Impact of Population Density on Spatial Differences in the Economic Growth of Urban Agglomerations: The Case of Guanzhong Plain Urban Agglomeration, China

Round 1

Reviewer 1 Report

The authors investigated the Impact of population density on spatial differences in economic growth of urban agglomerations: the case of Guanzhong Plain Urban Agglomeration, China.

1. Although 'agglomeration' is a keyword and has littered the paper, the reader is not educated on what 'agglomeration' is. 

2. I struggled to establish in one sentence, the main problem of this study.  

3. The problem statement should open the abstract.

4. One or two specific recommendations in the abstract will be in place.

5. The road map of the paper is missing at the end of the introduction.

6. In line 247, Q/A is a function of some variables. Is the letter 'k' or 'K'? Surprisingly, in line 253, the equation in line 247 is on the right-hand side. This is a big flaw. This flaw renders the rest of the derivations flawed! The results based on it are also unacceptable. 

7. In considering the robustness of estimates of key variables to the control variables, there must always be a model of the dependent variable and the key variable only. This is absent in Table 1 for example.

8. In applied research and/or empirical research, the results must be related to the existing literature. This is not the case in this paper. Such omission is fatal!

 

English language in the paper is a minor issue in my opinion. 

Author Response

For research article

Response to Reviewer 1 Comments

1. Summary

We feel great thanks for your professional review work on our article. As you are concerned, there are several problems that need to be addressed. According to your nice suggestions, we have made extensive corrections to our previous draft, and the detailed corrections are listed below.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Although ‘agglomeration’ is a keyword and has littered the paper, the reader is not educated on what 'agglomeration' is. 

Response 1: We think this is an excellent suggestion. We have added a definition of ‘agglomeration economy’ in the introduction section in our revised manuscript.

“Agglomeration economy” which is a high concentration of population, capital, and other factors in a specific space, produces the interaction process of spatial spillover effects such as sharing, matching, and learning [11].” (Line 59－62 )

The complementary literature is as follow.

11. Duranton, G.; Puga, D. Micro- foundations of urban agglomeration economies // Henderson, V.; Thisse, J. Handbook of regional and urban economics. Amsterdam, Netherlands: North-Holland, 2004, 2063-2117.

Comments 2: I struggled to establish in one sentence, the main problem of this study.  

Response 2: thanks for your suggestion. We found that we did not introduce the purpose of our study in the introduction, which could be the main reason for your confusion. Thus, we introduced the research question in the first paragraph of the introduction in our revised manuscript.

“Thus, we ask the question, does population agglomeration affect the spatial differences in the economic growth of urban agglomerations and how?” (Line 8－10)

Comments 3: The problem statement should open the abstract.

Response 3: thank you for your suggestion. We have added the problem statement at the beginning of the abstract.

“In the new period of ups and downs in the international environment, it is necessary to seek new endogenous impetus for the economic growth of urban agglomerations. Population agglomeration provides a new idea to explain the spatial differences in the economic growth of urban agglomerations. Thus, we ask the question, does population agglomeration affect the spatial differences in the economic growth of urban agglomerations and how?” (Line 5－10)

Comments 4: One or two specific recommendations in the abstract will be in place.

Response 4: thanks for your suggestion. We have added the problem statement at the end of the abstract.

“According to the research conclusion, this study suggests that local governments can continue to promote the regional development policy of spatial agglomeration and intensive land planning, strengthen the construction of the industrial chain and road network within the urban agglomeration, and deepen the network connection between districts and counties.” (Line 32－36)

In addition, we also added two specific policy recommendations at the end of our revised manuscript.

“According to the research conclusion, we put forward two policy recommendations. Firstly, we should continue to promote regional development policies of spatial agglomeration and intensive land planning. The conclusion of this study shows that population density has a significant and stable effect on the economic growth of urban agglomerations. However, China’s western region has long faced the phenomenon of allometric growth of urbanization, where land urbanization is beeter than population urbanization, and there are problems such as a large amount of idle construction land and low space utilization. To this end, urban and rural construction should take strengthening the stock planning of land potential as the main goal, and promote the intensive development of industry and service industries. Secondly, we should strengthen the construction of industrial chains and road networks within urban agglomerations, and deepen the network connection between districts and counties. This study found that the spatial agglomeration of economic, population, and other factors can produce benign spatial spillover effects. Strengthening the network connection between districts and counties through industrial cooperation and road network construction not only reduces the cost of spatial spillover in neighboring districts and counties, but also conforms to the new concept of network construction to promote common development between cities [22]. To this end, local governments need to strengthen the integration of industrial chains in urban agglomerations on the one hand, and improve the construction of road networks such as intercity highways and railways on the other hand. It should be pointed out that this study also found that the construction of high-speed rails has not yet promoted economic development in the Guanzhong Plain urban agglomeration. The possible reason for this is that most high-speed rail stations in western China are located in remote areas far from the economic center, and the surrounding industries and business districts are still not perfect. Therefore, it is necessary to effectively utilize the network-driven effect of high-speed rails by continuing to promote the planning and construction of industries or business districts around high-speed rail stations [53].” (Line 630－656)

Comments 5: The road map of the paper is missing at the end of the introduction.

Response 5: thanks for your professional suggestion. We have dedicated the end of the introduction to add a paragraph describing the specific structure of our article.

“Section 2 presents a literature review on the topic of spatial differences in the economic growth in urban agglomerations as a result of population agglomeration. Section 3 presents a formal framework for our empirical work and describes the data. Section 4 presents our results on the relationship between spatial differences in the economic growth in urban agglomerations and population agglomeration. Section 5 presents conclusion and discussion.” (Line 100－105)

Comments 6: In line 247, Q/A is a function of some variables. Is the letter 'k' or 'K'? Surprisingly, in line 253, the equation in line 247 is on the right-hand side. This is a big flaw. This flaw renders the rest of the derivations flawed! The results based on it are also unacceptable.

Response 6: thanks for your careful checks. We are sorry for our carelessness. Based on your comments, we have re-matched the Ciccone (2002) article, and found errors in our previous model. The corrected models are as Line 274－290 in revised manuscript.

