10 th National Convention on Statistics (NCS) EDSA Shangri-La Hotel October 1-2, 2007

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1 10 th National Convention on Statistics (NCS) EDSA Shangri-La Hotel October 1-2, 2007 Sub-National Determinants of the Digital Divide: The Case of the Philippines by Philip Arnold Tuaño For additional information, please contact: Author s name : Philip Arnold Tuaño Designation : Research Coordinator & Lecturer Affiliation : Foundation for Media Alternatives & Ateneo de Manila University Address : Ateneo de Manila University, Katipunan Road, QC Tel. no. : (0632) rtuano@fma.ph

2 Sub-national Determinants of the Digital Divide: The Case of the Philippines By Philip Arnold Tuaño 1 ABSTRACT Around the world, a divide exists among populations that have access to information and communications technologies, and these differences mirror the socioeconomic divide that had been pre-existing between and within countries. These disparities have effects on future economic growth and productivity, and also impact on social welfare of populations. While there have been many studies that have examined the digital divide across countries, there is a current dearth of studies that examine what accounts for the differences in access, utilization and ownership of these ICTs within countries. The paper being proposed is an extension of the study undertaken by Alegre, de Quiros and Tuaño (2006) for the Foundation for Media Alternatives, which undertook an analysis of household level determinants of the digital divide. The earlier paper found that, using probit regression analysis, specific characteristics of households and household members, such as age, sex, marital status, employment level and education level of the household head, the number of children and geographical location of the household, all determine the personal computer and telephone ownership. The data utilized for the study is derived from the 2000 and 2003 rounds of the Family Income and Expenditure Survey, which contain 40,000 respondents. The paper being proposed would examine provincial-level characteristics that affect gaps in personal computer and telephone ownership, as proposed in Chinn and Fairlie (2004). Some of these factors include economic variables (income and expenditure per capita, telephony rental prices), demographic variables (literacy rate, school participation and cohort survival ratios, labor dependency ratios, urbanization rate), and infrastructure (electricity coverage). The findings of the study would have implications on public spending on human capital and infrastructure that would reduce the differences in ICT ownership, and on the design of public access programs being undertaken by both the government and the private sector. This study would take advantage of the recent availability of sub-national data that are available in the national statistical system, especially the release of the 2003 round of the Functional Literacy, Education and Mass Media Survey; this survey now includes questions related to PC and telephony ownership. 1 Paper to be presented during the 10 th National Convention on Statistics, October 1-2, 2007, Manila. Portions of this paper were taken from the Foundation for Media Alternatives study on 'Locating the Digital Divide in the Philippines, and from an earlier paper presented at the ' during the 'Living the Information Society: The Impacts of Information and Communication Technology on People, Work and Communities in Asia' Conference, April 23-24, 2007, Renaissance Hotel, Makati.

3 I. Introduction Around the world, a divide exists among populations that have access to information and communications technologies, and these differences mirror the socio-economic divide that had been pre-existing between countries. The 2005 International Telecommunications Union (ITU) report for example showed that while Africa has telephone subscribers per 100 population, Europe has more than ten times the number at subscribers per 100 population. Burundi, the poorest country in the world, has only 1.82 landline phone subscribers and 2.03 cellular phone subscribers per 100 persons, and Luxemburg, has and landline and cellular phone subscribers per 100 persons, respectively. Asia, on the other hand, has landline subscribers and cellular phone subscribers per 100 persons. These disparities have effects on future economic growth and productivity, and also impact on social welfare of populations. The growth in productivity in the United States in the mid to late 1990s had been widely attributed to greater firm-level investments in ICTs; the capital investment in information technology had been said to account for two-thirds of productivity growth in this country. There is also some evidence of this phenomenon occurring in other developed countries. On the other hand, the growth of the IT sector in developing countries, such as India and the Philippines, where business process outsourcing had boomed, has resulted in greater export opportunities for these countries and improved incomes for workers employed in these sectors. Chinn and Fairlie (2004), assessing the determinants of the digital divide around the world, attributed higher communication efficiency, improved engagement in the political process and greater probability of leapfrogging traditional methods of increased productivity (i.e., allowing developed countries embrace higher levels of technologies to skip intermediate stages and save on development costs) to greater diffusion of ICTs worldwide. United Nations Development Program (2001) pointed out, for example, that the availability of ICTs had been said to increase accountability of governments to ordinary citizens, illustrated by the pivotal role of fax machines during the downfall of state socialism in Eastern Europe and that of cellular phones in the ouster of the Estrada administration from office. The same report point out that the availability of the internet had increased the diffusion of technical knowledge in the areas of agriculture and medicine, such that there has been cross-collaboration efforts among these countries in order to speed up the availability of new products and services in the developing

