Convergence Processes in the European Agriculture:
Analysis of the Total Factor of Productivity
PETER BIELIK1, STEFANIIA BELINSKA1*, TATIANA BULLOVÁ1, YANINA BELINSKA2,
IZABELA ADAMIČKOVÁ1, NATÁLIA TURČEKOVÁ1, ZUZANA BAJUSOVÁ1
1Faculty of Economics and Management,
Slovak University of Agriculture in Nitra,
Trieda Andreja Hlinku 2, 949 76 Nitra,
SLOVAKIA
2Department of International Economics,
University of the State Fiscal Service of Ukraine,
Universytetska str. 31, 08 201 Irpin,
UKRAINE
*Corresponding Author
Abstract: - The article explored labour productivity in agriculture and carried out a comparative analysis of the
achieved level of labour productivity in the countries of the European Union. The efficiency of agricultural
production needs to be measured to improve the productivity, competitiveness, and profitability of farms. The
multifactorial total factor productivity (MFP) of agricultural production evaluates the main and additional
sources of economic growth in the agricultural sector. Based on the analysis of beta convergence, we observe
an increase in total factor productivity (TFP) in Slovakia and the countries of the European Union. Based on the
obtained statistically significant regression analysis models, beta convergence and sigma divergence of the TFP
of the agricultural sector in the EU and Slovakia were identified. Thus, the process of positive convergence was
confirmed, which indicates the convergence of the economic development of Slovakia with highly developed
countries and the reduction of disparities between countries. State support and investment in innovative
technologies will stimulate the adoption of new technologies and at the same time ensure technological
progress and improve the impact of agriculture on the environment. We also concluded that it is important to
improve the skills of those working in the agricultural sector, and as a result, an increase in TFP is expected.
Key-Words: - agricultural productivity, labour productivity, total factor productivity, sigma-convergence, beta-
convergence model, rural economic growth
Received: November 28, 2022. Revised: August 24, 2023. Accepted: September 9, 2023. Published: September 15, 2023.
1 Introduction
The main objectives of the work are to study the
essence of agricultural productivity, a comparative
analysis of the dynamics of the level of labour
productivity, to substantiate the importance of
increasing total agricultural productivity as a key
factor in increasing farm incomes, national income
and increasing the competitiveness of the
agricultural sector. The study, [1], believes that the
analysis of the productivity of factors of production
is necessary because it is a tool for making decisions
and introducing changes at the economic level.
The countries of the European Union, which is most
agriculture-oriented, have begun to implement
policies to increase agricultural productivity and
implement the goals of the 2030 Agenda for
Sustainable Development. Because increasing the
efficiency and productivity of production in
agriculture will reduce poverty, increase food
security, and increase farm incomes, [2].
Also, the productive agricultural sector provides
a structural transformation of the country's
economy, leads to an increase in the welfare of the
population, and improves the diet of consumers
through a decrease in food prices, since low prices
increase real incomes, [3], [4].
Analysis of the level of agricultural productivity
is an information basis for management decisions
aimed at increasing the profitability and
competitiveness of the enterprise, organizational
improvement of production and technology, pricing,
and effective human resource management. The
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Peter Bielik, Stefaniia Belinska,
Tatiana Bullová, Yanina Belinska, Izabela Adamičková,
Natália Turčeková, Zuzana Bajusová
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ability to effectively utilization of production
factors, mainly capital, and human resources, is also
a measure of increased competitiveness, [5].
Productivity lies in the ability to efficiently use
resources to produce products, the technology used,
and economies of scale, [6]. According to the
European Commission, from a long-term
perspective, productivity is the most reliable factor
in increasing competitiveness, [7]. Thanks to
productivity growth, firms (industries, countries)
can more efficiently use and redistribute limited
factors of production for other purposes, which
ensures a high national income, [2].
Increases in agricultural productivity are an
essential condition for economic development. The
studies, [8], [9], [10], [11], [12], emphasize that the
growth of productivity is an indicator of
competitiveness, as well as a possible way to
achieve economic growth. In the study, [13], among
many other authors, have made essential
contributions towards developing a better
understanding, measuring, and analysing
agricultural productivity. The study, [14], pointed
out that economic development goes hand in hand
with TFP, and can increase only under, conditions
of intensive agricultural expansion. Agricultural
development is essential to economic growth,
leading to the perception of expanding opportunity,
[15]. The analysis of the productivity of production
factors is particularly important as it is a useful
management tool at any economic level, [1].
2 Literature Review of the Total
Factor Agricultural Productivity
(TFP)
Productivity is defined as the ratio of volume output
to resource use, [16]. The following factors
influence productivity growth:
Changes in farm physical productivity.
Changes in nominal prices for products
produced by farms.