Comments 7: In considering the robustness of estimates of key variables to the control variables, there must always be a model of the dependent variable and the key variable only. This is absent in Table 1 for example.

Response 7: We think this is an excellent suggestion. We have added the regression results for population density affecting economic growth only. They were model 1 and model 5 in table 1.

“Model 1 is the panel regression result, which considers population density as the only factor affecting GDP per capita. The p-value of the Hausman test was less than 0.1. We chose to use individual fixed effects. The coefficient of population density was positive and significant at a 1% level, which shows that population density had a significant positive impact on GDP per capita. We also added a quadratic term for population density to model 1, however, the quadratic term coefficient was not significant. Therefore, the significant positive effect of population density on GDP per capita was linear. Model 2 consists of a capital stock per capita element added to model 1 according to formula (2). The results show that the coefficient of population density was significantly increased from 0.004 to 0.069, and it is was still significant at the 1% level. Based on model 2, model 3 added the variable of key schools per capita to measure human capital. Model 4 further added the regional dummy variables. Since regional dummy variables do not vary over time, we chose the feasible generalized least squares estimation (FGLS) method to analyze panel data to control for possible heteroscedasticity and autocorrelation problems. The regression results for models 3 and 4 show that the effect of population density on GDP per capita remained significant at the 1% level. In order to test the robustness of the regression results of models 1-4 and to solve the endogeneity problem, according to equation (3), we further used GMM estimation to measure the extent of the effect of population density when adding the lagged term of GDP per capita (see Models 5 ~ 7). The results show that the coefficient of population density on GDP per capita stabilized around 0.06 and remained significant at the 1% level. Meanwhile, the p-value of the Sargan test was greater than 0.1, which cannot reject that the hypothesis that instrumental variables obey an exogenous chi-square distribution. This verifies the rationality of the dynamic panel model selection.” (Line 462－485)

Comments 8: In applied research and/or empirical research, the results must be related to the existing literature. This is not the case in this paper. Such omission is fatal!

Response 8: thanks for your expert suggestion. We’ve rewritten the discussion section of our article from Line 585 to 629.

“From the empirical results, we first found that, from 2005 to 2020, the Guanzhong Plain urban agglomeration had formed a “core-periphery” development pattern. The core is the main urban areas of Xi’an–Xianyang and Baoji, which gradually spread to the neighboring districts and counties within these three cities. From a static perspective, it is basically consistent with the spatial pattern of economic growth in other countries around the world and the eastern coastal urban agglomeration of China [4, 19], the Guanzhong Plain urban agglomeration in western China presents a standard “core-periphery“ spatial characteristic. However, in terms of dynamic development, compared with Hao [13] and Liu [20] on the spatial development pattern of the Guanzhong Plain urban agglomeration before and in 2010, the Guanzhong Plain urban agglomeration developed from a single center with the main urban area of Xi'an as the core to a polycentric development model with the main urban areas of Xi'an–Xianyang and Baoji as the core. It can be seen that the cities of the second- and third-order population sizes of the Guanzhong Plain urban agglomeration have developed rapidly [23], and this result basically follows the spatial development model of urban agglomerations in developed countries and eastern China, from monocentric to polycentric [17, 21]. Secondly, population density can significantly promote the economic growth of the districts and counties within the urban agglomeration, with a coefficient of approximately 0.06. When we used GMM estimation to measure the effect of population density with the addition of the lagged term of GDP per capita, the magnitude of its effect remained robust. This result is basically consistent with the current research conclusions on urban development in the United States, Europe, and Africa [38-40]. In addition, in order to verify the “inverted U” model that population density first promotes and then suppresses the economic growth of urban agglomerations [42], we added the squared term of population density to the explanatory variables of the basic model and found that the population density feature was still significant, but its squared term was not significant, and the GMM estimation results were the same. This indicated that the promotion effect of population density within the urban agglomerations of inland China was linear. Thirdly, the spatial agglomeration of economic and demographic factors in neighboring counties had a positive spillover effect on the local economy, while the significantly positive impact of population density on economic growth remained unchanged when it was integrated into the spillover effect of neighboring counties. In order to analyze the changes in the degree of local population density effects when integrating the influence of neighboring districts and counties, we simultaneously added local and surrounding population density factors which were used by Ciccone [38] to discuss five European countries; we also drew on the work of Zhang [43] and adopted spatial econometrics to measure the effect of urban population density. The results were basically consistent with the above studies, indicating that our model setting is reasonable and robust. In analyzing the impact of population agglomeration in surrounding districts and counties, our research conclusion supports the view that “borrowing size”, that is, areas with more economic and population agglomeration, can bring benefits to surrounding areas [46]. In fact, the phenomenon of polycentric spatial structures in the United States [45] and the rapid growth of second-rank cities in Europe [47] both verify the universality of “borrowing size”. In addition, the phenomenon of “agglomeration shadow” in the Beijing–Tianjin–Hebei urban agglomeration in China [49] did not occur in the Guanzhong Plain urban agglomeration.” (Line 585－629)

Comments 9: English language in the paper is a minor issue in my opinion. 

Response 9: thanks for your suggestion. We have enlisted the help of MDPI’s professional touch-up agency to help us touch up our English expressions. We hope the revised manuscript could be acceptable for you.

We hope the corrections will meet with approval. Once again, Thanks for your comments and suggestions.

Author Response File: Author Response.pdf

Reviewer 2 Report

I congratulate the authors for their paper entitled "Impact of population density on spatial differences in economic growth of urban agglomerations: the case of Guanzhong Plain Urban Agglomeration, China" which aims to analyze the impact of population density on economic growth within urban agglomerations, as well as the extent of the impact of population density on economic growth when incorporating spillover effects from neighboring districts and counties. Overall, I found your research to be quite intriguing, and I commend you on your work in this area. However, I would like to offer some constructive feedback that I believe will enhance the quality and impact of your article.

 

Firstly, your literature review primarily focuses on studies conducted in China. While this approach provides valuable insights into the specific context you are investigating, I believe it would be beneficial to expand the literature review to include international studies or research conducted in other countries. This would allow readers to gain a broader perspective on the subject matter and identify any potential variations or similarities in findings across different regions. By incorporating relevant international studies, you can strengthen the external validity of your research and provide a more comprehensive overview of the topic.

