Factors Influencing Labor Productivity in Modern Economies: A
Review and Qualitative Text Analysis
MARIUSZ-JAN RADŁO, ARTUR F. TOMECZEK
Global Economic Interdependence Department, Collegium of World Economy
SGH Warsaw School of Economics
al. Niepodległości 162, 02-554 Warsaw
POLAND
Abstract: We conduct a semi-systematic literature review and a qualitative text analysis of 141 publications on
labor productivity. We have identified 12 factors that play a leading role in economic research of labor
productivity: (i) agglomerations effect; (ii) business cycles and market selection; (iii) cross-country institutional
differences; (iv) environmental aspects; (v) foreign direct investment (FDI); (vi) globalization and international
trade; (vii) global value chains (GVC); (viii) human capital; (ix) information and communications technology
(ICT); (x) labor allocation; (xi) R&D and innovation; (xii) regional differences. When it comes to the quotes
count, the most prominent factor is (xi) R&D, followed by (vi) globalization and (viii) human capital. When it
comes to the co-occurrence and c-coefficient, the most prominent factor is (viii) human capital, closely
followed by (i) agglomerations, then either (xi) R&D or (vi) globalization. Network analysis reveals two
communities, the bigger one centered around (i) agglomerations, and the smaller one centered around (vi)
globalization.
Key-Words: labor productivity, factors of productivity, qualitative text analysis, network analysis, literature
review, bibliometric analysis
Received: June 27, 2021. Revised: December 27, 2021. Accepted: January 12, 2022. Published: February 4, 2022.
1 Introduction
Labor productivity is one of the tenets of
mainstream economic theory. For decades it has
been at the forefront of academic research, yet its
relevance remains as high as ever. It is also future-
proof, as even in a robot-dominated workplace
environment there will still be labor productivity to
be measured – albeit of a different kind. With a
subject so broad and important it is worthwhile to
systematize and predict the direction the specific
research field is going to take – and the following
review and qualitative text analysis represent our
attempt to provide that.
2 Methodology
The aim of this article is twofold. First, we want to
identify the factors that influence labor productivity
in modern economies. Second, an attempt is made to
create an original theoretical framework for future
empirical research. To fulfill the abovementioned
research aims, we put forward three research
questions: [RQ1] What are the key recent insights in
the literature? [RQ2] Are there significant themes in
the literature? [RQ3] Which factor is the most
prominent in the literature?
No single theoretical study can capture every
single topic in the history of labor productivity.
However, we believe that the factors identified in
our research are crucial for understanding the
changes in labor productivity in modern economies.
The categories proposed by us have been identified
as important factors during the initial review process
and word count analysis of collected publications
and confirmed as such through a more in-depth
qualitative study. Initially, we had identified 42
potential topics that we later combined into 12
categories used throughout this article (see: Table
1). The primary research methods used in this
article are literature review, bibliometric analysis,
and qualitative text analysis. Following the
methodology of Snyder [1] and motivated by the
overwhelming number of publications on the broad
topic of our research, we have adapted a semi-
systematic approach to the literature review.
Bibliometric analysis is rather brief and conducted
entirely using the Web of Science analyzing tool.
Furthermore, for the qualitative text analysis, we
utilize a computer-assisted qualitative data analysis
using Atlas.ti software. Following Kuckartz’s [2]
methodology, we chose a thematic qualitative text
analysis with quantitative elements. Finally, there
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Mariusz-Jan Radło, Artur F. Tomeczek
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are several network views constructed using Gephi
software. The qualitative text analysis is based on
the concepts of codes and sub-codes which are
described in detail in section four.
Analyzed scientific papers mostly concern more
than one issue, for example, it is hard to imagine
research about the impact of agglomerations that
disregards the role of human capital. This intuitive
interdependence will be put to test during the
qualitative text analysis. As such, for the sake of
clearness, each paper has been assigned to only one
category that best describes its subject matter. The
literature was collected using the Google Scholar
bibliometric database (general search term of “labor
OR labour productivity”) with a supplementary
search conducted in the Web of Science
bibliometric database. The primary criteria of
inclusion were relevance to the topic, language
(only publications in English), timeliness (how
recent is the research), and the importance of the
paper (measured by the number of citations in
Google Scholar). Some subjective judgments were
necessary for the selection of the literature due to
the overwhelming number of publications on this
topic. The exclusion of literature not in English is
motivated by the computer-assisted qualitative data
analysis, which would be much more complicated in
multiple languages. Future research could explore
potential differences present in qualitative text
analyses conducted in different languages. There is
a small number of works cited in this article that
play a supporting role (e.g. methodological context),
which are not included in the 141 publications
utilized in the qualitative text analysis.
During the course of the literature review, we
have divided the selected analyzed papers (n=141)
into 12 categories: (i) agglomerations effect; (ii)
business cycles and market selection; (iii) cross-
country institutional differences; (iv) environmental
aspects; (v) foreign direct investment (FDI); (vi)
globalization and international trade; (vii) global
value chains (GVC); (viii) human capital; (ix)
information and communications technology (ICT);
(x) labor allocation; (xi) R&D and innovation; (xii)
regional differences.
Table 2 shows the simplified versions of codes
used in the auto-coding process of the qualitative
text analysis. The results were manually revised.
Table 1. Initial categories/themes identified during
literature review
Initial
Fina
l
Initial
Fina
l
aging workforce
(viii)
ICT-intensive
(ix)
Initial
Fina
l
Initial
Fina
l
sectors/ICT
agglomerations
(i)
infrastructure
(viii)
agriculture sector
vario
us
innovation
(xi)
broadband access
(ix)
intangible
investment
(xi)
business cycles
(ii)
internal economies
of scale
(x)
climate
change/environm
ent
(iv)
international M&A
(vi)
construction
sector
(viii)
labor allocation
(x)
convergence
(xii)
labor mobility
(x)
countercyclical
behavior
(ii)
labor
unions/privatization
(x)
employment
density
(i)
liberalization
(vi)
employment type
(x)
manufacturing
sector
vario
us
exchange rate
(iii)
motivational
factors/morale/HR
management
(viii)
export/internation
al trade
(vi)
outsourcing
(vi)
FDI
(v)
pollution
(iv)
financial crisis
(ii)
production
specialization
(iii)
firm's lifecycle
(ii)
R&D
(xi)
globalization
(vi)
regional differences
(xii)
government
expenditure/publi
c policies
(viii)
services
vario
us
GVC
(vii)
theoretical
descriptions of
methods
vario
us
health
(viii)
vertical
specialization
(vii)
human
capital/education
quality
(viii)
violence/crime
(viii)
Source: Own preparation.
Table 2. Simplified codes for the qualitative text
analysis (codes include themselves and their every
sub-code)
Code/sub-code
Coding keywords
AGGLOMERATIONS
AGGLOMERATION|CITY|URBAN
AGGLOMERATIONS:
Congestion
congestion|traffic
jam|commute|gridlock|overcrowding
AGGLOMERATIONS:
Employment density
employment density
AGGLOMERATIONS:
Spillovers
spillover
BUSINESS CYCLES
BUSINESS CYCLE
BUSINESS CYCLES:
Crisis
crisis|economic downturn|economic
collapse|market crash|bubble burst
BUSINESS CYCLES:
Employment (total)
total employment|labor force
BUSINESS CYCLES:
Market selection
market selection|market forces|market
failure|reallocation of resources
BUSINESS CYCLES:
Procyclicality
procyclicality|cyclicality
COUNTRY
DIFFERENCES
COUNTRY
DIFFERENCES|NATIONAL
DIFFERENCES
COUNTRY
DIFFERENCES: Country
specialization
country specialization|specialized
economies|comparative
advantage|absolute advantage|localized
productivity
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Code/sub-code
Coding keywords
COUNTRY
DIFFERENCES:
Institutions
institution|institutional
COUNTRY
DIFFERENCES: Relative
prices
relative prices|comparative prices
ENVIRONMENT
ENVIRONMENT|ENVIRONMENTAL
ENVIRONMENT: Air
pollution
air pollution|air contamination|lung
disease|lung cancer|asthma|toxic
particles|fine particles|particulate matter
ENVIRONMENT:
Climate change
climate change|climate warming|global
warming
ENVIRONMENT: Green
investment
green investment|eco
investment|responsible
investment|sustainable
investment|ecological
investment|environmental regulation
FDI
FDI|Foreign direct investment
FDI: Investment policy
investment policy|FDI policy|Foreign
direct investment policy|FDI
policies|Foreign direct investment
policies|investment policies
FDI: Inward FDI
inward FDI| FDI inflow|investment
inflow|incoming investment|FDI
host|incoming FDI|iFDI
FDI: Outward FDI
outward FDI|FDI outflow|investment
outflow|outgoing investment|outgoing
FDI|oFDI
GLOBALIZATION
GLOBALIZATION|INTERNATIONALI
ZATION
GLOBALIZATION:
Financial liberalization
financial liberalization|financial
integration|capital flows|capital market
liberalization
GLOBALIZATION:
Outsourcing
outsourcing|offshoring
GLOBALIZATION:
Trade
trade
GLOBALIZATION:
Trade liberalization
trade liberalization|free trade|free
market|trade integration
GLOBALIZATION:
Transnational
corporations
transnational
corporations|TNC|multinational
corporations|MNC|international
firm|multinational
enterprise|MNE|transnational
enterprise|multinationals
HUMAN CAPITAL
HUMAN CAPITAL
HUMAN CAPITAL:
Education
education|schooling|scholarship|academic
HUMAN CAPITAL:
Health
health|disease|caloric
intake|nutrition|vaccine
HUMAN CAPITAL:
Healthcare
healthcare|medical
personnel|medics|nurse|medical
doctor|hospital|clinic
HUMAN CAPITAL:
Knowledge
knowledge|know-how
HUMAN CAPITAL: Life
expectancy
life expectancy|life span|lifespan
ICT
ICT|Information and Communications
Technology
ICT: ICT capital
ICT capital|Information Technology
capital|computers
ICT: ICT intensive
industry
ICT intensive industry|IT intensive
industry|ICT related industry|ICT firm
ICT: ICT investment
ICT investment|IT investment|computer
investment|ICT expenditure
ICT: Internet
Internet|world wide web|broadband|Wi-Fi
LABOR ALLOCATION
LABOR ALLOCATION
LABOR ALLOCATION:
Employment type
employment type|seasonal
employment|part-time|seasonal
work|formal employment|informal
employment|temporary employment
LABOR ALLOCATION:
Labor market
labor market
Code/sub-code
Coding keywords
LABOR ALLOCATION:
Labor mobility
labor mobility
LABOR ALLOCATION:
Migration
migration|emigration|immigration
LABOR ALLOCATION:
Worker reallocation
worker reallocation|retraining program|job
change|employment change|labor
reallocation
R&D
R&D|research and development|research
& development
R&D: Innovation
Innovation|innovative
R&D: Intangible
investment
Intangible investment|intellectual
property|intellectual
properties|intangibles|intangible capital
R&D: Process innovation
process innovation|innovative process
R&D: Product innovation
product innovation|innovative product
R&D: R&D expenditure
R&D expenditure|expenditure on
R&D|research expenditure|research and
development expenditure
R&D: R&D intensity
R&D intensity|research and development
intensity|research intensity
REGIONAL
DIFFERENCES
REGIONAL DIFFERENCES|high-
productivity regions|productive
region|regional growth|high-growth
region|specialized region
REGIONAL
DIFFERENCES: Beta
convergence
beta convergence|β-convergence
REGIONAL
DIFFERENCES:
Convergence
convergence
REGIONAL
DIFFERENCES:
Geographic location
geographic
location|North|South|West|East
REGIONAL
DIFFERENCES: Sigma
convergence
sigma convergence|σ-convergence
GVC
GLOBAL VALUE CHAINS|GVC
GVC: Upstream activities
upstream activities|upstreamness|upstream
flow|upstream stages
GVC: Downstream
activities
downstream
activities|downstreamness|downstream
flow|downstream stages
GVC: Vertical integration
vertical integration|vertical
specialization|vertically specialized
GVC: Linkage direction
forward linkage|backward linkage|forward
link|backward link|buyer linkage|seller
linkage
Source: Own preparation.
