investigating the impact of ict investments on human development

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INVESTIGATING THE IMPACT OF ICT INVESTMENTS ON HUMAN DEVELOPMENT Felix O. Bankole Department of Information Systems and The Centre for Information Technology and National Development in Africa, University of Cape Town South Africa [email protected]

Farid Shirazi Institute for Research on Innovation and Technology Management, Ryerson University, Toronto Canada [email protected]

Irwin Brown Department of Information Systems and The Centre for Information Technology and National Development in Africa University of Cape Town South Africa [email protected]

ABSTRACT In the last two decades, the worldwide information and communication technology (ICT) market has been growing at a rapid rate. This has led to a global net increase in ICT investments and usage. International organizations, ICT vendors and policy makers have been investigating whether such large investments are worthwhile. The results regarding this issue are inconclusive, for this research area is fraught with complexity, and existing empirical work is limited. This study investigates the impact of ICT investments on human development. Of particular interest are the relationships between different dimensions of ICT investment and the components of human development. ICT investments can be thought of as having four dimensions - hardware, software, internal spending and telecommunication investment, while typical human development indicators are standard of living (GDP per capita), education (literacy and school enrolments) and health (life expectancy). If these variables are not modelled correctly, their effect on each other can be either under- or overestimated. In this article, the line of enquiry from a study by Kim et al. (2008) is extended to investigate the relationship between the four aspects of ICT investments and the three key components of human development. The empirical analysis shows that the four dimensions of ICT investment have an impact in various ways on the components of human development, and that these impacts are different, in high income, mid income and low income countries. Based on these findings, this study suggests theoretical propositions to explain the impact of ICT investments on human development. Keywords: ICT, Structural equation modeling, Human Development Index, Education, Gross Domestic Product. 1. INTRODUCTION The rapid growth in the ICT sector is bringing digital opportunities to many countries, yet the general impact of increased ICT spending on a nation’s development is difficult to assess. The term ICT was born in the era of the Internet revolution, and encompasses telecommunications, computer networks, the Internet, radio and television (ITU, 2007). ICTs are used by people to seek information and to communicate it to those who appreciate its value. This becomes knowledge which is of great value to human development - the technology serves as a channel to disseminate the knowledge (UNDP, 2003). Simply put, ICTs are a means of disseminating information through a combination of complementary technologies (UNDP, 2003; ITU, 2007). This study aims to understand the impact of national ICT investments on human development. The rest of this article is organised as follows: Section two provides the conceptual background of the study. Section three The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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describes the research methodology, followed by empirical results in section four. Section five presents a theoretical elaboration of these results, while Section six discusses the implications of the findings. Section seven outlines limitations of the study and ideas for future research, and section eight concludes the paper. 2. CONCEPTUAL BACKGROUND The two major concepts to be investigated in this paper are ICT investments and human development. Each will be discussed in turn, followed by a discussion of the impact of ICTs on human development. 2.1 ICT Investments ICT investments have grown substantially in both developed and developing countries. Between 1993 and 2001 the cumulative annual growth rate of ICT spending in developing countries was 12 percent, while being 6 percent in developed countries (Qiang and Pitt, 2003). Worldwide ICT spending amounted to $1.8 trillion in 1997, 6% of the aggregate global GDP and 40% larger than it was in 1992. Asia, Latin America and Eastern Europe experienced the fastest growing ICT investments with five year annual growth rates of 14.5%, 13.6% and 9.5% respectively between 1992 to 1997 (WITSA, 1998; WITSA, 2008). Many developing countries in Africa, Asia and Latin America also increased investment in their ICT infrastructure in response to social and business demands (Shirazi et al., 2010; Morawczynski and Ngwenyama, 2007; Bollou and Ngwenyama, 2008). The global annual growth rate of ICT investments peaked in 2004 at 12.3 % following a slowdown in 2001. ICT growth moderated to 7.7% and 7.9 % in 2005 and 2006 respectively. In 2007 ICT investment did not rise as it had in previous years- the recession in the US economy and other developed nations affected the ICT sector, as it did other sectors (WITSA, 2008). ICT investments are referred to as second-order investments that, for example, create opportunities for people to overcome conditions of poverty and marginalization (Servon, 2002; Morawczynski and Ngwenyama, 2007). Investments in ICT can be thought of as consisting of four facets: hardware, software, internal spending and telecommunication investments (WITSA, 2008). For the purposes of this study, software investment refers to total country spending on software packages, database systems, utility software and programming tools. Hardware investment is the total computer hardware spending in a country (Kim et al., 2008). Internal spending refers to the total national amount spent on software customization, human capital development and other miscellaneous IT related expenses. Telecommunication spending refers to local and long distance wire-line and wireless communication investments in a country (ITU, 2007; WITSA, 2008). 2.2 Human Development Human development (HD) has been defined as “the process of enlarging people’s choices. Their three essential choices are to lead a long and healthy life, to acquire knowledge and to have access to the resources needed for a decent standard of living” (UNDP, 2003). The Human Development Index (HDI) is a measure used to assess human development in a nation. The HDI consists of three components corresponding to the three dimensions of human development. For assessing standard of living gross domestic product per capita (GDP per capita) is used; for assessing knowledge acquisition capability (education), national literacy rates and levels of school enrollment are used. For assessing health, longevity (life expectancy at birth) is used (UNDP, 1990, 1991, 2006). For each index, a 1 (100%) represents the highest score possible.

