Management & Economics Research Journal ISSN 2710-8856 (Online) ISSN 2676-184X (Print) Vol. 3 No. 2 (2021), pp. 1-21 https://doi.org/10.48100/merj.2021.159 Faculty of Economics, Commercial & Management Sciences, Ziane Achour University of Djelfa, BP 3117, Djelfa - Algeria 1 www.mer-j.com Sectoral Approach in Output Growth Decomposition and its Determinants in Europe Maja Bacovic 1 1 Prof. Dr., University of Montenegro, Faculty of Economics (Montenegro)  majab@ucg.ac.me Published: 01 -09 -2021 Available online: 23 -04-2021 Accepted: 23 -04 -2021 Received: 10 -03 -2021 How to Cite: Bacovic, M. (2021). Sectoral Approach in Output Growth Decomposition and its Determinants in Europe. Management & Economics Research Journal, 3(2), 1-21. https://doi.org/10.48100/merj.2021.159 Abstract: The structural changes in Europe have occurred over the last several decades, reducing output share in the goods sector, while increasing its share in overall services. Applying the growth accounting approach, we decomposed output growth in the economy while following the sectoral approach, in ten individual sectors in twenty-six European countries, from 2000 to 2019. Our analysis shows that total factor productivity has accounted for almost half of the European countries' growth in output in the last two decades, with the other half primarily accounted for by increases in fixed asset growth and employment growth, while its variations among sectors are significant. The output growth in the services sector is significantly more driven by employment growth than in the goods sector, leading to overall employment growth in the economy. Applying the panel pooled OLS model, we found that the relevance of expenditures for research Corresponding author: University of Montenegro, Faculty of Economics (Montenegro). [ majab@ucg.ac.me] ©2021 the Author(s). This is an open-access article distributed under the terms of (CC BY-NC 4.0) which permits use, distribution and reproduction in any medium, provided the original work is properly cited and is not used for commercial purposes. https://orcid.org/0000-0002-8865-3924 mailto:majab@ucg.ac.me https://doi.org/10.48100/merj.2021.159 mailto:salman.phdmgt80@iiu.edu.pk mailto:majab@ucg.ac.me https://creativecommons.org/licenses/by-nc/4.0/legalcode https://doi.org/10.48100/merj.2021.159 https://crossmark.crossref.org/dialog/?doi=10.48100/merj.2021.159&domain=pdf&date_stamp=2021-09-01 http://www.mer-j.com/ Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 2 and development is high and positive in all sectors, but higher in industry and knowledge-intensive services (information and communication, scientific activities) compared to all sectors average. To the best knowledge of the author, this is the first study presenting output growth decomposition estimates at the sectoral level for a selected group of countries, but also the first study presenting estimates of TFP in the total economy for a selected time period. In addition, this is the first study that presents the relevance of investment in research and development at the sectoral level in this specific time period and group of countries. Results of the study may be used in defining national policies priorities, as there are varieties among sectors in terms of their impact on employment and economic growth. Keywords: Output Growth Decomposition, Sectoral Approach, Total Factor Productivity, Research and Development. JEL Codes: O47, O52. 1. Introduction The structural changes in Europe have occurred over the last several decades, reducing output share in the goods sector, while increasing its share in overall services. The sectoral approach in the analysis of economic growth is relevant as it might be determined by its productive structure. Analysis at the sample of twenty-six European countries in the last two- decade period shows that the average output share in agriculture declined from 2.7% in 2000 to 2.18% in 2019; in the industry from 21.63% to 20.27% respectively; in construction from 6.36% to 5.4%, while it increased in the overall services sector from 71.1% to 72.5%. The fastest-growing services sectors are knowledge-intensive, such as information and communication, with output share growth from 3.3% in 2000 to 6.6% in 2019, and professional, scientific and technical activities, from 7.9% to 10.4% respectively1. The sectoral perspective in economic analysis is directly linked to structural changes. Kratena (2005), points out “the shift of resources, output and employment between different sectors accompanying the process of economic growth has been recognised as a possible challenge for adjustment in industrialised economies”. Moro (2015), suggested that both the growth rate and volatility of an economy might be related to its productive structure. 