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Licence fees and GDP per capita: The case for open source in developing countries by Rishab Aiyer Ghosh.

Licence fees and GDP per capita: The case for open source in developing countries

There is a strong case for free software (also known as open source or libre software) being deployed widely in developing countries. This paper describes three reasons in particular: free software is a skills enabling platform; it is far cheaper; and it is more adaptable to local needs. The free software development community provides an environment of intensive interactive skills development at little explicit cost, which is particularly useful for local development of skills, especially in economically disadvantaged regions. Meanwhile, the controversy over total costs of ownership (TCO) of free vs. proprietary software is not applicable to developing countries and other regions with low labour costs, where the TCO advantage lies with free software, and the share of licence fees in TCO is much higher than in (richer) high labour cost countries. The note concludes with a table comparing license fees for proprietary software against GDP per capita for 176 countries.


Free software communities — A platform for skills development
Total cost of ownership and low labour costs



Free software communities — A platform for skills development

“Access [to ICTs] is not enough, it is the ability to create, to add value, that is important.” — Felipe Gonzalez, former Spanish Prime Minister, Speaking at Open Source World Conference in Málaga, Spain (18 February 2004).

What former Spanish Prime Minister Felipe Gonzalez referred to as the ability to create and add value is particularly important for developing countries and other economically disadvantaged communities. Access alone limits them to the role of passive consumers in the knowledge economy; the ability to create transforms them into active participants. And as we shall see, free software appears to provide a training environment that enables this ability to create; it increases the earning capacity of community participants without any explicit investment in training and is perhaps a novel form of technology transfer.

Open source, or free software as it was originally called, has become in recent years one of the most talked about phenomena in the ICT world. This is remarkable, not only for the usual reasons — that open source has been around for many years as a volunteer driven success story before being discovered by big business and now government — but also because it has largely developed quietly on its own without the headline coverage and glare of international attention that it now receives.

This in turn makes it more attractive to governments and policy makers. Yet what is the special value of open source software, and how can it be harnessed? The Free/Libre/Open Source Software (FLOSS) study in 2002 [1], a comprehensive study of developers and users, showed that the most important reason for developers to participate in open source communities was to learn new skills — “for free”. These skills are valuable, help developers get jobs and can help create and sustain small businesses. The skills referred to here are not those required to use free software, but those learnt from participation in free software communities. Such skills include programming (of course), but also skills rarely taught in formal computer science courses, such as copyright law and licenses (a major topic of discussion in many free software projects). Teamwork and team management are also learnt — after all, the team management is required to coordinate the smooth collaboration of 1,500–plus people who rarely see each other is more intensive and far subtler than what is required to coordinate smaller teams employed in a single software company.

Some findings from the FLOSS survey are appropriate here: 78 percent of developers join the free software community “to learn and develop new skills”; 67 percent continue their participation to “share knowledge and skills”. These learnt skills have economic value to developers — 30 percent participate in the free software community to “improve job opportunities”; 30 percent derive income directly from this participation and a further 18 percent derive indirect income — such as getting a job unrelated to free software, thanks to their previous or current participation in free software developer communities. Being a Linux kernel developer proves a certain level of skills in many ways far better than having a computer science degree from MIT, and employers benefit from such informally learnt skills. Thirty–six percent of organisations polled in the FLOSS User Survey “totally” or “somewhat” agree that employees can work on free software projects on employer time. These are not necessarily IT companies — 16 percent of low IT–intensity companies (e.g., retail, automobiles, tourism, construction) “totally” agreed with this point.

Informal apprenticeships — Employers benefit, but don’t pay the cost

FLOSS communities are like informal apprenticeships — but the apprentice/students and master/teachers contribute their own time “for free”, without any monetary compensation for the training process. There is certainly a social cost, but it is borne voluntarily by the participants themselves and not paid for directly by those who benefit (such as current or future employers, or society at large). Everyone can benefit equally from this training — any employer can hire someone informally “trained” through participation in the free software developer community. However, not everyone invests equally in it. As many “teachers” may have been formally trained at university or at work, which is explicitly paid for, explicit costs are being borne for some proportion of community participants who have been formally trained.

