How meritocratic is the United States?

Research in Social Stratification and Mobility 24 (2006) 111–130

How meritocratic is the United States? Paul W. Kingston Department of Sociology, University of Virginia, 547 Cabell Hall, Charlottesville, VA 22903, United States Received 3 December 2003; received in revised form 3 November 2004; accepted 2 February 2005

Abstract This paper argues that a meritocracy exists to the extent that economically valued positions are allocated on the basis of educational attainment and performance, cognitive ability, and general personal dispositions like conscientiousness. The paper justifies these components of merit on the grounds that each is notably linked to job performance or is a reasonable predictor of performance. Drawing on the status attainment and wage determination literatures, this review establishes that in absolute terms the meritocratic impact is significant though not decisive, but the meritocratic impact is much more consequential than the impact of ascribed characteristics. © 2006 Elsevier Ltd. All rights reserved. Keywords: Performance; Status; Merit

1. How meritocratic is the United States? Is the American stratification system just? Explicitly or implicitly, this normative question animates the sociological study of inequality. Now, this larger question is often specifically framed by the argument about meritocracy: to what extent does merit shape the allocation of economic positions? Because meritocracy is generally deemed a social ideal, the answer to this question goes to the heart of judgments about the overall justice of social arrangements. The publication of The Bell Curve (Herrnstein & Murray, 1994) dramatically revealed how this question generates intense passion. In presenting a semi-popular brief for the existence of a meritocratic order, Herrnstein and Murray threw down the gauntlet. Their many critics responded forcefully, arguing that ascriptive factors substantially if not decisively affect allocation processes, and that the impact of intelligence is relatively minor (e.g., Fischer et al., 1996). The related disputes were

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not new, just more visible and perhaps more pointed. These disputes remained unresolved, largely because they reflect a welter of conceptual ambiguity, partial readings of often complex findings, and divergent ideological commitments. My aim here is to advance this debate by considering a wide range of existing findings in light of an explicit conceptualization of the issue. Too often, debaters talk by each other because they do not even attempt to answer the fundamental question, how would you recognize a meritocracy if you saw one? The coefficients in our multivariate models do not speak for themselves. We have to decide which data are most decisive and how to interpret their implications. At the same time, we must be attentive to the technical adequacy of relevant empirical analyses. To give a preliminary sense of the ambiguities involved, let me just note a few well-established findings that are related to the dispute: – Education is the single best predictor of economic position and the net effect of parental SES is relatively minor. On the common belief that education

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represents functional skill (achievement), this finding very substantially advances the meritocratic argument. Yet the equation of education and merit is not unproblematic. Some have argued that education essentially represents conformity to arbitrary middle class values. If so, the educational effect cannot be taken as evidence for meritocratic processes. – Combined together, the effects of all indicators of “merit” account for substantially less than one-half of the variance in various indicators of economic success. Critics of the meritocratic thesis might rightly question the argument that merit “rules” if so much depends on other undetermined factors. However, proponents might reasonably rejoin that the relative significance of achievement-related factors is greater than ascribed factors. – SES is related to educational attainment and academic performance, creating unequal chances to display merit. If school practices differentially promote attainment and performance, the meritocratic thesis is substantially compromised. Yet if school practices themselves reward academic skill, this thesis is advanced. Such interpretative issues are not easy to resolve. Still, social scientists have produced a wealth of relevant analyses that, if synthesized, provide a good basis for a summary assessment. In the following pages, I make this general argument: in absolute terms, meritocratic factors significantly though not decisively shape allocation processes; and in relative terms, meritocratic factors have substantially more impact than ascriptive factors. That generalization is subject to some qualifications, especially related to gender. Moreover, it critically depends on defining meritocracy in a relatively expansive light. This conceptual approach does not suggest a yes/no judgment about the existence of meritocracy; rather it defines the issue as one of degree. 2. Defining meritocracy Hoffer (2002, p. 255) usefully distills the conventional understanding of meritocracy: “The term meritocracy is usually understood to mean that individuals are selected for educational opportunities and jobs on the basis of demonstrated performance or ostensible predictors thereof.” Yet analysts have not agreed about what factors represent demonstrated performance or predict it—and thus cannot offer comparable assessments of the extent to which meritocratic processes affect allocation. Young (1958), who coined the term meritocracy in his satirical novel, took a somewhat restricted view. In

his formulation, merit was defined as “IQ plus effort,” though being “bright” seemed to be more central to his own discussion and to subsequent analysts who drew on it (see also Saunders, 1997). A common criticism of defining “merit” as IQ, even when coupled with a concern for effort, is that the definition is too narrow: job-related performance depends on other mental capacities. Classic IQ tests primarily measure analytical, logicbased reasoning, but other kinds of cognitive ability are also related to performance—and thus also represent merit. For instance: imagination, practical sense, and the ability to interpret others’ perspectives. By the same token, the effort component of Young’s formulation suggests that a number of personality factors may figure into a reasonable conception of merit. As Hauser et al. (2000, p. 203) usefully argue, “It is not clear why the term merit should be identified so closely with mental ability as distinct from many other conditions and traits that improve the chances of social and economic success.” For example, if you have a sense that your own actions can affect outcomes in your life (an internal locus of control), you may have an edge in job performance over those who are fatalistic. Of course, some individual traits/social skills may be rewarded because they reflect conformity to arbitrary group norms rather than productive capacities (e.g., the preference for dramatically displayed decisiveness in male-dominated corporate suites). Still, despite ambiguities, there seem to be some general psychological dispositions that warrant the term merit because they are linked to productivity in a wide range of settings. Furthermore, as Hoffer emphasizes, hiring on the basis of educational attainment is very commonly thought to represent the application of a meritocratic criterion. The presumption is that the more educated have either acquired productive skills through their academic experiences or are at least relatively endowed with them. Educational credentials thus are often presumed to identify merit. These preliminary considerations argue for an inclusive formulation, despite the ongoing attraction to the originator’s own emphasis on IQ scores. Hence, a meritocracy exists to the extent that economically valued positions are allocated on the basis of: (1) cognitive ability in any dimension related to job performance (i.e., general IQ, as well as measures of specific capacities like computational ability and scores on standardized tests of academic knowledge); (2) educational attainment (years of schooling and credentials) and educational performance (grades);

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(3) all general personal dispositions that are related to job performance in a wide range of settings and are not distinctly linked to particular ascribed groups (e.g., hard work and conscientiousness). While this formulation draws on common notions of what constitutes merit, it is distinct in being so explicitly inclusive. Ultimately, however, this inclusive conceptualization is compelling only if it can be demonstrated that each dimension is significantly related to job performance—the focus of Section 1. As I will review, research indicates that these three basic components of merit are related to allocation in most economic arenas in contemporary U.S. society, but they may be secondary or even irrelevant in a few well-rewarded sectors like professional sports and entertainment. In these sectors, a meritocratic order prevails if people are rewarded for individual prowess—hitting a curve ball or writing a catchy tune. Such special talents are crucial in particular niches of the economy, but their impact is hard to measure and, in any case, are relatively unimportant for assessing the general “rules of the game.” Of course, any notion of merit is culture dependent, even that linked to intelligence (Olneck & Crouse, 1979). The three personal factors invoked here represent merit in a particular context: each is presumably related to job performance in an advanced industrial, knowledgebased society. Although this assessment is focused on the U.S., these dimensions of merit seem sufficiently generic to apply broadly to all advanced societies—and thus be relevant for comparative analysis of these societies. To examine the rules of the game, the obvious point of comparison is merit (achievement) versus ascription: what you can do versus what groups you belong to. In concrete terms, ascription usually means the impact of socio-economic status/class, race, and gender on economic position. (Having the “right” connections may also be considered a non-merit based factor, although this advantage is often viewed as the mediating mechanism by which ascriptive advantages are realized.) So, assessment of the meritocratic thesis essentially involves comparing the impact of ascriptive and achieved factors on economic rewards. No particular magnitude of effect determines whether a society is meritocratic, however; it inevitably remains necessary to make judgments about the absolute and relative impact of merit. As part of this definitional discussion, however, let me stress that there is no implication that merit means deservingness. That implication may readily follow from a common connotation of the term “merit.” Yet the analytic issue here is whether certain personal capacities are

