The 'Digital Divide' Among Financially Disadvantaged Families in Australia by Jennifer McLaren and Gianni Zappalà
Despite figures suggesting that Australia is a high consumer of information and communication technologies (ICT), it is well documented that the pattern of this consumption is not spread evenly across the population; a 'digital divide' exists. In general, research suggests that people from higher socioeconomic backgrounds have greater access to ICT compared to those from lower socioeconomic backgrounds. A less well-researched area is the factors that may influence ICT access and usage within certain demographic and socioeconomic groups. This paper presents new data on the access and usage of ICT (computers and the Internet) by 3,404 households and 6,874 children from financially disadvantaged backgrounds. Fifty-nine per cent of the sample had a home computer and just under one-third had the Internet connected at home. The most common location for accessing the Internet was at school. A striking finding was the strong association between the level of parental education and ICT access and use. Schools are important in closing or levelling the access gap, as most students use computers and the Internet at school. However, considering the importance of having home Internet access for children's educational performance, the fact that almost three-quarters of students in this study did not use the Internet at home is of concern, particularly given that almost half of a comparable Australian population have home Internet access. Finding ways to increase the home access of low-income families to the Internet should therefore remain a policy priority for all sectors aiming to bridge the digital divide. Policies aimed at bridging the digital divide should also ensure that programs provide appropriate parenting support and emphasise the educational importance of having home access to computers and the Internet.
The 'digital divide' revisited
ICT Access and Usage in Australia
Background to the data
Profile of the Sample
Discussion and Conclusions
"Australia has another great dividing range. In the age of the information economy, modems - not mountains - separate the population" (Manktelow, 2001).
The release of the 2001 Australian Census figures this year has led to renewed concern among commentators that the gap between the 'haves' and 'have nots' with respect to the access and usage of information and communication technologies (ICT), commonly referred to as the 'digital divide', is widening (Mathewson, 2002). The Census revealed that nationally, an average of 42 per cent of Australians had used a computer at home in the week preceding the Census. With respect to the Internet, only 19 per cent of Australians had been online at home in the week before the Census. While these figures do not tell us how many households have a computer or the Internet at home they nevertheless provide a reasonable proxy that reveals that the 'digital divide' is still a real part of the Australian landscape. These figures also seem to dampen some of the more optimistic views that were being expressed about the 'digital divide' as recently as a year ago. A paper one of us presented at a forum on the new economy, for instance, stated:"... should we worry about it [the digital divide]? Is it not just a transitory phenomenon that will sort itself out in the medium to long-term? Like all new technologies, there will be some disparity of access, often due to cost initially, but as the costs of the technology become cheaper, it will be less of a problem. Evidence from the U.S. suggests that this may indeed be the case. Internet access among some disadvantaged groups that previously had low connection rates is now rising steadily. In Australia, the ABS projects that by the end of 2001 ... every second household in Australia will have home Internet access" .
While there is some evidence that an increasing number of people have access to ICT, this is occurring more slowly than predicted by some analysts. More importantly, the evidence confirms that the probability of households and children having home access to ICT is strongly related to socioeconomic status (SES), namely access increases with higher levels of SES. We know less, however, about the factors associated with home access and usage of ICT within certain SES groups.
This paper presents new data on the access and usage of ICT (computers and the Internet) by households and children from financially disadvantaged backgrounds. The next section outlines the concept of the 'digital divide' and its relationship to socioeconomic status. In particular, there are significant educational implications of not having home access to computers and the Internet for children from financially disadvantaged backgrounds. An overview of the nature and source of the data analysed in this paper is then presented. This is followed by a discussion of the key findings in terms of the factors associated with the ownership and access of ICT, and the frequency and location of ICT usage. The final section concludes by outlining some preliminary implications of the findings.
The 'digital divide' revisited
The 'digital divide' argument is well-known - namely, that the unequal access and usage of ICT across the population - is compounding disadvantage for some, because having access to ICT is becoming central to being able to fully participate in the economic, social, political and cultural spheres of society (Lee et al., 2002). The advent and increasing sophistication of ICT has changed, and will continue to change, the way in which businesses, governments, communities and individuals operate and interact with each other.
Some of the key spheres in which ICT is influencing participation (or lack thereof) in society include:
- Enabling people to search and apply for employment opportunities; and,
- Many jobs now involve having minimum levels of ICT competency as prerequisites.
Education & lifelong learning
- Opportunities for lifelong learning, especially for people who have not had experience of the formal education sector, are more easily accessed through distance and e-learning programs;
- Access to ICT is central for 'online schools' for children living in remote areas;
- Studies show that students, teachers and parents feel that computers have a positive effect on learning (Ainley et al., 2000);
- Recent research from the U.S. shows that the presence of computers and Internet at home are strongly and positively associated with the academic outcomes of school children, particularly children from disadvantaged backgrounds (Wilhelm et al., 2002);
- Given the increasing use of ICT by students at school, there is a risk that teachers and schools operate on the assumption that all children have access to computers and the Internet at home, which may influence their expectations of students' work and their computer literacy at school (Mathewson 2002); and,
- Different levels of ICT access, support and skills between private and public schools may further exacerbate public versus private school disparities.
