Structural Change in Central and South Eastern Europe—Does Technological Efficiency Harm the Labour Market?
Round 1
Reviewer 1 Report
GENERAL COMMENTS AND REVIEWER’S SUGGESTIONS FOR IMPROVEMENT OF THE ARTICLE
The paper is interesting and concentrate on structural change in Central and South Eastern European EU member economies. The theme elaborated is important for these countries. However, there are numerous weaknesses that must be addressed before paper can be considered for publication. In continuation I will outline my main comments to this paper:
Abstract
The abstract does not reflect accurately what is the aim of the study and what this study is about. The abstract needs to be re-written to reflect the new things the author is doing. Also the author describes the main objective of the 33 line (introduction) and it would be good to add such a sentence in the abstract.
Introduction
The introduction is acceptable as it explains fine the goal of the paper. The author combined introduction with literature review. I think that this combination is acceptable, however, it would be easier to read this article if these parts were divided. Moreover, author should concentrate to explain why Structural change in Central and South Eastern Europe have been taken for this analysis, etc. Maybe more appropriate would be if authors will focus only on Central Europe or South Eastern Europe? In this part the most important is to underline what authors tried to explain, what is innovative in this research, how results will fill the gap in the literature. Authors’ explanation is very sketchy. In my opinion this part needs to be extended. Despite some of these errors I think that this part is written correctly. In the part of introduction the literature review section is good written. The authors have reviewed leading journals and articles in this field. The volume of the article is probably limited, so I would not change anything in this part.
Materials and Methods
The explanation of the model is adequate and I have no objections to this part. I also think that methodology used in this paper is adequate for the part of empirical analysis. However, it might be worth adding some information about limitations of chosen methodology.
Results
The process of techniques used and tests performed are well done. However, due to the lack of data I was not able to evaluate the empirical part in this article. It would be good to include initial data in the appendix, in particular that the author has indicated that in all three datasets there were some missing data for some of the countries examined which were made up for by linear extrapolation or moving average estimates.
Discussion
I think that this part is acceptable, however it would be good to compare research results with other studies, even those that were described in the first part (introduction) of this article.
Conclusion
The conclusions must be related to the direct results and findings coming from the analysis. Conclusion should be rewritten in a way to provide policy recommendations. The author emphasize policy recommendations, however in this article the recommendations are too simplified. The resulting conclusions should be more specific for this case, and not general, as is currently the case in the article.
To sum up, in present form paper is almost ready for publication. I do think this study contains sufficient novelty and good empirical execution to be worthy of publication in Sustainability. Due to interesting topic I would recommend editors to give author a chance to improve and resubmit the paper. Therefore, my recommendation is: Accept after minor revision (corrections to minor methodological errors and text editing).
Author Response
Answers to Reviewer 1
Dear Madam/Sir,
Thank you for your constructive and useful comments on my paper and recommendations for further improvement. I am giving a detailed answer to all your comments below.
Abstract (revised version containing objectives)
According to Kuznets, modern economic growth entails structural change. The share of the broad economic sectors (agriculture, manufacturing and services) in value added and employment has undergone a significant transformation also in the post socialist Central Eastern European and the South Eastern European economies just like in the developed countries with somewhat lower dominance of the service sector. This phenomenon was widely explained by economists through technological development having a characteristically negative impact on employment within the same industry in which it is adopted. As preceding empirical research focused mainly on developd industrial countries including old EU member states, the purpose of the current paper is to examine structural change in 13 Central and South Eastern European EU member economies with special emphasis on the impact of own-industry productivity on employment with OLS and GMM panel regressions. The paper reveals that productivity increase in all the sectors goes together with the decrease in employment within the sector in the case of OLS estimations, whereas produces less evident results in the GMM model framework when controlled for other sectors’ and countries’ productivity and employment processes. Involving further country-, time- and industry-specific variables in the regression we find that it is mostly manufacturing that is negatively hit by these additional factors (such as relatively higher openness or EU level investment activity) whereas productivity does not necessarily harm the sustainability of workplaces in this sector. The paper also ascertains that there is a large diversity among the selected emerging European economies as regards economic structures.
Introduction
I fully agree with your comment. The original version of the paper contained a detailed and separate literature review but as I read in the „Instructions for Authors” and file formatting instructions that „The current state of the research field should be reviewed carefully and key publications cited”. Moreover, the latter did not include a separate chapter for Literature review, so thinking it is a prerequisite, I cut it shorter and included it in the introduction.
