WORKING PAPERS

Internet adoption and usage patterns in Africa: Evidence from

Thierry PENARD1 Nicolas POUSSING 2 Blaise MUKOKO3 Georges Bertrand TAMOKWE4

Université de Rennes 1, CREM-CNRS & Marsouin, France1 CEPS/INSTEAD, Luxembourg2 Université de Buea, Cameroun3 Université Douala, Cameroun4

Working Paper No 2013-22 November 2013 CEPS/INSTEAD Working Papers are intended to make research findings available and stimulate comments and discussion. They have been approved for circulation but are to be considered preliminary. They have not been edited and have not been subject to any peer review.

The views expressed in this paper are those of the author(s) and do not necessarily reflect views of CEPS/INSTEAD. Errors and omissions are the sole responsibility of the author(s).

INTERNET ADOPTION AND USAGE PATTERNS IN AFRICA: EVIDENCE FROM CAMEROON

Thierry PENARD UNIVERSITE DE RENNES 1 (FRANCE), CREM-CNRS & MARSOUIN

Nicolas POUSSING CEPS/INSTEAD (LUXEMBOURG) & CREM

Blaise MUKOKO UNIVERSITÉ DE BUEA (CAMEROUN) & GRETA

Georges Bertrand TAMOKWE UNIVERSITÉ DE DOUALA (CAMEROUN) & GRETA

ABSTRACT: The objective of this paper is to understand what factors stimulate or hinder the adoption and usage of the Internet in Africa. We adopt a micro-econometric approach and use household survey data from Cameroon. Our results show that Internet users in Cameroon tend to be young, educated and in employment. The probability of using the Internet is also higher for male, as well as for English-speaking and computer savvy individuals. Moreover, Internet users are more likely to have family abroad. We also find that Internet usage patterns differ across gender, age and education. For instance, young generations (below 21) tend to favor leisure usage (games) while older generations are more likely to use the Internet to search (local and international) information. Highly educated and computer savvy users are also more likely to use the Internet for professional purpose (information search) and less likely to have entertainment usage. These results provide evidence of digital divide in the Internet access, but also in the usage patterns on the African continent.

KEYWORDS: Internet Adoption, Internet Usage, Digital Divide, Africa, survey data, empirical analysis.

JEL code: L86, L96, O33, O57

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1. Introduction

In 2013, the rate of Internet use throughout Africa is estimated at 16% compared with 75% in Europe (source: ITU). The gap in Internet use rates between developed countries and the African continent has tended to widen in recent years. This "digital divide" has become exacerbated as regards the quality of Internet access. Most users in developed nations have a broadband connection at home whereas online speeds experienced in Africa are still very slow and impede access to certain uses requiring large bandwidth, e.g. video streaming.

The objective of this paper is to understand the process of Internet diffusion in Africa. What factors stimulate or hinder the adoption and usage of the Internet? Are these factors similar to those observed in studies conducted in developed countries? To address these questions, we adopt a micro-econometric approach and use household survey data from Cameroon. The survey was conducted on a representative sample of 2,650 residents of the country's three major cities (Douala, Buea and Limbe) in 2008. Cameroon1 with its 19.5 million of inhabitants offers several interesting features to study Internet diffusion and digital divide issues. First, Cameroun has a GDP per capita that is close to the average GDP per capita in Central Africa. Secondly, Cameroon has a weakly competitive telecommunications sector. Until 2012 it was one of the few countries in Africa with only two competing mobile networks, MTN and Orange2. That explains why the mobile penetration rate in Cameroon (45%) is below the African average.3 Fixed-line penetration is also extremely low (3%), and Internet penetration is about 4% in 2012. The price of broadband Internet connection (by optic fiber or satellite) is only affordable for a small percentage of Cameroon households and businesses. Most of the time, individuals have access to the Internet at their workplace or in Internet café.

Our survey allows to identify the profile of Internet users and to characterize the different patterns of Internet usage. Goldfarb and Prince (2008) showed that in the U.S.A

1 Cameroon is bordered by , Republic of Congo, , Chad and Equatorial Guinea. 2 The entry of the fixed-line incumbent Camtel has been delayed due to some controversy and legal issue on its license. The third mobile network license was finally awarded to the Vietnamese operator Viettel in 2012. 3 Third generation (3G) mobile service has still not been introduced apart from Camtel’s EV-DO fixed-wireless service.

