Analysis and visualisation of tax default list 2009

The final quarter of the list of tax defaulters fined or penalised by Revenue during 2009 was published last week. As usual details for name, address and penalties incurred for each defaulter, along with some information on the individual’s occupation, were included. I’ve used that to extrapolate further information and form statistics which can be seen in the graphs below.

Revenue gets our money back into the tax coffers in a number of ways. One is by fining people for, typically small, instances of tax avoidance. That type of case is often for cigarette or alcohol smuggling, failure to lodge income tax returns or the sale, delivery or use of laundered oil. In such circumstances the relevant individuals or companies are usually fined between €250 and €7,000.

In other cases companies make settlements to pay back taxes which, through an audit, Revenue has discovered they owe. These settlements usually involve much larger sums. Almost all of those who make settlements are companies or wealthy self-employed invididuals. Revenue does not publish most settlements of less than €30,000.

To give some idea of the scale between the values of fines and settlements; in Q4 of last year fines totalled €770,000, settlements reached nearly €18million. This was despite there being multiple times the number of cases where fines were made than settlements. It would be an oversimplification (but broadly accurate) to say fines are against the labourer, settlements are made by the developer.

Righty-o, you should now, if you did not have before, have a decent enough understanding of the ways Revenue claws back the few quid.

The first thing I did was attempt to categorise each defaulter into an employment sector by their occupation. As anyone who has attempted statistical analyses will tell you, the difficulty in categorising information is often in deciding how vague or specific the categories should be. I’ve used Building, Hospitality, Retail, Services, Property, Transport, Finance and Farming. Building includes anything from electricians to construction engineers, Services varies from web designers to mechanics while Retail goes from hairdressers to egg wholesalers. A further ‘Unknown’ category is for defaulters who were either listed by Revenue as ‘Unknown’ occupation or, much more commonly, as “Company director” which is too vague a term to attempt to categorise.

This chart shows the total amount reclaimed by Revenue broken down by sector in percentage terms. Hover the mouse over a slice to see the monetary value.

It’s interesting to view the Building (and transport) percentage in comparison with the next graph which places no value on the amount recouped but instead on the number of individuals charged. When compared with the first chart it indicates builders make up a larger percentage of charges than percentage of money recouped.

This is perhaps not disproportionate when evaluating the private sector labour market as a whole. It could be put down to the large number of tradesmen who are self-employed sub-contractors; the list of people fined is heavy with occupations like plasterer, plumber and electrician. The reasons listed for these fines are often non-, low- or late- income tax returns.

On the opposing side of the spectrum is Property, which makes up 11% of the recouped money but just 2% of the cases. In that sector a small number of individuals have paid large value sums.

In the next two graphs I’ve visualised the money recouped by sector for each type of return (fines and settlements). The first shows the percentages for fines, the second for settlements.

Property and Hospitality disproportionately large in the second graph while Transport (typically truckers and taxi drivers) makes up a massive number of percentage of cases relative to monies recouped.

The geographical/population breakdown is interesting because there is a clear out-lier, Offaly.

I first took the full list of defaulters for 2009 and categorised them by county as per addresses provided by Revenue. For each county I calculated a total value for all fines and settlements. Then population data was added to the mix (as provided by the CSO in the most recent census, 2006).

The total money recouped divided by the number of people in the county gives us a figure for what each person would have to pay if the population of the county collectively had to cover the money demanded by Revenue.

Offaly is an out-lier due to five large settlements made by five publicans, in addition to a above-average number of cases where fines were imposed. Interesting finding. Donegal is also an out-lier, due mainly to the number of small fines imposed for cigarette and alcohol smuggling.

The final graph gives us an indicator as to which counties you’re most (and least) likely to meet a tax defaulter. As with some of the earlier graphs the value is placed on the number of individuals charged as opposed to the amounts recouped.

Carlow and Laois stand out on the less-likely side. There were just 9 successful cases published for Carlow, compared to 62 for Monaghan, which has a similar population. In Laois there were less than 25. It’s interesting, though perhaps unsurprising, to observe that five of the top six are border counties.

It seems you’re most likely to pay up to Revenue if you’re working in the building trade and based in Louth. However, settlements are more likely to be made by those in the hospitality sector (hoteliers, publicans, restaurateurs) in Offaly.

The Google Document I used to extrapolate all of the above is available to view and download in Microsoft Excel format here.

3 thoughts on “Analysis and visualisation of tax default list 2009”

    1. They were done using pivots and Gadgets in the Google Docs portfolio. The info is sorted in spreadsheet format in Google Docs and then visualised with the Pivot and Gadget options.

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