Integrating “Neoliberal-Turn” and “Social-Turn” Constructs in Examining Sustainable Development and Happiness and Life Satisfaction: A Global-, Country Cluster-, and Country-Level Study

Round 1

Reviewer 1 Report

The current study entitled - Integrating “Neoliberal-Turn” and “Social-Turn” Constructs in Examining Sustainable Development and Happiness and Life Satisfaction: A Global, Country Cluster, and Country Level Study - addresses a topic of great interest both for the academic and for the socio-economic environment. Having a global approach, it is well structured and sufficiently explained through the lens of specialized literature published in the international data flow.

Considering the topicality of the research, the methodology used, the logical sequence and the results obtained support the publication of the article titled - Integrating “Neoliberal-Turn” and “Social-Turn” Constructs in Examining Sustainable Development and Happiness and Life Satisfaction: A Global, Country Cluster, and Country Level Study - in its current form, with small changes in form imposed by the journal's requirements.

I congratulate the research team on this occasion for their concerns and achievements.

Good luck!

Author Response

Response to Reviewer 1 comments.

We are very grateful for the generous comments of the reviewer. Surely, we look forward, if the paper is accepted, to following the guidelines of the journal’s editorial team.

Best regards,

Leonel Prieto

Author Response File: Author Response.pdf

Reviewer 2 Report

This study is of some interest, but throughout the text, the following recommendations are made:

1. The data used for the study is from 2002 to 2017, which is not sufficient to reveal the current situation, please update the data to 2022 or 201.

2. Table 2. Average Adjusted R-squares of selected models are low, not correlated and cannot express the relationship between individual variables.

3. An integrated approach is needed to integrate social, economic, economic and environmental indicators into a whole to reflect the current status of sustainable development in each country or region.

4. This study is static and it is recommended that relevant research be carried out from a dynamic perspective.

5. It is recommended that the results of the study be combined to give pathways and recommendations for the sustainable development of each country or region.

6. The wording of the text is inaccurate. For example, Taiwan is not a country, but a province of China.

7. The research is too scattered and not focused.

Better language expression.

Author Response

Responses to Reviewer 2.

We are very grateful for the reviewer’s comments which helped us to improve the paper. Below, we provide our responses.

1. Reviewer 2 comments:

 The data used for the study is from 2002 to 2017, which is not sufficient to reveal the current situation, please update the data to 2022 or 201.

1. Response:

Examining data from 125 countries with 108 social, environmental, and economic indicators entail a balance between the number of countries studied, the country clusters examined, the number of data years included, and the data quality.

This study tests the set of models at the global level, country cluster, and country level. To have a reasonable comparison among country clusters, it is necessary to have enough countries for each cluster. Therefore, it requires as many countries as possible. Maximizing the number of countries per cluster also contributes to a large global sample of countries. In this regard, our purpose is to be as inclusive as possible and to go away from many studies that mainly only study a few, usually developed countries.  Since worldwide each country must have a voice, development studies not including many countries lack global representativeness.  Given that sustainability ultimately requires a global scale, we made an effort to include as many countries as possible (125). Unfortunately, data may not be available for some indicators for all countries and years. Some indicators are derived from sporadic surveys and are only available every few years. Some countries do not regularly report data due to a lack of statistical capacity, conflict, or other reasons (World Bank, 2018). For example, there is missing  data for the following constructs and related indicators in the  time periods indicated for the named countries: goods market efficiency: Bhutan (2018-2022), Brunei (2018-2022), Guatemala (2019-2022), Burkina (2018-2022), and Burundi (2020-2022);  financial market efficiency: Jamaica (2019-2022), Mozambique (2018-2022),  and Namibia (2019-2022);  innovation: Lesotho (2020-2022), Kyrgyz Republic (2019-2022)); SDG11 Uganda (2020-2022), Mauritius (2019-2022), and Namibia (2018-2022); SDG 9: Tanzania (2020-2022), and Madagascar (2020-2022)); and SDG 5: Malawi (2019-2022), Ethiopia (2020-2022), Mongolia (2021-2022), and Bhutan (2019-2022)).  This example includes countries from Emerging & Developing Asia, Sub-Saharan Africa and Commonwealth country clusters which are critical country clusters.