Additionally, I recommend that you explicitly address the limitations of your study in the conclusion section. While your findings are undoubtedly valuable, acknowledging the boundaries of your research can enhance the credibility and transparency of your work. Discussing the limitations, whether they are related to data constraints, methodological choices, or other factors, demonstrates a thoughtful and honest approach to research. Furthermore, it provides a basis for future research directions and highlights areas where additional investigation is needed.

Finally, I encourage you to emphasize the potential importance of your study for decision-makers and local authorities in your conclusion. Clearly articulating the practical implications of your findings can help bridge the gap between academic research and real-world applications. Decision-makers often seek evidence-based insights to inform their choices, and by highlighting how your research can contribute to informed decision-making, you can increase the relevance and impact of your work.

Incorporating these suggestions into your article will strengthen its academic rigor and practical relevance. I believe that addressing these points will contribute to a more comprehensive and influential piece of research. I look forward to seeing your revised article.

The English needs some proofreading. 

Author Response

For research article

Response to Reviewer 2 Comments

1. Summary

We appreciate your recognition of our work and your professional suggestions. As you are concerned, there are several problems that need to be addressed. According to your nice suggestions, we have made extensive corrections to our previous draft, and the detailed corrections are listed below.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1: Firstly, your literature review primarily focuses on studies conducted in China. While this approach provides valuable insights into the specific context you are investigating, I believe it would be beneficial to expand the literature review to include international studies or research conducted in other countries. This would allow readers to gain a broader perspective on the subject matter and identify any potential variations or similarities in findings across different regions. By incorporating relevant international studies, you can strengthen the external validity of your research and provide a more comprehensive overview of the topic. 

Response 1: We think this is an excellent suggestion. We have read a great deal of relevant international studies for this purpose. We have revised the first four paragraphs of the literature review (changes are highlighted in red) and added 11 English-language papers.

In view of the spatial differences in the economic growth of urban agglomerations in China, academics have generally found that there is an obvious “core-periphery” spatial pattern within urban agglomerations. The central place theory, which discusses the spatial structure of German regions in the early stage, holds that the optimal urban system is a group composed of a series of non-central cities around a central city, and each city is interdependent and develops together. Krugman vividly describes the characteristics of the equilibrium space of the American metropolitan area by constructing a “center-periphery” endogenous development model [18]. Empirically, metropolitan areas in most countries in the world have or are experiencing a continuous transfer of population, capital, and other factors from the periphery to the core area [19]. An empirical study on the spatial pattern of urban agglomerations in China also found an obvious “core-periphery” spatial pattern. Hu studied the 1990-2007 district- and county-scale data and pointed out that the absolute core of the Yangtze River Delta is still Shanghai, its multilevel driven situation is not yet obvious [4]. In the early 2010s, the Guanzhong Plain urban agglomeration in western China still had Xi'an as its absolute core [13, 20]. In view of the spatial evolution characteristics of economic growth of urban agglomeration, the existing research conclusions have not reached a consensus. On the one hand, some studies suggest that the economic gap within urban agglomerations is narrowing. Brezzi found that the reduction in transport and communication costs has prompted the transformation of OECD countries from monocentric urban developments to integrated urban center and sub-center developments [21]. Compared with European countries that began to develop inter-city network systems, the United States is still dominated by large urban development, and the high cost of living has prompted the transformation of regional spaces to inter-city co-developments [22]. Huang pointed out that the Pearl River Delta's level of economic convergence was higher, and its manufacturing development had shifted from a one-way agglomeration stage to a stage where agglomeration and diffusion coexisted [17]. Moreover, the second- and third-order population size of cities within the urban agglomerations in western China also showed a rapid growth trend [23]. On the other hand, some studies show that the economic spatial differences between China's cross-provincial urban agglomerations are still increasing year by year. Peng found that the economic differences in the Chengdu–Chongqing urban agglomeration showed a fluctuating upward trend between 1995 and 2008 [24]. Yu pointed out that in the early 2010s, the Hubao–Eyu urban agglomeration was in an evolutionary pattern in which there was increasing polarization of the strong and marginalization of the weak cities [25]. Zeng used the more objective NPP/VIIRS nighttime lighting data to find that the economic level of counties in the provincial border area of Xiang-E-Gan showed obvious “pyramid” characteristics, and the top of the “pyramid” was becoming narrower [26].

Although traditional exogenous influencing factors of the spatial differences in the economic growth of urban agglomerations, such as capital stock, technological progress, and foreign trade dependence, still play an important role, they cannot perfectly explain the long-term development of regional economies [27]. In particular, for modern cities with a high concentration of population and capital, “endogenous development” has become an important engine for economic growth [9,10]. Agglomeration economy, as a classic theory that is intrinsically linked to urban economic growth, not only successfully incorporates spatial elements into the regional economic growth model [28], but also clearly describes the emergence and development of the intra-city space and the urban hierarchical system [29]. It provides a new way to study the explanatory mechanism of spatial differences in economic growth of urban agglomerations [12]. Location entropy, the Thiel index, and other indicators that measure the degree of single-industry agglomeration [30], as well as specialization, diversity, and other indicators reflecting the degree of agglomeration of core industries in the city [31], are the traditional criteria for measuring agglomeration economy. However, in modern urban agglomerations where science, communication, and other technologies are developing at a rapid rate, the role of service clusters and knowledge spillovers in urban agglomerations are becoming more and more prominent [22]. Indicators measuring the overall degree of urban agglomeration, such as population agglomeration, have begun to become the focus of attention in the study of the agglomeration economy of cities [19,32].

