Is there still son preference in the United States?

Abstract

In this paper, we use 2008–2013 American Community Survey data to update and further probe evidence on son preference in the USA. In light of the substantial increase in immigration, we examine this question separately for natives and immigrants. Dahl and Moretti (Review of Economic Studies 75, 1085-1120, 2008) found earlier evidence consistent with son preference in that having a female first child raised fertility and increased the probability that the family was living without a father. We find that for our more recent period, having a female first child still raises the likelihood of living without a father, but is instead associated with lower fertility, particularly for natives. Thus, by the 2008–2013 period, any apparent son preference in fertility decisions appears to have been outweighed by factors such as cost concerns in raising girls or increased female bargaining power. In contrast, some evidence for son preference in fertility persists among immigrants. Immigrant families that have a female first child have significantly higher fertility and are more likely to be living without a father (though not significantly so). Further, gender inequity in source countries is associated with son preference in fertility among immigrants. For both first- and second-generation immigrants, the impact of a female first-born child on fertility is more pronounced for immigrants from source countries with less gender equity. Finally, we find no evidence of sex selection for the general population of natives and immigrants, suggesting that it does not provide an alternative mechanism to account for the disappearance of a positive fertility effect for natives.

This is a preview of subscription content, log in to check access.

Fig. 1
Fig. 2
Fig. 3
Fig. 4
Fig. 5

Notes

  1. 1.

    See, for example, Dahl and Moretti (2008), Anderson and Ray (2010), Almond and Edlund (2008), Abrevaya (2009), Almond et al. (2013), and Lundberg (2005).

  2. 2.

    See, for example, Sen (2003) on India and Ebenstein (2010) on China. More recently, (male/female) sex ratios at birth in Korea, which used to also be extremely high, have declined to natural biological levels; other indicators of son preference have also decreased (Choi and Hwang, forthcoming).

  3. 3.

    Below, we discuss in detail whether one can in fact make such an assumption about the sex of the first child.

  4. 4.

    These outcomes include being kindergarten-ready, incidence of truancy and behavioral problems in elementary and middle school, performance on standardized tests, and high school graduation.

  5. 5.

    In an additional difference in design, Andersson et al. (2006) use the gender composition of the first two children as explanatory variables for the third birth, rather than sex of the first child.

  6. 6.

    See, for example, Fernández and Fogli (2006), Fernández and Fogli 2009), Blau (1992), Antecol (2000), Blau et al. (2011, 2013), and Blau and Kahn (2015) for studies of the impact of culture on female labor supply and fertility behavior among first and second generation immigrants. See also Nollenberger et al. (2016) for an impact on the gender math gap.

  7. 7.

    Others who employ this specification for similar reasons include, for example, Ichino et al. (2014) and Choi and Hwang (forthcoming). We present evidence on randomness below where we consider the possibility that maternal condition could affect the gender of the first child.

  8. 8.

    Information on parental birthplace became available in the CPS starting in 1994. We begin our analysis of the CPS with the March 1995 wave because the 1994 survey had insufficient detail on parents’ birthplaces.

  9. 9.

    While Dahl and Moretti (2008) only apply this restriction to the wife, it seems reasonable to apply it to the husband as well since a child or children born during a previous marriage could affect his preferences for the number and sex of children in the current marriage.

  10. 10.

    Nonetheless, when we include such a control in our analyses, the fertility results (available on request) are similar to those reported here.

  11. 11.

    For descriptions of and sources for the source country variables, see the Data Appendix.

  12. 12.

    This Index has been used as an indicator of gender equality in a number of other studies. See, for example, Guiso et al. (2008); Zentner and Mitura (2012); Fryer and Levitt (2010); and Nollenberger et al. (2016).

  13. 13.

    Note that apart from broad region controls, we do not control for an immigrant’s residence in an “enclave” with others from the same source country. This is because location is endogenous and part of the cultural or attitudinal effect we seek to capture with the source country variables.

  14. 14.

    We follow Dahl and Moretti (2008) in excluding never married fathers because they rarely have custody of their children.

  15. 15.

