Measuring Risk Allocation of Tax Burden for Small and Micro Enterprises

Transcription

1 sustainability Article Measuring Risk Allocation of Tax Burden for Small and Micro Enterprises Bing Xu *, Lili Li, Yan Liang and Mohib Ur Rahman Research Institute of Quantitative Economics, Zhejiang Gongshang University, Hangzhou 3118, China; (L.L.); (Y.L.); (M.U.R.) * Correspondence: bingxu@zjgsu.edu.cn Received: 29 December 218; Accepted: 28 January 219; Published: 31 January 219 Abstract: Tax burden outlier inhibits the growth of small and micro enterprises. This paper introduces the risk allocation of tax burden to measure the tax burden outlier. Using a time-varying nonparametric benchmark and path model, this paper measures the tax risk allocation of 3552 small and micro enterprises in the credit insurance fund from January 216 to August 218. This paper explores the configuration of tax burden risk allocation and discusses the changes along time and with the addition of other variables. Finally, this paper gives an analysis of strategies to improve tax burden risk allocation. The results provide decision support for reducing the tax burden and promoting the growth of small and micro enterprises. Keywords: Tax burden; Risk Allocation; Small and micro enterprise; Measure 1. Introduction As the most extensive and active production and management group in social and economic life, small and micro enterprises represent and even determine the development status of the entire social economy and their social and economic functions are highly correlated and interactive with public finance functions [1]. Small and medium enterprises (SMEs) are considered as the main pillars of almost every economy in the world. In most of the countries, they produce around 7% of jobs and account for 35% of GDP in developing countries, while 55% in developed countries [2]. In such, SMEs play a key role in social stability and contribute considerably to jobs creation, poverty alleviation, and economic growth. Regardless of the stage of economic development, SMEs enable innovation and foster creativity in the market. They transform the ideas from concept to production in a short period of time. Even though SMEs account for a high fraction of GDP and occupy a large portion in the market, yet, their failure rate is very high. Mason [3] documented that within the first five years, more than 9% of SMEs are failed to continue their businesses. According to the report of the U.S department of Commerce [4], SMEs need to develop new and/or enhance technologies to improve their competitiveness in order to survive. However, small and micro enterprises are facing the problems of financing constraints and expensive financing, as well as the unreasonable ratio of output value to taxation. SMEs face problems and difficulties when they deal with the government, particularly with the tax administrations. For most of the SMEs, it is difficult to attain or manage their growing profitability, due to different factors including tax policies [5]. Especially in times of inflation, capital and inventory-intensive companies face higher real tax burdens [6]. Increasing corporate taxes will shift the tax burden more towards low-skilled, young and female employees [7]. In case the tax structure is not effectively designed to the conditions of the particular environment in which the SMEs operate, it may produce a significant tax burden on the tax-paying enterprises and consequently will impact the end users, due to changing ability of taxes. Auerbach [8] considered Sustainability 219, 11, 741; doi:1.339/su

2 Sustainability 219, 11, of 2 that the US Tax Deduction and Employment Act of 217 will increase US capital investment, thereby increasing wages, but increasing the magnitude is difficult to predict. Chen, et al. [9] found that tax cuts can reduce unemployment by 7%. Most of these difficulties with the taxes regulators might be considered as the outcomes of inadequate tax policies, as well as with the uncertainty about the changes in future policy. SMEs are characterized as intense labor force and are considered as the key element of economic growth in China. Their flexible operating mechanism allowed them to have relative competitive advantages [1]. As a result, enhancing and promoting the growth of SMEs should be a key concern to enhance the development of the economy. Thus, the government as a regulator and policy maker should moderately reduce corporate tax burdens in order to improve the competitiveness of SMEs. Therefore, it is particularly important to correctly guide the reform of small and micro enterprises, vigorously support small and micro enterprises, and help small and micro enterprises to improve the quality of development. Given that friendly tax policy is instrumental to the growth and survival of SMEs, as taxes increase running cost and slow down the growth. Most of the SMEs face the problem of high tax rates, multiple taxations, lack of proper information and complex regulatory policies. Therefore, an appropriate tax policy is crucial for the survival of SMEs. The aim of our paper is to give an analysis of strategies to improve taxation policy. This paper introduces the risk allocation of the tax burden to measure the tax burden outlier. Using time-varying nonparametric benchmark and path model, this paper measures the tax risk allocation of 3552 small and micro enterprises from Taizhou city (China) in the credit insurance fund, from the period of January 216 to August 218 to identify excess taxation in the form of sales revenue drivers. First, we established a benchmark model to identify the driving pattern of taxation/sales revenue above 6% and below 4% taxation: Whether it is a linear drive of the trend or a nonlinear drive of vibration. Then, we added time and other variables to measure the configuration of tax burden risk allocation. Finally, we analyzed the strategies to improve the allocation of tax burden risk. The results of our study provide a flexible decision-making basis for the government to adopt flexible taxation at different points in time and supplemented by credit policy to help small and micro enterprises grow. The contents of this paper are organized as: Section 2, documents the related studies, Section 3, defines Tax Burden Outlier and proposes the problem to be solved in this paper. Section 4, analyzes the driving mode formed by the tax Burden Outlier. Section 5, adds time and other variables to analyze strategies for improving tax burden risk allocation. Section 6, summarizes the full text and answers the questions raised in this paper based on the research results. 2. Literature Review The promotion and development of SMEs is a crucial topic of economic growth. In the given scenario, it is an important issue for researchers to grasp the direction of Tax Policy and to enhance the sustainable development of the economy. To promote SMEs, many developed countries have formulated tax policies, in order to help them for their growth and survival Pizzacalla [11]. Many scholars believe that the provisions of taxes may have a significant impact on the innovation of SMEs (see for example [12,13]. The study of Atkinson [14] documented that tax incentives significantly and effectively promote SMEs innovations. Burlamaqui and Cimoli [15] documented that tax incentives and other subsidies by the government can be treated as developmental rents. Many other scholars have been actively promoting corporate tax cuts, but they have different opinions on the ways and effects of tax cuts. For example, Grubert and Altshuler [16] considered that changing the corporate income tax to personal income tax can significantly reduce the corporate tax rate; Hines Jr [17] considered that although the corporate tax rate in the United States provides a large number of additional deductions, tax exemptions, and tax credits, however, it creates the heaviest tax burden; Salaudeen and Atoyebi [18] demonstrated that the effect of tax reform was heterogeneous, which led to an increase or decrease in tax burdens in different industries.