In the larger perspective, this training system where all parts of society benefit from the products of the system, but only some explicitly pay for it, represents a subsidy — or technology transfer — from those who pay for formal training to those who do not (or cannot). Within countries, this represents a technology transfer from big companies who often formally pay for training to small and medium–sized enterprises (SMEs), who can less afford formal training expenses. Globally, this represents a technology transfer from the usually richer economies who can afford formal training, to the usually poorer ones who cannot.

There are also sectoral benefits, especially within poorer economies. Poor countries may have formal computer training during computer science degree courses, but perhaps not in other subjects, such as biology. Anecdotal evidence (in the case of biology, from India) suggests that the use of free software platforms during formal training in non–computer subjects may encourage informal learning of computer skills by students, thereby increasing their understanding of their own course subject (by better being able to conduct biology experiments through more sophisticated computer analysis). FLOSS usage can thus provide students of other subjects to informally learn computer skills, programming skills and enhance their competence in their formal training.

But do we all want to program?

The simple answer is “of course not”. But how will we know, unless we can try? This question is usually understood within the framework of proprietary software, where creativity requires climbing a very high barrier of investment — time and money — in order to become, say, a professional C programmer. However, HTML is in fact a programming language, and involves inserting “tags” such as “<b<” in the middle of text (this “command” would make text bold). The World Wide Web took off in the mid 1990s thanks to the hundreds of thousands of people — artists, scientists, children, poets, but not necessarily computer programmers — who created their own individual Web pages. They could do this because the structure of the Web was open, so people could learn to write their own sites just by copying and changing other sites. In other words, lay persons with few computer “programming” skills could write their own Web pages — HTML programs — by looking at someone else’s Web page (HTML program!) by clicking “View Source” on their browser. It was truly open source, in that the HTML source code for every Web page is visible to everyone who sees the finished Web page.

“Programming” covers a very broad range of skills from HTML to C. In a free software environment, users are allowed entry to the world of producing at any degree with little investment in time or effort. Of course, your level of creativity and your ability to create depends on the degree of effort you invest. But there is no bar over which you have to leap in order to be able to produce at all. In a proprietary environment, you have to decide to be a programmer, then buy development software, then spend lots of time and effort — all of which is contains risk and forms an entry barrier. With free software, you can tinker. You don’t need to buy tools. And you can use them to the extent you choose. Learning skills in this environment, you risk losing only your time and effort.

However, since the barrier to entry is low (HTML is the best example!) you can control the degree of your investment — paddle at the shallow end of the pool or dive in deeper. In proprietary environments, the dividing line between user and developer is much sharper — the pool has only a deep end, you have to dive in or stay out altogether. Of course an environment doesn’t have to be entirely proprietary or free — many learnt HTML on a proprietary Windows machine. But the library of Web pages available in HTML itself was all “free software”. The extent to which skills were easy to develop with a low barrier to entry was limited by the extent to which the environment was open; the barrier–free ability to create stops soon after HTML. In a fully free, open source environment, there is no such barrier; you can start with HTML and end up changing the Linux kernel, the core of the computer’s operating system.

Building local ICT competencies

The FLOSS study showed that developers who provided “learning new skills” as their reason for joining the community often show “sharing skills” as an equally or more important reason for continuing their community participation. This is correlated with the duration of their participation in the community, and thus represents a shift from “apprentice” to “mentor” roles. In a reflection of the development process for individuals, countries that profit most from open source are those that contribute back to the community and knowledge base, and there is a built–in incentive (and low barriers) for a shift from being a recipient of skills to being a skills donor. So the process of “subsidy” is very dynamic, and is likely to lead not to a dependency relationship but rather to an equal relationship based on, among other things, local specializations for locally relevant issues.

