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ICT and socio-economic development dynamics
CHAPTER FIVE
ICT and socio-economic development dynamics Contents 5.1 The context and data explanation 5.2 General picture 5.3 Economic development: Towards a structural shift? 5.3.1 The evidence 5.4 Social development patterns: Paving the road ahead 5.5 Gaps growing—Gaps narrowing? References Further reading
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5.1 The context and data explanation Since early 1980s all world regions have been rapidly and profoundly transforming due to an explosive growth of new information and communication technologies. Boosting demand for novel technological solutions offering cheap, fast, and unrestricted dissemination of knowledge and information has been gradually reshaping social and economic structures, thereby enforcing the emergence of new networks and facilitating immediate communication regardless of the physical location of agents. Available time series on changing ICT penetration rates suggests that all the European economies have been included in this overwhelming process. A rapidly growing interest in the adoption and broader usage for new technologies offering ‘connection with outside world’, mainly due to low-cost, distributable, and easily adaptable wireless solutions, has disruptively reshaped the world landscape. A fast spread of ICT has opened new windows of opportunities for technological catching-up, leapfrogging other countries technologically, or simply escaping from permanent, often historically conditioned, technological marginalisation. Still, as raised in different discussions, the role of ICT in enhancing social and economic development is undeniable. Although the ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00005-1
© 2019 Elsevier Inc. All rights reserved.
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fact that the impact of new technologies on the socio-economic aspects of life may be either direct or indirect, either short term or rather unveiled in the long-run time horizon, there are quite many channels through which this impact may be demonstrated. Needless to say, the seizing benefits of the adoption of ICT remain one of the greatest challenges of both developed and developing countries. New technologies offer unbounded opportunities to develop, regardless of physical location, gender, or other prerequisites. ICT may have a substantial impact on the economic welfare of people, performance of companies and whole nations, as well as on different spheres of social life. Numerous studies seem to confirm this; see, for instance, Avgerou (2003), Sein and Harindranath (2004), Cortes and Navarro (2011), Cruz-Jesus, Oliveira, Bacao, and Irani (2017), Niebel (2018), Stanley, Doucouliagos, and Steel (2018), Bhandari (2019), or Haftu (2019). In what follows we intend to address, at least partially, the questions on the impacts of new information and communication technologies in the social and economic performance of the European countries. We try to capture these impacts by running the analysis from two different perspectives: economic and social. With these aims, we selected a bundle of 15 economic variables and 9 social variables. Regarding economica variables, we chose the following: • GDP per capita, PPP (GDP)b; • GDP per person employed (GDP_empl); • contribution to the national output from three main sectors: agricultural sector (Agr_VA), industrial sector (Ind_VA), and service sector (Serv_VA); • employment structure by three main sectors: agricultural sector (Agr_empl), industrial sector (Ind_empl), and service sector (Serv_empl); • labour force participation rate for ages 15–24 (LF_15_24); • High-technology exports as a share of total manufactured exports (HT_exp); • ICT goods exports as a share of total goods exports (ICT_good_exp); and ICT goods imports as a share of total goods imports (ICT_good_imp); • ICT service exports as a share of total service exports (ICT_serv_exp); • communications, computer, etc., as a share of total service exports (Comp_serv_exp) and as a share of total service imports (Comp_serv_imp). a
For definitions of variables, see Appendix F. For abbreviations, see also Appendix H.
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As for the socialc variables, we decided to concentrate on the following: • school enrolment, tertiary as a share of gross (School); • female labour force as a share of total labour force (LE_female); • female labour force participation rate for ages 15–24 as a share of total female labour force at this age (LF_female_15_24); • contributing family workers as a share of total employment (Family_tot); • female contributing family workers as a share of total female employment (Family_female); • vulnerable employment as a share of total employment (Vulner_tot); • female vulnerable employment as a share of total female employment (Vulner_female); • waged and salaried workers as a share of total employment (Vulner_tot); • female waged and salaried workers as a share of total female employment (Vulner_female); • waged and salaried workers as a share of total employment (Wage_tot); • female waged and salaried workers as a share of total female employment (Wage_female). By convention, our empirical sample consists of 32 European countries, and the period of analysis covers the years between 1990 and 2017. All economic data used in this research are exclusively extracted from the World Development Indicators 2018 database. In what follows we aim to, first, examine in-time changes in each of the variables listed above and, second, to determine their statistical association with two core ICT indicators: mobile cellular subscriptions (MCS) and Internet user penetration rates (IU). By this we aim to show how the European countries were changing their economic and social performance between the years 1990 and 2017, as these years mark the period of extraordinary rapid expansion of new ICTs. Drawing this general picture of socioeconomic shifts in Europe, hopefully, shall allow concluding whether and to what extent these changes are accompanied by fast deployment of ICT. In this case, we expect to show that ICT’s growing adoption gives a strong impulse for growth in economic and social welfare approximated by a shift in, inter alia, national output per employee, the share of high-tech exports in total exported goods, or female engagement in labour market activities. On the other hand, we expect that positive changes in ICT’s increasing usage may be demonstrated indirectly through, for instance, drops in female labour force participation rate for ages 15–24, or female family workers as a share of c
For definitions of variables, see Appendix G.
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total female employment. In the final part of this chapter, we are examining the process of economic and social convergence that we expect to take place across the European countries. We challenge the process of convergence unconditionally, and our intention is to unveil if the process of rapid adoption of ICT is accompanied by gradually diminishing cross-country disparities with respect to both economic and social performance. In this case, we expect to uncover that the process of ICT diffusion enhances dropping economic and social inequalities among the European economies. Obviously, this astonishing rapid growth in ICT deployment observed over the last decades makes us think of the pervasive impact of ICT on the economic growth et alia, despite the fact that social performance is not merely detectable, but obvious. The absence of clear and easily identifiable impacts of new technologies on the economic and social performance has already been claimed in various researches and is widely recognised as the ‘productivity paradox’ (Brynjolfsson, 1993; David, 1989; Willcocks & Lester, 1999). Notably, even if technologies grow exponentially, the latter is not immediately or directly ‘converted’ into economic growth. Probably this is also the case with new information and communication technologies. We are fully aware that this picture is only partial, and concluding on the existence of casual relationships between the examined variables would be an overestimation in many cases. Obviously, detected correlations can be spurious, and the results of the research may be ambiguous.
5.2 General picture As shown in Chapter 4, the rate of technological progress that we observed across the European economies, in terms of increasing adoption of ICT, has been impressive over the last three decades. Facing these phenomenally explosive shifts in the adoption and usage of ICT can effectively stimulate the identification of its macroeconomic and social consequences, and potentially trace the extent to which the adoption of ICT affects, for instance, economic growth. Here, we briefly discuss the changes in economic and social performance, observed between 1990 and 2017, across the 32 European countries that come within the scope of this research. Figs. 5.1–5.3 show changes in the average values of each economic and social variable selected for the study. Our first general observation is that, during the period 1990–2017, there were significant fluctuations in the great majority of indicators. Fig. 5.1 reveals the main shifts in macroeconomic aggregate indicators, such as gross domestic output per head
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Fig. 5.1 Economic variables—in-time changes. Average values. Period 1990–2017. Note: On y-axis—raw values; GDPpc and GDP_empl— expressed in USD; other variables expressed as a percentage of total.
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Fig. 5.3 Social variables. Average values. Period 1990–2017. Note: On y-axis—raw values; all values expressed as a percentage of total.
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(GDPpc) and gross domestic output per person employed (GDP_empl), which shows a shift in the overall economic welfare of the European countries. Visibly, the growth of GDP per capita grew dynamically, especially since the mid-1990s, dropped slightly after the 2008 economic crisis, and has grown again since 2010. Surely a rapid growth of per capita income in the early 1990s has been associated with the collapse of the ‘Berlin Wall’, which began a new economic era for Central-Eastern European countries. Between 1990 and 2017, the GDP per capita increased by more than 10,000 USD. Similarly, strong improvements are observed when considering the national output per person employed. Basic numbers suggest that this grew by more than 50%, between 1990 and 2017, which supports the hypothesis of strong productivity shifts during this period. Enormous improvements in the general material well-being of the European countries were accompanied by massive and visible changes in the structure of national economies. That is to say that, between 1990 and 2017, we observe dynamic changes in the input from three main economic sectors—agriculture, industry, and services—to the national economies. On average, since 1990 onward in Europe, we observe continuous drops in the share of national output from agricultural and industrial sectors, whereas the total of value added services gains in importance. Analogous trends are demonstrated when visualising changes in employment by sectors. Again, there are radical drops in agriculture (from 14% to 7%) and industries (from 33% to 23%), accompanied by a growth in employment in the service sector of 15% during the period examined. Apparently, such radical changes in the structure of national economies, both in terms of valued added and employment, can be an effect of a rapid technological progress that the European countries underwent between 1990 and 2017. Growing importance in creating the national output of service sector can, unquestionably, be closely related and determined by the increasing deployment and usage of new ICTs. ICT has enabled the emergence of new types of products and services and further and more intensive mechanisation of production and, over the long term, drives structural changes in the national economies and domestic labour markets. As ICT is broadly claimed to be one of the ‘prime movers’ of access to education, this positive effect of the indirect impact of ICT on the labour market can be demonstrated through the decreasing share of the population aged between 15 and 24 who are actively engaged in the labour market. As displayed in Fig. 5.1, the participation of people aged 15–24 in the labour force (LF_15_24) dropped from 53% in 1990 to 42% in 2017. Such a change suggests that, on average, pupils leave the educational system late to enter the
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labour market. This view is supported by elementary data on the gross school tertiary-level enrolment rates; see Fig. 5.3. Next, Fig. 5.2 briefly explains trade-related macroeconomic indicators, which are potentially affected by technological progress. More specifically, we refer to high-technology exports expressed as a share of total exported goods (HT_exp) and to five other variables demonstrating export and import intensity with regard to ICT goods and services. As for the hightechnology exports, it can be seen that the path is highly unstable, marked by multiple ups and downs between 1990 and 2017. In the mid-1990s, after an abrupt shift in HT_exp, the general trend seems to be rather downward. Available time series for ICT goods’ exports and imports extend back to 2000. Between 2000 and 2017, massive drops in the values of these two indicators are revealed, which might suggest a diminishing role of ICT goods’ exports and imports in the total of ICT goods traded internationally. However, this surprising change is one of the effects of the globally recognised process of moving ICT production to Asian countries. The emergence of the so-called ‘Factory Asia’ (Ito & Vezina, 2016; Kam, 2017) at the beginning of 20th century is, inter alia, demonstrated through a falling share of ICT exports and imports with regard to high-income economies. When looking at the average changes in the exports of ICT services (explained as a share of total service exports), there has been an upward trend since the 1990s. Similar observations are valid for variables explaining communication and telecommunication services, both exported and imported (as a share of total service exports/imports). In the case of Comp_serv_imp, the growing patterns are relatively stable, and between 1990 and 2017 we observed a growth of total service imports, going from 30% to 45%. As for Comp_serv_exp, the path is less stable, with a dramatic drop in the late 1990s; however, since 1997, when average Comp_serv_exp was just 16% of total service exports, its share grew to 43% in 2017. Finally, let us move briefly to shifts observed on the social ground—see graphs in Fig. 5.3. In this case, we selected nine indicators that demonstrate the rather indirect impact of technological change on the social spheres of life, associated here mostly with female engagement in economic activities. As raised in many studies, a higher participation in the labour force is very likely to be the first and most important step in exploiting the full potential of ICTs. Its deployment permits a timely access to information, helping to overcome one of the fundamental barriers to the effective functioning of the market, namely, information asymmetries. Combined appropriately, these two elements—shifting labour force participation and removing
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constraints on information access—lead to increases in the number of transactions, enable participation in global markets, reduce transaction costs, and ensure worldwide visibility, all of which, in the longer run, offer better prospects for economic growth and development. One of the most serious problems for economically backward countries is low female participation in the formal market economy, i.e. in the job market and in entrepreneurial activities. Women’s relatively low rate of economic activity may be a direct effect of poor education, poor skills, and illiteracy. Women in less developed and tradition-oriented regions are often deprived of access to the financial system; they have no permanent income from contracted work. In traditional societies, a whole series of social or religious norms and attitudes often consign the female population to the status of ‘hidden and usually unpaid’ labour. Women are exposed to poverty more often than are men, and they are often denied basic rights. In fact, a significant share of women still constitute an unused labour force, which may significantly impede national growth and development. Additionally, women who want to engage in economic activity face various gender-specific constraints, such as barriers to education and lack of a combination of relevant education, professional skills, and work experience, which may be a severe handicap not only in seeking decently paid jobs but also in forming businesses of their own. Such claims may be traced in Lindio-McGovern and Wallimann (2016) and Sachs (2018), where the authors argue that the problem of marginal engagement of women in formal economic activities is especially severe in the rural areas. In Klasen, Lechtenfeld, and Povel (2015) and Klasen (2018), we find more arguments and explanations of female exclusion in the labour market. He shows that social values and norms, the structure of national economies, and also state political regimes in the developing countries can effectively hinder women’s participation in value creation. Ortiz Rodrı´guez and Pillai (2019) present evidence in the same vein. Fig. 5.3 summarises the general average tendencies in school enrolment in the tertiary level and female engagement in formal labour market activities. First, we observe an impressive shift in gross school enrolment at the tertiary level. Between 1990 and 2017 this grew by 40%—from 29% to 69%. This radical change in access to education, both for men and women, observed across Europe during the last three decades has created a structural shift in, inter alia, the labour market. These shifts are, probably, driven partially by a greater accessibility to the education system and, to some extent, by technological progress, which enforces multiple changes across the whole economic and social systems. Throughout this research, we claim that the impact of ICT on the social spheres of life associated with economic activities may, in the first place,
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be demonstrated through women’s participation in the labour market. To stay in line with the latter, along with increasing school enrolment, we examine shifts in female participation in the labour force (as a share of total; LF_female), participation in the labour force of females aged 15–24 (as a share of total; LF_female_15_24), and three indicators indicating the share of women falling into the categories of ‘contributing family workers’ and ‘vulnerable employment’, on the one hand, and ‘wage workers’, on the other. We argue that improving the overall economic performance along with the implementation of technological progress in economic and social systems shall have positive effects in terms of reducing female (as well as total) vulnerable employment and reducing the share of women working as contributing, usually unpaid, family workers; henceforth, the share of women being employed as waged and salaried workers shall increase. Such structural changes in labour markets would, evidently, be one of the most important manifestations of the positive impact of ICT on the overall socio-economic development, even though this impact may be hard to capture in numbers. Our preliminary evidence, summarised in Fig. 5.3, demonstrates clearly a strong drop in female labour participation (ages 15–24), from 49% to 38%, between 1990 and 2017. Moreover, we observe relatively substantial decreases both in the share of women working as contributing family workers and in the share of women who fall into vulnerable employment. These positive shifts are accompanied by a growth in the share of women being employed as waged and salaried workers. In what follows, we present and discuss the results of a more detailed analysis intended to uncover associations between growing adoption and usage of ICT and socio-economic development in the 32 European countries between 1990 and 2017.
5.3 Economic development: Towards a structural shift? As described briefly in Section 5.2, across the European countries, during 1990–2017, there have been massive changes in the levels of social and economic development. The general picture we described suggests that all the 32 economies examined between these years have experienced a rapid economic growth, which is demonstrated clearly through shifts in gross per capita income and final production value per person employed, to cite just two examples. Notably, based on the average values of consecutive variables, we also observed radical structural changes in the national output; the gradually declining share of value added in the agricultural and industrial
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sectors was compensated by a growth in the share of value created in services. Analogously, general tendencies were observed in employment in the three main sectors of national economies: significant drops in employment in the agricultural and industrial sectors but fast increases of employment in the services sector. Moreover, our general conclusion, based on a brief analysis of trade-related indicators, demonstrates important changes in this respect. International trade patterns showing ICT goods and services exports and imports are unstable but, at a time, they show dynamically changing situations with this respect. This picture of fast-changing structures of economies coincides with the debate on the potential benefits of new ICTs and the changes that it can bring to the societies and economies. This debate concentrates mainly on the productivity, economic growth, and overall welfare gains that the newly emerging digital economy may offer. Moreover, something that is relatively less intensively discussed, the broader adoption and usage of ICT may potentially create several structural shifts in the national economies. Obviously, these shifts are long-term processes that are dependent on various factors, and our supposition that some of them are generated by technological progress might be spurious. Still, there is not wide consensus over whether standard quantitative measures can fully demonstrate the impact of ICT on the economic performance of countries. Examining the relationships between the process of ICT diffusion and economic development is a challenging task. This is not only because countries included in empirical samples are relatively heterogeneous but also, and mostly, because these relationships are complex in their nature and are influenced by multiple external factors that may be even hard to identify. Statistical analysis and econometric modelling techniques are used conventionally to trace the relationships between variables. Still, some unique features of countries are often hard to capture and include in econometric models. Some elements and determinants that shape the process of economic development and either foster or hinder the impact of ICT on economic processes may be empirically intractable, but their influence is still massive. In what follows, we develop a quite intuitive framework analysing major economic areas that, potentially, may be affected by a rapid ICT expansion over the societies and economies that all the European countries have experienced during the last three decades. Our intention is, at least partially, to broaden our knowledge, draw a general picture of how the contours of the European economies were changing between 1990 and 2017, and associate those changes and structural shifts with the process of diffusion of new ICTs.
