Reducing Efficiency Loss Caused by Land Investment Introduction Based on Factor-Biased Technological Progress

Round 1

Reviewer 1 Report

Comments and Suggestions for Authors

This is a very interesting article, which examines the impact of the introduction of land investments on the loss of efficiency at both the business and urban levels. It also studies the role of technological progress in minimizing these losses. The article has scientific depth, a high level of analysis, proposes and uses a very interesting methodology and draws important conclusions from the application of the methodology.

I recommend the acceptance of the article, in its present form, in the Journal.

Comments for author File: Comments.pdf

Author Response

This is a very interesting article, which examines the impact of the introduction of land investments on the loss of efficiency at both the business and urban levels. It also studies the role of technological progress in minimizing these losses. The article has scientific depth, a high level of analysis, proposes and uses a very interesting methodology and draws important conclusions from the application of the methodology.

I recommend the acceptance of the article, in its present form, in the Journal.

Response: Thanks so much for your acknowledging our research work ^^

Reviewer 2 Report

Comments and Suggestions for Authors

This manuscript addresses a timely and policy-relevant issue—how land investment introduction in China influences both enterprise-level and urban-level efficiency and how factor-biased technological progress may mitigate these losses. The paper combines solid theoretical modeling using nested CES production functions with a large panel dataset and empirical strategy based on system GMM.

While the paper contributes important insights, it requires major revisions before it can be considered for publication. Improvements in academic English, theoretical clarity, engagement with international literature, and a more critical discussion of the results are necessary.

 

 Section-by-Section Review

1. Abstract

Corrections:

• Replace: “varying on the types” → “depending on the types”.
• Rephrase: “and offers valuable guidance…” → “and provides policy-oriented recommendations for enhancing production and efficiency...”.

Suggestions:

• Briefly specify what kinds of factor-biased technological progress are examined.
• Avoid repetition of "enterprise and urban levels."

 

2. Introduction

Strengths:

• Well-grounded in the literature on induced technological change and factor misallocation.

Corrections:

• “entity enterprise” should be corrected to “industrial enterprise”.
• The sentence on productive service enterprises is syntactically confusing and should be rewritten.

Suggestions:

• Include comparative international literature on land policies and efficiency (e.g., from OECD countries).
• Add references from post-2020 studies on land misallocation and TFP.

 

3. Methodology and Model Development

Strengths:

• Rigorous theoretical framework using nested CES functions.
• Clear mathematical derivations connecting land distortion and efficiency.

Corrections:

• Improve equation referencing; some equations (e.g., "(1)") are poorly formatted or missing.
• Clarify variables before their first use.

Suggestions:

• Add a schematic diagram showing relationships between land investment, TFP, and technology bias (the current Figure 1 is vague and needs visual clarity).
• Enhance the intuitive explanation of the model’s implications.

 

4. Data and Empirical Strategy

Strengths:

• Appropriate use of system GMM and dynamic panel modeling.
• Careful construction of control and instrumental variables.

Corrections:

• Clarify and justify the construction of the "land investment introduction" variable (pd).
• Provide a rationale for selecting the 2007–2019 time window.

Suggestions:

• Move descriptive statistics table (Table 2) closer to the data discussion.
• Justify the relevance and exogeneity of the instrumental variable (geographic slope × CPI) more clearly.

 

5. Results and Discussion

Strengths:

• Results are clearly structured across models.
• Empirical findings are consistent with theoretical expectations.

Corrections:

• Carefully verify coefficient signs in tables and text; some inconsistencies were noted.
• Interpret interactions in moderation models with more conceptual depth.

Suggestions:

• Discuss limitations of the results more critically.
• Include cross-country comparisons to broaden the relevance of findings.

 

6. Conclusion

Corrections:

• Redundant with the abstract and introduction—condense and rephrase.

Suggestions:

• Add a paragraph on limitations and future research directions.
• Discuss whether findings can generalize beyond China’s context.

Citations and Style

Issues:

• Inconsistent referencing and incomplete citation formatting.
• Predominant reliance on Chinese sources with limited international engagement.

Recommendations:

• Follow APA style or the specific reference style of Land.
• Supplement the bibliography with more global empirical studies on urban land allocation and efficiency.

 

 

 

 

Comments for author File: Comments.pdf

Author Response

2.1 Abstract

Corrections:

• Replace: “varying on the types” → “depending on the types”.
• Rephrase: “and offers valuable guidance…” → “and provides policy-oriented recommendations for enhancing production and efficiency...”.

Suggestions:

• Briefly specify what kinds of factor-biased technological progress are examined.
• Avoid repetition of "enterprise and urban levels."