The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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2.3 Impact of ICT Investments on Human Development International organizations such as the International Telecommunication Union (ITU), World Bank and International Monetary Fund (IMF), among others, have highlighted ICT as a potential tool for development in poor nations. There have been protests from some quarters, however, that poor nations should be utilising their limited resources on basic amenities like building schools and making provision for basic health, electricity and clean water, rather than ICT infrastructure (Ngwenyama et al., 2006; Morawczynski and Ngwenyama, 2007). Unraveling the impact of ICT investments on human development has therefore been receiving attention among policy makers, ICT practitioners and governments (UNDP, 2003). Most studies in this domain have investigated the economic impact of ICT investments (Jalava and Pohjola, 2002; Van Ark et al., 2002; Daveri, 2002 and Stiroh, 2000). Generally, findings point to the positive influence of ICT investment on economic development in a range of contexts, but particularly in developed and newly developed nations (Kim, 2003; Oulton, 2001; Wang, 1999; Colecchia and Schreyer, 2002; Kuppusamy and Santhapparaj, 2005). Less research has investigated these impacts in developing and the less/least developed countries (Mbarika et al., 2005; Bolou, 2006). There are even fewer studies looking at the impact of ICT investments on other facets of human development, such as education and health (Ngwenyama et al., 2006). Table 1 highlights some of the key studies conducted on ICT investments in both developed and developing countries that are important to this study and its findings. Of particular relevance is the study by Kim et al. (2008) which investigated the impact of different facets of ICT investment (hardware, software and internal spending) on economic development in 51 countries with the largest ICT markets. Kim et al. (2008) assessed these impacts in high income, middle income and low income countries recognising that national context plays a role in determining how ICT investments may impact economic development. The objective in this study was to extend this line of inquiry in two ways. Firstly, in addition to investments in hardware, software and internal spending, this study also considers telecommunication investments. Secondly, in addition to considering the impact of these ICT investments on standard of living (GDP per capita), this study seeks to explore the impact of these ICT investments on education (literacy rates and education enrolments) and health (life expectancy at birth) as well. As with Kim et al. (2008), these impacts are considered in high income, middle income and low income countries. This article makes three major contributions: (a.) It validates Kim et al.’s (2008) model while also introducing the human development concept, as in Morawczynski and Ngwenyama (2007). Kim et al. (2008) investigated only GDP per capita as the dependent variable. (b.) It introduces telecommunication investment into the model. Kim et al. (2008) looked only at hardware, software and internal spending as ICT investment components. (c.) It employs a new education index (edu= (primary + 2* secondary + 3* tertiary)/6) introduced by Orbicom (2005) and ITU to emphasize the impact of higher education on ICT development. This study aims to make a contribution to the body of knowledge by providing better explanation (theory of explanation as stated in Gregor, 2006) of the impacts of various dimensions of ICT investment on components of human development in high, mid and low income countries.


