Round 1

Reviewer 1 Report

This is an interesting paper using new tests on newish data that more or less confirms what we already know. The claims to novelty are only really relevant to the method.  The position of the US on this issue is already well known so the claims that the findings are surprising in this regard are not so well founded.  That doesn't count against the paper in my view but it would be better if this claim were removed or made more nuanced/specific.

The paper is fine and makes an empirical contriubution - especially the unpacking of the various tests of inequality in greater detail than in the well known work of Corak.  To improve the paper for the audience and give it more impact I would reccomend:

1. More lit review work at the outset explaining the existing positions in the literature in greater detail and also why this is an important poliucy issue - it is becoming more so bc of populism etc.

2. Explain the tests more simply and also the implications of the work on poverty and income shares. There is likely to be policy interest in these nuanced findings and they need unpacking in more accessible terms for non-statistical/economics audiences.

3. Beef up the conclusion to strengthen the implications and policy messages that arise from them.

Author Response

This is an interesting paper using new tests on newish data that more or less confirms what we already know. The claims to novelty are only really relevant to the method.  The position of the US on this issue is already well known so the claims that the findings are surprising in this regard are not so well founded.  That doesn't count against the paper in my view but it would be better if this claim were removed or made more nuanced/specific.

R/ taken into account, the claim was moderately nuanced!

The paper is fine and makes an empirical contribution - especially the unpacking of the various tests of inequality in greater detail than in the well known work of Corak.  To improve the paper for the audience and give it more impact I would reccomend:

1. More lit review work at the outset explaining the existing positions in the literature in greater detail and also why this is an important poliucy issue - it is becoming more so bc of populism etc.

R/ More literature review work was done, in fact the introduction was rewritten to accommodation the suggestions made by all three referees!

2. Explain the tests more simply and also the implications of the work on poverty and income shares. There is likely to be policy interest in these nuanced findings and they need unpacking in more accessible terms for non-statistical/economics audiences.

R/ Mode details were added to improve understanding especially for non-statistical/economics audience

3. Beef up the conclusion to strengthen the implications and policy messages that arise from them.

R/ We didn’t change much in the conclusion section since the word “beef up “ would have different meaning to us than to the referee.

 

Reviewer 2 Report

This paper uses Markov switching model to estimate the social mobility (transition probability) across 44 countries. The author(s) finds that developed and emerging countries exhibits lower social mobility, and some countries are abmornally immobile. This is a very important issue, however, I find the contribution of this paper is incremental.

 

There are many existing articles (both in economics and in sociology) that focus on the social mobility and the relation between equality and social mobility. While the result are similar, I was wondering what is the main contribution on top of the literature. Simply using the Markov switching and K-means does not sound enough. I would urge the author(s) to think about deeper, like, what factors lead to high correlation between equality and social mobility?

 

Some minor concerns are listed below.

-Are the results robust to the model selection and bin selection?

-Tables adnd figures should be self-explanatory.

-Figure 2 (and wrongly marked as Fig 1) is hard to read. I suggest separating the figure into several sub-figures, in which each of the indicies are compared across countries.

Author Response

This paper uses Markov switching model to estimate the social mobility (transition probability) across 44 countries. The author(s) finds that developed and emerging countries exhibits lower social mobility, and some countries are abmornally immobile. This is a very important issue, however, I find the contribution of this paper is incremental.

R/ noted with thanks 

There are many existing articles (both in economics and in sociology) that focus on the social mobility and the relation between equality and social mobility. While the result are similar, I was wondering what is the main contribution on top of the literature. Simply using the Markov switching and K-means does not sound enough. I would urge the author(s) to think about deeper, like, what factors lead to high correlation between equality and social mobility?

 R/ Our contribution is methodological, it proposes new tools (Markov Switching regression and K-Means) that have never been used before in this field. A different set of data and methodology would be needed to determine “what factors lead to high correlation between equality and social mobility”. We believe that that is out of the scope of our study

Some minor concerns are listed below.

-Are the results robust to the model selection and bin selection?

R/ Yes, the results are robust and involved the use of bootstrapping during model estimation

-Tables adnd figures should be self-explanatory.

R/ Thank you, we have added more details in the Tables and Figures. Please see revised Tables and Figures in the manuscript

-Figure 2 (and wrongly marked as Fig 1) is hard to read. I suggest separating the figure into several sub-figures, in which each of the indicies are compared across countries.

