ior 28 (2007) 392–398

Evolution and Human Behav

The effects of sex and childlessness on the association between status andreproductive output in modern society

Martin Fiedera,b,⁎, Susanne HubercaDepartment of Anthropology, University of Vienna, Vienna, Austria

bRector's Office, University of Vienna, Vienna, AustriacResearch Institute of Wildlife Ecology, University of Veterinary Medicine, Vienna, Austria

Initial receipt 9 November 2006; final revision received 4 May 2007

Abstract

Examining the association between socioeconomic status and reproductive output in modern societies has led to conflicting results. In thisstudy, we used a representative sample of contemporary Swedish men and women to analyze possible reasons for the contradictory results.We found that the relationship between socioeconomic status—described here by income and highest educational level attained—andreproductive output is dependent on sex and the inclusion or exclusion of childless individuals. In men, there is a strong positive associationbetween income/education and average offspring count if childless individuals are included in the analysis; this association is absent whensuch individuals are excluded. We attribute this reversal mainly to the higher proportion of childless individuals among men of lowersocioeconomic background. Among other factors, female choice appears to be a major cause of this association because the proportion ofmen who never married increased with decreasing income category and educational level. In women, however, including and excludingchildless individuals both yielded a negative association between income and average offspring count as well as a null or negativerelationship between education and average offspring count.© 2007 Elsevier Inc. All rights reserved.

Keywords: Human; Status; Income; Education; Reproduction; Evolution

1. Introduction

Life history theory predicts that individuals optimize theallocation of their available resources to maximize fitness. Indoing so, they experience a tradeoff between energyallocation for growth and survival versus reproduction aswell as that between the quantity and quality of offspring(Mace, 2000a,2007). These two tradeoffs are unveiled by anassociation between increasing offspring number andreduced parental and offspring survival (Penn & Smith,2007). Such an association has been demonstrated, forinstance, in preindustrial societies in which high-fertilitymothers experienced higher infant mortality (Mace, 2007;Strassmann & Gillespie, 2002). As a consequence, reducingoffspring count together with investing more in eachindividual child may be a means to increase individual

⁎ Corresponding author. Department of Anthropology and Rector'sOffice, University of Vienna, Dr. Karl Lueger Ring 1, 1010 Vienna, Austria.Tel.: +43 664 817 48 39; fax: +43 1 4277 9100.

E-mail address: martin.fieder@univie.ac.at (M. Fieder).

1090-5138/$ – see front matter © 2007 Elsevier Inc. All rights reserved.doi:10.1016/j.evolhumbehav.2007.05.004

fitness (Kaplan, Lancaster, Johnson, & Bock, 1995; Penn &Smith, 2007); this would hold true particularly for womenbecause of their high reproductive investment duringgestation and lactation.

Life history theory further predicts that resource avail-ability influences resource allocation. Accordingly, in lightof fitness maximization, women are expected to choosemates of high socioeconomic status: they offer greateraccess to resources as compared with those of lowersocioeconomic background (Buss, 1999). Consequently,men of higher socioeconomic status should have higheraccess to reproductive women, who, in turn, should adjusttheir reproductive decisions accordingly. The result is apositive association between male socioeconomic status andreproductive success.

Such a positive association among male status, resources,and reproduction has been demonstrated in a wide range ofpreindustrial societies (Borgerhoff-Mulder, 1988; Chagnon,1988; Cronk, 1989; Irons, 1979; Mealy, 1985; Voland,1990). In industrial societies since the end of the 19thcentury, however, a null or even a negative relationship has

Fig. 1. Offspring count (mean±SE) in men (solid circles) and that in women (open triangles) of different income categories: (A) including childless individualsand (C) excluding childless individuals. Offspring count (mean±SE) in men (solid circles) and that in women (open triangles) with different educational levelsattained: (B) including childless individuals and (D) excluding childless individuals. Percentage of childless individuals: among men (solid bars) and women(open bars) of different income categories (E) and different educational levels attained (F). Sample sizes: for Panels (A) and (E), men=1131/1176/2021/2648 andwomen=1990/2347/1669/987; for Panels (B) and (F), men=1894/3064/2018 and women=1422/3317/2254; for Panel (C), men=753/912/1646/2313 andwomen=1726/2088/1469/839; and for Panel (D), men=1474/2470/1680 and women=1250/2948/1924.

