Nonlinear Dynamics of the Development-Inequality Nexus in Emerging Countries: The Case of a Prudential Policy Regime

Suggestion by the reviewer(s)

Correction by the author(s)

I recommend the author(s) to compare the result obtained in this paper to the result of Chiu and Lee (2019). Chiu and Lee (2019) classified 59 countries into 32 high-income countries and 27 low-income countries for the period 1985-2015 (Table A1), showing that the Kuznets curve appears in low-income countries, but not in high-income countries (Tables 6 and 7).

Chiu Y-B, Lee C-C. (2019) Financial development, income inequality, and country risk. Journal of International Money and Finance. 93, 1-18.

Thank you so much for your valuable suggestion. However, the study by Chiu and Lee (2019) investigated the “Financial development, income inequality, and country risk”, while the current study looks at the “Nonlinear Dynamics of the Development-Inequality”.

Therefore, I think these two papers can’t not be compared

I would use a scatter diagram to make clear the structural change before and after 2005.

We really appreciate your varied suggestions. However, as the study aimed to investigate the non-linear dynamics of development inequality in a prudential-policy regime in emerging markets, a non-prudential policy regime (1985–1999) and a prudential policy regime (2000–2019) were adopted. The time span of our study is divided into Following the Cerutti data (Cerutti et al. 2017), Cerutti data dum-my-type variables for the implementation of various macroprudential instruments. Their data shows that most countries started introducing these kinds of policies in 2000 and have continued till now. However, even before 2000, these policies were adopted in various countries. We believe that there is a switch from a non-prudential policy regime to a prudential policy regime, which was divided

Suggestion by the reviewer(s)

Correction by the author(s)

I support the idea to publish the paper in the review.

The article is well constructed. The authors correctly describe the methodology and obtained results.

Maybe in the part of the robustness check, the estimations could have covered any analysis, including the cubic form of development.

Then, we might find out whether, after the reductions in inequality in the second stage, the third stage is characterised by growing disparities among households.

 Besides, the graphic part of the paper should be improved.

 I think that results can be presented more clearly.

Thank you so much for your valuable suggestion. However, we have contracted the second version of this paper, where our aim is to build from this paper.

One of the things we wanted to achieve in the second version of this paper is the cubic relationship between the two variables. We even further used more measures of macroprudential policies to single out their impact on the subject matter.

We then deeply scrutinize the relationship by looking at the poorest 20% of the population and the share of the total income accruing to 40% of the population, and further use the Atkinson index to measure income inequality.

In the second version of this paper, we incorporated variables such as the one mentioned in the reviewer’s report and others, such as the democracy index, a rule-of-law index, schooling, and trade openness.

Authors should also dwell on reducing the literature review.

 It would be enough to show what has been written till now about the subject, omitting the details on regressors used by other authors in their papers.

Summing up, the shorter version of the article could be prepared without losing its scientific dimension.

Thank you so much for your valuable suggestion. This comment was addressed properly by reducing the literature review.

Suggestion by the reviewer(s)

Correction by the author(s)

In the first round of revision process, I recommended the author(s) to compare the result obtained in this paper to the result of Chiu and Lee (2019), which I believe makes clear the contribution of this paper.

 Unfortunately, the author(s) did not follow the comment simply because Chiu and Lee (2019) cannot be compared to this paper. I suspect that the author(s) did not read Chiu and Lee (2019) carefully. The research question is the same as this paper, and they also control real GDP per capita as an index of economic development. Chiu and Lee (2019) employ a richer set of explanatory variables such as financial and political risks, which is a little bit different from this paper. However, this does not imply that this paper cannot be compared to Chiu and Lee (2019).

Thank you so much for your valuable suggestion, and we are very sorry about the response we provided in the first round. We have carefully checked the paper published by Chiu and Lee (2019), and the corrections have been made as follows:

We compare our results with the findings documented by Chiu and Lee (2019), who classified 59 countries into 32 high-income countries and 27 low-income countries for the period 1985–2015. When we look closer at the countries classified as low-income countries in their study, most of them are the ones we classified as emerging economies in our study. Their findings confirmed the evidence of the Kuznets hypothesis to hold for low-income countries, while for high-income countries it does not hold. Comparing their findings with our results reveals the evidence that more work needs to be done in implementing policies that will foster the distribution of income for these countries and push the level of GDP per capita to be above the estimated threshold.

Suggestion by the reviewer(s)

Correction by the author(s)

The authors claim that “The time span is divided into two regimes, following the work documented by Cerutti et al. (2017), which shows that most of the emerging markets started adopting these policies in 2000”. I believe that this explanation is not valid; the database by Cerutti et al. (2017) starts from 2000. This does not mean actually that macroprudential policies started to be implemented from 2000 onwards, but the availability of the data starts from this year onwards. Thus, the definition of the period from 2000 to 2019 as the prudential policy regime does not follow from the paper by Cerutti et al. 2017 but is has to do with the available data. I believe that authors should be more precise regarding this issue.

Thank you so much for your valuable suggestion. We have carefully checked the entire paper, and the corrections have been made.

The time span of our study is divided following the Cerutti data (Cerutti et al. 2017). Cerutti data dummy-type variables for the implementation of various macroprudential instruments. We define the period of the prudential policy regime starting from 2000 to onwards due to the availability of data starting from 2000 to onwards. While we then, from 1999 to backwards is classified as the period of the non-prudential policy regime. 

