## _wp06258 - conclusions of the paper highlight the role of financial development in the market for ## Source details **Canonical URL:** [_wp06258 - conclusions of the paper highlight the role of financial development in the market for](https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2006/_wp06258.pdf) ## Other formats - [Markdown version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2006/_wp06258.pdf.md) - [Structured JSON version](/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2006/_wp06258.pdf.json) --- ### Introduction and research scope - Objective: examine the role of financial development and financial structure in explaining cross-country diffusion of ICT. - Sample and period: panel data for 76 emerging and advanced countries for the period 1990–2003. - Explicit research question: "What is the role of financial development and financial structure in explaining cross-country diffusion of ICT?" ### Theoretical links and motivation - Strategic complementarity (Saint-Paul, 1992): underdeveloped financial markets → choice of poorly productive but flexible technologies; developed financial markets → more specialized and risky technologies. - Hicks (1969) vs. Robinson (1952) debate on whether finance leads industry or follows enterprise. - Allen and Gale (1999b): market-based systems vs. intermediated (bank) finance differ in handling diversity of opinion; market-based systems likely to finance more innovation though trade-offs exist. - Stock-market-based systems: argued to promote innovation through adaptive market for corporate control, incentives (stock options), and exit mechanisms (Black and Gilson, 1998; Gilson, 1996); critics warn of short-termism and undervaluation of long-term investments (Singh, 1997; Binswanger, 1999). ### Data, measures, and methodology - Dependent variables (per 1,000 people): telephone mainlines, personal computers, Internet users, mobile phones. - Financial development measures: - Stock market capitalization as percentage of GDP (stock market development). - Credit to the private sector as percentage of GDP (credit market development). - Stock market capitalization plus bank assets as percentage of GDP (financial development—size). - Stock market value traded plus credit to the private sector as percentage of GDP (financial development—activity). - Financial structure measure: ratio of bank assets to stock market capitalization. - Key controls: log GDP per capita, electricity consumption per capita, telephone cost (local call cost), urban population share, total population, trade as percentage of GDP, FDI as percentage of GDP, time dummies. - Diffusion model: Modified Gompertz technology diffusion model (Chow, 1983) leading to final estimating equation with lagged dependent variable, controls X, and FIN (equation (4) in paper). - Estimator: Arellano and Bond (1991) dynamic panel GMM (two-step used; hypothesis tests based on one-step statistics). - Specification tests: Sargan test of overidentifying restrictions; tests for second-order residual serial correlation (no significant second-order autocorrelation required). ### Cross-country ICT diffusion — key facts and magnitudes (1990–2003) - Mobile phones: - Subscribers: 11 million in 1990 → exceeded 1.4 billion by end of 2003. - Annual growth rate: 45 percent for mobile vs 6 percent for fixed telephone line subscribers. - Users: about 20 percent of world population had mobile phones by 2003 vs 0.3 percent in 1991. - Internet: - From 20 countries online in 1990 to virtually every country by 2003. - Estimated 691 million Internet users by end of 2003, representing about 11 percent of global population. - Personal computers: - Estimated number rose from about 120 million in 1990 to 650 million in 2003. - Worldwide penetration (personal computers per 100 people) stood at 10.1 at the end of 2003. - Developing vs developed world (end-2003): - Developing world: 85 percent of world population but hosted 55 percent of fixed lines, 53 percent of mobile subscribers, and 39 percent of Internet users. - Internet penetration rate of the developed world was eight times that of the developing world. - Regional disparities: - Advanced countries: almost half the population uses the Internet in many cases; sub-Saharan Africa: 1 percent online at end-2003. - Country examples (2003): Denmark: 512 Internet users per 1,000 people; Spain: 239 per 1,000. - Some countries had mobile subscribers exceeding population (e.g., Luxembourg, Italy); others (Democratic Republic of the Congo, Ethiopia, Lao PDR) had penetration rates less than 3 percent. ### Summary statistics (selected, per 1,000) - Computers: - 1990 mean: 39.61; Std. Deviation: 47.71; Minimum: 0.05; Maximum: 217.92; Number of observations: 52. - 2003 mean: 160.62; Std. Deviation: 181.39; Minimum: 7.77; Maximum: 557.97; Number of observations: 13. - Mobile phones: - 1990 mean: 5.91; Std. Deviation: 12.02; Minimum: 0.00; Maximum: 53.69; Number of observations: 75. - 2003 mean: 506.06; Std. Deviation: 340.55; Minimum: 10.10; Maximum: 