Automation Systems Implications on Economic Performance of Industrial Sectors in Selected European Union Countries

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

The aim of this study is to offer clear and sound evidence regarding the relationship between robot density and economic output, as measured by gross added value. The authors seek to examine how this association differs across sectors in the selected countries, considering the differences in the levels of technological adoption across countries. Using 12 European Union countries for the period 2016–2022, the authors find that robot density improves the economic performance of the construction, industrial, and manufacturing sectors. The conclusion is mentioned as follows: “the results underscore the transformative role of robotics in enhancing productivity and efficiency across these sectors, with varying effects depending on the sector and the level of industrial output.”

In the next revised version of their study, I recommend that the authors address and clarify several points I have noted below.

1.   The aim of this study is important, but it also requires convincing arguments and clear explanations on how to address the identified gap. The authors are expected to clearly explain how their methodology addresses the identification of the (significant) differences of sector-specific impacts across countries with diverse levels of technological adoption. This should be briefly explained in the introduction and detailed in the methodology section.

2.   A table containing clear definitions of variables and sector identification can help improve the understanding of the analysis.

3.   In Equations 5 to 8, the authors need to clarify whether they control for differences in countries by including country fixed effects, which capture unobserved characteristics other than those included in their analysis. Similarly, are year fixed effects included in these equations to control for differences across years? If not included, the authors can explain their reasoning.

4.   In all tables except Tables 2 and 3, the authors can insert the number of observations used in the analysis. In Tables 4, 5, and 6, it is important to include adjusted R-squares.

5.   The economic significance of the impact of robot density on the economic performance of the three sectors is important for clarifying the importance of this factor. 

6.   How are the statistical differences in the impact of robot density on sectoral economic performance across countries determined? This needs to be clarified by providing examples.

Author Response

Dear Reviewer,

We highly appreciate your suggestions which have proved very helpful in amending the manuscript and express our sincere appreciation for carefully reading our manuscript.

We have read the evaluation and, based on the review reports we received, we performed revisions of our manuscript, as requested, highlighted with red into the manuscript and summarized below:

1. The aim of this study is important, but it also requires convincing arguments and clear explanations on how to address the identified gap. The authors are expected to clearly explain how their methodology addresses the identification of the (significant) differences of sector-specific impacts across countries with diverse levels of technological adoption. This should be briefly explained in the introduction and detailed in the methodology section.

Thank you for highlighting the importance of clearly explaining how our methodology addresses the identified gap regarding the sector-specific impacts of robot density across countries with diverse levels of technological adoption. We have revised the manuscript to include a detailed explanation of how the methodology achieves this goal, both briefly in the Introduction and comprehensively in the Methodology section. We also added a flowchart of the methodological process to better express the methodological steps.

2. A table containing clear definitions of variables and sector identification can help improve the understanding of the analysis.

Thank you for your valuable suggestion to include a table with clear definitions of variables and sector identification. We have incorporated a table titled "Key Variables and Sector Classifications for Analyzing Automation Impacts" in the revised manuscript. This table provides concise definitions of all variables used in the analysis, including their measurement units and data sources, alongside a detailed description of the sectors studied (construction, industry, and manufacturing).

The inclusion of this table aims to enhance the clarity and accessibility of the analysis, ensuring that readers have a comprehensive understanding of the variables and sectors examined. We believe this addition significantly improves the manuscript's presentation and aligns with your recommendation.

3. In Equations 5 to 8, the authors need to clarify whether they control for differences in countries by including country fixed effects, which capture unobserved characteristics other than those included in their analysis. Similarly, are year fixed effects included in these equations to control for differences across years? If not included, the authors can explain their reasoning.

Thank you for your insightful observation regarding the inclusion of country and year fixed effects in Equations 5 to 8. In response to your comment, we have clarified the treatment of unobserved heterogeneity in our analysis and provided detailed reasoning for our methodological choices. We acknowledge the importance of controlling for unobserved heterogeneity that could arise from country-specific characteristics (e.g., institutional quality, industrial policies) or year-specific factors (e.g., global economic shocks). In the current analysis, explicit country and year fixed effects were not included in Equations 5 to 8. This decision was guided by the nature of the Method of Moments Quantile Regression (MMQR) methodology, which focuses on capturing distributional heterogeneity across quantiles rather than relying on fixed effects. The MMQR approach inherently accounts for variations within the data by modeling the relationships across different performance levels, reducing the necessity for explicit fixed effects. To address potential concerns regarding omitted variable bias, we have included key explanatory variables such as GDP growth, research and development expenditure, and human resources in science and technology. These variables serve as indirect controls for country-specific and temporal variations, capturing much of the heterogeneity that fixed effects would typically address.

4. In all tables except Tables 2 and 3, the authors can insert the number of observations used in the analysis. In Tables 4, 5, and 6, it is important to include adjusted R-squares.