1 Source: Author’s calculation based on Eurostat data. pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 3 In economic analysis, it is generally accepted that productivity growth is a major source of economic growth and welfare development. As in Margaritis et.al (2005), “labour productivity has accounted for roughly half of the growth in per capita GDP in OECD countries over the last two decades of the XX century, with the other half primarily accounted for by increases in labour utilisation (changes are the demographics, unemployment and labour force participation rates)”. Nelson (2000) points out that “in the early 1950s, empirical work made it clear that growth of total factor productivity accounted for the lion’s share of the measured increases in output per worker. Technological advancement has been proposed as a major force behind TFP growth’. Further, quoting work by Schmookler (1952), Schultz (1953), Fabricant (1954), Kendrick (1956) and Abramovitz (1956), concluded that “the growth output experienced in the United States after World War II was significantly greater than reasonably can be ascribed to input growth”. It was explained by technological advancement, changing composition of the labour, investments in human capital and reallocation of resources from lower to higher productivity activities and economies of scale. Observing structural changes toward rapid growth in the services sectors in European countries, this research aims to decompose output growth applying growth accounting approach, in all countries from the sample (twenty-six European countries) for the period from 2000 to 2019, at the economic level but also the individual sector level, into the following components: capital growth, labour growth, capacity utilisation growth and TFP growth. Therefore, we will be able to investigate whether the expansion of a specific sector is capital, labour or TFP growth driven. In addition, as the expansion of scientific and technical knowledge that raises the productivity of labour and other inputs of production have been seen as a source of persistent growth in income per person, applying a panel pooled OLS (fixed effects) model, we estimate the significance of the investment in research and development at the sectoral level. In this study, we use a sample of twenty-six European countries2 and the period from 2000 to 2019. The sample was reduced to twenty-six countries because of data availability. The growth accounting approach was applied to estimate production 2 Belgium, Czech republic, Denmark, Germany, Estonia, Ireland, Greece, France, Italy, Cyprus, Latvia, Lithuania, Luxemburg, Hungary, Malta, Netherlands, Austria, Poland, Portugal, Romania, Slovenia, Slovakia, Finland, Sweden, Norway United Kingdom. Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 4 input growth and total factor productivity (TFP) in the total economy and at the sectoral level, for ten sectors: agriculture, forestry and fishing; industry (except construction); construction; wholesale and retail trade, transport, accommodation and food service activities; financial and insurance activities; information and communication; real estate activities; professional, scientific and technical activities; administrative and support service activities; public administration, defence, education, human health and social work activities; arts, entertainment and recreation; other service activities; activities of the household and extra-territorial organisations and bodies. Estimation of the contribution from inputs and total factor productivity growth is important because of its contribution to long-term income per capita growth. From the computation methodological point of view, since the work of Abramovitz (1956) and Solow (1957), the growth accounting approach has been applied in a significant number of studies. To the best knowledge of the author, this is the first study presenting output growth decomposition estimates at the sectoral level for a selected group of countries, but also the first study presenting estimates of TFP in the total economy for a selected time period. In addition, this is the first study that presents the relevance of investment in research and development at the sectoral level in this specific time period and group of countries. This study is comprised of six sections. After the introduction, the second part provides an overview of the research available in the literature. The third section relates to the empirical analysis of structural changes and growth in Europe. The following section relates to the data, research methodology and empirical results. The fifth section provides comparisons with the results from other studies. The concluding remarks and recommendations for future research are presented in the final section. 