An analysis of the synergistic poverty reduction effectiveness of fiscal spending and digital inclusive finance from the standpoint of relative poverty was conducted

Abstract

This paper conducts an empirical investigation into the synergistic effects of fiscal spending and digital inclusive finance on poverty reduction. These two elements are noted as crucial linkages in the struggle against poverty. This paper employs the DEA-Malmquist index model and Tobit model analysis to assess the effectiveness of fiscal expenditure and digital inclusive finance synergy under relative poverty and the influencing factors in the central and western provinces of China using provincial panel data from 2014 to 2020. The study found that: first, the integrated effectiveness of fiscal spending and digital financial inclusion to reduce poverty is firstly higher than it is for fiscal spending alone; second, additional fiscal spending and technology for digital financial inclusion should be allocated to the central and western areas in particular; and third, for poverty reduction in central and western China, the level of financial development, financial payment capability, and industrial structure are the most crucial factors. The following recommendations are made based on the findings of the aforementioned research: overcoming the geographical restrictions; we will improve the transfer payment system’s top-level architecture for places with extreme poverty; the design of transfer payments to communities with extreme poverty will be improved; increasing access to digital financial services; we’ll boost technical and scientific innovation in underdeveloped areas; compensate for the lack of knowledge in underdeveloped areas; we will advance digital financial inclusion’s science, technology, and accuracy to lessen poverty; the combination of fiscal and financial policies should be put into practice in accordance with the level of poverty and the state of poverty in the places that are affected by it.

Keywords

Fiscal spending digital inclusive finance input-output analysis relative poverty

1. Introduction

In the wake of reform and opening, even if we have achieved some progress in reducing poverty, there have been notable changes in places where it is prevalent. In China, absolute poverty will be eradicated in 2020, and the number of absolute poor people will progressively decline. However, relative poverty is becoming a bigger issue as the rate of absolute poverty alleviation slows. The issue of hidden relative poverty, which cannot be resolved without financial assistance, has emerged as China’s most pressing obstacle in the fight against poverty. Digital inclusive financial investment and fiscal expenditure are crucial in the fight against poverty. Financial poverty reduction through tax breaks, improved infrastructure, support for arts and culture, access to health care, and other strategies to lift low-income families out of poverty. The goal of financial poverty alleviation, on the other hand, is to help poor families overcome their innate desire to remain poor. It does this through promoting industrial growth and using market-based financial tools to help poor households escape poverty and become wealthy. A network-based company called Digital Inclusive Finance can offer low-cost, high-quality financial services to low-income populations in underdeveloped areas [1, 2]. It is challenging to rely only on the market to make financial resources really available to underdeveloped regions and underdeveloped people because of the inherent conflict between the profit-seeking nature of finance and the fragility of poverty. Due to regional differences, irregularities in fiscal expenditure institutions, and significant differences in fiscal inputs between regions, fiscal policies restrict the poverty-reducing effects of fiscal expenditures in the implementation process. This is because of the fragmentation and inefficiency of the allocation of fiscal resources. Government finance must be used to repair the dysfunctions in digital inclusive finance for reducing poverty, and fiscal investment for reducing poverty has to be coordinated and reinforced by digital inclusive finance as well. This makes it necessary to fully exploit the complementary dynamics of fiscal and digital inclusive finance, develop a synergistic model of fiscal and digital inclusive finance in poverty reduction, and continuously increase the effectiveness of fiscal and financial synergy based on the benefits and drawbacks of digital inclusive finance and fiscal spending in poverty reduction.

Fiscal spending to combat poverty. Fiscal expenditure varies structurally and geographically, according to Gong et al. [3]. According to Zhang et al. [4], fiscal expenditures, improving resource utilization effectiveness, and optimizing the structure of fiscal expenditure may all be used to reduce poverty. Raising both the fiscal scale of physical capital spending and the scale of human capital expenditure will reduce poverty, according to Liu and Zhang [5]. Wang [6] asserts that government expenditure may reduce poverty both directly, through transfer payments, and indirectly, by raising the income of the poor through economic growth.

Alleviation of poverty through digital financial inclusion. Digital financial inclusion, according to Liang and Zhang et al. [7, 8], was helpful in lowering the prevalence of poverty and absolute poverty in this area. According to Pu et al. [9], there is a threshold effect on how the growth of digital inclusive finance affects long-term poverty reduction in low-, middle-, and high-income countries along the Belt and Road. Digital inclusive finance is helpful in lowering income and consumption poverty in rural areas, and its effectiveness in doing so is somewhat improving, claim Zeng and Zheng [10]. There are substantial regional variances, and digital inclusive finance has a limited impact on lowering relative poverty in China, according to Wu et al. [11] and Yao and Li [12]. People who live in the west, followed by those in the central area, and those who live in the east, have the biggest impact on reducing poverty. According to Liu and Liu [13], digital financial inclusion not only helps to increase the accessibility of rural financial services and the range of use by poor farmers, but it also, to some extent, encourages local economic and industrial development, providing poor farmers with more opportunities to earn a living and, thus, either directly or indirectly reducing rural poverty.

