5 Beyond Access: Social & Economic Impacts

Introduction

Access to technology and information resources and basic digital literacy skills, the first order effects, are essential prerequisites for experiencing impacts of ICT access in social and economic life. But does public access to ICTs in fact generate second-order effects—impacts on education, employment and income generation, civic engagement, or other areas of social and economic activity?

In answering this question, the study examined a number of categories of activity that fall within the study’s priority domains of Culture & Language, Education, Employment & Income, Governance, and Health. Communications & Leisure is also included, with special attention the role of email and social networking in building ICT skills and achieving specific tasks.

Due to the complex relationship between user needs, computer and internet usage, and downstream impacts, the study employs a number of analytical approaches to illuminate the nature of the impacts and how far-reaching they extend within different population groups.

The chapter begins with a general discussion of users’ perceptions of social and economic impacts. This is followed by a deeper analysis of impacts across the priority domains and user populations.

Impact domains and categories

The six impact domains (listed above) are used throughout this chapter to present usage data. However, a more fine-grained classification was needed to assess impact (particularly in the Communications & Leisure and Employment & Income domains). The impact domains are therefore supplemented by 13 impact categories — eleven categories within the six domains, plus two cross-cutting impact categories, financial savings and time savings. The cross-cutting categories are relevant to all domains: for example, people might save money by spending less to obtain a particular government service. The domains and categories are shown in Table 5.1.

Table 5.1: Impact categories and domains
CATEGORY DOMAIN
Communication with family & friends Communications & Leisure
Pursuing interests & hobbies Communications & Leisure
Meeting new people Communications & Leisure
Pursuing other leisure activities Communications & Leisure
Income Employment & Income
Access to employability resources Employment & Income
Sending or receiving remittances Employment & Income
Education Education
Health Health
Access to government information & services Governance
Local language & cultural activities Culture & Language
Financial savings Cross cutting
Time savings Cross cutting

Impact is measured in two ways:

  1. The proportion of users experiencing positive, negative or no impact from their use of public access venues (in the 13 impact categories)
  2. The proportion of users who have been able to successfully pursue particular information-seeking and communication goals at public access venues (within the six domains)

General impact perceptions

Table 5.2 gives an overview of users’ perceptions of impact across the 13 impact categories. Overall, 98% of users reported a positive impact in one or more of the 13 categories. This finding includes people who had not recently used a public access venue in the particular category of activity.

Table 5.2: Overall impact from using public access computers (%)
Positive No Impact Negative
Communication with family & friends 83 16 2
Education 79 20 2
Pursuing interests & hobbies 76 21 3
Meeting new people 74 24 3
Pursuing other leisure activities 68 29 4
Time savings 58 30 12
Access to employability resources 58 40 2
Financial savings 41 40 20
Access to government information & services 41 56 3
Local language & cultural activities 36 61 4
Health 38 56 7
Income 35 55 10
Sending or receiving remittances 23 72 5
Note: n=5,010

Over 50% of users reported positive impacts of public ICT access in their communication with family & friendsmeeting new peopleeducationpursuing interests & hobbies or other leisure activitiestime savings, and access to employability resources. The dominance of positive social and leisure-related impacts confirms a common narrative about public access use — that they are hubs for social networking and leisure pursuits (Sey and Fellows, 2009). Maintaining communication with family &friends tops the list, with 83% of users reporting positive impacts. But note that this first tier of positive impacts also includes areas related to education and employment, two of the priority impact domains.

A high proportion of users (25%–40%) also reported positive impacts on their financial savingsaccess to government information & serviceslocal language & cultural activitieshealthincome, and sending or receiving remittances. This second tier of impacts relates almost entirely to areas of traditional concern to international development, i.e., the priority domains of Health, Education, Governance, Culture, and Employment.

The highest negative impacts were associated with expenditures of time or money: financial savings (20%), time savings (12%), and income (10%). These users may have been reflecting on how much it costs them, in time and money, to use public access venues.

This first cut at the data appears to confirm prior research suggesting that people experience the most impact in the areas of communications and leisure. Nevertheless, impacts in other areas — educationhealth, access to employability resources, among others — remain extremely important, even if they appear less frequently in user responses. A closer look at the data shows a complex dynamic of usage and impacts. (See also Chapter 9, Public Access in a Development Context, for a discussion of “routine” vs. “episodic” uses.)

Usage patterns and impact

Table 5.2 shows high proportions of “no impact” reports in a number of domains, including those of priority concern to international development. A key question is whether the absence of impact meant that users had tried to obtain benefits in these areas and not succeeded, or that they were not in fact seeking benefits in these domains. To answer this question, first impacts between those who engaged in a particular domain (defined as use within the last 12 months) were compared with those who did not. Second, reasons for non-use were examined.