4 world. Greenberg (2004) also reviewed the importance of ICTs in poverty alleviation. Mobile telephones have been utilized by poor households to contact family members and to access government services, reducing the time and expense to make trips to the town center. Simple ICT enhancements have provided farmers with up-to-date weather and crop information, while new ICT-based businesses, such as telephone reselling, have increased incomes for informal sector workers. ICTs also have been utilized to improve collection, processing and retrieval of demographic data. Thus, during the 2003 World Summit for Information Society held in Geneva, and then again in 2005 in Tunis, governments around the world committed themselves to ending the digital divide and declared that they were fully aware that the benefits of the information technology revolution are today unevenly distributed between the developed and developing countries and within societies. We are fully committed to turning this digital divide into a digital opportunity for all, particularly for those who risk being left behind and being further marginalized (in WSIS (2003)). Implicit in this statement is the recognition by the international community has recognized the divide as one of the most pressing issues around the world that needs to be addressed. Various action lines, including those on the development of programs to build capacities, enhance confidence in the use of ICTs and create applications for different areas of society, were developed and being monitored. Currently, while it has been noted that ICTs are key in economic and social development of developing nations, there is also a more widespread recognition that that in order to take advantage of these technologies, countries have to invest in complimentary infrastructure such as education and skills development. But what programs are important? While there have been many studies that have examined the digital divide across countries, there is a current dearth of studies that scrutinize what accounts for the differences in access, utilization and ownership of these ICTs within countries. This is important to because cross-country studies do not take into account the particularities of policies and social norms among different countries; subnational studies may be better as they assume these variables are common across geographical units (see for example, Balisacan and Pernia (2002)).

5 The paper is an extension of the study undertaken by Alegre, de Quiros and Tuaño (2006) for the Foundation for Media Alternatives, which undertook an analysis of household level determinants of the digital divide. The earlier paper found that, using probit regression analysis, specific characteristics of households and household members, such as age, sex, marital status, employment level and education level of the household head, the number of children and geographical location of the household, all determine the personal computer and telephone ownership. The paper being proposed would examine provincial-level characteristics that affect gaps in personal computer and telephone ownership. The findings of the study would have implications on the development of policies and public spending on complimentary human capital and physical infrastructure that would reduce the differences in ICT use and ownership, and on the design of public access programs being undertaken by both the government and the private sector. II. Review of Literature The term digital divide was popularized in the mid 1990s by government technology advisers working in the United States and the United Kingdom to describe those who have access to information and communication technologies, such as computers and telephones. Selwyn (2002) reported that digital divide was an offshoot of the term digital exclusion that emerged to identify the differences between countries that had greater ICT densities (i.e., landlines per population) compared to countries that had lower densities; but later also focused on technology inequalities within individual countries, popularized by the terms information haves and have-nots, information poverty, and digital divide. These gaps have been caused by lack of access to income to purchase these ICTs, education to utilize them effectively, or infrastructure available to effectively connect between households and communities. The divide was characterized in developed countries as a spatial divide, but eventually, was broadened to other socio-political aspects, such as race, gender and age, within countries, and later, to differences between countries. The differences in access to quality and understandable content also characterize the divide.

6 The divide can be characterized by the following levels: First, differences in ownership of ICTs: this concept has been mainly utilized to define the digital divide mainly in developed countries where there has been a high level of penetration of ICTs. Thus, the differences that are being accounted by this concept are the number of households that have ICTs such as telephones, personal computers and internet access, or proportion of households that owned these ICTs. In the United States, this has been found to be attributed to the differences in race and incomes among households. Second, differences in access to ICTs, especially computers, telephony and internet and broadband technology, including within-country variations in terms of geographical areas, age, sex, culture and economic status. This includes the provision of ICTs in home, community and work environments for individuals. In many developing country studies on the digital divide, this has been characterized through the use of indicators, relating to teledensity (both fixed and wireless) and personal computer penetration. Third, differences in effective utilization of the ICTs. This can be characterized by the number of households and firms that are utilizing ICTs for specific ends, such as work-related tasks. At one level of discourse, there has been a discussion of the divide as within-country (across individuals and households) differences in outcomes and impacts, in the use of ICTs, including immediate effects of ICT use and consequences in participation in productive, political, social, consumption and savings activities, i.e., Selwyn (2003). These papers describe the digital divide in stages, or as the progression of theoretical access to effective/ perceived access to use of ICT to meaningful engagement to consequences of the use of ICTs. These stages include formal access to ICTs and content (i.e., formal access to ICTs at home) to effective access to ICT at home to use of ICTs to engagement of ICTs, then to outcomes and consequences of ICT use. This can be a useful framework in trying to describe the levels of ICT access among individuals and households. The characterization of ownership differences as the main criterion in measuring the