1193.80; Number of observations: 70. - Internet users: - 1990 mean: 4.48; Std. Deviation: 10.07; Minimum: 0.00; Maximum: 48.03; Number of observations: 22. - 2003 mean: 248.52; Std. Deviation: 190.11; Minimum: 1.80; Maximum: 674.74; Number of observations: 51. - Telephone mainlines: - 1990 mean: 215.92; Std. Deviation: 207.14; Minimum: 1.96; Maximum: 734.51; Number of observations: 76. - 2003 mean: 802.67; Std. Deviation: 555.43; Minimum: 4.84; Maximum: 1991.30; Number of observations: 65. ### GMM estimation — selected empirical results and coefficients (telephone mainlines per 1,000) - Model 1 (baseline, Number of observations: 540): - Lagged dependent: 0.8493 (51.87)∗∗∗ - Foreign direct investment: 2.9193 (7.07) ∗∗∗ - Trade openness: 0.9754 (3.98) ∗∗∗ - GDP per capita: 128.4285 (2.63)∗∗∗ - Population: -50.967 (-5.48)∗∗∗ - Stock market development: 0.4903 (7.73)∗∗∗ - Credit market development: 0.2814 (1.81)∗ - Sargan test: Chi2(65)=398.38 [0.0000]∗∗∗ - Test 1st order residual autocorrelation: Z=-5.75 [0.0000]∗∗∗ - Test 2nd order residual autocorrelation: Z=0.27 [0.7874] (not significant) - Model 2 (financial development—size included, Number of observations: 564): - Lagged dependent: 0.8684 (55.40)∗∗∗ - Foreign direct investment: 2.7110 (6.08) ∗∗∗ - Trade openness: 1.0106 (3.98) ∗∗∗ - GDP per capita: 133.70 (2.61)∗∗∗ - Population: -47.4735 (-5.11)∗∗∗ - Financial development (size): 14.8005 (7.51)∗∗∗ - Sargan test: Chi2(65)=393.48 [0.0000]∗∗∗ - Test 1st order residual autocorrelation: Z=-3.77 [0.0002]∗∗∗ - Test 2nd order residual autocorrelation: Z=-0.91 [0.3641] (not significant) - Model 3 (alternative specification, Number of observations: 515): - Lagged dependent: 0.8364 (49.18)∗∗∗ - Foreign direct investment: 2.7972 (6.86) ∗∗∗ - Trade openness: 0.9619 (3.59)∗∗∗ - GDP per capita: 102.309 (1.93) ∗∗ - Population: -53.7296 (-5.48) ∗∗∗ - Financial development (activity): 16.9854 (8.27) ∗∗∗ - Financial structure: 10.7383 (2.72) ∗∗∗ - Sargan test: Chi2(65)=385.90 [0.0000] ∗∗∗ - Test 1st order residual autocorrelation: Z=-6.30 [0.0000]∗∗∗ - Test 2nd order residual autocorrelation: Z=-1.27 [0.2041] (not significant) ### Empirical results — summary by ICT indicator (selected coefficients and sign patterns) - Telephone mainlines: - Stock market development coefficient example: 0.4903 (Model 1). - Credit market development example: 0.2814 (Model 1). - Financial development (activity) example: 0.33 percentage points (reported summary) per percentage point increase in financial activity (specified in overview). - Financial structure: positive and significant in some specifications (e.g., 10.7383 in Model 3) and negative (but statistically not significant) in others. - Population: negative and highly significant across specifications. - FDI, trade openness, GDP per capita: positive and significant. - Mobile phones: - Stock market development example (Model 1): 0.32 percentage points per percentage point increase. - Financial development (size) example (Model 2): 0.19 percentage points. - Financial development (activity) example (Model 3): 0.23 percentage points. - Telephone cost: negative and significant in multiple specifications. - Population and urban population: negative and significant in several specifications. - Per capita electricity consumption: negative sign and not statistically significant in some specifications. - Internet users per 1,000: - Stock market development (Model 1): 0.20 percentage points per percentage point increase. - Credit market development (Model 1): 0.17 percentage points. - Financial development (size) (Model 2): 0.07 percentage points. - Financial development (activity) (Model 3): 0.25 percentage points. - Population size: negative and statistically significant. - Financial structure: positive but often statistically not significant; positive and significant in some specifications. - Personal computers per 1,000: - Stock market development (Model 1): 0.11 percentage points per percentage point increase. - Financial development (size) (Model 2): 0.10 percentage points. - Financial development (activity) (Model 3): 0.12 percentage points. - Urban population: positive and statistically significant in some specifications. - Per capita electricity consumption: puzzling results (wrong negative sign and significance in some specifications). ### Robustness checks — financial structure in advanced countries - Interacted specification (advanced dummy × financial structure): - Telephone mainlines: coefficient of financial structure in advanced countries is positive and statistically significant. - Mobile phones: positive and significant result for advanced structure. - Internet users per 1,000: advanced structure positive but not statistically significant; test fails to reject equality inside vs. outside advanced countries. - Personal computers: advanced structure not statistically significant. - Overall robustness conclusion: no conclusive, significant, and robust role for financial structure in explaining cross-country diffusion of ICT even in advanced countries. ### Key