2. Literature review Due to its relevance, the availability of the literature on sources of growth and structural changes is vast. We list only selection from the most recent, tackling the issues studied in this paper. On the importance of total factor productivity (TFP) progress, there is much evidence in the literature. Barro (1998) points out that technological progress is, in fact, crucial to the long-term per capita growth that the U.S. economy has been able to sustain for two centuries. Based on an extensive literature review, Kim and Loayza (2017), categorised TFP determinants into five components: innovations, education, market efficiency, physical infrastructure and institutional infrastructure. In their research, they pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 5 concluded that “variation of TFP across countries for the last three decades is explained the most by the physical infrastructure index, followed by the education index and the market efficiency index at a similar level, the innovation index and the institutional infrastructure index”. Romer (2001) stressed that the growth of knowledge is the most important determinant of output growth for the last century. Becker (1993) concluded that “It is clear that all countries which have managed persistent growth in income have also had large increases in the education and training of their labour force. The systematic application of scientific knowledge to the production of goods has greatly increased the value of education, technical schooling, and on-the-job training as the growth of knowledge has been embodied in people – in scientists, scholars, technicians, managers, and other contributors to output”. In their study, Eichler et al. (2006) found that “higher taxes reduce productivity growth, increase innovation resources, and better intercontinental accessibility leads to higher productivity growth”. Productivity growth is also influenced by global trends, industrial structures and spatial spillover effects. Holmes and Schmitz (2010), found that industries experiencing dramatic changes in their competitive environment were forced to increase productivity, or only plants that have large productivity gains survive competition increases. Wu, Guo, and Marinova (2017) presented new evidence on TFP and economic growth in China and found that “productivity growth is the main driver of economic growth in all three sectors: agriculture, manufacturing and services”. They also found that technological progress influenced TFP growth significantly. The availability of literature on structural transformation and its impact on growth is also vast. Morro (2015), studied the impact of the sectoral composition of gross domestic product on cross-country differences in GDP growth and volatility, and concluded that “an increase in the share of services in GDP reduces both aggregate total factor productivity (TFP) growth and volatility, thus reducing GDP growth and volatility”. Foster- McGregor and Verspagen (2017) presented a study on decomposing TFP growth in manufacturing and services, with a sample of 40 countries and a period from 1995 to 2009. Their research has shown that “TFP growth in manufacturing tends to outpace that in services in most economies”. They found that some exceptions exist, particularly in Asian countries, suggesting that “productivity growth in services need not always be lower than that in manufacturing”. McMillan, Rodrik and Verduzco-Gallo (2014), analysing Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 6 structural changes in Africa and Latin America, found that labour flows from low productivity to high productivity activities are a key driver of development. In addition, since 1990, structural change has been growth reducing, with labour moving from low to high-productive sectors. Levenko et.al (2019) presented a study on TFP growth accounting in eleven countries from Central and Eastern Europe, for the period 1996- 2016. Their study showed that on average, TFP growth accounted for one- third of GDP growth, while capital deepening accounted for approximately half. The growth accounting methodology was also applied in research by Baier, Dwyer and Tamura (2006), using a sample of 145 countries and data for more than a hundred years for 23 countries in the sample, showing that total factor productivity explains 14% of average output growth per worker. By total factor productivity, they mean “changes in technology, institutional change, failure of the twin assumption of constant returns to scale and competitive factor markets, and other factors”. Bacovic (2021) presented a study on the total factor productivity in middle-income Balkan countries, applying a sectoral approach. Buera and Kaboski (2012) found a positive relationship between high-skilled labour and the services sector. They developed a theory in which “demand shifts toward more skill-intensive output as productivity rises, increasing the importance of market services relative to home production. Their theory predicts a rising level of skills, skills premium and the relative price of services that are linked to this skill premium”. 