Annals of the „Constantin Brâncuşi” University of Târgu Jiu, Economy Series, Issue 1, volume I/2015
HUMAN CAPITAL, THE DIGITAL DIVIDE, AND THE POSSIBLE
CONNECTION TO THE FLOW-FUND ANALYSIS OF SOCIOECONOMIC
METABOLISM
RALUCA I. IORGULESCU
SENIOR RESEARCHER, PH.D., INSTITUTE FOR ECONOMIC FORECASTING−NIER,
ROMANIAN ACADEMY, ROMANIA
e-mail: [email protected]
Abstract
Poverty and economic growth are interconnected research fields via the standard concept ‘human capital’ and
in modern societies their analysis is intertwined. In the past decades, more and more research has considered modern
economies as complex systems embedded in the biosystem. Under this assumption, scenario analysis based on the
concept of socioeconomic metabolism is used to complement more traditional forecasting methods.
Multi-Scale Integrated Analysis of Societal and Ecosystem Metabolism (MuSIASEM) is developed on the
framework of Georgescu-Roegen’s 'flow-fund' model of production and it characterizes socioeconomic systems as
metabolic systems. In the context of this novel approach, concepts like bio-economic pressure enable scenario analysis
for the evolution of the whole or partial socioeconomic system.
The Human Activity Fund is a concept central to the socioeconomic metabolic flow-fund framework and it is
related, besides human capital, to one of the newest sources of inequality, namely the ‘digital divide’. The digital divide
is a result of the IT Revolution and recent research has associated it with the ‘rewiring of the human brain’.
This paper introduces the concept of human capital and presents some of the measures employed in the
literature. It also investigates the possible change, due to the digital divide, in the metabolic pattern of a society
associated with ‘quality’ changes of the Human Activity Fund through demographic structure or poverty pattern
alterations. The ‘internet illiteracy’ indicator is used as a first step to investigate the connection between the
MuSIASEM concept Human Activity Fund and the standard concept Human Capital.
Keywords: human capital; societal metabolism; MuSIASEM; digital divide; demographic change; poverty; economic
growth
JEL Codes: J11, J24, Q57
1. Introduction
Human capital became a concept of interest in the past six decades, starting with the 1958 Journal of Political
Economy paper “Investment in Human Capital and Personal Income Distribution” by Jacob Mincer [1]. The impact of
human capital on economic growth, analysed using the Galor-Weil model [2], triggered a rich literature on the topic
[3]-[7] including an interesting critique by an experimental nuclear physicist turned economist [8]. Human capital is
also a vital ‘ingredient’ for studies discussing poverty traps [9]-[10].
Over the past 25 years, the concept of ‘social metabolism’ and the associated research became increasingly
stronger in socio-environmental studies. For example, de Molina and Toledo [11] offer an impressive and
comprehensive presentation of this idea. However, the flow-fund framework was first introduced by GeorgescuRoegen [12] to represent the economic production process subject to the entropy law; later, Giampietro and Mayumi
[13]-[15] developed on this framework a new approach, the Multi-Scale Integrated Analysis of Societal and Ecosystem
Metabolism (MuSIASEM). This method of analysis has been used to investigate, among other topics, multidimensional
poverty, land poverty and rural metabolism [16]-[18].
This paper investigates the connection between human capital and the demographic structure or poverty pattern
at country level. Both the demographic structure and poverty pattern are linked to the metabolic pattern of the society
under MuSIASEM scrutiny, among other factors, through the alteration of the ‘quality’ of the Human Activity Fund.
This discussion lays the foundation for further investigation of the way Human Activity Fund relates to human capital.
The rest of the paper is organized as follows: Section 2 presents an overview of the ‘human capital’ concept and
introduces some of the measures employed in the literature in Section 3; Section 4 discusses the ‘quality’ of the Human
Activity Fund through the connection with a generational demographic structure and the digital divide; Section 5
further investigates this connection by contrasting two EU cases: Romania and Sweden; Section 6 concludes.