The highest proportions of positive impacts, in almost all domains, were reported by people who had used that domain in the last 12 months (Figure 5.1). For example, almost 70% of those who had used the venue in the last 12 months for health reasons (sometimes, most times, or every time), either for themselves or for others, perceived positive impacts, compared to 25% of those who had not. Frequency of use was particularly important. All domains showed a dramatic increase in positive impact perceptions for the users who more frequently used a venue for that domain.

Figure 5.1: Frequency of use and perception of positive impacts, by domain

Figure 5.1: Frequency of use and perception of positive impacts, by domain

The results confirm the relationship between goals/needs and positive impact perceptions: those who came to the venue to seek information in a particular domain were more likely to perceive positive impacts in that domain. Staying with the health example, 82% of those who came to the venue to look for health information perceived positive overall health impacts, versus 42% of those who were not looking for health information.

Notably, non-use (in the previous 12 months) apparently did not mean an absence of impacts. People who had not used a venue for a specific domain still reported positive impacts, ranging from a high of 60% in Education to about 20% in Employment & Income. The explanation could be that respondents were thinking of a narrow set of activities when responding to the question about use of a particular domain. Or, it could be that some respondents were recalling an earlier time (prior to the past 12 months) when they experienced positive impacts. This would suggest that there may be some diffused and lingering forms of public access impacts.

Reasons for non-use

The second approach to understanding the incidence of “no impacts” was to examine why people did not use public access computers for a given domain. Respondents were given ten options. Across domains, the overwhelmingly dominant response was “didn’t have the need” (Figure 5.2).

Figure 5.2: Reasons for non-use, by domain

Figure 5.2: Reasons for non-use, by domain

Of course, non-use is not necessarily a bad thing: people who are not unemployed or unwell are likely not to be searching for a job or trying to find medical information. Moreover, the demographic composition of public access users — mostly young — would affect their impact responses. As a group, they may be less susceptible to illness, as well as (perhaps unfortunately) less engaged in civic and cultural activities.

At the same time, the high number of responses, “didn’t think of it,” could indicate that many users were unaware of the information resources available, or that those resources were not available. Public access venues might need to enhance their efforts at making users aware of the available resources and potential benefits.

Goal achievement and impact

Among those users who did engage in goal-oriented activities, how many actually achieved what they set out to accomplish? To probe goal achievement, a three-part logical question structure was used. For each priority domain, respondents were asked: (1) Did you search for information? (2) Did you find information? (3) Did you take action based on that information?

This structure is informed by two approaches to impact measurement. Outcome mapping (Earl, Carden, & Smutylo, 2001) is an evaluation framework that supports linking program activities to changes in the behaviors, activities, and relationships of program users. By focusing on “outcomes,” this approach identifies the contribution to development goals, without claiming causality. The situated logic model (Naumer, 2009) is an approach to evaluation that links program activities to high-level policy priorities (or development goals).

The analysis found support for the effectiveness of public access ICT as a resource for people pursuing specific goals. Across all priority domains, an overwhelming number of respondents were able to proceed through the progression of steps to achieve what they set out to do.

Figure 5.3 shows the proportion of users who engaged in each of the priority domains within the previous 12 months. Large percentages were engaged in all domains, and particularly in Education (66%) and Employment & Income (42%).

Figure 5.3: Proportion of users engaged in priority domains

Figure 5.3: Proportion of users engaged in priority domains

For each subset of the sample — those engaged in each of these five domains — a follow-up exercise examined how successful they were in satisfying information needs or completing key tasks, from two to four per domain. The assigned tasks are considered very common in all the countries studied, though they do not represent the full range of activities commonly performed in these areas and show only some of the tasks that public access users might engage in. Task completion, usually indicated by some action, marks the end of the activity sequence “search, find, act.”

Figures 5.4 through 5.8 show each task broken down into a series of steps. This demonstrates how task completion is not independent, but rather an outcome of other activities such as searching for information and finding information. Here, researchers specifically examine whether users actually followed through from information seeking to accomplish a task. Specifically, the figures show the number of users who engaged in each task (by domain), and the number who successfully completed the steps for that task. (Note that the charts show absolute numbers rather than percentages. The total number of respondents was 5000.)

Figures 5.4 through 5.8 show that an overwhelming proportion of people were able to complete the tasks they came to the public access venue to do — over 80% in each case. For example, the first block of Figure 5.4 shows that, out of 5,000 respondents, 1091 said they had searched for jobs. Of that group 1,027 (over 90%) said they found information on jobs. Of those 1,027, over 90% actually applied for a job.

