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Article from ejc/rec Digital Divide in the Netherlands
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The Electronic Journal of Communication / La Revue Electronique de Communication

Volume 12 Numbers 1 & 2, 2002


 

THE DIGITAL DIVIDE IN THE NETHERLANDS:

THE INFLUENCE OF MATERIAL, COGNITIVE AND SOCIAL RESOURCES

ON THE POSSESSION AND USE OF ICTS

 

Jos de Haan

Social and Cultural Planning Agency

The Netherlands

 

Susanne Rijken

Utrecht University

 

Abstract. In the Netherlands, like in other western countries, one observes social inequality in the possession and use of information and communication technology (ICT) and in digital skills. This digital divide between the information rich (such as whites, those with higher incomes, those more educated, and dual-parent households) and the information poor (such as certain minorities, those with lower incomes and lower education levels, and single-parent households) is part of a diffusion process. Some people take the lead, others follow. This paper addresses the mechanisms behind different kinds of digital inequality within households (possession, skills and use). The explanation of the differences between sections of the population focuses on the possession of three types of resources: material, social and cognitive resources. Differences in ICT access and use between population groups can sometimes be explained completely and sometimes only partly. Some resources primarily influence possession, while others mainly influence ICT use or digital skills.

Introduction: ICT and social inequality

 

The rise of the information society is facilitated by technological changes. The spread of PC ownership, innovations in the fields of media and telephony made it possible to communicate in new ways and to transport information through different channels. Those who possess and use the new information and communication technology (ICT) equipment enjoy benefits that others may lack. The question can be raised whether the rise of new ICT increases social inequality. The fear of new social inequality has given rise to alarming concepts such as the digital divide. This article will contribute to the discussion about this digital divide by analysing in detail the social backgrounds of the ‘haves’ and ‘have-nots’ and of the users and non-users of ICT.

 

According to the OECD (2001), the term ‘digital divide’ refers to the gap between individuals, households, businesses and geographic areas at different socio-economic levels with regard both to their opportunities to access information and communication technologies (ICTs) and to their use of the Internet for a wide variety of purposes. The digital divide can be measured in different ways, but is most often associated with PC and Internet access. PC diffusion shows the usual logistic curves for the diffusion of new technologies, although it is slower than some other technologies. The spread of Internet access is more rapid, because necessary complementary investments in PC equipment and the personal and business skill base have already been made (OECD, 2000). PC use in households is increasing in all OECD countries, with the highest rates in Nordic countries (Denmark, Finland), the Netherlands and English-language countries (Australia, Canada, United States). Internet access from home shows similar patterns (see Figure 1).

 

Figure 1: PC access and Internet access in households in selected OECD countries (most recent year)

Source: OECD (2000)

 

Patterns of both PC and Internet use show that more affluent and well-educated citizens are the first to acquire ICTs. Besides on income and education, the digital divide among households depends on variables such as household size and type, age, gender, racial and linguistic backgrounds and location (OECD, 2000). These differences are very consistent across countries. Although computer ownership and Internet access rates have increased rapidly in all OECD countries and among nearly all groups, several studies have indicated that the diffusion of ICTs has given rise to growing inequality among the populations in the countries studied (NTIA 1999, 2000; Perillieux et al., 2000; Van Dijk and Hacker, 2002). The digital divide between the information rich (such as whites, those with higher incomes, those more educated, and dual-parent households) and the information poor (such as certain minorities, those with lower incomes and lower education levels, and single-parent households) is growing. This paper addresses the mechanisms behind the social inequality in the possession and use of ICTs within households. It addresses the following question. How can the differences in the possession and use of ICT products between different sections of the population be explained?

 

The research was conducted in the Netherlands, one of the leading countries in the world concerning the diffusion of ICTs in households as figure 1 shows.  As the patterns of diffusion are very similar across countries, the explanation of differences that is offered here probably also holds for other countries. In the Netherlands, we have witnessed a quick diffusion of PC's in households. After a slow start in the first half of the eighties, the penetration of PCs grew rapidly in the 1990s of the 20th century, with the number of households having a computer rising from 9% in 1985 to 58% in 1998. In the autumn of 1998 56% of PC-owners had a modem, 34% an e-mail connection and 37% access to the Internet. Furthermore, 18% of the Dutch population had a fax machine connected to the telephone network. The number of Internet connections has increased fivefold in three years: from 4% of the population in 1995 to 21% in the autumn of 1998 (Van Dijk et al., 2000).

 

This study of social inequalities in the possession and use of ICTs is not restricted to PC use and Internet access, but also concerns innovations in the fields of media, electronic payments and telephony. In recent years more and more providers of mobile telephony services have appeared on the market, and the number of mobile telephones has increased rapidly. Mobile telephony is now one of the fastest growing and most visible new forms of communication. Payment methods have also been digitised in the past decade. Increasingly, people’s wallets are filled with cash/debit cards, credit cards and/or ‘chipcards’ (‘electronic purses’). In the Netherlands, the penetration of cash/debit cards is particularly high, with 94% of the population having such a card in the autumn of 1998. Compared with the cash/debit card, the penetration of credit cards – which have been around for longer – is modest (35% of the population in the autumn of 1998).

 

As in other countries, the spread of ICT innovations in the Netherlands is proceeding more rapidly in some sections of the population than in others. Certain groups crop up time and again when it comes to non-possession and non-use of the facilities described. The following are the groups that are falling behind in the access to these facilities, arranged in the order of how far behind they are on average: people in low-income households (who on average lag furthest behind); (single) women; people over-65; people with a lower (secondary) education level; the unemployed.

 

In order to explain the differences in the possession and use of ICT a theory on the possession of resources is discussed in paragraph 2. Subsequently, key concepts from this theory are measured in paragraph 3. In this paragraph the data collection is also described in some detail. The results of the multivariate analyses are presented in paragraph 4. In the final paragraph these results are interpreted in the light of the discussions about the digital divide.

 

An explanation: unequal possession of different types of resources

 

To explain the differences in the possession and use of modern ICT facilities, alignment was sought with earlier studies on the diffusion of innovations. According to this research tradition, the decision to accept a (technological) innovation is dependent on different characteristics of products, on personal, social and economic characteristics of consumers and on channels of information that provide relevant knowledge on new products. Here we present a theoretical framework that specifies the relationship between rational decisions of consumers and a mix of product and consumer characteristics. Our framework not only draws from literature on the diffusion of innovations, but also from social network analysis and from research on information processing theory.

