# Collection Trends, Classification of Expense Heads and Avoidance of Fringe Benefits Tax

Praveen Kishore

#### The government claims that the Fringe Benefits Tax has been introduced to tax those kinds of fringe benefits which are collectively enjoyed by employees in the form of facilities/amenities and therefore difficult to identify, segregate and apportion among beneficiaries for taxation. Accordingly, the tax liability has been fixed on employers, and not on the employees. FBT collection data for first two years (2005-06 and 2006-07) have been analysed to gain a deeper insight for fine-tuning. Some statistical tests have been conducted. The test of equality of two proportions for a large sample shows that the proportion of FBT collection under different heads has remained the same over the two years. The chi-square test for equality of proportion shows that this proportion has remained the same for most sectors. However, the chi-square test for homogeneity of sample data for each sector and each head indicates that sample data are not homogeneous. It points towards arbitrary booking of expenses under different heads, perhaps to avoid FBT.

The present article is a partial summary of an empirical research study on fringe beneﬁts tax conducted by the author on sponsorship from the Central Board of Direct Taxes, Ministry of Finance. The article summarises the results of advanced data analysis and statistical tests of the research report. This follows another article published in EPW (16 August 2008). The views expressed in this article are the personal opinions of the author and do not represent the views of the Government of India.

Praveen Kishore (praveenkishore@rediffmail.com) is a member of Indian Revenue Service and is presently with the Directorate of Income Tax (HRD), Ministry of Finance, Government of India, New Delhi.

##### 1 Introduction

here is no universally accepted deﬁnition of “fringe beneﬁts”. It is generally accepted that fringe beneﬁts provided by employer to employees cover all advantages, other then monetary salary and wages, in consequence to services rendered. Thus, they are part of employees’ overall remuneration packages, but they are mostly not in the form of cash p ayments. Some exception can also arise, for example entertainment allowances or other cash expense allowance granted/ reimbursed to an employee which exceeds his actual expenses incurred. Some time, an employer may have a statutory ob ligation also to provide a beneﬁt (for example, the Employees Provident Fund contribution by employers). In some countries, including India, a distinction is made between wages/salaries in kind (often called perquisites in those countries) and other fringe beneﬁts.The meaning of the Fringe Beneﬁts Tax (FBT), the tax payable, tax base and rates have been discussed in the article published in this journal on 16 August 2008. That article also discusses the research design and data collection of the larger study.

##### 2 Research Approach and Collection Pattern

We discuss below the approach in this paper and the col lection pattern.

##### 2.1 Collection Summary

FBT collection was Rs 4,772.3; Rs 5,323 and Rs 6,743 crore in the ﬁrst three years of its operations, the ﬁnancial years 2005-06, 2006-07 and 2007-08, respectively. As a percentage of total direct tax collection, it translates only to around 2.8%, 2.3%, and 2.2%, respectively in the three years. But, for a resource starved country, an additional Rs 6,000-7,000 crore is not a small sum. Further, FBT collections show an ABC pattern, similar to that of corporate income taxes. The collection data was a nalysed by classifying business/economic activities into 22 sectors of the economy. Banking, petrochemical, infotech, and insurance are found to be some of the important contributing sectors. Similarly, “Employee Welfare”, “Conveyance”, “Telephone”, “Running of Car”, “Sales Promotion” are some of the important contributing heads. It has been found that overall, the top 10 “heads” are contributing more than 90% and that the b ottom ﬁve “heads” are contributing around 2% of FBT collection. A “head-wise” and “sector-wise” summary has been incorporated in Tables 1 and 2 (p 61). A detailed analysis of sectorwise and head-wise FBT collection has already been done in a s eparate article (Kishore 2008).

Economic & Political Weekly

january 3, 2009##### 3 Statistical Tests and Inferences

There are certain questions and issues which have arisen during the preliminary analysis of the collection data. Some of these are: Are the overall collection patterns of FBT in the two years of its operation similar? Are these patterns similar even for different sectors of the economy? Can we conclude something about the collection pattern with certainty using some statistical test? Is the collection of FBT from an individual taxpayer dependent on the “economy sector” or on “heads” or on both? What do the top 10 and bottom ﬁve “heads” signify? Can the bottom ﬁve heads be removed from the FBT system without affecting the collection? Can we infer something about booking of expenses and uniformity of sample data? To address most of these issues, certain statistical tests have been conducted to get some rigorous and dependable inferences.

Statistical tests were conducted at three levels of collection data of the FBT. First, at the level of overall FBT collection to check equality of collection pattern under each head for the ﬁrst two years. Second, tests were conducted separately for each of the ‘economy sectors’ to test the equality of proportion of collection under each head for the ﬁrst two years. To measure the interaction between the “economy sectors” and “FBT heads”, a two- factor ANOVA was also conducted. Lastly, tests were conducted to see the homogeneity of proportion of collection in sample data for each combination of “head” and “sector”. For all the tests, collection of FBT from each “head” was converted into proportion of total FBT collection. Some other modiﬁcations were also made in the data which are explained in the coming paragraphs.