Figure 5.4: Tasks attempted and completed, employment & income domain

Figure 5.4: Tasks attempted and completed, employment & income domain

Figure 5.5: Tasks attempted and completed, education domain

Figure 5.5: Tasks attempted and completed, education domain

Figure 5.6: Tasks attempted and completed, health domain

Figure 5.6: Tasks attempted and completed, health domain

Figure 5.7: Tasks attempted and completed, governance domain

Figure 5.7: Tasks attempted and completed, governance domain

Figure 5.8: Tasks attempted and completed, culture & language domain

Figure 5.8: Tasks attempted and completed, culture & language domain

Usage patterns and impacts cannot be isolated from users’ needs and goals, as well as the availability of needed resources at the point of use. For example, considering the large number of students in the survey sample, it is not surprising that homework and searching for admissions information appeared as high priorities in the Education domain, as compared to taking an online course or workshop.

A number of interesting questions arise in this context. For instance, what factors enabled people to accomplish the attempted tasks with such high rates of success? What venue characteristics contribute to success, e.g., the presence of trained staff to assist users? Did all user groups achieve their goals equally, or are there differences among them? While they are beyond the scope of this report, these questions might be explored using the data collected.

Impacts by venue type

The analysis turns to whether these impacts were experienced equally across the three venue types. While the country variations in the distribution of libraries, telecenters, and cybercafés presents some analytical complications, some interesting features emerge from a general cross-venue analysis.

When users were asked whether they had used public access venues to access specific domains in the last 12 months, library and cybercafé users had a higher level of use over all domains, than telecenter users (Figure 5.9). In the highest-use domains — Communications & Leisure and Education — library and cybercafé users reported similar levels of use. Library users showed higher levels of use in the domains of Culture & Language, Health, and Governance.

Figure 5.9: Use of domains, by venue type

Figure 5.9: Use of domains, by venue type

When users were asked how often they had accessed each domain over the past 12 months, the picture became more complex. Three domains — Communications & Leisure, Employment & Income, and Governance — showed significant (P-value < 0.0001) differences in frequency of use. The other three — Culture & Language, Education, and Health — did not show significant differences.

Figure 5.10 shows overall perceived impact across the 13 impact categories. Again, higher proportions of library and cybercafé users showed positive impacts than did telecenter users. But in this case, there are greater differences — favoring libraries — between libraries and cybercafés than for usage. In other words, although library and cybercafé users have generally similar usage patterns, more library users report positive impacts than cybercafé users. Library users were more likely to report positive impacts than the other two venue types in the areas of educationtime savingsaccess to government information & services, local language and cultural activities, and health. In only three areas (communication with family & friends, meeting new people, pursuing leisure activities), higher proportions of cybercafé users reported positive impacts than library users, though by much smaller margins. Overall, lower proportions of telecenter users reported positive impacts, with some notable exceptions (access to employability resources, financial savings).

Figure 5.10: Perceived positive impact, by venue type

Figure 5.10: Perceived positive impact, by venue type

Impacts by user population

This section examines how different user populations perceive impact, sorted by socioeconomic variables — employment status, income level, and education level — and by gender, age, and location (urban or rural). Particular attention is given to findings for the populations of greater interest: lower socioeconomic status, females, older adults, and rural users.

Employment status

In order to compare employed and unemployed users, the sample for analysis excluded the categories of students, retired workers, homemakers, and “other.” It also excluded both the youngest and the oldest age categories, focusing instead on the two age categories that were mostly likely to constitute a working cohort (25–34 and 35–49) — people who had likely finished their education and were not yet approaching retirement. The resulting sample included 1,060 employed users and 180 unemployed users, in the 25–49 age range.

For usage, the data showed that a higher percentage of unemployed users were engaged in all priority domains, as compared to employed users (Figure 5.11). The greatest statistically significant differences were in the Education (P-value = 0.0054) and Communications & Leisure (P-value = 0.007) domains. In other domains, the differences were in the same direction but were not statistically significant.

Figure 5.11: Domain usage, by employed vs. unemployed users

Figure 5.11: Domain usage, by employed vs. unemployed users

Regarding perceived impacts, there was little difference between employed and unemployed users. Only one category showed a significant difference between the two groups — “maintaining communications” with family and friends (P-value = 0.024), where a higher percentage of the unemployed group reported positive impacts (61% vs. 49%). Other categories showed smaller differences that did not test significant.

