The per capita consumer spending on education ranking is led by Singapore with 1,640.84 U.S. dollars, while Australia is following with 1,290.37 U.S. dollars. In contrast, Ethiopia is at the bottom of the ranking with 0.68 U.S. dollars, showing a difference of 1,640.16 U.S. dollars to Singapore. Consumer spending, in this case education-related spending per capita, refers to the domestic demand of private households and non-profit institutions serving households (NPISHs). Spending by corporations and the state is not included. The forecast has been adjusted for the expected impact of COVID-19.Consumer spending is the biggest component of the gross domestic product as computed on an expenditure basis in the context of national accounts. The other components in this approach are consumption expenditure of the state, gross domestic investment as well as the net exports of goods and services. Consumer spending is broken down according to the United Nations' Classification of Individual Consumption By Purpose (COICOP). The shown data adheres broadly to group tenth As not all countries and regions report data in a harmonized way, all data shown here has been processed by Statista to allow the greatest level of comparability possible. The underlying input data are usually household budget surveys conducted by government agencies that track spending of selected households over a given period.The data is shown in nominal terms which means that monetary data is valued at prices of the respective year and has not been adjusted for inflation. For future years the price level has been projected as well. The data has been converted from local currencies to US$ using the average exchange rate of the respective year. For forecast years, the exchange rate has been projected as well. The timelines therefore incorporate currency effects.

Expenditure on Education: Madhya Pradesh: Revenue Account data was reported at 341,508,383.000 INR th in 2021. This records an increase from the previous number of 304,588,107.000 INR th for 2020. Expenditure on Education: Madhya Pradesh: Revenue Account data is updated yearly, averaging 119,999,337.000 INR th from Mar 2005 (Median) to 2021, with 17 observations. The data reached an all-time high of 341,508,383.000 INR th in 2021 and a record low of 35,034,700.000 INR th in 2005. Expenditure on Education: Madhya Pradesh: Revenue Account data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDE004: Memo Items: Expenditure on Education: Revenue Account: by States.

Expenditure on Education: Himachal Pradesh: Revenue Account data was reported at 84,132,779.000 INR th in 2021. This records an increase from the previous number of 77,216,367.000 INR th for 2020. Expenditure on Education: Himachal Pradesh: Revenue Account data is updated yearly, averaging 34,983,346.000 INR th from Mar 2005 (Median) to 2021, with 17 observations. The data reached an all-time high of 84,132,779.000 INR th in 2021 and a record low of 11,051,700.000 INR th in 2005. Expenditure on Education: Himachal Pradesh: Revenue Account data remains active status in CEIC and is reported by Ministry of Education. The data is categorized under India Premium Database’s Education Sector – Table IN.EDE004: Memo Items: Expenditure on Education: Revenue Account: by States.

Indian students studying abroad in higher education courses are estimated to spend around ****** U.S. dollars in 2023. The expenditure includes tuition, accommodation, and other living expenses. The average expenditure in Australia is also estimated to be over ****** U.S. dollars. English-speaking countries like the United States, Australia, Canada, the United Kingdom, are most preferred destinations by Indian students.

This layer shows Socio- Economic Profiles and Inter-State Comparison of Selected Major States of IndiaData source Url: https://www.indiabudget.gov.in/economicsurvey/doc/stat/tab89a.pdfSource: Office of Registrar General of India(RGI). Andhra Pradesh excludes Telangana for Sex ratio at birthNITI Aayog, Figures in parenthesis are rank amongst the selected 20 States Press Note on Poverty Estimates 2011-12, Government of India (2013)School Education in India, U-DISE + 2019-20 (Provisional)CSO, GSDP at constant prices and per capita income is at current prices ( Base Year 2011-12)National Statistical Office (NSO), Monthly per capita expenditure (MPCE) is based on mixed modified recall period,Periodic Labour Force Survey, 2019-20 (NSO); WPR (Worker Participation Rate) and Unemployment Rate are based on Usual Principal & Subsidiary Status (UPSS). This web layer is offered by Esri India, for ArcGIS Online subscribers. If you have any questions or comments, please let us know via content@esri.in.Note: Transition Rate: The number of new entrants admitted to the first grade of the next stage of school education in a given year, expressed as a percentage of number of pupils enrolled in the final grade of the current stage of school education in the previous year.