3 Review of the Factors
The following is an overview of the twelve factors
that have been identified in the course of the semi-
systematic literature review. The following sections
show the main factors (categories of the literature
review and qualitative text analysis), literature
assigned to each factor, and the main findings of the
literature review. There are several definitions of
labor productivity in economic literature, but they
are mostly similar (Table 3). Labor productivity is a
measure of how effective is the employed labor, be
it in an economy (aggregate labor productivity),
region (regional labor productivity), or sector
(sectoral labor productivity).
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Table 3. Definitions of labor productivity
Source
Definition
Cai et al. [3]
Sectoral value added divided by labor force in
a sector
Constantinescu et
al. [4]
“(…) real value‐added divided by the number
of persons employed”
Dietzenbacher et
al. [5]
“(…) we define labor productivity in vertically
integrated industry i as the ratio of value added
created in vertically integrated industry i and
the number of jobs in vertically integrated
industry i”
Eurostat [6]
“(…) value added per employed person”
Hatzikian [7]
“(…) ratio between turnover or annual sales
and the number to employees in the reference
year (…)”
Lemos [8]
“(…) output divided by number of employees”
McGowan et al.
[9]
“(…) gross output per employee (…)”
Mohnen & Hall
[10]
“(…) amount of output per labor (…)”
OECD [11]
“(…) output per unit of labour input”
Ortega-Argilés
[12]
“(…) GDP per hour worked (…)”
Source: Own preparation.
3.1 Agglomerations Effect
With rapidly increasing urbanization, the
importance of cities in economic sciences is hard to
overstate. Throughout numerous publications,
agglomerations have been shown to have a
significant positive impact on labor productivity.
Notable examples include studies conducted in
Sweden [13, 14], the Netherlands [15, 16], Italy
[17], China [18, 19], and the United States [20, 21].
Agglomerations strongly influence labor
productivity primarily because of the clustering of
human capital (job density), increased R&D
expenditures, the effectiveness of investment, and
multiple positive spillover effects. Agglomerations
have a very strong positive impact on levels of labor
productivity, mainly through the job density
channel; at the same time, they negatively impact
the future growth of labor productivity because of
the harmful congestion effect [15]. Crucially, R&D
expenditure has a higher impact on labor
productivity for firms located in agglomerations
[14]. From the macroeconomic perspective, they
almost universally increase the aggregate labor
productivity of a country [15]. The positive
spillovers have a surprisingly wide range, as the
cities can have a positive influence on other cities
located up to 100km from each other [19]. Both
urban areas and industrial districts have a positive
impact on labor productivity, but the effect is
stronger for urban areas as they increase the
resilience to financial shocks [17]. In the context of
urban planning, polycentric agglomerations have a
higher positive impact on labor productivity than
monocentric agglomerations [20].
There are, however, some significant caveats
when assessing their impact on the economy.
Firstly, agglomerations can also hurt the growth of
labor productivity in some regions. There are
productivity spillovers when surrounded by dense
agglomerations, which increase aggregate
productivity but hurt regional productivity;
additionally, the density of neighboring regions can
dampen the congestion channel [15]. Secondly,
while they have a significant impact on regional
labor productivity, they also actively increase
economic inequality [18]. Finally, there are
diminishing returns to labor productivity gain as
agglomerations become too congested in time [16].
The dangers of the congestion spillovers and
congestion in agglomeration themselves, as well as
the tendency to have greater economic inequality in
their populations, will only become exacerbated as a
larger percentage of the population will move to the
big cities.
3.2 Business Cycles and Market Selection
Business cycles periodically accelerate the process
of market selection during an economic downturn.
As such, their impact can be positive (due to
Schumpeter’s [22] creative destruction) or negative
(due to lower output and accumulation). Recent
studies have indeed confirmed the positive impact
of crises on innovation [23–25]. Still, the global
financial crisis and the following recession have had
a negative impact on productivity in Europe [26].
The process of market selection has a more
clear-cut impact on labor productivity. Market share
reallocations resulting from market selection are
important for labor productivity growth [27]. New
entrants initially lower industry productivity growth,
with time their contributions increase, and the
biggest contributors to productivity growth are old
and established firms experiencing productivity
renewal [28]. Old firms with persistently low
productivity (zombie firms) have a negative impact
on aggregate industry productivity because they
congest the market and waste invested capital [9].
According to a study conducted in Italy,
manufacturing industries are characterized by the
existence of several highly innovative firms and a
larger number of regressive firms that exploit local
markets, which can be described as neo-dualism of
market selection [29].
Plant-level labor productivity is more vulnerable
to business cycles than aggregate labor productivity
of an economy [30]. Labor productivity moved in a
countercyclical fashion during the Great Recession
[31]. The procyclicality of labor productivity has
declined greatly in the United States, at the same
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time there has been a rise in the relative volatility of
employment and the real wages – a possible cause is
a decline in the labor market turnover [32]. There is
a high correlation between employment growth and
business cycle vulnerability: long-run downsizers
experience a much higher drop in productivity than
long-run upsizers [30].
3.3 Cross-country Institutional Differences
Internationally, innovation is heavily localized and
occurs mostly in countries with high capital
intensity. Most productive firms are heavily
clustered in rich and developed economies with a
strong institutional framework. In 2008, the
percentage of country’s firms in the global top
decile of firms with the highest productivity level
was the largest in the following five countries:
35.5% in the United States, 27.3% in Sweden,
25.2% in Finland, 19.4% in France, and 16.5% in
the Netherlands; other notable examples include
11% in Germany, 4.2% in Poland, and 3.5% in
Japan [33]. Internationally, innovation is heavily
localized and occurs mostly in countries with high
capital intensity [34].
In advanced economies, a convergence in labor
productivity has occurred, however, its level differs
between industries; a probable cause of labor
productivity convergence is the convergence in the
capital-labor ratios [35]. In Western Europe, labor
productivity convergence has occurred on the
national and industry levels, especially in the
manufacturing sector [5]. In OECD economies,
relative prices and relative labor productivities are
proportional in the long run [36]. In the 1990s, the
relative demand for skilled labor increased in
Poland and decreased in Hungary and Czechia,
which was accompanied by growing wage
inequality in all three countries [37].
There is a substantial difference in priorities
(product innovation vs. process innovation) between
European countries. Northern European countries
(Germany, United Kingdom, and the Netherlands)
focus primarily on product innovation and new
technologies, while Southern European (France,
Italy, and Portugal) countries focus primarily on
process innovation and cost-minimization [38].
3.4 Environmental Aspects
In recent years, the environment, climate change,
and the shift to green energy have become some of
the most discussed topics in economic sciences.
When it comes to labor productivity, the analyses
focus on two primary issues: severe air pollution
and a high-temperature working environment, which
harm health-related labor productivity and product
quality, especially in the long run. The most
vulnerable sector is the construction industry. Given
time, the average GDP is also highly likely to drop
across the World.
Severe air pollution has a negative impact on
labor productivity and product quality [39].
Prolonged exposure to air pollution has a small
negative in the short run, however, long-term
adverse effects might be more significant [40]. By
2060, air pollution will lower GDP by an average of
1%, but this drop will be much more significant in
China and Eastern Europe. Additionally, labor
productivity will suffer because of the indirect
impact of worsening health [41].
In the coming decades, climate change will
most likely have a strong negative impact on labor
productivity, especially in Southeast Asia and
Central America [42]. Climate change-related labor
productivity loss is most pronounced in regions
where agriculture is dominant [43]. A high-
temperature working environment hurts construction
labor productivity [44, 45]. The least productive and
hazardous period of the day is between 2 pm and 3
pm [44].
Trade openness, as well as offshoring of
production by European countries to developing
countries with labor-intensive production, where
labor is cheaper but less efficient, hurt the
environment globally. Green investment has a
positive impact on labor productivity [46]. The
impact of stringent environmental regulation on
investment is a positive one, but with clear
diminishing returns at the higher levels of
environmental taxation [47]. Trade openness has a
negative impact on emissions efficiency, R&D
expenditure has no impact, and for manufacturing
the impact varies across sectors [48]. Offshoring of
production by European countries to developing
countries with labor-intensive production harms the
environment [49].
3.5 Foreign Direct Investment (FDI)
The impact of inward FDI on labor productivity is
generally positive, however, it depends on certain
factors like the development level of the receiving
country (GDP per capita), types of linkages
(positive for vertical and negative for horizontal),
type of production (services vs. manufacturing),
duration (positive for the long run), regional aspects,
and industry strength. The initial impact of inward
FDI on productivity is neutral, however, it shows a
positive effect in the long run [50]. The impact of
technology diffusion-related-FDI on productivity is
positive for vertical linkages and negative for
horizontal linkages [51]. The effect that FDI has on
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both host and home countries is undeniable, but its
character depends greatly on the types of activities,
level of competition, degree of internationalization,
and host potential [52].
Several studies have focused on the effect on
specific host countries. Inward FDI has no effect on
domestic manufacturing labor productivity in the
United States [53]. There has been a strong
convergence effect on national and industry levels
in Central and Eastern European countries with FDI
playing a crucial role in this process [54]. FDI have
a positive impact on productivity, but there is a big
difference between inflows of FDI between regions
in Romania: West is preferred to the East, and the
capital region of Bucharest-Ilfov is much more
heavily preferred than the rest of the country [55]. In
China, FDI can have a positive impact on regional
labor productivity and a negative impact on labor
productivity in a given industry [56]. Higher per
capita FDI inflow increases labor productivity in
Chinese cities [57].
The impact of outward FDI on labor
productivity is positive since it usually occurs in
countries with prosperous and productive firms that
extend or diversify their value chains. High FDI
outflows are related to high productivity, on the
other hand, high FDI inflows are related to an
increase in productivity only for the countries above
a certain GDP per capita threshold [58].
3.6 Globalization and International Trade
Firms that engage in international activities on
average pay higher wages, conduct more innovative
research, and have higher labor productivity.
Exporting has a positive impact on productivity, and
firms that export tend to pay higher wages and have
higher R&D expenditure [59, 60]. Transnational
corporations tend to have higher labor productivity
and R&D expenditure than domestic firms [61, 62].