Furthermore, to help us have good psychometric properties, we included many critical indicators for each construct, that is, a total of 108 indicators. In addition, to reduce the need for data imputation, we ensured that our missing data average was under 1% for each country and indicator. All the above restrictions have driven us to utilize data between 2007 and 2017, covering 11 years for 125 countries and 108 indicators. Given that our analyses are static, such a data set is strong enough to characterize the constructs and relationships studied.

We do not claim that our study reveals the current situation. Results are circumscribed to the 11-year data set used. However, adding two-year data is unlikely to drastically change the results based on 11-year data because it may take many years to substantially change most of the indicators used in the study (e.g., education, health, technology & innovation). In other words, adding two years data will reduce the total number of countries considered in the analyses and it will tell basically the same story.

To briefly reflect the above discussion in the paper, we added, in the revised version of the paper, the first two paragraphs of section 3.1.

2. Reviewer 2 comment

Table 2. Average Adjusted R-squares of selected models are low.

Judging adjusted R-squares is a function, among others, of the subject matter studied, the variables examined, the state of knowledge (e.g., availability of functional relationships depicting principles or laws with high predictive power, which we do not have), the quality of data, and the quality of the model. Nonetheless, low adjusted R-squares may be pretty informative, depending on the field/situation. Similarly, high adjusted R-squares may suggest a model that adds little to our understanding of the phenomenon studied. The adjusted R-squares values shown in Table 2 have very reasonable values given that we studied a system that is highly complex and variable, that is, sustainable development at the national and global levels. Such complexity is infinite! The phenomena studied may include uncontrollable, undeterminable, and unknown factors influencing the responses examined at different times and spaces. Because of the complicatedness and complexity of sustainable development, we use different models, scales, many countries, a large number of indicators, and a data set covering 11-year data. The explanatory power of the models studied is reasonable, given the nature of the constructs characterizing sustainable development as depicted in our models. Finance studies not infrequently have adjusted R-squares between 5% and 10%. In management, studies are common to have as acceptable adjusted R-squares between 30 % to 60%. Studying sustainable development relationships at the country, country cluster, and global levels is studying sustainable development management. Nonetheless, we sometimes looked for weak signals in a noisy context, as exemplified by weak or non-significant relationships which may call our attention to unexpected situations and may suggest future research. This acknowledgment is referred to several times in the paper.

Furthermore, we not only use adjusted R-squares to assess our models. Adjusted R-squares are just one among other measures used, including reliability and validity measures, variance inflation factors, and Tenenhaus goodness of fit (see section 3.5).

3. Reviewer 2 comments:

Average Adjusted R-squares do not correlated and cannot express the relationship between individual variables.

3.Response.

Given their purpose- indicating the percentage of variance explained by the model-average Adjusted R-squares do not require to be correlated among themselves, nor are they primarily meant to express relationships between individual variables. If the intention was to refer to correlations appearing in Table 4, 41 out of 66 are higher than .0.31, which in this field such values are categorized as moderate or strong. Certainly, the correlations are varied as may be expected from the nature of constructs and indicators studied. Moreover, even knowing that a relationship is weak or non-significant can be of value.

4. Reviewer 2 comments:

An integrated approach is needed to integrate social, economic, economic, and environmental indicators into a whole to reflect the current status of sustainable development in each country or region.

4. Response.

Our study aims to integrate social, economic, and environmental indicators into different wholes because we believe there is not just one whole. As pointed out several times in the paper as well as above, we used data from 125 countries, used 108 different indicators of social, economic, and environmental indicators at several scales. More integrations are needed; yes, we also pointed out such a need in the original paper several times.

5.Reviewer 2 comment:

 This study is static, and it is recommended that relevant research be carried out from a dynamic perspective.