Historical Dataset of Mid-State Education School District is provided by PublicSchoolReview and contain statistics on metrics:Comparison of Diversity Score Trends,Total Revenues Trends,Total Expenditure Trends,Average Revenue Per Student Trends,Average Expenditure Per Student Trends,Reading and Language Arts Proficiency Trends,Math Proficiency Trends,Science Proficiency Trends,Graduation Rate Trends,Overall School District Rank Trends,American Indian Student Percentage Comparison Over Years (2007-2023),Hispanic Student Percentage Comparison Over Years (2012-2023),Black Student Percentage Comparison Over Years (2008-2022),White Student Percentage Comparison Over Years (2007-2023),Two or More Races Student Percentage Comparison Over Years (2013-2023),Comparison of Students By Grade Trends

Explore the World Competitiveness Ranking dataset for 2016, including key indicators such as GDP per capita, fixed telephone tariffs, and pension funding. Discover insights on social cohesion, scientific research, and digital transformation in various countries.

Social cohesion, The image abroad of your country encourages business development, Scientific articles published by origin of author, International Telecommunication Union, World Telecommunication/ICT Indicators database, Data reproduced with the kind permission of ITU, National sources, Fixed telephone tariffs, GDP (PPP) per capita, Overall, Exports of goods - growth, Pension funding is adequately addressed for the future, Companies are very good at using big data and analytics to support decision-making, Gross fixed capital formation - real growth, Economic Performance, Scientific research legislation, Percentage of GDP, Health infrastructure meets the needs of society, Estimates based on preliminary data for the most recent year., Singapore: including re-exports., Value, Laws relating to scientific research do encourage innovation, % of GDP, Gross Domestic Product (GDP), Health Infrastructure, Digital transformation in companies is generally well understood, Industrial disputes, EE, Female / male ratio, State ownership of enterprises, Total expenditure on R&D (%), Score, Colombia, Estimates for the most recent year., Percentage change, based on US$ values, Number of listed domestic companies, Tax evasion is not a threat to your economy, Scientific articles, Tax evasion, % change, Use of big data and analytics, National sources, Disposable Income, Equal opportunity, Listed domestic companies, Government budget surplus/deficit (%), Pension funding, US$ per capita at purchasing power parity, Estimates; US$ per capita at purchasing power parity, Image abroad or branding, Equal opportunity legislation in your economy encourages economic development, Number, Article counts are from a selection of journals, books, and conference proceedings in S&E from Scopus. Articles are classified by their year of publication and are assigned to a region/country/economy on the basis of the institutional address(es) listed in the article. Articles are credited on a fractional-count basis. The sum of the countries/economies may not add to the world total because of rounding. Some publications have incomplete address information for coauthored publications in the Scopus database. The unassigned category count is the sum of fractional counts for publications that cannot be assigned to a country or economy. Hong Kong: research output items by the higher education institutions funded by the University Grants Committee only., State ownership of enterprises is not a threat to business activities, Protectionism does not impair the conduct of your business, Digital transformation in companies, Total final energy consumption per capita, Social cohesion is high, Rank, MTOE per capita, Percentage change, based on constant prices, US$ billions, National sources, World Trade Organization Statistics database, Rank, Score, Value, World Rankings

Argentina, Australia, Austria, Belgium, Brazil, Bulgaria, Canada, Chile, China, Colombia, Croatia, Cyprus, Denmark, Estonia, Finland, France, Germany, Greece, Hungary, Iceland, India, Indonesia, Ireland, Israel, Italy, Japan, Jordan, Kazakhstan, Latvia, Lithuania, Luxembourg, Malaysia, Mexico, Mongolia, Netherlands, New Zealand, Norway, Oman, Peru, Philippines, Poland, Portugal, Qatar, Romania, Russia, Saudi Arabia, Singapore, Slovenia, South Africa, Spain, Sweden, Switzerland, Thailand, Turkey, Ukraine, United Kingdom, Venezuela

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Abstract

Introduction: This is the 52nd round of NSS conducted from 1.7.1995 to 30.6.1996 . The National Sample Survey (NSS), set up by the Government of India in 1950 to collect socio-economic data employing scientific sampling methods.The third survey on social consumption. Two topics, viz. utilisation of the public distribution system and utilisation of family planning services, were dropped as these were covered in the NSS 50th round and by a nationwide survey by the Ministry of Health and Family Welfare, respectively.

