The Bell Curve: Intelligence and Class Structure in American Life (17 page)

We are not home yet, for although we know what it is worth to hire these more proficient dentists and receptionists, we have not yet factored in the validity of the selection test. The correlation between test score and proficiency is roughly .6 for dentists and .2 for receptionists, again based on observation and approximating the top and bottom of
the range illustrated in the figure below. Given that information, we may estimate the expected output difference between two dentists who score at the 50th and 84th percentiles on an intelligence test as being worth $30,000 a year.
⁵⁰
The corresponding difference between two receptionists who score at the 50th and 84th percentiles in intelligence is $750 a year. And this is what we meant by an “interaction effect”: the wage of the dentist is only four times that of the receptionist. But the value to the employer of hiring brighter dentists is
forty
times greater than the value of hiring comparably brighter receptionists.
⁵¹

In a real-life situation, the value of a test (or any other selection procedure) depends on another factor: How much choice does the employer have?
⁵²
There is no point in spending money on an intelligence test if only one applicant shows up. If ten applicants show up for the job, however, a test becomes attractive. The figure below illustrates the economic benefit of testing with different levels of competition for the job (from one to fifty applicants per job) and different tests (from a very poor one with a validity of .2 to a very strong one with a validity of .6).
⁵³
If everyone is hired, then, on average, the hired person is just at the average level of proficiency, which is a standard score of 0. But as soon as even two applicants are available per position, the value of testing rises quickly. With just two applicants per position, the employer gains 16 to 48 percent in productivity, depending on the validity of the test.
⁵⁴
The curve quickly begins to flatten out; much of the potential value of testing has already been captured when there are three applicants per job. The figure above is an answer to those who claim that a correlation of, say, .4 is too small to bother with.
⁵⁵
A validity of .4 (or even .6) may be unimportant if almost all applicants are hired, but even a correlation of .2 (or still smaller) may be important if only a small proportion gets hired.

Since the pivotal Supreme Court decision of
Griggs
v.
Duke Power Co.
in 1971, no large American employer has been able to hire from the top down based on intelligence tests. Estimates vary widely for how much the American economy loses by not doing so, from what Hunter and Hunter conclude is a minimum loss of $80 billion in 1980 (and in 1980 dollars) to what the Hartigan committee thought was a maximum loss of $13 billion for that year.
⁵⁶
The wide range reflects the many imponderables in making these calculations. For one thing, many attributes of an applicant other than a test score are correlated with intelligence—educational level, for example. Schooling captures some, but not all, of the predictive value of intelligence. Or consider an employer using family connections to hire instead of tests. A bright worker is likely to have a bright sister or brother. But the average IQ score difference between siblings is eleven or twelve points, so, again, test scores would predict proficiency better than judging an applicant by the work of a brother or sister.

Modeling the economic impact of testing has additional complexities. It has been noted that the applicant pool would gradually get depleted of the top scorers when every successive employer tries to hire top down.
⁵⁹
As the smart people are hired and thereby removed from the applicant pool, the validity of a test for those still on the job market may change because of, for example, restriction of range. The economic
benefit of using a test would then decline. But if testing tended to place the smartest people in the jobs where the test-job correlations are large, the spread of the productivity distributions is broad, the absolute levels of output value are high, and the proportions hired are small, the benefits could be huge, even if the economic effects of testing the last people in the pool are negligible. In short, figuring out the net effects of testing or not testing is no small matter. No one has yet done it conclusively.

When Only the Best Will Do

To recapitulate a complex discussion: Proficiency in most common civilian and military occupations can be predicted by IQ, with an over-all
validity that may conservatively be placed at .4. The more demanding a job is cognitively, the more predictive power such a test has, but no common job is so undemanding that the test totally lacks predictiveness. For the job market as a whole, cognitive ability predicts proficiency better than any other known variable describing an individual, including educational level. Intelligence tests are usually more predictive of proficiency than are paper-and-pencil tests that are specifically based on a job’s activities. For selecting large numbers of workers, there may be some added predictive power, usually small, when a score on a narrower test of performance is combined with an intelligence test. For low-complexity jobs, a test of motor skill often adds materially to predictiveness. The predictive power of IQ derives from its loading on
g,
in Spearman’s sense of general intelligence.

