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Purpose The objective of the study is to identify and explore factors affecting the productivity of companies in the Czech Republic with a focus on the role of firm size, firm age, indebtedness and long-term negative equity, efficiency of assets usage, liquidity, legal form, location and sector affiliation. Design/methodology/approach The study utilizes a large unbalanced panel dataset of 91,257 firms (548,998 observations in total) covering the period 2000–2019. The dependent variable, i.e. total factor productivity (TFP), reflecting the overall firm productivity, was estimated by ordinary least squares (OLS) regression. The main findings were obtained through the estimation of two econometric models explaining the effects of factors on firm-level TFP. First, the OLS regressions together with Nomenclature of Territorial Units for Statistics (NUTS) 3 regions, year dummies and robust standard errors were estimated. Second, as a robustness check, the very same model was estimated with the random effects (RE) generalized least squares (GLS) method. Findings The analysis has shown a statistically significant U -shaped relationship (with the turning point of 38, resp. 36 years) between firm age and the overall TFP among the Czech enterprises. The authors provide two key findings in terms of a firm size-productivity relationship. Firms with fewer employees, often officially registered as self-employed individuals/freelancers, report higher levels of productivity. Nevertheless, when it comes to firm property (assets), the authors find a positive relationship between firm size and TFP. A high proportion of debts in the capital structure of analysed companies, or even negative equity, has been negatively associated with TFP levels. Research limitations/implications More research is needed in the deeper exploration of sectoral and regional determinants of firm TFP, as both regional and sectoral heterogeneity were observed in the study. The authors propose the employment of a multi-level modelling approach, including a range of continuous variables and investigation of their role in shaping firm-level productivity. Practical implications Concerning the results, managers should be mindful of optimal capital structure principles due to the negative impact of a high level of debts on the productivity level. High indebtedness means high-interest payments drawing earnings off, which may be, especially in the long term, a hindrance to investments. The entrepreneurship and small- and medium-sized enterprise policies may be targeted at the soft policy actions, including advisory services and counselling on business development or risk and on the provision of financial capital allowing firms to strive for growth-oriented projects. Originality/value To the best of the authors' knowledge, this is the first attempt to provide insight into the firm-level productivity determinants, based on the large dataset covering enterprises across the whole economy over the long term, representing the structure of the country's entrepreneurial activity.
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Exploring firm-level and sectoral
variation in total factor
productivity (TFP)
Ond
rej Dvoulet
y
Department of Entrepreneurship, Prague University of Economics and Business,
Prague, Czech Republic, and
Ivana Bla
zkov
a
Department of Regional and Business Economics, Mendel University in Brno,
Brno, Czech Republic
Abstract
Purpose The objective of the study is to identify and explore factors affecting the productivity of companies
in the Czech Republic with a focus on the role of firm size, firm age, indebtedness and long-term negative equity,
efficiency of assets usage, liquidity, legal form, location and sector affiliation.
Design/methodology/approach The study utilizes a large unbalanced panel dataset of 91,257 firms
(548,998 observations in total) covering the period 20002019. The dependent variable, i.e. total factor
productivity (TFP), reflecting the overall firm productivity, was estimated by ordinary least squares (OLS)
regression. The main findings were obtained through the estimation of two econometric models explaining the
effects of factors on firm-level TFP. First, the OLS regressions together with Nomenclature of Territorial Units
for Statistics (NUTS) 3 regions, year dummies and robust standard errors were estimated. Second, as a
robustness check, the very same model was estimated with the random effects (RE) generalized least squares
(GLS) method.
Findings The analysis has shown a statistically significant U-shaped relationship (with the turning point of
38, resp. 36 years) between firm age and the overall TFP among the Czech enterprises. The authors provide two
key findings in terms of a firm size-productivity relationship. Firms with fewer employees, often officially
registered as self-employed individuals/freelancers, report higher levels of productivity. Nevertheless, when it
comes to firm property (assets), the authors find a positive relationship between firm size and TFP. A high
proportion of debts in the capital structure of analysed companies, or even negative equity, has been negatively
associated with TFP levels.
Research limitations/implications More research is needed in the deeper exploration of sectoral and
regional determinants of firm TFP, as both regional and sectoral heterogeneity were observed in the study. The
authors propose the employment of a multi-level modelling approach, including a range of continuous variables
and investigation of their role in shaping firm-level productivity.
Practical implications Concerning the results, managers should be mindful of optimal capital structure
principles due to the negative impact of a high level of debts on the productivity level. Highindebtedness means
high-interest payments drawing earnings off, which may be, especially in the long term, a hindrance to
investments. The entrepreneurship and small- and medium-sized enterprise policies may be targeted at the soft
policy actions, including advisory services and counselling on business development or risk and on the
provision of financial capital allowing firms to strive for growth-oriented projects.
Originality/value To the best of the authorsknowledge, this is the first attempt to provide insight into the
firm-level productivity determinants, based on the large dataset covering enterprises across the whole
economy over the long term, representing the structure of the countrys entrepreneurial activity.
Keywords Total factor productivity, TFP, Czech Republic, Firm-level analysis
Paper type Research paper
Firm-level and
sectoral
variation in
TFP
JEL Classification D24, L66, M21, O47
The authors express their thanks for the comments of both reviewers and the editor Paul Jones who
helped to improve the quality of the manuscript. This work was supported by the Internal Grant Agency
of Faculty of Business Administration, Prague University of Economics and Business, under no.:
IP300040 and the Internal Grant Agency of Faculty of Regional Development and International Studies,
Mendel University in Brno, under no.: 2020/003
The current issue and full text archive of this journal is available on Emerald Insight at:
https://www.emerald.com/insight/1355-2554.htm
Received 2 November 2020
Revised 22 April 2021
Accepted 19 May 2021
International Journal of
Entrepreneurial Behavior &
Research
© Emerald Publishing Limited
1355-2554
DOI 10.1108/IJEBR-11-2020-0744
1. Introduction
Determinants and variations in the overall productivity have been intensively studied in the
past decades, resulting in a plentiful number of both theoretical and empirical approaches to the
research of productivity within the numerous socio-economic contexts across the globe.
Productivity improvement is acknowledgedto be the key to sustained long-term growth (Dobbs
and Hamilton, 2007;Harris and Moffat, 2015;Innocenti and Zampi, 2019;Kim et al.,2016;
Williams et al., 2016) and one of the most reliable indicators for the competitiveness of a firm,
industry or country over the long term (Arranz et al., 2019;Castellacci, 2008;European
Commission, 2009;Feder, 2018). Moreover, Giang et al. (2019),amongothers,arguethatan
increase in the macroeconomic growth is conditioned by the rise in productivity at the micro-
level. Therefore, investigations into the factors of the variability of firm-level productivity are
critical, and also from the macroeconomic policy perspective, as continued sustainable
economic development is of interest to key policy actors, in designing relevant entrepreneurship
and innovation fostering policies (Dvoulet
yet al.,2020;Storey and Potter, 2020).
Previously published studies on productivity determinants (Botri
cet al., 2017;Cie
slik et al.,
2019;Du and Temouri, 2015) examine and confirm various factors that cause variability in
productivity levels, and agree on considerable and persistent heterogeneity in the firm-level
productivity, across countries and industries. For instance, the research based on the industrial
organisation literature usually addresses the relationship between productivity levels and
market structure (Syverson, 2004), the influence of competition (Galdon-Sanchez and Schmitz,
2002;Holmes and Schmitz, 2010), technology issues and technology spillovers (Bloom et al.,
2013;Castellacci, 2007;Los and Verspagen, 2007) or organizational structures (Chang and Lee,
2016;Lim and Sanidas, 2011;Schoar, 2002) in the context of productivity growth. Labour
economics investigates the effects of individual human capital on firm productivity and their
mutual relationships, e.g. effects of dispersion of wages (Arranz-Aperte, 2014), impacts of skills
and favourable working environment (Islam and Shazali, 2011), educational attainment (De La
Fuente, 2011;Isaksson, 2007) or managerial practices (Belay et al., 2014;Cowling, 2003;
Cucculelliet al., 2014). Within the international trade theory, the studies usually confirm that the
exporter status has significant contribution for firm-level productivity heterogeneity (Bravo-
Ortega et al., 2014;Manso Machado, 2019;Nunes et al.,2020;Salomon and Jin, 2008). Several
studies also examine the spatial dimension of the differences in firm-level productivity (Cie
slik
et al., 2019;Mart
ınez-Victoria et al., 2018) or productivity variations across industries (Biatour
et al., 2011;Botri
cet al.,2017). Although the existing research literature related to this topic is
very large in scale and scope, robust empirical analyses on the firm- and sectoral-level
productivity variations, based on large longitudinal and representative datasets, are still
sparse, as well as studies that examine the long-term relationships between economic variables
and productivity development (Erken et al., 2018).
Hence, our study is focussed on the examination of a comprehensive set of firm-level
productivity determinants using a large and sufficiently long panel dataset. The objective of
this study is to identify and explore factors affecting the productivity of companies in the
Czech Republic in the period of 20002019. As an outcome variable, we employ the most
appropriate indicator of productivity total factor productivity (TFP), which is used in the
majority of previous studies (Aiello et al., 2015;Bla
zkov
aet al., 2020;Botri
cet al., 2017;Cie
slik
et al., 2019; and others). TFP represents an increase in total production, which exceeds the
increase in production caused by an increase in the amount of inputs involved in this
production. Therefore, it provides an insight into the efficiency growth through intangible
factors such as technological change, research and development, accumulation of education
and skills, etc. (Sickles and Zelenyuk, 2019). We empirically test the significance and effects of
firm size, firm age, indebtedness and long-term negative equity, the efficiency of assets usage,
liquidity, legal form, location and sector affiliation on TFP, having controlled for cross-
regional and time variance. The robustness of the analysis is ensured by using a dataset
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containing 91,257 firms across all sectors of the Czech economy (except non-governmental
and non-profit organisations) over the period of 20 years, which represents 548,998
observations in total. Thus, this makes it possible to generalize the findings for the whole of
the Czech economy and provide managerial and economic-political implications supporting
such an external and internal environment that will be favourable for the growth of firms
productivity levels. Notably, our study also offers insights into the productivity based on
sectoral affiliation, assuming that a firms TFP differs from sector to sector.
There is also a broader motivation for doing this research. Despite the generally accepted
fact that productivity growth is a necessary condition for stable economic growth, the research
on productivity determinants in the Czech Republic is still underdeveloped. To date, most
studies on productivity have only dealt with selected sectors such as agriculture (e.g.
Cechura,
2012;Ratinger and Kristkova, 2015;
Zenka et al.,2015), manufacturing (e.g. Baghdasaryan and
La Cour, 2013) and the banking sector (e.g. Andries, 2011). Other studies principally employed a
single variable framework, such as foreign investments (Djankov and Hoekman, 2000;Ger
sl,
2008;Hampl et al., 2020) or information and communications technology (ICT) investments
(Piatkowski, 2003;Spiezia, 2013). An exception is a recent study conducted by Davies et al.