Access to services
- Many government services are being increasingly provided over the Internet, as are billing and banking services, which often offer discounts for paying or accessing services online. Using the Internet for these services not only saves time but is more cost-effective. A recent study found that most people (73 percent) who incorporated the Internet into their everyday lifestyle were able to reduce the time spent on errands by four hours per week, and many (40 percent) saved up to $A30 per week (Centre for International Economics, 2001).
Political participation & social inclusion
- Given the fact that the Internet is able to transmit information efficiently across geographical boundaries, it has the capacity to reduce some of the disadvantage associated with living in distant and remote locations;
- The Internet is becoming increasingly important for political participation and the democratic process, with several political movements or protests now occurring via e-mail campaigns. Similarly, most political parties and several political representatives now use the Internet as a key means of communication with the electorate and constituents (Curtin, 2001);
- Many cultural/leisure activities now involve or benefit from access to the Internet. In fact, the Internet is also "promoting social inclusion of traditionally marginalised groups such as the elderly, disabled and women with children " through facilitating communication and access to support networks .
The unequal access to ICT not only affects the lives of individuals who happen to be on the wrong side of the divide, but society as a whole (Perri 6 with Jupp, 2001). A 'technology' gap will have:
- Economic consequences
Australia will have lower productivity if fewer people have the opportunity to exploit the benefits of ICT (Lee et al., 2002); and,
- Social consequences
Australia will be less cohesive if the 'new' or 'information' economy/society becomes the preserve of an exclusive minority (Zappalà et al., 2002).
Furthermore, while having access to the Internet can bring several benefits such as those listed above, more recently, commentators have pointed out that the 'digital divide' is more than just a simple division between those with access to the physical hardware of the new ICT and those without. The concept needs to also encompass the broader social environment within which technologies operate. As one recent critic of the 'digital divide' label has argued:"[A]ccess to ICT is embedded in a complex array of factors encompassing physical, digital, human, and social resources and relationships. Content and language, literacy and education, and community and institutional structures must all be taken into account if meaningful access to new technologies is to be provided" .
A simple but useful concept that encapsulates this idea is what has been termed the 'ABCs of the digital divide' - Access, Basic Training and Content . It recognises that the divide is not solely about physical access to ICT, but also ensuring that people have the requisite resources and skills to use the technology appropriately. The data in this paper shed most light on the access issue .
ICT Access and Usage in Australia
Before we move onto examining the data in more detail, it is useful to briefly review some of the key studies and surveys that have sought to identify the extent of ICT usage by individuals in Australia as well as the factors that may be driving the differential access. The findings from four recent studies are summarised in Table 1, although comparisons are difficult because of the different sample sizes and timeframes of each particular survey.
The most reliable of the four is the survey conducted by the Australian Bureau of Statistics (ABS) in November 2000 (ABS, 2000a). It showed that just over half (56 percent) of all households in Australia had a computer in their home, and just over one-third (37 percent) had home Internet access. These figures represented a sharp increase in Internet access, as 1998 estimates by the ABS suggested that only one in eight households were connected to the Internet. Furthermore, on the basis of trends at the time, the ABS projected that every second household in Australia (or 50 percent) would have home Internet access by the end of 2001.
Table 1: Recent Australian data on Household ICT Access.
Study ABS (2000a) National Office for the Information Economy (NOIE) (2002) Casson et al. (2002) Ericsson (2002)
as cited in Connors (2002)
Time of survey November 2000 September 2001 2000-2001 May 2002 Sample 3,200 households 500 households 1,252 households 2,000 individuals Percent with Computer Percent with Internet Percent with Computer Percent with Internet Percent with Computer Percent with Internet Percent with Computer Percent with Internet All households 56 37 64 52 44 76 68 With children 74 48 58 No children 46 32 36 Household income $A0-49K 37 21 22-37 >$A50K 77 57 67
Table 1, suggests the ABS projection was accurate, as data collected in September 2001 by the National Office for the Information Economy (NOIE) estimated that almost two-thirds (64 percent) of Australian households owned or leased a computer, and just over half of all households (52 percent) were connected to the Internet (NOIE, 2002). The other two studies listed in Table 1, are less comparable as their samples were skewed towards people in capital cities in the case of Ericsson, and towards rural areas in the other (Casson et al., 2002). The most recent study, based on a sample of 2,000 individuals across five state capitals, conducted by Ericsson Australia, found that three-quarters of Australians have a PC at home and almost 70 percent have home Internet access (Connors, 2002). Overall, these surveys confirm that on a comparative basis, Australia ranks highly (third in the world) in adopting 'Information Economy enabling technologies' (NOIE, 2002; DITR, 2002).
