Formulation of Digitization Impact Index (DII): A Cross-Country Empirical Study (2009–2018)

Formulation of Digitization Impact Index (DII): A Cross-Country Empirical Study (2009–2018)

Copyright: © 2022 |Pages: 34
DOI: 10.4018/IJIDE.292628
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Abstract

This research paper is aimed at formulation of Digitization Impact Index (DII) based on five major dimensions, i.e., Share of Information and Communications Technology (ICT) goods in trade (SICT), Per capita Income Growth Rate (PCIGR), Labor Participation Growth Rate (LPGR), Share of Research and Development Expenditure to Income (RDI), and The Digital Economy and Society Index ® (DESI) respectively across 6 sample European countries for the time period of 10 years (2009 – 2018) (Base Year = 2008; DII = 100) by two methods to evaluate and analyze the impact of digitization especially in the perspective of two extremely important macroeconomic factors related to growth of per capita GDP and employment generation among the working population since the era of digitization is synonymous to economic growth and opening of new vistas of employment regardless of geographical locations.
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Introduction

The major problems regarding digital economy are associated with its identification and classification as per the standard norms of industrial classification. Tapscott’s (2015) groundbreaking work has also failed to offer a comprehensive structure of the digital economy other than ‘having to do something with internet, computer, and money’.

Marxian economists like Balkin (2008) and Zuboff (2019) have claimed that it is a new stage of capitalism that has emerged after the Fordist era which has led to “surveillance capitalism” and culminating into “surveillance state”. In view of the structural difficulties to define the nature of digital economy, Jordan (2020) has proposed to define the digital economy in terms of economic practices. He has treated “digital economic practices as repeated and patterned habits of creating, exchanging and consuming a huge range of goods and commodities that make up the wealth of society, while understanding that often the meanings of these commodities must themselves emerge from within those practices”. The basic difference between the digital economic practice and the common economic practice is that each digital practice is defined by a community of activities which offers a scope of monetization. He has further highlighted two forms of causations for digital economic practices. The first form of causation lies in the way when an action may be ‘formed and reformed in multiple settings and multiple meanings’ with different agents while these different uses and application do not necessarily reconcile to a single, consistent or unified moment or action. The second form of causation is related to collection and generation of information regardless of platforms which are not fully formed but ready to be distributed and termed as informational actions (Jordan, 2015). The key causal mechanism for both the digital economic practices is related to the segregation of the produced information (data) as property which originates from the informational actions. Jordan (2020) has mentioned three different ways in which information can be transformed into property such as public domain, private property, and distributed property. These processes are linked by three divisions: value, property, and profit. The relationship between these three divisions is shown in Figure 1.

Figure 1.

Digital economic practices (Source: Jordan, 2020; p. 185)

IJIDE.292628.f01

Tapscott (2015) has identified 12 salient themes of the new economy as:

  • Knowledge,

  • Digitization,

  • Virtualization,

  • Molecularization,

  • Integration / Internetworking,

  • Disintermediation,

  • Convergence,

  • Innovation,

  • Prosumption,

  • Immediacy,

  • Globalization, and

  • Discordance.

Thus taking into consideration the extremely complex and dynamic nature of digital economy in view, an attempt has been made to formulate a Digitization Impact Index (DII) based on two conventional statistical methods: first, by means of the Changing Base Year Method (Base Year: 2008) and second, by using the Fixed Base Year Method (Base Year: 2008) (Das, 2008) for a sample size of 6 European countries in a time series study of 10 years (2009 – 2018).

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