The size-wage effect shows that manufacturing
labor productivity increases with firm size [63]. A
firm’s R&D productivity has a positive relationship
with the globalization of its enterprises and a
negative relationship with industrial diversification
[64]. Firms’ high-growth status and their TFP
growth have a strong positive correlation, with one
reinforcing the other [65].
Trade liberalization has a positive impact on
manufacturing labor productivity in developing
countries [66, 67]. The positive impact of financial
liberalization on productivity is greater in the
manufacturing sector than in the service sector [68].
Trade liberalization in services has a positive impact
on the productivity of the manufacturing sectors
when manufacturers benefit from using these
services in the production process – this effect is
especially noticeable in countries with a strong
institutional environment [69]. Trade liberalization
should be accompanied by reforms aimed at
changes in ownership concentration; with regards to
labor productivity in manufacturing, low ownership
concentration is preferred with high tariffs,
concentrated ownership is preferred with low tariffs
[70].
A bilateral trade agreement between the United
States and Vietnam has increased labor productivity
in Vietnam by increasing employment in the more
productive export-oriented formal sectors at the cost
of a decrease in employment in the less productive
informal sectors [71]. Manufacturing industries in
Latin America have focused on raw material
processing and labor-intensive production, where
they held a natural comparative advantage; their
rapid specialization resulted in unemployment and
long-run external imbalances of the economies [72].
Cross-border acquisitions, outsourcing, and
offshoring help manufacturing productivity,
including low-skilled labor. Outsourcing and
offshoring have a positive impact on manufacturing
productivity [73, 74]. International outsourcing has
a positive impact on low-skilled labor productivity
in the long run [75]. Cross-border acquisitions have
a positive impact on domestic productivity,
especially when the acquired firm is located in a
more competitive market [76]. Additionally, high
domestic competition increases the chances of
cross-border acquisitions and the investment level of
the domestic firm, in general, has a positive
correlation with the increase in productivity
resulting from cross-border acquisitions [76].
3.7 Global value chains (GVC)
Global value chains are one of the most important,
and still relatively recent, additions to the literature
on international economics. The crucial conclusion
is that GVC participation has a significant positive
impact on labor productivity [4, 77–79]. GVCs have
a positive impact on labor productivity through four
primary channels: firm specialization, easier access
to inputs, knowledge spillovers, and increased
competition [80]. Furthermore, GVCs tend to form
as a consequence of regional clusters and activities
of multinational enterprises [80].
Position in the chain is a key distinction for the
estimation of the impact of GVCs. For upstream
GVC activities, business cycle-related demand
volatility has a negative impact on labor
productivity [81]. A study of enterprises belonging
to different GVC stages conducted in Italy and
Spain shows a positive impact of agglomerations on
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labor productivity only for supplier firms [82].
When trade barriers exist, downstream GVC
activities can be most cost-effectively placed in
relatively central economies, as their proximity to
locations of the upstream stages in the chain might
outweigh the higher marginal cost; e.g. a country
with lower labor productivity might still have a
comparative advantage due to its location [83].
When it comes to domestic value-added, forward
GVC linkages are more beneficial than backward
GVC linkages [84]. In recent decades, production
chains in China have lengthened, while those in the
United States have shortened [85].
Another aspect is the type of labor utilized in a
particular stage of the chain. According to Degain et
al. [86], with regards to the rise of GVCs in the
United States and other advanced economies, “(…)
the big winners appear to be high-skilled workers
and multinational corporations.” GVCs are greatly
beneficial to high-skilled workers with formal
employment [87]. Rapid technological progress
(Industry 4.0) has a chance to radically increase
labor productivity and demand for high-skilled labor
in GVCs [88]. However, in a study of GVCs in East
Asia, Choi [89] finds that high-skilled labor
productivity has not significantly contributed to
value-added activities, as they were linked to limited
technological innovation.
As usual, there are some important caveats. In
recent years, GVCs’ growth has slowed down [4,
80, 86]. As such, labor productivity growth has also
slowed down, partly because of the sluggish GVC
growth [4]. While GVCs have a positive impact on
labor productivity, they have little impact on actual
employment [79]. Ultimately, GVCs contribute to
the transmission of international economic shocks
[85].
GVCs are much more beneficial to advanced
economies while developing countries’ growth
might even be hindered by them – still, for
aggregate labor productivity alone their impact is
generally positive regardless of the development
level [90]. GVCs lead to an increase in labor
productivity of advanced economies, however, this
usually is accompanied by the outsourcing of the
low-skilled labor to developing economies and
increased unemployment in the former [86]. GVC
integration has increased labor productivity in the
Vietnamese garment and textile industries [91]. For
developing countries, the need for higher labor
productivity and the competitive pressure related to
supplying a GVC might lead to an increase in
informal employment with scarce work security
[92]. Finally, Kummritz [78] finds that productivity
gains are visible for both upstream/downstream
activities as well as developed/developing countries.
3.8 Human Capital
Human capital has, unsurprisingly, a strong positive
impact on labor productivity. Education (years of
schooling and % of tertiary education), health
(nutrition, vaccines, and life expectancy), and
technological progress are key elements of strong
productivity growth.
Education and technological progress are the
biggest contributors to labor productivity growth
[93]. Human capital (high level of education) has a
significant positive influence on labor productivity,
however, for a low level of education, there is a
significant negative relationship with labor
productivity [94]. Depending on the situation,
centralization or decentralization of education can
have a positive impact on its quality and human
capital. According to one study, to improve its
human capital, China should decentralize higher
education and centralize pre-tertiary rural education
[95]. While education has no strong impact on
agricultural productivity, it significantly increases
off-farm income generation capabilities [96].
Effective human resource management, greater
consideration for morale, and welfare all have a
positive impact on labor productivity [97–99].
Knowledge spillovers help regional growth,
which is most noticeable with close geographic
proximity between urban areas. There are tertiary
education spillovers with a highly positive impact
on labor productivity [94]. The degree of impact of
knowledge spillovers on regional productivity
depends on geographic proximity, with neighboring
regions benefiting the most from them [100]. An
increase in human capital has a significant positive
impact on regional labor productivity, however, this
is negated by a significant negative impact of spatial
spillovers [101]. From a perspective of a historically
divided country, human capital is very similar
between former West Germany and East Germany
regions, but labor productivity is still noticeably
higher in the West – potential explanations include
historically larger firms and better infrastructure in
the West [102].
Government expenditure can impact labor
productivity. Government expenditures on
education, agricultural research, and infrastructure
have a positive impact on the economic growth of
rural regions [103]. The use of vaccines has a
positive impact on labor productivity [104]. In
Mexico, violence and crime, and somewhat
surprisingly anti-crime government expenditure, all
negatively impact labor productivity [105]. Life
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expectancy and infrastructure have a significant
positive impact on labor productivity in the
agriculture sector [106]. Nutrition has a positive
impact on agricultural labor productivity [107].
3.9 Information and Communications
Technology (ICT)
The impact of information and communications
technology on the entire economy is undeniably
immense. Investment in ICT capital has a positive
impact on labor productivity [108–111]. Investment
in ICT capital has a bigger impact on labor
productivity than non-ICT capital by 25%-50%
[109]. ICT has been one of the primary causes of
major productivity trend breaks (positive shocks) for
the past decades. Major productivity trend breaks
include years following wars (WWII), global
financial crises (Great Depression, Great
Recession), global supply shocks (oil shocks),
idiosyncratic shocks (country level drastic policy
changes), and technological breakthroughs
(development of ICT in the USA) [112].
Internet access (as well as its quality and speed)
have a positive impact on labor productivity.
Digitalization and access to information made
possible by the Internet are important for inclusivity.
Internet access and data standardization have a
positive impact on labor productivity [110]. The
impact of broadband access on labor productivity is
positive, but its strength relies on better connection
quality and is more pronounced for less developed
regions – which makes it a tool for regional
convergence [113].
In the late 1990s and early 2000s, total labor
productivity in Germany, which at the time was one
of the global leaders, has suffered because of limited
gains in the ICT-intensive sectors despite large
investments [114]. However, for ITC-intensive
manufacturers since the 1990s in the United States,
labor productivity growth is influenced mostly by
the decline in output (Y) and decline in employment
(L), and not in real improvements [115].
3.10 Labor Allocation
Labor allocation includes primarily the processes of
labor mobility and migration. Skilled labor mobility
has a positive impact on the manufacturing
productivity of the receiving industry, especially in
the case of employment in the high-tech sectors
[116, 117]. Worker reallocation from less to more
innovative firms has a positive impact on aggregate
labor productivity [118]. Migration has a positive
impact on income convergence rate; immigration
into regions with high income per capita pushes the
value down, while emigration from low-income
regions pushes per capita values up [119].
Labor unions have a positive impact on
manufacturing labor productivity [120]. The use of
temporary contracts has a small negative impact on
labor productivity [121]. External labor flexibility
has a negative impact on productivity growth in
Italy, which is especially noticeable in SMEs [122].
Labor market distortions lower labor productivity
and the speed of convergence between regions in
China, which is especially exemplified by artificial
labor mobility barriers and inferior social security
systems in rural areas [3].
Vergeer and Kleinknecht [123] show that higher
wages lead to an increase in labor productivity,
while high labor turnover decreases productivity.
Consequently, a higher wage share of labor has a
positive impact on labor productivity, while income
inequality has a negative one [124]. Wage share in
advanced economies has decreased in the past
decades [125].
In the United States, plants that have increased
labor productivity done so either by downsizing or
by increasing their output with upsized employment
– manufacturers in mature industries tended to
downsize (i.e. steel industry), while there have been
some correlation of upsizing to a regional location
(i.e. New England) and firm size (i.e. large firms)
[126]. Also in the United States, Snowbelt states
have experienced a higher level of manufacturing
labor productivity growth than Sunbelt states, at the
same time they lost employment while sunbelt states
gained employment [127].
3.11 R&D and Innovation
Research and development expenditure and
intensity have a very strong positive impact on labor
productivity for both manufacturing and services.
R&D expenditure has a clear-cut positive impact on
labor productivity [10, 128–135]. R&D is positively
correlated with firm size, R&D intensity is
negatively correlated with firm size, and R&D
intensity is positively correlated with future
innovations [129]. The impact of R&D on labor
productivity is positive for both manufacturing and
services. Regional specialization is more important
for labor productivity in services than in
manufacturing [134]. Creative service industry
specializations have a positive impact on regional
labor productivity [136]. R&D intensity has a
positive impact on labor productivity; this effect is
especially noticeable in advanced industries [133].
Lack of availability of qualified personnel and the
availability of finance harm firms' productivity
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[137]. In general, R&D expenditure has a positive
influence on the innovativeness of MSMEs [129].
Intangible investments have a positive impact
on labor productivity [138–140]. The positive
impact of intangible investment on labor
productivity is the highest in the manufacturing and
finance industries [139]. Technology acquisition
expenditures have a positive impact on productivity
[130].
The impact of product innovation on
productivity is positive, but the impact of process
innovation is less unequivocal [141]. The impact of
innovation intensity on labor productivity follows a
U-curve [7]. The crucial element of inducing
productivity growth in the early stages of economic
development is the widespread use of technology
[142]. In Latin America, two determinants of
innovation crucial for the region have been
observed: public support and the intellectual
property rights system [131]. Technology shocks
have a positive impact on labor productivity [143].
