21 The proportions in 1960 were 66 percent (blacks) and 72 percent (whites). Computed from Tables 1 and 16, National Center for Health Statistics 1993, and comparable tables in earlier editions.
22 William Julius Wilson is best known for the lack-of-marriageable-males thesis (Wilson 1987), which is currently thought to have some explanatory power (like IQ) but leaves the bulk of the discrepancy unexplained (as does IQ). See South 1993; Fossett and Kiecolt 1993; Bulcroft and Bulcroft 1993; Schoen and Kleugel 1988; Lichter, LeClere, and McLaughlin 1991. For other empirical work bearing on the thesis, see Bennett, Bloom, and Craig 1989; Tucker and Taylor 1989; South and Lloyd 1992; Spanier and Glick 1980; Staples 1985.
23 National Center for Health Statistics, 1993, Table 26. Figures in the text are for live births.
24 E.g., Anderson 1989; Bumpass and McLanahan 1989; Duncan and Laren 1990; Ellwood and Crane 1990; Furstenberg et al. 1987; Hogan and Kitagawa 1985; Lundberg and Plotnick 1990; Murray 1993; Rowe and Rodgers 1992; Teachman 1985.
25 Computed from Committee on Ways and Means and U.S. House of Representatives 1993, pp. 688, 697; SAUS 1993, Table 23.
26 These figures, already high, are even higher when the analysis is limited to mothers. The percentages of mothers who had ever been on welfare for blacks, Latinos, and whites, were 65.0, 40.5 and 21.8, respectively. We conducted parallel analyses limited to women who had borne a child prior to 1986, giving at least five years’ “chance” for a woman to show up on the AFDC roles. This had the predictable effect of slightly increasing the percentages of women who had ever received AFDC, but yielded the same substantive conclusions.
27 Intergenerational transmission has some role. See McLanahan and Bumpass 1988; McLanahan 1988. For other discussions touching on racial differences in welfare recipiency, see An, Haveman, and Wolfe 1990; Bernstam and Swan 1986; Bianchi and Farley 1980; Donnelly and Voydanoff 1991; Duncan and Hoffman 1990; Hirschl and Rank 1991; Hofferth 1984; Hogan, Hao, and Paush 1990; Honig 1974; Hutchens, Jackson, and Schwartz 1987; Smith and Welch 1989; Wiseman 1984, Hoffman 1987; Rank 1988; Zabin et al. 1992.
28 National Center for Health Statistics 1993, Table 26.
29 Based on the Colorado Interuterine Growth Charts.
30 For discussions of reasons for the black-white gap in low-birth-weight babies see David 1990; Kempe et al. 1992; Mangold and Powell-Griner 1991.
31 U.S. Bureau of the Census 1993, Table 3. The Bureau of the Census does not break out “non-Latino whites” in the official statistics. If one assumes that all persons labeled as “Hispanic origin” were white, then 12.9 percent of non-Latino white children were under the poverty line. This is an underestimate for the actual figure, since many persons of Hispanic origin are classified as black. The figure of 14 percent in the text is an estimate that attempts to compensate roughly for the underestimate.
32 The reasons for the gap in black and white child poverty are discussed in the same literature that deals with differences in marriage rates and illegitimacy, which together account for much of the differing financial situations facing black and white mothers of young children.
33 Various approaches to ethnic differences in home environment are Heath 1982; Bardouille-Crema, Black, and Martin 1986; Field et al. 1993; Kelley, Power, and Wimbush 1992; McLoyd 1990; Moore 1985; Pearson et al. 1990; Radin 1971; Tolson and Wilson 1990; Wasserman et al. 1990. A useful older account is Davis and Havighurst 1946.
34 See Jones 1992 on abortion, Abramson and Claggett 1991 on voting, and Elliott and Ageton 1980 on delinquency.
35 See the references (note 33) regarding ethnic differences in home environment.
36 Refers to arrests for index crimes in 1992 relative to the size of the black and white populations. Computed from Federal Bureau of Investigation 1993, Table 43, and SAUS 1993, Table 22. See also Wilson and Herrnstein 1985, Chap. 18.
37 U.S. Bureau of the Census 1993b, Table 305.
38 R. Gordon 1976, 1987.
39 We cannot use the NLSY self-report data for inter-racial comparisons. Self-report crime measures have consistently revealed marked differences in the willingness of black and white youths to disclose crimes. See Elliott and Ageton 1980; Hindelang 1981; Hindelang, Hirschi, and Weis 1981.
