- Email: [email protected]
LGBT students: New evidence on demographics and educational outcomes
Economics of Education Review 73 (2019) 101933
Contents lists available at ScienceDirect
Economics of Education Review journal homepage: www.elsevier.com/locate/econedurev
Note
LGBT students: New evidence on demographics and educational outcomes #
Dario Sansone
T
University of Exeter and Vanderbilt University, Department of Economics, Nashville, TN37235 United States
ARTICLE INFO
ABSTRACT
Keywords: LGBT High school graduation College attendance Discrimination
This paper shows that LGBT students in the U.S. are less likely to graduate from high school and attend college. These differences persist after controlling for demographic characteristics, family background, state and school fixed effects. Relatedly, LGBT students have lower educational expectations, a lower sense of school belonging, and are more likely to have been affected by discrimination.
JEL classification: I20 J15 J24
1. Introduction The aim of this study is to provide new important descriptive statistics on LGBT students. By leveraging novel information contained in the restricted-use version of the High School Longitudinal Study of 2009 (HSLS:09), this paper investigates whether LGBT students are more or less likely to complete high school or attend college, and whether they have higher or lower performance while in school. In order to understand whether or not sexual minorities are a vulnerable sub-population that needs to be supported, policymakers need to know whether LGBT individuals are under-represented in secondary or tertiary education, whether they face specific challenges, and the key factors associated with their educational success. Without such data, policies may be driven by anecdotal evidence and the current political mood, or policymakers may believe that the invisibility of the LGBT population implies that these students do not require any specific intervention. Within economics, the only published paper on LGB students is by Carpenter (2009). He shows that gays have higher GPA in college; while bisexual females spend less time studying. Even in other social sciences, the evidence is scarce and mainly derived from health surveys (Pearson & Wilkinson, 2017). This paper makes three main contributions to this literature. First and foremost, even if researchers have recently started to analyze labor market outcomes for trans individuals (Geijtenbeek &
Plug, 2018), this is the first study to include any nationally representative statistics on trans students’ educational outcomes. Second, this analysis is based on recent data: the HSLS:09 surveys have been conducted between 2009 and 2016. Most of the previous analyses rely instead on data such as the Add Health containing information on individuals who attended high school in the 1990s. Especially after the decriminalization of homosexuality in 2003 (Lawrence v. Texas), and the progressive legalization of same-sex marriage between 2004 and 2015, the experiences of the LGBT individuals interviewed in the HSLS:09 might have been different due to improvements in social norms and attitudes towards the LGBT community (Flores & Barclay, 2016; Kenny & Patel, 2017; Kreitzer, Hamilton, & Tolbert, 2014; Sansone, 2018; Tankard & Paluck, 2017). Third, while other surveys focus on adolescent health and health behavior, the HSLS:09 provides an extremely rich set of educational variables relevant for this analysis. For instance, this dataset includes test scores such as the SAT, PSAT and ACT, information on students’ and parents’ educational expectation and aspirations, measures of school identity and discrimination, details on respondents’ college performance, and the main reason behind their decision not to attend college. This paper shows that LGBT students have poorer educational outcomes: even if they do not perform worse than their peers in several tests such as the SAT, they are less likely to graduate from high school,
I am grateful to the Editor McKinley Blackburn, two anonymous referees, Christopher Carpenter, Hiren Nisar, Deniz Sanin, and Allison Stashko for their helpful comments. I am also grateful to John Rust and Judith House for their technical support and their help in accessing the HSLS:09 restricted-use data. I conducted the empirical analysis while completing my Ph.D. at Georgetown University. The usual caveats apply. E-mail address: [email protected]. # Website: https://sites.google.com/view/dariosansone/ https://doi.org/10.1016/j.econedurev.2019.101933 Received 24 April 2019; Received in revised form 4 October 2019; Accepted 7 October 2019 0272-7757/ © 2019 Elsevier Ltd. All rights reserved.
Economics of Education Review 73 (2019) 101933
D. Sansone
have lower GPA, and accumulate fewer credits while in school. Furthermore, these students are less likely to apply and attend college. These gaps persist – especially for non-heterosexual students – after controlling for demographic characteristics, family background, state and school fixed effects. In line with these results, this study shows that LGBT students have lower educational expectations, lower motivation, school engagement and sense of belonging. In addition, these students believe that discrimination has impacted them both while in school and in the workplace. Therefore, future policies should ensure that students with similar ability have the same opportunities and can achieve the same educational outcomes irrespectively of their sexual orientation or gender identity. The findings in this paper are in contrast with previous studies highlighting higher educational achievements among LGBT adults (Black, Sanders, & Taylor, 2007). This could be due to the fact that some (highly educated) individuals start to identify as LGBT only in adulthood. It could also be that, despite the initial low academic performance in high school, LGBT students quickly catch-up once they live in a more welcoming environment such as a college campus. Another alternative explanation is that low-educated people were less likely to disclose their sexual behavior or relationship status in the past decades. Indeed, Millennials are much more willing to identify as LGBT than older generations (Newport, 2018). In line with this hypothesis, Sansone (2018) found a lower prevalence of individuals without tertiary education among self-reported same-sex couples before the introduction of marriage equality in the U.S.
Table 1 Sexual orientation and gender identity. Sexual orientation Heterosexual Lesbian or Gay Bisexual Another sexual orientation Don't know
Gender identity 90.0% 2.4% 4.6% 1.4% 1.6%
Male only Female only Trans/non-binary
47.7% 50.6% 1.7%
Unweighted percentages.
not reply. Among the respondents, 1.7% identify as both male and female, transgender male-to-female, transgender female-to-male, gender nonconforming, or they are not sure. Of these trans students, 25% identify as heterosexual.1 Online Appendix B compares demographic characteristics and family background among LGBT and non-LGBT students. Non-heterosexual and trans students are more likely to have a learning or physical disability, and to live in a poor household without a mother or a father. 3.2. LGBT students have lower educational achievements Table 2 compares high school educational outcomes by sexual orientation and gender identity. Both non-heterosexual and trans students are less likely to have a high school diploma, and more likely to have dropped out from high school at least once. LGBT students are more likely to receive a GED, a certificate legally – but not de facto – equivalent to a high school diploma (Heckman, Humphries, & Mader, 2011). Moreover, LGBT students have consistently lower achievement levels while in high school. Their GPA is lower, overall and in each single academic year, and they accumulate fewer credits. Nevertheless, these students do not perform worse than their peers when looking at certain ability and cognitive skill measures: their scores in the HSLS:09 math test are not statistically different from other students, and they actually achieve slightly higher scores in the PSAT, as well as in the SAT/ACT college entrance exams. Similar conclusions are reached when analyzing college outcomes (Table 3). LGBT respondents are less likely to apply for college, to have ever attended a post-secondary institution, and to attend college at the time of the survey. When asked to list the main reasons behind their decision not to attend college, these individuals are more likely than non-LGBT respondents to report academic, personal, and financial reasons rather than job-related explanations.