##### 4 Test of Equality of Proportion Over the Years

This section presents the results of test of equality over the two years, 2005-06 and 2006-07.

##### 4.1 The Test

The ﬁrst statistical test is a comparison of total FBT collection under each head for ﬁrst two years to check whether the p roportion contribution by each head has statistically remained the same. A hypothesis test called “large sample test for the d ifference between two population proportions” has been used. It is a parametric test which checks whether there is s tatistically signiﬁcant difference between two p opulation p roportions.

The raw data of FBT collection for two years are not strictly comparable. This is due to change in provisions relating to expense head “Contribution to Superannuation Fund” and due to breaking up of the head “Conveyance, Tour and Travel” into two heads, namely, “Conveyance” and “Tour and Travel” with a reduced base of 5%. Therefore, before conducting the tests, the data has been modiﬁed in the following way: t $PMMFDUJPO GSPN UIF IFBE DPOUSJCVUJPO UP 4VQFSBOOVBUJPO Fund has been taken out from the data before calculating the proportion and the proportions have been calculated on the reduced total. t 5PUBLFDBSFPGTFDPOEJTTVFUIFmHVSFTGPSGPSUIF head tour and travel was multiplied by four and then added to the collection ﬁgures of the head conveyance. In this way, the c ollection ﬁgures/proportions of 2006-07 under the heads c onveyance and tour and travel become comparable to the

Table 1: Results of 17 Tests for Equality of Proportion for Each Head of FBT

Sr FBT Heads Percentage Contribution Test Null Hypothesis No in Total Collection* Statistics (Z) Ho: p1=p

2

2006-07 2005-06

1 Employee welfare 22.1 10.7 0.3638 Accepted

2 Conveyance, tour and travel 20.6 18.2 0.1785 Accepted

3 Rep, runn, dep on car 9.3 4.7 0.1066 Accepted

4 Telephone 9.3 5.2 0.2568 Accepted

5 Sales promotion (and publicity) 8.9 4.9 0.5311 Accepted

6 Use of hotel, boarding 8.5 3.2 0.0155 Accepted

7 Gifts 5.5 2.2 0.0672 Accepted

8 Conference 3.8 1.7 0.9169 Accepted

9 Rep, runn, dep on aircraft 1.6 0.7 0.2063 Accepted

10 Hospitality 1.4 0.6 0.1396 Accepted

11 Entertainment 1.4 0.7 0.6027 Accepted

12 Maintenance of guest house 1.2 0.6 0.0085 Accepted

13 Scholarships 0.7 0.4 0.0609 Accepted

14 Other club 0.4 0.3 0.0846 Accepted

15 Festival celebration 0.4 0.2 0.2115 Accepted

16 Free/concessional ticket 0.3 0.2 0.5336 Accepted

17 Health club 0.1 0.1 0.0832 Accepted

* The figures are rounded and are also adjusted to the extent that “Conveyance, Tour and Travel” has been two separate heads in 2006-07, percentage contribution by head “Contribution to Superannuation Fund” was 4% and 45% during 2006-07 and 2005-06 respectively, which is not reflected in the table above. It should also be noted that these percentage figures are not the same as p and p used in conducting the tests.

12

c ollection ﬁgures/proportions of the head conveyance, tour and travel of 2005-06.

These two adjustments have made data fully comparable and has given 17 heads on which a test of equality of proportion has been performed. The sample size is large enough (350 each in both the years) so that the distribution of proportions of FBT from each head as a per cent of total FBT can be approximated by a normal distribution. Therefore, the difference between two s ample proportions (for the two years under consideration) is also approximately normally distributed and this gave rise to a test of equality of sample proportion based on the standard n ormal distribution.

We take p1 = proportion of FBT collection from a head to total FBT, for the year 2006-07 and p2 = Proportion of FBT collected from a head to total FBT, for the year 2005-06.

Then, we have the null hypothesis, H: p1 = p2 and the alternate

o1

hypothesis, H1: p1 ≠ p2

##### 4.2 Test Results and Conclusions

This test was conducted for each heads of FBT separately, thus totalling 17 tests. The results of these tests are shown in Table 1. (The table also shows percentage contribution by each head of FBT in the total collection for the ﬁnancial year 2006-07 and 2005-06, although with some adjustments.) It can be seen that the null hypothesis is accepted in case of all the heads of FBT. This means that in totality, the proportion of contribution by each head of FBT in the total FBT collection is not signiﬁcantly different for both the years. Further, the value of Z statistics is always less then

1.00 and in many cases it is even less than 0.50. This means that the null hypothesis would be accepted even at a stronger level of conﬁdence. Therefore, it can be said that the proportion of collection by different heads in the two years has remained the same and statistical evidence to support this hypothesis is very strong.

Economic Political Weekly

january 3, 2009This means that we have strong statistical evidence to conclude that the proportion of collection of FBT from different heads has remained same over the two years. This points towards an overall stability in FBT regime and FBT collection from the very ﬁrst year of its operation.

