The age pattern of first-birth rates among U.S. women: The bimodal 1990s

THE AGE PATTERN OF FIRST-BIRTH RATES AMONGU.S. WOMEN: THE BIMODAL 19905*

RACHEL SULLIVAN

Between 1990 and 2002, the age pattern of Type I first-birth rates (i.e., the hazard of a firstbirth) among u.s. women was bimodal. This pattern, driven by changing differential fertility pat-terns among racial and ethnic groups, reached its apex at the mid-1990s and had almost vanishedby the decade s end. Research on first-birth timing has tended to focus on Type II first-birth ratesand therefore has failed to identify this larger, bimodal pattern. This article presents the benefits ofusing Type I rates, documents the emergence of the bimodal pattern via two new measures ofbimo-dality, and uses a decomposition analysis to discuss the pattern s causes.

The age pattern of Type I first-birth rates was bimodal during the 1990s, but the agepattern ofType II age-specific first-birth rates was not. Type I rates, also known as hazardor occurrence/exposure rates, differ from Type II rates in that the denominator includesonly those people who are at risk of an event (cf. Kohler and Ortega 2002 and Wilmoth2005 for the use of terminology). In the case of Type I first-birth rates, the denominator ischildless women, the only group who are at risk of a first birth. Traditionally, however,demographers who have studied fertility have used Type II rates, such as age-specificfertility rates, because they are easy to calculate and thus widely available. For example,the total fertility rate is the sum of Type II age-specific fertility rates for women of child-bearing age. In this article, I show the benefit of using Type I first-birth rates for U.S.women. Because these rates capture women who are actually at risk of a first birth, theygive a clearer picture of the factors that drive first-birth patterns than do Type II first-birth rates.The age pattern of the Type II first-birth rate for contemporary U. S. women by age is

unimodal, with a peak at age 20 and a long tail into the thirties (see Figure 1). A graph ofType I first-birth rates reveals, however, that a bimodal pattern emerged and then recededduring the 1990s (see Figures 2 and 3). This article is the first to document explicitly theemergence of the bimodal pattern in Type I first-birth rates, which clarifies our under-standing of fertility. Analyses of first-birth timing often focus on one particular group ofwomen (e.g., teenage mothers, highly educated women, or African American women)whose age patterns of Type I and Type II first-birth rates are usually unimodal. Althoughsubgroup analysis is logical, it is also beneficial to consider overall age patterns of fertil-ity because these patterns serve as an important point of comparison to subgroup patterns.There are also advantages to focusing on a pattern across ages, rather than on indicators

*Rachel Sullivan, Graduate Group in Sociology and Demography, University of California, Berkeley, 2232Piedmont Avenue, Berkeley, CA 94720-2120; E-mail: [email protected] research was con-ducted with support from the National Institute of Child Health and Human Development (Grant T32-HD007275) and the National Science Foundation (graduate research fellowship). I extend my warmest thanksto Claude Fischer, Mike Hout, Jenna Johnson-Hanks, Ron Lee, Kristin Luker, Carl Mason, Ken Wachter, JohnWilmoth, the editors and two anonymous reviewers at Demography, and the participants in Session 68 at the2003 annual meeting of the Population Association of America, the University of California, Berkeley,Demography Department brown bag series, and the University of California, Berkeley, Sociology Department2003-2004 professional writing seminar.

Demography, Volume 42-Number 2, May 2005: 259-273 259

260 Demography, Volume 42-Number 2, May 2005

Figure 1. Type II First-Birth Rates, U.S. Women, 1980-1999

199819951992\989Year 19861983

Source: NCHS (1980-1999).

27 29 3123 25

17 19 2\ Age

60

50

40

30

20

10

that collapse age-specific information into one measure (like the total fertility rate).Specifically, these patterns can be compared across groups or time without losing theage-specific information. This use of patterns, rather than indicators, is reminiscent ofKnodel's (1977, 1983) reliance on age-specific patterns of marital fertility to indicatenatural fertility practice.Given that the bimodal pattern in the Type I first-birth rates emerged and receded, the

central question is why. There are two possible explanations: either the two modes are theresult of different life-course decision-making processes among women or the patternreflects extreme divergence among subgroups ofwomen. Ifthe age pattern ofType I first-birth rates of subgroups of women is bimodal, suggesting extreme heterogeneity withingroups, then the first explanation is more plausible. If, instead, subgroups of women haveunimodal age patterns, then the second explanation makes more sense. I first documentthe emergence and disappearance of the bimodal pattern in the graph of Type I first-birthrates to argue for the benefit of using Type I rates, as well as to introduce two measures ofbimodality. I then present a decomposition of these rates by race/ethnicity and educa-tional status to explain the driving force behind the bimodal pattern and to determinewhether a life-course or subgroup explanation fits the data better. I conclude with a dis-cussion of bimodality as an indicator of fertility transition.