The original Literature review is as follows. If possible and the reviewers support this alternative, I will insert this text in the revised version of the paper:
LITERATURE REVIEW
„Structural change can be captured in models by applying different rates of (labour augmenting) technological progress across sectors, change in the relative prices of inputs and (consequently) outputs, differences in input intensities and substitutability of capital and labour, as well as separating home production of services. From the consumer side non-homothetic preferences and differing income elasticities for different products are a prerequisite for explaining why certain sectors become more dominant and why others less. Referring to an early work by Clark (1940) Gabardo et al. (2017) conceptualise sectoral reallocations as the result of differential productivity growth and Engel (income) effects, that is sectoral deviations in productivity growth from the supply side and different income elasticities from the demand side. It was namely Engel who stated first that the lower income elasticity of demand leads to the drop in food prices and the shrinkage of the agricultural sector within the economy. His findings were later extended as a general law for consumption explaining other industries’ rise and downturn (see Houthakker 1987 among others). If income elasticity is greater than one in an industry (presuming non-homothetic preferences for consumers), then an increase in the per capita GDP leads to a higher expenditure share and also to the reallocation of labour in favour of the sector with higher efficiency. In the supply side or the technological explanation, relative price changes either derive from differences in productivity growth across sectors or the changes of relative prices of inputs (presuming different input intensities and changes in the relative supply of inputs). Foellmi and Zweimüeller’s (2008) model concentrating on the demand side is even able to capture the stylised facts on the three broad sectors, and at the same time, with a hierarchical representation of consumer preferences they show that goods when launched in the market are classified as luxury and later become necessity products due to changing income elasticities. In contrast, Acemoglu and Guerrieri (2008) construct a model which only takes into consideration supply side effects, varying input intensities and capital deepening as causes of relative price dynamics. Synthesising the two approaches, Boppart (2014) empirically proved that both demand and supply side effects are relevant which is then also confirmed in Gabardo et al. (2017).
Baumol (1967) was one of the first economists who explained changes in industrial proportions on value added with differences in technological progress using only labour input. Herrendorf et al. (2013) empirically examine how the weight of agriculture, manufacturing and services in value added, consumption and employment alternates at different welfare levels in industrialised countries with the analysis of historical time series. In addition, they also show that in the shorter run sectoral composition might also play an important role in business cycle fluctuations. Measured as a function of economic development (expressed as log of GDP per capita) they find that in most of the cases the sectoral shares of employment and nominal value added show a declining path in agriculture, a hump-shaped pattern in manufacturing and the service sector is continuously gaining ground in most developed countries. The share of the service sector shows a sharper increase from the point where manufacturing shifts to a decreasing from an increasing trend in the case of nominal value added shares. These patterns generally characterise the most industrialised countries whose data series from various data sources encompass a long enough period starting from the 19th century. Herrendorf et al. (2013) also reveal some methodological problems of measuring economic development and sectoral shares. Namely, the level of economic development is mostly expressed as GDP per capita which can show large deviations from GDP per hours worked in country rankings. As regards the measurement of sectoral shares, consumption may follow quite different patterns from value added as consumption measures final expenditure and not additional value created at different phases of production, and nominal versus real figures can also reflect contrasting price developments. Different data representations result in either a more accentuated effect of relative prices or that of income movements depending on the input-output relations of the economy. Thus for a better comparability of data, Herrendorf et al. (2013) mostly relied on the EU KLEMS databasis which offers methodologically harmonised data series on sectoral output, value added and employment covering the period between 1970-2007. Nevertheless, they detect similar results for the change in the sectoral reallocation of labour and income even for countries outside the set of rich countries for which EU KLEMS has data.
However, apart from the above general economic interpretations which macroeconomic models are based on and what data representations on sectoral shares reflect, there are still significant deviations in the way the three sectors transform with economic development as far as particular countries are concerned. These variability among countries can be attributed, among others, to different industrial policies, openness, the role of international trade in general, transportation costs, entry barriers in the service sector, the behaviour of new entrants in the labour market, the change in the number of skilled workers, female employment, as well as various economic policy measures (such as for instance employment protection rules) and other market distorting forces (externalities, public goods, market power etc.). Some of the researchers put special emphasis on the human determinants of sectoral transformation, the contribution of the skilled labour force (Buera and Kaboski 2012) and women (Rendall 2010, Olivetti 2013) to the greater share of the service sector in employment and value added. The open economy context of structural change is less elaborate but becomes more and more attractive. It is attributed a specific measurement, the so called Krugman index[1] and it is also characterised by the proportion of high- and medium-tech export in total exports.
