spatial inequality and the internet divide in indonesia

8/15/2019 Spatial Inequality and the Internet Divide in Indonesia

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Spatial inequality and the Internet divide in Indonesia2010–2012

Sujarwoto Sujarwoto a,n, Gindo Tampubolon b

a Brawijaya University, Malang, Indonesiab University of Manchester, United Kingdom

a r t i c l e i n f o

Keywords:

Spatial inequality

Internet divide

Indonesia

a b s t r a c t

Spatial inequality has been one of the key development characteristics considered across

developing countries. However, relatively few studies examine the mechanisms by which

spatial inequality explains the existing digital divide in a developing country. Applying the

normalisation and stratication thesis in diffusion theory, this study examines the ways in

which spatial inequality is related to the Internet divide in Indonesia, a developing

country that is currently growing in its use of Information and Communication Technol-

ogy (ICT), but that has experienced unequal regional development in the last three dec-

ades. Data comes from the Indonesian national socio-economic survey (Susenas) 2010–

2012, which comprises 3.3 million individuals, 750,000 households and 292 districts. Far

from moving towards convergence, the Internet divide expanded during this period; the

inequality of Internet access by age, gender, income, and education deepens and widens

across urban–rural, city–countryside, and remote island–mainland island areas. The

results of analyses using both stratied and multilevel models indicate that supply factors

across districts – particularly district disparities in telecommunications infrastructures,

human capital and education services – are associated with the Internet divide. The

results are robust against individual, household and district socio-economic character-

istics associated with the Internet divide. Enlarging the distribution of telecommunication

infrastructures and education facilities, particularly across districts in rural, countryside

and remote islands, may thus help to bridge the Internet divide in Indonesia.

& 2015 Elsevier Ltd. All rights reserved.

1. Introduction

The debate about the impact of the rise of the information society has produced deeply contested visions predicting thefuture direction of trends (Norris, 2001; Van Dijk & Hacker, 2003; Hargittai, 2002; Warschauer, 2003; Dutta & Mia, 2007).

Optimists hope that the development of the Internet will have the capacity to reduce, although not wholly eradicate, tra-

ditional inequalities between the information-rich and the information-poor both between and within societies ( Norris,

2001; Van Dijk & Hacker, 2003; Hargittai, 2002; Warschauer, 2003). In contrast, pessimists believe that ICTs will reinforce

and exacerbate existing disparities. Sceptics suggest that both the fears and hopes are exaggerated, with technologies

adapting to the social and political status quo, rather than vice-versa (Norris, 2001; Dutta & Mia, 2007).

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n Corresponding author.

E-mail addresses: [email protected] (S. Sujarwoto), [email protected] (G. Tampubolon).

Please cite this article as: Sujarwoto, S., & Tampubolon, G. Spatial inequality and the Internet divide in Indonesia 2010–2012. Telecommunications Policy (2016), http://dx.doi.org/10.1016/j.telpol.2015.08.008i

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Norris (2001), further makes a distinction between normalisation and stratication models of ICT diffusion. Normal-

isation thesis suggests that over time, access to the Internet will become widespread, overcoming social and other

boundaries, to make its day-to-day use appear normal. This thesis suggests that the prole of the online community will

come to reect society as a whole, given the wider availability of simpler and cheaper plug-and-play technologies and faster

broadband services, facilitating delivery of popular mass entertainment (Norris, 2001; Dutta & Mia, 2007). It is presupposed

that the differences between groups increase only in the early stages of adoption, and that those differences disappear with

saturation in the last stages. Certainly, the ubiquity of ready devices such as Wi-Fi networks, tablets and smartphones

enabling access to the Internet supports this notion.In contrast, the stratication thesis draws on experience with older technologies (such as the telegraph, automobiles, or

telephones in the twentieth century) to suggest that if Internet technology traces the same path, then the notion of a rise in

social inequality in terms of access cannot be easily dismissed ( Norris, 2001; Hargittai, 2002). Although social stratication is

not inevitable, depending as it does on, state intervention to provide enabling infrastructure and ensure equitable dis-

tribution and the nature of skills required to use the technology, digital access may persist in dividing groups in society. This

outcome is far from inevitable, because the conditions under which innovations implemented are also determined, in part,

by their social consequences. The existing social structure may thus, also play a role; as Rogers (2003) pointed out, inno-

vation in highly stratied societies usually reinforces existing socioeconomic inequalities. Norris (2001) provides empirical

evidence that, despite the high rate of penetration of ICTs in Europe and the United States, the digital divide between and

within countries is still perceptible.

This study attempts to make a distinction between normalisation and stratication theses in the context of a developing

country. A more nuanced understanding of the nature of ICT diffusion can be gained through examining the links between

widening spatial inequality within a developing country and the digital divide. Amidst growing concern about increasinginequality, the spatial dimensions of inequality have begun to attract considerable policy interest (Lessmann, 2014; Kanbur,

Rhee & Zhuang 2014; Tan & Zeng, 2014). In China, Russia, India, Mexico and South Africa, as well as most other developing

and transition economies, there is a sense that spatial and regional disparities in economic activity, incomes and social

indicators have been on the increase in the last two decades (Kanbur et al., 2014; Kanbur & Venables, 2005). For developing

countries that experience deep disparities across space, information and communication technology offers a sliver of hope

for bridging these disparities. However, spatial inequality with regard to ICT access may itself become a development

challenge, given the growth of Internet use. The gaps in physical access continue to grow in these developing countries; the

question is when and to what extent they will close again, equalising access for every social category (according to either

normalisation or stratication). A deeper analysis of these types of spatial inequality and the mechanisms explaining

unequal ICT access would prove a signicant contribution to developing countries’ efforts to address the digital divide.