3. The output, employment and labour productivity in Europe – an empirical analysis 3.1. Sector’s definition, data and sample Following NACE classification, sectors are defined as follows: agriculture, forestry and fishing (later agriculture), industry, construction and service sectors disaggregated into seven subsectors: wholesale and retail trade, transport, accommodation and food service activities; financial and insurance activities; information and communication; real estate activities; professional, scientific and technical activities; administrative and support service activities; public administration, defence, education, human health and social work activities; arts, entertainment and recreation; other service activities; activities of the household and extra-territorial organizations and bodies. All data were extracted from the Eurostat database. A detailed presentation of the indicators used in this research is presented in section 4.1. Analysis was based on the sample of 26 European countries, from 2000 pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 7 to 2019.3 3.2. Dynamics of the structural changes in Europe's output, labour productivity and employment Based on annual data on gross value added (output) in all NACE activities and specific sectors, from a sample of twenty-six European countries, from 2000 to 2019, we estimated the average share of the gross value added (GVA) in specific sectors concerning the economy’s gross value added. We observed that industry, trade (including transportation and tourism) and public administration have the largest share in total output, followed by construction and other services (finance, ICT, real estate). Sectors of public administration and trade, followed by industry are the largest employers, with the higher share of employed in relation to total employment. Labour productivity (measured as output per person employed) is the highest in the finance, information and communications and industry sector (above total average), while the lowest is in agriculture (Table 1). 3 All data are available on request. Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 8 Table 1. Structure of Europe's output and employment; labour productivity, sample average, 2000-2019 Share in total output Share in total employment Labour productivity (thousand 2010 constant euro) average annual 2000-2019 average annual 2000-2019 average annual 2000-2019 All = 100 All NACE activities 100.00% 100.00% 45,83 1.00 Agriculture, forestry and fishing 2.42% 6.62% 23,39 0.51 Industry (except construction) 20.38% 17.85% 57,70 1.26 Construction 6.05% 7.31% 36,16 0.79 Wholesale and retail trade, transport, accommodation and food service activities 20.89% 24.70% 37,11 0.81 Financial and insurance activities 5.89% 2.91% 91,57 2.00 Information and communication 4.90% 2.67% 81,57 1.78 Real estate activities 9.54% 0.99% - - Professional, scientific and technical activities; administrative and support service activities 9.16% 9.87% 41,91 0.91 Public administration, defence, education, human health and social work activities 18.01% 22.32% 36,66 0.80 Arts, entertainment and recreation; other service activities; activities of the household and extra- 3.12% 4.77% 28,70 0.63 Observations 498 459 459 459 Source: Author’s calculations, based on Eurostat data (common sample) Structural changes in Europe have an impact on GDP growth. As in Panel 1, industry growth has the strongest impact on GDP growth, while several services sectors also positively determine it, as information and communication, trade, finance and professional, scientific and technical services. The growth of the share of public administration in total output has a strong negative impact on GDP growth. pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 9 .0 .1 .2 .3 .4 .5 -.2 -.1 .0 .1 .2 .3 Output growth A griculture, % output Indus try, % output Cons truction, % output Trade at. el, % output Finance, % output ICT, % output Real es tate, % output Profess ional, s cience at. el, % output Public adminis tration et. al, % output A rts, % of output Panel 1. Structural changes and output growth, 1920-2019 Source: Author’s calculations (based on Eurostat data) Descriptive statistics analysis shows that investigation of the sources of output growth on a sectoral level is important, to understand future development trends. 