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2. The concept of human capital
The concept of human capital was mentioned by Adam Smith as one of the types of capital: “The acquisition of
such talents during … his education, study, or apprenticeship, always costs a real expense, which is a capital fixed and
realized, as it were, in his person. Those talents, as they make part of his fortune, so do they likewise of that of the
society to which he belongs.” [19] According to Goldin [20], the earliest the term “human capital” was used in
economics with a formal meaning was probably in 1897 by Irving Fisher [21].
The Oxford English Dictionary defines human capital as “the skills the labor force possesses and is regarded as
a resource or asset” and it relates to investments in people such as education, training, and health increasing that
person’s productivity. More than fifty years ago, Schultz [22] provided some examples of investment in human capital:
“Direct expenditures on education, health, and internal migration to take advantage of better job opportunities...
Earnings foregone by mature students attending school and by workers acquiring on-the-job training… The use of
leisure time to improve skills and knowledge...”
In the fifties and sixties, the term ‘capital’ in association with human beings was a sensitive issue as it could
take one to see people as slaves. This is the reason why Gary Becker [23] in his 1964 book Human Capital: A
Theoretical and Empirical Analysis, with Special Reference to Education added the long subtitle to prevent criticism
and he even mentioned he has been reluctant to use “human capital” in the title.
Robert Solow’s pioneering work on economic growth in the 1950s [24] led to the formulation of growth
accounting and the discovery (or uncovering) of the “residual”. Further, Mankiw, Romer and Weil [25] added human
capital growth to the Solow model to close the “residual” gap while Jones and Romer [26] discussed the growth of
knowledge and of other “non-rival” goods.
Goldin [27] used the research of Acemoglu, Johnson, and Robinson [28] to show how institutions in ex-colonies
were growth-dependent, and the research of Engerman and Sokoloff [29] and Sokoloff and Engerman [30] that adopted
a “similar logic and underscore the fact that the same European powers that brought bad institutions to some places
brought good institutions to others” to illustrate the connection between Human Capital – Institutions – Economic
Growth.
If no one contests the interconnection among education and technology on one side and human capital on the
other side, the the ever escalating education – technology race the world has witnessed in the past hundred years should
be no surprise. Goldin explains why using the standard supply and demand approach:
“That is, there is a race between the supply of skills and the demand for skills with the return to education as
the equilibrating price. When the return is high, the supply of new skills will be greater and when it is low,
the supply of new skills will be smaller.
New technologies increase the demand for superior skills… Technological advances throughout the last
century increased demands for yet more human capital.” [31]
In order to successfully increase human capital through education, the health of workers needs to be at least
maintained if not increased. Goldin [32] makes the point that increased access to resources is followed by an improved
nutrition that in turn increases health human capital and “more health human capital allows people to be more
productive” and, as a result, to earn more. Some of the measures for health human capital are mortality, heights and
weights for adults and for children during the growth spurt, infant weights, body mass index (BMI), chronic and
infectious disease rates, expectation of life at birth, and self-perceived health status.
3. Technology and Human Capital Measurement
Looking at the education aspect of human capital, Cohen and Soto [33] test the quality of their data in income
growth regressions using the significance of the schooling variable. They suggest that earlier literature has found that
increases in education are not associated with economic growth due to the low informational content of the schooling
series in first differences.
Equation (3) is the standard equation estimated in the literature:
Δ log(qt ) = π0 + π1Δ log(kt ) + π2Δ log(ht ) + Xt B + εt
(1)
where q is output per worker (or per capita), k physical capital per worker, h human capital per worker, X is a set of
additional variables (including the initial levels of income, physical capital or schooling) that are intended to capture
convergence or endogenous growth effects, and εt is the residual.
