Factor Analysis of Sustainable Livelihood Potential Development for Poverty Alleviation Using Structural Equation Modeling

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

In the abstract, unnecessary quantitative indicators (Results).

In more than 200 lines, the authors deal with Literature review. It is to be commended that the most recent literature is used, but in relation to the length of the paper, this section is too extensive.

The conclusion related to the relationship between the variables in relation to the primary model, it is important to note that the authors recognized a large number of Factors that significantly increase the potential of life capital, they considered different variables: By collecting data based on related questionnaires and recognized 37 indicators.

Authors used the index analysis method to evaluate the potential of poor house holds' living capital in 5 dimensions: natural capital, material capital, human capital, social capital, and financial capital.

The authors correctly noted and wrote that indicators must be reduced according to the recommendations of the statistical data analysis program (Mplus Program). Further research could address these limitations by collecting 589 more comprehensive and accurate data Using the alternative variable method or conducting a case study.

Author Response

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best regard,

Author Response File: Author Response.pdf

Reviewer 2 Report

Comments and Suggestions for Authors

Please open the attached file with comments and suggestions.

Comments for author File: Comments.pdf

Author Response

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best regard,

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

Review of Thailand Poverty Analysis

I start from the facts that agricultural labor and farmers are 31% of all workers but produce only 8.7% of all output. Thus, strategies that reduce poverty by focusing only on agriculture are likely to fail. The problem is there are too many workers in agriculture. The fact that rural to urban migration is continuing fairly rapidly ( The urbanization rate rose from 42% in 2010 to 55% in 2020 according to the Asian Development Bank Key Indicators – meaning migration of roughly ¾ million a year, more than 1% a year of population.) A perfectly rational anti-poverty approach is to educate your children enough so they can get a decent urban job and send money back home. Or to have government policies supporting that strategy.  The average farmer produces 28% of average GDP. The average non-farmer produces 132% of average GDP. Who would not want to enter a sector that offers 4.7 times the average income of the agricultural sector?

The analytical approach used implicitly takes the population and workforce as glued in place in each province or even village when that is not what is happening, nor should it happen. I am entirely sympathetic to many of the study’s suggestions – provide education, vocational training, electricity, transport infrastructure, rural credit, perhaps irrigation (but no High-Level Diversion Projects!) and so on. Asking poor families or villages to rank their priorities is usually a good place to start.  It is not clear to me if the poor villagers get any transfers from urban areas in estimating their income. Asking if they did and if they wanted their children to work in the village or move to a more thriving area might be a way to structure the needed investments.

The study itself could use a good English language editor. There are often non-sentences and unclear passages. Frequently, I am not sure what is meant. I did get the general drift of what was done and if the idea is to direct public investment, the list of variables is reasonable for those working in the villages. It may not be optimal for those who want to leave. It may not even be what the poor people prioritize.

The lists of variables with the boxes and arrows I found confusing. At least, put the variable description next to the box instead of C1, C3, and C6.

I might at least mention Thailand’s standing in the PISA tests (Program for International Student Assessment) since that is a weight on the ability of young rural workers to learn urban skills. National scores peaked about a decade ago and have fallen since. https://www.oecd.org/publication/pisa-2022-results/country-notes/thailand-6138f4af?mibextid=Zxz2cZ#section-d1e17 I do not have urban vs rural scores but would be shocked if the urban scores were not higher. This points to a national, not local, problem. It may require new technology since attracting really good teachers to rural areas is not always easy.

Comments on the Quality of English Language

If it is possible to work with an editor, the clarity of the writing would be much improved. Many times, you do not write a complete sentence and the meaning is unclear.

Author Response

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best regard,

Author Response File: Author Response.pdf

Reviewer 4 Report

Comments and Suggestions for Authors

The article "Factor Analysis of Sustainable Livelihood Potential Development for Poverty Alleviation with Structural Equation Modeling" presents an analysis of the potential for sustainable livelihood development to reduce poverty. The authors utilize factor analysis and structural equation modeling to examine the potential of natural, physical, human, social, and financial capital of households in Thailand.