(2019) analysing productivity determinants across a wider range of sectors in the Czech
Republic. Nevertheless, the research is based only on the Community Innovation Survey (CIS)
data from four waves (2010, 2012, 2014 and 2016) resulting in an analysis based on 7,891 firm-
level observations in total and covering mostly larger enterprises. Therefore, the studys
representativity of the whole economy is quite limited (for a discussion on limitations of the CIS,
see, e.g. Gault, 2013) as the Czech economy consists mainly of small- and medium-sized
enterprises (Dvoulet
y, 2019). To the best of our knowledge, this is the first attempt to provide
insight into the firm-level productivity determinants based on the large dataset covering
enterprises across the whole economy over the long term. Moreover, as stated by the
Organisation for Economic Co-operation and Development (OECD, 2018) and also highlighted
by Davies et al. (2019), low productivity level in the Czech Republic is still considered to be a
barrier, limiting convergence towards peer countries and OECD standards of living, which
emphasizes the need to explore the heterogeneity of the Czech companiesfurther and to identify
characteristics that are associated with higher levels of productivity.
2. Theoretical background
Different variables have previously been discussed to explain firm productivity variability,
from which some core determinants are addressed in this section.
Empirical evidence indicates that productivity is correlated with the firm size (e.g.
Bartelsman et al., 2009;Cie
slik et al., 2017;Davies et al., 2019), since small- and large-sized
firms organize their production processes differently (Brouwer et al., 2005). Generally, the
researchers (e.g. Ayyagari et al., 2011;Bartelsman et al., 2009) find a positive relationship
between firm size and productivity. Nevertheless, Brouwer et al. (2005) observed a non-linear
relationship among the firms in the Dutch manufacturing industries, and explain that due to
economies of scale, smaller firms initially experience a positive impact on their productivity,
however, when the firm size grows to a certain level, diseconomies of scale may dominate, and
the relationship between firm size and productivity may turn to negative. Dhawan (2001)
hypothesizes that smaller firms reach higher productivity due to their leaner organizational
structure, allowing them to act quickly in changing market conditions and exploit emerging
market opportunities and niche markets. Moreover, small firms are more often open to
innovations, since their managers are more likely risk-takers, which are a prerequisite for
their productivity growth (Nieto and Santamar
ıa, 2010).
The previous literature is also in consensus on the relationship between the firm age and
productivity. Most commonly, scholars (Alon et al., 2018;Brouwer et al., 2005 or Haldane,
2017) report a downward sloping and convex relationship. Brouwer et al. (2005) state that
Firm-level and
sectoral
variation in
TFP
young firms initially have low productivity levels, however, in order to survive and catch up
with established firms in the market (incumbents), they must naturally strive for productivity
growth, mainly through the learning processes and selection effects. This pattern could also
be explained by the body of literature relating to knowledge accumulation and productivity
growth, such as human capital theory (Becker, 1962) and learning-by-doingtheory (Arrow,
1962). It follows that productivity increases due to the skill accumulation and through being
on-the-job, including business and entrepreneurial experience, as empirically confirmed by
Burke et al. (2018),De La Fuente (2011) or Ramos et al. (2010). A considerable number of
empirical papers also examined how practical experience acquired during the production
process (more generally named as organizational learning) affects productivity at the firm
level, and confirmed the positive relationship (e.g. Geylani and Stefanou, 2013;Thornton and
Thompson, 2001). The above-mentioned explanations correspond with the convex shape of
the curve, capturing the relationship between firm age and productivity, where older
companies show above-average levels of productivity, since they are more experienced and
less exposed to newness, as documented by, for example Cucculelli et al. (2014) or Dabla-
Norris et al. (2012).Brouwer et al. (2005) state that companies that are surviving over a long
period of time, i.e. long-established firms, must be productive; otherwise, they would have left
the market already. Nevertheless, adverse empirical findings are also to be found in the
previous studies. For example, Power (1998) explained the opposite direction of the influence
through inertia effects, i.e. less flexibility and innovativeness of older firms.
Coricelli et al. (2012) further argue that there is a relationship between financial leverage and
productivity growth at the firm level. Financial conditions of firms influence not only firm
performance (Sivathaasan et al.,2013;Tsai and Luan, 2016;Bla
zkov
a and Dvoulet
y, 2019)but
their role is also seen in contributing to long-term enterprise growth through the growth of the
overall productivity (Calderon and Liu, 2003;Rioja and Valev, 2004). Coricelli et al. (2012)
estimated a threshold regression model that empirically confirmed a positive relationship
between TFP growth and financial leverage until a critical point, beyond which leverage
counteracts TFP growth. Such an observation may be explained by the trade-off theory of
optimal capital structure (Frank and Goyal, 2011), leading to the balance between the dead
weight costs of bankruptcy and the tax-saving benefits of debt. Therefore, the net effect of
indebtedness on firm productivity is an inconsistent empirical question. Regarding the role of
firmsleverage and indebtedness, there are two types of specific firms with different debt-
productivity ratios. Haldane (2017) identified high-productivity firms having high debts due to
high investments requiring capital borrowing. On the opposite side, Bla
zkov
a and Dvoulet
y
(2020) comment on the very low-productivity firms having high debts because of their low
profits and repayment capacity, including an extreme case oflow-productivity companies with
negative equity, referred to as zombies. Both of these groups of companies are likely to
provide conflicting results in terms of debt-productivity dependency. Nevertheless, we need to
point out that the optimal level of debt depends on many other factors, including the type of
business and entrepreneurial (growth) orientation of the firm (Vaznyte and Andries, 2019).
Hoogstra and van Dijk (2004) contributed to the literature by documenting an indisputable
role of location factors and growth and firm performance. Other studies on productivity have
also highlighted the role of spatial variation (Cie
slik et al., 2019;Giannakis and Bruggeman,
2015;Haldane, 2017;Mart
ınez-Victoria et al., 2018). It is widely acknowledged that firms are
dependent on external influences for their functioning (Henley, 2018) and are influenced by
spatially differentiating factors, such as geographical opportunities and constraints,
resources, spillover factors (Carlsson and Dahlberg, 2003) and public policy (Dvoulet
y
et al., 2020,2021;Harris and Moffat, 2020;Pickernell et al., 2013). The interconnection between
these factors has been described recently in the theory of entrepreneurship/entrepreneurial
ecosystem (Stam, 2015), describing the surroundings of entrepreneurial activity and its role in
the growth of a particular business. In the context of firm-level productivity, the question
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arises of whether circumstances for achieving higher productivity levels are more favourable
in some localities compared to others. Nystr
om (2007) draws attention to the agglomeration
effects due to the business density in specific areas that may acquire more comprehensive
benefits from (geographical) proximity to other enterprises (Acs et al., 2009;Rocha and
Sternberg, 2005). Karlsson and Stough (2005) explain that these effects include, for instance,
formal and financial relationships, such as subcontracting or cooperation on mutually
beneficial projects.
Zenka et al. (2015) state that urban proximity, population and firm density
may, however, influence productivity levels both positively and negatively. For example,
higher rents put pressure on firms to increase their productivity and, at the same time, they
mean higher costs. Likewise, higher wages on the metropolitan markets attract a highly
skilled labour force and thus allow firms to achieve higher productivity; nonetheless, firms
have to cope with higher personnel costs (Grigg, 2003).
When assessing the variability of firm productivity and its growth, we cannot neglect the
influence of industry-specific factors and market characteristics that are also of interest to the
research community and policymakers (Bartelsman et al., 2004;Castellacci, 2007;Harris and
Moffat, 2017;Jung and Lee, 2010). Differences in firm-level productivity across industries
depend on structural factors and sector-specific technological and economic conditions, such
as market concentration within an industry, demand conditions, degree of openness to
foreign competition, barriers of entrance, levels of technological opportunities and capital
requirements, or education and skill levels (Botriç et al., 2017;Castellacci, 2007;Jung and Lee,
2010). In the economic theory, there are two principal interpretations of sectoral variability in
productivity levels and dynamics (Castellacci, 2008)the mainstream view based on the new
growth theory (Romer, 1986) and the evolutionary view explaining this issue on the grounds
of technological regimes (Marsili, 1999). Studies based on the former approach focus on the
impact of research and development (R&D) activities and knowledge spillovers on sectoral
differences in productivity growth (Medda and Piga, 2014;Li and Bosworth, 2020). Applied
evolutionary studies explain sectoral differences through the characteristics of technological
regimes, which define a technological environment providing opportunities and constraints
for firms (Castellacci and Zheng, 2010;Lux et al., 2020). Besides, entrepreneurship scholars
(Block et al., 2017;Castellacci and Zheng, 2010) link the productivity growth of firms doing
business in a particular industry/sector with the share of Schumpeterian (innovation- and
growth-oriented) entrepreneurs. In summary, the findings of the previous studies based on
both approaches confirm that sector-specific technological and economic factors have a
significant impact on the opportunities and constraints faced by companies in different
industries, affecting innovation strategies and activities of firms with consequent effects on
firm productivity (e.g. Botriç et al., 2017;Castellacci, 2007;Castellacci and Zheng, 2010).
This literature review has demonstrated the complexity of the research on the determinants
of TFP. Research from the management, economics and entrepreneurship fields documented in
the above studies highlight the importance of firm-, industry- and location-specific factors
shaping enterprise productivity. Nevertheless, when studying the previous publications and
empiricalinsights, much less isknown about the industry dimension of the phenomenon (Botriç
et al.,2017;Harris and Moffat, 2017). Therefore, we aimto specifically explorewhether or not the
sectoral affiliation shapes the firm-level TFP, while being conditioned to other established
determinants. We formally state the following hypothesis to be tested:
H1. Firm-level total factor productivity is heterogeneous across sectors.
3. Data
Our research aimed to provide statistically robust empirical evidence on the factors shaping
TFP that might be generalizable for the whole of the country under investigation (i.e. the Czech
Republic). Therefore, we applied significant efforts to the maximisation of the investigated
Firm-level and
sectoral
variation in
TFP
period and sample size. We contacted a company called Bisnode (2020) which commercially
provides business purpose-related information on the companies (mainly via the databases
Albertina and MagnusWeb), including their profit and loss statements and balance sheets. We
obtained from them all historically available data for the period of 20002019, which initially
included information on around 330,050 firms and organisations (2,193,608 observations in
total). This initial dataset had to becomethe subject of careful screening, coding and checking for
outliers. We removed from the dataset all non-business-related organizational forms (such as
non-governmental organisations and non-profit organisations) and observations with missing
data for the key variables of interest (described in Table 1). The data processing accounted for a
total of 2,193,608 observations of activities that ended in having total data for 101,682 firms and
enterprises (in total 635,989 observations). After that, we used the Stata programme BACON
(for the details see Billor et al.,2000) to inspect our sample for outlier observations and strange
values (conditioned to all financial variables available in Table 1),andwefinishedwithasample
consisting of 91,257 firms, accounting for 548,998 observations in total.