 

The ICT innovations we studied are classified as relatively complex consumer goods. Besides complexity, we distinguished four other characteristics, following Rogers (1995: 207 ff.): compatibility, testability, visibility and the relative benefit. The rate of spread of different products offers some information on the influence of product characteristics. The spread of colour television was more rapid than that of the VCR, which in turn penetrated the community faster than the PC. These differences cannot be attributed entirely to average price levels: colour TV was an expensive product in the 1970s. Other product characteristics play an important role here. The relative benefit of colour television as compared to black and white sets was obvious for large sections of society. The user-unfriendliness of the PC, by contrast, contributes to the relatively slow spread of the personal computer.

 

We assume that the acceptance of ICT products is the result of choices made by consumers. In their decision-making process they are confronted with constraints for their choices. This constraint-driven approach assumes that the adoption of ICT products can be explained by differences in constraints between individuals. People are constrained in their possession of resources. Differences in this regard result not only from the quantity of these resources, but also from the type of resources. A distinction is drawn between material, cognitive and social resources. This distinction draws on the work of Bourdieu (1984) and (Coleman, 1990). Instead of cognitive resources, they refer to cultural resources or human capital. In order to stress that ICT skills are mental capabilities, we propose to use the term cognitive resources.

 

Material resources

 

Material resources in the strict sense include the financial budget of households but because in some senses time is money, the available number of leisure hours falls under this category too. Monetary and time constraints have been proven to be a part of the decision-making process associated with the adoption of new ICT products. Studies on consumer behaviour have shown that income and the costs of ICT products affect the purchase of these products. People with higher incomes spent a relatively large part of their financial budget on luxury consumer goods (Linder, 1971). It is very likely that this also holds true for ICT products. This relationship between individual or household income and consumer goods will be stronger if the prices of the goods are higher. This applies both to purchases of new goods and to replacements of old ones.

 

Available time is an incentive to buy and use ICT products. It has been shown that the amount of leisure time affects media consumption. Retired people who on average have most leisure time are the greatest consumers of television programmes (Rubin and Rubin, 1981; Knulst and Kalmijn, 1988). On the other hand, people with paid jobs and parents with young children have less free time and can be expected to have relatively little opportunity for using time-consuming ICT products.

 

Cognitive resources

 

Cognitive resources, also called human capital, can be defined as the ability to deal with symbols and information. In our study three types of cognitive resources are distinguished: literacy, numeracy and informacy. Which cognitive resources are primarily important? For a long time the ability to process written information was the most important skill for communication and processing information. These classic skills are called literacy, meaning the ability to use information from books, newspapers and magazines. Later came the ability to handle quantitative information, called numeracy or quantitative literacy (OECD, 1995). Nowadays another type of skills is becoming increasingly important, i.e. the skills to handle information and communication technologies. These digital skills or informacy refer to specific knowledge of and ability to deal with new technologies as well as with previous experiences.

 

People with a higher level of informacy will be better able to use ICTs. Digital skills enlarge the opportunity to use computers for a variety of applications such as word processing, programming and searching files (Rossom, 1984; Vincente et al., 1987; Czaja et al., 1998). The need for those skills increases if the ICT products or software become more complex (Steyaert, 2000). Thus once again consumer characteristics are related to the product characteristics.

 

Education is often used as an indicator of informacy. This can be easily understood because the school is the most important location were cognitive resources are acquired. This holds true for literacy, which is primarily attained during one’s early educational career. In higher forms of education more attention is paid to informacy. Cognitive resources (literacy, numeracy and informacy) are strongly related to the educational level people reach (OECD, 1997). During professional careers these skills will be used and further developed. Especially the use of a PC at the workplace enhances a positive attitude towards having and using a PC at home (De Haan, 2001).

 

Minding the importance of education, it is relevant to know in which period people went to school. Persons born after 1960 have a far greater chance to acquaint themselves with ICT during their educational career than those born before 1960. The generation born after 1960 has already been called the 'technical generation' (Sackmann and Weihmann, 1994) and the 'net generation' (Tapscott, 1998). However, there are more reasons why older people might use new media less often than young people. Older people less often followed higher types of education, they left the labour market before they could learn to work with computers, and their physical, sensory and cognitive skills decrease as they become older (OECD, 1997; Van Rijsselt en Weijers, 1997; Freudenthal, 1999). For this combination of reasons older people can be expected to have more difficulty processing complex information and using modern technology. For several reasons, there may also be a distinction between men and women. On average, women are lower educated, have participated less often in the labour market and may have acquired less affinity with technology during their gender specific socialisation (Rijken, 1999; Van Dijk and De Haan, 1999).

 

Social resources

 

Social resources consist of types of access people have to other people’s sources of help (Flap, 1987). In the field of ICT they are the number of people in someone’s social setting who themselves possess new ICT products, the skills (in particular informacy) to be addressed in this way, and the degree to which these persons are in a position to provide information on ICT. Someone will be more likely to buy and use new ICT products if more persons in his personal network can provide information on them and if they are able and willing to give advice. Computer use, for example, is stimulated in a social environment providing much support (Kling and Gerson, 1977).

 

Acquaintances can help reduce uncertainties often accompanying the purchase of new consumer goods (Bettman, 1979). Uncertainty may relate to the cost and quality of a product but also to the acceptance of that product in a social community. Products that can be tested, that fit within existing norms and values and that are more visible bring less uncertainty. People with more social resources will be better able to reduce their uncertainty. Again product characteristics (testability, compatibility and testability) are related to consumer characteristics (social resources which may decrease uncertainty).

 

Social contacts not only provide information but can also be a source of social approval and social distinction. In general, people tend to adopt the kind of behaviour that is expected to give them affirmation from the group in which they live and especially from the people they consider to be important (Burt, 1987). Positive feedback on the acquisition of ICT can provide them with social well-being. Possessing visible luxury goods they can also distinguish themselves from others. These products can give them a certain status. More than the cultural elite, the economic elite will try to attain this status of material ownership (cf. Bourdieu, 1984).

 

In this paragraph a distinction is drawn between material, cognitive and social resources. We will analyse to what extent the different resources are able to explain differences in possession and use of ICT between different sections of the population (see Figure 2). For example, to what extent can they explain differences between men and women and between different educational or different age groups.

