The Spatial Role and Influencing Mechanism of the Digital Economy in Empowering High-Quality Economic Development

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The Spatial Role and Influencing Mechanism of the Digital Economy in Empowering High-Quality Economic Development


1. Introduction

China’s economy has grown at a breakneck pace since enacting the reform and opening-up strategy. This has been made possible by the country’s reliance on the demographic dividend, factor inputs, the pull of external demand, and scale expansion. As a result, China’s gap with developed countries is closing in terms of urbanization and people’s incomes. Because of the objective existence of the law of diminishing marginalis, this extensive mode of economic growth will gradually reveal its drawbacks as the economy reaches a certain stage of development, even though it can quickly improve the social and economic level and has clear effects in promoting employment and social stability, such as a drop in production efficiency, significant resource loss, pollution of the environment, and other issues. Fast but not good, and big but not strong have become a shackle restricting the economy development of China. Against the current backdrop of accelerated globalization and an unstable global economy, China’s past economic growth dividend is gradually disappearing. “Demand contraction, supply shock, and weakening expectations” are the three pressures that China’s economy is currently experiencing. A high-quality and high-efficiency economic growth model has become an inevitable choice for transforming the development model, transforming growth drivers, and optimizing the economic structure. In this regard, the rise of the digital economy has not only spawned new business models, but has also grown to be a significant force behind the economy’s high-quality development. Its contribution to the transition to new from old economic engines as well as to the stimulation of consumption has become more and more apparent [1,2].
By applying and integrating digital technology with the traditional economy, the digital economy promotes a change in the mode of production and a reconfiguration of the innovation system [3]. Additionally, it has effectively addressed the issues of resource mismatch, uneven regional development, and a lackluster industrial innovation capacity in economic development by transforming the data generated and used by the whole society into key production factors [4,5]. Since the digital economy relies heavily on the modern information network, it is impossible to separate the discussion of the digital economy from the analysis of the modern information network. In China today, the internet is a significant component of the contemporary information network [6]. Researchers have discovered that the internet can raise economic development by increasing manufacturing productivity, human capital mismatch, and regional innovation efficiency [7,8,9]. Aside from this, “network externalities” and “Metcalfe’s Law” will cause the benefits of the internet to grow exponentially with the number of users, as well as cause value spillover, and such nonlinear effects and spillover effects will also promote high-quality economic development [7,10]. Thus, whether digital economy also has similar characteristics is worthy of in-depth discussion.
At present, there is a growing complexity in the mechanism path of the digital economy that facilitates high-quality economic development. Throughout existing research, the analysis of the mechanism of the two has mainly been carried out from the micro and macro dimensions. At the micro level, it can be discussed from two aspects: platform economy and enterprise digital transformation. In terms of platform economics, network economies and economies of scale have emerged as significant sources of supplementary profits for goods in the digital economy age. As digital technology has become more widely used, platform economies have grown quickly, and new growth and profit models have been created as a result of the interaction between network economies and economies of scale [11]. In terms of how the digital economy supports business digital transformation, business digital transformation can help businesses better understand consumer demand and maximize resource allocation [12]. At the macro level, it can be discussed from the two aspects of improving resource allocation efficiency and production efficiency. In terms of resource allocation efficiency improvement, the digital economy improves resource allocation efficiency by reducing reliance on traditional production factors in the manufacturing process through the integration of data and digital resources into data elements [13]. In terms of the improvement in TFP, a new technology revolution is underway, and emerging technologies—represented by big data, big models, blockchain, and other concepts of the production process—have brought about the improvement in TFP, which has transformed the traditional approach to enhancing production efficiency by relying solely on manpower and capital increases.
Scholars’ findings essentially agree on one thing when it comes to the digital economy’s influence: it can help to advance high-quality economic development. According to Shen et al. (2022), the effect is pronounced in eastern China [14]. However, Wu et al. (2023) have argued that it has a stronger influence on underdeveloped regions [15]. After that, researchers looked into how the digital economy influences high-quality development. For instance, according to Novikova and Strogonova (2020), the primary engine of economic development is innovation in digital technology and digital processes [16]. Gaziz S. et al. (2020) believe that the primary force driving the growth of SMEs is the digital economy [17]. The digital economy contributes positively to the advancement of sustainable economic growth, as noted by Aniqoh (2020) [18]. However, scholars have found that the unique characteristics of the digital economy also come with new risks and difficulties, like the security of cross-border information transmission, network security, the digital divide, and the dwindling bargaining power of workers [19,20,21]. All these may have a negative impact. In terms of the path of digital economy driving high-quality economic development, scholars’ research mainly focuses on technical innovation, modernizing industrial structures, and increasing production efficiency. According to Ding et al. (2022) and Lu et al. (2023), by increasing the level of technical innovation, the digital economy can promote the economy’s high-quality development [22,23]. Meanwhile, Liao (2023) and Guo et al. (2023) believe that this process has been greatly aided by the modernization of the industrial structure [24,25]. Zhao et al. (2023) have argued that digital economy can boost economic growth by raising businesses’ total factor productivity [26].