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Table 1: Specific Research and Findings. Title, Author and Year Findings Are ICT Investments Paying Off in Africa? The study revealed that ICT improved total An Analysis of Total Factor Productivity in factor productivity in six African countries. Six West African Countries from 1995-2002 (Bollou and Ngwenyama, 2008) Differential effects of IT Investments: The research indicates the importance of IT Complementarity and the effect on GDP in improving the gross domestic product of Level (Kim et al., 2008). countries (GDP). Investments in ICT and its payoff in The research shows that ICT investments are Malaysia (Kuppusanmy and Santhapparay, paying off in terms of economic 2005) development. Unravelling the Impacts of Investments in Complementary investment in ICT, ICTs, Education and Health on Development: Education and Health sectors improve the An Analysis of Archival Data of Five West level of human development. African Countries. (Morawczynski and Ngwenyama, 2007) Is there a Relationship between ICT, Health, The research indicates the relationships Education and Development? An Empirical between investments in ICT, health, Analysis of Five West African Countries. education and levels of human development. (Ngwenyama et al., 2006) An Exploration of the Effect of the The research shows that complementary Interaction between ICT and Labor Force on investments in ICT and labour are Economic Growth in Transition Economies prerequisites for the translation of ICT into (Samoilenko and Osei-Bryson, 2008). macroeconomic outcomes. ICT Investments and Economic Growth in The study reveals that ICT is contributing to 1990s: Is the United States a Unique Case? economic growth in the US and nine other (Colecchia and Schreyer, 2002). OECD countries. 3 RESEARCH METHODOLOGY In this study, exploratory research was conducted, using archival quantitative data concerning ICT investments and human development. The research was deductive in that it was presumed there was a relationship between ICT investments and development, as has been found in previous studies. The nature of the relationship between different aspects of ICT investments, and their impact on different components of human development is however not fully understood. Furthermore, the way these effects vary between countries with different incomes is not fully understood either. Hence the study was also inductive, in that from observations of relationships and patterns in the data, propositions and theory could be derived. Iivari and Huisman (2007) show that empirical quantitative research methods can be used for exploratory-theory building research. For the purpose of this research, the independent variables are the four facets of ICT investment and the dependent variables are the three components of human development. 3.1 Data Collection The data on ICT investments and human development (HDI) for each country were collected for the period 1994 to 2003 from three different sources - the UN (for HDI data), the ITU (for telecommunication investment data), and the WITSA databases (for IT investment data, i.e. hardware, software and internal spending). The period 1994 to 2003 was chosen as complete data sets on both ICT investment and human development for the 51 countries were available for this period only. The 51 countries included in this study are shown in Table 2. The Electronic Journal on Information Systems in Developing Countries http://www.ejisdc.org

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Table 2: Countries under Assessment (Kim et al., 2008) High Income

Middle Income

Low Income

Japan Switzerland Norway Denmark USA Sweden Germany Austria Singapore Netherlands Belgium France Hong Kong Finland UK Ireland Canada

Australia Italy Israel New Zealand Spain Taiwan Greece Portugal South Korea Slovenia Argentina Saudi Arabia Czech Mexico Hungary Chile Brazil

Malaysia Venezuela Poland Slovakia South Africa Turkey Egypt Colombia Thailand Russia Romania Bulgaria Philippines Indonesia China India Vietnam