3 Sustainability 219, 11, of 2 Among all taxes, corporate income tax is considered as the biggest burden for enterprises. Although the nominal corporate income tax rate is decreasing, in some cases, the actual tax burden has increased [19]. Therefore, tax reduction should also be tailored to local conditions, and targeted implementation of flexible tax reduction policies can achieve better results. For example, Fang, et al. [2] argued that the reform of streamlined management and tax reduction should be vigorously promoted in the service industry, and corresponding policies should be implemented according to different regions, time and industries. Ye, et al. [21] studied the relationship between corporate tax heterogeneity and firm size, ownership, export and location characteristics, and promoted China s structural tax reduction reform. Cai, et al. [22] found that various factors, such as tax rate, risk aversion, interest rate, stock return, and volatility, jointly affect optimal portfolio allocation. Most of the above researches suggest that tax incentives have a significant role in promoting the innovations and development of SMEs and the corporate income tax is considered as the biggest burden for enterprises. They suggest that the government should reduce corporate tax burdens in order to improve the competitiveness of SMEs. However, as a requirement for tax reduction and exemption, it is not merely enough to take into account the size of industries, regions, and enterprises, but rather it is necessary to identify the time period of excess taxation and formulate the policies and strategies for SMEs development. Therefore, an appropriate tax policy is crucial for the survival of SMEs. The aim of our paper is to give an analysis of strategies to improve taxation policy. Our paper introduces the risk allocation of the tax burden to measure the tax burden outlier. Using time-varying nonparametric benchmark and path model, this paper measures the tax risk allocation of 3552 small and micro enterprises in the credit insurance fund from January 216 to August 218. First, we established a benchmark model to identify the driving pattern of taxation/sales revenue above 6% and below 4% taxation: Whether it is a linear drive of the trend or a nonlinear drive of vibration. Then, we added time and other variables to measure the configuration of tax burden risk allocation. Finally, we analyzed the strategies to improve the allocation of tax burden risk. 3. Data and Model 3.1. Data Description The data used in this paper is a sample of 3552 small and micro enterprises from Taizhou city (China) of a certain credit insurance fund, from January 216 to August 218. This data is obtained from the government big data platform, which has comprehensive information about the lending, taxation, and output of more than 6, SMEs in all industries. The Observed variables in the model include: Company name, time, non-performing loan term, loan contract amount, loan balance, number of non-performing loans, loan form, actual tax payment, taxable amount, sales income, credit line, used credit line, average credit period, credit period, number of new credit grants, work injury subsidy, total wages of employees, social pension and medical insurance. Further, this paper divides 3552 small and micro enterprises into three categories, adjusting tax and credit lines according to different categories, and achieving the goal of improving tax risk allocation. We used the method of culling with nonparametric variables Hall, et al. [23] to carry out our analysis. Based entirely on data-driven, work injury subsidy, the total wages of employees, social pension and medical insurance are summed up, and the total wages of the employees are obtained. We exclude the credit period and the number of new credit grants that are weakly related to sales revenue, and finally, sales revenue is selected as the explanatory variable. The taxable amount, the credit line used, the credit period, and the total wages of the employees are used as linear explanatory variables. Further, actual tax payment and credit line are used as part of non-linear explanatory variables. The variables and symbolic representations are shown in Table 1:

4 Sustainability 219, 11, of 2 Table 1. Variable and symbolic representation. Variable Symbolic Representation Variable Symbolic Representation Sustainability 219, 11, Sales x FOR revenue PEER REVIEW Y Credit period X 4 4 of 19 Taxable amount X 1 Actual tax payment Z 5 Used credit Used credit line line X X 2 Credit Credit line line ZZ 6 The The total total wages wages of of employees employees X X Question 3.2. Question Using 216 to 218, setting the month to the horizontal axis, observing sales revenue and actual Using 216 to 218, setting the month to the horizontal axis, observing sales revenue and actual tax payment of selected 3552 companies time arrival changeover situation. tax payment of selected 3552 companies' time arrival changeover situation. The sales revenue reflects the economic benefits of the enterprise, and the tax reflects the corporate The sales revenue reflects the economic benefits of the enterprise, and the tax reflects the social responsibility. Intuitively, sales revenue and tax should be roughly synchronized, but as can corporate social responsibility. Intuitively, sales revenue and tax should be roughly synchronized, be seen from Figure 1, the increase in sales revenue is slower than the increase in actual tax payment. but as can be seen from Figure 1, the increase in sales revenue is slower than the increase in actual From January 216 to August 218, although the company s sales revenue and original tax payments tax payment. From January 216 to August 218, although the company's sales revenue and original are usually on the rise Thus, it is meaningful to study the formation mechanism of the outlier in which tax payments are usually on the rise Thus, it is meaningful to study the formation mechanism of the sales revenues are not synchronized with taxes, and how to improve these outliers. outlier in which sales revenues are not synchronized with taxes, and how to improve these outliers. Sales revenue and Actual tax payment Sales revenue Actual tax payment Figure 1. Sales revenue and actual tax payment trend chart. Figure 1. Sales revenue and actual tax payment trend chart. Specifically, in the month of December 216, sales revenue reached to its peak, but the actual tax payment did not reach the corresponding height; in May 217, the actual tax payment was at a high point, but the sales revenue at this point were not as high as it were in the month of of December 216. Assume that the tax burden of the enterprise is measured by the Actual tax payment divided by Sales Revenue. This article defines the Tax Burden Outlier as above 6%, and/or below 4%, i.e., when the ratio of actual tax payment to sales revenue is is above 6%, and/or below 4%. The rationality of the definition is based on the Law of the People's People s Republic of China on Tax Collection and Administration [24], accordingto towhich whichthe the total total value value of VAT, of VAT, stamp stamp duty, duty, urban urban construction construction tax, and tax, and education education surcharge surcharge is roughly is roughly 4%, 4%, plus plus income income tax should tax should exceed exceed 4%. 4%. As can be seen from Figure 2, 2, the the tax tax burden burden is is increasing increasing year year by by year, year, and and the the tax tax burden burden at the at the four four points: points: February February 217, 217, May May 217, 217, February February 218, and 218, May and 218 May is218 greater is greater than thethan other the periods. other periods. In February In February 217 and February 217 and 218, February the sales 218, revenues the sales at revenues these two at points these were two smaller points than were the smaller other than the other periods, but the tax revenue was not lowered at the same time, resulting in a large tax burden. The two points in May 217 and May 218 were due to the high tax revenue and the significant increase in sales revenue, which caused the tax burden to be too large. Management and investment can have adverse effects, resulting in a significant reduction in the cash flow of the company's investment, which in turn inhibits the improvement of financial performance [25].