Population size and population density are the two main indicators of population agglomeration in urban agglomerations. It has been found that population size contributed significantly to economic growth in U.S. cities [33]. For example, Duranton measured urban development in Colombia between 2008 and 2012 and found that a large population size resulted in a significant boost to labor output [34]. However, the relationship between population size and the economic growth of Chinese cities tends to be an “inverted U-shaped” [35]. This is partly due to the fact that China's mega-cities implement policies to control the size of building land and populations, and infrastructure and public services cannot meet the demands of population growth [36]. Compared with population size, population density can better reflect the spatial agglomeration characteristics of urban areas and better measure the urban agglomeration economy. Earlier, Ciccone pointed out the important role of population density and found that every doubling of the population density in U.S. cities increased labor productivity by 6% [37]. Ciccone further selected data from 628 regions in five European countries and noted that a doubling of population density resulted in a 4.5% increase in regional economies [38]. Faberman used the U.S. Bureau of Statistics business micro-data and found that after controlling for geology, climate, and other factors, the coefficient of the population density effect on labor income is 3% [39]. Henderson found that population density has a significant positive impact on household income by measuring the urban economic density in African countries [40]. Chen confirmed the significant positive impact of population density on the economic growth of Chinese cities by constructing the urban growth model of the effect of population density on per capita urban land revenue, which could be a solution to the endogeneity problem [41]. However, there is no consensus on the significant positive impact of population density on economic growth in Chinese cities because Chinese cities have a higher population density than other countries. Some scholars introduced the square term of population density into the income per capita model, and found that the impact of population density on China's urban economic growth is characterized by an “inverted U” shape, in which growth is first promoted and then suppressed [42].

The impact of population density on economic growth also plays a role across cities/regions. Firstly, when integrating the spatial spillovers from neighboring regions, the local population density still contributes significantly to economic growth. Based on the significant positive impact of population density on the economic level in each region of five European countries, Ciccone constructed a theoretical formula for the integration of neighboring regions and the results showed that the positive impact of local population density remained unchanged [38]. Indeed, spatial econometric techniques have produced a significant improvement in the accuracy and objectivity of these types of work [43]. Secondly, there are obvious spatial spillover effects of population density in neighboring cities. The higher population and agglomeration levels of surrounding cities often contribute to economic growth [38, 44]. Zhang used spatial dynamic panel estimation and found that the agglomeration economy of neighboring cities had a significant promoting effect on local economic growth, with a coefficient of 0.14% [43]. Meijers measured the spatial structure of metropolitan areas in the United States and found that polycentric spatial structures can produce higher labor productivity by avoiding the diseconomy of agglomeration and enjoy spillover effects in the process of agglomeration of different centers compared with monocentric structures [45]. Neighboring areas or more closely connected areas have a higher level of population density, which often has a certain spillover effect on the local area, prompting a greater inflow of population, capital, technology, etc., and academics defines this process as the “borrowing size” [46]. Camagni analyzed the reasons for the faster economic growth of second-rank cities in Western Europe between 1995 and 2006 and found that second-rank cities can achieve their own rapid growth through urban networks and borrowing the size of surrounding large cities [47]. In essence, for Chinese urban agglomerations, the “borrowing size” is more likely to form within Chinese urban agglomerations due to the close connection between districts and counties [48]. However, the close connection also brings the “agglomeration shadow” to the areas around the big cities within the urban agglomerations. Chen found that the core city of Beijing–Tianjin–Hebei inhibited the development of the neighboring cities, and there exists a “poverty belt around Beijing and Tianjin” [49]. (Line 107－220 )

The complementary literature are as follows.

11. Duranton, G.; Puga, D. Micro- foundations of urban agglomeration economies // Henderson, V.; Thisse, J. Handbook of regional and urban economics. Amsterdam, Netherlands: North-Holland, 2004, 2063-2117.
12. Krugman, P. Geography and trade; MIT press: Cambridge, USA, 1991.
13. Henderson, V. The urbanization process and economic growth: the so-what question. Journal of Economic growth. 2003, 8, 47-71.
14. Brezzi, M.; Veneri, P. Assessing polycentric urban systems in the OECD: country, regional and metropolitan perspectives. European planning studies. 2015, 23, 1128-1145.
15. Glaeser, E.; Ponzetto, G.; Zou, Y. Urban networks: connecting markets, people, and ideas. Papers in Regional Science. 2016, 95, 17-59.
16. Romer, P. Increasing returns and long-run growth. Journal of Political Economy. 1986, 94, 1002-1037.
17. Brulhart, M.; Sbergami, F. Agglomeration and growth: cross-country evidence. Journal of urban Economics. 2009, 65, 56-64+74.
18. Glaeser, E.; Gottleib, J. The wealth of cities: agglomeration economies and spatial equilibrium in the United States. Journal of Economic Literature. 2009, 47, 983-1028.
19. Duranton, G. Agglomeration effects in Colombia. Journal of Regional Science. 2016, 56, 210-238.
20. Henderson, V.; Nigmatulina, D.; Kriticos, S. Measuring urban economic density. Journal of Urban Economics. 2019, 125, 103188.
21. Meijers, E.; Burger, M. Spatial structure and productivity in US metropolitan areas. Environment and planning A. 2010, 42, 1383-1402.

Comments 2: Additionally, I recommend that you explicitly address the limitations of your study in the conclusion section. While your findings are undoubtedly valuable, acknowledging the boundaries of your research can enhance the credibility and transparency of your work. Discussing the limitations, whether they are related to data constraints, methodological choices, or other factors, demonstrates a thoughtful and honest approach to research. Furthermore, it provides a basis for future research directions and highlights areas where additional investigation is needed.

Response 2: Thank you for your professional advice. We have rewritten the last paragraph of our paper to highlight the limitations of our research.