    See also Schroeder (2019). To see how ambiguity in parent-child links may arise, consider a household in which a child who is the grandchild of the household head is present and the household also includes, e.g., (i) two unmarried daughters of the head of childbearing age or (ii) an unmarried son and daughter of the head of childbearing age; or (iii) a married and a single daughter of the head of childbearing age, etc. In instances like these, IPUMS uses information on age, sex, marital status and other variables with sequential assignment rules to link children to (possible) parents.

  16. 16.

    These countries include Antigua and Barbuda, Grenada, Bermuda, Micronesia, St. Kitts & Nevis, Marshall Islands, and Dominica. For CPS analyses, we also drop respondents born in countries not included in the 1995 list of countries. This restriction drops respondents born in Ivory Coast and Mongolia.

  17. 17.

    This information is also available for 1995, 1998, 2000, 2002, 2004, and 2006.

  18. 18.

    There is also some mixed evidence on the impact of economic prosperity (a perhaps negative indicator of stress) on the sex ratio at birth, with Catalano and Bruckner (2005) finding that prosperity raised the incidence of boys in Sweden, while Fernández et al. (2011) found that a recession also raised the incidence of boys in Cuba.

  19. 19.

    Although in principle, Assisted Reproductive Technologies (ART) can be used for nonmedical sex selection purposes, this potential method of sex selection is extremely rare. According to the Society for Assisted Reproductive Technology, a member organization that registers 95% of in vitro fertilization cycles in the United States (www.sart.org), 63,286 babies were born in the United States in 2013 using in vitro fertilization (http://www.sart.org/news/article.aspx?id=14570). In such cases, Pre-implantation Genetic Diagnosis (PGD)—the technology that in principle can be used for sex selection—is used 4–6% of the time, and of these, 9% involved nonmedical sex selection in 2005 (Baruch et al. 2008). These percentages imply that an upper bound of (0.06)·(0.09)·(63,286) = 342 babies born in the United States using nonmedical sex selection. This represents a miniscule fraction (0.00009) of the 3,912,181 births registered in the United States in 2013 (Martin et al. 2015).

  20. 20.

    We calculate these endpoints based on the endpoints of the 95% confidence intervals for the means of the fraction of first children who are boys.

  21. 21.

    Basu (2015) shows that the OLS bias = (Var(z)(1-r2zx)−1(a2/(1-a1a2))σ2, where z is the first child girl dummy variable, r2zx is the squared correlation coefficient between z and the inner product of coefficients and variables other than first child girl from the equation for living without a father, a2 is the effect of living without a father on first child girl, a1 is the coefficient of interest, and σ2 is the variance of the regression error. The figures in the text use the OLS estimates for a1 and Almond and Edlund’s (2007) estimate of 0.001 for a2. Because the OLS bias on a1 is positive, using the OLS a1 in Basu’s (2015) formula produces a slight overestimate of the OLS bias in this case.

  22. 22.

    Blau et al. (2011) found that immigrants had a more traditional division labor in the home than natives, as indicated by women’s labor supply behavior, which reflected the lower female- to-male labor supply ratios in immigrant source countries compared to the USA.

  23. 23.

    Not surprisingly, the impact of a first child girl tends to affect child spacing in the opposite direction as fertility. We found that a first child girl generally had a positive effect on child spacing for natives and a negative effect for immigrants in both all and first marriages (with the exception of a negative effect obtained for natives in first marriages in the extended sample). However, only the effect for natives in all marriages in the core sample was significant: 0.015 (se 0.008).

  24. 24.

    Dahl and Moretti control for decade of birth fixed effects, three (rather than four) categories of education (< HS. HS and College), and do not have a separate indicator for Hispanics. A further difference for the fertility regressions is that Dahl and Moretti do not control for spouse characteristics and include women who are married spouse absent; they also determine first marriage based solely on the wife’s marital history (rather than both the husband’s and wife’s as we do). Our results were virtually unaffected by the slight differences in specification.

  25. 25.

    For details on this decomposition, see the discussion in Dahl and Moretti (2008), p. 1088.

  26. 26.

    See, for example, Campbell and Horowitz (2016); and Davis and Greenstein (2009).

  27. 27.

    In the fertility regressions, we show results for all married couples but results were similar when immigrants were restricted to their first marriage.

  28. 28.

    These percentiles are implicitly weighted by the (weighted) frequency of immigrants from each source country.

