkey TR: Literacy Rate: Youth Female: % of Females Aged 15-24 data is updated yearly, averaging 94.289 % from Dec 1975 (Median) to 2015, with 15 observations. The data reached an all-time high of 99.167 % in 2015 and a record low of 68.253 % in 1975. Turkey TR: Literacy Rate: Youth Female: % of Females Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Turkey – Table TR.World Bank: Education Statistics. Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

Literacy rate Female in Swabi 2020

Iran IR: Literacy Rate: Youth Female: % of Females Aged 15-24 data was reported at 97.678 % in 2014. This records an increase from the previous number of 97.438 % for 2013. Iran IR: Literacy Rate: Youth Female: % of Females Aged 15-24 data is updated yearly, averaging 96.435 % from Dec 1976 (Median) to 2014, with 10 observations. The data reached an all-time high of 97.678 % in 2014 and a record low of 42.328 % in 1976. Iran IR: Literacy Rate: Youth Female: % of Females Aged 15-24 data remains active status in CEIC and is reported by World Bank. The data is categorized under Global Database’s Iran – Table IR.World Bank.WDI: Education Statistics. Youth literacy rate is the percentage of people ages 15-24 who can both read and write with understanding a short simple statement about their everyday life.; ; UNESCO Institute for Statistics; Weighted average; Each economy is classified based on the classification of World Bank Group's fiscal year 2018 (July 1, 2017-June 30, 2018).

This dataset presents the number of people attending evening schools and illiteracy eradication centers in the State of Qatar. The data is categorized by level of education (Primary, Preparatory, and Secondary) and gender (Male and Female). The dataset helps analyze trends in adult education and literacy efforts across different educational stages and genders.

Dataset from Singapore Department of Statistics. For more information, visit https://data.gov.sg/datasets/d_a98f2ae027bf4442d7250d50505feabe/view