The final sample consists mainly of small- and medium-sized enterprises (SMEs) which
represent 97.3% of the dataset (see Table 2). We believe that the sample closely reaches the
representativity of the Czech entrepreneurial activity, which is being characterized by the
dominance of SMEs, corresponding to 99.8% of the overall business activity (Dvoulet
y, 2019;
Kozub
ıkov
aet al., 2018;Luke
s, 2017) and strong orientation on manufacturing and the
automotive industry (Lefley et al., 2004;Pavl
ınek and
Z
ı
zalov
a, 2016;Prokop et al., 2017).
Dvoulet
y (2019, p. 4) used secondary data sources to document the overall entrepreneurial
activity in the country, showing that in 2017, there were in total 1,152,203 active enterprises in
the country. Although our final dataset includes roughly only about 8% of the overall
business activity, its characteristics are similar, especially in terms of the prevalence of SMEs
(see Table 2), which are often characterized as a backbone of the Czech economy (Davies et al.,
2019;Dvoulet
y, 2019) and with the highest representation of the firms in the manufacturing
(and especially in the automotive) and retailing sectors (see Table 3). Moreover, to the best of
our knowledge, this is the largest firm-level dataset compiled to date in the country.
Our dependent variable reflecting the overall firm productivity TFP, was estimated with
the help of ordinary least squares (OLS) regression (for details, see Appendix) and calculated
according to the step-by-step approach described in the highly cited review article by Van
Beveren (2012), for details see the respective Equations 1 and 2:
Equation (1): Estimation of CobbDouglas production function
(i) refers to a firm, tto time, and
ε
to the time and producer-specific deviation from the mean,
consisting of observable (v
it
) and unobservable components(u
qit
). All variables were
transformed into a form of natural logarithm and estimated with OLS regression with
robust standard errors based on Van Beveren (2012). For estimation output, see Appendix.
Total Salesit ¼β0þβ1Tangible Fixed Assetsit þβ2Personnel Costsit
þβ3Material Consumptionit þ
ε
it (1)
Then, we calculate firm-level TFP (
ω
it
) from the following Equation (2) (for details, please see
Van Beveren, 2012, p. 100; c.f. Bla
zkov
aet al. (2020), pp. 291292):
Equation (2): Calculation of total factor productivity
b
ω
it ¼
b
ν
it þ
b
β0¼Total Salesit
b
β1Tangible Fixed Assetsit
b
β2Personnel Costsit
b
β3Material Consumptionit (2)
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Table 1 further provides information about the available firm characteristics including
financial (Total Assets,Debt Ratio,Quick Ratio and Assets Turnover) and non-financial
variables (Age, Company Size, More than 5x Reported Negative Equity, Metropolitan Area,
Region and Sector). Continuous variables are then statistically summarized (in terms of mean,
median, SD, minimum and maximum) in Table 4.
Variable Definition
TFP estimation variables
Total sales Variable represents the value of total sales of a company and is used as a proxy for output
(in ths. CZK)
Tangible fixed assets Variable includes fixed assets such as machinery, buildings and land (in ths. CZK), and is
used as one of the three components creating inputs (together with Personnel Costs and
Material Consumption)
Personnel costs Variable is made up of wages, salaries and employerssocial security costs (in ths. CZK),
and is used as one of the three components creating inputs (together with Tangible Fixed
Assets and Material Consumption)
Material consumption Variable represents the costs related to the consumption of materials and services (in ths.
CZK), and is used as one of the three components creating inputs (together with Tangible
Fixed Assets and Personnel Costs)
Outcome variable
Total factor productivity (TFP) Variable is estimated by simple OLS regression with the use of CobbDouglas production
function (see Equation 1) and calculated from Equation (2) based on Van Beveren (2012)
Independent variables
Age The number of years during which the firm has officially registered (calculated as the
current year the year of incorporation)
Company size Variable divides firms into the four dummy categories according to the number of
employees reported: micro size (09 employees), small size (1049 employees), medium size
(50249 employees) and large size (250 and more employees)
Legal form Variable divides firms into the four dummy categories according to the legal form of the
firm: self-employed/freelancer, limited liabilities company, joint stock company and other
(mutual fund, syndicate)
Total assets Variable represents the value of total assets of a firm (in ths. CZK)
Debt ratio Variable is calculated as the share of liabilities of a firm on its assets (in %)
Quick ratio Variable is calculated as the ratio of current assets minus inventory to current liabilities
Assets turnover Variable measures the ration of the value of a firms sales to the value of its assets
More than 5x reported negative
equity
A dummy variable which takes the value of one if the firm reported six times during the
observation period and a more negative equity. The variable serves as an indicator of a non-
productive firm
Metropolitan area A dummy variable which takes the value of one when headquarters of a firm is located in
one of the ten largest cities of the Czech Republic from the perspectives of inhabitants (i.e.
Prague, Brno, Ostrava, Plze
n, Liberec, Olomouc,
Ust
ınad Labem, Hradec Kr
alov
e,
Cesk
e
Bud
ejovice, Pardubice)
Region Variable divides firms into the 14 NUTS III dummy categories according to the Czech
region in which they operate
Sector (NACE-2 REV. section) Variable divides firms into the 19 NACE-2 Rev. Section dummy categories according to
their main business activity
Note(s): CZK 5Czech crowns
Source(s): Own elaboration (based on Brealey et al., 2017)
Firm size (in terms of employment) NFreq. (%)
Micro (09 employees) 288,679 52.5
Small (1049 employees) 175,667 32.0
Medium (50249 employees) 70,068 12.8
Large (250þemployees) 14,584 2.7
Total 548,998 100.0
Source(s): Bisnode (2020); own elaboration
Table 1.
List of variables
Table 2.
Distribution of the
sample across firm size
Firm-level and
sectoral
variation in
TFP
4. Econometric analysis
The following econometric analysis aims to statistically capture and identify determinants of
TFP in the Czech Republic with the help of multivariate regression models. See the model
specification in Equation 3:
Model specification :
Total Factor Productivityit ¼β0þβ1Firm level Determinantsit
þβ2Industry level Determinantsit þβ3Location specific Determinantsit
þβ4Time specific Determinantsit þ
ε
it
(3)
where irefers to a firm, tto time, and
ε
to error term. Firm-level Determinants is a vector of
observed firm-level characteristics (age, company size, legal form, assets turnover, debt ratio
and negative equity company status), Industry-level Determinants is a vector of NACE-2 Rev.
NACE-2 rev. section NFreq. (%)
Accommodation and food service activities 19,238 3.5
Administrative and support service activities 14,284 2.6
Agriculture, forestry and fishing 26,599 4.8
Arts, entertainment and recreation 5,541 1.0
Construction 53,160 9.7
Education 3,828 0.7
Electricity, gas, steam and air conditioning supply 4,086 0.7
Financial and insurance activities 2,677 0.5
Human health and social work activities 10,623 1.9
Information and communication 21,141 3.9
Manufacturing 117,498 21.4
Mining and quarrying 1,233 0.2
Other services activities 3,935 0.7
Professional, scientific and technical activities 45,843 8.4
Public administration and defence; compulsory social security 40 0.0
Real estate activities 24,625 4.5
Transporting and storage 19,506 3.6
Water supply; sewerage; waste management and remediation activities 6,682 1.2
Wholesale and retail trade; repair of motor vehicles and motorcycles 168,299 30.7
Total 548,998 100.0
Source(s): Bisnode (2020); own elaboration
Indicator Mean Median Std. Dev Min Max N
Total sales 79342205.6 16972000.0 199609721.3 530.0 3412740000.0 548,998
Tangible fixed assets 23052652.6 2218000.0 76144142.3 1000.0 2002342000.0 548,998
Personnel costs 11677428.4 2922500.0 28892967.7 1000.0 667248000.0 548,998
Material
consumption
41424873.9 7318000.0 124463775.8 1000.0 2601334000.0 548,998
TFP (OLS) 1.1 1.3 1.6 10.5 7.8 548,998
Age 11.4 11.0 6.7 0.0 72.0 548,998
Total assets 60281466.9 13544.000.0 144887903.2 2000.0 2804558000.0 548,998
Asset turnover 1.9 1.5 2.8 0.0 904.5 548,998
Debt ratio 66.3 57.2 252.2 1456.7 91943.8 548,998
Quick ratio 3.7 1.4 63.7 25941.3 19399.0 548,998
Source(s): Bisnode (2020); own elaboration
Table 3.
Sectoral affiliation of
the firms in the sample
Table 4.
Summary statistics for
continuous variables
IJEBR
classification sectoral dummies, Location-specific Determinants is a vector of NUTS 3 regional
dummies and a metropolitan area variable, and Time-specific Determinants is a vector of year
dummies.
The models are estimated on an unbalanced panel dataset of 91,257 firms covering the
period of years 20002019. We follow the advice of Dana and Dana (2005) towards the aim of
relying on multiple statistical methods. Thus, we begin by estimating simple OLS regressions
together with NUTS 3 regions, year dummies and robust standard errors. Then, as a
robustness check, we estimate the very same model with the random effects (RE) generalized
least squares (GLS) method, following a recommendation of Baltagi (2008), who advises the
use of a RE model in the case of the relatively short time period and a large number of units
present in the panel. The estimated RE GLS model also includes dummies that were already
included in the OLS model. The estimated models are reported in Table 5. Both models were
found to be statistically significant (based on values of F/
χ
2
statistics). We further report the
statistical significance of estimated coefficients in a standardized way at the respective levels
(Baltagi, 2008). It is important to note that both models provided very similar empirical
results. The obtained findings are interpreted in the following way.
5. Results and discussion
First, we find a statistically significant U-shaped relationship between firm age and the
overall TFP (with a turning point at 38 years in Model 1 and 36 years in Model 2) in the Czech
economy. When inspecting Table 5, an explanation is straightforward the older the firm, the
lower the level of productivity (TFP). This applies until the age of 38, resp. 36 years, when
TFP starts to grow as the company ages. Similar results were confirmed, e.g. by Alon et al.
(2018) or Brouwer et al. (2005).Brouwer et al. (2005), who conducted their study for Dutch
manufacturing industries within the period of 19941999, also found out a non-linear
relationship between firm age and productivity, with a turning point at 50 years. Similarly,
Alon et al. (2018) observed a downward sloping and convex relationship between firm age
and productivity growth among firms in the US non-farm business sectors during the period
of 19962012. These findings document that productivity varies significantly over the firm
life cycle. As reported in some studies (e.g. Fackler et al., 2013;Mueller and Stegmaier, 2015),
firms achieve lower performance in the early years of their existence and may be more
vulnerable to bankruptcy, which leads to lower productivity of young firms. Their
productivity level gradually increases due to the accumulation of skill (De La Fuente, 2011),
reputation and accumulation of trust (Mueller and Stegmaier, 2015), and organizational
learning (Thornton and Thompson, 2001). More generally, mature firms may benefit from
experience and learning-by-doing (Arrow, 1962;Burke et al., 2018).