  29. 29.

    A similar exercise using the results of columns 5 and 6 yields a similar conclusion.

  30. 30.

    Although the fertility results for immigrants and natives in the CPS are similar in sign to the results we obtained with the ACS, the magnitudes of the estimated effects are larger in the CPS than in the ACS. This was not due to our wider time window in the CPS analysis (1995–2014): when we restricted the CPS analysis to the same years as the ACS—2008-2013, the effects for immigrants and 3rd + generation natives remained larger than in the ACS.

  31. 31.

    As was the case with the overall effects for immigrants and natives in Table 4, the interactions for second generation women shown in Table 5 are larger in magnitude for the Equity Index and LFP Ratio than for immigrants shown in Table 3; however, the effect for Girl*Sex Ratio at Birth is smaller for second generation women than for immigrants.

  32. 32.

    Some researchers have found such a correlation between the sex composition of previous births and the sex of future children (Ben-Porath and Welch 1976; Gellatly 2009), although some find no such pattern (Rodgers and Doughty 2001; Jacobsen et al. 1999).

  33. 33.

    Interestingly, while most authors found evidence of son preference, the recent paper by Persaud et al. (2015) reports results consistent with a demand for diversity.

  34. 34.

    In their online appendix (available at: https://link.springer.com/article/10.1007%2Fs13524-012-0146-4), the authors show that these patterns hold up in a regression context and for mixed gender families.

  35. 35.

    Specifically, in 2011, when asked about sex preference supposing that one could have only one child, men preferred a boy to a girl by a margin of 49 to 22%, whereas women were split roughly equally with 31% preferring a boy and 33% preferring a girl. (A higher proportion of women (36%) than men (28%) also said responded “Does not matter,” “Not sure,” or “No opinion.” See Newport (2011).

  36. 36.

    Lundberg (2005) notes that differences in material returns are not expected to play a large role in developed (“wealthy”) countries, although this factor receives considerable emphasis in analyses of son preference in developing countries.

References

  1. Abrevaya J (2009) Are there missing girls in the United States? Evidence from birth data. Am Econ J Appl Econ 1(2):1–34

    Google Scholar 

  2. Almond D, Edlund L (2007) Trivers-Willard at birth and one year: evidence from US natality data 1983-2001. Proc R Soc B 274(1624):2491–2496

    Google Scholar 

  3. Almond D, Edlund L (2008) Son-biased sex ratios in the 2000 United States census. Proc Natl Acad Sci 105(15):5681–5682

    Google Scholar 

  4. Almond D, Edlund L, Milligan K (2013) Son preference and the persistence of culture: evidence from South and East Asian immigrants to Canada. Popul Dev Rev 39(1):75–95

    Google Scholar 

  5. Anderson S, Ray D (2010) Missing women: age and disease. Rev Econ Stud 77(4):1262–1300

    Google Scholar 

  6. Andersson G, Hank K, Rønson M, Vikat A (2006) Gendering family composition: sex preferences for children and childbearing behavior in the Nordic countries. Demography 43(2):255–267

    Google Scholar 

  7. Antecol H (2000) An examination of cross-country differences in the gender gap in labor force participation rates. Labour Econ 7(4):409–426

    Google Scholar 

  8. Autor D, Figlio D, Karbownik K, Roth J, Wasserman M (2019) Family disadvantage and the gender gap in behavioral and educational outcomes. Am Econ J Appl Econ 11(3):338–381

    Google Scholar 

  9. Baker M, Milligan K (2016) Boy-girl differences in parental time investments: evidence from three countries. J Hum Cap 10(4):399–441

    Google Scholar 

  10. Barreca A, Page M (2015) A pint for a pound? Minimum drinking age laws and birth outcomes. Health Econ 24(4):400–418

    Google Scholar 

  11. Baruch S, Kaufman D, Hudson KL (2008) Genetic testing of embryos: practices and perspectives of US in vitro fertilization clinics. Fertil Steril 89(5):1053–1058

    Google Scholar 

  12. Basu D (2015) Asymptotic bias of OLS in the presence of reverse causality. Unpublished working paper, University of Massachusetts, Amherst, Department of Economics