7 divide in developing countries, including the Philippines, may be limited. Salazar (2006), for example, has pointed at the importance of utilizing the definition of access as a much more useful criterion in measuring the divide; many observers have pointed to the availability of community ICT centers, internet café and even schools as primary venues for computer and internet access rather than households given the relatively expensive prices of technology. What accounts for the differences for the digital divide among countries? Unsurprisingly, studies show that income differentials across countries seem to be the main factor; Chinn and Fairlie (2004) report that more than 50 percent of country differences in personal computer ownership can be accounted for by differences in income per capita of countries around the world. The Asian Development Bank study undertaken by Quibria, Ahmed, Tsang and Reyes- Macasaquit (2002), reported that ICT usage is strongly correlated with GDP per capita with income elasticities reported as greater than one. Education also is important; the 2002 ADB study report that tertiary schooling significantly accounts for differences in cellular and landline phones, fax machine, personal computer and internet use among countries in Asia, with university schooling strongly affecting computer and internet use. Kiiski and Pohjola (2001) report that average years of schooling and adult literacy rates are important determinants of personal computer adoption. Caselli and Coleman (2001) also find that a one percentage increase in the proportion of the population with higher education leads to roughly a one percentage increase in investments in computer technology. The quality of regulatory institutions are also determinants of ICT adoption; property rights protection and the quality of government institutions are important regressors of internet use in the Fairlie and Chinn study, and also by Wheeler, Lall and Dasgupta (2005) and Wallsten (2003). Computer use is also shown to be strongly complimentary with telephone access as 40 percent of the differences in PC ownership in these studies can be attributed to the variable landlines per 100 persons; this shows that internet access seems to be one of the main uses of personal computers. Other factors found to be Most of the intra-country studies on the factors affecting digital divide are based on developed country experiences. In developed countries, Novak and Hoffman (1998) and Fairlie (2003) find that there are racial disparities to personal computer ownership and internet access

8 in the United States are primarily due to income and education, and to some degree, language barriers. Ono and Zavodny (2004) found that there are gender differences in ICT usage in Japan due to employment of women in the non-formal sectors where ICT use is low. In the Philippines, Alampay, Heeks and Soliva (2003) found that income, education, age and geographical location are important indicators of the digital divide. III. Data and Methodology We utilize data on telephony and personal computer ownership data from the 2003 Family Income and Expenditure Survey (FIES); the last FIES round was undertaken in 2006 but the public use files for the survey had not yet been released. The FIES is a survey undertaken by the government every three years to measure income and spending patterns of households; there are around 40,000 household-respondents during each survey year. The FIES also has a module which allows it to measures access to and ownership of housing and household amenities (i.e., water, electricity, sanitary toilets) among these different households. The FIES questionnaire includes questions on ownership of telephones and personal computers; unfortunately does not distinguish between landline and cellular phone ownership. We compute for telephony and computer ownership per province from the FIES. We utilize a simple regression model to calculate the extent of relationship of the variables in the study to the independent variables, namely, telephony and personal computer ownership. As in Chinn and Fairlie (2004), we utilize a simple demand and supply framework to assess telephony and personal computer ownership rates in the different regions in the country. We estimate reduced form equations that includes several sets of variables. The first includes economic variables, such as the average provincial incomes and expenditures, and underemployment rates; incomes impact on the demand of ICT equipment since budget constraints limit the purchase of these items. The usefulness of these technologies for improving productivity could also impact on demand; thus we include underemployment rates and road density (a proxy for access to markets) in these regressions. The second are the human capital variables; education, for example, would impact on technological literacy, while preferences in using ICTs would be impacted by their expected usefulness, which would affected by age and the presence of children. And finally, infrastructure availability such as electricity connections are also complimentary factors affecting the use of these technologies.

9 The following are the variables and their sources of data: Average provincial income, which were taken from the FIES by calculating for the mean of total incomes of households within the province from the survey. Underemployment ratios. This is the proportion of workers that are working but need additional work to the total numbers that are working. This was taken from the 2005 Philippine Human Development Report from the 2003 quarterly Labor Force Surveys. The education proxy, the literacy rate, was taken from the 2000 Census of Population and Housing; this is computed as the ratio of the population ten years or older who are functionally literate. Other education variables such as rimary and secondary enrollment rates, cohort survival rates, drop-out rates and school leaver rates were obtained from the Department of Education; these are computed as the ratio of school aged children in school that are in school, have completed a school level or have left school, both voluntarily and involuntarily, compared to the total number of school aged children, i.e., 6 to 12 years old for primary school and 13 to 16 for secondary school. The proportion of children (population aged 0-14 years old) and senior citizens (population aged 65 years and above) were also utilized as human capital variables; these were taken from the 2000 Census of Population and Housing, which was the last census in the country; Proportion of households with electricity; these were taken from the 2002 Annual Poverty Indicators Survey by dividing households which utilize electricity for lighting divided by the total number of households in the province; Road infrastructure index; this is taken from the Department of Public Works and Highways Planning office. Total asphalted and cemented road in kilometers were divided by total area of the province in square kilometers in order to compute for the density of good quality roads.