40 See the sixteen studies reviewed in Osborne and McGurk, 1982. See also the results from the Philadelphia delinquency cohort (Wolfgang, Figlio, and Sellin 1972).
Chapter 15
1 We would, of course, need to know something about the fathers’ scores too. The more complete account comes later in the chapter.
2 Also see Ghiselin and Scudo 1986; Ingle 1973.
3 Soloway 1982.
4 Francis Galton’s coined the term eugenic. See Galton 1883.
5 The eugenicists were active, but, as we noted in the Introduction, the intelligence testers were not. For an account of what happened prior to the passage of the xenophobic and nativist Immigration Restriction Act of 1924 and how it has gotten distorted in the retelling, see Snyderman and Herrnstein 1983.
6 “Intrinsic birth rates” are birth rates corrected for age distributions. Death rates also decline during the demographic transition, but they will not be discussed in any detail here. Demographers generally believe that differential death rates cease to be a major factor in population growth in modernized societies like ours. This is a supposition that needs to be reassessed, given the probable differential impact of infant mortalities, homicide rates, and AIDS in relation to tested intelligence. Of all the studies we summarize below, only Retherford and Sewell 1988 takes mortality rates into account, but it did not have a nationally representative sample to analyze. We may surmise that the intergenerational decline in intelligence is being mitigated somewhat by differential intrinsic death rates.
7 Retherford 1986; Retherford and Sewell 1988; Vining 1986; Wrong 1980.
8 Retherford 1986; Retherford and Sewell 1988.
9 Becker 1981.
10 E.g., Retherford and Sewell 1988; Rindfuss, Bumpass, and John 1980.
11 Vining 1982a, Vining 1986.
12 Vining 1986.
13 For a sampling of studies that indicate the importance of attitudinal variables for motherhood in many nations, see Booth and Duvall 1981; Hass 1972; Krishnan 1990; Mason and Palan 1981; Youssef 1978.
14 Estimating the phenotypic, as distinguished from the genotypic, change in intelligence across generations is conceptually little more than a matter of toting up the population yielded across the distribution of intelligence, then aggregating the subtotals to get the overall distribution of scores in the next generation, after first taking account of regression to the mean (Andrews 1990; Falconer 1966; Retherford and Sewell 1988). It is not necessary to include any estimate for the heritability of intelligence. This simplicity in conception should not be confused with simplicity in actually making these calculations. Parents in, say, successive deciles of intelligence may have differing intrinsic rates of population growth (or decline) because of varying lifetime fertilities, varying ages at reproduction, and varying mortality rates. Assortative mating by the parents (see Chapter 4) matters in calculation only insofar as it influences the correlation between parents and children. Hence, if fertility is lower at higher levels of intelligence, then assortative mating for intelligence will speed the decline of the population intelligence because it increases the correlation between parents and children. Some of the studies that we cite focus on the genotypic decline rather than the phenotypic (e.g., Retherford and Sewell 1988). Since children resemble the parents who rear them for environmental reasons as well as genetic, the population phenotype will change more rapidly than the population genotype.
15 The best review of the early studies is Anastasi 1956. See also Duncan 1952; Olneck, Wolfe, and Dean 1980; Retherford and Sewell 1988; VanCourt and Bean 1985; Vining 1986.
16 Cattell 1936, Cattell 1937.
17 Retherford and Sewell 1988.
18 Cook, 1951 p. 6.
19 As Osborn and Bajema (1972) stated, “
The distribution of births in an industrial welfare-state democracy would become more eugenic as the environment improved with respect to health, educational, and occupational opportunities, and particularly with respect to the spread of birth control to the point where freedom of parenthood became a reality for all citizens” (p. 344). The Eugenic Hypothesis was first stated in Osborn 1940.
20 Maxwell 1954; Scottish Council for Research in Education 1949.
21 Cattell 1951. See also Tuddenham 1948.
22 Higgins, Reed, and Reed 1962.
23 Bajema 1963, 1971; Olneck, Wolfe, and Dean 1980; Waller 1971. In addition, as we explained in Chapter 13, the Flynn Effect would have masked any decline in IQ by demographic processes.
