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

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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.

Contents

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

 

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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

 

 

Conclusion

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.

 

About the author

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

 

Acknowledgements

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.

 

Notes

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.

 

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Editorial history

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

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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
http://firstmonday.org/ojs/index.php/fm/article/view/1103/1023