10 IV. Results A. Regional and Provincial Telephony and Personal Computer Ownership Rates Based on the Family Income and Expenditure Survey, computer telephone (both landline and cellular) ownership increased from 2.70 percent to 3.87 percent and percent to percent of households, respectively, from 2000 to 2003 (see Table 1). Almost a third of the growth of the number of households that own PCs has come from the National Capital Region, followed by Southern Tagalog, Cagayan Valley and Cordillera Administrative Region; the lowest growth rates have come from Central Mindanao and Northern Mindanao. Among income deciles in the period in Table 2, growth in households with PCs had been driven by those earning P 300,000 and above annually, while one-fifth of the growth in telephones had been driven by those earning P 150,000 to P 200,000 annually (threefourths by those earning P 95,000 to P 300,000). Personal computer and telephone ownership in Table 3 seems to increase monotonically compared to the number of years of study of household heads; less than four percent of households whose heads had no education own landline/ cellular phone, but more than 9 in 10 households with postgraduate degrees own landline/ cellular phones. A greater percentage of households with female heads (compared to households with male heads) have personal computers (in 2003, the gap was 5.9 percent vs. 3.4 percent), and telephone/ cellphones (39.1 percent vs percent) in Table 4. Households whose incomes are mainly sourced from agricultural activities have lower ownership rates of ICTs, while those whose heads are without jobs have higher ownership rates. In Table 5, The proportion of nonagricultural households that own personal computers outnumber the proportion of agricultural households by almost 18 to 1 in In 2003, less than one percent of households whose heads are economically classified as farmers own computers (see Table 6); around 11 percent of these households own landlines/ cellular phones. These figures are below the average for all households in the survey. Less than two percent of households whose heads are classified as industrial workers own computers and 31 percent of these own landlines/ cellular phones.

11 The following provinces (see Table 7) that has the highest level of ownership of phones: National Capital Region (69.2 percent), Cavite (59.9 percent), Laguna (56.9 percent), Benguet (52.4 percent), Pampanga (52.1 percent), Bataan (49.3 percent), Bulacan (49.1 percent), Rizal (45.8 percent), Batangas (45.0 percent), La Union (39.7 percent) and Zambales (38.8 percent). Interestingly, most of these areas are in or are close by the metropolitan capital of the country. Only Benguet and Zambales are not provinces in the Central Luzon and Southern Tagalog regions of the country. On the other hand, the following provinces have the lowest level of phone ownership: Sulu (0.8 percent), Tawi-tawi (3.1 percent), Guimaras (3.1 percent), Basilan (4.0 percent), Maguindanao (5.0 percent), Northern Samar (5.1 percent), Saranggani (7.1 percent), Apayao (7.1 percent), Mountain Province (7.5 percent) and Masbate (8.0 percent). Half of these areas are island provinces located in the Visayas and Mindanao island groups of the country. The following provinces have the highest levels of PC ownership: National Capital Region (15.1 percent), Benguet (13.7 percent), Rizal (7.8 percent), Palawan (7.8 percent), Pampanga (7.2 percent), Laguna (6.5 percent), Batangas (4.9 percent), Cebu (4.6 percent), Ilocos Norte (4.4 percent), Lanao del Norte (4.4 percent), and Nueva Vizcaya (4.0 percent). The areas with the highest levels of personal computer ownership did not follow the pattern as that of phone ownership, but half of them are also located in Central Luzon and Southern Tagalog. The provinces of Aklan, Basilan, Masbate, Cotabato, Aurora, Guimaras, Camiguin, and Davao Oriental have the lowest levels of PC ownership, as the FIES recorded zero percent ownership in B. Determinants of Telephony and Personal Computer Ownership The results of the regression analysis are provided in Tables 8A and 8B. The coefficients with asterisks correspond to the variables that are significant correlated with ICT ownership at the 10 percent significance level. Table 9 based on the previous FMA study is also provided so that the can compare the results of the provincial-level regressions in this study with the household-based regressions. The results in table show the following:a) sex of household head is a significant determinant of personal computer and telephone ownership which mean that more female-headed households

