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Article
Peer-Review Record

The Convergence Evolution in Europe from a Complex Networks Perspective

J. Risk Financial Manag. 2022, 15(10), 457; https://doi.org/10.3390/jrfm15100457
by Theophilos Papadimitriou, Periklis Gogas * and Fotios Gkatzoglou
Reviewer 1:
Reviewer 2: Anonymous
J. Risk Financial Manag. 2022, 15(10), 457; https://doi.org/10.3390/jrfm15100457
Submission received: 2 September 2022 / Revised: 6 October 2022 / Accepted: 8 October 2022 / Published: 12 October 2022
(This article belongs to the Special Issue Trends in Information Technology)

Round 1

Reviewer 1 Report

Referee Report

Journal: Journal of Risk and Financial Management

Title: The convergence evolution in Europe from a complex networks perspective

 

Comments:

·       The authors use a new complex networks algorithm the TW-MDS to study the evolution of the convergence process among the European countries.

·       The paper is well written and adheres to the standard manuscript format in terms of language and structure.

·       The methodological part seems well designed and sound.

·       The sample used for the study also looks adequate to draw the conclusions intended from such a study. The authors use 30 European countries and 8 macro variables to assess the situation and evolution of convergence.

·       The authors use a sample of 30 European countries. This sample includes all EU countries but also countries that are not members of the EU or the Eurosystem. It is interesting to see in this setting whether the conversion process that was designed and targeted to member-states, also dragged along the rest of the European countries. Is something like this evident from these results?

·       Why were these three periods of time selected by the authors?

·       The authors find that “The empirical findings provide a mixed pattern. The European countries exhibit a common behavior over time for some macroeconomic variables, but not for all of them”. Please discuss whether non-EU countries have the same mixed behavior with the EU-members.

·       Can there be chosen other metrics apart from the correlation coefficient to create these networks in this specific data set?

·       In Section 4.I, in the period 2011-2019, was Greece the only country that experienced a contraction to its GDP?

·       According to these results do we observe any geographical sub-groupings of countries that exhibit the similar behavior? i.e. north-south, Mediterranean and Atlantic countries, former communist countries and the rest, etc?

·       “There are signs of convergence during the crisis, that are reversed in the period after that.” We would expect that during the crisis we would see a negative impact to all the variables – thus, this is why we see this convergence during the crisis.

·       Leaving the crisis period aside and only focusing on the two periods before and after the crisis do we find any continuity in the process of convergence? Or the patterns are totally different signaling that the crisis impacted the qualitative and quantitative convergence?

·       The discussion in the conclusions after “Overall, the results highlight two important facts: First that..” highlight the notion that the EU is a well-designed institution to function in times of normality but fails in the face of crises as we have seen in the global financial crisis, the Greek debt crisis, the Covid-19 crisis and the recent energy crisis driven by the Russian invasion of Ukraine. There seems to be a lack of crisis handling institutions and processes.

Minor Comments:

·       The phrase “The dataset covers thirty European countries (Table 1), on the eight variables, recorded annually” should read “The dataset covers thirty European countries (Table 1), on the eight variables, in annual frequency.”

·       In the doiscussion of the TW-MDS methodology, the sentence should better read “The modification involves an additional phase that precedes the MDS identification: the imposition of a threshold on the edges’ strength (correlation values).”

·       In the conclusions use unemployment instead of Unemployment

Recommendation:

The topic is interesting with various obvious implications to both academics and policy makers. The paper is well written and structured. The authors use a new and innovative algorithm from the area of Graph Theory and more specifically Complex Networks. The methodology looks very suitable for this kind of analysis in economics. The results are interesting and provide some insight on the eight variables employed to study the conversion process. Nonetheless, I think that the authors should revise their paper along with my above comments before publishing.

Author Response

Please find attached.

Author Response File: Author Response.pdf

Reviewer 2 Report

Please see the attachment.

Comments for author File: Comments.pdf

Author Response

Please find attached.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

This version of the paper is much better.

Author Response

Please find attached.

Reviewer 2 Report

This paper has been modified based on the given comments, but there are still some deficiencies. So it can be accepted by Journal of Risk and Financial Management after minor revision.

Major commets

1.  The authors still do not explain the reasons for choosing eight macroeconomic variables such as GDP, HICP, Debt, that is, why not choose other macroeconomic variables.

2. About the descriptive statistical analysis of the eight macroeconomic variables, the authors add Table 2. In addition, the authors also needs to give necessary analysis of Table 2.

Minor Comments

1. There is an extra full stop in line 9, page 19.

Author Response

Please find attached.

Author Response File: Author Response.docx

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