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Table 1General linear model (main statistics and repeated contrasts) using offspring count as the dependent variable, income and education as fixed factors, and age as acovariate for men including childless individuals

Main statistics Corrected model Age Income Education Income×Education

Adjusted R2 0.027df 12/6975 1 3 2 6F 17.235 0.495 43.783 0.621 2.031p b.001 .482 b.001 .538 .058

Income Education

Repeated contrasts INC1 versus INC2 INC2 versus INC3 INC3 versus INC4 EDUC1 versus EDUC2 EDUC2 versus EDUC3

Contrast estimate −0.117 −0.068 −0.098 −0.016 −0.01195% confidence interval −0.177 to −0.057 −0.121 to −0.016 −0.140 to −0.057 −0.054 to 0.022 −0.057 to 0.035p b.001 .011 b.001 .414 .648

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emerged (Wrong, 1980). This challenges evolutionaryexplanations of human behavior (Borgerhoff-Mulder,1998). What might explain this null or negative relation-ship? A study on modern French Canadians (Pérusse,1993), for instance, may have been biased towardindividuals of higher income and education. Moreover,the used composite measure of socioeconomic status mayhave led to ambiguous results. Straightforward andseparated analyses of single factors could potentially leadto more consistent findings. Only when particular societysubsets are analyzed (Fieder et al., 2005; Weeden, Abrams,Green, & Sabini, 2006) might a combined indicator ofsocioeconomic status be reasonable: within any societysubset, income, formal position, and education are moreclearly interrelated than they are within whole societies.The study by Vining (1986) on social versus reproductivesuccess, which also reported on an overall negativerelationship between fertility and status, has severallimitations. It compared the reproductive output of menwith that of women, the study participants were still in theirreproductive years, and only indirect measures were used asa proxy for status. The study by Kanazawa (2003), basedon the General Social Survey from the late 1980s to themid 1990s, also reported on a negative association betweenmale status and reproduction but failed to consider that

Table 2General linear model (main statistics and repeated contrasts) using offspring count acovariate for women including childless individuals

Main statistics Corrected model Age Inc

Adjusted R2 0.002df 12/6992 1 3F 2.357 0.035 2p .005 .851

Income

Repeated contrasts INC1 versus INC2 INC2 versus INC3 IN

Contrast estimate −0.018 0.041 095% confidence interval −0.055 to 0.02 0.001 to 0.082 −0p .363 .042

highly educated individuals may delay their reproduction.Finally, the offspring count of men is very rarely sampledin censuses and other surveys. Thus, many of theassumptions made on how socioeconomic status influencesreproductive output rely solely on data from women.

Controlling for confounding variables, on the other hand,may yield results that meet evolutionary expectations even inmodern societies. Kaplan and Lancaster (2000), for instance,reported on a positive relationship between income andoffspring count in modern men. A similar positive associa-tion—among men but not women—was also found byHopcroft (2006), who analyzed General Social Survey dataas well. By analyzing a subset of society, Fieder et al. (2005)demonstrated a positive association between formal statusand reproduction in men as well as a negative association inwomen. These findings support the importance of societysubsets (Mace, 2000b) and homogeneous data sets (Voland,2007) when investigating evolutionary predictions in humanbehavior. Accordingly, even Vining (1986), who reported onan overall negative relationship between male status andreproductive output, described a positive association withinparticular cohorts of his sample.

Overall, these studies on the association between statusand reproductive output underline the following key issues:(1) sex differences; (2) the type of measure of socioeconomic

s the dependent variable, income and education as fixed factors, and age as a

ome Education Income×Education

2 6.812 0.860 0.975.038 .423 .441

Education

C3 versus INC4 EDUC1 versus EDUC2 EDUC2 versus EDUC3

.031 −0.009 0.023

.028 to 0.089 −0.055 to 0.037 −0.011 to 0.057

.301 .689 .190

Table 3General linear model (main statistics and repeated contrasts) using offspring count as the dependent variable, income and education as fixed factors, and age as acovariate for men excluding childless individuals

Main statistics Corrected model Age Income Education Income×Education

Adjusted R2 0.001df 12/5623 1 3 2 6F 1.670 4.604 0.717 1.091 0.451p .067 .032 .542 .336 .845

