Naresh Kumar

National Institute of Science Technology and development Studies

New Delhi, India e-mail: nareshqumar@yahoo.com

Shailaja Rego

NMIMS University, Mumbai, India e-mail: shailarego@gmail.com

Comparative enrolment growth at different levels of education and dissemination of ICT: A case of BRIC countries

Fast growth of Information and Communication Technology (ICT) has facilitated access to information as well as communication across the world. However, penetration of ICT is not homogeneous in the developed and developing countries consequently there is an increase in the information gap between these regions. There may be several reasons for asymmetrical dissemination like GDP, education etc. but education plays an important role in the penetration of a new product or technology. Therefore, in this paper an attempt is made to present a comparative growth of enrollment at different levels of education and linkages between levels of educational attainment and diffusion of ICT indicators. The paper is divided into two sections (i) to analyse enrollment trends at primary, secondary and tertiary level and (ii) to investigate relationship between levels of educational attainment and diffusion of ICT indicators; Internet user, Internet user and personal computers per 100 populations in Brazil, China, India which are the constituent of BRIC economies and USA.

Results show that enrollment at primary level in Brazil, China and USA is expected to decline whereas in the case of India it may increase. Likewise enrollment at secondary and tertiary level in China, India and USA may increase in future. Analysis also indicates positive correlation between enrollment at primary level and penetration of Internet users personal computers in India whereas in the case of tertiary level it shows weak correlation. In the case of tertiary level except India there is a strong correlation between penetration of Internet and personal computers. Further, it may be noticed that in the case of Brazil secondary education shows negative correlation in all cases.

Keywords: education, Information and Communication Technology (ICT), ICT indicators, BRIC countries.


Education plays a critical role in the process of economic development and new product growth and dissemination. Therefore, better understating of educational growth is required which can be achieved by increasing enrollment at different levels of education. Due to several socio-economic factors enrollment varies at different levels of education across the countries. Generally education is divided into three levels (i) primary level (ii) secondary level (iii) higher/tertiary level. Primary education is given more importance across the world as it establishes and builds basic skills such as literacy, mathematics, logic, and analysis that provide essential skills to children for constant learning. Recently for many countries higher education has become more important. So, there is a need to expand primary level education to strengthen secondary and tertiary education. However, over the last few years most of the countries are affected by financial and economic decisions made by their governments. This in turn affects growth of enrollment at different levels of education. Despite that enrollment has grown at an unprecedented pace. Consequently, over the year stock of human capital is increasing which is a direct measure of expansion of education. This has led several applications of ICT in education sector by promoting the multiple uses of Internet and computers.

ICT is defined1 as new information-processing and information-transmitting technologies that include computer-related commodities and technologies such as broadcasting and wireless mobile telecommunications etc. Personal computer (PC) that connects Internet has become a vital tool for communication during the past few decades since its increase among the masses. It is observed that penetration of ICT is faster in developed nations rather than developing nations. So, the penetration of ICT can be linked to various socio-economic factors such as education, income and promotion of basic telecommunications infrastructure and market. Therefore, the objective of the paper is to analyse growth trends of enrollment at different levels of education and the relationship between level of education and penetration of ICT; focuses on Internet and personal computers (PCs). The paper builds on empirical data pertaining to the enrollment at primary, secondary and tertiary level and ICT in Brazil, China, India and USA.

Literature review

The growth of a new product or technology depends upon several attributes and is asymmetrical across the world. Several empirical methods have been applied to analyze the relationship between ICT penetration and its various determinants but the main problem has been the choice of dependent variable. Attainment of education levels may be one of the important factors that affect the dissemination of ICT through various means. However, several other factors also affect diffusion of a technology for example GDP, culture and openness of a society. Nelson and Phelps2 (1966) explained that rate of technology penetration depends upon educational attainment. In their view education affects the process of technology dissemination by speeding up the rate at which new inventions are adopted. They were also concerned with the level of tertiary and specialized schooling. Contrary, Lucas3 (1988) advocated the improvement of basic skills, such as literacy and primary education. Similar other studies also illustrate a positive correlation between levels of educational attainment and penetration of computer and Internet4, 5. They argued that in developing

countries education has a significant impact on Internet access. Robinson et al6 found positive correlation between education and Internet penetration. Chinn and Fairlie7 (2004) & Guillen and Suarez8 (2005) also analysed the effect of education on diffusion of computers, Internet and digital divide. Recently, Wunnava and Leiter9 (2007), also argues that education has a positive effect on Internet diffusion.