12 have higher proportions of ownership of these ICTs; b) marital status is also a significant determinant of ICT ownership as households whose heads are married have significantly higher rates of ownership; c) formal employment status of the household head has a negative relationship with personal computer,and telephone ownership; those who are self-employed have higher rates of ownership of these ICTs; d) age of household heads is a significant factor in personal computer and radio ownership; household heads whose ages are older have higher ownership rates of these ICTs; e) education status is a significant predictor of ICT ownership; however, there is no significant difference between the ownership rates of personal computers of those without education and those with some secondary education or less because the rates of ownership of these population sub-groups are close to zero; f) there are no significant differences between ownership of personal computers among those in the National Capital Region, and the Cagayan Valley, the Cordillera Administrative Region and Southern Tagalog; between ownership of telephones among those in the National Capital Region and Western Visayas and Northern Mindanao; and g) the greater the number of children, the less likely would households have ownership of ICTs; this also follows the trend that the more number of children, the more likely the poorer the households are; but those with more children aged 8 to 15 are more likely to ICTs, except for telephones. Economic variables, including income and underemployment. Provincial per capita incomes are significantly correlated with both telephony and personal computer ownership. This also confirms the findings in household level regressions that household incomes have a high correlation with ICT utilization. For every 13,800 pesos increase in per capita income, the percentage of households with telephones increase by one percent. Underemployment rates affects only telephony utilization, and not personal computer use. Underemployment is defined as the proportion of the work force that is looking for additional hours of work, and it is this and not unemployment that is the main labor problem in developing countries. This may be due to the fact that underemployment rates are also high at those with high median incomes; these are also the households that only have the ability to purchase personal computers. This also compares favorably with the results of the household level regressions that show self-employment as having a positive impact on ICT ownership. Human capital variables including education. Also, as confirmed in other studies, education levels, as proxied by illiteracy rates, impact on telephony and personal computer

13 ownership. This is different from the finding in cross-country regressions that utilize secondary and tertiary education enrollment or even years of schooling that impact on ICT ownership and use. This shows that basic literacy skills are important for one to effectively utilize and own personal computers and telephones. This also confirms the results from household-level regressions. With regards to proportions of households with high youth and senior citizen dependency, these variables are significant in the telephony ownership regression but not in personal computer ownership. At the same time, in the telephony ownership results, the youth dependency variable is negative and not the expected sign, as we would suppose younger populations are better adopters of ICTs. But this also verifies the results of household-level regressions that families with more children are less likely to own ICTs. The effect may be only among a certain age (we find in the household regressions that only those with more 8 to 14 year old children are more likely to own telephones), but we do not test this effect in our study here. Infrastructure variables, including electricity connections and roads. Households with electricity connections have a positive relationship with telephony ownership. We also find a partial relationship between personal computer ownership and a mixed electricity and road index. The presence of electricity is important for the efficient utilization of ICTs; ICT equipment that utilize other means of power are very rare in developing country contexts. The road variable is also included in the regressions in order to test the relationship between physical infrastructure and ICTs; however, this is not significant in the telephony ownership regression. We note that both telephony and personal computer ownership regressions are highly significant as a cross-section econometric assessment with the correlation coefficient above 70 percent. V. Summary and Conclusion The paper provided a brief overview of the sub-national determinants of the digital divide in the Philippines While there are many definitions of the divide, and this include ownership, access, utilization and effective use and impacts of different information and communication technologies in the country, and that the variable used in this study is one of ownership, which

14 has some constrained definition in developing countries, unfortunately, this is the only subnational indicator that is publicly available. We note that ownership of telephony and personal computer ownership are widely dispersed in the country, ranging from almost 70 percent telephony household ownership and 15 percent personal computer ownership in Metro Manila to less than one percent telephony ownership in Sulu province (in the southernmost part of the country) and zero percent personal computer ownership in many provinces. We regress ICT ownership with specific provincial variables including income, education and infrastructure and we find that unsurprisingly ownership is correlated with many of these variables. The results also verify the household regression results undertaken in an earlier study. The study verifies that there is a need not only to address the digital divide in terms of household characteristics but also important is to invest in education and related infrastructure. In the future, work is also necessary to examine differences in utilization and access, which is a more relevant definition of the digital divide in the country; there are several national level surveys whose results will be available in the near future in which this can be undertaken, and the results of this study may be improved using this data. Issues such as whether affordability and quality of institutions at the local level related to the divide can also be assessed.