Despite these figures that suggest Australia is a high consumer of ICT, it is well documented that the pattern of this consumption is not spread evenly across the population (Zappalà et al., 2002). In brief, the 'usual suspects' of socioeconomic disadvantage are involved in the digital divide:
- Income: Level of income is an important factor in determining who benefits from the new technology. In 1998-1999, for instance only six percent of households on incomes less than $A19,000 were connected to the Internet compared to 47 percent of those on incomes of more than $A84,000 (Hellwig and Lloyd, 2000). In 2000 the disparity between income groups was still relatively high, with income earners in the top bracket 3.5 times more likely to have an Internet connection at home than those in the lowest bracket. The ABS survey found that households on incomes of $A50,000 or greater are twice as likely as households with incomes less than $A50,000 to have a home computer and Internet access (ABS, 2000a). A key reason why low-income households with computers do not have Internet access is due to the costs of connection (Curtin, 2001).
- Level of education: The study by researchers at the National Centre for Social and Economic Modelling (NATSEM) found that, with all else being equal, educational attainment of an individual was a stronger predictor of having home computers and the Internet than income (Hellwig and Lloyd, 2000). Individuals with a university education were 2.5 times more likely to have home access to the Internet than those without.
- Geographic location: Although the connection between the 'urban-rural divide' and the 'digital divide' is subject to debate, where a person lives does appear to influence their home access to the Internet. While the proportion of adults with Internet access at home in metropolitan areas grew from 24 to 30 percent between 1998 and 1999, the corresponding increase in non-metropolitan areas was from 15 to 18 percent (Hellwig and Lloyd, 2000). The latest figures from the ABS suggest that the gap between city and country in terms of Internet access is decreasing, with 40 percent of all metropolitan households having access compared to 32 percent of all households in non-metropolitan areas. Furthermore, once studies control for the influence of education and income, the influence of geographic location diminishes. This suggests that the observed differences between metropolitan and non-metropolitan areas is a function of the different socioeconomic characteristics of metropolitan and non-metropolitan populations, in particular, the lower income and qualification levels of the latter. As one researcher has stated, "Geography may not determine it [Internet access], but there is obviously a geographical dimension to it" .
- Age: Adults aged over 55 are significantly less likely to have Internet access compared to younger groups in the population (ABS, 2000a).
- Gender: The role of gender is unclear, with some studies finding that females have lower take-up rates for the Internet than males (ABS, 2000a) while other studies find that gender plays little to no role in access (NOIE, 2002).
- Occupation: Blue-collar workers are less likely to be connected to the Internet at home compared to other occupational groups after controlling for income and qualifications. Those in lower income jobs are also less likely to use a computer or access the Internet at work (Hellwig and Lloyd, 2000).
- Family type: Households with children are more likely to have home computers and Internet access compared to households without children. One-parent households, however, are far less likely to have access to the Internet (26 percent) than two-parent households (51 percent) (ABS, 2000c).
- Indigenous status: Indigenous Australians are less likely to have home computers and Internet access compared to non-indigenous Australians.
Most of these findings confirm that people from higher socioeconomic backgrounds have greater access to ICT compared to those from lower socioeconomic backgrounds. Another important dimension is the factors that may influence ICT access and usage within certain demographic and socioeconomic groups. In particular, what factors are associated with home computer and Internet access for children from low socioeconomic backgrounds? There is little research that has specifically addressed this particular issue (see Funston and Morrison, 2000 for an exception).
Background to the data
The data for this section come from administrative records of students and families on The Smith Family's (TSF) Learning for Life (LFL) program . The LFL program aims to increase the participation of children from financially disadvantaged families in the educational process by the provision of financial and educational support (see Zappalà and Parker, 2000; Smyth et al., 2002 for an overview of the program). As part of developments and enhancements to the program aimed at increasing access and usage of ICT by students, a small survey was included as part of the annual communication to families in October 2001. The main aim of the survey was to collect benchmark data on computer and Internet access and usage among LFL students. Although the survey was sent to parents in 5,850 households, they were asked to pass on the survey/s to their child/children to complete. Of the total students in the population (11,948), 7,226 completed the surveys, giving a response rate of 61 percent. Each survey contained a unique student code to enable responses to be matched to background information contained in TSF's Client Services Management Information System (CSMIS) database.
Following data entry and the matching of responses to the relevant background information, several steps were taken to clean the data and arrive at the two final samples used for this analysis. First, the 7,226 student responses were screened for internal inconsistencies. For instance, 352 cases were removed because the student had answered 'no' in response to the question 'Do you ever use the Internet?' but also answered 'sometimes', 'often' or 'regularly' to another question on how often they use the Internet. This left a final student database of 6,874 students.
Second, given that almost 85 percent of students had siblings who also took part in the survey, a database of 'households' that responded was created . This was particularly important for examining the extent of household access to ICT. Responses to questions such as 'Do you have a working computer in your home?' for instance, would be misleading if analysed on an individual basis, since two siblings answering 'yes' to this item does not mean that there are two households with a computer. The 'household' database allows the level of analysis to be the 'family unit' rather than the individual student.
Third, creating a household database enabled us to further filter and screen the sample so that inconsistent responses between siblings from the same household could be removed . This left a final sample of 3,404 households. This represents 58 percent of the total number of households that were on LFL at the time the data were collected. Fourth, as is discussed below, the respondents and non-respondents did not differ greatly in terms of the key characteristics.
Profile of the Sample
Table 2 presents the characteristics of the sample by a range of socio-demographic and socioeconomic characteristics:
- Almost half (47 percent) of the students were in Years 7-10 with just under one-third in Years 4 to 6. There was no difference between respondents and non-respondents in terms of student age.