Figure 5.12: Impact on employed vs. unemployed users, communications & leisure domain

Figure 5.12: Impact on employed vs. unemployed users, communications & leisure domain

Figure 5.13: Impact on employed vs. unemployed users, culture & language domain

Figure 5.13: Impact on employed vs. unemployed users, culture & language domain

Figure 5.14: Impact on employed vs. unemployed users, education domain

Figure 5.14: Impact on employed vs. unemployed users, education domain

Figure 5.15: Impact on employed vs. unemployed users, employment & income domain

Figure 5.15: Impact on employed vs. unemployed users, employment & income domain

Figure 5.16: Impact on employed vs. unemployed users, governance domain

Figure 5.16: Impact on employed vs. unemployed users, governance domain

Figure 5.17: Impact on employed vs. unemployed users, health domain

Figure 5.17: Impact on employed vs. unemployed users, health domain

Figure 5.18: Impact on employed vs. unemployed users, cross cutting

Figure 5.18: Impact on employed vs. unemployed users, cross cutting

How should these findings be interpreted? One possible reason for the overall similarity in perceived impacts is that the small sample size for unemployed users makes it more difficult to show statistical significance. Still, the fact that unemployed users were as likely as their employed counterparts to report positive impacts in Employment & Income gives support to a principal justification for supporting public access programs. Employed and unemployed people probably use public access to similar degrees but for different reasons, with different impacts in this domain. For employed users, the impacts may be in the provision of resources that help them improve their performance at work or earn more income. For the unemployed, the impacts might include access to information and resources that enable them to network with potential employers or that help to prepare them to re-enter the job market. The absolute scores on positive impact for the unemployed group, across all categories, suggest that public access indeed makes a difference for this vulnerable population. Further analysis of the data can yield more insights.

Income levels

This section examines the effect of income on perceived impacts, paying particular attention to those at the lower end of the income spectrum — the most vulnerable population. Users were identified as either above or below the national poverty line (based on personal income), and further classified by employment status: “employed” (full time, part time, or self-employed); “unemployed” (either looking or not looking for a job); and “student.” The result is six economic groups (see Table 5.3.) The “student” group was likely to report no personal income, and thus be classified as below the poverty line. Not surprisingly, employed users were more likely to be above the poverty line (76%), while unemployed users were more likely to be below it (78%). (Further analysis will include consideration for gender and age: for the employed-below-poverty group, for example, the gender split was 38% female and 62% male.)

Table 5.3: Users below/above poverty line, by employment status (%)
Below poverty line Above poverty line Total
Employed 24 76 100
Unemployed 78 22 100
Student 90 10 100
Note: n=4,138

Table 5.4 shows the percentage of users in each of the six employment groups reporting positive impacts, for each of the 13 impact categories. In relation to poverty status, the analysis reveals only minor differences in the percentages reporting positive impact. That is, except for the student group, users below the poverty line generally perceived positive impacts to the same extent as those above the poverty line, over all the impact categories. For students, however, higher percentages of those above the poverty line reported positive impacts in all categories — much higher, in some categories.

For education, the student group (both below and above the poverty line) was most likely to report positive impacts (85% for those below the poverty line and 87% above it). Unemployed users were the next most likely to report positive impact on education, at 80% for those below the poverty line and 71% for those above.

In the category of health impacts, the employed group below the poverty line had the highest proportion reporting positive impacts, at 43%, as compared for example to employed users above the poverty line, at 32%.

Table 5.4: Percent of employed and unemployed users reporting positive impact, by category (%)
Employed, Below
poverty line
Employed, Above poverty line Unemployed, Below
poverty line
Unemployed, Above poverty line Student, Below
poverty line
Student, Above poverty line
Income 42 46 36 34 20 38
Access to employability resources 68 67 60 63 39 63
Education 69 67 80 71 85 87
Health 43 32 38 32 33 34
Access to government information & services 44 42 38 40 26 42
Local language & cultural activities 30 34 30 39 27 43
Time savings 52 64 53 51 50 73
Financial savings 40 48 36 46 33 49
Meeting new people 74 69 74 68 72 79
Communication w/ family & friends 81 82 88 86 79 88
Sending or receiving remittances 23 20 20 18 19 25
Pursuing interests & hobbies 75 70 70 72 77 78
Pursuing other leisure activities 68 57 61 60 67 71
Note: n=4,138

Finally, users above the poverty line (employed, unemployed, and student) were more likely to report positive impacts on financial savings than those below the poverty line (ranging from 46% to 49%, as compared to 33% to 40%).

Overall, substantial percentages of the lowest income groups — those below the poverty line — report impacts in a number of areas of importance to their well-being, including the key instrumental categories of employability resources and education.

Education levels

Previous studies have found that public access users tend to be more educated than the general population, as also noted in Chapter 3. Overall, the data show that the higher their level of education, the more active users tended to be, in specific domains.

Usage increased with increasing education levels in two domains (Employment & Income and Governance). For the other four domains (Communications & Leisure, Culture & Language, Education, and Health), there was more variability. For example, college educated users were especially likely to use public ICT access for Health and Employment; trade school educated users were least likely to use public access for Education.

