Having spent an inordinate amount of time earlier this year figuring out an optimal family cell phone plan, it strikes me that an awful lot of American corporate activity these days consists of figuring out ways to nickel and dime people over complex monthly charges. It`s like a never-ending low intensity war between MBAs with computers versus customers, half of whom will be below average in intelligence, energy, or experience.
The MBA holy grail now is to figure out a way to get people to agree to pay an extra $9.99 per month for something they won`t use—especially, if the original process of coming up with their bill of $173.41 per month was so arduous that they won`t bother to go through all the work it would take to have it reduced to $163.42.
My impression is that American business has gotten more chisely over the years, probably because it`s now so easy for MBAs to model complicated variations of payment plans on spreadsheets and then have them coded into the software used by the $10/hour people at the customer service phone bank. In the old days, it was too complicated for a business to concoct and (especially) administer a whole bunch of different payment options, so corporations offered the equivalent of vanilla, chocolate, or strawberry, and didn`t change them too often. Hence, customers had a fighting chance to figure them out.
Nowadays, though, every MBA can whip up on his PC a million different options. That sounds good for consumers, right?
In theory, it`s also gotten easier for customers to whip up a spreadsheet of their own to figure out their optimal choice. Yet, what percentage of the public is proficient with Excel? Ten percent, maybe?
When I was a young MBA in 1984, the Lotus 1-2-3 spreadsheet was the first piece of software I used on an IBM PC-XT (10 meg hard disk). It was awesome. I figured at the time that half the public would be spreadsheet mavens within a decade.
I was wrong. Although spreadsheets were the glamor software in the mid-1980s, they rather quickly hit a ceiling in popularity and were supplanted by words and pictures software.
This trend toward complexity led us to disaster in the mortgage meltdown as the complexity of the new products covered up their implausibility. As products became more complicated, customers and investors became more dependents upon professional experts, whether mortgage brokers, appraisers, ratings firms, or investment bankers. Simultaneously, however, the various players figured out how to corrupt the people who were supposed to be the independent, neutral professional advisers. For example, mortgage brokers were offered payments to put their clients into higher interest products.
As daily business becomes more complicated, the intelligent and energetic, the ones who can figure out how to cut their bill to $163.42, benefit at the expense of the tired and dumb who can`t.
Here`s a new paper by two Chicago Fed researchers who looked up the military`s Armed Forces Qualifying Test entrance exam scores for people who had a chance to make two common mistakes with their finances.
Cognitive Abilities and Household Financial Decision Making
Sumit Agarwala and Bhashkar MazumderWe analyze the impact of cognitive skills on two specific examples of consumer financial decisions where suboptimal behavior is well defined: first, the use of a credit card for a transaction after making a balance transfer on the account, and second, cases where individuals are penalized for inaccurate estimation of the value of oneâ€™s home on home equity loan or line of credit application. We match individuals from the US military for whom we have detailed test scores from the Armed Services Vocational Aptitude Battery test (ASVAB), to administrative datasets of retail credit from a large financial institution. Our results show that consumers with higher overall composite test scores, and specifically those with higher math scores, are substantially less likely to make a financial mistake. Importantly no such effects are found for verbal or for most other component scores.
Here`s their description of the first of two potential mistakes they can track versus AFQT scores that consumers often make:
Credit card holders frequently receive offers to transfer account balances on their current cards to a new card. Borrowers pay substantially lower APRs on the balances transferred to the new card for a sixto-nine-month period (a â€?teaserâ€? rate). However, new purchases on the new card have high APRs. The catch is that payments on the new card first pay down the (low interest) transferred balances, and only subsequently pay down the (high interest) debt accumulated from new purchases.The optimal strategy during the teaser-rate period, is for the borrower to only make new purchases on the old credit card and to make all payments to the old card. To be clear, this implies that the borrower should make no new purchases with the new card to which balances have been transferred (unless she has already repaid her transferred balances on that card). Some borrowers will identify this optimal strategy immediately and will not make any new purchases using the new card. Some borrowers may not initially identify the optimal strategy, but will discover it after one or more pay cycles as they observe their (surprisingly) high interest charges. Those borrowers will make purchases for one or more months, then have what we refer to as a â€?eurekaâ€? moment, after which they will implement the optimal strategy. Some borrowers will never identify the optimal strategy.
This is the kind of puzzle I would have been fascinated by in 1984. I would have fired up Lotus 1-2-3 and worked it out. Today, though, it just makes my brain feel tired merely to start reading the above description. I just immediately get the impression "Somebody with a fresher MBA than mine has put a huge amount of energy into trying to confuse me into paying more,"
and I want to go lie down.
Conversely, in 1984, would I have fired up my PC to figure a way to chisel our customers out of an extra $9.99 per month? Well, in 1984 my customer was P&G, my employer`s biggest client, and we were all terrified of offending them in any way, so I wouldn`t have done it. But, if my customers were just a bunch of nobody consumers, well, yeah, I probably would have done it and then justified it to myself with some libertarian spiel. But now, I`m old, tired, not as smart, and not as persuaded by libertarian theories of ethics.
Not surprisingly, it`s easier to chisel people with two digit IQs than people with three digit IQs:
We find that among those with AFQT scores above 70 [i.e., the 70th percentile], everybody ultimately identifies the optimal strategy. In contrast, among those with an AFQT score below 50 [50th percentile, i.e., two-digit IQs], the majority will not identify the optimal strategy. ...
Interestingly, verbal intelligence doesn`t help much:
In columns (5) through (8) we use the four component scores (arithmetic reasoning, math knowledge, paragraph comprehension and word knowledge) that are used to calculate the AFQT score. In all four specifications the two math scores are both highly significant suggesting that quantitative skills are critical for avoiding suboptimal behavior. In contrast, we estimate that the effects of the two verbal test scores are a fairly precisely estimated 0. For example, the largest point estimate for a verbal score suggests that a one standard deviation increase in word knowledge would only increase the incidence of â€?eurekaâ€? moments by a little more than a tenth of a percentage point.
By the way, this should provide a cautionary note for using the ten-word vocabulary test score in the General Social Survey (GSS) as an automatic proxy for IQ. It`s often a decent proxy, no doubt, but there are certain situations in which it`s not.