empirical implications and magnitudes (policy-relevant) - Financial development is consistently associated with higher use of ICTs across ICT measures: - Financial development (activity) reported effects: 0.33 (telephone mainlines), 0.23 (mobile phones), 0.25 (Internet users per 1,000), 0.12 (personal computers per 1,000) percentage point increases in ICT per percentage point increase in financial activity (reported in respective model specifications). - Financial development (size) reported effects: 0.19 (mobile phones, model 2), 0.07 (Internet, model 2), 0.10 (personal computers, model 2). - Trade openness, FDI, and GDP per capita are robustly positive determinants of ICT development across specifications. - Financial structure (bank-dominated versus stock-market-dominated) generally does not have a robust, consistent role; it is positive and significant in some specifications and subsamples but insignificant in many others. - Population size typically enters negative and significant; urbanization effects vary across specifications, indicating context-specific dynamics. ### Concluding remarks and policy takeaway - Income influences ICT infrastructure: higher use of fixed and mobile phones, Internet, and personal computers is associated with higher income. - Government trade policies: openness is associated with adoption and adaptation of technology. - Financial development: the overall level of development of stock markets and banks tends to foster ICT development. - Financial structure: does not appear to have any consistent significant relationship with the level of ICT development. - Policy implication: promoting development of the financial system in emerging markets is motivated by its positive association with ICT diffusion; conversely, underdeveloped financial markets may exacerbate the digital divide. *Source: _wp06258 - conclusions of the paper highlight the role of financial development in the market for* ### conclusions of the paper highlight the role of financial development in the market for ### _wp06258 - conclusions of the paper highlight the role of financial development in the market for ### Introduction and research scope - Paper objective: examine the role of financial development and financial structure in explaining cross-country diffusion of ICT. - Sample and period: panel data for 76 emerging and advanced countries for the period 1990–2003. - Explicit research question (stated): "What is the role of financial development and financial structure in explaining cross-country diffusion of ICT?" ### Key findings and substantive conclusions - Financial development is an important determinant of ICT development. - The conclusions are consistent with theoretical predictions linking finance and the market for knowledge-based products. - Implication: countries with underdeveloped financial markets may continue to lag behind in the use of ICT. ### Empirical approach and methodology - Model and estimation: panel-data analysis with GMM estimation (see Tables 4–8 listed in the paper). - Dependent and explanatory variables: the paper specifies a dependent variable measuring ICT development and a set of explanatory variables including economic, infrastructural, external, demographic, and financial development and financial structure indicators. - Robustness checks: specific attention to financial structure and ICT in advanced countries (reported in Table 8). ### Contents and analytical structure (as presented) - Sections include: - I. Introduction - II. Finance, Financial Structure, and Technological Development (A. Theoretical Links; B. Stock Markets and Technological Development) - III. Cross-Country Diffusion of ICT - IV. Methodology (A. The Model; B. The Data; C. Estimation Method) - V. Empirical Results (A. Robustness—Financial Structure and ICT in Advanced Countries) - VI. Concluding Remarks - Tables and figures referenced: - Tables 1–8 (including "Access to ICTs by Region" and multiple "GMM Estimation" tables) - Figure 1: Number of Countries Connected to the Internet ### Keywords, classifications, and contact - JEL Classification Numbers: G23, G32 - Keywords: ICT, digital divide, financial development, financial structure - Author’s E‑Mail Address: cyartey@imf.org *Source: _wp06258 - conclusions of the paper highlight the role of financial development in the market for* ### 2. What kind of financial system or capital market organizations are most conducive to ### 2. What kind of financial system or capital market organizations are most conducive to fostering ICT and its use in the economy? ### Motivation and research focus - Recent empirical research documents technological diffusion hampered by limited network capacity and individual access to immaterial knowledge products; well-developed financial markets may affect both ICT diffusion and its impact on economic growth. - Paper extends prior work by covering four ICT indicators over 1990 to 2003 and using panel data and dynamic panel data techniques. - Analysis covers role of financial development and