2. Data The High School Longitudinal Study of 2009 is a nationally representative panel micro dataset. The survey design has two levels: first, around 940 private and public schools were selected at the national level. Second, around 20–30 9th grade students in each selected school were randomly chosen to participate in the survey. In the baseline year (2009), the selected 9th graders, their parents, math and science teachers, school administrators, and lead school counselors completed a survey. The first follow-up in 2012 included students (at that time in 11th grade), parent, school administrators, and counselors. A brief survey was conducted among the students and their parents in 2013 to record respondents’ post-secondary plans. Students were interviewed again in 2016. Students completed a math assessment while in grades 9 and 11. High school academic transcripts includes student GPA, AP class grades, SAT scores, and the number of credits taken in each subject. Most importantly, the last follow-up survey contains three questions on gender identity and sexual orientation. Online Appendix A provides a detailed description of all the variables used in the empirical analysis. For security reasons, all sample sizes have been rounded to the nearest 10. Additional documentation about the HSLS:09 can be found in Duprey et al. (2018).
4. Educational gaps persist in multivariate analysis 4.1. High school graduation and college attendance Table 4 estimates whether LGBT students are still less likely to complete high school after controlling for a rich set of controls. Both indicators for sexual orientation (Column 1) and gender identity (Column 2) are negatively associate with high school completion, but only sexual orientation remains statistically significant when including both measures in the same specification (Column 3). In addition, there is no evidence of intersectionality: the interaction term between sexual orientation and gender identity is not statistically significant (Column 4).
3. Descriptive statistics 3.1. How many students are LGBT? Around 15,870 students were asked to specify their sexual orientations. The non-response rate is 3.4%. This percentage is lower than in previous surveys such as the Add Health (Pearson & Wilkinson, 2017). Among the respondents, 10% of them do not identify as heterosexual. In particular, 2.4% of the students think of themselves as homosexual, 4.6% as bisexual, 1.6% do not know, while 1.4% of them identify with another sexual orientation (Table 1). Among nonheterosexual students, 29% identify only as men, 58% only as women, and the remaining 13% as trans. When asked about their gender identity, 2.9% of the students did
1 All summary statistics discussed in this and the next section are unweighted percentages. Similar descriptive statistics are obtained using sample weights and accounting for the HSLS:09 sample design (Online Appendix B). For brevity, individuals who identify as “transgender, male-to-female“, ”transgender, female-to-male”, “genderqueer or gender nonconforming, or some other gender”, or “not sure" have been referred as “trans” in the empirical analysis. Respondents who identify only as male or female has been categorized as “non-trans”, “cisgender”, or “M/F”.
2
Economics of Education Review 73 (2019) 101933
D. Sansone
Table 2 High school outcomes by sexual orientation and gender identity. Sexual orientation Non-hetero
Hetero
Gender identity Trans
M/F
HS diploma HS diploma/GED GED Ever dropped out HS
0.889 0.937 0.051 0.214
0.927 0.963 0.039 0.141
−0.038*** −0.026*** 0.012** 0.073***
0.870 0.927 0.065 0.238
0.924 0.961 0.040 0.147
−0.054*** −0.034*** 0.025** 0.091***
GPA 9th grade GPA 10th grade GPA 11th grade GPA 12th grade HS GPA HS credits Any credit AP/IB
2.743 2.793 2.872 2.984 2.749 24.289 0.433
2.834 2.843 2.896 3.024 2.833 25.113 0.413
−0.091*** −0.050** −0.024 −0.040* −0.084*** −0.824*** 0.020
2.688 2.657 2.781 2.958 2.696 23.911 0.408
2.825 2.840 2.894 3.021 2.825 25.040 0.414
−0.137** −0.183*** −0.113** −0.063 −0.129** −1.129*** −0.006
Math score 9th grade Math score 11th grade SAT/ACT math SAT/ACT read SAT/ACT PSAT
52.695 52.305 513.883 530.264 1044.15 154.541
52.304 52.526 505.632 499.635 1005.30 146.426
0.391 −0.221 8.251** 30.629*** 38.845*** 8.115***
52.295 52.564 516.012 547.202 1063.21 148.932
52.311 52.463 505.989 501.555 1007.58 147.215
−0.016 0.101 10.023 45.647*** 55.630*** 1.717
Observations
1530
13,800
260
15,140
Unweighted statistics. The number of observations refers to “HS diploma.” * p < 0.10,
⁎⁎
p < 0.05,
⁎⁎⁎
p < 0.01.
Table 3 College outcomes by sexual orientation and gender identity. Sexual orientation Non-hetero
Hetero
Attending college Ever applied college Ever attended 2-yr public institute Ever attended college
0.513 0.846 0.274 0.719
0.584 0.864 0.295 0.771
Reasons never attended college: Academic Personal Financial Job-related
0.112 0.519 0.516 0.243
0.086 0.410 0.440 0.336
Observations
1530
13,800
Gender identity Trans
M/F
−0.071*** −0.018** −0.021* −0.052***
0.483 0.808 0.250 0.678
0.577 0.863 0.294 0.766
−0.094*** −0.055** −0.044 −0.088***
0.026* 0.109*** 0.076*** −0.093***
0.157 0.542 0.446 0.265
0.088 0.420 0.448 0.324
0.069** 0.122** −0.002 −0.059
260
15,140
Unweighted statistics. The number of observations refers to “Attending college.” * p < 0.10,
⁎⁎
p < 0.05,
⁎⁎⁎
p < 0.01.