The existing literature provides abundant theoretical support for this paper, but it can yet be expanded upon. Firstly, the existing literature only pays attention to the mechanism of action of a certain dimension but does not systematically elaborate its internal mechanism of action. However, data elements can penetrate into every link of production and life, encouraging advancement at the innovation levels of resource allocation efficiency and production efficiency, which reshapes the traditional economic growth model. This is consistent with the concept of high-quality economic development, but the current literature has not incorporated the two into a unified framework to deeply explore their internal mechanism. Secondly, the existing literature fails to note the digital economy’s lag impact. Digital infrastructure construction, the accumulation and training of professional talents, and the popularization of digital technology are not achieved overnight; that is, there can be a lag effect with the digital economy, but few scholars have noticed this. Thirdly, building upon the existing literature, the nonlinear effects of the digital economy are expounded systematically. Because innovation is the digital economy’s primary engine, it will unavoidably have distinct effects on high-quality economic development depending on its stage of development. Furthermore, few researchers have looked into whether the digital economy also has nonlinear impacts because of “network externalities and Metcalfe’s law” in information and communication technologies. Based on this, firstly, the two are visually analyzed with the inclusion of exploratory spatial data analysis (ESDA), thereby revealing a more pronounced spatiotemporal differentiation between them; secondly, dynamic analysis divides the digital economy into several lag periods, and it looks at how the current period affects the high-quality economic development in the ensuing periods; finally, the threshold model is applied to examine the nonlinear consequences of different development stages of digital economy, using the digital economy as the threshold variable.

3. Model Construction and Data Sources

Given the frequent mention of the digital economy and high-quality economic development in this paper, for ease of comprehension, the terms DIGE and HQED will be utilized to represent these concepts, respectively, throughout subsequent analysis.

3.1. Modeling

To investigate study Hypothesis 1, scholars have typically used techniques like mixed regression, random effect models, and fixed effect models to confirm the linear effects of the DIGE. The autocorrelation of disturbance terms for the same individual in different periods in panel data, however, is frequently ignored by the first two models; however, the fixed effect model can avoid such problems well. So, this paper uses the panel fixed effect model. In light of this, the model was built as follows:

l n h q d i , t = α 0 + α 1 l n d i g e i , t + α c C i , t + μ i + δ t + ε i , t

In Equation (1), i and t are provinces and years, l n h q d i , t is the level of HQED, l n d i g e i , t is the level of the DIGE, C i , t are the control variables, μ i is the province fixed effect, δ t is the year fixed effect, and ε i , t is the random perturbation term.