Many other related studies have focused on education and the spread of Internet use, Internet hosts per 1,000 inhabitants. Crenshaw and Robison10 (2006), examine certain determinants including mass education, as drivers of Internet diffusion. They found that the most significant explanatory variables are development level, political freedom, and education. Moreover, Kiiski and Pohjolab11 (2002), analyse data from 60 countries over the years 1995-2000, and concluded that GDP per capita and Internet access cost are important factors in OECD countries, but education is not. However, in developing countries education becomes significant factor to adopt ICT. Of late, Dewan, Ganley and. Kraemer12 (2005), also advocated that education has a positive impact on IT penetration. Quibria et al13 (2003) have found that Internet use and tertiary education show significant statistical association.

Thus, there is a basic reason for assuming an association between levels of educational attainment and Internet and computers diffusion. Therefore, in this article an attempt is made to analyse cross-country growth of enrollment and association between the levels of education and ICT indicators.

Methodology and Data Analysis

The theoretical structure and analysis of the paper is based on the previous studies and literature. It is assumed that personal computers (PCs) and Internet usage are affected by level of educational attainment in a given society. Therefore, education is included as an independent variable and PCs and Internet penetration as dependent variables in the empirical model presented below. Hypothetically countries with higher educational levels and literacy rate are more likely to have higher penetration rate of PCs and Internet. The first reason is that the World Wide Web and email are completely text based which needs education at least primary level in order to be able to use the Internet. Secondly, academic institutions and universities play an important role in adopting new technology based on computers and Internet. Moreover, other important aspects of education like research and on line access of textbooks and reading material depend on the use of computers, which help in penetrating Internet. Therefore, it can be presumed that education promotes the adoption of the computers and Internet along with other factors such as GDP, high telecom infrastructure, urban population and openness of the society. Taking education, as independent variable is advantageous as data pertaining to enrollment is available easily.

For analyzing growth of enrollment at different levels in Brazil, China, India and USA data for the period 1999-2005 is used which is listed in Tables 1a-1c. Similarly data for personal computers per 100 population and Internet per 100 populations and Internet users is used for different years as given in Tables 2a-2c.

Table 1a: Total no. of enrolment in primary level (public & private)

Year India China Brazil USA

1999 110,985,877 138,556,000 20,939,076 24,937,931

2000 113,612,541 134,321,000 20,211,506 24,973,176

2001 113,826,978 130,132,548 19,727,684 25,297,600

2002 115,194,579 125,756,891 19,380,387 24,855,480

2003 125,568,597 121,662,360 18,919,122 24,848,518

2004 136,193,772 117,380,000 18,979,209 24,559,494

2005 113,787,993 113,145,000 18,661,105 24,454,602

2006 139,169,873 108,925,227 - 24,319,033

2007 140,357,454 107,394,752 17,996,083 24,492,041

2008 145,454,297 105,950,505 17,812,436 24,676,547

Source: http://stats.uis.unesco.org/unesco/TableViewer/tableView.aspx

Table 1b: Total no. of enrolment in Secondary level (public & private)

Year India China Brazil USA

1999 67,089,892 77,436,268 23462345 22,444,832

2000 71,030,515 77,436,268 25094296 22,593,562

2001 72,392,727 86,516,712 25126886 23,087,042

2002 76,215,685 90,722,795 26317983 23,196,310

2003 81,050,129 95,624,760 24140227 23,854,458

2004 84,569,081 100,446,000 24437536 24,185,786

2005 88,719,464 105,413,000 24,109,589 24,431,934

2006 90,779,920 - - 24,552,317

2007 95,306,729 - 22426612 24,731,027

2008 100,954,563 - 22,516,085 24,692,888

Source: http://stats.uis.unesco.org/unesco/TableViewer/tableView.aspx * Indicates computed

Table 1c: Total no. of enrolment in Tertiary level (public & private)