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there is strong inequality in Internet usage. Disparity in online usage is mainly explained by differences in Internet users’ skills, social support and opportunity cost of time (see also Drouard (2010) and Coneus and Schleife (2010) for similar evidence in Europe). Do Internet usage patterns in Cameroon exhibit similar disparity?

Our results show that Internet users in Cameroon tend to be young, educated and in employment. The probability of using the Internet is also higher for male, as well as for English-speaking and computer savvy individuals. Moreover, Internet users are more likely to have family abroad. We also find that Internet usage patterns differ across gender, age, and education. For instance, young generations (below 21) tend to favor leisure usage (games) while older generations are more likely to use the Internet to search (local and international) information. Highly educated and computer savvy users are also more likely to use the Internet for professional purpose (information search) and less likely to have entertainment usage. These results suggest that making Internet use affordable and accessible in African countries like Cameroon cannot be the only response to the existing digital divide. It is also important to educate Internet users and help them to improve their online experience and increase their potential benefits from using the Internet.

The next section of this paper will review the empirical studies performed on the determinants of Internet adoption in African countries. Section 3 will present the survey completed in Cameroon in 2008, along with the variables introduced into our econometric models. Section 4 will then comment on econometric results relative to Internet adoption and usage patterns. The final section will conclude on the policy implications.

2. Literature review

The majority of studies aimed at examining the determinants behind adopting and using the Internet have focused on the developed world. A handful of studies however have sought to explain the discrepancies in penetration rates between developed and emerging countries (Andres et al. 2008, Beilock and Dimitrova 2003, Chinn and Fairlie 2010, Kiiski and Pohjola 2002, Liu and San 2006, Madden et al. 2004, Mocnik and Sirec 2010, Quibria et al. 2003, Wuvanna and Leiter 2008). The main explanatory factors for Internet penetration are per capita

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income, average level of education (i.e. human capital), degree of competition among Internet Service Providers, and the density and quality of telecommunication infrastructure. For example, based on data from over 100 countries, Beilock and Dimitrova (2003) obtained a positive correlation between the rate of Internet penetration on the one hand and per capita income, rate of computer ownership and density of landlines on the other hand. These authors also found that Internet use is more widespread in countries that respect civil rights and liberties. Using more recent data, Chinn and Fairlie (2010) derived similar results; in particular, they demonstrated that the gap in Internet penetration between developed and emerging countries could be explained by the quality of the legal and institutional environment. Wide income disparities within a country also impede Internet diffusion (Mocnik and Sirec 2010). Furthermore, Wuvanna and Leiter (2008) reported that the command of English in a country exerts a positive influence on Internet adoption. This finding is explained by the relative abundance of English language content on the Web, thus enhancing its appeal to English-speaking populations4. Some research has also focused on the decision to have an Internet connection at home. This decision is positively correlated with household income, level of education attained by the head of household and the presence of children (Chaudhuri et al. 2005, Drouard 2011, Ghazzi and Vergara 2010). Research efforts have also been directed to the determinants of Internet usage (Goldfarb and Prince 2008, Drouard 2010, Coneus and Schleife 2010). These studies show that socioeconomic factors (age, income) exert a strong influence on the decision of using the Internet, but play no role in the selection of online usage (e-mail, games, social media, e- banking, etc.). Internet usage patterns depend to a much greater extent on time availability and computer skills. The body of studies focusing on African countries is less extensive. But the articles by Roycroft and Anantho (2003), Oyelaran-Oyeyinka and Lal (2005), and more recently Pénard et al. (2012) can be mentioned. Roycroft and Anantho (2003) found that regarding the expansion of Internet accessibility on the African continent, the most significant factors were the level of economic development, the country's Anglophone heritage, the capacity of Internet bandwidth, the density of Internet servers (an indirect measurement of both content quantity and locally-

4 More broadly, Viard and Economides (2011) revealed that the use of Internet in a country increased with the amount of content present on the Internet in the primary language spoken within the given country. This finding showcases one of the potential obstacles to spreading Internet access into African countries characterized by multiple local languages.

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offered services), and the intensity of competition among network access providers. In their research, Oyelaran-Oyeyinka and Lal (2005) indicated that the rate of Internet use in Sub- Saharan countries increased with the country's rate of computer ownership, the density of landline connections, and the number of Internet hosts. In addition, per capita income has a positive influence on Internet penetration, by means of stimulating telecommunications infrastructure investment. Oyelaran-Oyeyinka and Adeya (2004) also surveyed a sample of 200 individuals working in Kenyan and Nigerian universities, drawing the conclusion that Web users were younger than non-users without any significant differences existing between male and female use patterns. This sample however was very limited in scope and did not allow drawing conclusions on the entire population. Recently, Pénard et al. (2012) compared the determinants of both Internet and cell phone adoption, using household survey data from Gabon. They showed that the primary factors stimulating Internet use consist of a high level of education, young age and computer skills whereas cell phone use increases with age and income. To the best of our knowledge, no empirical studies have been conducted to closely identify and compare the determinants of both Internet adoption and usage patterns at the individual level in African countries. The objective and contribution of this paper is clearly to provide insights on the factors that explain Internet use and the disparities in online usage among Internet users in the African continent.