  

Figure 2: Explaining differences in ICT possession, use and skills

 

 


 

Data and operational definitions

 

Data were drawn from the Use of New Communication Resources (UNCR) Survey, carried out in the Netherlands in the autumn of 1998. This national survey not only allowed a detailed description of the Dutch situation in the autumn of 1998, but also enabled a close examination of the explanatory mechanisms behind the social inequality in the possession and use of ICT. In November 1998, mail questionnaires were sent to 6000 addresses taken from the administration of the national telephone company. Of these addresses, 109 could not be used. Within every household one person was asked to complete the questionnaire, being the person who was the first to have his/her birthday after a certain date (every quarter of the addresses had a different date: 1 March, 1 June, 1 September or 1 December). Ten days and three weeks after the distribution of the questionnaires reminders were sent to those households not having responded yet. In the end, we received 2538 completed questionnaires representing a 43% response.

 

The data were weighted for a number of variables known to have a distribution different from the distribution of the population at large. These variables were: gender, age, voting behaviour in 1998, registration in the telephone directory, residence in one of the four largest cities, and marital status. The weights were constructed using interactive proportional fitting on the distribution of marginal frequencies.

 

The introduction has already shown that there are strong similarities in the social distribution of the possession and use of  a whole range of ICT products. Our data show that the possession of each of eleven ICT products (television with teletext, VCR, cordless telephone, mobile telephone, fax, debit card, credit card, personal computer, laptop, Internet access and printer) is nearly always positively correlated to the others (table 1). People who have one product often also possess other ICT products. For this reason the explanation will not be sought for individual products. Instead we will use an additive score for all of them. In the scores concerning possession, three products from table 1 are not counted. Both the debit card and the television with teletext are present in nearly all households and do therefore not constitute a distinction between social groups. Access to Internet is not counted either because measuring it produced a high number of missing values.

 

Table 1 Correlations between the possession of ICT products, persons of 18 years of age and older, 1998

 

Teletext

VCR

Cordless phone

Mobile phone

Fax

Debit card

Credit card

PC

Laptop

Internet

Printer

Teletext

1  

 

 

 

 

 

 

 

 

 

 

VCR

0,21**

1  

 

 

 

 

 

 

 

 

 

Cordless phone

 

 

     0,13**

0,21**

1  

 

 

 

 

 

 

 

 

Mobile phone

      0,05* 

0,15**

0,17**

1  

 

 

 

 

 

 

 

Fax

0,08**

0,12**

0,09**

0,24**

1 

 

 

 

 

 

 

Debit card

0.02  

0,16**

0,05*

0,06**

0.04 

1 

 

 

 

 

 

Credit card

0,06**

0,12**

0,14**

0,22**

0,21**

0,12**

1 

 

 

 

 

PC

0,09**

0,25**

0,17**

0,21**

0,29**

0,17**

0,19**

1 

 

 

 

Laptop

      0,05* 

0,10**

0,07**

0,23**

0,25**

0,07**

0,20**

 0,20**

1 

 

 

Internet

0,11**

0,16**

0,07**

0,17**

0,29**

0,08**

0,20**

0,23**

0,21**

1 

 

Printer

0,08**

0.04  

0,07**

0.03  

0,19**

‑0.01 

0.02  

0,29**

‑0.03 

0,17**

1 

** significant (p < 0,01), * significant (p < 0,05)

 

Source: UNCR (1998)

 

The possession of ICT products does not necessarily mean that these products are used. The UNCR survey contains information on the extent to which six products are used (teletext, VCR, debit card, credit card, computer and Internet). Based on these variables, a new variable was constructed for the use of ICT products.

 

Below, we will use the cumulative variables of possession and use of ICT products and not deal with single products. As a result, we will not go into the influence of different product characteristics. The focus is completely on the influence of consumer characteristics. Before we can assess the impact of the different types of resources, we need to measure material, cognitive and social resources.

 

Social resources were measured in two ways. First, respondents were asked if three persons (significant others) of their personal network possessed seven ICT products. In the questionnaire the respondents were asked to give the names of these three persons (see note in table 2). Subsequently, more information was requested about these persons. Asking the respondents to supply such information for more than three persons was considered too demanding on them. We assume that the data on three persons gives a reliable picture of ICT possession in personal networks.

 



Table 2 Operational definition of social resources: possession of ICTs in the social network of the respondents (in percentages) a

 

Person 1

Person 2

Person 3

Personal computer

67

52

50

Internet/e-mail

40

24

24

Cordless telephone

62

47

45

Mobile telephone

43

38

26

Voice mail

55

40

37

Fax

33

17

17

Credit card

52

35

34

a Survey question: Most people discuss important subjects with someone else from time to time. Here, we are interested in everything that is important for people. Can you think of three persons with whom you frequently discuss important subjects? (...) Can you indicate whether these persons possess the following products?

 

Source: UNCR (1998)

 

Secondly, we measured the extent to which the social network is able to offer help in handling the personal computer. Here, it is important how much advice can be expected from persons in one’s direct social surroundings. Questions were posed regarding eight types of problems of computer use (table 3).

 

Table 3 Operational definition of social resources: number of network members that can give advice about computers (in percentages) a

 

No one

Some

Many

Operating system

7

47

37

Word processing

7

44

42

Internet

10

49

29

Spreadsheets

14

48

21

Telebanking

17

49

16

Keyboard

7

36

48

E-mail

12

45

29

Statistical programmes

26

36

8

a Survey question: If you have questions with regard to computers (at home or at work), how many relatives, friends, acquaintances or colleagues will be able to help you?

 

Source: UNCR (1998)

 

Adding the positive answers created two scales for social resources. We expected that possession in the social network would be positively related to both ICT possession and ICT use among the respondents. The capacity to give advice was considered to influence mostly the use of ICT.

 

We measured cognitive resources (literacy, numeracy and informacy), independently of educational level. Literacy and numeracy were measured by calculating the degree of understanding and application of written and numeric information. The literacy scale was based on answers to questions about the difficulties encountered in using a phone book, a dictionary, a television guide, filling out a tax form, understanding a legal contract, different kinds of newspaper articles and information brochures (see table 4).

 

Table 4 Operational definition of cognitive resources: literacy (in percentages) a

 

Cannot do it

With much difficulty

With trouble

Some trouble

No trouble

Look up a telephone number in a phone book

1

0

0

3

95

Look up a word in a dictionary

3

0

1

4

93

Find a programme in a tv guide

3

0

0

2

94

Fill out a tax form

39

6

10

22

23

Read a contract

10

6

13

34

37

Read a front page article

2

0

1

3

95

Read an information brochure on taxes

27

6

9

26

33

a Survey question: Can you indicate, for each action, how much trouble it gives you?

 

Source: UNCR (1998)

 

Numeracy was measured on the basis of the capacity to read and understand tables and graphs in newspapers and magazines, to roughly estimate the total price of groceries and to convert Dutch to foreign currency (table 5).

