Exploring the Impact of Digital Inclusive Finance and Industrial Structure Upgrading on High-Quality Economic Development: Evidence from a Spatial Durbin Model
Round 1
Reviewer 1 Report
Comments and Suggestions for AuthorsAttached file
Comments for author File: 
Comments.pdf
Author Response
Review Report: 1. This manuscript presents a compelling and well-structured investigation into the impact of digital inclusive finance (DIF) on high-quality economic development in China, employing a Spatial Durbin Model (SDM) and drawing on extensive panel data from 281 prefecture level cities between 2011 and 2021. The topic is timely and policy-relevant, particularly in light of China’s national strategy to shift from quantity-driven to quality-oriented economic growth (Shi et al., 2024; Zheng et al., 2025). Overall, the paper offers valuable insights and contributes meaningfully to the evolving literature on digital finance, spatial economics, and regional development.
Author’s response: We sincerely thank the reviewer for the positive and encouraging feedback. We are delighted that you found our research topic timely, the analysis well-structured, and the contribution to the literature meaningful. Your recognition motivates us to further improve the clarity and rigor of the manuscript.
- The abstract is informative and largely well-composed, although it would benefit from slight condensation to enhance clarity. Some expressions—such as “notable regional disparities exist”—could be more specific to sharpen the paper’s core message. Highlighting the novel identification of a “double-threshold effect” earlier in the abstract would help underline the study's originality.
Author’s response: We appreciate the reviewer’s thoughtful suggestions regarding the abstract. In response, we have revised the abstract to enhance clarity, conciseness, and specificity. Specifically, we (1) rephrased vague expressions such as “notable regional disparities exist” to reflect more precise findings on regional variation, and (2) highlighted the identification of the double-threshold effect earlier in the abstract to better emphasize the study’s originality. The revised abstract reads as follows:
This study investigates the impact and mechanisms of digital inclusive finance (DIF) on high-quality economic development in China. Drawing on panel data from 281 prefecture-level cities between 2011 and 2021, we employ a Spatial Durbin Model (SDM) to analyze both the direct effects and spatial spillovers of DIF. The results indicate that: (1) DIF has a significantly positive effect on high-quality development, which remains robust after conducting various stability and endogeneity tests; (2) DIF strongly contributes to economic upgrading in eastern regions, while its impact is weaker or even negative in central and western regions, revealing notable regional disparities exist; (3) A key finding is the identification of a double-threshold effect, suggesting that the positive influence of DIF only emerges when financial and industrial development surpass certain thresholds; (4) Results from the two-regime SDM further show that spillover effects are more prominent in non-central cities than in central ones; and (5) Mechanism analysis reveals that DIF facilitates high-quality growth primarily by promoting industrial structure upgrading. These findings underscore the importance of region-specific policy strategies to enhance the role of DIF and reduce spatial disparities in development across China.
- The introduction does a commendable job of motivating the research question and framing it within the broader context of national economic policy and digital transformation. The authors correctly identify DIF as an emerging engine of growth through its capacity to extend financial services using tools such as big data, AI, and blockchain (Lin & Peng, 2025). The key research questions are well-posed, and the section would be further strengthened by more clearly articulating the theoretical novelty of using spatial econometrics and multi-dimensional mechanisms, especially in comparison to prior studies limited to provincial or linear analyses (e.g., Xu et al., 2021; Zhang & Yang, 2020).
Author’s response: We sincerely thank the reviewer for the encouraging feedback and insightful suggestions. In response, we have revised the introduction to more clearly articulate the theoretical contributions and methodological innovation of our study. Specifically, we highlight the novelty of employing spatial econometric models (particularly the Spatial Durbin Model) to account for spatial spillover effects across cities—an advancement over existing literature which primarily relies on provincial-level or non-spatial linear frameworks. Furthermore, we emphasize the multidimensional mechanism analysis (e.g., threshold effects, industrial structure upgrading) that distinguishes our approach from prior studies. (See lines: 297-371)
- The literature review is comprehensive and thematically organized. It incorporates a rich selection of relevant studies, covering the direct effects of DIF, mediating pathways through industrial upgrading, nonlinear threshold effects, and spatial spillovers. A particularly strong point is the authors’ critical reflection on existing limitations—such as the overreliance on provincial-level data and the neglect of spatial heterogeneity. To further improve this section, the authors could integrate more synthesis at the end of each sub theme to link prior evidence with their hypotheses more directly.
