A study of the impact of financial new quality productivity on the modernization of the rural industrial chain supply chain

Considering the scientific and operability of the indicator data, this paper constructs a measurement system including four dimensions of first-level indicators of science and technology innovation, new quality industry, green quality state, and factors of production, as well as 12 second-level indicators and 20 third-level indicators, and the specific indicators are selected as shown in Table 1.

Based on the principles of scientific objectivity, comprehensive prominence of indicators and comparability and availability of data, the evaluation index system of industry chain modernization is constructed from five aspects of industry chain digitization, resilience, innovation, synergy and sustainability as shown in Table 2.

Considering the availability of data, this paper selects the panel data of 30 provinces in China (excluding Tibet, Hong Kong, Macao and Taiwan) from 2008-2022 as the sample for analysis, and the data are obtained from the CNRDS platform, the CSMAR database, the data published by the IFR, the annual reports of listed companies, the Peking University’s open research data platform, the China Statistical Yearbook, the China Environmental Statistical Yearbook of China, China Population and Employment Statistical Yearbook, and China Industrial Statistical Yearbook. For individual missing data, the interpolation method was applied to complete the data.

This paper chooses to use the entropy value method to determine the weight value of each indicator. The data are first standardized and normalized, and then a centralized summation is used to obtain a comprehensive score of financial new quality productivity, which can more objectively reflect the impact level of each indicator. The larger the value, the higher the development level of financial new quality productivity, and vice versa. The specific formulas and explanations of the variables are as follows. 1Yij={ Xij−min(Xij)max(Xij)−min(Xij),Xijisapositiveindicatormax(Xij)−Xijmax(Xij)−min(Xij),Xijisanegativeindicator$${Y_{ij}} = \left\{ {\matrix{ {{{{X_{ij}} - \min \left( {{X_{ij}}} \right)} \over {\max \left( {{X_{ij}}} \right) - \min \left( {{X_{ij}}} \right)}},{X_{ij}}\;is{\rm{ }}a{\rm{ }}positive{\rm{ }}indicator} \hfill \cr {{{\max \left( {{X_{ij}}} \right) - {X_{ij}}} \over {\max \left( {{X_{ij}}} \right) - \min \left( {{X_{ij}}} \right)}},{X_{ij}}{\rm{ }}is{\rm{ }}a{\rm{ }}negative{\rm{ }}indicator} \hfill \cr } } \right.$$ 2Ej=−lnln∑i=1n[ (Yij/∑i=1nYij)ln(Yij/∑i=1nYij) ]$${E_j} = - \ln {l \over n}\mathop \sum \limits_{i = 1}^n \left[ {\left( {{Y_{ij}}/\mathop \sum \limits_{i = 1}^n {Y_{ij}}} \right)\ln \left( {{Y_{ij}}/\mathop \sum \limits_{i = 1}^n {Y_{ij}}} \right)} \right]$$ 3wj=(1−Ej)∑j=1m(1−Ej)$${w_j} = \left( {1 - {E_j}} \right)\sum\limits_{j = 1}^m {\left( {1 - {E_j}} \right)} $$ 4Score=∑j=1n(wj×Yj)$$Score = \sum\limits_{j = 1}^n {\left( {{w_j} \times {Y_j}} \right)} $$

Where i denotes provinces; j denotes measurement indicators; X_ij denotes the value of the original financial new quality productivity measurement indicators; Y_ij denotes the value of the standardized financial new quality productivity measurement indicators; E_j denotes the information entropy of each indicator of the financial new quality productivity measurement system; and w_j denotes the weight of each indicator of the financial new quality productivity measurement system: Score denotes the composite score of the level of development of the financial new quality productivity.

After obtaining the level of financial new quality productivity of 30 provinces in China through entropy weight method, the regional differences in the level of financial new quality productivity and its sources are analyzed with the help of Dagum’s Gini coefficient, and the relevant formula is as follows [21]. 5G=∑j=1k∑h=1k∑i=1nj∑r=1nh| yji−yhr |2n2y¯$$G = {{\sum\limits_{j = 1}^k {\sum\limits_{h = 1}^k {\sum\limits_{i = 1}^{{n_j}} {\sum\limits_{r = 1}^{{n_h}} {\left| {{y_{ji}} - {y_{hr}}} \right|} } } } } \over {2{n^2}\bar y}}$$ 6Gw=∑j=1kGjjqjlj$${G_w} = \sum\limits_{j = 1}^k {{G_{jj}}} {q_j}{l_j}$$ 7Gb=∑j=2k∑h=1j−1Gjh(qjlh+qhlj)Djh$${G_b} = \sum\limits_{j = 2}^k {\sum\limits_{h = 1}^{j - 1} {{G_{jh}}} } \left( {{q_j}{l_h} + {q_h}{l_j}} \right){D_{jh}}$$ 8Gi=∑j=2h=1k∑jhj−1Gjh(qjlh+qhlj)(1−Djh)$${G_i} = \sum\limits_{j = 2h = 1}^k {\sum\limits_{jh}^{j - 1} {{G_{jh}}} } \left( {{q_j}{l_h} + {q_h}{l_j}} \right)\left( {1 - {D_{jh}}} \right)$$