15 References Alampay, Erwin, Richard Heeks and Peter Soliva (2003). Bridging the Information Divide: A Philippine Guidebook on ICTs. National College of Public Administration, University of the Philippines, and the Institute of Development Policy and Management, University of Manchester. Alegre, Alan, Jay de Quiros and Randy Tuaño (2006). Locating the Digital Divide in the Philippines, Foundation for Media Alternatives Discussion Paper, typescript. Balisacan, Arsenio and Ernesto Pernia (2002). Probing Beneath Cross-National Averages: Poverty, Inequality and Growth in the Philippines, Economics and Research Department Working Paper No. 7, Asian Development Bank. Caselli, Francesco and Wilbur John Coleman (2001). Cross-Country Technological Diffusion: The Case of Computers, National Bureau of Economic Research Working Paper Chinn, Menzie and Robert Farlie (2004). The Determinants of the Global Digital Divide: A Cross-Country Analysis of Computer and Internet Penetration, Economic Growth Center Discussion Paper No. 881, Yale University. Department of Education (2005). Elementary and Secondary School Education Indicators SY , computer file. Department of Public Works and Highways (2005). Inventory of Surface Type of National Roads, computer file. Fairlie, Robert (2003). Race and the Digital Divide, Joint Center for Poverty Research Working Paper. accessed April 28, Greenberg, Alan (2005). ICTs for Poverty Alleviation: Basic Tool and Enabling Sector, paper written for the ICT for Development Secretariat, Swedish International Development Agency, accessed January 16, National Statistics Office (2000) Census of Population and Housing: 10 percent sample, Public Use File. National Statistics Office (2000, 2003) and 2003 Family Income and Expenditure Survey, Public Use File. National Statistics Office (2002) Annual Poverty Indicators Survey, Public Use File. Hoffman, Donna and Thomas Novak (1998). Bridging the Digital Divide: Impact of Race on Computer Access and Internet Use, Science 280 (April), pp Ono, Hiroshi and Madeline Zavodny (2004). Gender Differences in Information Technology Usage: A US- Japan Comparison, Federal Reserve Bank of Atlanta Working Paper Pohjola, Matti (2003). The Adoption and Diffusion of ICT Across Countries: Patterns and Determinants, in the New Economy Handbook. New York: Academic Press. Quibria, M. G., Shamsun Ahmed, Ted Tschang and Mari-len Reyes Macasaquit (2002). Digital Divide: Determinants and Policies with Special Reference to Asia, Asian Development Bank Economics and Research Department No. 27. Selwyn, Neil (2003). Defining the Digital Divide : Developing a Theoretical Understanding of Inequalities in the Information Age, Cardiff University Adults Home Occasional Paper 49.

16 United Nations Information and Communications Task Force (2005). Measuring ICT: Global Status of ICT Indicators, Partnership on Measuring ICT for Development Monograph. Wallsten, Scott (2003). Regulation and Internet Use in Developing Countries, American Enterprise Institute- Brookings Joint Center accessed January 15, Wheeler, Lall and Dasgupta (2005). Policy Reform, Economic Growth and the Digital Divide: An Econometric Analysis, Oxford Development Studies 33 (2), pp

17 Table 1. Household ownership of personal computers and telephones, by regions, 2000 & 2003 Percentage of Households Source of Increase Region PC Phone PC Phone PC Phone Ilocos 1.82% 16.91% 2.49% 30.18% 5.1% 6.9% Cagayan Valley 1.54% 7.17% 3.38% 24.56% 9.0% 6.6% Central Luzon 2.79% 17.88% 4.31% 43.14% 7.8% 12.9% Southern Tagalog 3.00% 19.50% 4.60% 37.30% 17.0% 16.7% Bicol 1.48% 7.77% 2.33% 19.94% 5.3% 5.6% Western Visayas 1.86% 12.64% 2.36% 24.38% 2.7% 5.6% Central Visayas 2.70% 11.62% 3.42% 24.69% 6.9% 7.3% Eastern Visayas 1.55% 5.68% 1.61% 12.41% 0.4% 2.6% Western Mindanao 0.89% 4.89% 2.00% 16.31% 4.0% 3.5% Northern Mindanao 2.39% 8.78% 2.30% 24.50% 0.0% 5.5% Southern Mindanao 2.41% 12.55% 2.70% 25.30% 1.9% 4.9% Central Mindanao 2.29% 10.55% 1.01% 17.45% -3.2% 3.3% NCR 8.62% 42.12% 13.09% 59.09% 31.0% 9.9% CAR 2.83% 14.38% 5.74% 30.41% 8.8% 4.2% ARMM 0.22% 1.43% 0.78% 18.37% 1.9% 5.1% CARAGA 0.81% 7.92% 1.08% 4.61% 1.5% -0.6% National 2.79% 15.33% 3.87% 28.91% 100.0% 100.0% Note: Source of increase denotes the proportion of the increase in the household ownership of personal computers and telephones attributed to households in that specific region. Source of basic data: National Statistics Office (2000, 2003). Table 2. Household ownership of personal computes and telephones, by income decile, 2000 & 2003 Percentage of Households National income Source of Increase decile PC Phone PC Phone PC Phone Lowest 0.00% 0.00% 0.00% 0.50% 0.00% 0.4% Second 0.00% 0.05% 0.00% 1.49% 0.00% 1.1% Third 0.00% 0.03% 0.11% 3.91% 0.95% 3.0% Fourth 0.05% 0.88% 0.22% 8.22% 1.52% 5.5% Fifth 0.05% 1.92% 0.26% 15.43% 1.71% 9.4% Sixth 0.27% 4.67% 0.63% 25.34% 2.67% 13.5% Seventh 0.73% 11.54% 1.36% 40.41% 4.19% 17.6% Eight 1.64% 20.76% 3.15% 56.54% 10.10% 21.3% Ninth 4.43% 44.59% 7.51% 75.99% 18.29% 19.3% Tenth 20.27% 70.45% 30.18% 89.75% 60.57% 8.8% National 2.79% 15.33% 3.87% 28.91% Note: The range of annual incomes of households by income deciles are the following: lowest decile, 4,273 and 33,384 pesos in 2000 and 3,086 to 36,119 pesos in 2003); second, 33,385 and 45,366 pesos in 2000 and 36,128 to 49,157 pesos in 2003; third, 45,368 to 57,514 pesos in 2000, and 49,158 to 62,273 pesos in 2003; fourth, 57,517 to 89,800 pesos in 2000 and 62,274 to 77,010 pesos in 2003; fifth, 71,380 to 89,800 pesos in 2000 and 77,024 to 95,290 pesos in 2003; sixth, 89,810 to 112,529 pesos in 2000 and 95,291 to 119,031 pesos in 2003; seventh, 112,532 to 146,499 pesos in 2000 and 119,032 to 151,800 pesos in 2003; eight, 146,500 to 195,050 pesos in 2000 and 151,805 to 203,455 pesos in 2003; ninth, 195,060 to 294,077 pesos in 2000 and 203,470 to 300,300 pesos in 2003; and highest, 294,100 to 8.4 million in 2000 and 300,030 to 32.3 million pesos in Source of basic data: National Statistics Office (2000, 2003).