4. Data, methods and empirical results 4.1. Data Data used in the research were extracted from the Eurostat database (data on country’s level), with annual frequency. Following indicators were used: gross value added (all NACE activities, chain-linked volumes (2010), million euro), also for individual activities: agriculture, forestry and fishing; industry (except construction); construction; wholesale and retail trade, transport, accommodation and food service activities; financial and insurance activities; information and communication; real estate activities; professional, scientific and technical activities; administrative and support service activities; public administration, defence, education, human health and social work activities; arts, entertainment and recreation; other service Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 10 activities; activities of the household and extra-territorial organizations and bodies4. Following NACE classification, data on total fixed assets (current replacement costs, million euro)5 were adjusted to chain-linked volumes (2010), million euro, applying price index (implicit deflator), 2010=100, euro, for all activities individually. Employment data (total employment domestic concept) were used also classified in accordance with NACE6. Data on expenditures for research and development (originally extracted as share in GDP)7, were adjusted to its nominal values (constant prices, 2010 euro). 4.2. Methodology - Growth accounting approach Applying the growth accounting approach, we will decompose output growth rates in all sectors and ten individual sectors (as listed in section 3.1), into four components: capital stock growth, employment growth, capital utilization growth and total factor productivity growth. The growth accounting approach was selected due to its many advantages. “The growth accounting approach provides a filing system that is complete, in the sense that all phenomena that affect economic growth must do so through input factor quantities, relative factor intensities or total factor productivity growth, either singly or in combination. Second, the results of the growth accounting exercise may point to areas where parametric studies are likely to be fruitful” (Norsworthy, 2005). As in Levenko et.al (2019), the starting point is a general specification of aggregate production function: = (ℎ , ) (1) Where stands for output in the period t, is the capital stock in the period t, h ∈ (0,1) is the rate of capital utilization in the period t; L is employment and A is Total Factor Productivity (TFP). 4 National accounts aggregates by industry (up to NACE A*64) [nama_10_a64], extracted on 03/03/2021. 5 Cross-classification of fixed assets by industry and by asset (stocks) [nama_10_nfa_st], extracted on 04/03/2021. 6 National accounts employment data by industry (up to NACE A*64) [nama_10_a64_e], extracted on 04/03/2021. 7 Intramural R & D expenditure (GERD) by sectors of performance [rd_e_gerdtot], extracted on 04/03/2021. pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 11 If production function is time differenced and perfect competition and constant returns to scale are assumed, the growth rate of output is equal to (Levenko et.al, 2019): ∆ ≈ (1 − ) ∆ + ∆ + ∆ + ∆ (2) The operator ∆ denotes the first difference, and is the elasticity of output to capital utilized in the period t. the equation (2) “decomposes output growth into components steaming from growth in employment, growth in available capital, growth in capital utilization and TFP growth” (Levenko et.al, 2019). As in Bacovic (2021), at the sectoral level, the specification of aggregate production function is: , = , , (ℎ , , , , ) (3) The variable , is value added (output) in the period t in sector i, i=1, 10(1 - agriculture, forestry and fishing; 2- industry (except construction); 3 - construction; 4 - wholesale and retail trade, transport, accommodation and food service activities; 5 - financial and insurance activities; 6 - information and communication; 7 - real estate activities; 8 - professional, scientific and technical activities; administrative and support service activities; 9 - public administration, defence, education, human health and social work activities; 10 - arts, entertainment and recreation; other service activities; activities of the household and extra-territorial organizations and bodies). , is the sector’s capital stock (fixed asset) available at the beginning of the period t in sector i, while ℎ , ∈ (0,1) is the rate of capital utilization in the period t so ℎ , is the capital actually utilized for the production of , ; , is employment in sector i and , is Total Factor Productivity (TFP) in sector i. The growth rate of output is equal to: ∆ , , ≈ 1 − , ∆ , , + , ∆ , , + , ∆ , , + ∆ , , (4) Data for output, capital stock (total fixed asset) and employment were available, while