The obtained results show empirical support for the statistically significant relationship
between firm size and TFP. The coefficients related to explanatory variables representing the
size of the enterprise differ (see Table 5), for which the following explanation is offered. It is
evident from the estimated coefficients that the more employees a firm has, the lower the
productivity is. However, this only applies to the employment size, since in terms of the total
assets, as companies grow, their productivity grows as well. Once we inspected the firm
property operationalized as the size of total assets, we confirmed a positive relationship
between the firm size and TFP, which was also observed by, e.g. Ayyagari et al. (2011),
Bartelsman et al. (2009) or Sumner (2014). There are multiple explanations for this finding.
Larger firms may benefit from economies of scale, employment of more skilled managers and
a larger workforce, and diversification of risks (Bla
zkov
aet al., 2020). The evidence further
states that they can face economic shocks in a better way (Aldrich and Auster, 1986), and
thanks to more capital and easier access to financial resources they can also more easily
introduce new technologies and acquire equipment (Berko and Damoah, 2013) and engage in
Firm-level and
sectoral
variation in
TFP
R&D activities (Harris and Moffat, 2015). As further discovered, the most productive
enterprises, from the perspective of the legal form, are self-employed/freelancers, which may
correlate to the above-mentioned negative relationship between firm size as measured by the
number of employees and productivity these companies are usually among the smallest.
Model number Model (1) OLS Model (2) GLS RE
Independent variables/Dependent variable Total factor productivity (TFP)
Age 0.0177*** (0.00108) 0.0269*** (0.00146)
Age squared 0.000235*** (0.0000260) 0.000379*** (0.0000500)
Size small (1049 employees) 0.265*** (0.0253) 0.175*** (0.0293)
Size medium (50249 employees) 0.334*** (0.0453) 0.109* (0.0530)
Size large (250þemployees) 0.575*** (0.0634) 0.269*** (0.0755)
Self-employed/Freelancer 0.331*** (0.0391) 0.360*** (0.0767)
Limited liabilities company 0.133*** (0.0138) 0.0817* (0.0332)
Joint stock company 0.252*** (0.0138) 0.286*** (0.0335)
Log (total assets) 0.243*** (0.0145) 0.193*** (0.0168)
Asset turnover 0.179*** (0.0393) 0.109*** (0.0290)
Debt ratio 0.000368* (0.000159) 0.000254* (0.000117)
Quick ratio 0.0000258 (0.0000245) 0.0000413 (0.0000255)
More than 5x reported negative equity 0.186*** (0.0197) 0.243*** (0.0247)
Metropolitan area 0.00562 (0.00622) 0.0182 (0.01417)
Administrative and support service activities 0.104*** (0.0248) 0.165*** (0.0385)
Agriculture, forestry and fishing 0.176*** (0.0180) 0.195*** (0.0324)
Arts, entertainment and recreation 0.296*** (0.0251) 0.330*** (0.0555)
Construction 0.217*** (0.0199) 0.332*** (0.0294)
Education 0.758*** (0.0393) 0.858*** (0.0781)
Electricity, gas, steam and air conditioning supply 0.192*** (0.0236) 0.368*** (0.0582)
Financial and insurance activities 0.0289 (0.0377) 0.266** (0.0842)
Human health and social work activities 0.0888*** (0.0215) 0.0855* (0.0398)
Information and communication 0.00163 (0.0165) 0.0753* (0.0340)
Manufacturing 0.0728*** (0.0137) 0.148*** (0.0256)
Mining and quarrying 0.0961** (0.0331) 0.143 (0.0967)
Other services activities 0.0132 (0.0270) 0.0954 (0.0604)
Professional, scientific and technical activities 0.0658*** (0.0140) 0.0102 (0.0290)
Public administration and defence; compulsory
social security
0.404* (0.203) 0.417 (0.476)
Real estate activities 0.0488** (0.0186) 0.0477 (0.0314)
Transporting and storage 0.0811** (0.0266) 0.159*** (0.0364)
Water supply; sewerage; waste management and
remediation activities
0.0426þ(0.0244) 0.0211 (0.0528)
Wholesale and retail trade; repair of motor vehicles
and motorcycles
1.072*** (0.0467) 1.063*** (0.0395)
Constant 3.897*** (0.407) 2.912*** (0.398)
NUTS 3 regions dummies Yes Yes
Year dummies Yes Yes
Observations 548,998 548,998
Number of firms 91,257 91,257
R
2
0.255 0.242
Prob > F/ Prob >
χ
2
0.000 0.000
Akaike information criterion (AIC) 1938754.7 N/A
Bayesian information criterion (BIC) 1939483.7 N/A
Note(s): Reference groups for dummy variables: Size Micro (09 Employees), Legal form: Other, NACE-2 Rev.
Section: Accommodation and food service activities. Robust standard errors are in parentheses, stat. significance
is reported as follows: þp< 0.10, *p< 0.05, **p< 0.01, ***p< 0.001
Turning points for Age (and Squared): Model 1: 38; Model 2: 36
Source(s): Bisnode (2020); own elaboration
Table 5.
Determinants of the
total factor
productivity (TFP)
(based on firm-level
panel data for the
period of 20002019)
IJEBR
Generally, on the one hand, self-employed individuals/freelancers are among the most
productive companies, which are usually small in terms of the number of employees (mostly
working in a solo self-employment mode), and often paid on the basis of their productivity
(Burke and Van Stel, 2011;Van der Zwan et al., 2020). However, on the other hand, the level of
productivity increases with the size of the assets and, therefore, companies that are strong in
terms of assets and capital achieve higher levels of productivity in the Czech Republic.
We further discovered a positive relationship between a firms activity (measured as asset
turnover) and TFP, which corresponds to both the general theory of business economics
(Mankiw et al., 2016) and empirical evidence (Beinabaj et al., 2013;Tsai et al., 2006). Since asset
turnover measures the firmsasset utilization and the generating of revenues from their
assets, the increase in asset turnover reflects the higher productivity of firms and, as
highlighted by Fairfield and Yohn (2001), can also be useful for predicting future
productivity.
As our results further indicate, the firm-level productivity (TFP) is related to the capital
structure of the firm, namely, higher use of debt lowers TFP indicator. Although multiple
empirical studies emphasize the importance of using debt for investing in technical
equipment and modern technologies to support growth and thus productivity (Capitanio
et al., 2009;Dvoulet
y and Bla
zkov
a, 2019;
Spi
cka and Machek, 2015), the effects of
indebtedness can also be negative. For instance, Bla
zkov
aet al. (2020) discovered that the
high indebtedness of food processing firms in the Czech Republic is an obstacle to their
productivity growth. The high proportion of debt may lead to financial distress due to the
paying of high interests (Frank and Goyal, 2009), and subsequently to managerial decisions
restricting new investment and technological development as risky activities, usually
increasing the need for external sources of financing. Our study also demonstrates the
problem of firmsover-indebtedness by including a variable representing negative-equity
companies or what are also called zombie enterprises (Bla
zkov
a and Dvoulet
y, 2020;San-Jose
et al., 2021). These are a special case of high-risk companies that operate in the market even
though they have lost all their equity. The studies on zombie firms usually point to their
potentially high negative effects on the whole economy and its sustainable growth (e.g.
Asanuma, 2015;Fukuda and Nakamura, 2011;Urionabarrenetxea et al., 2017). We show that
the financial situation in these companies is not favourable for them either, since our models
confirmed a significant negative effect on productivity. Thus, they represent a harm/negative
burden on the overall entrepreneurial activity in the country.
Quite surprisingly, our results suggest that companies which are located in larger cities,
i.e. in metropolitan areas, achieve on average lower values of TFP, although this observation
was not found to be statistically significant. Therefore, the differences in environmental
conditions in urban and rural areas are not essential for the productivity of companies in the
Czech Republic, which is probably due to the relatively small area of the country.
The estimated models also allow us to inspect the cross-sectoral differences in overall
productivity. Based on the presented estimates, we may empirically support the validity of
our stated hypothesis regarding assumed heterogeneity in firm-level TFP across sectors. We
observe that the most productive firms (in terms of TFP) do business in the areas of wholesale
and retail trade; repair of motor vehicles and motorcycles and electricity, gas, steam and air
conditioning supply, whereas the least productive companies are represented in Education,
and also in the arts, entertainment and recreation and construction sectors.Generally,
differing TFP levels among particular sectors of the Czech economy reflect heterogeneous
market and production conditions, such as capital and workforce requirements, technological
opportunities, market concentration, intensity of export and innovation activities and others.
The top position of the Czech retailing sector in terms of productivity follows the
development of market conditions over the past years. Simov
a (2010) explains that the
expansion of foreign-owned multinational companies on the developing Czech market in the
Firm-level and
sectoral
variation in
TFP
1990s and the growth of internationalization of retailing made this sector among the most
productive of sectors. It is important to acknowledge, that processes of mergers and
acquisitions meant not an only inflow of foreign capital, but also the provision of know-how,
experienced management, the adoption and use of ICT. International competition within this
sector provides an essential factor for boosting the productivity of firms (Maris, 2019;Simov
a,
2010). This sector is also characterized by dynamic development and the introduction of new
technologies, in particular the digitization of business activities and the development of
online businesses (e-commerce), which enable it to achieve high efficiency, as also confirmed
by the analyses of the Czech Association of Small and Medium Enterprises (2019). As for the
sector electricity, gas, steam and air conditioning supply, we assume that such an empirical
observation might be associated with the liberalization of the Czech energy market back in
2002, which separated the distribution of energy (electricity and gas) and its sale to the end
customers (Arnold et al., 2006;Zemplinerov
a, 2000). Arnold et al. (2006) document that the
liberalization of the Czech energy market led to a significant reduction in customer prices, the
entrance of a number of new players purchasing energies on the energy stock markets and
resulting in higher pressure on the productivity of the sector, as also found in other countries
(Joskow, 2008;Oberndorfer, 2009). The least productive companies are observed in sectors
that are not innovative, are not major exporters, and do not make much use of public R&D
support, which is required as a key feature of the highest productivity sectors (Aiello et al.,
2015;Prokop et al., 2017;Ratinger et al., 2020).
Finally, our models support the cross-regional and time variance in the levels of TFP
documented in numerous papers (Cie
slik et al., 2019;Fattorini et al., 2020), but as they are not
the main interest of this article, we do not report on the details for parsimonious reasons.
6. Conclusions
The future of any enterprise is significantly correlated with the constant improvement of its
productivity which, in turn, provides an opportunity for its growth (Danlami et al., 2020).
Productivity improvements mean the cost reduction per unit of output and, thus, reduction in
product prices, increase in demand for these products and higher profits. In other words,
maintaining continuous productivity improvement is critical for enterprises in ensuring their
competitiveness (Auzina-Emsina, 2014). Moreover, maintaining productivity growth at the
firm-level is an essential prerequisite for increasing productivity at the national level, which
further affects long-term economic growth (Giang et al., 2019). And finally, regarding a
thorough understanding of firm-level productivity variability, we can contribute to the
formulation and adoption of relevant economic policies with desirable effects on the welfare
of the whole society.