3. Data and methodology 3.1 Description of the data Data come from a Cameroon survey relative to individual use of Information and Communication Technology (ICT) services5. The survey was conducted in the cities of Douala, Limbe and Buea between July 1st and November 30th, 2008. The selection of the surveyed individuals is based on the methodology used for the third nationwide household survey (ECAM3) sponsored by the World Bank. The surveyors started with a selection of Count Areas

5 This survey was part of a research project funded by the Agence Universitaire de la Francophonie (French language University Association), which associated the (Cameroun), University Omar Bongo (Gabon), University of Rennes 1 (France) and the CEPS/INSTEAD Institute (Luxembourg).

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based on the ECAM3 survey partition.6 Then in each area, the surveyors visited the same proportion of households. Finally, in each visited household, they randomly picked one member for a face-to-face interview. Our final sample contains 2,650 individuals that are representative of the population of Douala, Limbe and Buea.7 Data contains the respondent's socio-demographic characteristics (gender, age, languages spoken and read, education, marital status, occupational status, income, and association membership). The focus of the survey was on the adoption and usage of the Internet. Individuals were asked whether they regularly use the Internet, what they are doing online, their reasons to adopt the Internet… We also know their computer skills and what personal electronic devices they own (TV, personal computer, MP3 player, cell-phone). Table 1 displays the descriptive statistics of our sample. 53.2% of respondents are male. 44.6% are under 30 years-old. Regarding education, 54.2% of the respondents have only primary or lower secondary education, 13.2% completed "high school", and 8% have a university degree. 52.9% of our sample has a (formal or informal) job in the private sector (employee or self- employed) and 5.3% work in the public sector. The remaining respondents are mainly students and housewives. For 20.7% of the respondents, day-to-day life presents economic challenges.

[INSERT TABLE 1]

Even if only 1.3% of the respondents have an Internet connection at home, 33% have already used the Internet, and 20.6% had access to the Internet in the last three months (whatever the way of access - at their office, school or university, or in cybercafé…). The proportion of cell phone users is substantially high. 78.2% of the respondents owned at least one cell phone, with nearly one third owning more than one. But this is not surprising as our sample was selected in large cities in which the penetration rate of cellphone is high.

6 The ECAM3 survey partitioned the cities of Douala and Buea in Count Areas (using roads, rivers for the delimitations of the areas). Then, the census of households in a delimited area consisted of identifying the various structures (houses, buildings, offices or shops, compounds, villas or isolated houses) and the families living in these structures. Each household in a Count Area therefore, has a unique five-digit identification number, preceded by the ECAM3 abbreviation. The number is as follows: ECAM 3/ XXX/YY where XXX represents the house identification number and YY the identification of the family living in this house. 7 We didn’t conduct interviews in rural areas for two reasons. Firstly, it would have been costly to collect data in rural areas. Secondly, as the objective of this survey was to analyze the adoption and appropriation of the Internet, it was relevant to focus on urban areas in which the diffusion of the Internet is more widespread.