capital utilization must be computed. In case of missing data on capital utilization, there are few options. Levenko et.al (2019), used the capacity utilization in manufacturing as a proxy for the utilization of capital in the entire economy, but we will follow Solow’s Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 12 approach and use change in the unemployment rate (Solow, 1957) as a unique indicator in the estimation of TFP on total economy and sectoral level. In this research, we applied constant estimates for output to capital elasticity, equal to 0.33, as suggested as typical value by Romer (2001), on total economy and sectoral level, assuming that: , = (5) 4.3. Model, panel pooled OLS (simple and fixed effects) We may express aggregate production of an economy as a function of capital stock, labour and TFP: = ,0 < + < 1 (6) where Yt represents the aggregate production of the economy at the time t, At is the total factor productivity, and Kt and Lt represent the capital stock and labour, respectively. The constants α and β represent the share of capital and labour in income. Further, we will express TFP as a function of investment in research and development and other exogenous factors Ct: = ( , ) = (7) After combining equations 6 and 7, we express the production function as: = (8) where α, β and  represent the elasticities of production with respect to the inputs of production: Kt, Lt and . After taking natural logs, the following equation is obtained: = + + + + , (9) Where c is the intercept, α, β and  are constant elasticities and εt is the error term. pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 13 4.4. Empirical results 4.4.1. Growth accounting method Applying the growth accounting method, presented in section 4.2, on a sample of twenty-six European countries and the period from the year 2000 to 2019, we decomposed output growth rates in all NACE activities and ten individual NACE activities8 (Table 2). We estimated contribution to gross value added growth from capital stock growth, employment growth and total factor productivity growth, for the total economy and respective services sectors. In this section, we will present the average value for all variables. Average annual output growth (gross value added) in the overall sample from 2000 to 2019 was 2.27%, fixed asset growth 1.99%, employment growth 0.72% and total factor productivity growth 1.12%9. Comparative analysis of gross value added growth in all NACE activities (total economy) and respective sectors show that output growth was higher in relation to all sectors mean in information and communication sector (IT), professional, scientific and technical activities (PST) and trade, transport and accommodation (TTA), while in other sectors was below the average. Fixed asset growth (capital stock growth) was also the highest in those sectors (but also above average in agriculture and industry), while IT and PST activities generated the highest employment growth. Total factor productivity growth was the largest in the IT sector, followed by industry and financial and insurance activities. 8 Nomenclature statistique des activités économiques dans la Communauté européenne 9 Capacity utilization growth was (-0.01%) in all sectors Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 14 Table 2. Output growth decomposition in Europe, 2000-2019 Gross value added growth Fixed Asset growth Employment growth TFP growth mean mean 0.33% mean 0.67% mean All NACE activities 2.27% 1.99% 0.66% 0.72% 0.48% 1.12% Agriculture, forestry and fishing 0.75% 2.44% 0.81% -2.10% -1.41% 1.34% Industry (except construction) 1.93% 2.21% 0.73% -0.81% -0.54% 1.73% Construction 1.03% 1.29% 0.42% 0.63% 0.42% 0.18% Wholesale and retail trade, transport, accommodation and food service activities 2.30% 2.34% 0.77% 1.00% 0.67% 0.87% Financial and insurance activities 2.21% 0.66% 0.22% 0.53% 0.36% 1.63% Information and communication 4.99% 3.57% 1.18% 2.56% 1.71% 2.09% Real estate activities 2.06% 1.20% 0.40% 2.43% 1.63% 0.03% Professional, scientific and technical activities; administrative and support service activities 3.82% 3.77% 1.24% 3.50% 2.35% 0.22% Public administration, defence, education, human health and social work activities 1.20% 1.03% 0.34% 1.22% 0.82% 0.04% Arts, entertainment and recreation; other service activities 1.63% 1.71% 0.56% 1.61% 1.08% -0.02% Observations 399 399 399 399 399 399 Source: Author’s computation Decomposition of output growth for total economy and respective sectors has shown different significance from the production inputs (capital stock, employment and total factor productivity) if apply sectoral approach (Table 3). pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 15 Table 3. Share of the source of growth Fixed Asset growth