 

Table 5 Operational definition of cognitive resources: numeracy (in percentages) a

 

 

Cannot do it

With much difficulty

With trouble

Some trouble

No trouble

Read graph and tables

18

2

5

21

55

Estimate price of groceries

14

1

4

21

62

Convert Dutch to foreign currency

10

2

6

35

47

a Survey question: Can you indicate, for each action, how much trouble it gives you?

 

Source: UNCR (1998)

 

Informacy or ICT skills refer to the way information is retrieved (Internet or cd-rom versus telephone, telephone directory, etc.) and different kinds of ICT products are handled (television, VCR, personal computer) (see table 6).

 


Table 6 Operational definition of cognitive resources: informacy (in percentages)

 

 

Yes / done by PC

Have you done the following things (without the aid of others)?

programme frequencies on VCR

59

 

record a tv programme with VCR

74

 

programme the VCR

69

 

put telephone no. in memory

61

 

connect answering machine

35

 

connect a fax

20

 

adjust voice mail

32

 

 

 

Nowadays there are many ways to get information. We want to ask you where you look for information.

Suppose: You have to go by train from Utrecht to Amsterdam, but you do not know at what time the train leaves. Which information source do you consult first?

look for departure time in computerised version of timetable or on railway home page

18

 

 

 

If you need a someone’s phone number, where do you look first?

look up local phone number at WWW or in cd-phone guide

5

 

look up trunk phone number at WWW or in cd-phone guide

12

 

 

 

E-mail: do you know how to do the following? (e-mail owners)

make maps

43

 

create nicknames

21

 

make a distribution list

29

 

send attachments

34

 

decode messages

18

 

 

 

 

 

 

 

no

with help

yes

Television: Have you installed the tv channels yourself?

install tv channels

25

20

55

 

 

 

 

 

Did you install your PC yourself (PC owners)

install PC

1

63

37

 

 

 

 

 

 

If you need information from the Internet, can you find it easily? (Internet owners)

find information on Internet

hardly

with difficulty

some trouble

without trouble

 

   

5

10

53

32

Source: UNCR (1998)

 

In order to estimate the extent to which people have different types of cognitive resources, the positive answers to the questions in the tables were added.


Material resources refer to both the financial budget and the time budget. The household income is an important indicator of the financial budget. However, income has already been included in the set of background characteristics used to describe social inequality as regards ICT. It is therefore difficult to measure material resources separately. In order to arrive at an independent measurement we counted a number of luxury goods (non-ICT products) in the household (table 7). This scale expresses the ability and willingness to invest in expensive goods. Persons with the same income may still have different amounts of money left after paying fixed charges. They may also have different preferences and opinions where it concerns spending money. Unfortunately, it was not possible to construct a similar scale for the amount of leisure time on the basis of the UNCR survey.

 

Table 7 Operational definition of material resources (in percentages) a

 

Owns the product

Hi-fi set

87

CD player

85

Washing machine

92

Microwave oven

71

Drying machine

53

Kitchen equipment

49

Video camera

23

Piano

11

Central heating

86

Digital thermostat

35

Antique furniture

26

Garage

34

a Survey question: Do you possess any of the following?

 

Source: UNCR (1998)

 

On average, the more income people have, the higher the investment in luxury goods. The correlation between the possession of luxury goods and household income is 0.44 and between the possession of luxury goods and ICT products 0.45.

We expected that people who possess many luxury goods are more able and willing to buy ICT products. On the other hand, we did not expect that people who invest heavily in luxury goods also use their ICT products more often than others.

 

Results: the impact of resources

 

There are differences between households and persons as regards the extent to which they own and use ICT products. Both for ownership and for use, scales have been constructed. These scales do not have the same range or the same mean. To compare the distribution of ownership with that of use, the scores on the scales have been expressed in percentiles. These scores are orders of ranking with a fixed mean (50, the median) and a fixed standard deviation (26). Someone with a score of 10 belongs to the lowest 10% of the distribution and someone with a score of 90 to the highest 10% of the distribution. This score is very well suited to illustrate inequality in the distribution of different characteristics between groups.

The first column of table 8 shows the distribution of ICT possession among different population groups. The numbers reflect the differences already mentioned in the introduction. Socio-economic factors (such as income, level of education and marital status) are correlated with home ICT adoption. Men more often possess ICT products than women. The differences between age groups can be seen as well. Older people posses fewer ICT products than young people. Higher education groups and higher income groups possess relatively many ICT products. The education-related differences seem to be greater than the income-related differences. These differences reflect those found in other countries (OECD, 2000). In the United States noticeable differences were also found between different racial and ethnic groups, between single and dual-parent families, between those with and without disabilities and between rural areas and households nationwide (NTIA, 2000).

 

Table 8 includes not only the scores regarding possession and use but also those concerning informacy. Given the importance of digital skills in the information society, it seemed worthwhile to present these scores in some more detail and subsequently analyse the differences. Again, in this respect men and young people turn out to be more skilful than women and elderly people. As expected, there is a strong relationship between educational level and informacy. Income is less strongly but still positively connected to digital skills.
 


Table 8 Degree of possession and use of ICT products and informacy, according to background characteristics, persons 18 years of age and older, 1998 (in percentiles)

 

 

    Possession

 

    Use

 

    Informacy

Whole population

 

50

 

50

 

50

 

 

 

 

 

 

 

Man

 

57

 

55

 

60

Woman

 

43

 

44

 

40

 

 

 

 

 

 

 

Single man

 

51

 

55

 

62

Single woman

 

34

 

46

 

39

Married/living together without child

 

50

 

50

 

48

Family with child > 14 years

 

59

 

48

 

48

Family with youngest child 14 years

 

61

 

51

 

54

 

 

 

 

 

 

 

18-34 years

 

54

 

54

 

58

35-49 years

 

60

 

50

 

55

50-64 years

 

48

 

40

 

43

65 years and older

 

25

 

37

 

34

 

 

 

 

 

 

 

Primary education

 

30

 

34

 

29

Secondary education

 

45

 

48

 

44

Pre-university and senior vocational education

 

54

 

51

 

54

Higher professional and university education

 

60

 

54

 

59

 

 

 

 

 

 

 

Income 1st quartile

 

39

 

44

 

46

2nd quartile

 

50

 

48

 

51

3rd quartile

 

58

 

50

 

53

4th quartile

 

60

 

55

 

59

 

 

 

 

 

 

 

Working

 

59

 

52

 

59

Retired

 

31

 

55

 

32

Unemployed, disabled

 

39

 

49

 

43

Housewive/man

 

38

 

38

 

29

Student

 

52

 

57

 

50

Source: UNCR (1998)

 

To a certain extent the presented background characteristics overlap. People with a higher education often also enjoy a high income. And, on average, the 18-34 year group earns less than the 35-49 year group. It is therefore possible that the differences we found regarding one characteristic can in fact be attributed to another. In order to control for the influence of other variables and estimate the net effect of a variable, multivariate regression analyses have been conducted. The results are shown in table 9.