financial structure in cross-country diffusion of ICT. ### Theoretical links between finance and technological development - Historical debate: - Hicks (1969): large-scale capital requirements met by development of capital market institutions that pool small savings into large funds for industrial development. - Joan Robinson (1952): “where enterprise leads finance follows” (p. 123). - Strategic complementarity (Saint-Paul, 1992): - Underdeveloped financial markets → choice of poorly productive but flexible technologies → little incentive to develop financial markets. - Developed financial markets → more specialized and risky technologies → need for financial markets. - Allen and Gale (1999b): market-based systems and intermediated (bank) finance differ in handling diversity of opinion: - Market-based: many investors decide individually; costly information acquisition but enables “agree to disagree” and finance innovative projects. - Intermediated finance: delegated to a manager; economizes information acquisition but may underfund innovative projects when opinions diverge. - Conclusion: market-based systems likely to lead to more innovation than bank-based systems, though not unequivocal. ### Stock markets and technological development (theoretical perspectives and debates) - Advocates: stock-market-based systems conducive to innovation—adaptive market for corporate control, takeover mechanism aids selection, incentives (e.g., stock options) align employees and shareholders, exit mechanisms (IPOs, takeovers) improve rewards for innovation (Black and Gilson, 1998; Gilson, 1996; George and others, 1991). - Critics: pricing and takeover mechanisms can cause short-termism and undervaluation of long-term investment, lowering firm-specific human capital investment (Singh, 1997; Binswanger, 1999). - Net assessment: stock-market-based systems have both positive and negative features for promoting technological change; focus here is on information communication technology. ### Cross-country diffusion of ICT — key facts and magnitudes - Mobile phones: - Subscribers: 11 million in 1990 → exceeded 1.4 billion by end of 2003. - Annual growth rate: 45 percent for mobile vs 6 percent for fixed telephone line subscribers. - Users: about 20 percent of world population had mobile phones by 2003 vs 0.3 percent in 1991. - Internet: - From 20 countries online in 1990 to virtually every country by 2003. - Estimated 691 million Internet users by end of 2003, representing about 11 percent of global population. - Personal computers: - Estimated number rose from about 120 million in 1990 to 650 million in 2003. - Worldwide penetration (personal computers per 100 people) stood at 10.1 at the end of 2003. - Developing vs developed world (end-2003): - Developing world: 85 percent of world population but hosted 55 percent of fixed lines, 53 percent of mobile subscribers, and 39 percent of Internet users. - Internet penetration rate of the developed world was still eight times that of the developing world. - Regional disparities: - In many advanced countries almost half the population uses the Internet; sub-Saharan Africa had only 1 percent online at end-2003. - Some countries had mobile subscribers exceeding population (e.g., Luxembourg, Italy); others (Democratic Republic of the Congo, Ethiopia, Lao PDR) had penetration rates less than 3 percent. - Country examples (2003, selected): - Denmark: 512 Internet users per 1,000 people; Spain: 239 per 1,000. - Table 2 highlights disparities in Computers per 1,000 and Internet users per 1,000 across developed and developing countries (values shown in source). ### Methodology — diffusion model, variables, and expectations - Model: Modified Gompertz technology diffusion model (Chow, 1983). - Rate of change in technological adoption: equation (1) with speed of adjustment θ. - Equilibrium adoption level log-linear in controls X: equation (2). - Final estimating equation includes lagged dependent variable, control variables X, and FIN (financial development / structure): equation (4). - Key control variables (X): income level (log GDP per capita), electricity consumption per capita, telephone cost (local call cost), urban population share, total population, trade as percentage of GDP, FDI as percentage of GDP. - Financial development measures: - Stock market capitalization as percentage of GDP (stock market development). - Credit to the private sector as percentage of GDP (credit market development). - Stock market capitalization plus bank assets as percentage of GDP (financial development—size). - Stock market value traded plus credit to the private sector as percentage of GDP (financial development—activity). - Financial structure measure: - Ratio of bank assets to stock market capitalization (Demirguc-Kunt and Levine, 1999). - Expected signs: - Financial development coefficient expected positive. - Financial structure coefficient (bank-assets-to-stock-cap) expected negative in ICT equation (i.e., bank-dominated systems less conductive than market-based). ### Data — dependent variables and measurement caveats - Four ICT measures (per 1,000 people): telephone mainlines, personal computers, Internet users, mobile phones. - Data source: World Bank’s World Development Indicators; International Telecommunication Union (for some aggregates cited). - Measurement caveats: - Personal computers may understate total computer use where mainframes predominate (Wallsten, 2003). - Internet users estimates derived from ISP subscriber counts or multipliers; may understate use in developing countries where commercial subscribers rent connected computers. - Internet host counts may mis-assign hosts without country codes to United States (Wallsten, 2003). ### Estimation method - Estimation sample: panel data for 76 countries, 1990–2003. - Endogeneity concerns: simultaneous determination, two-way causality, unobserved country effects. - Estimator: Arellano and Bond (1991) dynamic panel GMM (one-step and two-step); two-step used but hypothesis tests based on one-step statistics. - Specification tests: Sargan test of overidentifying restrictions; tests for second-order residual serial correlation (no significant second-order autocorrelation required). ### Empirical results — summary and key quantitative findings - Summary statistics (Table 3, all measures per 1,000): - Computers: - 1990 mean: 39.61; Std. Deviation: 47.71; Minimum: 0.05; Maximum: 217.92; Number of observations: 52. - 2003 mean: 160.62; Std. Deviation: 181.39; Minimum: 7.77; Maximum: 557.97; Number of observations: 13. - Mobile phones: - 1990 mean: 5.91; Std. Deviation: 12.02; Minimum: 0.00; Maximum: 53.69; Number of observations: 75. - 2003 mean: 506.06; Std. Deviation: 340.55; Minimum: 10.10; Maximum: 1193.80; Number of observations: 70. - Internet users: - 1990 mean: 4.48; Std. Deviation: 10.07; Minimum: 0.00; Maximum: 48.03; Number of observations: 22. - 2003 mean: 248.52; Std. Deviation: 190.11; Minimum: 1.80; Maximum: 674.74; Number of observations: 51. - Telephone mainlines: - 1990 mean: 215.92; Std. Deviation: 207.14; Minimum: 1.96; Maximum: 734.51; Number of observations: 76. - 2003 mean: 802.67; Std. Deviation: 555.43; Minimum: 4.84; Maximum: 1991.30; Number of observations: 65. - GMM estimation results (selected findings from Table 4, dependent variable: Telephone mainlines per 1,000): - Model 1 (baseline): - Lagged dependent: 0.8493 (51.87)∗∗∗ - Foreign direct investment: 2.9193 (7.07) ∗∗∗ - Trade openness: 0.9754 (3.98) ∗∗∗ - GDP per capita: 128.4285 (2.63)∗∗∗ - Population: -50.967 (-5.48)∗∗∗ - Stock market development: 0.4903 (7.73)∗∗∗ — interpretation: a percentage point increase in stock market development increases telephone mainlines by 0.49 percentage points (per 1,000). - Credit market development: 0.2814 (1.81)∗ — interpretation: a percentage point increase in credit to the private sector as a percentage of GDP increases telephone mainlines per 1,000 by 0.28 percentage points. - Number of observations: 540. - Sargan test: Chi2(65)=398.38 [0.0000]∗∗∗. - Test of 1st order residual autocorrelation: Z=-5.75 [0.0000]∗∗∗. - Test of 2nd order residual autocorrelation: Z=0.27 [0.7874] (not significant). - Model 2 (including financial structure and financial development—size): - Lagged dependent: 0.8684 (55.40)∗∗∗ - Foreign direct investment: 2.7110 (6.08) ∗∗∗ - Trade openness: 1.0106 (3.98) ∗∗∗ - GDP per capita: 133.70 (2.61)∗∗∗ - Population: -47.4735 (-5.11)∗∗∗ - Financial development (size): 14.8005 (7.51)∗∗∗ (positive and significant). - Number of observations: 564. - Sargan test: Chi2(65)=393.48 [0.0000]∗∗∗. - Test of 1st order residual autocorrelation: Z=-3.77 [0.0002]∗∗∗. - Test of 2nd order residual autocorrelation: Z=-0.91 [0.3641] (not significant). - Model 3 (alternative specification reported in table): - Lagged dependent: 0.8364 (49.18)∗∗∗ - Foreign direct investment: 2.7972 (6.86) ∗∗∗ - Trade openness: 0.9619 (3.59)∗∗∗ - GDP per capita: 102.309 (1.93) ∗∗ - Population: -53.7296 (-5.48) ∗∗∗ - Financial development (activity): 16.9854 (8.27) ∗∗∗ - Financial structure: 10.7383 (2.72) ∗∗∗ (positive in this specification). - Number of observations: 515. - Sargan test: Chi2(65)=385.90 [0.0000] ∗∗∗. - Test of 1st order residual autocorrelation: Z=-6.30 [0.0000]∗∗∗. - Test of 2nd order residual autocorrelation: Z=-1.27 [0.2041] (not significant). - Interpretation highlights from results reported: - Lagged dependent variable is significant in all specifications, indicating diffusion dynamics are captured. - Stock market development and credit market development are positively associated with telephone mainlines per 1,000 (Model 1). - Financial development measures (size and activity) enter positively and significantly in alternative specifications (Models 2 and 3). - Population has a highly significant negative coefficient in telephone-mainline regressions (Models 1–3). - FDI, trade openness, and GDP per capita are consistently significant and positive in explaining telephone mainlines diffusion. - Specification tests indicate valid instruments with no evidence of problematic