Apologies for asking what is probably a basic question from someone that is not in the economics field.

But I was playing around with the idea of determining how a group of people could split a bill "fairly", meaning they each spend an equal amount of utility, calculated from their yearly income. And I did it using logarithms since that is the common recommendation.

I have seen many mentions that the utility of money is roughly logarithmic as it increases. And it is usually phrased as a rule of thumb, but I don't see actual justification. And the assumption seems embedded everywhere. For instance, a (real) flat income tax that taxes all income (not just consumption or earned income) is seen as neither regressive nor progressive - but the flat tax assumption (that everyone would pay the same percentage) again points to the idea of money having logarithmic utility.

My question is, has this actually been measured? I have seen indications that economists actually do studies where they analyze purchasing decisions across a wide range of income levels. It seems that with enough data points, a utility function could actually be calculated.

Has this been done? And is it actually logarithmic? What is it? Is there an up to date utility function for the value of money?

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    You claim "that is the common recommendation", "it is usually phrased as a rule of thumb", and "the assumption seems embedded everywhere". Who is doing the recommending, phrasing, and assuming/embedding? – Kenny LJ Aug 1 at 6:45
  • What is an "amount of utility"? How do you normalize utility functions to avoid manipulations by monotonic transformations? – denesp Aug 1 at 7:20
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    Hi: There's probably nothing terribly special about it ( that I know of ) except that it flattens out as x increases which is in sync with intuition in the sense that the more dollar bills one has, the less the utilitty of the next one he-she obtains.. Any curve tha flattens out ( decreasing marginal utility ) is probably okay but log(x) has a lot of nice properties such as the difference in the log of prices is ~ the rate of return for example. – mark leeds Aug 1 at 9:01

"Essentially, all models are wrong, but some are useful" George Box, Empirical Model-Building and Response Surfaces

To what approximation would one like to know the utility of wealth?

To be guaranteed that a utility function representation of preferences exists you need a number of assumptions about preferences to hold. Usually, these are Completeness and Transitivity with Continuity, Monotonicity, and Convexity common additional assumptions to give the nicely behaved preferences we see in most economics settings. Levin and Milgrom (2004) provides a technical, but short overview of the definitions and derivations in this branch of economics called "Choice Theory".

But that's just for ordinal utility functions (functions which rank bundles of goods but resulting the utils have no economic meaning beyond that a bundle with higher utils is preferred to one with lower utils) to exist. It turns out that the preferences of ordinal utility functions are preserved by any positive affine transformation. To get a particular functional form like $log(x)$ to matter you need cardinal utility where the values of the preferences matter (although even here they can be re-scaled by a positive constant and a constant added). Cardinal utility functions are more common in finance and macroeconomic settings even though more is required for such a utility function to exist. As Wikipedia says:

The idea of cardinal utility is considered outdated except for specific contexts such as decision making under risk, utilitarian welfare evaluations, and discounted utilities for intertemporal evaluations where it is still applied. Elsewhere, such as in general consumer theory, ordinal utility with its weaker assumptions is preferred because results that are just as strong can be derived.

Cardinal utility

Given the findings of behavioral economics experiments, which provide evidence that preferences may violate completeness and transitive axioms, it is likely that no such "true" function exists. However, as Box's quote indicates, they may still be useful.

The log utility function has nice properties. The logarithmic utility function is a special case of constant relative risk aversion (CRRA) utility function. Roughly speaking, this family of utility functions views risks in percents of wealth as constant for all levels of wealth. That is, rich and poor alike worry the same about a 10% shock to wealth. Equivalently, the utility "pain" of spending 10% of wealth on something are the same at all wealth levels. This is an example of a function with a declining marginal utility of consumption.

Log utility is particularly easy to work with because it has simple derivatives and reflects a "low" but non-zero level of preferences over risk. Also, under log utility the wealth and substitution effects of interest rates cancel out in intertemporal choice problems, which further simplifies some models. In portfolio allocation, log utility maximization is effectively maximizing the geometric mean return the return in the long run, which feels intuitively sensible as an investment goal for the long run investor. Also, when working with shocks to wealth that are log-normal and a log utility function, there are nice closed form solutions to the values of expected utility (they are slightly more complex in the CRRA case). Here is an example of trying to calibrate CRRA utility using experimental data:

For this specification of CRRA, a value of 0 denotes risk neutrality, negative values indicate risk-loving, and positive values indicate risk aversion. Thus we see clear evidence of risk aversion: the mean CRRA coefficient is 0.64. This distribution is consistent with comparable estimates obtained in the United States, using college students and an MPL design, by Holt and Laury [2002] and Harrison, Johnson, McInnes and Rutström [2003a][2003b].

Harrison, Lau, Rutstrom (2007)

If CRRA were the proper specification and the experimental point estimates recovered the true CRRA parameter, then we would be close to log utility which occurs when you take the limit of a CRRA utility function as the coefficient approaches $1$.

In conclusion, there is some empirical evidence that preferences can be approximated with a log utility function and it is very easy to work with.

It's next to impossible to empirically infer the approximate form of a utility function, because all data can tell us is how to order options by their mean utility, e.g. if people prefer one gamble to another. But there is a theoretical motivation. If I take successive independent gambles that each scale with an arbitrary ante I choose to put up, and I know my utility function and use it to calculate my optimum total investment, each gamble has a certain random multiplicative effect on my wealth, or equivalently a random additive effect on my log-wealth. Since my sum is to maximise my mean utility, a logarithmic utility function together with the theorem $\mathbb{E}(X+Y)=\mathbb{E}X+\mathbb{E}Y$ implies the optimum strategy is to take it one gamble at a time in the obvious way. Any other utility function would in general lack this desirable property.

In terms of generic human perceptions of information, you may want to look at "Information, Sensation and Perception" by KH Norwich, 2003 for a viewpoint on generalising the way human's perceive the information (value) of their observations.

It provides a useful link between various spot theories and the mathematics, but avoids the open-ended-ness of most 'psychologist' writings (hence it hasn't been popular..)

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