Table 9 Regression analysis of ICT possession, ICT use and informacy regarding background characteristics, persons of 18 years and older, 1998

 

Possession

 

Use

 

Informacy

 

beta (t-value)

 

beta (t-value)

 

beta (t-value)

Gender (woman)

-0,17** (8,6)

 

-0,17** (6,1)

 

-0,26** (12,5)

 

 

 

 

 

 

Single

reference group

 

reference group

 

reference group

Married/living together without child

0,08**  (2,9)

 

0,01     (0,1)

 

-0,00  (0,1)

Family with child > 14 years

0,10**  (4,4)

 

-0,00     (0,0)

 

-0,01  (0,6)

Family with youngest child  14 years

0,12**  (4,7)

 

0,00     (0,0)

 

-0,01  (0,3)

 

 

 

 

 

 

Age (linear)

  0,06    (0,7)

 

-0,20** (-5.3)

 

-0,35** (11,7)

Age (quadratic)

-0,33** (3,9)

 

 

 

 

 

 

 

 

 

 

Education

0,06**  (2,8)

 

0,08**  (2,8)

 

0,17**  (7,6)

 

 

 

 

 

 

Income

0,35** (13,7)

 

0,05     (1,4)

 

0,09** (3,4)

 

 

 

 

 

 

Work

reference group

 

reference group

 

reference group

Retired

-0,07*  (2,5)

 

0,05     (1,6)

 

-0,07*  (2,5)

Unemployed or disabled

-0,05*  (2,5)

 

0,08**   (3,0)

 

-0,06*   (2,9)

Housewive/man

‑0,02   (1.0)

 

0,01     (0,4)

 

‑0,07** (3,1)

Student

0,09**  (4,1)

 

0,06     (1,9)

 

-0,04    (1,9)

 

 

 

 

 

 

Possession

 

 

0,23**  (7,5)

 

 

 

 

 

 

 

 

Adj. R2

34

 

12

 

29

N

1891

 

1374

 

1873

* significant (p < 0,05), ** significant (p < 0,01)

 

Source: UNCR (1998)

 

 The multivariate analysis shows that the factor household income is of primary importance for variation in access to ICT. It should be pointed out that the income differentials are concentrated around the possession of a given facility: once someone has access to a piece of hardware or functionality, their income has little further effect on the actual use of that hardware or functionality. The differences in use of facilities already acquired, and the extent to which this use correlates with the background variables considered, are however markedly smaller than the differences in terms of acquisition and possession.

 

Where there are systematic differences in use, one striking conclusion is how strongly these differences are determined by the sex of the respondent. Women across the board have less access to ICT than men, and also make less use of it. The elderly (in particular the over-65s) are also at a systematic disadvantage when it comes to the use of new ICT facilities. Educational differences come only fourth in the list of important determinants of ICT inequality. Finally, evidence was found here and there that unemployed or disabled people also sometimes participate less in ICT usage. Given the importance of contacts at work, this is not very surprising.

 

ICT skills (informacy) are most strongly related to age and gender. Older people and women have far fewer ICT skills than young people and men. The education level also has a substantial influence on informacy. The influence of income is small but significant.

 

Possession of ICT

 

To what extent can social, cognitive and material resources explain the differences between the possession and use of ICT? To test these influences, the constructed scales of the three types of resources in table 10 have been added to the regression models in table 9. In table 10 on ICT possession, the base model from table 9 is shown again as model I. The next column presents a model with the separate resources only (model II). In the following models the resources have step by step been included in base model I: social resources (model III), cognitive resources (model IV) and material resources (model V). To estimate the influence of the work situation on the household, the use of a PC at work has been added to model VI.

 

Table 10 Regression analysis of ICT possession (0-100) regarding background characteristics and resources (beta and t-values)

Model

(I)

(II)

(III)

(IV)

(V)

(VI)

Gender

-0,17 (-8,6)**

 

-0,19 (-9,0)**

-0,10 (-4,7)**

-0,09 (-4,4)**

-0,10 (-4,6)**

 

 

 

 

 

 

 

Single

0

 

0

0

0

0

Married/living together without child

0,08 (2,9)**

 

0,05 (2,0)*

0,04 (1,6)

-0,01 (-0,4)

-0,01 (-0,4)

Family with child > 14 years

0,10 (4,4)**

 

0,10 (4,1)**

0,09 (3,9)**

0,05 (2,0)*

0,05 (1,9)

Family with youngest child   14 years

0,12 (4,7)**

 

0,11 (4,3)**

0,10 (3,9)**

0,03 (1,3)

0,03 (1,2)

 

 

 

 

 

 

 

Work

0

 

0

0

0

0

Retired

-0,07 (-2,6)*

 

-0,06 (-2,1)*

-0,05 (-1,8)

-0,06 (-2,1)*

-0,05 (-1,6)

Unemployed or disabled

-0,05 (-2,5)*

 

-0,02 (-1,2)

-0,03 (-1,3)

-0,02 (-1,0)

-0,01 (-0,5)

Housewive/man

-0,02 (-1,0)

 

0,01 (0,3)

0,03 (1,2)

0,02 (0,9)

0,03 (1,5)

Student

0,09 (4,1)**

 

0,10 (4,2)**

0,11 (4,5)**

0,09 (4,2)**

0,11 (4,5)**

 

 

 

 

 

 

 

Age (linear)

0,06 (0,7)

 

0,07 (0,9)

0,19 (2,3)*

0,08 (1,0)

0,09 (1,1)

Age (quadratic)

-0,33 (-3,9)**

 

-0,32 (-3,7)**

-0,31 (-3,6)**

-0,20 (-2,3)*

-0,20 (-2,4)*

 

 

 

 

 

 

 

Education

0,06 (2,8)**

 

0,02 (1,0)

-0,02 (-0,9)

-0,01 (-0,4)

-0,02 (-0,7)

 

 

 

 

 

 

 

Income

0,35 (13,7)**

 

0,32 (11,5)**

0,29 (10,5)**

0,23 (8,6)**

0,23 (8,4)**

 

 

 

 

 

 

 

Possession network

 