Employment growth TFP growth All NACE activities 28.9% 21.2% 50.0% Agriculture, forestry and fishing 107.7% -188.2% 180.5% Industry (except construction) 37.9% -28.0% 90.2% Construction 41.0% 40.7% 18.2% Wholesale and retail trade, transport, accommodation and food service activities 33.6% 29.2% 37.2% Financial and insurance activities 9.8% 16.1% 74.0% Information and communication 23.6% 34.4% 42.0% Real estate activities 19.2% 78.9% 1.9% Professional, scientific and technical activities; administrative and support service activities 32.6% 61.5% 5.9% Public administration, defence, education, human health and social work activities 28.3% 67.9% 3.8% Arts, entertainment and recreation; other service activities; activities of the household and extra- territorial organizations and bodies 34.7% 66.1% -0.8% Source: Author’s computation Our analysis has shown that total factor productivity determined almost half of the output growth in European countries over the last two decades, with the other half primarily determined by increases in fixed asset growth and employment growth. Similar findings were presented by Margaritis et.al (2005), showing that “labour productivity has accounted for roughly half of the growth in per capita GDP in OECD countries over the same period, with the other half primarily accounted for by increases in labour utilisation (changes in the demographics, unemployment and labour force participation rates)”. On a sectoral level, contribution to growth varies. In agriculture and industry employment growth was negative, while it is a significant source of growth in services. Opposite, TFP growth was the most significant source of growth in agriculture and industry, but also in financial and insurance activities. Capital growth was the most relevant source of growth in agriculture but contributed lesser to the growth in the services sector. 4.4.2. Panel pooled OLS (simple and fixed effects) model The properties of the variables (defined in section 4.1) were examined by determining the existence of unit-roots. The Hausman test is used in order to choose between the fixed effects model and the random- Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 16 effects model in panel data. Estimation results of Equation 9 (Table 4) show that growth in the capital stock (total fixed asset) by one unit leads to the output growth by 0.21, while the same rate of employment growth leads to the output growth by 0.84. An increase in expenditures for research and development by one percentage point determines output growth by 0.05 points. Growth in exogenous components of TFP is to 0.9%. Result obtained corresponds to the one estimated applying growth accounting approach, presented in the previous section. Table 4. Estimation results (equation 9), panel fixed effects OLS Dependent Variable: Sample (adjusted): 2001 2019 Cross-sections included: 26 Total panel (balanced) observations: 468 Variable Coefficient Std. Error 0.212717*** 0.036243 0.842992*** 0.062394 0.058221*** 0.012915 C 0.009205*** 0.001382 *** p<.01, ** p<.05, * p<.1 Source: Author’s estimations Estimation of Equation 9 at the industry level shows that the impact from expenditures for research and development is higher in the industry sector compared to the total economy (0.08), the exogenous component of TFP growth also (1.29%)10, as presented in Table 5. Table 5. Estimation results (equation 9), applied in the industry sector (panel pooled OLS) Dependent Variable: , Sample (adjusted): 2001 2019 Cross-sections included: 24 Total panel (balanced) observations: 424 Variable Coefficient Std. Error , 0.266830*** 0.054682 , 0.568351*** 0.081894 0.088898** 0.028636 C 0.012962*** 0.003208 *** p<.01, ** p<.05, * p<.1 Source: Author’s estimations Exogenous components of TFP growth is the highest in the 10 Result obtained corresponds to the one estimated applying growth accounting approach, presented in the previous section. pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 17 information and communication services sector (3.23%), which is higher than the value estimated applying the growth accounting approach, but with a comparable trend (information and communication sectors is the one with the highest TFP growth). Contribution from expenditures for research and development is higher in the information and communication sector in relation to the total economy, which is expected as this is the knowledge- intensive sector. Results are presented in Table 6. Table 6. Estimation results (equation 9), applied in the ICT sector (panel fixed effects OLS) Dependent Variable: , Sample (adjusted): 2001 2019 Cross-sections included: 25 Total panel (balanced) observations: 446 Variable Coefficient Std. Error , 0.339460*** 0.041459 , 0.104957** 0.051201 0.073761*** 0.025753 C 0.032395*** 