Labor productivity in the agriculture, structural shifts and economic growth in the central and eastern European countries

Research and assessment of the scope and direction of changes in labor productivity in agriculture compared to other sectors of the economy. Analysis of the process of convergence of sectoral labor productivity and its impact on economic growth.

Рубрика Экономика и экономическая теория
Вид статья
Язык английский
Дата добавления 06.06.2023
Размер файла 212,9 K

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Static

Total growth

Within

Dynamic

Static

Total growth

growth

shift

shift

growth

shift

shift

growth

shift

shift

effect

effect

effect

effect

effect

effect

effect

effect

effect

Albania

0.52

-0.18

-0.13

0.21

0.13

0.09

0.22

0.44

0.93

0.32

0.11

1.36

Belarus

0.17

-0.05

-0.06

0.07

0.73

-0.08

-0.03

0.62

0.49

0.09

0.09

0.67

Bulgaria

0.08

-0.05

-0.04

-0.01

0.32

-0.06

-0.04

0.22

0.11

0.04

0.24

0.39

Czech

Republic

0.04

-0.02

-0.02

0.00

0.32

-0.04

-0.04

0.24

0.27

0.04

0.10

0.42

Estonia

0.16

-0.11

-0.02

0.03

0.55

-0.08

-0.04

0.43

0.66

0.13

0.14

0.94

Hungary

0.04

-0.02

-0.02

0.00

0.18

-0.01

-0.01

0.16

0.26

0.02

0.05

0.34

Latvia

0.19

-0.11

-0.03

0.05

0.34

-0.04

-0.04

0.26

0.80

0.20

0.16

1.16

Lithuania

0.19

-0.13

-0.05

0.01

0.64

-0.05

-0.02

0.56

0.67

0.24

0.23

1.15

Moldova

0.49

-0.25

-0.08

0.16

0.08

0.03

0.15

0.27

0.55

0.20

0.18

0.93

Poland

0.04

-0.02

-0.03

-0.02

0.35

-0.01

-0.01

0.34

0.43

0.13

0.18

0.74

Romania

0.15

-0.07

-0.05

0.04

0.49

-0.02

-0.02

0.45

0.59

0.35

0.30

1.24

Russian

Federation

0.17

-0.10

-0.04

0.03

0.47

-0.08

-0.06

0.32

0.35

0.10

0.17

0.62

Slovak

Republic

0.18

-0.12

-0.01

0.05

0.51

-0.04

-0.02

0.44

0.32

0.06

0.13

0.51

Slovenia

0.06

-0.03

-0.02

0.01

0.34

-0.06

-0.06

0.22

0.21

0.06

0.19

0.47

Ukraine

0.31

-0.15

-0.05

0.12

0.14

-0.01

-0.01

0.13

0.27

0.08

0.19

0.54

USA

0.01

0.00

0.00

0.00

0.11

-0.02

-0.05

0.04

0.23

0.02

0.06

0.30

France

0.02

-0.01

-0.01

0.00

0.07

-0.02

-0.05

0.00

0.09

0.01

0.09

0.19

Source: author's estimation

Industry is dominated by growth within the sector, dynamic and static structural effects are negative in all countries, except Albania and Moldova, where industrialization has taken place in a certain way. After the crisis, although productivity growth rates in industry have decreased, especially negative indicators of growth are visible within industry, dynamic structural effects have increased and amounted to 0.068, exceeding the overall growth of labor productivity in the industry by 0.04 on average across countries. Consequently, labor productivity in industry is growing at a higher rate than in the service sector and an influx of labor has begun.

In agriculture, where employment has fallen the most, the structural effects are negative in all countries, but the increase in labor productivity within this sector significantly exceeds their negative impact. The agricultural sector is the most dynamic sector of the economy in terms of labor productivity in the countries of Central and Eastern Europe. The Baltic States, Ukraine, Moldova, Slovakia, and Albania are among the leaders in terms of labor productivity growth in agriculture. Among the lagging countries there are Bulgaria, the Czech Republic, Hungary and Poland.

Similar studies were conducted by A. Dieppe and H. Matsuoka and their database consists of sectoral and aggregate labor productivity statistics for 91 countries, and 8 sectors (agriculture, mining, manufacturing, utilities, construction, trade services, transport and financial services, and government and personal services) covering the period from 1975 up to 2018. The results of the survey show that productivity growth in advanced economies had been almost entirely driven by within-sector productivity growth mainly in the manufacturing, transport and finance sectors. However, since the 2000s both within-sector and between-sector productivity growth have slowed. During the 2010s, the contribution of between-sectors slowed down due to small movement to higher productivity sectors such as manufacturing and trade [34]. Our results are very similar to surveys by M. McMillan et al. [32], and P. Dobrzanski and W. Grabowski [35].

