2.1

Variable selection and data source

When at the forefront, the less investment in technology development, the more output. Only by getting higher output through relatively low investment in technological transformation can we achieve high efficiency in technological transformation, and get greater commercial value and innovation returns, which is the common pursuit of the whole industry. Financial agglomeration can further serve and support technological innovation activities by relying on the functions of the financial system itself. If the financial industry wants to play these functions, it is inseparable from improving its own operating efficiency.

The basic function of financial sector to mobilize savings plays a key role in economic growth, mainly manifested in the expansion of credit scale. Banks have obvious advantages in this function and are dominant in the financial system ⁹. In this process, with the emergence of emerging industries or some core technologies of traditional industries at the forefront of world technology, the real economy has the need to avoid and spread risks, the capital market has begun to develop rapidly, and the function of financial system to avoid and spread risks has gradually taken shape; Therefore, the process of financial development from the basic function of mobilizing savings to the core function of allocating capital, and then to the derivative function of dispersing risks plays a leading role, is also the process of upgrading the financial level, and its deepening result is the dynamic evolution from financial quantitative growth to quality improvement.

Financial agglomeration indicators: This paper uses the location entropy method to measure the indicators:

FA :Financial industry agglomeration;

BA :Banking agglomeration;

SA :Securities industry agglomeration;

IA:Insurance industry agglomeration;

FVA :Development level of financial industry;

HSD :The development level of the banking industry;

SMV :Development of securities industry;

PMI :Development level of insurance industry.

Index of innovation efficiency of high-tech industry: This paper uses data envelopment method to measure the innovation efficiency TIE of high-tech industry in China. In this paper, the input-output system is constructed when measuring the innovation efficiency of high-tech industries ^10-11.

Control variables: Ranging from macro-environment and market environment to enterprises themselves. Such as enterprise scale, government support and market. Drawing lessons from the existing research, this paper finally determines the following two influencing factors as control variables, namely, the regional economic level GDP as control variable 1 and the foreign investment amount FDI as control variable 2.

The sample of this paper is the relevant data of 11 cities in GBA. This paper adopts a two-stage SDM model that matches the chain process of innovation activities. At the same time, considering the time lag of innovation activities, this paper sets a lag period for the efficiency of technological innovation, and selects two years as the lag period, that is, from initial input to intermediate output to final output, with an interval of one year. Therefore, the time for capital investment and manpower investment in technological innovation is 2010-2018, and the time for data of knowledge output and economic output is 2012-2020. Therefore, 2012-2020 is chosen as the time span for research.

2.2

Model construction

The panel data is analyzed by multiple regression. The models are as follows (1) and (2), in which all variables are logarithmically processed. At the same time, considering the lag of input and output, the lag period is 2 years, that is, the initial input index is t period, the intermediate output is t + 1 period, and the final output is t + 2 period.

Model 1:

Model 2:

Where subscript i stands for region, subscript t stands for year, γ, β stands for parameter to be estimated, and ε stands for error term. Model (1) is used to investigate the influence of financial agglomeration on the innovation efficiency of high-tech enterprises, and model (2) is used to analyze the influence of the respective agglomeration of banks, securities, insurance and venture capital on the innovation efficiency of high-tech enterprises.

The collection of enterprises constitutes an industry that produces the same type of products or provides the same type of services, and the innovative behavior of enterprises also reflects the characteristics of the industry, while the characteristics of market structure and market demand are important factors that affect the innovative behavior of enterprises. Because of the externality and uncertainty of innovation investment, in theory, the government subsidy can overcome the market failure, and governments all over the world have adopted innovative incentives or policies to guide enterprises to engage in innovation activities. The results also show that both tax relief and government financial subsidies have played a positive role in promoting the innovation investment level of enterprises.

When there is a difference in the cost of using external funds and internal funds, it is considered that there is a capital-knocking constraint. If the gap between internal and external capital costs is large, the degree of financing constraint will be greater. The special nature of innovative investment, agency conflict and information asymmetry are the root causes of financing constraints ¹². When analyzing the spatial effect, it is impossible to judge the most suitable spatial regression model intuitively, so statistical test should be carried out to select the appropriate spatial panel model. LM test can’t identify the applicability of SDM, so Wald test and LR test can be carried out according to Burridge’s test criterion to determine whether SDM can be simplified to SLM or SEM model. The specific form of the SDM model established in this paper is shown in the following formula:

Among them, i, t represents the region and time respectively, ρ is the lag coefficient of spatial regression of the explained variable technology development efficiency, and μ_i corresponds to the spatial effect; v_t corresponds to the time effect; ε_it is a random error term.