Sudden losses in human capital have a much
stronger short-run and long-run impact on
innovation than physical capital [144].
3.12 Regional Differences
Regional differences can be difficult to measure, as
the quantitative analysis of institutions and other
factors is more difficult than in the international
comparisons. Consequently, further exploration of
regional differences and convergence/divergence
trends could be an important topic of future
research.
In the 1980s, there has been a significant
regional labor productivity convergence across
Europe [145]. The polarization was present in the
regional labor productivity of the European Union.
At the sectoral level, regional labor productivity
polarization was present in the services, but not in
manufacturing [146]. This can be explained by the
concentration of highly productive tradable services
in agglomerations and interregional differences in
productivity in non-tradable services. Productivity
of non-tradable services results to a greater extent
from the level of wages in a given region, as the
demand for them is local and they can be provided
locally. While tradable services can be provided
remotely, which is often done, for example, in
service centers located in agglomerations. There are
two equilibria in regional productivity growth, with
high-productivity regions converging in the center
of Europe, and low-productivity regions converging
in the peripheries. The low-productivity
convergence is a type of low-productivity trap
[147]. Since the early 1980s, regional convergence
in labor productivity in Spain had stopped as there
has been limited technological diffusion between the
regions; convergence occurs in aggregate labor
productivity at the regional level but not at the
sectoral level [148, 149]. Regional labor
productivity in Russia has converged [150].
Productivity levels across regions in the United
States are highly differentiated. The main forces in
labor productivity convergence have been the
manufacturing and mining sectors [151].
Differences in productivity across regions were
caused by different growth rates of capital and labor
input [152].
In the past, the labor productivity and
agricultural surplus in China were high and
unevenly spread between regions. Labor
productivity in agriculture was much lower than in
manufacturing; increased urbanization improves
labor productivity in rural areas [153]. China’s East
Rim provinces have had higher labor productivity
and labor productivity growth than the rest of the
country [154]. Increases in regional labor
productivity in China were mostly the results of new
labor-saving processes [155]. In 1995, the labor
surplus in agriculture in China was substantial (120
million), with very large differences between
regions: 44.8% of the agriculture labor force for the
Southwest region and 0.3% for the Northeast region
[153].
4 Qualitative Text Analysis
The qualitative text analysis section of this article
presents the results of the computer-assisted
qualitative data analysis using Atlas.ti software.
There are 141 publications used in the qualitative
text analysis. A small but notable number of works
cited in this article play a supporting role (e.g.
provide methodological context), as such, they are
not included in the qualitative analysis [1, 2, 8, 12,
22–25, 52, 123–125]. This analysis is based on the
concepts of codes and sub-codes: they are assigned
quotes (portions of the reviewed literature). If their
quotes overlap it means that the codes (or sub-
codes) co-occur with other codes (or sub-codes).
The co-occurrence is measured using the co-
occurrence count (the number of times they co-
occur) and the c-coefficient (the number of times
they co-occur adjusted for the size of each code).
The former is an integer, and the latter is a
standardized coefficient (between 0 and 1). If the
codes co-occur, we can assume that there is some
interdependence between them.
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4.1 Bibliometric Analysis
The bibliometric analysis is using the Web of
Science analyzing tool. Fig. 1 shows the number of
articles per year for the search term “labo*r
productivity” (considering both spellings) available
in the Web of Science Core Collection (WoS-CC)
and Web of Science all databases (WoS-AD). Even
though labor productivity is a very old concept, the
trend clearly shows a rapidly growing interest,
indicating its timelessness. As of today, and the
number is sure to increase as the database gets
updated, there have been 1,013 publications in the
WoS-AD and 572 publications in the WoS-CC in
2019 alone. For the entire analyzed period, there are
8,962 publications in the WoS-AD and 5,373 in the
WoS-CC. The numbers emphasize how vast this
topic is, and that the semi-systematic approach is
advantageous.
Fig. 1: Bibliometric trends of labor productivity,
1980-2019
Source: Own preparation based on the Web of
Science [156].
Table 4Σφάλμα! Το αρχείο προέλευσης της
αναφοράς δεν βρέθηκε. presents the number of
publications on selected search topics in the WoS-
CC for the 1980-2019 period. The percentage
increase is the number of publications compared to
the preceding decade. Notable figures include the
increase in ICT-related publications, especially in
2000-2009 (1,229%), the explosion of popularity of
globalization since the 1990s, and the consistent
popularity of R&D. For GVC, 2019 alone brought
106 publications (15% of total). Of course, it is hard
to directly compare these topics, as some of them
are much broader, but it should give a general idea
of how crucial they are to any economic analysis –
in particular that of labor productivity.
Table 4. Number of publications on selected search
topics, WoS-CC, 1980-2019
Search topic
1980-
1989
1990-
1999
2000-2009
2010-2019
Total
number
% increase
number
% increase
“labo*r productivity”
144
427
1,021
139
3,519
245
5,111
“agglomerations”
18
216
605
180
2,495
312
3,334
“business cycle”
225
1,128
2,388
112
5,371
125
9,112
“market selection”
7
20
116
480
271
134
414
“international
differences”
44
147
251
71
472
88
914
“environmental aspects”
120
606
1,097
81
3,425
212
5,248
“FDI”
27
621
3,075
395
8,940
191
12,663
“human capital”
222
1,601
4,786
199
15,026
214
21,635
“GVC”
94
113
81
-28
420
419
708
“ICT”
37
503
6,684
1,229
32,129
381
39,353
“international trade”
695
2,085
4,236
103
12,224
189
19,240
“globali*ation”
58
3,452
19,638
469
45,210
130
68,358
“labo*r mobility”
48
156
353
126
987
180
1,544
“labo*r allocation”
5
40
77
93
135
75
257
“regional convergence”
1
41
161
293
321
99
524
“R&D”
123
8,696
19,258
121
30,317
57
58,394
Source: Own preparation based on the Web of
Science database (Accessed 17.07.2020).
4.2 Codes
Fig. 2 and Table 5 show the word count for the
reviewed literature (n=141). For clarity, we omit the
common words irrelevant to the topic like “the,”
“we,” “and,” etc. This figure and table are the only
ones that differentiate between different spellings
(e.g. labor/labour). As expected, “productivity” is by
far the most common word, followed by “labor”
(American spelling), “growth,” “capital,” and
“data.” The word count list is interesting in its own
right, but its primary function is to help with the
initial exploration of literature and the creation of
codes (factors) and sub-codes.
Fig. 2: Word count
Source: Own preparation.
Table 5. Word count
productivity
11,871
share
1,626
global
1,095
labor
5,724
regions
1,614
estimates
1,091
growth
5,583
regional
1,603
business
1,090
capital
3,810
market
1,579
OECD
1,083
data
3,698
across
1,534
FDI
1,060
level
3,564
labour
1,529
empirical
1,006
industry
3,450
sectors
1,503
worker
1,005
countries
3,363
foreign
1,429
knowledge
983
value
3,031
variable
1,426
relative
976
economic
2,937
research
1,388
region
969
innovation
2,913
rate
1,375
increase
968
production
2,858
impact
1,365
product
967
output
2,638
size
1,303
input
961
results
2,543
international
1,302
process
955
manufacturing
2,541
economics
1,289
European
944
trade
2,534
domestic
1,287
measures
942
model
2,323
technology
1,266
aggregate
935
industries
2,260
convergence
1,263
world
934
0
200
400
600
800
1000
1980 1987 1994 2001 2008 2015
WoS-CC
0
2.000
4.000
6.000
8.000
10.000
12.000
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country
2,235
work
1,259
China
932
firms
2,211
they
1,253
economies
930
effects
2,193
percent
1,188
performance
924
sector
2,105
income
1,165
costs
919
employment
2,033
development
1,161
inputs
914
average
2,010
activities
1,124
spatial
914
effect
1,822
products
1,120
factor
859
variables
1,806
firm
1,119
information
859
investment
1,795
levels
1,116
EU
845
services
1,716
TFP
1,114
GVC
843
workers
1,702
human
1,111
industrial
839
analysis
1,676
ICT
1,104
national
836
Source: Own preparation.
Table 6 shows the number of quotes per code
(n=12) and sub-code (n=49) for the reviewed
literature (n=141). The number in the brackets is the
number of papers with at least one quote for this
code (the maximum possible value would be 141).
The assigned codes are: (i) agglomerations effect;
(ii) business cycles and market selection; (iii) cross-
country institutional differences; (iv) environmental
aspects; (v) foreign direct investment (FDI); (vi)
globalization and international trade; (vii) global
value chains (GVC); (viii) human capital; (ix)
information and communications technology (ICT);
(x) labor allocation; (xi) R&D and innovation; (xii)
regional differences.
The number of quotes per code is different than
the sum of its sub-codes since the codes also include
generic terms (e.g. R&D). The more general sub-
codes (e.g. innovation) have a higher number of
quotes than the more specialized sub-codes (e.g.
product innovation). There are is one dominant
factor with more than three thousand quotes: (xi)
R&D. Other prominent codes are (vi) globalization
and (viii) human capital, with more than two
thousand quotes. The code with the lowest quote
count is (iii) country differences. (i) Agglomerations
are especially interesting, as most of its quotes co-
occur with other codes.
While the publications are assigned to a single
code in the review, the qualitative analysis shows
that these topics are difficult to isolate. Most of the
codes occur at least once in the majority of the
analyzed literature, e.g. the code for human capital
occurs in 133 publications. When it comes to quotes
per individual publications, the average is 218 and
the median is 181. There are seven publications with
more than 500 quotes: Antràs & Gortari [83], Crespi
& Zuniga [131], Criscuolo & Timmis [80], Degain
et al. [86], Foster-McGregor & Pöschl [116], Fuchs-
Schündeln & Izem [102], and Kummritz et al. [84].
Table 6. Number of quotes per code and sub-code
(i)
AGGLOMERAT
IONS
1,75
1
[99]
(vi)
GLOBALIZA
TION
2,67
6
[11
5]
Internet
250
Congestion
115
Financial
liberalization
20
(x) LABOR
ALLOCATI
ON
615
[72]
Employment
density
60
Outsourcing
376
Employment
type
51
Spillovers
666
Trade
2,04
2
Labor market
265
(ii) BUSINESS
CYCLES
823
[11
5]
Trade
liberalization
137
Labor
mobility
94
Crisis
261
Transnational
corporations
148
Migration
191
Employment
(total)
559
(vii) GVC
1,06
7
[24]
Worker
reallocation
47
Market selection
46
Downstream
activities
59
(xi) R&D
3,11
7
[108
]
Procyclicality
74
Linkage
direction
56
Innovation
2,09
1
(iii) COUNTRY
DIFFERENCES
479
[97]
Upstream
activities
84
Intangible
investment
287
Country
specialization
75
Vertical
integration
53
Process
innovation
105
Institutions
350
(viii) HUMAN
CAPITAL
2,41
7
[13
3]
Product
innovation
92
Relative prices
42
Education
678
R&D
expenditure
40
(iv)
ENVIRONMEN
T
813
[76]
Health
480
R&D
intensity
81
Air pollution
117
Healthcare
38
(xii)
REGIONAL
DIFFEREN
CES
1,41
6
[102
]
Climate change
70
Knowledge
889
Beta
convergence
14
Green investment
63
Life expectancy
19
Convergence
808
(v) FDI
827
[51]
(ix) ICT
1,06
2
[66]
Geographic
location
1,12
8
Investment policy
12
ICT capital
247
Sigma
convergence
32
Inward FDI
79
ICT intensive
industry
43
TOTALS:
30,6
75
Outward FDI
8
ICT investment
70
Source: Own preparation.