  13. Ben-Porath Y, Welch F (1976) Do sex preferences really matter? Q J Econ 90(2):285–307

    Google Scholar 

  14. Bertrand M, Pan J (2013) The trouble with boys: social influences and the gender gap in disruptive behavior. Am Econ J Appl Econ 5(1):32–64

    Google Scholar 

  15. Blau FD (1992) The fertility of immigrant women: evidence from high-fertility source countries. In: Borjas GJ, Freeman RB (eds) Immigration and the work force: economic consequences for the United States and source areas. University of Chicago Press, Chicago, pp 93–133

    Google Scholar 

  16. Blau FD, Kahn LM (2015) Substitution between Individual and source country characteristics: social capital, culture, and US labor market outcomes among immigrant women. J Hum Cap 9(4):439–482

    Google Scholar 

  17. Blau FD, Kahn LM (2017) The gender wage gap: extent, trends, and explanations. J Econ Lit 55(3):789–865

    Google Scholar 

  18. Blau FD, Kahn LM, Papps KL (2011) Gender, source country characteristics, and labor market assimilation among immigrants. Rev Econ Stat 93(1):43–58

    Google Scholar 

  19. Blau FD, Kahn LM, Liu AY-H, Papps KL (2013) The transmission of women’s fertility, human capital and work orientation across immigrant generations. J Popul Econ 26(2):405–435

    Google Scholar 

  20. Bongaarts J (2013) The implementation of preferences for male offspring. Popul Dev Rev 39(2):185–208

    Google Scholar 

  21. Campbell C, Horowitz J (2016) Does college influence sociopolitical attitudes? Sociol Educ 89(1):40–58

    Google Scholar 

  22. Catalano RA, Bruckner T (2005) Economic antecedents of the Swedish sex ratio. Soc Sci Med 60(3):537–543

    Google Scholar 

  23. Catalano R, Bruckner T, Marks AR, Eskenazi B (2006) Exogenous shocks to the human sex ratio: the case of September 11, 2001 in New York City. Human Reproduction 21(12):3127–3131

    Google Scholar 

  24. Choi EJ, Hwang J (forthcoming) Transition of son preference: evidence from South Korea. Demography

  25. Dahl GB, Moretti E (2008) The demand for sons. Rev Econ Stud 75(4):1085–1120

    Google Scholar 

  26. Davis SN, Greenstein TN (2009) Gender ideology: components, predictors, and consequences. Annu Rev Sociol 35:87–105

    Google Scholar 

  27. Ebenstein A (2010) The “Missing Girls” of China and the unintended consequences of the one child policy. J Hum Resour 45(1):87–115

    Google Scholar 

  28. Fernández R, Fogli A (2006) Fertility: the role of culture and family experience. J Eur Econ Assoc 4(2/3):552–561

    Google Scholar 

  29. Fernández R, Fogli A (2009) Culture: an empirical investigation of beliefs, work, and fertility. Am Econ J Macroecon 1(1):146–177

    Google Scholar 

  30. Fernández SJV, Medina RS, Britton J, Fogarty AW (2011) American Journal of Epidemiology 174(12):1327–1331

    Google Scholar 

  31. Fryer RG, Levitt SD (2010) An empirical analysis of the gender gap in mathematics. Am Econ J Appl Econ 2(April):210–240

    Google Scholar 

  32. Fukuda M, Fukuda K, Shimizu T, Møller H (1998) Decline in sex ratio at birth after Kobe earthquake. Hum Reprod 13(8):2321–2322

    Google Scholar 

  33. Gellatly C (2009) Trends in population sex ratios may be explained by changes in the frequencies of polymorphic alleles of a sex ratio gene. Evol Biol 36(2):190–200

    Google Scholar 

  34. Goldin C, Katz LF, Kuziemko I (2006) The homecoming of American College women: the reversal of the college gender gap. J Econ Perspect 20(4):133–156

    Google Scholar 

  35. Grigoryeva A (2017) Own gender, sibling’s gender, parent’s gender: the division of elderly parent care among adult children. Am Sociol Rev 82(1):116–146

