Can the digital economy promote fiscal effort?: Empirical evidence from Chinese cities

Over the last few years, the governments’ fiscal revenue and expenditure have been significantly affected by the rapid growth of global digital economy. Despite the significant role that digital economy plays in improving the ability of the government to generate fiscal revenue, there is relatively little empirical evidence of this. Therefore, this study aims to reveal the impact of digital economy on the urban fiscal effort of China by analyzing the data recorded from 2011 to 2019. According to the empirical findings, digital economy can significantly improve the level of fiscal effort. Meanwhile, digital economy has a significant nonlinear effect on fiscal effort. As suggested by the results of expansion analysis, the effect of digital economy on fiscal effort shows significant regional heterogeneity and the spatial spillover effect is positive. Based on the findings of this study, it is recommended to strengthen the construction of digital infrastructure, optimize the practice of fiscal revenue management under the context of digital economy, and improve the governments’ fiscal situation by paying attention to the coordinated development of digital economy among various regions.


Introduction
The fiscal effort can be used to assess the ability of an individual country to make use of its fiscal revenues (Zhang and Wu 2009;Xu and Warner 2016).Since the outbreak of the COVID-19 pandemic, there has been an increase in the downward pressure on the economy.Meanwhile, the contradiction has become more prominent between government fiscal revenues and expenditures around the world (Chernick et al. 2020;Guo and Shi 2021).On the one hand, the outbreak of COVID-19, high energy prices, and continuously rising inflation have prompted many countries around the world, including the USA, Germany, and the UK, to introduce a series of supportive fiscal policies for their citizens and businesses to overcome economic difficulties through fiscal expenditures and subsidies.On the other hand, the government's fiscal revenue has been significantly affected by the weakness of economic activities such as investment, consumption, and exports.Thus, how to raise fiscal revenues in the new era has become the focus of widespread discussion among governments and all sectors of society.
As the largest developing country as well as the second-largest economy worldwide, China has a substantial influence on the development of global economy as its national economy grows.In the past few decades, a potentially vital impact has been caused by the increasing fiscal pressure from urbanization strategy on China in terms of its sustainable economic development.Especially after the taxsharing reform initiated across China in 1994, there have been many cities facing a fiscal pressure caused by the mismatch between their powers and expenditure responsibilities.Consequently, local governments overly focused on short-term economic benefits while overlooking the long-term social benefits in their development philosophy, which impairs the sustainability of economic development (Li and Du 2021;Lin and Zhou 2021).Therefore, in recent years, the central scheme of Chinese governments is to explore the new paths and mode of growth.
As a new round of technological revolution, emerging digital economy is considered a potential driving force for the development of Chinese social economy.In recent literature (e.g., Abendin and Duan 2021;Niyazbekova et al. 2021), it is suggested that the development of digital economy is a key measure that countries should take to promote rapid economic recovery in the face of increasing global uncertainty risks.Having demonstrated their enormous strength in breaking time and space limits, digitization-based activities play a vital role in promoting economic growth, lowering transaction costs, and enhancing the efficiency of government management (Carlsson 2004;Li et al. 2020).For the governments, developing digital economy is expected to play a critical role in alleviating fiscal pressures and further promoting regional economic growth.As one core mechanism of fiscal governance at macro level, the fiscal revenue would be potentially impacted by the rapid growth of digital economy, in such aspects as collection management, data information, resource cluster, and policies governance.This leads to an interesting question, that is, whether and how the digital economy in China facilitates the governmental fiscal effort? 1 3 Economic Change and Restructuring (2023) 56:3501-3525 In this study, the impact of digital economy on governmental fiscal efforts will be empirically tested by analyzing Chinese urban data for theoretical contributions.Firstly, a theoretical analysis is conducted as to the impact of digital economy on fiscal efforts.Secondly, the impact of digital economy on governmental fiscal efforts is empirically tested using China's urban data, and robustness test is performed through a variety of methods.Thirdly, the research in related fields is deepened by expanding it from the perspectives of nonlinear effects, regional heterogeneity and spatial spillover effects.The results of empirical test are expected to improve our understanding of the relationship between digital economy and fiscal effort in China, which is of theoretical and practical significance to guiding digital economy and enhancing fiscal sustainability in the era of digitization for the governments of both developed or developing economies.
2 Literature review and theoretical hypothesis