Subject Coverage:

The four main topics covered in the 52nd round are:

(i) Utilisation of maternity and child health care services (ii) Morbidity and utilisation of medical services (iii) Problems of aged persons (iv) Participation in education

In addition, the topics of household consumer expenditure and employment unemployment were covered as is usual with every round.

Geographical coverage:

The survey covered the whole of the Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir (for central sample), (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Period of survey and work programme:

The period of survey is from 1.7.1995 to 30.6.1996.

The survey period is divided into four sub-rounds of three months' duration each as follows:

sub-round 1 : July - September 1995
sub-round 2 : October - December 1995 sub-round 3 : January - March 1996
sub-round 4 : April - June 1996

In each of these four sub-rounds equal number of sample villages/ blocks (FSUs) was covered to ensuring uniform spread of sample FSUs over the entire survey period.

Geographic coverage

The survey covered the whole of the Indian Union except (i) Leh (Ladakh) and Kargil districts of Jammu & Kashmir (for central sample), (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Households and household members.

Universe

Households and members of the household

Kind of data

Sample survey data [ssd]

Sampling procedure

Sample Design

General:

A stratified two-stage design was adopted in this round. The first-stage units were the census villages for the rural areas (panchayat wards in case of Kerala) and the NSSO Urban Frame Survey(UFS) blocks for the urban areas. The second stage units were the households in both the cases.

Sampling Frame for First-Stage Units (FSUs):

The list of census villages of the 1991 census (1981 census list for Jammu & Kashmir) constituted the sampling frame for the rural areas. For Kerala, however, the list of panchayat wards was used as the sampling frame for the selection of first stage units in the rural areas. For Nagaland, the villages located within 5 km of a bus route constituted the sampling frame, whereas for Andaman & Nicobar Islands, the list of 'accessible' villages formed the sampling frame. For the urban areas, the list of NSSO Urban Frame Survey(UFS) blocks has been used as the sampling frame.

Stratification:

For the socio-economic surveys of the NSSO, each state or union territory (u.t.) is divided into one or more agro-climatic regions by grouping contiguous districts which are similar with respect to population density and crop pattern. In Gujarat, however, some districts are subdivided for the purpose of region formation on the basis of location of dry areas and the distribution of tribal population in the state. In all, there are 78 regions covering the entire geographical area of the country.

Stratification for Rural areas:

In the rural areas, each district within a region was treated as a separate stratum. However, if the 1991 census population of the district was found to be greater than or equal to 2 million (1.8 million population as per 1981 census for Jammu & Kashmir), the district was split into two or more strata, by grouping contiguous tehsils to form strata. In Gujarat, in the case of districts extending over more than one NSS region, the part of a district falling within each NSS region formed a separate stratum.

Stratification for Urban areas :

In the urban areas, strata were formed, within each NSS region, by grouping towns on the basis of the population of towns. The urban strata were formed as shown in Table.

Table: Composition of urban strata in an NSS region

Stratum No. Population size class (as per 1991 census) of towns

1 all towns with population less than 50,000 2 all towns with population 50,000 - 1, 99,999 3 all towns with population 2, 00,000 - 9, 99,999 4, 5... each city with population 10,00,000 or more

Allocation of First Stage Units (FSUs):

A sample of 13,000 FSUs (rural & urban combined) was selected as the 'central sample' at the all-India level. The sample size of FSUs (rural & urban combined) for the central sample for a state/u.t. was allocated to its rural and urban areas considering the relative sizes of the rural and urban population with double weightage to the urban areas. The state level rural sample size was allocated to the rural strata in proportion to their rural population figures as per the census. Similarly, urban sample size of the state/u.t. was allocated to the urban strata in proportion to urban population figures as per the census. All the stratum-level allocations were adjusted to multiples of 8 as far as possible (otherwise to multiples of 4) in order to allocate them equally in each sub-sample x sub-round combination (2 sub-samples x 4 sub-rounds).

Selection of First-Stage Units:

The sample FSUs in the rural areas were selected circular systematically with equal probability. In the Union Territory of Daman & Diu, the district Diu consists of only two villages. These two were selected for the survey in both the central and the state sample. Sample blocks in the urban areas were also selected circular systematically with equal probability. Sample FSUs of both the rural and urban areas were selected in the form of two independent sub-samples. The only departure from the general procedure of selection of FSUs was made for the rural areas of Arunachal Pradesh for which the procedure of cluster sampling was followed. The nucleus villages were selected circular systematically with equal probability, in the form of two independent sub-samples. A cluster, generally of 4 to 6 villages, was formed around each nucleus village.