Although the authors included an additional subsection about the effects of macroprudential policies on income inequality in the literature review section, I missed a discussion whether the positive relationship of macroprudential policy and income inequality depends on the level of economic and financial development (for example, this relationship is stronger for less developed financial markets and lower degrees of economic openness (see e.g., Konstantinou et al, 2021))

Thank you so much for your valuable suggestion. We responded to your comments as follows:

This study is the first to examine the impact of macroprudential policies on the development-inequality relationship. Therefore, we briefly explain the empirical relationship between macroprudential policies and income inequality. The existing empirical literature on the distributional impact of moocrprudential policies shows that an increase in the adoption of these policies increases income inequality. After scrutinizing the empirical literature on this subject matter, the researcher revealed five relevant empirical papers that examine the impact of macroprudential policy on inequality (Zinman, 2010; Tzur-Ilan, 2016; Frost and van Stralen, 2017; Acharya et al., 2017; Carpantier et al., 2018). The study by Zinman (2010) investigated wealth and income inequality and the consumption effects of macroprudential measures in the case of the state of Oregon in the USA. The empirical evidence shows that macroprudential policies have a redistributive effect on wealth and income inequality. The argument was further taken by Tzur-Ilan (2016) following a borrower-related argument using a macro-analytical framework to examine the introduction effect of the loan-to-value (LTV) limit in Israel. The empirical findings show that consumer credit is a form of unsecured debt associated with higher rates. Borrowers increase the economy’s overall exposure to the risk of recession and unemployment. The results support the argument that LTV macroprudential instruments are likely to make less-wealthy borrowers more vulnerable.

Furthermore, Acharya et al. (2017) studied the effect on residential mortgage credit of the introduction of DTI and LTV caps in Ireland. The results of this study support the argument that borrower-related macroprudential instruments make the wealthy group wealthier, thus increasing wealth inequality. Frost and van Stralen (2017) used the database of Cerutti et al. (2016) for 69 countries over the time span 2000–2013 to investigate the casual relationship between macroprudential instruments and the Gini coefficients of net and market inequality. The findings reveal evidence for the redistributive effects of macroprudential policy. Precisely, the finding shows that tighter measures such as higher reserve requirements, LTV caps, as well as concentration and interbank exposure limits, increase income inequality. Carpantier et al. (2018) used a household survey for 12 European Area countries employing HFCS data. The author finds that caps on LVT ratios may reduce wealth inequality in a sense that households find it tougher to get a mortgage, which results in low indebtedness by pushing wealth inequality low.

Konstantinou et al. (2021) investigated the impact of macroprudential policies and income inequality in former transition economies. Their results indicate that, in general, the adoption of these policies exacerbates income inequality. The effect, however, is contingent on the extent of the degree of financial development and globalization; low levels of openness and financial development exacerbate inequality. However, some macroprudential measures may result in lower income inequality, provided the adopting economy is sufficiently open and has a developed or unrestricted financial system.

The authors claim that they further use different variables to measure income inequality instead of Gini, such as total income accruing to the poorest 20% of the population and the share of the total income accruing to 40% of the population, and further use the Atkinson index. However, there is no table in the paper that presents these results (for instance, Table 4 presents the results for the aforementioned dependent variables? It is not clear to me)

Thank you so much for your valuable suggestion. In response to your comments, We adopted the pre-tax national income top 10% from the world inequality database to measure income inequality to capture income inequality. Please check section 4.4.

I missed again a discussion about the GMM regression. It would be useful if the authors could provide some additional clarifications about the GMM estimation, for example the number of lags used or how many and what are the endogenous variables.

Thank you so much for your valuable suggestion. We responded to your comments as follows:

Finally, we evaluate the correlation between economic development and income inequality using the Difference Generalized Method of Moments (Difference-GMM) (Arellano and Bond 1991; Blundell and Bond 1998) and fixed-effect models (FE). We adopted the Difference-GMM because we wanted to remove the problem of the individual effect. In these techniques, we generated the squared term of economic development to capture the nonlinear form of development inequality in emerging economies. The dependent variable will be further included in the GMM estimator as a lagged explanatory variable. This estimation approach is utilized since the economic development variable has an endogeneity problem, as the expansion of income inequality may have an effect on the level of development. Furthermore, for some of our control variables, the idea of double causation cannot be ruled out. Finally, the GMM estimator has two types of instruments: the external instrument and the internal instrument. It has been argued in the literature that internal instruments are recommended for the GMM system, compared to external instruments. This is because choosing an external instrument for the GMM is the most difficult part of the estimation. The internal instruments are instruments for the data the researcher is working with, such as the lag values of the regressors. We take advantage of the ability to build instruments internally for the current study. Endogenous variables are, therefore, instrumented by their lagged values. In a nutshell, this means that the instrument of this analysis must come from within.

For our Difference-GMM, we set the number of lags to 1 for yearly differences in our yearly data, and we further cut our time period for the prudential policy regime to start from 2005–2019 in order to comply with the conditions of the GMM that T should not be greater than N. 

It would be useful if the authors could provide some stylized facts or discuss the per-country variation of the income inequality indices across time.

Thank you so much for your valuable suggestion. We responded to your comments as follows: (Please check figure 1 on page 2 that corresponds with the explanation below).

Figure 1 graphically demonstrates the mean Gini coefficient for both the prudential (1985–1999) and non-prudential policy regimes (2000–2019).

The insight gained from these two regimes (prudential and non-prudential policy regimes) demonstrates that during the non-prudential policy regime, about four countries, namely, Brazil, Chile, Malaysia, Mexico, Thailand, and Peru, experienced high income inequality as the Gini coefficient is high above that of the prudential policy regime. During the prudential period.

While the remaining nine countries that are included in the sample are experiencing the challenge of high income inequality during the prudential policy regime, as the mean Gini coefficient is above the mean reported during the non-prudential policy regime.