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5.3.1 The evidence The following empirical evidence confronts the process of ICT diffusion vs economic development across the 32 European economies, between 1990 and 2017. Put differently, we trace the statistical relationships between ICT deployment and the process of economic development and investigate whether the impact of ICT has been either positive and strong, or, conversely, negligible. To this end, we use two core ICT indicators: mobilecellular telephony subscription rates (MCS) and Internet users penetration rates (IU), which we claim are good proxies of access to and use of ICT.d Economic development is approximated by a bundle of arbitrary variables (see Section 5.2), which, we believe, allow us to demonstrate the types of changes and structural shifts that have been observed during the last three decades in countries that are within the scope of this research. The results of our analysis are demonstrated in Figs. 5.4–5.7, which display the statistical associations between mobile-cellular telephony and Internet users penetration rates and selected economic variables. Intentionally, we develop separate graphs for each pair of variables, which allows for a detailed analysis of the issue. Our graphical evidence is then enriched by panel regression estimates; the results of these analyses are summarised in Tables 5.1 and 5.2. Inspecting the empirical findings reveals that certain regularities can be identified with respect to the relationships examined. As presented briefly in Section 5.2, many European countries experienced a rapid economic development, on the one hand, and significant structural shifts regarding, for instance, value creation and/or employment structure of the main economic sectors, agricultural, industrial, and services, on the other. Obviously, the average annual speed of these changes differs across the countries. Central-Eastern European countries, the economies of which were boosted in 1990, experienced much faster growth than did wellestablished Western European countries. For instance, in Albania, Latvia, Lithuania, and Poland, the average annual GDP per capita growth ratese between 1990 and 2017 were 3.4%, 3.9%, 4.1%, and 3.5%, respectively. During an analogous period, countries such as France and the United Kingdom grew at a slower rate, hardly reaching 1% per annum. Similarly, dynamic changes were observed with respect to the gross national output per person employed; the calculated correlation between GDPpc and GDP_empl is almost 0.95. Rapid economic advances, measured in gross d
In Chapter 4, we described extensively the process of ICT diffusion, providing country-wise evidence. Authors’ calculations based on data derived from the World Development Indicators 2018.
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Fig. 5.5 Mobile cellular subscription vs trade-related variables. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; on xaxis—MCS; all values expressed as a percentage of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov.
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Fig. 5.6 Internet user penetration rate vs economic variables. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; on xaxis—IU, GDPpc, and GDP_empl—expressed in USD; other variables expressed as a percentage of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov.
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Fig. 5.7 Internet user penetration rate vs trade-related variables. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; on x-axis—IU; all values expressed as a percentage of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov.
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Table 5.1 Mobile cellular subscriptions vs economic and trade-related variables. Panel regression estimates. Period 1990–2017 GDP GDP_empl Agric_VA Ind_VA Serv_VA Agric_empl Ind_empl Serv_empl
MCS Rho R2 (within) F (prob > F) No. of obs.
0.07 [0.00] 0.96 0.69 1772 [0.00] 833
20.15 [0.00] 0.93 0.63 1332 [0.00] 799
20.03 [0.00] 0.72 0.21 202 [0.00] 799
0.02 [0.00] 0.82 0.45 620 [0.00] 794
20.11 [0.00] 0.94 0.57 1108 [0.00] 846
20.02 [0.00] 0.81 0.26 284 [0.00] 846
0.03 [0.00] 0.92 0.64 1436 [0.00] 845
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20.03 [0.00] 0.89 0.41 586 [0.00] 865
0.09 [0.00] 0.78 0.17 165 [0.00] 830
0.02 [0.04] 0.86 0.00 0.42 [0.51] 544
20.16 [0.02] 0.77 0.09 52.1 [0.00] 544
0.24 [0.01] 0.55 0.41 477.6 [0.00] 721
0.04 [0.00] 0.55 0.07 52 [0.00] 779
0.04 [0.00] 0.64 0.09 78 [0.00] 766
Note: All values are logged; fixed-effects panel regression applied; constant—not reported; SE below coefficients; in bold—results statistically significant at 5% level of significance; panel—strongly balanced.
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MCS Rho R2 (within) F (prob > F) No. of obs.
0.08 [0.00] 0.95 0.65 1516 [0.00] 838
IU Rho R2 (within) F (prob > F) No. of obs.
IU Rho R2 (within) F (prob > F) No. of obs.
0.07 [0.00] 0.95 0.63 1330 [0.00] 824
0.06 [0.00] 0.95 0.62 1311 [0.00] 809
20.14 [0.00] 0.93 0.65 1450 [0.00] 789
20.02 [0.00] 0.78 0.23 226 [0.00] 789
0.02 [0.00] 0.84 0.41 544 [0.00] 786
20.11 [0.00] 0.95 0.62 1297 [0.00] 824
20.03 [0.00] 0.82 0.30 344 [0.00] 824
0.03 [0.00] 0.92 0.64 1415 [0.00] 823
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20.03 [0.00] 0.91 0.36 454 [0.00] 837
0.08 [0.00] 0.78 0.14 129 [0.00] 812
20.08 [0.03] 0.87 0.01 7.5 [0.00] 544
20.15 [0.01] 0.79 0.14 84 [0.00] 544
0.23 [0.00] 0.61 0.46 575 [0.00] 703
0.03 [0.00] 0.55 0.03 26.9 [0.00] 762
0.03 [0.00] 0.63 0.10 79.8 [0.00] 749
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Table 5.2 Internet user penetration rates vs economic and trade-related variables. Panel regression estimates. Period 1990–2017 GDP GDP_empl Agric_VA Ind_VA Serv_VA Agric_empl Ind_empl Serv_empl
Note: All values are logged; fixed-effects panel regression applied; constant—not reported; SE below coefficients; in bold—results statistically significant at 5% level of significance; panel—strongly balanced.
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per capita income, were accompanied by structural shifts in terms of the contribution of agriculture, industry, and service sectors to the GDP. Massive increases in GDP created in the service sector were observed, for instance, in Albania (from 16% to 47% of gross value added), Malta (from 35% to 75% of gross value added), and Moldova (from 33% to 56% of gross value added). In other countries, these changes were not that radical, but the trend was positive in each country examined. On the other hand, a rapidly falling role of the agricultural sector was traced in each country. Again, analogous structural shifts were observed with respect to employment in consecutive economic sectors—a rapidly dropping share of people being employed in agricultural and industrial activities, and an increasing share of employees in the services sector. Fig. 5.8 represents this type of structural changes in terms of employment across sectors. Correlation coefficients for Agr_empl vs Serv_empl and Agr_empl vs Ind_empl are (0.83) and (0.56f), which directs our attention towards the fact that radical flows of the labour force are observed across the sectors. Obviously, this general view does not necessarily show that the labour force previously employed in the agricultural sector moves directly to the service sector, effectively ‘jumping over’ industry. Labour force leaving the agricultural sector moves towards both industry and services, but, unquestionably, a broader deployment of new ICT opens multiple opportunities to operate in the service sector. These opportunities are opened not only in the ICT service sector itself, despite the fact that the ICT service sector is recognised globally as a key driving sector of economies, but also in other types of services that need ICT to work effectively. It is needless to say that ICT positively impacts firm performance, and this impact is even stronger when accompanied either by other types of investments or by organisational changes in companies. These investments and other expenditures on skills improvements or, inter alia, firm market reorientation and external market expansion, together with ICT deployment, may lead to a kind of synergism that boosts economic performance. Those synergies will lead inevitably to the development of new products and services and their introduction to the market. Moreover, changes in production and the service sector, from the long-run perspective, will generate profound changes in the labour market, and, above all, in the structure of labour demand and supply. Those effects are, to some extent, also traceable at the macro level.
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Fig. 5.8 Employment in agricultural, industrial, and service sectors. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; all values expressed as a percentage of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov. 163
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Graphical evidence summarised in Figs. 5.4–5.7 demonstrates the statistical relationships between the two core ICT indicators and select macroeconomic variables that approximate the economic performance of countries. Not surprisingly, mobile-cellular telephony penetration rates plotted vs GDPpc, GDP_empl, Serv_VA, and Serv_empl show a positive statistical relationship. Statistically, the strongest association is demonstrated in the case of changes in the share of labour force employed in the service sector (correlation coefficient is 0.41). This result confirms our general intuition that a rapid expansion of ICT and a broader adoption and extensive deployment of new technologies across both social and business activities constitutes a strong stimulus for moving from an agriculture- and industry-oriented economy towards a service-based economy. Respective graph in Fig. 5.6 displaying analogous evidence, confronting Internet users vs economic variables as in Fig. 5.4, supports this supposition. The shift in the share of labour force being employed in the service sector is strongly and positively correlated with growing IU. In this case, the correlation coefficient is even higher, at 0.57, and it represents the strongest positive statistical relationship among the others examined and presented in Figs. 5.4–5.7. The growing role of ICT in enhancing structural changes in the national and global economies is demonstrated effectively not only through changes in the structure of production and employment across the main economic sectors but also through changes in the composition of the labour force. As economies and societies head towards full saturation with new ICT, it becomes quite natural that ICTs proliferate not only social life, but, above all, in the way of running business, structure of consumption patterns and habits, and thus structure of production, just to cite a few examples. Obviously, a broader deployment of ICT offers new opportunities to intensify business activities, internationalise companies, and enhance the penetration of new markets through e-platforms, et alia; and as raised by some scholars, ICT usage increases storability and tradability (e.g. Boden & Miles, 2000), thereby positively affecting the emergence of a service-based economy. It is needless to emphasise that the expansion of ICT generates changes in demand for different skill levels. As raised by many, technological progress in the field of ICT drives massive robotisation and automatisation of production processes. This is then reflected in growing job polarisation and a divide between the low-skilled and the high-skilled labour force. Low-skilled and non-routine manual jobs decline, but the demand for high-skilled professionals who can do non-routine cognitive jobs grows. Technological progress drives the demand for new types of jobs and skills, which allows the
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potential of ICT to be exploited fully. In that context, reorientation towards a service-based economy seems to be one of the manifestations of the growing importance of technologies offered by the Digital Revolution. Referring back to the evidence in Fig. 5.4, conversely to what was observed with respect to the service sector’s contribution to gross value addition and the share of labour force working in the service sector, the statistical relationships revealed between mobile-cellular telephony vs employment in the agricultural and industrial sectors are negative. Analogous observations are reported when confronting MCS vs Ind_VA and Agr_VA. In each of the countries examined, except Albania, between 1990 and 2017, the share of employment in the industrial sector showed negative average annual growth rates. The highest average annual growth rates were observed in Cyprus (2.0% per annum), Malta (2.7% per annum), Spain, and the United Kingdom (1.9% per annum for both). Average annual drops in the share of labour force working in the agricultural sector were even more radical in Germany (4.3% per annum), Estonia (5.9% per annum), the Slovak Republic (4.4% per annum), and also in many other countries (approximately 3.5% per annum). Fig. 5.6, which offers a graphical explanation of the relationships between IU and economic variables, supports our previous findings. In this case, the statistical associations between IU vs Serv_VA and IU vs Serv_empl are also positive; the correlation coefficients are 0.33 and 0.57, respectively. When considering IU vs GDP per capita and IU vs GDP per person employed, the picture that emerges shows clearly that there is a close statistical relationship between these variables. During 1990–2017, in the European countries, gross per capita input and gross input per person employed was accompanied by growing ICT usage. Obviously, economic growth was driven by many factors, but, as claimed in many studies, in that period, the growth of ICT investments, ICT per capita, and ICT-driven international trade, in addition to changing consumption patterns and consumer preferences, effectively enhanced—although not always directly—economic growth and productivity shifts (Latif et al., 2018; Niebel, 2018). Additionally, a broader deployment and usage of new technological solutions contributed to the emergence of knowledge-based and technology-based economic structures, which, by definition, are characterised by a relatively higher productivity and efficiency than are non-technology-intensive sectors (Singh, Dı´az Andrade, & Techatassanasoontorn, 2018). The positive role of ICT in boosting economic growth has been recognised not only the area of dynamically increasing investments, but also in growing role of ICT manufacturing sector that
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contributes to aggregate productivity shifts (Pradhan, Arvin, & Norman, 2015). A dynamic development and broader implementation of ICT in the various economic sectors has been linked partially to the liberalisation of national telecommunication markets, which resulted in rapidly growing competition and reduction in the prices of access to and use of ICT tools and services. On the other hand, due to the unlimited opportunities and benefits offered by ICT, economic players began to adapt new technologies rapidly into daily usage (Luo & Bu, 2016). That greater use of ICT contributed to the emergence of network effects and lowered transaction costs, which improved the overall efficiency of the economy (Arthur, 2018; Edquist, Goodridge, Haskel, Li, & Lindquist, 2018). Today, many economic organisations use ICT not only in the production process but also in the services sector, such as, inter alia, finance services, retail trade, and insurance. The overall use of capital and labour has been much improved due to ICT usage, and growths of national per capita output are one of its major manifestations. Figs. 5.5 and 5.7 provide additional evidence of the statistical relationships between MCS/IU and international trade-related variables across the European economies. In this case, the period of analysis covers our standard period between 1990 and 2017 for high technology exports (HT_exp), ICT service exports (ICT_serv_exp), communications, and computer services exports and imports (Comp_serv_exp/Comp_serv_imp), although there are significant breaks in the time series. As the available time series for ICT goods exports and imports (ICT_good_exp/ICT_good_imp) go back only to 2000, the period of analysis is significantly shorter in this case. In Section 5.2, Fig. 5.2 demonstrates changes in the average values of international trade-related variables and allows conclusions to be drawn on relatively unstable development patterns marked by multiple ups and downs in short time intervals and, above all, on significant drops in ICT goods exports and importsg observed since 2000. At first glance, the evidence summarised in Fig. 5.5 does not suggest the existence of strong and positive relationships between mobile telephony accessibility and consecutive international traderelated variables, as might be expected. The statistical relationship between MCS vs HT_exp, ICT_good_exp, and ICT_good_imp seems to be negligible, and correlation coefficients are 0.06, (0.05), and (0.11), respectively. However, when we view these results in light of the general tendencies observed across the European countries in regard to the radically falling role of ICT goods exports in total goods exports and ICT goods g
Calculated as a share of total value of services export, BoP.
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imports in total goods imports, with the fast-growing deployment of ICT, this zero relationship becomes obvious. Note that, between 2000 and 2017, the share of ICT goods exports in the total value of exports of goods fell from 10% to 5%, whereas the share of ICT goods imports in the total value of imports of goods fell from barely 12% to below 7%. Next, when we look at the high-tech goods export pattern (see Fig. 5.2), we see that, on average, this value is stable over time, despite the HT_exp pattern showing in-time instability. However, for Internet users, analogous relationships are similarly weak (see Fig. 5.7). As mentioned in Section 5.2, the falling ‘contribution’ of ICT goods exports and imports to the total value of exported and imported goods is the ostensible signal of the dynamic ‘moving to Asia’ of ICT goods production. A radically different situation is reported when considering ICT variables vs ICT_serv_exp, Comp_serv_exp, and Comp_serv_imp. In these cases, the relationship is positive; the strongest relationship is revealed for MCS/IU vs ICT_serv_exp, where the correlation coefficients are 0.37 and 0.41, respectively. This boosting of ICT services sector and its role in the international trade is driven mostly by the profound impact of the Digital Transformation of various spheres of life. The use of IT platforms and cloud solutions is growing, and, according to various sources, the demand for IoT platforms is characterised by the most dynamic shifts. Moreover, Web solutions for managing organisations, e-commerce, et alia, and platforms to manage relationships with consumers, social platforms, Data Centre services, and e-governance solutions, and many other ICT implementations effectively drive the demand for ICT services. Finally, our graphical evidence is enriched by panel regression estimates summarised in Tables 5.1 and 5.2. We define two separate panels. In the first panel (estimates are presented in Table 5.1), MCS is the treated regressor, and all the economic variables examined above are considered as regressands. In the second panel (estimates are presented in Table 5.2), IU is defined as a regressor, and all the economic variables examined above are dependent variables. Both panels are balanced strongly. By convention, the empirical sample covers 32 European countries, and the time span of analysis is set as 1990–2017. Bearing in mind the results presented graphically, uncovering the statistical association between the two core ICT variables and economic variables, we expect qualitatively analogous outcomes from the estimated panels. Panel analysis results summarised in Table 5.1 demonstrate how strongly the growing deployment of mobile-cellular telephony impacted the macroeconomic performance of the European countries. However, Table 5.2 shows analogous results in examining the impact
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of increasing usage of the Internet by the members of the society. In general, all estimates confirm the type of ‘linkages’ between variables previously reported in Figs. 5.4–5.7. Even more interesting is the observation that, as reported in Table 5.1 and 5.2, the estimated coefficients that MCS and IU variables hold are barely the same for the respective panel modelsh; for instance, when estimating the impact of ICT on GDP per capita, MCS holds the coefficient 0.08, whereas IU holds the coefficient (0.07). When looking at the results of consecutive estimated coefficients standing by MCS and IU, we find similar analogies. The latter suggest that the potential impact of both MCS and IU on the examined economic variables is equally strong (or weak). When concentrating exclusively on the impact of Internet penetration rates on the economic performance, we see that growing Internet usage demonstrates, relatively, the strongest positive associations with ICT_serv_exp; in this case, the estimated coefficient is 0.23. Hence, the growth of IU at 1% may potentially enhance the growth of ICT_serv_exp at 0.23%. Tracing the potential impact of IU on remaining trade-related variables, we observe that, in the case of high-technology manufacturing exports, ICT goods exports, and computers services exports and imports, the estimated coefficients are close to zero, although still statistically significant. That might suggest that no clearly traceable and direct relationships between these variables may be identified. The coefficients that IU holds when estimating its impact on ICT goods import are statistically significant and negative. Its estimated value would suggest that a 1% IU increase generates a drop of 0.15% in ICT_goods_imp. Clearly, this result should not be interpreted in a straightforward way. In fact, drawing the conclusion that a shift in Internet usage in a country determines falling imports of ICT goods is at odds with general logic. Observed across the European economies, a drop in ICT_goods_imp expressed as a share of the total value of imported goods does not show a diminishing role of international trade in ICT goods. This ‘strange’ effect is rather associated with the changing structure of goods imported into the European countries. According to UNCTAD (2017) data, during the last three decades, international trade flows in CT goods have grown dramatically, driven by multiple factors, the most important of which seem to be the WTO Information Technology Agreement, boosting bilateral trade arrangements, dynamic technological changes, consumer demand for technological novelties (both tools and services), liberalisation of national telecommunication h
Note that MCS and IU are highly correlated. The calculated correlation coefficient is 0.92.