Table 4 High school diploma. (1) Non-hetero Trans
−0.038 (0.008)
(2)
(3)
−0.054⁎⁎⁎ (0.020)
−0.034 (0.008) −0.034 (0.021)
⁎⁎⁎
Non-hetero*Trans
(4) ⁎⁎⁎
−0.034 (0.009) −0.037 (0.039) 0.004 (0.048)
Demographic Family School FE (9th grade) State FE (college) Observations R2 Adjusted-R2
15,330 0.002 0.002
Average dep var
0.923
15,400 0.001 0.001
15,310 0.002 0.002
(5) ⁎⁎⁎
15,310 0.002 0.002
−0.020 (0.008) −0.015 (0.020)
(6) ⁎⁎
−0.018 (0.008) −0.017 (0.021)
(7) ⁎⁎
−0.016* (0.008) −0.023 (0.021)
✓ ✓
✓ ✓ ✓
✓ ✓ ✓ ✓
14,380 0.042 0.041
14,380 0.152 0.091
14,250 0.156 0.092
This table shows how sexual orientation and gender identity are associated with the probability of receiving a high school diploma. Demographic controls: race and ethnicity, US born, disability. Family controls: no mother/father in the household, parental education and employment status, household size, and poverty indicator. School fixed effects are computed from the baseline interview, while state fixed effects are derived from the 2016 follow-up survey. All controls are described in Online Appendix A. Reported coefficient from Linear Probability Models. Standard errors in parentheses clustered at the 9th grade school level. Source: HSLS:09. * p < 0.10, ⁎⁎ p < 0.05, ⁎⁎⁎ p < 0.01. 3
Economics of Education Review 73 (2019) 101933
D. Sansone
Although the magnitude of the coefficient decreases, non-heterosexual students are still less likely to graduate from high school after accounting for individual and household characteristics such as ethnicity, race, nationality, disability, presence of a mother and a father in the household, parental education and employment status, household size, and a poverty indicator (Column 5). The specification can be expanded by including 9th grade school fixed effects (computed from the baseline interviews, Column 6), and state fixed effects (derived from the 2016 follow-up survey, Column 7). Even after accounting for all these observable characteristics, the average probability of a non-heterosexual student to graduate from high school is 2 percentage points lower than the corresponding probability for their heterosexual peers. While the coefficient associated with the trans indicator is negative and has the same magnitude as the coefficient of sexual orientation, it is not statistically significant. It is worth emphasizing that this coefficient is not a precisely estimated zero. The 95% confidence interval includes large negative values: it is not possible to rule out that trans students are up to 6 percentage points less likely to graduate from high school than their cisgender classmates. The large standard errors could be due to the high correlation between the gender identity and the sexual orientation indicators. However, as already discussed in Section 3.1, a substantial fraction of trans students identifies as heterosexual. Alternatively, it is possible that the relatively small sample of trans students (around 260) does not provide enough power to precisely estimate the relationship between gender identity and educational outcomes. As shown in Table 5, even larger differences are obtained when examining the likelihood of attending college. Non-heterosexual students are more than 7 percentage points less likely to attend college than their heterosexual peers (Column 1). The chances of attending college are even lower – by 9 percentage points – among trans students (Column 2). In line with the results on high school completion, only the sexual orientation indicator remains statistically significant when including both LGBT measures in the same specification (Column 3). The coefficient of the interaction between the sexual orientation and gender identity indicators is not precisely estimated (Column 4). Furthermore, non-heterosexual students remain almost 3 percentage points less likely to attend college even after accounting for individual and family characteristics (Column 5), 9th grade school fixed effects (Column 6), and state fixed effects (Column 7). After accounting for all observable characteristics, one may argue that the differences between heterosexual and non-heterosexual students – less than 2 percentage points when examining high school graduation rates, and around 3 percentage points when analyzing
college attendance – are not large. Nevertheless, these gaps are of the same order of magnitude as the racial and ethnic ones highlighted in the education literature (Murnane, 2013). After controlling for individual variables, household characteristics, school and state fixed effects, Asian students are almost 3 percentage points more likely to graduate from high school than white students. The differences between Hispanic, African-American and white students are around 1 percentage points and are not statistically significant (Column 3 Table C1 in the Online Appendix). When focusing on college attendance, Hispanic respondents are almost 4 percentage points less likely to be enrolled at a postsecondary institution than white respondents. A similar gap is found when comparing African-American and white respondents (Column 6 Table C1 in the Online Appendix). 4.2. Sample selection It is important to emphasize that this paper reports descriptive statistics on individuals who identify as LGBT in the HSLS:09 survey. As usual when analyzing data on sexual minorities, one may worry that some individuals may have decided not to truthfully declare their sexual orientation and gender identity, and that such sample selection may not be random. Despite this valid concern, there are a few tests and extensions that can be done to check the robustness of the results highlighted in the previous sections. First, the size of the LGBT population can be validated using surveys weights and other datasets. The estimated fraction of non-heterosexual and trans students in the HSLS:09 does not change substantially when comparing unweighted (Table 1) and weighted (Table B1 in the Online Appendix) percentages. As mention by Newport (2018), while only 4.5% of U.S. adults identified as LGBT in 2017, Gallup's LGBT estimates are much higher among Millennials. Among those born between 1980 and 1999, i.e. the same birth cohort of the students included in the HSLS:09, 8.2% identify as LGBT. This figure is close to the estimates from the HSLS:09 shown in Table 1. In addition, Coffman, Coffman, and Ericson (2017) summarize other existing measures of the LGBT population – ranging from 1.7% to 11% – while also emphasizing that current estimates may be considerably downward biased. One way to include LGBT individuals who decided not to disclose their sexual orientation in the analysis is by looking at item non-respondents. Indeed, some individuals took part to the 2016 follow-up survey, but skipped the question on sexual orientation. So far, these respondents have been excluded from the analysis. One could instead
Table 5 College attendance. (1) Non-hetero Trans
−0.071⁎⁎⁎ (0.014)
Non-hetero*Trans
(2)
(3)
(4)
(5)
(6)
(7)
−0.094⁎⁎⁎ (0.031)
−0.065⁎⁎⁎ (0.015) −0.049 (0.033)
−0.068⁎⁎⁎ (0.015) −0.100 (0.062) 0.070 (0.075)
−0.029⁎⁎ (0.014) −0.035 (0.032)
−0.028⁎⁎ (0.014) −0.027 (0.033)
−0.028⁎⁎ (0.014) −0.029 (0.034)
✓ ✓
✓ ✓ ✓
✓ ✓ ✓ ✓
14,380 0.146 0.145
14,380 0.267 0.214
14,250 0.268 0.212
Demographic Family School FE (9th grade) State FE (College) Observations R2 Adjusted-R2
15,330 0.002 0.002
Average dep var
0.577
15,400 0.001 0.001
15,310 0.002 0.002
15,310 0.002 0.002
This table shows how sexual orientation and gender identity are associated with the probability of attending college at the time of the last interview (February 2016). Reported coefficient from Linear Probability models. Same controls, fixed effects, and standard errors as Table 4. Source: HSLS:09. * p < 0.10, ** p < 0.05, *** p < 0.01. 4
Economics of Education Review 73 (2019) 101933
D. Sansone
Table 6 Expectations, school identity, and discrimination. Student expectation
Sexual orientation Non-hetero
Hetero
Gender identity Trans
M/F
Confident HS graduation (9th) Expect to complete BA (9th) Expect to complete BA (11th) Expect to complete BA (2016) Aspire to complete BA (11th) Think parents aspire BA (2016)
0.821 0.591 0.659 0.604 0.808 0.811
0.868 0.626 0.682 0.663 0.799 0.826
−0.047*** −0.035*** −0.023* −0.059*** 0.009 −0.015
0.762 0.617 0.617 0.604 0.732 0.787
0.865 0.621 0.680 0.657 0.800 0.824
−0.103*** −0.004 −0.063** −0.053* −0.068** −0.037
Parental expectations Aspire to complete BA (9th) Expect to complete BA (9th) Expect to complete BA (11th)
0.962 0.766 0.677
0.951 0.762 0.710
0.011 0.004 −0.033
0.960 0.749 0.639
0.952 0.762 0.707
0.008 −0.013 −0.068
School identity School belonging (9th) School engagement (9th) School motivation (11th)
−0.072 0.067 0.017
0.131 0.120 0.100
−0.203*** −0.053* −0.083***
−0.157 −0.036 −0.030
0.114 0.116 0.095
−0.271*** −0.152** −0.125**
Discrimination Education discrimination Work discrimination
0.179 0.279
0.112 0.160
0.067*** 0.119***
0.274 0.434
0.117 0.168
0.157*** 0.266***
Observations
1530
13,800
260
15,080
Unweighted statistics. The number of observations refers to “Education discrimination.” BA stands for “Bachelor's degree.” * p < 0.10,
⁎⁎
p < 0.05,
⁎⁎⁎
p < 0.01.