This study aims to answer some of the questions raised by examining the mechanism linking determinants producing

spatial inequality with respect to the Internet divide in Indonesia. It is also often cited as an emerging economic success

(World Bank, 2008). However, its economic development is characterised by an endemic problem of spatial inequalities(Akita & Lukman, 1995; Hill, 1996; Resosudarmo & Vidyattama, 2006; Hill, Resosudarmo & Vidyattama, 2008; Yusuf, Sumer

& Rum, 2014). We thus, also consider in some depth whether these spatial inequalities reinforce the social inequalities in

Internet access. In this study, spatial inequality means a disparity in resources and services due to discrepancies in social and

economic factors across geography (Kanbur & Venables, 2005). Four measures of spatial inequality related to Internet access

are used: economy, human capital, telecommunication infrastructure, and education services. In order to achieve the aim of

this study, annual data from Susenas 2010–2012 were studied using multilevel models to account for the effect of spatial

inequality across districts on unequal Internet access among individuals. The next section presents a synthesis of the lit-

erature on spatial inequality and the digital divide.

2. Spatial inequality and the digital divide

The notion that the Internet could reduce the economic importance of geographic distance has been discussed in theliterature (Negroponte, 1995; Kelly, 1998; Cairncross, 2001). Cairncross (2001) describes the narrative of the Internet as the

death of distance. The Internet, many believe, will level the playing eld for people both near and far from the centre. It

allows people to communicate over distance and thus, lifts the constraints of geography. Some believe that this will not only

change the social world but will also effectively eliminate distance as a cost factor (Grimes, 2000). According to this view,

the economy would work in a space, rather than a place; the cost of transport would be drastically reduced, distance would

become less important, and peripheral regions would benet from opportunities that were not available in the economy

based on the manufacturing industry (Negroponte, 1995; Kelly, 1998). Since ICTs are mainly based on immaterial and human

capital investments, regions or areas that have historically suffered from isolation, high transportation costs, or a lack of

physical private and public infrastructure might nd new paths for growth. Consequently, according to this view, the

concentration of income opportunities and wealth should decrease over time (Compaine, 2001). Although other predictions

were also present in the debate on the impact of the digital economy ( Norris, 2001), this view was largely dominant.

However, in the same way that normalisation is not only the thesis used to organise evidence regarding Internet access,

the death of distance is not the only narrative by which to explain the nature of ICT access across and within countries. Infact, this relationship is not merely about geographic proximity but also reects spatial inequalities. Indeed, spatial

Please cite this article as: Sujarwoto, S., & Tampubolon, G. Spatial inequality and the Internet divide in Indonesia 2010 –2012. Telecommunications Policy (2016), http://dx.doi.org/10.1016/j.telpol.2015.08.008i

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inequalities themselves are an endemic feature of regional development in most developing and transition countries

(Kanbur et al., 2014; Kanbur & Venables, 2005). In these countries, differences in economic development still shape the rate

of the diffusion of technologies at the rm, regional and country levels. The reasons behind these stylised facts have been

investigated at length in recent times. The geo-spatial digital divide could emerge as a consequence of the rise of ICTs

(Rallet & Rochelandet, 2007). In addition, the rise of Internet use may also have the potential to exacerbate any existing

spatial inequality in developing and transition countries (Norris, 2001).

The overall empirical reality is one of large geographic differences in the rate of diffusion of ICTs, with the result that

disparities and inequalities seem to be reinforced, rather than reduced by these technologies. Cross-country studies haverevealed the disparities in ICT between North America and Europe, on the one hand, and African and Asian countries on the

other (Chinn & Fairlie, 2007; Oyelaran-Oyeyinka & Lal, 2005; Pohjola, 2003). These studies have identied some mechan-

isms by which determinants producing spatial inequality relate to the digital divide. First, these large digital disparities have

been explained by differences in economic capital. Disparities of economic capital distribution such as income and gross

domestic product are positively associated with the Internet divide ( Norris, 2001; Van Dijk, 2012). As the Internet has

become increasingly central to life, work and play – providing job opportunities, strengthening community networks and

facilitating educational advancement – the systematic exclusion of certain groups and areas, such as poorer regions and

communities, becomes even more important (Norris, 2001).

Second, inequality in human capital increases unequal access to the Internet. The unequal development of human capital –

meaning investment in digital skills and capacities through education, training, and lifelong learning – represents one of the

most important factors that facilitate Internet access. Education is one of the most signicant forms of social development,

producing the skills and experiences that are most likely to contribute to the use of ICTs. Academic institutions may also play

an important role in spreading ICTs because they are often among the rst institutions in a nation to become wired. Unequaldistribution and unequal access to education may therefore lead to a digital divide. Wilson, Wallin, and Reiser (2003),

explained that the numbers of Internet users are greatly affected by whether access is offered in schools, community centres,

cybercafés, and/or post-of ces, especially in poor countries where computer access at work and home is highly limited.

Third, disparities in the distribution of telecommunications infrastructures result in a digital divide ( Rao, 2005; Mariscal,

2005). Individuals need access to computers, landlines, mobile phones and networks in order to access the Internet.

Landline networks and satellite facilities cannot be deployed as extensively as they can in developed countries. In many poor

countries such as Indonesia, basic telecommunications services are still unavailable to some people on remote islands.