Income Education

Repeated contrasts INC1 versus INC2 INC2 versus INC3 INC3 versus INC4 EDUC1 versus EDUC2 EDUC2 versus EDUC3

Contrast estimate 0.015 0.002 −0.013 0.009 −0.01995% confidence interval −0.019 to 0.05 −0.027 to 0.031 −0.035 to 0.009 −0.012 to 0.031 −0.044 to 0.007p .388 .893 .245 .394 .157

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status; (3) the reproductive age of the study population; and(4) the homogeneity of the data set. Further potentiallyimportant issues concern (5) the effects of childlessness andmarital status: both parameters may determine fertilitypatterns (Fieder et al., 2005; Forsberg & Tullberg, 1995;Oppenheimer, 2003; Weeden et al., 2006) but have not beensufficiently considered in previous studies on the associationbetween status and reproduction.

This study therefore investigated separately for post-reproductive men and women (1) the association amongincome, education, and reproductive output (both includingand excluding childless individuals) and (2) the associationamong income, education, marital status, and childlessness.

2. Methods

We used a representative data set of 7000 Swedish menand 7000 Swedish women from the Total PopulationRegister of the year 2000 obtained from Statistics Sweden.The sample has been matched by Statistics Sweden with theMultigeneration Register, Register of Population Changes,Register of Income and Wealth, and Register of Education.The sample contains only those individuals born between1945 and 1955 and thus aged between 45 and 55 years. Thisyields data about lifetime reproductive success because, in

Table 4General linear model (main statistics and repeated contrasts) using offspring count acovariate for women excluding childless individuals

Main statistics Corrected model Age In

Adjusted R2 0.01df 12/6121 1F 6.223 7.770 1p b.001 .005

Income

Repeated contrasts INC1 versus INC2 INC2 versus INC3 IN

Contrast estimate 0.029 0.04595% confidence interval 0.007 to 0.051 0.023 to 0.068 −p .009 b.001

this sample, more than 99.7% of women and more than96.5% of men have finished reproduction by age 45 years.The main advantage of this data set is that it is one of the veryrare data sets that include highly accurate data about theoffspring count of men and women and that cover society asa whole. The created file has been depersonalized andcontains the following variables: sex; age; number ofbiological children born up to 2003; individual-level incomein 2000 (classified by Statistics Sweden into four incomecategories: INC1, 0–156,002 Skr; INC2, 156,003–211,444Skr; INC3, 211,445–272,471 Skr; and INC4, ≥272,472Skr); highest education achieved by 2000 (classified intothree categories: EDUC1, primary and lower secondaryeducation; EDUC2, upper secondary education; andEDUC3, tertiary education); and marital status (nevermarried vs. married at some point).

We used the SPSS 12.1 statistical package for Windows(SPSS, Chicago, IL). We performed each analysis separatelyfor men and women. Offspring count was not normallydistributed. We therefore performed a square root transfor-mation of offspring count before the analyses. We analyzedthe relationship among income, education, and offspringcount including and excluding childless individuals with thegeneral linear model (calculating main statistics and repeatedcontrasts). Offspring count was used as the dependentvariable; income and education were used as fixed factors;

s the dependent variable, income and education as fixed factors, and age as a

come Education Income×Education

3 2 67.726 8.042 2.836b.001 b.001 .009

Education

C3 versus INC4 EDUC1 versus EDUC2 EDUC2 versus EDUC3

0.017 −0.02 −0.0290.016 to 0.05 −0.046 to 0.006 −0.049 to −0.01.302 .126 .003

Table 5Percentage of individuals who were married at some point among men andwomen of different income categories and educational levels attained,including childless individuals

Men married at some point Women married at some point

INC1 1069 (40.1) 1866 (48.4)INC2 1110 (44.9) 2215 (50.7)INC3 1919 (54.8) 1591 (56.3)INC4 2524 (66.8) 939 (62.2)EDUC1 1782 (48.1) 1343 (39.2)EDUC2 2893 (53.4) 3117 (50.9)EDUC3 1923 (67.0) 2144 (64.7)

Data are expressed as n (%).