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markets, and the emergence of new technology-led business models. However, as noted in UNCTAD (2017) ‘for the first time since 2009, global imports of ICT goods declined in 2015 by 3.6 per cent in current prices, to just over $2 trillion. Most of this decline was due to lower imports from developed economies in Asia and Europe, which fell by 11 per cent and 7 per cent, respectively, and also to the decline in imports of computers and peripherals as well as consumer electronic equipment’ (p. 49). Despite the fact that a huge share of the trade in ICT goods, including finished and intermediate goods, is hosted between Europe, Asia, and the United States, the UNCTAD (2017) data reveal that, in 2015, developing Asian countries in which huge manufacturing facilities are located account for nearly 50% of the global ICT goods imports, and China alone accounts for 20% of the latter. Our estimates also show significant and relatively strong negative associations for IU vs valued addition created in the agricultural sector (Agric_VA)—the estimated coefficient is (0.14)—and for the share of the labour force employed in agricultural activities (Agric_empl)—estimated coefficient is (0.11). These results support the evidence revealed previously in this section. A direct interpretation of the estimated coefficients would show that, for instance, a 1% growth of IU induces a drop of 0.14% in valued addition created in the agricultural sector. However, it is rather obvious that ICT itself does not drive a decrease in valued addition in the agricultural sector as such, but enhances economic activities in other sectors. Henceforth, this impact should be treated as indirect. Technological advances that allow for the emergence of new types of products and services, professional skill development, firm reorientations, and many other things effectively generate structural shifts that are seen throughout the economy. Labour force and financial capital move to dynamically developing sectors, offering new opportunities and bringing benefits and profits. In that sense, technological progress, and, here, ICT-driven changes in particular, have far-reaching implications, for, inter alia, computerisation of jobs, automatisation of production, and the emergence of new types of plants where all the work is done by robots. From the longer-term perspective, these changes are transmitted into structural shifts; the labour force moves from low-technology to technology-intensive sectors, and manufacturing and services in which production is strictly ICT-dependent play a growing and pivotal role in the economic systems. In the long term, these shifts are converted into economic welfare.
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5.4 Social development patterns: Paving the road ahead Many countries face the problem of relatively poor participation of women in the formal market economy, i.e. in the job market and in entrepreneurial activities. The low rate of female economic activity may be identified as one of the negative effects of difficult access to the education system, poorly developed professional skills, and high illiteracy. It happens that women are massively deprived of access to financial systems, and they have no permanent income from contracted work. In rural areas and/or less developed European economies, inhabited by more traditional societies, the existing social, religious norms, and attitudes often consign the female population to the status of ‘hidden and usually unpaid’ labour. Even in high-income and well-developed countries, where female population seems to be treated ‘equally’ to men in terms of free access to labour market, the basic national statistics speak in support of the supposition on the existence of a relatively huge gender wage gap. Undeniably, women belonging to traditional societies face deprivation from unrestricted access to the labour market and, thus, constitute an unused labour force, which impedes economic growth and development. On the other hand, female population is often engaged in informal home-based businesses, occupying traditional activities. These, however, require less seed capital and professional experience, yielding lower returns and benefits. Women without access to the formal labour market tend to run home-based businesses in traditional and sometimes in informal sectors, characterised by low effective demand, low profits, and high exposure to risks and external shocks. They are highly vulnerable inside workers, suffering from permanent material and institutional exclusion (Klasen et al., 2015). Across the countries, we observe a gradually increasing number of women running their own businesses; however, it should be noted that in many economies the main enhancement for women to set up a new business is necessity-, and not opportunity, driven. Facing the absence of alternative ways of supplementing household income, entrepreneurship or self-employment is the only viable option. Across less advanced regions, restricted access to the technology and difficulty in financial market participation (financial exclusion) are large barriers for women seeking to escape vulnerable, low-paid, and indecent employment (Benerı´a, Berik, & Floro, 2015). Still, pretty often, women’s labour and entrepreneurial activities are the ‘untapped resource’, and ICTs, if used properly, can unlock the potential of female population, mainly by making it easier to overcome
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various gender-specific constraints on entrepreneurial behaviour. A broader deployment of ICT may have either direct or indirect effects on social development by mobilising resources and reinforcing market activities. ICT may foster the mobilisation of savings and offer opportunities to convert these savings into investment; ICT may facilitate a greater mobilisation of the labour force. All these have far-reaching consequences. Increasing active engagement in the formal labour markets constitutes a solid base for earning a regular income, which relieves people living in material deprivation out of the subsistence economy. Having a regular salaried work effectively reduces vulnerability to risks and external shocks, which bring a danger of falling into poverty. A greater engagement in labour markets, both through salaried employment and through small business start-ups, produces economic gains and wealth in the long-run perspective. A higher participation in the labour force is very likely to be the first, most important step for developing countries to exploit the full potential of ICT. Additionally, a broader usage of ICT allows timely access to various types of information, helping to combat one of the fundamental impediments to the effective functioning of the market, that is, information asymmetries. A growing labour force participation and the eradication of multiple barriers on information access shall drive an increase in the number of market transactions, boost the presence in global markets, and reduce transaction costs. Indirectly, unbounded access to the ICT drives socio-economic development through a better access to education and knowledge, more effective functioning of healthcare systems, and many other ways where new technologies may support the functioning of different organisations and mechanisms. That the positive effects of ICTs on, inter alia, education and healthcare systems are qualitative in nature will be unveiled in the long-time horizon, but surely the positive gains emerge progressively in the form of social and economic advance. Capturing the effects of the implementation of new ICTs on social development in numbers remains a very challenging task. This is not only because the availability of long and complete time series in this case is limited but also, and above all, because social development is an extremely complex process, preconditioned by multiple unquantifiable elements, such as culture, religion, social norms and attitudes, tradition, and history. The process of social development is characterised by long-time inertia. Changes are usually slow, and only detectable over the long term. In what follows, we present empirical evidence intended to reveal, at least partially, the relationships between growing ICT deployment and social development. By convention, we concentrate on the 32 European
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economies between 1990 and 2017. ICT development is represented by the two core indicators: mobile-cellular telephony and IU. As for the social development, we concentrate on select elements of socially related economic aspects, which we suppose may be, at least to some extent, affected by the Digital Revolution; we intended to use those indicators, by which changes potentially may be driven by technological progress. In particular, we concentrate on female-related indicators,i as these aspects are often raised in the context of the ‘Opportunity Windows’ that ICT may bring to the overall socio-economic development. Figs. 5.9 and 5.10 present graphical evidence of the statistical relationships of MCS vs social variables and IU vs social variables, respectively. Examining the relationships between consecutive pairs of variables suggests a relatively strong statistical association between ICT and school enrolment (tertiary, as a share of gross). Clearly, the growth of both MCS and IU is accompanied by a fast shift in school enrolments across the European economies. The basic statistics on tertiary school enrolment show rapid changes in this respect that are easily observable since 1990. In some of the countries analysed, the average annual growth rate was extraordinarily high; for instance, Albania (7.6% per annum), Cyprus (7.3% per annum), and Greece and Romania, with analogously high dynamics. The correlation coefficients for MCS vs School and IU vs School are 0.76 and 0.73, respectively. Of course, it is hard to agree that the impact of ICT diffusion on tertiary school enrolment is direct. It should be borne in mind that a high correlation in this respect may be spurious. Growing tertiary school enrolment is, above all, an effect of long-term state education policies. However, it is needless to explain that a positive impact of increasing tertiary school enrolment may, although again indirectly, positively influence female labour market participation. Next, the two analysed indicators are LF_female and LF_female_15_24. In-time dynamics of female labour force participation rates (a share of total labour force) are comparably slow. Significant inertia and time lags characterise the process of changing (herein, growing) women’s share in the total labour force. Across the countries examined, the highest average annual dynamics were observed in Malta (1.2% per annum; a change from 28% to 38%) and Spain (1.05% per annum; a change from 34% to 46%). In the remaining economies, these changes were significantly slower (e.g. Hungary and Slovenia—0.02% per annum), and, in some cases, even negative (Bulgaria, the Czech Republic, Moldova, Poland, Romania, the Slovak i
For details, see Section 5.2 and Appendix G for explanation.
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Republic, and Sweden). Still, as demonstrated in Figs. 5.9 and 5.10, statistical associations between MCS/IU and LF_female are relatively strong; the correlation coefficients are (0.26) and (0.33), respectively. The situation is different when the LF_female_15_24 indicator is considered; the correlation coefficients for MCS vs LF_female_15_24 and IU vs LF_female_15_24 are 0.11 and 0.13, respectively. Initially, we would expect that both growing ICT deployment and increasing scholarisation rates would contribute effectively to dynamically reduce the engagement of young women (between 15 and 24 years) in labour markets. However, when looking at preliminary descriptive evidence summarised in Section 5.2, the time trend (see Fig. 5.3) for averaged values shows a massive drop in LF_female_15_24 by more than 10%. This evidence suggests that, in the European countries, between 1990 and 2017, a significant share of women, instead of entering the labour market between the ages of 15 and 24, stayed in the education system, at university (tertiary) level. This structural break plays a crucial role in the society; well-educated people may foster innovation, which increases economic development and growth over the long term, contributing to the overall welfare of societies. The other side of the story is that, as predicted by many, in the forthcoming years, there will obviously be a significant growth in demand for highly skilled labour forces. Technological progress, especially a broader adoption and usage of digital technologies, generates changes in labour markets—jobs are increasingly flexible and complex. People working in knowledge- and technology-intensive sectors must be able to deal with complex and fast-changing information and provide technological solutions; they must become ‘autonomous and smart workers’. Evidently, technological progress effectively reshapes economies and labour markets, but it also drives the demand for skilled and specialised workers. All these elements, mentioned briefly above, induce people to continue in education instead of joining the labour market. That change is especially visible with respect to female engagement in labour market activities. The behaviour patterns of female labour markets have changed significantly with higher educational attainments and better access to the education system. Faced with a rapid ICT development, labour market pressure for highly skilled workers who can exploit the potential offered by new technologies, and social policies directed towards a higher educational attainment, there have been massive drops in the rates of participation by those aged 15–24 in the labour force. Next, we considered changes in contributing family workers (total/ female), vulnerable employment (total/female), and waged and salaried workers (total/female). We expected that, with increasing ICT deployment and usage, the first two variables might fall, whereas the share in total
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employment of waged and salaried workers might rise. Fig. 5.3 in Section 5.2 reveals a relatively significant drop in all the indicators cited above; however, it is important to emphasise that falls in Family_female and Vulner_female are more radical than are changes referred to the total population. Analogously, increases in upward trends in Wage_female are greater than those observed for Wage_tot. On inspecting the graphical evidence in Fig. 5.9, it may be noted that IU is negatively correlated both with contributing family workers (total/female) and with vulnerable employment (total/female). The correlation coefficients for consecutive pairs of variables are slightly higher for female-related variables. Apparently, there is one outlying country for which both contributing family workers and vulnerable employment shares in total are significantly higher; on the other hand, waged and salaried workers make up a very low share of total employment. This country is Albania, where, for instance, vulnerable female employment increased from 48% in 1990 to 57% in 2017, reaching a peak of 78% in 1999. In the remaining countries in the group, these variables were significantly lower during the whole period analysed. If we exclude Albania from the sample, the correlation coefficients for IU vs Family_female and IU vs Vulner_female are (0.42) and (0.41), respectively. Apparently, drops in contributing family workers and female vulnerable employment are a direct consequence of women moving towards waged and salaried work. These conclusions are supported by panel regression results summarised in Tables 5.3 and 5.4. Similar to the empirical evidence provided in Table 5.3 MCS vs social and quasi-social variables. Panel regression estimates. Period 1990–2017 MCS Rho R2 (within) F (prob > F) No. of obs.
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LF_female
LF_female_15_24 Family_female Family_tot
0.14 [0.00] 0.61 0.74 2228 [0.00] 793
0.008 [0.00] 0.83 0.21 213 [0.00] 865
20.04 [0.00] 0.89 0.38 514 [0.00] 865
20.18 [0.01] 0.81 0.27 304 [0.00] 845
20.15 [0.00] 0.83 0.28 327 [0.00] 846
Vulner_female
Vulner_tot
Wage_female
Wage_tot
20.04 [0.00] 0.91 0.16 154 [0.00] 846
20.02 [0.00] 0.89 0.08 73.9 [0.00] 846
0.01 [0.00] 0.94 0.22 235 [0.00] 846
0.008 [0.00] 0.95 0.16 164 [0.00] 846
Note: All values are logged; fixed-effects panel model applied; constant—not reported; SE below coefficients; in bold— results statistically significant at 5% level of significance; panel—strongly balanced.
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Table 5.4 IU vs social and quasi-social variables. Panel regression estimates. Period 1990–2017 IU Rho R2 (within) F (prob > F) No. of obs.
IU Rho R2 (within) F (prob > F) No. of obs.
School
LF_female
LF_female_15_24 Family_female Family_tot
0.13 [0.00] 0.63 0.74 2189 [0.00] 771
0.009 [0.00] 0.84 0.28 322 [0.00] 837
20.03 [0.00] 0.89 0.33 401 [0.00] 837
20.21 [0.00] 0.83 0.36 443 [0.00] 821
20.18 [0.00] 0.86 0.37 468 [0.00] 824
Vulner_female
Vulner_tot
Wage_female
Wage_tot
20.06 [0.00] 0.91 0.27 292 [0.00] 824
20.03 [0.00] 0.90 0.17 171 [0.00] 824
0.01 [0.00] 0.95 0.31 358 [0.00] 824
0.01 [0.00] 0.96 0.26 288 [0.00] 824
Note: All values are logged; fixed-effects panel model applied; constant—not reported; SE below coefficients; in bold— results statistically significant at 5% level of significance; panel—strongly balanced.
