Tax structure and corporate tax compliance: evidence from China

Figures & data

Figure 1. Correlation between tax structure and tax complexity.

Notes: Tax_struc_1 and Tax_struc_2 represent the proportion of direct taxes in tax revenues and the ratio of direct to indirect taxes, respectively. Tax complexity is calculated using the sum of the squares of the total tax revenue proportion of each tax revenue and then taking its reciprocal.

Source: Authors’ Estimation.

Table 1. Summary of statistics.

Variable	Observations	Mean	St. dev.	Minimum	Maximum
Pro_ratio	1369482	0.2895	0.5095	−0.4641	3.7256
Profit_ratio	1369482	0.1036	0.1423	0.0002	0.9552
Capital_density	1369483	0.3486	0.2029	0.0164	0.8840
Lnemployees	1371469	4.8573	1.0348	2.7081	8.0173
Loan_capacity	1369482	0.0154	0.0202	−0.0072	0.1459
Lnage	1379798	3.2335	0.3166	2.7081	4.2341
Cit_ratio	1382769	0.1696	0.1641	0.0000	0.8413
Lnpgdp	1302093	0.6448	0.7347	−1.0984	2.0399
Urban	1331646	0.3893	0.1900	0.0000	0.8576
IS	1350603	0.7921	0.2382	0.3953	1.7011
Lnpopden	1275402	6.2769	0.4931	4.3296	6.9366
Fiscalratio	1357140	0.0818	0.0254	0.0374	0.1766
Tax_burden	1339004	0.0554	0.0169	0.0242	0.0958
Tax_struc_1	1360264	0.4512	0.1932	0.1374	1.2003
Tax_struc_2	1337863	0.1708	0.0721	0.0508	0.3857

Notes: Pro_ratio, Profit_ratio are the imputed profits and actual profits computed by using Cai and Liu(2009); Capital_density is the ratio of net fixed assets to total assets; loan_capacity is the ratio of debt to total assets; lnemployees, lnage, lnpgdp, lnpopden are logarithms of number of employees, year of sample minus year of establishment, real per capita GDP, people per square kilometre; IS is the city’s industry structure calculated using the ratio of the third industry to GDP, and the Urban is the ratio of city population to total population; Fiscalratio, Tax_burden are the ratio of fiscal expenditure and tax income to GDP, respectively.

Source: Authors’ calculation.

Table 2. Baseline results: fixed effect.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
tax_struc_1	0.013***	0.010***	0.008***
	(0.002)	(0.002)	(0.003)
tax_struc_2				0.053***	0.048***	0.024***
				(0.008)	(0.008)	(0.009)
capital_density		−0.013***	−0.010***		−0.012***	−0.011***
		(0.002)	(0.002)		(0.002)	(0.002)
lnemployees		0.010***	0.009***		0.010***	0.009***
		(0.001)	(0.001)		(0.001)	(0.001)
loan_capacity		0.037**	0.024		0.044***	0.028*
		(0.015)	(0.017)		(0.015)	(0.017)
lnage		0.016***	0.012***		0.015***	0.012***
		(0.002)	(0.002)		(0.002)	(0.002)
lnpgdp			0.002			0.003
			(0.005)			(0.005)
urban			−0.002			−0.001
			(0.005)			(0.005)
IS			0.004			0.006**
			(0.003)			(0.003)
lnpopden			0.035***			0.027**
			(0.012)			(0.011)
fiscalratio			−0.219***			−0.235***
			(0.051)			(0.052)
tax_burden			0.390***			0.425***
			(0.061)			(0.062)
Individual effect	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
Regional effect	No	Yes	Yes	No	Yes	Yes
Observations	1226415	1140006	886932	1203261	1119064	877286
R^2	0.548	0.552	0.553	0.548	0.552	0.553
Adjusted R^2	0.408	0.409	0.402	0.406	0.408	0.401

Note: Standard errors are clustered at the city-industry level for all regressions. The dependent variable is the ratio of taxes to profit. ***p < 0.01, **p < 0.05, *p < 0.1.

Source: Authors’ calculation.

Table 3. Robustness: national income accounting method.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
tax_struc_1	0.009***	0.008***	0.012***
	(0.002)	(0.002)	(0.003)
tax_struc_2				−0.006	−0.005	0.04***
				(0.008)	(0.008)	(0.009)
PRO_ratio	0.046***	0.046***	0.055***	0.047***	0.046***	0.049***
	(0.004)	(0.004)	(0.009)	(0.004)	(0.005)	(0.009)
PRO_ratio × tax_struc_1	0.035***	0.038***	0.044***
	(0.007)	(0.007)	(0.009)
PRO_ratio × tax_struc_2				0.039*	0.067***	0.105***
				(0.023)	(0.024)	(0.034)
Enterprise controls	No	Yes	Yes	No	Yes	Yes
Regional controls	No	No	Yes	No	No	Yes
Individual effect	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
Observations	1310590	1224768	963740	1288655	1204859	954707
R^2	0.144	0.150	0.169	0.145	0.151	0.170
Adjusted R^2	0.144	0.150	0.169	0.145	0.151	0.170

Note: Standard errors are clustered at the city-industry level for all regressions. The dependent variable is the ratio of taxes to profit. ***p < 0.01, **p < 0.05, *p < 0.1.