5. Response.

There are different models and models with different purposes. We recognize that our models are static and that because of their static nature, they cannot provide some of the insights that dynamic models may produce. However, static models comprise more than 95% of articles published in development studies. Reasons include, among others, the lack of quality time series for most development indicators, the lack of developed and reliable functional relationships, our inability to have models evolving in terms of changing functional relationships and parameters, and our lack of knowledge as well as of principles or laws which may have high predictive power. In the paper, we referred to integrated assessment models, which have been very informative but mainly at the macro level. Currently, in most cases, we are not in a stage of scientific development in sustainable development studies, like the one reported in the paper, to properly operationalize dynamic models. Besides, greater numerical and or analytical details will be secondary, in sustainable development matters, to the effects of politics and social organizing or to insights gathered at the meso and macro levels because adding details here and there will mostly disappear in a vast set of complex relationships at higher levels.

6. Reviewer 2 comment:

 It is recommended that the results of the study be combined to give pathways and recommendations for the sustainable development of each country or region.

6. Response.

Recognizing the specificity of all model results as well as their variability, we are reluctant to express recommendations. In fact, given the complexity of sustainable development, in most cases, giving recommendations will amount to being irresponsible. We just sketch a few patterns contingent to the particulars of the study. As a result, in the section "5.2. Practical implications", we are generic and brief; namely, we refer to the need for policymakers to be aware of models' assumptions and limitations- an infrequent occurrence! -and also, the need to be humble in using models for policy recommendations and instead call for further development increasing the diversity of time, space, and perspectives considered. Furthermore, genuine collaboration among all stakeholders is the key to sustainable development which leaves modeling just as one tool to be used within frameworks collaboratively determined.

7. Reviewer 2 comment:

The wording of the text is inaccurate. For example, Taiwan is not a country, but a province of China.

7.Response.

We fully agree with this observation. Our apologies for our mistake.  We are grateful to the reviewer for this observation because it led us to thoroughly review the paper again. As a result, we realized that last year, in adding to the analysis the construct Happiness & Life Satisfaction (H&LS), we dropped from the analysis, because of the lack of H&LS data, four geographical entities: Austria, Albania, Cabo Verde, and Taiwan. However, in the paper version submitted to Sustainability, we overlooked erasing Taiwan from Table 1 as well as to correct the total number of countries. This can be verified by looking at Figure 7, on page 23 of the submitted paper in which it can be observed that in the Advanced Economies cluster Taiwan does not appear. In other words, Taiwan was discarded in 2022 and it was not included in any of our analyses. We also corrected, in the revised version of the paper, the total number of countries included, from 129, which was mistaken, to 125, which is the correct number of countries.

8.Reviewer 2 comment:

The research is too scattered and not focused.

8.Response.

Our purpose was to operationalize a set of models with a certain degree of complicatedness. Expanding the set of variables considered in most models is badly needed for no other reason than "clean"; sketchy models miss most of the context, and because of constructs' interdependence, such models are rather simplistic and far from the realities of sustainable development. Thus, our model set depicting different perspectives, integrating "classical" neoliberal constructs and a set of five sustainable development pillars synthesizing the 17 United Nations development goals, is an attempt to go beyond the restrictive realm of too much simplicity. In studies of nations and the world, most modeling efforts usually ignore many indicators and perspectives. Suppose we get results from simplistic models and make recommendations based on such models. In that case, we are most likely wrong because we ignore many other important constituents of the realities of countries and the world.  We believe that rather than continue with oversimplified models, we should seek to incorporate more constructs and relationships and try to reflect at higher levels of complicatedness and complexity. Such an approach would be more realistic and potentially more helpful in trying to reduce the gap between theory and practice or between scientists’ work and practitioners’ interests. The comprehensiveness of the models studied is one of the significant strengths of this paper. Based on their comments, other scholars appreciate this unique contribution and, and thus, the value of the paper.

Author Response File: Author Response.pdf

Reviewer 3 Report

The article is an interesting take on the subject.