Section 5.3, in this case, the estimated parameters held by MCS and IU are very close for the respective model specifications. For instance, when estimating the impact of ICT on school enrolment, the parameters standing by MCS and IU are (0.14) and (0.13), respectively. Apparently, all the estimated parameters are statistically significant and hold the expected sign. As might be expected, the highest estimated parameters are demonstrated when examining the impact of ICT on contributing family workers: both total (Family_tot) and female (Family_female). The highest parameter (0.21) is held by IU when estimating its impact on changes in Family_female. The potential impact of IU on the size of vulnerable employment is significantly weaker; however, it is still significant. The International Labour Organisation defines vulnerable employment as ‘the sum of the employment status groups of own account workers and contributing family workers. They are less likely to have formal work arrangements, and are therefore more likely to lack decent working conditions, adequate social security (…). Vulnerable employment is often characterised by inadequate earnings, low productivity and difficult conditions of work that undermine workers’ fundamental rights’ (ILO, 2010). Obviously, the problem of vulnerable employment, and a high share of the labour force being ‘employed’ as contributing family workers, is not evenly distributed across the European countries and regions. In high-income, well-developed economies, the problem is marginal. However, in more backward regions, both in terms of overall economics and of social and institutional development,
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the problem does exist. The latter is closely associated with a lack of adequate labour market legal frameworks, established social habits and norms, poor education, and—in pure economic terms—a lack of opportunities to find a regular job. Regions suffering from a massive vulnerable employment usually are those where the shadow economy is extensive, and a significant share of the national output is being produced outside of the formal economy. The problem of female vulnerable employment is urgent, as national statistics tend to be significantly higher for women than men in this case. This is the case not only for Albania but also for Romania, Moldova, Greece, and Italy. ICTs as enabling technologies may play a pivotal role in women’s economic empowerment, mainly by opening the ‘opportunity windows’ to different forms of economic and social activities. The ongoing digital revolution will, inevitably, have a positive influence on gender equality. This may happen, potentially, in two different ways. First, technological change, and the economic structural changes that it brings, radically changes the composition of jobs and the skills that are required to perform those jobs. Enabled by the digital technologies, the growing work automation may potentially enhance women’s labour market inclusion by restructuring the demand for typical women’s jobs differently from that for typical men’s jobs. What is observed across branches is that, on the one hand, robots and algorithms simply substitute for different jobs, but, on the other hand, they complement jobs in occupations such as management, research, engineering, legal services, and many others. These shifts bring new opportunities not only to women, of course, but also to the whole labour force. Next, digital technologies enable ‘home-based’ jobs in the area of trade or services, which constitutes an important alternative for women willing to leave ‘vulnerable sectors’ and be engaged in formal market activities. Digital technologies allow the elimination of multiple barriers to full-female economic activity. To a large extent, ICT offers opportunities for one to become fully active financially, entrepreneurially, and economically (Gardey, 2015; Goldin, 2006). Newly emerged services, due to the implementation of ICT solutions, may effectively help women to access new markets, work flexibly and distantly, acquire and interact with customers, receive training and provide mentoring, improve their financial autonomy, and gain access to finance for their ventures. Apparently, the Digital Revolution seems to favour the female labour force, since women face, on average, a lower risk of being replaced by machines than do men. Women’s often superior social skills represent a comparative advantage in the digital age, and this is
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particularly so when social skills are complemented with higher education and advanced digital literacy (Vazquez & Winkler, 2017).
5.5 Gaps growing—Gaps narrowing? This last section is dedicated entirely to the examination of the process of convergence among the 32 European countries between 1990 and 2017. We aim to verify the hypotheses on the existence of economic and social convergence across the countries. To this end, we use three different empirical approaches allowing us to determine whether economic and social development gaps are diminishing across the European countries (hence, the process of convergence is reported) or, vice versa, cross-country gaps are growing (hence, the process of divergence is reported). More specifically, we calculate the standard deviation (SD), the Gini coefficient (classical inequality measure), and coefficients of variation (CV) to verify the hypothesis on σ-convergence, and based on the neoclassical growth theory, we estimate regression models to verify the hypothesis on β-convergence. To enrich the whole picture, for individual variables, we draw density functions to examine changes in the distribution of variables during the period examined (see Appendix I). Figs. 5.11 and 5.12 show the process of economic σ-convergence and changes in economic cross-country inequalities, and Fig. 5.13 shows social σ-convergence and changes in cross-country social inequalities. To provide an exhaustive picture, we examine each variable separately. A brief look at the consecutive graphs in Fig. 5.10 enables several specific tendencies in cross-country inequalities to be observed. The first interesting observation is that, with respect to the gross per capita income and gross income per person employed, drops in cross-country disparities are noted; however, in both cases, drops in the Gini coefficient and the coefficients of variations are relatively weak. For GDP per capita, the Gini coefficient falls by only 0.02 (from 0.40 in 1990 to 0.38 in 2017); however, there are significant rises in the mid-1990s. Notably, despite dropping Gini coefficients and coefficients of variations, we observe massive increases in standard deviations (from approximately 10,000 in 1990 to almost 14,000 in 2017). The latter suggests that, despite the fall in cross-country inequalities in relative terms, in absolute terms, the GDP per capita gaps are growing. Analogous tendencies are detected for GDP_empl and also for Ind_VA and Ind_empl. Looking at inequality changes in regard to Serv_VA and Serv_empl, we observe that, in these cases, the drops in cross-country disparities are massive. The Serv_VA
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GDP_empl 14,000
.5
13,000
.4
27,000
2
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26,000
1.5
12,000
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25,000 .3
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.3 .25 .2 .15 .1 .05
1990 1995 2000 2005 2010 2015
.25
1990 1995 2000 2005 2010 2015
.35 .3 .25 .2 .15 .1
15 14 13 12 11 10 1990 1995 2000 2005 2010 2015
Fig. 5.11 Economic variables. σ-convergence, SD, and Gini coefficient. Period 1990–2017. Note: σ-Convergence represented by the coefficient of variation; on x-axis—Gini coefficient and SD; on y-axis—coefficient of variation; solid line—SD; long-dash line—Gini coefficient; shortdash line—coefficient of variation.
ICT-Driven economic and financial development
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.05
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Serv_VA 10
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ICT_good_imp
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ICT and socio-economic development dynamics
HT_exp 1
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Fig. 5.12 Economic (trade-related) variables. σ-convergence, SD, and Gini coefficient. Period 1990–2017. Note: σ-Convergence represented by the coefficient of variation; on x-axis—Gini coefficient and SD; on y-axis—coefficient of variation; solid line—SD; long-dash line—Gini coefficient; short-dash line—coefficient of variation.
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School
LF_female
.4
18 16
.3
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12
.1
10 1990
1995
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LF_female_15_24
.12 .1 .08 .06 .04 .02
2015
5 4.5 4 3.5 3 2.5
Family_tot
12
.1
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6
1.5
9
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.15 .1 .05 1990 1995 2000 2005 2010 2015
12 11 10 1990 1995 2000 2005 2010 2015
Vulner_female
.8 .7 .6 .5 .4 .3 1990
1995
2000
2005
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13
Vulner_tot 15 14 13 12 11 10
2010
.16 .14 .12 .1 .08 .06 .04
1990 1995 2000 2005 2010 2015
Wage_female .2
2005
2010
2015
13
1
12
.8
11
.6
10
.4
15 14 13 12 11 10 1990
1995
2000
2005
2010
2015
Fig. 5.13 Social variables. σ-Convergence, SD, and Gini coefficient. Period 1990–2017. Note: σ-Convergence represented by the coefficient of variation; on x-axis—Gini coefficient and SD; on y-axis—coefficient of variation; solid line—SD; long-dash line—Gini coefficient; short-dash line—coefficient of variation.
ICT-Driven economic and financial development
2
2010
2000
Wage_tot 11
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14
.2
Family_female 2.5
2000
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coefficient of variation fell from 0.31 to 0.10 in 2017; for Serv_empl, the fall was from 0.25 to 0.14.j In these two cases, we also note diminishing standard deviations, which may confirm our supposition of the gradual eradication of cross-country differences as all the European countries head rapidly towards service-based economies (see also graphs in Appendix I). These results are consistent with what is reported for the agricultural sector in terms of the gross value addition created there. Notably, since 1990, the role of the agricultural sector has been declining in Europe in terms of its total contribution to countries’ GDPs, and that tendency is also demonstrated in falling crosscountry inequalities in this respect. A rapid fall in the coefficients of variation and Gini coefficients for Agr_VA and Agr_empl supports this view. The European economies are becoming more and more similar in terms of the role that the agricultural sector plays in creating their national wealth. Unexpectedly, changes in LF_15_24 show growing cross-country disparities. Massive increases in all the three considered inequality measures clearly show increasing gaps in this respect, both in relative and absolute terms. Growing inequalities in LF_15_24 may be caused by significant intercountry disparities in the dynamics of change in this regard. Notably, some countries move fast ahead and fast diminish the labour force participation rate for those aged 15–24 (as a share of total), whereas others demonstrate negligible dynamics in this process. In effect, countries differ more in this respect in 2017 than was observed in 1990. Turning to a brief analysis of trade-related economic variables (see Fig. 5.12), we observe that, in the cases of three variables (HT_exp, ICT_good_exp, and ICT_imp_exp) falls in cross-country gaps are striking. The coefficients of variation for the respective variables dropped radically; from 1.3 to 0.8 for ICT_good_exp and from 0.7 to 0.4 for ICT_good_imp. A similar tendency, although decreases are less dynamic, is reported for Comp_serv_exp. For the remaining two indicators, ICT_serv_exp and Comp_serv_exp, despite an abrupt shift in 1997, declines in inequalities are observable, although less radical than in the case of other economic variables. The graphical evidence summarised in Fig. 5.13 shows changes in crosscountry social inequalities. With regard to LF_female and LF_female_15_24, general tendencies and direction of changes are clearly identifiable, however, in the cases of the remaining variables, the picture is scattered and unclear and does not allow rigid conclusions to be drawn. In the case of LF_female and j
Authors’ calculations.
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LF_female_15_24, the Gini coefficient, coefficients of variation, and standard deviations calculated for the respective variables move in the same direction; although gradual decreases in cross-country disparities are manifested for LF_female, the opposite is the case for LF_female_15_24. Our preliminary evidence on the process of economic and social convergence seems to be slightly confusing, especially in the cases of the indicators examined. On the one hand, we observe fast-dropping cross-country inequalities, expressed as the Gini coefficient, whereas, on the other hand, the coefficients of variation seem to go in the opposite direction. Hence, to re-examine the existence of the process of σ-convergence, we provide additional empirical evidence on economic and social β-convergence. Figs. 5.14–5.16 plot the average annual growth rates vs the level of variable in the initial year of analysis. By definition, the process of β-convergence is demonstrated when this relationship is negative, which shows that initially poorer countries tend to grow faster compared to initially richer countries. Inevitably, this shall lead to a gradual eradication of cross-country disparities. The graphical evidence on economic and social β-convergence is next enriched by regression estimates—see Appendix J. Figs. 5.14 and 5.15 display the scatterplot for the average annual rates of growth of the respective economic variables vs their initial level in either 1990 or the earliest year available. Both suggest unquestionably that, among the group of European countries analysed between 1990 and 2017, the process of economic β-convergence is unambiguous with regard to all the variables examined. In all the cases, the statistical relationship is negative, which initially enables confirmation of the existence of the process of convergence. According to these data, we may conclude that, among these 32 European countries, economic gaps (disparities) were diminishing gradually between 1990 and 2017. This may support our initial supposition that a rapid ICT deployment enhances and accelerates the growth of relatively more backward regions. As expected, the process of gap elimination seems to be the fastest in the cases of Serv_VA and Serv_empl. This coincides with our initial evidence, as provided at the beginning of this section. A relatively fast process of convergence is also revealed for GDP per capita, HT_exp, and all the remaining trade-related variables. This graphical evidence demonstrates evidently how dynamic changes in this respect were. Hence, the period between 1990 and 2017 is revealed not only as a period of rapid shifts in economic material wealth—see gross per capita income and growth in per employee final production—but also as a period of dynamically diminishing cross-country disparities. Obviously, these economic disparities
2 1 0
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ICT and socio-economic development dynamics
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1 0 –1 –2 –3
2.5
3
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4
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3
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LF_15_24
185
Fig. 5.14 Economic β-convergence. Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.
5
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–5 2.5
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4
4.5
1
2
3 Comp_serv_exp
4
Fig. 5.15 Economic β-convergence (trade-related variables). Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.
ICT-Driven economic and financial development
ICT_serv_exp_gr
.2
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.5 0
3 School
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4
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–1
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2
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2 0 –2 –4 1.5
0 –5
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Wage_tot_gr
5
.5 0 –.5 –1
4
4
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6 4
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–2
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Vulner_female_gr
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School_gr
6
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ICT and socio-economic development dynamics
1.5
8
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4
4.2 Wage_female
4.4
4.6
187
Fig. 5.16 Social β-convergence. Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.
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were not eradicated totally, but significantly higher average annual growth rates observed for the relatively poorer countries pave the way for a less differentiated Europe, at least in economic terms. We claim, that, despite the positive impact of a broader deployment of new technologies being neither direct nor immediate in the cases of the variables examined in Fig. 5.14, the impact of ICT is more direct and obvious in the cases of ICT trade-related indicators (see Fig. 5.15). A fast diffusion of new technologies enabled all these economies to intensify their international trade activities in this respect. The growing domestic demand for ICT tools and services, boosting the demand from firms for ICT solutions, evidently was the driving factor of export-import flows. Our graphical evidence on economic β-convergence is then enriched by panel regression estimates summarised in Appendix J. As in previous cases, we examine each variable separately. The sample composition and period of analysis are analogous, as in the analysis above. In 10 out of the 15 cases examined, the estimated regression coefficients hold the expected negative sign and are statistically significant. Both OLS and robust regression estimates returned analogous (in qualitative terms) results, with the only exception being HT_exp, which confirms the relative stability of these estimates. The highest parameters were obtained for Ind_VA (2.6), Serv_VA (2.83), ICT_good_imp (3.68), Comp_serv_exp (3.08), and Comp_serv_imp (3.45), which shows that the process of β-convergence is fastest in this regard. The estimated parameters allow the calculation of specific half-time, hence the time needed to diminish cross-country inequalities by half. The shortest half-times are 14.4 years for ICT_good_imp and 14.8 years for Comp_serv_imp. Next, there is 15.8 years for Comp_serv_exp, 16.5years for Serv_VA, and 17.3years for Ind_VA. This means that, for instance, for ICT_good_imp, only 14.4 years are needed to diminish cross-country disparities by 50%. For GDP per capita and GDP_empl these half-times are approximately 30 years. These results allow for one very important observation; between 1990 and 2017 economic disparities among the European countries have declined massively. Finally, we take a look at the process of social β-convergence. The graphical evidence in Fig. 5.16 also suggests the existence of the process of social convergence among the European economies, with reported falling inequalities in this regard. However, for some of examined indicators— FL_female_15_24, Family_tot, and Family_female—the statistical relationship between average annual growth rates and the initial value of a given variable is negative, although not that ‘impressive’, as for School, FL_female,
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or Vulnet_tot. The table in Appendix J summarises regression estimates from which we may deduce more precisely the speed of the process of social βconvergence. Considering OLS estimates, in only four out of nine cases, the parameters of estimates are negative (as expected) and statistically significant. These cases are School, LF_female, Vulner_tot, and Vulner_female. The highest parameters are for School (2.91) and LF_female (2.67), which automatically implies the shortest half-times—16.2 and 17.1 years, respectively. However, these results are not surprising. Profound changes in the European education system encouraged more girls to stay at school, which, on the one hand, increased the tertiary enrolment rate, but, on the other hand, inspired women to get more actively involved in labour market activities. For the remaining social indicators, the process of social β-convergence was not confirmed by OLS estimates. Although the returned parameters were negative (with the only exception being LF_female_15_24), they were statistically insignificant. When we ran robust regressions, all parameters of the estimates, except for LF_female_15_24, were statistically significant again, which suggests that the examination of the process of social βconvergence does not give unambiguous and robust results. However, it should be noted that the process of declining cross-country disparities in the level of social development is, by its nature, much slower than is the process of economic convergence. Social convergence requires, to a large extent, changes in social norms, attitudes, and expectations. It is also preconditioned by economic shifts, legal regulations, and state policies. Social changes are always slow and characterised by high in-time inertia. The primary goal of this chapter was to identify whether the process of ICT diffusion and deployment of new technologies enhances two important processes across the European countries. We aimed to determine whether ICT may be claimed as the driving force of socio-economic development and whether ICT changes are accompanied by declining cross-country disparities in respect of social and economic development levels. To meet these goals, we selected a bundle of social and economic indicators that approximate, at least to some extent, the overall socio-economic welfare. Our general findings provide support for the hypothesis that the growing deployment of ICT is associated positively with the process of growing socio-economic development and also contributes to various structural shifts and declining inter-country inequalities. It seems likely that this ICT contribution is either direct or immediate. Nevertheless, our empirical evidence detects the statistical relationships exclusively, and one must remember that these correlations may be simply spurious. A strict identification of the ICT
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impact channels is a challenging task, and the processes of social and economic development and their determinants are hard to capture in numbers. No equation can show fully and profoundly the complexity and multidimensionality of the impact of ICT on the economic sphere of life. The causality between ICT diffusion and socio-economic development seems to be obvious but hard to quantify. Finally, countries have been carrying out rapid ICT deployment only since 1990; hence, there is limited availability of time series to facilitate a more profound analysis of the relationships between ICT and socio-economic development. This is a serious limitation that our results may lack robustness; severe time lags may emerge between the root causes, i.e. ICT, and the outcomes, i.e. leveraging the overall socioeconomic welfare.