Response 2.1: The incorrect words have been corrected. And the land-biased technological progress in the revision has been defined. Enterprise and urban levels have been replaced.

 

2.2 Introduction

Strengths:

• Well-grounded in the literature on induced technological change and factor misallocation.

Corrections:

• “entity enterprise” should be corrected to“industrial enterprise”.
• The sentence on productive service enterprises is syntactically confusing and should be rewritten.

Suggestions:

• Include comparative international literature on land policies and efficiency (e.g., from OECD countries).
• Add references from post-2020 studies on land misallocation and TFP.

Response 2.2: The word “entity enterprise” has been corrected. And the confusing sentences have been rewritten. The references have been updated, and the literature on land policies,  land misallocation and TFP have been cited in the revision. 

2.3 Methodology and Model Development

Strengths:

• Rigorous theoretical framework using nested CES functions.
• Clear mathematical derivations connecting land distortion and efficiency.

Corrections:

• Improve equation referencing; some equations (e.g., "(1)") are poorly formatted or missing.
• Clarify variables before their first use.

Suggestions:

• Add a schematic diagram showing relationships between land investment, TFP, and technology bias (the current Figure 1 is vague and needs visual clarity).
• Enhance the intuitive explanation of the model’s implications.

Response 2.3: The equations have been reformatted, and all the variables are defined in the revision. Fig. 1 has been depicted, and the relationships between land investment, TFP, and technology bias have been shown in Fig. 1. The intuitive explanation of the model has been given in revision.

 

2.4 Data and Empirical Strategy

Strengths:

• Appropriate use of system GMM and dynamic panel modeling.
• Careful construction of control and instrumental variables.

Corrections:

• Clarify and justify the construction of the "land investment introduction" variable (pd).
• Provide a rationale for selecting the 2007–2019 time window.

Suggestions:

• Move descriptive statistics table (Table 2) closer to the data discussion.
• Justify the relevance and exogeneity of the instrumental variable (geographic slope × CPI) more clearly.

 

Response 2.4: The core explanatory variable (pd) has been clarified, and the construction of pd has been shown in the revision. The reason for selecting the 2007–2019 time window has been given in the revision; Table 2 has been moved to closer to the data discussion; The relevance and exogeneity of the instrumental variable has been explained in the revision. 

 

 

2.5 Results and Discussion

Strengths:

• Results are clearly structured across models.
• Empirical findings are consistent with theoretical expectations.

Corrections:

• Carefully verify coefficient signs in tables and text; some inconsistencies were noted.
• Interpret interactions in moderation models with more conceptual depth.

Suggestions:

• Discuss limitations of the results more critically.
• Include cross-country comparisons to broaden the relevance of findings.

Response 2.5: The tables and the text has been revised. The Interpret interactions in moderation models has been rewritten in the revision. The limitations of the results have been discussed in the revision. It shall be noted that land investment introduction is the special product of special system in China, so the discussion mainly focuses on Chinese research.

 

2.6 Conclusion

Corrections:

• Redundant with the abstract and introduction—condense and rephrase.

Suggestions:

• Add a paragraph on limitations and future research directions.
• Discuss whether findings can generalize beyond China’s context.

 

Response 2.6: The abstract and introduction have been rewritten. The limitations and future research directions have been added in the revision. Although it is widely recognized that resource misallocation can result in efficiency losses, the choice of biased-technological progress will be influenced by the national industrial development status, factor prices, factor supply and so on. Moreover, land investment introduction is the special product of special system in China. Thus, the findings can not beyond China’s context. 

 

 

2.7 Citations and Style

Issues:

• Inconsistent referencing and incomplete citation formatting.
• Predominant reliance on Chinese sources with limited international engagement.

Recommendations:

• Follow APA style or the specific reference style of Land.
• Supplement the bibliography with more global empirical studies on urban land allocation and efficiency.

Response 2.7: The references have been updated.

 

Thanks so much for your helpful comments^^

Reviewer 3 Report

Comments and Suggestions for Authors

This manuscript focuses on the impact of land investment on the efficiency losses of enterprises and urban areas, and discusses the role of factor-biased technological progress in mitigating these losses. It holds certain theoretical value and practical significance. The structure of the article is reasonable, and the methodology is appropriate, but there are issues that need revision, such as unclear model parameters and a lack of discussion sections.