Selection of Hamlet-Groups/ Sub-Blocks (for 'large' FSUs only):

A large FSU was divided into a suitable number of hamlet-groups/ sub-blocks having equal population content. Two hamlet-groups were selected from each large FSU in the rural areas and only one sub-block was selected from each large FSU of the urban areas.

Selection of Households (Second-Stage Units):

In each of the selected FSUs, three different enquiries, viz. "Survey on Health Care", "Survey on Participation in Education" and "Survey on Consumer Expenditure", were conducted on three independent samples of the households. For the present enquiry, i.e. survey on education, a sample of 6 households was selected for the detailed enquiry. However, before selection, the listed households were first grouped into two second-stage strata. The composition of the second-stage strata and the number of sample households selected from each of them are shown in following table.

Table: Second-stage stratum composition and number of households selected

No.   Second-stage stratum Composition selected    Number of households

1  households with at least one member of age group 5-24          3
  years and studying in post-primary level
2  remaining households                                  3

Mode of data collection

Face-to-face [f2f]

Research instrument

In the present round, Schedule 25.2 on participation and expenditure in education consists of following 7 blocks which comprised of questions on:

BLOCK 1 - IDENTIFICATION OF SAMPLE HOUSEHOLD: Sample(Central, State), Sector(Rural, Urban), FOD sub region, found number, schedule number, state region, stratum number, district code, sub sample, sub round, serial number of sample village/block, hamlet group/sub block number, second stage stratum number, sample household number, serial number of informant, response code, reason for substitution of original household.

BLOCK 2 - HOUSEHOLD PARTICULARS size, social group, expenditure on members staying away from home, average monthly expenditure, monthly per capita consumption expenditure, distance to nearest primary school

BLOCK 3 - DEMOGRAPHIC PARTICULARS OF PERSONS IN HOUSEHOLD serial number, name, relation to head, sex, age, literate, education level, status of current attendance, current enrolment status.

BLOCK 4 - EDUCATION PARTICULRS FOR CURRENTLY ATTENDING AT PRIMARY & POST PRIMARY Serial number, age at entry in school, no of courses attended, type of current education, level of current attendance, present class / grade / year of study, type of institution, nature of institution, medium of instruction, type of course, is education free, whether tuition fee waived, annual amount waived, reason for waiver, received scholarship / stipend, annual amount of scholarship / stipend received with reason for receiving, received textbooks, received stationery, agency if any provided free mid-day meal / tiffin / nutrition, distance of institution from place of residence, mode of transport, whether concession received in case of public

BackgroundIndia is unlikely to meet the Millennium Development Goal for child mortality. As public policy impacts child mortality, we assessed the association of social sector expenditure with child mortality in India. Methods and FindingsMixed-effects regression models were used to assess the relationship of state-level overall social sector expenditure and its major components (health, health-related, education, and other) with mortality by sex among infants and children aged 1–4 years from 1997 to 2009, adjusting for potential confounders. Counterfactual models were constructed to estimate deaths averted due to overall social sector increases since 1997. Increases in per capita overall social sector expenditure were slightly higher in less developed than in more developed states from 1997 to 2009 (2.4-fold versus 2-fold), but the level of expenditure remained 36% lower in the former in 2009. Increase in public expenditure on health was not significantly associated with mortality reduction in infants or at ages 1–4 years, but a 10% increase in health-related public expenditure was associated with a 3.6% mortality reduction (95% confidence interval 0.2–6.9%) in 1–4 years old boys. A 10% increase in overall social sector expenditure was associated with a mortality reduction in both boys (6.8%, 3.5–10.0%) and girls (4.1%, 0.8–7.5%) aged 1–4 years. We estimated 119,807 (95% uncertainty interval 53,409 – 214,662) averted deaths in boys aged 1–4 years and 94,037 (14,725 – 206,684) in girls in India in 2009 that could be attributed to increases in overall social sector expenditure since 1997. ConclusionsFurther reduction in child mortality in India would be facilitated if policymakers give high priority to the social sector as a whole for resource allocation in the country’s 5-year plan for 2012–2017, as public expenditure on health alone has not had major impact on reducing child mortality.

