The document summarizes a presentation on measuring the digital economy using evidence from BRICS countries. It finds that increased investment in digital technologies is positively associated with labor productivity, though the impact may be delayed due to measurement challenges and a gestation period. While digitalization has expanded rapidly, productivity gains are not always immediately visible in economic statistics, consistent with the "productivity paradox". The study aims to help inform economic policymaking, business decisions, and investment within and between BRICS nations.

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Digitalisation, Productivity, and Measurability of Digital
Economy: Evidence from BRICS
Thabo Gopane
University of Johannesburg
South Africa
5th International Conference in Digital Economy
11-13 June 2020, Virtual Presentation

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 DEFINITION: New Digital Economy … [is] the combination of mobile technology, ubiquitous access
to the internet, and the shift toward storage, analysis, and development of new applications in the
cloud…. Van Ark (2016:3).
 To Elaborate further,
… core activities of digitalisation [are], ICT goods and services, online platforms, and platform-
enabled activities such as the sharing economy … [therefore] … digitalisation encompasses a
wide range of new applications of information technology in business models and products that are
transforming the economy and social interactions. International Monetary Fund (2018, bold added)
 EXAMPLES: For good or bad, there is digitalisation everywhere:
o e-commerce: Alibaba, Amazon, Rakuten,
o search engine with online advertising: Google, Yahoo; free search engines: Wikipedia,
o social networks: Twitter, Facebook, LinkedIn, YouTube;
o cloud computing platforms: Amazon, Apple, Cisco, IBM, Google
 But digitalisation innovation brings along undesirables:
o Criminality: 419 Scams, Tender fraud, celebrity identity fraud, business fraud, money
laundering, immorality, publication fraud, identity fraud, public indecency,
o International spillover: financial risk, economic contagion, financial crises
 DIGITALISATION: positive disruptions dominate, economy-wide sectors, e.g: education,
agriculture, financial, Broader economic linkages, (Rinaldo et al, 2014; Gopane, 2018)
INTRODUCTION
CONCEPT AND DEFINITIONS

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 HYPOTHESIS: If digitalisation is good to the economy overall, then we should observe:
o Beneficiation towards firms, workers, and consumers- because of- easier market access; better
technology; innovation improvements; trust; and competitiveness, among others.
o All this should translate into improved labour productivity (higher output per worker). Does it?
 Historical puzzles:
o [1980s - 1990]: persistent increase in computer purchase in USA – no corresponding
increase in productivity. Snippet: We’d Better Watch out, New York Times Book Review,
Economics Nobel Laureate, Robert Merton Solow (1987: 6)
‘we see computers, everywhere except in the productivity statistics’.
Now known as: ‘productivity paradox’
o [1990’s - early 2000’s]: New economy – positively related to productivity, but short lived.
Eliasson et al. (2004)
o Current: “Despite a rapid increase in business spending on capital and services in ICT, the
New Digital Economy … has not yet generated any visible improvement in productivity growth”.
Van Ark, (2016).
 PARADOX: Why is digitalisation not matched with increased productivity?
EMPIRICAL PROBLEM

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PRODUCTIVITY PARADOX
Original quotation

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CONCEPTUALISING DIGITAL ECONOMY MEASUREMENT: SOME PERSPECTIVES
 The Organisation for Economic Cooperation and Development (OECD, 2011, 2019a)
o e-commerce (electronic commerce), and measurement focus on digitally-delivered service.
 Voorburg Group on Service Statistic (Barrera, 2018) focuses on:
o Digitally-ordered
o Digitally-delivered
o Platform-enabled transaction
DIGITAL ECONMY
MEASUREMENT PROBLEM

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Conceptualising digital economy measurement:
o The inner-most: e-commerce is suggested by OECD
o Second layer: Voorburg Group: digital transactions.
o The third layer: Cooper and Xu (2018).
DIGITAL ECONOMY
MEASUREMENT PROBLEM

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Sources of measurement problems for digital economy
International Monetary Fund (2018)
o lack of adequate sectorising or industry classification for internet platforms;
o adjustment errors in deflators for digital products;
o gaps in measuring activities of online platforms;
o understatement of growth and productivity;
o services that are self-produced,
o complexities of services that are volunteer-produced,
o Services that are platforms produced;
o limitation on measuring cross border remittances,
o the need to treat data as product conflicts with current practice,
o the seeming arbitrariness in distinguishing: digital sector vs digital economy,
o old unsolved difficulty : should household non-market production be counted?
DIGITAL ECONMY
MEASUREMENT PROBLEM

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 Numerous terms used to describe digital economy,
o New economy, Eliasson et al (2004)
o Internet economy, Tapscott (1994,
o Web economy, Watanabea et al. (2018)
o New Digital Economy, Van Ark (2016)
 This paper compliments studies that investigate
o Digital adoption (Andrews et al, 2018),
o Digital contribution to productivity (Gal et al, 2019),
o Digital adoption policies and online platforms (Bailin et al, 2019)
 Economy’s existing measurement problems aggravated
o Measurability problem is matter of inadequacies in national accounting systems (NSA)
(Ahmad & Schreyer, 2016).
o This means that GDP’s measurement weaknesses are provoked by digitalisation challenges
 GDP was originally conceived for non-digital economy
o Inspection conventional NSA’s now broadened to scrutinise digital economy
(Groshen et al, 2017; Feldstein, 2017; Syverson, 2017)
LITERATURE REVIEW: Context