1. Literature review is not comprehensive enough. In the second section, "2.1Research Review," the article briefly mentions studies on the direct and indirect effects of land investment on TFP, but does not systematically review and analyze these studies. It lacks a summary and critique of existing research and does not clearly indicate the differences and contributions of this study compared to previous works.
2. In the section "2.3 Mathematical Derivation Based on the CES Production Function," although the author constructs a theoretical model based on the CES production function, the explanation of some key assumptions and derivation processes is unclear. For example, when setting the production function, the specific meanings and value basis of parameters m, α, β, and σ are not detailed. In the derivation of enterprise efficiency loss and urban efficiency loss, some formula derivations are brief and hard to understand.
3. The construction of the land investment introduction variable (pd) is based on the deviation in land transfer prices. However, the specific definitions and differences between agreement transfers and auctioned land transfers are not explained, and it is not clear whether this measurement method accurately reflects the degree of land investment introduction.
4. In the econometric model section, the system GMM method is used, but the reasons for selecting this method are not explained in detail, nor are the results of a series of hypothesis tests provided. Additionally, for the moderation effect model, the choice of specific moderator variables and their relationships with the explained variable and core explanatory variables are not explained. For the moderation effect model, theoretical support should be provided for the rationale behind the chosen moderator variables, and the potential relationships between the moderator variables and the explained variable and core explanatory variables should be explained.
5. The manuscript should include a separate discussion section. In the discussion, the research results should be compared with similar studies, highlighting the similarities and differences between this study and others, as well as discussing the limitations of this study and future research directions.
6. The abstract and keywords need to be optimized.
7. The literature is outdated and does not track the latest research developments.

Author Response

3.1 Literature review is not comprehensive enough. In the second section, "2.1Research Review," the article briefly mentions studies on the direct and indirect effects of land investment on TFP, but does not systematically review and analyze these studies. It lacks a summary and critique of existing research and does not clearly indicate the differences and contributions of this study compared to previous works.

Response 3.1: The literature review has been updated, and the contributions of these studies have been concluded in the revision. 

3.2 In the section "2.3 Mathematical Derivation Based on the CES Production Function," although the author constructs a theoretical model based on the CES production function, the explanation of some key assumptions and derivation processes is unclear. For example, when setting the production function, the specific meanings and value basis of parameters m, α, β, and σ are not detailed. In the derivation of enterprise efficiency loss and urban efficiency loss, some formula derivations are brief and hard to understand.

Response 3.2: The parameters in the equations have been defined in the revision. And the derivation processes have been updated. 

 

3.3 The construction of the land investment introduction variable (pd) is based on the deviation in land transfer prices. However, the specific definitions and differences between agreement transfers and auctioned land transfers are not explained, and it is not clear whether this measurement method accurately reflects the degree of land investment introduction.

Response 3.3: Agreement land transfer refers to the government and land users negotiating and signing a transfer contract to determine the land price, and the transfers of bidding& auction& hang signs means the land price is determined based on marketization approach through public bidding. Compared to the transfer price based on bidding, auction and hang signs, there is a large bargaining space by agreement-based land grant involved in negotiating land price with land-users, which is considered as the main non-marketization approach. Therefore, the larger price difference between the agreement and bidding& auction& hang signs, the more severe the land price deviation under government intervention, and the greater the efforts to attract land investment. According to the research of Liu and Yan (2016), land investment introduction can be represented by pd=ln[(Pzpg-Pxy)/Pxy], where: Pzpg is the average price of land transfer on bidding& auction& hang signs, Pxy is the average price of land transfer on agreement. 

 

Liu, W.; Yan, X. The crowding in/out effects of FDI against the background of local governments’ land investment introduction. Journal of Finance and Economics, 2016, 42, 17-29.

 

3.4 In the econometric model section, the system GMM method is used, but the reasons for selecting this method are not explained in detail, nor are the results of a series of hypothesis tests provided. Additionally, for the moderation effect model, the choice of specific moderator variables and their relationships with the explained variable and core explanatory variables are not explained. For the moderation effect model, theoretical support should be provided for the rationale behind the chosen moderator variables, and the potential relationships between the moderator variables and the explained variable and core explanatory variables should be explained.

Response 3.4: The reason for selecting GMM has been given in the revision. The variables and their relationships in the moderation effect model has been defined in the revision.  

3.5 The manuscript should include a separate discussion section. In the discussion, the research results should be compared with similar studies, highlighting the similarities and differences between this study and others, as well as discussing the limitations of this study and future research directions.

Response 3.5: The discussion section has been added in the revision, and research limitations and future are also added at the end of paper. 

3.6 The abstract and keywords need to be optimized.

Response 3.6: The abstract and keywords have been revised. 

3.7 The literature is outdated and does not track the latest research developments.

Response 3.7: The references has been updated in the revision.