- There was an even split between male and female students. Once again, there was no difference between respondents and non-respondents in terms of student sex.
- Most of the students that responded (59 percent) lived in non-metropolitan areas . Students who lived in metropolitan areas were slightly less likely to have responded (46 percent of non-respondents came from metropolitan areas compared to 41 percent of respondents).
- Over two-thirds (68 percent) of students lived in areas that were below the median level of locational disadvantage in Australia as measured by the Index of Relative Socio-Economic Disadvantage (IRSED). The IRSED is one of five Socio-economic Indexes for Areas (SEIFA) derived from the 1996 Census of Population and Housing. The indexes relate to socio-economic aspects of geographical areas. The IRSED is derived from features such as low income, lack of English language fluency, low educational attainment and high unemployment. A low score on this index indicates that the area has high levels of low-income families and individuals in unskilled occupations with little training. The percentile indicates the relative extent of disadvantage compared with other communities in Australia. For example, living in an area that scored in the bottom decile indicates that the families in the area are on average worse off than 90 per cent of the rest of the families in Australia. An IRSED score was calculated for each case in the sample based on their post-code and then converted into percentile bands. There was no difference between respondents and non-respondents in terms of this indicator.
- The majority (59 percent) of students that responded came from one-parent families. Students from one-parent families were also less likely to have responded (66 percent of non-respondents came from one-parent families).
- Over two-thirds (69 percent) of the students have parents with ten or less years of education (i.e. Completed up to or less than Year 10). There was no difference between respondents and non-respondents in terms of level of parental education.
- Approximately five out of every six students were from an English-speaking background. There was little difference between respondents and non-respondents in terms of ethnic and cultural background.
- An overwhelming majority (90 percent) of the students came from households where social security was the main source of income. There was no difference between respondents and non-respondents in terms of this indicator (91 percent of non-respondents were also from households where social security was the main source of income).
- Just under half of the students (44 percent) lived in public housing, just over one-third (36 percent) were from families that lived in privately-rented accommodation and one-fifth were from families that either owned or were paying off their own homes. Students who lived in public housing were less likely to have responded (51 percent of non-respondents), while those whose parents owned or were paying off their own homes were slightly more likely to have responded (20 percent compared to 13 percent of non-respondents).
Table 2: Socio-demographic characteristics of survey sample.
Student Characteristic Number  Percent Year level at school 1-3 886 13 4-6 2,023 30 7-10 3,214 47 11-12 701 10 Total 6,824 100 Sex Male 3,407 50 Female 3,461 50 Total 6,868 100 Location Metropolitan 2,800 41 Non-metropolitan 4,074 59 Total 6,874 100 Level of locational disadvantage (IRSED)  Bottom 1,273 19 10-25 percent 1,484 22 25-50 percent 1,874 27 50-75 percent 1,432 21 75-90 percent 570 8 Top 10 percent 195 3 Total 6,828 100 Family type One-parent 3,933 59 Two-parent 2,787 41 Total 6,720 100 Parental education  < Year 10 1,183 22 Year 10 2,592 47 Year 12 698 13 TAFE/Other post-secondary 608 11 University degree 378 7 Total 5,459 100 Ethnic/cultural background  Anglo-Australian 5,348 79 Aboriginal/Torres Strait Islander (ATSI) 100 1 English speaking background (ESB) 201 3 Europe 332 5 Asia 138 2 Middle East & Africa 517 8 Central & South America 99 1 Pacific Islands 68 1 Total 6,803 100 Main source of income Social security 5,980 90 Employment 630 10 Total 6,610 100 Housing type Public rental 2,986 44 Private rental 2,388 36 Owns/purchasing house 1,356 20 Total 6,730 100
Home access to computers and the Internet
Overall, using the household sample, 59 percent of families had a computer at home. At first, this appears to be a higher level of ownership than that revealed by the ABS survey cited in Table 1. A more appropriate comparison, given that our sample comprises only households with school-aged children, would be with computer ownership among households with dependent children under the age of 18 who have access to a computer. This suggests that LFL families are significantly below the national average, as almost three-quarters (74 percent) of all Australian households with dependent children have a home computer.
Table 3: Home computer and Internet access (LFL Households).
Computer Internet Percentage Number Percentage Number Yes 59 2,006 32 1,085 No 41 1,398 68 2,319 Total 100 3,404 100 3,404
Table 3 also shows that just under one-third (32 percent) of families were connected to the Internet at home . These results are not too dissimilar to the level of home access revealed by the 2000 ABS survey, where 37 percent of households had access to the Internet. However, a greater level of disparity is revealed through the more meaningful comparison with households with dependent children, since 48 percent of all Australian households with dependent children under the age of 18 had home Internet access. Furthermore, it is also below the 58 percent of households with children that had home Internet access revealed by the more recent CLC survey (see Table 1).
Given that our sample comprises households that are all financially disadvantaged, it is not surprising that we would find lower levels of home access to computers and the Internet compared to families in the wider population. The remainder of this section examines the extent to which certain socio-demographic and socioeconomic factors are associated with home access of computers and the Internet within this group of financially disadvantaged households.
