The relation between the number of countries-Rich Files on the web and countries-economic

development

Hamzehali Nourmohammad1

Abdalsamad Keramatfar2

Economic Development, GDP, Rich Files, Science Indicators, Webometrics

Abstract

Price demonstrated the correlation between countries‟ scientific productivity and their GDP and presented

the relation between scientific dynamism and economic development to show the important of scientific

researches and to verify the paper counting approach and, Nourmohammadi and Keramatfar

demonstrated that there exists a correlation between countries‟ scientific production rank and their Rich

Files rank on the web and concluded that scientific evaluation of countries could be done based on the

number of their Rich Files on the web. In this paper we examine the correlation between countries‟ Rich

Files and their GDP. The results show that, there is a higher correlation between them than previous

correlation. The high degree of correlation between rank base on Rich Files and rank base on economic

development signifies the significance of web as the context of research and free access to information

resources so policy makers in every country should be informed of it.

Introduction

All the activities related to measuring science started in early 20th century with the works of people like

Holm(Braun & others,1985), following Price‟s attempts to display the relation between scientific products

and countries‟ scientific development, using citation indexes for examining countries‟ scientific

development expanded rapidly. In addition, late in 1960s, Price demonstrated the correlation between

countries‟ scientific productivity and their GDP and presented the relation between scientific dynamism

and economic development (Noroozi Chakoli, 2012). Within the past years this correlation has been

1 Shahed University, Persian Gulf Highway, Tehran, Iran

2 Scientific Information Database SID, Tehran, Iran

confirmed by many researchers like, Vinkler (2008) and Lee & others (2011), which both indicates the

significance of evaluation of researches‟ findings and verifies its method which is using excessive c itation

indexes. On the other hand, since the mid-1990s has emerged a new research field, webometrics-

“webometrics” itself was coined in 1997 (Almind and Ingwersen, 1997), investigating the nature and

properties of the Web drawing on modern informetric methodologies (Björneborn & Ingwersen, 2001).

The value of webometrics quickly became established through the Web Impact Factor, the key metric for

measuring and analyzing website hyperlinks (Thelwall, 2012). Also the need for timely and relevant web-

based S&T indicators has become more urgent (Scharnhorst & Wouters, 2006). Nourmohammadi and

Keramatfar (2013) demonstrated that there exists a correlation between countries scientific production

rank and their Rich Files rank on the web and concluded that scientific evaluation of countries could be

done based on the number of their Rich Files on the web. According to what was mentioned above, the

main problem this study seeks to address is this; is there any relation between countries‟ Rich Files on

the web and their economic development?

Therefore, the questions this study addresses are as follows:

- What is the number of scientific production of world‟s different countries?

- What is the number of different countries‟ Rich Files on the web?

- What is the amount of GDP indicator of world‟s different countries?

- What is the amount of correlation between countries‟ scientific production rank and their GDP rank in

comparison with the correlation between countries‟ Rich Files rank and their GDP rank?

- How is the linear relation between the number of countries‟ Rich Files and their GDP?

Methodology

This study is descriptive -based and due to its use of Scientometrics methods lies within scope of

Webometrics Researches. Countries‟ scientific production data was extracted from SCImago and

countries‟ GDP data was extracted from World Bank. Countries‟ Rich Files data was extracted from Bing

search engine in the following way; in order to search, the name of a given country was chosen in the

Advance Search section then using the formulae: filetype:pdf, filetype:doc, filetype:ppt, the number of

Rich Files was determined. Correlation Test was carried out using SPSS19, and Regression Test was

carried out using Excel 2007. Research Society included all the world countries for which there is the

possibility of specific search in Bing search engine. Data was extracted in the second half of August 2013.

Theoretical framework

Nowadays scientific production is measured based on excessive citation indexes that present

bibliographical information of different kinds of scientific productions, because citation index makes

identifying and recovering valid information about subject areas possible and provides citation information

that relates papers and indicates the degree of validity of papers to a great extent (Noroozi Chakoli,

2013). Using the number of countries‟ scientific productions in order to evaluate their scientific

development by experts is done by the two large databases ESA and SCImago, the former using Web of

Knowledge data and the latter using Scopus data.