    Google Scholar 

  36. Guiso L, Monte F, Sapienza P, Zingales L (2008) Education forum: culture, gender, and math. Science 320:1164–1165

    Google Scholar 

  37. Ichino A, Lindström E-A, Viviano E (2014) Hidden consequences of a first-born boy for mothers. Econ Lett 123:274–278

    Google Scholar 

  38. Jacobsen R, Møller H, Mouritsen A (1999) Natural variation in the human sex ratio. Hum Reprod 14(12):3120–3125

    Google Scholar 

  39. Kornrich S, Furstenberg F (2013) Investing in children: changes in parental spending on children, 1972–2007. Demography 50(1):1–23

    Google Scholar 

  40. Louis GM, Buck KJ, Lum RS, Chen Z, Kim S, Courtney D, Lynch E, Schisterman F, Pyper C (2011) Stress reduces conception probabilities across the fertile window: evidence in support of relaxation. Fertil Steril 95(7):2184–2189

    Google Scholar 

  41. Lundberg S (2005) Sons, daughters, and parental behavior. Oxford Review of Economic Policy 21(3):340–356

    Google Scholar 

  42. Martin JA, Hamilton BE, Osterman MJK, Curtin SC, Matthews TJ (2015) Births: final data for 2013. National Vital Statistics Reports 64, No. 1 (January): 1–68. (U.S. Department of Health and Human Services, Centers for Disease Control and Prevention, National Center for Health Statistics, National Vital Statistics System)

  43. Newport F (2011) Americans prefer boys to girls, just as they did in 1941. Gallup (June 23) available at: http://www.gallup.com/poll/148187/americans-prefer-boys-girls-1941.aspx

  44. Nollenberger N, Rodríguez-Planas N, Sevilla A (2016) The math gender gap: the role of culture. Am Econ Rev 106(May):257–261

    Google Scholar 

  45. Norberg K (2004) Partnership status and the human sex ratio at birth. Proc R Soc Lond B 271:2403–2410

    Google Scholar 

  46. Persaud A, Kalantry S, Citro B, Nandi A (2015) Do Asian Americans practice male-biased selection in the United States? New evidence from the 2008–2012 American community survey and 2010 US census. Unpublished Working Paper, Cornell University

  47. Plana-Ripoli O, Li J, Kesmodel US, Olsen J, Parner E, Basso O (2016) Maternal stress before and during pregnancy and subsequent infertility in daughters: a nationwide population-based cohort study. Hum Reprod 31(2):454–462

    Google Scholar 

  48. Rodgers JL, Doughty D (2001) Does having boys or girls run in the family? Chance 14(4):8–13

    Google Scholar 

  49. Ruder EH, Hartman TJ, Goldman MB (2009) Impact of oxidative stress on female fertility. Current Opinion in Obstetrics and Gynecology 21(3):219–222

    Google Scholar 

  50. Sanders NJ, Stoecker C (2015) Where have all the young men gone? Using sex ratios to measure fetal death rates. J Health Econ 41(May):30–45

    Google Scholar 

  51. Schroeder MB (2019) undated. The (Mis)measurement of subfamilies in U.S. census data. Unpublished Working Paper, Minnesota Population Center, accessed at: https://usa.ipums.org/usa/resources/volii/subfamily_measurement_final.pdf, 9/16/19

  52. Sen A (1990) More than 100 million women are missing. The New York Review of Books 37, No. 20 (Dec 20) available at http://www.nybooks.com/articles/1990/12/20/more-than-100-million-women-are-missing/

  53. Sen A (2003) Missing women—revisited. Br Med J 327(Dec 4) available at http://www.bmj.com/content/327/7427/1297

  54. Subbaraman MS, Goldman-Mellor SJ, Anderson ES, LeWinn KZ, Saxton KB, Shumway M, Catalano R (2010) An exploration of secondary sex ratios among women diagnosed with anxiety disorders. Hum Reprod 25(8):2084–2010

    Google Scholar 

  55. Trivers RL, Willard DE (1973) Natural selection of parental ability to vary the sex ratio of offspring. Science 179(4068):90–92

    Google Scholar 

  56. Zentner M, Mitura K (2012) Stepping out of the caveman's shadow: nations' gender gap predicts degree of sex differentiation in mate preferences. Psychol Sci 25:1176–1185

    Google Scholar 

Download references

Acknowledgements

We are indebted to Nikolai Boboshko, Amanda Eng, Alexander Willén, and Matthew Comey for excellent research assistance, as well as useful comments and input into this work. We thank Pamela Meyerhofer, Sital Kalantry, Shelly Lundberg, David Deming, Angela Cools, seminar participants at the University of Toronto, the editor, Junsen Zhang and three anonymous referees for their helpful comments and suggestions. The views expressed in this paper are those of the authors and do not necessarily reflect those of the Federal Trade Commission. Replication data and programs for this paper are archived at https://doi.org/10.7910/DVN/KEQLMK

Author information

Affiliations

Authors

Corresponding author

Correspondence to Francine D. Blau.

