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Does the number of sex partners affect educational attainment? Evidence from female respondents to the Add Health

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Abstract

We use data on young women from the National Longitudinal Study of Adolescent Health to explore the relationship between number of sex partners and educational attainment. Using the average physical development of male schoolmates to generate plausibly exogenous variation in number of sex partners, instrumental variables estimates suggest that number of sex partners is negatively related to educational attainment. This result is consistent with the argument that romantic involvements are time consuming and can impose substantial emotional costs on young women.

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Notes

  1. Juster et al. (2004) analyzed data from the Child Development Supplement to the Panel Study of Income Dynamics. They found that, during the 2002–2003 academic year, the average 15–17-year-old spent 4 h 59 min/week studying and an additional 49 min reading. In comparison, Kooreman (2007) found that the average Dutch teenager spent 5.8 h/week reading but did not distinguish between studying and reading for pleasure.

  2. Specifically, Hallfors et al. (2005, p. 168) wrote, girls’ greater interpersonal sensitivity contributes to higher levels of interpersonal stress during adolescence. Substance use and sexual activity likely contribute to experienced stress. The greater exposure to stress due to risk behavior, and girls’ more negative reactivity to interpersonal stressors, may partially account for demonstrated gender differences in depression.

  3. We are not the first to hypothesize that such a tradeoff exists. Freud hypothesized that, if repressed, sexual energy (or libido) could be transformed into creative energy (Gay 1992). In Think and Grow Rich, Hill (1937) famously asserted that:

    So strong and impelling is the desire for sexual contact that men freely run the risk of life and reputation to indulge in it. When...redirected along other lines, this motivating force maintains all of its attributes of keenness of imagination, courage, etc., which may be used as powerful creative forces in literature, art, or in any other profession or calling, including, of course, the accumulation of riches.

    In a similar vein, Rector and Johnson (2005, p. 20) wrote:

    Human attention and motivation are finite; when greater energy and interest are invested in sexual activity, the drive for academic performance is likely to diminish. Sexually active teens may become preoccupied with the present; long-term academic goals may have diminished importance.

  4. A sample of 80 high schools and 52 middle schools from the USA was selected with unequal probability. Incorporating systematic sampling methods and implicit stratification into the Add Health study design ensured this sample is representative of US schools with respect to region of country, urbanicity, school size, school type, and ethnicity (see Harris et al. 2003 for more information on the research design).

  5. At Wave III, Add Health respondents were between the ages of 18 and 28. In the interest of keeping our sample homogeneous and old enough to have completed high school and begun college, we focused on individuals between the ages of 22 and 24. However, our main results are robust to examining a larger sample that includes younger females aged 18–21 (see Supplementary Appendix Table 2). Only 5.2% of female Add Health respondents were older than 24 at Wave III.

  6. Nine percent of 22- to 24-year-olds reported having zero sexual partners in their lifetime at the time of the Wave III survey. These individuals are omitted from our sample in order to avoid having to estimate abstinence effect. However, their inclusion does not appreciably change the results presented below. Estimates including virgins are available upon request.

  7. Self-reports of sexual behavior have been found to be relatively accurate, despite the sensitive nature of such subject matter. See, for example, Jaccard and Wan (1995) and Jaccard et al. (2002).

  8. Tables 1 and 2 present descriptive statistics for the all the variables used in the analysis.

  9. Although there is evidence that economic conditions impact sexual activity (Arkes and Klerman 2009), this specification will capture characteristics of the community common to siblings such as local economic conditions.

  10. The early onset of sexual maturation may be associated with obesity or being overweight (Adair and Gordon-Larsen 2001; Anderson et al. 2001), and late onset of sexual maturation may be associated with bulimia or being extremely underweight (Striegel-Moore et al. 2001). It is also possible that early maturation may be associated with greater self-esteem and better mental health (Booth 1990; Prieto and Robbins 1975).

  11. Many studies have examined the correlates and causes of age at menarche, but only a relative handful have examined the correlates of male pubertal development. From these studies we know that there is substantial regional variation in age at menarche (Saar et al. 1988; Boldsen and Mascie-Taylor 1992; Rimpela and Rimpela 1993; Parent et al. 2003; Juul et al. 2006). There is also evidence, albeit weaker, of regional variation in male pubertal development (Juul et al. 2006, 2007). Regional variation in both male and female pubertal development could be due to socioeconomic differences, genetic factors, or environmental conditions such as light, temperature, altitude, and contaminants (Zacharias and Wurtman 1969; Parent et al. 2003; Monosson et al. 1999; Golub et al. 2004; Matchock et al. 2004). Field and Ambrus (2008, p. 895) have argued persuasively that most of the observed variation in age at menarche is genetic in nature and therefore can be thought of as random. Presumably, genetic-based variation in male pubertal development can also be thought of as random, and it is difficult to believe that factors such as light, temperature, altitude, and contaminants are related systematically to both number of sex partners and educational attainment. However, if the school-level variation in male physical development is a reflection of regional differences in socioeconomic status, and if socioeconomic status in turn affects both number of partners and educational attainment, this would bias our 2SLS estimates. In an effort to address this potential problem, we experiment with controlling for the social economic status of the respondent’s schoolmates. The results are reported in column (5) of Table 8.