Since the DIGE is the product of information technology, the “network effect” and “Metcalfe’s law” pertaining to that field may also apply to the DIGE. To confirm a nonlinear impact, panel threshold models are frequently used in research processes by academics to investigate the phased impact of variables; this work likewise employs similar methodology. The panel threshold model was constructed as follows:

l n h q d i , t = φ 0 + φ 1 l n d i g e i , t × I l n d i g e i , t θ + φ 2 l n d i g e i , t × I l n d i g e i , t > θ + φ c C i , t + μ i + ε i , t

In Equation (2), l n d i g e i , t is the threshold variable, I(•) is the indicator function taking the value 0 or 1, and all other variables are consistent with Equation (1).

The spatial econometric model is an effective method to test the spatial spillover effect. Common spatial models include the spatial autoregressive model (SAR), the spatial error model (SEM), and the spatial Durbin model (SDM). The SAR and SEM models assume that the spillover effect is generated by the spatial interaction of the explained variable and the error term, respectively, while the SDM model adds spatial interaction terms of explanatory variables on the basis of considering these two spatial mechanisms; that is, high-quality economic development is not only affected by the DIGE of the province, but also by neighboring provinces. Consequently, the SDM was built as follows:

l n h q d i , t = α 0 + ρ W l n h q d i , t + β 1 W l n d i g e i , t + α 1 l n d i g e i , t + β 2 W C i , t + α c C i , t + μ i + δ t + ε i , t

In Equation (3), ρ is the autoregressive coefficient, β 1 , β 2 are the elasticity coefficients of the spatial interaction terms of the DIGE and the control variable, and W is the spatial weighting matrix. To ensure the robustness of the results, this paper constructed the adjacency matrix ( W 1 ) and the geographic distance matrix ( W 2 ) for the subsequent regression analysis, respectively.

3.2. Variable Description and Measurement

  • Explained variable: HQED (lnhqd). HQED requires innovative, coordinated, green, open, and shared development. Drawing on the framework proposed by Zhang et al. (2022) [37], and building upon findings from Sun et al. (2020) [38], this paper constructs an evaluation index system based on five concepts (Table 1), and adopts the entropy method for measurement.
  • Core explanatory variables: DIGE (lndige). There is no unanimous agreement on what constitutes the DIGE. Thus, based on Wang et al.’s (2021) [39] study and integrating the theoretical analysis presented in this paper, an evaluation index system (Table 2) was constructed encompassing digital technology innovation, digital infrastructure, digital industrialization, and industrial digitalization, adopting the entropy method for measurement.
  • Control variables. In addition, a set of control variables were established to reduce the bias due to missing variables. These variables are as follows: economic development level (del), economic development level has an important impact on local innovation capacity, education, human resources, and market mechanisms, which in turn affects high-quality economic development, using GDP per capita. Government intervention degree (gov): government fiscal expenditure can interfere with the spontaneous regulation of the market, and the intensity of government fiscal intervention affects regional economic development to a certain extent, expressed by the proportion of local fiscal expenditure in GDP. Foreign investment (fdi): foreign investment is an indispensable factor in China’s economic expansion, job creation, and reform advocacy, using the proportion of total foreign investment to GDP. Advanced industrial structure (isa): industrial development structure can directly affect the environment, sustainable development, and international competitiveness, expressed by the proportion of value-added of the tertiary industry to value-added of the secondary industry. Technological innovation (ti): technological innovation is the key to whether China’s economy can cross the middle-income trap, and directly affects the transformation of the production mode and the improvement in resource utilization efficiency, etc.; its importance is self-evident, and the proportion of authorized domestic patent applications to the number of domestic patent applications is expressed.

3.3. Data Sources and Descriptive Statistics of Variables

Panel data from 30 Chinese administrative regions at the provincial level, spanning the years 2012 to 2022, are analyzed in this article (data for Tibet Autonomous Region is absent). To prevent the effect of heteroskedasticity, the main explanatory variables and explanatory variables are taken as logarithmic treatment, and some of the missing data are interpolated to make up for the missing data. The research data come from the China Statistical Yearbook, the National Bureau of Statistics, the Statistical Yearbook of China’s Tertiary Industry, and the Peking University Digital Financial Inclusion Index [40].
Table 3 shows that the indices of HQED and the DIGE differ greatly among different provinces. These results align with Zhao et al.’s (2020) findings [31]. The control variables also exhibit this characteristic.