second-order autocorrelation (p-values for second-order tests not significant). ### Key empirical implications (as inferred from reported results) - Financial development (stock market and credit market measures, and aggregate measures of financial development) is positively associated with ICT diffusion (example: stock market development coefficient 0.4903; credit market development coefficient 0.2814 in the telephone-mainlines regressions). - FDI, trade openness, and income (GDP per capita) are robustly positively associated with ICT diffusion. - The role of financial structure is model-dependent; some specifications show positive coefficients for financial structure or related aggregates, indicating complexity in how bank-dominated versus market-dominated systems relate to ICT diffusion. - Dynamic diffusion process is important given significant lagged dependent coefficients across specifications. *Source: _wp06258 - 2. What kind of financial system or capital market organizations are most conducive to fostering ICT and its use in the economy? (IMF working paper content provided).* ### 0.45 percentage points. Financial structure, however, is negative, indicating that economies with ### _wp06258 - 0.45 percentage points. Financial structure, however, is negative, indicating that economies with ### Overview of empirical approach - Panel GMM estimations are used to examine ICT diffusion (telephone mainlines, mobile phones, Internet users per 1,000, personal computers per 1,000). - Financial development is measured two ways: (a) financial development (size) — sum of credit to the private sector as a percentage of GDP and stock market capitalization as a percentage of GDP; (b) financial development (activity) — sum of credit to the private sector as a percentage of GDP and stock market value traded as a percentage of GDP. - Financial structure is measured as the ratio of bank assets to stock market capitalization. - Controls include lagged dependent variable, FDI, trade openness, GDP per capita, telephone cost, urban population, electricity consumption, population, and time dummies. ### Telephone mainlines — key empirical results - Financial development (activity): a percentage point increase in the activities of financial intermediaries and stock markets increases ICT development by 0.33 percentage points. - Financial structure: a percentage point increase in the ratio of bank assets to stock market capitalization increases ICT development by 10.74 percentage points (reported positive and significant in the specification described). - Urban population: negative and significant relationship with telephone mainlines development. - Per capita electricity consumption: wrong negative sign and statistically not significant. - Trade openness, FDI, and GDP per capita: all positive and significant. - Population: negative and highly significant. - Some specifications show financial structure negative (indicating economies with a high ratio of banks to stock markets tend to have a lower value for telephone mainlines), but that relationship is statistically not significant in that case. ### Mobile phone diffusion — key empirical results - Model 1: stock market development — a percentage point increase increases ICT diffusion by 0.32 percentage points. Credit market development not significant in this specification. - Telephone cost: significant with expected negative sign. - Urban population and total population: negative and significant. - Model 2: financial development (size) — a percentage point increase in financial development increases ICT development by 0.19 percentage points. Financial structure: positive but statistically insignificant. - Model 3: financial development (activity) — a percentage point increase in the activities of both banks and stock markets increases ICT development by 0.23 percentage points. Financial structure: positive and statistically significant in this specification. - Population and telephone cost: negative and significant. - Per capita electricity consumption: wrong negative sign and not statistically significant. ### Internet usage per 1,000 — key empirical results - Model 1: stock market development — a percentage point increase increases Internet usage per 1,000 by 0.20 percentage point. Credit market development — a percentage point increase increases Internet usage by 0.17 percentage points. - Population size: negative and statistically significant. - Telephone cost, electricity consumption, urban population: negative but statistically insignificant in model 1. - Model 2: financial development (size) — a percentage point increase in financial development induces a 0.07 percentage increase in ICT development. Financial structure: positive but statistically not significant. - Model 3: financial development (activity) — a percentage point increase in the activities of financial intermediaries and banks induces 0.25 percentage point increase in ICT development. Financial structure: positive and statistically significant. Population size: negative