18 Table 3. Household ownership of ICTs, by education of household head, 2000 & 2003 Percentage of Households Education of Household Head PC Phone Radio Television PC Phone Radio Television None 0.11% 1.76% 59.36% 18.45% 0.06% 3.67% 44.49% 14.32% Some Elementary 0.23% 3.56% 65.56% 36.76% 0.18% 9.34% 58.17% 34.42% Elementary Graduate 0.56% 6.06% 70.36% 53.18% 0.33% 16.78% 69.44% 53.47% Some High School 0.69% 8.09% 71.25% 57.21% 0.73% 21.51% 65.07% 58.37% High School Graduate 1.73% 15.63% 76.55% 73.59% 1.43% 35.89% 69.56% 73.29% Some College 4.09% 27.92% 78.63% 82.75% 4.04% 55.29% 71.05% 84.16% College Graduate 12.54% 46.60% 81.54% 94.08% 13.61% 76.42% 73.48% 93.08% Postgraduate 27.75% 62.83% 80.30% 88.96% 39.13% 90.24% 79.27% 98.78% Source of basic data: National Statistics Office (2000, 2003). Table 4. Household ownership of ICTs, by sex of household head, 2000 & 2003 Sex of Household Head PC Phone Radio Television PC Phone Radio Television Source of basic data: National Statistics Office (2000, 2003). Table 5. Household ownership of ICTs, by agricultural status, 2000 & 2003 Source of basic: National Statistics Office (2000, 2003). Percentage of Households Male 2.59% 13.78% 72.37% 60.04% 3.48% 26.98% 64.94% 58.07% Female 3.72% 22.58% 73.74% 66.32% 5.90% 39.10% 65.38% 65.60% Agricultural employment status of Household Percentage of Households PC Phone Radio Television PC Phone Radio Television Head Agricultural 0.14% 2.81% 64.44% 22.17% 0.33% 4.45% 55.89% 24.86% Non-agricultural 3.64% 30.02% 75.25% 73.74% 5.36% 39.24% 68.86% 73.79% Table 6. Household ownership of ICTs, by economic occupation of household head, 2000 & Socio-economic occupation of household head Percentage of Households PC Phone Radio Television PC Phone Radio Television Farmers 0.34% 3.19% 69.88% 32.50% 0.68% 11.25% 60.96% 35.06% Forest workers 1.90% 2.86% 50.48% 29.52% n. a. n. a. n. a. n. a. Fisherfolk 0.18% 1.46% 60.09% 27.27% n. a. n. a. n. a. n. a. Trades & Industrial workers 1.25% 11.49% 72.59% 68.79% 1.96% 31.09% 67.46% 68.18% Other workers 0.65% 5.56% 75.98% 77.93% 0.67% 12.81% 62.29% 57.89% Source of basic data: National Statistics Office (2000, 2003).