This paper contributes to an ongoing discussion on the determinants of firm-level
productivity by presenting a microeconomic study of the Czech economy during the period of
20002019. Drawing on a large firm-level dataset consisting of 91,257 firms, accounting for
548,998 observations in total and including mostly SMEs, which were often missing in the
previous research endeavours due to the lack data. The study ensures the representativeness
and robustness of the presented results that are more likely to be generalizable for the whole
country. As a measure of productivity, i.e. our main variable of interest, we used TFP, which
is generally considered as an indicator of technological efficiency and a crucial determinant of
economic welfare (Comin, 2010). We identified several determinants with a significant impact
on the TFP, such as firm size, firm age, capital structure and long-term negative equity, i.e. a
condition referred to as a zombie company. Moreover, even though we applied the micro-level
observation, we also inspected the sub-sector variability in productivity, while controlling for
the time and regional dimension by including yearly and regional dummies in the estimated
regression models. To the best of our knowledge, our study is the first to exploit such a unique
IJEBR
dataset in terms of scope and representativeness in order to explore firm-level productivity
determinants.
We have found a statistically significant U-shaped relationship between firm age and the
overall TFP in the Czech economy during the period of 20002019. Older firms reached a
lower productivity level than younger firms until the age of 38, resp. 36 years, then the
opposite relationship applies, which corresponds to the learning-by-doing theory (Arrow,
1962). Such an observation is also related to the accumulation of skills and knowledge,
building of a market position to be more competitive, since only after the accumulation of
relevant experience and equipment, may firm productivity begin to grow (Burke et al., 2018).
Once we studied the role of firm size, we confirmed two key findings. First, smaller firms in
terms of the number of employees, often legally registered as self-employed individuals/
freelancers, report higher levels of productivity, which may be related to their innovativeness,
creativity, flexibility and response-ability (Nieto and Santamar
ıa, 2010). Second, we observe a
positive relationship between firm size measured in terms of the overall property (assets) and
TFP. We match this finding with the possibilities of larger firms to benefit from economies of
scale, employ a more qualified workforce and skilled managers or ability to easier
accommodate the risks and economic shocks when compared with low-capital intensity
companies (Bla
zkov
aet al., 2020). Our results also document a negative relationship between
indebtedness and TFP. Besides, long-term problems related to the capital structure of
companies, such as negative equity, are a serious obstacle to achieving higher levels of
productivity. Finally, the sector affiliation matters for firm-level productivity. Our study
reveals differences in firm-level productivity across the Czech sectors.
After identifying the determinants of productivity, the next step is to discuss what
recommendations to offer business managers and what policies to propose to increase
productivity levels in the Czech Republic. Concerning our results, managers should be
mindful of optimal capital structure principles due to the negative impact of high debt at the
productivity level. High indebtedness means high-interest payments drawing earnings off,
which may be a hindrance to investments, especially in the long term (Brealey et al., 2017;
Frank and Goyal, 2011). Moreover, enterprise growth, in terms of increasing firm property,
which is certainly not possible without investments, was found to be positively associated
with productivity growth. Based on that, productivity can be further (potentially) increased
through the acquisition of skills, the employment or contracting of experienced managers
from mature companies or business developers that may advise Czech entrepreneurs and
business owners on growth-related investment decisions.
The entrepreneurship and SME policies aiming to stimulate Czech entrepreneurial
activity (and productivity of the Czech firms) may be targeted at the soft policy actions,
including advisory services and counselling on business development or risk management
(Hoogendoorn et al., 2019) and on the provision of financial capital allowing firms to strive for
the more challenging and growth-oriented projects with the help of public aid (Dvoulet
yet al.,
2020). Furthermore, TFP should be used as an outcome indicator in evaluation studies,
informing policymakers whether the policy action delivered the expected change in the firm
productivity, as noted recently by Dvoulet
yet al. (2021) or Harris and Moffat (2020). We also
note that the Czech policymakers should become interested in the characteristics of
unproductive firms and zombie companies (which are over-indebted or even without equity)
through the adjustment of the appropriate bankruptcy rules and more frequent (and carefully
targeted) checks of firm accounting, which may result in the elimination of these companies
from the market and consequent in reallocations of resources towards the better
alternative usage.
Finally, we are fully aware of the limitations of our study, especially involving the range of
factors that have a potential impact on a firmsproductivity level, and that could not have
been included in the present study due to the lack of data, such as information about the
Firm-level and
sectoral
variation in
TFP
business owner and management of the company or ownership structure of the business. We
also believe that more research is needed in the deeper exploration of sectoral and regional
determinants of firm TFP, by the employment of a multi-level modelling approach, including
a range of continuous variables and investigation of their role in shaping firm-level
productivity. Given the absence of these (and other possible) determinants, we need to
conclude that our estimates in the present study are limited and would be better when they
are further expanded in future studies. Thus, we may only encourage future researchers to
expand their own empirical analyses in this regard.
References
Acs, Z.J., Audretsch, D.B., Braunerhjelm, P. and Carlsson, B. (2009), The knowledge spillover theory
of entrepreneurship,Small Business Economics, Vol. 32 No. 2, pp. 15-30.
Aiello, F., Pupo, V. and Ricotta, F. (2015), Firm heterogeneity in TFP, sectoral innovation and
location. Evidence from Italy,International Review of Applied Economics, Vol. 29 No. 5,
pp. 579-607.
Aldrich, H. and Auster, E.A. (1986), Even dwarfs started small: liabilities of age and size and their
strategic implications,Research in Organizational Behavior, Vol. 8, pp. 165-198.
Alon, T., Berger, D., Dent, R. and Pugsley, B. (2018), Older and slower: the startup deficits lasting
effects on aggregate productivity growth,Journal of Monetary Economics, Vol. 93, pp. 68-85.
Andries, A.M. (2011), The determinants of bank efficiency and productivity growth in the Central
and Eastern European banking systems,Eastern European Economics, Vol. 49 No. 6, pp. 38-59.
Arnold, J., Javorcik, B. and Mattoo, A. (2006), The productivity effects of services liberalization:
evidence from the Czech Republic, Working paper, World Bank and Bocconi University,
Washington, District of Colombia.
Arranz, N., Arroyabe, M.F., Li, J. and de Arroyabe, J.F. (2019), An integrated model of organisational
innovation and firm performance: generation, persistence and complementarity,Journal of
Business Research, Vol. 105, pp. 270-282.
Arranz-Aperte, L. (2014), The Impact of wage dispersion on labor productivity: evidence from
Finnish workers, in Polacheck, S.W. (Ed.), New Analysis of Workers Well-Being, Emerald Group
Publishing, pp. 77-103.
Arrow, K.J. (1962), The economic implications of learning by doing,The Review of Economic Studies,
Vol. 29 No. 3, pp. 155-173.
Asanuma, D. (2015), An examination on the zombie theory: an agent-based-approach,International
Business Management, Vol. 9 No. 5, pp. 719-725.
Association of Small and Medium Enterprises of the Czech Republic (2019), Anal
yza maloobchodu
2019, available at: https://amsp.cz/wp-content/uploads/2019/04/Anal%C3%BDza-Maloobchod-
4_2019.pdf (accessed 20 September 2020).
Auzina-Emsina, A. (2014), Labor productivity, economic growth and global competitiveness in post-
crisis period,Procedia Social and Behavioral Sciences, Vol. 156, pp. 317-321.
Ayyagari, M., Demirg
urç-Kunt, A. and Maksimovic, V. (2011), Firm innovation in emerging markets:
the role of governance and finance,Journal of Financial and Quantitative Analysis, Vol. 46
No. 6, pp. 1545-1580.
Baghdasaryan, D. and La Cour, L. (2013), Competition, ownership and productivity. A panel analysis
of Czech firms,Journal of Economics and Business, Vol. 69, pp. 86-100.
Baltagi, B. (2008), Econometric Analysis of Panel Data, John Wiley and Sons, Hoboken, New Jersey.
Bartelsman, E., Haltiwanger, J. and Scarpetta, S. (2004), Microeconomic evidence of creative
destruction in industrial and developing countries, Policy Research Working Paper No. 3464,
The World Bank, December.
IJEBR
Bartelsman, E., Haltiwanger, J. and Scarpetta, S. (2009), Cross-country differences in productivity: the
role of allocation and selection, Working paper, NBER Working Paper Series 15490, National
Bureau for Economic Research, Cambridge, Massachusetts.
Becker, G.S. (1962), Investment in human capital: a theoretical analysis,Journal of Political Economy,
Vol. 70 No. 5 (Part 2), pp. 9-49.
Beinabaj, M.H., Soleimani, M. and Rashidi, M. (2013), The relationship between total asset turnover
and productivity indicators of companies listed in Tehran stock exchange,Advances in
Environmental Biology, Vol. 7 No. 8, pp. 1648-1653.
Belay, A.M., Kasie, F.M., Helo, P., Takala, J. and Powell, D.J. (2014), Adoption of quality management
practice: an investigation of its relationship with labor productivity for labor-intensive
manufacturing companies,Benchmarking: An International Journal, Vol. 21 No. 1, pp. 77-100.
Berko, O. and Damoah, O. (2013), Strategic factors and firm performance in an emerging economy,
African Journal of Economic and Management Studies, Vol. 4 No. 2, pp. 267-287.
Biatour, B., Dumont, M. and Kegels, C. (2011), The determinants of industry-level total factor
productivity in Belgium, Working Paper 711, Federal Planning Bureau, Brussels.
Billor, N., Hadi, A.S. and Velleman, P.F. (2000), BACON: blocked adaptive computationally efficient
outlier nominators,Computational Statistics and Data Analysis, Vol. 34 No. 3, pp. 279-298.
Bisnode (2020), Commercial Databases MagnusWeb and Albertina, Bisnode
Cesk
a republika, a.s.,
available at: https://www.bisnode.cz/produkty/albertina/ (accessed 12 October 2020).
Bla
zkov
a, I. and Dvoulet
y, O. (2019), Investigating the differences in entrepreneurial success through
the firm-specific factors: microeconomic evidence from the Czech food industry,Journal of
Entrepreneurship in Emerging Economies, Vol. 11 No. 2, pp. 154-176.
Bla
zkov
a, I. and Dvoulet
y, O. (2020), Zombies: who are they and how do firms become zombies?,
Journal of Small Business Management. doi: 10.1080/00472778.2019.1696100 (forthcoming).
Bla
zkov
a, I., Dvoulet
y, O. and Machek, O. (2020), What drives total factor productivity and its growth
in post-communist countries? The case of the Czech food industry,Journal of Agribusiness in
Developing and Emerging Economies, Vol. 10 No. 3, pp. 285-305.