Such analyzes are needed and can add value to science.

The article is written in a correct way, the concept, literature review, description of methodological assumptions and analysis of research results are made in accordance with the standards adopted for this type of studies.

The article fully meets the quality criteria of the journal. The publication is at a very high substantive level. The included analyzes are a new element of knowledge that broadens the horizons of science. The authors did a great job. In view of the above, I have only a few minor suggestions for further improvement of the article:

1. Check the entire study, taking into account the editorial guidelines of the journal, in particular the font and its size for figures (No. 1-4).

2. Extend part 6. Conclusion, many interesting and valuable analyzes were carried out in the article, so it is worth extending the final summary of the article - especially referring to the hypotheses put forward.

3. Add a DISCIUSSION part, in which a scientific discussion will be initiated with similar authors or in some field of similar research.

Author Response

Responses to reviewer 3’s  comments.

We greatly appreciate and are very grateful for the reviewer’s positive and kind assessment of our work.  Below, we provide answers to the reviewer’s questions/suggestions/comments.

Reviewer comment.

1. Check the entire study, taking into account the editorial guidelines of the journal, in particular the font and its size for figures (No. 1-4).

Response.

We checked thoroughly the paper in accordance with the journal editing guidelines. To the best of our knowledge, we followed such guidelines. However, if there is still anything that may need correcting, please let us know. If the paper is accepted we expect fine editing details from the editing/production team.

Reviewer comment.

2. Extend part 6. Conclusion, many interesting and valuable analyzes were carried out in the article, so it is worth extending the final summary of the article - especially referring to the hypotheses put forward.

Response.

Thank you very much for your suggestion. We did expand the conclusion trying to make it more informative. The final version of the Conclusion appears below..

This study shows that most relationships depicted in the “Social-turn 1," “Social-turn 1.2”, and “Social-turn 2” models differ in these configurations. In addition to model configuration differences, our research shows relationship differences between the global, country cluster, and country levels. Planet, an SD pillar constituted by biophysical variables, is the least of the five SD pillars positively related to the "neoliberal-turn" constructs and H&LS. Results show model configurations and scale effects. Furthermore, findings reveal synergies between neoliberal and the five SD pillars and both negative and non-significant relationships. Results’ diversity suggests the centrality of context and that both research and institutions; reports about development may be biased towards positive outcomes. Similarly, the diversity of results calls for further model integration and specificity. Diverse findings entail caution and difficulties in generalizing knowledge. Similarly, results suggest that it is not advisable, since there may be many different and partly legitimate alternatives, to rely on just one perspective (e.g., giving primacy to economic-based analyses) or in analyses at only one scale. The perspective effects and the stakes involved in SD urgently call for more collaborative efforts at all levels. Thus, deficits appear to be worse for the environmental and social dimensions reaffirming the need to continue taking a “Social-turn” approach. Addressing the complex challenges of SD entails strengthening, reconfiguring, and or creating new relationships. Paraphrasing [18], in co-creating societies and nature, we urgently need to do it creatively and more effectively than in the past. This study shows that most relationships depicted in the “Social-turn 1," “Social-turn 1.2”, and “Social-turn 2” models differ in these configurations.

Reviewer comment.

3. Add a DISCIUSSION part, in which a scientific discussion will be initiated with similar authors or in some field of similar research.

Response.

 As a result of the reviewer suggestion, we edited many parts of the paper. Such changes appear below. In addition, we provide, after the changes appearing below, a sample of instances where, in the text, we discuss the results.

We decided to consider together results and discussion (see Section 4) to avoid repeating the results and to save space. Our discussion part combines 1) our own thinking attempting to succinctly interpret the study results, and 2) relates our work to extant research which may agree or disagree with our results.  Following the reviewer’s suggestion, we expanded the discussion component. In this regard, we made the additions to the text which also appears below. Thereafter, we provide, after the changes appearing below, a sample of instances where, in the text, we discuss the results.