ICT home access and socio-demographic variables
Table 4 shows the proportion of households that had computer and Internet access at home according to a number of socio-demographic variables. Several points stand out:
- The geographic location of the household had no influence in terms of having a home computer; households in metropolitan areas were only slightly more likely to have Internet access compared to those in non-metropolitan areas. This finding may seem to go against the commonly held view that the 'digital divide' has a spatial dimension (Curtin, 2001). Studies that have used multivariate techniques in examining Internet access, however, have found that the influence of geography disappears once variables such as education level and income are controlled for (Hellwig and Lloyd, 2000). Given that this sample comprises only low-income households, these initial findings suggest that geographic location per se is not a significant influence with respect to access to ICT (see also Curtin, 2001 on this point) .
Table 4: ICT home access and socio-demographic variables.
Characteristic Computer (percentage) Internet (percentage) Overall distribution 59 32 Location Metropolitan 59 34 Non-metropolitan 59 30 Ethnic Background Anglo-Australian 58 30 ATSI 25 15 ESB 50 30 Europe 71 53 Asia 81 43 Middle East & Africa 64 42 Central & South America 66 43 Pacific Islands 28 8 Family type One-parent 55 28 Two-parent 66 39
- The ethnic/cultural background of the household seems to be associated with levels of ICT access. While caution is needed with respect to some groups given the small cell sizes (see Table 2), Indigenous households were much less likely to have a computer or Internet access at home compared to other groups, with the exception of households from the 'Pacific Islands' background. Households where the parent/s were either Australian-born or born overseas but from English-speaking backgrounds had similar levels of computer and Internet access to the overall mean. In contrast, households from Non-English Speaking Backgrounds (NESB), especially European, had higher levels of computer and Internet access than the overall mean.
- Finally, family structure seems to be associated with access levels, with one-parent households having lower levels of access to a home computer (55 percent) and the Internet (28 percent) compared to two-parent households (66 percent and 39 percent respectively).
ICT home access and socioeconomic variables
Table 5 shows the percentage of households with home computer and Internet access according to a range of socioeconomic variables. It suggests that all these variables were associated with the level of ICT access, although some variables appear to have a stronger association than others.
Table 5: ICT home access and socio-economic variables.
Characteristic Computer (percentage) Internet (percentage) Overall distribution 59 32 Level of disadvantage Bottom 10 percent 52 27 10-25 percent 59 30 25-50 percent 59 32 50-75 percent 60 33 75-90 percent 67 40 Top 10 percent 67 35 Housing type Public rental 53 26 Private rental 58 33 Owns/purchasing 73 43 Main source of income Social security 58 31 Employment 72 44 Parental education < Year 10 43 18 Year 10 58 31 Year 12 68 42 TAFE/Other post-secondary 68 38 University degree 88 57
Not surprisingly, households that were located in the most disadvantaged areas, were less likely to have a home computer (52 percent) and home Internet access (27 percent), compared to households situated in the least disadvantaged areas (67 percent and 35 percent respectively). In terms of the type of housing that families lived in, households that owned or were purchasing their homes were more likely to own a computer (73 percent) than households that were renting privately (58 percent) or living in public housing (53 percent). Owners/purchasers were also more likely to have Internet access (43 percent) compared to those renting privately (33 percent) or in public housing (26 percent). A household's main source of income was also associated with home computer ownership and Internet access. Households whose main source of income was social security were far less likely to have computer at home compared to those whose main source of income came from employment (58 percent v. 72 percent). Similarly, home Internet access was higher for households whose primary income was from employment (44 percent) compared to those on social security (31 percent). A striking finding was the strong association between the level of parental education and computer and Internet access. This is illustrated further in Figure 1. In households where the parent/s had less than ten years of education, 43 percent had a computer at home; this increased to 88 percent for households where the parent/s were university-educated. In households where parent/s had less than ten years of education, only 18 percent had Internet access at home; this increased to 57 percent with university-educated parent/s. This finding is consistent with previous studies that have found education level to be the key driver of Internet access, followed in importance only by income level (Hellwig and Lloyd, 2000).
Figure 1: Households with home computers and Internet by education level.
Frequency of usage of ICT by LFL students
In addition to examining the ownership and access of ICT by households, we also examined how frequently students use computers and the Internet as well as where they access them.Computer Usage
An overwhelming majority of students (98 percent) indicated that they used a computer. This is comparable to Australia-wide surveys that have found that 95 percent of children aged five to 14 used a computer in the last 12 months (ABS, 2000b). Of those that used a computer, Table 6 presents the variation in the frequency of usage by a range of demographic variables. Overall, most students stated that they use a computer 'sometimes' (33 percent) or 'often' (28 percent), with one quarter of students stating that they use a computer 'regularly'.