Block, J.H., Fisch, C.O. and Van Praag, M. (2017), The Schumpeterian entrepreneur: a review of the
empirical evidence on the antecedents, behaviour and consequences of innovative
entrepreneurship,Industry and Innovation, Vol. 24 No. 1, pp. 61-95.
Bloom, N., Schankerman, M. and Van Reenen, J. (2013), Identifying technology spillovers and product
market rivalry,Econometrica, Vol. 81 No. 4, pp. 1347-1393.
Botri
c, V., Bo
zi
c, L. and Broz, T. (2017), Explaining firm-level total factor productivity in post-
transition: manufacturing vs. services sector,Journal of International Studies, Vol. 10 No. 3,
pp. 77-90, doi: 10.14254/2071-8330.2017/10-3/6.
Bravo-Ortega, C., Benavente, J.M. and Gonz
alez,
A. (2014), Innovation, exports, and productivity:
learning and self-selection in Chile,Emerging Markets Finance and Trade, Vol. 50 No. Sup1,
pp. 68-95.
Brealey, R., Myers, S. and Allen, F. (2017), Principles of Corporate Finance, 12th ed., McGraw-Hill
Education, New York.
Brouwer, P., De Kok, J. and Fris, P. (2005), Can firm age account for productivity differences, EIM
SCALES-paper N 200421, Zoetermeer.
Burke, A., Mill
an, J.M., Rom
an, C. and van Stel, A. (2018), Exploring the impact of different types of
prior entrepreneurial experience on employer firm performance,Journal of Business Research,
Vol. 90, pp. 107-122.
Burke, A. and Van Stel, A. (2011), The entrepreneurship enabling role of freelancers: theory with
evidence from the construction industry,International Review of Entrepreneurship, Vol. 9
No. 3, pp. 131-158.
Firm-level and
sectoral
variation in
TFP
Calderon, C. and Liu, L. (2003), The direction of causality between financial development and
economic growth,Journal of Development Economics, Vol. 72 No. 1, pp. 321-334.
Capitanio, F., Coppola, A. and Pascucci, S. (2009), Indications for drivers of innovation in the food
sector,British Food Journal, Vol. 111 No. 8, pp. 820-838.
Carlsson, C. and Dahlberg, R. (2003), Entrepreneurship, firm growth and regional economic
development in the new economic geography: introduction,Small Business Economics, Vol. 21
No. 2, pp. 73-76.
Castellacci, F. (2007), Technological regimes and sectoral differences in productivity growth,
Industrial and Corporate Change, Vol. 16 No. 6, pp. 1105-1145.
Castellacci, F. (2008), Innovation and the competitiveness of industries: comparing the mainstream
and the evolutionary approaches,Technological Forecasting and Social Change, Vol. 75 No. 7,
pp. 984-1006.
Castellacci, F. and Zheng, J. (2010), Technological regimes, Schumpeterian patterns of innovation and
firm-level productivity growth,Industrial and Corporate Change, Vol. 19 No. 6, pp. 1829-1865.
Cechura, L. (2012), Technical efficiency and total factor productivity in Czech agriculture,
Agricultural Economics, Vol. 58 No. 4, pp. 147-156.
Chang, H.Y. and Lee, A.Y.P. (2016), The relationship between business diversification and
productivity: considering the impact of process innovation at different corporate life cycles,
Technology Analysis and Strategic Management, Vol. 28 No. 7, pp. 827-840.
Cie
slik, A., Gauger, I. and Michałek, J.J. (2017), Determinants of productivity of Ukrainian firms,
Comparative Economic Research, Vol. 20 No. 1, pp. 5-19.
Cie
slik, A., Michałek, J.J. and Gauger, I. (2019), Regional dimension of firm level productivity
determinants: the case of manufacturing and service firms in Ukraine,Central European
Economic Journal, Vol. 5 No. 52, pp. 81-95.
Comin, D. (2010), Total factor productivity, in Durlauf, S.N. and Blume, L.E. (Eds), Economic Growth,
the New Palgrave Economics Collection, Palgrave Macmillan, London.
Coricelli, F., Driffield, N., Pal, S. and Roland, I. (2012), When does leverage hurt productivity growth?
A firm-level analysis,Journal of International Money and Finance, Vol. 31 No. 6, pp. 1674-1694.
Cowling, M. (2003), Productivity and corporate governance in smaller firms,Small Business
Economics, Vol. 20 No. 4, pp. 335-344.
Cucculelli, M., Mannarino, L., Pupo, V. and Ricotta, F. (2014), Owner-management, firm age, and
productivity in Italian family firms,Journal of Small Business Management, Vol. 52 No. 2,
pp. 325-343.
Dabla-Norris, E., Kersting, E.K. and Verdier, G. (2012), Firm productivity, innovation, and financial
development,Southern Economic Journal, Vol. 79 No. 2, pp. 422-449.
Dana, L.P. and Dana, T.E. (2005), Expanding the scope of methodologies used in entrepreneurship
research,International Journal of Entrepreneurship and Small Business, Vol. 2 No. 1, pp. 79-88.
Danlami, I.A., Hidthiir, M.H. and Hassan, S. (2020), Determinants of productivity: a conceptual review
of economic and social factors,Journal of Business Management and Accounting, Vol. 8 No. 1,
pp. 63-71.
Davies, E., Iootty, M. and Zouhar, J. (2019), Productivity and Innovation in the Czech Republic: A Firm-
Level Perspective, World Bank, available at: http://documents1.worldbank.org/curated/en/
157911576708421803/pdf/Background-Note-Productivity-and-Innovation-in-the-Czech-Republic-
A-Firm-Level-Perspective.pdf (accessed 12 August 2020).
De La Fuente, A. (2011), Human capital and productivity,Nordic Economic Policy Review, Vol. 2
No. 2, pp. 103-132.
Dhawan, R. (2001), Firm size and productivity differential: theory and evidence from a panel of US
firms,Journal of Economic Behavior and Organization, Vol. 44 No. 3, pp. 269-293.
IJEBR
Djankov, S. and Hoekman, B. (2000), Foreign investment and productivity growth in Czech
enterprises,The World Bank Economic Review, Vol. 14 No. 1, pp. 49-64.
Dobbs, M. and Hamilton, R.T. (2007), Small business growth: recent evidence and new directions,
International Journal of Entrepreneurial Behavior and Research, Vol. 13 No. 5, pp. 296-322.
Du, J. and Temouri, Y. (2015), High-growth firms and productivity: evidence from the United
Kingdom,Small Business Economics, Vol. 44 No. 1, pp. 123-143.
Dvoulet
y, O. (2019), Development of entrepreneurial activity in the Czech republic over the years
20052017,Journal of Open Innovation: Technology, Market, and Complexity, Vol. 5 No. 3, p. 38.
Dvoulet
y, O. and Bla
zkov
a, I. (2019), The impact of public grants on firm-level productivity: findings
from the Czech food industry,Sustainability, Vol. 11 No. 2, p. 552.
Dvoulet
y, O., Bla
zkov
a, I. and Potluka, O. (2021), Estimating the effects of public subsidies on the
performance of supported enterprises across firm sizes, (forthcoming), Research Evaluation.
doi: 10.1093/reseval/rvab004.
Dvoulet
y, O., Srhoj, S. and Pantea, S. (2020), Public SME grants and firm performance in European
union: a systematic review of empirical evidence, (forthcoming), Small Business Economics. doi:
10.1007/s11187-019-00306-x.
Erken, H., Donselaar, P. and Thurik, R. (2018), Total factor productivity and the role of
entrepreneurship,The Journal of Technology Transfer, Vol. 43 No. 6, pp. 1493-1521.
European Commission (2009), European Competitiveness Report 2008, Office for Official Publications
of the European Communities, Luxembourg.
Fackler, D., Schnabel, C. and Wagner, J. (2013), Establishment exits in Germany: the role of size and
age,Small Business Economics, Vol. 41 No. 3, pp. 683-700.
Fairfield, P.M. and Yohn, T.L. (2001), Using asset turnover and profit margin to forecast changes in
profitability,Review of Accounting Studies, Vol. 6 No. 4, pp. 371-385.
Fattorini, L., Ghodsi, M. and Rungi, A. (2020), Cohesion policy meets heterogeneous firms,JCMS:
Journal of Common Market Studies, Vol. 58 No. 4, pp. 803-817.
Feder, C. (2018), A measure of total factor productivity with biased technological change,Economics
of Innovation and New Technology, Vol. 27 No. 3, pp. 243-253.
Frank, M.Z. and Goyal, V.K. (2009), Capital structure decisions: which factors are reliably important?,
Financial Management, Vol. 38 No. 1, pp. 1-37.
Frank, M.Z. and Goyal, V.K. (2011), Trade-off and pecking order theories of debt, in Espen, B.E.
(Ed.), Handbook of Empirical Corporate Finance: Empirical Corporate Finance, Elsevier,
pp. 135-202.
Fukuda, S.I. and Nakamura, J.I. (2011), Why did zombiefirms recover in Japan?,The World
Economy, Vol. 34 No. 7, pp. 1124-1137.
Galdon-Sanchez, J.E. and Schmitz, J.A., Jr (2002), Competitive pressure and labor productivity:
world iron-ore markets in the 1980s,The American Economic Review, Vol. 92 No. 4,
pp. 1222-1235.
Gault, F. (Ed.), (2013), Handbook of Innovation Indicators and Measurement, Edward Elgar Publishing,
Cheltenham.
Ger
sl, A. (2008), Productivity, export performance, and financing of the Czech corporate sector: the
effects of foreign direct investment,Czech Journal of Economics and Finance (Finance a Uver),
Vol. 58 Nos 05-06, pp. 232-247.
Geylani, P.C. and Stefanou, S.E. (2013), Linking investment spikes and productivity growth,
Empirical Economics, Vol. 45 No. 1, pp. 157-178.
Giang, M.H., Xuan, T.D., Trung, B.H. and Que, M.T. (2019), Total factor productivity of agricultural
firms in Vietnam and its relevant determinants,Economies, Vol. 7 No. 1, p. 4.
Firm-level and
sectoral
variation in
TFP
Giannakis, E. and Bruggeman, A. (2015), The highly variable economic performance of European
agriculture,Land Use Policy, Vol. 45, pp. 26-35.
Grigg, D. (2003), An Introduction to Agricultural Geography, Routledge, London.
Haldane, A. (2017), Productivity puzzles, Speech given at London School of Economics, London,
March 27, available at: http://worldmanagementsurvey.org/wp-content/uploads/2017/03/
boespeech_220317.pdf (accessed 24 July 2020).
Hampl, M., Havranek, T. and Irsova, Z. (2020), Foreign capital and domestic productivity in the Czech
Republic: a meta-regression analysis,Applied Economics, Vol. 52 No. 18, pp. 1949-1958.
Harris, R. and Moffat, J. (2015), Plant-level determinants of total factor productivity in Great Britain,
19972008,Journal of Productivity Analysis, Vol. 44 No. 1, pp. 1-20.