Ethics declarations

Conflict of interest

The authors declare that they have no conflict of interest.

Additional information

Publisher’s note

Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Responsible editor: Junsen Zhang

Appendix

Appendix

Variable definitions

Variables from the ACS and CPS

Race and ethnicity

  • We control for race and ethnicity using a set of indicator variables for five mutually exclusive categories: White non-Hispanic, Black non-Hispanic, Asian non-Hispanic, other non-Hispanic and Hispanic.

  • Respondent is classified as Hispanic if the respondent reports being Hispanic or reports race as Spanish, Portuguese, Mexican, Puerto Rican, Latin American Indian, South American Indian, or Mexican American Indian.

  • Respondent is classified as black non-Hispanic if the respondent reports being any detailed race that include black (except for Black and Chinese, Black and Asian Indian, or Black and Korean) and is not classified as Hispanic.

  • Respondent is classified as Asian non-Hispanic if the respondent is not classified as Hispanic or black non-Hispanic and reports race as Asian or any mixed race including Asian.

  • Respondent is classified as white non-Hispanic if the respondent is not classified as Hispanic, black non-Hispanic, or Asian non-Hispanic and reports race as white.

  • Respondent is classified as other non-Hispanic if none of the above classifications apply.

Immigrant status and years since migration

  • Respondents are classified as natives if their birthplace is one of the fifty states or the District of Columbia.

  • For foreign-born persons we define years since migration as the lesser of age or reported years in the United States.

First, second, third + generation (CPS only)

  • Respondents are classified as 1st generation if they report their birthplace as outside the fifty states or the District of Columbia.

  • Respondents are classified as third + generation if they report that they and both of their parents were born in the fifty states or the District of Columbia.

  • Respondents are classified as second generation if they were born in the fifty states or the District of Columbia and they report that either of their parents was born outside the United States. Parental source country characteristics are allocated based on mother’s birthplace, if she is foreign born, and father’s birthplace otherwise.

Living without a father

  • We classify a respondent as living without a father if the respondent is female, unmarried (where married, spouse absent is considered married), and has at least one child. (The oldest child must be 12 years of age or younger for sample inclusion.) In the core sample, we include only women who meet these requirements and are listed as head of household. Note that if the respondent has an unmarried partner present, she can still be classified as a female head of household.

  • Single fathers are included in the extended sample. Single fathers are included and given a 0 for living without a father only if their children do not have a mother in the household and if they are ever married (i.e., never married men are excluded).

First marriage

  • Respondents are classified as being in a first marriage if both the respondent and her spouse report that their current marriage is their first marriage.

Country characteristics variables

Total fertility

Total fertility data comes from the World Bank, available at http://data.worldbank.org/indicator/SP.DYN.TFRT.IN. In the regressions with country characteristics, we include 2000–2007 country averages of total fertility.

GDP per capita

Most GDP per capita data comes from the World Bank, available at http://data.worldbank.org/indicator/NY.GDP.PCAP.PP.KD. For Taiwan, data comes from the Chinese Statistical Yearbook 2013, available at http://ebook.dgbas.gov.tw/public/Data/3117141132EDNZ45LR.pdf. GDP for Argentina, Burma and Syria is constructed from UN Stats data on GDP by Type of Expenditure at current prices and at constant 2005 prices in national currency units, available at http://data.un.org/Data.aspx?d=SNAAMA&f=grID%3A101%3BcurrID%3ANCU%3BpcFlag%3A0 and http://data.un.org/Data.aspx?q=gdp&d=SNAAMA&f=grID%3a102%3bcurrID%3aNCU%3bpcFlag%3a0, respectively. PPP conversion rates come from http://icp.worldbank.org/icp/QueryResults.aspx?r=-1&ds=0&y=3&ws=1. We use the World Bank methodology to convert to GDP per capita, PPP. In the regressions with country characteristics, we include the natural log of 2000–2007 country averages of GDP per capita.