The question of the convergence of labor productivity levels in various sectors of the economy is of serious interest among economists. As explained above, an increase in output per worker in the agricultural sector is key for structural transformation. Yet, agricultural productivity in developing countries tends to be significantly lower relative to the non-agricultural sectors. This fact is known as the agricultural productivity gap [38]. Implications for development are huge as reducing the gap may increase aggregate productivity and be growth-enhancing.

Considering the significant surge in labor productivity in agriculture, the convergence of this indicator with the economy average was expected. A. Dieppe and H. Matsuoka emphasize that agricultural productivity growth has been a significant contributor to aggregate convergence, whereas a catch-up in other sectors has only contributed a small amount to convergence [34]. The process of convergence of the level of productivity in the sectors of the economy means the equalization of the level of productivity in industry, the service sector and agriculture and converging with the average level of labor productivity in the economy as a whole. We analyzed this process using two indicators: 1) the ratio of labor productivity indicators in the agricultural sector to this indicator in the economy as a whole; 2) as well as o - convergence, which is determined by the ratio of the standard deviation of labor productivity indicators in each industry to the average value. Regarding the last indicator: the lower it is, the higher the level of convergence.

The data in Table 7 show the rapid convergence of labor productivity indicators by industry in the Baltic countries, Slovakia and Ukraine. The lack of convergence is observed in Belarus, Poland and Romania. Consequently, significant labor flows from agriculture to other industries are possible in these countries. If we compare the processes of convergence and overcoming the gap between labor productivity in agriculture and other sectors of the economy of the CEE countries with developed economies, we can conclude that in the USA these processes are carried out at a very fast pace, the o-convergence rate is especially rapidly decreasing, in France the convergence of productivity is somewhat slower. Estonia, Slovakia, and Ukraine have reached US convergence rates.

At the end of the study, we determined the strength of the impact of labor productivity in the sectors of the economy on the well-being of the population, which we measure through GDP per capita and also on the economic growth. We used the technique of panel regressions using GLS (generalized least square) estimator.

Models based on panel data include a number of observations of the same units (firms, industries, countries) that are collected over a period of time. Panel data are typically collected at the microeconomic level, but it is becoming increasingly popular at the applied level to combine the time series of several countries or industries to analyze them simultaneously. Having a series of time-repeated observations of the same structural units allows economists to define specifications and calculate more complex and realistic models than cross-sectional or time series databases and models allow [39, pp. 310-320].

Table 7. Convergence of sectoral labor productivity in CEE countries, 1996-2019

Country

Ratio of agriculture productivity to total productivity of economy

o-convergence of sectoral labor productivity

1996

2019

1996

2019

Albania

0.668

0.520

0.749

0.417

Belarus

1.146

0.926

0.186

0.190

Bulgaria

0.501

0.738

0.681

0.260

Czech Republic

0.422

0.724

0.484

0.245

Estonia

0.333

0.872

0.582

0.111

Hungary

0.610

0.830

0.329

0.180

Latvia

0.343

0.679

0.524

0.285

Lithuania

0.328

0.511

0.583

0.438

Moldova

0.378

0.673

0.762

0.295

Poland

0.270

0.276

0.622

0.629

Romania

0.280

0.288

0.669

0.641

russian

federation

0.372

0.764

0.494

0.286

Slovak Republic

0.183

1.073

0.818

0.064

Slovenia

0.321

0.626

0.662

0.340

Ukraine

0.356

0.908

0.553

0.126

USA

0.612

0.860

0.258

0.084

France

0.426

0.658

0.441

0.223

Source: author's estimation

An important advantage of panel data compared to time series or cross-sectional data sets is that they allow identifying certain parameters or questions, without the need to make restrictive assumptions. Panel data make it possible to analyze changes on the individual level. That is, panel data are not only suitable to model or explain why economic units behave differently, but also to model why a given unit behaves differently at different time periods.

We apply for all variables the index i for economic units (countries) i = 1,…, N and t for the time period (t =1., T). In general, the linear model has the form:

where Pit measures the partial effects of xit in period t for country i. The standard assumption used in many empirical studies is that pitis a constant for all i and t. This can be written:

where xit - is a vector of explanatory variables that does not include a constant.