While the article provides valuable insights into the potential development of livelihood capital in the context of poverty alleviation, it falls short in certain aspects of analysis. Primarily, the authors do not provide a sufficiently broad discussion of their research findings. The lack of an in-depth analysis of the results and their implications may limit understanding of potential consequences for practice and social policy.

Additionally, the authors omit a review of European literature, which serves as a significant reference point for the topic of poverty and methods of its analysis. Works by researchers such as Szarfenberg, van Praag, Kepteyn and Aleksandra Łuczak, the last author explored subjective poverty and multidimensional poverty analysis methods, could enrich the discussion and contextualize the article's findings.

One significant missing element in the article is an analysis of the potential applicability of research findings to other countries. Since each society faces unique challenges related to poverty, it would be valuable for the authors to consider how their findings may be applied in the context of other countries, especially those with similar socio-economic conditions.

Despite these shortcomings, the article contributes an important insight into the discussion on the potential of livelihood capital in poverty reduction. It represents a step towards understanding the complex mechanisms influencing the material situation of poor households. However, to fully appreciate the significance of these findings and their practical application, a more comprehensive discussion of the results and consideration of a broader theoretical and empirical context is necessary.

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in track changes in the re-submitted files.

Author Response File: Author Response.pdf

Reviewer 5 Report

Comments and Suggestions for Authors Thank you very much for sending this interesting article and for the possibility to review it. In itself, the discussion of structural equations applied to multidimensional poverty is interesting mainly because of the application of the structural equations to poverty. In general terms, the method is understood, it is explained. However, the explanation of figure 18, which is key to understand the whole analysis, needs to be improved. In mathematical terms there is a clear, although not conclusive analysis of the results. In terms of graphs, it is clearly necessary to consider an improvement to the graphs such as 18 and an order in the tables adjusted to the format of the review.
However, table 14 should be improved for your comments in the same way as your explanation. The conclusions are consistent and associated with the estimated models. The article requires minor revisions to make it suitable for publication.

 

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in track changes in the re-submitted files.

Author Response File: Author Response.pdf

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

Thank You to the Authors for the responses. Please see the attachment below.

With best regards.

Comments for author File: Comments.pdf

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in track changes in the re-submitted files.

Author Response File: Author Response.pdf

Reviewer 3 Report

Comments and Suggestions for Authors

My continuing major concern with the analysis in this paper is that of reverse causality. If someone owns land for their home, is that because they were lucky (good parents left them a home) or because they did well in life and the home is the result of that? If someone uses irrigation and gets higher income and health and education, does that mean that areas not fit for irrigation should strive for it? Again, they may have been lucky to get an irrigated farm (parents) or had been able to buy one and benefit from it. Correlation does not indicate the direction of causality.

Comments on the Quality of English Language

Check line 90 where the sentence has no verb.

Author Response

Thank you very much for taking the time to review this manuscript. Please find the detailed responses below and the corresponding revisions and corrections highlighted in track changes in the re-submitted files.

Author Response File: Author Response.pdf

Round 3

Reviewer 3 Report

Comments and Suggestions for Authors

My concern remains that most people escape poverty by moving from low to high productivity sectors. This may involve internal migration or could involve the expansion of more productive firms moving into poorer areas. The focus on providing natural capital is understandable but limited. Of course, having money, an owned home, a good social network (often in cities, not just in one's home province) both reduces poverty and is - if inherited or due to prior success - is a result of having escaped or not having been in poverty. Your analysis (I think) tries to tie policy interventions to people in or near poverty. Ignoring the "exit" option suggests a secondary priority of investing in a specific place rather than just reducing poverty. This should, at least, be acknowledged. Since 2000, the urban share of population has been rising 1% a year in Thailand and is now more than 50%.

Comments on the Quality of English Language

This is readable but has occasional glitches.

Author Response

Thank you for pointing this out. We agree with this comment that most people escape poverty by moving from low to high-productivity sectors, which often involves internal migration or the expansion of more productive firms into poorer areas. While providing natural capital is important, it's not the only solution. Having money, owning a home, and having a good social network can also reduce poverty, but these are often the result of having already escaped poverty or inherited wealth. It's important to acknowledge that policy interventions should focus on reducing poverty in a specific place and creating opportunities for people to escape poverty through other means such as job growth or urbanization.

Author Response File: Author Response.pdf