PONE-D-24-21206Policy Effects of Reduced Pension Contribution Rate on Innovation Input —— Empirical Evidence from Zhejiang and Shandong ProvincesPLOS ONE

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Reviewer #1: Yes

Reviewer #2: Partly

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2. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: Yes

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Reviewer #1: Yes

Reviewer #2: No

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5. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (1) Why select Zhejiang and Shandong Provinces as research objectives

(2) Please provide literature references for explaining why taking R&D intensity as a proxy for innovation input

(3) Research gaps among prior works should be presented

(4) Theoretical implications should be provided

Reviewer #2: The paper investigates the impact of reduced pension insurance contribution rates on enterprise innovation in Zhejiang and Shandong provinces in China, using data from 2000 to 2017. The final results indicate that lowering pension insurance contribution rates significantly positively affect enterprise innovation in these provinces. This policy measure increases R&D intensity by alleviating financial constraints and enhancing firm profitability, though the effects vary by region and over time.

The empirical strategy is based on provincial panel data from 31 Chinese provinces and employs the synthetic control method (SCM) to construct a counterfactual scenario. This approach helps estimate the impact of reduced pension contributions on R&D intensity. To ensure robustness, the authors also use placebo tests, permutation tests, and the Difference-in-Differences (DID) method.

In Zhejiang province, the reduction in pension contribution rates led to a significant increase in R&D intensity, although this effect exhibited a lag of about 2-3 years post-implementation. The average R&D intensity increased by approximately 0.07% compared to the synthetic control. The impact was more immediate in Shandong province, with the R&D intensity increasing by about 0.21% after the policy implementation. Despite a smaller reduction in pension contributions compared to Zhejiang, Shandong showed a stronger response.

The study makes important contributions to the existing literature. However, before publication, some important limitations need to be addressed. These include its focus on only two provinces, which may limit the generalizability of the findings, and the potential trade-off between reduced mechanization and increased R&D investment that should be further explored.

Why should reducing labor costs through a cut in pension insurance contribution rates incentivize more R&D investments? Reducing labor costs through a cut in pension insurance contribution rates can incentivize more R&D investments for several reasons rooted in economic theory and empirical observations: enhanced liquidity, reduced financial constraints, increased profitability, reinvestment of profits, cost-competitive innovations, strategic shifts, and a buffer against uncertainty. The paper needs to discuss all these aspects to unveil the potential mechanism behind the final results.

The paper does not sufficiently address the long-term sustainability of reduced pension contributions. What are the potential risks or downsides of this policy in terms of pension fund solvency or social security? Policymakers must balance the short-term economic benefits with the long-term sustainability of pension funds. To explain the positive effects of pension insurance cuts, the authors should discuss complementary measures in more detail, such as improving the overall business environment and providing targeted R&D subsidies. An aging population increases the fiscal pressure on public pension systems, and as the number of retirees grows, the demand for pension benefits rises, potentially leading to higher deficits if the contribution base is simultaneously reduced. This demographic shift necessitates reforms that ensure the pension system's sustainability without compromising economic growth. To offset the reduced contributions, governments may need to explore alternative funding mechanisms for pension systems. This could include increased general taxation, introducing or expanding private pension schemes, or implementing policies encouraging higher labor force participation among older workers.

The sample size of 72 units is quite small, and it is unclear how it was achieved, given that the entire analysis is based on provincial panel data from 31 provinces (cities) in China from 2000 to 2017.

I suggest discussing in more detail the assumptions behind the exogeneity of the explanatory variables adopted to control for regional economic development, investment level, openness, population factors, industrial structure, financial level, and financial support with respect to the dependent variable used to capture the R&D intensity.

Although the study aligns with the broader literature demonstrating that innovation is a key driver of economic growth, as highlighted by Griliches (1984) and Hall & Rosenberg (2010), I suggest including additional relevant literature that was not covered in the initial version of the paper, which could provide a broader context or deeper insights into the relationship between fiscal policies, labor costs, and innovation. Some examples are Aghion, P., & Howitt, P. (1992), Romer, P. M. (1990), Klette, T. J., & Kortum, S. (2004), Zwick, E., & Mahon, J. (2017), Bloom, N., Sadun, R., & Van Reenen, J. (2012), Garicano, L., Lelarge, C., & Van Reenen, J. (2016) and Autor, D. H., Dorn, D., & Hanson, G. H. (2013). Incorporating some of these additional references may provide a more comprehensive understanding of the interplay between fiscal policies, labor costs, and innovation. They offer theoretical frameworks, empirical evidence, and methodological advancements that can deepen the analysis presented in the paper. As a minor point, references should be reorganized in alphabetical order.

Finally, I have included a list of additional areas where the paper could be improved before finalizing it for publication:

1. Time Frame: The study uses data from 2000 to 2017, which provides a solid historical perspective but may not capture the most recent trends or long-term effects of the policy changes.