Changes in labour productivity
At a fundamental level, productivity measures
the amount of output produced in a country,
industry, sector, or farm given a set of resources and
inputs. Productivity can be measured for each
subject separately or for the group, [2].
Productivity in agriculture is calculated as partial
productivity related to one factor or as total
productivity (multi-factor). Multi-factor or total
factor productivity growth (MFP or TFP) is a
change in production that is not the result of a
change in all or several factors of production, which
in agriculture are usually land, labour, and capital.
The most comprehensive indicator of productivity is
total factor productivity (TFP), which measures the
efficiency with which producers combine resources
to produce output. The total factor productivity is
defined as the aggregated output-input ratio, [1].
According to a generic definition, productivity is the
ability of production factors to produce output, [17].
Total factor productivity measures the ratio of total
marketable output (plants and livestock) to inputs
(land, labour, capital, and materials), but does not
consider inputs and outputs that have no economic
value to the producer. Following the methodology
for assessing the factors of economic growth
(growth accounting) TFP is calculated as the
remainder of the difference between the growth
rates of output and the sum of the growth rates of
capital and labour, weighted by the corresponding
elasticities. The TFP growth rate is calculated as the
difference in the average growth rate of combined
outputs and inputs. If total output grows faster than
total input, then each unit of output is produced
using fewer total inputs, and average cost
productivity, or TFP, increases, [18]. Total Factor
Productivity reveals how efficiently farmers are
producing it and indicates how well they are
conserving available resources to meet future needs,
[19].
TFP, measured at the industry level, reflects the
most complete measure of efficiency, [20]. TFP
demonstrates the efficiency of the agricultural sector
in using available resources to turn inputs into
finished products. Many factors, such as new
technologies, efficiency gains, economies of scale,
managerial skills, and changes in the organization of
production, have a complex influence on the growth
of TFP, [21].
The total factor productivity index is the relation
of total production to total expenditure on
production, [22]. The index of TFP growth can be
defined as the ratio between the change in
production volumes over a period and the
corresponding change of inputs to produce them. An
increase in TFP reflects a gain in output that does
not originate from an increase in input use. The
productivity of the agricultural sector is quite
differentiated in the respective member states of the
EU, [23], [24]. Identification of the determinants of
growth in agricultural productivity is the
precondition to make up for differences in TFP
between member states.
Data Envelopment Analysis (DEA) use for
determining technical efficiency and use of the
Malmquist productivity index. The Malmquist index
shows how the change in parameters (inputs and
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outputs) in two different periods affects the total
factor productivity of agriculture and differentiates
the sources of productivity growth, [25].
TFP gives a complete picture of performance and
is linked to technical changes and economic
performance. Increases in single-factor productivity
measures such as output per worker or yield per
hectare may simply be due to increases in the use of
other inputs (capital per worker, fertilizer per
hectare, etc.), that do not reduce costs or reflect
changes in the underlying technology. However, a
1% increase in TFP at fixed is equivalent to a 1%
decrease in the unit cost of production and
represents a real welfare gain to society, [18].
TFP is influenced by several factors, such as the
introduction of new technologies, better
management of resource use and the choice of
agricultural practices, economies of scale, and
efficiency gains from trade. TFP will also be
affected by long-term changes in environmental
factors, such as changes in soil fertility, water
quality, and climate, which can reduce TFP in the
event of environmental degradation. TFP analysis is
especially important as it is a useful management
tool at any economic level, [19]. Performance
evaluation creates the basis for continual
improvement, so an accurate analysis of TFP growth
is essential to develop appropriate policies to access
its performance, [26].
Agricultural productivity growth is a key source
of economic growth in the EU agricultural sector.
Growth in agricultural productivity is associated
with new research and development, growth in
human capital, innovation, and improvements in
technology. Technical change has been the main
source of productivity growth, not efficiency
change. The agricultural industry uses digital
(information) technologies to create additional
opportunities to increase productivity and diversify
incomes. Multi-product performance metrics can be
used to measure farm productivity growth that
demonstrates the impact of new technologies,
economies of scale, and management practices on
productivity, [27].
Rising agricultural productivity affects the
welfare and structural transformation of the
economy, contributes to overall economic growth,
and can reduce poverty; leads to the release of
labour from agriculture to the manufacturing
industry and other industries; reduces food prices
and thus increases real incomes, [3], [4].
The development of the agricultural sector has
brought numerous benefits to society. The growing
availability of food has allowed people to overcome
the problems associated with inappropriate levels of
food security and thus improved the standard of
living of the rural population, [28]. It is important to
strive for cost-effective agriculture based on
knowledge and innovation, with a focus on the well-
being of farmers and increasing the potential for
yields and a positive impact on the environment. In
this context, the analysis of agricultural productivity
is important for producers and the government, [29].