5 Sustainability 219, 11, x FOR PEER REVIEW 5 of 19 Sustainability The higher 219, 11, points 741 include February 216, February 217, May 217, February 218, March 5218, of May 218, and August 218; while The lower points include June 216, July 216, September 216, October 216, November 216, periods, but the tax revenue was not lowered at the same time, resulting in a large tax burden. The two December 216, January 217, March 217, June 217, July 217, August 217, September 217, points in May 217 and May 218 were due to the high tax revenue and the significant increase in sales October 217, and November 217. revenue, which caused the tax burden to be too large. Management and investment can have adverse The following points will explore the configuration of tax risk allocation, and discusses the effects, resulting in a significant reduction in the cash flow of the company s investment, which in turn changes along time and with the addition of other variables. Finally, this paper gives an analysis of inhibits the improvement of financial performance [25]. strategies to improve tax risk allocation. Figure Figure Actual Actual tax tax payment/sales payment/sales revenue. revenue Initial BasedModel on the above discussion, the following points mainly analyze risk allocation of the tax burden Using outliers: Hall, Li and Racine [23] data-driven variable selection approach, this paper uses four variables The higher namely, points taxable include amount February X, used 216, credit February line X 217,, total May wage 217, X February, and credit 218, period March X 218, as control May 218, variables and August of the 218; linear while. part, while the actual tax payment Z, the credit line Z and time as control The variables lower points of the include nonlinear June part, 216, and July the 216, sales September income Y 216, is taken October as the 216, explanatory November variable. 216, The December variables 216, and January symbolic 217, representations March 217, are June shown 217, in Table July 217, 1. August 217, September 217, October Establishing 217, and the November time-varying 217. semi-parameter initial benchmark model of sales revenue is as follows: The following points will explore the configuration of tax risk allocation, and discusses the changes along time and with the addition of other variables. Finally, this paper gives an analysis of strategies to improve tax Yrisk =a allocation. X +b X +c X +d X +G (t) +ε, (1) where G (t) represents the non-linear part of the model. We added the actual tax payment Z as a 3.3. Initial Model nonlinear part of the variable to get the actual tax payment path model: Using Hall, Li and Racine [23] data-driven variable selection approach, this paper uses four Y =a X +b X +c X +d X +G (t, z ) +ε. (2) variables namely, taxable amount X 1, used credit line X 2, total wage X 3, and credit period X 4 as control Further, variables we added of the linear the credit part, line while Z the as the actual variable tax payment of the nonlinear Z 5, the credit part to line get Zthe 6 and credit timeline as path control model: variables of the nonlinear part, and the sales income Y is taken as the explanatory variable. The variables and symbolic Y =a representations X +b X +c are shown X +d in Table X +G 1. (t, z ) +ε. (3) Establishing the time-varying semi-parameter initial benchmark model of sales revenue is as follows: 3.4. Error Test Y t = a t X 1t + b t X 2t + c t X 3t + d t X 4t + G (t) + ε t, (1) In order to establish the relationship between the above-mentioned variables, it is necessary to where G (t) represents the non-linear part of the model. We added the actual tax payment Z 5 as a test the fitting effect of the model. According to the sample data, through regression analysis we can nonlinear part of the variable to get the actual tax payment path model: get the relative error of the model, as shown in the Figures 3 5. Y t = a 1t X 1t + b 1t X 2t + c 1t X 3t + d 1t X 4t + G 1 (t, z 5 ) + ε 1t. (2) Further, we added the credit line Z 6 as the variable of the nonlinear part to get the credit line path model: Y t = a 2t X 1t + b 2t X 2t + c 2t X 3t + d 2t X 4t + G 2 (t, z 6 ) + ε 2t. (3)

6 Figure 5. The credit line path model relative error. Sustainability 219, 11, of Error Test In order to establish the relationship between the above-mentioned variables, it is necessary to test the fitting effect of the model. According to the sample data, through regression analysis we can get the relative error of the model, as shown in the Figures 3 5. Sustainability 219, 11, x FOR PEER REVIEW 6 of 19 Sustainability 219, 11, x FOR PEER REVIEW 6 of Relative Relative error error Figure 3. Basic model relative error Relative Relative error error Figure 4. The actual tax payment path model relative error. Figure Figure4. 4. The The actual tax payment path model relative error. Relative error The regression estimation results of the three models show that, except a few individual points, the relative errors of the sample points fluctuate Relative around errorzero as shown in Figures 3 5. The mean values of.5the relative errors of the three models are.84,.173, and.113, respectively. Removing the two.4 points January 218 and April 218 in the base model, while the two points February 217 and February in the RKSE path model, and the point February 217 in the RKSE path model, the relative.3 error means are calculated again which are.67,.91, and.51 respectively. The results indicate that the model of this paper has a good fitting effect

7 Sustainability 219, 11, of 2 Figure 4. The actual tax payment path model relative error Relative error Figure Figure The Thecredit line line path path model model relative relative error. error. 4. Measuring The regression Risk Allocation estimation of results the Tax of the Burden three models show that, except a few individual points, the relative errors of the sample points fluctuate around zero as shown in Figures 3 5. The mean This section uses the driving form of sales revenue to understand the formation mechanism of the tax burden outliers, along time and non-time path, where non-time includes linear and non-linear path Volatility or Trend From the initial model i.e. Equation (1), the equation of the t 1 phase can be obtained: Y t 1 = a t 1 X 1t 1 + b t 1 X 2t 1 + c t 1 X 3t 1 + d t 1 X 4t 1 + G (t 1) + ε t 1, (4) Equations (1) (4) can obtain: Y t = Y t 1 + σ (t) + ε t. (5) According to the data, the model can be used to calculate the trend linearity, volatility nonlinearity, and unobservable three parts: σ (t) = Y t Y t 1 ε t = I 1 + I 2 ε t, where I 1 is the trend linearity and I 2 is volatility nonlinearity, ε t is the unobservable part. I 1 = a t X 1t + b t X 2t + c t X 3t + d t X 4t (a t 1 X 1t 1 + b t 1 X 2t 1 + c t 1 X 3t 1 + d t 1 X 4t 1 ) I 2 = G (t) G (t 1) ε t = ε t ε t 1. It can be seen from Figure 6 that the unobservable volatility is almost oscillating up and down around, and the relative volatility is small, so it can be ignored. The volatility caused by the time variables shows a mutual alternating trend. The positive and negative values of the volatility represent the direction of the influence, and the magnitude of the volatility determines the intensity of the influence. If the tax burden fluctuates to the outliers, and if it is caused by a time variable, after some period of time, it may return to a reasonable level again. As shown in Figure 6 in point January 217, it is negatively affected by time variables, corresponding to the sales income curve in Figure 1, it can be found that the sales revenue at this point in January 217 shows a downward trend; in addition, as in