Year India China Brazil USA

1999 9,171,986 6,365,625 2,456,961 13,769,362

2000 9,404,460 7,364,111 2,781,328 13,202,880

2001 9,834,046 9,398,581 3,125,745 13,595,580

2002 10,576,653 12,143,723 3,582,105 15,927,987

2003 11,295,041 15,186,217 3,994,422 16,611,711

2004 10,009,137 18,090,814 4,275,027 16,900,471

2005 11,777,296 20,601,219 4,572,297 17,272,044

2006 12,852,684 23,360,535 - 17,487,474

2007 14,862,965 25,346,279 5,272,877 17,758,870

Source: http://stats.uis.unesco.org/unesco/TableViewer/tableView.aspx

Table 2a: Density of Internet users per 100 population

Year India China Brazil USA

1991 0.00 1.16

1992 0.00 0.01 1.72

1993 0.00 0.00 0.03 2.27

1994 0.00 0.00 0.04 4.86

1995 0.03 0.00 0.11 9.24

1996 0.05 0.01 0.45 16.42

1997 0.07 0.03 0.79 21.62

1998 0.14 0.17 1.48 30.09

1999 0.27 0.71 2.04 35.85

2000 0.53 1.78 2.87 43.08

2001 0.66 2.64 4.53 49.08

2002 1.54 4.60 9.15 58.79

2003 1.69 6.14 13.21 61.70

2004 1.98 7.21 19.07 64.76

2005 2.39 8.52 21.02 67.97

2006 2.81 10.52 28.18 68.93

2007 3.95 15.99 30.88 71.83

2008 4.38 22.28 37.52 74.00

Source: http://mdgs.un.org/unsd/mdg/SeriesDetail.aspx?srid=605

Table 2b: Total number of Internet users

Year India China Brazil USA

1990 2,000,000

1991 5000 3,000,000

1992 1000 20,000 4,500,000

1993 2000 2000 40,000 6,000,000

1994 10,000 14,000 60,000 13,000,000

1995 250,000 60,000 170,000 25,000,000

1996 450,000 160,000 740,000 45,000,000

1997 700,000 400,000 1 310,000 60,000,000

1998 1,400,000 2,100,000 2 500,000 84,587 000

1999 2,800,000 8,900,000 3,500,000 102,000,000

2000 5,500,000 22,500,000 5,000,000 124,000,000

2001 7,000,000 33,700,000 8,000,000 142,823,000

2002 16,580,000 59,100,000 16,388,758 172,834,267

2003 18,481,000 79,500,000 23,976,703 183,195,742

2004 22,000,000 94,000,000 35,069,526 194,158,959

2005 27,000,000 111,846,701 39,118,000 205,766,898

2006 32,200,000 138,981,770 53,020,000 210,720,370

2007 46,000,000 212,580,786 58,717,000 221,724,027

Source: http://mdgs.un.org/unsd/mdg/SeriesDetail.aspx?srid=608

Table 2c: Number of personal computers per 100 population

Year India China Brazil USA

1990 0.03 0.04 0.30 21.17

1991 0.04 0.07 0.43 22.88

1992 0.05 0.09 0.61 24.71

1993 0.06 0.12 0.83 26.58

1994 0.09 0.17 1.13 28.99

1995 0.13 0.23 1.67 31.89

1996 0.15 0.37 2.07 35.25

1997 0.20 0.61 2.52 39.27

1998 0.27 0.90 2.96 44.12

1999 0.32 1.23 3.55 49.56

2000 0.44 1.63 4.88 55.93

2001 0.57 1.96 6.11 61.17

2002 0.70 2.76 7.26 67.67

2003 0.86 3.89 8.62 72.45

2004 1.17 4.06 13.05 74.65

2005 1.50 4.84 16.12 76.10

2006 2.69 5.61 78.67

Source: http://mdgs.un.org/unsd/mdg/SeriesDetail.aspx?srid=607

To analyse enrollment trends linear growth model; y=a+bx is applied. Logistic function is also used to study growth pattern but in this case empirical data exhibits linear trend. For establishing correlation between levels of educational attainment and diffusion of Internet and computers Pearson correlation and t-test technique are applied, which are mathematically represented below:



Similarly, t-test is used to test the hypothesis and t-values are calculated by using following formula:


where symbols have their usual meanings. For parameter estimation and regression analysis SYSTAT14 package is used. Parameter estimates and projections of enrollment at different levels are listed in Tables 3a-3c. Similarly, the correlation and t-test analysis matrix is given in Tables 4a-4c.