The lessons learned from theories on structural change can also be applied to the differences between developing and developed countries. Caselli (2005) and Restuccia et al. (2008), among others, emphasise the importance of agriculture in economic development. In their view, the lower level of productivity in agriculture and the greater share of agriculture in employment prevent developing economies to reach a higher living standard (Herrendorf et al. 2013). Another reason why less developed countries might not catch up with the more developed ones lies in the fact that the difference between the two country groups in the level and growth rates of producitvity in agriculture and services is greater than in manufacturing, therefore the shift from the dominant role of manufacturing to that of services does not support convergence in terms of aggregate productivity (Duarte and Restuccia 2010).
Apart from their deep analysis of determinants behind sectoral transformation and a comprehensive review of models dealing with it, Herrendorf et al. (2013) also pointed to the limitations of the three-sectoral approach of economic development. Jorgenson and Timmer (2011) questioned whether the classical trichotomy among agriculture, manufacturing and services well captures the structural processes of an economy. This is reasonable if one thinks about significant discrepancies in productivity, value added, consumption and employment patterns within sectors, such as for instance the service sector. The service sector can be divided into traditional and non-traditional, high-skill and low-skill services etc. among which relative price changes, real expenditure and labour shares can show large deviations. Gabardo et al. (2017) underline that structural change cannot be restricted to the three broad sectors but instead it covers the change in the structure of production and employment between and within sectors as well.
Kuznets (1966) emphasised the positive overall productivity effect of the move of labour from less productive sectors to more productive ones as a favourable phenomenon of structural change. At the same time, the technological development of the given industry (measured as increase in productivity) has a characteristically negative impact on the employment of the same industry (Baumol-hypothesis). This technology determined own-industry employment deterioration might be overcompensated or at least counterbalanced by positive spillover effects originating from the technologically advanced industry affecting overall consumption, income and employment. Recent research has shed light on the fact that the contribution of industries using higher technology to employment shows a declining trend. At the national economy level technological unemployment can be mitigated by continuous product innovation according to Saviotti and Pyka (2004). Nordhaus (2005) confuted that own-industry technological advancement (with special regard to the New Economy of semiconductors, softwares and telecommunication) would cause job losses and detected a positive relationship between productivity and employment even within manufacturing for the period between 1955-2001 and 1998-2003. The outcome was opposite to what the observed shrinkage in manufacturing employment suggested which Nordhaus explained with the more rapid productivity growth and price decline from foreign competitors, thus competing imported goods can more than offset labour augmenting technological development in the domestic economy. Concerning the employment effect of technology in the various sectors of the economy, Bessen (2017) reveals more nuanced relations: employment shows a dramatical increase at the early stages of innovation then starts declining in later stages of maturity due to market saturation and the widespread use of the new technology thanks to mass production and price reduction. Therefore, an initial favourable employment impact of product innovation ends up in employment depressing processes within the innovative sector. Productivity increase induced by automatisation, at the same time, does not impact employees with different qualifications uniformly which was underscored, among others, by the examination of Autor and Wasserman (2013) and Dustmann et al. (2014), who revealed that the salary of low-wage, less educated workers further decreased in the USA and Germany in the last two to three decades considered. These labour market effects are explained by the shift in demand, thus the aggregate favourable labour market effect of productivity can go together with contradicting processes within the industry but can affect employees at various skill levels also differently. That is changes in labour demand (often referred to as „skill-biased demand shifts”) can have an adverse impact on broad skill groups even if technological advance does not harm the labour market in aggregate. Autor and Salomons (2017) confirmed both presumptions in their investigation encompassing 37 years and the statistics of 19 developed countries. They test the employment effects of productivity (as a widely accepted indicator of technological progress) with regards to change in employment both expressed as the number of persons engaged in work and as industrial share of working age population across industries. They point out that the employment decrease resulting from the productivity increase within the same industry is outperformed by the positive, employment augmenting spillover effect of the productivity growth of a given industry appearing in other industries. These intersectoral advantages stem partly from final output demand increases (income effect), partly from interindustry demand connections. Furthermore, they provide a statistical proof of that the change in employment appears in employee groups with differing skills in a different way and polarises the labour market. Autor and Salomons rank 28 industries in five categories: mining, utiities and construction; manufacturing; education and health services; capital intensive (‘high tech’) services; and labor-intensive (‘low tech’) services. The interdependence between employment and productivity