8 where I is the trend linearity and I is volatility nonlinearity, ε is the unobservable part. I =a X +b X +c X +d X (a X +b X +c X +d X ) I =G (t) G (t 1) ε =ε ε. Sustainability It can 219, be seen 11, 741 from Figure 6 that the unobservable volatility is almost oscillating up and down 8 of 2 around, and the relative volatility is small, so it can be ignored. The volatility caused by the time variables shows a mutual alternating trend. The positive and negative values of the volatility November 216 and March 217, it is positively affected by the time variable, and the tax burden of represent the direction of the influence, and the magnitude of the volatility determines the intensity this point in Figure 2 is also less than 4%. of the influence. Various parts of the Volatility linear part nonlinear part unobservable part Figure Figure Various Various parts parts of of the the volatility. volatility. If it the is the tax non-time-induced burden fluctuates tax to volatility the outliers, fluctuates and if toit the is outliers, caused by then a time further variable, analysisafter of taxsome cuts period or credit of lines time, isit needed may return to improve to a reasonable the abnormal level volatility again. As of shown the taxin burden. Figure 6 As in shown point January in Figure217, 6 in February 216, June 216, February 217 and June 217, the fluctuations caused by non-time taxable amount, used credit line, credit period, and the total wages have a negative impact. In Figure 2, the tax burden for February 216 and February 217 are both greater than 6%, and in Figure 1, it can be seen that the sales revenue at these four points is at a low level. Of course, there are some outliers, such as December 216, July 216, September 216, October 216, May 217, July 217, August 217, September 217, October 217, November 217, February 218, March 218, May 218, and August 218, which cannot be directly explained in Figure 6, and further exploration is needed Tax Burden along the Time Path Figure 7 depicts the proportion of the volatility of the various parts received by each point and specifically illustrates the magnitude of the influence of the volatility of each part received by each point. When judging the driving mode, it is necessary to judge by means of the ratio of I 2 /I 1. If this ratio is greater than one i.e. I 2 /I 1 > 1, it means that the influence of nonlinear volatility is greater at this time, it is an oscillation drive. On the other hand, if I 2 /I 1 < 1, it means that the linear volatility is more affected at this time, it is a trend-driven. It can be seen from Figure 8 that the absolute value of the ratio of 8 points is greater than 1, and the ratio of the remaining points is less than 1. Observing the outliers mentioned in the second part: February 216, June 216, July 216, September 216, October 216, November 216, December 216, Month, February 217, March 217, May 217, June 217, July 217, August 217, September 217, October 217, November 217, Month, March 218, May 218, August 218. At the lower points, it can be found that I 2 /I 1 of June 216, October 216, November 216, December 216, June 217, October 217, and November 217 are greater than 1, and the driving form is time-oscillation driven and can wait for the decision after regression; and I 2 /I 1 of July 216, September 216, January 217, March 217, July 217, August 217, September 217 are all less than 1,

9 non-time taxable amount, used credit line, credit period, and the total wages have a negative impact. In Figure 2, the tax burden for February 216 and February 217 are both greater than 6%, and in Figure 1, it can be seen that the sales revenue at these four points is at a low level. Of course, there are some outliers, such as December 216, July 216, September 216, October Sustainability 216, May 219, 217, 11, July , August 217, September 217, October 217, November 217, February 9 of 2 218, March 218, May 218, and August 218, which cannot be directly explained in Figure 6, and further exploration is needed. the driving forms are trend-driven, and the volatility in the control variables are caused by actual tax payments 4.2. Tax Burden amount along andthe credit time path line. AT the higher points, I 2 /I 1 of February 216, February 217, February 217, February 218, Figure 7 depicts the proportion of the volatility of the various parts received by each point and March 218, May 218, and August 218 are all less than 1, driving forms are trend-driven, and the specifically illustrates the magnitude of the influence of the volatility of each part received by each volatility is caused by actual tax payments and credit lines. point Volatility ratio of each part Sustainability 219, 11, x FOR PEER REVIEW 9 of 19 Linear Volatility ratio Nonlinear Volatility ratio Unobservable Volatility ratio AT the higher points, I /I of February 216, February 217, February 217, February 218, March 218, May 218, and August 218 are all less than 1, driving forms are trend-driven, and the Figure 7. Volatility ratio of each part. volatility is caused by actual tax payments Figure 7. Volatility and credit ratio lines. of each part When judging the driving mode, it is necessary to judge by means of the ratio of I /I. If this ratio is greater than one i.e. I /I >1, it means that the influence of nonlinear volatility is greater at Linear ratio of nonlinearity(time path) this time, it is an oscillation drive. On the other hand, if I /I <1, it means that the linear volatility is more affected 7 at this time, it is a trend-driven. It can be seen from Figure 8 that the absolute value of the ratio of 6 8 points is greater than 1, and the ratio of the remaining points is less than 1. Observing 5 the outliers mentioned in the second part: February 216, June 216, July 216, 4 September 216, October 216, November 216, December 216, Month, February 217, 3 March 217, May 217, June 217, July 217, August 217, September 217, October 217, November 2 217, Month, March 218, May 218, August At the lower points, it can be found that I /I of June 216, October 216, November 216, December 216, June 217, October 217, and November 217 are greater than 1, and the driving -1 form is time-oscillation -2 driven and can wait for the decision after regression; and I /I of July 216, September , January 217, March 217, July 217, August 217, September 217 are all less than 1, the driving -4 forms are trend-driven, and the volatility in the control variables are caused by actual tax payments -5 amount and credit line Figure 8. The Linear ratio of nonlinearity (time path). Figure 8. The Linear ratio of nonlinearity (time path) Tax burden Burden alongactual Actualtax Taxpayment Payment or or credit Credit path Path Figures9 9and andfigure 1 are1 the are volatility the volatility diagrams diagrams of I 2 /I of 1 I based /I based on the on actual the actual tax payment tax payment and and the the credit credit line. line. Observing the outliers mentioned in the second part: February 216, June 216, July 216, September 216, October 216, November 216, December 216, Month, February 217, Linear ratio of nonlinearity (actual tax payment path) March 217, May 217, June 217, July 217, August 217, September 217, October 217, November 217, February 7 218, March 218, May 218, August