##### 5 Chi-Square Tests

This section carries out a test of equality of proportion over the years for each economy sector.

##### 5.1 The Test

The next logical step in this direction is to test the equality of proportion of FBT for each head over the two years, as done previously, but separately for each of the 22 sectors of the economy. In this way, we would be attempting to ascertain as to whether the collection pattern of FBT for each head over the two years has statistically remained the same or not, separately for each of the economy sectors.

At ﬁrst instance, it would appear that the same test as done previously can be conducted in this case also, separately for all the 22 sectors of the economy. However, doing the same test as in the previous section is not appropriate in the present situation because the sample size for each sector of economy is small, often less then 30 which violates the pre sumption of large sample size and assumption of resultant normality of distribution. Further, sample sizes are also not equal for different sector of the economy as well as across years for each sector of the economy. Therefore, a non-parametric test, the chi-square test for equality of proportion has been conducted. Many of these stringent assumptions of parametric tests are not necessary in chi-square test and it is more appropriate in the present situation.

Table 2: Summary Results of Chi-Square Test for Equality of Proportions of Head-wise FBT Collection for Each Economy Sector

Sr | Economy Sector | Percentage | X2 Statistics | Null |
---|---|---|---|---|

No | Contribution in | Hypothesis | ||

Total Collection | Ho: p1 = p2 | |||

(2006-07) | ||||

1 | Banking | 15.5 | 2.2030 | Accepted |

2 | Petrochemical | 8.7 | 4.1032 | Accepted |

3 | Infotech-software | 8.5 | 5.9995 | Accepted |

4 | Infotech-ITES | 6.5 | 19.7771 | Accepted |

5 | Insurance | 6.1 | 29.0183 | Rejected |

6 | Elect/electronics manufacturing | 5.4 | 3.9897 | Accepted |

7 | Services-financial-consultancy | 5.2 | 3.3607 | Accepted |

8 | Telecom service | 5.1 | 9.2342 | Accepted |

9 | Engg manufacturing | 4.4 | 43.1121 | Rejected |

10 | Pharma-drugs-biotech | 4.1 | 15.0360 | Accepted |

11 | Power-energy | 3.9 | 41.4008 | Rejected |

12 | Diversified | 3.8 | 4.6591 | Accepted |

13 | Automobile-ancillary | 3.3 | 7.7707 | Accepted |

14 | Minerals-metals | 3.3 | 3.3925 | Accepted |

15 | Steel | 2.7 | 1.6843 | Accepted |

16 | FMCG-consumer goods | 2.5 | 4.9539 | Accepted |

17 | Transport-hotel-storage | 2.3 | 17.9197 | Accepted |

18 | Agro-food-beverage | 2.3 | 6.8788 | Accepted |

19 | Construction | 2.0 | 16.3344 | Accepted |

20 | Trading-retail | 1.5 | 5.4992 | Accepted |

21 | Media-entertainment | 1.4 | 10.2717 | Accepted |

22 | Chemical-fertiliser | 1.3 | 5.8329 | Accepted |

Twenty-two separate chi-square (X2) tests have been conducted, one each for each of the sectors of the economy. For each test, in this model, there are two populations, being the proportion of FBT for two years and there are 17 categories of proportions within each population, one each for each head of FBT. Null hypothesis in this case is that the proportion of each head of FBT is equal across both the populations. The a lternative hypothesis is that not all proportions are equal across all populations.2

In the present analysis, conﬁdence level of 95% is taken and the critical value at this conﬁdence level with 16 degrees of freedom is 26.2962. Thus, if the test statistics, i e, X2 value calculated is less then the critical value, the null hypothesis is accepted,

o therwise it is rejected. When the null hypothesis is accepted for a particular sector of economy, it is concluded that the proportion of FBT collection for each head is statistically same for both years being compared. Otherwise, it is not the same.

Here again, before conducting the tests, the collection data has been modiﬁed in the same way as done in the previous test to make the ﬁgures/proportion of collection of two years c omparable.

##### 5.2 Test Results and Conclusions

Separate tests have been performed for each sector of the e conomy. Thus, in effect, 22 chi-square tests were conducted. The results are summarised in Table 2. (The table also incorporates percentage contribution by each sector of the economy in total collection of FBT for the ﬁnancial year 2006-07.) It is found that in 19 sectors, the null hypothesis has been accepted whereas it has been rejected only for three sectors. Therefore, it can be c oncluded that the proportion of collection form different heads of FBT has more or less remained the same over the two years even when the data is examined for each of the economy sector separately.

The null hypothesis has been rejected for insurance, e ngineering manufacturing’ and the power-energy sectors. It points towards the fact that for these sectors of the economy, FBT collection data for different heads of expenses are s howing a large variation in two years thereby leading to more variability. A further look into major contributor for the high value of X2 statistics has shown the heads gifts, employee w elfare and sales promotion with major ﬂuctuation in per cent contribution, year to year, thereby contributing to rejection of null hypothesis for these three economy sectors. Further, the least value of X2 statistics has been found for sectors like steel, b anking, minerals and metals, services-ﬁnancial-consultancy, which imply that FBT collection pattern under different heads has shown very strong homo geneity during the two years for these sectors.