5. Conclusions and Policy Recommendations

5.1. Conclusions

The coordinated development and construction of the DIGE are in line with the concept of HQED, and are a vital measure to implementing the new development concept. Therefore, this study takes the reality that the DIGE has a great impact on social and economic development as an entry point and uses the ESDA, fixed panel effect model, panel threshold model, and SDM based on the panel data from 30 Chinese administrative regions at the provincial level, spanning the years 2012 to 2022, on the basis of constructing a HQED and DIGE indicator system. This paper verifies the spatiotemporal heterogeneity of China’s DIGE and HQED, and empirically tests the internal mechanism and action mechanism of the DIGE on HQED in multiple dimensions. The conclusions are as follows.

First, China’s DIGE underwent rapid expansion during the study period, and as time went on, the overall center of gravity started to shift to the southeast, eventually forming the features of the southeast region cluster. At the same time, a qualitative jump in HQED also occurred throughout the research period, forming a spatial development pattern with the development of central and western regions driven by the radiation of the Yangtze River Delta, Pearl River Delta, and Beijing–Tianjin–Hebei region.

Second, the DIGE significantly promoted the improvement in the level of HQED, and the conclusion remained stable after adding the fixed effects of time and province. The dynamic analysis results show that the DIGE has a lagging effect on HQED, and when the DIGE lags to the sixth stage, the effect on HQED is not significant.

Third, the DIGE has a nonlinear effect of a single threshold on HQED. The DIGE development index can significantly promote an improvement in HQED at both sides of the threshold value, and the improvement effect on the left side of the threshold value is greater than that on the right side of the threshold value, showing a nonlinear feature that is first strong and then weak.

Fourth, the point estimation results of the SDM show that the DIGE has a spatial spillover effect, which is manifested in that the DIGE can not only significantly improve the local HQED, but also significantly promote HQED in neighboring provinces. The results of the partial differential estimation show that both direct and indirect effects of the DIGE can promote HQED, which further supports the conclusion of the point estimation results. After replacing the space matrix, the results are still consistent with the above, which indicates that the estimation results of the SDM are robust.

5.2. Policy Recommendations

This study’s findings lead this paper to propose the following policy recommendations:

First, implementing regional differentiation and dynamic development, allowing the Beijing and Tianjin regions, as well as the eastern coastal provinces and regions, to fully adopt their leading and radiating roles, forming a development pattern where the eastern region radiates to drive the development of the central and western regions and further eliminate the digital divide. At the same time, in order to undertake industrial transfer from the eastern region, the central and western regions should simultaneously modernize their infrastructure.

Second, given that the DIGE has the potential to enhance HQED, the government ought to augment its investment and backing for digital technology, persist in endorsing innovative digital technology, develop digital infrastructure, train talent in digital technology, facilitate digital sharing, and implement other measures to foster the digital economy. Additionally, the process of digital industrialization and industrial digitalization should be further advanced.

Third, create plans for the DIGE and HQED that take local factors and the region’s actual development into consideration. In order to strengthen the weak points in the central and western regions’ DIGE development and close the gap with the eastern region, it is important to expedite the implementation of supporting policies like building DIGE infrastructure, bringing in digital talent, and offering tax breaks. The eastern area should concentrate on the advancement of digital core technologies, improve resource integration and critical technical support capabilities, and offer policy assurances to better encourage the beneficial contribution of the DIGE to HQED.

Fourth, promote regional integrated development. The DIGE has the characteristics of spatial spillover. The driving and radiating role of neighboring regions should be fully embraced by regions with a high degree of DIGE. Local governments should strengthen exchanges and cooperation, establish platforms for sharing digital factors, and smooth the flow of digital factor resources.


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