Along with developments in bibliometrics and emergence of Webometrics some attempts were made to

use the web for scientific evaluation. Webometrics is the quantitative analysis of web phenomenon using

informetric methods (Noroozi Chakoli, 2012). A useful database in this field is Webometrics3 that has

been evaluating universities across the world according to their website since2007. One of the indicators

of this database is the number of universities‟ Rich Files on the web. Rich Files include PDF, DOC, and

PPT; these files have been chosen because the majority of scientific productions are published in one of

these formats. Nourmohammadi & Keramatfar (2013) by demonstrating the correlation between the

number of countries‟ Rich Files on the web and the number of their scientific production proposed that

Rich Files can be used for evaluating countries‟ scientific development. In this study, the authors examine

Nourmohammadi & Keramatfar‟s proposal and by examining its correlation with countries‟ economic

development compare this method with excessive citation indexes method.

Findings:

3 Webometrics.info

The findings will be presented in four sections according to the questions put forward in the introduction.

1. What is the number of scientific production of world’s different countries?

Table No1 shows the number of world countries‟ scientific productions in SCImago. USA, UK, and

Japan are ranked first, second, and third.

Table No1. The number of countries’ document in SCImago

Country Document

s

Country Documen

ts

Country Documents

United

States

6,149,455 Portugal 117,469 Philippines 11,326

United

Kingdom

1,711,878 New

Zealand

114,495 Puerto Rico 9,862

Japan 1,604,017 South

Africa

107,976 Iceland 9,285

Germany 1,581,429 Argentina 105,216 Latvia 8,396

France 1,141,005 Hungary 100,137 Armenia 8,054

Canada 885,197 Ukraine 98,083 Peru 7,516

Italy 851,692 Ireland 91,125 Oman 6,875

Spain 665,977 Romania 76,361 Georgia 6,381

India 634,472 Egypt 75,610 Azerbaijan 6,135

Australia 592,533 Malaysia 75,530 Costa Rica 5,711

Russian

Federation

527,442 Thailand 69,637 Luxembour

g

5,121

South Korea 497,681 Chile 58,768 Iraq 4,420

Netherlands 487,784 Slovakia 49,863 Macedonia 4,401

Brazil 391,589 Croatia 49,462 Qatar 4,398

Taiwan 351,610 Pakistan 47,443 Ecuador 3,887

Switzerland 350,253 Saudi

Arabia

46,167 Bosnia and

Herzegovin

a

3,524

Sweden 337,135 Slovenia 44,142 Syrian Arab

Republic

3,379

Poland 304,003 Tunisia 32,250 Panama 3,043

Turkey 267,902 Colombia 28,817 Bahrain 2,817

Belgium 265,913 Morocco 23,446 Libyan

Arab

Jamahiriya

2,304

Israel 204,262 Lithuania 21,098 Bolivia 2,298

Austria 188,440 Algeria 21,059 Malta 2,029

Denmark 183,880 Serbia 21,011 Yemen 1,395

Finland 170,476 Jordan 17,126 Guatemala 1,296

Greece 160,760 Estonia 16,573 Albania 1,229

Iran 159,046 Indonesia 16,139 Nicaragua 818

Mexico 144,997 United

Arab

Emirates

15,698 Paraguay 776

Hong Kong 144,935 Kenya 14,765 El Salvador 768

Czech

Republic

142,090 Viet Nam 13,172 Dominican

Republic

606

Norway 141,143 Kuwait 12,254 Honduras 595

Singapore 126,881 Lebanon 11,672

2. What is the number of different countries’ Rich Files on the web?

Table No2 shows the number of Rich Files for different world countries, with USA, Japan, and Italy

having the highest number of Rich Files on the web respectively.