This means that the effect of changes in хis the same for all economic units and periods. The value of ai reflects the influence of these variables, which are unique to each unit іand is constant throughout the period. In the standard case, let's assume that sit is an error that is an independent quantity that is identically distributed among countries and time periods with zero mean and variance a2. If we consider at as N fixed unknown parameters, model (2) refers to the standard model with fixed effects.

The computation of goodness-of-fit measures in panel data application is somewhat uncommon. One reason is the fact that one may attach different importance to explaining the within and between variation in the data.

The total variation in yit can be written as the sum of the within variation and the between variation, that is:

where ? - denotes the overall sample average.

For example, the fixed effects estimator is chosen to explain the within variation as well as possible, and thus maximizes the «within R2» given by

where and corr2 denotes the squared correlation coefficient.

The between estimator, being an OLS estimator in the model in terms of individual means, maximizes the «between R2»

rural economy labor productivity

where The OLS estimator maximizes the overall goodness-of-fit and thus the overall, which is defined as:

where Studies have shown that panel data estimates are more effective in most cases than when the same amount of data is available, and the data is generated by selecting different units in each time period. Models with panel data are more stable with respect to missed variables, measurement errors and the presence of endogenous variables among regressors.

For the analysis, we took several periods of time: the entire period from the beginning of the reforms to now 1991-2020, the period of rapid economic growth 2002-2008, as well as the broader period from macroeconomic stabilization to the world currency crisis of 1996-2008 and the period after the world crisis of 2009-2020 (Table 8). The study included the same 15 countries as before. We use the World Development Indicators Database of the World Bank for this period [13].

For estimation of this model we used panel GLS regression with fixed effects for all periods. Dependent variable is GDPCti - Gross Domestic Product per capita in period t; independent variables: agriproductivti - labor productivity in agriculture in period t; indproductiva - labor productivity in industry in period t; servproductivti - labor productivity in service in period t.

Table 8. Level of welfare and sectoral labor productivity in CEE countries, 1991-2020

Independent

variables

Dependent variable GDPCti

For period 1991-2020

For period 2002-2008

For period 1996-2008

For period 2002-2020

For period 2008-2020

agriproductivti

0.058

0.04

0.07

0.037

0.058

(6.01)

(2.13)

(4.47)

(2.86)

(3.35)

indproductivti

0.25

0.16

0.23

0.22

0.25

(19.68)

(4.99)

(11.84)

(10.52)

(8.77)

servproductivti

0.266

0.50

0.29

0.36

0.39

(15.84)

(11.07)

(10.76)

(13.2)

(11.85)

Constantti

-1710.7

-5190.4

-2157.9

-3104.4

-5227

(-7.57)

(-8.5)

(-6.8)

(-6.1)

(-6.87)

Within R2

0.94

0.92

0.93

0.84

0.80

Between R2

0.97

0.95

0.96

0.96

0.96

Overall R2

0.96

0.95

0.96

0.95

0.96

F-test

2188.8

293.9

45.93

398

199.3

Number of

397

90

152

269

179

observations

Note. In parenthesis t-statistic. Source: author's estimation

The results of the study showed that over the entire period, the impact of the level of labor productivity in the service sector is almost the same as the impact of labor productivity in industry: an increase of 1000 USD in labor productivity increases GDP per capita by 250-260 USD (Table 8). The impact of labor productivity in agriculture is five times lower. The statistical significance of the results is quite high. If we consider the period of economic growth (2002-2008), the impact of labor productivity in the service sector on GDP per capita increases by 2 times, and the impact of a similar indicator of industry falls significantly. In general, in the pre-crisis period of 1996-2008, the impact of sectoral labor productivity on general well-being is similar to the indicators for the entire period of survey. In the post-crisis period, the coefficient determining the impact of labor productivity in the service sector increased from 0.29 to 0.39.

We obtained slightly different results when studying the impact of the growth of value added in the economic sectors on the economic growth rates in the economy (Table 9). The periods during which the research was conducted were the same as in the previous case. Dependent variable is GDPCGti - Gross Domestic Product growth in period t; independent variables: agrigrowthti - growth of value added in agriculture in period t; industrygrowthti - growth of value added in industry in period t; servicegrowthti - growth of value added in service in period t.