Figure 5.19: Public access usage, by education level and domain

Figure 5.19: Public access usage, by education level and domain

For perceived impact as well, perceptions of positive impact increased with the education level of users. Moving from grade school to high school to college education level, there was a generally consistent increase in the proportions of respondents at each level reporting positive impacts, for most of the 13 impact categories.

This trend was less evident for certain categories, such as pursuing interests & hobbiespursuing leisure activitiesmeeting people, and maintaining communication with family & friends. However, for most of the key instrumental categories — income, access to employability resources, education, health, government information & services, and time savings — the proportions of positive impacts generally increased with education level. Note that these categories are in the specific areas of higher priority to international development. Figures 5.14 to 5.16 illustrate this trend in three key areas: employability resources, health, and government information & services.

Figure 5.20: Users reporting positive impact on employability resources, by education level

Figure 5.20: Users reporting positive impact on employability resources, by education level

Figure 5.21: Users reporting positive impact on health, by education level

Figure 5.21: Users reporting positive impact on health, by education level

Figure 5.22: Users reporting positive impact on government, by education level

Figure 5.22: Users reporting positive impact on government, by education level

An exception to this overall trend is the pattern of responses from trade school educated users, who often had a lower percentage of reports of positive impacts as compared with either high school or college level respondents (but higher percentages than high school level respondents for some categories). One possible factor is that many high school students are preparing to enter college, making them more similar to college level users than trade school users. Overall, however, there seems to be a relationship between education level and perceived impacts of using public access venues.

Gender

The survey design aimed at sampling females and males in equal proportion, in order to have a sizable female sample for statistical analysis — although this strategy would not reflect the actual proportion of female versus male users. Unfortunately, the goal of stratifying equally by gender was not achieved in some countries, notably Bangladesh and Ghana, perhaps because there are actually low numbers of female users in these countries.

Overall, while there were some statistically significant differences by gender, they did not indicate dramatically different perceptions of impacts. Figure 5.17 shows impact categories by gender, with two rows for each of the 13 impact categories, showing male and female responses.

Figure 5.23: Perceived impacts, by gender, communications & leisure domain

Figure 5.23: Perceived impacts, by gender, communications & leisure domain

Figure 5.24: Perceived impacts, by gender, culture & language domain

Figure 5.24: Perceived impacts, by gender, culture & language domain

Figure 5.25: Perceived impacts, by gender, education domain

Figure 5.25: Perceived impacts, by gender, education domain

Figure 5.26: Perceived impacts, by gender, employment & income domain

Figure 5.26: Perceived impacts, by gender, employment & income domain

Figure 5.27: Perceived impacts, by gender, governance domain

Figure 5.27: Perceived impacts, by gender, governance domain

Figure 5.28: Perceived impacts, by gender, health domain

Figure 5.28: Perceived impacts, by gender, health domain

Figure 5.29: Perceived impacts, by gender, cross-cutting

Figure 5.29: Perceived impacts, by gender, cross-cutting

For four of the impact categories, there was no significant difference between male and female users: time savingsmeeting peoplemaintaining communication with family & friends, and pursuing interests & hobbies. For the other nine impact categories, the differences between male and female users were statistically significant (using a chi-square test); however, these differences were not all in the same direction.

Male users were significantly more likely to report positive impacts in income and access to employability resources. They were also more likely to report positive impacts (and less likely to report negative impacts) in financial savings.

Female users, on the other hand, tended to report positive impacts in education, healthaccess to government information & servicesculture, and pursuing leisure activities. Higher proportions of women also reported positive impacts (and also negative impacts) in sending & receiving remittances.

Overall, male users tended to perceive more positive impacts in the economic categories, while female users seemed to perceive more positive impacts in the social categories.

Even when the differences between male and female users were statistically significant, however, the actual observed differences still tended to be small in relation to the rather large sample size. Overall, female users are not uniformly disadvantaged in terms of using technology, to the extent that they perceive positive impacts in roughly similar proportion to male users. Of course, females do not visit public access venues in equal proportions — particularly in Bangladesh and Ghana — and barriers to participation clearly persist. Among the population of users, however, female users benefit similarly to their male counterparts. Further analysis would be needed to determine whether female users have characteristics that differentiate them from female non-users.

Age

Young people comprised over 50% of users in every country (see Chapter 3). This analysis examined seven age groups, from ages 12–15 to over 65, revealing a number of trends in reported impacts.

Distinctions between age groups had statistical support: all impact categories except culture showed significant differences across age groups (using Chi-squared tests). In some categories, such as health, the difference between older and younger users was not great. (See Figures 5.18 to 5.25.)

