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It is well known that multinational enterprises (MNEs) gain tax advantages by playing off the synergies they enjoy as a single economic entity against the legal structure of multiple related entities across many jurisdictions. The disconnect between the economic and legal structures enables MNEs to shift profits via internal transactions to tax-advantageous jurisdictions. It is challenging for outsiders to identify and measure the extent of tax aggressive behaviour since all the information needed for this purpose remains confidential to the MNEs.

In the absence of direct information on MNE tax behaviour, scholars have sought ways to estimate tax aggressiveness using alternative public data sources and surrogate measurements that they hope might reflect actual behaviour. Unfortunately, in the absence of information on true practices within MNEs, it is impossible to measure the accuracy of the proxies by comparing the proxy estimates of tax aggressiveness to the actual behaviour of these entities.

A possible alternative to a benchmarking exercise that would compare proxy findings to actual behaviour is to compare the proxies against each other by applying them to a sample set of large corporations and determine whether there is any consistency in the outcomes. This is, admittedly, a second-best approach that is incapable of showing with confidence that proxies are definitively accurate measures of corporate tax aggressiveness.

If all tests ranked MNEs in terms of tax aggressiveness differently, we would at least know that all but one at most were invalid proxies for some companies. Ironically, we would know even less about their accuracy if all came up with the same result as this may illustrate that they are all wrong or all right or all both wrong and right with respect to different MNEs.

What the comparison can do is provide insights into the assumptions researchers have made when devising proxy tests to measure tax aggressiveness.

Research design

In our study, entitled Identifying Tax Aggressive Behaviour: Testing the Proxies, we examine two types of proxies and apply them to the largest 200 companies by market capitalisation on the Australian Stock Exchange (ASX). The two types of proxies commonly applied to a dataset of corporate taxpayers are continuous proxies and dichotomous proxies.

Continuous proxies, of which there are numerous variations, generally compare a company’s taxable income to its net profits calculated for accounting purposes. The logic in using this type of proxy is that it may measure tax aggressiveness by revealing the ability of a company to reduce its net taxable income from its net pre-tax profits for financial accounting purposes. These proxies usually consider the book-tax difference or an effective tax rate. Published studies apply their selected proxies to a dataset of firms and then rank those firms according to tax aggressiveness based on the assumptions surrounding the proxies. The second set of proxies look for the presence of certain company characteristics such as having affiliates in tax havens or involvement in publicly disclosed tax disputes as indicators of tax aggressiveness.

A total of 16 different proxies are used in our study, 14 continuous proxies and 2 dichotomous proxies. Of the 14 continuous proxies, seven are based on book-tax difference and seven are based on effective tax rates. The 16 proxies, set out in an appendix to the study, were applied to the largest 200 companies on Australia’s stock exchange with the 10 most aggressive and 10 least aggressive companies extracted for comparative purposes. Consistency in ranking would mean that at least the proxies are measuring the same thing while inconsistency would show each researcher has different ideas about optimal proxy to identify tax aggressiveness.

The results of the application of the proxies to the ASX top 200 were analysed in three stages.  First, the outcomes of the 14 continuous tests were compared with each other and analysed for consistency. Second, the outcomes of the continuous tests were compared with the dichotomous tests. Third, the results were analysed in terms of the industry types of companies that had been identified as very tax aggressive or least tax aggressive using the different tests.

Inconsistency between tests

The study suggests that there is little consistency in the results obtained using the different proxies. Not one company was classified as a top 10 tax aggressive company using all proxies. In fact, when applying the continuous tests, a total of 31 companies were found to be highly tax aggressive using the 14 proxies. The most times a company appeared in the continuous proxy top 10 lists was 7 times and that was only one company. We found even less consistency with the bottom 10 least tax aggressive companies with 38 companies ranked as low tax aggressive companies using the 14 proxies. The most times a company appeared in the 14 different continuous proxy bottom 10 lists was 6 times and that was only one company.

A comparison of the continuous and dichotomous test rankings also failed to reveal any discernible relationship. We found that many companies with affiliates in tax havens were not in the most aggressive group using the continuous proxies and, indeed, 13 companies with tax havens fell into the least aggressive group under at least some of the continuous proxies.

An analysis of industry types provided no more insight into the accuracy of the different proxies. For example, the dominant industry in the top 10 aggressive group is the mining industry. There are, however, far more mining companies outside the continuous proxy top 10 tax aggressive companies than are in it. Less than one in five mining companies considered very tax aggressive using a continuous proxy had a tax haven affiliate.

What have we learnt?

The wide span of rankings across the 16 proxy tests researchers have used to identify tax aggressive companies confirms two facts. The first is that there is a very wide range of views by researchers devising proxies to test for tax aggressiveness on the indicators that might best reflect proclivity to engage in tax minimisation tactics. The second is that it is highly unlikely any proxy can provide an accurate indication of any particular company’s tax aggressiveness or their tax aggressiveness relative to other companies. At the end of the day, the level of any taxpayer’s tax aggressiveness is known only to the taxpayer. The rest of us will never know if any proxy comes close to measuring and ranking tax aggressive behaviour.

Notwithstanding the enthusiasm of the proponents of the various proxies used in studies of tax aggressiveness, the use of proxies to identify tax aggressive firms is unlikely to be valuable in the study of tax aggressive behaviour.



This article has 1 comment

  1. But can we identify and punish aggressive and unlawful behaviour by tax offices which can create divorces, suicides and bankruptcies on asserted “tax debts” which turn out to be never based on sound assessments? This is a serious and increasingly important question. If taxes are the price we pay for a civilised society, as Oliver Wendell Holmes ahistorically commented, then abusive taxation is the end of civilisations, as with the Later Roman Empire. The price of legalised injustice becomes not worth paying.

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