10 Figure 8. The Linear ratio of nonlinearity (time path) Tax burden along actual tax payment or credit path Sustainability 219, 11, of 2 Figure 9 and Figure 1 are the volatility diagrams of I /I based on the actual tax payment and the credit line. Linear ratio of nonlinearity (actual tax payment path) Figure 9. Linear ratio of nonlinearity (actual tax payment path). Sustainability 219, 11, x FOR Figure PEER 9. REVIEW Linear ratio of nonlinearity (actual tax payment path). 1 of 19 Linear ratio of nonlinearity (credit path) Figure Figure1. Linear Linearratio ratioof of nonlinearity (credit path). Observing Analysis of the actual outliers taxmentioned payment path in the cansecond be found part: in Figure February 9. It is 216, clearjune that 216, the ratio July I 2 /I 216, 1 of September all the lower 216, points, October including, 216, September November 216, November December 216, March , 1 Month, June February 217, July 217, March and August 217, 217 May is217, greater June than 217, 1, and July the 217, driving August form217, is driven September by time 217, oscillation, October which 217, can November wait for 217, the decision February after 218, regression; March 218, I 2 /I 1 May of June 218, 216, August July 216, 218. October 216, December 216, January 217, September Analysis 217, of the October actual 217, tax payment and November path can 217 be are found lessin than Figure 1, the 9. driving It is clear forms that the are ratio trend-driven, I /I of all andthe thelower control points, variables including, volatility September is caused 216, bynovember actual tax 216, payment March and217, credit June lines. 217, The July I 2 /I 217, 1 of and higher August points, 217 including is greater February than 1, and 216the anddriving February form 218, is driven are allby less time than oscillation, 1. The driving which forms can wait are for trend-driven, the decision and after theregression; control variables I /I fluctuations of June 216, are July caused 216, by October the actual 216, tax December payment216, and the January credit 217, line. InSeptember February 217, May October 217, 217, Marchand 218, November May 218, 217 and are August less 218, than I1, 2 /I the 1 are driving all greater forms than are 1. trend-driven, The driving forms and the are time-oscillation control variables driven volatility and can is caused wait for by the actual decision tax payment after regression. and credit lines. The IAnalysis /I of higher of the credit points, path including can be found February in Figure 2161, and It is February clear that 218, the ratio are all I 2 /Iless 1 of than all the1. lower The driving points, including, forms are trend-driven, September 216, and December the control 216, variables January fluctuations 217, September are caused 217, by November the actual 217, tax payment and the credit line. In February 217, May 217, March 218, May 218, and August 218, I /I are all greater than 1. The driving forms are time-oscillation driven and can wait for the decision after regression. Analysis of the credit path can be found in Figure 1, It is clear that the ratio I /I of all the lower points, including, September 216, December 216, January 217, September 217, November

11 Sustainability 219, 11, of 2 is greater than 1, and the driving form is time-oscillation driven, and can wait for the decision after regression; and June 216, July 216, October 216, November 216, March 217, June 217, July 217, August 217 and October 217, I 2 /I 1 is less than 1. The driving form is trend-driven, and the control variables volatility is caused by the actual tax payment and the credit line. The I 2 /I 1 of higher points, including February 216, February 217, May 217, February 218, March 218, and August 218, are all less than 1 and the drive forms are trend-driven. The control variables volatility is caused by the actual tax payment and the credit line; while I 2 /I 1 in May 218 is greater than 1, the driving form is driven by time oscillation and can wait for the decision after regression. It is clear from the above analysis, based on the actual tax payment or the credit line path; there is a change in the driving form of the outliers. Specifically, the time path to the actual tax payment path, the lower points, and the driving form of nine (9) outliers has been changed. Among those points, June 216, October 216, December 216, October 217, and November 217 were changed from the time oscillation drive to the trend drive, while, the four points of September 216, March 217, July 217 and August 217, were changed from the trend drive to the time oscillation drive. At the higher points, there are changes in the driving form of five (5) outliers. In February 217, May 217, March 218, May 218, and August 218, the trend drive becomes time oscillation drive. The time path to the credit path, the lower points, the driving form of seven (7) outliers has been changed, including June 216, October 216, November 216, June 217, and October 217. It is changed from the time oscillation drive to the trend drive. In September 216 and September 217, the trend drive was changed to the time oscillation drive. Among the higher points, changed can be observed only at point May 218, which is from the trend drive to the time oscillation drive. 5. Improving Tax Risk Allocation This section analyzes improvement in tax burden risk allocation by reducing taxes or increasing credit. The results provide decision support for promoting the growth of small and micro enterprises Model Description Equation (6) represents the Benchmark model, which only takes the time t as a nonlinear variable and compares the results with the path model. The actual tax payment refers to the amount of income tax in the previous year that appears in the financial statement of the following year; The original credit line refers to the stock management index of the short-term credit business approved by the commercial bank for the customer, so that the customer s requirements for the quickness and convenience of financial services are met within the credit line. This article refers to the stock of credit business of commercial banks under the principle of underwriting by Credit Insurance Fund. For the convenience of description. Y t = a t X 1t + b t X 2t + c t X 3t + d t X 4t + G (t) + ε t. (6) By adding z5 and z6 as nonlinear variables to the benchmark model, the path model is obtained, that is, Equation (7) Y t 1 = a t 1 X 1t 1 + b t 1 X 2t 1 + c t X 3t 1 + d t 1 X 4t 1 + G (t 1) + ε t 1, (7) σ (t, p) be the sum of linear and nonlinear parts of the path model, σ (t) be the sum of linear and nonlinear of the Benchmark model. In order to explore the role of the path model, by combining Equation (6) and Equation (7), we can get Equation (8) Y t = Y t 1 + σ p (t, p) + ε pt. (8) Now analyze Equation (8), if the error is small, its impact will be negligible, and the larger σ (t, p) represents greater sales income, so we can approximate the size of sales revenue according to

