Exploring firm-level and sectoral
variation in total factor
Department of Entrepreneurship, Prague University of Economics and Business,
Prague, Czech Republic, and
Department of Regional and Business Economics, Mendel University in Brno,
Brno, Czech Republic
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
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 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.
Keywords Total factor productivity, TFP, Czech Republic, Firm-level analysis
Paper type Research paper
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:
Received 2 November 2020
Revised 22 April 2021
Accepted 19 May 2021
International Journal of
Entrepreneurial Behavior &
© Emerald Publishing Limited
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
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 2000–2019. 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
aet al., 2020;Botri
cet al., 2017;Cie
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
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 firm’s 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.
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
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 study’s
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
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
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
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-doing”theory (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
a and Dvoulet
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
firms’leverage 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
a and Dvoulet
(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,
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
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
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
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.
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
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 2000–2019, 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
aet al., 2018;Luke
s, 2017) and strong orientation on manufacturing and the
automotive industry (Lefley et al., 2004;Pavl
a, 2016;Prokop et al., 2017).
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.,
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 Cobb–Douglas production function
(i) refers to a firm, tto time, and
to the time and producer-specific deviation from the mean,
consisting of observable (v
) and unobservable components(u
). 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
Then, we calculate firm-level TFP (
) from the following Equation (2) (for details, please see
Van Beveren, 2012, p. 100; c.f. Bla
aet al. (2020), pp. 291–292):
Equation (2): Calculation of total factor productivity
β1Tangible Fixed Assetsit
β3Material Consumptionit (2)
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.
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
Personnel costs Variable is made up of wages, salaries and employers’social 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)
Total factor productivity (TFP) Variable is estimated by simple OLS regression with the use of Cobb–Douglas production
function (see Equation 1) and calculated from Equation (2) based on Van Beveren (2012)
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 (0–9 employees), small size (10–49 employees), medium size
(50–249 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 firm’s sales to the value of its assets
More than 5x reported negative
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-
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,
ınad Labem, Hradec Kr
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 (0–9 employees) 288,679 52.5
Small (10–49 employees) 175,667 32.0
Medium (50–249 employees) 70,068 12.8
Large (250þemployees) 14,584 2.7
Total 548,998 100.0
Source(s): Bisnode (2020); own elaboration
List of variables
Distribution of the
sample across firm size
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
where irefers to a firm, tto time, and
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
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
Sectoral affiliation of
the firms in the sample
Summary statistics for
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
The models are estimated on an unbalanced panel dataset of 91,257 firms covering the
period of years 2000–2019. 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/
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 1994–1999, 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 1996–2012. 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
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
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 (10–49 employees) 0.265*** (0.0253) 0.175*** (0.0293)
Size medium (50–249 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
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
0.0426þ(0.0244) 0.0211 (0.0528)
Wholesale and retail trade; repair of motor vehicles
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
Prob > F/ Prob >
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 (0–9 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
Determinants of the
(based on firm-level
panel data for the
period of 2000–2019)
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 firm’s 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 firms’asset 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
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
cka and Machek, 2015), the effects of
indebtedness can also be negative. For instance, Bla
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 firms’over-indebtedness by including a variable representing negative-equity
companies or what are also called zombie enterprises (Bla
a and Dvoulet
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
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
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.
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
2000–2019. 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
dataset in terms of scope and representativeness in order to explore firm-level productivity
We have found a statistically significant U-shaped relationship between firm age and the
overall TFP in the Czech economy during the period of 2000–2019. 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
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
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
Finally, we are fully aware of the limitations of our study, especially involving the range of
factors that have a potential impact on a firms’productivity level, and that could not have
been included in the present study due to the lack of data, such as information about the
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.
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About the authors
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)
p-value F-test 0.000
Note(s): Standard errors in parentheses: ***p< 0.001
Source(s): Bisnode (2020); own elaboration
Estimation of total
with OLS (based on
firm-level data for the
period of 2000–2019)
Entrepreneurship and Sustainability (IMES) conference. Ond
y is the corresponding author
and can be contacted at: firstname.lastname@example.org
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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