##### 6 Test of Homogeneity of Sample Data

This section presents the analysis of variance (Anova) and chi-square test for the e conomy sectors and FBT heads.

TIFAC

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The objective of the training workshop is to educate and train the budding scientists/engineers/researchers/policymakers in the ﬁeld of mathematical modelling of real life application oriented problems in their respective areas of work. The workshop includes a series of lectures on the techniques of mathematical modelling in conjugation with sophisticated software packages by eminent scientists. Limited numbers of seats are available. Boarding and lodging arrangement would be made available to registered participants. Arrangement for travel cost is to be made by candidates themselves.

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K. S. Krishnan Marg, New Delhi-110 012, INDIA

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Table 3: Banking Sector – Summarised Results of Chi-Square Tests for Homogeneity of Sample Data

Sr FBT Head X2 Statistics Null Hypothesis: No p = p = … = p

01n

Employee welfare 231.53 Rejected

Conveyance 241.82 Rejected

Telephone 139.04 Rejected

Rep, runn, dep on car 221.23 Rejected

Sales promotion (and publicity) 204.17 Rejected

Use of hotel, boarding, etc 467.94 Rejected

Tour and travel 73.21 Rejected

8 Gifts 337.39 Rejected

Contribution to Superannuation Fund 1388.68 Rejected

10 Conference 133.67 Rejected

11 Rep, runn, dep on aircraft – –

12 Entertainment 231.53 Rejected

13 Hospitality 241.82 Rejected

14 Maintenance of guest house 139.04 Rejected

15 Scholarships 221.23 Rejected

16 Festival celebration 204.17 Rejected

17 Other club 467.94 Rejected

18 Free/concessional ticket 73.21 Rejected

19 Health club 337.39 Rejected

Sample size (n) = 38 Degrees of Freedom = (n-1) = 37 Critical value of X2 at 95% confidence level = 52.16

##### 6.1 Chi-Square Test for Each Combination

The ﬁrst two levels of tests have shown broad homogeneity of collection data. The last levels of tests have been conducted to check the homogeneity of collection proportion in case of individual samples in a particular combination of sector and head. All 22 economy sectors are taken one by one and chi-square test for homogeneity of sample data has been conducted for each of its 19 FBT heads. It has been checked whether individual FBT p ayers in a particular combination of the economy sector and FBT head have a statistically similar pattern of contribution. For example, in the automobile-ancillary sector, wherein the sample consisted of data from 17 individual FBT payers, it is checked whether per cent contribution3 from a FBT head, say employee welfare from each of these 17 taxpayers in sample, is statistically homogeneous or different. In essence, it is tested whether the FBT collection proportion shown by individual samples in a particular combination of sector and head are statistically equal to the average p roportion of that particular combination. Since same types of business generally have similar kind of expense patterns, in the ideal situation, the sample data are expected to show some s tatistical homogeneity.

##### 6.2 Two-Factor ANOVA

However, before doing these chi-square tests, a two factor ANOVA has been conducted to test whether there are differences in proportion of FBT collection from different heads and from different economy sectors – being two factors. The main idea is to test whether there are differences in proportion of FBT collection from different heads and from different economy sectors. In effect, it has been tested as to whether collection of FBT from a taxpayer is dependent on “economy sectors” or on FBT heads or on both and it what fashion. For this purpose, a two-factor ANOVA

Economic Political Weekly

january 3, 2009with different observation per cell has been conducted. The combined effect of both these factors, beyond what is expected from the consideration of each effect separately, called the interaction effect, has been found by calculating mean squares of two factors and mean squares error.

The F-ratio for interaction effect has been calculated as 10.002. The critical value of F-ratio has been found to be 1.22 at the 0.05 level of signiﬁcance, with the given level of numerator and denominator degrees of freedom.4 Thus, test statistics of F ration of 10.002 indicated a very strong interaction effect among the two factors – sector and head, leading to separate chi-square tests for all possible combinations of these two factors.

##### 6.3 Classification of Expenses

The chi-square test is relevant in analysing another signiﬁcant issue being classiﬁcation and booking of expense. As of now, there is no standard procedure or classiﬁcation system for booking of expenses and its accounting treatment by business organisations. It depends solely on the accounts/ﬁnance department to devise/determine a head, and book an expense under a head. No guidelines or accounting standard have been issued by Institute of Chartered Accountants of India (ICAI) for this purpose. What is generally found in the books of account of large organisations is a broad four or ﬁvefold classiﬁcation of all expenses into manufacturing, selling, employees, administrative and miscellaneous expenses heads. These broad heads are then s ubdivided into v arious speciﬁc heads for booking of expense and there is no uniformity even for naming of such a speciﬁc head. This gives complete discretion to an organisation for classifying and booking an expense. The classiﬁcation issue is a complex one. It should also be realised that due to complex nature of modern business enterprises and diversity in nature and type of expenses incurred in the course of business, it is