Changes to the prior version of the paper. The line numbers referred correspond to the revised version of the paper.

Deleted

Line #

16                           and

197                        Taiwan

Text added

Line #

6 - 9    † Authorship note

426      in addition to H&LS

482      and discussion

485      and discussion

522      and discussion

871      positively

971      Authorship note

Edited

125 countries instead of 129

108 indicators instead of 107

Table A1. Pillars and indicators (Number of indicators)

Line # 128 – 129: INTRODUCTION                                                                                             (Removed this line)

Similarly, results suggest that it is not advisable, since there may be many different and partly legitimate alternatives, to rely on just one perspective (e.g., giving primacy to economic-based analyses) and or on analyses at only one scale.

Line # 134 – 137               : INTRODUCTION                                                                                                             (Added text in yellow)

Finally, these findings suggest that it is not advisable, since there may be many different and legitimate alternatives, to rely on just one perspective (e.g., giving primacy to the economic dimension), highlighting the need for collaboratively constructed perspectives and or analyses at only one scale.

Line # METHOD (3.1 Measures selection and data sources) 360 – 368                                     (Text added)

Examining data from 125 countries with 108 social, environmental, and economic indicators entails balancing the number of countries studied, the number of data years included, and the data quality.

This study tests the set of models at the global level, country cluster, and country level. To have a reasonable comparison among country clusters, having enough countries for each cluster is necessary. Therefore, it requires as many countries as possible. Maximizing the number of countries per cluster also contributes to a large global sample of countries. Since sustainability ultimately requires a global scale, we made an effort to include as many countries as possible (125).

Line # 480 RESULTS AND DISCUSSION Table 4.                                      Deleted the last column

Line # 540 – 541               (4.1.2 GLOBAL LEVEL RESULTS AND DISCUSSION FOR “SOCIAL-TURN 2”                                                                                                                                                                                 (Text added)

These findings may be explained by the more comprehensive and enduring effects of Institutional Enhancers than those of Financial Enhancers.

Line 559 – 562 (4.1.2 GLOBAL LEVEL RESULTS AND DISCUSSION FOR “SOCIAL-TURN 2”                                                                                                                   (Deleted the repeated words appearing in yellow)

1. b) since results are a function of the type and number of relationships, in a study as comprehensive as ours, it is only possible to find past research, either partly supporting or not, specific relationships of our models, as discussed in the literature review section.

Line# 637 – 653 (4.2 COMPARISON OF COUNTRY CLUSTER)                                                          (Text added)

Cluster country results indicating model differences in only about one third of the relationships studied reaffirm our hypotheses because the remainder constitutes a set of shared relationships or a commonality existing despite model configuration differences. Most differences are concentrated in relationships involving FDIO, GC-Ins, and GC-InsS, which are constructs that manifest to a greater degree in developed countries. Since developed countries are the minority, the strength of their relationships involving the referred constructs is relatively infrequent. In addition to commonalities, the relationships between SD pillars and neoliberal constructs vary substantially among clusters reaffirming the usefulness of clustering countries. Likewise, the results’ variability, and, as a result, the need for model specificity, is greater than that usually acknowledged by international and national institutions involved in national (sustainable) development. Thus, lack of contextuality, including deficits in the voices considered in development efforts, may partly explain development efforts’ failures. In addition, published research and reports are biased toward significant and positive results. Our findings depict a mixture of positive, non-significant, and a few negative relationships, which, judging by the varied state of development worldwide, seem to somewhat reflect the diversity of development realities.

Line # 689 – 692               (4.3 Country cluster level comparison SD Pillars’ ….)                                       (Text added)

These findings reaffirm the disconnect between the environmental and both the social and economic dimensions and point out the need for a better balance between the three dimensions, particularly for less developed countries, the imbalance reflects a lack of development.

Line # 698 – 702 (4.3 Country cluster level comparison SD Pillars’ ….)                                      (Text added)

Furthermore, our models include “hard data” and indicators based on perceptions. Such diversity of data types and data sources, while helpful for triangulating and for reducing common method bias, may reflect a disconnect between, for instance, the SD pillars and H&LS, that is, there may be happy individuals that are negatively impacting the environment, the social, and the economic realms.