Research on the role of financial cooperation and fiscal spending in reducing poverty. Fiscal spending finance has a threshold impact on multidimensional poverty alleviation, claim Wu and Whang [14]. Fiscal policies in undeveloped regions severely limit the ability of rural finance to reduce poverty [15]. According to Zhang and Li [16], the expansion of agricultural family income is significantly influenced favorably by fiscal investment and inclusive financial development. Using the DEA-Malmquist efficiency index, Zhao [17] calculated the synergistic efficiency of the two and their differences in two dimensions: space and time. According to Chen et al. [18], fiscal spending and financial development poverty reduction, both of which have a significant influence on decreasing rural poverty, differ geographically. Using the DEA-Malmquist index model, Ma and Wu [19] investigated the impact of financial and fiscal synergy in decreasing poverty and found that this effect was stronger than that of financial and fiscal alone.

2. Measuring the effectiveness of fiscal spending and digital inclusive finance in reducing poverty

2.1 Setting a model

The non-parametric efficiency assessment approach known as data envelopment analysis (DEA) is adaptable to numerous inputs and outputs and is based on the construction frontier of linear programming. Keeping the input or output indicators of a decision making unit (DMU) constant, valid production frontiers are identified based on statistical data and mathematical planning, each decision making unit is projected onto the production frontier surface of DEA, and finally, their relative effectiveness is evaluated by comparing the degree to which the decision making units deviate from the frontier. This method was first proposed by Charens et al. [20] in 1978. Two models exist for the analysis of its efficiency measures: The first is the DEA analysis model of the constant returns to scale (CRS) model, the CCR model [20]. The model is based on the assumption that all decision units operate at optimal size; however, in reality, the model is flawed due to factors such as unfair competition, economic environment and policy changes that may prevent a decision unit from reaching optimal size. The second type of DEA analysis model is the variable returns to scale (VRS) model, the class BBC model [21]. However, traditional DEA models are static methods of analysis and cannot analyse the dynamic efficiency changes in each decision unit, nor can they provide in-depth identification of the causes of efficiency changes. The model yields a total factor productivity index equal to the product of the combined efficiency technical efficiency and technical progress indices, is TFP $=$ EFF $\times$ TE, and combined technical efficiency (TE) can be further decomposed into pure technical efficiency (PET) and scale efficiency (SE), that is, the combined technical efficiency is equal to the product of pure technical efficiency and scale efficiency, EFF $=$ PTE $\times$ SE. Among them, pure technical efficiency reflects the technical level of synergistic poverty reduction efficiency of fiscal expenditure and digital inclusive finance in China’s central and western regions after excluding the influence of payoffs of scale, while scale efficiency reflects the degree of optimisation of synergistic poverty reduction scale.

At present, most of this paper also focuses on the output effects under multiple types of fiscal and digital financial inputs, and therefore, chooses the input-oriented BBC model to measure the synergistic poverty reduction efficiency of fiscal spending and digital financial inclusion in China’s central and western regions, which is formulated as follows:

Using the residual variable S $-$ and the slack variable S $+$ , the BBC model is:

$\displaystyle(D)\left\{{{\begin{array}[]{l}{\min[{\theta-\varepsilon(e^{-T}s^{% -}+e^{+T}s^{+})}],}\\ {s.t\sum\limits_{j=1}^{n}{x_{j}\lambda_{j}-s^{-}=\theta x_{0},}}\\ {\sum\limits_{j=1}^{n}{x_{j}\lambda_{j}-s^{+}=y_{0},}}\\ {j=\sum\limits_{j=1}^{n}{\lambda_{j}=1,}}\\ {\lambda_{j}\geqslant 0,n=1,2,\ldots,n,s^{+}\geqslant 0,s^{-}\geqslant 0.}\\ \end{array}}}\right.$ (1)

However, traditional DEA models are static and cannot analyse the dynamic efficiency changes of various decision units, nor can they identify in depth the causes of efficiency changes. So Färe et al. [22] combined the non-parametric linear programming method proposed by Malmquist to calculate productivity with the data envelopment analysis (DEA) model to propose the DEA-Malmquist model for analysing the dynamics of Malmquist’s total factor productivity (TFP).

In order to estimate productivity from period $t$ to period $t+1$ , Färe et al. [22] updated it, changing it from a theoretical index to an empirical index. Under the assumption of changing size, Malmquist’s productivity index from period $t$ to period $t+1$ may be expressed as follows:

$\displaystyle m_{0}({x^{t+1},y^{t+1},x^{t},y^{t}})=\left[{\frac{d_{0}^{t}({x^{% t+1},y^{t+1}})}{d_{0}^{t}({x^{t},y^{t}})}\times\frac{d_{0}^{t+1}({x^{t+1},y^{t% +1}}t)}{d^{t+1}({x^{t},y^{t}})}}\right]^{\frac{1}{2}}$ (2)

The Malmquist index can be broken down further into:

$\displaystyle m_{0}({x^{t+1},y^{t+1},x^{t},y^{t}})=\frac{d_{0}^{t}({x^{t+1},y^% {t+1}/\textit{VRS}})}{d_{0}^{t}({x^{t},y^{t}/\textit{VRS}})}\times\frac{d_{0}^% {t+1}({x^{t+1},y^{t+1}/\textit{CRS}})}{d_{0}^{t+1}({x^{t+1},y^{t+1}/\textit{% VRS}})}$ (3) $\displaystyle\quad{}\times\frac{d_{0}^{t}({x^{t},y^{t}/\textit{VRS}})}{d_{t}^{% 0}({x^{t},y^{t}/\textit{CRS}})}\times\left({\frac{d_{0}^{t}({x^{t+1},y^{t+1}})% }{d_{0}^{t+1}({x^{t+1},y^{t+1}})}\times\frac{d_{0}^{t}({x^{t},y^{t}})}{d^{t+1}% ({x^{t},y^{t}})}}\right)^{\frac{1}{2}}=\textit{Pech}\times\textit{Sech}\times% \textit{Tech}$

The technological progress index (Techch) and the technical efficiency variation index (Effch), which may be described as the outcome of pure technical efficiency (Pech) and scale efficiency, are identical to the Malmquist productivity index (Sech).

2.2 Data sources and indicator choices

2.2.1 Choosing the right indications

In terms of input, considering the availability of data, the financial input index is selected as “material capital”, “social security” and “human capital”. Data from the China Statistical Yearbook are used as input. The data is derived from the Chinese statistics yearbook, and the indicators of financial input for digital inclusion are chosen based on the breadth and depth of financial coverage of digital inclusion. The indicators of digital inclusive finance were chosen as inputs from the Digital Inclusion Index produced by the Digital Research Center of Peking University taking into account the breadth and depth of coverage of digital inclusive finance. Relative poverty is used as the starting point in terms of output in earlier studies like Wu et al. [23], and the impact of reducing poverty is assessed in four areas: relative poverty in terms of income, relative poverty in terms of consumption, relative poverty in terms of living standards, and relative poverty in terms of public services.

(1) Input indicators

Table 1
The DEA model’s input indicators

Selected dimensions	Indicator name	Description of the indicator
Financial inputs	Inputs of physical capital	Forestry, agriculture, and water issues/total spending from the municipal general budget
	Input from social security	Employability and social security/total spending from the municipal general budget
	Infusion of human capital	(education expenditure $+$ cost of healthcare $+$ science and technology)/total spending from the municipal general budget
Digital financial inclusion	The breadth of comprehensive financial coverage in the digital era	Coverage rate, includes the ratio of Alipay card-tied users, the average number of bank cards linked to each Alipay account, and the number of Alipay accounts per 10,000 individuals
input	Depth of inclusive digital finance	Coverage depth of usage, including the number and amount of payments made per person, the number and amount of loans taken out, the number and amount of investments made per person, the number of credit requests made per person who is a natural person, etc.

(2) Results markers

Table 2

The DEA model’s output indicators

Indicator name	Description of the indicator
Income compared to poverty	Disposable income per person in cities/the average disposable income of rural inhabitants (income gap between urban and rural residents)
Relative deprivation of necessities	Urban dwellers’ Engel coefficient/rural inhabitants’ Engel coefficient (difference in living standards between urban and rural populations)
Relative consumption poverty	Spending on goods and services by urban dwellers per capita/consumption costs per person in remote areas (consumption gap between urban and rural residents)
Poverty relative to public services	Beds in hospitals and other medical facilities per 1,000 people (gap in public services between urban and rural residents)

2.2.2 Sources of data

Due to the consistency of the data and the reality that some economically successful regions have long since completely risen from poverty, data from 22 provinces in China’s central and western provinces were selected. The financial data comes from the National Bureau of Statistics; the financial data comes from the Digital Financial Inclusion Index compiled by the Digital Research Center of Peking University; the China Financial Yearbook, China Statistical Yearbook, China Rural Statistical Yearbook, and China Poverty Monitoring Report are the main sources of other statistics.

2.3 Empirical findings

2.3.1 The effectiveness of fiscal and financial digital synergy in eradicating poverty

The DEAP2.1 program was used to evaluate and dissect the efficacy of fiscal expenditure poverty reduction, digital financial poverty reduction, and their synergistic poverty reduction in 22 provinces and cities between 2014 and 2020. The results are shown in the table.

Table 3 above demonstrates that the synergistic effect of fiscal and digital finance in reducing poverty is superior to the role of fiscal and digital finance alone in doing so. It also shows that the effectiveness of fiscal and digital finance synergy is greater than the effectiveness of fiscal and digital finance alone. According to the breakdown of the combined efficiency, the fiscal poverty reduction efficiency for the years 2014 to 2020 is less than the mean 1, and both the technical and scale fiscal efficiency are also less than 1. This demonstrates that there is a problem with input scale and the application of technical tools in the reduction of fiscal poverty, but the mean scale efficiency is at 0.986 and the mean technical efficiency is at 0.973, which is closer to the production frontier side than in the reduction of digital financial poverty. Indicating that the size of digital finance is lower than the scale of fiscal inputs, the scale efficiency for eradicating financial poverty through digital methods is at a mean value of 0.964; technical poverty reduction measures have a mean value of 0.949, which shows that they are underused and less effective than the mean value of technical efficiency of financial inputs, i.e. Fiscal poverty reduction’s scale efficiency has been increasing over time, reaching 0.998 in 2020, suggesting that it is approaching the production frontier. Technical efficiency falls, then rises to 0.993 in 2020, showing that there is still a lot of space for growth in this area. Fiscal-digital financial poverty reduction has a scale efficiency and a technical efficiency that are both below one. Although there is still room for improvement from the production frontier surface, the scale efficiency of digital financial investment to eliminate poverty has been increasing, reaching a value of 0.972 efficiencies in 2020; although technical efficiency has increased recently from 0.93 in 2014 to 0.96 in 2020, the increase has not been significant. This indicates that it is important to increase technological use, especially with an emphasis on increasing technical efficiency, in order to reduce poverty more effectively.