  • Overall, younger users were more likely than older users to report positive impacts in categories related to social interaction and leisure (although many older users also saw positive benefits in these categories).
  • Older users had a greater tendency to see positive impacts in the priority domains generally (notably in income, health, access to government information & services, and access to employability resources).
  • Working-age users tended to see positive impacts in employment-related domains (income and access to employability resources).
  • Older adults tended to see positive benefits in the health and government categories.
  • Younger users also reported positive benefits in some of the priority categories — particularly in education, where they had an equal or greater tendency to see positive impacts.

Regarding education impacts, since younger users are typically in school it can be expected that they would report positive impacts. However, a high proportion of older users (50–65 and, especially, 65 and up) also reported positive education impacts, reflecting the availability of educational resources and activities through computer and internet access.

With regard to communication with family and friends, there were substantial impacts across age groups. Older users generally reported positive impacts more frequently in categories outside the social and leisure categories.

The 25–34 age group reported the highest percentages of positive impacts on income, as well as the highest percentages of positive impacts in access to employability resources. Users older than 50 reported the highest percentages of positive impacts on health.

Figure 5.30: Perceived impacts on hobbies & interests, by age

Figure 5.30: Perceived impacts on hobbies & interests, by age

Figure 5.31: Perceived impacts on meeting people, by age

Figure 5.31: Perceived impacts on meeting people, by age

Figure 5.32: Perceived impacts on education, by age

Figure 5.32: Perceived impacts on education, by age

Figure 5.33: Perceived impacts on communications with family & friends, by age

Figure 5.33: Perceived impacts on communications with family & friends, by age

Figure 5.34: Perceived impacts on income, by age

Figure 5.34: Perceived impacts on income, by age

Figure 5.35: Perceived impacts on access to employability resources, by age

Figure 5.35: Perceived impacts on access to employability resources, by age

Figure 5.36: Perceived impacts on health, by age

Figure 5.36: Perceived impacts on health, by age

Figure 5.37: Perceived impacts on government information & services, by age

Figure 5.37: Perceived impacts on government information & services, by age

Rural users: A case study of Bangladesh

Compared with urban users, rural users typically have fewer public access options, as commercial cybercafés are mostly concentrated in higher density areas. This lack of market provision is the most common public policy justification for investing in rural public access infrastructure.

This analysis of rural users focuses on Bangladesh. As discussed in Chapter 3, the definition of “rural” varies too widely between countries to allow for cross-country analysis. In Chile, for instance, there are no “rural” public access facilities because (by national definition) an area is considered urban if it has basic infrastructure such as electricity — so that every public access venue in Chile is by default in an urban area, since they use electricity to power the computers. The Bangladesh definition of rural, in contrast, is based on an area’s physical aspect, amenities, and population characteristics.[1] According to this definition, about 46% of the public access users in Bangladesh were classified as rural, yielding a significantly large population for study.

Since urban users tend to have greater social assets (education, income, etc.), it seems likely that they would make greater use of — and possibly derive greater benefits from — public ICT venues. Conversely, because of their deficits in these areas, rural users may have more to gain from public access ICTs and may therefore place higher value on their gains from public access use. This section begins with a look at usage, then shows the results of more in-depth analysis controlling for the variables of level of computer experience, seeking staff assistance, and venue type.

Figure 5.26 compares rural and urban users in Bangladesh in their use of public access venues in each of the six impact domains. Rural users trailed their urban counterparts in four out of the six domains, particularly in Communications & Leisure and Education, and secondarily in Employment and Culture & Language.

Figure 5.38: Domain usage, urban vs. rural (Bangladesh)

Figure 5.38: Domain usage, urban vs. rural (Bangladesh)

Another difference between these two groups is type of venue. Rural users largely visited telecenters, while urban users frequented cybercafés. Telecenter users tended to report lower usage, as well as lower levels of positive impacts, than cybercafé users in general.

To isolate the differences associated with urban and rural settings, the analysis controlled for three cross-cutting variables. The first was the users’ level of computer experience. This possibility was addressed by comparing the percentages of rural and urban users reporting positive impacts at a given level of computer experience (say, six months). The results showed that within a given level of computer experience, there was little difference between rural and urban users in their rate of use and impact reporting. That is, it seems likely that the level of computer experience contributes to the observed rural/urban differences. Rural users, who are more likely to be inexperienced telecenter users, offer fewer reports of positive impacts compared with more experienced urban cybercafé users.