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and age was used as a covariate. The association amongincome, education, marital status, age, and childlessness—aswell as income, education, age, and marital status—wasanalyzed using logistic regression.

able 6ogistic regression using marital status as the dependent variable andcome, education, and age as independent variables for men and womencluding childless individuals

n Nagelkerke's R2 Income Education Age

en 6598 0.06 B=0.313 B=0.259 B=0.000pb.001 pb.001 p=.973

omen 6604 0.088 B=0.076 B=0.454 B=−0.12p=.005 pb.001 pb.001

3. Results

We found that in the male sample including childlessindividuals, men of higher income categories have, onaverage, more children as compared with those of lowerincome categories (Fig. 1A). The average offspring countincreased with increasing income category and was, in eachincome category, significantly lower than that in the nexthigher category (Table 1, contrasts). In the female sampleincluding childless individuals, an inverse pattern emerged.Wealthier individuals have, on average, fewer children ascompared with those of lower income categories, with theaverage offspring count decreasing as income increased(Fig. 1A). The overall differences in average offspring countamong income categories were less pronounced in thefemale sample as compared with the male sample. Inaddition, in the female sample, contrasts between adjacentincome categories were nonsignificant, except for signifi-cantly more offspring among women of Income Category 2as compared with those of Income Category 3 (Table 2).

The highest educational level attained and averageoffspring count were also positively associated in menincluding childless individuals (Fig. 1B), although contrastsbetween adjacent education categories were nonsignificant(Table 1). In the female sample including childless indivi-duals, however, moderately educated women have, onaverage, the most children and the best educated womenhave, on average, the least children (Fig. 1B). Again, contrastsbetween adjacent education categories were nonsignificant(Table 2). When examining the effects of income andeducation on offspring count and correcting for age, onlyincome was significantly associated with offspring count inthe male and female samples including childless individuals.The interaction between income and education in the malesample was marginally significant (Tables 1 and 2).

After excluding childless individuals, the overall varianceof average offspring count among different income cate-gories and educational levels decreased in men and women

(cf. Fig. 1A and C as well as Fig. 1B and D). Men with thelowest income have, on average, more children as comparedwith wealthier men if childless individuals were excluded,with the fewest offspring in the second highest income(Fig. 1C) and nonsignificant contrasts between adjacentincome categories (Table 3). In the female sample, a similarpattern emerged regardless of whether childless individualswere included or excluded, with the average offspring countdecreasing with wealth (cf. Fig. 1A and C). After excludingchildless individuals, however, contrasts between adjacentincome categories were significant (except between Cate-gories 3 and 4; Table 4).

The highest educational level attained and averageoffspring count were not significantly associated in menexcluding childless individuals (Fig. 1D; Table 3), whereas inwomen of this category, the best educated have significantlymore children as compared with the moderately educated(Fig. 1D; Table 4). In men, only age was significantlyassociated with offspring count when examining the effectsof income and education on offspring count, corrected for ageand excluding childless individuals (Table 3). In women,however, each parameter as well as the interaction betweenincome and education were significantly associated withoffspring number (Table 4).

In the male sample, the proportion of childless individualsdecreased with wealth and educational level (Fig. 1E and F).In the female sample, the lowest percentage of childlessindividuals was found within the second lowest incomecategory and medium educational level; the highestpercentage, within the highest income category and educa-tional level (Fig. 1E and F).

Marital status was also associated with income andeducation. In the male and female samples includingchildless individuals, the percentage of individuals marriedat some point increased with wealth and educational level(Table 5). A logistic regression using marital status (encodedas 1=married at some point or 0=never married) as thedependent variable and income, education, and age asindependent variables showed that income and educationwere significantly positively associated with marital status inmen and women. Age was significantly negatively asso-ciated with marital status in women (Table 6). We claim thatthe age effect in the female sample could be attributed to acohort phenomenon of changing martial patterns.

Marital status and childlessness were associated. In men,62.5% of reproducing individuals but only 30% of childless

TLinin

M

W

Table 7Logistic regression using childlessness as the dependent variable and income, education, marital status, and age as independent variables for men and womenincluding childless individuals

n Nagelkerke's R2 Income Education Martial status Age

Men 6598 0.135 B=−0.345 B=0.070 B=−1.237 B=−0.017pb.001 p=.126 pb.001 p=.091

Women 6604 0.026 B=0.004 B=0.208 B=−0.689 B=−0.037p=.916 pb.001 pb.001 p=.001

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individuals were married at some point. In women, thedifference was less pronounced: 55.0% of reproducingindividuals and 40.3% of childless individuals weremarried at some point. A logistic regression revealed thatincome and marital status were significantly negativelyassociated with childlessness (encoded as 1=childless or0=reproducing) in the male sample. In the female sample,education was significantly positively but age and maritalstatus were significantly negatively associated with child-lessness (Table 7).