Among them, G is the overall Gini coefficient, n is the number of provinces, k is the number of regions, y_ij is the level of the i th province in region j, y¯$${\bar y}$$ is the national average of the level of new financial productivity, G_jj is the Gini coefficient of region j, q_j and l_j represent the number of provinces in region j and the proportion of the level of new financial productivity, D_jh = (d_jh – q_jh)/(d_jh + q_jh) measures the degree of interaction between different regions, and d_j,h is the difference between the levels of financial productivity in region j,h. q_jh represents the first-order moment of supervariability, G_w represents the contribution of intra-regional differences, G_b represents the contribution of inter-regional differences, and G_i represents the contribution of super-variable density.

In this paper, the logarithmic standard deviation of the financial new quality productivity score is chosen to reflect the change in regional differences with the following formula: 9σt=∑i=1n(lnMi−lnMi¯)2/(n−1)$${\sigma _t} = \sqrt {\mathop \sum \limits_{i = 1}^n {{\left( {\ln {M_i} - \overline {\ln {M_i}} } \right)}^2}/\left( {n - 1} \right)} $$

Absolute β -convergence tests for the level of financial new quality productivity are computed using the following model: 101Tln(Mi,t+1/Mi,t)=α+βln(Mi,t)+εi,t$${1 \over T}\ln \left( {{M_{i,t + 1}}/{M_{i,t}}} \right) = \alpha + \beta \ln \left( {{M_{i,t}}} \right) + {\varepsilon _{i,t}}$$

Where: M_i,1 and M_u are the financial new quality productivity scores at the end and beginning of a time period in region i, T is the number of years in the study period (T = 1 in this paper), ln(M_i,+1/M_ij) represents the average growth level of financial new quality productivity in region i; α is a constant term, ε_is is a random error term, and if β in the formula is significantly negative, then it indicates that there is an absolute β convergence of the changes in the financial new quality productivity.

In this paper, on the basis of the absolute β convergence test, we further test whether there is condition β convergence of financial new quality productivity in China, synthesize the findings of related literature, and choose the following five factors as control variables to be substituted into the model (Eq. 11), so that we can get the condition β convergence test model: 111Tln(Miu+1/Mu)=α+βln(Mu)+β1X1+β2X2+β3X3+β4X4+β3X5+εiu$${1 \over T}\ln \left( {{M_{iu + 1}}/{M_u}} \right) = \alpha + \beta \ln \left( {{M_u}} \right) + {\beta _1}{X_1} + {\beta _2}{X_2} + {\beta _3}{X_3} + {\beta _4}{X_4} + {\beta _3}{X_5} + {\varepsilon _{iu}}$$

Where: X₁, X₂, X₃, X₄, X₅ represent the level of regional financial and economic development (Ogdp), measured by the per capita gross domestic product (GDP); the level of industrialization (Ind); the level of government expenditure (Gov), measured by the share of local financial expenditure in GDP; the level of openness to the outside world (Open), measured by the logarithm of the share of import trade in the regional GDP; and the level of agricultural resource endowment (Ons), measured by the logarithm of the agricultural production in each region. Ons, measured by the logarithm of agricultural production in each region. When β is significantly negative, it indicates the existence of a condition β convergence of financial NQP.

Table 3 shows the financial new quality productivity development index of 30 provinces and four regions in China from 2008 to 2022. As can be seen from the table, the national financial new quality productivity development index grows at an average annual rate of 0.991% during the period under examination, and improves by 0.043 in 2022 compared with 2008. This shows that the level of financial new quality productivity development is increasing at a slow rate. The Financial New Quality Productivity Development Index (FNQPDI) for the Eastern Region increases from 0.288 in 2008 to 0.308 in 2022. The Financial New Quality Productivity Development Index (FNQPDI) for the Central Region increases from 0.24 in 2008 to 0.276 in 2022. The Financial New Quality Productivity Development Index (FNQPDI) for the western region increases from 0.207 in 2008 to 0.242 in 2022. The Financial New Quality Productivity Development Index (FNQPDI) for the Northeast declined from 0.189 in 2008 to 0.239 in 2022. In summary, it can be seen that China’s financial new quality productivity development index shows the characteristics of the eastern, central, western, and northeastern regions in decreasing order.

Through the Dagum Gini coefficient and decomposition method, the Gini index and decomposition results of financial new quality productivity development of China’s four major segments from 2015 to 2022 are calculated as shown in Table 4 [22]. The overall difference in the level of financial new quality productivity development in China shows a clear downward trend, and this difference mainly comes from the differences between regions. Specifically, in the observation sample, the gap in the level of financial new quality productivity development of the 30 provinces shows a decreasing trend, from 0.229 in 2017 to 0.201 in 2023.