Literature review
The term "digital economy" dates back to the 1990s when the Internet remained an add-on to analog products and services.With the evolution of the epoch, digital economy has become increasingly significant.According to the research of the US Department of Commerce and G20 Group, digital economy can be defined as the main economic form following the agricultural economy and industrial economy.As a new economic form, it takes data resources as the core element, modern information networks as the primary carrier, and the integration and application of information technology and digital transformation of all elements as the major driving force to promote the unified fairness and efficiency.
Currently, there has been plenty of research conducted on digital economy.It has been found out in most studies that digital economy can promote the growth of modern economy.On the one hand, the advancement of emerging technologies such as the Internet creates an economic environment characterized by economies of scale, economies of scope, and long tail effects, which is conducive to reducing information asymmetry, matching supply and demand, and improving the level of balanced economic output (Pradhan et al. 2019;Bulturbayevich and Jurayevich 2020;Shodiev et al. 2021).On the other hand, digital economy can help improve the quality of various production factors, break the geographical limits of traditional economies, promote the free flow of production factors between various regions, and enhance total factor productivity, thus driving economic growth (Chakpitak et al. 2018;Barata 2019;Wu and Yu 2022).Some scholars focus attention on the correlation between digital economy and green development.As discovered by Moyer and Hughes (2012), the application of ICT contributes to improving production efficiency, reducing energy intensity, and promoting carbon emission reduction.As revealed by Nguyen et al. (2020) in their research on G20 countries, the development of information and communication technology can not only promote economic growth for these countries but also enhance carbon productivity.In addition, it is demonstrated in the research carried out by Bhattacharya et al. (2015), Zhang et al. (2018), Li et al. (2021a, b), Dou and Gao(2022) that digital economy can improve the regulatory capacity of governments, lead to a better control on the overall energy supply through pricing, cross-subsidies, and curtail carbon emissions, and promote green development.Finally, some scholars have explored the relationship between digital financial inclusion and economic growth.Ahmad et al. (2021) and Liu et al. (2021) conducted research in China, finding out that digital financial inclusion can significantly promote economic growth.
At the same time, many scholars argue that the development of digital economy will also have negative impact on economic development to a certain extent, as manifested in three aspects.The first one is the substitution of digital production for job positions.In the context of digital economy, the labor market presents a completely different picture from before, which causes a significant change in the opportunity structure for social members to enter the labor market.This change stems from the "creative destruction" of digital production on employment, which means new jobs are created, while existing ones are vanishing (Greve 2019).According to the International Social Security Association, there were 7.1 million jobs lost to digital transformation worldwide between 2010 and 2015.It is projected that the number of operable industrial robots across the globe will approach 11.3 million by 2030, an increase in 10.26 million compared to 2010 (Bloom et al. 2019).In this circumstance, workers have to compete with such digital production tools as robots and artificial intelligence, but it is inevitable that they would fail due to the advantage of the latter in productivity, which leads to a "brand new and huge class" without any value or contribution (Harari 2017).The second one is the social protection dilemma facing workers in those new forms of employment.In the context of digital economy, there has been an unprecedented expansion of labor time and space for workers.However, the more flexible means of employment have created a significant obstacle to their access to adequate social protection.In the era of digital economy, it is highly likely that three types of labor problems arise simultaneously: unemployment, overwork, and work poverty.The third one is the negative impact of digital economy development on environmental protection.The digital economy will increase energy consumption and carbon emissions due to the replacement of production equipment, the increase in electricity consumption, and the expansion of mineral resource consumption, which is detrimental to the environment (Salahuddin and Alam 2015;Li et al. 2021a, b).
Herein, fiscal effort is defined as the capability of local governments to generate fiscal revenues.As for the concept of fiscal effort, it originates from tax effort.Bahl (1971) is the first to define tax effort as the degree to which a country makes use of its tax capacity, which can be measured by the ratio of actual tax revenues to expected tax revenues.In those advanced countries, tax revenues are relied on by the governments at all levels as the major source of fiscal revenues.Tax effort is effective in measuring the level of fiscal effort, for which it has been adopted by many scholars.In the view of Chervin (2007), fiscal efforts refer to the degree to which local governments make use of their revenue sources or fiscal capacity.It is commonly used to evaluate how much effort a regional government invests in raising fiscal revenues relative to those 1 3 Economic Change and Restructuring (2023) 56:3501-3525 competitive regions.By applying conditional nonparametric frontier methods, Pedraja et al. (2020) evaluate fiscal effort in heterogeneous environments.
At present, the literature on fiscal effort focuses mainly on the impact of transfer payment and tax decentralization on the fiscal efforts of local governments.For example, Inman (1988) and Peterson (1997) propose the rational income maximization hypothesis, indicating that the tax cost cannot be internalized by local governments.Therefore, on the premise of fixed expenditure, local governments tend to reduce fiscal effort by replacing local taxes with higher financing costs through the transfer payments with lower financing costs.In the empirical research conducted by Litvack et al. (1998) and Panda (2009), it is shown that transfer payments can reduce the fiscal efforts of local governments.However, Smart (1998) and Egger et al. (2010) suggest that improving the transfer payment system plays a role in enhancing the enthusiasm of local governments about raising fiscal revenues to a certain extent.By using Chinese provincial data to test the relationship between fiscal revenues share and fiscal effort, Liu (2012), Qiao (2018), and Cui and Li (2019) discover a significant positive correlation between them.In addition, some other scholars focus their study on the impact of soft budget constraints, fiscal imbalance, international finance, and expenditure demand on the fiscal efforts of local governments (Di Liddo et al. 2019;Queralt 2019;Shanmugam 2022).
Despite plenty of results in the research on digital economy and fiscal effort, there remain a massive gap to fill for further research.Firstly, there are few studies paying attention to the impact of digital economy on government finance because a large majority of the existing research focuses mainly on economic growth, production factor matching, government regulation, and green development.Secondly, there is insufficient consideration given to the development of digital economy as the research on the influencing factors of fiscal effort mostly focuses on such traditional aspects as fiscal decentralization and transfer payment.Due to the decoupling of research between the two, the government fails to accurately assess the impact of digital economy on government finance.Allowing for the rapid development of digital economy and the increasing fiscal pressure of governments in various countries, it is of practical significance to systematically analyze the impact of digital economy on fiscal effort in this study.
Specifically, its impact on fiscal effort is theoretically analyzed in this study, which is based on the intrinsic characteristics of digital economy.Then, the developmental level of digital economy and fiscal efforts of 286 cities in China from 2011 to 2019 is measured, with a variety of measurement methods used to test the impact of digital economy on fiscal efforts empirically.According to the research results, digital economy can improve the level of fiscal efforts significantly.Meanwhile, there are significant nonlinear effect, regional heterogeneity, and spatial spillover effect shown by the impact of digital economy on fiscal effort.