12 Sustainability 219, 11, of 2 the size of σ (t, p). Where σ (t, p) σ (t) represents the difference between the Path model and the Benchmark model, σ (t, z5) σ (t) represents the impact of government taxation on the company s sales revenue; and σ (t, z6) σ (t) reflects the impact of credit guarantee fund on the company s sales revenue; σ (t, z5, z6) σ (t) represents the government s tax reduction while the Credit Guarantee Fund increases credit, the impact on corporate sales revenue. The ultimate goal of the three methods is to help in the faster development and growth of small and medium-sized enterprises, and to maximize social responsibility so that enterprises can obtain maximum economic benefits. Since σ (t, p) σ (t) is an objectively calculated value, so the positive and negative effects of policy can be judged according to its value, then to choose the corresponding policy, that is, the policy which is most conducive to improving economic efficiency. If σ (t, z5) σ (t) or σ (t, z6) σ (t) is greater than, it indicates that the adjustment of the government and the letter insurance fund does not adversely affect the economic benefits of the enterprise; if it is significantly less than, it indicates that there is a negative impact which cannot be ignored, and it is necessary to change the adjustment mode and intensity to further integrate and optimize social responsibility and economic benefits Results and Discussions Sustainability Outliers 219, Improvement 11, x FOR PEER REVIEW (Full Sample Analysis) 12 of 19 From Figure 11, it can be seen that the negative effects of the four sample points, 21612, 2173, From Figure 11, it can be seen that the negative effects of the four sample points, 21612, 2173, 2182, and 2185 are relatively large, which are.245,.26,.217,.384, respectively. 2182, and 2185 are relatively large, which are -.245, -.26, -.217, -.384, respectively. The The positive effects of the two sample points, 2171 and 2184, are.356 and.314 respectively, positive effects of the two sample points, 2171 and 2184, are.356 and.314 respectively, which which are relatively large, and the rest of the values fluctuate around, indicating that the government s are relatively large, and the rest of the values fluctuate around, indicating that the government's tax policy adjustment effect is ideal. tax policy adjustment effect is ideal..4 б(t,z5)-б(t) Figure Figure The The actual actual tax tax payment payment path path model model minus minus the the benchmark benchmark model. model. According to Figure 12, the negative effects of the four sample points, 21612, 2173, 2182, and 2185 are are relatively large, large, namely namely.22, -.22,.24, -.24,.224, -.224, -.32,.32, so so credit should be moderately increased to promote improvement in economic benefits of the enterprise. The three sample points 2171, 2172, and and have have relatively relatively positive positive effect, which effect, are which.22, are.239,.22, and , respectively, and.336 and respectively, it indicates and that it the indicates credit line that at the this credit timeline is relatively this time reasonable. is relatively The rest reasonable. of the values The rest fluctuated of the around values fluctuated, indicating around that the, indicating credit limit that adjustment the credit oflimit the Credit adjustment Guarantee of the Fund Credit isguarantee ideal. Fund is ideal..3 б(t,z6)-б(t).2.1

13 According to Figure 12, the negative effects of the four sample points, 21612, 2173, 2182, and 2185 are relatively large, namely -.22, -.24, -.224, -.32, so credit should be moderately increased to promote improvement in economic benefits of the enterprise. The three sample points 2171, 2172, and 2184 have relatively positive effect, which are.22,.239, and.336 respectively, and it indicates that the credit line at this time is relatively reasonable. The rest of the Sustainability 219, 11, of 2 values fluctuated around, indicating that the credit limit adjustment of the Credit Guarantee Fund is ideal..3 б(t,z6)-б(t) Figure 12. The credit line path model minus the benchmark model. Figure 12. The credit line path model minus the benchmark model. As can be seen from Figures 11 and 12, the negative impacts of the above four outliers on tax As can be seen from Figures 11 and 12, the negative impacts of the above four outliers on tax and credit are more obvious. Therefore, it is necessary to reduce taxes and increase credit to promote andpositive credit are growth more in obvious. output value. Therefore, For the itabove is necessary four outliers, to reduce reduce taxes the and tax increase by 2, credit Yuan to and promote positive increase growth the credit in output investment value. of For two themillion above Yuan, four outliers, that is, integrate reduce the the tax actual by 2, tax payment Yuan and and increase the credit line investment to the new of path two model, million and Yuan, compare that the is, results integrate with the actual benchmark tax payment model whether and credit has line to thea new positive patheffect model, or vice and versa. compare the results with the benchmark model whether it has a positive Sustainability 219, 11, x FOR PEER REVIEW 13 of 19 effect or vice versa. It can It can be be seen seen from Figure 13 that the thenegative effects effects of the of the two two outliers outliers at point at point and and are are significantly improved, and the negative effect effect at point at point is -.6, is.6, which which is nearly is nearly 2% 2% lower than the negative effect produced by bythe the single single policy; policy; The The negative negative effect effect at point at2185 point 2185 became became.272, -.272, which was 11.2 percentage points points lower lower than than the negative the negative effect effect before before the tax the cut, tax cut, and and the the negative negative effect effect on on the the credit credit of of Credit Credit Guarantee Fund Fund was was reduced by by three percentage points. points б(t,z5,z6)-б(t) Figure 13. The adjusted path model minus the Benchmark model. Figure 13. The adjusted path model minus the Benchmark model. It is obvious from Table 2, different sample points apply to different models, and different path It is obvious from Table 2, different sample points apply to different models, and different path models should be adopted to reduce the implications of negative effects. Increasing credit at points models 2173 should and be adopted will increase to reduce the negative the implications effect, and at ofthat negative time the effects. tax is more Increasing reasonable; credit and at points 2173 the policy and of reducing will increase taxes and the increasing negative credit effect, at the andtwo at points time the tax and is 2185 morewill reasonable; minimize and the policy the negative of reducing effect, taxes and and its volatility increasing is small. credit at the two points and 2185 will minimize the negative effect, and its volatility is small. Table 2. The value of different path models minus benchmark models. Path benchmark σ (t, z5) σ (t) σ (t, z6) σ (t) σ (t, z5, z6) σ (t)

14 Sustainability 219, 11, of 2 Table 2. The value of different path models minus benchmark models. Path Benchmark σ (t, z5) σ (t) σ (t, z6) σ (t) σ (t, z5, z6) σ (t) According to the income range of employee wages/sales income, the enterprises are divided into three categories on average, and the three types of enterprises are discussed separately. After adopting the policy of tax reduction and credit increase, the extraordinary tax burden at points 2182 and 2185 were improved, as shown in Table 3. Table 3. Tax burden improvement comparison. t Before Improvement Improved % 5.32% % 9.52% It can be seen from Table 3 that after reducing the tax of 2, Yuan and increasing the credit of two million Yuan, the tax burden will fall to 5.32% at point 2182, reaching the reasonable range of 4% 6% as defined in Section 3; it has also dropped by 2.66% at point Therefore, our supernormal improvement results are effective Classified Enterprise Analysis Sustainability 219, 11, x FOR PEER REVIEW 14 of 19 The FirstThe Category First Category of Enterprises of Enterprises As As can can be be seen from Figure 14, in the first category of ofenterprises, the the path path models of z5 ofand z5 and z6 z6 are added, are added, and their and values their values fluctuate fluctuate around around,, indicating that that for for the the first first type of of enterprises, the actual taxactual payment tax payment and credit and line credit haveline no have adverse no adverse effects on effects the economic the economic benefits. benefits. It is more It is reasonable. more reasonable б(t,z5)-б(t) б(t,z6)-б(t) Figure 14. The first type of path model minus the benchmark model. Figure 14. The first type of path model minus the benchmark model. The first category of enterprises will also adjust the tax and credit in the same proportion. After adjustment, it can be seen in Figure 15, that more values have a relatively large negative effect. It can be seen that the first type of enterprise is not suitable for tax reduction and increasing credit simultaneously, but is more suitable for a single type policy. б(t,z5,z6)-б(t)