Snapshot img

Education Apps Market Size 2025-2029

The education apps market size is forecast to increase by USD 6.08 billion, at a CAGR of 14.5% between 2024 and 2029.

The market is witnessing significant growth, driven by increasing government initiatives for digital learning and the expanding focus on wearable technology in education. These trends reflect a global shift towards more accessible, flexible, and technology-enhanced educational experiences. However, the market faces challenges related to data security and privacy concerns, as sensitive student information is increasingly being stored and transmitted through these applications.
Companies seeking to capitalize on this market's opportunities must prioritize robust data protection measures and transparent privacy policies to build trust with users and regulatory bodies. By addressing these challenges effectively, organizations can differentiate themselves and thrive in the competitive the market.

What will be the Size of the Education Apps Market during the forecast period?

Explore in-depth regional segment analysis with market size data - historical 2019-2023 and forecasts 2025-2029 - in the full report.
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The market continues to evolve, driven by the integration of various technologies and innovative approaches to teaching and learning. Classroom management tools facilitate efficient organization and monitoring of student progress, while student engagement is enhanced through gamification design and educational games. Behavioral analytics and machine learning algorithms provide valuable insights for personalized feedback and learning paths, enabling teachers to tailor instruction to individual students' needs. Content creation and curation are essential components, with user experience (UX) playing a crucial role in ensuring ease of use and accessibility. Data security and privacy are paramount, as user data is collected and analyzed for progress tracking and compliance regulations.

Blended learning models and automated grading streamline administrative tasks, allowing educators to focus on instruction. Corporate training and professional development applications offer flexibility and affordability, while microlearning modules and monetization strategies cater to diverse learning styles and budgets. Early childhood education, K-12 (game learning) special education, and higher education sectors all benefit from these advancements, with adaptive learning platforms and interactive simulations providing effective solutions for numeracy development, literacy, STEM education, and social-emotional learning (SEL). Remote learning solutions and subscription models offer accessibility and flexibility, making education more accessible to a broader audience. Continuous innovation in educational technology, user interface (UI), and compliance regulations ensures a dynamic and evolving market landscape.Personalized learning paths and assessment tools enable teachers to cater to individual students' needs, fostering a more effective and engaging learning experience.

How is this Education Apps Industry segmented?

The education apps industry research report provides comprehensive data (region-wise segment analysis), with forecasts and estimates in 'USD million' for the period 2025-2029, as well as historical data from 2019-2023 for the following segments.

End-user

  High education
  Pre K-12


Product

  Web-based
  Mobile-based


App Type

  Learning Management Systems (LMS)
  Adaptive Learning Apps
  Educational Games
  Assessment and Grading


Delivery Mode

  SaaS (Software as a Service)
  On-Premise
  Cloud-Based


Target Audience

  Students
  Teachers
  Parents
  Administrators


Geography

  North America

    US
    Canada


  Europe

    France
    Germany
    Italy
    UK


  APAC

    China
    India
    Japan


  South America

    Brazil


  Rest of World (ROW)

By End-user Insights

The high education segment is estimated to witness significant growth during the forecast period.

The market encompasses a wide range of digital solutions for various learning stages, from early childhood to higher education. Multi-platform compatibility ensures accessibility, while behavioral analytics and personalized feedback cater to individual learning needs. Compliance regulations are addressed to ensure secure data and privacy. Professional development apps offer corporate training and certification programs, integrating machine learning algorithms for efficient learning. Blended learning models combine traditional and digital methods, with automated grading and progress tracking. Educational games and interactive simulations engage students, while curriculum alignment and content curation ensure academic rigor. Monetization strategies include freemium models, in-app purchases, and subscription models.

User interface design prioritizes

Abstract

The National Sample Survey Organisation (NSSO) has been carrying out All-India surveys on consumer expenditure. While some of these smaller-scale surveys are spread over a full year and others over six months only, the quinquennial (full-scale) surveys have all been of a full year's duration. Household consumer expenditure is measured as the expenditure incurred by a household on domestic account during a specified period, called reference period. It includes the imputed values of goods and services, which are not purchased but procured otherwise for consumption. In other words, it is the sum total of monetary values of all the items (i.e. goods and services) consumed by the household on domestic account during the reference period. Any expenditure incurred towards the productive enterprises of the households is also excluded from household consumer expenditure. To minimise recall errors, a very detailed item classification is adopted to collect information, including items of food, items of fuel, items of clothing, bedding and footwear, items of educational and medical expenses, items of durable goods and other items. The schedule has also collected some other household particulars including age, sex and educational level etc. of each household member.