During the entire period of post-socialist economic growth, the dominant factor was the growth of value added in industry with a coefficient of 0.52, which indicates that an increase of value added in industry by 1 percentage point will cause an increase of 0.5 percentage points in the rate of economic growth, an increase in value added in the service sector affects it with a coefficient of 0.11, in the agricultural sector with a coefficient of 0.04.

We also analyzed the period before the accession of CEE countries to the European Union and after the accession to the present time and compared. Thus, the impact of the growth of value added in industry decreased from 0.54 to 0.41, and the growth of value added in the service sector increased from 0.06 to 0.41, and in the agricultural sector, the growth of value added changed from a negative and statistically insignificant impact before the accession of countries to the EU to a positive and statistically significant impact on economic growth with a coefficient of 0.065.

Table 9. Economic growth and growth of sectors of economy in CEE countries, 1991-2020

Independent

variables

Dependent variable GDPCGti

For period 1991-2020

For period 1991-2003

For period 1996-2008

For period 2002-2020

For period 2008-2020

agrigrowthti

0.04

-0.001

0.027

0.065

0.066

(4.34)

(0.95)

(1.70)

(9.88)

(7.92)

industrygrow thti

0 52

0 54

0 44

041

0 42

(30.56)

(15.84)

(12.90)

(29.8)

(26.14)

servicegrowthti

0.11

0.06

0.07

0.41

0.33

(9.15)

(3.71)

(4.72)

(21.2)

(15.45)

Constantti

1.15

0.82

2.19

0.64

0.568

(8.83)

(3.01)

(7.72)

(6.33)

(5.47)

Within R2

0.78

0.75

0.56

0.91

0.91

Between R2

0.76

0.85

0.62

0.58

0.67

Overall R2

0.78

0.78

0.57

0.89

0.89

F-test

474.7

113.4

203.11

882.14

545.1

Number of

397

128

152

269

179

observations

Note. In parenthesis t-statistic. Source: author's estimation

The results of the study of the pre-crisis period from 1996 to 2008 and after the crisis period indicate an increasing impact of the growth of value added in the services sector (the coefficient increased by 4 times) and agriculture (the coefficient increased by 2 times). The impact of the increase in value added in industry is decreasing, but remains the highest on the economic growth of countries.

Therefore, we have confirmed that the agricultural sector is gaining weight in the economic growth of the CEE countries, the influence of the service sector is increasing, although together they do not exceed the influence of the growth of value added in industry. Such research results encourage the implementation of an effective industrial policy that would contribute to raising labor productivity in the agricultural sector and the service sector. Governments should stimulate the inflow of investment in agriculture to catch up with productivity in industry, as well as to preserve and increase the fertility of arable land in the long term, as well as to improve cropping patterns.

In general, structural changes in the economy are a rather painful process that affects the way of life of large groups of the population, changes in their place of residence and employment. S. Kuznets pointed out that government policy and institutional change must minimize costs and reduce resistance to the structural changes needed to achieve high economic growth [1]. M. McMillan et al. emphasized that the speed with which this structural transformation takes place is the key factor that differentiates successful countries from unsuccessful ones [32]. Structural changes are designed to reduce the level of imbalances in the economy, use resources more efficiently and allocate them in certain areas to accelerate technical change and achieve dynamic efficiency of society.

Our research made it possible to obtain significant results regarding the evolution of labor productivity and structural shifts in the countries of Central and Eastern Europe for the entire period from the beginning of market reforms in the 1990s. We paid special attention to the study of the structural transformation of agriculture and its impact on changes in labor productivity, identified the reasons for the initial decline of the agricultural sector, and then for its sharp rise. The research period covered the time of the economic boom - 2002-2008, as well as the post-crisis recession, which covered the whole world. The currency and financial crisis of 2008, as our research showed, was a turning point and the reason for the slowdown in economic growth and, accordingly, a decrease in labor productivity in the countries of Central and Eastern Europe. This global shock caused significant structural transformations in the sources of labor productivity growth in general in the economy. Labor productivity in agriculture grew in a number of countries at a significant pace and, in fact, agriculture became the dominant sector along with services in raising the productivity of the economies of Central and Eastern Europe.

Industry got the biggest hit, although the structural dynamic effect began to grow, and in certain branches of industrial production, there was an increase in labor productivity while the general process of decreasing employment was observed.