19 Table 7. Telephony and Personal Computer Ownership Rates, 2003, in percent Region Province Telephony PC Region Province Telephony PC Ilocos Ilocos Norte Western Visayas Aklan Ilocos Sur Antique La Union Capiz Pangasinan Guimaras Cagayan Valley Batanes Iloilo Cagayan Negros Occidental Isabela Central Visayas Bohol Nueva Vizcaya Cebu Quirino Negros Oriental CAR Abra Siquijor Apayao Eastern Visayas Eastern Samar Benguet Leyte Ifugao Northern Samar Kalinga Western Samar Mt. Province Southern Leyte Central Luzon Aurora Biliran Bataan Western Mindanao Zamboanga del Norte Bulacan Zamboanga del Sur Nueva Ecija Zamboanga Sibugay Pampanga Northern Mindanao Bukidnon Tarlac Camiguin Zambales Lanao del Norte NCR Misamis Occidental CALABARZON Batangas Misamis Oriental Cavite CARAGA Agusan del Norte Laguna Agusan del Sur Quezon Surigao del Norte Rizal Surigao del Sur MIMAROPA Marinduque Southern Mindanao Davao del Norte Occidental Mindoro Davao del Sur Oriental Mindoro Davao Oriental Palawan Compostela Valley Romblon Central Mindanao North Cotabato Bicol Albay Saranggani Camarines Norte South Cotabato Camarines Sur Sultan Kudarat Catanduanes ARMM Basilan Masbate Lanao del Sur Sorsogon Maguindanao Sulu Tawi-tawi Source of basic data: National Statistics Office (2003).

20 Table 8A. Telephony Ownership Regression Results Independent Variables Coefficient Std. Error T- statistic Provincial per capita income Provincial underemployment rate Provincial illiteracy rate Proportion of population under 15 years old Proportion of population over 60 years old Proportion of households with electricity Road infrastructure index Constant R2= ; Adjusted R2= Note: Variables in italics are significant at the 10 percent level. Table 8B. Personal Computer Ownership Regression Results Independent Variables Coefficient Std. Error T- statistic Proportion of households with telephones Provincial per capita income Provincial underemployment rate Provincial illiteracy rate Proportion of population under 15 years old Proportion of population over 60 years old Electricity*road index Constant R2= ; Adjusted R2= Note: Variables in italics are significant at the 10 percent level.

21 Table 9 Probit Regression Results of ICT Ownership Probit Regressions Dependent Variables Explanatory Variables PC Telephone TV Radio HH head female (0.0682)*** (0.0409)*** (0.0547) (0.0404) HH head married (0.0810)*** (0.0534)*** (0.0533)*** (0.0409)*** HH head employed (0.0602)* (0.0411)*** (0.0463)*** (0.0353) HH head age (0.0021)*** (0.0012) ( ) (0.0009)*** HH head elementary (0.2887) (0.1286)*** (0.0729)*** (0.0527)*** HH head elementary grad (0.2908) (0.1281)*** (0.0736)*** (0.0538)*** HH head secondary (0.2899) (0.1295)*** (0.0759)*** (0.0563)*** HH head secondary grad (0.2827)** (0.1282)*** (0.0755)*** (0.0554)*** HH head college (0.2830)*** (0.1299)*** (0.0818)*** (0.0593)*** HH head college grad (0.2817)*** (0.1323)*** (0.0948)*** (0.0622)*** Ilocos (0.1112)*** (0.0587)* (0.0738)*** (0.0526)** Cagayan Valley (0.1019) (0.0640) (0.0747)*** (0.0540) Central Luzon (0.0782)* (0.0502)*** (0.0735) (0.0470) Southern Tagalog (0.0659) (0.0459)** (0.0657)*** (0.0411)*** Bicol (0.1104)*** (0.0623)*** (0.0714)*** (0.0488)*** Western Visayas (0.1038)*** (0.0577) (0.0703)*** (0.0480)*** Central Visayas (0.0955)*** (0.0588)** (0.0716)*** (0.0483)*** Eastern Visayas (0.1225)*** (0.0695)*** (0.0728)*** (0.0496)*** Western Mindanao (0.1221)*** (0.0743)*** (0.0781)*** (0.0538)*** Northern Mindanao ().1150)*** (0.0635) (0.0742)*** (0.0511)*** Southern Mindanao (0.1092)** (0.0635)* (0.0749)*** (0.0514)*** Central Mindanao (0.1623)*** (0.0663)* (0.0737)*** (0.0513)*** CAR (0.1025) (0.0684)* (0.0799)*** (0.0608)*** ARMM (0.1623)** (0.1027)*** (0.0815)*** (0.0553)*** CARAGA (0.1387)*** (0.0684)* (0.0752)*** (0.0522)*** HH income ( )*** ( )*** ( )*** ( )*** No of children (0.0247)*** (0.0127)*** (0.0108)*** (0.0090)*** No. of children aged 8 to (0.0341)** (0.0183) (0.0160)*** (0.0133)*** HH agriculture (0.0933)*** (0.0361)*** (0.0245)*** (0.0218) Note: Coefficients are listed above. One asterisk (*) means that the explanatory variable is significant up to the 10 percent level, two asterisks (**) means that variables are significant to 5 percent level, while three asterisks (***) means that variables are significant to 1 percent level. Source: Alegre, de Quiros and Tuaño (2006).