Harris, R. and Moffat, J. (2017), The UK productivity puzzle, 20082012: evidence using plant-level
estimates of total factor productivity,Oxford Economic Papers, Vol. 69 No. 3, pp. 529-549.
Harris, R. and Moffat, J. (2020), The impact of product subsidies on plant-level total factor
productivity in Britain, 19972014,Scottish Journal of Political Economy, Vol. 67 No. 4,
pp. 387-403.
Henley, A. (2018), Small Business Growth and Productivity: Evidence Review, Productivity Insight
Network, available at: https://productivityinsightsnetwork.co.uk/app/uploads/2018/07/
Evidence-Review_Small-Business-Growth-and-Productivity.pdf (accessed 5 July 2019).
Hogendoorn, B., Rud, I., Groot, W. and Maassen van den Brink, H. (2019), The effects of human
capital interventions on entrepreneurial performance in industrialized countries, (forthcoming),
Journal of Economic Surveys, Vol. 33 No. 3, pp. 798-826, doi: 10.1111/joes.12308 (accessed 15
August 2020).
Holmes, T.J. and Schmitz, J.A., Jr (2010), Competition and productivity: a review of evidence,Annual
Review of Economics, Vol. 2 No. 1, pp. 619-642.
Hoogstra, G.J. and van Dijk, J. (2004), Explaining firm employment growth: does location matter?,
Small Business Economics, Vol. 22 Nos 3-4, pp. 179-192.
Innocenti, N. and Zampi, V. (2019), What does a start-up need to grow? An empirical approach for
Italian innovative start-ups,International Journal of Entrepreneurial Behavior and Research,
Vol. 25 No. 2, pp. 376-393.
Isaksson, A. (2007), Determinants of Total Factor Productivity: A Literature Review, Research and
Statistics Branch, UNIDO, Vienna.
Islam, S. and Shazali, S.T.S. (2011), Determinants of manufacturing productivity: pilot study on labor-
intensive industries,International Journal of Productivity and Performance Management,
Vol. 60 No. 6, pp. 567-582.
Joskow, P.L. (2008), Lessons learned from electricity market liberalization,The Energy Journal,
Vol. 29 No. SI2, pp. 9-42.
Jung, M. and Lee, K. (2010), Sectoral systems of innovation and productivity catch-up: determinants
of the productivity gap between Korean and Japanese firms,Industrial and Corporate Change,
Vol. 19 No. 4, pp. 1037-1069.
Karlsson, C. and Stough, R. (2005), Industrial Clusters and Inter-firm Networks, Edward Elgar,
Cheltenham.
Kim, Y.E., Loayza, N. and Meza Cuadra Balcazar, C.M. (2016), Productivity as the Key to Economic
Growth and Development, World Bank Research and Policy Briefs, 108092, Kuala Lumpur.
Kozub
ıkov
a, L.,
Cepel, M. and Zl
amalov
a, M. (2018), Attitude toward innovativeness based on
personality traits in the SME sector. Czech Republic case study,Management and Marketing.
Challenges for the Knowledge Society, Vol. 13 No. 2, pp. 913-928.
Lefley, F., Wharton, F., H
ajek, L., Hynek, J. and Janecek, V. (2004), Manufacturing investments in the
Czech Republic: an international comparison,International Journal of Production Economics,
Vol. 88 No. 1, pp. 1-14.
IJEBR
Li, Y. and Bosworth, D. (2020), R&D spillovers in a supply chain and productivity performance in
British firms,The Journal of Technology Transfer, Vol. 45 No. 1, pp. 177-204.
Lim, J. and Sanidas, E. (2011), The impact of organisational and technical innovations on
productivity: the case of Korean firms and sectors,Asian Journal of Technology Innovation,
Vol. 19 No. 1, pp. 21-35.
Los, B. and Verspagen, B. (2007), Technology spillovers and their impact on productivity,in
Hanusch, H. and Pyka, A. (Eds), Elgar Companion to Neo-Schumpeterian Economics, Edward
Elgar Publishing, chapter 35.
Luke
s, M. (2017), Entrepreneurship development in the Czech republic, in Sauka, A. and
Chepurenko, A. (Eds), Entrepreneurship in Transition Economies, Springer, Cham, pp. 209-224.
Lux, A.A., Macau, F.R. and Brown, K.A. (2020), Putting the entrepreneur back into entrepreneurial
ecosystems,International Journal of Entrepreneurial Behavior and Research, Vol. 26 No. 5,
pp. 1011-1041.
Mankiw, N.G., Taylor, M.P. and Ashwin, A. (2016), Business Economics, 2nd ed., Cengage Learning
EMEA, Boston.
Manso Machado, L.P. (2019), Exporter firms behaviour, evidence from Portuguese firms using
microdata,GEE paper 136, Gabinete de Estrat
egia e Estudos, Minist
erio da Economia,
available at: https://www.gee.gov.pt//RePEc/WorkingPapers/GEE_PAPERS_136.pdf (accessed
16 June 2020).
Maris, M. (2019), Structural and productivity shift of industries in Slovakia and Czech Republic: a
comparative study,Journal of International Studies, Vol. 12 No. 1, pp. 313-323.
Marsili, O. (1999), Technological Regimes: Theory and Evidence, Dynacom Project TSER European
Union, available at: http://www.lem.sssup.it/Dynacom/files/D20_0.pdf (accessed 16 June 2020).
Mart
ınez-Victoria, M., Mat
eS
anchez-Val, M. and Arcas-Lario, N. (2018), Spatial determinants of
productivity growth on agri-food Spanish firms: a comparison between cooperatives and
investor-owned firms,Agricultural Economics, Vol. 49 No. 2, pp. 213-223.
Medda, G. and Piga, C.A. (2014), Technological spillovers and productivity in Italian manufacturing
firms,Journal of Productivity Analysis, Vol. 41 No. 3, pp. 419-434.
Mueller, S. and Stegmaier, J. (2015), Economic failure and the role of plant age and size,Small
Business Economics, Vol. 44 No. 3, pp. 621-638.
Nieto, M.J. and Santamar
ıa, L. (2010), Technological collaboration: bridging the innovation gap
between small and large firms,Journal of Small Business Management, Vol. 48 No. 1, pp. 44-69.
Nunes, C., Cordeiro, J. and Martins, J. (2020), Productivity of Portuguese Firms: Are Exporter Firms
More, Nova Economics Club, available at: http://novaeconomicsclub.pt/wp-content/uploads/
2020/06/GPEARI-Produtividade.pdf (accessed 03 September 2020).
Nystr
om, K. (2007), Interdependencies in the dynamics of firm entry and exit,Journal of Industry,
Competition and Trade, Vol. 7 No. 2, pp. 113-130.
Oberndorfer, U. (2009), Energy prices, volatility, and the stock market: evidence from the Eurozone,
Energy Policy, Vol. 37 No. 12, pp. 5787-5795.
OECD (2018), OECD economic surveys: Czech republic (2018), available at: http://www.oecd.org/
economy/surveys/Czech-Republic-2018-OECDeconomic-survey-overview.pdf (accessed 11
November 2019).
Pavl
ınek, P. and
Z
ı
zalov
a, P. (2016), Linkages and spillovers in global production networks: firm-level
analysis of the Czech automotive industry,Journal of Economic Geography, Vol. 16 No. 2,
pp. 331-363.
Piatkowski, M. (2003), Does ICT investment matter for growth and labor productivity in transition
economies?, TIGER Working Paper Series, No. 47, Warsaw.
Pickernell, D., Senyard, J., Jones, P., Packham, G. and Ramsey, E. (2013), New and young firms:
entrepreneurship policy and the role of government evidence from the federation of small
Firm-level and
sectoral
variation in
TFP
businesses survey,Journal of Small Business and Enterprise Development, Vol. 20 No. 2,
pp. 358-382.
Power, L. (1998), The missing link: technology, investment and productivity,The Review of
Economics and Statistics, Vol. 80 No. 2, pp. 300-313.
Prokop, V., Stejskal, J. and Kuv
ıkov
a, H. (2017), The different drivers of innovation activities in
European countries: a comparative study of Czech, Slovak, and Hungarian manufacturing
firms,Ekonomick
y
Casopis, Vol. 65 No. 1, pp. 31-45.
Ramos, R., Suri~
nach, J. and Art
ıs, M. (2010), Human capital spillovers, productivity and regional
convergence in Spain,Papers in Regional Science, Vol. 89 No. 2, pp. 435-447.
Ratinger, T.,
Cadil, V. and Agyemang, S.A. (2020), Are there any economic impacts of business R&D
support? The case of the Czech republic,Central European Business Review, Vol. 2020 No. 5,
pp. 45-62.
Ratinger, T. and Kristkova, Z. (2015), R&D Investments, technology spillovers and agricultural
productivity, case of the Czech Republic,Agricultural Economics, Vol. 61 No. 7, pp. 297-313.
Rioja, F. and Valev, N. (2004), Does one size fit all?: a reexamination of the finance and growth
relationship,Journal of Development Economics, Vol. 74, pp. 429-447.
Rocha, H.O. and Sternberg, R. (2005), Entrepreneurship: the role of clusters, theoretical perspectives
and empirical evidence from Germany,Small Business Economics, Vol. 15 No. 3, pp. 193-208.
Romer, P.M. (1986), Increasing returns and long-run growth,Journal of Political Economy, Vol. 94
No. 5, pp. 1002-1037.
Salomon, R. and Jin, B. (2008), Does knowledge spill to leaders or laggards? Exploring industry
heterogeneity in learning by exporting,Journal of International Business Studies, Vol. 39,
pp. 132-150.
San-Jose, L., Urionabarrenetxea, S. and Garc
ıa-Merino (2021), Zombie firms and corporate
governance: what room for maneuver do companies have to avoid becoming zombies?,
forthcoming, Review of Managerial Science. doi: 10.1007/s11846-021-00462-z.
Schoar, A. (2002), Effects of corporate diversification on productivity,The Journal of Finance,
Vol. 57 No. 6, pp. 2379-2403.
Sickles, R.C. and Zelenyuk, V. (2019), Measurement of Productivity and Efficiency, Cambridge
University Press, Cambridge.
Simov
a, J. (2010), Internationalization in the process of the Czech retail development,EþM.
Ekonomie a Management, Vol. 2010 No. 2, pp. 78-91.
Sivathaasan, N., Tharanika, R., Sinthuja, M. and Hanitha, V. (2013), Factors determining profitability:
a study of selected manufacturing companies listed on Colombo Stock Exchange in Sri Lanka,
European Journal of Business and Management, Vol. 5 No. 27, pp. 99-108.
Spi
cka, J. and Machek, O. (2015), Change in the production efficiency of European specialized milk
farming,Agricultural Economics, Vol. 61 No. 1, pp. 1-13.
Spiezia, V. (2013), ICT investments and productivity: measuring the contribution of ICTS to growth,
OECD Journal: Economic Studies, Vol. 2012 No. 1, pp. 199-211.