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Kam, A. (2017). Dynamics of trade in value-added in ‘Factory Asia’. Journal of Contemporary Asia, 47(5), 704–727. Klasen, S. (2018). What explains uneven female labor force participation levels and trends in developing countries? (No. 246). Courant research centre: Poverty, equity and growthdiscussion papers. Klasen, S., Lechtenfeld, T., & Povel, F. (2015). A feminization of vulnerability? Female headship, poverty, and vulnerability in Thailand and Vietnam. World Development, 71, 36–53. Latif, Z., Latif, S., Ximei, L., Pathan, Z. H., Salam, S., & Jianqiu, Z. (2018). The dynamics of ICT, foreign direct investment, globalization and economic growth: Panel estimation robust to heterogeneity and cross-sectional dependence. Telematics and Informatics, 35 (2), 318–328. Lindio-McGovern, L., & Wallimann, I. (2016). Globalization and third world women: Exploitation, coping and resistance. Routledge. Luo, Y., & Bu, J. (2016). How valuable is information and communication technology? A study of emerging economy enterprises. Journal of World Business, 51(2), 200–211. Niebel, T. (2018). ICT and economic growth–comparing developing, emerging and developed countries. World Development, 104, 197–211. Ortiz Rodrı´guez, J., & Pillai, V. K. (2019). Advancing support for gender equality among women in Mexico: Significance of labor force participation. International Social Work, 62(1), 172–184. Pradhan, R. P., Arvin, M. B., & Norman, N. R. (2015). The dynamics of information and communications technologies infrastructure, economic growth, and financial development: Evidence from Asian countries. Technology in Society, 42, 135–149. Sachs, C. E. (2018). Gendered fields: Rural women, agriculture, and environment. Routledge. Sein, M. K., & Harindranath, G. (2004). Conceptualizing the ICT artifact: Toward understanding the role of ICT in national development. The Information Society, 20(1), 15–24. Singh, H., Dı´az Andrade, A., & Techatassanasoontorn, A. A. (2018). The practice of ICTenabled development. Information Technology for Development, 24(1), 37–62. Stanley, T. D., Doucouliagos, H., & Steel, P. (2018). Does ICT generate economic growth? A meta-regression analysis. Journal of Economic Surveys, 32(3), 705–726. UNCTAD. (2017). Information economy report 2017. Digitalization, trade and development. Switzerland: United Nations/UNCTAD. Vazquez, E., & Winkler, H. (2017). How is the internet changing labor market arrangements? Evidence from telecommunications reforms in Europe. The World Bank. Willcocks, L. P., & Lester, S. (1999). Beyond the IT productivity paradox. John Wiley & Sons Inc.
Further reading Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I., et al. (2017). Women’s empowerment in action: Evidence from a randomized control trial in Africa. World Bank.
ICT and socio-economic development dynamics Contents 5.1 The context and data explanation 5.2 General picture 5.3 Economic development: Towards a structural shift? 5.3.1 The evidence 5.4 Social development patterns: Paving the road ahead 5.5 Gaps growing—Gaps narrowing? References Further reading
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5.1 The context and data explanation Since early 1980s all world regions have been rapidly and profoundly transforming due to an explosive growth of new information and communication technologies. Boosting demand for novel technological solutions offering cheap, fast, and unrestricted dissemination of knowledge and information has been gradually reshaping social and economic structures, thereby enforcing the emergence of new networks and facilitating immediate communication regardless of the physical location of agents. Available time series on changing ICT penetration rates suggests that all the European economies have been included in this overwhelming process. A rapidly growing interest in the adoption and broader usage for new technologies offering ‘connection with outside world’, mainly due to low-cost, distributable, and easily adaptable wireless solutions, has disruptively reshaped the world landscape. A fast spread of ICT has opened new windows of opportunities for technological catching-up, leapfrogging other countries technologically, or simply escaping from permanent, often historically conditioned, technological marginalisation. Still, as raised in different discussions, the role of ICT in enhancing social and economic development is undeniable. Although the ICT-Driven Economic and Financial Development https://doi.org/10.1016/B978-0-12-813798-7.00005-1
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fact that the impact of new technologies on the socio-economic aspects of life may be either direct or indirect, either short term or rather unveiled in the long-run time horizon, there are quite many channels through which this impact may be demonstrated. Needless to say, the seizing benefits of the adoption of ICT remain one of the greatest challenges of both developed and developing countries. New technologies offer unbounded opportunities to develop, regardless of physical location, gender, or other prerequisites. ICT may have a substantial impact on the economic welfare of people, performance of companies and whole nations, as well as on different spheres of social life. Numerous studies seem to confirm this; see, for instance, Avgerou (2003), Sein and Harindranath (2004), Cortes and Navarro (2011), Cruz-Jesus, Oliveira, Bacao, and Irani (2017), Niebel (2018), Stanley, Doucouliagos, and Steel (2018), Bhandari (2019), or Haftu (2019). In what follows we intend to address, at least partially, the questions on the impacts of new information and communication technologies in the social and economic performance of the European countries. We try to capture these impacts by running the analysis from two different perspectives: economic and social. With these aims, we selected a bundle of 15 economic variables and 9 social variables. Regarding economica variables, we chose the following: • GDP per capita, PPP (GDP)b; • GDP per person employed (GDP_empl); • contribution to the national output from three main sectors: agricultural sector (Agr_VA), industrial sector (Ind_VA), and service sector (Serv_VA); • employment structure by three main sectors: agricultural sector (Agr_empl), industrial sector (Ind_empl), and service sector (Serv_empl); • labour force participation rate for ages 15–24 (LF_15_24); • High-technology exports as a share of total manufactured exports (HT_exp); • ICT goods exports as a share of total goods exports (ICT_good_exp); and ICT goods imports as a share of total goods imports (ICT_good_imp); • ICT service exports as a share of total service exports (ICT_serv_exp); • communications, computer, etc., as a share of total service exports (Comp_serv_exp) and as a share of total service imports (Comp_serv_imp). a
For definitions of variables, see Appendix F. For abbreviations, see also Appendix H.
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As for the socialc variables, we decided to concentrate on the following: • school enrolment, tertiary as a share of gross (School); • female labour force as a share of total labour force (LE_female); • female labour force participation rate for ages 15–24 as a share of total female labour force at this age (LF_female_15_24); • contributing family workers as a share of total employment (Family_tot); • female contributing family workers as a share of total female employment (Family_female); • vulnerable employment as a share of total employment (Vulner_tot); • female vulnerable employment as a share of total female employment (Vulner_female); • waged and salaried workers as a share of total employment (Vulner_tot); • female waged and salaried workers as a share of total female employment (Vulner_female); • waged and salaried workers as a share of total employment (Wage_tot); • female waged and salaried workers as a share of total female employment (Wage_female). By convention, our empirical sample consists of 32 European countries, and the period of analysis covers the years between 1990 and 2017. All economic data used in this research are exclusively extracted from the World Development Indicators 2018 database. In what follows we aim to, first, examine in-time changes in each of the variables listed above and, second, to determine their statistical association with two core ICT indicators: mobile cellular subscriptions (MCS) and Internet user penetration rates (IU). By this we aim to show how the European countries were changing their economic and social performance between the years 1990 and 2017, as these years mark the period of extraordinary rapid expansion of new ICTs. Drawing this general picture of socioeconomic shifts in Europe, hopefully, shall allow concluding whether and to what extent these changes are accompanied by fast deployment of ICT. In this case, we expect to show that ICT’s growing adoption gives a strong impulse for growth in economic and social welfare approximated by a shift in, inter alia, national output per employee, the share of high-tech exports in total exported goods, or female engagement in labour market activities. On the other hand, we expect that positive changes in ICT’s increasing usage may be demonstrated indirectly through, for instance, drops in female labour force participation rate for ages 15–24, or female family workers as a share of c
For definitions of variables, see Appendix G.
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total female employment. In the final part of this chapter, we are examining the process of economic and social convergence that we expect to take place across the European countries. We challenge the process of convergence unconditionally, and our intention is to unveil if the process of rapid adoption of ICT is accompanied by gradually diminishing cross-country disparities with respect to both economic and social performance. In this case, we expect to uncover that the process of ICT diffusion enhances dropping economic and social inequalities among the European economies. Obviously, this astonishing rapid growth in ICT deployment observed over the last decades makes us think of the pervasive impact of ICT on the economic growth et alia, despite the fact that social performance is not merely detectable, but obvious. The absence of clear and easily identifiable impacts of new technologies on the economic and social performance has already been claimed in various researches and is widely recognised as the ‘productivity paradox’ (Brynjolfsson, 1993; David, 1989; Willcocks & Lester, 1999). Notably, even if technologies grow exponentially, the latter is not immediately or directly ‘converted’ into economic growth. Probably this is also the case with new information and communication technologies. We are fully aware that this picture is only partial, and concluding on the existence of casual relationships between the examined variables would be an overestimation in many cases. Obviously, detected correlations can be spurious, and the results of the research may be ambiguous.
5.2 General picture As shown in Chapter 4, the rate of technological progress that we observed across the European economies, in terms of increasing adoption of ICT, has been impressive over the last three decades. Facing these phenomenally explosive shifts in the adoption and usage of ICT can effectively stimulate the identification of its macroeconomic and social consequences, and potentially trace the extent to which the adoption of ICT affects, for instance, economic growth. Here, we briefly discuss the changes in economic and social performance, observed between 1990 and 2017, across the 32 European countries that come within the scope of this research. Figs. 5.1–5.3 show changes in the average values of each economic and social variable selected for the study. Our first general observation is that, during the period 1990–2017, there were significant fluctuations in the great majority of indicators. Fig. 5.1 reveals the main shifts in macroeconomic aggregate indicators, such as gross domestic output per head
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(GDPpc) and gross domestic output per person employed (GDP_empl), which shows a shift in the overall economic welfare of the European countries. Visibly, the growth of GDP per capita grew dynamically, especially since the mid-1990s, dropped slightly after the 2008 economic crisis, and has grown again since 2010. Surely a rapid growth of per capita income in the early 1990s has been associated with the collapse of the ‘Berlin Wall’, which began a new economic era for Central-Eastern European countries. Between 1990 and 2017, the GDP per capita increased by more than 10,000 USD. Similarly, strong improvements are observed when considering the national output per person employed. Basic numbers suggest that this grew by more than 50%, between 1990 and 2017, which supports the hypothesis of strong productivity shifts during this period. Enormous improvements in the general material well-being of the European countries were accompanied by massive and visible changes in the structure of national economies. That is to say that, between 1990 and 2017, we observe dynamic changes in the input from three main economic sectors—agriculture, industry, and services—to the national economies. On average, since 1990 onward in Europe, we observe continuous drops in the share of national output from agricultural and industrial sectors, whereas the total of value added services gains in importance. Analogous trends are demonstrated when visualising changes in employment by sectors. Again, there are radical drops in agriculture (from 14% to 7%) and industries (from 33% to 23%), accompanied by a growth in employment in the service sector of 15% during the period examined. Apparently, such radical changes in the structure of national economies, both in terms of valued added and employment, can be an effect of a rapid technological progress that the European countries underwent between 1990 and 2017. Growing importance in creating the national output of service sector can, unquestionably, be closely related and determined by the increasing deployment and usage of new ICTs. ICT has enabled the emergence of new types of products and services and further and more intensive mechanisation of production and, over the long term, drives structural changes in the national economies and domestic labour markets. As ICT is broadly claimed to be one of the ‘prime movers’ of access to education, this positive effect of the indirect impact of ICT on the labour market can be demonstrated through the decreasing share of the population aged between 15 and 24 who are actively engaged in the labour market. As displayed in Fig. 5.1, the participation of people aged 15–24 in the labour force (LF_15_24) dropped from 53% in 1990 to 42% in 2017. Such a change suggests that, on average, pupils leave the educational system late to enter the
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labour market. This view is supported by elementary data on the gross school tertiary-level enrolment rates; see Fig. 5.3. Next, Fig. 5.2 briefly explains trade-related macroeconomic indicators, which are potentially affected by technological progress. More specifically, we refer to high-technology exports expressed as a share of total exported goods (HT_exp) and to five other variables demonstrating export and import intensity with regard to ICT goods and services. As for the hightechnology exports, it can be seen that the path is highly unstable, marked by multiple ups and downs between 1990 and 2017. In the mid-1990s, after an abrupt shift in HT_exp, the general trend seems to be rather downward. Available time series for ICT goods’ exports and imports extend back to 2000. Between 2000 and 2017, massive drops in the values of these two indicators are revealed, which might suggest a diminishing role of ICT goods’ exports and imports in the total of ICT goods traded internationally. However, this surprising change is one of the effects of the globally recognised process of moving ICT production to Asian countries. The emergence of the so-called ‘Factory Asia’ (Ito & Vezina, 2016; Kam, 2017) at the beginning of 20th century is, inter alia, demonstrated through a falling share of ICT exports and imports with regard to high-income economies. When looking at the average changes in the exports of ICT services (explained as a share of total service exports), there has been an upward trend since the 1990s. Similar observations are valid for variables explaining communication and telecommunication services, both exported and imported (as a share of total service exports/imports). In the case of Comp_serv_imp, the growing patterns are relatively stable, and between 1990 and 2017 we observed a growth of total service imports, going from 30% to 45%. As for Comp_serv_exp, the path is less stable, with a dramatic drop in the late 1990s; however, since 1997, when average Comp_serv_exp was just 16% of total service exports, its share grew to 43% in 2017. Finally, let us move briefly to shifts observed on the social ground—see graphs in Fig. 5.3. In this case, we selected nine indicators that demonstrate the rather indirect impact of technological change on the social spheres of life, associated here mostly with female engagement in economic activities. As raised in many studies, a higher participation in the labour force is very likely to be the first and most important step in exploiting the full potential of ICTs. Its deployment permits a timely access to information, helping to overcome one of the fundamental barriers to the effective functioning of the market, namely, information asymmetries. Combined appropriately, these two elements—shifting labour force participation and removing
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constraints on information access—lead to increases in the number of transactions, enable participation in global markets, reduce transaction costs, and ensure worldwide visibility, all of which, in the longer run, offer better prospects for economic growth and development. One of the most serious problems for economically backward countries is low female participation in the formal market economy, i.e. in the job market and in entrepreneurial activities. Women’s relatively low rate of economic activity may be a direct effect of poor education, poor skills, and illiteracy. Women in less developed and tradition-oriented regions are often deprived of access to the financial system; they have no permanent income from contracted work. In traditional societies, a whole series of social or religious norms and attitudes often consign the female population to the status of ‘hidden and usually unpaid’ labour. Women are exposed to poverty more often than are men, and they are often denied basic rights. In fact, a significant share of women still constitute an unused labour force, which may significantly impede national growth and development. Additionally, women who want to engage in economic activity face various gender-specific constraints, such as barriers to education and lack of a combination of relevant education, professional skills, and work experience, which may be a severe handicap not only in seeking decently paid jobs but also in forming businesses of their own. Such claims may be traced in Lindio-McGovern and Wallimann (2016) and Sachs (2018), where the authors argue that the problem of marginal engagement of women in formal economic activities is especially severe in the rural areas. In Klasen, Lechtenfeld, and Povel (2015) and Klasen (2018), we find more arguments and explanations of female exclusion in the labour market. He shows that social values and norms, the structure of national economies, and also state political regimes in the developing countries can effectively hinder women’s participation in value creation. Ortiz Rodrı´guez and Pillai (2019) present evidence in the same vein. Fig. 5.3 summarises the general average tendencies in school enrolment in the tertiary level and female engagement in formal labour market activities. First, we observe an impressive shift in gross school enrolment at the tertiary level. Between 1990 and 2017 this grew by 40%—from 29% to 69%. This radical change in access to education, both for men and women, observed across Europe during the last three decades has created a structural shift in, inter alia, the labour market. These shifts are, probably, driven partially by a greater accessibility to the education system and, to some extent, by technological progress, which enforces multiple changes across the whole economic and social systems. Throughout this research, we claim that the impact of ICT on the social spheres of life associated with economic activities may, in the first place,
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be demonstrated through women’s participation in the labour market. To stay in line with the latter, along with increasing school enrolment, we examine shifts in female participation in the labour force (as a share of total; LF_female), participation in the labour force of females aged 15–24 (as a share of total; LF_female_15_24), and three indicators indicating the share of women falling into the categories of ‘contributing family workers’ and ‘vulnerable employment’, on the one hand, and ‘wage workers’, on the other. We argue that improving the overall economic performance along with the implementation of technological progress in economic and social systems shall have positive effects in terms of reducing female (as well as total) vulnerable employment and reducing the share of women working as contributing, usually unpaid, family workers; henceforth, the share of women being employed as waged and salaried workers shall increase. Such structural changes in labour markets would, evidently, be one of the most important manifestations of the positive impact of ICT on the overall socio-economic development, even though this impact may be hard to capture in numbers. Our preliminary evidence, summarised in Fig. 5.3, demonstrates clearly a strong drop in female labour participation (ages 15–24), from 49% to 38%, between 1990 and 2017. Moreover, we observe relatively substantial decreases both in the share of women working as contributing family workers and in the share of women who fall into vulnerable employment. These positive shifts are accompanied by a growth in the share of women being employed as waged and salaried workers. In what follows, we present and discuss the results of a more detailed analysis intended to uncover associations between growing adoption and usage of ICT and socio-economic development in the 32 European countries between 1990 and 2017.