STATE OF KNOWLEDGEUnderstanding the pattern of ages at first birth matters to demographers, sociologists, andpolicy makers. Because the majority of births are first and second births, the timing ofthese births directly drives current and future overall fertility trends (Morgan 1996).Therefore, measuring period fertility and projecting completed cohort fertility requires

The Age Pattern of First-Birth Rates 261

Figure 2. Type I First-Birth Rates, U.S. Women, 1980-1999

0.10

008

006 ::0~'"

0.04

002

Source: NCHS (1980-1999).

Figure 3. Bimodality in Type I First-Birth Rates, U.S. Women, 1990s

012r----------------------------,

0.08 t---....,~:::::::::~-.:::...---------::~~~--____1

002ff~;================;_----------I1-1991 ---1995 -19991

0.10 +------------~;:...--.....:::..~~~------__f

0.04+---f-l~-------------------~~:r__;

~ 0.06+---H......------------------........~~'c_----;i:l:;

o.00 -l--.-~__.___._--r___._____.____.-.__..___.__~__.___.___.____._____.____.-.___~..--~""T"""__l15 16 17 18 19 20 21 22 23 24 25 26 27 28 29 30 31 32 33 34 35 36 37 38 39

Age

Source: NCHS (1991, 1995, and 1999).


accurately modeling the timing of first births. The first birth signifies the transition tomotherhood, and although the meaning of motherhood has been changing rapidly overthe past half century, this transition remains a major life-course event worthy of study.Policy makers also rely on projections of fertility trends in designing social security andhealth care support programs, for example.Most research on first-birth timing has addressed either the causes or the effects of

having a first birth at a particular age and has divided the population of interest betweenyounger and older mothers. Studies on the causes of early or late first births have fo-cused largely on socioeconomic status (Luker 1996; Teachman and Polonko 1985;Teachman and Schollaert 1989), education (Edwards 2002; Martin 2000; Rindfuss, Mor-gan, and Offut 1996; Rindfuss, Morgan, and Swicegood 1984; Skirbekk, Kohler, andPrskawetz 2004), race (Bloom 1982; Chen and Morgan 1991; Teachman 1985;Teachman and Schollaert 1989), union status (Manning 1995; Teachman 1985), experi-ences in adolescence (Barber 2001; Miller and Heaton 1991; Powers 1993), attitudestoward the proper timing of life events (East 1998; Mahaffy and Ward 2002; Morgan1996), and parental attitudes (Barber 2000; Barber and Axinn 1998). Although this re-search has provided comprehensive analyses of the factors that affect first-birth timing,the focus on subgroups removes the emphasis from the overall pattern. As Rindfuss,Morgan, and Swicegood (1988) pointed out, to understand both early and late fertility, itis necessary to understand fertility at all ages. Using Type I birth rates increases theability to do so by incorporating information on the group at risk.Age at first birth was a popular research topic during the early 1980s, when delayed

fertility was first recognized as a permanent trend (Bloom 1982; Bumpass, Rindfuss, andJanosik 1978; Rindfuss and St. John 1983; Wilkie 1981). Initial interest focused on thefactors that affect birth timing. By the late 1990s, attention had shifted to accountingproperly for the effect of tempo on the projected cohort quantum of fertility, a body ofresearch that emphasizes the importance of first-birth timing (e.g., Bongaarts 2002;Bongaarts and Feeney 1998; Kohler, Billari, and Ortega 2002). Yet, there are no specificreferences in the literature to a bimodal pattern of Type-l first-birth rates or first-birthprobabilities. Morgan (1996) reported an increasing dispersion of the age at first birthover time that would accompany the emergence of such a pattern. Other authors havenoted specific variables that divide women into distinct groups on the basis of age atfirst birth, such as education (Edwards 2002; Rindfuss et al. 1996), marital status (Wu,Bumpass, and Musick 2001), and income (Luker 1996). These variables, as I will show,help explain the bimodal pattern, but none of these authors described these fertilitycurves as bimodal.The current literature adequately describes why some women have a first birth early

