Pattern of Revenue Generation and Revenue Efforts of Government of Assam
3.6 Panel Regression Analysis of Revenue Effort of the State Government
It is evident from figure 3.15, 3.16 and 3.17 that cost recovery of selected services of the state is much less than all states average. Urgent steps are required on the part of the administrative machinery for realisation of larger revenue from these services through appropriate user charges.
The above discussion gives an idea about the efforts of the state government to mobilise more revenues. As discussed in the previous sections, revenue effort of the government depends upon revenue potential of the government which in turn is determined by growth of GSDP of the state. Under these circumstances, it is necessary to study the impact of the GSDP and other relevant factors on the revenue effort of the government which is carried out in the next section.
revenue of the government is mainly contributed by royalty on petroleum which in turn depends on the international prices on crude oil. The royalty on petroleum, on an average, has contributed 75 percent of the total non-tax revenue of the state government. The contribution of the other sources of non-tax revenue is found to be insignificant during the period of study. In other words, major portion of the non-tax revenue is found to be independent of revenue effort of the government. Considering the above fact, per-capita own tax revenue is taken as a dependent variable of the model.
Available literature on revenue effort has identified various relevant variables which have an impact on revenue generation of the state government such as GSDP, rate of urbanization, literacy rate and revenue expenditure of the previous year etc. As year wise data on urbanization and literacy rate are not available for all the states, these two variables are not considered for the analysis. The explanatory variables which have been considered initially for the analysis are given below:
i. Per-Capita GSDP (X1): GSDP is considered to be main determinant of the tax revenue of a government. An increase in GSDP is expected to increase the tax revenue of the state government if proper effort is put up by the government. In this regression model, GSDP is considered in terms of per-capita basis as states with higher per-capita income have more revenue potential with same level of GSDP. Considering the above fact, the Finance Commissions of India also use per-capita GSDP as a proxy for revenue potential while measuring the tax effort of the state governments.
ii. Per-capita Revenue Expenditure of the Previous Year (X2): Another variable, per- capita revenue expenditure of the previous year is also considered to observe the sense of urgency of the state government to control revenue expenditure for fiscal stability. It also represents the state’s fiscal situation and the need for augmenting resources. Available literature on government finances states that excessive increase in revenue expenditure is one of the reasons for fiscal imbalances. High revenue expenditure in the previous year should generally compel the states to augment revenue in the current year. This indicates the demand side of increasing the tax effort (Panda, 2009).
The time period taken for the analysis was from 1990-91 to 2009-10. To have a comparative analysis with other states, 15 major states have been considered along with Assam. Data on Gross Domestic Product of the states at current and constant prices in the series 1980-81, 1993-94, 1999-00 and 2004-05 are obtained from the Central Statistical Organization. All the data are converted into 2004-05 prices by splicing to make it comparable with the above mentioned series. Mid-year population figures have been taken from CSO and price deflators have been computed from the ratio of current to constant price GSDP figures. This is used to convert the fiscal data into constant price term (with 2004-05 prices as base) and per-capita term whenever necessary. On initial estimation of the model, the value of the coefficient of the variable ‘revenue expenditure of the previous year’ has come out positive as expected but insignificant. Therefore, to facilitate better treatment of the model, the analysis has been reworked by dropping the above variable. Thus, a panel regression analysis is constructed as follows:
Yit = α0 + α1 Xit + uit...(1) Here,
Y is the per-capita own tax revenue of the states.
X is the per capita GSDP of the states.
i stands for the ith cross sectional unit and t for the tth time period.
u is random disturbance or error term.
α0 and α1 are the parameters to be estimated in the model.
To decide between fixed or random effects, Hausman test is used where the null hypothesis is that the preferred model is random effect versus the alternative the fixed effect (Greene, 2008). By applying this test, it has been ascertained that a random effect model will be more appropriate for the present data set. Accordingly, the panel regression analysis has been carried out by applying random effect model. Presence of autocorrelation and heteroscedascity has been checked by using Pearson cross sectional dependence test and Modified Wald test for group wise heteroscedasticity respectively. The panel regression is estimated by using the econometric software STATA 11. The results of the panel regression are provided in table 3.19.
Table 3.19
Results of the Random Effect Model on Revenue Effort of the Government Variables Estimated
Coefficients t-statistic Per- capita GSDP .0849279
(.001582)*** 53.68
Constant -244.2238
(74.05468)*** -3.30 Hausman test
(p-value)
0.4873***
(.000531) Wald chi2 2881.80***
R2 within 0.8946 R2 between 0.9561 R2 overall 0.9395
Figures in parentheses represent standard error of the estimated coefficients
***, ** and * indicate significant at 0.01, 0.05 and 0.10 level respectively
The value of the coefficient of determination (R2) of the model is found to be very high suggesting a good fit of the model. The coefficient estimates obtained from the above panel data model reflects the average relationship across states. It implies that, on an average, 8.5 percent of the per capita GSDP has been mobilised by the states governments as tax revenue during the period under study. However, to evaluate the performance of Assam in relation to the other Indian states, the errors have been estimated as the difference between the observed and estimated values of ‘per capita own tax revenue’ of the state. The estimated errors for the state for the period under study are presented in table 3.20.
Table 3.20
Calculated Values of the Estimated Errors for the State
Year
Actual Per Capita Own Tax Revenue
(1)
Fitted Per Capita Own Tax Revenue
(2)
Estimated Error (1-2)
1990-91 503 958 -456
1991-92 559 983 -424
1992-93 515 969 -454
1993-94 546 989 -443
1994-95 490 1001 -510
1995-96 497 1006 -508
1996-97 508 1013 -505
1997-98 534 1013 -479
1998-99 519 989 -470
1999-00 570 1011 -441
2000-01 631 1028 -397
2001-02 678 1036 -359
2002-03 778 1092 -314
2003-04 799 1153 -354
2004-05 965 1181 -216
2005-06 1051 1204 -153
2006-07 1074 1249 -175
2007-08 975 1292 -317
2008-09 1069 1363 -294
2009-10 986 1471 -485
It has been found from table 3.20 that the error terms for the state are negative for all the years taken for the analysis. It implies that Assam has been underperforming as compared to the other (average) states during the period taken for the analysis. Thus, it suggests that state has to put more effort for mobilization of additional revenue.