Table 4: Petrochemical Sector – Summarised Results of Chi-Square Tests for Homogeneity of Sample Data

Sr | FBT Head | X2 Statistics | Null Hypothesis: |
---|---|---|---|

No | p0 = p1 = … = pn | ||

1 | Employee welfare | 85.96 | Rejected |

2 | Conveyance | 34.24 | Rejected |

3 | Telephone | 7.15 | Accepted |

4 | Rep, runn, dep on car | 104.00 | Rejected |

5 | Sales promotion (and publicity) | 891.79 | Rejected |

6 | Use of hotel, boarding, etc | 285.98 | Rejected |

7 | Tour and travel | 175.21 | Rejected |

8 | Gifts | 56.76 | Rejected |

9 | Contribution to Superannuation Fund | 2045.67 | Rejected |

10 | Conference | 54.83 | Rejected |

11 | Rep, runn, dep on aircraft | 98.02 | Rejected |

12 | Entertainment | 2.05 | Accepted |

13 | Hospitality | 22.51 | Rejected |

14 | Maintenance of guest house | 32.68 | Rejected |

15 | Scholarships | 15.94 | Accepted |

16 | Festival celebration | 7.25 | Accepted |

17 | Other club | 4.75 | Accepted |

18 | Free/concessional ticket | 47.80 | Rejected |

19 | Health club | 9.11 | Accepted |

Sample size (n) = 10 | Degrees of Freedom = (n-1) = 9 | ||

Critical value of X2 at 95% confidence level = 16.92 |

very difﬁcult to have a practicable classiﬁcation system and standardised b ooking of expense.

It has been noted during preliminary data analysis that out of the bottom ﬁve heads, four heads are those heads where the base/valuation rate is 50% of total expense. Further, these four heads are such where the expense made for these purposes can also be booked under other heads of expense, notably under employee welfare. The head employee welfare is a general type of head which can include expenses incurred for providing scholarship, festival celebration, etc, and doing so would perfectly be within the four corners of law. However, when done so, it will have the impact of reducing the FBT liability due to d ifferential valuation base for these heads of expense. Prima facie, it may be the reason why the heads with 50% base are least contributing and also why the head employee welfare is the largest contributing head. However, it would have been too naïve to arrive at such a conclusion on the basis of above simplistic notion.

The present tests could give us the required insight. It is safe to assume that there would be a kind of homogeneity in the nature of expense incurred by entities engaged in same e conomic/ b usiness activity. That is, for infotech-ITES sector as a whole, it can be assumed that expenditure incurred on some head, say tele phones as a proportion of total expense or some other similar parameter would be similar for many/most of individual taxpaying entities. Following this logic, if this being the case, collection of FBT from a head as a proportion of total FBT collection in case of each individual taxpayer in the sample should show statistical homogeneity/equality for each com bination of head and sector. If this is not the case, there is some indication to believe that the sample data are h eterogeneous and that perhaps booking of expense is arbitrary.

The chi-square statistics calculated are compared with the critical value of chi-square distribution for the required degree of freedom and conﬁdence level (taken to be 95% in this case). For each of the economy sectors, sample sizes are different which gave different n and different values for degree of freedom as (n-1). The sample size of different sectors of the economy varies between 38 (banking) and 7 (steel). Accordingly, the critical v alues of X2 are different for different sectors of economy as they depend on size of sample also. Finally, if the test statistics, i e, X2 value calculated is less than the critical value, null hypothesis is accepted, otherwise it is rejected.

##### 6.5 Test Results and Findings

A total of 418 chi-square tests were to be conducted, one each for each possible combination of sector and head (22 sectors × 19 heads). However, in 18 instances involving the heads “Free ticket” and “Rep, Runn, Dep of Aircraft” for different economy sectors, there were no data points and therefore, no test could be conducted. Thus total number of chi-square tests actually conducted are (400 = (22 × 19) – 18).

Test results for two sectors of the economy banking (18 tests) and petrochemical (19 tests), being the two largest contributing sectors, for all the heads are given in Tables 3 and 4 (p 63). In case of banking, null hypothesis has been rejected in all the 18 tests, i e, for all the heads implying thereby that the s ample data is not homogeneous even for a single combination. For the petrochemical sector, the null hypothesis has been rejected in 13 tests, i e, for 13 heads and accepted in six tests, i e, for six heads showing some homogeneity in sample data for accepted heads.