Such skills development extends to the creation of new, local businesses, which are able to provide commercial support for and build upon open source software thanks to its low entry barriers, in a way that would not be possible with proprietary software. This effect is heightened by any public support of the open source software sector. For example, the take–up by the Extremadura Region in Spain of open source through its support for the LinEx project (a localised, Spanish–language version of the GNU/Linux operating environment) has led to an economic regeneration in a relatively poor region of the E.U. (and, in April 2004, the award of the European Regional Innovation Award). This has not just allowed the implementation of activities for a lower price, but activities especially in education and training which were simply not possible with proprietary software; it has also led to the growth of a number of small businesses to provide commercial support, since with open source there is no need to approach one sole vendor for support — approaching local entrepreneurs is possible and an obvious choice.

This is especially important given the tendency of proprietary vendors to ignore local needs especially in developing regions. As proprietary vendors are motivated by global profit–maximisation strategies they often don’t care about local issues and user needs — unless they matter in “a global context”. So, for instance, a large multinational software company may not be interested in supporting Xhosa speakers. And since their software is proprietary, no local user or local business is in a position to add such support.

Many FLOSS developers may have absolutely no interest in software usability for Xhosa speakers. However, FLOSS developers allow and encourage those with locally relevant motives to adapt their software. So local users — and, importantly for developing local ICT economies — small local businesses are entirely capable of providing services and adapting the software to local needs. In the case of Xhosa and several other southern African languages this is being done for Linux by the South Africa–based project translate.org.za.

Should a society encourage passive users of “black–box” software or active participants in the global ICT community? Being active requires being able to create — and choose with the least barriers the level of creativity. Clearly, the lower the entry barrier for creativity, the higher the potential creativity that will occur. Developing countries need to avoid being locked out of skills and competencies. Skills development requires access to the ability to create — so while you don’t have to be a programmer, you should have a choice.



Total cost of ownership and low labour costs

Inexpensive skills development is an important reason for developing countries to promote open source software. But, in contrast to the situation in richer countries, another reason is simply cost. Total cost of ownership (TCO) studies show varying results in rich countries, where labour costs are high and the relative low license fees of open source software need not necessarily reduce total costs of using and maintaining systems. Typically, TCO studies in rich countries show that licence fees account for five percent to 10 percent of total costs, while maintenance, integration, support and training — all labour costs — represent 60 percent to 85 percent of total costs (the remainder is the cost of related hardware and software).

Clearly, when labour costs are high, labour–intensive components of the total cost represent a high share of the total cost, making the licence fee itself less crucial. As a result, since the only certain saving with open source software is the (zero) licence fee, the cost advantages are not necessarily always clear. In contrast, when labour costs are low, the share of the licence fee in the total cost of ownership is much more significant, even prohibitively so.

This relationship is neatly demonstrated by comparing licence fees with a country’s GDP per capita (i.e., the average individual income; of course labour costs related to software ownership — maintenance staff — receive well above average income, but they receive a premium in rich countries too, allowing the use of the GDP per capita as a proxy index). As is quickly apparent, in developing countries, even after software price discounts, the price tag for proprietary software is enormous in purchasing power terms. The price of a typical, basic proprietary toolset required for any ICT infrastructure, Windows XP together with Office XP, is US$560 in the U.S. [2]. This is over 2.5 months of GDP/capita in South Africa and over 16 months of GDP/capita in Vietnam. This is the equivalent of charging a single–user licence fee in the U.S. of US$7,541 and US$48,011 respectively, which is clearly unaffordable. Moreover, no likely discount would significantly reduce this cost, and in any case the simple fact that a single vendor controls any single proprietary software application means that there can never be a guarantee that any discount offered is intended to be sustained for the long term, rather than as a temporary measure used to tempt consumers into a lock–in situation at which point in time the discount can be reduced.