Unequal access to telecommunications infrastructures and services constitutes one of the challenges to bridging the digital

divide, and remedying it should be an objective of all stakeholders in developing countries.

3. Internet growth and regional development in Indonesia

Indonesia provides an interesting case for the examination of spatial inequality and the Internet divide in developing

countries. Internet growth in Indonesia has shown promising trends in the last two decades. Nielsen’s Southeast Asian

Digital Consumer (2013), reports that Indonesia’s Internet penetration rate is at 21%, growing at 20% annually since 2003.

Semiocast (2013), reported that the country was one of the biggest users of Twitter and Facebook after the United States,

India and Brazil. In 2012, there were 71.19 million Internet users in Indonesia or about 28% of Indonesia ’s population; these

numbers are in line with world Internet growth. The country is also often cited as an emerging economic success in

Southeast Asia (World Bank, 2008). Indonesia’s economic growth has been robust since the Asian nancial crisis in 1998,

and it appears well positioned with an average annual growth of 4–6% since 2002 (World Bank, 2008). The poverty

headcount ratio at $1.25 (PPP) decreased sharply from 47.7% in 1999 to 16.2% in 2011. The Human Development Index also

increased sharply from 0.479 in 1990 to 0.624 in 2011 ( World Bank, 2013).

Despite its impressive economic growth, Indonesia’s socio-economic development has been characterised by deep

spatial inequalities across regions. Biro Pusat Statistik (2014), recently reported that the overall Gini Index increased from

0.33 in 2002 to 0.41 point in 2013. Inequality is greater in urban areas with patterns closely aligned to total trends, whilerural inequality is consistently lower than urban inequality by approximately 7 points between 2002 and 2013 (Biro Pusat

Statistik, 2014). As Indonesia is characterised by many remote and isolated areas due to the archipelagic nature of the

country, geographic location, along with the relative openness of trade and market operations, can restrict growth and the

development process, allowing some areas to develop faster than others (Vidyattama, 2010). Gaps in human capital and

infrastructure endowments, which are often themselves a product of differing development experiences, hinder current and

future development and thereby exacerbate regional disparities (Vidyattama, 2010). Equality in access to education is still an

issue in Indonesia (Suryadarma, Suryahadi, Sumarto & Rogers, 2006). Although national data show a net school enrolment

ratio of 98% for primary schools in 2010, enrolment is still only 86.2% for junior high school and 56% for senior high school.

Gaps in telecommunications infrastructure across regions also appear, with 80% of the country’s Internet activities taking

place in Java while 60% of the country’s population occupies the island. Indonesia has six major islands and thousands of

smaller ones, and the majority of the central and eastern parts of the country have yet to see consistent electricity con-

nections, let alone Internet (Biro Pusat Statistik, 2012).

The socio-economic history of each of Indonesia’s regions plays a role in the disparities seen among them, particularly

with regard to the presence or lack of political and governing institutions to facilitate development (Hill et al., 2008). The


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presence of natural resources also plays a signicant role in regional disparities in Indonesia, with regions that are heavily

endowed with abundant natural resources such as oil and gas more likely to have higher GDP per capita than those without.

This study aims to understand whether and to what extent these determinants producing spatial inequality are associated

with the Internet divide in Indonesia.

Table 1

Sample characteristics.

2010 2011 2012

Mean7SD or % Correlation with

Internet divide


Internet divide


Internet divide

Internet divide

Access Internet 9% 10% 12%Not access Internet 91% 90% 88%

District

GDP (in Trillion rupiah) 18.8730.2 0.320* 19.9731.2 0.411* 21.2732.4 0.121*

Gini index 0.3870.042 0.216* 0.4170.046 0.221* 0.4170.048 0.190*

Disparity in human capital 0.2670.06 0.210* 0.2570.07 0.310* 0.2570.06 0.198*

Disparity in telecommunication

infrastructures

Electricity access 0.567 0.12 0.561* 0.420* 0.217 0.29 0.431*

Landline networks 0.687 0.35 0.213* 0.210* 0.677 0.34 0.180*

Mobile phone access 0.237 0.15 0.242* 0.210* 0.167 0.15 0.210*

Cybercafé access 0.897 0.19 0.104* 0.105* 0.727 0.30 0.113*

Base transceiver station 0.897 0.14 0.113* 0.141* 0.827 0.13 0.128*

Mobile phone signal coverage 0.357 0.27 0.321* 0.220* 0.117 0.21 0.230*

Disparity in education services 0.927 0.12 0.210* 0.231* 0.907 0.13 0.214*

Spending for telecommunicationservices and infrastructures (in

Billion rupiah)

140722.5 0.109* 130722.1 0.105* 142729.7 0.110*

Spending for education services (in

Billion rupiah)

2087149 0.218* 2087153 0.320* 2847215 0.211*

Household

Household expenditure (in Million

rupiah)