Geographic coverage

The survey covered the whole of the Indian Union except (i) Leh and Kargil districts of Jammu & Kashmir, (ii) interior villages of Nagaland situated beyond five kilometres of the bus route and (iii) villages in Andaman and Nicobar Islands which remain inaccessible throughout the year.

Analysis unit

Randomly selected households based on sampling procedure and members of the household

Universe

The survey used the interview method of data collection from a sample of randomly selected households and members of the household.

Kind of data

Sample survey data [ssd]

Sampling procedure

An outline of sampling design: A stratified sampling design has been adopted for selection of the sample first-stage units (FSU's). The FSU's are villages (panchayat wards for Kerala) for rural areas and Urban Frame Survey (UFS) blocks for urban areas. The Ultimate stage units (USU's) are households which are selected by the method of circular systematic sampling from the corresponding frame in the FSU. Large FSU's are subdivided into hamlet groups (rural)/ sub-blocks (urban), that are grouped into two segments, and USU's are selected independently from each of these segments.

Sampling Frame: List of villages (panchayat wards for Kerala) as per 1991Census and latest lists of UFS blocks are respectively used for selection of rural and urban sample FSU's. For selection of sample villages from the State of Jammu & Kashmir, list of villages as per 1981Census has been used as the sampling frame.

Sample size (FSU's):
The total sample size for Central Sample was fixed at 15032. Total State sample size was fixed at 17096 taking care of prevalent matching pattern for almost all states. Sample size for the whole round for each State/UT x Sector (i.e. rural/ urban) are allocated equally among the 4 sub-rounds. Sample FSU's for each sub-round are selected afresh in the form of 2 independent sub-samples.

Selection of FSU's: For each sub-round, sample FSU's from each stratum are selected in the form of 2 independent sub-samples by following circular systematic sampling with (a) probability proportional to population for all rural strata other than stratum 1, and (b) equal probability for rural stratum 1 as well as all urban strata.

Sampling deviation

There was no deviation from the original sampling design.

Mode of data collection

Face-to-face [f2f]

Research instrument

Schedule 1.0 of the 56th NSS round consists of the following blocks:

Block 0: Descriptive identification of sample household: This block is meant for recording descriptive identification particulars of a sample household.

Block 1: Identification of sample household

Block 2: Particulars of field operation: The identity of the Investigator, Assistant Superintendent and Superintendent associated, date of survey/inspection/scrutiny of schedules, despatch, etc., has been recorded in this block against the appropriate items in the relevant columns.

Block 3: Household characteristics:
Characteristics which are mainly intended to be used to classify the households for tabulation has been recorded in this block.

Block 4: Demographic and other particulars of household members: All members of the sample household will be listed in this block. Demographic particulars (viz., relation to head, sex, age, marital status and general education), working status, type of income received and number of meals taken will be recorded for each member using one line for one member. Block 5: Consumption of food, pan, tobacco and intoxicants. Information on an item has been recorded only if it is consumed. Block 5.1: Consumption of fuel and light. Information on an item has been recorded only if it is consumed. Block 6: Consumption of clothing, bedding, etc. Value of an item has been recorded only if it is brought into first-use during the reference period. Block 7: Consumption of footwear. Value of an item has been recorded only if it is brought into first-use during the reference period. Block 8.1: Expenditure on education & medical (institutional) goods and services. Expenditure has been recorded if it is incurred on any item during the reference period. Block 8.2: Expenditure on miscellaneous goods and services including medical (non-institutional), rents and taxes. Expenditure has been recorded if it is incurred on any item during the reference period. Block 9: Expenditure for purchase and construction (including repair and maintenance) of durable goods for domestic use. Expenditure has been recorded if it is incurred on any item during the reference period. Block 10: Perception of household regarding sufficiency of food Block 11: Summary of consumer expenditure: This block is meant to derive the value of household per capita consumption expenditure for a period of 30 days. Most entries in this block are transfer entries from blocks 5 to 9.