Line # 845 – 850 (4.4.8 Country outliers, globally and per country cluster)              (Text added)

The country cluster outliers refer, by definition, only extreme cases. However, there are other countries with substantial deficits in the 5 SD pillars. As pointed out above, such deficits relate not only to different scales (e.g., different scales may produce deficits in different geographical scales) but also to specific contexts. For example, air pollution problems greatly differ, despite the same geographical scales, between agricultural land and heavily populated areas.

Line # 871 – 875 (4.4.8 Country outliers, globally and per country cluster)               (Edited and text added)

In turn, this represents a challenge for SD because factors positively contributing to H&LS may, at the same time, degrade the natural environment. These results suggest that in integrating different geographical scales and dimensions, the degree of consistency between both constructs and construct indicators should be decided by collaboratively determined agreements.

Line # 885 – 888 (4.4.8 Country outliers, globally and per country cluster)               (Edited and text added)

All cases above (e.g., different scales, different model configurations, different constructs, different construct indicators) reaffirm the perspective (parallax) effect. Because of such effects as well as the collective and complex nature of global and national SD, the only way forward is to create, and work with, collaboratively determined perspectives.

Line # 955- 965 (Conclusion)                        (Expanded the information provided. Final text appear below)

This study shows that most relationships depicted in the “Social-turn 1," “Social-turn 1.2”, and “Social-turn 2” models differ in these configurations. In addition to model configuration differences, our research shows relationship differences between the global, country cluster, and country levels. Planet, an SD pillar constituted by biophysical variables, is the least of the five SD pillars positively related to the "neoliberal-turn" constructs and H&LS. Results show model configurations and scale effects. Furthermore, findings reveal synergies between neoliberal and the five SD pillars and both negative and non-significant relationships. Results’ diversity suggests the centrality of context and that both research and institutions; reports about development may be biased towards positive outcomes. Similarly, the diversity of results calls for further model integration and specificity. Diverse findings entail caution and difficulties in generalizing knowledge. Similarly, results suggest that it is not advisable, since there may be many different and partly legitimate alternatives, to rely on just one perspective (e.g., giving primacy to economic-based analyses) or in analyses at only one scale. The perspective effects and the stakes involved in SD urgently call for more collaborative efforts at all levels. Thus, deficits appear to be worse for the environmental and social dimensions reaffirming the need to continue taking a “Social-turn” approach. Addressing the complex challenges of SD entails strengthening, reconfiguring, and or creating new relationships. Paraphrasing [18], in co-creating societies and nature, we urgently need to do it creatively and more effectively than in the past. This study shows that most relationships depicted in the “Social-turn 1," “Social-turn 1.2”, and “Social-turn 2” models differ in these configurations.

Examples of discussion of results. The line numbers refer to the revised version of the paper.

Lines 489 to 506:

These findings are partly supported by [119,120], who found that institutions' overall efficiency and effectiveness increase the SD level. In contrast, Institutional Enhancers are negatively associated with Planet. In this regard, [121] proposed that although democratic institutions attract investments, most of the investments hurt the quality of the environment.

Financial Enhancers (i.e., government spending, monetary freedom, trade freedom, investment freedom, and financial freedom) are positively associated with People, Prosperity, and Resources. This result is partly in line with the findings of [122], who suggest that the role of government and government spending preferences on education, unemployment, safe water usage, and renewable energy consumption is vital for SD. Financial Enhancers are not associated with Planet but are negatively associated with Peace. The latter findings partly disagree with [123], who noted that economic freedom is negatively associated with homicides. Overall, Financial Enhancers have a weaker association with SD pillars compared to Institutional Enhancers, confirming the importance of countries' institutional soundness [124]. Both Institutional and Financial Enhancers have a small positive association with H&LS. These findings partly agree with [125], who proposed that both institutional and financial soundness positively influence countries' multidimensional well-being inequalities.