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3.2. Methodology

To analyze the determinants of Internet adoption and usage patterns, we estimate a two stage model. In the first stage, we estimate a binary probit model with “having used the Internet in the previous 3 months” (USE3M) as the dependent variable. Then, conditional to the decision to use the Internet, we estimate several ordered probit models on a selection of Internet usages. Nine usages are considered namely: emailing (MAIL); instant messaging with people living in Cameroon (LOCALMESSAGING); instant messaging with people living abroad (INTMESSAGING); local content search (LOCALSEARCH); international content search (INTSEARCH); search for school or job related content (JOBSEARCH); watching videos (VIDEO); downloading music or films (MUSIC) and playing games (GAME). Each of these nine usages is coded as a three-scale dependent variable (no use, occasional and regular use). This two-stage estimation procedure, called Heckman model, allows us to overcome the selection bias problem that appears in the second stage. The explanatory variables used in the first stage (Internet use model) and the second stage (Online usage model) can be grouped into three categories: the individual's socioeconomic characteristics, IT (information technology) skills and equipment, and social capital. The socioeconomic characteristics taken into account herein include: gender, age, level of education, marital status, living conditions, occupation, and lifestyle. Regarding the impact of gender, a number of studies (e.g. Bimber 2000, Schumacher and Morahan-Martin 2001, Gillwald et al. 2010) have demonstrated that during the initial phases of introducing a new technology, the early adopters tend most often to be male. Over time however as the technology is disseminated, the gap between men and women narrows. Several studies have shown that Internet users are young (Rice and Katz 2003, Oyelaran- Oyeyinka and Adeya 2004). To test the effect of age in the case of Cameroon, we create four binary age group variables: 15 to 21-year-olds (AGE15-21), 22 to 29-year-olds (AGE22-29), 30 to 44-year-olds (AGE30-44), and over 44 (AGE45). Another important factor concerns the level of education, which is expected to be positively correlated with Internet use given that the Internet requires at the very least being able to read and write (i.e. literacy). Yet a higher level of education can be associated with greater

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benefits from using the Internet and lower adoption costs (Goldfarb and Prince 2008). We measure the level of education through four binary variables: primary education or first cycle of secondary education (PRIMARY), second cycle of secondary education (SECONDARY), first cycle of post-secondary education (TERTIARY1), and second cycle of post-secondary education (TERTIARY2). Income is another key factor in explaining Internet adoption (Gillwald et al. 2010). Without any reliable data on individual income, we introduce a subjective assessment of each respondent’s financial situation via three scale variables indicating whether the respondent finds their living conditions very difficult (DIFFICULTY2), difficult (DIFFICULTY) or rather easy or comfortable (EASY). We assume this variable to be a suitable proxy for the individual’s income as it somehow indicates the personal perception of his/her own living conditions. We also control for marital status through the variable (PARTNER) that is equal to 1 if the respondent is married or living with a partner. Similarly, employment status is taken into account via the following variables: unemployed (NOJOB), employed in the public sector (PUBLICJOB), employed in the private sector (PRIVATEJOB). Having a job could provide more opportunity for Internet access at the workplace and should increase the likelihood to use the Internet. The level of computer skills is also expected to have a positive impact on Internet use. This skill level is measured by the capacity to operate word processing or spreadsheet software (USESOFTWARE) and install a piece of software on a computer (INSTALLSOFTWARE). One third of respondents know how to use spreadsheet or word processor software, while 13.6% is capable of installing software. Due to the quantity of English language content found on the Internet, individuals able to read English should be more attracted to the Internet and its diverse uses (Viard and Economides 2011, Wuvanna and Leite 2008). Command of the English language is measured by introducing a binary variable (ENGLISH), which equals 1 if the respondent has a good reading knowledge of English. We also control for the ownership of computing and electronic devices. 12% of interviewees had access to a computer, 57% to a CD player and 19% to an MP3 player. The presence of these devices turns out to be complementary to Internet use or an indicator of a taste for digital technologies and, in either case, should increase the probability of Internet use.

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A considerable body of work has underlined the influence of social networks in the decision to adopt new technologies, especially when network effects play a substantial role (Goolsbee and Zittrain 1999, Coneus and Schleife 2010, Liu and San 2006, Ward 2010). Social interactions and social learning are determinant factors, especially during the early (startup) phase. Along these lines, Goldfarb (2006) showed that the use of e-mail services in the United States began in universities and spread via students who went on to become influencers within their own households. Moreover, the density of an individual's social network (or his/her amount of social capital) can also facilitate Internet adoption, by strengthening network externalities and thus raising the gains expected from these technologies (Franzen 2003, Penard and Poussing 2010). In order to measure the impact of social network, we created three binary variables: FAMILYAFRICA (does the respondent have at least one member of family living abroad in an African country or not?), FAMILYWORLD (does the respondent have at least one member of family living abroad out of Africa or not?) and MEMBERSHIP (is respondent member of at least one voluntary organization?). We expect that, having family members abroad favors the adoption of the Internet, especially if they are in countries where the Internet is widely diffused. The explanatory variables for online usages are the same except that the three above variables related to social network are excluded. Instead we use the variables FAMILY INTERNET and FRIEND INTERNET that respectively indicate whether the individual has many family members or many friends that are using the Internet. We expect that having a lot of friends and relative connected should increase the frequency and variety of Internet usage, especially communication-oriented usage (email, instant messaging). For the socio-demographic and Information Technology variables, Goldfarb and Prince (2008) have shown that they can indirectly influence usage through the differences in the opportunity cost of time and Internet skills.