and others have a first birth later. But this simple dichotomy obscures how the overallpattern offertility is related to subgroup particularities. Exploring the bimodal pattern thatexisted during the 1990s will thus help to clarify contemporary fertility trends and directfuture research on the topic. The analysis presented here documents the emergence andrecession of the bimodal pattern during the 1990s using two new measures of bimodality.I then explore the causes of this pattern, with a particular emphasis on whether a life-course or a subgroup analysis reflects the data better, and conclude by touching on some ofthe pattern's implications.

DATA AND METHODSIn the main, I use a period perspective to discuss the bimodal shape of the age pattern ofType I first-birth rates by age, largely because these data are more readily available.However, the bimodal pattern appears in cohort data as well, as I show for all women.The decision to use a cohort or a period perspective is primary to any analysis of demo-graphic phenomena. Regarding fertility and first-birth timing, Morgan (1996) suggested


that the period perspective gives the most leverage on the causes of different patterns,since period effects are integral to the changes that have occurred in U.S. fertility duringthe twentieth century. Similarly, Rindfuss et al. (1988) and Ni Bhrolchain (1992) alsoespoused the importance of period effects relative to cohort effects. Even Ryder,demography's greatest champion of the cohort perspective (Ryder 1965), acknowledgedthe relevance of a period perspective on age at first birth because age at first birth influ-ences period fertility, which, in tum, affects cohort fertility (Ryder 1980). Although pe-riod data inform researchers about the causes of the bimodal pattern, in interpretingthese data, it is often easier to use a cohort-based explanation, which, in the case ofperiod data, can refer only to the synthetic cohorts that are composed of women of allages in the year of interest.Following Rindfuss et al. (1988) and Chen and Morgan (1991), I selected Type I

first-birth rates at age x during year t as the main dependent variable. These Type I ratesare published annually for all women, by exact age and by the race of the child, by theNational Center for Health Statistics (NCHS 1980-1999) and are referred to as "prob-abilities." For first births, they have the form

(1)

where BxP) is the number of first births to women aged x during year t and E)O) is thenumber of childless women aged x in year t. Although h)l) looks similar to a probability,it is not: the combination of values across all x does not result in a distribution curvewhose integral is 1. Because h)l) accounts for the age structure of the population, thepatterns in the graph of h)l) over time are not biased because of differences in cohortsize. All Type I first-birth rates that are presented are for women aged 15-39. The num-bers of women who have first births before and after these ages are small and are there-fore disregarded. The birth data used by NCHS come from birth certificates and are thus a100% sample. The counts of childless women that are used in the NCHS first-birth prob-abilities are based on cumulative birth rates by parity (NCHS 1999).The analysis of Type I first-birth rates by cohort used data from Wilmoth (2005),

which, in tum, came from a multistate life-table model based on NCHS data, by parity, for1917-2000. These data are available only for all women, so they could not be used for thedecomposition analysis. The analysis of Type I first-birth rates by race, ethnicity, andeducational level used data from the extracts of the June Current Population Survey (CPS)and the NCHS natality files that were restricted to the years that the CPS fertility supple-ment was collected and bracketed the period of bimodality (NCHS 1990, 1992, 1994,1995, 1998,2000; U.S. Census Bureau 1990, 1992, 1994, 1995, 1998,2000). The NCHSdata give counts of the number of first births to women by age, race/ethnicity, and educa-tionallevel and the CPS data provide the number of childless women by these same char-acteristics. I combined the NCHS and CPS data to calculate the Type I first-birth ratesbecause doing so obviated the need to construct retrospective birth histories and becausethe NCHS offers a 100% sample. I then calculated Type I first-birth rates from the NCHSand CPS data in the same manner as was just described and smoothed them using a lowessprocedure in R software (R Development Core Team 2004) with a bandwidth of 1/4. Thisprocess uses locally weighted regressions with approximately 6 of the 25 data points ineach graph. Smoothing was necessary because of some small cell sizes in the unweightedCPS data among women who were younger than 20 and older than 30. I also include a briefanalysis of childlessness that was based on the CPS data to explain why there is no bimo-dal pattern in the graph of Type II first-birth rates by age.After presenting the graphs ofType I first-birth rates by age, the next step is to assess

the presence and strength of bimodality. Kurtosis, sometimes used by social scientists toindicate bimodality (DiMaggio, Evans, and Bryson 1996; Downey and Huffman 2001) is

264

Table 1.