Fig. 3 shows a simple network view of the
codes and sub-codes. Fig. 4 shows a network view
with weighted edges (using the number of quotes as
weights). Fig. 5 shows the connections between the
12 codes (factors of labor productivity) and 141
publications; the exact number of edges per
code/node is provided in Table 6. The factors and
literature are highly interconnected and virtually
impossible to analyze in a vacuum. The next section
of this article deals with co-occurrence measures for
the reviewed literature.
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Fig. 3: Network view: overview
Source: Own preparation.
Fig. 4: Network view: weighted edges
Source: Own preparation.
Fig. 5: Network view: codes and publications
Source: Own preparation.
4.3 Co-occurrence
While the number of quotes is determined by the
choice of publications, the co-occurrence analysis
should provide a more nuanced result.
Table 7 presents the co-occurrence between codes
and the c-coefficient (n=12) for the reviewed
literature (n=141). The upper-right portion of the
table shows the co-occurrence count, while the
bottom-left portion shows the c-coefficient. The use
of the c-coefficient allows for a fairer comparison of
co-occurrence since it takes into consideration how
numerous the code count is for each code.
The following two codes have the highest co-
occurrence count and c-coefficient: (viii) human
capital and (i) agglomerations. The code with the
third-highest co-occurrence count is (xi) R&D, and
the code with the third-highest c-coefficient is (vi)
globalization. Among which, (viii) human capital
leads the overall total co-occurrence count with
1,231 and the c-coefficient with 0.32 – the latter is
tied with the result of (i) agglomerations. In general,
the four leading codes (human capital,
agglomerations, R&D, globalization) are the most
important in this analysis and the differences in their
scores are relatively small.
There is a notable cluster of scientific interest
between R&D/human capital/agglomerations,
visible in the crucial code dyads of (viii) human
capital and (i) agglomerations, as well as (viii)
human capital and (xi) R&D. There is also a high
co-occurrence dyad of (i) agglomerations and (v)
FDI. Another one is between (vi) globalization and
(vii) GVC. This shows how the literature on
international trade, the role of large multinational
corporations, and knowledge spillovers play a
crucial part in explaining labor productivity and its
growth. These results are rather intuitive, as
agglomerations have been shown to concentrate and
enhance the regional human capital in densely
populated, small areas, which in turn attracts
investments. Globalization and global value chains
are similarly interconnected. Noteworthy code
dyads (with c-coefficient of 0.04 or higher) include:
(i) agglomerations and (viii) human capital
(294, 0.08);
(i) agglomerations and (v) FDI (163, 0.07);
(viii) human capital and (xi) R&D (301, 0.06);
(vi) globalization and (vii) GVC (190, 0.05);
(i) agglomerations and (x) labor allocation
(85, 0.04).
On the other hand, and somewhat surprisingly,
the total co-occurrence count and c-coefficient for
(ix) ICT are the lowest (224, 0.08). ICT is
ubiquitous and has a profound effect on almost
every aspect of modern business life, yet the
literature on its impact on labor productivity is
somewhat confined to a relatively modest number of
papers. A possible explanation could be that the
impact is so great, that general economic models are
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taking it for a given (exogenous variable) – or
perhaps there are issues with quantifying it. Other
codes with relatively low total co-occurrence count
are (x) labor allocation (350), (iii) country
differences (387), and the (iv) environment (392).
For the latter, most of its co-occurrences (88) are
related to (viii) human capital, which makes sense
as the environmental damage is sure to cause health
problems, which in turn lowers labor productivity.
The environmental aspects are relatively new to
economic analysis – at least compared to other
factors presented in this review. Their analysis is
sometimes pushed outside of the mainstream
macroeconomic and microeconomic models, be it
for methodological difficulties or ideological
differences.
Table 7. Co-occurrence count between codes and the c-coefficient
CODE
(i)
(ii)
(iii)
(iv)
(v)
(vi)
(vii)
(viii)
(ix)
(x)
(xi)
(xii)
TOTALS
(i)
-
48
32
32
163
89
41
294
23
85
149
70
1,026
(ii)
0.02
-
13
10
17
72
22
86
18
36
61
31
414
(iii)
0.01
0.01
-
39
21
92
34
53
9
16
58
20
387
(iv)
0.01
0.01
0.03
-
26
77
10
88
4
6
90
10
392
(v)
0.07
0.01
0.02
0.02
-
95
28
66
6
10
50
33
515
(vi)
0.02
0.02
0.03
0.02
0.03
-
190
107
25
32
153
61
993
(vii)
0.01
0.01
0.02
0.01
0.02
0.05
-
31
18
7
23
1
405
(viii)
0.08
0.03
0.02
0.03
0.02
0.02
0.01
-
50
79
301
76
1,231
(ix)
0.01
0.01
0.01
0
0
0.01
0.01
0.01
-
4
55
12
224
(x)
0.04
0.03
0.01
0
0.01
0.01
0
0.03
0
-
18
57
350
(xi)
0.03
0.02
0.02
0.02
0.01
0.03
0.01
0.06
0.01
0
-
48
1,006
(xii)
0.02
0.01
0.01
0
0.01
0.02
0
0.02
0
0.03
0.01
-
419
TOTALS
0.32
0.17
0.19
0.16
0.21
0.26
0.15
0.32
0.08
0.17
0.22
0.15
-
Source: Own preparation.
Fig. 6 presents the network view of the co-occurrence count, while
Σφάλμα! Το αρχείο προέλευσης της αναφοράς δεν βρέθηκε.
visualizes one for the c-coefficient. The exact numbers for both figures
are given in
Table 7. Both graphs have size-scaled nodes (by
weighted degree) and edges (by weight value). We
use modularity analysis to reveal two communities.
The first community is centered around (i)
agglomerations and (viii) human capital. The second
one is centered around (vi) globalization. The node
for (xi) R&D is visibly smaller when analyzing the
c-coefficient. If codes are in the same community
(modularity class) it means they are more likely to
co-occur with other codes in the same community
(modularity class). The figures show that whether
we use the co-occurrence count or the c-coefficient
the results are very similar, and only one code (ix)
changes its community.
Fig. 6: Network view: co-occurrence count
Source: Own preparation.
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Fig. 7: Network view: c-coefficient
Source: Own preparation.
Finally, we focus on the interdependencies
between the sub-codes.
visualizes the co-occurrence between all sub-
codes. The colors are determined by modularity
class (codes in the same community that are more
likely to co-occur with each other). This time there
are seven modularity classes (communities). Table 8
shows the co-occurrence between 39 selected sub-
code dyads for the reviewed literature (n=141). The
pairs were selected in the following way:
the 3 pairs with the highest co-occurrence
count per category (factor) are chosen;
due to some degree of overlap, both sub-codes
in the pair cannot be from the same category;
when both categories share each other’s
highest pair, the pair is repeated but in reverse
order;
if there is an equal number of co-occurrences,
all pairs are included and the category has
more than 3 pairs.
As per above, every dyad in the table is notable
because of its placement in the top three according
to the co-occurrence count for its factor (code).
Many of them are seemingly obvious, like
spillovers/knowledge or innovation/knowledge.
Some, however, should and have been of particular
interest to economic research. The close relation
between institutions, trade, and innovation signifies
the ever-growing importance of a strong
institutional framework for modern economies.
While globalization is fraught with dangers, from a
strictly theoretical standpoint, its benefits are almost
overwhelming when implemented correctly. For
business cycles, the main questions are how
geography, trade, and education determine
differences in total employment between regions.
Another important cluster of interest is formed by
the relations between labor mobility and knowledge
spillovers as well as between migration and regional
convergence. Migration, both interregional and
international, has become of the most hot-button
issues in political discourse, so economic analyses
must present its impact as clear as possible. Finally,
the link between the environmental aspects (air
pollution and climate change) and their impact on
the decline in labor productivity due to worsening
health has to become one of the paramount issues in
economics.
Table 8 Co-occurrence between selected sub-codes
Sub-code 1
Sub-code 2
Coun
t
Spillovers
Knowledge
166
Spillovers
Innovation
63
Spillovers
Trade
37
Employment
(total)
Geographic
location
45
Employment
(total)
Education
45
Employment
(total)
Trade
30
Institutions
Trade
54
Institutions
Innovation
35
Country
specialization
Trade
20
Air pollution
Health
24
Climate change
Health
15
Green investment
Innovation
11
Inward FDI
Spillovers
8
Inward FDI
Convergence
7
Inward FDI
Geographic
location
7
Trade
Geographic
location
70
Trade
Innovation
60
Trade
Institutions
54
Vertical
integration
Trade
19
Downstream
activities
Trade
13
Upstream
activities
Trade
12
Knowledge
Spillovers
166
Knowledge
Innovation
166
Education
Employment
(total)
45
ICT capital
Innovation
9
ICT capital
Intangible
investment
9
ICT capital
Knowledge
7
ICT capital
Trade
7
Internet
Knowledge
7
Labor mobility
Spillovers
31
Migration
Convergence
29
Migration
Geographic
location
26
Labor mobility
Knowledge
26
Innovation
Knowledge
166
Innovation
Spillovers
63
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Sub-code 1
Sub-code 2
Coun
t
Innovation
Trade
60
Geographic
location
Trade
70
Geographic
location
Employment
(total)
45
Convergence
Trade
38
Source: Own preparation.
Fig. 8: Network view: overview
Source: Own preparation.
5 Discussion and Conclusions
Our study aimed to answer three key research
questions. [RQ1] What are the key recent insights in
the literature? [RQ2] Are there significant themes in
the literature? [RQ3] Which factor is the most
prominent in the literature?
When answering the first two questions, it
should be noted that during the study we identified
twelve areas related to various factors influencing
labor productivity. Among the identified factors
influencing labor productivity we have identified
and categorized such factors like: (i) agglomerations
effect; (ii) business cycles and market selection; (iii)
country differences; (iv) environmental aspects; (v)
foreign direct investment (FDI); (vi) globalization
and international trade; (vii) global value chains
(GVC); (viii) human capital; (ix) information and
communications technology (ICT); (x) labor
allocation; (xi) R&D and innovation; (xii) regional
differences.
Summarizing the analysis of these factors, it
should be pointed out that for each of them the
impact is differentiated and sometimes conditional
and dependent on other factors. Agglomerations
effect (i) has a strong positive impact on labor
productivity, but it may be negative if there is a
problem of congestion. Moreover, agglomeration
may contribute to greater economic inequality. The
impact of business cycle (ii) on labor productivity in
short-run perspective is procyclical (negative during
a recession, positive during expansion), however,
the actual impact on labor productivity depends on
the industry/market or the level of analysis (plant
versus general economy). In the long-run
perspective, a recession may foster creative
destruction which causes the j-curve effect related to
the entry of new companies on the market, i.e.
initially productivity drops and then begins to rise.