R/ We have corrected it, thank you for picking this up. However, separating this figure into several sub-figures would require more space.

 

Reviewer 3 Report

Comments

 

Economies-1918256

Does Economic Inequality Account for Cross-Country Discrepancies in Relative Social Mobility: An Empirical Investigation

 

This paper makes use of the Markov Switching model and the K-Means Cluster analysis to estimate the transition probabilities of social mobility and to analyze the impact of social inequalities on intergenerational social mobility. The dataset is a sample of 44 countries and comprises the 2018 social mobility indices, and the 2018 or latest income inequality measures. The data is collected from the OECD Income and Wealth Distribution Databases, and from the world economic forum. It was found that the likelihood of moving upward or downward the social ladder is minimal in both developed and emerging countries. In addition, the paper found that the hypothesis according to which high-income countries have higher relative social mobility is not necessarily true. The United States, a high-income country was found to have a lower social mobility similar to that of Turkey and South Africa. Furthermore, it was found that when poverty decreases intergenerational social mobility increases in both lower and higher mobility countries. Policies that promote equality of opportunities at all stages of life are therefore recommended to improve intergenerational social mobility.

 

1.       The article is interesting, and its topic is very timely. This paper is well developed, and enjoyable to read.

2.       The authors should clarify if and how their approach differs from the previous literature. The authors should be better motivated. What is a methodological innovation in their studies? How do they overcome the methodological issue in the previous research?

3.       The authors enrich their references. In what way does inequality affect economic variables (Konstantakopoulou, 2016).

4.       In all tables, the significance levels should be included in the t-statistic (i.e., t=5.212***, where : *** indicates significance at 1% level; ** to be significance at 5% level and * significance at 10% level.)

 

Reference

Konstantakopoulou I. Further Evidence on Import Demand Function and Income Inequality. Economies. 2020; 8(4):91. https://doi.org/10.3390/economies8040091

 

 

Author Response

This paper makes use of the Markov Switching model and the K-Means Cluster analysis to estimate the transition probabilities of social mobility and to analyze the impact of social inequalities on intergenerational social mobility. The dataset is a sample of 44 countries and comprises the 2018 social mobility indices, and the 2018 or latest income inequality measures. The data is collected from the OECD Income and Wealth Distribution Databases, and from the world economic forum. It was found that the likelihood of moving upward or downward the social ladder is minimal in both developed and emerging countries. In addition, the paper found that the hypothesis according to which high-income countries have higher relative social mobility is not necessarily true. The United States, a high-income country was found to have a lower social mobility similar to that of Turkey and South Africa. Furthermore, it was found that when poverty decreases intergenerational social mobility increases in both lower and higher mobility countries. Policies that promote equality of opportunities at all stages of life are therefore recommended to improve intergenerational social mobility.

 

1. The article is interesting, and its topic is very timely. This paper is well developed, and enjoyable to read.

R/ Thank you so much!

2. The authors should clarify if and how their approach differs from the previous literature. The authors should be better motivated. What is a methodological innovation in their studies? How do they overcome the methodological issue in the previous research?

R/ We have rewritten the introduction to cater for this important suggestion and the suggestions of other referees. The revised manuscript extends the literature and spells out the contribution of this paper.

3. The authors enrich their references. In what way does inequality affect economic variables (Konstantakopoulou, 2016).

R/ Konstantakopoulou (2016) used annual panel data on OECD countries to demonstrates the impact of inequality on import demand. Our paper approaches the issues of inequality and social mobility from the hidden Markov process standpoint which is a bit parallel to Konstantakopoulou (2016). Thank you for proposing the paper

 

4. In all tables, the significance levels should be included in the t-statistic (i.e., t=5.212***, where : *** indicates significance at 1% level; ** to be significance at 5% level and * significance at 10% level.)

R/ Thank you for proposing this, we have added the t-statistics with their significance to the tables

 

Reference

Konstantakopoulou I. Further Evidence on Import Demand Function and Income Inequality. Economies. 2020; 8(4):91. https://doi.org/10.3390/economies8040091

 

Round 2

Reviewer 2 Report

I have no more questions. The revised version looks much clearer to me.