Lines 517 to 521:

“Neoliberal-turn” constructs either do not relate (e.g., GC-Ins, FDIO, Financial Enhancers) or relate negatively (FDII, Institutional Enhancers) to Planet. These results illustrate the disconnect between the environmental and economic dimensions. Similarly, “Neoliberal-turn” constructs relate more among themselves and with SD pillars, including economically related indicators.

Lines 526 to 530

In accordance with our expectations, advanced economies, which are more likely to be FDIO sources, are to have a higher degree of GC-InnS. However, the insignificant relationship between FDIO and GC-Ins is against expectations. It may have resulted due to canceling out effects.

Lines 532 to 535

In accordance with our expectations, advanced economies, which are more likely to be FDIO sources, are to have a higher degree of GC-InnS. However, the insignificant relationship between FDIO and GC-Ins is against expectations. It may have resulted due to canceling out effects.

Lines 542 to 549

Prosperity, Resources, and Peace have the most significant overall effect sizes on GC-Ins, GC-InnS, and H&LS, whereas the corresponding relationships with People and Planet were insignificant. Thus, Prosperity, Resources, and Peace may be viewed as reflecting the degree of national development, which will also include GC-Ins, GC-InnS. These findings suggest that, at the global level, the earth’s biophysical environment is given less importance than wealth generation, building resources, and having safe conditions. These results support, again, the disconnect between the environmental and economic dimensions.

Lines 550 to 555

Generally, the results of the “Social-turn 1” and “Social-turn 2” models partly affirm the relationships posed among the socio-ecological constructs in our study, thereby partly supporting Hypothesis 1a and 1b. However, it must be pointed out that 23 out of 30 common relationships between the “Social-turn 1” and “Social-turn 2” models are significantly different. This confirms, like a parallax, the perspective effect. Results’ variability makes it difficult to generalize findings.

Lines 557 to 571.

Generally, the results of the “Social-turn 1” and “Social-turn 2” models partly affirm the relationships posed among the socio-ecological constructs in our study, thereby partly supporting Hypothesis 1a and 1b. However, it must be pointed out that 23 out of 30 common relationships between the “Social-turn 1” and “Social-turn 2” models are significantly different. This confirms, like a parallax, the perspective effect. Results’ variability makes it difficult to generalize findings.

Lines 625 to 636

Out of nine common relationships between “Social-turn 1.2” and Social-turn 2” models in seven country clusters, that is, a total of 63 relationships, only about one third of them significantly differ between the two models. Most of such differences involve FDIO relating to GC-Ins and GC-InsS. FDIO variability among 125 countries may explain these results. Common results between the “Social-turn 1.2” and Social-turn 2” models seem to support our expectations expressed in the hypotheses. Similarly, specific model results suggest moderate to strong relationships between SD Pillars and socioeconomic constructs (i.e., Financial Enhancers, Institutional Enhancers, FDII, FDIO, GC-Ins, and GC-InnS). However, the model-specific relationships between SD pillars and neoliberal constructs drastically vary according to the country cluster. Thus, common and different relationships within and between country clusters suggest the need to analyze every single country cluster separately.

Lines 693 to 715

Overall, only 39 out of 105 relationships between any of the five SD pillars and H&LS were positive and significant. In other words, at the cluster level, only 37% of expected positive relationships were supported. Possible explanations include a lack of expected desirable development, canceling out effects due to aggregation, wrong expectations posed, disassociations between H & LS and the five SD pillars, and or the type of models’ functional relationships. Furthermore, our models include “hard data” and indicators based on perceptions. Such diversity of data types and data sources, while helpful for triangulating and for reducing common method bias, may reflect a disconnect between, for instance, the SD pillars and H&LS, that is, there may be happy individuals that are negatively impacting the environment, the social, and the economic realms.

Path coefficient differences occur not only between the “Social-turn 1” and Social-turn 2” models but also between the “Social-turn 1” and “Social-turn 1.2” models.