the Online Appendix). This result is particularly relevant when discussing attrition. Among the 15,330 students analyzed in Table 4 Column 1, 92.3% of them had completed high school. Among the 2010 students not included in the sample, but for whom high school graduation information is available, the completion rate is 86%. Therefore, the final sample analyzed in Table 4 seems to contain relatively higher-achieving students. This is a limitation of the study, although Online Appendix Table C5 suggests that this attrition may not vary between LGBT and non-LGBT individuals: there is no reason to believe that the high school dropouts for whom there is no information on sexual orientation are more or less likely to be LGBT. To further support this claim, one could include the aforementioned 2010 individuals in the analysis. The sexual orientation indicator is missing for these students either because they did not answer this specific question (540 respondents, as previously considered in Section 4.2 and Table C2 in the Online Appendix), or because the question was not administered (1470 individuals). Indeed, an abbreviated survey was sent to individuals who had not completed the fulllength survey in order to reduce non-response rates. The robustness of the estimates in Tables 4 and 5 can then be tested by randomly assigning a value for the sexual orientation indicator to a varying percentage of these individuals. As shown in Tables C6–C7 in the Online Appendix, non-heterosexual individuals remain less likely to graduate from high school or to attend college even when including all the 2010 non-respondents in the analysis and counting between 1% and 15% of them as non-heterosexuals.
include these individuals in the non-heterosexual sub-sample. Despite this strong assumption, it is reassuring to note that the estimated coefficients of sexual orientation remain negative and statistically significant when analyzing the probabilities of completing high school and attending college with this expanded sample size (Table C2 in the Online Appendix). The magnitude of the coefficients in Online Appendix Table C2 are even larger than those in Tables 4 and 5. The stability of the estimated coefficient of the sexual orientation indicator in case of omitted variables can be further tested by following Oster (2019). The key assumption in this case is that the bias due to unobservable components is correlated with the observed controls. This hypothesis is plausible in this application since the unobserved propensity to disclose one's sexual orientation is likely to be correlated with the included individual and household controls. Oster's method and suggested calibration implies that the unobservables would need to be 2.4 times as important as the observables to push the relationship between sexual orientation and high school graduation (Column 7 Table 4) to 0, well above the heuristic threshold of 1 . A close value (1.84) is obtained when testing the robustness of the relationship between sexual orientation and the probability of attending college (Column 7 Table 5). 4.3. Non-response rates Due to some respondents participating in the survey but not answering certain questions, the full set of controls is not available for all individuals. Consequently, one may wonder whether the changes across specifications in the magnitude of the coefficients in Tables 4 and 5 are due to the additional covariates being added, or to the underlying shifts in sample size. Tables C3–C4 in the Online Appendix replicate the main Tables 4 and 5 by estimating all models using the same core analytic sample, i.e. the one in Column 7 Table 4. When comparing Tables C3–C4 with Tables 4 and 5, the coefficients associated with the sexual orientation indicator remain negative, statistically significant, and with similar magnitudes. Item non-response rates might also vary by sexual orientation or gender identity. Nevertheless, among those who participated in the last follow-up survey, few did not reply to the questions about their educational attainment. In particular, the non-response rates do not differ systematically between LGBT and non-LGBT individuals (Table C5 in
4.4. Additional extensions and sensitivity analyses Sexual minorities have lower educational achievements also when looking at additional high school outcomes (Table C8 in the Online Appendix). As already shown in Table 4, non-heterosexual students are less likely to graduate from high school (replicated in Column 1). This finding holds even when counting GED recipients as high school graduates (Column 2). Furthermore, non-heterosexual students are more likely to dropout from high school at least once (Column 3), have lower GPA (Column 4), and accumulate a lower number of credits while in school (Column 5). However, these students have on average slightly higher SAT/ACT (Column 6) and PSAT scores (Column 7) than their 5
Economics of Education Review 73 (2019) 101933
D. Sansone
heterosexual peers. As in the main Tables 4 and 5, the coefficient associated with the gender identity indicator is imprecisely estimated in most specifications. For the sake of completeness, Online Appendix Table C9 replicates Online Appendix Table C8 by estimating weighted instead of unweighted regressions. For most outcomes, the conclusions do not change. There is still evidence that non-heterosexual students are less likely to graduate and have lower performance in terms of GPA and number of credits. In contrast instead with Online Appendix Table C8, the weighted gaps in SAT/ACT and PSAT scores are no longer statistically significant. In order to test the robustness of the main estimates on high school graduation and college attendance to different functional form assumptions, Online Appendix Tables C10–C11 reports the marginal effects from Logit models rather than Linear Probability models. The coefficients associated with the sexual orientation indicator remain negative, statistically significant, and with magnitude similar to those in Tables 4 and 5.
less willing to identify as LGBT in the past. Clearly, more data and research is needed on individuals who identify as LGBT at different points in their lives.2 5.2. Expectations, school identity, and discrimination In order to shed light on the experiences of LGBT students in high school, and to explore potential factors driving their low educational achievements, Table 6 compare LGBT students with their peers in terms of educational expectations, school identity, and experienced discrimination. LGBT students have lower expectations. Fewer LGBT than non-LGBT students are very sure about graduating from high school. Similarly, these students are less likely to think that they will complete a Bachelor's degree. These gaps can be found when comparing students’ responses in 9th grade, 11th grade, and in the last follow-up interview. On the other hand, while fewer non-heterosexual students expect to achieve a tertiary educational level, there is no significant difference between heterosexual and non-heterosexual students when asking them how far in school they would like to go if there were no barriers. Parental expectations are not different between LGBT and non-LGBT students. LGBT students are not less likely to believe that their parents would like them to complete a Bachelor's degree if there were no barriers. Parents (or guardians) of LGBT students also provide similar answers to their peers when asked in the baseline interviews how far they would like their 9th graders to go if there were no barriers, and how far in school they think their offspring would actually get. As mentioned in the previous section, marginalization of LGBT students might affect their sense of school identity. Indeed, LGBT 9th graders have a lower sense of school belonging: they score lower on average in a comprehensive index measuring whether students feel safe at school, are proud of their school, have an adult in school they feel comfortable talking with about their problems, do not believe that school is a waste of time, and think that getting good grades is important. Moreover, LGBT students have lower levels of school engagement, as measured using different indicators such as whether respondents go to class late or do not complete their homework. Such indices of school identity do not improve over time. LGBT 11th graders have lower motivation: their in-school behavior is worse than their peers. These findings are in line with previous literature showing that certain lesbian, gay, and bisexual adolescents can be at a greater risk for substance abuse (Goldbach, Tanner-Smith, Bagwell, & Dunlap, 2014; Russell, Driscoll, & Truong, 2002). Last but not least, LGBT students are much more likely to have suffered from discrimination. A substantial fraction of non-heterosexual and trans students feel that discrimination or unfair treatment due to their personal characteristics has limited their educational and work opportunities. In particular, 43% of trans students in the sample have experienced discrimination in the workplace, compared with less than 17% among non-trans respondents. In conclusion, while there is no evidence that parents have different expectations and aspirations for LGBT and non-LGBT students, nonheterosexual and trans students have lower educational expectations, lower levels of school identity, and are severely impacted by discrimination. Similar conclusions are reached when comparing weighted instead of unweighted statistics (Table C12 in the Online Appendix).