Demography, Volume 42-Number 2, May 2005

BIMDistribution Under the Null Hypothesis

ElM

Percentage 59.4

2

36.1

3

4.4

Source:Author's simulations, based on NCHS data for 1990, 1992, 1994,1995, and 1998.

not well defined for this analysis because the graphs are of rates by age, not probabilitydensities. In the absence of standard measures of bimodality, I introduce two measures,BIM and BIMalt , which were designed specifically for this article, but are intended formore general application.

BIM can be calculated for any sequence of numbers, regardless of its initial pattern.It is calculated by successively taking moving averages of the data over widening inter-vals until all but one local maximum (peak) has been "smoothed" away-that is, whenthere is only one sign change in the first differences of the averaged data. BIM is definedto equal the smallest width window that produces a pattern with only one peak. Thus, agraph that is already unimodal will have a BIM of 1, because a one-point moving averageof a graph is just the graph itself. A BIM value of 2 means that it was necessary to takemoving averages of two points at a time to produce a graph with one maximum. BIMwillbe larger for graphs with peaks that are farther apart and that have deeper valleys betweenthem because it will take a wider window to smooth the peaks away. The methodologybehind BIM can be adapted to measure whether a curve has three, four, or more modes,but the focus here is on bimodality.

BIM can be used either as a descriptive statistic or as a measure that is suitable fortesting for statistical significance. There is a good deal of latitude in specifying a nullhypothesis to represent the absence of bimodality, so I present one of a number of pos-sible significance tests. I opted for a null hypothesis that took expected values by agefrom the latest of the observed unimodal graphs ofNCHS Type I first-birth rates, that for1989, and assumed independent binomial sampling at each age with a sample size equalto the minimum value of the population count of childless women (from the NCHS data)at each age over the years 1990, 1992, 1994, 1995, and 1998. Table 1 presents the distri-bution of BIM values under this null hypothesis. In all cases, one would reject the nullhypothesis of unimodality at levels at least as strict as 4.4%. Given that BIM values of 2and 3 are not uncommon randomly, I assumed that graphs must have a BIM of at least 4 tobe considered strongly bimodal.On the basis of the test of statistical significance, BIM is a good measure of bimo-

dality. However, given a bimodal curve, it is desirable to have a descriptive statistic thatis tailored to characterizing the shape of the age pattern of Type I first-birth rates. Hence,I present BIMalt , a measure that is applicable only to patterns with two local maxima.BIMalt relies on the coordinates of the three points of interest: the peak of the first mode(Plx, P ly), the valley between the modes (Vx, Vy), and the peak of the second mode(P2x, P2y). All x-values are in units of years, and all y-values are values of hx/i). BIMalttakes into account the degree to which the two modes are centered around the valley(VC), the distance between the peaks (RS), the relative height of the two modes (RH),and the height of the valley relative to the peaks (PV). Thus,

VC = Min (IP1x - Vxl, IP2x - Vxl) / (P2x - PIx)

RS = (P2x - PIx) / (Max (x-values) - Min (x-values))

(2)

(3)

The Age Pattern of First-Birth Rates

RH= Min (Ply - ry, P2y - ry) 1Max (Ply - Vy, P2y - ry)PV= ry 1Avg (Ply, P2y)

BIMalt = 4/3 VC + 2/3RS + 2/3RH - 2/3PV.

265

(4)

(5)

(6)

VC is equal to 0.5 for a valley that is perfectly centered between the two peaks, RS ap-proaches 1 for peaks that are at completely opposite ends of the x-values, RH is equal to 1when both peaks are of the same height, and PV approaches 1 as the valley gets shal-lower. Because of the weighting factors, under these criteria of bimodality, as a graphbecomes more bimodal, its BIMalt approaches 2.The analysis presented next examines the bimodality of the age pattern of Type I

first-birth rates between 1980 and 2002. With the appearance of two local maxima in1990, I expected to see both BIM and BIMalt increase and then decrease as the first peakcollapsed during the second half of the decade. The analysis also explores some of thecauses of the bimodal pattern through a cohort analysis that is based on all women bornfrom 1956 to 1975 and a decomposition of the data by race and educational status for1990 to 2000, including an examination of childlessness.