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rewarded, not whether they should be. Despite the common embrace of meritocracy as a good thing, its ethical claims are not so clear. For one matter, while cognitive ability is linked to economic success, a substantial part of an individual’s cognitive ability represents genetic inheritance. A person did not do anything to earn this genetically rooted endowment: it was the luck of the draw. Why, then, should a person be rewarded for good luck? That is a philosophically contentious issue.1 Assessing the meritocratic tendencies in American society involves both conceptual and empirical issues. My argument about these interrelated matters is developed as follows: Section 1 justifies each of the three components of merit by establishing that each is significantly related to performance and thus meets Hoffer’s standard. While seemly focused on a preliminary concern, this section is critical because the assessment of meritocracy ultimately depends on the conceptual meaning of empirical relationships such as that between education and earnings. Section 2 reviews relevant empirical research in the sociological, economic, and psychological literatures on the determinants of economic success. Section 3 assesses the meritocratic underpinnings of educational attainment. This concern follows from the central role of education in the allocation process. Section 4 considers the implication of “effect sizes” for the overall assessment along with the social implications of the argument. 3. Section 1: justifying indicators of merit Because cognitive ability, educational attainment, and (probably to a lesser extent) personality traits are systematically related to economic success, any compelling assessment of the meritocratic issue must consider whether they are valid indicators of merit. Economists, in line with the neoclassical model, assume that any characteristic that is rewarded is a productive skill: in a competitive labor market, workers’ earnings equal their marginal productivity. Yet, as the economists Bowles et al. (2001, p. 1137) have aptly observed, “. . . we know surprisingly little about what skills make up the vector of individual capabilities contributing to higher earnings . . ..” And in a contrary perspective, many critical sociologists see alleged “abilities” or “skills” as arbitrary social constructs largely divorced from productivity, but their arguments rely on limited empirical analysis. Given this

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The irony here is that many social critics have indicted American society for having fallen short of meritocratic ideals, while Young presumed that a meritocratic order was emerging and would be very unappealing.

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dispute, the empirical case for these three components of merit must be made. 3.1. Cognitive ability I start with cognitive ability because it has place of honor in Young’s original formulation, even if sociologists have been ambivalent or even resistant to considering its social impact (Hauser, 2002). Are “smarter” workers better workers? The best evidence overwhelmingly indicates that workers with higher IQ’s, better scores on tests of general cognitive, or indeed any test of verbal or quantitative skill are more productive workers. Hunter (1986) has effectively summarized the research – much in the applied psychology literature – about the correlation between cognitive ability and performance ratings and training success: – hundreds of validation studies of general cognitive ability, corrected for measurement error and restricted variance in ability, showed substantial average correlations between cognitive ability and performance ratings and training success. For performance ratings, the average correlations ranged from a low of .27 for sales clerks to .46 for trade and crafts workers to .53 for managers; – the U.S. Employment Service conducted 515 validation studies of the General Aptitude Test in its first 30 years of use. For job proficiency ratings, it always had substantial predictive validity, and the greater the job complexity, the greater the correlation with cognitive ability (.58 for highly complex jobs). For training success, the correlation was above .5 for all levels of job complexity; – an even larger body of studies conducted by the U.S. military indicated correlations of about .6 across a wide range of job families. Moreover, studies of actual performance at work stations, obviously a preferable indicator, also indicate the significance of being “smart”: the average validity for civilian work was .75. Why does general cognitive ability lead to better job performance? The best answer appears to be those with greater ability more readily acquire specific job knowledge (i.e., techniques, protocols, information retention) that in turn leads to better performance. However, the ability effect is not entirely mediated through job knowledge, suggesting the direct impact of general capacities (e.g., the ability to synthesize data) on performance in diverse settings (Gottfredson, 1997; Hunter & Schmidt, 1996).

True, scores on all such tests are substantially associated with family background; for example, Raudenbush and Kasim (1998) report a correlation of .51 between family SES and adult literacy, and Jencks et al. (1979) report correlations in the .2–.3 range between paternal education and occupation and adolescent test scores in a number of data sets. But such correlations in themselves do not mean that test scores are rewarded as cultural markers in an ascriptive process. High scorers rarely benefit from the label of being a high scorer: employers generally do not have access to test scores and do not promote on the basis of them (Hunter & Schmidt, 1996; Rosenbaum, 2001). The argument that test scores represent a cultural marker falters on two other grounds. First, as to be detailed later, the statistical “effects” of scores are net of family SES and race. Were scores simply a proxy for class or race, the bivariate association between scores and economic outcomes should be substantially reduced with controls for ascriptive factors. That is not the case. Second, scores have predictive, though somewhat variable, impact on economic outcomes across social groups, suggesting that higher scores are not a cultural resource of particular groups (Raudenbush & Kasim, 1998). If “smarter” people are more productive and being “smart” is generally beneficial to everyone irrespective of their social affiliations, then test scores are a valid indicator of merit by Hoffer’s definition. Let me stress that the case for a substantial connection between cognitive ability and productivity does not imply any particular stance on contentious issues about intelligence—its inheritability, malleability, or dimensionality. The relevant point here is simply that basic verbal and mathematical competence – whatever its source – is a productive capacity. For the purpose of assessing the meritocracy, the “nature/nurture” debate is irrelevant. What is relevant are the consequences of cognitive ability, not its cause. 3.1.1. Common sense too The test for meritocratic allocation should not be limited to assessing the explanatory power of strictly construed IQ scores. It seems entirely consistent with the general implications of the meritocratic thesis to find that a wide range of cognitive capacities are related to economic success, even what has come to be called practical intelligence (Sternberg et al., 1995)—or in lay terms, common sense. Practical intelligence has not been measured with the same psychometric precision as cognitive ability, nor has it been as systematically validated as a predictor of job performance. However, a fair number of small-scale studies, conducted in a variety of work

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situations, suggest that the ability to handle complex on-the-job problem solving is notably related to practical intelligence and only weakly related to conventional measures of cognitive ability (see summary in Sternberg et al., 1995). Complementary to analyses of practical intelligence, psychologists have also turned to considering tacit knowledge—“action-oriented knowledge, acquired without direct help from others, that allows individuals to achieve goals they personally value” (Sternberg et al., 1995, p. 916). It is about knowing how to get things done. While such knowledge would seem to be domainspecific (e.g., knowing how to work the system in a particular office), some research indicates that measures of tacit knowledge tap a fairly general construct (Wagner, 1987). Like measures of practical intelligence, measures of tacit knowledge are only weakly related to measures of cognitive ability and to ascriptive attributes—and yet they are substantially related to measures of job performance. Admittedly, the connection between tacit knowledge and job performance rests on a thin evidentiary base, and the role of such “other intelligences” in the larger stratification process has barely been explored. As a result, when I later empirically assess the extent of meritocratic commitments, I cannot systematically incorporate these concerns. By drawing attention to common sense and tacit knowledge, however, I hope to underscore how the debate about the meaning of merit is unduly restricted—and thus how the assessment of meritocracy should involve more than comparing the net effects of IQ scores and SES measures. 3.1.2. Interpreting education Because educational attainment is the prime determinant of occupational success, the debate about meritocracy critically hinges on how to interpret the “education effect” in multivariate models. The theoretical debate about the connection between education and career success is basically defined by two competing explanations: the socialization and allocation models (Bidwell, 1989; Kerckhoff, 1976). Within both the status attainment (e.g., Blau & Duncan, 1967) and human capital literatures (Blaug, 1972) it is presumed that the experience of going to school transforms the capacities of individuals in productive ways that are rewarded in the labor market. While education is not a direct indicator of human capital (i.e., productive capacities), economists typically treat it as a good proxy. In these socialization models, the clear implication is that the “education effect” reflects the workings of a meritocratic order.

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In the allocation model education is portrayed as a sorting device, with gatekeepers using academic credentials to identify the desired group and exclude others. Depending on their educational certification, individuals can thereby benefit or lose out, whatever their own personal capacities. Why are credentials rewarded in the labor market? Answers differ, essentially reflecting arguments about the connection between credentials and productive skills (Bridges, 1994). By some accounts credentials provide low-cost signals of unmeasured productivity (Spence, 1974) or desirable characteristics like punctuality and perseverance (Berg, 1971). On the other hand, Bourdieu (1977) and Collins (1979) have argued that the educational signal has little to do with employers’ concerns for productivity. They have similarly contended that particular levels of educational attainment represent certification of different sorts of class-rooted cultural capital—and secondarily, may help create it (a socialization effect). Thus, in their homes and elite schools, privileged children presumably learn the personal styles and cultural dispositions that fit elite occupational positions—and being certified by the right academic credentials, they are slotted to top positions by elite gatekeepers. To the extent that the educational signal (credential) involves concerns for productivity instead of cultural capital, the meritocratic argument is advanced. Although not all the individual beneficiaries of this credentialism are equally productive (remember, all degree holders are presumed to benefit), the selection process would seem governed by the goal of efficiently hiring productive workers. On the other hand, to the extent that the education effect reflects the pay-off to cultural capital, the meritocratic argument is undermined. Education, then, would be an indicator of ascriptive status, a mechanism whereby privilege is transmitted across generations. This dispute is difficult to arbitrate decisively because the related analyses are surprisingly sparse. But the best evidence does fairly consistently suggest that education is associated with productive skills or at least signals them. 3.1.3. The case for productivity The case for the education–productivity link most strongly rests on the fact that educational attainment is substantially associated with measures of cognitive ability—and as detailed above, greater cognitive ability is substantially associated with greater productivity. Hauser et al. (2000), for example, report a correlation of .66 between AFQT scores and educational attainment for young non-black men. Yet, the educational effect on career success cannot simply be attributed to the fact that the more educated are smarter and thus more productive.