24 Cattell 1974; Osborne 1975.
25 Retherford and Sewell 1988.
26 Vining 1982b.
27 VanCourt and Bean 1985.
28 Retherford and Sewell 1988.
29 Ree and Earles 1991a.
30 The simplest way to get around the estimates that scholars have derived would be to measure the IQs of successive generations, following parents and their children, but surprisingly few studies of any size measure cognitive ability in both parents and children, and those few have always been small studies conducted for specific purposes; none has met the crucial criterion of national representativeness. In the United States, the NLSY has the potential to yield such estimates, if the study continues long enough, because it has already initiated a program of testing the children of the NLSY mothers. As of now, however, it provides no interpretable data about the national population as a whole. The women of the NLSY are only partway through their childbearing years (ages 25 to 33 as of our last observation), and the children of the sample are atypical in that they were disproportionately born to young mothers, who may differ in their child-rearing practices from older mothers. The sample is still missing altogether many of the children of women who delay childbearing, who in turn are disproportionately women with advanced education—and high IQs. We can use the mother-child testing data to extract a few clues about ethnic differences, described later in this chapter.
31 See Chapter 17.
32 Not everyone agrees that it is worrisome. In a recent contribution to the fertility debate, Samuel Preston and Cameron Campbell (1993) challenge the premise that negative differential fertility on the microlevel must mean falling national intelligence on the macrolevel. Such negative differentials are compatible, they argue, with a constant, improving, or deteriorating intelligence distribution in the population as a whole. It all depends on how the current differentials relate to past and future fertility patterns. The argument is densely mathematical, and neither the article nor the two accompanying commentaries lend themselves to easy summary. Interpreting the argument is complicated by the fact that the authors operationalized their model with one of the only data sets in which the fertility differential is not negative. However, the narrowest mathematical implication of their model remains accurate: It is possible to postulate conditions that produce a constant or even rising IQ in the face of negative fertility differentials. There is no reason to suppose that those special conditions prevail now or have in the recent past. James Coleman (1993) similarly points out in his commentary that these hypothetical conditions do not have much to do with what is known about the history of fertility, concluding that “their rejection of the common belief about the effect of fertility differences is not warranted. What they have done is not to answer the questions involved, but to frame the problem in a most useful way” (p. 1032).
33 A population has a limited number of ova and an unlimited number of sperm. Therefore, what matters for replacement (net of migration) is how many females are born and what their fertilities are. Hence, since slightly more than 50 percent of births are males and since a few of the females do not reach the age of reproduction, the average woman needs to have approximately 2.1 births to attain replacement fertility.
34 Sweet and Rindfuss 1983, Fig. 2. Other countries similarly show the impact of education on fertility. A study of Mexican women in which urbanization, occupation, migration, and education were examined for their effects on fertility found that education was the main depressant. See Pick, Butler, and Pavgi 1988.
35 Based on completed fertility for women ages 35 to 44 in the Bureau of the Census’s Current Population Survey, a nationally representative sample, in June 1992 (Bachu 1993, Table 2). The mean IQ represents the aggregated means by educational level. This calculation assumes that the mean IQ of women at various educational levels is the same for women born from 1948 to 1957 (the national sample represented in the figure on page 349) as it was for the NLSY women born from 1957 to 1964. Is this plausible? Women born from 1948 to 1957 graduated from high school from 1966 to 1975, after the percentage of students finishing high school had hit its peak, after the major shifts in educational recruitment to college had already changed for whites, and after aggressive affirmative action had begun for blacks and to some extent for Latinos. We can think of no reason to assume that the mean IQ of NLSY women (born from 1957 to 1964) at different levels of educational attainment was systematically different than for the cohort of women born from 1948 to 1957, though it could have been.