  12. Using a single-equation probit model to estimate effects on high school completion and college attendance produced similar results. Each additional sex partner is associated with a 0.004 increase in the probability of high school diploma receipt (standard error = 0.001; p = 0.00) and a 0.006 increase in the probability of college attendance (standard error = 0.001; p = 0.00).

  13. The sample includes 70 twin pairs and one set of triplets. Because Eq. 2 includes family fixed effects, there are only four controls in the vector X i : the respondent’s attractiveness as assessed by the Add Health interviewer, bodyweight, PPVT score, and marital status at Wave III.

  14. If the own physical development index is dropped as an instrument, the coefficient of age of menarche becomes positive, but is not significant at the 5% level.

  15. IV probit estimates of the effect of multiple sex partners on high school completion and college attendance produce similar results. We also experimented with splitting the sample based on race and ethnicity. The 2SLS estimate indicated a positive relationship between number of partners and the educational attainment of black females. However, because the first-stage F-statistics on the instruments were considerably below 10 when black females were examined separately, we are reluctant to infer too much from this estimate. Finally, we also experimented with examining the relationship between number of sex partners and male educational attainment. 2SLS estimates, while imprecise and statistically insignificant, were positive.

  16. The bulimia indicator was based on responses to the Wave III Adolescent Health survey. The weight indicators were based on the respondent’s Body Mass Index at the time of the Wave I survey and CDC charts available at: http://www.cdc.gov/growthcharts/.

  17. The answers to the peer substance use questions were dichotomized. Specifically, X i was augmented by: an indicator equal to 1 if the respondent reported having 1 friend who smoked, and equal to 0 otherwise; an indicator equal to 1 if the respondent reported having 2 friends who smoked, and equal to 0 otherwise; an indicator equal to 1 if the respondent reported having 3 friends who smoked, and equal to 0 otherwise; an indicator equal to 1 if the respondent reported having 1 friend who drank, and equal to 0 otherwise; and so forth.

  18. We also experimented with using peer characteristics measured at Wave II, but the results were qualitatively similar to those reported in Table 6.

  19. We also experimented with controlling for cognitive ability of the respondent’s male schoolmates to ensure that self-reported puberty measured were not reflecting unmeasured school-differences in knowledge or ability. When we added the mean Peabody Picture Vocabulary Test (PPVT) score of the respondent’s male schoolmates and the male as an additional control in X i , 2SLS are qualitatively similar to those reported in Table 6.

  20. Supplementary Appendix Table 4 presents the robustness checks for the outcomes high school graduation and college attendance.

  21. In contrast, Hotz et al. (1997, 2005) find that much of the adverse effects of teen childbearing can be explained by selection.

  22. Note that the sample size is slightly different because we require all respondents to have provided information on their age at first intercourse.

  23. Our measure of STDs is dichotomous, created from respondents’ answers to the following question:

    In the past 12 months, have you been told by a doctor or nurse that you had the following sexually transmitted diseases: Chlamydia, gonorrhea, trichomoniasis, genital herpes, genital warts, human papilloma virus, bacterial vaginosis, pelvic inflammatory disease, cervicitis, HIV/AIDS, urethritis, vaginitis, or other sexually transmitted infections?

  24. Add Health respondents were asked the following question at Wave III: “How old were you the first time you had vaginal intercourse?”

  25. Supplementary Appendix Table 5 shows the first-stage results. Sabia and Rees (2009) used age of menarche to instrument for age at first intercourse. Although age of menarche is a strong predictor of age at first intercourse, it does not provide much predictive power for number of sex partners. Similarly, the mean male physical development index is a strong predictor of number of sex partners, but does not have as much predictive power for age at first intercourse.

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Acknowledgements

The views expressed herein are those of the author and do not reflect the position of the United States Military Academy, the Department of the Army, or the Department of Defense. The authors thank David Ribar, Erdal Tekin, and participants at the 2009 IZA Economics of Risky Behaviors conference and the 2010 Population Association of America meetings for useful comments on earlier drafts of this paper. We also thank Junsen Zhang and two anonymous referees for their helpful suggestions. This research uses data from the National Longitudinal Study of Adolescent Health, designed by J. Richard Udry, Peter S. Bearman, and Kathleen Mullan Harris, and funded by a grant P01-HD31921 from the National Institute of Child Health and Human Development, with cooperative funding from 17 other agencies. Special acknowledgment is due Ronald R. Rindfuss and Barbara Entwisle for assistance in the original design. Persons interested in obtaining data files from the National Longitudinal Study of Adolescent Health should contact Add Health, Carolina Population Center, 123 W. Franklin Street, Chapel Hill, NC 27516-2524 (http://www.cpc.unc.edu/addhealth/contract.html).

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  1. Department of Social Sciences, Office of Economic and Manpower Analysis, United States Military Academy, West Point, NY, 10996, USA

    Joseph J. Sabia

  2. Department of Economics, Department of Economics, San Diego, CA, 92812, USA

    Joseph J. Sabia & Daniel I. Rees

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  1. Joseph J. Sabia
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Sabia, J.J., Rees, D.I. Does the number of sex partners affect educational attainment? Evidence from female respondents to the Add Health. J Popul Econ 25, 89–118 (2012). https://doi.org/10.1007/s00148-011-0354-z

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