5. Discussion on potential mechanisms and limitations 5.1. Sexual orientation and gender identity is reported only once One limitation of the HSLS:09 is that information on sexual orientation and gender identity has been collected only in the last followup survey. Individuals who identify as LGBT in high school might have different experiences, personal and family characteristics than those who identify as LGBT in college or later in adulthood, thus resulting in different educational outcomes. Whether individuals who identify as LGBT earlier in life are more or less likely to have lower educational level is not clear ex-ante. Indeed, individuals who start to identify as LGBT in college may not experience the strong stigma often associated in high school with their sexual and gender identity, or they might have had the time to develop the skills to cope and confront such stigma and biases (Ueno, Roach, & PeñaTalamantes, 2013). The stress associated with the coming out or transition process might prevent LGBT high school students from focusing on their education. In addition, LGBT students might be marginalized while in high school. This process could lead them to become detached from their teachers and coursework (Poteat & Espelage, 2007; Rostosky, Owens, Zimmerman, & Ellen, 2003), and could reduce their sense of school belonging (Akerlof & Kranton, 2002). At the same time, these LGBT students may become resilient, develop their grittiness (Duckworth, Peterson, Matthews, & Kelly, 2007), and thrive once they move to a more welcoming environment such as a college campus. Furthermore, individuals who identify as LGBT only later in life might still suffer while in high school because of stigma, heteronormativity, implicit and explicit biases. Previous studies have found a positive association between educational attainment and the age at which individuals identify as LGBT (Barrett, Pollack, & Tilden, 2002; Pearson & Wilkinson, 2017), thus supporting the hypothesis that individuals who identify as LGBT earlier in life are more likely to struggle in school. The HSLS:09 sample includes individuals who started to identify as LGBT either in high school or in college. Since the first group seems to have been more penalized in terms of educational achievements, it is plausible that the educational gaps found in Tables 4 and 5 would have been even larger if the analysis had focused only on students who started to identify as LGBT in high school. It is possible that additional – highly educated – respondents might identify as LGBT later in life. These progressive variations in the LGBT sample might help explaining why LGBT students appear to underperform in school surveys such as the HSLS:09, while adults in same-sex couples have higher educational achievements in general datasets such as the American Community Survey. Alternatively, as discussed in the Introduction, there is evidence suggesting that low-educated adults were
2 An additional factor explaining the differences in educational levels among LGBT individuals reported in the HSLS:09 and in previous studies could be that most statistics on LGBT adults are computed from data on same-sex couples in the Census or the American Community Survey. Since college graduates are more likely to be married (Fry and Cohn, 2011), omitting single individuals might inflate the estimated average educational level of LGBT adults. Nevertheless, similar educational patterns have also been highlighted in a few studies analyzing (smaller) datasets including sexual minority individuals without a partner (Black et al., 2007; Carpenter and Gates, 2008).
6
Economics of Education Review 73 (2019) 101933
D. Sansone
6. Conclusions
families. Journal of Economic Perspectives, 21(2), 53–70. Carpenter, C. S. (2007). Revisiting the income penalty for behaviorally gay men: Evidence from NHANES III. Labour Economics, 14(1), 25–34. Carpenter, C. S. (2009). Sexual orientation and outcomes in college. Economics of Education Review, 28(6), 693–703. Carpenter, C. S., & Gates, G. J. (2008). Gay and lesbian partnership: Evidence from California. Demography, 45(3), 573–590. Coffman, K. B., Coffman, L. C., & Ericson, K. M. M. (2017). The Size of the LGBT population and the magnitude of antigay sentiment are substantially underestimated. Management Science, 63(10), 3168–3186. Drydakis, N. (2009). Sexual orientation discrimination in the labour market. Labour Economics, 16(4), 364–372. Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. Duprey, M. A., Pratt, D. J., Jewell, D. M., Cominole, M. B., Fritch, L. B., Ritchie, E. A., et al. (2018). High school longitudinal study of 2009 (HSLS: 09) base-year to second follow-up, Vol. 2018-140 Washington, D.C. Flores, A. R., & Barclay, S. (2016). Backlash, consensus, legitimacy, or polarization: The effect of same-sex marriage policy on mass attitudes. Political Research Quarterly, 69(1), 43–56. Fry, R., & Cohn, D. (2011). Prevalence and growth of cohabitation. In P. Taylor, R. Fry, D. Cohn, W. Wang, G. Velasco, & D. Dockterman (Eds.). Living together: The economics of cohabitation (pp. 7–10). Washington, D.C: Pew Social & Demographic Trends. Geijtenbeek, L., & Plug, E. (2018). Is there a penalty for registered women? Is there a premium for registered men? Evidence from a sample of transsexual workers. European Economic Review, 109(October), 334–347. Genicot, G., & Ray, D. (2017). Aspirations and Inequality. Econometrica, 85(2), 489–519. Goldbach, J. T., Tanner-Smith, E. E., Bagwell, M., & Dunlap, S. (2014). Minority stress and substance use in sexual minority adolescents: A meta-analysis. Prevention Science, 15(3), 350–363. Heckman, J. J., Humphries, J. E., & Mader, N. S. (2011). The GED. In E. A. Hanushek, S. Machin, & L. Woessmann (Vol. Eds.), Handbooks in economics: Economics of education: 3, (pp. 423–484). North-Holland. Kenny, C., & Patel, D. (2017). Norms and reform: Legalizing homosexuality improves attitudes. CGD Working Paper465. Kreitzer, R. J., Hamilton, A. J., & Tolbert, C. J. (2014). Does policy adoption change opinions on minority rights? The effects of legalizing same-sex marriage. Political Research Quarterly, 67(4), 795–808. Murnane, R. J. (2013). U.S. high school graduation rates: Patterns and explanations. Journal of Economic Literature, 51(2), 370–422. Newport, F. (2018). In U.S., estimate of LGBT population rises to 4.5%. Gallup May(22). Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business and Economic Statistics, 37(2), 187–204. Papageorge, N. W., Gershenson, S., & Kang, K. (2019). Teacher expectations matter. Review of Economics and Statistics Accepted https://doi.org/10.1162/rest_a_00838. Pearson, J., & Wilkinson, L. (2017). Same-sex sexuality and educational attainment: The pathway to college. Journal of Homosexuality, 64(4), 538–576. Plug, E., & Berkhout, P. (2004). Effects of sexual preferences on earnings in the Netherlands. Journal of Population Economics, 17(1), 117–131. Poteat, P. V., & Espelage, D. L. (2007). Predicting psychosocial consequences of homophobic victimization in middle school students. Journal of Early Adolescence, 27(2), 175–191. Rostosky, S. S., Owens, G. P., Zimmerman, R. S., & Ellen, D. B. R. (2003). Associations among sexual attraction status, school belonging, and alcohol and marijuana use in rural high school students. Journal of Adolescence, 26(6), 741–751. Russell, S. T., Driscoll, A. K., & Truong, N. (2002). Adolescent same-sex romantic attractions and relationships: Implications for substance use and abuse. American Journal of Public Health, 92(2), 198–202. Sansone, D. (2018). Pink work: Same-sex marriage. Employment and Discrimination ArXivWorking Paper, 1807.06698. Tankard, M. E., & Paluck, E. L. (2017). The effect of a Supreme Court decision regarding gay marriage on social norms and personal attitudes. Psychological Science, 28(9), 1334–1344. Ueno, K., Roach, T. A., & Peña-Talamantes, A. E. (2013). The dynamic association between same-sex contact and educational attainment. Advances in Life Course Research, 18(2), 127–140.