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Bowles and Gintis (2001) review a large set of studies and find that, on average, measures of cognitive ability account for about one-fifth of the relationship between education and log earnings. Jencks et al. (1979; Table 6.2) found that test scores accounted for even less of the association between years of education and occupational status. These large residuals mean that there is something about education other than its association with cognitive ability that leads to economic success. Although the connection is less well-established, educational attainment seems to be associated with other productivity-enhancing characteristics. For one, managers’ tacit knowledge was found to be notably related to years of education (.37), self-reported school performance (.26), and college quality (.34) (Sternberg et al., 1995). Recall that tacit knowledge is related to indicators of job performance and is only weakly related to measures of cognitive ability. In effect, then, greater cognitive ability and tacit knowledge are additive resources of the more educated. In addition, education is related to a number of personality characteristics that are related to job performance. Dunifon and Duncan (1998, unpublished results provided by authors) find that education is related to efficacy (.31) and an orientation to challenge (.22). Similarly, Kohn and Slomczynski (1990; Table 4.5) report that education is quite strongly related to “intellectual flexibility” (.55) and “self-directedness” (.52). As detailed in the later section “Section 3.2,” such personality characteristics are notably associated with performance. Whether the actual content of school knowledge enhances productivity is not fully settled, though it probably has more impact than is commonly believed. Even if many job skills are largely learned on the job (Berg, 1971), those with vocation-specific training in a number of technical fields outperform their untrained counterparts, even in fields like teaching where its efficacy is commonly doubted (Laczko-Kerr & Berliner, 2002). Moreover, in a number of lucrative credential-controlled professions such as law or engineering, it is hard to imagine that a person could be an effective practitioner without professional training. The connection between academic performance and job performance, even if modest, further suggests that the content of schooling is relevant to productivity. In their meta-analysis, Hunter and Hunter (1984; Table 8) reported that college G.P.A. had validities of .11 for supervisor ratings, .21 for promotion, and .30 for training success. Further, as many others, Rosenbaum (2001) documented that among recent high school graduates, grades are unrelated to early earnings – often taken as a

sign that the content of schooling does not matter – but his important finding is that high school grades are notably associated with earnings 9 years after graduation. In line with this finding, Jencks, Crouse, and Mueser (1983) found that high school grades, net educational attainment, and cognitive ability, were associated with higher occupational status. Considered together, such findings suggest that education is more than an arbitrary screen: those who have the knowledge, skills, or dispositions to do well in school are rewarded for these capacities in their careers, despite the fact that employers typically know little about school performance and thus do not pre-label employees by their grades. Even with better and more comprehensive measures of productivity-related skills and traits, however, it is unlikely that the education effect on career outcomes could be fully accounted for by the individual productivity-related differences of the more and less educated. Surely a good part of this effect is attributable to the fact that educational attainment is a prominent signal of productive characteristics. Given the significant association between education and such productivityrelated traits as cognitive ability, practical sense, work habits, and personality dispositions, education provides employers with “an inexpensive and efficient way of creating acceptable applicant pools” (Gottfredson, 1985, p. 154). That is, by selecting on education, employers increase their odds of getting productive workers without incurring the high costs of other selection procedures. Screening on education, then, seems consistent with Hoffer’s definition of meritocracy because it involves “ostensible predictors” of performance, even if it does not necessarily distinguish differences in individual merit. 3.1.4. The status culture case The critics advance a very different screening argument. As represented by Bourdieu (1970), Brown (1995), and Collins (1979), the counter-argument denies that education is associated with inherently productive capacities and claims instead that education is rewarded because it reflects status cultures. Thus, in Brown’s formulation, the highly credentialed are prized because they are conversant in the “language group” of those who control professions and organizations and are inclined to accept their culture and interests. By screening on education, then, employers presumably reduce the uncertainties related to the control of new entrants. Perhaps so in some sectors of the labor market, but the supporting evidence is very limited. For example, Neckerman and Krischenman (1991) document that a number of employers in Chicago were more attracted to Catholic

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than public school graduates—a fact that seems related to racially based behavioral concerns, not any greater appreciation of the former’s technical skills. Undoubtedly, some other supportive studies could be adduced, but the case has yet to be made that status cultures/control arguments apply broadly throughout the labor market or that education is primarily an ascriptive marker. The critical perspective becomes particularly problematic as it invokes class-based dynamics. If education is essentially a marker of class affiliation, used for both exclusionary and inclusionary practices, we should expect the following: (1) a fairly tight link between class origins and educational attainment and (2) controlling for class origins, a substantial reduction in the association between education and economic success. The correlation between father’s occupation and educational attainment generally is about .4, consequential but short of tight (e.g., Featherman & Hauser, 1978). (Recall, for point of comparison, the previously cited correlation of .66 between cognitive ability scores and educational attainment.) As to the second condition, the argument cannot be sustained. The education effect is largely independent of family origins (Jencks et al., 1979; Tables 6.2 and 6.3). Critics of the human capital/productivity argument have also often emphasized that educational effects are non-linear—that is, net of years of education and cognitive ability, having particular degrees (credentials) provides economic advantage. Multivariate analyses clearly reveal credential effects especially for college degrees, but the underlying basis for these effects is not resolved (Jaeger & Page, 1996; Jencks et al., 1979).2 To the critics, credential effects suggest an allocation process that is devoid of economic rationality: the credentialed are favored above those of presumably equal productive capacity. That is possibly true; for instance, credentialed gatekeepers may favor similarly credentialed applicants as a way to bid up the value of their own degrees. Yet degree holders may well differ from non-degree holders in productive but unmeasured ways—for example, having greater tenacity or attention to detail. Similar considerations apply to research indicating that degree prestige, net measures of cognitive ability, affects earnings (Kingston & Smart, 1990). The returns of having prestigious degrees may reflect the social cachet and 2

While it is true that there are “credential effects” net of cognitive ability, it is also the case that, net of years of schooling, those with credentials have notably greater cognitive ability than those without credentials. This latter point suggests that if degrees are used as a screen, employers may be using a screen that is related to productivity (Arkes, 1999).

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connections that such graduates enjoy, but they also may reflect unmeasured productive capacities of the prestigiously credentialed. This matter is not settled.3 In short, it is impossible to specify what exact proportion of observed education effects on careers reflect the greater productive capacities of the more educated. Surely it is not total, as suggested in many standard human capital accounts. But the significance of the education–productivity connection seems very substantial because (1) education is reciprocally related to cognitive ability which is related to job performance and earnings, (2) education is related to other productivityenhancing personal characteristics, and (3) better school performance is modestly related to job performance and to later rewards despite employers being unaware of grades. So, if employers screen on education, they enhance the productivity of their work force in a socially acceptable, inexpensive way. Even though the greater (measured) productive capacities of the more educated do not fully account for the education effect on careers, that does not mean, by a simple default logic, that the residual effects reflect processes of class reproduction. That argument is significantly undermined by the fact that education effects are largely independent of SES/class origins. In the final analysis, I conclude that we need to apply only a modest discount on educational effects as an indicator of meritocratic processes. 3.2. Personality measures Common sense suggests that personality (e.g., being outgoing) affects career outcomes, as does having certain attitudes (e.g., a willingness to defer gratification) and dispositions (e.g., being on time). However, before considering whether this is so, it is first essential to establish whether personal capacities can be considered aspects of merit in the sense of being productivity-enhancing. This is no simple task: the range of personal capacities that might affect productivity is vast, and the distinction between what is a genuine productive capacity and what is a culturally preferred style can be blurry. 3 Studies of the “prestige effect” are always open to the charge of the omitted variable bias: any apparent effect may reflect unmeasured characteristics of the graduates of different institutions. In an innovative attempt to reduce this bias, Dale and Krueger (1999) compared the postgraduation incomes of the graduates of the most elite colleges to the graduates of somewhat less elite institutions who had been accepted at a very elite institution but did not attend. In effect, the admissions offices at the most elite institutions are holding constant a wide range of tangible and intangible attributes that are presumably related to academic and career success. The result: a slight advantage to the less prestigiously credentialed.