This simple calculation is presented below in the table for 176 countries, together with 10 geographical and political aggregates. The table also includes the piracy figures published by the Business Software Alliance (BSA). It should be noted that there is a correlation between the piracy rate and the effective software licence fee — the more expensive software is, the higher the piracy rate. This is common sense, but does not seem to be reflected in the BSA estimates of the “losses” to the software industry based on piracy, which assume that all the estimated unlicensed copies of software in a country should (or could) be replaced with paid licensed copies. Ironically, the logical conclusion of the increasingly stringent international campaign for strong enforcement of copyright is the reduction of piracy rates not through the take–up of licensed, proprietary software, but through the use of open source software. Anecdotal evidence shows this is the case in Argentina, Peru and other countries especially in Latin America, where a campaign for strong copyright enforcement has coincided with poor economic conditions.


Table 1: Licence fee relative to GDP/capita.
Source: World Bank World Development Indicators Database, 2001; Piracy data from Business Software Alliance
GDP/capita in US$, Windows + Office XP cost in effective US$ equivalent.
Country GDP/cap PCs (‘000s) Piracy WinXP Cost [3]
Effective $ GDP months
Albania 1300 24 n.a. 15196 5.17
Algeria 1773 220 n.a. 11140 3.79
Angola 701 17 n.a. 28184 9.59
Antigua and Barbuda 9961 n.a. n.a. 1983 0.67
Argentina 7166 3415 62% 2757 0.94
Armenia 686 24 n.a. 28806 9.80
Australia 19019 10000 27% 1039 0.35
Austria 23186 2727 33% 852 0.29
Azerbaijan 688 n.a. n.a. 28708 9.77
Bahrain 12189 92 77% 1621 0.55
Bangladesh 350 254 n.a. 56401 19.19
Barbados 10281 25 n.a. 1921 0.65
Belarus 1226 n.a. n.a. 16120 5.48
Belgium 22323 2394 n.a. 885 0.30
Belize 3258 33 n.a. 6064 2.06
Benin 368 11 n.a. 53613 18.24
Bhutan 644 5 n.a. 30668 10.43
Bolivia 936 175 77% 21109 7.18
Bosnia and Herzegovina 1175 n.a. n.a. 16818 5.72
Botswana 3066 66 n.a. 6444 2.19
Brazil 2915 10835 56% 6777 2.31
Bulgaria 1713 n.a. 75% 11534 3.92
Burkina Faso 215 17 n.a. 91801 31.23
Burundi 99 n.a. n.a. 198864 67.65
Cambodia 278 18 n.a. 71184 24.21
Cameroon 559 60 n.a. 35319 12.01
Canada 22343 14294 38% 884 0.30
Cape Verde 1317 31 n.a. 14998 5.10
Central African Republic 257 7 n.a. 76998 26.19
Chad 202 12 n.a. 97728 33.24
Chile 4314 1640 51% 4579 1.56
China 911 24222 92% 21678 7.37
Colombia 1915 1810 52% 10316 3.51
Comoros 386 3 n.a. 51208 17.42
Congo, Dem. Rep. 99 n.a. n.a. 199394 67.83