shows a great diversity in intersectoral relations as well: the most positive external effects can be detected due to health care, education and other (low- and high-tech) services, in contrast productivity in utility sevices, mining and construction causes no sizable intersectoral spillover effects. (The low-tech sector merits attention on account of its share in employment, whereas the manufacturing industry due to the highest efficiency increase in respect of all the industries examined.) The differences in the response to the change in productivity can be explained by the presence of sector specific technology, the level of saturation of the market, and how demand effects are shared between domestic and foreign markets. Furthermore, it is generally observable that a powerful efficiency in the primary and secondary sectors will cause an expansion in employment in the tertiary sector, which principally affects low skilled and high skilled labour force, medium skilled will mostly be excluded from the favourable labour market developments. Moreover, Autor and Salomons (2017) conclude that other factors, above all, population changes have caused significant employment effects beside the favourable overall employment effect of productivity increase. Though this favourable impact can be still recognised for the average of the whole period examined but has been moderating (or even turned negative) in recent decades just like the interaction between productivity and employment, as labelled by the authors as decoupling. A possible reason for productivity growth exercising a less forceful effect on domestic employment is that the ensuing expansion in demand is partly satisfied by foreign producers, thus trade openness might explain the change in the intensity of the productivity-employment relationship just like the patterns of structural change.
As regards structural change in the emerging countries of Europe, Bah and Brada (2009) found that post socialist countries, due to the former planned economic system, tend to have a higher employment level in agriculture and manufacturing than the service sector compared to industrialised countries. Furthermore, the service sector in these emerging economies is less productive, having a significantly lower TFP, thus the expansion in the service sector does not entail growth in GDP per capita. Dobrzanski and Grabowski (2019) give a detailed review on research papers discussing productivity processes in the CEECs with special emphasis on the sectoral reallocation of employment. With the help of shift-share analysis and panel data methods, Dobrzanski and Grabowski (2019) decompose NACE level industrial productivity growth into pure and structural productivity – the former captures productivity driven by technological progress, the latter by changes in the industrial shares of employment – for the period between 2004 (the date of EU accession of the first group of countries) and 2018. They find that in the CEECs it is structural productivity, that is the move of labour force to more productive branches of the economy that dominates efficiency increases due to technology modernisation. Moreover, within the general productivity increase since EU accession, significant deviations exist among the sectors and the countries examined. The services sector (especially ICT, financial and real estate services) had outstanding dynamics while others less, and Slovenia and to a lesser extent, the Baltic countries, have made the greatest progress while Hungary leads the group of economies with the lowest productivity growth – especially in terms of structural productivity – showing also negative tendencies in recent periods. Structural productivity and thus structural change is largely influenced by R&D expenditure, while within sector productivity growth much less in these countries. Dobrzanski and Grabowski (2019) also noted that employment has mostly broadened in the service sector, with special regard to the Professional, scientific research, technical, administrative, and support service activities. Correia et al. (2018) by analysing innovative processes in Central, Eastern and South Eastern European economies (CESEE), point to the shift between the period before the 2007-2008 global financial crisis and the period after the crisis. Before 2008 the region was attractive for skilled labour force and low wages which lured foreign investment and thus innovation spurring productivity was mostly imported or foreign (dominantly EU) financed (supplemented with government funds) and concentrated in the manufacturing sector. After the crisis, however, productivity has decelerated and the region has lost its attractiveness it earlier enjoyed due to the relatively high level of educational attainment and unexploited capacities of its labour force. Aging, the bad health conditions and the outward migration of the labour force resulted in tight labour market conditions in the last couple of years. Therefore, the innovative ability of these economies should rely more on internal financing and a more practical use of home inventions or innovative efforts, on a new growth model in general as the EC and EIB experts concluded. Galgóczi (2017), in contrast, argues that there has not been a low wage strategy in the CEE countries, but instead there was a large decline in real wages in the region in the transformation period followed by an evident convergence in wages between 2000 and 2010. However, the global financial crisis shattered the favourable process and EU crisis management policies had again a dampening effect on wages which have prevented the formation of a new strategy based on structural change towards high-skilled labour force and higher value added activities and has kept these countries in a „low-wage trap”. Finally, Novák (2019) could not unequivocally prove an overall positive productivity induced employment growth in 14 Central and South Eastern European countries for the period between 1995-2015 but instead OLS panel regressions all resulted in a negative productivity-employment relationship at the national economy level also when controlling for demographic changes.”