The number of years of schooling has a strong impact on the measured growth rate of human capital and it can
be used as a proxy for human capital, assuming a linear relationship between both variables. Pritchett [34] defines the
stock of educational capital (with workers without education explicitly excluded from the variable h) as the discounted
wage premium of education over raw labor: h = Cw0(e0.1ys − 1) where C is the discounting factor, w0 is the wage of
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labor with no education, and the wage of a worker with ys years of schooling is assumed equal to w0e0.1ys and the log of
h at date t is given by:
log (ht) = log (C) + log (w0) + log (e0.1yst − 1)
(2)
If yst is the number of years of schooling of the labour force, with the role of experience ignored, another
macroeconomic measure is based on Mincer’s wage regressions [35] that consider the logarithm of human capital as:
log (ht) = a + b × yst + et
(3)
This measure of human capital which includes the human capital of non-educated workers was adopted in
empirical macro studies through the works of Bils and Klenow [36], Heckman and Klenow [37] and Krueger and
Lindahl [38].
Glomm and Ravikumar [39] introduce an overlapping generations model with heterogeneous agents in which
human capital investment through formal schooling is the engine of growth while Blankenau and Simpson [40] explore
the expenditure–growth relationship in the context of an endogenous growth model in which private and public
investment are inputs to human capital accumulation.
Building on their results, Carraro and his collaborators [41] use the hybrid model WITCH in which the
production of human capital (education expenditure) and knowledge (general purpose R&D expenditure) are affected
by intertemporal spillovers, as “the stock available in the economy at each point in time contributes to the creation of
the future stock.” The increase of human capital (ZEDU) and, respectively, knowledge (ZR&D) is represented using a
Cobb-Douglas function with diminishing returns.
!!"# !, ! = ∝!"# !!"# !!"# !" !!"#
(4)
!!&! !, ! = ∝!&! !!&! !!&! !&! !!&!
(5)
!!"# + !!"# < 1
(6)
!!"# + !!"# < 1
(7)
If HK stands for the existing stock of human capital, IEDU for current expenditure in education, R&D for
available knowledge stock and IEDU for current R&D investments, following Jorgenson and Fraumeni [42], both stocks
depreciate over time with a 2% per annum depreciation rate for human capital (δEDU) lower than 5% per annum for
knowledge (δR&D).
!" !, ! + 1 = !"(!, !)(1 − !!"# ) + !!"# !, !
(8)
!&! !, ! + 1 = !&!(!, !)(1 − !!&! ) + !!&! !, !
(9)
Acemoglu and Autor [43] discuss Goldin and Katz's The Race between Education and Technology [44] where
they quantify the contribution of human capital to economic growth using an accounting framework with human capital
represented as a single aggregate factor (in place of separate measures of low skilled and high skilled workers L and H).
The separate measures L and H, when assuming competitive factor markets, are replaced by unskilled and skilled
wages ωL and ωH and given by their marginal products. The skill premium ω can be obtained as the high wage divided
by the low wage:
!=
!"
!"
(10)
The following measure for the aggregate stock of human capital (E) as efficiency units of labor results:
! = ! +
!"
!"
! = ! + !"
(11)
In what regards aggregate output (Q), it depends on the stock of capital (K), resources (X), and the level of
technology (A). Human capital (E) is amplifying labor expressed as a function of the level of population (P) and the
aggregate labor force participation rate (λ). The aggregate output equation [45] becomes:
! = !(!, ! ∙ !" , !, !)
(12)
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4. The ‘quality’ of Human Activity Fund and the connection THA−Generations−The digital
divide
The MuSIASEM variable Human Activity Fund is the fund of total human activity (THA) expressed as number
of hours of activity available per year for a given population. If different populations have the same number of
individuals their THA is the same:
THA = 365 (days in a year) × 24 ( hours per day ) × POPULATION ( number of people )
(13)
THA = 8760 ( hr of total activity per year per capita ) × POPULATION ( number of people)
(14)
Individuals born between 1925 and 2009 can be grouped as five generations (Table 1), each with its own traits.