Congo, Rep. 886 12 n.a. 22288 7.58
Costa Rica 4159 659 64% 4750 1.62
Cote d’Ivoire 634 118 n.a. 31140 10.59
Croatia 4625 376 67% 4272 1.45
Cyprus 12004 188 61% 1646 0.56
Czech Republic 5554 1490 43% 3557 1.21
Denmark 30144 2896 26% 655 0.22
Djibouti 894 7 n.a. 22107 7.52
Dominica 3661 5 n.a. 5396 1.84
Dominican Republic 2494 n.a. 64% 7922 2.69
Ecuador 1396 300 62% 14149 4.81
Egypt, Arab Rep. 1511 1010 58% 13075 4.45
El Salvador 2147 140 73% 9203 3.13
Equatorial Guinea 3935 2 n.a. 5021 1.71
Eritrea 164 8 n.a. 120613 41.03
Estonia 4051 238 53% 4877 1.66
Ethiopia 95 75 n.a. 208612 70.96
Fiji 2061 50 n.a. 9584 3.26
Finland 23295 2197 27% 848 0.29
France 22129 19949 46% 893 0.30
Gabon 3437 15 n.a. 5747 1.96
Gambia 291 17 n.a. 67847 23.08
Georgia 601 n.a. n.a. 32884 11.19
Germany 22422 31471 34% 881 0.30
Ghana 269 66 n.a. 73442 24.98
Greece 11063 860 64% 1786 0.61
Grenada 3965 13 n.a. 4982 1.69
Guatemala 1754 150 73% 11261 3.83
Guinea 394 30 n.a. 50090 17.04
Guinea–Bissau 162 n.a. n.a. 121634 41.38
Guyana 912 20 n.a. 21670 7.37
Haiti 460 n.a. n.a. 42984 14.62
Honduras 970 80 68% 20371 6.93
Hong Kong, China 24074 2600 53% 821 0.28
Hungary 5097 1021 48% 3876 1.32
Iceland 27312 118 n.a. 723 0.25
India 462 6031 70% 42725 14.53
Indonesia 695 2298 88% 28412 9.66
Iran, Islamic Rep. 1767 4495 n.a. 11177 3.80
Ireland 26908 1500 42% 734 0.25
Israel 17024 1564 40% 1160 0.39
Italy 18788 11286 45% 1051 0.36
Jamaica 3005 130 n.a. 6573 2.24
Japan 32601 44311 37% 606 0.21
Jordan 1755 165 67% 11257 3.83
Kazakhstan 1503 n.a. n.a. 13143 4.47
Kenya 371 172 77% 53283 18.12
Kiribati 430 2 n.a. 45919 15.62
Korea, Rep. 8917 12142 48% 2215 0.75
Kuwait 16048 270 76% 1231 0.42
Kyrgyz Republic 308 n.a. n.a. 64178 21.83
Lao PDR 326 16 n.a. 60625 20.62
Latvia 3200 361 59% 6173 2.10
Lebanon 3811 247 79% 5184 1.76
Lesotho 386 n.a. n.a. 51122 17.39
Liberia 163 n.a. n.a. 121417 41.30
Lithuania 3444 246 56% 5736 1.95
Luxembourg 42041 228 n.a. 470 0.16
Macao, China 14089 79 n.a. 1402 0.48
Macedonia, FYR 1684 n.a. n.a. 11735 3.99
Madagascar 288 39 n.a. 68550 23.32
Malawi 166 13 n.a. 118904 40.45
Malaysia 3699 3000 70% 5341 1.82
Maldives 2082 6 n.a. 9487 3.23
Mali 239 13 n.a. 82801 28.17
Malta 9172 91 53% 2154 0.73
Marshall Islands 1830 3 n.a. 10795 3.67
Mauritania 366 28 n.a. 53959 18.35
Mauritius 3750 131 65% 5268 1.79
Mexico 6214 6835 55% 3179 1.08
Micronesia, Fed. Sts. 1973 n.a. n.a. 10012 3.41
Moldova 346 68 n.a. 57020 19.40
Mongolia 433 35 n.a. 45598 15.51
Morocco 1173 400 61% 16840 5.73
Mozambique 200 63 n.a. 98978 33.67
Namibia 1730 65 n.a. 11420 3.88
Nepal 236 83 n.a. 83770 28.50
Netherlands 23701 6872 39% 834 0.28