In Emerging Asia and Emerging Europe, the “Social-turn 2” model has a higher explanatory power of H&LS than the “Social-turn 1” model. The ‘Social-turn 2” model’s comprehensiveness and paths’ configurations may explain such results.

The “Social-turn 2” model (Model 14) reflects the comprehensive effects of the SD’s economic, social, and environmental dimensions on H&LS. In contrast, the “Social-turn 1 and Social-turn 1.2” models give primacy to the economic dimension. The “Social-turn 2” model may be considered superior to the “Social-turn 1 and Social-turn 1.2” models because it has higher explanatory power than the “Social-turn 1 and Social-turn 1.2” models, and conceptually, it circumscribes the economic dimension within a more encompassing socio-ecological framework. Therefore, we base the remainder of our analyses on the “Social-turn 2” model.

Lines 721 to 729

This result is also partly supported by [126] findings, who, in 23 developed economies, found that the environmental degradation relationship with people’s happiness was not significant. This cluster has two country outliers: Australia (Planet) and Portugal (People) (Fig. 6). In Australia, Planet negatively relates to H&LS. Long-term climate change projections show that climate change is more extensive than expected and that irreversibility of environmental changes is highly recognized [127]. Similarly, in Portugal, the People’s pillar is one of the central developmental issues in the country due to the low completion rate of higher education [128,129]. 

Lines 772 to 779

In this cluster, Planet is negatively related to H&LS. This may be so because the cultural-economic syndrome of oil-wealthy countries has a negative effect on democratic performance, which subsequently reduces the country's H&LS [137]. (Fig. 12 and 13). There are two negative country outliers: Tunisia and the United Arab Emirates. Tunisia is an outlier in terms of People (Fig. 12) since a low level of secondary education is negatively associated with disparity of socio-economic factors, causing unemployment and extreme poverty [138]. The United Arab Emirates is an outlier in terms of Resources since its water resources are scarce [139].

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

I still think the data is selected from 2007-2017 and is not realistic.

Author Response

Responses to Reviewer 2 comments about the revised version of the paper.

Reviewer comment.

I still think the data is selected from 2007-2017 and is not realistic.

Response.

• We priorly explained that we selected the period of the data used in our analyses to maximize the number of construct indicators as well as the number of countries. A large number of indicators is more likely to achieve a higher degree of construct content validity than just a few. However, neat and clean indicators usually depict very narrow realities. Including 108 indicators, most of them widely used worldwide in development studies, suggest that the study will somewhat depict some of the realities of the world. Similarly, in considering 125 countries, we sought to approximate a relatively high degree of world representativeness compared to hitherto research.
• The constructs and their indicators have been used worldwide for centuries. Therefore, they may depict a worldwide reality.
• The nature of the constructs and their indicators is such that it may take many years to change them, hopefully for the better substantially.
• The data used in our analyses is publicly available. Anyone at any time can access it. Therefore, data reflects the strengths and shortcomings of the different sources.
• The data sources, among others, the World Bank, the Footprint Network, and the Heritage Foundation, are used worldwide by a large number of researchers. In other words, the data sources used in our study have one of the highest credibility in the world.
• We indicated in the paper that the findings are circumscribed to the data period studied, the constructs considered, the operationalization of the constructs, and the configuration and nature of the models used in the analyses.
• Indeed, all models, be they academic, policy-focused, or those in the minds of social actors, are fragmented, incomplete models, as are ours. They depict only one or a few perspectives. As such, they are inaccurate from the viewpoint of other observers. However, a fully integrated and completely realistic development model of the 125 countries studied seems out of reach for humans. It is only approximations that we, all researchers, do. Such approximations are evidently from a certain angle, from a specific viewpoint. A mighty God may realize a fully integrated and utterly realistic model of the 125 countries studied, but it will not be useful for humans because our ability to deal with and understand complexity is limited; we have different beliefs as well as a vast diversity of how we look at things.

Author Response File: Author Response.pdf