The effect of digital economy on fiscal effort in China
Digital economy, which relies on 5G, big data, cloud computing, artificial intelligence, and other cutting-edge technologies, is conducive to seizing the opportunities of global scientific and technological revolution and industrial revolution, which not only facilitates the transformation of old and new economic drivers but also creates competitive advantages.The advantages of digitization can break the constraints of time and space across economic activities.It plays a significant role in promoting the economic cycle (Mardonakulovich and Bulturbayevich 2020;Jamshid et al. 2020) by improving the efficiency of resource allocation (Chen 2022), reconstructing organizational mode (Malecki and Moriset 2007), supplying emerging products/services (Chen and Wang 2019), upgrading industrial chains (Miao 2021), and creating new market segments (Ritter and Schanz 2019).The emerging advantages highlight digital economy as a new force to drive economic growth and build modern economic system (Carlsson 2004;Limna et al. 2022).
The rapid development of digital economy in China has increasingly innovated various modes of economic production, consumption, and distribution.Accordingly, the space of fiscal revenues for local governments in China has also been expanded.However, due to the emerging features of digital economy, local governments still face uncertainties to capture the opportunities of generating fiscal revenues.In this case, digital economy is likely to impact governmental fiscal effort.It is argued that digital economy improves the fiscal effort of local government in China in two ways.
Firstly, digital economy expands the total economic scale and improves efficiency.Traditional industries rely on digital means for active transformation and upgrade, emerging industries are rapidly cultivated, and there is an increase in industrial scale and profit (Liu et al. 2022;Kan et al. 2022).Digital economy plays a crucial role in conserving tax sources, expanding the tax base, and improving fiscal revenues.Secondly, digital economy promotes the digital transformation of government finances from the perspective of social governance.As the system of digital economy infrastructure improves and the behavior of economic subjects changes, local governments can gain access to all kinds of economic data in more advanced ways and through more diversified channels (Lips 2019).At the same time, advanced analysis enables the government to better analyze the collected data, mine the potential information carried by economic data, and raise fiscal revenues more effectively.
Based on the above analysis, the first research hypothesis is proposed as follows: H1 Digital economy can significantly improve the fiscal effort of local governments in China.