6.4 The Test and the Model | Table 5: Summary Results of 400 Chi-Square Test for Equality of Sample Proportion | |||||||||||||||||||
---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|

Sectors of | Heads of FBT | |||||||||||||||||||

The chi-square test has been used again as test of | Economy | EW | Cnv | Tel | Car | Slp | Hot | Tor | Gft | Sup | Con | Air | Ent | Hos | GH | Sch | Fes | OC | Tkt | HC |

homogeneity of sample data. The test has been | Bnk | R | R | R | R | R | R | R | R | R | R | x | R | R | R | R | R | R | R | R |

conduced for the year 2006-07 only because the | Petr | R | R | A | R | R | R | R | R | R | R | R | A | R | R | A | A | A | R | A |

data for this year is more stable and balanced. | Inf-S | R | R | R | R | R | R | R | R | R | R | x | R | R | R | R | R | R | R | A |

The null hypothesis in this case is that the proportions of FBT collection from all individual tax | Inf-I Insur EleM | R R R | R R R | R R R | R R R | R R R | R R R | R R R | R R R | R R R | R R R | R x x | R A R | R R R | R A R | R R R | R A R | R R A | x x x | R R R |

payers in the sample of a given combination of | SeFC | R | R | R | R | R | R | R | R | R | R | x | R | R | R | R | A | A | R | R |

sector and head are statistically similar (to that of | Tele | R | R | R | R | R | R | A | R | R | R | x | A | R | R | A | A | A | x | A |

the average value). Alternatively, at least one | EngM | R | R | R | R | R | R | R | R | R | R | R | R | R | R | A | R | R | x | A |

sample proportions is not equal. Separate chi- | PhDr | R | R | R | R | R | R | R | R | R | R | R | A | R | A | A | A | R | x | A |

square tests have to be conducted on sample data | PowE | R | R | R | R | A | R | R | R | R | R | R | A | R | A | A | A | R | x | R |

set/cells present in each possible combination of | Divr | R | R | R | R | R | R | R | R | R | R | R | R | R | A | A | A | A | R | R |

head and sector.5 | Auto | R | R | R | R | R | R | R | R | R | R | R | R | R | R | A | A | R | R | A |

We deﬁne p0 as the sample average of propor- | MinM | R | R | A | R | A | R | A | R | R | A | R | A | R | A | R | A | A | A | A |

tion of FBT collection for a particular combination | Stl | R | R | A | R | A | R | A | R | R | A | R | A | A | A | A | A | A | x | A |

of sector and head. p0 can also be called expected proportion or average proportion. The chi-square (X2 ) statistics is then calculated | FMCg TrHC AgFd Const | R R R R | R R R R | R R A A | R R R R | R R R R | R R R R | R R R A | R R R R | R R R A | R R R R | x R R R | A R R R | R R A R | A R R R | A A A A | A A R R | A A R A | x R x x | A A R A |

as follows | TrRtl | R | R | R | R | R | R | R | R | R | R | R | A | R | R | A | A | R | A | A |

n (pi – p0)2 X2 = Σ ——— | MeEn ChFrt | A R | R R | A A | R R | R R | A R | R R | R R | R R | R A | R R | A A | R R | A R | A A | A A | A A | R A | R A |

i=1 | p0 | R = Null hypothesis Rejected | ||||||||||||||||||

with (n–1) degrees of freedom. | A = Null hypothesis Accepted X = No test conducted due to absence of data | |||||||||||||||||||

64 | january 3, 2009 | Economic Political Weekly |

##### 6.6 Summary of Results and Inferences

Thus, in total, 400 chi-square tests have been conducted for all combinations of sectors and heads. However, it is difﬁcult to ﬁnd meaningful patterns, inferences and insights form the results of such a large number of tests at ﬁrst sight. Therefore, we look deeper into the results by summarising them. Table 5 (p 64) presents the results of all the 400 chi-square tests in the form of a matrix. The heads of FBT has been listed horizontally while the sectors of the economy have been shown in vertical fashion. Thus, each cell of the matrix represents the result of chi-square test for the sample data represented by that cell. R represents the cases when the null hypotheses have been rejected (and therefore, we concluded that the sample data is heterogeneous) whereas A represents the cases where the null hypotheses have been accepted (and we concluded that the sample data is showing homo geneity). X marks the cells where no test has been conducted due to lack of data. Out of total tests, in 99 instances, which is around 25% of total number of tests, the null hypothesis has been accepted. Therefore, overall, it can be concluded that there is not much statistical evidence to accept the null hypothesis and accordingly it is difﬁcult to conclude that the sample data is homogeneous.

It is not the individual test results that are signiﬁcant but the summary of these and the pattern of these results which throws valuable insights. From Table 5, we notice that the upper left corner has very few acceptances of null hypothesis whereas the occurrences of acceptances of null hypothesis increases in the right side of the matrix, which represents the least contributing heads, thereby implying that the sample data is more homogeneous for this portion of the matrix. We also note that the head-wise pattern is more discernible than the sector-wise pattern. Accordingly, Table 6 further s ummarises the occurrences of acceptance of null hypothesis in absolute and percentage terms for each of the FBT heads. The heads has been listed in decreasing order of their contribution in total collection. The table has been horizontally divided into two parts, thus listing the top 10 and bottom nine heads separately.

Now, two distinct patterns are clearly discernable. In the case of the top 10 heads, the occurrences of acceptance of null hypothesis are mostly between 0% and 15% except for the head T elephone. For this group of top 10 heads, overall, the null hypothesis has been accepted in 20 out of 220 (22 sectors × top 10 heads) instances of tests which give an acceptance of 9%. For the heads conveyance, gift and maintenance of car, the null hypothesis has not been accepted even once. This shows that for these heads of expenses, the data of individual taxpayers are very heterogeneous.