0,21 (10,9)**

0,24 (12,0)**

0,21 (10,2)**

0,19 (9,7)**

0,19 (9,5)**

Help network

 

0,03 (1,5)

-

-

-

-

 

 

 

 

 

 

 

Numeracy

 

0,04 (1,9)

 

0,04 (2,1)*

0,04 (1,9)

0,04 (1,9)

Literacy

 

0,01 (0,5)

 

-0,01 (-0,5)

-0,02 (-0,8)

-0,02 (-0,9)

Informacy

 

0,38 (19,2)**

 

0,32 (13,8)**

0,30 (13,4)**

0,29 (12,5)**

 

 

 

 

 

 

 

Material possession

 

0,30 (15,9)**

 

 

0,20 (8,6)**

0,20 (8,6)**

 

 

 

 

 

 

 

PC at work

 

 

 

 

 

0,05 (2,0)*

 

 

 

 

 

 

 

R2

0.34

0.40

0.38

0.44

0.46

0.46

N

1891

1794

1665

1565

1565

1565

DF

12

6

13

16

17

18

* significant (p < 0,05), ** significant (p < 0,01);   Source: UNCR (1998)

 

To some extent, the difference in ICT possession between those in work and the unemployed can be attributed to divergent social resources. The correlation between income and ICT possession also reduces when allowance is made for the difference in social resources. The same applies to the relation between education levels and ICT possession. Higher status groups evidently have access to well-informed networks so that, irrespective of their own skills, they are more likely to acquire ICT products. Access to material resources also influences the possession of ICT facilities. Using a PC at work hardly influences the possession of a wide range of ICT products. However, other research shows that this influence on having a PC at home is indeed substantial (De Haan, 2001). Ethnic minority groups not only have less PC and Internet access at home, but they also use a PC at work less often (Hoffman en Novak, 1999).

 

The high percentage of explained variance (40%) in model II indicates that the resources strongly determine the possession of ICT. Especially the degree of informacy, ICT possession in the personal network and the possession of many luxury consumer goods prove to be strong determinants. More informacy, more ICT in the network and more consumer goods increase the likelihood of ICT possession.

 

The increase in the explained variance (R2 ) in model III to V compared to model I indicates that the three types of resources have additional explanatory power compared to the background characteristics. Furthermore, the decrease in the betas referring to social characteristics in these models indicates the degree to which the resources explain the relationship between ICT possession and social characteristics. For example: after adding the social resources in model III, the beta of education (.02) is lower than the beta of education in model I (.06). Furthermore, the beta of education in model III no longer has a significant effect on the possession of ICT. This implies that the differences between educational groups can largely be attributed to the social support the higher educated can get from their social network.

 

Use of ICT

 

The use of ICT is less strongly determined by social background than ICT possession. Only 12% of the variance in ICT use is explained (table 9 and table 11, model I). The resources prove to have more explanatory power than social background characteristics since their explained variance is 19% (model II).

 

The cognitive resources (informacy) have the strongest influence on the use of ICT. The explained variance increases from 12% to 20% and the cognitive resources are largely responsible for differences in use between different educational groups. To a lesser extent, differences in use between age groups and between men and women also relate to differing access to cognitive resources. The level of informacy explains approximately half of these differences.

 

The social resources have little influence on ICT use. After controlling for differences in social resources, the relationship between age groups and ICT use slightly reduces.

Access to material resources has no effect on the social distribution of the use of ICT facilities.

 

The use of a PC at work stimulates the use of ICT, independent from the influence of resources (model VI). A small part of the influence of informacy can be attributed to PC use at work. Evidently, people acquire digital skills at the workplace, which in turn increases the frequency of ICT use at home or for private purposes.


Table 11 Regression analysis of ICT use (0-100) regarding background characteristics and resources (beta and t-values)

Model

(I)

(II)

(III)

(IV)

(V)

(VI)

Gender

-0,17 (-6,1)**

 

-0,17 (-6,0)**

-0,10 (-3,38)**

-0,09 (-3,3)**

-0,10 (-3,6)**

 

 

 

 

 

 

 

Single

0 

 

0 

0 

0 

0 

Married/living together without child

0,01 (0,2) 

 

0,02 (0,4) 

0,01 (0,2) 

-0,01 (-0,3) 

-0,02 (-0,4) 

Family with child > 14 years

0,00 (0,0) 

 

-0,01 (-0,2) 

-0,01 (-0,2) 

-0,03 (-0,8) 

-0,03 (-1,0) 

Family with youngest child  14 years

-0,00 (-0,00) 

 

0,01 (0,3) 

0,04 (1,0) 

0,00 (0,0) 

-0,01 (-0,2) 

 

 

 

 

 

 

 

Work

0

 

0 

0 

0 

0 

Retired

0,05 (1,6) 

 

 0,05 (1,4) 

0,03 (1,0) 

0,03 (1,0) 

0,06 (1,8) 

Unemployed or disabled

 0,08 (3,0)**

 

 0,07 (2,6)**

0,08 (3,0)**

0,08 (3,1)**

0,11 (3,9)**

Housewive/man

0,01 (0,4) 

 

0,01 (0,3) 

0,02 (0,6) 

0,02 (0,5) 

0,06 (1,8) 

Student

0,06 (1,9) 

 

0,06 (1,8) 

0,09 (3,1)**

0,09 (2,9)**

0,12 (3,9)**

 

 

 

 

 

 

 

Age

-0,20 (-5,3)**

 

-0,18 (-4,7)**

-0,09 (-2,5)*

-0,11 (-2,9)**

-0,10 (-2,6)**

 

 

 

 

 

 

 

Education

0,08 (2,8)**

 

0,07 (2,4)*

0,01 (0,4) 

0,02 (0,6) 

0,00 (0,0) 

 

 

 

 

 

 

 

Income

0,05 (1,4) 

 

0,03 (0,9) 

0,07 (1,9) 

0,05 (1,3) 

0,04 (1,0) 

 

 

 

 

 

 

 

Possession

0,21 (7,5)**

 

0,19 (6,3)**

0,09 (2,8)**

0,07 (2,1)*

0,06 (2,1)*

 

 

 

 

 

 

 

Possession network

 

0,02 (1,0) 

0,04 (1,2) 

0,02 (0,6) 

0,01 (0,5) 

0,01 (0,3) 

Aid from network

 

0,03 (1,1) 

- 

- 

- 

- 

 

 

 

 

 

 

 

Numeracy

 

0,02 (0,9) 

 

0,05 (1,7) 

0,05 (1,7) 

0,05 (1,9) 

Literacy

 

0,00 (-0,1) 