 

Special thanks for your good comments and helpful suggestions^^

Reviewer 4 Report

Comments and Suggestions for Authors

This study explores how land investment affects efficiency at both enterprise and urban levels, and how factor-biased technological progress can help mitigate these effects. Using a theoretical model based on a nested constant elasticity of substitution production function and empirical data from Chinese cities (2007–2019), the study finds that land investment disrupts optimal allocation of productive factors, causing efficiency losses. While extended land investment worsens enterprise efficiency, urban efficiency may improve temporarily but deteriorates over time. Technological progress tailored to specific production factors can reduce enterprise-level losses, while its effect on urban efficiency is unclear. The study tends to offer guidance for policy and technology choices to enhance productivity under land investment strategies.  

The authors have elaborated the methodology in detail and explained the objectives of the work. The obtained results are well presented. However, the paper is dense and highly technical, which may hinder accessibility for wider audience such as policy makers and interdisciplinary readers. Therefore, a more concise structure and clearer visualisation of key points would improve readability. The findings of the research are specific to China's economy and land governance, so the applicability of the approach to other economies may also be discussed.

There are also several key points that need to be discussed. It should be noted how robust the assumptions behind using the proposed methodology are, and to what extent variations in governance capacity across cities influence the efficiency impact of land investment strategies. Furthermore, it should be discussed whether the heterogeneity of the companies such as their size, sector or ownership structure alter the relationship between land cost and technological bias. Is the assumption that capital and labor are substitutable with land in a uniform way realistic across different industries? Additionally, it would be useful to explain how policy makers can practically use this framework to guide land pricing or technology incentives, especially in regions without clear and sufficient data.

Author Response

4.1 The authors have elaborated the methodology in detail and explained the objectives of the work. The obtained results are well presented. However, the paper is dense and highly technical, which may hinder accessibility for wider audience such as policy makers and interdisciplinary readers. Therefore, a more concise structure and clearer visualisation of key points would improve readability. The findings of the research are specific to China's economy and land governance, so the applicability of the approach to other economies may also be discussed.

Response 4.1: The structure of the manuscript has been revised, and the limitations of our research have been discussed in the revision.  

4.2 There are also several key points that need to be discussed. It should be noted how robust the assumptions behind using the proposed methodology are, and to what extent variations in governance capacity across cities influence the efficiency impact of land investment strategies.

Response 4.2: The robust the assumptions have been validated in the revision. According to Wang (2019), the transfer price and scale of industrial land are linked to the evaluation of local government officials, and the evaluation is correlated with the local governance capacity. If the governance capacity is considered in the relationship between land investment introduction and TFP, there may be collinearity problems. In future research, we will focus on the different impacts of land investment introduction on TFP in cities with different levels of local governance capability.

 

Wang, C. Land use control, fiscal revenue and land transfer: an exploration of central and local land use governance. Economic Research, 2019, 54(12), 54-69. 

4.3 Furthermore, it should be discussed whether the heterogeneity of the companies such as their size, sector or ownership structure alter the relationship between land cost and technological bias.

Response 4.3: The heterogeneity analysis of company need the detailed data, which mainly comes from China Industrial Enterprise Database. However, it is only updated until 2014. In this manuscript, we choose the data from Statistical Yearbook of Urban Construction in China to extend the research period and ensure the result stability, which does not contain the heterogeneity index of enterprise. Thus, in this manuscript, we did not discuss the effect of enterprise heterogeneity, and we will study this point thoroughly in future research.  

4.4 Is the assumption that capital and labor are substitutable with land in a uniform way realistic across different industries?

Response 4.4: No. In our view, capital and labor cannot be substituted by the land. The effects of the capital-biased and labor-biased on TFP are discussed in the manuscript independently.

 4.5 Additionally, it would be useful to explain how policy makers can practically use this framework to guide land pricing or technology incentives, especially in regions without clear and sufficient data.

Response 4.5: As stated in the revision, local governments can adopt appropriate land investment strategies to attract enterprise when local development momentum is insufficient; while when local economy has sufficiently progressed, local government should actively adopt market-oriented bidding methods to transfer industrial land to avoid the negative consequences of indiscriminate and disorderly land investment strategies. When capital price and quantity are relatively high, enterprises should focus on labor-biased technological progress, such as increasing the proportion of highly skilled and educated human capital. Conversely, when labor price and labor quantity are high, enterprise should prioritize capital-biased technological progress, such as strengthening the intensity of capital investment in independent research or technology acquisition. 

Special thanks for your good comments and helpful suggestions ^^

Round 2

Reviewer 2 Report

Comments and Suggestions for Authors

The authors made the suggestions made previously, making the article suitable for publication.

Reviewer 3 Report

Comments and Suggestions for Authors

The authors have addressed the issues I raised, and the quality of the manuscript has improved.