4. Results

Table 2 shows the results on the determinants of internet adoption. We find that the probability to use the Internet is higher for male, young and educated people. We observe a clear digital divide between those who are below 30 and have a post-secondary education and those who are 9

above 30 and less educated. These results are not surprising. Young people are usually more technologically savvy.

[INSERT TABLE 2]

Internet accessibility and affordability are also important factors for Internet adoption. We find that Internet users tend to have better living conditions. Individuals, who have a job, especially in the public sector, are also more likely to adopt the Internet. The main reason is that they are more exposed to the Internet at their workplace. In many companies and public administrations, employees may have free Internet access and be required to use it for some tasks.

As expected the probability of Internet use increases with computing skills (as measured by the ability to use a word processor or spreadsheet or to install a piece of software). Owning a PC and a MP3 player appears to be complementary with Internet use. Moreover, command of the English language is positively correlated with Internet use. This finding was also observed by Wunnava and Leifer (2008) and Roycroft and Anantho (2003) in African countries and may be explained by the greater availability of English language content on the Web. Finally, to have some family members abroad has a positive impact on Internet use as the Internet is a convenient tool to communicate or stay in touch with relatives abroad. But being member of a voluntary association has no impact on the decision to be online.

To sum up, the decision whether to adopt the Internet or not is mainly driven by the expected benefits/utility of being connected (age, English fluency, family abroad) and the monetary and cognitive costs (education, computing skills, income, Internet accessibility).

The second objective of this paper is to understand whether the same variables explain the choices of Internet usages. Table 3 shows the results for the determinants of nine Internet usages. We find that Internet usage patterns are mainly influenced by gender, age, education and computer skills. For instance, young generations (below 21) tend to favor leisure usage (games) while older generations are more likely to use the Internet to search (local and international)

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information. Highly educated and computer savvy users are also more likely to use the Internet for professional purpose (information search) and less likely to have entertainment usage. Video, Music and Game are more common among Internet users with primary or secondary education.

Command of the English language plays no role in the choice of Internet usage whereas it increases the propensity to use the Internet. Similarly, occupational status has barely any influence on Internet usage patterns. Finally, individuals declare more intensive usage of emailing, instant messaging and video when some friends or family members are connected. Our results suggests that network externalities play a role in the diffusion of communication tools like email or instant messaging, but also for the consumption of online video. The propensity to watch online video is higher when at least one family member is also using the Internet. This relation is not observed for other leisure usages like online music or game.

[INSERT TABLE 3]

5. Conclusion

The objective of this article was to compare the determinants of Internet adoption and usage patterns. The literature has revealed the existence of two levels of digital divide: a first- level divide between those who have already adopted information technologies and those who (still) have not (i.e. an accessibility divide); and a second-level divide within the adopters, between those able to master use of these technologies and those with a skills deficit in operating these technologies (i.e. a usage divide) (Hargittai 2002). According to DiMaggio et al. (2004), this second-level divide would be explained by inequalities in the quality of access to the Internet, as well as in the skill levels of Internet users and their social entourage. Our study provides additional evidence that this dual digital divide exists in developing countries. The first-level divide remains considerable on the African continent, especially in matter of Internet access. Some rural zones are still barely covered by the cell phone network and poorly connected to the landline network. Yet the second-level divide gives rise to an equally important challenge, as a large portion of the population is illiterate with no exposure to Information Technology. Bridging these divides entails not only improving Internet access conditions (better 11

infrastructure, high-speed service, etc.) and cell phone network coverage, through cutting the price paid for network access (achieved by authorizing the market entry of new telecom operators and service providers), but also upgrading technological training (with as prerequisites raising the average level of education and lowering the illiteracy rate). Such training would allow showcasing the advantages and returns derived from Internet and cell phone usage. Despite the important benefits of information technology, digital policies are actually nonexistent or limited in many African countries. Probably because these technologies can serve as a force of opposition in non-democratic countries by providing access to information outside the country and helping disseminate news without having to rely on official communication channels (which are often subject to censorship). Not only have they allowed hosting discussion forums and played a vital role in the Arab spring uprisings (Tunisia, , ), but these technologies also came to the fore during elections held in several African countries (to ensure more transparency on voting processes). Reducing the digital divide in the African continent requires taking into consideration technological, economic and political challenges.

