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

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 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.

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 \({\mathrm{Effort}}_{i,t}\) represents the fiscal effort of the i city in year t, \({\mathrm{Dige}}_{i,t}\) 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 \({\varepsilon }_{i,t}\) 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 \({\mathrm{Dige}}_{it}\) in this study; s indicates the threshold value; \(I(\cdot )\) 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.

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 \(\rho\) exceed 85%, indicating that the variance of the composite disturbance term \({u}_{i}+{\varepsilon }_{it}\) 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.

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.

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.

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 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.