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?
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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
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a middle-class individual
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a working-class individual
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a financial institution
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a factory
The jury would vote on a tax rate for each entity. It doesn't follow
that the corporations would pay a higher rate than the individuals. The
jury might find that the corporation truly is a public-spirited
organization committed to sustainability. And it might find that the individual
is a fuel hog in taking unnecessary trips, and not taking care of self.
(See my blog entry on the badness tax.)
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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.
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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.
Computer scientists and statisticians have developed many different
ways that one could learn functions from example data. These relate from
such standby's as
multiple regression to the new methods such as
neural
networks, and
machine learning techniques. Thus, assume that we
have juror ratings of one thousand tax payers. We can apply these
techniques to generate a function between the parameters and the tax
assigned. Obviously, one could replace the jurors by the formula.
Alternatively, people could use such capabilities to estimate how much
tax they would have to pay. This would be how "tax planning" would be
done.
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
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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.
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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.