Climate change negatively affects agricultural
productivity due to rising temperatures and changes
in weather conditions, which makes it difficult to
grow and develop crops and livestock, and for
agricultural workers to endure the physical
challenges, [19].
Thus, it is necessary to develop effective policy
regulation focused on environmental TFP, which
will have a significant impact on the reduction of
greenhouse gas emissions, thereby significantly
contributing to a sustainable and productive
agricultural sector, [26].
3 Methodology
We performed an analysis of the regional disparities
between Slovakia and EU countries based on the
TFP using the Gini coefficient and the Theil index.
The dynamics of the indices help to identify the
processes of convergence or divergence and, as a
result, the presence of regional disparities in TFP in
the EU and Slovakia from 2007-2018. We verified
the presence of convergence processes, or
divergence using regression models.
The Gini coefficient, the best-known and most
widely used measure of inequality, is a measure of
statistical dispersion used to express the distribution
of a set of values and is calculated as the mean of
the absolute differences between all pairs of values
for a given variable. It compares the distribution of a
variable to theoretical perfect equality.
Regional disparities are measured using the
unweighted Gini coefficient, which is calculated
using the formula, [33]:



 (1)
where:
(2)

 (3)
N is the number of regions;
is the value of the variable y (e.g. GDP per
capita, TFP) in country j when evaluated from the
lowest (y1) to the highest (yN) among all countries.
The Gini coefficient ranges between 0 % and
100 % (perfect equality or inequality: y is the same
in all countries or zero in all regions except one).
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The Theil index measures total disparities
between all countries (GDP, income, TFP, labour
productivity). It divides total inequality into
inequality due to differences within countries and
inequality due to differences between countries.
The Theil index is calculated by the formula,
[33]:

󰇡
󰇢
 (4)
where:
N is the number of regions/countries;
yi is a variable in the i-th region (i.e. GDP per
capita, household income, life expectancy, etc.);
is the average value of the variable over all
regions.
The Theil index ranges from zero to  ∞,
where zero is the same distribution, the higher the
value, the higher the level of disparities, and the
value of  represents perfect inequality.
Convergence refers to the convergence of the
levels of development of countries or regions over
time. The opposite process is called divergence. The
concept of convergence is linked to the Solow
model of economic growth, which refers to higher
rates of economic growth in countries that are far
from a steady state (a state in which the capital-
labour ratio is constant) compared to countries that
are closer to him. Therefore, lagging economies are
gradually catching up with developed countries.
Sigma convergence is a gradual decrease in
variation (inequality, differentiation) in the levels of
economic development of countries or enterprises.
Sigma convergence occurs when the value of the
variance of the indicator under consideration
decreases/increases over time for a group of
countries (including compared to the average value),
[31]. The standard deviation or coefficient of
variation is the most used indicator for testing the
convergence hypothesis. It is more advantageous to
use a coefficient of variation which, unlike the
standard deviation, will not depend on the
spatiotemporal dimension.
This type of convergence means that the values
obtained by the calculation according to the
following formula (2) are constantly decreasing:



 (5)
where:
standard deviation;
arithmetic mean of the indicator;
yi,t level (value) of the indicator in the i-th
region at time t;
yt is the average level of the indicator in the
group of the countries;
N is the number of countries.
We confirm the hypothesis of the presence of
sigma convergence if there is a downward trend in
inequality across countries. The higher the values of
the coefficient of variation, the greater the
disparities between countries.
The term beta convergence was introduced by R.
Barro and X. Sala-i-Martin, [30]. Beta convergence
is the negative dependence of the rate of economic
growth on the initial level of development of
countries.
Poor regions (or countries) have a higher rate of
economic growth than the rich, respectively. richer
countries are growing more slowly, which in the
long run should lead to a leveling off of regional
levels of economic development.
Absolute convergence is understood as the
convergence of the levels of development of regions
over time, without this process being affected by
other conditions.
For empirical verification of absolute beta
convergence, a regression of the logarithm of the
average baseline growth rate of the observed
property and the logarithm of the initial level of the
investigated property is compiled. The absolute beta
convergence model can be quantified as follows:


󰇛󰇜 (6)
where:
󰇛󰇜 a  is a random
error;
yit, yi,t-1 the initial (final) amount of well-being
(salary, income, etc.) in the i-th region;
t year.
Regression analysis is used to model -
convergence. For absolute beta convergence, we can
write a regression function in the form:

 (7)
If the coefficient is significant for the
explanatory variable and has a negative sign, the
hypothesis of absolute convergence is not rejected
and regions with worse initial conditions will have a
higher growth rate. In this way, the presence of a
negative correlation between the rates of economic
growth and the initial level of development of the
regional economy is checked. With a positive
coefficient, -divergence is observed.