Ratio of female to male labor force participation

Data on male and female labor force participation come from the International Labor Organization’s Key Indicators of the Labor Market. We use labor force participation for the population 15 years of age and older. In the regressions with country characteristics, we include 2000–2007 country averages of the ratio of female to male labor force participation.

Sex ratio at birth

Sex ratio at birth comes from UN Data, available at http://data.un.org/Data.aspx?q=sex+ratio+at+birth&d=PopDiv&f=variableID%3a52. We follow the WEF in censoring the sex ratio at birth at 1.059 to identify son preference. In the regressions with country characteristics, we include 2000–2007 country averages of sex ratio at birth.

Equity index

The equity index is based on the World Economic Forum’s Global Gender Gap Index from “The Global Gender Gap Report, 2012,” available at http://www3.weforum.org/docs/WEF_GenderGap_Report_2012.pdf. In the regressions with country characteristics, we include 2006–2007 country averages of the index, unless a 2006 value is not available, in which case we use the earliest value available up until 2012. (Note that the index first became available in 2006.)

Sample selection and weighting

Unless otherwise noted, analyses with the American Community Survey (ACS) use data from the 2008–2013 waves and analyses with the Current Population Survey (CPS) use data from the 1995–2014 March CPS. Regressions for the core sample are weighted by household weights that are normalized to provide equal weighting for each sample year; regressions for the extended sample are weighted by person weights (as suggested by IPUMS when one is analyzing members of subfamilies: https://usa.ipums.org/usa/volii/subfamilies.shtml, accessed 9/10/19) that are normalized to provide equal weighting for each sample year.

Our core sample includes women between the ages of 18 and 40, who are the head of household or spouse of the household head, with one or more children, where the oldest child is twelve years old or younger and all children are born in the U.S. Households with adopted, step, or foster children are dropped in the ACS. We are unable to identify step or adopted children in the CPS, but we are able to drop CPS households with foster children. Same-sex couples, respondents living in group quarters, respondents born abroad to American parents, widows, as well as mothers with multiple children born in the same year and quarter (ACS) or same year (CPS) are excluded. When the dependent variable is fertility, the sample is additionally limited to married women with a spouse present, and, in some specifications, to women in their first marriage who are married to men also in their first marriage.

The extended sample expands the core sample by including father-only families and parents who are not the household head or spouse of the head (i.e., in subfamilies). Men are included in the sample only if their children do not have a mother in the household and if they are ever married (i.e., never married men are excluded). We also expand the sample to include step and adopted children since we are not able to identify these categories of children for subfamilies. We continue to exclude foster children, but, in the spirit of inclusiveness, not their households.

In analyses that include country characteristics, we exclude respondents who report being born in US territories or country aggregates. We also exclude respondents born in countries with low frequency and a high number of missing values in the data or countries with missing data on labor force participation. These countries include Antigua and Barbuda, Grenada, Bermuda, Micronesia, St. Kitts & Nevis, Marshall Islands, and Dominica. For CPS analyses, we also drop respondents born in countries not included in the 1995 list of countries. This restriction drops respondents born in Ivory Coast and Mongolia.

Table 7 Children in sample compared to reported live births
Table 8 Boy/girl ratio, first child
Table 9 Probability that first child is a girl (linear Prob models)
Table 10 Effects of a female first child on the probability of being in the labor force: married women and all women (linear probability models)

Rights and permissions

Reprints and Permissions

About this article

Verify currency and authenticity via CrossMark

Cite this article

Blau, F.D., Kahn, L.M., Brummund, P. et al. Is there still son preference in the United States?. J Popul Econ 33, 709–750 (2020). https://doi.org/10.1007/s00148-019-00760-7

Download citation

Keywords

  • Gender
  • Son preference
  • Family structure
  • Fertility
  • Sex selection
  • Immigrants

JEL classifications

  • J11
  • J12
  • J13
  • J15
  • J16