Table 6 shows that student age is a key factor in discriminating among LFL students in terms of frequency of computer usage. The older students use computers more frequently than younger students. While none of the other demographic characteristics seem to be strongly associated with the frequency of computer use, boys were more likely to state they used a computer 'regularly' (28 percent) compared to girls (22 percent). In terms of ethnic/cultural background, students from a European background were most likely (32 percent) to use a computer 'regularly' compared to other groups; students from two-parent families were also more likely to use a computer 'regularly' (27 percent) compared to students from one-parent families (24 percent). Students from metropolitan areas were slightly more likely to use a computer 'regularly' (26 percent) compared to those from non-metropolitan areas (24 percent).
Table 6: Frequency of computer use by demographic variables.
Characteristic Frequency of computer use (percentage) Rarely Sometimes Often Regularly Number  Overall distribution 14 33 28 25 6,694 Year 1-3 18 47 20 15 846 4-6 16 36 27 21 1,981 7-10 12 29 32 28 3,131 11-12 13 21 30 36 690 Sex Male 14 31 27 28 3,310 Female 14 34 30 22 3,378 Ethnic Background Anglo-Australian 14 33 28 25 5,213 ATSI 12 32 29 27 96 ESB 10 32 32 26 196 Europe 12 29 26 32 325 Asia 10 30 33 27 133 Middle East & Africa 20 31 26 23 501 Central & South America 11 36 28 25 97 Pacific Islands 29 29 24 18 62 Family type One-parent 15 33 29 24 3,824 Two-parent 13 33 27 27 2,723 Location Metropolitan 15 32 27 26 2,727 Non-metropolitan 14 33 29 24 3,967
Table 7 examines frequency of computer use by socioeconomic status. Once again, parental level of education seemed to have the most influence, with over one-third (35 percent) of students whose parents were university educated using a computer 'regularly' compared to 23 percent of students whose parents had not completed Year 10. Similarly, students whose parents' main source of income was from employment were more likely to state they used a computer regularly (29 percent), compared to students whose parents' main source of income was from social security (24 percent). Regular usage was also higher for students who lived in a house that was owned or being paid off compared to those in private or public rental accommodation and for those who lived in the more advantaged areas based on the IRSED.
Table 7: Frequency of computer use and socioeconomic variables.
Characteristic Frequency of computer use (percentage) Rarely Sometimes Often Regularly Number  Overall distribution 14 33 28 25 6,694 Parental education < Year 10 18 35 25 23 1,136 Year 10 14 34 29 24 2,526 Year 12 16 29 27 28 688 TAFE/Other post-secondary 13 34 31 22 597 University degree 8 25 33 35 375 Main source of income Social security 15 33 28 24 5,815 Employment 11 30 31 29 620 Housing type Public rental 16 34 27 23 2,901 Private rental 14 32 29 25 2,319 Owns/purchasing 10 30 30 29 1,335 Level of disadvantage Bottom 10 percent 13 33 30 25 1,239 10-25 percent 15 33 29 24 1,446 25-50 percent 16 33 26 26 1,831 50-75 percent 16 31 30 24 1,382 75-90 percent 11 33 29 27 560 Top 10 percent 12 33 27 29 191
Just over four-fifths of students (82 percent) indicated that they had used the Internet. Consistent with other studies (see ABS, 2000b), Figure 2 shows that older students were significantly more likely to state that they had used the Internet (95 percent for those in Years 11 and 12) compared to younger students (49 percent for those in Years 1 to 3).
Figure 2: Internet use by age.
Once again, the level of parental education was a key factor in the use of the Internet by students (Figure 3). While 92 percent of students whose parent/s were university educated had used the Internet, this fell to 76 percent for students whose parents had not completed Year 10. In terms of odds ratios, students whose parents completed Year 12 were one and a half times more likely to have stated that they had used the Internet than students whose parents did not complete Year 12. Those students whose parents had a university degree were almost three times more likely to have ever used the Internet than those whose parents did not have a university degree.
Figure 3: Internet use by parental education level.
Of those that used the Internet, Table 8 presents the variation in the frequency of usage by a range of demographic variables. Overall, only a small proportion of students stated that they used the Internet 'regularly' (11 percent), with just over one-fifth stating they used the Internet 'often' (22 percent), and almost two-thirds of students stating that they used the Internet either 'rarely' or 'sometimes'. Overall then, LFL students use the Internet less frequently than computers.
Table 8 also suggests that student age is a key factor in discriminating between the frequency of Internet usage among LFL students, with older students using the Internet more frequently than younger students. Male students were more likely to state they used the Internet 'regularly' (13 percent) compared to female students (10 percent). In terms of ethnic/cultural background, students from a European background were most likely (17 percent) to use the Internet 'regularly' compared to other groups; there was little difference in Internet usage by students according to family structure. Students from metropolitan areas were slightly more likely to use the Internet 'regularly' (14 percent) compared to those from non-metropolitan areas (10 percent).
Table 8: Frequency of Internet use and demographic variables.