Table 3
22 provinces and cities’ effectiveness in reducing financial and fiscal poverty between 2014 and 2020

Year	Financial efficiency			Digital financial efficiency			Finance digital finance synergy efficiency
	Integrated efficiency	Technical efficiency	Scale efficiency	Integrated efficiency	Technical efficiency	Scale efficiency	Integrated efficiency	Technical efficiency	Scale efficiency
2014	0.969	0.982	0.986	0.898	0.933	0.963	0.979	0.989	0.990
2015	0.933	0.961	0.971	0.901	0.930	0.969	0.968	0.986	0.982
2016	0.946	0.967	0.978	0.893	0.942	0.948	0.969	0.985	0.983
2017	0.939	0.957	0.981	0.930	0.955	0.973	0.964	0.991	0.973
2018	0.969	0.978	0.990	0.917	0.959	0.955	0.987	0.998	0.989
2019	0.978	0.983	0.995	0.936	0.963	0.971	0.994	0.999	0.996
2020	0.981	0.983	0.998	0.936	0.963	0.972	0.991	0.996	0.995
Average	0.959	0.973	0.986	0.916	0.949	0.964	0.979	0.992	0.986

Table 4

Effectiveness of digital financial synergies in 22 provinces and municipalities for reducing poverty between 2014 and 2020

Province	Year							Average
	2014	2015	2016	2017	2018	2019	2020
Hebei	0.926	0.916	0.918	0.889	0.959	0.981	1.000	0.941
Shanxi	1.000	0.967	0.910	0.873	1.000	1.000	1.000	0.964
Inner Mongolia	1.000	1.000	0.984	1.000	1.000	1.000	1.000	0.998
Jilin	0.994	0.900	0.880	0.907	0.932	0.996	0.974	0.940
Heilongjiang	0.974	0.961	0.956	1.000	1.000	1.000	1.000	0.984
Anhui	0.975	0.983	0.927	0.923	0.978	1.000	0.931	0.960
Jiangxi	0.948	0.917	0.921	0.918	1.000	1.000	1.000	0.958
Henan	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Hubei	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Hunan	0.993	0.981	1.000	0.953	1.000	1.000	1.000	0.990
Guangxi	1.000	0.998	0.985	0.989	0.997	0.985	0.961	0.988
Hainan	0.882	0.888	0.938	0.892	1.000	1.000	1.000	0.943
Chongqing	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Sichuan	0.910	0.896	0.906	0.876	0.868	0.934	0.977	0.910
Guizhou	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Yunnan	0.988	0.967	0.985	1.000	1.000	0.981	1.000	0.989
Tibet	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Shaanxi	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Gansu	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Qinghai	1.000	1.000	1.000	1.000	1.000	1.000	1.000	1.000
Ningxia	0.954	0.927	1.000	1.000	0.980	1.000	0.986	0.978
Xinjiang	1.000	0.991	0.945	0.991	1.000	1.000	0.977	0.986
Mean	0.979	0.968	0.969	0.964	0.987	0.994	0.991	0.979

Table 4 above reports the effectiveness of fiscal digital finance synergy for reducing poverty in each province. With an efficiency of somewhat over 0.960, it appears that 22 provinces and cities have a high degree of fiscal digital financial synergy. Fiscal digital financial investment may significantly reduce relative rural poverty, and poverty reduction has had impressive achievements. Second, from a spatial standpoint, every year from 2014 to 2020, there are provinces with an efficiency value of 1 for fiscal digital financial synergy to reduce poverty. From 12 provinces with an efficiency value of 1 in 2014 to 9 provinces with an efficiency value of 1 in 2015 to 10 provinces with an efficiency value of 1 in 2016, 12 provinces have an efficiency value of 1 for fiscal digital financial synergy to reduce poverty. In 2018, 16 provinces had an efficiency value of 1, in 2019, 17 provinces did the same, and in 2020, 16 provinces did as well. In Jilin, Anhui, Guangxi, Sichuan, Ningxia, and Sichuan, only a few provinces lacked an efficiency score of 1. The efficiency value response in recent years has revealed an increase in the number of provinces with an efficiency value of 1, which illustrates the success of measures to reduce poverty. Although there is considerable regional variation in the efficiency values of poverty alleviation, Hainan Province has succeeded in reducing poverty, going from an efficiency value of 0.882 in 2014 to 1 in 2020. However, the gap between the comprehensive efficiency values among various regions is relatively wide. However, the time required to reach an efficiency value of 1 from 2014 to 2020 is quite short, unlike in Hebei, Heilongjiang, Jiangxi, Hunan, and Yunnan where the combined efficiency value in 2014 is already fairly near to the cutting edge of production. Finally, from a temporal viewpoint, the proportion of provinces with a fiscal-digital-finance synergistic efficiency value of 1 for reducing poverty is the growing year, and even while some still have efficiency values below 1, the total efficiency value in 2020 is greater than in 2014. This shows that, even though certain provinces have efficiency values below 1, overall efficiency is rising. Therefore, in the future, efforts should be concentrated on strengthening the provinces where fiscal digital finance synergy for poverty reduction does not yet reach 1 so that they can achieve an efficiency effective state, as well as continuing to uphold the provinces where the efficiency value is already 1, and moving forward ever further on the road of poverty reduction to prevent a return to poverty.