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Table 1 - Summary statistics for all individuals and only Internet users VARIABLE DEFINITION POPULATION INTERNET USER Mean (Standard error) Mean (Standard error) GENDER Male (yes = 1) 0.4753687493 0.5946743681 (1.610386894) (1.5262950158) AGE15-21 Age of the respondent from 15 to 21 (yes = 1) 0.16161717 0.1794068373 (1.1870056107) (1.1928347538) AGE22-29 Age of the respondent from 22 to 29 (yes = 1) 0.2802340162 0.4234933401 (1.4482512755) (1.5361099375) AGE30-44 Age of the respondent from 30 to 44 (yes = 1) 0.3081684719 0.2896384247 (1.4889568514) (1.4101478874) AGE45 Age of the respondent more than 45 (yes = 1) 0.2499803419 0.1074613979 (1.3962946927) (0.9628011248) PRIMARY Primary or first stage of secondary education 0.5750463797 0.1470128935 (yes = 1) (1.5940797275) (1.1008947178) SECONDARY Upper secondary education (yes = 1) 0.2315136941 0.2907107747 (1.3601732956) (1.4116891845) TERTIARY1 The first stage of Tertiary education: University 0.1220041742 0.3064346289 License or Bachelor (yes = 1) (1.055410488) (1.4332087907) TERTIARY2 The second stage of Tertiary education: Master, 0.071435752 0.2558417029 Doctorate (yes = 1) (0.8305229247) (1.3564855588) PARTNER Married or with a partner (yes = 1) 0.4983934505 0.3885698363 (1.6123361734) (1.5153216007) DIFFICULTY2 Living conditions are very difficult with my 0.2710254673 0.1840111526 income (yes = 1) (1.4333394831) ( 1.204650392) DIFFICULTY Living conditions are difficult with my income 0.5265178947 0.4590513799 (yes = 1) (1.6100753023) (1.5491929044) EASY Living conditions are easy with my income (yes 0.1674160417 0.3236716027 = 1) (1.2039276517) ( 1.454547648) FAMILYAFRICA At least one member of the family living in 0.1190476341 0.132626742 another African country (yes = 1) (1.0442979676) (1.0544243015) FAMILYWORLD At least one member of the family living in 0.499546301 0.6837601119 another country out of Africa (yes = 1) (1.6123438326) ( 1.445629649) PRIVATEJOB In employment in the private sector (yes = 1) 0.5176539896 0.4297259432 (1.6123008525) (1.5389851743) PUBLICJOB In employment in the public sector (yes = 1) 0.044540347 0.1083846556 (0.6652284425) (0.9664280125) NOJOB Retired/pensioned, Housewife, Unemployed, 0.4378056634 0.4530436448 student or at school (yes = 1) (1.5998223362) (1.5475446907) ENGLISH Fluent in reading and speaking English (yes = 1) 0.5065188828 0.7915501238 (1.6122074547) (1.2628066879) USESOFTWARE Able to use an office software suite (yes = 1) 0.127840267 0.3838554306 (1.0767619135) (1.5118962819) INSTALLSOFTWARE Able to install software (yes = 1) 0.089165072 0.3139536718 (0.9189778749) ( 1.44280071) PC Having a personal computer (yes = 1) 0.1264738429 0.3126165138 (1.0718305951) (1.4411273053) CD Having a CD reader (yes = 1) 0.5714655413 0.7025787027 (1.5957899807) (1.4211187325) MP3 Having a MP3 player (yes = 1) 0.1914172029 0.335401279 (1.2686463279) (1.4677731865) MEMBERSHIP Membership in at least one voluntary 0.6265519789 0.5910231893 organization (yes = 1) (1.5598450871) (1.5284401744)

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VARIABLE DEFINITION POPULATION INTERNET USER Mean (Standard error) Mean (Standard error) FAMILY INTERNET At least one member of the family is using the 0.5427724536 0.9941328787 Internet (yes = 1) (1.6064341507) (0.2374278137) FRIEND INTERNET Many friends use the Internet (yes = 1) 0.3031675941 0.7630222773 (1.482154215) (1.3219627311) USE3M Have used the Internet in the last 3 months (yes 0.1912196669 1 = 1) (1.2681464343) (0) MAIL Send email (never = 0; every month = 1; every / 1.1429573871 week = 2) (2.0834283837) LOCALMESSAGING Use Instant messaging software for local relation / 0.9428640981 (never=0; less than once a month = 1; more than (2.8168990919) once a month = 2) INTMESSAGING Use Instant messaging software for international / 1.0668205384 relation (never=0; less than once a month = 1; (2.7554507667) more than once a month = 2) LOCALSEARCH Search for local information (never=0; less than / 1.0716782438 once a month = 1; more than once a month = 2) (2.6728263066) INTSEARCH Search for international information (never=0; / 1.1701109208 less than once a month = 1; more than once a (2.6322603202) month = 2) JOBSEARCH Search for job or school-related information / 1.1187453125 (never=0; less than once a month = 1; more than (2.8011351299) once a month = 2) VIDEO Watch online video (never=0; less than once a / 0.4469088736 month = 1; more than once a month = 2) (2.2849779366) MUSIC Download music or film (never=0; less than once / 0.7435099415 a month = 1; more than once a month = 2) (2.6092485067) GAME Play online (never=0; less than once a month = / 0.496287166 1; more than once a month = 2) (2.378053398)