Figures 5.27 through 5.31 show the perceived impact variables for rural and urban users in five specific impact categories, compared over levels of experience (determined by how long ago the respondent first used a computer). At each level (6 months or less, 7–11 months ago, etc.) the percentage of positive impacts reported by urban users and rural users is shown. In most cases, rural and urban users with the same level of computer experience generally showed a similar tendency to perceive positive impacts (with differences ranging from 1%-17%). This held for the priority categories of incomeaccess to government information & serviceshealth, and local language & culture. The only category showing a marked difference was communications, with higher proportions of urban users consistently reporting positive impacts as compared to rural users, regardless of computer experience level (differences ranged from 14%-34%).

Figure 5.39: Perceived positive impacts on income, by computer experience, Bangladesh

Figure 5.39: Perceived positive impacts on income, by computer experience, Bangladesh

Figure 5.40: Perceived positive impacts on government, by computer experience, Bangladesh

Figure 5.40: Perceived positive impacts on government, by computer experience, Bangladesh

Figure 5.41: Perceived positive impacts on health, by computer experience, Bangladesh

Figure 5.41: Perceived positive impacts on health, by computer experience, Bangladesh

Figure 5.42: Perceived positive impacts on language & culture, by computer experience, Bangladesh

Figure 5.42: Perceived positive impacts on language & culture, by computer experience, Bangladesh

Figure 5.43: Perceived positive impacts on communication, by computer experience, Bangladesh

Figure 5.43: Perceived positive impacts on communication, by computer experience, Bangladesh

The second variable controlled for was usage frequency. In this analysis, rural and urban users with similar usage frequencies showed similar percentages of perceived positive impacts in most impact categories. Particularly in the categories of incomeeducation, and health, urban and rural users had quite similar proportions of positive impact perceptions. For access to government information &serviceslocal language & cultural activities, and communication with family & friends, the results were more varied, possibly because of small sample sizes (after non-responses and logical skips).[2]

Comparing the third variable — venue type — the differences between urban and rural users’ perceptions of impact were generally small in most impact categories. Interestingly, in communication with family & friends, rural telecenter users were much less likely to report positive impacts than either rural or urban cybercafé users, who had similar percentages of positive impacts. This could be because cybercafés are more open to their patrons’ using social networking and other communication media.

The sixth domain: Communications & Leisure

Communication-related activities, including social networking, have an important place in any discussion of the value of public access venues. The data show that communicating with family and friends is the single activity with the greatest overall use, usage frequency, and perceived impact (see Table 5.2 and Figure 5.1 in Chapter 5). But are these activities as beneficial as other digital pursuits? What about leisure activities, such as gaming? Those questions frame this section.

In the conventional view, email and social networking, as well as leisure activities such as games and multimedia, are considered less productive activities than the five priority domains (Culture & Language, Education, Employment & Income, Governance, and Health). At the same time, there is growing recognition that Communications & Leisure is itself a worthy and legitimate domain. Two arguments support this view. First, communication activities are often a means to achieving outcomes in other areas, including in the five priority domains. And second, leisure digital activities may offer an “on ramp” for new users, developing important ICT skills that are transferable to other more instrumental uses.

Three specific questions were framed in order to investigate these broad claims.

  1. How important are communications and leisure activities to acquiring ICT skills?
  2. How strong is the association between frequency of communication and positive impacts in the priority impact categories?
  3. How often were communications used to obtain information in health, education, etc.?

Two in-depth studies were also devoted to this topic. Non-Instrumental Use: Skills Acquisition, Self-confidence, and Community-based Technology Teaching explores the extent to which gaming and social networking enhance generic computer skills. Interpersonal: The Impact of Cybercafés on the Connectedness of Children Left Behind by Overseas Filipino Workers examines the impact of communications for families in which a parent works abroad.[3]

Communications and leisure activities for ICT skills building

Do communication and leisure activities contribute to building ICT skills? The self-reported survey responses were unambiguous. When asked whether use of public access computers for communication and leisure activities had improved their overall ICT skills, 94% of the users who used public access for communications and leisure activities in the last 12 months claimed it had improved their skills (Figure 5.32).

Frequency of use and impact

This section presents the percentages of reported positive impacts for several variables at four levels of communications frequency, in nine of the 13 impact categories. Seven of these nine impact categories are associated with the priority domains. The other two are the cross-cutting categories, financial savings and time savings. (The four communication and leisure categories are omitted: one would naturally expect to see a close association between frequency of communication and impact in the communication categories, such as meeting new people.)

Figure 5.44: Users who report communications and leisure activities improved their ICT skills

Figure 5.44: Users who report communications and leisure activities improved their ICT skills

The results, shown in Figure 5.33, are challenging to interpret. For most of the impact categories there was an increasing percentage of positive impacts with increased frequency of use for communication, for the first three frequency levels (i.e., through “most times you visit”). The main exceptions were the categories of financial savings impacts and sending or receiving remittances. However, there was a slight decrease in perceived positive impacts for the very highest frequency of communication use (“every time you visit”). It is possible that the most frequent communication users tend to be younger users or students, who would be less likely to report positive impacts in areas such as income and employability resources.