It is easy to notice that in case of the bottom nine heads, the occurrences of acceptance of null hypothesis have suddenly increased and are in the range of 30% to 70% for all the heads except for hospitality and rep, runn, dep on aircraft. O verall, for the bottom nine heads, 180 chi-square tests have been conducted out of which, in 79 instances (43%), null hypothesis of equality/homogeneity of sample data has been accepted.

Economic Political Weekly

january 3, 2009Table 6: Chi-Square Test: Occurrence of Acceptance of Null Hypothesis for FBT Heads

Hd FBT Head | Valuation Base | No of | Number of | Percentage of |

Rk | (As % of | Tests | Instances of | Acceptance |

Expense) | Conducted | Accepting | of Null | |

Null Hypothesis | Hypothesis |

1 | Employee welfare | 20 | 22 | 1 | 4.5 |
---|---|---|---|---|---|

2 | Conveyance | 20 | 22 | 0 | 0 |

3 | Telephone | 20 | 22 | 7 | 31.8 |

4 | Rep, runn, dep on car | 20 | 22 | 0 | 0 |

5 | Sales promotion (and publicity) | 20 | 22 | 3 | 13.6 |

6 | Use of hotel, boarding, etc | 20 | 22 | 1 | 4.5 |

7 | Tour and travel | 5 | 22 | 4 | 18.2 |

8 | Gifts | 50 | 22 | 0 | 0 |

9 | Contribution to Superannuation Fund 100@ | 22 | 1 | 4.5 | |

10 Conference | 20 | 22 | 3 | 13.6 | |

Total | 220 | 20 | 9.1 | ||

11 Rep, runn, dep on aircraft | 20 | 15* | 0 | 0 | |

12 Entertainment | 20 | 22 | 11 | 50.0 | |

13 Hospitality | 20 | 22 | 2 | 9.1 | |

14 Maintenance of guest house | 20 | 22 | 8 | 36.3 | |

15 Scholarships | 50 | 22 | 15 | 68.2 | |

16 Festival celebration | 50 | 22 | 15 | 68.2 | |

17 Other club | 50 | 22 | 12 | 54.5 | |

18 Free/concessional ticket | 100 | 11** | 3 | 27.3 | |

19 Health club | 50 | 22 | 13 | 59.1 | |

Total | 180 | 79 | 43.1 | ||

Grand total | 400 | 99 | 24.7 |

*There are only 15 economy sectors for which this head has shown any collection and in none of the instances, the null hypothesis has been accepted. ** There are only 11 economy sectors for which this head has shown any collection and out of these 11 tests, in 3 instances, null hypothesis has been accepted. @ Contribution up to Rs 1 lakh per employee per year exempt.

If we analyse the distribution of acceptance of null hypothesis for different sectors of the economy, it is seen that there are some sectors where the sample data has been found to be more h omogeneous, for example in case of minerals-metals, steel, chemical-fertiliser, and media-entertainment sectors, where null hypothesis have been accepted in eight to 11 instances of test out of 19 tests, i e, about 50% times. For two sectors, namely, banking and infotech-ITES; the null hypothesis has not been accepted even once, showing high heterogeneity of sample data for these sectors.

However, it is the distribution of test results as per the heads of FBT which throws some interesting results. We have seen that for top 10 heads, sample data are not homogeneous whereas for the bottom nine heads, sample data are more h omogeneous. Table 6 also shows the valuation base for each head of expense. It is also easy to note that in case of heads with 50% valuation base, most of which form the bottom ﬁve heads, occurrences of acceptance of null hypothesis are signiﬁcantly more. Similarly, the top 10 heads, most of which have 20% v aluation base, are most heterogeneous. The type of heterogeneity shown in the test by the top 10 heads is difﬁcult to explain only on the basis of internal diversity and differences in individual organisations/taxpayers. What can we infer form this analysis?

It is natural for the taxpayers to attempt reducing their FBT liability, if that is possible within the four corners of law. Due to the available discretion for booking expenses under

EPWRF

d ifferent heads, it is logical to expect that taxpayers would be motivated to book more and more expenses under those heads where the valuation base for FBT is lower. From the results of the chi-square test, this indeed appears to be the case. Taxpayers are perhaps taking beneﬁts of absence of any standardised method for c lassiﬁcation and booking of expenses. This is the reason data for heads with a lower base are the most heterogeneous representing arbitrary booking practices and are the largest contributing heads. Such a p ractice is not illegal and to some extent natural also since it is always possible that some particular expenses have the p ossibility of being included in more than one category. On the same logic, generally the heads with a higher valuation base are the least contributing and the individual sample data is also more homogeneous because these are not experiencing any arbitrary booking. It should also be noted that an heterogeneity/arbitrary pattern emerges due to the fact that not all taxpayers would be indulging in cross booking of expenses and that the cross booking/shifting of expense would be quite random.