36 The data report the education of the mother at the time she has a child, but a very young mother may later go back to finish high school, and a woman with a bachelor’s degree may return for a master’s or a Ph.D. In ascribing IQs based on educational attainment, it is important to base them on the final attainment, not just on the years of education at the time of birth. Our procedure for doing so was as follows: Using the NLSY, we first established the difference between education at the time of birth and education as of 1990, when the youngest woman in the NLSY was reaching 26. In the first version of our procedure, it was assumed that the proportion of women who gave birth at ages 26 to 33 (the age range of 98 percent of NLSY women by the 1990 interview) who would subsequently move into a new educational category (the categories were 0-11, 12, 13-15, 16, and 17 or more years of education) was extremely small. We then computed an adjusted version of the table showing births by age by race in National Center for Health Statistics 1993, Table 20, assuming eventual educational attainment equal to that observed in the NLSY (for example, 36.1 percent of NLSY women who had ten years of education when they first gave birth reported twelve years of education by 1990; we recomputed the NCHS cell assuming that 36.1 percent of the women in the NCHS figures who were shown as having ten years of education would eventually get twelve). We then used the adjusted matrix of births by age by race to estimate IQs, using the NLSY mean IQs for women with equivalent years of education. Note that this computation must be done using separate estimates by race, because of the large discrepancy between the IQs of blacks and whites of equivalent years of education. This first: iteration yielded an estimated mean IQ of mothers for the 1991 U.S. birth cohort of 97.9. We then repeated the process, using a sample limited to births that occurred by the end of 1986, meaning that each mother had at least four years of postbirth observation to see if she went back to school. This version avoided the assumption that women ages 26 and over seldom go back to school, at the cost of reducing sample sizes and perhaps introducing some unrepresentativeness into the truncated sample. The estimated IQ for the mothers of 1991 U.S. birth cohort using this procedure was 98.0.
37 The actual figure, based on all births through 1990, was 95.7. It is produced by taking the mean (using sample weights as always) of the IQ associated with the mother of each child born to an NLSY mother.
38 Out of every 100 women ages 30 to 34 in 1990, only 2 had their first birth that year; after age 34, the proportion fell rapidly to near zero. See Bachu 1991, Table 4. We realize that many readers know personally of numerous women who had their first babies in their late thirties. It is one more useful example of the difference between the world in which most of our readers live and the rest of the country.
39 Women of the NLSY who had reached ages
32 to 33 may be expected to have borne about 83 percent of all the babies they will ever bear (interpolated from National Center for Health Statistics 1991, Table 2).
40 The biases will understate the age differential by cognitive class because (based on known patterns of childbearing by women of different educational groups) the largest change in the final mean age of births will occur among the brightest women.
41 Bachu 1993, Table 2.
42 This finding echoes points made in other places. We showed earlier (see Chapter 8) that it is not IQ per se that depresses fertility but the things that a higher IQ results in, such as more education (see Retherford and Sewell 1989; Rindfuss, Morgan, and Spicegood 1980). At given IQ scores, blacks get more schooling than either whites or Latinos (Chapters 13,18). Hence we should not be surprised that, at given IQ scores, blacks have lower fertility than either of the other groups; they are more likely to be still in school.
43 Rindfuss, Morgan, and Spicegood 1980; Osborne 1973; Chen and Morgan 1991b.
44 Chen and Morgan 1991a; Rindfuss, Morgan, and Spicegood 1988.
45 The quotation is taken from Baker and Mott 1989, p. 24.
46 To mention just one of the most important reasons to hedge, the participation of Latino mothers in the NLSY testing program was comparatively low, making the white-Latino comparison quite tentative. And as we cautioned in Chapter 14, the PPVT is probably less valid for Latinos than for other groups. This may bear on the comparison between Latino-white differences among mothers and among children. In any case, the figure for the apparent dysgenic effect for the Latino-white comparison is small enough to deter strong conclusions.
In contrast, the black-white apparent dysgenic effect is large, and we examined it using several methods to see if it might be spurious. The table on page 356 reports the results using the children’s sample weights, and comparing tested children with the mothers of those children, counting a mother more than once if she had more than one child and counting the same child more than once if he or she had been tested in more than one year (after turning 6). If we repeat the same calculation but including all children who were tested (including those under the age of 6), the black-white difference among the mothers is 13.9 points, compared to a difference among the children of 20.0 points, an even larger dysgenic difference than the one produced by the children ages 6 and older. Another approach is to discard the sample weights (which are problematic in several respects, when comparing across test years) and instead restrict the sample to children born to mothers who were in the cross-sectional NLSY sample. Doing so for all children who took the PPVT after the age of 6 produces a B/W difference of 14.8 points for the mothers and 18.1 points for the children, or a dysgenic difference of 3.3 points. Doing so for all children who took the PPVT produces a B/W difference of 14.9 points for the mothers and 19.4 for the children, or a dysgenic difference of 4.5 points.
The Bell Curve: Intelligence and Class Structure in American Life Page 87