2. Geographical Focus: The analysis is limited to Zhejiang and Shandong provinces. While these are significant regions, the study could benefit from including more provinces to provide a broader view of the policy's impact across China.

3. Regional Differences: The paper acknowledges heterogeneity in economic development but could delve deeper into how specific regional characteristics (e.g., industrial structure and enterprise size) affect policy outcomes. This would help understand why the policy impact differs between Zhejiang and Shandong.

4. Mechanistic Explanation: The study shows that reduced pension contribution rates lead to increased R&D intensity, but it lacks a detailed discussion of the causal mechanisms. How exactly do reduced costs translate into higher innovation investment? Are there intermediate steps or specific areas within R&D that benefit more?

5. Sectoral Analysis: A more detailed breakdown of how different sectors respond to the policy change could provide insights into sector-specific effects and guide more targeted policy recommendations.

6. Theoretical Framework: The theoretical framework could be expanded to include more detailed discussions of related economic theories, particularly those addressing the relationship between labor costs and innovation.

7. Complexity of Methodology: The synthetic control method and other statistical techniques used are complex and might not be fully accessible to all readers. Simplifying the explanation or providing a more detailed methodological appendix could help.

8. Data Presentation: The paper includes various figures and tables, but their presentation could be clarified. More intuitive visualizations or summaries could make the results easier to interpret.

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Reviewer #1: Yes: YANG Z

Reviewer #2: Yes: Francesco Porcelli

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Policy Effects of Reduced Pension Contribution Rate on Innovation Input —— Empirical Evidence from Zhejiang and Shandong Provinces

PONE-D-24-21206R1

Dear Dr. Xu,

We’re pleased to inform you that your manuscript has been judged scientifically suitable for publication and will be formally accepted for publication once it meets all outstanding technical requirements.

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Prof. Donato Morea

Academic Editor

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Reviewers' comments:

Reviewer's Responses to Questions

Comments to the Author

1. If the authors have adequately addressed your comments raised in a previous round of review and you feel that this manuscript is now acceptable for publication, you may indicate that here to bypass the “Comments to the Author” section, enter your conflict of interest statement in the “Confidential to Editor” section, and submit your "Accept" recommendation.

Reviewer #1: All comments have been addressed

Reviewer #2: All comments have been addressed

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2. Is the manuscript technically sound, and do the data support the conclusions?

The manuscript must describe a technically sound piece of scientific research with data that supports the conclusions. Experiments must have been conducted rigorously, with appropriate controls, replication, and sample sizes. The conclusions must be drawn appropriately based on the data presented.

Reviewer #1: Yes

Reviewer #2: Yes

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3. Has the statistical analysis been performed appropriately and rigorously?

Reviewer #1: Yes

Reviewer #2: Yes

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4. Have the authors made all data underlying the findings in their manuscript fully available?

The PLOS Data policy requires authors to make all data underlying the findings described in their manuscript fully available without restriction, with rare exception (please refer to the Data Availability Statement in the manuscript PDF file). The data should be provided as part of the manuscript or its supporting information, or deposited to a public repository. For example, in addition to summary statistics, the data points behind means, medians and variance measures should be available. If there are restrictions on publicly sharing data—e.g. participant privacy or use of data from a third party—those must be specified.

Reviewer #1: Yes

Reviewer #2: Yes

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5. Is the manuscript presented in an intelligible fashion and written in standard English?

PLOS ONE does not copyedit accepted manuscripts, so the language in submitted articles must be clear, correct, and unambiguous. Any typographical or grammatical errors should be corrected at revision, so please note any specific errors here.

Reviewer #1: Yes

Reviewer #2: Yes

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6. Review Comments to the Author

Please use the space provided to explain your answers to the questions above. You may also include additional comments for the author, including concerns about dual publication, research ethics, or publication ethics. (Please upload your review as an attachment if it exceeds 20,000 characters)

Reviewer #1: (No Response)

Reviewer #2: After conducting a thorough examination of the revised version of the manuscript, I am pleased to acknowledge the substantial improvements made by the authors in addressing the majority of the requested changes. While some revisions may not perfectly align with the specifics of my initial suggestions, I recognize that the authors have thoughtfully incorporated these adjustments in ways that enhance the overall quality and coherence of the paper. The revisions demonstrate a commendable effort to refine the arguments, improve the structure, and ensure clarity in the presentation of the research findings.

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7. PLOS authors have the option to publish the peer review history of their article (what does this mean?). If published, this will include your full peer review and any attached files.

If you choose “no”, your identity will remain anonymous but your review may still be made public.

Do you want your identity to be public for this peer review? For information about this choice, including consent withdrawal, please see our Privacy Policy.

Reviewer #1: Yes: YANG ZHEN

Reviewer #2: Yes: Francesco Porcelli

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