4. Discussion

We show that the association between socioeconomicstatus—as indicated by income and education—and off-spring count differs markedly between the sexes. Offspringnumber increased with wealth and educational level in men,whereas an inverse pattern emerged in women: feweroffspring as income and education increased. We attributethe positive association in the male sample mainly to theassociation between income/education and childlessnessbecause the proportion of childless men increased as incomeand educational level fell. Accordingly, in men, therelationship between income/education and average off-spring count differed markedly depending on whetherchildless individuals were included or excluded. Only ifthey were included in the analysis did a positive associationemerge; when they were excluded, no significant associationbetween income/education and offspring count was found.

We assume that female choice is a major reason for thereversed association when including and excluding childlessmen. To maximize fitness, women are expected to prefermen of high socioeconomic status as mates because they canoffer more resources, protection, and earning potential ascompared with those of lower status (Buss, 1999; Buss &Barnes, 1986). Accordingly, the latter group should havelower and less regular access to reproductive females and,consequently, a higher chance to remain childless. This viewis supported by our findings of an increasing proportion ofmen who were married at some point as wealth andeducation increased as well as a higher proportion ofindividuals who never married among childless versusreproducing men. These data are consistent with marriagepatterns reported thus far: Nakosteen and Zimmer (1997), forinstance, showed that a higher percentage of lower-incomemen remained unmarried. In addition, the percentage of

childless individuals and that of individuals without a partnerare higher among unskilled male laborers than in menworking in other occupations (Forsberg & Tullberg, 1995).Finally, Burgess, Propper, and Aassve (2003) reported thathigh earnings increased the probability of marriage anddecreased the probability of divorce in young men.

In reproducing men (i.e., excluding childless individuals),we found the most offspring among the poorest and leasteducated individuals—a pattern similar to that in the femalesample. Thus, it is reasonable to assume that this femaleassociation underlies the pattern found in men after excludingchildless individuals.

In contrast to the male sample, including and excludingchildless individuals in the female sample did not substan-tially change the relationship between income and offspring.Wealth remained to be associated with fewer childrenregardless of whether childless individuals were includedor excluded. However, greater education was correlated withslightly more children only after childless women wereexcluded. On the one hand, these data point to the well-known difficulties of balancing motherhood and a career inmodern societies as well as to the risk of missing the fertilitywindow when reproduction is postponed during education(Goldstein & Kenney, 2001). From an evolutionaryperspective, on the other hand, the negative associationbetween income/education and offspring count in womenmay be attributed to a life history strategy. This predicts atradeoff between offspring quality and quantity, yielding an“optimum number of offspring” (Mace, 2007). Accordingly,women increase their individual fitness by having fewerchildren yet investing more in each individual child viaincreased education and personal income. Note, however,that empirical evidence for such a quality–quantity tradeoffin fitness outcomes over generations is not clear in modernsocieties (Kaplan et al., 1995).

In addition, women may be victims of their evolutionaryadaptations to prefer men of high socioeconomic status(Buss, 1999). Particularly, successful professional womenare known to be very interested in the high income of apotential husband (Wiederman, 1993). This may complicatefinding an adequate spouse because few high-status men areavailable. In fact, high income in women has been reportedto decrease their probability of marriage (Burgess et al.,2003). Lack of an adequate partner, in turn, may limit awoman's reproductive output, although marriage is appar-ently becoming less important for reproductive decisions,particularly in women. Our study found the highest

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percentage of unmarried individuals among the poorest andleast educated women, but these women had, on average, themost offspring.

This study has limitations as it lacked data on the spouses'income and education level. However, reproductive deci-sions involve two people—or at least the resources availableto two people.

Acknowledgments

Data Source: Statistics Sweden. We thank the twoanonymous reviewers for their valuable suggestions.

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