These names and characteristics are adopted in the U.S. and other Western countries. Although, some researchers [46]
argue that different cultures might have a different grouping of generations and that any analysis of the global
workforce should take this issue into consideration, this paper will use these commonly referred to generational divides.
Table 1. The ‘Generations’ and their names according to Western listing
Today’s Generations
Born
Age (in 2013)
1
Silent Generation
1925–1946
67–88
2
Baby Boom Generation
1946–1964
49–66
3
Generation X
1965–1979
34–48
4
Millennial Generation (Generation Y)
1980–1994
14–33
5
Generation Z
1995–2009
4–13
Source: Adapted from U.S. Chamber of Commerce Foundation (2012) [47]
The IT revolution is getting more and more global; even in the poorest countries people manage to use social
networking as a new way to communicate. After more than thirty years of multitasking and internet access, scientists
are advancing the idea that the rewiring of the human brain is actually taking place; this is the ‘physical’ effect of the
IT revolution.
Trans-national corporations, with their incessant chase for efficiency and profits, are the agents and
beneficiaries of globalization who call for a levelled ‘playground’. In theory, the free expansion of the IT revolution
would bring uniformity all over the world but in reality it also nurtures a digital divide [48] which is the most recent
cause for increasing inequality.
The structure of the population and implicitly of the workforce as IT-related generations is connected to the
digital divide/brain rewiring as a possible source of inequality with respect to the speed/quality of the same type of
activity. In other words, and in Georgescu-Roegen’s terms, this is a change of the exosomatic metabolism (the energy
used by man outside his body in the socioeconomic and environmental metabolism). At the same time, the biological
processes associated with the structure generations → digital divide/brain rewiring are also related to a change of the
individual endosomatic metabolism (the energy/matter processed within the human body and implicitly the energy
consumed by the brain to function with new wiring).
In time, the Human Activity Fund, for an assumed stable and constant population but with a different brain
wiring due to IT, would have a different ‘quality’ even if the Human Activity Fund (number of hours) remains
constant. Giampietro et al. [49] further analyze this fund considering that
“At the hierarchical level of individual human beings we can define a set of structural types determining
what the system is. Put another way, we characterize the population as being made up of a set of structural
types (categories) of individuals, each category having a determined size depending on the demographic
structure.”… “At the same time, we have to define what the system does at the level of individual human
beings. In other words, we have to define categories, or functional types of human activity” …
“physiological overhead, comprising activities essential to human survival such as sleeping, eating and
personal care; paid work; and other activities such as household chores, leisure and education.”
Different demographic structures corresponding to different structural types could be considered, such as age groups or
gender discussed by Giampietro and his collaborators [50].
When human activity is seen as a fund for societal metabolism, it has to be emphasised that the fund in order to
maintain itself needs to replace itself. Consequently, the ‘quality’ of the Human Activity Fund could be related, among
other factors, to education and health/mental health. Giampietro suggests the concept of bio-economic pressure (PBE)
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to construct scenarios to analyze, for example, the impact of poverty on the entire or partial socioeconomic system
evolution using “the existence of a link between changes in the economic performance of an economy and changes in
the metabolic pattern of society [...] a richer society will demand more services and will work less because of ageing,
increased education and leisure time. Facing these changes the productive sector must be able to supply a larger amount
of products, energy and materials to the rest of society using only a very limited amount of work hours.” [51] Further
research could investigate the connection between changes of the demographic structure or poverty pattern and changes
of the socioeconomic metabolic pattern.
5. Two opposite cases in the European Union: Romania and Sweden
The digital divide in the European Union can be analyzed using Eurostat data [52] that are collected annually by
the National Statistical Institutes and are based on Eurostat's annual model questionnaires on ICT (Information and
Communication Technologies) usage in households and by individuals. For example, the evolution of ‘internet
illiteracy’ (the percentage of all individuals who never used the internet) is one way to approach the digital divide topic.