This effect is biggest when established firms
experience productivity renewal. There are many
institutional differences (iii) affecting the
differences in productivity between countries.
Innovation and most productive firms are heavily
localized and occur mostly in developed economies
with high capital intensity and strong institutional
frameworks. Environmental (iv) impact on labor
productivity aspects represented by climate change
will most likely have to be strong and negative,
especially in regions where agriculture is dominant,
and in some vulnerable sectors like the construction
industry. However, green investments may have a
positive impact on labor productivity. In the case of
foreign direct investment (v), inward FDI has a
generally positive impact on labor, however, it
depends on the development level of the receiving
country, types of linkages, type of production,
duration, regional aspects, and industry strength.
The impact of outward FDI on labor productivity is
positive. Globalization and international trade (vi)
have a positive impact on manufacturing labor
productivity in developing countries is accompanied
by reforms aimed at changes in ownership
concentration, and – in case of liberalization of trade
in services - a positive impact on manufacturing
labor productivity when manufacturers benefit from
using these services. Exporting, cross-border
acquisitions, outsourcing, and offshoring have a
positive impact on labor productivity. Labor
productivity gains from participation in global value
chains (vii) are higher in developed countries than in
developing countries. Similarly, the labor
productivity of people with higher education grows
more as a result of participation in the GVC than
that of people with lower education. Clusters and
multinational enterprises are crucial for GVC
formation. Human capital (viii) has a strong positive
impact on labor productivity. Knowledge spillovers
help regional growth, which is most noticeable with
close geographic proximity between urban areas.
Investment in ICT capital (ix) has a positive impact
on labor productivity, more so than in non-ICT
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capital. Digitalization and access to information
made possible by the Internet make it a tool for
regional convergence and inclusivity. Skilled labor
mobility (x) has a positive impact on the
manufacturing productivity of the receiving
industry, especially in the case of employment in the
high-tech sectors. Labor unions have a positive
impact on manufacturing labor productivity. R&D
and innovation (xi) have a very strong positive
impact on labor productivity for both manufacturing
and services. The impact of innovation intensity on
labor productivity follows a U-curve. In the regional
dimension (xii), economies experience fairly
persistent interregional differences in labor
productivity. On a regional level, there are two
equilibria in regional productivity growth, with
high-productivity regions converging in the center
of Europe, and low-productivity regions converging
in the peripheries. The polarization was present in
the regional labor productivity of the European
Union. Historically, the productivity levels across
regions in the United States have been highly
differentiated, while in Russia they have converged.
China’s East Rim provinces have had higher labor
productivity and labor productivity growth than the
rest of the country. In the past, the labor surplus in
agriculture in China was high and unevenly spread
between regions. Key recent insights from the above
review concern the observed growing importance of
global value chains and the impact of environmental
and climate change factors.
The main limitation of this study is that it has
not yet captured the impact of COVID-19 on labor
productivity. Although we believe it may be
partially internalized by the codes describing the
business cycle and the development of global value
chains, the full long-term impact of the pandemic is
still unknown as of the writing of this article.
However, these areas certainly require further and
more in-depth exploration in future research.
Another potential direction of future research could
be a qualitative and quantitative text analysis of the
differences present in literature published in
different languages.
When analyzing significant themes in the
literature, it is necessary to indicate the intensity of
relationships between the identified research areas
or factors. The network analysis allowed for the
estimation of co-occurrence and c-coefficient
indicators (see: network visualizations: Fig. 6 and
Σφάλμα! Το αρχείο προέλευσης της αναφοράς
δεν βρέθηκε.). As a result, pairs of connections
within the network were identified, which then form
the key links of the areas. Among closely related
pairs, one should mention: (i) agglomerations +
(viii) human capital; (i) agglomerations + (v) FDI;
(viii) human capital + (xi) R&D; (vi) globalization +
(vii) GVC; (i) agglomerations + (x) labor allocation;
and (i) agglomerations + (xi) R&D. These
connections may indicate a strong correlation
between the discussed factors. This is especially true
for agglomerations which are linked to human
capital, foreign investment, workforce allocation,
and research and development. All these factors are
part of the agglomeration phenomenon. The strong
links between human capital and R&D are not
surprising either, as high-quality human capital is
essential for research development. Similarly, strong
relationships between globalization and GVC are
quite obvious.
The assessment of the most prominent factors in
the literature to evaluate these factors is based on
two criteria: quotes count and the above-mentioned
co-occurrence and c-coefficient. When it comes to
the quotes count, the most prominent factor is (xi)
R&D, followed by (vi) globalization and (viii)
human capital. When it comes to the co-occurrence
and c-coefficient, the most prominent factor is (viii)
human capital, closely followed by (i)
agglomerations, then either (xi) R&D or (vi)
globalization. Network analysis reveals two
communities, the bigger one centered around (i)
agglomerations, and the smaller one centered
around (vi) globalization.
References:
[1] Snyder, H., Literature review as a
research methodology: An overview and
guidelines. Journal of Business
Research 2019, 104, 333–339.
[2] Kuckartz, U., Qualitative text analysis: a
guide to methods, practice & using
software, SAGE, Los Angeles 2014.
[3] Cai, F., Wang, D., Du, Y., Regional
disparity and economic growth in China:
The impact of labor market distortions.
China Economic Review 2002, 13, 197–
212.
[4] Constantinescu, C., Mattoo, A., Ruta,
M., Does vertical specialisation increase
productivity? World Econ 2019, 42,
2385–2402.
[5] Dietzenbacher, E., Hoen, A.R., Los, B.,
Labor productivity in Western Europe
1975–1985: an intercountry,
interindustry analysis. Journal of
Regional Science 2000, 40, 425–452.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
Mariusz-Jan Radło, Artur F. Tomeczek
E-ISSN: 2224-3496
306
Volume 18, 2022
[6] Eurostat, Glossary. 2020.
[7] Hatzikian, Y., Exploring the link
between innovation and firm
performance. J Knowl Econ 2015, 6,
749–768.
[8] Lemos, S., A survey of the effects of the
minimum wage on prices. J Economic
Surveys 2008, 22, 187–212.
[9] McGowan, M.A., Andrews, D., Millot,
V., The walking dead? Zombie firms
and productivity performance in OECD
countries. OECD Economic Department
Working Papers 2017, No. 1372.
[10] Mohnen, P., Hall, B.H., Innovation and
productivity: an update. Eurasian
Business Review 2013, 3, 47–65.
[11] OECD, Glossary of Statistical Terms.
2020.
[12] Ortega-Argilés, R., The transatlantic
productivity gap: a survey of the main
causes: the transatlantic productivity
gap. Journal of Economic Surveys 2012,
26, 395–419.
[13] Andersson, M., Lööf, H.,
Agglomeration and productivity:
evidence from firm-level data. Ann Reg
Sci 2011, 46, 601–620.
[14] Lööf, H., Johansson, B., R&D strategy,
metropolitan externalities and
productivity: evidence from Sweden.
Industry and Innovation 2014, 21, 141–
154.
[15] Broersma, L., Oosterhaven, J., Regional
labor productivity in the Netherlands:
evidence of agglomeration and
congestion effects. Journal of Regional
Science 2009, 49, 483–511.
[16] Rizov, M., Oskam, A., Walsh, P., Is
there a limit to agglomeration? Evidence
from productivity of Dutch firms.
Regional Science and Urban Economics
2012, 42, 595–606.
[17] Di Giacinto, V., Gomellini, M.,
Micucci, G., Pagnini, M., Mapping local
productivity advantages in Italy:
industrial districts, cities or both?
Journal of Economic Geography 2014,
14, 365–394.
[18] Fan, J., Industrial agglomeration and
difference of regional productivity.
Front. Econ. China 2007, 2, 346–361.
[19] Ke, S., Agglomeration, productivity, and
spatial spillovers across Chinese cities.
Ann Reg Sci 2010, 45, 157–179.
[20] Meijers, E.J., Burger, M.J., Spatial
structure and productivity in US
metropolitan areas. Environ Plan A
2010, 42, 1383–1402.
[21] Rigby, D.L., Essletzbichler, J.,
Agglomeration economies and
productivity differences in US cities.
Journal of Economic Geography 2002,
2, 407–432.
[22] Schumpeter, J.A., Capitalism, socialism
and democracy, Routledge, New York,
NY 2003.
[23] Archibugi, D., Filippetti, A., Frenz, M.,
Economic crisis and innovation: Is
destruction prevailing over
accumulation? Research Policy 2013,
42, 303–314.
[24] Archibugi, D., Filippetti, A., Frenz, M.,
The impact of the economic crisis on
innovation: Evidence from Europe.
Technological Forecasting and Social
Change 2013, 80, 1247–1260.
[25] Devece, C., Peris-Ortiz, M., Rueda-
Armengot, C., Entrepreneurship during
economic crisis: Success factors and
paths to failure. Journal of Business
Research 2016, 69, 5366–5370.
[26] van Ark, B., Jäger, K., Recent trends in
Europe’s output and productivity growth
performance at the sector level,.
International Productivity Monitor
2017, 33, 8–23.
[27] Melitz, M.J., Polanec, S., Dynamic
Olley-Pakes productivity decomposition
with entry and exit. The RAND Journal
of Economics 2015, 46, 362–375.
[28] Hyytinen, A., Maliranta, M., Firm
lifecycles and evolution of industry
productivity. Research Policy 2013, 42,
1080–1098.
[29] Dosi, G., Grazzi, M., Tomasi, C., Zeli,
A., Turbulence underneath the big calm?
The micro-evidence behind Italian
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
Mariusz-Jan Radło, Artur F. Tomeczek
E-ISSN: 2224-3496
307
Volume 18, 2022
productivity dynamics. Small Bus Econ
2012, 39, 1043–1067.
[30] Baily, M.N., Bartelsman, E.J.,
Haltiwanger, J., Labor productivity:
structural change and cyclical dynamics.
Review of Economics and Statistics
2001, 83, 420–433.
[31] Burda, M.C., Hamermesh, D.S.,
Stewart, J., Cyclical Variation in Labor
Hours and Productivity Using the
ATUS. American Economic Review
2013, 103, 99–104.
[32] Galí, J., van Rens, T., The vanishing
procyclicality of labor productivity.
Warwick Economic Research Papers
2014, No. 1061.
[33] Gal, P.N., Measuring total factor
productivity at the firm level using
OECD-ORBIS. OECD Economics
Department Working Papers 2013, No.
1040.
[34] Timmer, M.P., Los, B., Localized
innovation and productivity growth in
Asia: an intertemporal DEA approach. J
Prod Anal 2005, 23, 47–64.
[35] Dollar, D., Wolff, E.N., Covergence of
industry labor productivity among
advanced economies, 1963-1982. The
Review of Economics and Statistics
1988, 70, 549.
[36] Canzoneri, M.B., Cumby, R.E., Diba,
B., Relative labor productivity and the
real exchange rate in the long run:
evidence for a panel of OECD countries.