ANALYSISFigure 2 shows the age pattern ofNCHS Type I first-birth rates for 1980 to 1999 in threedimensions. It clearly shows the emergence of the bimodal pattern starting in 1990, whichis also visible from a two-dimensional perspective (see Figure 3). The first peak remainscentered at age 20, though its height increases through 1994 and then decreases. Thesecond peak shifts to the right by a year (from age 28 to 29), and its height increases from1991 onward. There is little change, either lateral or vertical, in the valley. The patterns ofthe curves in Figures 2 and 3 are distinctly different from those in Figure 1, showing thathow the event of a first birth is conceptualized greatly matters to the picture that emerges.The bimodal pattern appears only when Type I first-birth rates are used as the dependentvariable, not when the more standard Type II first-birth rates are used, highlighting theimpact on the interpretation of small differences in the choice of variable. The samebimodal pattern appears in graphs that were made with retrospective Type I first-birthrates by age, constructed from both the National Survey of Families and Households(Sweet and Bumpass 1996) and the 1995 CPS Fertility and Marital History Supplement(results not shown).Figure 4 shows BIM for 1980 to 2002 (which ranges between 1 and 6) and BIMalt for

1990 to 2002 (which ranges between -0.17 and 0.34). Both measures tell a consistentstory of an increase and then a decrease in bimodality. BIM increases from 1990 to 1993,the first year in which it is higher than 3, is constant at 6 through 1995, and then de-creases. The last year that BIM is strongly bimodal is 1999. BIMalt also increases through1993 and then decreases through 2002, with a slight fluctuation in 1998. The post-1993drops in BIM and BIMalt are caused largely by the second peak getting higher relative tothe first as the first peak "collapses" into the second peak. The end result, by 2002, is aslightly bimodal curve with a peak at an older age than in the 1980s.Figure 5 shows the graph analogous to Figure 2, but for cohorts born between 1956

and 1975. I expect the period and cohort data to parallel one another because a periodgraph with a peak at age x for year t consists of the fertility experiences ofwomen born inthe year t - x who were aged x in year t. The bimodal pattern first appears for the cohortborn between 1970 and 1974. These women were aged 20-24 in 1990, when the bimodalpattern first appears in the period data, which is what I would expect.On the basis of Figures 2-4, it is clear that in 1990 a bimodal pattern emerged in the

age pattern of Type I first-birth rates. I now explore whether the differential fertility of


Figure 4. RIM and RIMalt Values for the Age Pattern of Type I First-Birth Rates, U.S. Women,

1980-2002

......0

......

0

0.... .... 1;1 ....LJ T T

00 0

00

7

6

5

4

2

o1975 1980 1985 1990

Year

I.ElM OElMaIt I1995 2000

0.40

0.30

0.20

0.10 ~~'"

0.00

-0.10

-0.202005

Source: Author's calculations from CPS, NCHS, and Wilmoth (2005).

subgroups is the cause. Even in the presence of heterogeneity, bimodality is rare: thegraph of Type I rates for 1980 to 1989 (see Figure 2) is unimodal, even though fertilitybehavior varied by groups. Bimodality is thus the extreme version of an age pattern offertility with a shoulder at young ages. To achieve a bimodal pattern through subgroupsrequires the superimposition of two highly and oppositely skewed unimodal age patterns.That the first peak of the graph remained centered at age 20 suggests that the bimodalpattern resulted from one group ofwomen delaying their first birth (becoming the secondpeak) and a second group continuing the fertility patterns that are more closely related tothose observed in the 1980s (the first peak, centered at age 20). Based on the literature,two characteristics of women that are most associated with the timing of first birth arerace/ethnicity and education, so the subgroup analysis is based on these characteristics.Figure 6 decomposes the age pattern of Type I first-birth rates for 1990-2000 for

non-Hispanic whites, Hispanic whites, and non-Hispanic African Americans. Withineach figure, each of the curves is the average of two years' worth of data: 1990 and1992, 1994 and 1995, and 1998 and 2000. The peak of the curve for non-Hispanic whites(see Figure 6a) shifts to the right over time, ultimately ending up higher in 1998-2000than in 1990-1992. The main shift for Hispanic whites (see Figure 6b) is down, but thecurve also spreads out and flattens. In Figure 6c, the curve for African American womenshifts down over time, particularly among women in their twenties. The increase in thehazard of a first birth for non-Hispanic whites in their thirties (indicating a delay in theonset of childbearing) pushed up the older peak of the curve for all women. The Type I