 

-0,00 (-0,2) 

-0,01 (-0,3) 

-0,02 (-0,5) 

Informacy

 

0,41 (16,1)**

 

0,35 (11,5)**

0,34 (11,4)**

0,32 (10,5)**

 

 

 

 

 

 

 

Material possession

 

0,08 (3,1)**

 

 

0,10 (3,1)**

0,11 (3,2)**

 

 

 

 

 

 

 

PC at work

 

 

 

 

 

0,12 (3,6)**

 

 

 

 

 

 

 

R2

12 

19 

12 

2 

2 

21 

N

1374 

1388 

1257 

1212 

1212 

1212 

DF

 12 

6 

13 

16 

17 

18 

* significant (p < 0,05), ** significant (p < 0,01); Source: UNCR (1998)

 

Informacy

 

The level of informacy strongly determines both the possession and the use of ICT. Of course, we cannot conclude from our data that high levels of informacy are the reason why ICT products are acquired. It is just as likely that ICT owners have learned to work with their products after purchasing them. To a certain extent this also holds true for the relationship between ICT use and informacy. Yet, it is more likely that the frequency, as well as variety, of use will increase if people have more digital skills. If the diffusion of ICT among larger sections of the population continues, possession may become less of a distinction between social groups. In the future, the level of informacy may become a key determinant of ICT use and may become mainly responsible for differences in participation in the information society. Therefore, we will consider the determinants of informacy in detail. Table 12 shows the results from our analysis.

 

The same procedure has been followed as in the previous tables. Base model I explains 29% of the differences in informacy between social groups. This means that the level of informacy is strongly related to the social characteristics.

 

Table 12 Regression analysis of informacy (1-100) regarding background characteristics and resources (beta and t-values)

Model

(I)

(II)

(III)

(IV)

(V)

(VI)

Gender (women)

-0,26 (-12,5)**

 

-0,27 (-11,7)**

-0,26 (-11,0)**

-0,25 (-10,8)**

-0,25 (-11,2)**

 

 

 

 

 

 

 

Single

0

 

0

0

0

0

Married/living together without child

0,00 (0,1)

 

0,01 (0,4)

0,00 (0,0)

-0,03 (-0,8)

-0,03 (-1,0)

Family with child > 14 years

-0,01 (-0,3)

 

0,00 (0,1)

-0,01 (-0,5)

-0,04 (-1,5)

-0,05 (-1,7)

Family with youngest child  14 years

-0,01 (-0,6)

 

-0,01 (-0,3)

-0,02 (-0,5)

-0,06 (-1,8)

-0,06 (-2,1)*

 

 

 

 

 

 

 

Work

0

 

0

0

0

0

Retired

-0,07 (-2,5)*

 

 -0,04 (-1,4)

-0,04 (-1,5)

-0,04 (-1,3)

0,02 (0,8)

Unemployed or disabled

 -0,06 (-3,0)**

 

 -0,05 (-2,2)*

-0,04 (-1,9)

-0,04 (-1,8)

0,01 (0,5)

Housewife/man

-0,07 (-3,1)**

 

-0,03 (-1,4)

-0,01 (-0,5)

-0,01 (-0,6)

0,06 (2,3)*

Student

-0,04 (-1,9)

 

-0,05 (-2,1)*

-0,05 (-1,9)

-0,05 (-1,9)

0,01 (0,5)

 

 

 

 

 

 

 

Age

-0,35 (-11,7)**

 

-0,31 (-9,8)**

-0,31 (-9,9)**

-0,31 (-9,9)**

-0,27 (-9,0)**

 

 

 

 

 

 

 

Education

0,17 (7,6)**

 

0,14 (5,8)**

0,10 (4,1)**

0,11 (4,3)**

0,06 (2,4)**

 

 

 

 

 

 

 

Income

0,09 (3,4)**

 

0,04 (1,5)

0,04 (1,3)

0,01 (0,4)

-0,01 (-0,3)

 

 

 

 

 

 

 

Possession network

 

0,17 (7,5)**

0,14 (6,3)**

0,15 (6,5)**

0,14 (6,1)**

0,12 (5,3)**

Aid network

 

0,19 (8,6)**

0,09 (3,7)**

0,08 (3,3)**

0,07 (3,2)**

0,07 (2,9)**

 

 

 

 

 

 

 

Numeracy

 

0,04 (1,6)

 

0,04 (2,0)*

0,04 (1,9)

0,05 (2,2)*

Literacy

 

0,20 (8,9)**

 

0,15 (6,4)**

0,15 (6,3)**

0,13 (5,8)**

 

 

 

 

 

 

 

Material possession

 

0,10 (4,4)**

 

 

0,11 (4,0)**

0,10 (4,0)**

 

 

 

 

 

 

 

PC at work

 

 

 

 

 

0,25 (9,2)**

 

 

 

 

 

 

 

R2

0.29

0.16

0.28

30

0.31

0.34

N

1873

1830

1580

1526

1526

1526

DF

11

5

13

15

16

17

* significant (p < 0,05), ** significant (p < 0,01),

 

Source: UNCR (1998)

 

Informacy positively relates to other types of resources, most strongly to literacy and social resources. However, these relationships are not very strong (R2=.16). On average, people who handle written information well, also know how to use digital information and communication devices. And they find themselves surrounded by people who show similar capabilities. Remarkably, numeracy is not positively related to informacy. Being able to make calculations does not coincide with a better command of ICT.

 

Social resources do not contribute to the explanation of informacy. However, coefficients of background variables change after adding social resources to the model. The effect of income disappears and the effects of age and educational level are somewhat less. Furthermore, when social resources are controlled there is no longer any difference in informacy between people who are retired or do household work and those who are in paid employment. The cognitive resources (literacy) are largely responsible for differences in ICT skills between different educational groups. Access to material resources does not influence differences in ICT skills between social groups.

 

Using a PC at work strongly influences informacy (model VI). The explained variance increases by another 3 percent points and the standardised effect (beta) of a PC at work (0,25) is also high. By adding this variable to the model, the differences between age groups and educational groups further decrease, indicating that young people and higher educated people acquire digital skills at work and also use these skills outside the workplace.