The convergence process is characterized by two
indicators:
the degree of convergence (), which
indicates how many fractions of a unit the
gap between regions decreases over time. If
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it has the opposite sign to the coefficient b,
that is, if the coefficient b is negative, then
the velocity is higher than zero;
the time () needed for the regions to cross
halfway to equilibrium.
These indicators can be calculated based on an
estimate of the coefficient b, which can be
expressed as follows:

(8)
Then: 󰇛󰇜
(9)
󰇛󰇜
󰇛󰇜 (10)
The presence of convergence/divergence
processes indicates whether regional disparities
decreased or increased during the analysed period.
4 Practical Research Results
TFP is a key indicator of the effective
implementation of the overall objectives of the
CAP. To analyse the dynamics of productivity
changes, the TFP is used, which also evaluates the
European Innovation Partnership for Agricultural
Productivity and Sustainability (EIP-Agri3). Thus,
the TFP indicator is the main factor in the growth of
agricultural production and farm incomes.
Productivity in the EU has increased over time.
(Figure 1). TFP grew by 14.69% in 2018 compared
to 2000, and by 8.93% in 2018 compared to 2008.
In 2019 TFP decreased by 2.5%, which is related to
the coronavirus and indicates the growth of
problems in agriculture, which continued in 2020
and became the basis for increasing the danger of a
world food crisis in 2022. In general, the behaviour
of TFP is different in different periods periods of
growth alternate with decline. In Figure 1, we
observe a drop in TFP growth after the financial and
economic crisis in 2009-2012, as well as in 2015-
2017, after the start of the implementation of the
new CAP 2014-2019 program. In 2012-2015, there
was an acceleration in the growth of TFP due to
favourable climatic conditions for growing crops,
which led to an increase in agricultural production.
It follows from this that the growth of total output
has a positive effect on TFP, but the associated
increase in costs, on the contrary, hurts TFP, [31].
Fig. 1: Total factor productivity of EU-28 in 2000-2019 years (index)
Source: Authors’ calculations based on data from the Agri-food data portal
85,00
90,00
95,00
100,00
105,00
110,00
115,00
TFP TFP growth
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The total TFP index has a growing trend, but
TFP in EU countries is different. That demonstrates
the results in Figure 2. Therefore, we are dealing
with a significant increase in the diversification of
productivity. The average level estimated for all
European Union-28 countries only in the case of 18
countries, where the TFP index was the highest or
equal, obtained a result higher of 109.81%
compared to EU-28. In other countries, where TFP
is lower than the EU-28 average level, this may be
explained by the fact that implementation of
technological progress in these countries not only
requires structural transformations including
optimization of work resources, but also quality
changes, and especially an improvement in farmers’
knowledge and qualifications.
Fig. 2: Total factor productivity in EU’s countries in 2007-2019 (index)
Source: Authors’ calculations based on data from the Agri-food data portal
Figure 3 was built without Cyprus, Croatia, and
Malta, as they have significantly broken away from
the rest of the EU-27 countries. Average annual
rates of TFP decline were in Croatia (39.86%),
Malta ( 28.75%) and Cyprus increased by
155.05%.
The results in Figure 3 show that Belgium (156),
Slovenia (123.6), Hungary (126.3), and Latvia
(123.7) are the four countries with the maximum
total factor productivity and total factor productivity
growth in 2007-2019. Belgium, Latvia, and
Slovenia show a 47.1%, 42.2%, and 40.7 % average
growth in total factor productivity change. It should
be noticed that a productivity increase in the
mentioned countries was affected to a great degree
by technological changes. The lowest average rates
for 2007-2019 were in Germany (3.09%). Only in
the Czech Republic, Germany, Greece, Estonia,
France, Italy, the Netherlands, Austria, Finland, and
Sweden were TFP lower than the European average.
In Slovakia, the TFP was 109 compared to the
average of EU-27 (113). After the transition of the
economy, there had been a change in the structure of
the agricultural sector which affected Slovakia and
the Czech Republic, causing the TFP to lag in the
EU average TFP growth.
There was a decrease in TFP in Germany, Malta,
and Croatia. The most powerful agrarian countries
-50,00
0,00
50,00
100,00
150,00
200,00
0
50
100
150
200
250
Belgium
Bulgaria
Czechia
Denmark
Germany
Estonia
Ireland
Greece
Spain
France
Croatia
Italy
Cyprus
Latvia
Lithuania
Luxembourg
Hungary
Malta
Netherlands
Austria
Poland
Portugal
Romania
Slovenia
Slovakia
Finland
Sweden
TFP average annual change
TFP index
European Union 27 Average annual change in TFP, 2007-2019
EU average annual change in TFP, 2007-2019
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in Europe Belgium, Latvia, and Estonia
respectively had the highest growth rates of TFP,
the level of which exceeded the average European
level.