5.3 Economic development: Towards a structural shift? As described briefly in Section 5.2, across the European countries, during 1990–2017, there have been massive changes in the levels of social and economic development. The general picture we described suggests that all the 32 economies examined between these years have experienced a rapid economic growth, which is demonstrated clearly through shifts in gross per capita income and final production value per person employed, to cite just two examples. Notably, based on the average values of consecutive variables, we also observed radical structural changes in the national output; the gradually declining share of value added in the agricultural and industrial
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sectors was compensated by a growth in the share of value created in services. Analogously, general tendencies were observed in employment in the three main sectors of national economies: significant drops in employment in the agricultural and industrial sectors but fast increases of employment in the services sector. Moreover, our general conclusion, based on a brief analysis of trade-related indicators, demonstrates important changes in this respect. International trade patterns showing ICT goods and services exports and imports are unstable but, at a time, they show dynamically changing situations with this respect. This picture of fast-changing structures of economies coincides with the debate on the potential benefits of new ICTs and the changes that it can bring to the societies and economies. This debate concentrates mainly on the productivity, economic growth, and overall welfare gains that the newly emerging digital economy may offer. Moreover, something that is relatively less intensively discussed, the broader adoption and usage of ICT may potentially create several structural shifts in the national economies. Obviously, these shifts are long-term processes that are dependent on various factors, and our supposition that some of them are generated by technological progress might be spurious. Still, there is not wide consensus over whether standard quantitative measures can fully demonstrate the impact of ICT on the economic performance of countries. Examining the relationships between the process of ICT diffusion and economic development is a challenging task. This is not only because countries included in empirical samples are relatively heterogeneous but also, and mostly, because these relationships are complex in their nature and are influenced by multiple external factors that may be even hard to identify. Statistical analysis and econometric modelling techniques are used conventionally to trace the relationships between variables. Still, some unique features of countries are often hard to capture and include in econometric models. Some elements and determinants that shape the process of economic development and either foster or hinder the impact of ICT on economic processes may be empirically intractable, but their influence is still massive. In what follows, we develop a quite intuitive framework analysing major economic areas that, potentially, may be affected by a rapid ICT expansion over the societies and economies that all the European countries have experienced during the last three decades. Our intention is, at least partially, to broaden our knowledge, draw a general picture of how the contours of the European economies were changing between 1990 and 2017, and associate those changes and structural shifts with the process of diffusion of new ICTs.
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5.3.1 The evidence The following empirical evidence confronts the process of ICT diffusion vs economic development across the 32 European economies, between 1990 and 2017. Put differently, we trace the statistical relationships between ICT deployment and the process of economic development and investigate whether the impact of ICT has been either positive and strong, or, conversely, negligible. To this end, we use two core ICT indicators: mobilecellular telephony subscription rates (MCS) and Internet users penetration rates (IU), which we claim are good proxies of access to and use of ICT.d Economic development is approximated by a bundle of arbitrary variables (see Section 5.2), which, we believe, allow us to demonstrate the types of changes and structural shifts that have been observed during the last three decades in countries that are within the scope of this research. The results of our analysis are demonstrated in Figs. 5.4–5.7, which display the statistical associations between mobile-cellular telephony and Internet users penetration rates and selected economic variables. Intentionally, we develop separate graphs for each pair of variables, which allows for a detailed analysis of the issue. Our graphical evidence is then enriched by panel regression estimates; the results of these analyses are summarised in Tables 5.1 and 5.2. Inspecting the empirical findings reveals that certain regularities can be identified with respect to the relationships examined. As presented briefly in Section 5.2, many European countries experienced a rapid economic development, on the one hand, and significant structural shifts regarding, for instance, value creation and/or employment structure of the main economic sectors, agricultural, industrial, and services, on the other. Obviously, the average annual speed of these changes differs across the countries. Central-Eastern European countries, the economies of which were boosted in 1990, experienced much faster growth than did wellestablished Western European countries. For instance, in Albania, Latvia, Lithuania, and Poland, the average annual GDP per capita growth ratese between 1990 and 2017 were 3.4%, 3.9%, 4.1%, and 3.5%, respectively. During an analogous period, countries such as France and the United Kingdom grew at a slower rate, hardly reaching 1% per annum. Similarly, dynamic changes were observed with respect to the gross national output per person employed; the calculated correlation between GDPpc and GDP_empl is almost 0.95. Rapid economic advances, measured in gross d
In Chapter 4, we described extensively the process of ICT diffusion, providing country-wise evidence. Authors’ calculations based on data derived from the World Development Indicators 2018.
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Fig. 5.7 Internet user penetration rate vs trade-related variables. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; on x-axis—IU; all values expressed as a percentage of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov.
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Table 5.1 Mobile cellular subscriptions vs economic and trade-related variables. Panel regression estimates. Period 1990–2017 GDP GDP_empl Agric_VA Ind_VA Serv_VA Agric_empl Ind_empl Serv_empl
MCS Rho R2 (within) F (prob > F) No. of obs.
0.07 [0.00] 0.96 0.69 1772 [0.00] 833
20.15 [0.00] 0.93 0.63 1332 [0.00] 799
20.03 [0.00] 0.72 0.21 202 [0.00] 799
0.02 [0.00] 0.82 0.45 620 [0.00] 794
20.11 [0.00] 0.94 0.57 1108 [0.00] 846
20.02 [0.00] 0.81 0.26 284 [0.00] 846
0.03 [0.00] 0.92 0.64 1436 [0.00] 845
FL_15_24
HT_exp
ICT_good_exp
ICT_good_imp
ICT_serv_exp
Comp_serv_exp
Comp_Serv_imp
20.03 [0.00] 0.89 0.41 586 [0.00] 865
0.09 [0.00] 0.78 0.17 165 [0.00] 830
0.02 [0.04] 0.86 0.00 0.42 [0.51] 544
20.16 [0.02] 0.77 0.09 52.1 [0.00] 544
0.24 [0.01] 0.55 0.41 477.6 [0.00] 721
0.04 [0.00] 0.55 0.07 52 [0.00] 779
0.04 [0.00] 0.64 0.09 78 [0.00] 766
Note: All values are logged; fixed-effects panel regression applied; constant—not reported; SE below coefficients; in bold—results statistically significant at 5% level of significance; panel—strongly balanced.
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MCS Rho R2 (within) F (prob > F) No. of obs.
0.08 [0.00] 0.95 0.65 1516 [0.00] 838
IU Rho R2 (within) F (prob > F) No. of obs.
IU Rho R2 (within) F (prob > F) No. of obs.
0.07 [0.00] 0.95 0.63 1330 [0.00] 824
0.06 [0.00] 0.95 0.62 1311 [0.00] 809
20.14 [0.00] 0.93 0.65 1450 [0.00] 789
20.02 [0.00] 0.78 0.23 226 [0.00] 789
0.02 [0.00] 0.84 0.41 544 [0.00] 786
20.11 [0.00] 0.95 0.62 1297 [0.00] 824
20.03 [0.00] 0.82 0.30 344 [0.00] 824
0.03 [0.00] 0.92 0.64 1415 [0.00] 823
FL_15_24
HT_exp
ICT_good_exp
ICT_good_imp
ICT_serv_exp
Comp_serv_exp
Comp_serv_imp
20.03 [0.00] 0.91 0.36 454 [0.00] 837
0.08 [0.00] 0.78 0.14 129 [0.00] 812
20.08 [0.03] 0.87 0.01 7.5 [0.00] 544
20.15 [0.01] 0.79 0.14 84 [0.00] 544
0.23 [0.00] 0.61 0.46 575 [0.00] 703
0.03 [0.00] 0.55 0.03 26.9 [0.00] 762
0.03 [0.00] 0.63 0.10 79.8 [0.00] 749
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Table 5.2 Internet user penetration rates vs economic and trade-related variables. Panel regression estimates. Period 1990–2017 GDP GDP_empl Agric_VA Ind_VA Serv_VA Agric_empl Ind_empl Serv_empl
Note: All values are logged; fixed-effects panel regression applied; constant—not reported; SE below coefficients; in bold—results statistically significant at 5% level of significance; panel—strongly balanced.
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per capita income, were accompanied by structural shifts in terms of the contribution of agriculture, industry, and service sectors to the GDP. Massive increases in GDP created in the service sector were observed, for instance, in Albania (from 16% to 47% of gross value added), Malta (from 35% to 75% of gross value added), and Moldova (from 33% to 56% of gross value added). In other countries, these changes were not that radical, but the trend was positive in each country examined. On the other hand, a rapidly falling role of the agricultural sector was traced in each country. Again, analogous structural shifts were observed with respect to employment in consecutive economic sectors—a rapidly dropping share of people being employed in agricultural and industrial activities, and an increasing share of employees in the services sector. Fig. 5.8 represents this type of structural changes in terms of employment across sectors. Correlation coefficients for Agr_empl vs Serv_empl and Agr_empl vs Ind_empl are (0.83) and (0.56f), which directs our attention towards the fact that radical flows of the labour force are observed across the sectors. Obviously, this general view does not necessarily show that the labour force previously employed in the agricultural sector moves directly to the service sector, effectively ‘jumping over’ industry. Labour force leaving the agricultural sector moves towards both industry and services, but, unquestionably, a broader deployment of new ICT opens multiple opportunities to operate in the service sector. These opportunities are opened not only in the ICT service sector itself, despite the fact that the ICT service sector is recognised globally as a key driving sector of economies, but also in other types of services that need ICT to work effectively. It is needless to say that ICT positively impacts firm performance, and this impact is even stronger when accompanied either by other types of investments or by organisational changes in companies. These investments and other expenditures on skills improvements or, inter alia, firm market reorientation and external market expansion, together with ICT deployment, may lead to a kind of synergism that boosts economic performance. Those synergies will lead inevitably to the development of new products and services and their introduction to the market. Moreover, changes in production and the service sector, from the long-run perspective, will generate profound changes in the labour market, and, above all, in the structure of labour demand and supply. Those effects are, to some extent, also traceable at the macro level.
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Fig. 5.8 Employment in agricultural, industrial, and service sectors. Period 1990–2017. Note: Raw data used; on x-axis and y-axis—raw values; all values expressed as a percentage of total; for non-parametric visualisation, kernel-weighted local polynomial smoothing applied; kernel ¼ epanechnikov. 163
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Graphical evidence summarised in Figs. 5.4–5.7 demonstrates the statistical relationships between the two core ICT indicators and select macroeconomic variables that approximate the economic performance of countries. Not surprisingly, mobile-cellular telephony penetration rates plotted vs GDPpc, GDP_empl, Serv_VA, and Serv_empl show a positive statistical relationship. Statistically, the strongest association is demonstrated in the case of changes in the share of labour force employed in the service sector (correlation coefficient is 0.41). This result confirms our general intuition that a rapid expansion of ICT and a broader adoption and extensive deployment of new technologies across both social and business activities constitutes a strong stimulus for moving from an agriculture- and industry-oriented economy towards a service-based economy. Respective graph in Fig. 5.6 displaying analogous evidence, confronting Internet users vs economic variables as in Fig. 5.4, supports this supposition. The shift in the share of labour force being employed in the service sector is strongly and positively correlated with growing IU. In this case, the correlation coefficient is even higher, at 0.57, and it represents the strongest positive statistical relationship among the others examined and presented in Figs. 5.4–5.7. The growing role of ICT in enhancing structural changes in the national and global economies is demonstrated effectively not only through changes in the structure of production and employment across the main economic sectors but also through changes in the composition of the labour force. As economies and societies head towards full saturation with new ICT, it becomes quite natural that ICTs proliferate not only social life, but, above all, in the way of running business, structure of consumption patterns and habits, and thus structure of production, just to cite a few examples. Obviously, a broader deployment of ICT offers new opportunities to intensify business activities, internationalise companies, and enhance the penetration of new markets through e-platforms, et alia; and as raised by some scholars, ICT usage increases storability and tradability (e.g. Boden & Miles, 2000), thereby positively affecting the emergence of a service-based economy. It is needless to emphasise that the expansion of ICT generates changes in demand for different skill levels. As raised by many, technological progress in the field of ICT drives massive robotisation and automatisation of production processes. This is then reflected in growing job polarisation and a divide between the low-skilled and the high-skilled labour force. Low-skilled and non-routine manual jobs decline, but the demand for high-skilled professionals who can do non-routine cognitive jobs grows. Technological progress drives the demand for new types of jobs and skills, which allows the
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potential of ICT to be exploited fully. In that context, reorientation towards a service-based economy seems to be one of the manifestations of the growing importance of technologies offered by the Digital Revolution. Referring back to the evidence in Fig. 5.4, conversely to what was observed with respect to the service sector’s contribution to gross value addition and the share of labour force working in the service sector, the statistical relationships revealed between mobile-cellular telephony vs employment in the agricultural and industrial sectors are negative. Analogous observations are reported when confronting MCS vs Ind_VA and Agr_VA. In each of the countries examined, except Albania, between 1990 and 2017, the share of employment in the industrial sector showed negative average annual growth rates. The highest average annual growth rates were observed in Cyprus (2.0% per annum), Malta (2.7% per annum), Spain, and the United Kingdom (1.9% per annum for both). Average annual drops in the share of labour force working in the agricultural sector were even more radical in Germany (4.3% per annum), Estonia (5.9% per annum), the Slovak Republic (4.4% per annum), and also in many other countries (approximately 3.5% per annum). Fig. 5.6, which offers a graphical explanation of the relationships between IU and economic variables, supports our previous findings. In this case, the statistical associations between IU vs Serv_VA and IU vs Serv_empl are also positive; the correlation coefficients are 0.33 and 0.57, respectively. When considering IU vs GDP per capita and IU vs GDP per person employed, the picture that emerges shows clearly that there is a close statistical relationship between these variables. During 1990–2017, in the European countries, gross per capita input and gross input per person employed was accompanied by growing ICT usage. Obviously, economic growth was driven by many factors, but, as claimed in many studies, in that period, the growth of ICT investments, ICT per capita, and ICT-driven international trade, in addition to changing consumption patterns and consumer preferences, effectively enhanced—although not always directly—economic growth and productivity shifts (Latif et al., 2018; Niebel, 2018). Additionally, a broader deployment and usage of new technological solutions contributed to the emergence of knowledge-based and technology-based economic structures, which, by definition, are characterised by a relatively higher productivity and efficiency than are non-technology-intensive sectors (Singh, Dı´az Andrade, & Techatassanasoontorn, 2018). The positive role of ICT in boosting economic growth has been recognised not only the area of dynamically increasing investments, but also in growing role of ICT manufacturing sector that
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contributes to aggregate productivity shifts (Pradhan, Arvin, & Norman, 2015). A dynamic development and broader implementation of ICT in the various economic sectors has been linked partially to the liberalisation of national telecommunication markets, which resulted in rapidly growing competition and reduction in the prices of access to and use of ICT tools and services. On the other hand, due to the unlimited opportunities and benefits offered by ICT, economic players began to adapt new technologies rapidly into daily usage (Luo & Bu, 2016). That greater use of ICT contributed to the emergence of network effects and lowered transaction costs, which improved the overall efficiency of the economy (Arthur, 2018; Edquist, Goodridge, Haskel, Li, & Lindquist, 2018). Today, many economic organisations use ICT not only in the production process but also in the services sector, such as, inter alia, finance services, retail trade, and insurance. The overall use of capital and labour has been much improved due to ICT usage, and growths of national per capita output are one of its major manifestations. Figs. 5.5 and 5.7 provide additional evidence of the statistical relationships between MCS/IU and international trade-related variables across the European economies. In this case, the period of analysis covers our standard period between 1990 and 2017 for high technology exports (HT_exp), ICT service exports (ICT_serv_exp), communications, and computer services exports and imports (Comp_serv_exp/Comp_serv_imp), although there are significant breaks in the time series. As the available time series for ICT goods exports and imports (ICT_good_exp/ICT_good_imp) go back only to 2000, the period of analysis is significantly shorter in this case. In Section 5.2, Fig. 5.2 demonstrates changes in the average values of international trade-related variables and allows conclusions to be drawn on relatively unstable development patterns marked by multiple ups and downs in short time intervals and, above all, on significant drops in ICT goods exports and importsg observed since 2000. At first glance, the evidence summarised in Fig. 5.5 does not suggest the existence of strong and positive relationships between mobile telephony accessibility and consecutive international traderelated variables, as might be expected. The statistical relationship between MCS vs HT_exp, ICT_good_exp, and ICT_good_imp seems to be negligible, and correlation coefficients are 0.06, (0.05), and (0.11), respectively. However, when we view these results in light of the general tendencies observed across the European countries in regard to the radically falling role of ICT goods exports in total goods exports and ICT goods g
Calculated as a share of total value of services export, BoP.