“We only used the Guanzhong Plain urban agglomeration as a case study to explore the impact of population agglomeration on the economic growth of urban agglomerations. However, due to the differences in climate, topography, history and other aspects in China, and the obvious differences in population agglomeration patterns between different urban agglomerations, the Guanzhong Plain urban agglomeration cannot fully represent the inland urban agglomerations of China. To this end, we still need to carry out comparative research on multiple urban agglomerations to identify the commonalities and differences between population agglomeration and economic growth models among different urban agglomerations. On the other hand, we found that since 2018, the economic spatial differences in the Guanzhong Plain urban agglomeration have been obvious, but this study did not explore this finding in depth. In reality, 2018 is a period of transition for policy making in the Guanzhong Plain urban agglomeration, that is, from the "Guanzhong City Agglomeration" that only includes only the Guanzhong region of Shaanxi Province to the "Guanzhong Plain Urban Agglomeration", which includes the three provinces of Shaanxi, Shanxi, and Gansu. Therefore, whether the policy's improvement reduces the economic differences in urban agglomerations can become another future research direction. In addition, the global environment has experienced ups and downs in recent years, especially due to the global COVID-19 epidemic. Although the COVID-19 epidemic has basically been controlled, people's bottom-line thinking of the normalization of epidemic prevention and control will continue for many years. In the post-epidemic era of the full resumption of work and production, how to perfectly integrate the spatial agglomeration construction mode of urban agglomeration with resilient city construction in response to emergencies will be a research topic of great significance.” (Line 657－679 )

Comments 3: Finally, I encourage you to emphasize the potential importance of your study for decision-makers and local authorities in your conclusion. Clearly articulating the practical implications of your findings can help bridge the gap between academic research and real-world applications. Decision-makers often seek evidence-based insights to inform their choices, and by highlighting how your research can contribute to informed decision-making, you can increase the relevance and impact of your work.

Response 3: Thank you for your professional advice. We have added policy recommend-dations specifically in our conclusions.

“According to the research conclusion, we put forward two policy recommendations. Firstly, we should continue to promote regional development policies of spatial agglomeration and intensive land planning. The conclusion of this study shows that population density has a significant and stable effect on the economic growth of urban agglomerations. However, China’s western region has long faced the phenomenon of allometric growth of urbanization, where land urbanization is beeter than population urbanization, and there are problems such as a large amount of idle construction land and low space utilization. To this end, urban and rural construction should take strengthening the stock planning of land potential as the main goal, and promote the intensive development of industry and service industries. Secondly, we should strengthen the construction of industrial chains and road networks within urban agglomerations, and deepen the network connection between districts and counties. This study found that the spatial agglomeration of economic, population, and other factors can produce benign spatial spillover effects. Strengthening the network connection between districts and counties through industrial cooperation and road network construction not only reduces the cost of spatial spillover in neighboring districts and counties, but also conforms to the new concept of network construction to promote common development between cities [22]. To this end, local governments need to strengthen the integration of industrial chains in urban agglomerations on the one hand, and improve the construction of road networks such as intercity highways and railways on the other hand. It should be pointed out that this study also found that the construction of high-speed rails has not yet promoted economic development in the Guanzhong Plain urban agglomeration. The possible reason for this is that most high-speed rail stations in western China are located in remote areas far from the economic center, and the surrounding industries and business districts are still not perfect. Therefore, it is necessary to effectively utilize the network-driven effect of high-speed rails by continuing to promote the planning and construction of industries or business districts around high-speed rail stations [53].” (Line 630－656 )

The complementary literature is as follow.

53. Ren, T.; Lin, J. Research on the spatial spillover effect of high-speed rasilway on urban economic growth: based on evidence from 277 core cities. Journal of Applied Statistics and Management. 2022, 41, 444-459.

Comments 4: The English needs some proofreading. 

Response 4: thanks for your suggestion. We have enlisted the help of MDPI's professional touch-up agency to help us touch up our English expressions. We hope the revised manuscript could be acceptable for you.

We hope the corrections will meet with approval. Once again, Thanks for your comments and suggestions.

Author Response File: Author Response.pdf

Reviewer 3 Report

The paper makes a valuable contribution to the field by examining spatial variations in economic growth within China's Guanzhong Plain Urban Agglomeration. The study presents a comprehensive methodology for data collection and analysis, focusing on the impact of population density on urban agglomeration's economic growth, considering both intra- and inter-district dynamics. The research employs innovative methods, utilizing nighttime lighting data to extract population density information.

The paper is well-organized and effectively introduces its subject matter and relevance in the initial sections. The significance of the research is evident, as it explores the relationship between population density and economic growth within an urban agglomeration context. The study sheds light on the core-periphery development pattern within the Guanzhong Plain Urban Agglomeration, highlighting the importance of certain core areas and the spillover effects on surrounding districts and counties.

The methodology and data presentation are strong points of the paper. The author articulates the process clearly, from data extraction to the construction of the empirical formula. The use of nighttime lighting data to measure population density is a novel approach that adds to the originality of the study. The step-by-step explanation enhances the paper's credibility, allowing readers to understand and potentially replicate the methodology.

The results are detailed and comprehensible, providing insights into the significant positive relationship between population density and economic growth. Moreover, the consideration of spillover effects from neighboring areas further enriches the findings. This aspect highlights the broader impacts of population density on the entire urban agglomeration and demonstrates the author's thorough analysis.

However, the conclusion section of the paper seems relatively concise compared to the thoroughness of the rest of the work. A more comprehensive elaboration on the implications of the results and their potential applications could further enhance the paper's value. A more extensive exploration of the conclusion would provide a satisfying closure to the study and solidify its contributions.

Additionally, while the paper's literature review effectively contextualizes the study within the Chinese context, a broader international perspective could add depth to the paper's significance. Incorporating international publications that discuss similar themes of urban agglomeration, population density, and economic growth would position the paper within a global academic discourse.

In summary, the recommendations for improvement include a more comprehensive conclusion section and a broader international perspective in the literature review. 

Author Response

For research article

 

Response to Reviewer 3 Comments

1. Summary

We appreciate your recognition of our work and your excellent suggestions. As you are concerned, there are several problems that need to be addressed. According to your professional suggestions, we have made extensive corrections to our previous draft, and the detailed corrections are listed below.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1: However, the conclusion section of the paper seems relatively concise compared to the thoroughness of the rest of the work. A more comprehensive elaboration on the implications of the results and their potential applications could further enhance the paper’s value. A more extensive exploration of the conclusion would provide a satisfying closure to the study and solidify its contributions.

Response 1: thanks for your professional suggestion. We’ve rewritten the discussion section of our article (changes are highlighted in red). There we compared our findings in detail with existing studies.