Fig. 3: Total factor productivity in EU’s countries in 2007-2019 (index)
(without Malta, Cyprus, and Croatia)
Source: Authors’ calculations based on data from the Agri-food data portal
The highest productivity increase during the
examined period may be observed in agriculture,
where the examined index reached 108.92 %
(113.19 % without three countries that significantly
differed from the average values) and annual
average growth of 110.53% (118% without three
countries) and was an effect of both production
technology changes and technical efficiency, and
natural conditions. Accordingly, such countries as
Cyprus, Germany, and Malta exhibit the lowest total
factor productivity in the agriculture sector.
A general indicator of labour performance that
characterizes the efficiency of its production costs is
labour productivity. A significant part of the
difference in agricultural TFP is caused by changes
in labour productivity. This relationship between
total factor productivity and labour productivity is
confirmed by the high correlation coefficient
(0.9567).
To determine the factors that have the greatest
influence on the TFP of individual countries we
have estimated a linear regression model for the
studied countries from the agricultural sector. The
regression model describes the dependence between
the total factor productivity and labour productivity
in 2005-2018 years. The coefficient of
determination is equal to 95.83%. The estimated
model explains 91.53% of the variability of the
dependent variable, and the model is high statistical
significance. With 95% confidence, we can state,
that if labour productivity increases by 1 unit, then
we can expect total factor productivity to increase
by 0.16. Considering the small dependence of TFP
in rural areas on technologies, equipment, and
natural conditions, the organization is quite a good
result.
Compared to the base year 2005 in the EU-28,
labour productivity in agriculture rose by 68.73% in
2019. At present, labour productivity in agriculture
is an increase in all EU Member States. The highest
productivity growth was in the years 2010 to 2012
after the financial crisis, and then since 2016, it has
been growing slightly. Between 2013 and 2015,
most EU regions saw a slight annual decline in
productivity (Figure 4).
The growth of labour productivity in absolute
terms, which to some extent is the result of a
decrease in the number of employees in rural areas.
During 2000-2020 the biggest drop was observed in
Europe, the number of people working in agriculture
decreased by 50 % from about 35 million, which
represents a decrease of 18 million people, [32].
Regarding the price trend, then prices decreased
annually by 1-7 % from 2013-2019 (World Bank
-10,00
0,00
10,00
20,00
30,00
40,00
50,00
0
20
40
60
80
100
120
140
160
180
TFP average annual change
TFP index
European Union 27 Average annual change in TFP, 2007-2019
EU average annual change in TFP, 2007-2019
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Commodity Price Data Agricultural) and jumped
by 5 and 22 percent in 2020-2021 due to the corona
crisis. Annual fluctuations in labour productivity
demonstrate its unstable dynamics, which to some
extent reflects the influence of natural factors on
labour productivity in agriculture. In general, some
inconsistencies between the results of labour
productivity growth and the state of the food market
and farmers' incomes should be washed away.
Despite the positive effects of increased labour
productivity, and therefore agricultural production
and more complete satisfaction of society's needs,
the increase in supply puts downward pressure on
prices. This has a negative impact on farmers'
incomes if they are not balanced by the growing
volume of sold products. Therefore, regulatory
actions by the state to balance the food market by
year and smooth out market shocks are extremely
necessary.
In the EU countries, the level of labour
productivity in agriculture is highly differentiated
and lower than in other sectors of the economy,
which is a negative reason for slow intensive
growth. The current trend associated with low levels
of labour productivity has a negative impact on
sustainable economic growth, the creation of a
competitive economy, and the improvement of the
standard and quality of life.
As part of the agroecological transition and
integration on a European and global scale,
disparities in the level of socio-economic
development of countries will be affected by
changes in labour productivity. Increasing labour
productivity is one of the decisive conditions for the
development of agricultural production, the
implementation of social transformations in rural
regions, and the improvement of the material well-
being of the population.
Fig. 4: Changes in labour productivity in EU-28 agriculture in 2005-2019
Source: Authors’ calculations based on data from the Agri-food data portal
Fig. 5: Labour productivity in EU-28 countries in 2019, EUR/AWU
Source: Authors’ calculations based on data from the Agri-food data portal
0,00
10.000,00
20.000,00
30.000,00
40.000,00
50.000,00
60.000,00
70.000,00
80.000,00
European Union 28
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Peter Bielik, Stefaniia Belinska,
Tatiana Bullová, Yanina Belinska, Izabela Adamičková,
Natália Turčeková, Zuzana Bajusová
E-ISSN: 2224-2899
2030
Volume 20, 2023
In Figure 5 agricultural labour productivity
varies greatly across EU countries. The highest
labour productivity in agriculture in 2019 was in the
Netherlands, Denmark, France, Germany, and
Belgium. In these countries, labour productivity is
higher than the EU-28 average. And the lowest
labour productivity in agriculture was in Latvia,
Poland, Croatia, and Romania. That is, in the most
agrarian-oriented countries of Europe, relatively low
labour productivity was to some extent compensated
by favourable natural conditions. At the same time,
rather high rates of their growth prove that probably
fewer positive changes in labour productivity have
taken place in agriculture in most of the other new
EU Member States, notably Estonia, Croatia,
Slovenia, and Malta.