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Consider the very bright hardworking executive who alienates co-workers because of repeated failure to acknowledge others’ contributions. Now compare this executive to the equally bright, hardworking colleague who has the capacity to work cooperatively and even to inspire the work efforts of others. You might say that this second executive has better social skills—a matter to consider in comparing the merit of the two. Yet, what counts as a social skill is hardly a culturefree construct. As suggested before, you can also readily imagine that certain personal styles like dramatically displayed decisiveness are favored in corporate suites. In a sense that style is a “skill” that is connected to performance in that context, but it also seems to be a “male skill.” It is certainly questionable whether this “skill” represents some true productivity-enhancing capacity or is just an expression of maleness in a context dominated by male gatekeepers. That is, more generally, what passes for social skills may represent, at least in part, conformity to the norms of dominant groups—and thus cannot be construed as merit. The analytical challenge is to identify personal capacities that are rewarded largely because they represent productive ability, not because they reflect conformity with prevailing group norms. The distinction cannot be hard and fast: your productivity is inherently affected by the constraints of particular social pressures (do you fit in?). Even so, some capacities seem relatively functional for actually getting a job done—for example, a willingness to work hard. On the other hand, some capacities seem relatively a matter of cultural conformity even if economically rewarded—for example, the ability to affect a breezy yet assured style in interactions. Having a breezy, assured style does not seem necessary for productive performance. To help make this distinction, I propose a threepronged test to detect merit among personal attributes: (1) a prima facie connection between the personal attribute and effective job performance across a range of jobs (or better yet a demonstrated correlation); (2) no more than a modest association between the attribute and ascriptive factors (this undercuts concerns that the attribute is an aspect of class-based cultural capital); (3) evidence that the personal attribute is associated with career success across demographic groups (e.g., women and men both benefit). Given the limits of existing research, this test cannot often be fully met. Yet it sets a standard pointing to the kinds of personal attributes that reflect merit. 3.2.1. Big Five personality traits Many psychologists endorse the view that the key dimensions of personality can be boiled down to a

five-factor model of personality, often deemed the Big Five (Goldberg, 1990). These are: (1) neuroticism— essentially emotional stability, (2) extraversion—not only sociability but an orientation to activity and dominance, (3) conscientiousness—an achievement orientation, dependability and orderliness, (4) openness to experience—an intellectual orientation and unconventionality, and (5) agreeableness—cooperative and likeable. Variations in measures of these traits seem to reflect basic features of personality that are not dependent on social context. The dimensionality of the Big Five holds in many cultures and is quite stable over time (McCrae & Costa, 1992; Salgado, 1997). Some evidence indicates that basic traits, especially dimensions of neuroticism and conscientiousness, are associated with better job performance. Based on largescale meta-analytical findings about supervisor’s ratings, Schmidt and Hunter (1998) report that the predictive ability for integrity and conscientiousness tests are .41 and .31, respectively. Similarly, for a set of Army jobs, McHenry et al. (1990) report substantial correlations between assessments of achievement orientation, dependability, and emotional adjustment and such outcomes as job proficiency. 3.2.2. Characteristics While psychologists have studied traits as relatively stable and enduring aspects of personality, they also consider features of personality that are affected by circumstances. A prime example is self-esteem which can be influenced by events and the reactions of particular others. Brockner (1988) found that workers with high self-esteem were judged to use time more efficiently, to consider a wide range of solutions to problems, and to be more confident decision makers.4 In short, more conscientious, dependable and emotionally stable people tend to do better at work. The fact that these personal capacities reflect what appear to be universal dimensions of basic personality and are consequential in diverse work settings undercuts the argument that they are arbitrary social constructions. 4. Section 2: battle of the coefficients To broadly set the context of this assessment, it is relevant to note that for men the intergenerational correlation of occupational status is about .4 (Hauser et al., 2000) 4 This finding suggests the general difficulty of establishing the causal relationship between some personality measures and success because they are jointly determined.

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and for earnings about .5 (Mulligan, 1997).5 As should be obvious, however, the fact that there is an association between the economic position of parents and their children does not make the case against the meritocratic thesis (Saunders, 1997, 2002). That would follow only on the unrealistic assumption that productivity-related capacities (merit) are evenly distributed across social groups. Any reasonable assessment of the meritocratic thesis must acknowledge the fact that indicators of merit are related to ascribed characteristics. That linkage per se does not invalidate these indicators. Since Blau and Duncan’s (1967) pioneering work, the status attainment literature has been impressively elaborated and provides the most relevant findings for assessing the meritocracy debate.6 Its bedrock assumption is that an individual’s occupational success is determined by a concatenation of ascriptive and achieved factors over the life course. This theoretical perspective is captured in path-analytic models that seek to show the direct (net) and indirect (mediated) effects of these factors on economic success. These models can therefore be used to “explain” the association between origins and distinctions—that is, to specify the extent to which the offspring of higher status families enjoy greater success because they are more “meritorious” or have other advantages. In this section, I summarize key findings from the best analyses in this literature and from the related economic literature on wage determination. Both of these literatures portray personal economic outcomes as largely the product of differential individual resources and, indeed, tend to incorporate many of the same variables in their models. Yet sociologists and economists focus on different outcomes. Sociologists have conceptually favored a multi-faceted sense of socio-economic status, but operationally they have most frequently employed indices of occupational status.7 For economists, the metric is obvi-

5 This latter estimate based on the Panel Study of Income Dynamics is substantially higher than earlier estimates, a difference that appears attributable to better measurement including corrections for measurement error. 6 In making this argument, I am following the lead of Saunders (1997) who makes a very impressive empirical case for the meritocratic underpinnings of British society. See also Bond and Saunders (1999). 7 In brief, the scores for occupations in these indices are based on a weighted average of occupational educational attainment and occupational income (or earnings) for census occupation lines. The weights for occupational education and income are usually derived from regressions of popular ratings of occupational prestige on these factors. The theoretical meaning of these indices is not clear—why is prestige the basis of the ranking scheme, what does it signify?

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ous and intuitively understandable: various dimensions of monetary reward—yearly or hourly earnings, family or personal income. (In practice, economists tend to rely on log values.) On the presumption that each measures a significant dimension of economic life, I will simply report analyses of both and compare results as possible. These are not proxy measures: Jencks et al. (1979) report a correlation of about .5 between Duncan SEI scores and income. As a further preliminary point, I should highlight the fact that no analysis, either of status or earnings, is ideal for assessing the meritocratic thesis. Necessarily, this assessment involves piecing together many imperfect analyses and noting their limitations. Even so, the accumulated weight of this research points to reasonably consistent, coherent patterns. This assessment does not rest on the precise size of coefficients so much as the rough magnitude of effects. Rather than cite all related studies and try to reconcile all discrepancies, I focus on prominent, technically sound analyses. A set of three basic relationships (specified in bold below) is central to this assessment. For these basic findings, I consider net direct effects in models with a relatively full set of ascribed and achieved characteristics. Inelegantly put, this is a battle of the coefficients. Obviously as an “explanation” of economic success, this approach is inadequate. It wholly ignores the structural context that shapes individual careers—e.g., variations in demand for particular skills, changing cohort sizes and the forces of globalization. Nor does it reveal how ascribed characteristics may be related to the development of merit, or how various dimensions of merit may be jointly determined through the life course. Also, despite some modest efforts to measure ambition, these models presume that individuals are motivated by the desire to maximize status or income—not always a reasonable presumption. What these models simply show is the independent effects of separate achieved and ascribed characteristics at the time the economic outcome is measured, not the source of these effects. This is the acid test of the debate: to what extent is “merit” per se rewarded once people are in the labor market; to what extent is family privilege rewarded, over and above whatever merit they may have acquired? 1. Controlling for measured background, the net effects of education and cognitive ability on economic outcomes are substantial. A vexing issue in analyzing the determinants of wages or status is that education and cognitive ability are jointly determined, but for the limited purpose of assessing the