Figure 5.45: Percentage of perceived positive impacts, by frequency of using public ICT access for communication

Figure 5.45: Percentage of perceived positive impacts, by frequency of using public ICT access for communication

Communications as a path to achieving impact in priority domains

How important are communication activities for obtaining information in instrumental areas? Information objective achievement rates (the search, find, act progression) were explored earlier, finding that in general people were quite successful in tasks across all domains. For each task, respondents were asked what was the most important online source in being able to complete the task: website, friends (via email or social networking sources), family (via email or social networking sources), or other (open answer).

The findings paint an interesting picture. While websites were generally the most important resource for every task, a number of respondents reported that email and social networking were of paramount importance (Figure 5.34).

Figure 5.46: Users identifying email/social networking as most important resource to complete task

Figure 5.46: Users identifying email/social networking as most important resource to complete task

Of those tasks with large percentages of users identifying communications as most important, the top four tasks related to the domain of Culture & Language. The next two tasks, with nearly one in four such responses, were health-related: accessing online health services and choosing a doctor. This may reflect a tendency for people to seek the assistance of people they know for personal health issues. One in six respondents relied most heavily on email and social networking for searching for and applying for jobs.

These findings point to the major role that communication activities play in enabling task completion in virtually all of the priority domains. In fact, these figures understate the importance of communication tools, since respondents could select only one online resource as “most important.” Communication activities such as email and networking undoubtedly have a place as one of the combination of tools — along with websites and offline sources — that help people navigate these tasks.

Summary

Public access ICT users perceive impacts in a number of areas of importance to their lives, in all five priority domains. Not everyone uses public access for every activity: lower usage was reported for the areas of Culture &amp; Language, Governance, and Health. Among those users, however, high proportions considered the impacts positive. Moreover, beyond perceived impact, most users were not only able to find relevant information, but successfully took some sort of action based on that information (such as applying for a job). This was shown for a number of tasks across all domains.

With regard to venue type, although library and cybercafé users had generally similar usage patterns, library users reported positive impacts in higher proportions than cybercafé users. Moreover, library users were also more likely than cybercafé or telecenter users to report positive impacts in such priority categories as education, health, access to government information & services, local language & cultural activities, and time savings.

Overall, there was little difference among various user populations in the rate of perceiving positive impacts. People of lower socioeconomic status, as well as females, older adults, and rural residents, in general perceived positive impacts in similar percentages to other groups. Similarly, unemployed users reported positive impacts almost as frequently as employed users, as did those below the poverty line compared to those above the poverty line. Education level, however, did make a difference in perceived impacts: the more educated users reported positive impacts more frequently than less educated users.

Although females in some areas are less likely to use public access facilities, the females who use them perceive positive impacts in proportions equal to males, with somewhat higher rates in the social categories and slightly lower rates in the economic categories. Older users, who constitute a minority of users (see Table 3.8), reported high levels of impact, particularly in the priority domains.

Rural users had lower overall usage rates in some domains than urban users. However, controlling for level of computer experience, usage frequency, and venue type, rural users mostly experienced positive impacts in similar proportions to urban users.

As a domain, Communications &amp; Leisure showed the highest scores for usage and impact. This finding, already familiar from other research, is of concern to the extent that communications activities are considered not to contribute to developmental outcomes. There are reasons to question this view. First, nearly all users reported that communication and leisure activities improved their ICT skills. A test of skills of instrumental and non-instrumental users confirmed that non-instrumental ICT skills do indeed enable users to perform instrumental tasks. Second, a correlation, albeit weak, exists between communications usage and perceived impact. That is, the more frequently people communicated, the more likely they were to report positive impacts in other domains (with the exception of the most frequent communication users). Third, a substantial proportion of users reported that communications and social networking sites were the most important resource for achieving specific tasks, across a number of domains. These findings, along with those from the in-depth studies, suggest a prominent and positive role for communications and leisure within the development context.


  1. Urban and rural definitions for Bangladesh come from the Bangladesh Bureau of Statistics (BBS). The BBS defines an urban area as the developed area (i) around an identifiable central place, (ii) where amenities like metalized (paved) roads, communication facilities, electricity, gas, water supply, sewerage connections usually exist, and (iii) which is densely populated and a majority of the population involved in non-agricultural occupations.
  2. “Logical skips” are skipped questions that result when a respondent is dropped from a particular question because of a filtering process to decide who needs to answer certain questions.
  3. See Chapter 7 for an overview of these studies.

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