For the European Union, between 2007 and 2013, Romania seems to be the country with the highest ‘internet
illiteracy’, Sweden has the lowest ‘internet illiteracy’, and the EU-27 could be considered as the average. Romania is an
ex-transitional Southeastern Central European country who joined the EU in 2007; Sweden joined the EU in 1995.
Since the ‘quality’ of the Human Activity Fund depends, among other factors, on individuals’ health and
wealth, it is worth contrasting for the three entities (EU-27, Romania and Sweden) the percentage of the population
which is at risk of poverty or social exclusion (Figure 1) and the share of it that self-perceives its health as “very good”
(Figure 2).
Source: Eurostat (online data code: ilc_peps01)
Figure 1. People at risk of poverty or social exclusion in EU-27, Romanian and Sweden (% of total population)
Despite having almost double the risk of poverty in Romania than for the average EU-27 citizen, more
Romanians feel in “very good” health as compared to the average European citizen.
Source: Eurostat (online data code: hlth_silc_01)
Figure 2. Self-perceived health status ‘very good’ in EU-27, Romanian and Sweden (% of total population)
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The evolution of ‘internet illiteracy’ for the three entities is captured by households’ access to internet (Figure
3) and the percentage of individuals who never used the internet (Figure 4).
(1) 2011, not available.
Source: Eurostat. Information Society Statistics, Figure 2 (online data code: isoc_ci_in_h) [53]
Figure 3. Internet access of households, 2010 and 2011 (% of all households)
For the case of Sweden, the sharp increase in 2007 of the percentage of individuals who never used the internet
corresponds to the EU expansion with Bulgaria and Romania at January 1, 2007 and the following migration of the
labor force from the newest EU Member States.
Source: Authors’ elaboration based on EUROSTAT data
Figure 4. Percentage of individuals who never used the internet in EU-27, Romania and Sweden
Table 2 presents the reduction, after Romania became a Member State, of the digital divide gap for different age
groups (related to the generational structure). The sharper decrease for Romania is also consistent with the advent of
last generation mobile phones which allow internet access.
Table 2. Decrease of ‘internet illiteracy’ between 2007 and 2013 (% of total population)
Age group
16 to 24 years old
25 to 34 years old
35 to 44 years old
Sweden
6
5
9
Romania
21
35
33
EU-27
6
15
19
45 to 54 years old
55 to 64 years old
12
16
29
26
21
21
Source: Author’s elaboration based on EUROSTAT data
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The connection between the demographic structure as generations and the digital divide is shown using the
percentage of individuals who never used the internet by age group (Figure 5).
Source: Eurostat (online data code: isoc_ci_ifp_iu)
Figure 5. Percentage of individuals who never used the internet by age group in EU-27, Romania and Sweden
The connection to income inequality or to urban-rural inequality is revealed when analyzing the percentage of
individuals who never used the internet by household income group (Table 3 and Figure 6) and the percentage of
individuals who never used the internet by area of residence (Table 4 and Figure 7).
Table 3. Comparative change of ‘internet illiteracy’ by household income group between 2008 and 2012 (ratio
Romania to Sweden)
Year
2008
2009
1st quartile
4.6
4.6
2nd quartile
7.4
9.0
3rd quartile
13.8
16.3
4th quartile
23.0
41.0
2010
2011
2012
4.4
5.2
3.9
9.0
9.6
6.9
18.3
13.5
15.0
39.0
37.0
33.0
Source: Author’s elaboration based on EUROSTAT data
Table 3 shows how the situation changed after Romania joined the EU in 2007. The different speed, due to
poverty, of the digital divide gap reduction between Romania and Sweden is visible when analyzing the situation for
individuals living in households with an income in the first and fourth quartiles in Sweden and in Romania.