Materials and Methods
Both the OLS and GMM methodology have their drawback.
I refer to this in lines 135-142 by stating: „the OLS test might underestimate the coefficient of the main explanatory variable (productivity) due to statistical errors and the biased relationship between productivity and employment (as productivity is calculated using the same employment figures). At the same time, the GMM model is susceptible to overestimate the coefficient of the explanatory variables if controlled for by relevant instrumental factors, like in our case by other countries’ and sectors’ productivity. To mitigate the effect of the employment-productivity bias, additional variables are involved in the regression capturing country-, industry- and time-specific factors.”
My supplementary comments are (if you find it necessary to be involved in the paper):
„The econometric literature suggests that in the case of possible non-stationarity of the data, as very often is the case with GDP per capita and other productivity figures, spurious regression can be the outcome, which can be mitigated by panel estimation resulting in consistent estimates of the regressors if N and T is large enough (Baltagi 2012). GMM is widely used in econometric analysis to tackle the shortcomings of the OLS estimation such as the endogeneity among regressors and the falilure to meet the normal distribution condition. GMM uses instruments strongly related to regressors, helping eliminate endogeneity problems and does not require the knowledge of the distribution of the data, only the moments derived from models and has good large sample properties (Zivot–Wang 2006). However, its shortcomings lie in the same features, the lack of adequate sample size can lead to statistically less significant parameter estimates and their 1-step application does not produce optimal results when the set of instruments initially selected is only valid when particular initial conditions hold (see Kiviet 2009 among others for further details).”
Results
As I use various data series, my suggestion is that I attach the final table with data and mark the estimated (missing) statistics and the links to the original dataset. If necessary I can also include the original data in the Appendix though I am not sure if TheGlobalEconomy data can be disclosed as their data service is subject to fees.
Discussion
I refer to research results in other studies in lines 340-345 and lines 350-351 as quoted below:
„Economic openness might also contribute to the diversion of workforce from manufacturing as proposed by the literature (see [19]) and this effect is recognisable in the regressions and even present in the ’low-tech’ services industry apart from manufacturing. The other theoretically underpinned fact (see [9,16]), the relative advantage of low-skilled and high-skilled employees in the labour market is conspicuous in the ’high-tech’ services area.”
„The own-sector productivity regressor only has the expected robust negative coefficient as recorded by Autor and Salomons (2017) [16] in agriculture.”
For additional comparison I would supplement the above in the following way, if accepted:
„Autor and Salomons (2017) [16] detected a negative productivity-employment relationship in mining, utilities and construction, in manufacturing, in education and health, in high-tech (excluding education and health) and low-tech services with OLS regression, and in the frames of a general own-industry analysis using industrial emplyoment weights in both OLS an IV estimations. The sector by sector analysis in this paper also revealed the negative own-industry link between the two variables in the focus of the examination in the OLS approach, however, the own-sector productivity regressor only has the expected robust negative coefficient as recorded by Autor and Salomons (2017) [16] in agriculture when the GMM method is applied. Moreover, Autor and Salomons (2017) found that irrespective of the employment variable used (either change in the number of employed persons or the share of employment) we obtain the same results, while the GMM estimation in this paper has brought varying signs for the productivity coefficients depending on which employment variable was used.”
In addition, the statement in lines 376-378 could be supplemented in the following way:
„This means that both EU accession and the global financial crisis had an overall positive bearing on agricultural and ’high-tech’ employment, these sectors could absorb some of the redundant workforce, and deteriorating the situation in manufacturing during the recessionary years after 2008. This can be partly attributed to government policy intervention and also to the change in foreign inestors’ attitude as the region has partly lost foreign investors’ interest in investing in productive investment, mainly in the field of agrcultre as suggested by Correia et al. (2018).”