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meritocratic thesis, the relevant consideration is the net direct effects of each of these indicators of merit. The combined net effects of education and ability reflect the significance of meritocratic processes. Education and ability are quite strongly related, but these two dimensions of merit are not proxies for each other. Indeed, Bowles et al. (2001) review a large body of studies and estimate that 82 percent of the education effect on earnings is independent of the co-variation of ability and earnings. And this effect is sizeable. As Jencks et al. (1979) wrote, “The best readily observable predictor of young man’s eventual status or earnings is the amount of schooling he has had (p. 230).” That conclusion remains true, though it may be amended to include women as well. To appreciate the magnitude of this impact, consider the findings in Hauser, Warren, Huang, and Cooper (2002) (HWHC) recent analysis of occupational status using the Wisconsin Longitudinal Survey.8 In regression analyses that reflected the full Wisconsin social psychological model of achievement (origins, school performance and IQ, others’ influence, aspirations, and schooling—16 variables in all), they found that for men a standard deviation increase in education was associated with a 8.8 point increase in status (Beta = .36). For women, the comparable increase was 4.6 points (Beta = .22). Jencks et al. (1983) further support this finding in their replications of the Wisconsin Model. The standardized coefficient of education (years) is remarkably similar across surveys: .51, .45, and .45. The educational effect on earnings is also substantial. Korenman and Winship (2000) used the National Longitudinal Survey of Youth to examine the determinants of early career annual earnings. In models with just AFQT scores, a SES index, and education (years), they report a net effect for z ED of US$ 3445 (Beta = .21).9 Analyzing the 1992 NALS survey, Kerckhoff, Raudenbush, and Glennie (2001) (KRG) further highlight the impact of education on both occupational status and earnings. They use a multi-dimensional measure of cognitive skills (literacy) that complements all the other analyses that rely on AFQT scores, more a measure of general cognitive ability. These analyses show that education has a substantially large net impact on both

8 Among the virtues of this survey are the unusually good measures of adolescent mental ability (Henman-Nelson) and parental income (natural log of the 4-year average of income reported in 1957–1960 state tax returns. 9 Table 7.3 with reliability ratios of .95 for AFQT, .76 for SES, and .90 for education; standard deviation for earnings as reported in Table 7.1.

status and log earnings, and these results hold within each of six gender/race groups. To show the magnitude of this impact on status, standardized coefficients among men are .559 for whites, .604 for blacks, and .453 for Hispanics. KRG’s within-group analyses do not lend themselves to simple summary, but they do underscore two important points. First, both education and skills have notable independent impacts on careers. Second, these effects hold within different demographic groups, despite some variations, suggesting that these indicators of merit do not represent a masked ascriptive resource. Jencks et al.’s (1979) earlier work indicates that the education effect is non-linear, so that years of higher education and a B.A. degree provide a greater pay-off. Across five surveys, the estimated effect on occupational status of 4 years of college versus a high school education is in a range of 23.2–29.9, net measured family background—and only slightly lower with additional controls for test scores. Effects of this size are substantial, about equal to a standard deviation increase in occupational status. 4.1. Test scores As previously noted, many assessments of the meritocracy thesis have been based on the career impact of test scores—one-half of Young’s “ability plus effort” formulation. With that limited approach, it is fair to conclude that the U.S. is not “very meritocratic” (Jencks et al., 1979, p. 230). Indeed, the research that I review here clearly indicates that measured ability in itself does not decisively shape career outcomes. At the same time, however, ability does make a consequential difference in careers—a difference that adds to the overall impact of meritocratic processes. HWHC’s analysis indicates that a 1 S.D. increase in ability yields a predicted 2.7 points increase in occupational status for men (Beta = .22) and a 3.1 increase for women (Beta = .15).10 And to highlight their main point about ability, KRG similarly estimate that a standard deviation increase in their “literacy”/skills measure is associated with a 3.4 point increase in status (Beta = .18). The extensive literature on the impact of cognitive ability on earnings is difficult to summarize because the findings are not entirely consistent. Fortunately, Bowles et al. (2001) have reviewed 24 studies that estimate the impact of cognitive ability in a standard earnings

10

The standardized coefficients were calculated from data presented in Tables 8.8 and 8.9.

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equation (65 estimates in total). The mean standardized coefficient is .15; the range is considerable, −.09 to .33. (They calculate that the mean standardized coefficient for years of education is somewhat larger, .22.) In another light, a standard deviation increase in cognitive ability is associated with less than a 10 percent increase in wages, about equal to the impact of 1 additional year of schooling. Taken together, these studies of wage determination indicate that the net impact of cognitive ability is consequential but is far from determinative of economic success. 2. Controlling for education and cognitive ability, the net effects of measured family SES – within each sex – are minor. (I separately though briefly consider the impact of race and gender below.) The net impact of measured family background on economic success is easy to summarize: very little. This conclusion holds across different data sets with different model specifications and measurements and applies to both occupational status and earnings. Consider again Hauser et al.’s (2000) analysis of occupational status using the Wisconsin Longitudinal Survey. In their most complete models, among men, father’s occupational status and parental income had small net effects (Beta = .04 for each); among women only parental income had any effect.11 Neither mother’s nor father’s education had any independent consequence. After analyzing other data sets, even without controls for ability, they conclude, “Whatever the explanatory power of schooling, social background adds little to it. With few exceptions, the vectors of social background variables add no more than one or two percentage points to the explained variance in occupational status (p. 194).” The impact of measured background on earnings also appears minor, though results are not as uniform. For instance, Jencks et al. (1983) analysis of three surveys found mother’s education and father’s occupation had no net effect on earnings in any of the three surveys; father’s education had a modest effect (Beta = .12) in one sample; and parents’ income had a modest effect in two surveys (Beta = .14 each). Korenman and Winship (2000) provide perhaps the most technically sophisticated recent assessment of background effects on earnings. Controlling for education and AFQT scores, they report an earnings “boost” of US$ 910 associated with a standard 11

The standardized coefficients were calculated from data presented in Tables 8.8 and 8.9.

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deviation increase in SES. This amounts to a standardized coefficient of .06.12 In referring to family background, the qualifier “measured” is crucial here. By “measured” background analysts typically mean such factors as parental education, family income, parental occupational status, and race—the dimensions of background life that presumably create ascriptive advantages and disadvantages. I have deliberately not considered studies based on socalled sibling analysis that attempt to depict the total effect (measured and unmeasured) of growing up in one family instead of another (see, e.g., Jencks et al., 1979).13 Predictably enough, this total effect is greater than the effect attributable to the measured SES-related dimensions of family background. Yet it is far from clear that this unspecified extra advantage represents unmeasured ascriptive influences. Indeed, it seems likely that much of this “family advantage” is attributable to genetic endowments that favor success, as well as environmental reinforcement of cognitive development, work-oriented values and the like. By excluding sibling analyses, then, some small part of the ascriptive aspects of background may be missed; but the far greater likelihood is that the total family effect incorporates substantial merit (and matters unrelated to ascription). It is therefore highly problematic to link the difference between the total family effect and the measured family effect to ascriptive processes. 3. Controlling for measured family background, education and cognitive ability, merit-related dimensions of personality may have some impact on economic outcomes. There is no evidence that non-merit related dimensions of personality have any systematic impact. Of the three major components of merit, personality has surely been the least frequently studied and the least well-measured. Although it is difficult to draw firm conclusions about the “personality effect” given the limited and somewhat inconsistent findings – ranging from

12

The standardized coefficient was calculated from Tables 7.1 and 7.2. Adjusting for possible measurement error with reliability coefficients of .95 for AFQT, .90 for education, and .76 for SES, this coefficient increases slightly, .07 (Tables 7.1 and 7.3). 13 To estimate the total family effect, sibling analyses are based on the resemblance between sibling pairs in a sample. For economic outcomes, analysts estimate the variance of family means and compare it to the total variance of individual success, thus indicating the percentage of total variance attributable to family background (Jencks et al., 1979).