The nonlinear effect of digital economy on fiscal effort in China
According to Metcalfe's law, the value of the network is proportional to the square of the number of connected users.As the number of people and equipment accessing digital platforms increases continuously, there is a decline in the average cost and marginal cost of economic activities such as software, chips, online services, etc., while the digital economy exerts significant network effects and scale effects (Chen 2020;Tong and Zhang 2022).Meanwhile, in terms of digital technology applications, the transformation of government departments from "passive response" to "active action" has improved the level of fiscal management progressively (Li and 1 3 Economic Change and Restructuring ( 2023) 56:3501-3525 Zhang 2021; Wang and Wang 2022).Therefore, its impact on fiscal effort varies at different stages of development of the digital economy.
In the early stage of development, the scale effect of digital economy is insignificant and the overall benefit is limited, which restricts it from improving productivity.Thus, the impact of digital economy on fiscal effort is insignificant at this time.
As digital economy grows, the government is disadvantaged in the collection and analysis of data compared with the enterprises following the latest trend of technology.For example, such taxpayers as Internet enterprises and digital platforms have naturally accumulated a large amount of data in the course of conducting business.With sufficient incentives to develop and apply more advanced technology for data analysis, they seek to increase both market share and profits.In contrast, the government has relatively limited access to data, and lags behind in the application of technology, which makes it difficult to achieve the effective management of fiscal revenues (Mulaydinov 2021;Laguna 2022).Meanwhile, data are the core production factor in the context of digital economy.There is uncertainty in value creation, and it cannot be accurately measured by the traditional accounting system.Also, it is difficult to accurately determine the scale of the tax base and apply the principle of tax territoriality.In the meantime, the transactions conducted in digital economy are characterized by networking, remoteness, and virtualization, which makes it difficult to define the tax subject, tax base erosion, and profit transfer (Gulkova et al. 2019).These problems reduce the ability of the government to raise fiscal revenues.Finally, due to the monopoly caused by digital economy, competition is suppressed, market efficiency is affected, economic growth is constrained, and fiscal revenues diminish (Pan et al. 2022).
However, the marginal use cost of data, as a key factor of production, approaches zero when digital economy develops to a certain level.Meanwhile, the marginal income of data continues to increase.The related industries are also characterized by the increasing returns to scale.Therefore, the release of digital dividend continues, and digital economy permeates the economy in all aspects.What's more, ICT and government finance are closely integrated, and the fiscal management capacity of governments is gradually improved.This is beneficial for local governments to conserve high-quality tax sources, expand the tax base, and raise fiscal revenues more effectively.
Based on the above analysis, the second research hypothesis is proposed as follows: H2 Digital economy has a significant nonlinear effect on the fiscal effort of local governments in China.

Model building
In order to empirically test the impact of digital economy on fiscal effort, a panel linear model is constructed as the benchmark of analysis.The model is expressed as Eq.(1).
where Ef fort i,t represents the fiscal effort of the i city in year t, Dige i,t denotes the digital economy development level of the i city in year t, Z i,t refers to the introduced control variable system, u i indicates the individual fixed effect of city i that does not change with time, year t means the time fixed effect that changes with time but not with the city, and i,t stands for an error term.
On this basis, the robustness is verified by four methods.Firstly, the fixed effect control method is applied to control the changes in the macro environment in different cities since the cities in the same province will face a similar macro environment.Secondly, the instrumental variable method is adopted to deal with the possible endogenous problems.Thirdly, system GMM is used to estimate Eq. (1).Fourthly, the "Broadband China" pilot policy is taken as a quasi-natural experiment to construct a difference-in-differences (DID) model for the test on the effect of the exogenous policy.
Meanwhile, a panel threshold model is constructed to conduct empirical test on nonlinear effects of the digital economy on fiscal efforts.The model is expressed as Eq. ( 2).
where q it represents the threshold variable, which is Dige it in this study; s indicates the threshold value; I(⋅) denotes a schematic function.When the condition in the brackets is true, the function takes the value of 1, otherwise it takes the value of 0. Other symbols have the same meanings as Eq.(1).In Eq. ( 2), the single threshold case is considered, which can be extended to the multi-threshold case.
In addition, as an expansion analysis, this study relies on sub-sample regression and panel space model to conduct empirical test on the regional heterogeneity and spatial spillover effects of the digital economy on fiscal efforts.
For different purposes, all econometric models used in this article are shown in Table 1.