15 -.3 б(t,z5)-б(t) б(t,z6)-б(t) Sustainability 219, 11, of 2 Figure 14. The first type of path model minus the benchmark model. The first category of enterprises will also adjust the tax and credit in the same proportion. After The adjustment, first category it of canenterprises be seen inwill Figure also adjust 15, that the more tax and values credit have in the a relatively same proportion. large negative After adjustment, effect. It can it can be seen be seen that in the Figure first 15, type that of more enterprise values ishave not suitable a relatively for tax large reduction negative and effect. increasing It can be credit seen simultaneously, that the first type but isof more enterprise suitableis for not a single suitable type for policy. tax reduction and increasing credit simultaneously, but is more suitable for a single type policy б(t,z5,z6)-б(t) Figure 15. The first type of adjusted path model minus the benchmark model. Figure 15. The first type of adjusted path model minus the benchmark model. After adopting the policy of tax reduction and credit increase, the extraordinary tax burden at points 2182 and 2185 were improved, as shown in Table 4. Table 4. Tax burden improvement comparison. t Before Improvement Improved % 4.69% % 6.72% It can be seen from Table 4, that after reducing the tax by 2, Yuan and increasing the credit by two million Yuan, the tax burden will fall to 4.69% at point 2182, reaching the reasonable range of 4% 6% as defined in Section 3; it has also dropped by 2.54% at point Therefore, our supernormal improvement results are effective. The Second Category of Enterprises For the second type of enterprises, it is clear from Figure 16, that most of the time, the positive effect of credit is more significant, but at point 2176, the negative effect of the actual tax payment is smaller, which is.178, and the negative effect of the credit line is quite large, which is.28, so it is better to reduce the actual tax payment at this point than to increase the credit. Running the program again with the same revision rate also found that the results have not a single tax exemption policy effect as shown in Figure 17. Since the tax burden at point 2176 is 4.34%, within the reasonable tax gap, tax adjustments are no longer carried out.

16 The Second Category of Enterprises For the second type of enterprises, it is clear from Figure 16, that most of the time, the positive effect of credit is more significant, but at point 2176, the negative effect of the actual tax payment is smaller, which is.178, and the negative effect of the credit line is quite large, which is.28, so it is better to reduce the actual tax payment at this point than to increase the credit. Sustainability 219, 11, of б(t,z5)-б(t) б(t,z6)-б(t) Figure Figure The The second second type type of path of path model model minus minus the the benchmark benchmark model. Sustainability 219, 11, x FOR PEER REVIEW model. 16 of 19 Running the program again with the same revision rate also found that the results have not a single tax exemption policy effect as shown б(t,z5,z6)-б(t) in Figure 17. Since the tax burden at point 2176 is 4.34%, within the reasonable tax gap, tax adjustments are no longer carried.4 out Figure 17. The second type of adjusted path model minus the benchmark model. Figure 17. The second type of adjusted path model minus the benchmark model The Third The Third Category Category of Enterprises of Enterprises As As can canbe be seen from Figure 18, in in the the third third category category of enterprises, of enterprises, the path the model path model of z6 isof relatively z6 is relatively stable instable the early in the stage, early but stage, thebut negative the negative effect suddenly effect suddenly becomes becomes larger larger at point at 2182, point 2182, and the and negative the negative effect effect of the of actual the actual tax payment tax payment is less is than less the than credit the line. credit Since line. asince relatively a relatively large negative large negative effect can effect only can beonly observed be observed at pointat 2182, point 2182, so the tax so and the credit tax and arecredit modified are modified at that point at that only. point It can only. be seen It can from be seen Figure from 19, Figure that a19, single that a tax single reduction tax reduction policy is policy moreis conducive more conducive to obtaining to obtaining a larger a larger output output value. value

17 This paper explores the configuration of tax burden risk allocation, and discuss the changes along time and with the addition of other variables. According to the results, the study found that the calculations of σ (t) corresponding to the extraordinary time points often have more extreme values, either caused by nonlinear time variables or by linear control variables. If the ratio of I /I >1, it indicates that this is an oscillation drive; if the ratio of I /I <1, it indicates that this is a trend drive. As can be seen from Figure 18, in the third category of enterprises, the path model of z6 is relatively stable in the early stage, but the negative effect suddenly becomes larger at point 2182, and the negative effect of the actual tax payment is less than the credit line. Since a relatively large negative effect can only be observed at point 2182, so the tax and credit are modified at that point only. It can be seen from Figure 19, that a single tax reduction policy is more conducive to obtaining a larger output value. Sustainability 219, 11, of б(t,z5)-б(t) б(t,z6)-б(t) Figure 18. The third type of path model minus the benchmark model. Sustainability 219, 11, x FOR Figure PEER 18. REVIEW The third type of path model minus the benchmark model. 17 of 19 Figure 19. The third type of adjusted path model minus the benchmark model. Figure 19. The third type of adjusted path model minus the benchmark model. After adopting the policy of tax reduction and credit increase, the extraordinary tax burden at After adopting the policy of tax reduction and credit increase, the extraordinary tax burden at point 2182 was improved, as shown in Table 5 point 2182 was improved, as shown in Table 5 It can be seen from Table 5 that after reducing the tax by 2, Yuan and increasing the credit by two million Yuan, the tax burden Table 5. falls Taxto burden 5.98% improvement at point 2182, comparison. reaching the reasonable range of 4 6% as defined in Section 3. Therefore, our supernormal improvement results are effective. t Before Improvement Improved Table Tax burden 12.7% improvement comparison. 5.98% Before t improved It can be seen from Table 5 that after improvement reducing the tax by 2, Yuan and increasing the credit by two million Yuan, the tax burden2182 falls to 5.98% 12.7% at point 2182, 5.98% reaching the reasonable range of 4 6% as defined in Section 3. Therefore, our supernormal improvement results are effective Discussions б(t,z5,z6)-б(t)