Author’s response: We thank the reviewer for the positive evaluation of our literature review and for the constructive suggestion. In response, we have revised the end of each sub-section to include more integrative synthesis, explicitly connecting the reviewed studies to our research hypotheses. These additions enhance the logical flow of the literature review and clarify how each thematic area informs our study design.
(See lines: 246-253; 359-370; 408-417)
- The theoretical framework and hypotheses are thoughtful and well-articulated. The dual pathway logic—focusing on both local and spatial spillover effects—is convincing, and the emphasis on industrial structure upgrading as a mediating variable is highly relevant given China’s ongoing transition toward innovation-driven and green development (Liu et al., 2022; Sun et al., 2025). The use of clearly stated hypotheses (H1–H3) is appreciated. However, some of the mechanisms, such as human capital enhancement and the demonstration-learning effect, could be described with slightly more nuance and empirical grounding.
Author’s response: We sincerely thank the reviewer for the positive feedback on our theoretical framework and hypotheses. In response to your constructive suggestion, we have further refined the descriptions of the human capital enhancement and demonstration–learning effect mechanisms in the revised manuscript. (See lines: 234-245; 327-338; 348-358; 408-417)
- The methodology is a strong component of the paper. The use of the Spatial Durbin Model is appropriate and well justified, particularly in addressing spatial autocorrelation that would otherwise bias OLS results (Anselin, 1988). The paper also goes beyond standard SDM by applying a two-regime approach (Elhorst & Frère, 2009), mediation analysis, and threshold regression (Hansen, 1999), all of which are executed rigorously. The entropy-weighted composite indicator of “high-quality economic development” is carefully constructed, although a brief reflection on the validity and limitations of using this synthetic index would enhance transparency.
Author’s response: We sincerely thank the reviewer for their recognition of our methodological framework. We are pleased that the application of the Spatial Durbin Model (SDM), two-regime analysis, mediation testing, and threshold regression was well received.
In response to your helpful suggestion, we have added a brief discussion on the validity and potential limitations of the entropy-weighted composite index for high-quality economic development in the revised manuscript. This addition acknowledges that while the index provides a comprehensive and objective measure, it may be sensitive to the selection and scaling of component indicators. We also mention that the entropy method relies on statistical variability rather than theoretical weights, which could affect the interpretation of indicator importance. At the same time, we have also included the limitations of entropy methods in measuring high-quality economic development in our future research directions, hoping that further research will find better composite index methods. (See lines: 500-505)
- The empirical results are well-documented and effectively interpreted. The finding that DIF has a significant and positive effect on economic quality—with stronger spatial spillovers than local impacts—is particularly noteworthy. The regional heterogeneity analysis adds richness to the study by revealing that while eastern cities benefit substantially from DIF, its impact is negative or negligible in the central and western regions. The explanations offered—ranging from infrastructural deficits to the risk of over financialization (Li & Chen, 2023)—are plausible and well-grounded.
Author’s response: Thank you very much for your positive evaluation of our empirical analysis. We are pleased that you found the interpretation of the results—particularly the stronger spatial spillover effects and the regional heterogeneity—to be clear and meaningful. In response to your comments, we have further emphasized the differentiated impacts of digital inclusive finance (DIF) across regions, especially in central and western China. We elaborated on the underlying mechanisms that may explain the weaker or even negative effects observed in these areas, such as infrastructure deficits, limited financial literacy, and the potential risk of over-financialization. In addition, we clarified that these findings highlight the importance of tailoring DIF-related policies to regional development capacities and readiness levels.
- The mediation analysis confirms the role of industrial structure upgrading as a significant channel through which DIF enhances economic quality. The authors successfully quantify the mediating effect and present results transparently. A visual representation, such as a causal path diagram, could make the mediation pathway even more accessible to readers.