The Age Pattern of First-Birth Rates

Figure 5. Type I First-Birth Rates, U.S. Women, Cohorts Born 1956-1975

Source:Wilmoth (2005).

0.12

0.10

0.08~~~

0.06

0.04

0.02

0.00

267

first-birth rates for Hispanic whites, on the other hand, first lowered (see 1994-1995 inFigure 6b), making the bimodal pattern more extreme, but then spread out and increasedfor women in their thirties, adding to the older peak and taking away from the youngerpeak. In so doing, the bimodal pattern weakened. The lack of change in the shape of thecurves for African American women implies that the fertility of non-Hispanic andHispanic white women primarily influenced the appearance and disappearance of thebimodal pattern. Figure 6 thus supports the hypothesis that racial/ethnic differences infertility timing drove the bimodal pattern.Figure 7 includes curves for two groups of women over the same time period as

Figure 6: those with a high school degree or less and those with a BA or higher degree.The peak for women with a high school degree or less is much earlier than that for womenwith a BA or higher degree. There is virtually no movement in the curves for women witha high school degree or less, while there is an increase in the likelihood of a first birth forwomen aged 29 and older with a BA or higher degree. l The lack of change for womenwith a high school degree or less indicates that their fertility has little influence on thebimodal pattern. That the increase in first-birth rates among women aged 29 and older

1. There is also an increase in the Type I first-birth rate for women aged 20-24, which is interesting, giventhat most women do not receive a BA degree until their 22nd year.


Figure 7. Type I First-Birth Rates, by Education, U.S. Women, 1990-2000

a. Women with a high school degree or less

0,180,160,140,12

.. 0,10....

osQ::; 0,080,060,040,020,0015 17 19 21 23 25 27 29 31 33 35 37 39

Age

b. Women with a BA or higher degree

o12r--------------------

0081--------""""2lp;.....---~~~---

o1O.----------.,..,...........,.:::~..3ror----

~~ 006+--------~."...--------~~~-

004.------""7.l~------------:O"~

23 25 27 29 31 33 35 37 39Age

0,0015 17

002.----+r--------------

1- 1990-1992 --- 1994-1995 - 1998-2000 I

with a BA or higher degree is constantly up helps explain the appearance, but not thedisappearance, of the bimodal pattern. The disappearance is still best explained by changesin the fertility of white women, both Hispanic and non-Hispanic.As I discussed at the beginning of this article, the fact that Type I first-birth rates

incorporate the number of childless women both differentiates them from Type II rates andinfluences their values. Because racial/ethnic differences in Type I first-birth rates seem todo the best job of explaining changes in the age pattern, I present data on the levels ofchildlessness by age for these racial/ethnic categories over the same period. Figure 8 showsthe percentage of women who were childless for non-Hispanic whites, Hispanic whites,and non-Hispanic African Americans over time. Each curve is the average of two years'


Figure 8. Percentage of Women Who Were Childless, by Race/Ethnicity, 1990-2000

1- 1990-1992 --- 1994-1995 1998-2000 1015 17 19 21 23 25 27 29

Age31 33 35 37 39

b, Hispanic white women

1- 1990-1992 --- 1994-1995 - 1998-2000 1

fo 60'"....5'"iil 40Q.,

20 ~

17 19 21 23 25 27 29 31 33 35 37 39Age

c. Non-Hispanic African American women

3937o\....;1~~:;:1=99;:::O:;:-1=99=2;::::;-=-;:- :;19=9;:::4-:;:19=9;:::5;::::;-==1:;99=8;:-2:;:0=00;::1~~15 17 19 21 23 25 27 29 31 33 35

