Possible Mechanisms for Participatory Democracy for Taxes
I proposed and our group at Western Illinois University are implementing techniques for people to vote on the laws that constitute the tax code. The tax code could be represented as a decision tree which in the end leads to the amount of tax or a simple formula such as a linear function of income that gives one tax. A decision tree is a set of divisions, each of which will have other divisions. For example, we might vote to divide people on the basis of the number of children. Thus, we would look separately at those having zero children, one child, two children, three or four children, five to eight children, etc. This has been referred to in the literature (Reference One) as Recursive Partitioning.
Then, we might look at the ratio of earned (wages) to unearned income (bank interest, dividends, bond coupons). Thus, the tree might have a division for those having no children and eighty to one hundred percent wage income. Another branch for those having no children and sixty to eighty percent wage income, etc.
Then for each of these divisions, we would have a formula or graph relating income to division.
At each stage in the process, individuals would vote on what divisions to make and eventually the ratio of income.
Another student is working on applying genetic algorithms to determing a tax code.
But do we need rules? Do we need a tax code?
One could envision a taxing system that simply said: each individual and each entity would go before a jury. They would determine the tax they pay. How can we make it less arbitrary?- Tax rates would not vary by more than ten percent year to year without a supermajority. To transition to that system, we would start with whatever tax the entity paid under our complicated tax code. Thus, if a firm paid four millions in taxes the previous year, their tax this year would be between 3.6 million and 4.4 million. However, sixty percent of the jury could vote to change it by twenty percent, in the example from 3.2 million to 4.8 million. Seventy percent could vote to change it by fourty percent, etc.
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Several entities could be grouped together for comparison. One possibility
is to group them randomly. Thus, a jury would see a disparate
group of entities, say
- a middle-class individual
- a working-class individual
- a financial institution
- a factory
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The above scenario assumes no attempt to group elements. One certainly
could group members. This can done by rules. That is, we could vote
as discussed at the beginning for classifications. We could vote on dividing
by corporation, partnership or individual. We could vote to divide by
their net income, gross income or
number of employees.. Thus, one would not be comparing
small businesses with large businesses.
But we wouldn't vote on a tax rate for the category, simply a number of sets
to be collected from each group.
A sample of let's say twenty individuals or entities or corporations would go before the tax jury. The tax jury would know that they have to collect a specific amount revenue from each set. (The computer would divide the total to be collected in each category by the amount of revenue).
Example, we vote that we want to group all those married individuals earning between sixty-thousand dollars and eighty-thousand dollars and having two children in one group. We would look at the total income earned and decide that gather all such people should pay twenty billion in revenue. Assume this group was two-million families. Thus, on average each family would pay ten-thousand in taxes. And thus, each group of twenty would pay $200,000 in taxes. The jury would tnen adjust the $10,000 that each should pay based upon all kinds of other factors: how much have they given in charity, which have high medical bills or suffered other disasters this year, etc. etc.
Each family would be given a chance to explain their financial situation and any reason why they should be given special consideration.
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The alternative to rules for categorization
is clustering. This would introduce a second type of
jury. This jury would be given pairs of individuals. They would
get financial information for the two individuals in the pair.
These jurors would indicate how similar they are; not how much taxes
they should pay.
There are many algorithms available that cluster items into similar groups including Self-organizing maps. In two or three spatial dimensions, this would be groups of points that are very close, forming a clump on a scattergraph. This could be extended to a tax situation in that the software would treat numbers such as number of children, incomes, as spatial dimension and place each taxpayer as a point in the "n-dimensional space." The clustering algorithm would find groups of tax payers that are similar in input characteristics. The taxpayers would go before groups of the first juror types to explain those special tax considerations that would be lower than individuals that are similar.
- There are many algorithms to take sets of example data and create a function out of them. (See the discussion below and references one and two for a good summary.) Thus, the jurors could rate several tax payers as to how much tax they should pay. This, of course, assumes that the characteristics that determine how much tax an individual or entity should pay are all quantitative or captured by the collected parameters (income, medical expenses, etc.) Are we better off allowing people to present these issues and construct the rules interactively and collaboratively, or merely say what the tax should be for various tax payers and construct the rules mechanically.
This is similar to the various services that report jury verdicts in tort litigation to help trial lawywers decide when and for how much to settle their cases.
Classification Procedures and Tax
James E. Parker and Kenneth F. Abromowicz tried both statistical methods and recursive participation to see if they could discover tax law from examples. They tried it on decision rules of a straightforward tax law provisions for whetehr somebody should be considered a dependent. They also compared the ID3 results with those of a common statistical techniques such as regression. The ID3 based approach did slightly better than other techniques.
They then looked at one hundred decisoins regarding "tax home." Taxpayers can deduct traveling expenses when away from home. But where is one's home? If I claim my home near Western Illinois University where I am an Associate Professor, I cannot deduct the cost to go to and from. But if I am temporarily in Pennsylvania during my sabbatical, can I take my carfare off my taxes? It depends on what is considered my home. The Researchers looked at such factors as where the person filed state tax, have income producing property and where one's children resided. The ID3 algorithm correctly correctly classified twenty-seven out of thirty tax cases while the statistical techniques did only 26 to 30.
The tax law considers a scholarship or fellowship not income. If a profit-making corporation pays one's tuition in exchange for working for them latger, that is income. But what about the assistantships that Ph.D. students often get. There are some duties, but it is more awarded to support promising students Garrison and Michelsen looked at one hundred cases and then presented a holdout sample. The ID3 correctly classified all twelve cases as compared to one two or three from statistical approaches.
The Internal Revenue Service has been looking into using machine learning techniques to identify tax returns that are potentially fraudelent. See94ARD 030-1, Statement of Margaret Milner Richardson on February 10, 1994. NCR Terradata division and States has been using such techniques to help catch those who don't file their State Income Tax or don't pay it problperly. And in 2006, the National Taxpayer Advocate reported on using machine learning to identify abusive tax returns.)
Proposed Work
We can simulate the ID3 algorithm and use Lindahl Equilibrium as a figure of merit. That is, I will hypothesize individuals with various incomes and characteristics. I will also hypothesize a distribution for their preference for public goods (defense, public libraries, public education, national and state parks). We will assume some percentage of them behave strategically and some percentage simply vote their true preferences. Dr. Wally Smith did some excellent simulations for multiple-candidate elections, which will be another Thoughtful Thursday.As an empirical work, we will solicit stories from taxpayers where they will provide their tax return, other information that the jurors would consider relevant to how much tax they should pay and possibly have them give a video clip explaining how much tax they feel they should pay and why.
We will try two different sets of jurors. One set will simply vote on the taxes to be paid by each taxpayer. They will have a training set of half the sample tax data. Other groups will develop a classification scheme by ID3 or Genetic Algorithms.
Then, we will generate a tax code using the examples from the first set of jurors, and classify the holdout set, the other half. We will ask the first set of jurors how they liked the results for the second half. The second group will have the holdout set classified by the rules on which they just voted. We will see how they like the result.
We may have to make up some hypotheticals for the very rich as we probably could not get them to give us their data. Although some politicians are wealthy and do make their taxes public.
References
- Parker, James E. "Predictive Abilities of Three Modeling Procedures" The Journal of the American Taxation Association, 37 to 53, Volume 11 Number One Fall 1989.
- Garrison, Larry R. and Robert H. Michaelsen, "Symbolic Concept Acquisition: A New Approach to Determining Underlying Tax Constructs" Journal of the American Taxation Association, 77 to 91, Volume 11 Number One Fall 1989.