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 Empirical Cases of individual countries: Do you find paradox?
o Swiss: micro-to-macro calibration of New Economy => delayed economic effect or gestation,
Eliasson et al (2004) – no
o New Zealand: TFP fails detect the arrival of GPTs appropriately, as big technological shocks,
Carlawa & Oxley (2008) – yes
o Canada: firm level data for 1999 to 2005 using generalized method of moments, Li (2014) –
yes
o Korea: dynamic general equilibrium, impact of ICT on economy, 1996-2005, & 2006 2015
=> economic stagnation, Chung (2018) – no
o Partial equilibrium analysis => uncaptured GDP, Watanabea et al. (2018) – yes
o Conceptual graph analysis => lagged economic effect, Brynjolfsson et al. (2019) – yes
 Global studies (multiple countries together)
o Countries x 42: estimated translog stochastic production frontier, 1993–2001,
Dimelis & Papaioannou (2015) – yes. USA dominants
o Digital economy: Measurement & mkt share: 15%: 2016, & 2025: 24%, Copper & Xu (2018)
LITERATURE REVIEW: Prior Studies

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Contribution of the study: case productivity paradox based in a regional economic bloc setting
 Regional economic bloc: by design, provides some value of from economic integration.
 Conjecture: this case of synergy linked economic situation provides alternative test
environment to evaluate digital economy
 BRICS, an acronym: Brazil, Russia, India, China, and South Africa.
 BRICS account for more than 40 percent of world population and 24 percent of total world
Gross Domestic Product (before COVID19 effect)
 BRICS has ongoing digitalisation programmes at national, regional bloc levels
CONTRIBUTION

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𝑙𝑛
𝑌𝑡
𝐿 𝑡
− 𝑙𝑛
𝑌0
𝐿0
= 1 − 𝑒−𝜆𝑡
𝛼
1 − 𝛼 − 𝛽
𝑙𝑛
𝐼𝑡
𝐷
𝑌𝑡
+ 1 − 𝑒−𝜆𝑡
𝛽
1 − 𝛼 − 𝛽
𝑙𝑛
𝐼𝑡
𝑁
𝑌𝑡
− 1 − 𝑒−𝜆𝑡 𝑙𝑛 𝐴0 − 1 − 𝑒−𝜆𝑡 𝛼+𝛽
1−𝛼−𝛽
𝑙𝑛 𝑛 + 𝑒𝑡
VARIABLES
o change in labour productivity where 𝑌, and 𝐿 are GDP and labour quantities,
o The subscripts 𝑡 and 0 represents time.
o Investment (𝐼) relative to GDP in digital technologies
𝐼𝑡
𝐷
𝑌𝑡
,
o Investment relative to GDP in none-digital technologies
𝐼𝑡
𝑁
𝑌𝑡
,
o Initial measure of technological efficiency (𝐴),
o Population growth rate (𝑛).
o All variables are transformed with natural logs (𝑙𝑛).
o The parameters, 𝜆, 𝛼, and 𝛽 are estimated in the model
o Speed of digital diffusion : 1 − 𝑒−𝜆𝑡
ECONOMETRIC MODEL

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DATA: Background

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DATA: Summary Descriptive Statistics
Table 1: Summary Descriptive Statistics
Variable Observation Mean Std Deviation Minimum Maximum
Labour productivity 145 -0.2388 1.8680 -3.8606 3.0000
Digital 145 4.3149 2.5677 0.0000 9.4376
None digital 145 0.8986 0.8936 -1.5000 3.7000
Technical efficiency 145 0.1765 0.7901 -2.3900 1.5200
Population growth 145 -0.0068 0.2576 -2.1717 1.6812
 The data set sourced from: Conference Board (2019’s Total Economy Database.
 Numbers are deflated using deflators by Byrne and Corrado (2017).

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RESULTS I: Regression Output
Table 2: Empirical Results
Variable Coefficient Robust Std Errors P_value
Technical efficiency 1.4116 0.2420 0.0000***
Digital investment 0.2325 0.0832 0.0060***
None digital
investment 0.4861 0.1637 0.0040***
Population growth -0.9051 0.3479 0.0100***
Implied:
𝜆
𝛼
𝛽
0.6557
0.1691
0.3537
Constant -1.7069 0.5121
0.0000***
F_statistic (8, 136) 43.0200 0.0000***
R_squared 71.68
Observations 145.00
Notes: Statistical significance at, ***1%, **5%, *10%

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RESULTS: Marginal Analysis

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RESULTS: Summary
 Statistical model was validated through standard econometric techniques:
o F-statistic satisfies the model at less 1% percent level.
o All coefficients are statistically significant at less than 1% level.
 The economic significance of the model is consistent with expectations.
o Positive technical efficiency may imply non convergence (or catch up) in the sense of
growth model theory (Mankiw et al. 1992).
o Coefficient on population growth is negative as expected since an increase in population
decreases labour productivity due to thin spread for given investment magnitude
 Coefficients on both digital and none digital technologies have a positive association with
labour productivity in line with economic intuition.
 Implied parameter estimates, α, and β are marginal productivity for digital and none digital
technologies respectively.
 Regarding digitalisation: the empirical results say that for a 1% increase in digital
technology, labour productivity should increase by 16.91% across BRICS countries

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CONCLUSION
Findings of the study:
o Consistent with the studies of new productivity paradox.
Reasons:
o Puzzle
o Measurement problem
o Gestation period: time lag between new technology impact on productivity, and
economic measurement.
Policy Implication:
o Economic policy application at country, and regional bloc level.
o Business decision making, product marketing, and expansion information
o Global investors in technology sector
o BRICS is a relatively new economic bloc: repeat study with expanded set
recommended, as well as similar test in other economic blocs.

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PRODUCTIVITY PARADOX
Economics Nobel Laureate, Robert Merton Solow

19. Thank you