Journal of International Economics
1999, 47, 245–266.
[37] Crinò, R., Wages, skills, and integration
in Poland, Hungary and the Czech
Republic: an industry-level analysis.
Transition Stud Rev 2005, 12, 432–459.
[38] Crespi, F., Pianta, M., Diversity in
innovation and productivity in Europe. J
Evol Econ 2008, 18, 529–545.
[39] Li, T., Liu, H., Salvo, A., Severe air
pollution and labor productivity. SSRN
Journal 2015, No. 8916.
[40] He, J., Liu, H., Salvo, A., Severe air
pollution and labor productivity:
evidence from industrial towns in China.
American Economic Journal: Applied
Economics 2019, 11, 173–201.
[41] Lanzi, E., Dellink, R., Chateau, J., The
sectoral and regional economic
consequences of outdoor air pollution to
2060. Energy Economics 2018, 71, 89–
113.
[42] Kjellstrom, T., Kovats, R.S., Lloyd, S.J.,
Holt, T., et al., The direct impact of
climate change on regional labor
productivity. Archives of Environmental
& Occupational Health 2009, 64, 217–
227.
[43] Altinsoy, H., Yildirim, H.A., Labor
productivity losses over western Turkey
in the twenty-first century as a result of
alteration in WBGT. Int J Biometeorol
2015, 59, 463–471.
[44] Li, X., Chow, K.H., Zhu, Y., Lin, Y.,
Evaluating the impacts of high-
temperature outdoor working
environments on construction labor
productivity in China: A case study of
rebar workers. Building and
Environment 2016, 95, 42–52.
[45] Yi, W., Chan, A., Effects of heat stress
on construction labor productivity in
Hong Kong: a case study of rebar
workers. IJERPH 2017, 14, 1055–1069.
[46] Woo, C., Chung, Y., Chun, D., Han, S.,
et al., Impact of green innovation on
labor productivity and its determinants:
an analysis of the Korean manufacturing
industry. Bus. Strat. Env. 2014, 23, 567–
576.
[47] Leiter, A.M., Parolini, A., Winner, H.,
Environmental regulation and
investment: Evidence from European
industry data. Ecological Economics
2011, 70, 759–770.
[48] Marin, G., Mazzanti, M., The evolution
of environmental and labor productivity
dynamics: Sector based evidence from
Italy. J Evol Econ 2013, 23, 357–399.
[49] Simas, M., Wood, R., Hertwich, E.,
Labor embodied in trade: the role of
labor and energy productivity and
implications for greenhouse gas
emissions. Journal of Industrial Ecology
2015, 19, 343–356.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
Mariusz-Jan Radło, Artur F. Tomeczek
E-ISSN: 2224-3496
308
Volume 18, 2022
[50] Vahter, P., Does FDI spur productivity,
knowledge sourcing and innovation by
incumbent firms? Evidence from
manufacturing industry in Estonia. The
World Economy 2011, 34, 1308–1326.
[51] Jabbour, L., Mucchielli, J.L.,
Technology transfer through vertical
linkages: the case of the Spanish
manufacturing industry. Journal of
Applied Economics 2007, 10, 115–136.
[52] Blomstrom, M., Kokko, A.,
Multinational Corporations and
Spillovers. J Economic Surveys 1998,
12, 247–277.
[53] Mullen, J.K., Williams, M., Foreign
direct investment and regional
productivity spillovers in US
manufacturing. Review of Urban &
Regional Development Studies 2007, 19,
185–196.
[54] Bijsterbosch, M., Kolasa, M., FDI and
productivity convergence in Central and
Eastern Europe: an industry-level
investigation. Review of World
Economics 2010, 145, 689–712.
[55] Antonescu, D., Empirical analysis of
foreign direct investments at NUTS 2
region, in European Union and
Romania. Procedia Economics and
Finance 2015, 22, 681–689.
[56] Zhou, D., Li, S., Tse, D.K., The impact
of FDI on the productivity of domestic
firms: the case of China. International
Business Review 2002, 11, 465–484.
[57] Zhu, G., Tan, K.Y., Foreign direct
investment and labor productivity: New
evidence from China as the host.
Thunderbird International Business
Review 2000, 42, 507–528.
[58] Boghean, C., State, M., The relation
between foreign direct investments
(FDI) and labour productivity in the
European Union countries. Procedia
Economics and Finance 2015, 32, 278–
285.
[59] Fariñas, J.C., Martín-Marcos, A.,
Exporting and economic performance:
firm‐level evidence of Spanish
manufacturing. World Economy 2007,
30, 618–646.
[60] Wagner, J., The causal effects of exports
on firm size and labor productivity: first
evidence from a matching approach.
Economics Letters 2002, 77, 287–292.
[61] Ramstetter, E.D., Labor productivity,
wages, nationality, and foreign
ownership shares in Thai manufacturing,
1996–2000. Journal of Asian Economics
2004, 14, 861–884.
[62] Siedschlag, I., Zhang, X.,
Internationalisation of firms and their
innovation and productivity. Economics
of Innovation and New Technology
2015, 24, 183–203.
[63] Bottazzi, G., Grazzi, M., Dynamics of
productivity and cost of labour in Italian
manufacturing firms. Bulletin of
Economic Research 2014, 66, S55–S73.
[64] Cincera, M., Ravet, J., Globalisation,
industrial diversification and
productivity growth in large European
R&D companies. J Prod Anal 2014, 41,
227–246.
[65] Du, J., Temouri, Y., High-growth firms
and productivity: evidence from the
United Kingdom. Small Bus Econ 2015,
44, 123–143.
[66] Parteka, A., Wolszczak-Derlacz, J., The
impact of trade integration with the
European Union on productivity in a
posttransition economy: the case of
Polish manufacturing sectors. Emerging
Markets Finance and Trade 2013, 49,
84–104.
[67] Paus, E., Reinhardt, N., Robinson, M.,
Trade liberalization and productivity
growth in Latin American
manufacturing, 1970-98. The Journal of
Policy Reform 2003, 6, 1–15.
[68] Gehringer, A., Uneven effects of
financial liberalization on productivity
growth in the EU: Evidence from a
dynamic panel investigation.
International Journal of Production
Economics 2015, 159, 334–346.
[69] Beverelli, C., Fiorini, M., Hoekman, B.,
Services trade policy and manufacturing
productivity: The role of institutions.
Journal of International Economics
2017, 104, 166–182.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
Mariusz-Jan Radło, Artur F. Tomeczek
E-ISSN: 2224-3496
309
Volume 18, 2022
[70] Baghdasaryan, D., la Cour, L.,
Competition, ownership and
productivity. A panel analysis of Czech
firms. Journal of Economics and
Business 2013, 69, 86–100.
[71] McCaig, B., Pavcnik, N., Export
markets and labor allocation in a low-
income country. American Economic
Review 2018, 108, 1899–1941.
[72] Katz, J., Structural change and labor
productivity growth in Latin American
manufacturing industries 1970–96.
World Development 2000, 28, 1583–
1596.
[73] Fariñas, J.C., López, A., Martín-Marcos,
A., Assessing the impact of domestic
outsourcing and offshoring on
productivity at the firm level. Applied
Economics 2014, 46, 1814–1828.
[74] Girma, S., Gorg, H., Outsourcing,
foreign ownership, and productivity:
evidence from UK establishment‐level
data. Rev International Economics 2004,
12, 817–832.
[75] Egger, H., Egger, P., International
outsourcing and the productivity of
low‐skilled labor in the EU. Economic
Inquiry 2006, 44, 98–108.
[76] Bertrand, O., Capron, L., Productivity
enhancement at home via cross-border
acquisitions: The roles of learning and
contemporaneous domestic investments:
Cross-Border Acquisitions and
Domestic Productivity. Strat. Mgmt. J.
2015, 36, 640–658.
[77] Hagemejer, J., Trade and growth in the
new member states: the role of global
value chains. Emerging Markets
Finance and Trade 2018, 54, 2630–
2649.
[78] Kummritz, V., Do global value chains
cause industrial development? CTEI
Working Paper 2016, No. 2016-01.
[79] Pahl, S., Timmer, M.P., Do global value
chains enhance economic upgrading? A
long view. The Journal of Development
Studies 2020, 56, 1683–1705.
[80] Criscuolo, C., Timmis, J., The
relationship between global value chains
and productivity. International
Productivity Monitor 2017, 61–83.
[81] Anderson, E.G., Fine, C.H., Parker,
G.G., Upstream volatility in the supply
chain: the machine tool industry as a
case study. Production and Operations
Management 2000, 9, 239–261.
[82] Cainelli, G., Ganau, R., Giunta, A.,
Spatial agglomeration, global value
chains, and productivity. Micro-
evidence from Italy and Spain.
Economics Letters 2018, 169, 43–46.
[83] Antràs, P., Gortari, A., On the
geography of global value chains. ECTA
2020, 88, 1553–1598.
[84] Kummritz, V., Taglioni, D., Winkler,
D., Economic upgrading through global
value chain participation: which policies
increase the value added gains? Policy
Research Working Paper 2017, No.
8007.
[85] Wang, Z., Wei, S.-J., Yu, X., Zhu, K.,
Characterizing global value chains:
production length and upstreamness.
NBER Working Paper Series 2017, No.
w23261.
[86] Degain, C., Meng, B., Wang, Z., Recent
trends in global trade and global value
chains, in: Global Value Chain
Development Report 2017: Measuring
and Analyzing the Impact of GVCs On
Economic Development, Word Bank,
2017, pp. 37–68.
[87] Gereffi, G., Luo, X., Risks and
opportunities of participation in global
value chains. JBFE 2015, 2, 51–63.
[88] Strange, R., Zucchella, A., Industry 4.0,
global value chains and international
business. Multinational Business Review
2017, 25, 174–184.
[89] Choi, N., Global value chains and East
Asian trade in value-added. Asian
Economic Papers 2015, 14, 129–144.
[90] Rodrik, D., New technologies, global
value chains, and developing
economies. NBER Working Paper Series
2018, No. w25164.
[91] Nadvi, K., Thoburn, J.T., Thang, B.T.,
Ha, N.T.T., et al., Vietnam in the global
garment and textile value chain: impacts
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
Mariusz-Jan Radło, Artur F. Tomeczek
E-ISSN: 2224-3496
310
Volume 18, 2022
on firms and workers. J. Int. Dev. 2004,
16, 111–123.
[92] Barrientos, S., Kritzinger, A., Squaring
the circle: global production and the
informalization of work in South
African fruit exports. J. Int. Dev. 2004,
16, 81–92.
[93] Chansarn, S., Labor productivity
growth, education, health and
technological progress: a cross-country
analysis. Economic Analysis and Policy
2010, 40, 249–261.
[94] Benos, N., Karagiannis, S., Do
education quality and spillovers matter?
Evidence on human capital and
productivity in Greece. Economic
Modelling 2016, 54, 563–573.
[95] Li, H., Loyalka, P., Rozelle, S., Wu, B.,
Human capital and China’s future
growth. Journal of Economic
Perspectives 2017, 31, 25–48.
[96] Fafchamps, M., Quisumbing, A.R.,
Human capital, productivity, and labor
allocation in rural Pakistan. The Journal
of Human Resources 1999, 34, 369.