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imports in total goods imports, with the fast-growing deployment of ICT, this zero relationship becomes obvious. Note that, between 2000 and 2017, the share of ICT goods exports in the total value of exports of goods fell from 10% to 5%, whereas the share of ICT goods imports in the total value of imports of goods fell from barely 12% to below 7%. Next, when we look at the high-tech goods export pattern (see Fig. 5.2), we see that, on average, this value is stable over time, despite the HT_exp pattern showing in-time instability. However, for Internet users, analogous relationships are similarly weak (see Fig. 5.7). As mentioned in Section 5.2, the falling ‘contribution’ of ICT goods exports and imports to the total value of exported and imported goods is the ostensible signal of the dynamic ‘moving to Asia’ of ICT goods production. A radically different situation is reported when considering ICT variables vs ICT_serv_exp, Comp_serv_exp, and Comp_serv_imp. In these cases, the relationship is positive; the strongest relationship is revealed for MCS/IU vs ICT_serv_exp, where the correlation coefficients are 0.37 and 0.41, respectively. This boosting of ICT services sector and its role in the international trade is driven mostly by the profound impact of the Digital Transformation of various spheres of life. The use of IT platforms and cloud solutions is growing, and, according to various sources, the demand for IoT platforms is characterised by the most dynamic shifts. Moreover, Web solutions for managing organisations, e-commerce, et alia, and platforms to manage relationships with consumers, social platforms, Data Centre services, and e-governance solutions, and many other ICT implementations effectively drive the demand for ICT services. Finally, our graphical evidence is enriched by panel regression estimates summarised in Tables 5.1 and 5.2. We define two separate panels. In the first panel (estimates are presented in Table 5.1), MCS is the treated regressor, and all the economic variables examined above are considered as regressands. In the second panel (estimates are presented in Table 5.2), IU is defined as a regressor, and all the economic variables examined above are dependent variables. Both panels are balanced strongly. By convention, the empirical sample covers 32 European countries, and the time span of analysis is set as 1990–2017. Bearing in mind the results presented graphically, uncovering the statistical association between the two core ICT variables and economic variables, we expect qualitatively analogous outcomes from the estimated panels. Panel analysis results summarised in Table 5.1 demonstrate how strongly the growing deployment of mobile-cellular telephony impacted the macroeconomic performance of the European countries. However, Table 5.2 shows analogous results in examining the impact
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of increasing usage of the Internet by the members of the society. In general, all estimates confirm the type of ‘linkages’ between variables previously reported in Figs. 5.4–5.7. Even more interesting is the observation that, as reported in Table 5.1 and 5.2, the estimated coefficients that MCS and IU variables hold are barely the same for the respective panel modelsh; for instance, when estimating the impact of ICT on GDP per capita, MCS holds the coefficient 0.08, whereas IU holds the coefficient (0.07). When looking at the results of consecutive estimated coefficients standing by MCS and IU, we find similar analogies. The latter suggest that the potential impact of both MCS and IU on the examined economic variables is equally strong (or weak). When concentrating exclusively on the impact of Internet penetration rates on the economic performance, we see that growing Internet usage demonstrates, relatively, the strongest positive associations with ICT_serv_exp; in this case, the estimated coefficient is 0.23. Hence, the growth of IU at 1% may potentially enhance the growth of ICT_serv_exp at 0.23%. Tracing the potential impact of IU on remaining trade-related variables, we observe that, in the case of high-technology manufacturing exports, ICT goods exports, and computers services exports and imports, the estimated coefficients are close to zero, although still statistically significant. That might suggest that no clearly traceable and direct relationships between these variables may be identified. The coefficients that IU holds when estimating its impact on ICT goods import are statistically significant and negative. Its estimated value would suggest that a 1% IU increase generates a drop of 0.15% in ICT_goods_imp. Clearly, this result should not be interpreted in a straightforward way. In fact, drawing the conclusion that a shift in Internet usage in a country determines falling imports of ICT goods is at odds with general logic. Observed across the European economies, a drop in ICT_goods_imp expressed as a share of the total value of imported goods does not show a diminishing role of international trade in ICT goods. This ‘strange’ effect is rather associated with the changing structure of goods imported into the European countries. According to UNCTAD (2017) data, during the last three decades, international trade flows in CT goods have grown dramatically, driven by multiple factors, the most important of which seem to be the WTO Information Technology Agreement, boosting bilateral trade arrangements, dynamic technological changes, consumer demand for technological novelties (both tools and services), liberalisation of national telecommunication h
Note that MCS and IU are highly correlated. The calculated correlation coefficient is 0.92.
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markets, and the emergence of new technology-led business models. However, as noted in UNCTAD (2017) ‘for the first time since 2009, global imports of ICT goods declined in 2015 by 3.6 per cent in current prices, to just over $2 trillion. Most of this decline was due to lower imports from developed economies in Asia and Europe, which fell by 11 per cent and 7 per cent, respectively, and also to the decline in imports of computers and peripherals as well as consumer electronic equipment’ (p. 49). Despite the fact that a huge share of the trade in ICT goods, including finished and intermediate goods, is hosted between Europe, Asia, and the United States, the UNCTAD (2017) data reveal that, in 2015, developing Asian countries in which huge manufacturing facilities are located account for nearly 50% of the global ICT goods imports, and China alone accounts for 20% of the latter. Our estimates also show significant and relatively strong negative associations for IU vs valued addition created in the agricultural sector (Agric_VA)—the estimated coefficient is (0.14)—and for the share of the labour force employed in agricultural activities (Agric_empl)—estimated coefficient is (0.11). These results support the evidence revealed previously in this section. A direct interpretation of the estimated coefficients would show that, for instance, a 1% growth of IU induces a drop of 0.14% in valued addition created in the agricultural sector. However, it is rather obvious that ICT itself does not drive a decrease in valued addition in the agricultural sector as such, but enhances economic activities in other sectors. Henceforth, this impact should be treated as indirect. Technological advances that allow for the emergence of new types of products and services, professional skill development, firm reorientations, and many other things effectively generate structural shifts that are seen throughout the economy. Labour force and financial capital move to dynamically developing sectors, offering new opportunities and bringing benefits and profits. In that sense, technological progress, and, here, ICT-driven changes in particular, have far-reaching implications, for, inter alia, computerisation of jobs, automatisation of production, and the emergence of new types of plants where all the work is done by robots. From the longer-term perspective, these changes are transmitted into structural shifts; the labour force moves from low-technology to technology-intensive sectors, and manufacturing and services in which production is strictly ICT-dependent play a growing and pivotal role in the economic systems. In the long term, these shifts are converted into economic welfare.
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5.4 Social development patterns: Paving the road ahead Many countries face the problem of relatively poor participation of women in the formal market economy, i.e. in the job market and in entrepreneurial activities. The low rate of female economic activity may be identified as one of the negative effects of difficult access to the education system, poorly developed professional skills, and high illiteracy. It happens that women are massively deprived of access to financial systems, and they have no permanent income from contracted work. In rural areas and/or less developed European economies, inhabited by more traditional societies, the existing social, religious norms, and attitudes often consign the female population to the status of ‘hidden and usually unpaid’ labour. Even in high-income and well-developed countries, where female population seems to be treated ‘equally’ to men in terms of free access to labour market, the basic national statistics speak in support of the supposition on the existence of a relatively huge gender wage gap. Undeniably, women belonging to traditional societies face deprivation from unrestricted access to the labour market and, thus, constitute an unused labour force, which impedes economic growth and development. On the other hand, female population is often engaged in informal home-based businesses, occupying traditional activities. These, however, require less seed capital and professional experience, yielding lower returns and benefits. Women without access to the formal labour market tend to run home-based businesses in traditional and sometimes in informal sectors, characterised by low effective demand, low profits, and high exposure to risks and external shocks. They are highly vulnerable inside workers, suffering from permanent material and institutional exclusion (Klasen et al., 2015). Across the countries, we observe a gradually increasing number of women running their own businesses; however, it should be noted that in many economies the main enhancement for women to set up a new business is necessity-, and not opportunity, driven. Facing the absence of alternative ways of supplementing household income, entrepreneurship or self-employment is the only viable option. Across less advanced regions, restricted access to the technology and difficulty in financial market participation (financial exclusion) are large barriers for women seeking to escape vulnerable, low-paid, and indecent employment (Benerı´a, Berik, & Floro, 2015). Still, pretty often, women’s labour and entrepreneurial activities are the ‘untapped resource’, and ICTs, if used properly, can unlock the potential of female population, mainly by making it easier to overcome
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various gender-specific constraints on entrepreneurial behaviour. A broader deployment of ICT may have either direct or indirect effects on social development by mobilising resources and reinforcing market activities. ICT may foster the mobilisation of savings and offer opportunities to convert these savings into investment; ICT may facilitate a greater mobilisation of the labour force. All these have far-reaching consequences. Increasing active engagement in the formal labour markets constitutes a solid base for earning a regular income, which relieves people living in material deprivation out of the subsistence economy. Having a regular salaried work effectively reduces vulnerability to risks and external shocks, which bring a danger of falling into poverty. A greater engagement in labour markets, both through salaried employment and through small business start-ups, produces economic gains and wealth in the long-run perspective. A higher participation in the labour force is very likely to be the first, most important step for developing countries to exploit the full potential of ICT. Additionally, a broader usage of ICT allows timely access to various types of information, helping to combat one of the fundamental impediments to the effective functioning of the market, that is, information asymmetries. A growing labour force participation and the eradication of multiple barriers on information access shall drive an increase in the number of market transactions, boost the presence in global markets, and reduce transaction costs. Indirectly, unbounded access to the ICT drives socio-economic development through a better access to education and knowledge, more effective functioning of healthcare systems, and many other ways where new technologies may support the functioning of different organisations and mechanisms. That the positive effects of ICTs on, inter alia, education and healthcare systems are qualitative in nature will be unveiled in the long-time horizon, but surely the positive gains emerge progressively in the form of social and economic advance. Capturing the effects of the implementation of new ICTs on social development in numbers remains a very challenging task. This is not only because the availability of long and complete time series in this case is limited but also, and above all, because social development is an extremely complex process, preconditioned by multiple unquantifiable elements, such as culture, religion, social norms and attitudes, tradition, and history. The process of social development is characterised by long-time inertia. Changes are usually slow, and only detectable over the long term. In what follows, we present empirical evidence intended to reveal, at least partially, the relationships between growing ICT deployment and social development. By convention, we concentrate on the 32 European
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economies between 1990 and 2017. ICT development is represented by the two core indicators: mobile-cellular telephony and IU. As for the social development, we concentrate on select elements of socially related economic aspects, which we suppose may be, at least to some extent, affected by the Digital Revolution; we intended to use those indicators, by which changes potentially may be driven by technological progress. In particular, we concentrate on female-related indicators,i as these aspects are often raised in the context of the ‘Opportunity Windows’ that ICT may bring to the overall socio-economic development. Figs. 5.9 and 5.10 present graphical evidence of the statistical relationships of MCS vs social variables and IU vs social variables, respectively. Examining the relationships between consecutive pairs of variables suggests a relatively strong statistical association between ICT and school enrolment (tertiary, as a share of gross). Clearly, the growth of both MCS and IU is accompanied by a fast shift in school enrolments across the European economies. The basic statistics on tertiary school enrolment show rapid changes in this respect that are easily observable since 1990. In some of the countries analysed, the average annual growth rate was extraordinarily high; for instance, Albania (7.6% per annum), Cyprus (7.3% per annum), and Greece and Romania, with analogously high dynamics. The correlation coefficients for MCS vs School and IU vs School are 0.76 and 0.73, respectively. Of course, it is hard to agree that the impact of ICT diffusion on tertiary school enrolment is direct. It should be borne in mind that a high correlation in this respect may be spurious. Growing tertiary school enrolment is, above all, an effect of long-term state education policies. However, it is needless to explain that a positive impact of increasing tertiary school enrolment may, although again indirectly, positively influence female labour market participation. Next, the two analysed indicators are LF_female and LF_female_15_24. In-time dynamics of female labour force participation rates (a share of total labour force) are comparably slow. Significant inertia and time lags characterise the process of changing (herein, growing) women’s share in the total labour force. Across the countries examined, the highest average annual dynamics were observed in Malta (1.2% per annum; a change from 28% to 38%) and Spain (1.05% per annum; a change from 34% to 46%). In the remaining economies, these changes were significantly slower (e.g. Hungary and Slovenia—0.02% per annum), and, in some cases, even negative (Bulgaria, the Czech Republic, Moldova, Poland, Romania, the Slovak i
For details, see Section 5.2 and Appendix G for explanation.
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Republic, and Sweden). Still, as demonstrated in Figs. 5.9 and 5.10, statistical associations between MCS/IU and LF_female are relatively strong; the correlation coefficients are (0.26) and (0.33), respectively. The situation is different when the LF_female_15_24 indicator is considered; the correlation coefficients for MCS vs LF_female_15_24 and IU vs LF_female_15_24 are 0.11 and 0.13, respectively. Initially, we would expect that both growing ICT deployment and increasing scholarisation rates would contribute effectively to dynamically reduce the engagement of young women (between 15 and 24 years) in labour markets. However, when looking at preliminary descriptive evidence summarised in Section 5.2, the time trend (see Fig. 5.3) for averaged values shows a massive drop in LF_female_15_24 by more than 10%. This evidence suggests that, in the European countries, between 1990 and 2017, a significant share of women, instead of entering the labour market between the ages of 15 and 24, stayed in the education system, at university (tertiary) level. This structural break plays a crucial role in the society; well-educated people may foster innovation, which increases economic development and growth over the long term, contributing to the overall welfare of societies. The other side of the story is that, as predicted by many, in the forthcoming years, there will obviously be a significant growth in demand for highly skilled labour forces. Technological progress, especially a broader adoption and usage of digital technologies, generates changes in labour markets—jobs are increasingly flexible and complex. People working in knowledge- and technology-intensive sectors must be able to deal with complex and fast-changing information and provide technological solutions; they must become ‘autonomous and smart workers’. Evidently, technological progress effectively reshapes economies and labour markets, but it also drives the demand for skilled and specialised workers. All these elements, mentioned briefly above, induce people to continue in education instead of joining the labour market. That change is especially visible with respect to female engagement in labour market activities. The behaviour patterns of female labour markets have changed significantly with higher educational attainments and better access to the education system. Faced with a rapid ICT development, labour market pressure for highly skilled workers who can exploit the potential offered by new technologies, and social policies directed towards a higher educational attainment, there have been massive drops in the rates of participation by those aged 15–24 in the labour force. Next, we considered changes in contributing family workers (total/ female), vulnerable employment (total/female), and waged and salaried workers (total/female). We expected that, with increasing ICT deployment and usage, the first two variables might fall, whereas the share in total
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employment of waged and salaried workers might rise. Fig. 5.3 in Section 5.2 reveals a relatively significant drop in all the indicators cited above; however, it is important to emphasise that falls in Family_female and Vulner_female are more radical than are changes referred to the total population. Analogously, increases in upward trends in Wage_female are greater than those observed for Wage_tot. On inspecting the graphical evidence in Fig. 5.9, it may be noted that IU is negatively correlated both with contributing family workers (total/female) and with vulnerable employment (total/female). The correlation coefficients for consecutive pairs of variables are slightly higher for female-related variables. Apparently, there is one outlying country for which both contributing family workers and vulnerable employment shares in total are significantly higher; on the other hand, waged and salaried workers make up a very low share of total employment. This country is Albania, where, for instance, vulnerable female employment increased from 48% in 1990 to 57% in 2017, reaching a peak of 78% in 1999. In the remaining countries in the group, these variables were significantly lower during the whole period analysed. If we exclude Albania from the sample, the correlation coefficients for IU vs Family_female and IU vs Vulner_female are (0.42) and (0.41), respectively. Apparently, drops in contributing family workers and female vulnerable employment are a direct consequence of women moving towards waged and salaried work. These conclusions are supported by panel regression results summarised in Tables 5.3 and 5.4. Similar to the empirical evidence provided in Table 5.3 MCS vs social and quasi-social variables. Panel regression estimates. Period 1990–2017 MCS Rho R2 (within) F (prob > F) No. of obs.
MCS Rho R2 (within) F (prob > F) No. of obs.
School
LF_female
LF_female_15_24 Family_female Family_tot
0.14 [0.00] 0.61 0.74 2228 [0.00] 793
0.008 [0.00] 0.83 0.21 213 [0.00] 865
20.04 [0.00] 0.89 0.38 514 [0.00] 865
20.18 [0.01] 0.81 0.27 304 [0.00] 845
20.15 [0.00] 0.83 0.28 327 [0.00] 846
Vulner_female
Vulner_tot
Wage_female
Wage_tot
20.04 [0.00] 0.91 0.16 154 [0.00] 846
20.02 [0.00] 0.89 0.08 73.9 [0.00] 846
0.01 [0.00] 0.94 0.22 235 [0.00] 846
0.008 [0.00] 0.95 0.16 164 [0.00] 846
Note: All values are logged; fixed-effects panel model applied; constant—not reported; SE below coefficients; in bold— results statistically significant at 5% level of significance; panel—strongly balanced.
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Table 5.4 IU vs social and quasi-social variables. Panel regression estimates. Period 1990–2017 IU Rho R2 (within) F (prob > F) No. of obs.
IU Rho R2 (within) F (prob > F) No. of obs.