Stam, E. (2015), Entrepreneurial ecosystems and regional policy: a sympathetic critique,European
Planning Studies, Vol. 23 No. 9, pp. 1759-1769.
Storey, D. and Potter, J. (2020), A research agenda for entrepreneurship policy, in Smallbone, D. and
Welter, F. (Eds), A Research Agenda for Entrepreneurship Policy, Edward Elgar Publishing.
Sumner, D.A. (2014), American farms keep growing: size, productivity, and policy,Journal of
Economic Perspectives, Vol. 28 No. 1, pp. 147-66.
Syverson, C. (2004), Market structure and productivity: a concrete example,Journal of Political
Economy, Vol. 112 No. 6, pp. 1181-1222.
IJEBR
Thornton, R.A. and Thompson, P. (2001), Learning from experience and learning from others: an
exploration of learning and spillovers in wartime shipbuilding,The American Economic
Review, Vol. 91 No. 5, pp. 1350-1368.
Tsai, H.C., Chen, C.M. and Tzeng, G.H. (2006), The comparative productivity efficiency for global
telecoms,International Journal of Production Economics, Vol. 103 No. 2, pp. 509-526.
Tsai, H.F. and Luan, C.J. (2016), What makes firms embrace risks? A risk-taking capability
perspective,BRQ Business Research Quarterly, Vol. 19 No. 3, pp. 219-231.
Urionabarrenetxea, S., Garcia-Merino, J.D., San-Jose, L. and Retolaza, J.L. (2017), Living with zombie
companies: do we know where the threat lies?,European Management Journal, Vol. 36 No. 3,
pp. 408-420.
Van Beveren, I. (2012), Total factor productivity estimation: a practical review,Journal of Economic
Surveys, Vol. 26 No. 1, pp. 98-128.
Van der Zwan, P., Hessels, J. and Burger, M. (2020), Happy free willies? Investigating the relationship
between freelancing and subjective well-being,Small Business Economics, Vol. 55 No. 2,
pp. 475-491.
Vaznyte, E. and Andries, P. (2019), Entrepreneurial orientation and start-upsexternal financing,
Journal of Business Venturing, Vol. 34 No. 3, pp. 439-458.
Williams, C.C., Martinez-Perez, A. and Kedir, A. (2016), Does bribery have a negative impact on firm
performance? A firm-level analysis across 132 developing countries,International Journal of
Entrepreneurial Behavior and Research, Vol. 22 No. 3, pp. 398-415.
Zemplinerov
a, A. (2000), Privatisation of network industries: the case of the Czech energy sector,
Paper Presented at Advisory Group on Privatisation OECD, Budapest, 1920 September,
available at: https://www.oecd.org/corporate/ca/corporategovernanceofstate-ownedenterprises/
1929608.pdf (accessed 10 March 2020).
Zenka, J.,
Zufan, P., Krti
cka, L. and Slach, O. (2015), Labour productivity of agricultural business
companies and cooperatives in the Czech Republic: a micro-regional level analysis,Moravian
Geographical Reports, Vol. 23 No. 4, pp. 14-25.
Appendix
About the authors
Ond
rej Dvoulet
y is the Associate Professor at the Department of Entrepreneurship, Prague University
of Economics and Business. His research focuses on heterogeneity of self-employment, entrepreneurship
and SME policy evaluation and entrepreneurial economics. He is the Associate Editor of the Journal of
Small Business Management, Journal of Entrepreneurship in Emerging Economies and Central
European Business Review. Ond
rej is also an Editor of the annual Innovation Management,
Independent/Dependent variable Log (total sales)
Log (tangible fixed assets) 0.065*** (0.001)
Log (personnel costs) 0.436*** (0.002)
Log (material consumption) 0.481*** (0.002)
Constant 1.299*** (0.017)
Observations 548,998
R
2
0.507
p-value F-test 0.000
Note(s): Standard errors in parentheses: ***p< 0.001
Source(s): Bisnode (2020); own elaboration
Table A1.
Estimation of total
factor productivity
with OLS (based on
firm-level data for the
period of 20002019)
Firm-level and
sectoral
variation in
TFP
Entrepreneurship and Sustainability (IMES) conference. Ond
rej Dvoulet
y is the corresponding author
and can be contacted at: ondrej.dvoulety@vse.cz
Ivana Bla
zkov
a is the Associate Professor at the Department of Regional and Business Economics,
Faculty of Regional Development and International Studies, Mendel University in Brno, the Czech
Republic. Her research focuses on determinants of firm profitability and productivity, business
performance, industrial organisation and agribusiness. She is involved in various projects, and she also
acts as a reviewer for several journals and conferences. Ivana is also engaged in international teaching
activities, and she is a member of the Czech Academy of Agricultural Sciences.
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Purpose This study offers a focus on the income dynamics of the sector, analyzing the evolution of the main profitability indicators (ROE, ROA, ROI and ROS) of 457 Italian companies in the 2008–2020 period. So, it is possible to verify the reactivity to the global financial crisis that began in 2008 and the first indications on the 2020 pandemic. Design/methodology/approach The analysis uses descriptive statistics tools and the ANOVA method of analysis of variance completed by Tukey's test, useful for identifying the existence of significant differences between geographical macro-areas of the country. Findings The results show positive dynamics in a sector that has been able to absorb the negative consequences of the great global crisis, improving its profitability over the years, albeit with differences in the macro-regions of Italy. Research limitations/implications The study considers only the companies that survived the crisis, so, presumably, the strongest. In the future, other ratios should be considered to have a more complete view. It is a quantitative study based on the financial report data that neglects other important economic factors. Practical implications Public policies might use this study for a better intervention in support of the sector. Besides, internal management may compare company outcomes with average sector outcomes to identify improvement prospects. Social implications The research represents a significant basis considering the risks deriving from the supply of low-cost Asian products that could significantly affect the profitability of Italian companies in the future. Originality/value The study contributes to the literature by providing a quantitative analysis of the dynamics of the sector, through the comparative information that may be deduced of balanced sheets in the course of the years.
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This paper is an attempt to empirically examine the influence of corporate governance on zombie theory. Recent data indicates that the problem affects more than 10% of all firms and is worth close to a billion Euros in the European Union. It is thus an important problem worth analysing in European countries, and Spain is one of the worst affected. The sample studied here thus consists of Spanish companies with negative equity over the five business years from 2013 to 2017. The analysis focuses on establishing how corporate government aspects-board size, dedication to board meetings, concentration of ownership, owners and gender of the CEO-, affect the severity of extreme zombie firms. It uses the amount and temporality dimensions of EZIndex. The results confirm that internal dimensions such as corporate governance are important aspects to be introduced into zombie analysis. To control zombiness our results suggest structuring boards of directors with more than one member, CEOs with non-exclusive dedication, non-concentrated ownership and female CEOs. We also find that all these factors affect both amount and temporality, so factors do not affect zombie companies partially but totally. The contributions of the paper are twofold: first it includes corporate governance factors as a relevant approach for understanding zombie theory; and second, it encourages companies to manage risk-taking decisions efficiently so as to avoid the proliferation of zombies.
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Only a few studies consider heterogeneity in the effects of investment grants (subsidies) across firm size. Therefore, we investigate the effects of particular public policy on firm-level performance with a focus on firm size heterogeneity as an important determinant. We aim to investigate whether the larger-sized firms benefit from the direct financial assistance to a lesser extent when compared with micro- and small-sized firms. Specifically, we study the microeconomic effects of the Czech Operational Programme Enterprise and Innovation (OPEI) that was implemented during the period of 2007-2013. Compared to previous studies, we work with a large firm-level dataset, consisting of 13,924 firms, of which 3,572 are supported firms (57% of beneficiaries of the OPEI programme). We implement the propensity score matching (PSM) in combination with a difference in differences (DID) approach. We measure the overall financial performance of firms by using tangible fixed assets and their depreciation, sales, return on assets (ROA), and total factor productivity (TFP). While there are improvements in all evaluated indicators for microenterprises two years after the end of the programme, there are only minor positive effects of subsidies for larger enterprises.
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Evaluation of the economic impact of public research and development (R&D) support has become an integral part of the policymaking process. The paper examines the economic effects of the Czech programme TIP supporting R&D in private companies in the recent decade and aspires to contribute to the debate on quantitative methods for assessing outputs additionality of public R&D programmes. The analysis is based on the counter-factual econometric approach, because an appropriate control group could not be found among the unsupported companies, a so-called generalized propensity score matching (GPSM) was used. Two data sources were used: monitoring data of the programme TIP and economic data from the database Bisnode-MagnusWeb. The impact of R&D support on the firms' performance was measured by output indicators: gross value added, profit and productivity. Treatment effects are diverse depending on firm sizes. Generally, the R&D support effects are positive and more substantial in small and medium firms (SMEs) while there are no such effects confirmed for large firms. The support could only impact positively on the output (profit, productivity and gross value added) of small firms only after reaching a certain minimum threshold of the support whereas output diminishes for medium firms after a point of saturation.
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Purpose This paper extends entrepreneurial ecosystems theory by testing how aspects of the local business environment affect individual entrepreneurs' ability to translate their personal resources into firm performance. Design/methodology/approach Data were collected from 223 business owners across Australia. Moderation hypotheses were tested using multiple hierarchical regression and confirmed with the Preacher and Hayes (2004) bootstrapping method. Findings The results show that business owners' psychological capital, social capital and entrepreneurial education directly affect their individual firm performance. These positive relations are moderated by specific aspects of the business environment, such that they are stronger when the environment is more favorable. Originality/value This study puts individual business owners back into entrepreneurial ecosystems theory and explains how they can make the most of their personal resources, suggesting a complex interplay where one size does not fit all. Far-reaching practical implications for policymakers are discussed.
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The United Kingdom’s imminent departure from the European Union provides the opportunity for a more selective industrial strategy. This paper therefore analyses the effect of product subsidies on productivity in British manufacturing plants between 1997 and 2014 in order to provide evidence on the desirability of extending their use. The results suggest that low rates of subsidisation had either a positive or no effect but higher rates had a negative effect on total factor productivity in some sectors. This implies that increasing the generosity of subsidies does not offer a means of improving the United Kingdom’s productivity performance.
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Our paper encompasses an integral view of organisational innovation (OI), covering from the generation of OI to the effect of OI on firm performance, tackling the gap identified by Damanpour, Walker, and Avellaneda (2009) about the lack of studies that comprehensively study OI. We empirically test this question using data from the Spanish Technological Innovation Panel (PITEC, 2016) carried out in the years 2008–2013, focusing on a sample of 3795 manufacturing firms. The results provide empirical evidence that confirms OI as an innovation capability. We conclude that innovation capabilities operate through a reciprocity and complementarity relation, where technological, product and process, innovations and OI are all determining factors of one another. Last, our paper explores the effect of OI on firm performance, expanding the current analysis of the effect of process and product innovation on firm performance.