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quite strong effects to null effects – there is good reason to believe personality matters. In the most supportive study, Dunifon and Duncan (1998) showed that for young men measures of “personal control” and a preference for “challenge versus affiliation” during late adolescence were substantially related to earnings some 15–25 years later.14 Here is the net “personality effect”: a 1 S.D. increase in “personal control” was associated with a 14 percent increase in earnings; a similar increase in a preference for “challenge” was associated with a 7 percent increase in earnings. Moreover, in models with interaction terms, the impact of motivation did not differ by SES or race, suggesting that a general dimension of merit is being considered. Goldsmith, Veum, and Darity (1997) provide further reason to believe that psychological capital counts. They added good measures of locus of control and self-esteem to standard models of wage determination. Like Dunifon and Duncan (1998), they find that personal control matters, but the effect is indirect through self-esteem. The direct impact of self-esteem is substantial: a 10 percent increase in self-esteem is associated with a 13.3 percent increase in earnings for young workers. Jencks et al. (1979), though drawing on older data sets, conducted the most wide-ranging analysis of “personality,” including self-assessments of 10 personal characteristics like self-confidence, leadership, and sociability; adolescent activities and attitudes; and teacher assessments of characteristics. At most, it presents some evidence of small effects. Considered together, the many analyses in Who Gets Ahead? make the case that there is something about being a leader and working hard in adolescence that modestly pays off in careers, over and above whatever impact those dispositions have on educational attainment. All other dispositions do not appear consequential. To place this impact in perspective, Jencks et al. (1979) find that R2 increases by only a few percent if all personality measures are added to status or income models with background, ability and education. The implications for the meritocracy debate are not clear. The two recent studies indicate that well-measured indicators of personality are associated with sizeable boosts in earnings, thus suggesting that other findings of null or very small effects may reflect poor measurement. Given the strengths of these studies, I am inclined

14

Their measure is based on 5-year mean scores on three questions like, “When you make plans ahead, do you usually get to carry out things the way you expected, or do things usually come up to make you change your plans?” (Alpha = .79).

to say that merit-related dimensions of personality (e.g., dispositions to work hard and to lead) seem likely to add (an additive effect) at least some modest increment to the overall impact of merit factors on status and earnings. And, it bears repeating, the dispositions that appear to make a difference are not notably related to family background. On the other hand, although not thoroughly studied, having a “good personality” (sociability) does not seem to systematically matter, nor do the class-related personality traits that are central to Bowles and Gintis (1976) influential theory of class reproduction through education (Olneck & Bills, 1980). 4.2. Race effects America’s history of deep and pervasive racial discrimination is an obvious challenge to the meritocratic thesis, yet recent research calls for a revision in thinking about how racial background affects economic success. Perhaps the most notable, uncontested finding in The Bell Curve is that group differences in AFQT scores largely account for, in a statistical sense, the annual earnings gap between young black and white males who were employed full-time. By Herrnstein and Murray’s (1994) calculation, the gap between blacks and whites with similar scores is 2 percent. Their basic finding, though not the precise “race penalty,” had been previously reported (O’Neil, 1990) and subsequently corroborated (Farkas, England, Vicknair, & Kilbourne, 1997; Fischer et al., 1996; Johnson & Neal, 1997).15 Racial–ethnic differences in human capital, including cognitive ability, also largely account for the relative economic fortunes of other groups: black versus white women, and Mexican men and women versus their white counterparts (Farkas et al., 1997). On the other hand, however, Raudenbush and Kasim (1998) find a 15 percent difference in log wages between comparable black and white males, though no difference for black and white women. Recall that they measured adult literacy, not adolescent AFQT scores. Overall, then, among recent cohorts measures of human capital that include cognitive ability very substantially “explain” racial–ethnic differences in earnings for men, even if estimates of the “black penalty” 15 As Farkas et al. (1997) explain, if you use blacks’ slopes and intercept, the results indicate how much of the gap would be reduced if blacks had the mean values for whites on the independent variables. However, if you use whites’ slopes and intercept, the results indicate how much of the pay gap would be reduced if whites had the mean values for blacks on the independent variables. Neither procedure is inherently better than the other; both are informative.

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vary. Moreover, among comparably capable women, minorities appear to earn more than their white counterparts. These findings force a fundamental rethinking of how race currently affects economic standing. Earlier research (Jencks, 1972) had indicated that, net of cognitive ability and education, blacks paid a large “race penalty.” Now, Jencks (1998, p. 6) states that he must change this conclusion, not because of mistakes in the prior analysis but because “Today’s world is different.” This is not to say, of course, that the labor market itself is now free of racial discrimination (e.g., Neckerman & Krischenman, 1991). But these findings do suggest that explanations of racial disparities that invoke racist employment decisions, institutional discrimination and differential access to favorable connections may be missing the main dynamic. 4.3. The gender gap In 2002, according to Census Bureau statistics, median earnings among full-time female workers were US$ 29,215 and 38,275 for male workers. Averaging 76 cents for every dollar men received, women had never done better. Yet, by any estimation, this gap is still sizeable and cannot be fully explained as the outcome of gender differences in merit. The reason is obvious, especially among younger cohorts: women exceed or at least match the educational attainment, academic performance, and cognitive ability of men. Male economic advantage, then, persists in the face of no greater merit on these key dimensions. True, women have lesser human capital in the form of job experience and on-the-job training and also work fewer hours, but these differences account for only a fraction of the gender gap in pay. Kilbourne, Farkas, Beron, Weir, & England (1994) found that sex differences in the means of human capital variables (including experience) accounted for only about a quarter of the wage gap. As Petersen and Morgan (1995) demonstrate, within-job wage differences between women and men in the same occupation and establishment are minor. What’s critical is that in a substantially sex-segregated labor force women are disproportionately employed in low-paying occupations and industries. In light of these findings, the role of gender discrimination is difficult to interpret. Many analysts have inferred gender (and racial) discrimination from the unexplained residual in wage models—i.e., the gap that remains net of controls for human capital. The logic is this: what else could it be, even if we do not directly observe discrimination? That logic is reasonable if human capital and other earnings-related factors are fully

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accounted for and groups equally seek to maximize their earnings. The latter assumption seems sound for analyzing the earnings of black and white men. But there is at least reason to question whether women have the same commitment to income (or status) maximization as men. Do women “choose” some types of jobs, often lower paying, because they are relatively accommodating to the demands of family life? If so, the residual may overstate the gender discrimination in labor market decisions. Of course, choices – the supply side of the labor market – are socially conditioned, and it is difficult to say conclusively why women and men follow such different career paths. These choices are conditioned by differences in socialization, expected and current family responsibilities, and anticipated and actual experiences in the workplace. In an important sense you could say that the social structure constrains choices in a discriminatory way, even if women and men are paid the same for the same work. Indeed, the historical record suggests that the emergence and perpetuation of the sex-segregated labor market – the demand side – involved active gender-based discrimination (Baron, 1991). Even setting aside this macro-level demand effect, however, labor market allocation processes at the microlevel still disadvantage women. The gender gap in wages is even apparent at the very beginning of careers when gender differences in on-the-job training and work experience have yet to merge. Marini and Fan (1997) find an initial 16 percent male–female wage gap, less than the aggregate difference in the full-time labor force but still substantial; but net of a very full set of human capital variables and work-family aspirations, the male–female gap is still about 11 cents on the dollar.16 The ongoing male–female wage gap represents a challenge to the meritocratic thesis. 5. Section 3: educational processes Although meritocratic factors have a much more significant impact on labor market outcomes than ascriptive factors, you could argue that this analysis just did not start early enough. Perhaps in the earlier rounds, when pre-market merit was created or certified, the socially privileged enjoyed great ascriptive advantages. If so, the apparent meritocracy is a rigged contest, even a sham for privilege. Because education is the prime determinant of economic success, we also need to ask, “What determines educational attainment?” 16

On the other hand, to present the impact of gender in a less dramatic light, the net effect (beta) of sex in Marini and Fan’s model is .02.

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This issue cannot be fully resolved as a simple regression-based battle of coefficients—the impact of ascriptive factors versus cognitive ability/effort on educational attainment. For one, education and ability are jointly determined (Neal & Johnson, 1995), contradicting the contention that AFQT scores represent a fixed endowment (Herrnstein & Murray, 1994). In addition, SES and race affect early school performance and ability, both of which affect subsequent educational attainment—an indirect effect. Yet, even if privilege enhances the probability of acquiring productive capacities, the issue is whether privileged school kids get rewarded for these capacities or their favored status. And, as I argue below, academic performance is not some arbitrary measure of middle class status. A simple straight up comparison shows that cognitive ability is a much more significant predictor of educational attainment than SES. Analyzing years of schooling, Korenman and Winship (1997) report a standardized coefficient of .62 for AFQT scores, controlling SES; and a coefficient of .20 for SES, controlling scores. They also find that scores have much greater impact than SES on B.A. attainment; in a logistic regression model, the respective coefficients are 1.72 and .67. This latter finding is particularly notable because higher education involves substantial costs that might be presumed to deter lower income students, even bright ones. The strong association between ability and educational attainment reflects the fact that internal processes in schools are largely meritocratic. Here, I simply cite some illustrative findings, highlighting key evaluative junctures that shape subsequent attainment.17 5.1. Grades Although students with higher grades are more apt to pursue higher education, researchers have given surprisingly little attention to how teachers assign grades. The limited evidence that exists suggests the teachers do what they are supposed to do—grade on ability and effort. In an unusually sophisticated study, Farkas, Grobe, Sheehan, and Shuan (1990) find that, by far, the best predictors of grades in Dallas middle schools are academic competence (scores on tests that were not