“From the empirical results, we first found that, from 2005 to 2020, the Guanzhong Plain urban agglomeration had formed a “core-periphery” development pattern. The core is the main urban areas of Xi’an–Xianyang and Baoji, which gradually spread to the neighboring districts and counties within these three cities. From a static perspective, it is basically consistent with the spatial pattern of economic growth in other countries around the world and the eastern coastal urban agglomeration of China [4, 19], the Guanzhong Plain urban agglomeration in western China presents a standard “core-periphery“ spatial characteristic. However, in terms of dynamic development, compared with Hao [13] and Liu [20] on the spatial development pattern of the Guanzhong Plain urban agglomeration before and in 2010, the Guanzhong Plain urban agglomeration developed from a single center with the main urban area of Xi'an as the core to a polycentric development model with the main urban areas of Xi'an–Xianyang and Baoji as the core. It can be seen that the cities of the second- and third-order population sizes of the Guanzhong Plain urban agglomeration have developed rapidly [23], and this result basically follows the spatial development model of urban agglomerations in developed countries and eastern China, from monocentric to polycentric [17, 21]. Secondly, population density can significantly promote the economic growth of the districts and counties within the urban agglomeration, with a coefficient of approximately 0.06. When we used GMM estimation to measure the effect of population density with the addition of the lagged term of GDP per capita, the magnitude of its effect remained robust. This result is basically consistent with the current research conclusions on urban development in the United States, Europe, and Africa [38-40]. In addition, in order to verify the “inverted U” model that population density first promotes and then suppresses the economic growth of urban agglomerations [42], we added the squared term of population density to the explanatory variables of the basic model and found that the population density feature was still significant, but its squared term was not significant, and the GMM estimation results were the same. This indicated that the promotion effect of population density within the urban agglomerations of inland China was linear. Thirdly, the spatial agglomeration of economic and demographic factors in neighboring counties had a positive spillover effect on the local economy, while the significantly positive impact of population density on economic growth remained unchanged when it was integrated into the spillover effect of neighboring counties. In order to analyze the changes in the degree of local population density effects when integrating the influence of neighboring districts and counties, we simultaneously added local and surrounding population density factors which were used by Ciccone [38] to discuss five European countries; we also drew on the work of Zhang [43] and adopted spatial econometrics to measure the effect of urban population density. The results were basically consistent with the above studies, indicating that our model setting is reasonable and robust. In analyzing the impact of population agglomeration in surrounding districts and counties, our research conclusion supports the view that “borrowing size”, that is, areas with more economic and population agglomeration, can bring benefits to surrounding areas [46]. In fact, the phenomenon of polycentric spatial structures in the United States [45] and the rapid growth of second-rank cities in Europe [47] both verify the universality of “borrowing size”. In addition, the phenomenon of “agglomeration shadow” in the Beijing–Tianjin–Hebei urban agglomeration in China [49] did not occur in the Guanzhong Plain urban agglomeration.” (Line 585－629)

Comments 2: Additionally, while the paper’s literature review effectively contextualizes the study within the Chinese context, a broader international perspective could add depth to the paper's significance. Incorporating international publications that discuss similar themes of urban agglomeration, population density, and economic growth would position the paper within a global academic discourse.

Response 2: We think this is an excellent suggestion. We have read a great deal of relevant international studies for this purpose. We have revised the first four paragraphs of the literature review (changes are highlighted in red) and added 11 English-language papers.

In view of the spatial differences in the economic growth of urban agglomerations in China, academics have generally found that there is an obvious “core-periphery” spatial pattern within urban agglomerations. The central place theory, which discusses the spatial structure of German regions in the early stage, holds that the optimal urban system is a group composed of a series of non-central cities around a central city, and each city is interdependent and develops together. Krugman vividly describes the characteristics of the equilibrium space of the American metropolitan area by constructing a “center-periphery” endogenous development model [18]. Empirically, metropolitan areas in most countries in the world have or are experiencing a continuous transfer of population, capital, and other factors from the periphery to the core area [19]. An empirical study on the spatial pattern of urban agglomerations in China also found an obvious “core-periphery” spatial pattern. Hu studied the 1990-2007 district- and county-scale data and pointed out that the absolute core of the Yangtze River Delta is still Shanghai, its multilevel driven situation is not yet obvious [4]. In the early 2010s, the Guanzhong Plain urban agglomeration in western China still had Xi'an as its absolute core [13, 20]. In view of the spatial evolution characteristics of economic growth of urban agglomeration, the existing research conclusions have not reached a consensus. On the one hand, some studies suggest that the economic gap within urban agglomerations is narrowing. Brezzi found that the reduction in transport and communication costs has prompted the transformation of OECD countries from monocentric urban developments to integrated urban center and sub-center developments [21]. Compared with European countries that began to develop inter-city network systems, the United States is still dominated by large urban development, and the high cost of living has prompted the transformation of regional spaces to inter-city co-developments [22]. Huang pointed out that the Pearl River Delta's level of economic convergence was higher, and its manufacturing development had shifted from a one-way agglomeration stage to a stage where agglomeration and diffusion coexisted [17]. Moreover, the second- and third-order population size of cities within the urban agglomerations in western China also showed a rapid growth trend [23]. On the other hand, some studies show that the economic spatial differences between China's cross-provincial urban agglomerations are still increasing year by year. Peng found that the economic differences in the Chengdu–Chongqing urban agglomeration showed a fluctuating upward trend between 1995 and 2008 [24]. Yu pointed out that in the early 2010s, the Hubao–Eyu urban agglomeration was in an evolutionary pattern in which there was increasing polarization of the strong and marginalization of the weak cities [25]. Zeng used the more objective NPP/VIIRS nighttime lighting data to find that the economic level of counties in the provincial border area of Xiang-E-Gan showed obvious “pyramid” characteristics, and the top of the “pyramid” was becoming narrower [26].