The 2020-2021 School Learning Modalities dataset provides weekly estimates of school learning modality (including in-person, remote, or hybrid learning) for U.S. K-12 public and independent charter school districts for the 2020-2021 school year, from August 2020 – June 2021. These data were modeled using multiple sources of input data (see below) to infer the most likely learning modality of a school district for a given week. These data should be considered district-level estimates and may not always reflect true learning modality, particularly for districts in which data are unavailable. If a district reports multiple modality types within the same week, the modality offered for the majority of those days is reflected in the weekly estimate. All school district metadata are sourced from the National Center for Educational Statistics (NCES) for 2020-2021. School learning modality types are defined as follows: In-Person: All schools within the district offer face-to-face instruction 5 days per week to all students at all available grade levels. Remote: Schools within the district do not offer face-to-face instruction; all learning is conducted online/remotely to all students at all available grade levels. Hybrid: Schools within the district offer a combination of in-person and remote learning; face-to-face instruction is offered less than 5 days per week, or only to a subset of students. Data Information School learning modality data provided here are model estimates using combined input data and are not guaranteed to be 100% accurate. This learning modality dataset was generated by combining data from four different sources: Burbio [1], MCH Strategic Data [2], the AEI/Return to Learn Tracker [3], and state dashboards [4-20]. These data were combined using a Hidden Markov model which infers the sequence of learning modalities (In-Person, Hybrid, or Remote) for each district that is most likely to produce the modalities reported by these sources. This model was trained using data from the 2020-2021 school year. Metadata describing the _location, number of schools and number of students in each district comes from NCES [21]. You can read more about the model in the CDC MMWR: COVID-19–Related School Closures and Learning Modality Changes — United States, August 1–September 17, 2021. The metrics listed for each school learning modality reflect totals by district and the number of enrolled students per district for which data are available. School districts represented here exclude private schools and include the following NCES subtypes: Public school district that is NOT a component of a supervisory union Public school district that is a component of a supervisory union Independent charter district “BI” in the state column refers to school districts funded by the Bureau of Indian Education. Technical Notes Data from September 1, 2020 to June 25, 2021 correspond to the 2020-2021 school year. During this timeframe, all four sources of data were available. Inferred modalities with a probability below 0.75 were deemed inconclusive and were omitted. Data for the month of July may show “In Person” status although most school districts are effectively closed during this time for summer break. Users may wish to exclude July data from use for this reason where applicable. Sources K-12 School Opening Tracker. Burbio 2021; https

In the academic year 2023/24, there were 331,602 international students from India studying in the United States. International students The majority of international students studying in the United States are originally from India and China, totaling 331,602 students and 277,398 students respectively in the 2023/24 school year. In 2022/23, there were 467,027 international graduate students , which accounted for over one third of the international students in the country. Typically, engineering and math & computer science programs were among the most common fields of study for these students. The United States is home to many world-renowned schools, most notably, the Ivy League Colleges which provide education that is sought after by both foreign and local students. International students and college Foreign students in the United States pay some of the highest fees in the United States, with an average of 24,914 U.S. dollars. American students attending a college in New England paid an average of 14,900 U.S. dollars for tuition alone and there were about 79,751 international students in Massachusetts . Among high-income families, U.S. students paid an average of 34,700 U.S. dollars for college, whereas the average for all U.S. families reached only 28,026 U.S. dollars. Typically, 40 percent of families paid for college tuition through parent income and savings, while 29 percent relied on grants and scholarships.

In 2022, Canada had the highest share of adults with a university degree, at over 60 percent of those between the ages of 25 and 64. India had the smallest share of people with a university degree, at 13 percent of the adult population. University around the world Deciding which university to attend can be a difficult decision for some and in today’s world, people are not left wanting for choice. There are thousands of universities around the world, with the highest number found in India and Indonesia. When picking which school to attend, some look to university rankings, where Harvard University in the United States consistently comes in on top. Moving on up One of the major perks of attending university is that it enables people to move up in the world. Getting a good education is generally seen as a giant step along the path to success and opens up doors for future employment. Future earnings potential can be determined by which university one attends, whether by the prestige of the university or the connections that have been made there. For instance, graduates from the Stanford Graduate School of Business can expect to earn around 250,000 U.S. dollars annually.

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.