1.0570.97 0.340* 1.0770.98 0.217* 1.0970.97 0.210*

Residential status

Urban areas 45% 0.210* 41% 0.311* 49% 0.231*

Rural areas 55% 0.210* 59% 0.311* 57% 0.231*

Landline telephone

Yes 8% 0.320* 7% 0.221* 6% 0.120*

No 92% 0.320* 93% 0.221* 94% 0.120*

Computer ownership

Yes 11% 0.421* 12% 0.320* 15% 0.312*

No 89% 0.421* 88% 0.320* 85% 0.321*Mobile phone ownership

Yes 75% 0.310* 78% 0.230* 84% 0.321*

No 25% 0.310* 22% 0.230* 16% 0.321*

Individual

Age 28719 0.210* 28719 0.312* 29719 0.221*

Gender

Female 50% 0.101* 50% 0.123* 52% 0.108*

Male 50% 0.101* 50% 0.123* 48% 0.108*

Education

University 13% 0.312* 13% 0.362* 14% 0.381*

High school 35% 0.142* 36% 0.160* 37% 0.132*

Secondary school and below 52% 0.210* 51% 0.120* 49% 0.231*

Employment status

Employed 93.9% 0.111* 93.8% 0.101* 93.7% 0.104*

Unemployed 6.1%

0.111* 6.2%

0.101* 6.3%

0.104*Poverty

oUS 2$ per day 46.1% 0.310* 43.3% 0.245* 41.1% 0.110*

4US 2$ per day 53.9% 0.310* 56.7% 0.245* 58.9% 0.110*

Reported * po0.05.





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4. Data and method

4.1. Susenas 2010– 2012 and of cial statistics

The individual data used in the analysis are taken from the National Socioeconomic Survey (Susenas) 2010–2012. Susenas

is one of the oldest and the best-regarded national representative surveys in developing countries ( Friedman & Levinsohn,

2002; Ravallion & Lokshin, 2007); it is also the only one in Indonesia that covers the whole archipelago (Pradhan, Suryahadi,

Sumarto, & Pritchett, 2001; Biro Pusat Statistik, 2009). Conducted by the government’s Central Bureau of Statistics, it has

been elded yearly since 1993 and is representative at the district level. The annual sample size is about 250,000 households

(close to 1.2 million individuals) (Biro Pusat Statistik, 2009), in all districts in the country. During the three years analysed,

more than one hundred new districts emerged as a result of district splits. In such cases, the data from the split districts

were aggregated and assigned to the original district denition. Variables to track the year and the number of ‘child’ districts

for each split were used. The 1998, pre-decentralisation, district denition frame, which comprised 292 districts were

applied. This practise follows Dreze and Sen (2002), and Kruse, Pradhan, and Sparrow (2012).

The survey instrument contains a core questionnaire, which collects information regarding the socio-demographic char-

acteristics of individuals and households, their education, labour market activities, and access to various ICTs including

landlines, mobile phones, personal computers/laptops, and the Internet. The Susenas data is linked with the Indonesian Village

Potential Census (PODES) and of cial statistics. First, PODES data provides information about the distribution of tele-

communications infrastructures such as electricity, landline networks, internet cafés, and mobile phone signal networks across

districts. PODES data also provides information on the distribution of education facilities across districts. Second, the district

GDP and Gini index data 2010–

2012 were used, all retrieved from the government’s Central Bureau of Statistics. Third, district

spending on telecommunications and education services was included in the model to determine whether more district

spending on human capital and telecommunications development relates to Internet access. Data on district spending on

telecommunications services and infrastructures as well as education services were retrieved from the Ministry of Finance.

4.2. Measures of the Internet divide

The Internet divide is measured by a dummy variable indicating individual access to the Internet. In the survey,

respondents were asked whether they had accessed the Internet in the last three months with the question “have you

accessed the Internet within the last three months?”) (Biro Pusat Statistik, 2010). The survey denes access to the Internet as

a connection made by respondents to an Internet enabling system such as a computer terminal, laptop, PC/computer, and

mobile device (Biro Pusat Statistik, 2010). The Internet divide is described using the socio-demographic characteristics of

individuals who either had or did not have access to the Internet. Differential access to the Internet is related to individuals

and their characteristics, including income level, education, employment, age and gender (Van Dijk, 2012). Table 1 presentssample characteristics in this study.

4.3. Measures of spatial inequality

Spatial inequality is dened as a disparity in resources and services due to discrepancies in social and economic factors

across geography (Kanbur & Venables, 2005). In this study, “across geography” means districts within a country. Four

measures of spatial inequality related to Internet access are used in this study. First, the Gini Index is a standard measure of

spatial inequality of economic capital (Kanbur & Venables, 2005). Studies found that a higher Gini Index related to a digital

divide (Kiiski & Matti, 2002). Second, to examine whether spatial inequality in human capital relates to a digital divide,

district disparities in education outcomes were used as a proxy for spatial inequality in human capital Kanbur and Venables

(2005). Third, spatial inequality in telecommunication infrastructure is measured by district disparities in the availability of

telecommunication infrastructures such as electricity, landline networks, mobile phones, cybercafés, mobile phone signal

networks, and base transceiver stations. Prior studies found that the Internet access between and within countries arerelated to the availability of these telecommunication infrastructures (Norris, 2001; Quibria, Ahmed, Tschang & Reyes-

Macasaquit, 2003). Fourth, spatial inequality in education services is measured by district disparities in higher education

facilities (colleges and universities). Studies suggest that the presence of colleges and universities is important support for

Internet penetration (Norris, 2001; Wilson et al., 2003; Mossberger, Tolbert & Stansbury, 2003).

The Palma ratio is adopted to measure district disparities in human capital, telecommunications services and education

services (Palma, 2011). In this study, district disparity in human capital is measured by the ratio of the largest 10% of

university graduates divided by the smallest 40%’s share within a district. Likewise, district disparity in electricity services is

measured by the ratio of the largest 10% of people who have access to electricity divided by the smallest 40% ’s share within a

district.