School
LF_female
LF_female_15_24 Family_female Family_tot
0.13 [0.00] 0.63 0.74 2189 [0.00] 771
0.009 [0.00] 0.84 0.28 322 [0.00] 837
20.03 [0.00] 0.89 0.33 401 [0.00] 837
20.21 [0.00] 0.83 0.36 443 [0.00] 821
20.18 [0.00] 0.86 0.37 468 [0.00] 824
Vulner_female
Vulner_tot
Wage_female
Wage_tot
20.06 [0.00] 0.91 0.27 292 [0.00] 824
20.03 [0.00] 0.90 0.17 171 [0.00] 824
0.01 [0.00] 0.95 0.31 358 [0.00] 824
0.01 [0.00] 0.96 0.26 288 [0.00] 824
Note: All values are logged; fixed-effects panel model applied; constant—not reported; SE below coefficients; in bold— results statistically significant at 5% level of significance; panel—strongly balanced.
Section 5.3, in this case, the estimated parameters held by MCS and IU are very close for the respective model specifications. For instance, when estimating the impact of ICT on school enrolment, the parameters standing by MCS and IU are (0.14) and (0.13), respectively. Apparently, all the estimated parameters are statistically significant and hold the expected sign. As might be expected, the highest estimated parameters are demonstrated when examining the impact of ICT on contributing family workers: both total (Family_tot) and female (Family_female). The highest parameter (0.21) is held by IU when estimating its impact on changes in Family_female. The potential impact of IU on the size of vulnerable employment is significantly weaker; however, it is still significant. The International Labour Organisation defines vulnerable employment as ‘the sum of the employment status groups of own account workers and contributing family workers. They are less likely to have formal work arrangements, and are therefore more likely to lack decent working conditions, adequate social security (…). Vulnerable employment is often characterised by inadequate earnings, low productivity and difficult conditions of work that undermine workers’ fundamental rights’ (ILO, 2010). Obviously, the problem of vulnerable employment, and a high share of the labour force being ‘employed’ as contributing family workers, is not evenly distributed across the European countries and regions. In high-income, well-developed economies, the problem is marginal. However, in more backward regions, both in terms of overall economics and of social and institutional development,
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the problem does exist. The latter is closely associated with a lack of adequate labour market legal frameworks, established social habits and norms, poor education, and—in pure economic terms—a lack of opportunities to find a regular job. Regions suffering from a massive vulnerable employment usually are those where the shadow economy is extensive, and a significant share of the national output is being produced outside of the formal economy. The problem of female vulnerable employment is urgent, as national statistics tend to be significantly higher for women than men in this case. This is the case not only for Albania but also for Romania, Moldova, Greece, and Italy. ICTs as enabling technologies may play a pivotal role in women’s economic empowerment, mainly by opening the ‘opportunity windows’ to different forms of economic and social activities. The ongoing digital revolution will, inevitably, have a positive influence on gender equality. This may happen, potentially, in two different ways. First, technological change, and the economic structural changes that it brings, radically changes the composition of jobs and the skills that are required to perform those jobs. Enabled by the digital technologies, the growing work automation may potentially enhance women’s labour market inclusion by restructuring the demand for typical women’s jobs differently from that for typical men’s jobs. What is observed across branches is that, on the one hand, robots and algorithms simply substitute for different jobs, but, on the other hand, they complement jobs in occupations such as management, research, engineering, legal services, and many others. These shifts bring new opportunities not only to women, of course, but also to the whole labour force. Next, digital technologies enable ‘home-based’ jobs in the area of trade or services, which constitutes an important alternative for women willing to leave ‘vulnerable sectors’ and be engaged in formal market activities. Digital technologies allow the elimination of multiple barriers to full-female economic activity. To a large extent, ICT offers opportunities for one to become fully active financially, entrepreneurially, and economically (Gardey, 2015; Goldin, 2006). Newly emerged services, due to the implementation of ICT solutions, may effectively help women to access new markets, work flexibly and distantly, acquire and interact with customers, receive training and provide mentoring, improve their financial autonomy, and gain access to finance for their ventures. Apparently, the Digital Revolution seems to favour the female labour force, since women face, on average, a lower risk of being replaced by machines than do men. Women’s often superior social skills represent a comparative advantage in the digital age, and this is
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particularly so when social skills are complemented with higher education and advanced digital literacy (Vazquez & Winkler, 2017).
5.5 Gaps growing—Gaps narrowing? This last section is dedicated entirely to the examination of the process of convergence among the 32 European countries between 1990 and 2017. We aim to verify the hypotheses on the existence of economic and social convergence across the countries. To this end, we use three different empirical approaches allowing us to determine whether economic and social development gaps are diminishing across the European countries (hence, the process of convergence is reported) or, vice versa, cross-country gaps are growing (hence, the process of divergence is reported). More specifically, we calculate the standard deviation (SD), the Gini coefficient (classical inequality measure), and coefficients of variation (CV) to verify the hypothesis on σ-convergence, and based on the neoclassical growth theory, we estimate regression models to verify the hypothesis on β-convergence. To enrich the whole picture, for individual variables, we draw density functions to examine changes in the distribution of variables during the period examined (see Appendix I). Figs. 5.11 and 5.12 show the process of economic σ-convergence and changes in economic cross-country inequalities, and Fig. 5.13 shows social σ-convergence and changes in cross-country social inequalities. To provide an exhaustive picture, we examine each variable separately. A brief look at the consecutive graphs in Fig. 5.10 enables several specific tendencies in cross-country inequalities to be observed. The first interesting observation is that, with respect to the gross per capita income and gross income per person employed, drops in cross-country disparities are noted; however, in both cases, drops in the Gini coefficient and the coefficients of variations are relatively weak. For GDP per capita, the Gini coefficient falls by only 0.02 (from 0.40 in 1990 to 0.38 in 2017); however, there are significant rises in the mid-1990s. Notably, despite dropping Gini coefficients and coefficients of variations, we observe massive increases in standard deviations (from approximately 10,000 in 1990 to almost 14,000 in 2017). The latter suggests that, despite the fall in cross-country inequalities in relative terms, in absolute terms, the GDP per capita gaps are growing. Analogous tendencies are detected for GDP_empl and also for Ind_VA and Ind_empl. Looking at inequality changes in regard to Serv_VA and Serv_empl, we observe that, in these cases, the drops in cross-country disparities are massive. The Serv_VA
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.3 .25 .2 .15 .1 .05
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1990 1995 2000 2005 2010 2015
.35 .3 .25 .2 .15 .1
15 14 13 12 11 10 1990 1995 2000 2005 2010 2015
Fig. 5.11 Economic variables. σ-convergence, SD, and Gini coefficient. Period 1990–2017. Note: σ-Convergence represented by the coefficient of variation; on x-axis—Gini coefficient and SD; on y-axis—coefficient of variation; solid line—SD; long-dash line—Gini coefficient; shortdash line—coefficient of variation.
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Fig. 5.12 Economic (trade-related) variables. σ-convergence, SD, and Gini coefficient. Period 1990–2017. Note: σ-Convergence represented by the coefficient of variation; on x-axis—Gini coefficient and SD; on y-axis—coefficient of variation; solid line—SD; long-dash line—Gini coefficient; short-dash line—coefficient of variation.
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Fig. 5.13 Social variables. σ-Convergence, SD, and Gini coefficient. Period 1990–2017. Note: σ-Convergence represented by the coefficient of variation; on x-axis—Gini coefficient and SD; on y-axis—coefficient of variation; solid line—SD; long-dash line—Gini coefficient; short-dash line—coefficient of variation.
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coefficient of variation fell from 0.31 to 0.10 in 2017; for Serv_empl, the fall was from 0.25 to 0.14.j In these two cases, we also note diminishing standard deviations, which may confirm our supposition of the gradual eradication of cross-country differences as all the European countries head rapidly towards service-based economies (see also graphs in Appendix I). These results are consistent with what is reported for the agricultural sector in terms of the gross value addition created there. Notably, since 1990, the role of the agricultural sector has been declining in Europe in terms of its total contribution to countries’ GDPs, and that tendency is also demonstrated in falling crosscountry inequalities in this respect. A rapid fall in the coefficients of variation and Gini coefficients for Agr_VA and Agr_empl supports this view. The European economies are becoming more and more similar in terms of the role that the agricultural sector plays in creating their national wealth. Unexpectedly, changes in LF_15_24 show growing cross-country disparities. Massive increases in all the three considered inequality measures clearly show increasing gaps in this respect, both in relative and absolute terms. Growing inequalities in LF_15_24 may be caused by significant intercountry disparities in the dynamics of change in this regard. Notably, some countries move fast ahead and fast diminish the labour force participation rate for those aged 15–24 (as a share of total), whereas others demonstrate negligible dynamics in this process. In effect, countries differ more in this respect in 2017 than was observed in 1990. Turning to a brief analysis of trade-related economic variables (see Fig. 5.12), we observe that, in the cases of three variables (HT_exp, ICT_good_exp, and ICT_imp_exp) falls in cross-country gaps are striking. The coefficients of variation for the respective variables dropped radically; from 1.3 to 0.8 for ICT_good_exp and from 0.7 to 0.4 for ICT_good_imp. A similar tendency, although decreases are less dynamic, is reported for Comp_serv_exp. For the remaining two indicators, ICT_serv_exp and Comp_serv_exp, despite an abrupt shift in 1997, declines in inequalities are observable, although less radical than in the case of other economic variables. The graphical evidence summarised in Fig. 5.13 shows changes in crosscountry social inequalities. With regard to LF_female and LF_female_15_24, general tendencies and direction of changes are clearly identifiable, however, in the cases of the remaining variables, the picture is scattered and unclear and does not allow rigid conclusions to be drawn. In the case of LF_female and j
Authors’ calculations.
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LF_female_15_24, the Gini coefficient, coefficients of variation, and standard deviations calculated for the respective variables move in the same direction; although gradual decreases in cross-country disparities are manifested for LF_female, the opposite is the case for LF_female_15_24. Our preliminary evidence on the process of economic and social convergence seems to be slightly confusing, especially in the cases of the indicators examined. On the one hand, we observe fast-dropping cross-country inequalities, expressed as the Gini coefficient, whereas, on the other hand, the coefficients of variation seem to go in the opposite direction. Hence, to re-examine the existence of the process of σ-convergence, we provide additional empirical evidence on economic and social β-convergence. Figs. 5.14–5.16 plot the average annual growth rates vs the level of variable in the initial year of analysis. By definition, the process of β-convergence is demonstrated when this relationship is negative, which shows that initially poorer countries tend to grow faster compared to initially richer countries. Inevitably, this shall lead to a gradual eradication of cross-country disparities. The graphical evidence on economic and social β-convergence is next enriched by regression estimates—see Appendix J. Figs. 5.14 and 5.15 display the scatterplot for the average annual rates of growth of the respective economic variables vs their initial level in either 1990 or the earliest year available. Both suggest unquestionably that, among the group of European countries analysed between 1990 and 2017, the process of economic β-convergence is unambiguous with regard to all the variables examined. In all the cases, the statistical relationship is negative, which initially enables confirmation of the existence of the process of convergence. According to these data, we may conclude that, among these 32 European countries, economic gaps (disparities) were diminishing gradually between 1990 and 2017. This may support our initial supposition that a rapid ICT deployment enhances and accelerates the growth of relatively more backward regions. As expected, the process of gap elimination seems to be the fastest in the cases of Serv_VA and Serv_empl. This coincides with our initial evidence, as provided at the beginning of this section. A relatively fast process of convergence is also revealed for GDP per capita, HT_exp, and all the remaining trade-related variables. This graphical evidence demonstrates evidently how dynamic changes in this respect were. Hence, the period between 1990 and 2017 is revealed not only as a period of rapid shifts in economic material wealth—see gross per capita income and growth in per employee final production—but also as a period of dynamically diminishing cross-country disparities. Obviously, these economic disparities
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Fig. 5.14 Economic β-convergence. Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.
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Fig. 5.15 Economic β-convergence (trade-related variables). Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.
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Fig. 5.16 Social β-convergence. Period 1990–2017. Note: All values are logged; on x-axis—variables (all logged) in 1990 or the earliest available year; on y-axis—average annual growth; kernel-weighted local polynomial smoothing applied with confidence interval at 5% significance.
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were not eradicated totally, but significantly higher average annual growth rates observed for the relatively poorer countries pave the way for a less differentiated Europe, at least in economic terms. We claim, that, despite the positive impact of a broader deployment of new technologies being neither direct nor immediate in the cases of the variables examined in Fig. 5.14, the impact of ICT is more direct and obvious in the cases of ICT trade-related indicators (see Fig. 5.15). A fast diffusion of new technologies enabled all these economies to intensify their international trade activities in this respect. The growing domestic demand for ICT tools and services, boosting the demand from firms for ICT solutions, evidently was the driving factor of export-import flows. Our graphical evidence on economic β-convergence is then enriched by panel regression estimates summarised in Appendix J. As in previous cases, we examine each variable separately. The sample composition and period of analysis are analogous, as in the analysis above. In 10 out of the 15 cases examined, the estimated regression coefficients hold the expected negative sign and are statistically significant. Both OLS and robust regression estimates returned analogous (in qualitative terms) results, with the only exception being HT_exp, which confirms the relative stability of these estimates. The highest parameters were obtained for Ind_VA (2.6), Serv_VA (2.83), ICT_good_imp (3.68), Comp_serv_exp (3.08), and Comp_serv_imp (3.45), which shows that the process of β-convergence is fastest in this regard. The estimated parameters allow the calculation of specific half-time, hence the time needed to diminish cross-country inequalities by half. The shortest half-times are 14.4 years for ICT_good_imp and 14.8 years for Comp_serv_imp. Next, there is 15.8 years for Comp_serv_exp, 16.5years for Serv_VA, and 17.3years for Ind_VA. This means that, for instance, for ICT_good_imp, only 14.4 years are needed to diminish cross-country disparities by 50%. For GDP per capita and GDP_empl these half-times are approximately 30 years. These results allow for one very important observation; between 1990 and 2017 economic disparities among the European countries have declined massively. Finally, we take a look at the process of social β-convergence. The graphical evidence in Fig. 5.16 also suggests the existence of the process of social convergence among the European economies, with reported falling inequalities in this regard. However, for some of examined indicators— FL_female_15_24, Family_tot, and Family_female—the statistical relationship between average annual growth rates and the initial value of a given variable is negative, although not that ‘impressive’, as for School, FL_female,
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or Vulnet_tot. The table in Appendix J summarises regression estimates from which we may deduce more precisely the speed of the process of social βconvergence. Considering OLS estimates, in only four out of nine cases, the parameters of estimates are negative (as expected) and statistically significant. These cases are School, LF_female, Vulner_tot, and Vulner_female. The highest parameters are for School (2.91) and LF_female (2.67), which automatically implies the shortest half-times—16.2 and 17.1 years, respectively. However, these results are not surprising. Profound changes in the European education system encouraged more girls to stay at school, which, on the one hand, increased the tertiary enrolment rate, but, on the other hand, inspired women to get more actively involved in labour market activities. For the remaining social indicators, the process of social β-convergence was not confirmed by OLS estimates. Although the returned parameters were negative (with the only exception being LF_female_15_24), they were statistically insignificant. When we ran robust regressions, all parameters of the estimates, except for LF_female_15_24, were statistically significant again, which suggests that the examination of the process of social βconvergence does not give unambiguous and robust results. However, it should be noted that the process of declining cross-country disparities in the level of social development is, by its nature, much slower than is the process of economic convergence. Social convergence requires, to a large extent, changes in social norms, attitudes, and expectations. It is also preconditioned by economic shifts, legal regulations, and state policies. Social changes are always slow and characterised by high in-time inertia. The primary goal of this chapter was to identify whether the process of ICT diffusion and deployment of new technologies enhances two important processes across the European countries. We aimed to determine whether ICT may be claimed as the driving force of socio-economic development and whether ICT changes are accompanied by declining cross-country disparities in respect of social and economic development levels. To meet these goals, we selected a bundle of social and economic indicators that approximate, at least to some extent, the overall socio-economic welfare. Our general findings provide support for the hypothesis that the growing deployment of ICT is associated positively with the process of growing socio-economic development and also contributes to various structural shifts and declining inter-country inequalities. It seems likely that this ICT contribution is either direct or immediate. Nevertheless, our empirical evidence detects the statistical relationships exclusively, and one must remember that these correlations may be simply spurious. A strict identification of the ICT
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impact channels is a challenging task, and the processes of social and economic development and their determinants are hard to capture in numbers. No equation can show fully and profoundly the complexity and multidimensionality of the impact of ICT on the economic sphere of life. The causality between ICT diffusion and socio-economic development seems to be obvious but hard to quantify. Finally, countries have been carrying out rapid ICT deployment only since 1990; hence, there is limited availability of time series to facilitate a more profound analysis of the relationships between ICT and socio-economic development. This is a serious limitation that our results may lack robustness; severe time lags may emerge between the root causes, i.e. ICT, and the outcomes, i.e. leveraging the overall socioeconomic welfare.
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Further reading Bandiera, O., Buehren, N., Burgess, R., Goldstein, M., Gulesci, S., Rasul, I., et al. (2017). Women’s empowerment in action: Evidence from a randomized control trial in Africa. World Bank.