Thank you for your observation. We added the number of observations as you suggested.  Regarding the inclusion of adjusted R-squared values in Tables 4, 5, and 6, we would like to clarify that the Method of Moments Quantile Regression (MMQR) methodology does not produce adjusted R-squared values as part of its standard output. This is because MMQR focuses on estimating quantile-specific effects rather than providing a single goodness-of-fit measure applicable to the entire model, as is common in ordinary least squares (OLS) regression. Instead of adjusted R-squared, MMQR provides a nuanced view of the relationships between variables by estimating the effects across different conditional quantiles of the dependent variable. This allows for a richer understanding of how robot density impacts sectoral performance at various levels (e.g., low-performing vs. high-performing sectors), which cannot be captured by a single goodness-of-fit statistic like adjusted R-squared. To address concerns about model robustness and explanatory power, we have conducted sensitivity analyses and alternative specifications (e.g., fixed-effects models) to validate the consistency of our results. These checks demonstrate the reliability of our findings and provide reassurance about the robustness of the methodology employed. We appreciate your comment and hope this clarification adequately addresses your concern.

 

5. The economic significance of the impact of robot density on the economic performance of the three sectors is important for clarifying the importance of this factor. 

Thank you for emphasizing the importance of discussing the economic significance of robot density's impact on the economic performance of the three sectors. We agree that providing a detailed interpretation of the results is essential for clarifying the practical implications of our findings. We have introduced in the Discussion section and the Conclusion section an explicit discussion of the economic significance of robot density in each sector.

6. How are the statistical differences in the impact of robot density on sectoral economic performance across countries determined? This needs to be clarified by providing examples.

Thank you for highlighting the need to clarify how the statistical differences in the impact of robot density on sectoral economic performance across countries are determined. We appreciate the opportunity to elaborate on our methodology and provide illustrative examples to enhance clarity.

The statistical differences in the impact of robot density are determined through a combination of econometric techniques that capture variations across countries with different levels of technological adoption. Specifically:

• Slope heterogeneity tests, based on the method by Pesaran and Yamagata, are employed to assess whether the relationship between robot density and economic outcomes varies significantly across countries. These tests identify whether sectoral impacts differ due to country-specific characteristics such as industrial structure, workforce skills, or technological readiness. For example, in advanced economies like Germany, higher coefficients for robot density reflect the sector’s ability to leverage automation effectively, while moderate-adoption economies like Slovakia exhibit smaller coefficients, indicating structural barriers.
• The Method of Moments Quantile Regression (MMQR) framework captures the effects of robot density across different quantiles of sectoral performance, providing insights into cross-country differences at various levels of economic activity. For instance, in high-performing manufacturing sectors in countries like Sweden, significant positive effects are observed at higher quantiles (e.g., Q75 or Q90). Conversely, in lower-performing sectors in countries like Poland, moderate effects are seen at median quantiles (e.g., Q50), highlighting disparities in technological adoption and sectoral efficiency.
• Cross-sectional dependency tests are used to account for shared external factors, such as global economic trends or regional policies, that might influence all countries. While these factors create overarching trends, individual country differences emerge due to unique industrial capacities and policy environments.

These methodological tools and examples are explicitly discussed in the revised manuscript to address this concern. We have elaborated on the statistical techniques in the Methodology section and incorporated specific examples in the Discussion and Conclusion sections to illustrate the findings clearly. Thank you for this valuable feedback, which has helped us enhance the clarity and robustness of our analysis.

Author Response File: Author Response.docx

Reviewer 2 Report

Comments and Suggestions for Authors

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Comments for author File: Comments.pdf

Author Response

Dear Reviewer,

We highly appreciate your suggestions which have proved very helpful in amending the manuscript and express our sincere appreciation for carefully reading our manuscript.

We have read the evaluation and, based on the review reports we received, we performed revisions of our manuscript, as requested, highlighted with red into the manuscript and summarized below:

1. Abstract: Improve the flow, ensuring smooth transitions between the purpose, methods, results, and implications. Avoid the qualification as „robust”. 

Thank you for your valuable feedback regarding the abstract. We have revised the abstract to improve its flow, ensuring smooth transitions between the purpose, methods, results, and implications. The updated version avoids the term "robust" and instead focuses on clearly presenting the study's key elements in a logical sequence.

2. Introduction: The introduction starts usually with the claim of the paper, please consider writing it. Be specific about what variables you're focusing on. List specific research questions, being mandatory for this part of the paper.

Thank you for your insightful comments regarding the introduction. We have revised the introduction to address your recommendations and enhance its clarity and focus. The revised introduction now provides a clear roadmap for the paper, outlining its purpose, key variables, and research questions in a logical and engaging manner. We believe these changes align with your recommendations and strengthen the manuscript. Thank you again for your thoughtful feedback.

3. Literature review: Explain more how literature relates to research questions and hypotheses.

Thank you for your observation regarding the need to explain how the literature relates to the research questions and hypotheses. We have revised the conclusion of the literature review section to explicitly connect the insights and gaps identified in prior studies to the research questions and hypotheses formulated in this study.

4. Methodology: Justify the sample size and explain how it relates to the research question and study design. Avoid the extensive use of „robust”. Describe the specific software or tools employed for data analysis.