and statistically significant. Urban population: positive but statistically insignificant. ### Personal computer penetration — key empirical results - Model 1: stock market development — a percentage point increase increases computer penetration rate by 0.11 percentage points. Credit market development not significant in that specification. - Per capita electricity consumption: generally insignificant; one specification reports a wrong negative sign and significance, suggesting a puzzling and nonmonotonic relationship. - Urban population: positive and statistically significant in some specifications. - Model 2: financial development (size) — a percentage point increase in financial development increases personal computer penetration by 0.10 percentage points. Financial structure: statistically insignificant in this model. - Model 3: financial development (activity) — a percentage point increase in the activities of financial intermediaries and stock markets increases computer penetration by 0.12 percentage points. Financial structure: positive and statistically significant in this specification. Electricity consumption: wrong negative sign and significant in one specification. ### Robustness: financial structure in advanced countries - An interacted specification (advanced dummy × financial structure) is estimated to test whether the coefficient of financial structure differs inside vs. outside advanced countries. - Telephone mainlines: coefficient of financial structure in advanced countries is positive and statistically significant (implying that in advanced countries where the banking system is relatively more important, the stock market tends to have greater use of ICT measured by telephone mainlines). - Mobile phones: a similar positive and significant result for advanced structure is found. - Internet users per 1,000: advanced structure is not statistically significant though positive; test fails to reject the null that coefficients are the same inside and outside advanced countries. - Personal computer users: advanced structure is not statistically significant. - Overall conclusion from robustness checks: no conclusive, significant, and robust role for financial structure in explaining cross-country diffusion of ICT even in advanced countries. ### Aggregate interpretation and policy-relevant points - Financial development is consistently associated with higher use of ICTs across ICT measures: - financial development (activity) effects reported: 0.33 (telephone mainlines), 0.23 (mobile phones), 0.25 (Internet users per 1,000), 0.12 (personal computers per 1,000) percentage point increases in ICT per percentage point increase in financial activity (reported in respective model specifications). - financial development (size) effects reported: 0.19 (mobile phones, model 2), 0.07 (Internet, model 2), 0.10 (personal computers, model 2). - Trade openness, FDI, and GDP per capita are robustly positive determinants of ICT development across specifications. - Financial structure (bank-dominated versus stock-market-dominated) generally does not have a robust, consistent role in explaining cross-country ICT diffusion; it is positive and significant in some specifications and subsamples (notably telephone mainlines and mobile phones in advanced-country interactions) but insignificant in many others. - Population size typically enters negative and significant, while urbanization effects vary across specifications (negative and significant for mobile phones in some models; positive and significant for personal computers in others), suggesting substitution effects and context-specific dynamics (the paper links a negative urban coefficient for mobile phones to the “global village” theory vs. urban density theory). ### Concluding remarks (authors’ summary) - First, income influences ICT infrastructure because it relates to higher use of fixed and mobile phones, Internet, and personal computers. - Second, there is a positive impact of government trade policies on ICT. Openness is associated with the adoption and adaptation of technology. - Third, financial development is associated with higher use of ICTs. In particular, the overall level of development of stock markets and banks tends to foster ICT development. - Last, the structure of a country’s capital market does not appear to have any significant relationship with the level of ICT development. - Policy implication: promoting development of the financial system in emerging markets is motivated by its positive association with ICT diffusion; conversely, underdeveloped financial markets may exacerbate the digital divide. *Source: _wp06258 - 0.45 percentage points. Financial structure, however, is negative, indicating that economies with (IMF working paper content provided).* ### References ### References ### Information and Communication Technology (ICT) diffusion, digital divide, and IT impact - Adriani, Fabrizio, and Leonardo Becchetti, 2003, “Does the Digital Divide Matter? 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