Table 3a: Projections for enrollment at primary level

Year India China Brazil USA

2008 1.58E+08 1.03E+08 1.72E+07 2.43E+07

2009 1.65E+08 9.92E+07 1.69E+07 2.42E+07

2010 1.72E+08 9.59E+07 1.65E+07 2.41E+07

2011 1.80E+08 9.27E+07 1.62E+07 2.40E+07

2012 1.88E+08 8.96E+07 1.59E+07 2.39E+07

2013 1.96E+08 8.66E+07 1.55E+07 2.38E+07

2014 2.05E+08 8.38E+07 1.52E+07 2.37E+07

2015 2.14E+08 8.10E+07 1.49E+07 2.36E+07

a 18.447 18.783 16.867 17.044

b 0.043 -0.034 -0.020 -0.004

MSE 1213.443 1217.177 986.043 1014.857

Table 3b: Projections for enrollment at secondary level

Year India China Brazil USA

2008 1.01E+08 1.25E+08 2.51E+07 2.56E+07

2009 1.05E+08 1.32E+08 2.50E+07 2.60E+07

2010 1.10E+08 1.40E+08 2.49E+07 2.64E+07

2011 1.15E+08 1.48E+08 2.48E+07 2.68E+07

2012 1.20E+08 1.56E+08 2.47E+07 2.72E+07

2013 1.26E+08 1.65E+08 2.46E+07 2.76E+07

2014 1.32E+08 1.74E+08 2.45E+07 2.80E+07

2015 1.38E+08 1.84E+08 2.44E+07 2.85E+07

a 17.997 18.094 17.073 16.907

b 0.045 0.055 -0.004 0.015

MSE 1153.876 1174.054 1018.510 1007.668

Table 3c: Projections for enrollment at tertiary level

Year India China Brazil USA

2008 1.27E+07 3.66E+07 6671391.326 2.05E+07

2009 1.32E+07 4.45E+07 7437321.148 2.15E+07

2010 1.37E+07 5.40E+07 8291185.924 2.26E+07

2011 1.42E+07 6.55E+07 9243081.301 2.37E+07

2012 1.47E+07 7.95E+07 1.03E+07 2.49E+07

2013 1.53E+07 9.65E+07 1.15E+07 2.61E+07

2014 1.58E+07 1.17E+08 1.28E+07 2.75E+07

2015 1.64E+07 1.42E+08 1.43E+07 2.88E+07

a 15.999 15.480 14.622 16.343

b 0.036 0.194 0.109 0.049

MSE 912.162 925.295 794.109 957.465

Table 4a: Values for r and t for educational attainment V/S Internet users

Education Level India China Brazil USA

r t R t r t r t

Primary 0.950 6.09** -0.994 -17.46** -0.925 -4.88** -0.544 -1.3

Secondary 0.950 6.08** 0.979 9.51** -0.280 -0.58 0.945 5.76**

Tertiary 0.490 1.12 0.998 29.44** 0.992 16.02** 0.854 3.29*

Note: 1. t — table; 5 % ,4 = 2.776, 1 %, 4 =4.604 2. ** — Indicates significant at both 5 % and 1 % level of significance, 3. * — Indicates significant at only 5 % level of significance

Table 4b: Values for r and t for educational attainment V/S Internet user /100 population

Education Level India China Brazil USA

r t r t r t r t

Primary 0.955 5.95** -0.996 -17.88** -0.943 -5.02** -0.693 -1.24

Secondary 0.920 6.19** 0.986 9 49** -0.293 -0.56 0.957 5.31**

Tertiary 0.766 1.15 0.998 28.23** 0.981 16.94** 0.883 3.13*

Note: 1. t-table; 5 % , 4 = 2.776, 1 %, 4 = 4.604 2. ** — Indicates significant at both 5 % and 1 % level of significance, 3. * — Indicates significant at only 5 % level of significance

Table 4c: Values for r and t for educational attainment V/S PCs per 100 population