The growth rate of labor productivity for the period 1996-2019 was very high, on average - 5%, which was 3.3 times higher than in the USA and 6 times higher than in France, which indicates a catch-up, which the CEE countries have achieved in their economic development, increasing the level of added value per employee by 2.2 times on average over 23 years. In the pre-crisis period of 1996-2008, the CEE countries demonstrated extremely high growth rates of labor productivity - 6.25%, which sharply decreased in the post-crisis period by almost 3 times to 2.09% per year. In the USA, the rate of labor productivity fell by only 1.5 times, and in France, such a phenomenon was not observed at all.

What sectors of the economy were dominant in this impressive increase in labor productivity in the economy? Labor productivity grew at the fastest rate in the agricultural sector of the economy, on average almost 12% per year. The highest achievements were in Slovakia - 48.2%, Estonia - 21.5%, Latvia - 14.8%, Lithuania - 11.6%, Ukraine - 14.2%. Analysis by country shows that agriculture is dominant in terms of labor productivity growth rates in almost all countries, except for Poland, Belarus, Bulgaria and Albania. Slovakia, Estonia and Ukraine showed particularly phenomenal results.

In general, during the period from 1996 to 2019, the growth of labor productivity in the economy by almost 66% was provided by the service sector, the contribution of industry is measured by 30.4%, and agriculture - 3.6%, respectively. If we consider the period before and after the crisis, we will find certain differences in the sectoral structure of labor productivity growth. During the period of economic boom of 1996-2008, the contribution of the service sector was 59%, industry - 38%, and agriculture 2.3% on average for the sample. After the crisis of 2008, the contribution of the service sector increased significantly, to almost 78%, and industry fell more than 2 times, to 18.8%, and the contribution of agriculture grew to 3,4%, and in almost all countries except Estonia and Hungary. It can be stated that the growth of labor productivity in agriculture in the post-crisis period was the highest. The growth within the industry in all sectors of the economy in the CEE countries occupies a dominant position and its share is on average 88.5%, and the structural effects are as follows: the dynamic effect is almost 1%, the static effect is 10,4%.

After the crisis, the dynamic effect has become negative, the static effect has increased to 14%, in the pre-crisis period the dynamic structural effect was almost 2%, and the static effect was 9.9%. In most countries, the dynamic effect has negative values. This means that labor force flows to the sector with lower productivity.

During the entire period of post-socialist economic growth, the dominant factor was the growth of value added in industry with a coefficient of 0.52, which indicates that an increase of value added in industry by 1% point will cause an increase of 0,5% points in the rate of economic growth, an increase in value added in the service sector it affects with a coefficient of 0.11, in the agricultural sector with a coefficient of 0.04.

We also analyzed the period before the accession of CEE countries to the European Union and after the accession to date and compared. Analysis showed that the impact of the growth of value added in industry decreased from 0.54 to 0.41, and the growth of value added in the service sector increased from 0.06 to 0.41, and in the agricultural sector, the growth of value added changed from a negative and statistically insignificant impact before the entry of countries the EU to a positive and statistically significant impact on economic growth with a coefficient of 0.065. The results of the study of the pre-crisis period from 1996 to 2008 and after the crisis period indicate an increasing impact of the growth of value added in the services sector (the coefficient increased by 4 times) and agriculture (the coefficient increased by 2 times). The impact of the increase in value added in industry is decreasing, but remains the highest on the economic growth of countries.

Especially interesting processes took place in the agriculture of Ukraine. We carried out a detailed analysis of Ukrainian agricultural development, starting from 2000. The increase in world prices for food products in 2008-2010 and maintaining its high level during the following years, as well as the growing demand for technical crops for the needs of the energy sector acted as the main factors of demand, in our opinion, caused the rise of the agricultural sector in Ukraine. In addition, agricultural production does not require significant investments in fixed capital, as well as access to new technologies of grain production, high-yield crop varieties, seed material, pesticides and mineral fertilizers. Besides, new imported agricultural machinery allowed increasing dramatically labor productivity in crop production. All the above - mentioned factors affected the supply of agricultural products and increased efficiency of their production. The change in the structure of cultivated areas in favor of industrial crops increased the export potential and profitability of agricultural production.

In almost all CEE countries, the contribution of agricultural production to total labor productivity has increased over the past 12 years, although the level of labor productivity still falls short of the level of other sectors. In further research, it will be valuable to determine spillovers of technology between industrial sector and agricultural sector, as well as the impact of innovative technologies on employment in agricultural production in the countries of Central and Eastern Europe, influence of global COVID-19 pandemic on agriculture development and also the prospects of structural changes in the agricultural sector of Ukraine in the post-war period.

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