The high income countries in this study have populations varying from 4.1 million (Ireland) to 299.8 million (United States). These countries have 60-100 % urban populations. The average GDP index of the HDI for these countries is 95%; the average education index is 95%, and the health index average is 89%. The mid income countries have populations ranging from 2 million (Slovenia) to 22.2 million (Taiwan), with 40-92% of the people living in urban areas. The average GDP index is 86%; the average education index is 90%, and the average health index is 83%. The low income countries have populations ranging from 5.4 million (Slovakia) to 1,313.0 million (China), with 26-94% of the population being urban. The average GDP index is 74%; the average education index is 82%, and the average health index is 72% (See Appendix A). 3.2 Data Classification The countries in this study represent the largest ICT market nations around the world (Kim et al., 2008). They were selected and grouped into high income, middle income and low income countries as per Kim et al. (2008), based on GDP per capita. The first 17 countries above the two-third percentile of GDP per capita were defined as high income countries and the second 17 countries within the one- third percentile were defined as mid- income countries. The last 17 countries below the one- third percentile were defined as low-income countries. The appropriateness of this grouping was assessed, based on the human development index (HDI) and it was observed that the majority of the countries fall within the same GDP group. Data envelopment analysis (DEA) was performed to compare the average efficiency score of each group. The results show that the average performance of the High group is superior to that of the Medium group which is superior to that of the Low group (see Table 3). These results (of the DEA analysis) can be considered as offering additional support to the appropriateness of using Kim et al’s (2008) classification for this study. It is important to mention that the interest here is to explain the rational for the classification of the countries into high, medium


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and low groups rather than to discuss DEA methodology. For a detailed discussion on DEA approach, interested readers are encouraged to examine Charnes et al. (1978). Table 3: Average Relative Efficiency Scores Group Average Relative Efficiency Score High 0.88 Medium 0.79 Low 0.69 3.3 Measurement Model Morawczynski and Ngwenyama (2007) conducted a study to unravel the impact of investments in ICT, education and health on human development in West Africa. This research focuses on the impact of ICT investment specifically. Human development is expressed in terms of GDP per capita (for standard of living), literacy rates and school enrolments (for education) and life expectancy rates (for health) [UNDP, 2003]. Kim et al. (2008) investigated the impact of ICT investment on economic development (GDP per capita) and considered the impact and interaction of three facets of ICT investment. The interaction effect was introduced into the model because the deployment of each component of ICT needs to be accompanied by complementary investments in order for the total ICT investments to impact on development (Kim et al., 2008; Samoilenko and Osei-Bryson, 2008). This study extends the Kim et al. (2008) model by using four facets of ICT investment (i.e. including telecommunications), and by considering their influence, inclusive of interaction effects, on the three components of human development. Kim et al.’s (2008) model is expressed as: Impact of IT on GDP per capita = f[Software Investment (SI) + Hardware Investment (HI) + Internal IT Spending (IS) + (SI *IS) + (HI*IS) + (SI*HI)] Morawczynski & Ngwenyama’s (2007) study expressed human development as: HDI = f[Standard of living (GDP per capita), Education (Literacy rates / Enrolments) and Health (life expectancy)] The model for this study is the following: 1. The impact of ICT investment on Standard of Living (GDP per capita) = f[Software Investment (SI) + Hardware Investment (HI) + Telecommunication Investment (TI) + Internal Spending (IS) + (SI*IS) + (HI*IS) + (TI*IS) + (SI*HI)]. 2. The impact on Education (Literacy rates / Enrolments) = f[Software Investment (SI) + Hardware Investment (HI) + Telecommunication Investment (TI) + Internal Spending (IS) + (SI*IS) + (HI*IS) + (TI*IS) + (SI*HI)]. 3. The impact on Health (Life expectancy) = f[Software Investment (SI) + Hardware Investment (HI) + Telecommunication Investment (TI) + Internal Spending (IS) + (SI*IS) + (HI*IS) + (TI*IS) + (SI*HI)]. 3.4 Structural Equation Model (SEM) The data were used to perform computations using STAT software (Version 11.0). A threestage least squares (3SLS) estimation model was used to test the above three equations. 3SLS estimates a system of structural equations, where some equations contain endogenous


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variables (e.g., human development indices) among the independent variables (e.g., ICTs). In essence, this estimation model is a multivariate regression capable of having multiple endogenous regressors. The 3SLS as proposed by Zellner and Theil (1962) is an effective method for systems of structural equations, where some equations contain endogenous variables among the explanatory variables. In this context, the endogenous explanatory variables are dependent variables from other equations in the system. As noted by Zellner and Theil (1962), the model is tested in three stages as follows:  