Author’s response: Thank you for your valuable feedback on the mediation analysis. We are pleased that you found the identification and quantification of the mediating role of industrial structure upgrading to be clear and transparent. In response to your helpful suggestion, we have added a causal path diagram to visually illustrate the mediation pathway through which digital inclusive finance (DIF) influences high-quality economic development via industrial upgrading. We believe this addition enhances reader comprehension by providing a more intuitive understanding of the underlying mechanism.(See Fig 1.)
- The threshold effect analysis is another highlight, revealing a nonlinear relationship between DIF and economic development. This is an important finding, suggesting that certain technological or financial literacy thresholds must be crossed for DIF to yield meaningful benefits—especially in less-developed regions (Qian & Fang, 2024). It would be helpful for readers if the authors could provide an interpretative benchmark of the DIF index values associated with these thresholds.
Author’s response: Thank you for highlighting the importance of the threshold effect analysis. We agree that identifying the nonlinear relationship between DIF and high-quality economic development is a valuable contribution, particularly for understanding regional disparities. In response to your suggestion, we have added interpretative benchmarks of the estimated DIF threshold values to enhance clarity. Specifically, we found that the positive impact of DIF emerges only when the DIF index exceeds the threshold value of 5.213. This indicates that regions with low DIF levels—typically due to insufficient digital infrastructure or financial literacy—may not yet benefit significantly from digital financial development. Therefore, improving technological readiness and digital education in these areas is essential for unlocking the potential of DIF.
- Finally, the conclusion and policy implications are thoughtful and well-aligned with the findings. The emphasis on tailoring regional DIF policies—rather than adopting a one-size fits-all approach—is well justified. It may be useful to include a brief discussion on the risks of digital exclusion and the potential unintended consequences of aggressive DIF expansion in fragile regions.
Author’s response: Thank you very much for your insightful suggestion regarding the potential risks of digital exclusion and unintended consequences associated with aggressive DIF expansion in fragile regions. We fully agree that while promoting the development of Digital Inclusive Finance (DIF), it is equally important to guard against the risk of deepening inequality due to infrastructural or digital literacy gaps. In response, we have added a brief but focused discussion in the policy implication section (Section 5.2) under a new subsection titled “Preventing Digital Exclusion and Inequality.”（See lines: 951-960）
Author Response File: Author Response.pdf
Reviewer 2 Report
Comments and Suggestions for AuthorsThe article addresses a highly relevant topic, namely digital financial inclusion (DIF) and how it contributes to economic development. The methodology seems appropriate to me, particularly the use of the Spatial Durbin Model (SDM), and both the sample and the chosen time period are sufficiently broad. On the other hand, I find it pertinent to analyze the existing regional heterogeneity.
As I have been able to verify, the bibliography in this journal is ordered alphabetically, so this point should be reviewed. The references are very focused on the case of China, and there is a lack of international comparison. In this regard, I suggest mentioning experiences such as M-Pesa in Kenya, among others. I also miss a comparison between DIF and the digital yuan. I understand that DIF is a broader concept that includes digital wallets, mobile banking, fintech applications, etc., but also cryptocurrencies such as Bitcoin—which have been regulated or banned in China—or central bank digital currencies (CBDCs). To what extent has the launch of the digital yuan contributed to financial inclusion and economic development?
Although an index from the Digital Finance Research Center at Peking University is used as a reference, I would like to know what components this index measures, how it is constructed, and which DIF tools have the greatest impact.
Some figures lack source citations, which should be added. I find the conclusions to be appropriate, as well as the inclusion of future research lines, since the absence of micro-level data can be considered a limitation of the study.
Author Response
Reviewer 2
- The article addresses a highly relevant topic, namely digital financial inclusion (DIF) and how it contributes to economic development. The methodology seems appropriate to me, particularly the use of the Spatial Durbin Model (SDM), and both the sample and the chosen time period are sufficiently broad. On the other hand, I find it pertinent to analyze the existing regional heterogeneity.