Characteristic Frequency of Internet use (percentage) Rarely Sometimes Often Regularly Number  Overall distribution 32 35 22 11 5,565 Year 1-3 44 38 13 5 423 4-6 36 39 18 7 1,606 7-10 28 35 24 13 2,861 11-12 29 25 27 19 658 Sex Male 31 35 22 13 2,731 Female 33 35 22 10 2,830 Ethnic Background Anglo-Australian 32 36 21 11 4,293 ATSI 33 38 21 9 77 ESB 31 30 23 16 172 Europe 24 32 27 17 287 Asia 38 33 18 11 118 Middle East & Africa 31 31 25 13 424 Central & South America 19 38 33 10 81 Pacific Islands 36 36 21 7 58 Family type One-parent 33 36 21 11 3,163 Two-parent 30 35 23 12 2,274 Location Metropolitan 30 34 23 14 2,301 Non-metropolitan 33 36 22 10 3,264
Table 9 examines frequency of Internet use by socioeconomic status. In contrast to computer usage, there did not appear to be a strong relationship between socioeconomic variables and the regularity of Internet usage.
Table 9: Frequency of Internet use and socioeconomic variables.
Location of Internet use
Characteristic Frequency of computer use (percentage) Rarely Sometimes Often Regularly Number  Overall distribution 32 35 22 11 5,565 Parental education < Year 10 33 38 18 11 894 Year 10 32 36 21 11 2,090 Year 12 32 32 24 13 587 TAFE/Other post-secondary 34 33 21 12 500 University degree 26 35 29 10 347 Main source of income Social security 32 35 22 11 4,819 Employment 32 32 25 10 536 Housing type Public rental 33 36 20 12 2,342 Private rental 32 34 23 11 1,945 Owns/purchasing 29 36 24 12 1,159 Level of disadvantage Bottom 10 percent 32 34 22 12 1,037 10-25 percent 32 37 21 10 1,204 25-50 percent 32 35 22 12 1,474 50-75 percent 31 34 23 12 1,167 75-90 percent 31 35 21 13 481 Top 10 percent 29 35 22 14 162
Almost three-quarters (70 percent) of students that used the Internet did so at school. Table 10 shows that the next most common location for Internet use was at home (29 percent). While the importance of school as a site for Internet use is consistent with other surveys in Australia, the proportion of students who indicated they used the Internet at home is lower compared to the national average. For instance, the ABS found that 67 percent of children aged between five and 14 used the Internet at school and 56 percent used the Internet at home (ABS, 2000b). Looking at a similar age group among the LFL students shows that while the same proportion (67 percent) was found to use the Internet at school, the rate for using the Internet at home was only 27 percent. Given the relatively low rates of home Internet access discussed earlier (32 percent), these findings are not that surprising, but more importantly, they suggest the important role that schools have as a means of providing access and training in ICT for students of disadvantaged backgrounds (Zappalà et al., 2002).
It is also interesting to note that using the Internet at school was also related to the level of parental education. While two-thirds of students whose parents' had not completed Year 10 stated they used the Internet at school, this increased to almost four-fifths of students whose parents were university educated. Apart from the level of parental education, student age was the only other variable that influenced use of the Internet at school, with usage increasing for older students (38 percent of students in Years 1-3 used the Internet at school compared to 76 percent for students in Years 11-12).
Table 10: Location of Internet Use.
Place Internet used Number Percentage  During school 4,790 70 Home 2,024 29 Friend's House 1,466 21 Public library 1,113 16 At school after hours 327 5 Youth/community centre 90 1 Other 252 4 Internet café 66 1
Discussion and Conclusions
This paper focused on what was termed the 'A' of the 'ABCs of the digital divide' - Access, Basic Training and Content . Although the results have not employed multivariate techniques to isolate the effects of particular variables, they nevertheless point to several preliminary research and policy implications that will be pursued in more detail in forthcoming TSF publications and programs.
First, while the access gap has been narrowing over the last few years, only one-third of families who were on the LFL program at the end of 2001 had home Internet access. This compares to almost half of the comparable (i.e. families with children) population Australia-wide. While some may not consider this finding to be that alarming, when seen in the context that having home Internet access is increasingly important for children's educational performance, then the fact that almost three-quarters of students did not use the Internet at home is of concern. Finding ways to increase the home access of low-income families to the Internet should therefore remain a policy priority for all sectors (government, private and nonprofit) aiming to bridge the digital divide.
Second, the results are particularly interesting given that our sample controls for one of the key socioeconomic factors known to be associated with lack of access - income. All families on the LFL program are by definition low-income families. Despite this, several other dimensions of socioeconomic status seemed to be related to home access of computers and the Internet, and in some instances, the usage of computers and the Internet. In particular, the level of parental education was most strongly associated with home access to computers and Internet as well as computer and Internet usage. This finding is consistent with the key role found for educational level in home access to ICT in the multivariate analysis conducted by NATSEM (Hellwig and Lloyd, 2000).
This finding also bears a remarkable similarity to other studies that examined the relationship between the educational performance of students on LFL and socioeconomic status (Zappalà and Considine, 2001; Considine and Zappalà, 2002). Controlling for other variables, the authors found that a student whose parent/s were university educated had a 39 percent predicted probability of attaining 'outstanding' results compared to nine percent for students whose parent/s had not completed Year 10. A key reason posited to explain that finding was that the levels of parental education acts as a proxy for the degree of educational support parents provide for their children. Previous studies show that the level of parental education is strongly associated with factors such as the home literacy environment, parents' teaching styles and investment in resources that promote learning (Shonkoff and Phillips, 2000). Key resources for learning in today's information society also include computers and the Internet.