New Zealand 13101 1511 26% 1508 0.51
Niger 175 6 n.a. 113078 38.46
Nigeria 319 889 71% 62014 21.09
Norway 36815 2292 34% 537 0.18
Pakistan 415 585 83% 47630 16.20
Palau 6280 n.a. n.a. 3146 1.07
Panama 3511 110 61% 5627 1.91
Papua New Guinea 563 298 n.a. 35071 11.93
Paraguay 1337 76 72% 14777 5.03
Peru 2051 1262 60% 9630 3.28
Philippines 912 1702 63% 21658 7.37
Poland 4561 3301 53% 4331 1.47
Portugal 10954 1177 43% 1803 0.61
Puerto Rico 17682 n.a. 47% 1117 0.38
Romania 1728 801 75% 11433 3.89
Russian Federation 2141 7200 87% 9226 3.14
Rwanda 215 n.a. n.a. 92034 31.31
Samoa 1465 1 n.a. 13485 4.59
Sao Tome and Principe 311 n.a. n.a. 63600 21.63
Saudi Arabia 8711 1343 52% 2268 0.77
Senegal 476 182 n.a. 41539 14.13
Seychelles 6912 12 n.a. 2858 0.97
Sierra Leone 146 n.a. n.a. 135380 46.05
Singapore 20733 2100 51% 953 0.32
Slovak Republic 3786 800 46% 5218 1.77
Slovenia 9443 549 60% 2092 0.71
Solomon Islands 614 22 n.a. 32173 10.94
South Africa 2620 2962 38% 7541 2.57
Spain 14150 6916 49% 1396 0.47
Sri Lanka 849 175 n.a. 23257 7.91
St. Kitts and Nevis 7609 8 n.a. 2596 0.88
St. Lucia 4222 23 n.a. 4679 1.59
St. Vincent and the Grenadines 3047 13 n.a. 6483 2.21
Sudan 395 115 n.a. 49990 17.00
Suriname 1803 19 n.a. 10955 3.73
Swaziland 1175 n.a. n.a. 16816 5.72
Sweden 23590 4991 31% 837 0.28
Switzerland 34171 3906 33% 578 0.20
Syrian Arab Republic 1175 270 n.a. 16815 5.72
Tajikistan 169 n.a. n.a. 116879 39.76
Tanzania 271 115 n.a. 72860 24.78
Thailand 1874 1698 77% 10540 3.59
Timor–Leste 517 n.a. n.a. 38212 13.00
Togo 270 100 n.a. 73033 24.84
Tonga 1406 n.a. n.a. 14054 4.78
Trinidad and Tobago 6752 91 n.a. 2926 1.00
Tunisia 2066 229 n.a. 9560 3.25
Turkey 2155 2792 58% 9167 3.12
Turkmenistan 1097 n.a. n.a. 18010 6.13
Uganda 249 71 n.a. 79324 26.98
Ukraine 766 898 86% 25802 8.78
United Kingdom 24219 21533 25% 816 0.28
United States 35277 178326 25% 560 0.19
Uruguay 5554 370 63% 3557 1.21
Uzbekistan 450 n.a. n.a. 43943 14.95
Vanuatu 1058 n.a. n.a. 18677 6.35
Venezuela, RB 5073 1300 55% 3895 1.32
Vietnam 411 933 94% 48011 16.33
West Bank and Gaza 1286 n.a. n.a. 15366 5.23
Yemen, Rep. 514 35 n.a. 38434 13.07
Yugoslavia, Fed. Rep. 1020 249 n.a. 19373 6.59
Zambia 354 72 n.a. 55824 18.99
Zimbabwe 706 155 68% 27965 9.51
Regional aggregates [4]
European Union 20863 116997 n.a. 947 0.32
EU accession countries 4840 8286 n.a. 4082 1.39
EU applicant countries 2023 3592 n.a. 9766 3.32
The Caribbean 4560 308 n.a. 4332 1.47
Latin America 4335 18703 n.a. 4557 1.55
Africa 652 7636 n.a. 30297 10.31
Middle East 2679 9708 n.a. 7375 2.51
Asia 2128 102229 n.a. 9282 3.16
Oceania 13946 11886 n.a. 1417 0.48