graded by the teacher) and teacher perceptions of effort. Ascribed characteristics have no net impact, except that better dress somehow depresses grades. At the high school level, Broderick and Hubbard (2000) found that standard measures of cultural capital have no impact on teachers’ perceptions of students and only trivial associations with grades (see also Roscigno & Ainsworth-Darnell, 1999). Teachers are impressed by students who have high test scores and stay out of trouble. Were cultural certification central to school processes, you would imagine that teachers would pay more attention to it. Indeed, no recent research makes a convincing case that cultural capital – presumably the mechanism by which privilege is transmitted in schools – has more than a very minor impact on grades or educational attainment, much less accounts for the relationship between academic success and SES. The seemingly supportive analyses are beset by obvious omitted variable bias (Kingston, 2001). 5.2. Teachers expectations Ever since the publication of Pygmalion in the Classroom (Rosenthal & Jacobson, 1968), it has been commonly assumed that teacher expectations about academic performance have a self-fulfilling impact, so that teachers’ lower expectations for lesser privileged students mean that they end up performing less well than their more privileged counterparts. Ferguson’s (1997) review of the literature indicates that teacher expectations may be consequential but that these expectations are generally accurately related to students’ prior performance and do not reflect ascriptive bias. 5.3. Tracking Although it is impossible to make a short blanket generalization about the impact on learning of all the school practices that are called “tracking,” it is fair to say that they tend to exacerbate inequalities in academic performance and that socially privileged students are disproportionately enrolled in the more rigorous courses (Gamoran, 1992; Loveless, 1998).18 However, track placement is most significantly determined by prior performance (grades and test scores); the net impact of SES is relatively minor, reflecting some undetermined mix-

17

In this review, I highlight the findings of quantitative research. The qualitative literature finds more indication of ascriptive processes, though the generalizability of these findings is questionable. Even though the indicators in survey research are not always subtle, it seems unlikely that the relative strength of merit versus ascriptive factors can be attributed to measurement error.

18

It is increasingly difficult to say that a student’s “track” affects academic performance because students are enrolled in courses at different levels in any 1 year, and particular subject areas, they often change levels from year to year (Lucas, 1999).

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ture of student/family choice and institutional decisions (Gamoran & Mare, 1989). If anything, black students are enrolled in higher tracks than predicted by prior performance. 5.4. Increasing test score gaps Racial and economic gaps in academic proficiency are substantial at entry into the school system and become larger as students move through the system, suggesting to many observers that school practices must be socially biased in some systematic way. However, the so-called “summer learning” literature calls that conclusion into serious question. What this research rather consistently shows is that during the school year learning rates are similar across groups and that the increasing gaps are attributable to differential learning rates during the summer when the impacts of family and local community are relatively large (see, e.g., Alexander & Entwisle, 1995; Alexander, Entwisle, & Olson, 2001; Gamoran, 1995; Heyns, 1978). 5.5. Higher education admissions As Korenman and Winship’s (1997) previously cited study indicates, academic ability is much more strongly associated with college graduation than family background, but given the highly varied prestige of American universities and colleges (Princeton versus Podunk), it is also relevant to consider where people go because that is related to economic outcomes, even if the magnitude of the “prestige effect” is disputed (Kingston & Smart, 1990). Karen’s (2002) analysis of the college destinations, defined in terms of institutional selectivity, for the high school class of 1992 indicates that background still matters (adjusted R2 = .16 for a standard set of seven background variables). However, net these background variables, measures of academic performance are about equally potent, and the net effects of test scores (.25) and grades (.18) exceed the effects of parental income (.13) and each other background effect.19 At the graduate level, the impact of academic merit is dominant. Analyzing college graduates, Mullen, 19

This analysis on enrollments does not directly speak to the admissions process at individual colleges. Enrollments reflect patterns of application, institutional decisions on these applications, and students’ response to offers. As Karen observes, the aggregate pattern of effects does not fit a meritocratically-based distribution, but it is uncertain whether these outcomes were the result of non-meritocratic admissions decisions or student “choices” surely conditioned by the fact that selectivity and cost are substantially related.

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Goyette, and Soares (2003) find that any association between parental education and enrollment in MBA, first professional, and doctoral programs is entirely mediated by academic performance, academic expectations and career goals, and type of undergraduate institution. Even if the evaluative processes within schools have a strong meritocratic thrust, the fact remains that the school system is marked by a highly unequal distribution of educational resources—and thus would seemingly create unequal opportunities to develop academic merit. Some qualitative “inputs” as teacher quality (Haycock, 1998) and organizational practices (Lee & Smith, 2001) appear to affect students’ learning, as may direct expenditures on instruction (Greenwald, Hedges, & Lane, 1996). Yet the so-called “production function” approach does not suggest that measurable inputs like school expenditures (or the inputs they presumably purchase) have substantial, consistent impacts on achievement (Greenwald et al., 1996; Hanushek, 1995, 1997).20 Nor does this research indicate that any differences in measured resources substantially account for economic and racial gaps in academic achievement and attainment. This is likely not the last word on the impact of resources, but so far it does not appear that between-school differences are more than a modest determinant of academic merit, certainly by comparison to home influences (Riordan, 2004).21 Yet, many have argued (e.g., Lareau, 1987; MacLeod, 1987), the culture and practices of schools generally have a strong middle class bias. Schools are alleged to be most receptive to the linguistic and interactional styles, cultural interests, and values of middle class students. Presumably being “at home” in school, middle class students outperform their less privileged classmates who confront an unfamiliar world. This large argument is difficult to evaluate thoroughly in a short space, but the meaning of this alleged cultural bias might be seriously questioned. First, while schools

20 Greenwald et al. (1996) and Hanushek (1995) use different analytical procedures to summarize the large, inconsistent literature. I am inclined to the latter’s more sophisticated meta-analysis showing that money matters to a degree (a lesser degree than they initially reported). In any case, the failure to find strong, consistent findings argues for interpretative caution. 21 The conclusion does not deny the possibility of making greater educational commitments that would promote the learning of those not advantaged by family background. Indeed, that is my personal hope. Yet the production function research on the current educational system does not suggest that this system is structured so as to produce a socially biased distribution of merit. As the summer learning research indicates, schools do relatively little to create that outcome or to alleviate it.

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obviously do reward such matters as diligence, orderliness and timeliness, we should ask, are these truly middle class values (in opposition to the values of other classes)? Is it possible to imagine a school system in a modern society that doesn’t reward these commitments? Second, is there any evidence that curricula or pedagogy with different “class biases” have substantially altered the connection between economic advantage and academic performance? The short answer to each question is no. The fault lines of class and race do not determine what techniques work best for teaching algebra, and no one can reasonably argue that algebraic reasoning is some arbitrary, class-based cultural preference divorced from the demands of many higher level jobs. Undoubtedly, some interventions can help poor kids read and mathematically reason better (Farkas, 1996), but these involve efforts like intensive drilling, not the removal of middle class bias from the curricula. This brief review suggests that education is largely a meritocratic institution; indeed, both schools and the labor market similarly reward cognitive ability and traits like conscientiousness and dependability (Farkas, 2003). Admittedly, this section did not note all of schools’ divergences from meritocratic procedures, e.g., the considerable advantages for alumni “legacies” and minorities in elite college admissions or greater use of out-of-field teachers in low-income schools. That acknowledged, however, the compelling point is that such non-meritocratic procedures represent deviations from the prevailing meritocratic patterns in the main operations of schools. By and large, achievement in school is not a contrived certification of privilege. 6. Section 4: concluding perspectives After the barrage of coefficients in the preceding sections, it is time to make sense of the overall patterns, with particular attention to the rough magnitude of effects. Unfortunately, no analysis adequately incorporates all relevant factors, thus allowing a precise delineation of the net impact of the many merit and ascriptive factors. Even so, I think everyone would agree that social scientists should be pretty humble about the explanatory power of their models. In economists’ wage determination models for people of the same sex and race “between two-thirds and four-fifths of the variance of the natural logarithm of hourly wages or annual earnings is unexplained . . .” (Bowles et al., 2001, p. 1137). The full Wisconsin model (not including “unmeasured” family background) of male occupational status leaves about