Author’s response: Thank you very much for your positive evaluation of our manuscript. We appreciate your recognition of the study's relevance and methodological rigor, including the appropriateness of the Spatial Durbin Model (SDM), the robustness of the sample, and the sufficiency of the time period covered. We are especially grateful for your acknowledgement of the importance of analyzing regional heterogeneity. In response to this, we have further emphasized the implications of regional disparities in the discussion and policy recommendation sections. This includes highlighting the differentiated effects of DIF across eastern, central, and western regions, and advocating for region-specific policy approaches tailored to local infrastructure, financial literacy, and economic readiness.
- As I have been able to verify, the bibliography in this journal is ordered alphabetically, so this point should be reviewed. The references are very focused on the case of China, and there is a lack of international comparison. In this regard, I suggest mentioning experiences such as M-Pesa in Kenya, among others. I also miss a comparison between DIF and the digital yuan. I understand that DIF is a broader concept that includes digital wallets, mobile banking, fintech applications, etc., but also cryptocurrencies such as Bitcoin—which have been regulated or banned in China—or central bank digital currencies (CBDCs). To what extent has the launch of the digital yuan contributed to financial inclusion and economic development?
Author’s response: Thank you for your insightful observation. In accordance with your suggestion, we have carefully reviewed the reference section and adjusted the bibliography to ensure alphabetical ordering, consistent with the journal’s formatting requirements. Additionally, we acknowledge the need to broaden the international scope of our literature review. We have now supplemented the revised manuscript with references to international experiences, particularly the case of M-Pesa in Kenya, a pioneering mobile money platform. Studies such as Suri and Jack (2016) and Tiony (2023) have been cited to illustrate how mobile financial services significantly improved financial inclusion, enhanced productivity, and contributed to poverty reduction in Kenya. These examples help to enrich the global perspective of our study and provide comparative insights into how DIF operates in diverse socio-economic contexts.
You are absolutely right that a comparative analysis between digital inclusive finance (DIF) and central bank digital currencies (CBDCs), such as the digital yuan, is highly relevant and valuable.
We sincerely apologize for the confusion caused by not explicitly listing the specific indicators used to measure DIF in our manuscript. This omission may have led to a certain degree of misunderstanding regarding the scope of our study. In our current research, we rely on the Digital Inclusive Finance Index developed by Peking University, which primarily captures dimensions such as account coverage, usage depth, and the degree of digitalization—mainly based on data from platforms like Alipay. It does not yet cover the digital yuan or cryptocurrencies like Bitcoin.
Your suggestion is greatly appreciated and points to an important avenue for future exploration. In the revised version, we have added this as a potential direction for future research—namely, to investigate the differential impacts of various digital financial instruments, including CBDCs like the digital yuan, on financial inclusion and economic development. (See lines 978-985)
- Although an index from the Digital Finance Research Center at Peking University is used as a reference, I would like to know what components this index measures, how it is constructed, and which DIF tools have the greatest impact.
Author’s response:
The Digital Inclusive Finance (DIF) Index developed by the Digital Finance Research Center at Peking University is a comprehensive measure widely used in empirical research to assess the development level of digital inclusive finance across Chinese cities and provinces. It consists of three first-level dimensions, each comprising multiple second- and third-level indicators. For details, please refer to Table 2.(See line: 599)
- Some figures lack source citations, which should be added. I find the conclusions to be appropriate, as well as the inclusion of future research lines, since the absence of micro-level data can be considered a limitation of the study.
Author’s response: Thank you for your constructive feedback. We appreciate your acknowledgment of the conclusions and future research directions presented in the manuscript. In response to your suggestion: Source Citations for Figures: We have carefully reviewed all figures in the manuscript and added missing source citations where appropriate. Each figure now clearly references the original data source or author, ensuring transparency and academic rigor. Future Research and Micro-Level Data: We agree that the absence of micro-level data represents a limitation. This point has now been explicitly acknowledged in the Future Research Directions section.
Author Response File: Author Response.pdf
Round 2
Reviewer 1 Report
Comments and Suggestions for AuthorsThe paper is well-revised and well-improved