4.4. Individual and district control variables

Control variables include individual and district variables related to the Internet divide. Studies have identied thataccess to the Internet has been linked to a number of individual demographic and socio-economic characteristics, among





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them age, income, education, gender, and geographic location (i.e. urban–rural, big cities-small cities, and mainland-remote-

island) (Wilson et al., 2003; Warschauer, 2003; Dutton, Shepherd & di-Gennaro, 2007; Mosberger et al., 2003). Multiple

regression analysis across countries has shown that income levels and educational attainment are identied as providing

the most powerful explanatory variables for digital media access (Quibria et al., 2003; Hilbert, 2010). As for geographic

location, people living in urban centres have more access to computer services than those in rural areas. Gender was

previously thought to provide an explanation for the digital divide, with many believing that digital media use male

dominated; however, controlled statistical analysis has shown that income, education and employment act as confounding

variables and that females actually tend to embrace digital media to a greater degree than do males with the same level of income, education and employment (Ono & Zavodny, 2003; Hilbert, 2010; Brannstrom, 2012).

Household expenditure is used as proxy of income, this information is biased and dif cult to assess in many developing

countries, particularly in subsistence farming households. Income data is typically prone to under-reporting and mea-

surement error, with the contribution of individual production and in-kind transfers often overlooked. Household expen-

diture is, thus a more accurate measure of household economic resources, both in developing and developed countries

(Deaton & Zaidi, 2002; Jorgensen, 2002). Since price levels of consumer goods and services in Indonesia vary across the

country (Strauss et al., 2004), the amount of household expenditure has been deated with the consumer price index for

urban and rural regions. Rural ination is taken to be 5% higher than urban ination (Resosudarmo & Jotzo, 2009). This

calculation produces real spending adjusted for urban and rural ination. The Consumer Prices Index data were retrieved

from the government’s Central Bureau of Statistics (Biro Pusat Statistik, 2009).

District gross domestic product is used to determine whether district economic development may relate to the Internet

divide. District spending on education and telecommunications services and infrastructures was used to examine whether

district spending capacity on telecommunications services and infrastructures, and human capital development relates to

the Internet divide. These variables are particularly important given the decentralisation process that Indonesia has been

undergoing since 2001. Decentralisation is one of the well-known features in contemporary Indonesia that cannot be

ignored, especially when discussing spatial disparity among districts in the country (World Bank, 2007). Appendix A pro-

vides detailed information about each variable.

Table 2

Internet access across social groups and geography 2010–2012.

Variables 2010 (%) 2011 (%) 2012 (%)

Age

Young (o25 ) 11 16 18

Middle (25–50) 8 8 10

Old (450) 1 1 1 Δ young –middle age 3 8 8

Δ young –old 8 15 15

Gender

Male 9 11 14

Female 8 9 11

Δmale– female 1 2 3

Education

University 36 39 44

High school 17 19 21

Secondary school and below 2 3 4

Δuniversity–secondary school 34 36 40

Δhigh school–secondary school 15 16 17

Poverty

Non poor (42US$ per day) 9 14 17

Poor (o2US$ per day) 1 4 5

Δnon-poor – poor 8 11 12 Job status

Employed 13 15 17

Unemployed 11 11 11

Δemployed–unemployed 2 4 6

City areas

Cities 21 27 31

Country side 7 7 9

Δcities–countryside 133 20 22

Main–remote islands

Main islands 9 11 13

Remote islands 1 1 3

Δmainland–remote islands 8 10 10

Urban–rural areas

Urban areas 15 19 22

Rural areas 4 4 5

ΔUrban–rural areas 11 15 17





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The regression equation of the models can be written as follows. Considering an individual i nested in district j, the

model is:

β β β * = + ∑ + + µ + ϵE W X ij o j j ij ij j ij

With E ij*¼ logit (P (E ij*¼1)), W j is a set of district characteristics (i.e. Gini index, GDP, district disparity in electricity, landlinenetworks, internet cafés, etc.), X ij is a set of individual characteristics (i.e. age, gender, job status, education and household

expenditure), m j is a random intercept varying over districts with mean zero and variance sm2, ϵij is normally distributed with

zero and variance sϵ

2.

Multilevel models were carried out using Generalised Linear Latent and Mixed Models (GLLAMM) commands using Stata

13. Rabe-Hesketh and Skrondal (2012) explained that GLLAMMs are a class of multilevel latent variable models for (mul-

tivariate) responses of mixed type, including continuous responses, counts, duration/survival data, dichotomous, ordered

and unordered categorical responses and rankings. In this analysis, GLLAMM is used with logit link as the dependant

variable (Internet divide) which is binary.

The multilevel models were carried out in several steps. First, multilevel stratied models were carried out to examine

whether the effect of various types of spatial inequality on the Internet divide differs across urban–rural, city–countryside,

and remote islands–mainland islands. Second, multilevel models for pooled data between 2010 and 2012 were carried out

to estimate the effect of various types of spatial inequality on the Internet divide across years and the sample population.Third, to determine whether spatial inequalities and socio-economic groups substitute or reinforce each other, we estimated

Table 3

Results of stratied analysis.

