momopi wrote:Note: this study is done on dating web site "meat market". It's questionable as to how much it'd apply to society overall since most people don't use dating sites. But you can use the data for example or reference.
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http://www.asian-nation.org/docs/online ... -study.pdf
Attribute Trade-Offs
In order to obtain a better understanding of the relative magnitude of the attribute preferences
we consider the implied trade-offs between different traits. We focus on the trade-offs
between income and several other attributes.
First, we look at the trade-off between looks and income. Consider a woman evaluating
the profile of a man whose looks rating is in the nth decile (n < 10) of all looks scores among
men. We would like to know the amount of additional income this man would need to be
as “successful� with the woman as another man whose looks rating is in the top decile. To
that end, we calculate the income variation such that the woman’s utility index for either
man is equal. Remember that the utility index allows for preference heterogeneity through
attribute distance terms, and hence we also need to specify the income of the woman and
the “baseline man� in the top looks decile. We assume (here and below) that the woman
has an annual income of $42,500 and that the man has an annual income of $62,500. These
are the median income levels for men and women among the dating site users in our data.
Table 5.4 shows the income tradeoffs for all looks deciles. A man in the bottom decile,
for example, needs an additional income of $186,000 (a total annual income of $248,500)
to compensate for his poor looks. The table also shows that women cannot make up for
their looks at all. The reason is that our preference estimates indicate that men’s marginal
utility from income is approximately flat between income levels of $100,000 and $200,000
and declining for income levels higher than $200,000. Hence, even for a woman in the 9th
decile of looks there is no amount of additional income that could make her as attractive
in a man’s eyes as a woman in the top decile. Of course, these results should not be taken
fully literally—functional form assumptions, distributional assumptions, and sampling error
will generally influence the precise income compensation numbers. Hence, for example, our
model will not be able to accurately predict how a man evaluates a woman with an annual
income of $2 million. However, the results strongly indicate two basic messages: preferences
for looks are quantitatively important, and there are strong gender differences in the relative
preference of looks versus income.
Table 5.5 shows the trade-offs between height and income. A man who is 5 feet 6
inches tall, for example, needs an additional $175,000 to be as desirable as a man who is
approximately 6 feet tall (the median height in our sample) and who makes $62,500 per
year.
Maybe the most striking numbers are with regard to income-ethnicity trade-offs, as
shown in Table 5.6. For equal success with a white woman, an African-American man needs
to earn $154,000 more than a white man. Hispanic men need an additional $77,000, and
Asian men need an additional $247,000 in annual income. In contrast to men, women mostly
cannot compensate for their ethnicity with a higher income.
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