Slovakia also exhibits labour productivity which
is lower than the EU-28 average. It was caused by
the decrease in labour input in the agricultural
sector. The constant decline in the number of
employees in agriculture ranks Slovakia among the
countries with the lowest share of agricultural
workers in the total number of employees.
The gradual stabilization of the dynamics of
labour productivity may be evidence of a certain
exhaustion of the potential for increasing the
volume of agricultural production in the existing
conditions. This reinforces the need to find new
opportunities for increasing labour productivity, not
by reducing the number of people employed in the
labour market, but by intensification and
diversification of production.
Differences in labour productivity growth
between regions are the result of many national and
local factors, including labour market policies and
institutions, as well as innovation and the use of new
technologies. Despite clear progress in labour
productivity, the level of agriculture in the new
Member States is still significantly lower. This
means that convergence processes are taking place
in the field of labour productivity in EU agriculture.
Improving labour productivity in agriculture is a
multifactorial task that includes institutional
mechanisms that will strengthen the material and
technical base, deepen specialization, and strengthen
the concentration of agricultural production through
the development of economic cross-sectoral ties,
development of rural infrastructure, and
development of adequate pricing policy. agricultural
products, which will open opportunities to increase
the level of real incomes of the personnel of
agricultural enterprises and raise the prestige of
agricultural labour.
Figure 6 shows the dynamics of the Gini
coefficient (right scale) and the Theil index and
coefficient of variation (left scale), which point out
a significant increase in the difference between
countries in terms of TFP in the period 2007-2018.
We can see a reduction during the financial crisis on
the stock market in 2008-2009. After this period, we
can see a constant increase in these coefficients,
which confirms the presence of divergence
processes. Dynamics of indicators of the coefficient
of variation of TFP in the period 2007-2018 Indicate
a gradual process of increasing differences between
the levels of development of countries
(convergence). The main fluctuations in the
coefficient of variation occur during the crisis
period.
In 2014-2018 disparities between countries also
increased, partly due to the implementation of the
next phase of EU agricultural policy reform. The
Gini coefficient, coefficient of variation, and Theil
index clearly show that disparities between
countries are getting bigger. This means that
disparities in the EU are increasing, mainly due to
the divergence of countries.
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Peter Bielik, Stefaniia Belinska,
Tatiana Bullová, Yanina Belinska, Izabela Adamičková,
Natália Turčeková, Zuzana Bajusová
E-ISSN: 2224-2899
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Volume 20, 2023
Fig. 6: Sigma-convergence between EU-28 countries in the years 2007-2018
Source: Authors’ calculations based on data from the Agri-food data portal
The large correlation between the Gini
coefficient, Theil index, and the coefficient of
variation between 2007 and 2018 increases the
validity of our assumption about the presence of
sigma divergence processes between the EU
countries. Thus, we can say that the sigma-
convergence processes were gradually replaced by
diversification processes since the countries began
to develop according to their development trajectory
and found their place in the EU's agriculture system.
Table 1. Summary of the estimation of the
parameters of the beta-convergence model
Coefficient
Std. Error
t-ratio
p-value
Statistical
significance
const
4.06328
1.01761
3.993
0.0005
***
(Very
significant)
ln_TFP_
2007
−0.860789
0.222403
−3.870
0.0007
***
(Very
significant)
Source: Authors’ calculations based on data from the
Agri-food data portal
Based on the data obtained in Figure 6 and Table
1, the negative dependence of the studied indicators
is manifested in all countries of the European
Union. We confirmed the process of beta
convergence in the EU countries, as evidenced by
the negative and statically significant value of the
beta coefficient (-86.08%) in the calculated
econometric model based on regression analysis.
significant, which confirms the absolute beta
convergence (Table 1). The coefficient of
determination is equal to 36.55%. Homoskedasticity
is one of the classical conditions of the linear
econometric model and is the requirement of finite
and constant variance of random perturbations and
residuals. Identifying homoscedasticity is necessary
to evaluate, that parameter estimates by the
econometric model did not lose some optimal
properties. We used the Breusch-Pagan test and
White's test to verify homoskedasticity, and we did
not reject the null hypothesis, so the model is
statistically significant and optimal. We can confirm
catching up with the better-developed countries by
the less developed countries.