Variables Urban Rural Cities Countryside Remote islands Main islands

Coef se Coef se Coef se Coef se Coef se Coef se

District

GDP 1.251* 0.004 1.501* 0.004 1.101* 0.002 1.411* 0.011 1.611* 0.012 1.200* 0.009

Index Gini 0.245* 0.002 0.345* 0.002 0.115* 0.001 0.211* 0.012 0.361* 0.002 0.210* 0.006Disparity in human capital 0.367* 0.004 0.470* 0.004 0.210* 0.002 0.350* 0.011 0.521* 0.011 0.350* 0.007

Disparity in telecommunication

infrastructures

Electricity access 0.251* 0.011 0.250* 0.010 0.130 0.110 0.171* 0.013 0.311* 0.011 0.250* 0.015

Landline networks 0.267* 0.012 0.361* 0.013 0.221 0.111 0.261* 0.010 0.333* 0.011 0.261* 0.016

Mobile phone access 0.354* 0.014 0.611* 0.012 0.211* 0.010 0.351* 0.011 0.612* 0.010 0.330* 0.017

Cybercafe access 0.123* 0.002 0.213* 0.002 0.121* 0.002 0.212* 0.010 0.421* 0.005 0.110* 0.008

Base transceiver station 0.156* 0.003 0.341* 0.003 0.126* 0.003 0.341* 0.009 0.456* 0.002 0.121* 0.003

Mobile phone signal coverage 0.021* 0.002 0.221* 0.001 0.022* 0.002 0.120* 0.009 0.321* 0.004 0.020* 0.004

Disparity in education services 0.132* 0.007 0.234* 0.002 0.023* 0.005 0.091* 0.006 0.422* 0.007 0.130* 0.008

Spending for telecommunication

services and infrastructures

0.111 0.108 0.201 0.101 0.100 0.101 0.110 0.209 0.311 0.136 0.120 0.108

Spending for education services 0.412* 0.006 0.512* 0.003 0.410* 0.003 0.510* 0.012 0.531* 0.013 0.410* 0.002

Household

Household expenditure 0.616* 0.0 08 0.723* 0.013 0.603* 0.014 0.812* 0.003 0.788* 0.041 0.708* 0.007Connected to landline telephone 0.483* 0.015 0.326* 0.039 0.367* 0.022 0.681* 0.011 0.659* 0.108 0.549* 0.013

Have PC/laptop 1.412* 0.011 1.468* 0.018 1.428* 0.019 1.7012* 0.010 1.459* 0.056 1.541* 0.010

Have mobile phone 1.280* 0.051 2.146* 0.058 1.096* 0.097 1.611* 0.012 1.869* 0.142 1.895* 0.039

Individual

Age 0.106* 0.000 0.095* 0.000 0.110* 0.000 0.201* 0.001 0.087* 0.002 0.101* 0.000

Female 0.462* 0.014 0.231* 0.014 0.642* 0.017 0.260* 0.011 0.298* 0.048 0.382* 0.008

University 3.480* 0.019 4.071* 0.031 3.471* 0.032 4.481* 0.012 4.379* 0.127 3.678* 0.016

High school 2.466* 0.019 2.990* 0.031 2.416* 0.031 3.463* 0.021 3.253* 0.125 2.638* 0.016

Secondary school 1.663* 0.201 2.060* 0.032 1.543* 0.032 1.762* 0.023 1.979* 0.048 1.767* 0.017

Employed 0.020* 0.001 0.037 0.017 0.058* 0.019 0.020 0.012 0.083 0.057 0.022* 0.001

Poverty 0.201* 0.003 0.011 0.013 0.204* 0.002 0.202 0.105 0.022 0.021 0.211* 0.003

Years

2011 0.010* 0.001 0.017* 0.002 0.009* 0.002 0.015* 0.002 0.014* 0.002 0.013* 0.002

2012 0.012* 0.001 0.010* 0.001 0.010* 0.002 0.010* 0.001 0.011* 0.001 0.011* 0.001

Constants 2.716* 0.053 5.407* 0.064 1.852* 0.100 2.818* 0.041 6.133 0.181 4.055*

Variance between districts 0.18 0.16 0.15 0.19 0.12 0.20

N 1144809 1450941 375141 1103421 225501 2370249

Log likelihood 123782 65752 46093 123782 6606 186987

Reported * po0.05.





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the association of the interaction between various forms of spatial inequality and individual socio-economic characteristics

on the Internet divide. For each of the models, the estimated coef cient, standard errors, district variances, and log like-

lihood as an indicator of model t are reported. All models were estimated using maximum likelihood estimation.

5. Results

5.1. Descriptive analysis

The descriptive statistics show that the dataset is relatively balanced across the study period. The average district GDPremained relatively similar, at about IDR 18.8–21.2 trillion (1.9–2.2 billion US$). The percentage of poor people across

Table 4

Results of multilevel analysis with cross-level interaction.

Model 1 Model 2 Model 3

Coef se Coef se Coef se

District

GDP 1.121* 0.005 1.101* 0.004 1.100* 0.003

Index Gini 0.201* 0.002 0.190* 0.002 0.189* 0.001Disparity in human capital 0.211* 0.001 0.200* 0.002 0.202* 0.003

Disparity in telecommunication infrastructures

Electricity access 0.232* 0.010 0.210* 0.009 0.212* 0.012

Landline networks 0.211* 0.020 0.201* 0.021 0.209* 0.020

Mobile phone access 0.311* 0.010 0.312* 0.011 0.314* 0.010

Cybercafé access 0.124* 0.006 0.115* 0.005 0.116* 0.004

Base transceiver station 0.256* 0.003 0.143* 0.002 0.165* 0.002

Mobile phone signal coverage 0.021* 0.004 0.019* 0.003 0.018* 0.004

Disparity in education services 0.141* 0.006 0.132* 0.004 0.134* 0.002

Spending for telecommunication services and infrastructures 0.131 0.112 0.141 0.110 0.143 0.111