Thank you for your comment regarding the justification of the sample size and its relation to the research questions and study design, as well as your request to describe the specific software or tools employed for data analysis. The sample consists of panel data from 12 European Union countries spanning 2016–2022, chosen to capture a diverse range of technological readiness, industrial structures, and automation adoption rates. This selection aligns with the research questions by providing sufficient variation to analyze both sectoral differences and cross-country disparities in the impacts of robot density on economic performance. The panel structure enables the study to explore temporal trends and long-term relationships, essential for assessing how automation influences sectoral gross value added (GVA) across manufacturing, industry, and construction. The longitudinal coverage and heterogeneity of the sample ensure that the analysis captures meaningful variations and relationships across contexts.

For data analysis, the study employs Stata software, which provides a comprehensive suite of econometric tools suitable for panel data analysis. Specifically, the Method of Moments Quantile Regression (MMQR) methodology is implemented to examine distributional effects across different quantiles of sectoral performance. Stata's capabilities are leveraged to conduct slope heterogeneity and cross-sectional dependency tests, ensuring that the model appropriately accounts for variations and interdependencies within the data. By utilizing Stata, the study achieves precise and reliable estimation of the relationships between robot density and economic performance, consistent with the study’s objectives and design. We have avoided the use of overly broad terms and focused on providing clear and specific descriptions to address your comments. Thank you for your valuable feedback, which has strengthened the manuscript.

 

5. Discussions: It is mandatory to write this part! Provide your comments on the points discussed & justified!!!. 

Thank you for highlighting the need to include a comprehensive Discussion section in the paper. We have now explicitly addressed and justified the key findings of the study, providing detailed comments on their implications, relevance, and alignment with the research objectives. The revised Discussion section has been added, integrating the following points:

• We provide a detailed interpretation of the varying impacts of robot density on the manufacturing, industrial, and construction sectors. The discussion highlights how advanced automation integration in manufacturing leads to significant economic benefits, while structural barriers in construction limit the scalability of robotics. These findings are supported by existing literature and justified using sectoral-specific characteristics observed in the data.
• The discussion elaborates on the disparities in the impacts of robot density across countries, linking these to differences in technological readiness, industrial maturity, and policy environments. For instance, advanced economies like Germany exhibit stronger positive effects due to their infrastructure and workforce adaptability, while moderate-adoption economies show smaller but still meaningful gains, reflecting untapped potential.
• The discussion emphasizes the importance of tailored automation strategies and policies to maximize the benefits of robotics. Policymakers in advanced economies are encouraged to sustain growth through innovation and workforce upskilling, while in moderate-adoption economies, targeted investments in technology and infrastructure are critical to overcoming barriers to adoption.
• Each finding is explicitly linked to the research questions, providing a clear and justified narrative that aligns the results with the study’s objectives. The discussion also considers alternative explanations and contextual factors, ensuring a balanced interpretation of the results.

These revisions ensure that the Discussion section is comprehensive, fully developed, and directly addresses the key points raised by the reviewer. We believe this addition strengthens the manuscript significantly and provides valuable insights into the implications of the study. Thank you for this important observation. Please let us know if additional clarifications are needed.

6. Conclusion: Provide your comments on the research limitations and future research.

Thank you for your valuable feedback regarding the inclusion of research limitations and directions for future research in the conclusion. We have revised the Conclusion section to incorporate a clear and comprehensive discussion of the study's limitations and suggestions for future research.

 

Author Response File: Author Response.docx

Reviewer 3 Report

Comments and Suggestions for Authors

The paper aims “to contribute to the expansion of knowledge in the field of robotization by focusing on a comprehensive analysis of its economic effects.”

The results of the study provide a comprehensive view of ​​the impact of density of robotization in the three industries’ performance across 12 EU countries.

The article is logically structured; the chosen research methodology is substantiated by a literature review. One of the industries under study is construction, which is a positive point given the lack of studies in this area of ​​research. The selected literature corresponds to the topic of the paper, obviously helped the authors in the theoretical justification of the relevance of the research topic, the choice of research methods and its application, the development of conclusions. The authors proposed 3 hypotheses aimed at answering the research aim. The hypotheses were tested using statistical and econometric analysis methods.  For statistical analysis, economic indicators were selected that characterize the impact of robotization on the case industries performance as a whole.  Therefore, the results of the analysis are general. However, the results of the study may be a good starting point for further research, for example, on the impact of robotics on the development of a particular industry.

Sometimes there are editorial typos: the text mentions 4 hypotheses; authors should check the text preceding them. 

 

Author Response

Dear Reviewer,

We highly appreciate your suggestions which have proved very helpful in amending the manuscript and express our sincere appreciation for carefully reading our manuscript.

We made the corrections in the manuscript and replaced four hypotheses with three. 

We also improved the Results section and developed the Discussion section. 

All changes are in red in the manuscript.

Thank you again and Happy holidays to you.

The authors

Round 2

Reviewer 1 Report

Comments and Suggestions for Authors

I appreciate your efforts in revising your study and addressing my feedback. Your responses are valued.