Digital economy development index
According to the aforementioned definition of digital economy, it is a comprehensive concept that cannot be measured by a single index.Therefore, the development level of digital economy is measured this study by constructing a multidimensional index system.
At present, the research carried out by most scholars on measuring the developmental level of digital economy in China focuses on the national level and provincial levels, with less attention paid to the urban level.However, it is common to have unbalanced regional development in most provinces.Therefore, a more credible conclusion can be reached only by refining the research to the urban level.Based (1) Economic Change and Restructuring (2023) 56:3501-3525 on the research of Zhao et al. (2020), the availability of urban data is considered in this study to construct the development index system of China's urban digital economy from digital inclusive financial development and Internet development.As for the development of digital inclusive finance, it is measured by the digital inclusive finance index published by the Institute of Digital Finance Peking University.Taking into account the breadth and depth of digital financial services, this index is applicable to construct a digital inclusive financial index system from three dimensions: the coverage of digital finance, the use depth of digital finance, and the digitization degree of inclusive finance.It is comparable both vertically and horizontally, which makes it suitable for comprehensively measuring the development of digital inclusive finance in Chinese cities.Based on the research of Huang et al. ( 2019) and other scholars, Internet development is measured in this study by four indicators: Internet penetration rate, mobile phone penetration rate, relevant output, and relevant employees.The specifics are as follows: the number of Internet broadband access users among 100 people, the number of mobile phone users among 100 people, the total amount of telecom services per capita, and the proportion of employees in the computer software industry in total employees.
In this study, principal component analysis is conducted to calculate the digital economy development index.The primary aim of principal component analysis is to simplify complexity and reduce the dimensionality of indicators.Drawing on the discussions of Ringnér (2008) and Bro and Smilde (2014), principal component analysis is conducted to transform correlated original random variables into new random variables with unrelated components through an orthogonal transformation.Algebraically, it is purposed to transform the covariance matrix of the original random variable into a diagonal matrix.In geometry, it is intended to convert the original coordinate system into a new orthogonal coordinate system, for the maximum dispersion of the sample at the new random variable points in the n orthogonal directions.Then, the multidimensional variable system is dimensionally reduced to a low dimensional variable system.Finally, an appropriate value function is constructed to further transform the low dimensional system into a one-dimensional system.In short, principal component analysis is a linear transformation with relatively few independent comprehensive indicators used to reflect the overall information and the developmental status of a system.Therefore, the general process of principal component analysis is as follows.Firstly, the raw data of each indicator is standardized to reduce the impact of different dimensions of indicators on the evaluation results.Secondly, the covariance matrix or correlation coefficient matrix based on standardized indicator data is calculated.Thirdly, the eigenvalues and eigenvectors of the covariance matrix or correlation coefficient matrix are calculated.Fourthly, the contribution rate and cumulative contribution rate of the principal components is calculated, and the number of principal components is determined.Fifthly, the load of the principal component is calculated.Sixthly, the comprehensive score is calculated.Finally, the digital economy development index is obtained.

Fiscal effort
In the above, fiscal effort has been defined as the ability of local governments to raise fiscal revenues.Therefore, fiscal effort is measured as the ratio of actual fiscal revenues to expected fiscal revenues.Besides, the practices of Lots and Morss (1967) and Bahl (1971) are used for reference to estimate the fiscal revenues through Eq. ( 3).
ln Rev it and ln GDP it represent the logarithm of fiscal revenues and GDP of the i city in year t, respectively.The meanings of other symbols are consistent with that of Eq. ( 1).According to the regression results of Eq. ( 3), the fitting value of the logarithm of fiscal revenues of each city over the years can be obtained, that is, the logarithm of expected fiscal revenues.Then, the value of fiscal effort of each city is obtained.

Control variables
To avoid missing variables, and taking into account the availability of data, a series of control variables are introduced into the model, as shown in Table 2.

Data sources and descriptive statistics
The data sources of this study include China Urban Statistical Yearbook, China Regional Economic Statistical Yearbook, provincial and municipal statistical yearbooks, the CEIC database, and the EPS database.Considering the availability of data and research objectives, those cities lacking data for three years or more are excluded.Finally, a research sample containing the data from 289 cities in China from 2011 to 2019 is formed, and all monetary variables are translated into actual values at the prices of 2011.The descriptive statistics of each variable are shown in Table 3.

Benchmark regression results
The sample data is used to regress Eq. ( 1) as the benchmark of empirical analysis.The results are shown in Table 4.
As shown in Table 4, the regression results of Eq. ( 1) suggest good characteristics.On the one hand, the values of exceed 85%, indicating that the variance of the composite disturbance term u i + it is largely attributed to individual effects, which means the fixed effect model is reasonable.One the other hand, the F statistic is of much significance, which indicates that the model is highly significant on the whole and has strong fitting ability to the data.
The estimated coefficient of Dige is significantly positive, indicating that digital economy significantly promotes the fiscal effort of local governments in China.Such results are consistent with our expectation.As indicated in the theoretical analysis section, digital economy can break the constraints of time and space on the flow of production factors, improve the efficiency of resource allocation, and promote economic growth, thus laying a solid foundation for the government to raise fiscal revenues.At the same time, digital economy facilitates the digital transformation of fiscal system, which allows the government to raise revenues more efficiently.Theoretically, digital economy may also reduce the ability of governments to raise fiscal revenues, but it exerts a more catalytic than the inhibitory effect on fiscal effort in the context of fast-growing global digital economy.
For the control variables, the estimated coefficients of Sec_GDP, Ter_GDP, pop, and Gov are significantly positive, which indicates that the increase in fiscal effort can be significantly promoted by the growth of secondary industry and tertiary industry, the increase in population density, and the expansion of government scale.In addition, there is no significant statistical correlation found between per capita real GDP, financial development, social security, and fiscal effort.
According to the above analysis, digital economy has a significantly positive effect on the fiscal effort of local governments in China.