Spending for education services 0.211* 0.001 0.215* 0.002 0.217* 0.003

Household

Household expenditure 0.617* 0.007 0.620* 0.006 0.531* 0.005

Rural areas 0.679* 0.009 0.681* 0.006 0.682* 0.007

Remote islands

0.800* 0.023

0.811* 0.022

0.781* 0.023Cities 0.555* 0.010 0.561* 0.011 0.573* 0.012

Connected to landline telephone 0.353* 0.014 0.358* 0.015 0.342* 0.014

Have PC/laptop 1.424* 0.010 1.428* 0.011 1.425* 0.012

Have mobile phone 1.718* 0.038 1.721* 0.038 1.722* 0.037

Individual

Age 0.101* 0.000 0.111* 0.000 0.123* 0.000

Female 0.378* 0.008 0.371* 0.007 0.322* 0.006

University 3.690* 0.016 3.691* 0.014 3.692* 0.013

High school 2.635* 0.015 2.641* 0.012 2.542* 0.010

Secondary school 1.781* 0.016 1.791* 0.013 1.692* 0.011

Employed 0.032* 0.001 0.037* 0.001 0.039* 0.002

Poverty 0.211* 0.001 0.200* 0.002 0.201* 0.003

Years 0.011* 0.003 0.012* 0.004 0.012* 0.004

2011 0.013* 0.002 0.011* 0.003 0.013* 0.003

2012

First quartile of household expenditureBottom 10 electricity access 0.017* 0.001

First quartile of household expenditureBottom 10 landline networks 0.019* 0.009

First quartile of household expenditureBottom 10 mobile phone access 0.010 0.013

First quartile of household expenditureBottom 10 cybercafe access 0.101* 0.002

First quartile of household expenditureBottom 10 mobile phone signal coverage 0.050* 0.002

First quartile of household expenditureBottom 10 base transceiver station access 0.045* 0.001

High school and aboveBottom 10 electricity access 0.011* 0.001

High school and aboveBottom 10 landline networks 0.025* 0.002

High school and aboveBottom 10 mobile phone access 0.132* 0.000

High school and aboveBottom 10 cybercafé access 0.010* 0.001

High school and aboveBottom 10 base transceiver station access 0.019* 0.001

High school and aboveBottom 10 mobile phone signal coverage 0.112* 0.004

Constants 3.522* 0.040 3.522* 0.040 3.522* 0.040

Variance between districts 0.22 0.22 0.22

N 3334533 3334533 3334533

Log likelihood

187933

189910

189910

Reported *po0.05.





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districts remained large, with about 41–46% of people consuming less than US $2 a day. Averages of age, gender, and

employment status in the survey did not change signicantly during the three years studied. Household expenditure is

shown to have increased slightly. Only a small proportion of individuals had attained university education (13–14%); the

education level of the majority was secondary school or below (49–52%). About 15% and 7% of respondents lived in big cities

and remote islands respectively.

The Gini index increases by 0.3 points during the three-year study period. The gap in the Gini index across districts is

quite large at SD 0.42–0.48. District disparities in human capital, landline networks and cybercafé presence remained large.

However, the spatial gap in electricity, mobile phones, and mobile phone signal access decreased. District spending oneducation and on telecommunications services and infrastructures varied across districts with a range between IDR 208–

284 billion (21–29 million US$) and IDR 140–142 billion (14–15 million US$) respectively.

Table 2 shows the distribution of Internet access across years, socio-economic groups and geography. Internet access is

unequal, showing divergent trends among education and poverty levels as well as across generations. The deepest divide

between highly educated and less educated individuals in Internet access in 2012 is at 40%. Inequality in the Internet access

of poor and non-poor people also deepened substantially (from 8% in 2010 to 12% in 2012). A deepening gap in Internet

access between the younger and older generations also appears, from 8% in 2010 to 15% in 2012. Gaps in Internet access also

increased between females and males (from 1% in 2010 to 3% in 2012).

5.2. Spatial distribution of Internet access

A sense of the importance of area variations in Internet access can be gained from the map in Fig. 1, which highlights

geographical disparities across districts. Most districts in urban areas in Central Java, East Kalimantan, and North Sumatrahave more widespread Internet access than other regions in Indonesia, in particular Papua, Sulawesi and small islands across

Maluku and Ambon.

Fig. 2 shows disparities in telecommunications infrastructures across districts. It indicates that districts in Papua, Kali-

mantan, South Sumatra, and Central Sulawesi have greater disparities in telecommunications infrastructures than do dis-

tricts across Java and Bali.

The next section presents the results of multilevel analyses that reveal whether spatial disparities in telecommunications

infrastructures are related to the Internet divide in Indonesia.

5.3. Strati ed analysis

Table 3 shows the results of stratied analysis. The estimates for the stratied models suggest a substantial urban–rural,

city–countryside and remote island–main island difference in Internet access. The coef cient of spatial inequality indicators

for rural, countryside and remote island areas is higher than the coef cient of urban, city and main island areas.In all models, the association of GDP on the Internet divide is signicant at 5%. Likewise, higher district spending for

education services is likely to increase individual access to the Internet with statistical signi cance at 5%. However, district

spending on telecommunications services and infrastructures appears to have no signicant effect on the Internet divide.

Having a mobile phone signicantly increases an individual’s likelihood of having Internet access. The substantial difference

in the degree to which mobile phone ownership affects Internet access is also shown between urban–rural, city–countryside

and remote island–main island areas. However, signicant associations of poverty and unemployment with the Internet

divide are only shown in urban, city and main island areas. Poverty and unemployment appear not to be statistically

signicant inuences on Internet access for rural, countryside and remote island samples.