0.003081 *** p<.01, ** p<.05, * p<.1 Source: Author’s estimations Contribution from expenditures for research and development is the highest in the sector of professional, scientific and technical services, as expected (Table 7). Exogenous components of TFP growth determines output growth in this sector at a slower rate than in the total economy. Table 7. Estimation results (equation 9), applied in the sector of professional, scientific and technical services (panel fixed effects OLS) Dependent Variable: , Sample (adjusted): 2001 2019 Cross-sections included: 23 Total panel (balanced) observations: 407 Variable Coefficient Std. Error , 0.252440*** 0.030831 , E 0.423837*** 0.061037 0.149740*** 0.026578 C 0.007116** 0.003480 *** p<.01, ** p<.05, * p<.1 Source: Author’s estimations Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 18 4.4.3. Comparisons with results with other studies Measuring Total factor productivity applying the growth accounting approach has its difficulties. Fairly innocuous deference in assumptions can lead to very different estimates of TFP growth. (Prem notes, 2000) That is why is important to emphasize the sensitivity of the results by changing assumptions. Although earlier research on output growth decomposition applying the growth accounting method does not cover the same sample and time frame, some overlaps make comparison possible. Work done by Foster- McGregor and Verspagen (2017) and Levenko et.al (2019) are the most comparable one, in terms of a time frame but also methodology and sample (eighteen countries from our sample match the sample in Foster-McGregor and Verspagen study, while seven countries from our sample match the sample in the study by Levenko et.al). Despite different methodological approaches and time covered, results from all studies are similar for most countries (Table 8) and show a high degree of consistency. Results from both studies on the sectoral level show higher TFP growth in the industry than in the services sector. pp. 1-21 Vol. 3 No. 2 (2021) Management & Economics Research Journal 19 Table 8. Comparisons of the results with the study by Foster-McGregor and Verspagen (2017) and Levenko et.al (2019) Bacovic (2021) Foster-McGregor, Verspagen (2017) Levenko et.al (2019) 2001-2019 average annual growth 2001-2019 cumulative growth 1995-2009 cumulative growth 1996-2016 average annual growth Belgium 0.40% 7.53% 3.96% 0.63% Czechia 1.88% 35.78% 23.99% Germany 0.39% 7.44% 9.19% Estonia 2.04% 38.75% 34.14% 1.77% Ireland 1.36% 25.78% 7.00% Greece -0.15% -2.93% 3.67% France 0.11% 2.17% 17.94% Italy -0.48% -9.16% -5.04% Lithuania 3.28% 62.28% 29.36% 1.24% Luxembou rg -0.18% -3.40% 6.01% Hungary 1.57% 29.81% 30.20% 0.89% Poland 2.43% 46.16% 52.30% 1.80% Slovenia 1.47% 27.92% 29.24% 1.22% Slovakia 2.61% 49.61% 27.10% 0.96% Finland 0.07% 1.27% 19.60% Sweden 0.44% 8.35% 16.98% United Kingdom 0.28% 5.29% 13.13% Source: Author’s calculations, Foster-McGregor and Verspagen (2017), Levenko et.al (2019) 5. Conclusion The structural changes in Europe have occurred over the last several decades, reducing output share in the goods sector, while increasing its share in overall services. The sectoral perspective in economic analysis is important as both the growth rate and volatility of an economy might be related to its productive structure. Estimation of the contribution from inputs and total factor productivity growth at the sectoral level provides valuable insights into the impact of structural changes on the overall growth. Applying the growth accounting approach, we decomposed output growth rates in twenty-six European countries from the year 2000 to 2019, in all sectors aggregated, and ten individual services sectors (agriculture, industry, construction and seven services sectors). Output growth was Sectoral approach in output growth decomposition and its determinants in Europe Bacovic, M. 20 decomposed into 4 components: capital stock growth, employment growth, capital utilization growth and total factor productivity growth. Computation of the share of the source of growth in total growth in the economy and specific sectors has shown that the total factor productivity is the major source of growth, accounting for 50% in all sectors, but more significant in agriculture and industry. Employment growth is the most significant source of growth in services. The capital stock growth has the largest relevance in agriculture, industry and construction. Applying the panel pooled OLS model, we found that the relevance of expenditures for research and development is high and positive in all sectors, but higher in industry and knowledge-intensive services (information and communication, scientific activities) compared to all sectors average. 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