Control province fixed effect
There is a similar macro environment faced by the cities located in the same province.For this reason, it is controlled by setting the fixed effect of provinces.The regression results are shown in Column (1) of Table 5.
It can be seen from above that with control applied on the systematic changes in the macro environment, the symbol and significance of the estimated coefficient of the Dige are consistent with the results of benchmark regression.It indicates the robustness of the results of our previous empirical analysis.

Instrumental variable method
Higher fiscal effort is beneficial in making the government raise fiscal resources more effectively, promoting the construction of digital economy infrastructure, and then stimulating the development of digital economy.Therefore, endogenous problems may arise from the judgment of causality in the empirical analysis.
The instrumental variable method used in this study is intended mainly to solve endogenous problems.An effective instrumental variable is supposed to satisfy both correlation and externality, which means it is related to endogenous explanatory variables instead of random disturbance terms.Herein, the number of Internet broadband access ports per capita is taken as the instrumental variable of digital economy development index.On the one hand, the development of digital economy relies on the construction of network infrastructure and the progress in modern information technology.The number of Internet broadband access ports is a measure that directly reflects the level of Internet infrastructure construction.In general, the more well-developed the Internet technology facilities built in a region, the higher the developmental level of digital economy.On the other hand, the fiscal effort is affected mainly by the government's behavior, and the change in the number of Internet broadband access ports is attributed more to the adjustment made by telecom operators for active adaptation to the changes in the production, work, consumption, and living habits of people for commercial purposes.This is barely relevant to the government, meeting the externality.The regression results are shown in Column (2) of Table 5.
According to the results, the estimation coefficient of instrumental variables is positive and highly significant at the level of 1% after the endogenous problem is taken into consideration.The conclusion remains valid that digital economy is effective in improving fiscal effort in China.The test results of the instrumental variable demonstrate their good properties.The Kleibergen-Paaprk LM statistic is of significance, which rejects the original hypothesis of "insufficient identification of instrumental variable" at the level of 1%.Also, the Kleibergen-Paaprk Wald F statistic reaches above the critical value at the level of 10%, indicating that the instrumental variable constructed in this study is reasonable.

System GMM
To further enhance the robustness of the findings, Eq. ( 1) is estimated using system GMM, where the endogenous variable is Dige.Also, the first-order lag term of Effort is introduced into the model.The estimation results are shown in Column (3) of Table 5.
According to the results of AR (1), AR (2), and Hansen Test, the system GMM model is reasonable.The estimated coefficient of Dige remains significantly

Quasi-natural experiment
The rapid development of digital economy is attributed to the popularization of Internet technology.For a more steady evaluation of whether digital economy can effectively improve the fiscal effort of local governments in China, the "Broadband China" pilot policy is taken as the exogenous policy to test it with the difference-in-differences (DID) model.
On August 17, 2013, China's State Council issued the implementation plan of the "Broadband China" strategy, and broadband has since become strategic public infrastructure in China.So far, there have been 120 cities designated in three batches in 2014, 2015, and 2016 by the Ministry of Industry and Information Technology, and the National Development and Reform Commission as the pilot of the "Broadband China", which provides an ideal quasi-natural experimental basis for this study.Among them, the experimental group includes the cities entering the pilot, and the control group includes other cities.Since the pilot cities do not enter the experimental group at the same time point, the multi-stage DID model is adopted.Furthermore, it is worth noting that the constructed samples are mainly prefecture-level city data, and that Beijing, Tianjin, Shanghai, and Chongqing are special as four municipalities directly under the central government.For this reason, it is considered to eliminate them and carry out regression again to ensure the robustness of the results.Columns (4) and (5) of Table 5 show the estimated results of including and excluding the municipalities directly under the central government, respectively.
It can be found out that regardless of the exclusion of those municipalities governed directly by the central government, the parallel trend tests show a parallel trend between the experimental group and the control group before introduction of the policy.Therefore, the DID model is verified as reasonable.The estimated coefficients of the "Broadband China" pilot are significantly positive at the 1% level, indicating that the implementation of the "Broadband China" pilot policy leads to a significant improvement in the fiscal effort of local governments.
In addition, the regression results of the model may also be affected by the timevarying unobservable factors of each city.To solve this problem, a placebo test is conducted by setting the number of sampling times to 4000. Figure 1 shows the kernel density of the estimated coefficient.
As shown in Fig. 1, the estimated coefficient is close to 0 and the distribution basically conforms to the normal distribution, indicating that other non-observable factors have no significant impact on the regression results.
The above results show the robustness of our empirical test results, which means digital economy can significantly improve the fiscal effort of local governments in China.Thus, the research hypothesis H1 is supported.