18 Sustainability 219, 11, of Discussions This paper explores the configuration of tax burden risk allocation, and discuss the changes along time and with the addition of other variables. According to the results, the study found that the calculations of σ (t) corresponding to the extraordinary time points often have more extreme values, either caused by nonlinear time variables or by linear control variables. If the ratio of I 2 /I 1 > 1, it indicates that this is an oscillation drive; if the ratio of I 2 /I 1 < 1, it indicates that this is a trend drive. In the tax exemption and credit increase sections, by adding nonlinear variables, the path model established by the actual tax payment and the credit line, compare with the benchmark model to reflect the government s tax and credit lines, indicate that the impact of the economic benefits of small and micro enterprises gives a scenario analysis to improve the abnormal point of taxation. Through research, it is found that the government s tax policy adjustment and the credit line adjustment of the credit insurance fund have a relatively satisfactory effect. Further, this paper divides 3552 small and micro enterprises into three categories, adjusting tax and credit lines according to different categories, and achieving the goal of improving tax risk allocation. Trying to find a reduction of 2, Yuan in actual tax payment while increasing the credit investment of two million Yuan, that is, integrating the actual tax payment and credit line to the new path model, the comparison benchmark model has produced a positive improvement effect. The conclusions of this paper provide empirical evidence for policy decision support. 6. Conclusions SMEs occupy a very important position in the national economy. At present, SMEs registered in the industrial and commercial sector account for 99% of the total number of registered enterprises. As the most extensive and active production and management group in social and economic life, SMEs have become a new growth point for the economy. In the rapid economic growth since the 199s, 76.7% of the newly added industrial output value comes from SMEs [26]. At the same time, SMEs are still the basic force to ease employment pressure and maintain social stability. Therefore, it is particularly important to correctly guide the reform of small and micro enterprises, vigorously support small and micro enterprises, and help small and micro enterprises to improve the quality of development. Given that friendly tax policy is instrumental to the growth and survival of SMEs, as taxes increase running cost and slow down the growth. Most of the SMEs face the problem of high tax rates, multiple taxations, lack of proper information and complex regulatory policies. Therefore, an appropriate tax policy is crucial for the survival of SMEs. The aim of our paper is to give an analysis of strategies to improve taxation policy. This paper introduces the risk allocation of the tax burden to measure the tax burden outlier. Using time-varying nonparametric benchmark and path model, this paper measures the tax risk allocation of 3552 small and micro enterprises in the credit insurance fund from January 216 to August 218. First, we established a benchmark model to identify the driving pattern of taxation/sales revenue above 6% and below 4% taxation: Whether it is a linear drive of the trend or a nonlinear drive of vibration. Then, we added time and other variables to measure the configuration of tax burden risk allocation. Finally, we analyzed the strategies to improve the allocation of tax burden risk. For taxation abnormalities above 6% and below 4%, the study found that these outliers have a trend of linear drive and nonlinear drive of vibration, and there is a certain interaction between the two. In the time path, and if it is caused by a time variable, after some period of time, it may return to a reasonable level again. Under the path of credit and taxation, the excess tax drive mode will change and it is difficult to wait for the return to normal. Further, this paper gives an analysis of strategies to improve tax burden risk allocation. The results provide decision support for reducing the tax burden and promoting the growth of small and micro enterprises. The results of our study provide a flexible decision-making basis for the government to adopt flexible taxation at different points in time and supplemented by credit policy to help small and micro

19 Sustainability 219, 11, of 2 enterprises growth. However, there are some problems to be further studied. This paper only chooses the path of time and tax. Different paths will have different results. How to integrate as many paths as possible to get a robust result? Furthermore, how to use the conservatism results to warn the excess tax burden in advance? Further study of these questions will be more meaningful. Author Contributions: B.X. put forward this idea and conceived the framework; L.L. and Y.L. processed and analyzed the data; the four together wrote this paper. In addition, M.U.R. modified the English wording. Acknowledgments: We would like to express great appreciation to First Class Discipline of Zhejiang-A (Zhejiang Gongshang University-Statistics) for its support of our research. Conflicts of Interest: The author declares that there is no conflict of interest. References 1. Jiang, J.Q. Problems and Countermeasures in the Operation of Government-Supported Funds. Priv. Sci. Technol. Phase 214, 1, World Trade Report 216 Levelling the Trading Field for SMEs Available online: org/english/res_e/booksp_e/world_trade_report16_e.pdf (accessed on 12 December 218). 3. Mason, M.K. What Causes Small Businesses to Fail; MKM Research Publishers: Agnantero Karditsa, Greece, 29; Available online: (accessed on 1 December 218). 4. Commerce, U.D.O. The competitiveness and innovative capacity of the United States. Us Dep. Commer. 212, 1, Mnenwa, R.; Maliti, E. The Role of Small Businesses in Poverty Alleviation: The Case of Dar es Salaam, Tanzania. 28. Available online: (accessed on 11 December 218). 6. Dhaliwal, D.S.; Gaertner, F.B.; Seung, H.; Trezevant, R. Historical cost, inflation, and the US corporate tax burden. J. Account. Public Policy 215, 34, [CrossRef] 7. Fuest, C.; Peichl, A.; Siegloch, S. Do higher corporate taxes reduce wages? Micro evidence from Germany. Am. Econ. Rev. 218, 18, [CrossRef] 8. Auerbach, A.J. Measuring the Effects of Corporate Tax Cuts. J. Econ. Perspect. 218, 32, [CrossRef] 9. Chen, D.; Qi, S.; Schlagenhauf, D. Corporate income tax, legal form of organization, and employment. Am. Econ. J. Macroecon. 218, 1, [CrossRef] 1. Luo, S.; Zhang, Y.; Zhou, G. Financial Structure and Financing Constraints: Evidence on Small-and Medium-Sized Enterprises in China. Sustainability 218, 1, [CrossRef] 11. Pizzacalla, M. Global SME tax policy conundrum. Aust. Tax Forum 28, 23, Greenhalgh, C.; Rogers, M. Innovation, Intellectual Property, and Economic Growth; Princeton University Press: Princeton, NJ, USA, Xu, B.; Xiao, Y.; Rahman, M.U. Enterprise level cluster innovation with policy design. Entrep. Reg. Dev. 218, 31, [CrossRef] 14. Atkinson, R.D. Expanding the R&E tax credit to drive innovation, competitiveness and prosperity. J. Technol. Transf. 27, 32, Burlamaqui, L.; Cimoli, M. Industrial policy and IPR: A knowledge governance approach. Intellect. Prop. Rights: Leg. Econ. Chall. Dev. 214, 214, Grubert, H.; Altshuler, R. Shifting the burden of taxation from the corporate to the personal level and getting the corporate tax rate down to 15 percent. In Annual Conference on Taxation and Minutes of the Annual Meeting of the National Tax Association; National Tax Association: Columbus, OH, USA, 215; pp Hines, J.R., Jr. Business Tax Burdens and Tax Reform. Brook. Pap. Econ. Act. 217, 217, [CrossRef] 18. Salaudeen, Y.M.; Atoyebi, T.A. Tax Burden Implication of Tax Reform. Open J. Bus. Manag. 218, 6, 761. [CrossRef] 19. Czékus, Á. The Impact of Reducing the Corporate Income Tax Rate on the State Aid Practices of EU Member States (2 to 215). Public Financ. Q. 218, 63, Fang, H.; Yu, L.; Hong, Y.; Zhang, J. Tax Burden, Regulations and the Development of the Service Sector. Emerg. Mark. Financ. Trade 219, 55, [CrossRef]

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