Source: Authors’ calculation.

Table 4. Regression with new variables and different time intervals.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
	Change independent variable		1998-2001		2003-2007
Tax_struc_1			0.010***		0.014**
			(0.003)		(0.006)
Tax_struc_2				0.044***		0.081***
				(0.016)		(0.025)
Tax_struc_3	0.058***
	(0.004)
Tax_struc_4		0.112***
		(0.008)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Individual effect	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
Regional effect	Yes	Yes	Yes	Yes	Yes	Yes
Observations	913061	913061	225135	225143	676558	672392
R^2	0.552	0.552	0.700	0.696	0.611	0.611
Adjusted R^2	0.403	0.403	0.533	0.530	0.446	0.446

Note: Standard errors are clustered at city-industry level for all regressions. The dependent variable is the ratio of taxes to profit. ***p < 0.01, **p < 0.05, *p < 0.1.

Source: Authors’ calculation.

Table 5. Replacement of the interpreted variable.

Variable	(1)	(2)	(3)	(4)
	Overall tax compliance		Indirect tax compliance
Tax_struc_1	0.004***		0.002***
	(0.001)		(0.001)
Tax_struc_2		0.014***		0.010***
		(0.002)		(0.003)
Controls	Yes	Yes	Yes	Yes
Individual effect	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes
Regional effect	Yes	Yes	Yes	Yes
Observations	882866	873209	896852	887117
R^2	0.694	0.694	0.571	0.569
Adjusted R^2	0.590	0.589	0.427	0.423

Note: Standard errors are clustered at the city-industry level for all regressions. The dependent variable is the ratio of taxes to profit. ***p < 0.01, **p< 0.05, *p< 0.1.

Source: Authors’ calculation.

Table 6. Considering the full sample with negative profits.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
tax_struc_1	0.013***	0.012***	0.010***
	(0.002)	(0.002)	(0.002)
tax_struc_2				0.045***	0.041***	0.028***
				(0.007)	(0.007)	(0.007)
Controls	Yes	Yes	Yes	Yes	Yes	Yes
Individual effect	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
Regional effect	No	Yes	Yes	No	Yes	Yes
Observations	1490252	1385877	1090676	1464796	1362938	1082419
R^2	0.556	0.565	0.567	0.555	0.565	0.567
Adjusted R^2	0.428	0.434	0.428	0.427	0.433	0.427

Note: Standard errors are clustered at city-industry level for all regressions. The dependent variable is the ratio of taxes to profit. ***p< 0.01, **p < 0.05, *p < 0.1.

Source: Authors’ calculation.

Table 7. Instrumental variable method.

Variable	(1)	(2)	(3)	(4)	(5)	(6)
Tax_struc_1	0.020***	0.016***	0.016***
	(0.004)	(0.004)	(0.004)
Tax_struc_1_iv	0.8550***	0.8611***	0.8014***
	(0.0115)	(0.0117)	(0.0133)
Tax_struc_2				0.116***	0.105***	0.060***
				(0.014)	(0.014)	(0.016)
Tax_struc_2_iv				0.9122***	0.9178***	0.8661***
				(0.0163)	(0.0168)	(0.0192)
Individual controls	No	Yes	Yes	No	Yes	Yes
Regional controls	No	No	Yes	No	No	Yes
Individual effect	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
Regional effect	No	Yes	Yes	No	Yes	Yes
Observations	1226415	1140006	886932	1203261	1119064	877286
Unidentification test	138.335	135.459	146.533	135.321	131.721	146.472
F values	7844.757	7828.239	4321.065	3488.511	3373.839	2441.923

Note: Standard errors are clustered at city-industry level for all regressions. The dependent variable is the ratio of taxes to profit. ***p < 0.01, **p < 0.05, *p < 0.1. All the explanatory variables are enterprise effective tax rates. The explained variables of the instrumental variable method are the corresponding tax structure variables. For space considerations, the instrumental variables and the core explanatory variables are placed in the same column.

Source: Authors’ calculation.

Table 8. Impact mechanism.

Variable	Tax efforts		Corruption		Tax complexity
	(1)	(2)	(3)	(4)	(5)	(6)
tax_struc_1	0.2609***		−0.3616***		−0.0812***
	(0.0559)		(0.0043)		(0.0039)
tax_struc_2		0.5716**		−0.6447***		−2.1198***
		(0.2371)		(0.0076)		(0.0124)
Regional effect	Yes	Yes	Yes	Yes	Yes	Yes
Time effect	Yes	Yes	Yes	Yes	Yes	Yes
R^2	0.2236	0.1949	0.6235	0.6238	0.1642	0.3271

Note: Standard errors are clustered at the prefecture-industry level for all regressions. ***p< 0.01, **p< 0.05, *p< 0.1. In columns 1 and 2 and columns 5 and 6, prefecture-level controls are considered in the baseline regression. In columns 3 and 4, provincial-level controls are considered that correspond to the provincial-level variables. Corruption data are open at the provincial level to represent the governance quality.

Source: Authors’ calculation.

Cai, H., & Liu, Q. (2009). Competition and corporate tax avoidance: Evidence from Chinese industrial firms. The Economic Journal, 119(537), 764–795. https://doi.org/10.1111/j.1468-0297.2009.02217.x

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