Nonlinear effect
As shown in Eq. ( 2), a panel threshold model is constructed to conduct empirical test on the nonlinear effects of digital economy on fiscal effort.Before the panel threshold model is estimated, the hypotheses of no threshold, single threshold, and double threshold are tested in turn by setting the number of sampling times to 3000, which is in line with the method of Hansen (1999).Table 6 lists the results.
As shown in Table 6, digital economy development index is taken as the threshold variable.The results of single threshold and double threshold tests are highly significant at the level of 1%, but the triple threshold fails to pass the significance test.Therefore, the number of thresholds is set to 2. Table 7 shows the regression results.
As shown in Table 7, there is a significant nonlinear relationship between digital economy and fiscal effort in China.When digital economy development index falls below the first threshold, the estimated coefficient is insignificant, indicating the inability of digital economy to have a significant impact on the fiscal effort at this time.When the digital economy development index ranges between the first threshold and the second threshold, the estimation coefficient is significantly negative.That is to say, digital economy hinders local governments from raising fiscal revenues and reduces the level of fiscal effort at this time.However, the estimation coefficient is significantly positive when the digital economy development index reaches above the second threshold, indicating the development of digital economy to a certain scale and the full release of potential.This is effective in improving the efficiency of raising fiscal revenues and the level of fiscal effort for local governments.
The above results show that digital economy exerts a significant nonlinear effect on fiscal effort in China.With the constant development of digital economy, its impact on the fiscal effort of local governments has shifted from neutral to negative and then to positive.Thus, the research hypothesis H2 is supported.

Regional heterogeneity
Due to the differences in resource endowment, development stage, and historical evolution, there is a significant heterogeneity shown in regional distribution by the developmental levels of digital economy and fiscal effort.Therefore, the impact of digital economy on fiscal effort may show regional heterogeneity in China as well, which requires further analysis.In terms of region, it is divided into the eastern region and the non-eastern region according to the general literature division method.The regression results of subsamples are shown in Table 8.As shown in Table 8, digital economy improves the fiscal effort of local governments significantly in the non-eastern region, but the effect is found to be insignificant in the eastern region.The possible reason for these results is as follows.The digital economy in the eastern region developed earlier, its dividends have been fully released, and the further development of digital economy makes little difference to fiscal effort.Differently, the development of digital economy is relatively sluggish in the non-eastern regions, but its development dividend is gradually released, which improves the level of fiscal effort significantly.

Spatial spillover effect
Digital economy reduces the space-time distance through efficient information transmission, increases the breadth and depth of economic activities among different regions, and promotes the flow of various production factors.On this basis, digital economy will have effect not only on the fiscal situation of this region to a significant extent but also on the fiscal situation of other regions.Thus, a panel space model is used to test this spatial spillover effect empirically.The model is expressed as Eq. ( 4).
where W represents the spatial weight matrix used to measure the spatial distance between regions.In this study, the geographical inverse distance matrix is treated as (4) the spatial weight matrix.In the matrix W, the main diagonal element is 0 and the non-diagonal element is the inverse of the geographical distance between regions.Prior to regression, the method proposed by Moran ( 1950) is applied to test the spatial effect.Table 9 lists the test results.
According to Table 9, the Moran I index of fiscal effort and digital economy development index from 2011 to 2019 exceeds 0, both of which are highly significant at the level of 1%.It is indicated that fiscal effort and digital economy show significant positive spatial autocorrelation.At the same time, the result of testing the spatial effect of the random disturbance term is highly significant at the level of 1%.These findings are consistent with the spatial aggregation characteristics of China's economic development.It is also shown that there is a necessity to introduce the spatial lag term of fiscal effort, digital economy development index, and random disturbance term into the model.
On this basis, the panel space model is regressed, the results of which are shown in Table 10.
As shown in Table 10, there are good characteristics exhibited by the regression results.From the perspective of interaction terms, there is significance shown at the level of 1% by the interaction terms between digital economy development index, fiscal effort, random disturbance term, and spatial weight matrix, which confirms the significance of the spatial spillover effect of these factors.The direct effect of digital economy development index is significantly positive, indicating that the digital economy in a region significantly promotes the increase in local fiscal effort.This is consistent with the results of benchmark regression.From the perspective of indirect effect, the indirect effect of digital economy development index is significantly positive under the spatial weight matrix, which indicates the positive spillover effect of digital economy.That is to say, the development of digital economy in a region is conducive to increasing fiscal efforts in its adjacent regions.
A potential contributor to this spatial spillover effect is that the technological advances induced by digital economy will spread to other regions through the flow of talents and production factors, which facilitates the transformation and upgrading of industries in each region, thus stimulating the potential of economic growth.At the same time, the development of digital economy in one region, as driven by the competitive pressure on local governments and incentives for official promotion, prompts other regional governments to follow suit, which accelerates inter-regional synergistic development, thus increasing fiscal efforts in all regions.
As confirmed by the empirical results of this study, digital economy exerts a significant spatial spillover effect on the fiscal efforts of local governments, which is consistent with the characteristics of spatial aggregation as shown by the development of China's digital economy.