There is much current debate on whether governments should promote or encourage in any way the take–up of open source software in their economies, such as through public procurement practices. While there are a number of reasons cited for and against such action, any policy based purely on value–for–money considerations would, faced with a choice of spending either 0 or 16 months of GDP, necessarily prefer the former.

ICTs are supposed to be an “enabler” for growth in developing countries. Such growth cannot spread much beyond a very small elite if the basic enabling software infrastructure requires the investment of several months’ worth of GDP on software license fees, repeatedly, every few years in an upgrade cycle beyond the control of users. To illustrate, the reasoning given by the government of Extremadura for adopting free software was, ab initio, that they wanted everyone in the region to have access to ICTs. They realised that they could use proprietary software to provide access to all government officials, or even all doctors and engineers and universities; but in order to provide full ICT access to all citizens there was no choice but to use free software [5].

Moreover, economic growth driven by ICT depends on the wide dissemination of ICT usage and competences. The skills development aspects of open source encourage this, provide support for the generation of local ICT industries, and furthermore facilitate a reciprocal relationship where developing economies and local players can quickly start contributing to the global software developer community, and hence to the global economy.

To conclude, in the interest of sustainable, long–term and widespread economic growth and ICT development, developing countries need to seriously consider the adoption and promote open source software in order to develop local skills and businesses, actively participate in the global ICT economy, and avoid unnecessary expenditure. End of article


About the author

Rishab Aiyer Ghosh is Programme Leader, FLOSS, at MERIT/Infonomics, University of Maastricht, Netherlands.
E–mail: Rishab [at] dxm [dot] org



Kirsten Haaland at MERIT compiled the data for Table 1. The table was inspired by the “equivalent cost” calculation for Vietnam by Jordi Carrasco–Muñoz in his paper, “Free/Libre/Open Source Software as Official Development Aid,” available at http://jordiweb.net/docs/IT/Presentation_eGOVOS_-_FLOSS_as_ODA.pdf.



1. MERIT/Infonomics and Berlecon Research 2002, “Free/Libre/Open Source Software Study — Final Report,” at http://www.flossproject.org/report/.

2. Price from amazon.com in June 2003.

3. Windows + Office XP equivalent US$ cost calculation = $560 * (U.S. GDP per capita/Country GDP per capita).

4. Aggregates of: European Union (15 member states); EU Accession countries (Czech Republic, Estonia, Cyprus, Latvia, Lithuania, Hungary, Malta, Poland, Slovenia, Slovakia); EU applicant countries (Bulgaria, Romania, Turkey); Caribbean (Antigua and Barbuda, Barbados, Dominica, Dominican Republic, Grenada, Haiti, Jamaica,Puerto Rico, St. Kitts and Nevis, St. Lucia, St. Vincent and the Grenadines, Trinidad and Tobago); Latin America (Argentina, Belize, Bolivia, Brazil, Chile, Colombia, Costa Rica, Ecuador, El Salvador, Guatemala, Guyana, Honduras, Panama, Paraguay, Peru, Suriname, Uruguay, Venezuela, Mexico); Africa (geographical); Middle East (Bahrain, Iran, Israel, Jordan, Kuwait, Lebanon, Saudi Arabia, Syrian Arab Republic, Turkey, West Bank and Gaza, Yemen); Asia (geographical); Oceania (Australia, Fiji, Kiribati, Marshall Islands, Micronesia, New Zealand, Papua New Guinea, Samoa, Solomon Islands, Tonga, Vanuatu).

5. Interview with Carlos Castro Castro, Director General for Information Society at the Junta de Extremadura — on file with the author. Intended for future publication at flossproject.org/papers.htm.


Editorial history

Paper received 25 November 2003; accepted 28 November 2003; revised 7 May 2004.

Copyright © 2003, First Monday.

Copyright © 2003, Rishab Aiyer Ghosh.

Licence fees and GDP per capita: The case for open source in developing countries
by Rishab Aiyer Ghosh.
First Monday, Volume 8, Number 12 – 1 December 2003


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