2.3.2 The effectiveness of fiscal digital finance in reducing poverty

To further investigate the law of time evolution of fiscal digital financial synergistic poverty reduction efficiency under relative poverty, the change in synergistic poverty reduction efficiency was estimated and decomposed using the Malmquist productivity index model. The calculation results are shown in the following table.

Table 5
Time evolution of fiscal digital finance efficiency in reducing poverty between 2014 and 2020

Year	Changes in technical effectiveness	Technology development causes modifications	Changes in pure technological efficiency	Changes in scale efficiency	Change in total factor productivity
2014–2015	0.988	1.279	0.997	0.991	1.264
2015–2016	1.001	0.743	1.000	1.001	0.774
2016–2017	0.995	0.866	1.006	0.989	0.862
2017–2018	1.024	0.947	1.000	1.007	0.943
2018–2019	1.008	0.936	1.001	1.001	0.975
2019–2020	0.997	0.923	0.998	0.999	0.920
Mean	1.002	0.936	1.001	1.001	0.928

Total factor productivity fell to 0.928 in 2019–2020, showing that over time, the variables influencing poverty in the overall environment of relative poverty have become more complicated, resulting in a decline in degree of efficacy. The general trend of dropping and then growing, albeit the overall increase and decrease are relatively minor, is less than 1, with the mean value of the change in total factor productivity of fiscal digital financial synergy for poverty reduction. There is a decrease.

According to the breakdown of the change in total factor productivity, the mean value of the change in technical efficiency is greater than one, with an overall trend of falling and then rising, whereas the mean value of the change in technical progress is less than one, with an overall trend of falling. This suggests that changes in technological advancement are the primary source of the loss in total factor productivity and that changes in technical efficiency are not greater than changes in technical progress, which also induce a decline in total factor productivity. There is no discernible downward or upward trend between the pure technical efficiency change and the scale efficiency change, and both are greater than one among the technical efficiency changes, indicating that the increase in technical efficiency change is the result of both of their combined effects. In conclusion, despite the fact that technology progress is not always obvious, it is crucial for reducing poverty that financial digital synergy’s technical efficiency increases. Expanding technology investments is essential, especially in the central and western regions, to improve the efficiency of financial digital synergy for reducing poverty and to support the quick and effective development of reducing relative poverty.

Table 6

Malmquist index breakdown of 22 provinces and municipalities’ fiscal digital finance synergy efficiency for reducing poverty

Province	Changes in technical effectiveness	Technology development causes modifications	Changes in pure technological efficiency	Changes in scale efficiency	Change in total factor productivity
Hebei	1.013	0.929	1.005	1.008	0.941
Shanxi	1.000	0.935	1.000	1.000	0.935
Inner Mongolia	1.000	0.920	1.000	1.000	0.920
Jilin	0.997	0.941	1.000	0.997	0.938
Heilongjiang	1.004	0.932	1.000	1.004	0.936
Anhui	0.992	0.949	0.991	1.001	0.941
Jiangxi	1.009	0.949	1.005	1.004	0.957
Henan	1.000	0.946	1.000	1.000	0.946
Hubei	1.000	0.962	1.000	1.000	0.962
Hunan	1.001	0.940	1.000	1.001	0.941
Guangxi	0.993	0.936	0.996	0.998	0.929
Hainan	1.021	0.926	1.021	1.000	0.946
Chongqing	1.000	0.987	1.000	1.000	0.987
Sichuan	1.012	0.948	1.011	1.001	0.960
Guizhou	1.000	0.922	1.000	1.000	0.922
Yunnan	1.002	0.933	1.000	1.002	0.935
Tibet	1.000	0.915	1.000	1.000	0.915
Shaanxi	1.000	0.949	1.000	1.000	0.949
Gansu	1.000	0.918	1.000	1.000	0.918
Qinghai	1.000	0.896	1.000	1.000	0.896
Ningxia	1.006	0.928	1.000	1.005	0.933
Xinjiang	0.996	0.941	1.000	0.996	0.937
Mean	1.002	0.936	1.001	1.001	0.938

Table 6 displays the overall factor productivity index results and its breakdown for the elimination of poverty in each province and city from 2014 to 2020. The overall factor productivity change is less than 1 for all of the provinces in the central and western areas of the study. The average change in total factor productivity across all provinces is 0.938, indicating that the efficacy of fiscal digital financial synergy for alleviating poverty has somewhat declined in the majority of provinces and cities.