Although traditional exogenous influencing factors of the spatial differences in the economic growth of urban agglomerations, such as capital stock, technological progress, and foreign trade dependence, still play an important role, they cannot perfectly explain the long-term development of regional economies [27]. In particular, for modern cities with a high concentration of population and capital, “endogenous development” has become an important engine for economic growth [9,10]. Agglomeration economy, as a classic theory that is intrinsically linked to urban economic growth, not only successfully incorporates spatial elements into the regional economic growth model [28], but also clearly describes the emergence and development of the intra-city space and the urban hierarchical system [29]. It provides a new way to study the explanatory mechanism of spatial differences in economic growth of urban agglomerations [12]. Location entropy, the Thiel index, and other indicators that measure the degree of single-industry agglomeration [30], as well as specialization, diversity, and other indicators reflecting the degree of agglomeration of core industries in the city [31], are the traditional criteria for measuring agglomeration economy. However, in modern urban agglomerations where science, communication, and other technologies are developing at a rapid rate, the role of service clusters and knowledge spillovers in urban agglomerations are becoming more and more prominent [22]. Indicators measuring the overall degree of urban agglomeration, such as population agglomeration, have begun to become the focus of attention in the study of the agglomeration economy of cities [19,32].

Population size and population density are the two main indicators of population agglomeration in urban agglomerations. It has been found that population size contributed significantly to economic growth in U.S. cities [33]. For example, Duranton measured urban development in Colombia between 2008 and 2012 and found that a large population size resulted in a significant boost to labor output [34]. However, the relationship between population size and the economic growth of Chinese cities tends to be an “inverted U-shaped” [35]. This is partly due to the fact that China's mega-cities implement policies to control the size of building land and populations, and infrastructure and public services cannot meet the demands of population growth [36]. Compared with population size, population density can better reflect the spatial agglomeration characteristics of urban areas and better measure the urban agglomeration economy. Earlier, Ciccone pointed out the important role of population density and found that every doubling of the population density in U.S. cities increased labor productivity by 6% [37]. Ciccone further selected data from 628 regions in five European countries and noted that a doubling of population density resulted in a 4.5% increase in regional economies [38]. Faberman used the U.S. Bureau of Statistics business micro-data and found that after controlling for geology, climate, and other factors, the coefficient of the population density effect on labor income is 3% [39]. Henderson found that population density has a significant positive impact on household income by measuring the urban economic density in African countries [40]. Chen confirmed the significant positive impact of population density on the economic growth of Chinese cities by constructing the urban growth model of the effect of population density on per capita urban land revenue, which could be a solution to the endogeneity problem [41]. However, there is no consensus on the significant positive impact of population density on economic growth in Chinese cities because Chinese cities have a higher population density than other countries. Some scholars introduced the square term of population density into the income per capita model, and found that the impact of population density on China's urban economic growth is characterized by an “inverted U” shape, in which growth is first promoted and then suppressed [42].

The impact of population density on economic growth also plays a role across cities/regions. Firstly, when integrating the spatial spillovers from neighboring regions, the local population density still contributes significantly to economic growth. Based on the significant positive impact of population density on the economic level in each region of five European countries, Ciccone constructed a theoretical formula for the integration of neighboring regions and the results showed that the positive impact of local population density remained unchanged [38]. Indeed, spatial econometric techniques have produced a significant improvement in the accuracy and objectivity of these types of work [43]. Secondly, there are obvious spatial spillover effects of population density in neighboring cities. The higher population and agglomeration levels of surrounding cities often contribute to economic growth [38, 44]. Zhang used spatial dynamic panel estimation and found that the agglomeration economy of neighboring cities had a significant promoting effect on local economic growth, with a coefficient of 0.14% [43]. Meijers measured the spatial structure of metropolitan areas in the United States and found that polycentric spatial structures can produce higher labor productivity by avoiding the diseconomy of agglomeration and enjoy spillover effects in the process of agglomeration of different centers compared with monocentric structures [45]. Neighboring areas or more closely connected areas have a higher level of population density, which often has a certain spillover effect on the local area, prompting a greater inflow of population, capital, technology, etc., and academics defines this process as the “borrowing size” [46]. Camagni analyzed the reasons for the faster economic growth of second-rank cities in Western Europe between 1995 and 2006 and found that second-rank cities can achieve their own rapid growth through urban networks and borrowing the size of surrounding large cities [47]. In essence, for Chinese urban agglomerations, the “borrowing size” is more likely to form within Chinese urban agglomerations due to the close connection between districts and counties [48]. However, the close connection also brings the “agglomeration shadow” to the areas around the big cities within the urban agglomerations. Chen found that the core city of Beijing–Tianjin–Hebei inhibited the development of the neighboring cities, and there exists a “poverty belt around Beijing and Tianjin” [49]. (Line 107－220 )

The complementary literature are as follows.

11. Duranton, G.; Puga, D. Micro- foundations of urban agglomeration economies // Henderson, V.; Thisse, J. Handbook of regional and urban economics. Amsterdam, Netherlands: North-Holland, 2004, 2063-2117.
12. Krugman, P. Geography and trade; MIT press: Cambridge, USA, 1991.
13. Henderson, V. The urbanization process and economic growth: the so-what question. Journal of Economic growth. 2003, 8, 47-71.
14. Brezzi, M.; Veneri, P. Assessing polycentric urban systems in the OECD: country, regional and metropolitan perspectives. European planning studies. 2015, 23, 1128-1145.
15. Glaeser, E.; Ponzetto, G.; Zou, Y. Urban networks: connecting markets, people, and ideas. Papers in Regional Science. 2016, 95, 17-59.
16. Romer, P. Increasing returns and long-run growth. Journal of Political Economy. 1986, 94, 1002-1037.
17. Brulhart, M.; Sbergami, F. Agglomeration and growth: cross-country evidence. Journal of urban Economics. 2009, 65, 56-64+74.
18. Glaeser, E.; Gottleib, J. The wealth of cities: agglomeration economies and spatial equilibrium in the United States. Journal of Economic Literature. 2009, 47, 983-1028.
19. Duranton, G. Agglomeration effects in Colombia. Journal of Regional Science. 2016, 56, 210-238.
20. Henderson, V.; Nigmatulina, D.; Kriticos, S. Measuring urban economic density. Journal of Urban Economics. 2019, 125, 103188.
21. Meijers, E.; Burger, M. Spatial structure and productivity in US metropolitan areas. Environment and planning A. 2010, 42, 1383-1402.