5.4. Pooled analysis

Table 4 shows the results of multilevel analysis. Model 1 presents results from multilevel logit regression before cross

variable interaction. The portrait of the digital divide across social and economic groups is explicit. The digital divide acrossgeneration and gender is shown from the negative and signicant associations of increased age and being female. Across the

models, the older generation and females are less likely to access the Internet than the younger generation and males.

Human capital in the form of education is signicantly associated with digital access; those who have graduated at the

university level have substantially wider access to the Internet. Economic capital is also strongly and signi cantly related to

digital access. Households with higher monthly expenditures are more likely have Internet access than those with less.

Likewise, having a mobile phone, personal computer/telephone networks at home increases the likelihood of having

Internet access. The geographic divide in digital access is also found in all models. Those living in rural, countryside, and

remote areas are less likely to have access to the Internet.

5.5. Interaction analysis

Interaction terms between indicators of telecommunications infrastructure inequalities and indicators of individual

socio-economic groups enable us to examine whether spatial inequalities and socio-economic groups reinforce each other





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Appendix A. Variables denition

Variables name Questions/explanation Sources

Internet divide Apakah pernah mengakses internet dalam 3 bulan terakhir?” (“Have you accessed the Internet

within the last three months?”). The survey denes access to the Internet as a connection made

by respondents toward Internet enabling system such as computer terminals, laptop, PC/com-

puters, and mobile devices (Biro Pusat Statistik, 2010).

Susenas

2010/12

Coding 1¼Yes 0¼No

GDP District Gross Domestic Product (in Trillion rupiah) BPS 2010/12

Gini Index Gini ratio BPS 2010/12

Disparity in human capital The ratio of the largest 10% of the people graduated from university divided by the smallest

40%’s share within a district.

Susenas

2010/12

Disparity in electricity access The ratio of the largest 10% of the people who having access to electricity divided by the

smallest 40%’s share within a district.

PODES 2010/

12

Disparity in landline networks The ratio of the largest 10% of the people who having access to landline networks divided by the


Susenas

2010/12

Disparity in mobile phone access The ratio of the largest 10% of the people who having access to mobile phone divided by the


Susenas

2010/12

Disparity in cybercafé access The ratio of the largest 10% of the villages that having access to cybercafé divided by the


PODES 2010/

12

Disparity in base transceiver station

distribution

The ratio of the largest 10% of the villages that having access to BTS divided by the smallest

40%’s share within a district.

PODES 2010/

12

Disparity in mobile phone signalcoverage

The ratio of the largest 10% of the villages that having been coverage by mobile phone signaldivided by the smallest 40%’s share within a district.

PODES 2010/12

Disparity in education services The ratios of the largest 10% of the villages have university divided by the smallest 40%’s share

within a district.

PODES 2010/

12

Spending for telecommunication

services and infrastructures

Total amount of budget spend for telecommunication services and infrastructures (in Billion

rupiah).

SIKD 2009/

11

Spending for education services Total amount of budget spend for education services and infrastructures (in Billion rupiah). SIKD 2009/

11

Household expenditure Monthly household expenditure (in Million rupiah) Susenas

2010/12

Rural areas An urban area is dened as the areas that have a major non-agricultural activity and function as

the urban settlements, concentration and distribution of government services, social services,

and economic activities. A rural areas is dened as the areas that have a major agricultural

activity, including the management of natural resources in the region, and function as rural

settlements, government services, social services, and economic services (Biro Pusat Statistik,

2009). Respondent lives in rural areas, coding 1¼rural 0¼urban

Susenas

2010/12

Landline telephone Respondents have landline telephone, coding 1¼Yes 0¼No Susenas2010/12

Computer ownership Respondents have computer or laptop, coding 1¼Yes 0¼No Susenas

2010/12

Mobile phone ownership Respondents have mobile phone, coding 1¼Yes 0¼No Susenas

2010/12

Age Age of respondents Susenas

2010/12

Female Respondent is female, coding 1¼female 0¼male Susenas

2010/12

University Respondents have university education, coding 1¼University 0¼others Susenas

2010/12

High school Respondents only have high school education, coding 1¼High school 0¼others Susenas

2010/12

Secondary school and below Respondents only have secondary school/primary school education, coding 1¼secondary

school/primary school education 0¼others

Susenas

2010/12

Employed Respondents are being employed, coding 1¼employed 0¼employed Susenas2010/12

Poverty Household consumes less than US $2 per day Susenas

2010/12

Cities Biro Pusat Statistik (2009) classies 60 districts in Indonesia, which is categorised as big cities.

Big cities refer to districts within a region which have function as a centre of population, gov-

ernment, commerce, and culture. Population within big cities is above 2 million people.

Countryside is district with have population 10.000 and less.

BPS 2010

district code

Main islands Mainland is districts located at ve big islands in Indonesia (Java-Bali, Sumatra, Kalimantan,

Sulawesi, and Papua). Remote island is districts, which located at small islands outside ve big

islands in Indonesia. District in mainlands in Indonesia is generally more developed than district

within remote islands due to they have better access of infrastructures, facilities and services

(Biro Pusat Statistik, 2009).

BPS 2010

district code

Remote islands (1) Districts at small islands across Sumatra, Kalimantan, Sulawesi, Maluku, Nusa Tenggara, and

Papua (e.g. Kepulauan Nias, Raja Ampat, etc.)

(2) Districts at remote areas in Papua, Kalimantan, Sulawesi, Sumatra and Nusa Tenggara main

islands (i.e. Kabupaten Puncak, Yahukimo, etc.)

BPS 2010

district code





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