Choosing Police Applicants by IQ

A case study of what happens when a public service is able to hire from the top down on a test of cognitive ability, drawing on a large applicant pool, comes out of New York City. In April 1939, after a decade of economic depression, New York City attracted almost 30,000 men to a written and physical examination for 300 openings in the city’s police force, a selection ratio of approximately one in a hundred:
⁵⁷
The written test was similar to the intelligence test then being given by the federal civil service. Positions were offered top down for a composite score on the mental and physical tests, with the mental test more heavily weighted by more than two to one. Not everyone accepted the offer, but, times being what they were, the 300 slots were filled by men who earned the top 350 scores. Inasmuch as the performance of police officers has been shown to correlate significantly with scores on intelligence tests,
⁵⁸
this group of men should have made outstanding policemen. And they did, achieving extraordinarily successful careers in and out of policing. They attained far higher than average rank as police officers. Of the entire group, four have been police chiefs, four deputy commissioners, two chiefs of personnel, one a chief inspector, and one became commissioner of the New York Police Department. They suffered far fewer disciplinary penalties, and they contributed significantly to the study and teaching of policing and law enforcement. Many also had successful careers as lawyers, businessmen, and academics after leaving the police department.

If we were writing a monograph for personnel managers, the appropriate next step would be to present a handbook of tables for computing when it makes economic sense to test new applicants (ignoring for the moment legislative and judicial restrictions on such testing). Such a calculation would be based on four variables: the predictive power of the test for the job at hand, the variation in worker productivity for the job at hand, the proportion of job applicants that are to be selected, and the cost of testing. The conclusion would often be that testing is profitable. Even a marginally predictive test can be economically important if only a small fraction of applicants is to be selected. Even a marginally predictive test may have a telling economic impact if the variation in productivity is wide. And for most occupations, the test is more than marginally predictive. In the average case, a test with a .4 validity, the employer who uses a cognitive test captures 40 percent of the profit that would be realized from a perfectly predictive test—no small advantage. In an era when a reliable intelligence test can be administered in twelve minutes, the costs of testing can be low—lower in terms of labor than, for example, conducting an interview or checking references.

We are not writing a monograph for personnel managers, however, and the main point has nothing to do with whether one favors or opposes the use of tests as a hiring device. The main point is rather that intelligence itself is importantly related to job performance.
Getting rid of intelligence tests in hiring—as policy is trying to do—will not get rid of the importance of intelligence.
The alternatives that employers have available to them—biographical data, reference checks, educational record, and so forth—are valid predictors of job performance in part because they imperfectly reflect something about the applicant’s intelligence. Employers who are forbidden to obtain test scores nonetheless strive to obtain the best possible work force, and it so happens that the way to get the best possible work force, other things equal, is to hire the smartest people they can find. It is not even necessary for employers to be aware that intelligence is the attribute they are looking for. As employers check their hiring procedures against the quality of their employees and refine their procedures accordingly, the importance of intelligence in the selection process converges on whatever real importance it has for the job in question, whether or not they use a formal test.

Because the economic value of their employees is linked to intelligence, so ultimately are their wages. Let us consider that issue in the next chapter, along with some others that have interlocking implications as we try to foresee, however dimly, what the future holds for the cognitive elite.

Cognitive partitioning through education and occupations will continue, and there is not much that the government or anyone else can do about it. Economics will be the main reason. At the same time that elite colleges and professional schools are turning out brighter and brighter graduates, the value of intelligence in the marketplace is rising. Wages earned by people in high-IQ occupations have pulled away from the wages in low-IQ occupations, and differences in education cannot explain most of this change.

Another force for cognitive partitioning is the increasing physical segregation of the cognitive elite from the rest of society. Members of the cognitive elite work in jobs that usually keep them off the shop floor, away from the construction site, and close to others who also tend to be smart. Computers and electronic communication make it increasingly likely that people who work mainly with their minds collaborate only with other such people. The isolation of the cognitive elite is compounded by its choices of where to live, shop, play, worship, and send its children to school.

Its isolation is intensified by an irony of a mobile and democratic society like America’s. Cognitive ability is a function of both genes and environment, with implications for egalitarian social policies. The more we succeed in giving every youngster a chance to develop his or her latent cognitive ability, the more we equalize the environmental sources of differences in intelligence. The irony is that as America equalizes the circumstances of people’s lives, the remaining differences in intelligence are increasingly determined by differences in genes. Meanwhile, high cognitive ability means, more than ever before, that the chances of success in life are good and getting better all the time. Putting it all together, success and failure in the American economy, and all that goes with it, are increasingly a matter of the genes that people inherit.