Every province, with the exception of Jilin, Anhui, Guangxi, and Xinjiang, has a technological efficiency change of at least 1, indicating a general increasing trend in technical efficiency change in most provinces and cities. Pure technical efficiency in Anhui and Guangxi is less than 1, demonstrating that the majority of central and western provinces’ financial digital resources for decreasing poverty are fully used with the existing technology support. Every other province – aside from Jilin, Guangxi, and Xinjiang – has a scaling efficiency that is more than 1, demonstrating the high scale efficiency of fiscal digital financial poverty reduction in the majority of provinces. The size of fiscal and financial investment in Jilin, Guangxi, and Xinjiang is modest owing to economic development and geographic location characteristics, which should drive the economic growth of the area, since only those three regions have a scaling efficiency below 1.

Only Qinghai has a change in the technical development of less than 0.9, suggesting a significant technological regression. All provinces and cities in the central and western regions have a change in the technological advancement of less than 1, all on a declining trend. This shows that the great majority of central and western Chinese provinces pay little attention to the use of technology tools in fiscal digital finance poverty reduction and that these tools have not been developed, leading to a drop in the efficacy of collaborative poverty reduction. This demonstrates that limiting the increase in total factor productivity requires a focus on technical advancement. The total factor productivity is less than one in provinces where both the change in technical efficiency and the change in technical progress are less than one. This is because the loss in technological progress is higher than the decline in technical efficiency. Therefore, technological investment in fiscal digital finance should be given more attention in the future to increase the efficacy of its synergistic action in reducing poverty.

3. Analysis of factors influencing the effectiveness of fiscal spending and synergistic poverty reduction through digital inclusive finance

In comparison to each technique used independently, fiscal and financial synergy eliminates poverty more effectively. To effectively utilize fiscal and financial in reducing relative poverty, more study should be done on the influencing factors that affect the synergistic poverty reduction of fiscal expenditure and digital inclusive finance.

3.1 Choosing the variables

Variables are chosen based on prior research, taking into consideration the availability of indicators, from the mechanism of the engagement of fiscal spending and digital inclusive finance in poverty reduction. Financial payment capacity has an impact on how effectively poverty is reduced, and the amount of fiscal expenditure directly affects the income level of disadvantaged farmers. The more the fiscal payment capacity, the more the financial scale is encouraged, which might lead to more financial resources traveling to rural areas. The degree of financial development has a direct impact on how effectively poverty is reduced. The level of financial development directly affects the price and threshold of financial services, which also affects how easily accessible financial services are. The effectiveness of reducing poverty is directly impacted by the scope and effectiveness of financial development. The cost and threshold of financial services are directly impacted by the level of financial development, which also has an impact on the accessibility of financial services; select “Balance of financial institutions’ loans for agriculture/Balance of financial institutions’ loans at year’s conclusion”, to gauge how effectively digital financial inclusion reduces poverty in relation to the level of financial development [19]; the ability of financial institutions to convert credit funds is what is meant by the efficiency of financial development; select “Balance of financial institutions’ loans at year’s conclusion/Deposits held in financial institutions as a whole”, to assess the effect of financial development efficiency on the effectiveness of digital financial inclusion in reducing poverty [24]. In addition, human capital is essential for reducing poverty. The degree of emphasis on education and the level of education will have an impact on the efficiency of fiscal and financial coordination.

The “trickle-down theory” and the “pro-poor effect” are the foundations for the indirect poverty reduction effects of financial support for agriculture and digital inclusive finance, but Wu Qingtian and Du Xingyang et al. contend that by fostering economic development, the divide between urban and rural areas can be reduced and poverty alleviated. As a result, the economic development level of the region per capita is chosen to influence the synergistic poverty reduction efficiency. The effectiveness of synergistic poverty reduction will depend on the amount of economic development per capita.

The development of an information technology infrastructure and the nurturing of future financial markets are inextricably linked to the growth of the reach of digital inclusive finance. To assess the effect of financial support for agricultural and digital inclusive finance on synergistic poverty reduction effectiveness, Peking University’s Digital Inclusive Finance Index was used. The growth of primary and tertiary industries in underdeveloped rural regions is primarily driven by fiscal transfers, special payments, and digital inclusive financing. As a result, the industrial structure of underdeveloped rural areas affects how effectively poverty is reduced. To assess the effect of industrial structure on the synergistic effects of fiscal expenditure and digital financial inclusion in reducing poverty, select the “total value of primary and tertiary industries/regional GDP” is used.