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Source: Eurostat (online data code: isoc_ci_ifp_iu)
Figure 6. Individuals who never used the internet by household income group in EU-27, Romania and Sweden
(% of population)
For incomes in the first quartile, in Sweden, the share of people who never used the internet is between 3.9 and
5.4 times higher than in Romania. Not surprisingly, for people in the fourth quartile this ratio is almost six to eight
times larger.
Table 4. Comparative change of ‘internet illiteracy’ by area of residence between 2008 and 2013 (ratio Romania
to Sweden)
2008
2009
Densely populated area (at
least 500 inhabitants/km2)
9.4
11.0
Intermediate urbanized area (between
100 and 499 inhabitant/km2)
10.3
7.3
Sparsely populated area (less
than 100 inhabitants/km2)
6.9
8.2
2010
2011
2012
7.6
18.0
7.0
7.6
14.0
6.1
8.8
11.0
10.2
Year
Source: Author’s elaboration based on EUROSTAT data
In what regards the digital divide depending on the area of residence, there are three types of areas based on
population density (Table 4 and Figure 7).
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After 2007, the effect of the massive migration of the most skilled people from Romania’s rural areas (to
countries all over Europe) is visible on Table 4 as the share of people who never used the internet in rural Romania
compared to rural Sweden; their ratio increased from 6.9 times in 2008 to 10.2 times in 2012.
Source: Eurostat (online data code: isoc_ci_ifp_iu)
Figure 7. Individuals who never used the internet by area of residence in EU-27, Romania and Sweden (% of
population)
The analysis of the socioeconomic metabolism at a local level depends on the type of residential area. For
Sweden, for all three types of areas of residence, 2007 is the year of a high immigration with possibly low IT skills.
After 2008, in Romania, the dynamics of internet illiteracy for rural (sparsely urbanized) areas is more similar to that in
Sweden than the dynamics for urban (densely populated) areas. The dynamics of internet illiteracy in Romania’s
intermediate areas is U-shaped and there are more factors explaining it; a more thorough analysis will be a topic of
future research. One of the factors is the increasing residential trend towards family houses (which means in most of the
situations moving outside the city) as opposed to apartments (in the city); this effect is visible as a sharp reduction of
the illiteracy rate before the effects of the Great Recession were felt. The stagnation after the 2008 financial crisis was
followed by a sharp increase of the internet illiteracy rate that might be related to a massive migration of the more
skilled residents.
6. Conclusions
Economic growth and poverty are complex concepts and alleviating the effects of economic and financial crises in
modern societies requires a clear definition of these concepts and a set of accurate indicators. Human capital is a central
concept in standard economics for modelling economic growth and, obviously, poverty traps and poverty forecasting.
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The research presented above introduces a new way of thinking about changes of the local demographic structure or
poverty pattern and their impact on the socioeconomic metabolism. Of particular importance is the consideration of the
mix IT−poverty and analyzing this concept further than the standard approach of internet users by considering Total
Human Activity Fund.
This paper gives a brief overview of the interplay between the digital divide as a source of inequality generated by
the IT revolution and its impact on human capital and Human Activity Fund. The rewiring of the human brain is
indicated to be the carrier for this effect. A European Union two-country analysis is used to discuss the existence of the
digital divide. Additional research would allow decision-makers and researchers to delve further into the interaction
between technology and poverty. Future work will further investigate the connection between the MuSIASEM concept
Human Activity Fund and the standard concept Human Capital.
7. Acknowledgements
This paper presents some results of the study “Poverty analysis and forecasting models: From econometric
modeling to multi-scale integrated analysis with a fund-flow model” coordinator Raluca I. Iorgulescu, part of the
annual research program of the Institute for Economic Forecasting-NIER, Romanian Academy, Strategic Area no. 6,
Priority Direction “Development and improvement of tools for economic and social forecasting. Models, scenarios,
numerical and qualitative analysis”.
The author wishes to thank Prof. John Polimeni who kindly offered his support.
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