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Table 2 - The determinants of Internet use (First stage) Variable Estimate (Standard Error) GENDER 0.2471754791*** (0.0227268414) AGE15-21 0.3984170793*** (0.0408495254) AGE22-29 0.5312738032*** (0.0304081726) AGE30-44 Ref. AGE45 -0.417564127*** ( 0.037414971) PRIMARY -0.687723576*** (0.027578407) SECONDARY Ref. TERTIARY1 0.525143163*** (0.0314159622) TERTIARY2 0.8781079146*** (0.0404977275) PARTNER -0.282145597*** (0.0254726663) DIFFICULTY2 Ref. DIFFICULTY -0.031810704 (0.0267021547) EASY 0.2150673392*** (0.0327423716) FAMILYAFRICA 0.0872506983*** (0.0331123532) FAMILYWORLD 0.2644746952*** (0.022978642) PRIVATE JOB Ref. PUBLIC JOB 0.2700218341*** (0.0482468896) NOJOB -0.150617786*** (0.0286600802) ENGLISH 0.180395397*** (0.0249566653) USESOFTWARE 0.3006455466*** (0.0380860541) INSTALLSOFTWARE 0.584075106*** (0.0431665729) PC 0.2478937003*** (0.0308082817) CD -0.006975259 (0.0248386915) MP3 0.2995515189*** (0.0269190899) MEMBERSHIP -0.010288214 (0.0250859159) INTERCEPT -1.439875812*** (0.0483282104) Number of obs. 2650 -2 Log L 17314.509 Percent Concordant 88.1 Note: *** coefficients significant at 1%, ** significant at 5%, * significant at 10%

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Table 3 - The determinants of Internet usages (Second stage)

Variable MAIL LOCALMESSAGING INTMESSAGING LOCALSEARCH INTSEARCH GENDER -0.0718613 -0.198525** -0.1347899 0.1639857** 0.238901*** (0.0615594) (0.0907644) (0.089247) (0.0763758) (0.0775918) AGE15-21 -0.0447527 -0.0138456 0.1146877 0.2112023 0.2471467 (0.1212771) (0.1670872) (0.165131) (0.152176) (0.1549916) AGE22-29 0.0413925 0.1592448 0.2314901* 0.0985454 0.2363672** (0.0868529) (0.127738) (0.1241338) (0.1094945) (0.1100363) AGE30-44 Ref. Ref. Ref. Ref. Ref. AGE45 -0.0467917 0.2577026* 0.3150007** 0.2548025** 0.3226736*** (0.0949271) (0.1396638) (0.1410956) (0.1054646) (0.1073068) PRIMARY 0.2287115** 0.0556054 0.1173813 -0.3583632*** -0.295502** (0.1118163) (0.1561844) (0.1406236) (0.1294316) (0.1382391) SECONDARY Ref. Ref. Ref. Ref. Ref. TERTIARY1 0.0574487 0.0220465 -0.1222822 0.2115371* .0352033 (0.0857084) (0.119264) (0.1190836) (0.1098487) (0.1105181) TERTIARY2 0.1741495 -0.1411312 -0.2448887* 0.3799737*** .0735436 (0.10653) (0.1491433) (0.14675) (0.1240659) (0.1296895) 0.0200148 0.1830461* 0.1895437* 0.0111352 0.0482492 PARTNER (0.0691109) (0.1026297) (0.1003402) (0.0861353) (0.089253) PRIVATE JOB Ref. Ref. Ref. Ref. Ref. PUBLIC JOB 0.0916377 0.1777268 0.1733653 0.193099* 0.0457915 (0.0911294) (0.1441274) (0.1355355) (0.1068866) (0.1059078) NO JOB -0.182107** -.0047628 -0.0301557 -0.0888002 -0.2509223** (0.0793513) (0.1100507) (0.1073376) (0.1056485) (0.1004832) ENGLISH 0.0870339 -0.0448773 -0.0617438 -0.1360926 -0.2311432** (0.0743294) (0.1028126) (0.1025038) (0.0990591) (0.0947189) USESOFTWARE 0.1607425 0.1494657 -0.025679 0.3116375*** 0.3058334*** (0.0833021) (0.1251224) (0.1287057) (0.1036762) (0.0976042) 0.1842013** -0.0702417 0.0439077 0.2710117** 0.1264466 NSTALLSOFTWARE (0.0925611) (0.1417956) (0.1453025) (0.1162585) (0.1105699) FAMILY INTERNET 0.3105347 0.8548204*** 0.6202135** 0.1316022 0.5197603 (0.3779136) (0.1932563) (0.2891849) (0.2708262) (0.3290302) FRIEND INTERNET 0.1046976* 0.1379803 0.0724537 -0.0155233 0.0537534 (0.0632079) (0.08884) (0.0867731) (0.0841524) (0.0835831) 0.6894352* 0.2144148 0.861393** 0.4228591 0.4795214 CONSTANT (0.4169658) (0.3388576) (0.3735044) (0.3419992) (0.3993869) Number of obs. 2650 2650 2650 2650 2650 Log Pseudo Likelihood -13527.9 -15400.58 -15218.5 -14855.75 -14789.1 Note: *** coefficients significant at 1%, ** significant at 5%, * significant at 10%