Education Level India China Brazil USA

r t R t r t r t

Primary 0.958 6.66** -0.976 -8.93** -0.943 -5.83** -0.559 -1.35

Secondary 0.985 11.58** 0.964 7.25** -0.216 -0.44 0.970 7 91**

Tertiary 0.591 1.46 0.994 17.88** 0.993 16.58** 0.891 3.93*

Note: 1. t-table; 5 % , 4 = 2.776, 1 %, 4 = 4.604 2. ** — Indicates significant at both 5 % and 1 % level of significance, 3. * — Indicates significant at only 5 % level of significance

Results and Discussion

Brazil, India and China are the emerging economies, which constitute a larger share of the world population. Structure of enrollment in these countries is a crucial indicator of expansion of education and these countries may be good source of human capital stock in the future. Therefore, the comparative estimations of enrollment with USA at different levels will be a significant to cope with future expansion and challenges in education sectors. Analysis shows that enrollment at primary level is showing declining trends except India. The major reason behind this is likely the decline in the population of the age group 5-10 years in countries like China and Brazil15 while in the case of India it is increasing. However, enrollment at secondary is expected to increase except in the case of Brazil. It is noticeable that enrollment at tertiary level in India, China, Brazil and USA is increasing

equally. It may be concluded that the growth in enrollment provides supports the growth of higher and tertiary education. However, growth of enrollment at all levels is not identical among Brazil, India, China and USA. For example enrollment at primary reflects increasing trends due to government policy of free education to all children upto primary and upper primary levels under Serva Shiksha Abhiyan (SSA). Though Brazil, China, India and USA are promoting tertiary education to have sufficient higher qualified human capital for encouraging research. Growth in enrollment at tertiary level in these countries indicates.

Analysis indicates strong positive correlation between all levels of educational attainment and Internet users and PCs users. However, at tertiary level there is a weak correlation between enrollment and Internet users. Conversely, China shows significant negative correlation between Primary enrollment and Internet users but shows significant positive correlation between Secondary enrollment and tertiary level for Internet users and PCs as well. The same results follows in the case of USA. However, In the case of Brazil There is a negative correlation between primary and secondary level enrollment and Internet users and PCs whereas at tertiary level the association is positive. Evidently negative correlation is due to decline of enrollment at primary level at in the case of China and USA and at primary and secondary level in the case of Brazil. This supports the view that education is the most significant variables for Internet penetration1216. Thus level of educational attainment influences the adoption and penetration ICT indicators such as Internet users and PCs. Though degree of significance may vary among the countries. This also confirms past findings that education enrollment have a significant impact on penetration rate and usage of computers and Internet.


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Ракитов Анатолий Иванович

главный научный сотрудник ИНИОН РАН, зав. лабораторией современной стратегии образования МГПУ, доктор философских наук, профессор, заслуженный деятель науки РФ, Москва, Россия e-mail: rakit1@yandex.ru

Образовательные стратегии

Главная задача современной России — модернизация экономики, социальной сферы, государственного управления. Для ее решения необходимо радикальное улучшение системы среднего специального и, особенно, высшего образования. На протяжении XX века стратегия образования в России существовала. В настоящее время ее предстоит создать. Современная наука стала синтагматической, то есть задачно-ориентированной. Это же должно произойти и с системой образования. За последнюю четверть века в высшем образовании России выросла доля социально-гуманитарных дисциплин (85—90 %). Следует же поднять удельный вес математики, естествознания и инженерных дисциплин. Россия — суперэтатистское общество, поэтому научно фундированную стратегию образования должно вырабатывать государство с учетом результатов социологии науки, образования и науковедения в целом.

Ключевые слова: стратегия, стратегия образования, синтагма и парадигма, кадровый потенциал, модернизация, организация науки и высшего образования, инженерное образование.

Несколько лет назад я был свидетелем разговора солидного сотрудника казенной палаты и совсем молодой девушки, работавшей в какой-то бюджетной организации, принесшей в казенную палату финансовый отчет и выслушивающей замечания и возражения чиновника. «Вот эти таблички скучные, — спокойно вещал чиновник, — не годятся: они не по форме составлены, не по инструкции. И вот здесь — все циферки надо согласно инструкции перестроить и переставить, так что, надеюсь, Вы все-таки через пару дней этот документ приведете в порядок». Несколько испуганная девушка возразила: «Но я сделала отчет так, как нас учили в университете на финансовом факультете». «Неправильно вас в университете учили», — провозгласил чиновник. — «Делать нужно, как мы требуем, а не так, как вас учили».