Age

Source: Author's calculations from the CPS.


worth of data, and each curve is smoothed using a two-point moving average. For non-Hispanic white women (see Figure 8a), the percentage of childless women aged 23-31shifts up over time. Assuming that the number of first births increases at a slower rate thanthe change in the percentage ofchildless women, the Type I first-birth rate should go downover time, which is roughly what Figure 6a shows. No such correspondence is seen betweenFigures 8b and 6b (Hispanic white women), but there is a parallel between the increase inthe percentage of childless women for non-Hispanic African Americans (see Figure 8c)from 25 to 32 and the decrease in the Type I first-birth rate over time for the same agegroups, although it starts much younger. The lack of complete correspondence betweenType I first-birth rates and childlessness is not surprising, however, since it is unlikely thatthe number of first births changes at the same rate over time. Figure 8 shows thatchildlessness matters, but is only part of the story in explaining bimodality and Type Ifirst-birth rates.On the basis of the analysis displayed in Figures 6-8, fertility differences by race/

ethnicity and education appear to explain much of the bimodal pattern in the graphs ofType-I first-birth rates by age. There is undoubtedly an interaction between race/ethnicityand education; however, the CPS cell sizes become prohibitively small at that level ofdetail to explore this possibility. It is most likely that Hispanic whites and AfricanAmericans who delay their first birth have higher levels of education. A decompositionby marital status (not shown) indicated that the two peaks are also driven, to a certainextent, by whether a woman is married, although the extremely high likelihood of a firstbirth among married women younger than 20 makes this decomposition less conclusive.The minimal degree of bimodality within subgroups of women shows that a life-courseexplanation does not fit the data well, while the changes in the position of unimodalityacross racial/ethnic subgroups help to explain both the appearance and the disappearanceof the bimodal pattern.

CONCLUSIONThese data demonstrate the emergence and disappearance of a bimodal pattern in the agepattern of Type I first-birth rates during the 1990s. On the basis of a visual examination,as well as BIM and BIMalt values, the bimodal pattern was present by 1990, reached itsapex from 1994 to 1996, and declined for the rest of the decade. The implications of thisfinding are both methodological and substantive. Methodologically, statistical analysesthat use first-birth hazards as the dependent variable cannot assume that the distributionof the variable is normal. Substantively, the bimodal pattern reflects a divergence andthen a convergence in the fertility behavior of non-Hispanic and Hispanic white women.Although Type II rates are beneficial for certain purposes, demographers' use of them

and their tendency to focus fertility analyses on subgroups ofwomen have prevented theirobservation of the larger trend toward, and then away from, bimodality. Analyzing firstbirths as Type I rates rather than Type II rates provides a deeper understanding of overallfertility patterns as being so bifurcated that subgroup differences are visible at the popula-tion level. What is liable to happen in the future? The bimodal pattern appears to have beena temporary phenomenon based on the uneven adoption of fertility delay across groups ofwomen. The end result, after further diffusion, is likely to be a unimodal pattern with amode closer to age 30. However, if the women who are delaying their first birth were toalter their behavior (perhaps because ofmore public provision of child care or an increas-ing sense of the risk of waiting too long), the curve may become bimdodal again.Given that the decomposition analysis indicates that a subgroup explanation, rather

than a life-course explanation, best fits the data, bimodality can best be thought of as anindicator of bifurcation among subgroups. Changing bimodality, in turn, reflects a shift-ing relationship among subgroups that occurred during a transition in fertility timing. Justas there are number differences associated with the demographic transition, bimodality


could be the indicator that best captures phases in a timing transition whereby the major-ity ofwomen shift from early to later first births. Using Type I rates provides an excellentway to measure the location of subgroups within this transition, as well as to track theprogress of the overall transition.

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Barber, J.S. and W.G. Axinn. 1998. "The Impact of Parental Pressure for Grandchildren on YoungPeople's Entry into Cohabitation and Marriage." Population Studies 52: 129-44.

Bloom, D.E. 1982. "What's Happening to the Age at First Birth in the United States? A Study ofRecent Cohorts." Demography 19:351-70.

Bongaarts, J. 2002. "The End of the Fertility Transition in the Developed World." Population andDevelopment Review 28:419-43.

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Documents

The age pattern of first-birth rates among U.S. women: The bimodal 1990s