Table 7
Factors impacting the effectiveness of financial support for agriculture that works in concert to reduce poverty

Influential variables	Calculator for indicators
Capacity for financial expenditures	General budgetary income/general government spending
Financial growth on a broad scale	Farm-related loans from banking institutions/the banking institutions’ loan balance
The efficiency of financial	The banking institutions’ loan balance/financial institutions’ balances in their deposits
development
Human resources	Number of higher education students/overall population of the region
Economic development level	Per-capita gross regional product
Digitalization level	University of Beihang Financial Index
Industrial organization	Total economic output from basic and tertiary sectors/GDP

Table 8

Variables’ descriptive statistical analysis

Variables	Sample	Average	Standard deviation	Minimum value	Maximum value
Fis	154	0.3752	0.1118	0.0982	0.5901
FDS	154	0.3269	0.0765	0.0377	0.4918
FDE	154	0.8255	0.1316	0.5242	1.1438
PQ	154	0.0381	0.1611	0.0019	1.8
LN (RGDP)	154	10.7299	0.2421	10.17	11.27
LN (DOD)	154	5.8544	0.1502	5.46	6.12
IS	154	0.5896	0.0717	0.458	0.809

Note: Sample size 154, calculated using Stata 17.

3.2 Tobit regression model construction

The Tobit model, first proposed by Tobin [25], is a model for the case where the dependent variable is partially continuous or discrete distributed data. When the dependent variable is constrained, estimating the regression wash using ordinary least squares (OLS) is likely to lead to biased results. From the results of the study’s DEA measurement, the combined efficiency value of fiscal spending and digital inclusive finance in synergistic poverty reduction ranged from 0 to 1. Therefore, the Tobit model with maximum likelihood coefficients was chosen to give a more complete picture of the data results and to improve the precision of the estimates. Introducing $x_{i}y^{\ast}$ to denote the truncated dependent variable, the Tobit model is set up as follows:

$\displaystyle y=\left\{{{\begin{array}[]{l}{y*=\beta^{\prime}x_{i}+\mu_{i},y>0% }\\ {0,y\leqslant 0}\\ \end{array}}}\right.$ (4)

where: $y$ denotes the combined efficiency value, $\beta_{i}$ ; the regression parameter vector is the follow and error term and $\sim$ (0, ${\sigma}^{2}$ ).

Based on the results of the above DEA-Malmquist analysis, the study used the combined efficiency of fiscal spending and digital inclusion in synergizing poverty reduction as the explanatory variables, the Tobit regression model was constructed using fiscal capacity to pay (FIS), financial development size (FDS), financial development efficiency (FDE), human capital (PQ), economic development level (REL), digitalization (DOD) and industrial structure (IS) as explanatory variables as follows:

$\displaystyle TE=\alpha+\beta_{1}\textit{FIS}+\beta_{2}\textit{FDS}+\beta_{3}% \textit{FDE}+\beta_{4}PQ+\beta_{5}\textit{REL}+\beta_{6}\textit{DOD }+\beta_{7% }IS+\mu$ (5)

Where: TE is the explanatory variable, FIS, FDS, FDE, PQ, REL, DOD, IS are explanatory variables indicating the influencing factors affecting synergy efficiency, are constant terms, are regression coefficients for each explanatory variable, and indicate random disturbance terms.

3.3 Examining the empirical finding

Table 9
Findings from tobit regression

y	Coef.	St. Err.	$t$ -value	$p$ -value	[95% Conf	Interval]	Sig
Fis	$-$ 0.548	0.163	$-$ 3.36	0.001	$-$ 0.869	$-$ 0.228	${}^{***}$
FDS	$-$ 0.499	0.168	$-$ 2.97	0.003	$-$ 0.828	$-$ 0.17	${}^{***}$
FDE	$-$ 0.094	0.106	$-$ 0.89	0.375	$-$ 0.302	0.114
PQ	2.164	2.503	0.86	0.387	$-$ 2.741	7.069
REL	0.049	0.04	1.21	0.226	$-$ 0.03	0.128
DOD	$-$ 0.033	0.031	$-$ 1.05	0.292	$-$ 0.094	0.028
IS	$-$ 0.361	0.161	$-$ 2.24	0.025	$-$ 0.677	$-$ 0.045	${}^{**}$

${}^{***}p<$ 01, ${}^{**}p<$ 05, ${}^{*}p<$ 1, indicate significant at 1%, 5%, 10% significance level, respectively, non-significant without markers.

Table 9 demonstrates the strong beneficial benefits of financial development size, financial payment capability, and industrial structure on the synergistic poverty reduction effectiveness of financial digital inclusion. The more primary and tertiary industries there are in the industrial structure, the better the financial payment capability, and the more digital financial growth there is, the more effectively poverty is reduced through synergy. Synergistic poverty reduction is adversely correlated with the scale and effectiveness of financial development; however, the magnitude of financial development is significant while the latter is not. This suggests that while the effectiveness of local financial development has little bearing on the efficiency of synergistic poverty reduction, the scope of agriculture-related financial development can increase its effectiveness. This is because agricultural loans are specifically intended for farmers and may significantly reduce their level of poverty, whereas financial development efficiency refers to the capacity to turn borrowed money into savings. Farmers’ savings will not rise in the short run because poor farmers acquire loans more frequently to enhance their subsistence level or to engage in investment activities.