Conclusions
Based on the typical fact that digital economy has a significant effect on social and economic development, Chinese urban data is used in this study to explore the impact of digital economy on fiscal effort from multiple perspectives.The present study achieves the expected objectives, and reaches the following conclusions.
Firstly, on the whole, digital economy has a significant positive effect on fiscal effort and plays an important role in improving the fiscal situation of local governments in China.The conclusion remains valid through the robustness tests of provincial fixed effect, instrumental variable, system GMM, and exogenous policy impact.In terms of regional heterogeneity, the digital economy in non-eastern regions can significantly improve the fiscal efforts of local governments, but such effect is insignificant in eastern regions.
Secondly, the impact of digital economy on fiscal effort shows significant nonlinearity in China.The development of digital economy has changed its impact on fiscal effort from neutral to negative and then to positive.This is consistent with the characteristics of digital economy network effect and economies of scale, which demonstrates the significance of Metcalfe's law to the impact of digital economy on fiscal effort.Thirdly, digital economy exerts a significant positive spatial spillover effect on the fiscal efforts of local governments in China, which means the development of digital economy in a region can increase the fiscal efforts in its adjacent regions, which is consistent with the spatial aggregation characteristics of digital economy development in China.In the meantime, it also suggests that digital economy is contributory to the formation of a new pattern of healthy fiscal development among various regions.
Based on these research conclusions, the targeted policy recommendations are made as follows: Firstly, local governments should accelerate the construction of digital infrastructure, and cultivate digital industries by focusing on artificial intelligence, big data, and other frontier areas.In particular, the local governments in underdeveloped regions should accelerate the development of digital economy, fully release the potential of digital economy for fiscal management, and reduce the tax loss caused by resource endowment and geographical location.In addition, it is necessary for local governments to seamlessly connect new technologies with fiscal business for the improved efficiency of government management.In this way, fiscal revenues can be raised in a more effective way and the healthy development of government finance can be promoted.
Secondly, it is inevitable that digital economy inhibits the fiscal efforts of local governments on a temporary basis during the development of digital economy.Therefore, when promoting the development of digital economy, local governments should accelerate the implementation of modern fiscal management methods, optimize the tax administration under digital economy to pass through the stage where digital economy will reduce fiscal efforts smoothly, and give full play to the positive effect of digital economy on the fiscal revenues.
Thirdly, more attention should be paid to top-level design to reduce duplication and inefficient construction and avoid overcapacity and the degradation of the highend industry, given the spatial spillover effect of digital economy on the fiscal efforts of local governments.At the same time, the coordination of new infrastructure construction and industrial cultivation should be enhanced by the government in all regions.Meanwhile, local governments at all levels are supposed to promote data integration, business integration, and technology integration in fiscal management.This is beneficial to the improvement of fiscal efforts through a full release of the spatial contribution from digital economy.
Finally, it is worth noting that the limitation of this study is a lack of more detailed research conducted on fiscal effort.Fiscal revenues are divided mainly into tax revenues and non-tax revenues.Due to the significant difference in sources, characteristics, and management between the two, there may be significant variations in the impact of digital economy on the government's ability to collect tax revenues and non-tax revenues.This is the next step to be taken in our research for exploring the impact of digital economy on fiscal effort in more depth.
EconomicChange and Restructuring (2023) 56:3501-3525    positive, indicating the robustness of the conclusion that digital economy can increase the fiscal effort of local governments in China.

Table 1
All econometric models used in the study

Table 2
Control variable system

Table 5
Robustness tests ***, **, * indicate significant at the level of 1%, 5%, and 10%, respectively.The value in () is the value of t, and the value in [] is the critical value at the level of 10% of Stock-Yogo weak identification test.P-values are shown in AR (1), AR (2), and Hansen test Variables

Table 6
Threshold effect test