In this paper, the 2012-2022 data of 30 Chinese provinces (autonomous regions and municipalities directly under the central government), except Tibet and Hong Kong, Macao, and Taiwan, are selected as the research object. The raw data in this paper come from provincial statistical yearbooks, enterprise annual reports, the Ministry of Industry and Information Technology of the People’s Republic of China, the State Intellectual Property Office, the China Science and Technology Statistical Yearbook, the China Tertiary Industry Statistical Yearbook, the China Industrial Statistical Yearbook, the High-technology Industry Statistical Yearbook, the China Energy Statistical Yearbook, the China Environment Statistical Yearbook, and the China Statistical Yearbook, among others. Linear interpolation was employed to address missing values.

After Hausman test as well as correlation test, there is no multicollinearity problem for each variable and the F-statistic and P-value are 35.0123 and 0.0000 respectively rejecting the original hypothesis of random effect. Based on this, this paper chooses the fixed-effect model for empirical testing. The specific test results are shown in Table 7. Analysis of the resultant data shows that the coefficients of the impact of financial new quality productivity on the modernization of the rural industrial chain supply chain are significantly positive after controlling for the control variables, individual as well as time fixed effects. Further analysis of the data in column (3) shows that the regression coefficient value of financial new quality productivity decreases after the introduction of both control variables and fixed effects, which indicates that the omitted variables affect the test results to a certain extent, and the effect is further mitigated after the introduction of control variables. In summary, financial new quality productivity has a significant role in promoting the modernization of the rural industrial chain supply chain, and for every 1% increase in the development level of financial new quality productivity, the process of modernization of the rural industrial chain supply chain is enhanced by 0.5731%. Hypothesis H1 is confirmed.

Considering that there are obvious differences in the level of development of digital economy and the level of scientific and technological innovation in each region, the whole sample is divided into four regions, namely, East, Central, West and Northeast, in accordance with the division standard of China Statistical Bureau to examine the heterogeneity of the impact of the new financial productivity on the modernization of the rural industrial chain supply chain, and the results are shown in Columns (1) to (4) of Table 10. It can be seen that the empowering effect of new financial productivity on the modernization of the rural industrial chain supply chain in the four regions is “East (0.377) > Central (0.171) > Northeast (0.158) > West (0.129)”. This phenomenon may be due to the fact that the eastern region has the development advantages of abundant high-quality talent reserves and strong R&D capabilities in advanced production technologies, which can provide a strong impetus for the development of new financial productivity, enhance the degree of integration of advanced factors of production with the whole process of agricultural production and operation, and empower the modernization of the rural industrial chain supply chain. As China’s key crop production bases, the central and northeastern regions have formed a relatively mature system for the whole process of agricultural production and operation, which can give full play to the enabling effect of new financial productivity on the modernization of the rural industrial chain supply chain. In the western region, there is still much room for improvement in the level of innovative human capital and scientific and technological innovation capacity, resulting in a slow growth trend in the development of financial new quality productivity and a weaker facilitating effect on the modernization of the rural industrial chain supply chain compared with other regions. Taking the average value added per capita of the primary industry as the cut-off point, the total sample is divided into regions with higher agricultural labor productivity and regions with lower agricultural labor productivity, and regression analysis is carried out respectively, and the results are shown in columns (5) and (6) of Table 10. It can be seen that the promotion effect of financial productivity of new quality on modernizing the rural industrial chain supply chain in regions with higher agricultural labor productivity is more evident. Compared with regions with lower agricultural labor productivity, regions with higher agricultural labor productivity have the advantages of better agricultural labor structure and higher level of scientific and technological innovation, which can accelerate the transformation of advanced human capital and digitalization of production tools, and promote the development of new financial productivity to optimize the various aspects of agricultural production and operation, and empower the modernization of rural industrial chain supply chain.

Column (1) of Table 11 shows the test results of the mediating effect of agricultural technological progress. Financial new quality productivity can have a positive impact on the modernization of the rural industrial chain supply chain by promoting agricultural technological progress, proving that hypothesis H3 is valid. The development of financial new quality productivity can provide more high-quality innovative talents for agricultural science and technology innovation, consolidate the foundation of innovative talents, promote the progress of agricultural technology, enhance the degree of integration of advanced digital technology and the whole process of agricultural production and operation, drive the development of intelligent agriculture, and empower the modernization of the rural industrial chain supply chain. Table 11 column (2) shows the results of the mediation effect test of rural industrial integration. It can be seen that financial new-quality productivity can promote rural industrial chain supply chain modernization by driving rural industrial integration, and hypothesis H2 is verified. This is because the development of financial new quality productivity can provide high-quality labor and high-technology production tools for the whole process of agricultural production and operation, improve the degree of interaction in various fields, and promote the integration of rural industries, so as to expand the scale of agricultural production and operation, promote the vertical integration and horizontal synergies of the rural industrial chain supply chain, and enhance the modernization of the rural industrial chain supply chain.