Gendered Dimensions of Poverty in Indonesia: A Study of Financial Inclusion and the Influence of Female-Headed Households
, Ketut Sukiyono 2, Yefriza Yefriza 1, Ratu Eva Febriani 1 and Ririn Nopiah 1Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsThis research examines the feminization of poverty in Indonesia, with a focus on the distinct vulnerabilities faced by female-headed households using logistic regression model.
Please refer below for my comments:
It’s important to review and discuss the past studies in the field of study. Thus, a new section “Literature Review” should be included. Besides, the types of models should also be included and discussed, such as logistic regression and other statistical models.
For the research gap, research on the effect of financial inclusion on female-headed households in Indonesia are limited, particularly regarding poverty. What is the novelty of this study? Is there any new framework, model improvement etc.? The novelty of the study should be strengthened and emphasized.
Why do the authors use logistic regression model and not others?
Is there any missing value/data? Is yes, how do the authors handle it?
Do the authors consider interaction in this study? Kindly provide justification.
The equation of logit model should be labelled. Besides, the notation should also be provided.
More statistical analyses should be included in the results.
The limitations of this study should be presented in the paper.
The future research should be discussed in the conclusion section.
The citation format is incorrect.
Author Response
Dear Reviewer 1,
Please see the attachment.
Thank you,
Author Response File: 
Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThis study tackles an important topic by analyzing the intersection of gender, poverty, and financial inclusion in Indonesia. While the paper provides empirical evidence using a large dataset (SUSENAS 2023) and a logistic regression approach, there are several critical shortcomings that limit the study’s overall contribution and methodological rigor. Below are key weaknesses and areas where the manuscript needs improvement:
1. Overstated Novelty and Unclear Research Gap
The introduction claims novelty in exploring digital literacy and financial inclusion in female-headed households, yet fails to clearly differentiate this study from prior work. Many cited studies (e.g., Demirgüç-Kunt et al., 2020; Mabrouk, 2023) have already explored these intersections.
Articulate a sharper, evidence-backed research gap. Explain how this study advances theory or policy beyond prior literature. Consider including a comparative table of past studies and their limitations to strengthen your position.
2. Methodological Concerns and Model Justification
The paper relies solely on logistic regression without addressing key issues such as endogeneity, omitted variable bias, or model robustness beyond marginal effects and link tests.
Consider more rigorous econometric techniques (e.g., instrumental variables, propensity score matching) to address potential endogeneity—particularly around access to credit and bank account ownership. Also, provide VIF or correlation matrix results to justify multicollinearity tests.
3. Inconsistencies in Interpretation of Female-Headed Household Results
The paper inconsistently interprets the results related to female-headed households. In some sections, they are found less likely to be poor (β = -0.317), while elsewhere, the discussion reverts to traditional claims of their higher vulnerability.
Clarify this contradiction. If the finding contradicts literature, explore why—e.g., sample composition, social program effects, or measurement limitations.
4. Overreliance on Descriptive Analysis
Weakness: Much of the discussion section restates descriptive statistics and known theories rather than interpreting the regression results rigorously.
Suggestion: Focus on marginal effects and statistical significance from the model output to derive insights. Avoid overgeneralizations based on untested hypotheses.
5. Poorly Structured Tables and Model Presentation
Table 4 mixes regression coefficients and marginal effects without a clear structure or appropriate labeling. No separate models are presented for female vs. male-headed households, despite this being the central theme.
Reformat Table 4. Clearly distinguish marginal effects from coefficients. More importantly, run and present separate regressions by household gender to identify heterogeneous effects. This is crucial to support your gendered poverty argument.
6. Weak Operationalization of Key Concepts
Constructs like digital literacy, financial inclusion, and poverty are only briefly defined and not rigorously operationalized. For instance, binary IT skill and account ownership variables oversimplify multidimensional concepts.
Justify and explain how these variables are measured. Consider using indices (e.g., Financial Inclusion Index) or multiple-item measures if the dataset allows.
7. Writing Quality
The manuscript is verbose, repetitive, and in some areas poorly structured. For example, similar findings about education and poverty are repeated across pages with minor variation.
Substantially reduce redundancy. Improve the academic tone and clarity. Consider professional editing for grammar, syntax, and flow.
8. Policy Recommendations Lack Depth and Feasibility
While the conclusion calls for “gender-sensitive financial inclusion” and “multisectoral approaches,” it lacks specificity on implementation.
Propose concrete and context-relevant policies based on the empirical findings. For example, what kind of digital literacy programs? Delivered by whom? Targeted how?
9. No Robustness Checks or Sensitivity Analyses
The study does not provide robustness tests (e.g., alternative poverty thresholds, variable transformations, or sub-sample analysis).
Run robustness checks to enhance credibility. At a minimum, show how results change (or remain stable) for urban vs. rural or male- vs. female-headed households separately.
10. Literature Integration Is Dense but Superficial
The discussion references an overwhelming number of studies, often in passing, without critical engagement or synthesis.
Engage fewer but more relevant sources in greater depth. Build a coherent theoretical narrative to explain the observed results.
Comments on the Quality of English LanguageThe manuscript is verbose, repetitive, and in some areas poorly structured. For example, similar findings about education and poverty are repeated across pages with minor variation.
Substantially reduce redundancy. Improve the academic tone and clarity. Consider professional editing for grammar, syntax, and flow.
Author Response
Dear Reviewer 2,
Please see attachment.
Thank you.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe authors have revised and improved the manuscript based on the given comments. Hence, I have no further comments.
Reviewer 2 Report
Comments and Suggestions for AuthorsAcceptable
Comments on the Quality of English LanguageLanguage needs more attention. The manuscript is riddled with grammatical errors, awkward phrasing, run-on sentences, and inconsistent use of tenses. Numerous instances of repetitive, verbose, and unclear wording hinder readability and diminish the professional quality of the paper. A comprehensive language edit is essential to improve clarity, academic tone, and flow.