The absolute beta convergence rate is 17.93%
and is not constant over time. However, the rate of
convergence is faster in the poorer and less
economically developed regions of the EU. Within
the EU, there is a process of beta convergence
across EU regions, and the general trend of
narrowing TFP differences continues. The time for
passing half the distance to the convergence of the
levels of regional development in terms of TFP is τ
= 4.21, and this is a fairly short period to achieve
sustainable economic development of countries,
[31].
0
2
4
6
8
10
12
14
16
0
0,01
0,02
0,03
0,04
0,05
0,06
0,07
0,08
2007 2008 2009 2010 2011 2012 2013 2014 2015 2016 2017 2018
Theil index Gini coefficient Coefficient of variation
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Peter Bielik, Stefaniia Belinska,
Tatiana Bullová, Yanina Belinska, Izabela Adamičková,
Natália Turčeková, Zuzana Bajusová
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Volume 20, 2023
Fig. 7: Beta-convergence in the EU-28 in years 2007-2018
Source: Authors’ calculations based on data from the Agri-food data portal
As can be seen in Figure 7, the change in the
level of TFP in the years 2007-2018 allows the
grouping of countries. The first group includes
countries with a high level of development (France,
Germany, Belgium, the Netherlands, and the United
Kingdom). They did not significantly change the
level of development but remained at the upper
stages of development. These countries lie under the
regression line. Slovak Republic is in the second
group, which includes countries with a relatively
lower level of development in comparison with the
EU average (Latvia, Lithuania, Estonia, Poland,
Hungary, Bulgaria), which developed at an
accelerated pace. These countries are above the
regression line. Thus, it can be assumed that these
countries are using the catch-up development
strategy within the framework of the convergence
policy applied in the European Union, [31].
Based on this, we can say that Slovakia is
convergent in the sense of absolute beta
convergence. For Slovakia, this means
strengthening the agrarian nature of the country's
economy, primarily through improving the quality
of agricultural development, especially labour
productivity and total factor productivity. An
increase in TFP is one of the decisive conditions for
the development of agricultural production, the
implementation of social transformations in rural
areas, and the improvement of the environmental
conditions, and material well-being of the
population. The dynamics of positive changes in
TFP in agriculture in Slovakia indicate significant
reserves for its increase: an increase in the share of
value added in the value of GDP; renewal of long-
term funds, the introduction of energy-saving
technologies, technical innovations; changes in
environmental factors; improvement of the
organization of production and institutional
infrastructure. Important criteria for technical
progress in the agricultural production of Slovakia
are the efficient use of land and animal husbandry.
Their rational use increases the efficiency of all
factors of production. It is technological progress
that increases the TFP and leads to savings in labour
time and the growth of agricultural production while
increasing the income of farmers.
The empirical assessment of economic
convergence and growth carried out points to
economic convergence of total factor productivity in
the EU countries. Countries initially characterized
by low productivity are catching up with countries
having high productivity. Despite a significant
disparity in TFP levels and growth rates among
countries, agricultural productivity in the EU-28
countries has grown. In addition to technological
progress, changing labour factors also contribute to
inter-country disparities in TFP levels and growth,
[31].
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Peter Bielik, Stefaniia Belinska,
Tatiana Bullová, Yanina Belinska, Izabela Adamičková,
Natália Turčeková, Zuzana Bajusová
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Volume 20, 2023
The TFP indices for the EU countries have
positive increased trends and similar dynamics of
the Gini index, Theil index, and Coefficient of
Variation support the presence of long-run
divergence among the EU countries. Absolute beta
convergence in the EU countries continued
continuously before, during, and after the financial
crisis. The absolute beta convergence among the
EU-28 countries is a consequence of the
transformational processes of the agroecological
transition in agriculture and requires state support
for the further growth of TFP, farm incomes,
improvement of the well-being of the population
and the environment, and in general the economic
development of agriculture and rural areas.
5 Discussion
A study, [34], based on unit root tests confirms the
evidence for TFP convergence in developing
countries. They found a trend towards TFP
convergence with the average TFP of OECD
countries. The study, [34], recommends that
countries should take care to improve TFP by
boosting R&D and improving technological
progress to further accelerate productivity growth.
The study, [35], also tests for absolute and
conditional convergence of total factor productivity
and real GDP per worker, using cross-section and
cross-section, time-series data. The findings support
both absolute and conditional β-convergence of total
factor productivity in 83 countries.
In, [36], explored trade openness and foreign
direct investment as the main determinants of TFP
convergence across 91 emerging economies over the
period 19602015. They concluded that a high
degree of openness promotes the growth and
convergence of TFPs, but policy action is needed to
stimulate trade activity and FDI flows.