This has at least two implications. First, the costs of these resources, as with other educational costs in general, are increasingly being pushed onto individual families. This further compounds the problem for families in financial disadvantage who often struggle to meet the basic costs of their children's education. It therefore reinforces the need for programs such as Learning for Life that aim to assist families in financial disadvantage to meet some of the costs associated with their children's education. Second, policies aimed at bridging the digital divide should not only focus on reducing the cost of ICT, but also on ensuring that programs that provide appropriate parenting support also emphasise the educational importance of having home access to computers and the Internet. This may also mean that access and training programs should focus just as much on parents as they do on children. Once again, the dual-generation approach (focus on parents and children) of programs such as Learning for Life provide an appropriate framework within which to embed such initiatives.
Third, other key factors associated with home access were ethnic and cultural background, family structure, housing type and regional disadvantage. The findings with respect to ethnicity were also consistent with the above-mentioned study on educational outcomes of LFL students. Namely, students from NESB (with the exception of those from Middle East/Africa) were significantly more likely to achieve outstanding results compared to students from English-speaking backgrounds. Similarly, the findings with respect to access suggest that families from some NESB groups have higher levels of home access compared to those that were either Australian born or born overseas from English speaking countries.
Fourth, schools are important in closing or leveling the access gap, as most students use computers and the Internet at school. Reinforcing the role of parental education, however, the likelihood of students using the Internet at school also increased in line with the educational level of their parents. Greater research and policy attention needs to be given to the role of schools, teachers and parents in the 'ABC of the digital divide'.
About the Authors
Jennifer McLaren is a Research Assistant in the Research & Social Policy Team, The Smith Family. Prior to joining the team in March 2002, she completed one year of a combined PhD/Masters in neuropsychology at Macquarie University, where she also worked as a research assistant.
Dr. Gianni Zappalà is Research Manager in the Research & Social Policy Team, The Smith Family. Prior to joining The Smith Family, he held various teaching and research positions at the Universities of Sydney, Cambridge (Fellow of Emmanuel College), Wollongong and the Research School of Social Sciences at the Australian National University. His research at TSF has included: the social impact of the new economy, the relationship between socioeconomic status and educational outcomes, volunteering, assessing the social impact of companies, and school-to-work transitions.
We thank our colleagues Kirsten Buwalda, Martin Laverty, Ciara Smyth, and Mike Wilson for comments and assistance. We also thank Peter Huta from the National Office of the Information Economy (NOIE) for comments and suggestions on an earlier draft of this paper. The usual disclaimers apply.
1. Zappalà, 2001, p. 3.
2. Robbins, 2000, p. 14.
3. Warschauer, 2002, p. 6.
4. Wilhelm et al., 2002, p. 2.
5. See Smyth and Zappalà (2002) for an overview of TSF's Computer Clubs that while also aimed at providing access, have a focus on training and content.
6. Curtin cited in Manktelow, 2001, p. 4; Curtin, 2001.
7. Robbins, 2000, p. 14.
8. Of the 6,874 students 5,818 were members of families that had more than one child on LFL.
9. There were 114 households where the response of one sibling was inconsistent with that of another sibling for the question 'Do you have a working computer in your home?' This corresponded to 266 individual cases that were deleted from the database. There were 187 cases where the question 'Where do you use the Internet?' - 'At home' was endorsed by one sibling and not by the other. These cases were not deleted, as it is possible that one child used the Internet at home while their sibling did not.
10. Geographic location coding was based on the household's post code and refers to the classification used by Australia Post; Capital city post codes are classed as Metropolitan and all other areas as Non-metropolitan.
11. Total number of cases vary for each variable due to missing data.
12. Percentiles indicate level of disadvantage relative to Australia as a whole. For example, 10-25 percent encompasses areas that are better off than at least 10 percent of Australia and at most 25 percent of Australia. An area falling in the 50-75 percent band is less disadvantaged than one falling in the 25-50 percent band.
13. Education level of most highly educated parent.
14. Refers to both first- and second-generation Australians.
15. This figure was based on responses that endorsed the option 'At home' to the question 'Where do you use the Internet?' This proxy may underestimate the level of household access as there may be cases where a household had the Internet at home but the parent/s did not allow their child/children to use it.
16. A possible reason for the apparent lack of a geographic location effect is the coding system used (see note ), which does not allow a sharper differentiation of the 'non-metropolitan' category. This category includes, for instance, all areas other than a capital city (e.g. cities such as Newcastle in New South Wales). This was one reason that post codes were linked to IRSED scores, thus providing another proxy for geographical location.
17. Number of cases may vary for each variable due to missing cases.
18. Does not add up to 100 percent because participants could endorse more that one option.
19. Wilhelm et al., 2002, p. 2.
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Paper received 11 October 2002; accepted 25 October 2002.
Copyright ©2002, First Monday
Copyright ©2002, Jennifer McLaren
Copyright ©2002, Gianni Zappalà
The 'Digital Divide' Among Financially Disadvantaged Families in Australia by Jennifer McLaren and Gianni Zappalà
First Monday, volume 7, number 11 (November 2002),