3.1

Statistical description of samples

The data used in this paper are panel data. From the descriptive statistics of various data of 11 cities in GBA (Table 1), it can be seen that the average GDP growth rate is 11.1768, the minimum is -17.0121, the maximum is 28.5394, and the standard deviation is 7.7988, which shows that the economic development in different regions is very different in different periods. The maximum and minimum FA are 6.6325 and 0.0224, respectively. The range of other variables is also very large, which shows that the financial development and economic growth in GBA are unbalanced, and some cities are far ahead of other cities in terms of financial scale, opening up and human capital investment.

Table 1.

Statistical description of samples

3.2

SDM model regression results

Because the SDM model contains the spatial lag term of the explained variables, OLS estimation is prone to result bias. In order to ensure the accuracy of the regression results, this paper adopts maximum likelihood estimation method for analysis, and the specific regression results are shown in Table 2.

Table 2.

SDM model estimation results

Note: *, * * and * * represent significant at 10%, 5% and 1% levels respectively.

The regression results of SDM model show that the spatial autocorrelation coefficient ρ passes the significance level of 1% and 10% under the 0-1 adjacency weight matrix and geo-economic distance weight matrix, which shows that there is obvious correlation between the technical development efficiency of GBA high-tech industries, but they are not independent of each other.

Under the two spatial weight matrices, financial aggregation has a positive effect on the efficiency of technology development, and both of them have been verified at 1% significance, which shows that financial aggregation can promote the efficiency of technology development to some extent.

3.3

Robustness test

In this paper, the robustness of model 1 is tested, the explained variables are replaced, and the technological innovation efficiency obtained by multiplying the comprehensive efficiency of the first stage and the second stage in the two-stage SDM model is used. The robustness test results of Model 1 are shown in Table 3:

Table 3.

Robustness test result

It can be found that the regression results of the two models are basically the same as those of the original model. Model 1 and Model 2 draw the conclusion that financial agglomeration can promote the technological innovation efficiency of high-tech enterprises at 1% significance level, and Model 1 also draws the same conclusion at 5% significance level.

4.1

Industry effect of coordinating financial aggregation

The single financial system structure makes the financing channels of technological innovation of high-tech enterprises single, mainly including bank loans, lack of equity, bond financing and other direct financing methods. At this time, financial aggregation has a negative squeezing effect on high-tech enterprises. Therefore, on the one hand, we should maintain the steady growth of the banking industry, carry out the shareholding system reform of the banking industry, promote the improvement of banking performance, thus injecting new vitality into the real economy. At the same time, we should give full play to the government’s policy advantages, attract securities companies, venture capital institutions and other institutions to enter industrial clusters, and constantly expand the scale and coverage of industrial clusters.

4.2

Improve GBA financial development and in-depth cooperation policies and regulations

There are differences in the distribution and allocation of financial resources in 11 cities in GBA, and each city is also different in the operation and management of financial institutions and the construction of financial market system. Therefore, we should first actively promote regional financial cooperation at the national level, build the framework and agreement of GBA financial cooperation, make the financial development in GBA have laws to follow, strengthen the breadth and depth of GBA financial industry in the open field, appropriately reduce the threshold and restrictions of mutual penetration of related financial enterprises in GBA, and improve the convenience of financing. In addition, the convergence and integration of financial laws and regulations in GBA should be strengthened, and a multi-level security system for financial industries, financial institutions and financial consumers should be formulated from top to bottom to improve the maturity and integration of GBA policies and regulations.

4.3

Strengthen the strategic cooperation of financial innovation within GBA with financial agglomeration as the medium

We will strengthen the strategic cooperation of financial innovation in GBA, finally, the coordinated development of financial product design and internet finance and other financial innovations will be achieved, and the purpose of improving the efficiency of financial innovation in GBA system will be achieved. On this basis, further deepen GBA’s financing innovation strategy, and enriching financing business, and improve the efficiency of financing system. Pay attention to the spatial spillover characteristics, degree and effect of financial agglomeration in GBA, so as to guide the innovation of financial system, financial products and business and promote the coordinated development of financial industry.