The paper uses data from a recent cohort of U.S. students to show that non-heterosexual and trans individuals have lower educational outcomes both in high school and college. Individual, family, school and state characteristics cannot fully explain these differences. These results emphasize the vulnerability of LGBT students, and the need to take this sub-population into account when designing educational policies. In addition, given the pivotal role played by subjective expectations and aspirations (Beaman, Duflo, Pande, & Topalova, 2012; Genicot & Ray, 2017; Papageorge, Gershenson, & Kang, 2019), the fact that LGBT students are less likely to believe that they can graduate from high school and complete a Bachelor's degree is particularly worrisome. Similarly, the lower sense of school belonging and engagement is striking given the importance of school identity highlighted by Akerlof and Kranton (2002). Finally, there is ample evidence that sexual minorities experience discrimination from employers, consumers and coworkers (Aksoy, Carpenter, Frank, & Huffman, 2019; Carpenter, 2007; Drydakis, 2009; Plug & Berkhout, 2004). This paper provides suggestive evidence that discrimination might also limit educational opportunities for LGBT students. Future research is needed to conciliate the findings in this paper with the higher educational levels among adults in same-sex couples estimated in previous studies. Longitudinal datasets in which the sexual orientation and gender identity is recorded more than once during a respondent's lifetime would be particularly useful to understand the educational and work career trajectories of LGBT individuals, and the effect of identifying as LGBT earlier in life. In this context, the HSLS:09 follow-up data collection scheduled in 2025 will provide valuable information on the transition of LGBT individuals into adulthood. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.econedurev.2019.101933. References Akerlof, G. A., & Kranton, R. E. (2002). Identity and schooling: Some lession from the economics of education. Journal of Economic Literature, 40(4), 1167–1201. Aksoy, C. G., Carpenter, C. S., Frank, J., & Huffman, M. L. (2019). Gay glass ceilings : Sexual orientation and workplace authority in the UK. Journal of Economic Behavior & Organization, 159(March), 167–180. Barrett, D. C., Pollack, L. M., & Tilden, M. L. (2002). Teenage sexual orientation, adult openness, and status attainment in gay males. Sociological Perspectives, 45(2), 163–182. Beaman, L., Duflo, E., Pande, R., & Topalova, P. (2012). Female leadership raises aspirations and educational attainment for girls: A policy experiment in India. Science Magazine, 335, 582–586. Black, D. A., Sanders, S. G., & Taylor, L. J. (2007). The economics of lesbian and gay
7
Contents lists available at ScienceDirect
Economics of Education Review journal homepage: www.elsevier.com/locate/econedurev
Note
LGBT students: New evidence on demographics and educational outcomes #
Dario Sansone
T
University of Exeter and Vanderbilt University, Department of Economics, Nashville, TN37235 United States
ARTICLE INFO
ABSTRACT
Keywords: LGBT High school graduation College attendance Discrimination
This paper shows that LGBT students in the U.S. are less likely to graduate from high school and attend college. These differences persist after controlling for demographic characteristics, family background, state and school fixed effects. Relatedly, LGBT students have lower educational expectations, a lower sense of school belonging, and are more likely to have been affected by discrimination.
JEL classification: I20 J15 J24
1. Introduction The aim of this study is to provide new important descriptive statistics on LGBT students. By leveraging novel information contained in the restricted-use version of the High School Longitudinal Study of 2009 (HSLS:09), this paper investigates whether LGBT students are more or less likely to complete high school or attend college, and whether they have higher or lower performance while in school. In order to understand whether or not sexual minorities are a vulnerable sub-population that needs to be supported, policymakers need to know whether LGBT individuals are under-represented in secondary or tertiary education, whether they face specific challenges, and the key factors associated with their educational success. Without such data, policies may be driven by anecdotal evidence and the current political mood, or policymakers may believe that the invisibility of the LGBT population implies that these students do not require any specific intervention. Within economics, the only published paper on LGB students is by Carpenter (2009). He shows that gays have higher GPA in college; while bisexual females spend less time studying. Even in other social sciences, the evidence is scarce and mainly derived from health surveys (Pearson & Wilkinson, 2017). This paper makes three main contributions to this literature. First and foremost, even if researchers have recently started to analyze labor market outcomes for trans individuals (Geijtenbeek &
Plug, 2018), this is the first study to include any nationally representative statistics on trans students’ educational outcomes. Second, this analysis is based on recent data: the HSLS:09 surveys have been conducted between 2009 and 2016. Most of the previous analyses rely instead on data such as the Add Health containing information on individuals who attended high school in the 1990s. Especially after the decriminalization of homosexuality in 2003 (Lawrence v. Texas), and the progressive legalization of same-sex marriage between 2004 and 2015, the experiences of the LGBT individuals interviewed in the HSLS:09 might have been different due to improvements in social norms and attitudes towards the LGBT community (Flores & Barclay, 2016; Kenny & Patel, 2017; Kreitzer, Hamilton, & Tolbert, 2014; Sansone, 2018; Tankard & Paluck, 2017). Third, while other surveys focus on adolescent health and health behavior, the HSLS:09 provides an extremely rich set of educational variables relevant for this analysis. For instance, this dataset includes test scores such as the SAT, PSAT and ACT, information on students’ and parents’ educational expectation and aspirations, measures of school identity and discrimination, details on respondents’ college performance, and the main reason behind their decision not to attend college. This paper shows that LGBT students have poorer educational outcomes: even if they do not perform worse than their peers in several tests such as the SAT, they are less likely to graduate from high school,
I am grateful to the Editor McKinley Blackburn, two anonymous referees, Christopher Carpenter, Hiren Nisar, Deniz Sanin, and Allison Stashko for their helpful comments. I am also grateful to John Rust and Judith House for their technical support and their help in accessing the HSLS:09 restricted-use data. I conducted the empirical analysis while completing my Ph.D. at Georgetown University. The usual caveats apply. E-mail address: [email protected]. # Website: https://sites.google.com/view/dariosansone/ https://doi.org/10.1016/j.econedurev.2019.101933 Received 24 April 2019; Received in revised form 4 October 2019; Accepted 7 October 2019 0272-7757/ © 2019 Elsevier Ltd. All rights reserved.
Economics of Education Review 73 (2019) 101933
D. Sansone
have lower GPA, and accumulate fewer credits while in school. Furthermore, these students are less likely to apply and attend college. These gaps persist – especially for non-heterosexual students – after controlling for demographic characteristics, family background, state and school fixed effects. In line with these results, this study shows that LGBT students have lower educational expectations, lower motivation, school engagement and sense of belonging. In addition, these students believe that discrimination has impacted them both while in school and in the workplace. Therefore, future policies should ensure that students with similar ability have the same opportunities and can achieve the same educational outcomes irrespectively of their sexual orientation or gender identity. The findings in this paper are in contrast with previous studies highlighting higher educational achievements among LGBT adults (Black, Sanders, & Taylor, 2007). This could be due to the fact that some (highly educated) individuals start to identify as LGBT only in adulthood. It could also be that, despite the initial low academic performance in high school, LGBT students quickly catch-up once they live in a more welcoming environment such as a college campus. Another alternative explanation is that low-educated people were less likely to disclose their sexual behavior or relationship status in the past decades. Indeed, Millennials are much more willing to identify as LGBT than older generations (Newport, 2018). In line with this hypothesis, Sansone (2018) found a lower prevalence of individuals without tertiary education among self-reported same-sex couples before the introduction of marriage equality in the U.S.