 

Summary and discussion

 

The analyses of ICT possession and use and of informacy show that individual resources influence existing social differences. Differences in ICT access and use between population groups can sometimes be explained completely and sometimes only partly. Some resources primarily influence possession, while others mainly influence ICT use or digital skills (informacy). However, the explanation based on the different types of resources is not adequate for all sections of the population. This applies in particular to the wide difference between the sexes. In the area of skills, the differences between men and women hardly reduce at all when allowance is made for divergent access to resources. With regard to possession and use of ICT, more than half the differences remain unexplained. The explanation for these differences must therefore be sought in factors other than the resources measured. The differences between age groups in all three domains also largely remain after control for the influence of resources. Lack of experience with modern technology on the labour market plays an important role here, but an anxious attitude towards unknown products and few opportunities for use could also be important. The absence of local information on the Internet can be an additional reason why the elderly are not on line (Lazarus en Mora, 2000).

 

In the debate on the digital divide there are two positions. According to some, there is a growing divide between the information rich and the information poor (NTIA, 1999). Others do not see a permanent divide between population groups, but rather a stage difference in the same process of diffusion (Van Dijk et al., 2000). Is the digital divide a matter of lasting social inequality?

 

The existence of a stable divide assumes a gulf that is difficult to bridge. Although the different rates of possession of ICT products in the different sections of the population have increased since their market introduction, this does not mean that these differences are here to stay. The chance of ownership for separate individuals is not fixed; someone who today is a “non-possessor” may not be tomorrow. In general, new products spread in accordance with the ‘trickle-down’ principle: the affluent and well educated are among the early adopters and the less affluent and lower educated follow in time. If the spread of existing ICT products continues under the influence of falling prices and increasing user-friendliness, the PC and mobile telephone will eventually become everyday possessions.

 

An updated snapshot of the digital divide in the Netherlands already points to decreasing differences in PC possession. Judging by a comparison of data from 1995 and 2000, these differences decline between men and women. As for education, persons who did not finish higher education have slowly but surely been gaining upon the higher educated since 1995 (De Haan and Huysmans, 2001). Possession among the elderly still lags far behind that of young people, but there has been a strong increase in possession among the 50-64-year olds. Many of the working people of this age group have gathered experience with PC use at the workplace. The principle of cohort replacement combined with the continuation of the diffusion process is expected to lead to a further spread of PC possession among the elderly (De Haan, 2001).

 

This turn in the diffusion process has also been witnessed in the United States. The General Accounting Office (GAO, 2001) finds that the ‘digital divide’ is shrinking as more Americans gain access to the Internet. Particularly usage gaps in gender and geography have notably declined. However, the National Telecommunications and Information Administration (NTIA, 2000) still concludes that a digital divide remains or has expanded slightly in some cases, even while Internet access and computer ownership are rising rapidly for almost all groups. Yet, the change in terminology in the latest ‘Falling through the Net – toward digital inclusion’ study also indicates that NTIA foresees shrinking divides in the time to come. NTIA recognises that groups that have traditionally been digital ‘have-nots’ are now making ‘dramatic’ gains.

 

It is likely that several groups, even if they have equal access to ICT products, will make differing use of them in the future. Groups which are already over represented among the possessors also make more use of the facilities in question. This study has shown that usage differences are smaller than the differences in possession. Nonetheless, this finding suggests that differences in usage (frequencies and above all type of usage) remain once the ICT products described have become very widely distributed. These differences will probably be strongly related to digital skills (informacy). And differences in digital skills are likely to remain. This calls for a more complex picture of the digital divide than a mere look at access. We have done this by systematically comparing differences in access, use and digital skills. Van Dijk and Hacker (2002) make a comparable distinction: digital experience, possession of computers and network connections, digital skills and usage opportunities. New inequalities are not expected to arise when digital skills are taken into account. The literate elite will gradually change into an elite, which is capable of handling new technologies.

 

The differences in computer use and digital skills will be related to needs for digital information. The significance of the possession of modern ICT facilities for the acquisition of other scarce goods should not be overestimated. The labour market needs more and more people with computer skills; a person with such skills has an advantage in this situation. However, a substantial proportion of jobs still do not require computer training. And a large section of the population, such as retired people, has no labour market ambitions. For this group, the possession or lack of computer skills is therefore not instrumental in the generation of income. Once again the research in the Netherlands and that in the United States correspond: in both countries there is quite a large group of information want nots. Early research on the adoption of the PC showed that nearly one fifth of families had quit using the PC entirely within two years of purchase (NSF, 2000). When asked why they are not online, almost half of the Dutch and one third of the American non-users say that they are ‘not interested’ (Van Dijk et al., 2000; UCLA, 2000); many surfers went back to the beach (Wyatt, 2000)

 

Finally, consideration needs to be given to the extent to which government intervention is desirable to ameliorate ICT inequality, as well as the related question of what type of policy would then be most appropriate. The recommendations can be relatively short. The greatest inequality, which relates to the possession of ICT facilities, can be attributed primarily to income differentials, and to some extent these are the easiest to repair (through income and price measures). Is it desirable to do so, however? The above description of diffusion processes suggests that the answer is no. It is after all reasonable to assume that the consequences of income differentials are ephemeral in nature. Higher income groups lead the field, but they will not be able to retain this lead. In particular, falling prices and increasing user-friendliness mean that the spread of these products among those who do not currently have them will continue. Moreover, among those who do not possess the various products are many people who do not see any opportunities for using them. Encouraging the possession of ICT products would therefore not appear to be a task for the government. It is likely that market forces will lead to the further spread of these products among people who stand to gain from possessing them.

 

Once a product has become widespread in society, it is primarily the differences in use and utilisation that count. And it is precisely these differences that, according to our study, are not exceptionally large; they are in any event smaller than the differences in possession. Differences in frequency of use, types of use and skills will however continue to exist. These differences are not connected to income and can therefore not be tackled through income and prices policy. Here again, the question can be asked as to whether there is a task for the government in stimulating the acquisition of user skills, and if so, how the government should set about this.

 

Government policy with respect to social inequality in the use of modern ICT facilities in the Netherlands focuses primarily on education, where large-scale operations and catching-up exercises are being implemented in order to ‘get everyone in front of a computer’. There is a relatively strong belief that instruction via new ICT technology offers major advantages and that it is necessary for the collective well-being to train the population in this way. It is however debatable whether this belief is correct, or whether it is based primarily on an unreasonable fear of missing the connection to the electronic superhighway.

 

Not all consequences of technological development can be tackled through education policy. Forms of social exclusion among the elderly cannot be resolved at all in this way. Here again, however, it is unclear to what extent people with few digital skills perceive this as a disadvantage. It is quite plausible that older people can manage perfectly well without a great deal of modern apparatus. However, the commotion surrounding the closure of ‘physical points of contact’ is an indication that keeping open traditional channels for communication and information transfer deserves attention, as long as there is a group of citizens who do not possess modern technology or are unable to use it adequately.


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