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Variable JOBSEARCH VIDEO MUSIC GAME

GENDER 0.1411305* -0.0424544 -0.0153543 0.0674558 (0.0817988) (0.0676772) (0.0797343) (0.0688958) AGE15-21 0.2849802* 0.0491589 0.1876922 0.4361076*** (0.154911) (0.1346934) (0.1500252) (0.1480817) AGE22-29 0.2754324** 0.049297 0.2088654* 0.2832821*** (0.1130976) (0.0992979) (0.11083) (0.101378)

AGE30-44 Ref. Ref. Ref. Ref.

AGE45 0.1820918 0.1607803* -0.010261 0.0099954 (0.1196488) (0.0844466) (0.11633) (0.0884351) PRIMARY -0.1204516 0.2840877** 0.1509499 0.0789218 (0.1480146) (0.1121197) (0.1329129) (0.1260535)

SECONDARY Ref. Ref. Ref. Ref.

TERTIARY1 0.2824202** -0.1581366* -0.2354408** -0.1885544* (0.1177147) (0.0940487) (0.1153099) (0.0975502) TERTIARY2 0.4294755*** -0.3322009*** -0.4442898*** -0.5080534*** (0.1352514) (0.1011266) (0.1292436) (0.1055077) 0.0055556 -0.0609406 -0.0534176 -0.0436349 PARTNER (0.0933852) (0.0824764) (0.0914847) (0.0803637)

PRIVATE JOB Ref. Ref. Ref. Ref.

PUBLIC JOB -0.0302864 0.1368413 -0.1125183 0.0197909 (0.1156046) (0.1127787) (0.1230043) (0.1015429) NO JOB -.0255683 0.0166075 -0.0088776 -0.0433009 (0.1048047) (0.0929932) (0.1018766) (0.0974535) ENGLISH -0.1123449 0.1151094 0.035078 0.0042581 (0.1028451) (0.0776033) (0.0954989) (0.0898567) USESOFTWARE 0.2047121* 0.0941532 -0.0534862 0.1249573 (0.1177898) (0.0953076) (0.1111427) (0.0929379) INSTALLSOFTWARE 0.2976204** 0.2046006* 0.3903083*** 0.1813512* (0.12795) (0.1053357) (0.1215903) (0.0998368) FAMILY INTERNET -0.3075016*** 0.4042889*** -0.0796674 0.082629 (0.088712) (0.0903421) (0.4515339) (0.1892779) FRIEND INTERNET -0.1371114 0.0559986 0.0385859 -0.0681433 (0.090002) (0.0717221) (0.0824998) (0.0807889) 1.134.673*** 0.2471008* 107.791** 0.5161961** CONSTANT (0.2527693) (0.1481684) (0.4965188) (0.2441832)

Number of obs. 2650 2650 2650 2650

Log Pseudo Likelihood -15078.46 -14105.52 -14844.46 -14288.6

Note: *** coefficients significant at 1%, ** significant at 5%, * significant at 10%

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