Table No2. The number of countries‟ Rich Files on the web

Country PDF DOC PPT SUM

Albania 16100 6720 71 22891

Algeria 46200 5130 1220 52550

Argentina 1190000 158000 25400 1373400

Armenia 13300 3190 1530 18020

Australia 2960000 171000 18800 3149800

Austria 1090000 42800 8560 1141360

Azerbaijan 12000 4490 61 16551

Bahrain 7820 101 44 7965

Belgium 1280000 98900 16600 1395500

Bolivia 64200 7610 1350 73160

Bosnia and

Herzegovina

68300 11000 1480 80780

Brazil 4800000 399000 100000 5299000

Canada 4370000 202000 67900 4639900

Chile 639000 67400 22500 728900

Colombia 872000 93800 14100 979900

Costa Rica 127000 24600 14100 165700

Croatia 377000 54200 13700 444900

Czech Republic 708000 101000 22700 831700

Denmark 1070000 74200 11500 1155700

Dominican Republic 42900 2490 734 46124

Ecuador 169000 17200 3970 190170

Egypt 49400 11400 4550 65350

El Salvador 51700 2810 1040 55550

Estonia 161000 23500 8010 192510

Finland 883000 46300 11900 941200

France 5930000 351000 88300 6369300

Georgia 25500 4530 641 30671

Germany 8320000 264000 121000 8705000

Greece 553000 89900 11500 654400

Guatemala 69400 3610 1090 74100

Honduras 24800 1000 90 25890

Hong Kong S.A.R. 704000 60000 21000 785000

Hungary 672000 137000 24500 833500

Iceland 54200 4270 2230 60700

India 1500000 105000 2230 1607230

Indonesia 669000 83400 27600 780000

Iran 536000 93400 19800 649200

Iraq 14800 5460 66 20326

Ireland 528000 47700 8940 584640

Israel 387000 215000 35800 637800

Italy 9660000 978000 120000 10758000

Japan 13100000 444000 39400 13583400

Jordan 24700 9700 4050 38450

Kenya 27700 2490 792 30982

Kuwait 13100 1890 63 15053

Latvia 114000 51700 3510 169210

Lebanon 23300 3070 1150 27520

Libya 5230 81 31 5342

Lithuania 210000 63600 7910 281510

Luxembourg 78800 3640 766 83206

Macedonia 33100 4660 600 38360

Malaysia 378000 28600 6720 413320

Malta 24800 1690 1860 28350

Mexico 2770000 308000 40400 3118400

Morocco 64600 6720 1570 72890

Netherlands 3570000 252000 36800 3858800

New Zealand 592000 48100 8730 648830

Nicaragua 24800 2060 911 27771

Norway 682000 57600 15000 754600

Oman 7630 2390 47 10067

Pakistan 101000 11400 2100 114500

Panama 60100 4380 1230 65710

Paraguay 23900 2650 1570 28120

Peru 633000 80500 11700 725200

Philippines 77200 4990 1510 83700

Poland 3400000 715000 45800 4160800

Portugal 934000 33900 10200 978100

Puerto Rico 84000 8580 4970 97550

Qatar 12500 1490 61 14051

Romania 737000 152000 18500 907500

Russia 2140000 2150000 147000 4437000

Saudi Arabia 88400 38400 20800 147600

Serbia 187000 20000 6300 213300

Singapore 352000 19000 3960 374960

Slovakia 397000 63800 10400 471200

Slovenia 284000 45200 20800 350000

South Africa 852000 71700 11800 935500

South Korea 686000 30900 67700 784600

Spain 6310000 334000 80500 6724500

Sweden 2540000 148000 21200 2709200

Switzerland 2420000 88300 22100 2530400

Syria 9330 980 45 10355

Taiwan 1320000 603000 127000 2050000

Thailand 1220000 310000 57000 1587000

Tunisia 36100 2350 891 39341

Turkey 1030000 229000 44900 1303900

United Arab Emirates 47700 4830 1520 54050

Ukraine 243000 128000 7490 378490

United Kingdom 6730000 626000 108000 7464000

United States 47500000 3870000 1380000 52750000

Vietnam 141000 135000 4030 280030

Yemen 710 43 3 756

3. What is the amount of GDP indicator of world’s different countries?

Table No3 shows countries‟ GDP with USA, Japan, and Germany having the highest GDP

respectively.