We hope the corrections will meet with approval. Once again, Thanks for your comments and suggestions.

Author Response File: Author Response.pdf

Reviewer 4 Report

Dear authors,  we appreciate the idea of such a research endeavor  focused on the impact of population density on spatial differences in eco- 2 nomic growth of urban agglomerations. We recommend that the authors to address (related to the proposed topic), in the discussion / conclusion section, some potential trends that have emerged from the global environment's recent dynamics, during the pandemic, and to underline which may be the main further developments in the post-pandemic context.   Moreover, the limits of the research should be mentioned in the discussion / conclusion section. The continuous development of the analyzed context will definitely impact the the future dynamics of the urban regions, therefore the authors might  formulate more clearly some recommendations for decision makers.       

Author Response

For research article

 

Response to Reviewer 4 Comments

1. Summary

We appreciate your recognition of our work and your professional suggestions. As you are concerned, there are several problems that need to be addressed. According to your nice suggestions, we have made extensive corrections to our previous draft, and the detailed corrections are listed below.

2. Point-by-point response to Comments and Suggestions for Authors

Comments 1: We recommend that the authors to address (related to the proposed topic), in the discussion / conclusion section, some potential trends that have emerged from the global environment's recent dynamics, during the pandemic, and to underline which may be the main further developments in the post-pandemic context. 

Response 1: Thank you for your professional advice. Your suggestion makes us realize that the global environment is having a significant impact on the economic growth of China’s urban agglomerations. For this reason, we have concluded our article by writing about possible research topic relevant to the post-pandemic era.

“In addition, the global environment has experienced ups and downs in recent years, especially due to the global COVID-19 epidemic. Although the COVID-19 epidemic has basically been controlled, people’s bottom-line thinking of the normalization of epidemic prevention and control will continue for many years. In the post-epidemic era of the full resumption of work and production, how to perfectly integrate the spatial agglomeration construction mode of urban agglomeration with resilient city construction in response to emergencies will be a research topic of great significance.” (Line 673－679 )

Comments 2: Moreover, the limits of the research should be mentioned in the discussion / conclusion section.

Response 2: Thank you for your professional suggestion. We have rewritten the last paragraph of our article to highlight the limitations of our research.

“We only used the Guanzhong Plain urban agglomeration as a case study to explore the impact of population agglomeration on the economic growth of urban agglomerations. However, due to the differences in climate, topography, history and other aspects in China, and the obvious differences in population agglomeration patterns between different urban agglomerations, the Guanzhong Plain urban agglomeration cannot fully represent the inland urban agglomerations of China. To this end, we still need to carry out comparative research on multiple urban agglomerations to identify the commonalities and differences between population agglomeration and economic growth models among different urban agglomerations. On the other hand, we found that since 2018, the economic spatial differences in the Guanzhong Plain urban agglomeration have been obvious, but this study did not explore this finding in depth. In reality, 2018 is a period of transition for policy making in the Guanzhong Plain urban agglomeration, that is, from the "Guanzhong City Agglomeration" that only includes only the Guanzhong region of Shaanxi Province to the "Guanzhong Plain Urban Agglomeration", which includes the three provinces of Shaanxi, Shanxi, and Gansu. Therefore, whether the policy's improvement reduces the economic differences in urban agglomerations can become another future research direction. In addition, the global environment has experienced ups and downs in recent years, especially due to the global COVID-19 epidemic. Although the COVID-19 epidemic has basically been controlled, people’s bottom-line thinking of the normalization of epidemic prevention and control will continue for many years. In the post-epidemic era of the full resumption of work and production, how to perfectly integrate the spatial agglomeration construction mode of urban agglomeration with resilient city construction in response to emergencies will be a research topic of great significance.” (Line 657－679 )

Comments 3: The continuous development of the analyzed context will definitely impact the future dynamics of the urban regions, therefore the authors might formulate more clearly some recommendations for decision makers.

Response 3: We think this is an excellent suggestion. We have added the policy recommend-dations specifically in our conclusions.

“According to the research conclusion, we put forward two policy recommendations. Firstly, we should continue to promote regional development policies of spatial agglomeration and intensive land planning. The conclusion of this study shows that population density has a significant and stable effect on the economic growth of urban agglomerations. However, China’s western region has long faced the phenomenon of allometric growth of urbanization, where land urbanization is beeter than population urbanization, and there are problems such as a large amount of idle construction land and low space utilization. To this end, urban and rural construction should take strengthening the stock planning of land potential as the main goal, and promote the intensive development of industry and service industries. Secondly, we should strengthen the construction of industrial chains and road networks within urban agglomerations, and deepen the network connection between districts and counties. This study found that the spatial agglomeration of economic, population, and other factors can produce benign spatial spillover effects. Strengthening the network connection between districts and counties through industrial cooperation and road network construction not only reduces the cost of spatial spillover in neighboring districts and counties, but also conforms to the new concept of network construction to promote common development between cities [22]. To this end, local governments need to strengthen the integration of industrial chains in urban agglomerations on the one hand, and improve the construction of road networks such as intercity highways and railways on the other hand. It should be pointed out that this study also found that the construction of high-speed rails has not yet promoted economic development in the Guanzhong Plain urban agglomeration. The possible reason for this is that most high-speed rail stations in western China are located in remote areas far from the economic center, and the surrounding industries and business districts are still not perfect. Therefore, it is necessary to effectively utilize the network-driven effect of high-speed rails by continuing to promote the planning and construction of industries or business districts around high-speed rail stations [53].” (Line 630－656 )

The complementary literature are as follows.

22. Glaeser, E.; Ponzetto, G.; Zou, Y. Urban networks: connecting markets, people, and ideas. Papers in Regional Science. 2016, 95, 17-59.
23. Ren, T.; Lin, J. Research on the spatial spillover effect of high-speed rasilway on urban economic growth: based on evidence from 277 core cities. Journal of Applied Statistics and Management. 2022, 41, 444-459.

We hope the corrections will meet with approval. Once again, Thanks for your comments and suggestions.

Author Response File: Author Response.pdf

Round 2

Reviewer 1 Report

I am satisfied with the corrections.