In stage one, the simulation processes regression of each endogenous variable is applied on all exogenous variable of the entire system to build the "moment matrix of the reduced-form" (p.54); In stage two, the estimates of the coefficient of a single structural equation based on the results from the first stage were computed. In stage three, the model is tested by using the resultant matrix of the first two stages. The last stage produces estimates for all the coefficients of the entire system (for more details see Zellner and Theil, 1962). Rao (1974) points out that the 3SLS estimator is based on Aitken's generalized least square application after a suitable transformation of a structural equation within the system. Belsley (1998) notes that 3SLS can be more efficient when compared with other linear regressions. It provides the advantage that increases with the strength of the interrelations among the error terms.

In particular, its efficiency is achieved by exploiting non-zero cross-equation covariation (Belsley, 1998). In addition, as noted by Stoian and Vickerman (2005), this model is an effective method for testing the bi-directional relationship between variables (in this case the endogenous variables such as GDP per capita (GDPP), Education, and Health) through a single equation estimation method. This model investigates how changes in three key components of human development GDP per capita (GDPP), Education (edu) and Health correlate with changes in four dimensions of ICT investment. 4 RESULTS Before the regression model was run, a series of tests on panel data were conducted to find out about possible multicollinearity and autocorrelation issues among variables. Table 1 in Appendix B reports the results of multicollinearity test as measured by tolerance Variance Inflation Factor (VIF) and the autocorrelation test. The panel data shows a mean VIF value of 10.95 which is lower than the server multicollinearity issue (value 30). The result of crosssectional time-series for the homoskedastic panel shows that there is no autocorrelation problem among variables as reported by the Wald chi-squared test. The impact of the four independent variables (ICT investments) on the three components of the dependent variable (human development index) and the impact of interaction terms of telecommunications and internal spending; software and internal spending; software and hardware; and hardware and internal spending on the three components of human development (See Appendix B) are presented in Table 4 The impacts for each of the high income, mid-income and low-income countries were determined. Only coefficients that were positive and significant with a p-value less than 0.1 were examined in further detail to understand how ICT investments contribute positively to human development.


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Table 4: Results of 3SLS Regression Analysis (1994-2003) Variables

Standard of Living

Education

Health

β β β Telecoms HI:0.137*** HI:6.710*** HI:1.011* MI:0.094*** MI:6.139*** MI:-0.147 LI:0.2007*** LI:8.503*** LI:0.385* HI:0.000 HI:0.000 Hardware HI:0.0001* MI:0.000 MI:0.004*** MI:-0.002 LI:-0.000 LI:.-0.005 LI:-0.003 HI:-0.000 HI:-0.000 HI:0.000 Software MI:0.000 MI:-0.004 MI:0.000 LI:0.0003** LI:0.0165** LI:0.004** HI:0.000 HI:-0.000 HI:-0.000 Internal Spending MI:0.000 MI:-0.000 MI:0.000 LI:-0.000 LI:0.000 LI:0.000 HI:-1.306 Telecoms x Internal HI:0.036** HI:0.728** MI:0.016 MI:0.016 MI:-1.447 Spending LI:-0.062 LI:0.440 LI:-6.238 HI:-0.000 HI:-0.056 Software х Internal HI:5.79*** MI:0.041 Spending MI:9.116*** MI:-1.447 LI:-8.312 LI:0.051 LI:-1.648 HI:-0.327 Software х Hardware HI:0.042*** HI:0.728** MI:-0.80 MI:0.041 MI:2.17*** LI:-0.062 LI:4.495** LI:1.240** HI:-0.085 HI:-4.167 HI:-1.622 Hardware х Internal MI:-0.117 MI:-8.424 MI:-0.571 Spending LI:--0.338 LI:0.073** LI:8.48*** Note: HI: High income, MI: Mid income, LI: Low income *p