Table No.3. Countries‟ GDP

Country GDP Country GDP

Albania 13119013351.4499 Lebanon 42945273631.8408

Algeria 207955103846.43 Libya -

Argentina 474865096195.534 Lithuania 42245532390.1713

Armenia 9910387657.35811 Luxembourg 57117125224.9936

Australia 1520608083022.1 Macedonia 9663203711.45536

Austria 399649131196.966 Malaysia 303526203366.211

Azerbaijan 67197738734.7695 Malta 8721923076.92308

Bahrain - Mexico 1177271329643.86

Belgium 483709179737.722 Morocco 96729450169.498

Bolivia 27035110167.0902 Netherlands 772226793520.185

Bosnia and

Herzegovina

17047582419.997 New Zealand -

Brazil 2252664120777.39 Nicaragua 10507356837.651

Canada 1821424139311.45 Norway 499667211001.289

Chile 268313656098.796 Oman -

Colombia 369812739540.023 Pakistan 231181921489.54

Costa Rica 45127292711.0687 Panama 36252500000

Croatia 56441607483.0696 Paraguay 25502060502.1181

Czech Republic 195656544502.618 Peru 197110985681.958

Denmark 314242037116.962 Philippines 250265341493.171

Dominican

Republic

58951239185.7506 Poland 489795486644.151

Ecuador 84532444000 Portugal 212454101311.391

Egypt 257285845358.245 Puerto Rico 101495811266

El Salvador 23786800000 Qatar -

Estonia 21854197100.7971 Romania 169395940257.194

Finland 250024427873.489 Russia 2014774938341.85

France 2612878387760.35 Saudi Arabia -

Georgia 15829300978.6172 Serbia 37488935009.7878

Germany 3399588583183.34 Singapore 274701299733.694

Greece 249098684277.449 Slovakia 91619230769.2308

Guatemala 50806430481.5925 Slovenia 45469230769.5781

Honduras 17967497441.1464 South Africa 384312674445.534

Hong Kong S.A.R. 263259372904.956 South Korea 1129598273324.48

Hungary 125507525410.477 Spain 1349350732836.2

Iceland 13656532879.6765 Sweden 525742140221.402

India 1841717371769.71 Switzerland 632193558707.476

Indonesia 878043028442.369 Syria -

Iran - Taiwan -

Iraq 210279947255.575 Thailand 365564375701.58

Ireland 210330986079.969 Tunisia 45662043358.0705

Israel - Turkey 789257487307.029

Italy 2013263114238.88 Ukraine 176308825694.203

Japan 5959718262199.13 United Arab

Emirates

-

Jordan 31243324000 United

Kingdom

2435173775671.41

Kenya 37229405066.6773 United States 15684800000000

Kuwait - Vietnam 141669099289.418

Latvia 28373857404.0219 Yemen 35645823131.5726

4. What is the amount of correlation between countries’ scientific production rank and their

GDP rank in comparison with the correlation between countries’ Rich Files rank and their

GDP rank?

Tables No.4 and No.5 show the correlation between GDP and the two indicators of countries scientific

production rank and countries Rich Files rank.

Table No.4. correlation between countries‟ scientific

production rank and their GDP rank in comparison

GDP

DOC Correlation

Coefficient

.879**

Sig. (2-tailed) .000

N 80

Correlation is significant at the 0.01 level (2-tailed)

Table No.5. correlation between countries‟ Rich Files rank

and their GDP rank.

GDP

RICH Correlation

Coefficient

.897**

Sig. (2-tailed) .000

N 80

Correlation is significant at the 0.01 level (2-tailed)E

Conclusion and Discussion

Nowadays web and web databases are the first and the most important source for researchers to find

information and web richness of every country as its scientific backbone is of highest importance.

Moreover, free access to information resources is the context for expanding researches. Existence of

scientific resources could be used as a criterion for scientific evaluation (Nourmohammadi & Keramatfar,

2013). The present study sought to investigate the correlation between countries‟ Rich Files rank and

their economic development rank. The findings indicate that there is a high degree of correlation between

the rankings of these two variables. Compared with the correlation between countries‟ scientific

development Ranking and countries‟ economic development ranking (that also has been showed by

(King, 2004) and (Price, 1978) and (Kealey, 1996)), this correlation does have a higher amount that

means this variable has a greater correlation with economic development than science production

indicator. The high degree of correlation between this variable and economic development signifies the

significance of web as the context of research and free access to information resources. Moreover this

correlation demonstrates that this variable can be used along with other indicators to evaluate countries‟

scientific development. Another point worth noticing is the fact that having access to web, disregarding

the initial expenses, is free and evaluation according to this can be easily done, while having access to

databases like Web of Knowledge and Scopus involves expenditure; however, it should be taken into

account that due to the dynamic nature of web and its constant and rapid changes, Webometric results

have always been tentative. Other researches following this study can be concerned with the evaluation

of the nature of these files and their types –article, manual, handbook, book, etc.; meanwhile conducting

causality test between these two variables can result in helpful findings.

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