[97] Berumen, S.A., Pérez-Megino, L.P.,
Ibarra, K.A., Extrinsic motivation index:
A new tool for managing labor
productivity. International Journal of
Business Science & Applied
Management 2016, 11, 1–17.
[98] Iverson, R.D., Zatzick, C.D., The effects
of downsizing on labor productivity:
The value of showing consideration for
employees’ morale and welfare in high-
performance work systems. Hum.
Resour. Manage. 2011, 50, 29–44.
[99] Koch, M.J., McGrath, R.G., Improving
labor productivity: Human resource
management policies do matter.
Strategic Management Journal 1996,
17, 335–354.
[100] Fischer, M.M., Scherngell, T.,
Reismann, M., Knowledge spillovers
and total factor productivity: evidence
using a spatial panel data model:
knowledge spillovers and total factor
productivity. Geographical Analysis
2009, 41, 204–220.
[101] Fischer, M.M., Bartkowska, M., Riedl,
A., Sardadvar, S., et al., The impact of
human capital on regional labor
productivity in Europe. Lett Spat Resour
Sci 2009, 2, 97–108.
[102] Fuchs-Schündeln, N., Izem, R.,
Explaining the low labor productivity in
East Germany – A spatial analysis.
Journal of Comparative Economics
2012, 40, 1–21.
[103] Fan, S., Gulati, A., Thorat, S.,
Investment, subsidies, and pro-poor
growth in rural India. Agricultural
Economics 2008, 39, 163–170.
[104] Stack, M.L., Ozawa, S., Bishai, D.M.,
Mirelman, A., et al., Estimated
economic benefits during the ‘decade of
vaccines’ include treatment savings,
gains in labor productivity. Health
Affairs 2011, 30, 1021–1028.
[105] Cabral, R., Mollick, A.V., Saucedo, E.,
Violence in Mexico and its effects on
labor productivity. Ann Reg Sci 2016,
56, 317–339.
[106] Craig, B.J., Pardey, P.G., Roseboom, J.,
International productivity patterns:
accounting for input quality,
infrastructure, and research. American
Journal of Agricultural Economics
1997, 79, 1064–1076.
[107] Strauss, J., Does better nutrition raise
farm productivity? Journal of Political
Economy 1986, 94, 297–320.
[108] Belorgey, N., Lecat, R., Maury, T.,
Determinants of productivity per
employee: An empirical estimation
using panel data. SSRN Journal 2004,
No. 110.
[109] Kılıçaslan, Y., Sickles, R.C., Atay
Kayış, A., Üçdoğruk Gürel, Y., Impact
of ICT on the productivity of the firm:
evidence from Turkish manufacturing. J
Prod Anal 2017, 47, 277–289.
[110] Nurmilaakso, J.-M., ICT solutions and
labor productivity: evidence from firm-
level data. Electron Commer Res 2009,
9, 173–181.
[111] Piatkowski, M., The contribution of ICT
investment to economic growth and
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
Mariusz-Jan Radło, Artur F. Tomeczek
E-ISSN: 2224-3496
311
Volume 18, 2022
labor productivity in Poland 1995-2000.
SSRN Journal 2003, No. 43.
[112] Bergeaud, A., Cette, G., Lecat, R.,
Productivity trends in advanced
countries between 1890 and 2012.
Review of Income and Wealth 2016, 62,
420–444.
[113] Jung, J., López-Bazo, E., On the
regional impact of broadband on
productivity: The case of Brazil.
Telecommunications Policy 2019.
[114] Eicher, T.S., Roehn, O., Sources of the
German productivity demise: tracing the
effects of industry‐level information
and communication technology
investment. German Economic Review
2007, 8, 211–236.
[115] Acemoglu, D., Autor, D., Dorn, D.,
Hanson, G.H., et al., Return of the
Solow Paradox? IT, productivity, and
employment in US manufacturing.
American Economic Review 2014, 104,
394–399.
[116] Foster-McGregor, N., Pöschl, J.,
Productivity effects of knowledge
transfers through labour mobility. J
Prod Anal 2016, 46, 169–184.
[117] Stoyanov, A., Zubanov, N., Productivity
spillovers across firms through worker
mobility. American Economic Journal:
Applied Economics 2012, 4, 168–198.
[118] Lentz, R., Mortensen, D.T., Productivity
growth and worker reallocation. Int
Economic Rev 2005, 46, 731–749.
[119] Fischer, L.B., Pfaffermayr, M., The
more the merrier? Migration and
convergence among European regions.
Regional Science and Urban Economics
2018, 72, 103–114.
[120] Mefford, R.N., The effect of unions on
productivity in a multinational
manufacturing firm. ILR Review 1986,
40, 105–114.
[121] Lisi, D., The impact of temporary
employment and employment protection
on labour productivity: evidence from
an industry-level panel of EU countries.
J Labour Market Res 2013, 46, 119–
144.
[122] Bardazzi, R., Duranti, S., Atypical work:
a threat to labour productivity growth?
Some evidence from Italy. International
Review of Applied Economics 2016, 30,
620–643.
[123] Vergeer, R., Kleinknecht, A., The
impact of labor market deregulation on
productivity: a panel data analysis of 19
OECD countries (1960-2004). Journal
of Post Keynesian Economics 2010, 33,
371–408.
[124] Tridico, P., Pariboni, R., Inequality,
financialization, and economic decline.
Journal of Post Keynesian Economics
2018, 41, 236–259.
[125] Carter, S., Real wage productivity
elasticity across advanced economies,
1963-1996. Journal of Post Keynesian
Economics 2007, 29, 573–600.
[126] Baily, M.N., Bartelsman, E.J.,
Haltiwanger, J., Downsizing and
productivity growth: myth or reality?
Small Bus Econ 1996, 8, 259–278.
[127] Haynes, K.E., Dinc, M., Productivity
Change in Manufacturing Regions: A
Multifactor/Shift-Share Approach.
Growth and Change 1997, 28, 201–221.
[128] Apergis, N., Economidou, C., Filippidis,
I., Innovation, technology transfer and
labor productivity linkages: evidence
from a panel of manufacturing
industries. Rev World Econ 2008, 144,
491–508.
[129] Baumann, J., Kritikos, A.S., The link
between R&D, innovation and
productivity: Are micro firms different?
Research Policy 2016, 45, 1263–1274.
[130] Chudnovsky, D., López, A., Pupato, G.,
Innovation and productivity in
developing countries: A study of
Argentine manufacturing firms’
behavior (1992–2001). Research Policy
2006, 35, 266–288.
[131] Crespi, G., Zuniga, P., Innovation and
productivity: evidence from six Latin
American countries. SSRN Journal
2010, No. IDB-WP-218.
[132] De Fuentes, C., Dutrenit, G., Santiago,
F., Gras, N., Determinants of Innovation
and Productivity in the Service Sector in
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
Mariusz-Jan Radło, Artur F. Tomeczek
E-ISSN: 2224-3496
312
Volume 18, 2022
Mexico. Emerging Markets Finance and
Trade 2015, 51, 578–592.
[133] Lee, D., Role of R&D in the
productivity growth of Korean
industries: Technology gap and business
cycle. Journal of Asian Economics
2016, 45, 31–45.
[134] Piras, G., Postiglione, P., Aroca, P.,
Specialization, R&D and productivity
growth: evidence from EU regions. Ann
Reg Sci 2012, 49, 35–51.
[135] Sondermann, D., Productivity in the
euro area: any evidence of convergence?
Empir Econ 2014, 47, 999–1027.
[136] Boix-Domenech, R., Soler-Marco, V.,
Creative service industries and regional
productivity: CSI and the productivity of
the regions. Papers in Regional Science
2017, 96, 261–279.
[137] Coad, A., Pellegrino, G., Savona, M.,
Barriers to innovation and firm
productivity. Economics of Innovation
and New Technology 2016, 25, 321–
334.
[138] Borgo, M.D., Goodridge, P., Haskel, J.,
Pesole, A., Productivity and growth in
UK industries: an intangible investment
approach. Oxford Bulletin of Economics
and Statistics 2013, 75, 806–834.
[139] Niebel, T., O’Mahony, M., Saam, M.,
The contribution of intangible assets to
sectoral productivity growth in the EU.
Review of Income and Wealth 2017, 63,
S49–S67.
[140] Roth, F., Thum, A.-E., Intangible capital
and labor productivity growth: Panel
evidence for the EU from 1998-2005.
Review of Income and Wealth 2013, 59,
486–508.
[141] Hall, B., Innovation and productivity.
NBER Working Paper Series 2011, No.
w17178, w17178.
[142] Galenson, W., Leibenstein, H.,
Investment criteria, productivity, and
economic development. The Quarterly
Journal of Economics 1955, 69, 343.
[143] Travaglini, G., Trade-off between labor
productivity and capital accumulation in
Italian energy sector. Journal of Policy
Modeling 2012, 34, 35–48.
[144] Waldinger, F., Bombs, brains, and
science: the role of human and physical
capital for the creation of scientific
knowledge. Review of Economics and
Statistics 2016, 98, 811–831.
[145] Paci, R., More similar and less equal:
Economic growth in the European
regions. Weltwirtschaftliches Archiv
1997, 133, 609–634.
[146] Fotopoulos, G., European union regional
productivity dynamics: a
“distributional” approach. J Regional
Sci 2008, 48, 419–454.
[147] Basile, R., Productivity polarization
across regions in Europe: the role of
nonlinearities and spatial dependence.
International Regional Science Review
2009, 32, 92–115.
[148] Cuadrado-Roura, J.R., García-Greciano,
B., Raymond, J.L., Regional
convergence in productivity and
productive structure: The Spanish case.
International Regional Science Review
1999, 22, 35–53.
[149] Dall’erba, S., Productivity convergence
and spatial dependence among Spanish
regions. J Geograph Syst 2005, 7, 207–
227.
[150] Mikheeva, N.N., Comparative analysis
of labor productivity in Russian regions.
Reg. Res. Russ. 2016, 6, 105–114.
[151] Bernard, A.B., Jones, C.I., Productivity
and convergence across U.S. States and
industries. Empirical Economics 1996,
21, 113–135.
[152] Hulten, C.R., Schwab, R.M., Regional
productivity growth in US
manufacturing: 1951-78. The American
Economic Review 1984, 74, 152–162.
[153] Bhattacharyya, A., Parker, E., Labor
productivity and migration in Chinese
agriculture A stochastic frontier
approach. China Economic Review
1999, 10, 59–74.
[154] Jiang, X., Dietzenbacher, E., Los, B., A
dissection of the growth of regional
disparities in Chinese labor productivity
between 1997 and 2002. Ann Reg Sci
2014, 52, 513–536.
WSEAS TRANSACTIONS on ENVIRONMENT and DEVELOPMENT
DOI: 10.37394/232015.2022.18.30
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E-ISSN: 2224-3496
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Volume 18, 2022
[155] Yang, L., Lahr, M.L., Labor
productivity differences in China 1987-
1997: an interregional decomposition
analysis. The Review of Regional
Studies 2008, 38, 319–341.
[156] Web of Science, Database. 2020.
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