two-thirds of the variance unaccounted for, more for women (Hauser et al., 2000).22 As I have detailed, the very great part of our “one-third plus” explanation is attributable to merit factors—primarily education and secondarily cognitive ability. While there are female and black male “penalties,” the net impact of socio-economic background is minor. Something other than merit and ascriptive background accounts for most of the individual differences in economic outcomes. A key analytical issue is whether the meritocracy thesis should be assessed in absolute or relative terms. The literal connotation of meritocracy is that merit rules the allocation process—that is, in an absolute sense merit largely determines where people end up. Against that strong standard we fall very short, whatever reasonable statistical cut-off is used to indicate a determinative effect. But the presumption that merit actually rules exists only in Young’s dystopia, not in any sociologically informed argument. It seems premature to close the case on these grounds because meritocratic factors have so much more impact on careers than ascriptive factors. As a distributional principle, merit is relatively significant; to the extent that the allocation process is rule-governed, meritocratic rules predominate and their impact is consequential. Indeed, existing empirical analyses seem to understate the impact of merit on career outcomes. First, wage determination models typically have crude or no measures of on-the-job training. Neglecting this form of education means overlooking about a quarter of all human capital investment (Heckman, Lochner, & Tober, 1998 cited in Cowley, Heckman, Lochner, & Vytlacil, 2000). Second, these models do not include measures of either practical intelligence or tacit knowledge. Third, they rarely incorporate good measures of productive personal traits like conscientiousness. Were these additional merit factors to be included, we could reasonably expect some notable increase in the predictive power of the models because these factors are only modestly or even weakly correlated with education and cognitive ability. On the other hand, although corrections for measurement error may increase the apparent weak impact of SES/class, it is difficult to argue that any important dimension of this factor – perhaps with the exception of wealth – has been neglected or is not largely reflected in the effects of the

22 Models that include total family background (by considering resemblance of brothers) still leave more than one-half of the variance unexplained (Jencks et al., 1979).

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included dimensions. Having more complete measures of ascription and merit, then, would likely boost the case for the meritocratic argument. Furthermore, in absolute terms, endowments of merit make a meaningful difference in how well people live, even if they are not determinative. Recognize the reality that can get overlooked in the reporting of standardized coefficients. For instance, a standard deviation difference in education is associated with almost nine points difference in occupational status (HWHC). A similar educational difference meant about a US$ 3500 difference in earnings among young workers back in the early 1990s. Although the net pay-off to cognitive ability may not be “impressively large” (HWHC, p. 209), it is still about one-half the educational effect on status and about two-thirds of education’s impact on earnings.23 Moreover, even the few studies documenting personality effects suggest a substantial impact—for example, a standard deviation increase in personal control is associated with a 14 percent increase in earnings (Dunifon & Duncan, 1998). Yet I should emphasize that the coefficients in our status attainment and wage determination models do not themselves make the case for meritocracy. To recall the pattern: (1) education and cognitive ability have substantial net effects, (2) ascribed characteristics have small direct effects, and (3) the indirect effects of status operate, in a statistical sense, through education and cognitive ability. To be sure, this pattern is consistent with the meritocratic thesis, but only if education and ability are interpreted as dimensions of merit. Indeed, the distinctive contribution of this analysis is to view the cumulative findings of the status attainment literature in light of the impact of validated indicators of merit. Imagine, instead, that education is primarily a marker of status cultures and ability is an arbitrary social construct, as many sociologists seem inclined to believe. If so, this same pattern can be taken as evidence of a substantially ascriptive-based society. However, as appealing as that perspective may be to our debunking sensibilities, it falters on the fact that both education and ability are related to job performance—and thus if employers screen on education, they have a reasonable proxy for productivity. This is not to deny that in certain segments of the labor market education may also involve ascriptive signals or that employers may overestimate the greater productivity of the more educated. But even with these

23

The two-thirds figure represents the ratio of the average coefficients reported in Bowles et al. (2001): .15/.22.

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qualifications, allocation by education is preponderantly the hallmark of meritocratic considerations. The upshot is that the offspring of socially privileged families become privileged themselves largely because they have acquired genuinely productive skills and dispositions. The fact that background and such capacities are moderately linked does not compromise the relatively meritocratic nature of the allocation process. And the minor size of net ascriptive effects in these models suggests that cultural capital and differential access to networks play a minor role in reproducing privilege across generations. Some limits of this analysis point to the needed direction of future research. For one, it should be recognized that regression-based analyses of the total distributions of income or occupational status indicate only the average impact of selected variables. For the purpose of indicating the social processes that are generally operative, these coefficients are useful because they concisely summarize a complex reality. And thus for good reason researchers in this area have looked first to general models. Yet obviously averages can conceal distinctive processes at various levels of the stratification system and, indeed, may not apply to lives of many people, especially as increasing proportions are located at the extremes. To have a complete fix on the interplay of ascriptive and merit factors, it is necessary to take a disaggregated approach, being open to the possibility that this interplay operates differently in various “sub-markets” (e.g., art directors at elite museums, construction electricians in Chicago, network server repair people, hightech venture capitalists). This approach would involve analyzing institutional processes and the activities of gatekeepers (see, for example, Kingston & Clawson, 1990). Secondly, this analysis calls for a comparative perspective. Is the U.S. more or less meritocratic than other advanced societies? Saunders (1998) has made an impressive start, showing that British society is notably meritocratic in the sense that cognitive ability and effort have much more impact than socio-economic background on economic outcomes. The obvious extension is to consider more countries with similar coverage of all dimensions of merit—cognitive capacities, education, and productivity-related aspects of personality. Yet it is not enough to simply compare coefficients. It remains crucial to establish that all dimensions of merit are, in fact, related to job performance in other settings. Moreover, given the crucial allocative role of education in all advanced societies, it is essential to comparatively examine whether allocative processes within schools are themselves meritocratic.

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In this concluding section, I should underscore an important restriction of this analysis. Recognize that it is limited to considering the allocation process, the “rules” by which individuals acquire positions within the existing hierarchy. What it does not consider is the underlying basis of this hierarchy. Conceptually, these are separate matters—each important in its own right, each requiring distinctive analyses. My argument that the allocation process is relatively meritocratic does not necessarily imply that the stratification system represents a hierarchy of skills/talent/motivation. Indeed, the “contested terrain” of the occupational world has been significantly shaped by the political maneuverings of self-interested groupings seeking to create privilege (Collins, 1979; Edwards, 1978).24 At the same time, however, these political struggles take place in a context in which economic competition induces strong pressures to increase productivity—hence fostering a hierarchy that substantially reflects differences in talent, especially intelligence (Gottfredson, 1985). Here is not the place to address this debate about the macro-level structure. Yet a total assessment of societal fairness should consider both allocation and structural processes. As a final note, I am acutely aware that this analysis can be read as a defense of the status quo, but my own motivation has not been to deliver an all-is-well message. Rather, it is to undermine misdiagnoses of our inequalities so that we can pursue sensible strategies to alleviate them. We create undue obstacles and misdirect efforts if we deny the important role of merit in contemporary allocation processes and imagine that ascriptive biases are the fundamental cause of differential success and the intergenerational transmission of privilege. Our research shows that merit generally trumps the advantages and disadvantages of birth. For those who favor greater opportunity, that point should guide policy analysis. The challenge is to devise strategies to broaden the social distribution of merit—in effect, to decouple social privilege and human capital. That challenge may appear formidable, but the optimistic note is that the cultivation of human capital seems more amenable to policy intervention than a restructuring of labor market operations.25

24

This perspective is suggested in my brief discussion of the sexsegregated occupational structure. At the macro-level, “women’s occupations” are devalued without any evident connection to their relative contribution to overall economic performance. 25 Jencks and Phillips (1998) similarly argue that a supply side solution – reducing the test score gap between blacks and whites – would do more to reduce our racial problems than any other strategy that is politically palatable.

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Further reading Becker, G. (1964). Human capital. New York: National Bureau of Economic Research. Breen, R., & Goldthorpe, J. (1999). Class inequality and meritocracy: A critique of Saunders and an alternative measure. British Journal of Sociology, 50, 1–27. Cornelius, S. W., & Caspi, A. (1987). Everyday problem solving in adulthood and old age. Psychology and Aging, 2, 144–153. Hout, M. (1980). More universalism, less structural mobility: The American occupational structure in the 1980s. American Journal of Sociology, 93, 1358–1400. Jencks, C., Smith, M., Acland, H., Bane, M. J., Cohen, D., Gintis, H., et al. (1972). Inequality: A reassessment of the effect of family and schooling in America. New York: Basic Books. Lareau, A. (2000). Home advantage: Social class and parental involvement in elementary education (Second ed.). Lanham, MD: Rowman & Littlefield. McCrae, R., & Costa, P. (1997). Personality trait structure as human universal. American Psychologist, 52(5), 509–516.