Table 1 Sexual orientation and gender identity. Sexual orientation Heterosexual Lesbian or Gay Bisexual Another sexual orientation Don't know
Gender identity 90.0% 2.4% 4.6% 1.4% 1.6%
Male only Female only Trans/non-binary
47.7% 50.6% 1.7%
Unweighted percentages.
not reply. Among the respondents, 1.7% identify as both male and female, transgender male-to-female, transgender female-to-male, gender nonconforming, or they are not sure. Of these trans students, 25% identify as heterosexual.1 Online Appendix B compares demographic characteristics and family background among LGBT and non-LGBT students. Non-heterosexual and trans students are more likely to have a learning or physical disability, and to live in a poor household without a mother or a father. 3.2. LGBT students have lower educational achievements Table 2 compares high school educational outcomes by sexual orientation and gender identity. Both non-heterosexual and trans students are less likely to have a high school diploma, and more likely to have dropped out from high school at least once. LGBT students are more likely to receive a GED, a certificate legally – but not de facto – equivalent to a high school diploma (Heckman, Humphries, & Mader, 2011). Moreover, LGBT students have consistently lower achievement levels while in high school. Their GPA is lower, overall and in each single academic year, and they accumulate fewer credits. Nevertheless, these students do not perform worse than their peers when looking at certain ability and cognitive skill measures: their scores in the HSLS:09 math test are not statistically different from other students, and they actually achieve slightly higher scores in the PSAT, as well as in the SAT/ACT college entrance exams. Similar conclusions are reached when analyzing college outcomes (Table 3). LGBT respondents are less likely to apply for college, to have ever attended a post-secondary institution, and to attend college at the time of the survey. When asked to list the main reasons behind their decision not to attend college, these individuals are more likely than non-LGBT respondents to report academic, personal, and financial reasons rather than job-related explanations.
2. Data The High School Longitudinal Study of 2009 is a nationally representative panel micro dataset. The survey design has two levels: first, around 940 private and public schools were selected at the national level. Second, around 20–30 9th grade students in each selected school were randomly chosen to participate in the survey. In the baseline year (2009), the selected 9th graders, their parents, math and science teachers, school administrators, and lead school counselors completed a survey. The first follow-up in 2012 included students (at that time in 11th grade), parent, school administrators, and counselors. A brief survey was conducted among the students and their parents in 2013 to record respondents’ post-secondary plans. Students were interviewed again in 2016. Students completed a math assessment while in grades 9 and 11. High school academic transcripts includes student GPA, AP class grades, SAT scores, and the number of credits taken in each subject. Most importantly, the last follow-up survey contains three questions on gender identity and sexual orientation. Online Appendix A provides a detailed description of all the variables used in the empirical analysis. For security reasons, all sample sizes have been rounded to the nearest 10. Additional documentation about the HSLS:09 can be found in Duprey et al. (2018).
4. Educational gaps persist in multivariate analysis 4.1. High school graduation and college attendance Table 4 estimates whether LGBT students are still less likely to complete high school after controlling for a rich set of controls. Both indicators for sexual orientation (Column 1) and gender identity (Column 2) are negatively associate with high school completion, but only sexual orientation remains statistically significant when including both measures in the same specification (Column 3). In addition, there is no evidence of intersectionality: the interaction term between sexual orientation and gender identity is not statistically significant (Column 4).
3. Descriptive statistics 3.1. How many students are LGBT? Around 15,870 students were asked to specify their sexual orientations. The non-response rate is 3.4%. This percentage is lower than in previous surveys such as the Add Health (Pearson & Wilkinson, 2017). Among the respondents, 10% of them do not identify as heterosexual. In particular, 2.4% of the students think of themselves as homosexual, 4.6% as bisexual, 1.6% do not know, while 1.4% of them identify with another sexual orientation (Table 1). Among nonheterosexual students, 29% identify only as men, 58% only as women, and the remaining 13% as trans. When asked about their gender identity, 2.9% of the students did
1 All summary statistics discussed in this and the next section are unweighted percentages. Similar descriptive statistics are obtained using sample weights and accounting for the HSLS:09 sample design (Online Appendix B). For brevity, individuals who identify as “transgender, male-to-female“, ”transgender, female-to-male”, “genderqueer or gender nonconforming, or some other gender”, or “not sure" have been referred as “trans” in the empirical analysis. Respondents who identify only as male or female has been categorized as “non-trans”, “cisgender”, or “M/F”.
2
Economics of Education Review 73 (2019) 101933
D. Sansone
Table 2 High school outcomes by sexual orientation and gender identity. Sexual orientation Non-hetero
Hetero
Gender identity Trans
M/F
HS diploma HS diploma/GED GED Ever dropped out HS
0.889 0.937 0.051 0.214
0.927 0.963 0.039 0.141
−0.038*** −0.026*** 0.012** 0.073***
0.870 0.927 0.065 0.238
0.924 0.961 0.040 0.147
−0.054*** −0.034*** 0.025** 0.091***
GPA 9th grade GPA 10th grade GPA 11th grade GPA 12th grade HS GPA HS credits Any credit AP/IB
2.743 2.793 2.872 2.984 2.749 24.289 0.433
2.834 2.843 2.896 3.024 2.833 25.113 0.413
−0.091*** −0.050** −0.024 −0.040* −0.084*** −0.824*** 0.020
2.688 2.657 2.781 2.958 2.696 23.911 0.408
2.825 2.840 2.894 3.021 2.825 25.040 0.414
−0.137** −0.183*** −0.113** −0.063 −0.129** −1.129*** −0.006
Math score 9th grade Math score 11th grade SAT/ACT math SAT/ACT read SAT/ACT PSAT
52.695 52.305 513.883 530.264 1044.15 154.541
52.304 52.526 505.632 499.635 1005.30 146.426
0.391 −0.221 8.251** 30.629*** 38.845*** 8.115***
52.295 52.564 516.012 547.202 1063.21 148.932
52.311 52.463 505.989 501.555 1007.58 147.215
−0.016 0.101 10.023 45.647*** 55.630*** 1.717
Observations
1530
13,800
260
15,140
Unweighted statistics. The number of observations refers to “HS diploma.” * p < 0.10,
⁎⁎
p < 0.05,
⁎⁎⁎
p < 0.01.