The article, [37], applies conditional quantile
regression to a panel dataset of 17 OECD countries
to examine relative factor endowments and
technological advances, which are important drivers
of convergence in agricultural productivity levels in
developed countries. Capital deepening has been
found to have an impact on the technological gap in
different clusters of countries, but to increase the
TFP of agriculture in countries with a large amount
of land than in countries with a relatively large
amount of labor. That is, differences in relative
factor endowments within countries influence
domestic technological progress through capital
deepening and contribute to the growth of TFP in
agriculture and the convergence of productivity
across countries, [37].
The study, [38], examines the process of global
TFP convergence in the EU-15 regions in 1985-
2006 and finds that there is no overall process of
TFP convergence, as the dispersion of estimated
TFP levels has remained stable and constant over
time. Spatial dependence has been proven to be a
constant feature of the distribution of TFP over
time, but technology and the IT revolution also
affect regional disparities and convergence
processes.
Thus, our conclusions about the presence of
conversion processes in TFP between the countries
of the European Union and Slovakia coincide with
the studies of other scientific works.
6 Conclusion
Agriculture is a specific sector of the national
economy, which, is extremely important for the
economic development of the country as a whole, so
an objective assessment of the productivity of this
industry is an important economic task.
Several factors play a role in the development of
TFP (climatic, capital, land, and labour). To develop
effective measures of state regulation of the
agricultural sector, it is necessary to investigate the
causes and factors that cause changes in the level of
agriculture and conduct an in-depth analysis of the
use of production resources.
Labour productivity is the main driver of TFP
growth in the EU. Changes in the agricultural labour
force, jobs, and rural economic growth, are among
the main policy directions under the CAP and the
main drivers of TFP.
According to calculated regression models, we
are dealing with statistically significant TFP beta-
convergence and sigma-divergence of the
agricultural sector across the EU. We can see an
increase in TFP in analyse period in EU-28
countries and confirm a convergence process.
However, the slowdown of this process may
indicate the exhaustion of its potential, which will
require excessive costs for its continuation.
Therefore, it is possible to predict the onset of a
period of divergence when countries will maintain
differences in labour productivity levels and will use
this difference to develop integration and trade.
Given the fact that the TFP is also affected by
long-term changes in environmental factors, such as
changes in soil fertility, water quality, and climate,
the efforts for climate change mitigation could also
lead to TFP growth.
To further increase the level of TFP there is a
need for public support for investments in research
and development to help enhance the technical
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Peter Bielik, Stefaniia Belinska,
Tatiana Bullová, Yanina Belinska, Izabela Adamičková,
Natália Turčeková, Zuzana Bajusová
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Volume 20, 2023
progress in agriculture. The final policy
recommendations are to invest in innovation, in the
development and retraining of farm management
personnel. The introduction of new innovative
technologies and their competent use increases the
productivity of agriculture through the qualitative
use of production factors.
Acknowledgment:
This publication is the result of the project
implementation: „Scientific support of climate
change adaptation in agriculture and mitigation of
soil degradation” (ITMS2014+ 313011W580)
supported by the Integrated Infrastructure
Operational Programme funded by the European
Regional Development Fund (ERDF).
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Peter Bielik, Stefaniia Belinska,
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Natália Turčeková, Zuzana Bajusová
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Contribution of Individual Authors to the
Creation of a Scientific Article (Ghostwriting
Policy)
- Peter Bielik, Izabela Adamičková were engaged in
the collection of literature and material for writing
the theoretical part and made formal amendments
to the article.
- Stefaniia Belinska (*corresponding author),
Tatiana Bullová, and Natália Turčeková was
responsible for formulating and setting the goals
and objectives of the study. Was engaged in the
collection and processing of data, the choice of
methodology, and its description. Carried out the
statistical data analysis using the chosen
methodology and was engaged in the construction
of graphs. Writing the practical part and
conclusion. Engaged in the design and preparation
of articles for publication.
- Zuzana Bajusová, and Yanina Belinska prepared
and writing of the initial draft of the published
work, wrote an abstract and introduction, and
assisted in the analysis of graphs and structuring
of the theoretical part.
Sources of Funding for Research Presented in a
Scientific Article or Scientific Article Itself
This publication is the result of the project
implementation: „Scientific support of climate
change adaptation in agriculture and mitigation of
soil degradation” (ITMS2014+ 313011W580)
supported by the Integrated Infrastructure
Operational Programme funded the by the European
Regional Development Fund (ERDF).
Conflict of Interest
The authors have no conflict of interest to declare.
Creative Commons Attribution License 4.0
(Attribution 4.0 International, CC BY 4.0)
This article is published under the terms of the
Creative Commons Attribution License 4.0
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Peter Bielik, Stefaniia Belinska,
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Natália Turčeková, Zuzana Bajusová
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