And as regards lines 424-425 the following amendments could be made:
„Manufacturing employment is boosted by intramural R&D spending on both natural sciences and other research areas in all the regressions, which is in line with Dobrzanski and Grabowski (2019) emphasising that structural productvity growth and thus the move of labour to more productive sectors is significantly affected by R&D expenditure. Furthermore, should intramural R&D really contribute to employment in manufacturing, the necessity of a shift to a more self-financed approach to innovation as proposed by Correia et al. (2018) could also be justified. ”
Conclusion
Taking into the the reviewer’s recommendation, the conclusion part from line 475 has been rewritten in the following way:
„The above results lead us to make two important conclusions. First of all, EU industrial policy principles (like those laid down in Europe 2020 for instance) should put more emphasis on the diverse industrial structure of member states. Reconsidering reindustrialisation potentials in the CEECs offering more high-skilled activities is one of the alternatives. The region traditionally has a relatively well-educated labour force, however, manufacturing has not offered broadening employment opportunities for them. Notwithstanding, the greater reliance on the service sector in Baltic countries, Malta and Cyprus partly does not necessarily contribute to the creation of new jobs within the sector in different service branches of the high-tech area but can still result in positive labour market spillover effects. Secondly, a new domestic innovation policy focusing on high-skilled and high-quality workforce together with a stronger inward financing of investment and R&D could bring a renewed productivity stimulus in these economies. These together could help to preserve their labour force especially in manufacturing where technological progress does not seem to harm employment as underpinned by the empirical analysis above. Higher EU investment and trade openness, just like EU accession (especially the after-crisis period) has created new job opportunities for the ‘high-tech’ services, while badly affected manufacturing, which might contradict to EU efforts aimed at reviving industrial activity in Europe. This can partly explain why higher female participation generally restrain further employment creation, the structure of the economy in the last two decades might not have allowed for job creation benefiting male workers.
For a deeper understanding of structural processes in emerging European economies, however, relative price effects and intersectoral employment („spillover”) effects need to be further scrutinised backed by cluster analysis to account for the differences among economic structures of the countries in Central and South Eastern Europe.”
[1] The Krugman index is a relative specialisation index where a given sector’s importance is compared to that of a country or a reference group (Mongelli et al., 2016).
Reviewer 2 Report
Dear author,
I have carefully read your article that aims at investigating the relationship between structural change and employment in the central and south European countries. As reading this title, I am curious about how do you contribute new staff to this traditional research question. Sadly, I did not find something new from your study to the literature. Here are some general concerns,
How do you define and measure productivity? Can you specify the research question in depth, for example, if you look at the difference among those countries, it would be something exciting, or if you want to concentrate on the regression methods, and argue that methods matter in results, it also could be an option? You argue that the GMM leads to a different result, but you do not explain the reason. Regarding this approach has been widely applied in other researches already, you must explain because somehow you are challenging those studies.
The following are my detailed comments.
- Introduction
In the paragraphs for reviewing the literature, I cannot grasp the logic as you organize the literature. I had a feeling that you just list some literature that is relevant to productivity and employment. And do the literature have the same measurement of productivity?
The literature about the central and south European countries is missing, if there is rare, reviewing the literature about structural change in developing economies and in many post-socialist countries such as China, Vietnam, etc., will be helpful.
Lines 68-71, “Autor and Salomons (2017) find that changes in labour demand…… in aggregate [21,22,16]”, why there are three references for one article?
We usually use the present tense in the literature review, although some suggest using the past tense. However, you cannot use both in the same paper, and please check with the journal which time tense they prefer.
Line 83, What is “CEECs”?
- Materials and Methods
Please add a section to introduce your variables and measurement; otherwise, it is too difficult to follow and understand your results in section 3.2. When I read your table, I hope that I have known your variables, rather than searching for that in the note.
and explain the propositions you want to investigate.
- Results
The section “Structural Change in the Light of Descriptive Statistics” is difficult to read. I cannot get your points beyond the general trend. Please discuss more the insights by comparing the sampling countries and other EU, OECD countries. It is too heavy for reading if you just present numbers without a precise aim. And, why you include the comparison in the brackets.
Table 1, what does “const/agriculture/” mean? Why put these two together?
Line 318, how do you identify the “spillover effects”?
Overall, this study has the potentials to enrich our knowledge about structural change and employment in the central and south European countries. Nevertheless, I would suggest thinking about the following questions as revising your paper. How does this study contribute to the literature in this well-established research question? Does this study contribute insights for understanding the structural change in the emerging European economies? Is it possible to refine your research question? And have you discussed the structural change in the central and south European economies adequately?
And I find it is difficult to read this paper, perhaps our writing styles do not match, but anyway, please reduce the paragraphs that provide massive information without explanations.
Good luck!
Author Response
Please see the attachment.
Author Response File: 
Author Response.pdf
Round 2
Reviewer 2 Report
The author has improved the paper significantly. I noticed there are some typos, please check them carefully before publishing it.