Further, perhaps due to this reason, the head employee welfare which is a wider category and which can accommodate a large number of other expenses like gifts, other beneﬁts, scholarships, etc, is the largest contributing head for FBT. Other heads in the top 10, like “conveyance”, “sales promotion”, “tour and travel” are also general in nature making it possible to book different kind of expenses under them and therefore, show high variability in booking practices.

The heads which are least contributing like gift, scholarships, health club, etc, are speciﬁc in nature and it is difﬁcult and too blatant to accommodate and book other expenses under these heads, though it is easy and perfectly legal to book such expenses under employee welfare. Further, there is no beneﬁt of reduced tax liability by booking other expenses under these heads having a 50% valuation base. Similarly, the head t elephone is quite conspicuous and speciﬁc and it would be very blatant to book other expenses under this head which is the r eason for its high h omogeneity, although it has a low valuation base of 20%.

On the basis of above analysis, one is tempted to conclude that perhaps there are deliberate booking of expenses in such a way to reduce the FBT liability by business organisations, which is being reﬂected in higher heterogeneity in sample data in case of wider FBT heads having 20% valuation base.

##### 7 Conclusions

What has been done through statistical tests can be termed as an introductory data mining and the ﬁndings are only preliminary in nature. Although, at the aggregate level, the collection pattern has shown stability and homogeneity, at the level of individual taxpayers, the data is showing a high level of heterogeneity. It only indicates that there may be attempts by individual taxpayers to book expenses in such a way which reduces their total FBT liability. There is a vast opportunity to further dig into data to gain a deeper insight. However, one suggestion which could be made on the basis of the above analysis is to r ecommend a uniform valuation base for all heads of expenses to remove the opportunity of tax avoidance. However, it would bring into question the very basic logic of bring FBT in the present from as the valuation bases are stated to be decided taking into consideration the nature of the expense head. F urther, recommending removal of the least contributing heads are not as easy because it may lead to shifting in the expense booking pattern to avoid FBT, thereby leading to a signiﬁcant loss of revenue.

Some alternatives of FBT have been suggested like imposition of a ﬂat rate of surcharge on corporate tax or making valuation rules of perquisite more comprehensive and incorporating FBT provisions in them, thereby only taxing employees and not the employers. All this requires further deliberation, informed discussion and empirical analysis of FBT collection data through involvement of all the stakeholders. Only then will it help in reform of the FBT regime and an overall i mprovement of tax policy formulation and taxation structure of our economy.

Notes

(n1 p1 + n2 p2 ) 1 We deﬁne p = —————— where p is com(n1 + n2)

bined population proportion, n1 = No of observation in 2006-07 = 350 and n2 = No of observations in 2005-06 = 350. The sample standard deviation S is given by

1 n 12)S = √ p 1–p) — + — .n1

(p1 – p2) We calculate Z statistics as Z = ——— . S

The Z statistic so calculated is then compared to the critical value of Z statistics. The critical value for a given level of conﬁdence (it has been taken at 5% in this case) is found by looking at the normal distribution table. It is a two-tailed test of hypothesis and the corresponding critical value which leaves 5% area of the Standard Normal Distribution in each of its tails (thus keeping 90% area within the acceptable limits of critical value) is 1.645. Thus, if we have value of test statistics below the critical value (1.645), we accept the null hypothesis and if it is more than critical value, the null hypothesis is rejected.

2 Mathematically, H: p1i = p2i for all i

o

H1: At least one i not the same, where p1i is the proportion of FBT collection for i th head for the year 2006-07, and p2i is the proportion of FBT collection for the i th head for the year 2005-06, there are in total 17 heads, i e, i varies from 1 to 17. Chi-Square (X2) test Statistics is calculated as

n ( p1i – p2i)2 X2 = Σ i=1 p1i

with (n-1) degrees of freedom, where n is total number of FBT categories, i e, 17, and the degree of freedom, thus, is 16. The X2 statistics so calculated is compared from the critical value of chi-square distribution for given degree of freedom and conﬁdence level.

3 “Per cent contribution” here means per cent contribution by a FBT head as a per cent of total FBT collection for a particular taxpayer. It has also been called “proportion of FBT collection” in subsequent paragraphs.

4 A detailed discussion of ANOVA is too complicated to be elaborated here. It would be sufﬁcient to note that the ANOVA conducted in this case has indicated a strong interaction effect, which directed towards conducting one-way ANOVA or separate chi-square tests.

5 Mathematically, H: p1 = p2 = p3 = …. = p

on

: At least one p not the same. Where pis the

H1nn

proportion of FBT collection from n th FBT payer in the sample for a particular combination of sector and head; and n is total number of FBT payers in the sample representing a particular combination of sector and head.

Reference

Kishore, Praveen (2008): “Analysis of Fringe Beneﬁts Tax and Its Collection Pattern”, Economic Political Weekly, Mumbai, Vol XLIII, No 33, Issue 16-22 August, p 41.

Economic Political Weekly

january 3, 2009