There is a popular genre of commentary which wishes to show that bright people make as many errors as less bright people, perhaps as a consequence of divine retribution. “Einstein made an error in maths which was spotted by a bus conductor” lifts the hearts of some readers. Of course, bright people make errors. Do they do so at a higher, lower, or the same rate as everyone else?

Can we test this in a way that favours the average citizen? How about ignoring high finance and stock picking and just concentrating on basic aspects of household financial decision-making, the nitty gritty of so many of our lives? Can we also avoid the charge that some have levelled against the correlation of intelligence and earnings and wealth, that some people just don’t want to be rich? Let us restrict the focus to seeing whether people, whatever their income, can avoid costly financial mistakes. I assume that even those who don’t wish to pursue inordinate wealth, but instead want to live happily on an average income, still wish to handle their meagre emoluments in a sensible fashion, avoiding hare-brained incompetence and fiscal irresponsibility.

Agarwal and Mazumder think this matter is worth exploring.

Sumit Agarwal and Bhashkar Mazumder. Cognitive Abilities and Household Financial Decision Making. American Economic Journal: Applied Economics 2013, 5(1): 193–207http://dx.doi.org/10.1257/app.5.1.193

https://drive.google.com/file/d/1tYKD-6nZnIJd9H2Pt2-bXWFCcIkEthWQ/view?usp=sharing

We analyze the effects of cognitive abilities on two examples of consumer financial decisions where suboptimal behavior is well defined. The first example features the optimal use of credit cards for convenience transactions after a balance transfer and the second involves a financial mistake on a home equity loan application. We find that consumers with higher overall test scores, and specifically those with higher math scores, are substantially less likely to make a financial mistake. These mistakes are generally not associated with non-math test scores.

A 1 standard deviation increase in the composite AFQT score is associated with a 24 percentage point increase in the probability that a consumer will discover the optimal balance transfer strategy and an 11 percentage point decrease in the likelihood of making a rate-changing mistake in the home loan application process. Interestingly, we find that verbal scores are not at all associated with balance transfer mistakes and are much less strongly associated with rate-changing mistakes.

This was based on military personnel on whom there were results available on the Armed Forces Qualifying Test, then linked to data from a credit card finance company, so these are real data, able to pick up sub-optimal choices, more commonly known as mistakes.

The “balance-transfer mistake” is to be fooled by a “teaser” low APR rate on a new card into using the new card rather than the old one during the transfer period. This is because while the old purchase balance is transferred at the new low rate, new purchases on the new card are at a new much higher rate. Sneaky. Some will learn from their mistake as the monthly bills come in with surprisingly high interest rates, and have a “eureka” moment. Others will take more time to learn their mistake.

Lower ability people get fooled more often, brighter people learn about their mistake more often, and the brightest (those above 70th percentile) never make the mistake in the first place, so there is a hierarchy of “eureka” moments.

The next issue the authors consider is the mistake of picking the wrong rate when requesting a loan based on a home valuation. This is another opportunity for the lender to crank up the interest rate, but in this case the rates are visible before the loan is taken out, so it is possible to turn down a poor offer, and try another lender.

Again, the mistake (which increases the cost of the loan by 2.7%) is made at lower ability levels, and not in those above the 70th percentile.

Interestingly, given that the 2008 financial crisis was sometimes portrayed as being particularly unfair to black borrowers, the race difference in this study is in the opposite direction, so long as one controls for intelligence.

Interestingly, we find that the effect on being black is actually positive conditional on AFQT scores and education. This finding is interesting in light of the theoretical model developed by Lang and Manove (2011) who argue that blacks of similar ability to whites may need to signal their productivity to employers by acquiring more education. They cite studies suggesting that blacks are not rewarded the same as whites in the labor market for equivalent AFQT scores. It is possible that the increased likelihood of discovering the optimal balance transfer strategy among blacks who have the same measured ability as whites, reflects their greater investments along other dimensions of human capital.

Being intelligent implies an ability to learn quickly, so it is interesting to plot out how long it takes people to recognize their financial errors, and move to an optimal strategy. High scorers avoid any errors (they can see the error in advance, and avoid it) while those of lower ability take 5 months to correct their mistakes.

To illustrate the effects of AFQT scores on the speed at which individuals learn, we plot in Figure 3 the unadjusted mean AFQT scores for borrowers based on how many months it took them to discover the optimal strategy. The chart shows that AFQT is monotonically decreasing in the number of months it takes borrowers to learn. We estimate that a 1 standard deviation increase in AFQT scores is associated with a 1.5 month reduction in the time it takes to achieve optimal behavior speed. This analysis suggests that cognitive skills also affect the “intensive” margin of optimal financial decision-making behavior.

These two real-life financial errors show the importance of cognitive ability, and may explain a wider range of bad choices regarding finance.

In any case, we think that our analysis likely only touches the tip of the iceberg in terms of the effects of poor financial decision making, due to low cognitive ability, on individual and social welfare. It is highly plausible that similar types of financial mistakes have played a role in explaining loan default, foreclosures, and bankruptcies. In a highly complementary paper to ours, Gerardi, Goette, and Meier (2010) find a strong association between numerical ability and mortgage delinquency and default during the recent financial crisis. Future research may shed more light on the quantitative importance of cognitive ability.

In summary, I think that there are plentiful studies showing that intelligence tests produce scores which are predictive of a wide varied of important real-life measures: earnings, savings and managing loans.