GDP per capita, which measures economic development, has a strong and positive relationship with the effectiveness of synergistic poverty reduction because better regional economic development has a larger impact on economic growth’s trickle-down effects and a more effective reduction of poverty overall. Contrarily, there is no correlation between fiscal digital financial synergistic poverty reduction efficiency and GDP per capita, digital level, human capital, or any other of these variables. Gross regional product per capita is a measure of a region’s economic growth, and the higher the level, the more pronounced the impact on reducing poverty will be. Human capital is a measure of a region’s education level; the higher the education level per capita, the more positively correlated with the effectiveness of reducing regional poverty, and the better the effect of reducing regional poverty; however, as the Internet and other technologies have not fully developed in central and western regions, the digital level is typically not high, and it does not have a very significant impact.

4. Conclusions and advice

4.1 The key findings

This study evaluates the effectiveness of fiscal spending and digital inclusive finance in eradicating poverty from both static and dynamic angles, and it also examines the elements that influence it. The findings demonstrate that: from a static perspective, the efficiency of fiscal expenditure and digital inclusive finance synergistic poverty reduction is higher than the efficiency of fiscal expenditure poverty reduction and digital inclusive finance poverty reduction, but there are clear regional differences, with significant differences in efficiency among provinces and municipalities; with the investment of fiscal finance, there is an increase in the number of provinces with effective fiscal expenditure and digital inclusive finance poverty reduction. Technical efficiency among them is greater than scale efficiency and nearer the production frontier, although scale efficiency still has potential for improvement. From a dynamic perspective, the mean value of total factor productivity changes in fiscal expenditure and digital financial inclusion’s synergistic poverty reduction efficiency from 2014 to 2020 is 0.938, and the overall trend is down. The efficiency of technical advancement changes is what causes total factor productivity, according to the breakdown of the Malmquist index of synergistic poverty reduction efficiency. The scale of financial development, financial payment capacity, and industrial structure are significantly and favorably related to the effectiveness of fiscal digital financial synergy in reducing poverty, but there is no significant correlation between the effectiveness of financial development, economic development, digital development, or human capital.

4.2 Valuable suggestions

In the light of the above study, recommendations are made on the synergy of fiscal spending digital inclusive finance for poverty reduction under relative poverty:

First, break through geographical and spatial limitations, strengthen the top-level design of the financial transfer system in poor areas, optimize the structure of financial transfers in poor areas, and enhance the accessibility of financial services. Increase the quality and long-term performance of fiscal transfer systems and digital inclusive finance in poverty governance, appropriately allocate a portion of the financial resources to support the improvement of the level of basic financial services in poor areas. Promote the construction and expansion of financial outlets of commercial banks and agricultural and commercial banks in the backward areas of the central and western regions, promote the digital transformation of financial institutions to streamline transaction processes and enhance the affordability of financial services. Second, increase scientific and technological innovation in poor areas to make up for the lack of information and improve the science, technology and precision of digital inclusive finance for poverty alleviation. Using technologies such as big data and cloud computing to fully mine and analyse data on farmers’ transactions in poor areas, accurately identify the risks and needs of farmers in poor areas, reduce service costs, improve the accessibility of financial resources for the poor, promote the diversification of financial product supply, use digital technologies such as online payments to facilitate financial services and increase the breadth and depth of financial services for the poor. Thirdly, a combination of fiscal and financial policies should be implemented according to the current state of poverty and the stage of poverty in poor areas. Identify mechanisms to convert fiscal and financial poverty reduction effects, promote high-quality economic development in poor areas, increase the level of economic development in the central and western regions, and narrow the economic development gap between the central and western regions and the east. Focusing on the educational attainment of people living in poor areas and improving understanding of fiscal and financial policies for poverty reduction. Promoting economic development in poor areas, which can lead to employment, income, infrastructure development, etc. in poor areas. Maximise the synergy between finance and digital finance to reduce poverty, raise incomes and increase wealth, thus leading to poverty reduction in poor areas.

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An analysis of the synergistic poverty reduction effectiveness of fiscal spending and digital inclusive finance from the standpoint of relative poverty was conducted

Abstract

Keywords

1. Introduction

2. Measuring the effectiveness of fiscal spending and digital inclusive finance in reducing poverty

2.1 Setting a model

2.2.1 Choosing the right indications

(1) Input indicators

Table 1 The DEA model’s input indicators

(2) Results markers

2.3 Empirical findings

2.3.1 The effectiveness of fiscal and financial digital synergy in eradicating poverty

Table 3 22 provinces and cities’ effectiveness in reducing financial and fiscal poverty between 2014 and 2020

Table 5 Time evolution of fiscal digital finance efficiency in reducing poverty between 2014 and 2020

3.1 Choosing the variables

Table 7 Factors impacting the effectiveness of financial support for agriculture that works in concert to reduce poverty

Table 9 Findings from tobit regression

4.1 The key findings

4.2 Valuable suggestions

References

Table 1
The DEA model’s input indicators

Table 3
22 provinces and cities’ effectiveness in reducing financial and fiscal poverty between 2014 and 2020

Table 5
Time evolution of fiscal digital finance efficiency in reducing poverty between 2014 and 2020

Table 7
Factors impacting the effectiveness of financial support for agriculture that works in concert to reduce poverty

Table 9
Findings from tobit regression