Table 3 College outcomes by sexual orientation and gender identity. Sexual orientation Non-hetero
Hetero
Attending college Ever applied college Ever attended 2-yr public institute Ever attended college
0.513 0.846 0.274 0.719
0.584 0.864 0.295 0.771
Reasons never attended college: Academic Personal Financial Job-related
0.112 0.519 0.516 0.243
0.086 0.410 0.440 0.336
Observations
1530
13,800
Gender identity Trans
M/F
−0.071*** −0.018** −0.021* −0.052***
0.483 0.808 0.250 0.678
0.577 0.863 0.294 0.766
−0.094*** −0.055** −0.044 −0.088***
0.026* 0.109*** 0.076*** −0.093***
0.157 0.542 0.446 0.265
0.088 0.420 0.448 0.324
0.069** 0.122** −0.002 −0.059
260
15,140
Unweighted statistics. The number of observations refers to “Attending college.” * p < 0.10,
⁎⁎
p < 0.05,
⁎⁎⁎
p < 0.01.
Table 4 High school diploma. (1) Non-hetero Trans
−0.038 (0.008)
(2)
(3)
−0.054⁎⁎⁎ (0.020)
−0.034 (0.008) −0.034 (0.021)
⁎⁎⁎
Non-hetero*Trans
(4) ⁎⁎⁎
−0.034 (0.009) −0.037 (0.039) 0.004 (0.048)
Demographic Family School FE (9th grade) State FE (college) Observations R2 Adjusted-R2
15,330 0.002 0.002
Average dep var
0.923
15,400 0.001 0.001
15,310 0.002 0.002
(5) ⁎⁎⁎
15,310 0.002 0.002
−0.020 (0.008) −0.015 (0.020)
(6) ⁎⁎
−0.018 (0.008) −0.017 (0.021)
(7) ⁎⁎
−0.016* (0.008) −0.023 (0.021)
✓ ✓
✓ ✓ ✓
✓ ✓ ✓ ✓
14,380 0.042 0.041
14,380 0.152 0.091
14,250 0.156 0.092
This table shows how sexual orientation and gender identity are associated with the probability of receiving a high school diploma. Demographic controls: race and ethnicity, US born, disability. Family controls: no mother/father in the household, parental education and employment status, household size, and poverty indicator. School fixed effects are computed from the baseline interview, while state fixed effects are derived from the 2016 follow-up survey. All controls are described in Online Appendix A. Reported coefficient from Linear Probability Models. Standard errors in parentheses clustered at the 9th grade school level. Source: HSLS:09. * p < 0.10, ⁎⁎ p < 0.05, ⁎⁎⁎ p < 0.01. 3
Economics of Education Review 73 (2019) 101933
D. Sansone
Although the magnitude of the coefficient decreases, non-heterosexual students are still less likely to graduate from high school after accounting for individual and household characteristics such as ethnicity, race, nationality, disability, presence of a mother and a father in the household, parental education and employment status, household size, and a poverty indicator (Column 5). The specification can be expanded by including 9th grade school fixed effects (computed from the baseline interviews, Column 6), and state fixed effects (derived from the 2016 follow-up survey, Column 7). Even after accounting for all these observable characteristics, the average probability of a non-heterosexual student to graduate from high school is 2 percentage points lower than the corresponding probability for their heterosexual peers. While the coefficient associated with the trans indicator is negative and has the same magnitude as the coefficient of sexual orientation, it is not statistically significant. It is worth emphasizing that this coefficient is not a precisely estimated zero. The 95% confidence interval includes large negative values: it is not possible to rule out that trans students are up to 6 percentage points less likely to graduate from high school than their cisgender classmates. The large standard errors could be due to the high correlation between the gender identity and the sexual orientation indicators. However, as already discussed in Section 3.1, a substantial fraction of trans students identifies as heterosexual. Alternatively, it is possible that the relatively small sample of trans students (around 260) does not provide enough power to precisely estimate the relationship between gender identity and educational outcomes. As shown in Table 5, even larger differences are obtained when examining the likelihood of attending college. Non-heterosexual students are more than 7 percentage points less likely to attend college than their heterosexual peers (Column 1). The chances of attending college are even lower – by 9 percentage points – among trans students (Column 2). In line with the results on high school completion, only the sexual orientation indicator remains statistically significant when including both LGBT measures in the same specification (Column 3). The coefficient of the interaction between the sexual orientation and gender identity indicators is not precisely estimated (Column 4). Furthermore, non-heterosexual students remain almost 3 percentage points less likely to attend college even after accounting for individual and family characteristics (Column 5), 9th grade school fixed effects (Column 6), and state fixed effects (Column 7). After accounting for all observable characteristics, one may argue that the differences between heterosexual and non-heterosexual students – less than 2 percentage points when examining high school graduation rates, and around 3 percentage points when analyzing
college attendance – are not large. Nevertheless, these gaps are of the same order of magnitude as the racial and ethnic ones highlighted in the education literature (Murnane, 2013). After controlling for individual variables, household characteristics, school and state fixed effects, Asian students are almost 3 percentage points more likely to graduate from high school than white students. The differences between Hispanic, African-American and white students are around 1 percentage points and are not statistically significant (Column 3 Table C1 in the Online Appendix). When focusing on college attendance, Hispanic respondents are almost 4 percentage points less likely to be enrolled at a postsecondary institution than white respondents. A similar gap is found when comparing African-American and white respondents (Column 6 Table C1 in the Online Appendix). 4.2. Sample selection It is important to emphasize that this paper reports descriptive statistics on individuals who identify as LGBT in the HSLS:09 survey. As usual when analyzing data on sexual minorities, one may worry that some individuals may have decided not to truthfully declare their sexual orientation and gender identity, and that such sample selection may not be random. Despite this valid concern, there are a few tests and extensions that can be done to check the robustness of the results highlighted in the previous sections. First, the size of the LGBT population can be validated using surveys weights and other datasets. The estimated fraction of non-heterosexual and trans students in the HSLS:09 does not change substantially when comparing unweighted (Table 1) and weighted (Table B1 in the Online Appendix) percentages. As mention by Newport (2018), while only 4.5% of U.S. adults identified as LGBT in 2017, Gallup's LGBT estimates are much higher among Millennials. Among those born between 1980 and 1999, i.e. the same birth cohort of the students included in the HSLS:09, 8.2% identify as LGBT. This figure is close to the estimates from the HSLS:09 shown in Table 1. In addition, Coffman, Coffman, and Ericson (2017) summarize other existing measures of the LGBT population – ranging from 1.7% to 11% – while also emphasizing that current estimates may be considerably downward biased. One way to include LGBT individuals who decided not to disclose their sexual orientation in the analysis is by looking at item non-respondents. Indeed, some individuals took part to the 2016 follow-up survey, but skipped the question on sexual orientation. So far, these respondents have been excluded from the analysis. One could instead
Table 5 College attendance. (1) Non-hetero Trans
−0.071⁎⁎⁎ (0.014)
Non-hetero*Trans
(2)
(3)
(4)
(5)
(6)
(7)
−0.094⁎⁎⁎ (0.031)
−0.065⁎⁎⁎ (0.015) −0.049 (0.033)
−0.068⁎⁎⁎ (0.015) −0.100 (0.062) 0.070 (0.075)
−0.029⁎⁎ (0.014) −0.035 (0.032)
−0.028⁎⁎ (0.014) −0.027 (0.033)
−0.028⁎⁎ (0.014) −0.029 (0.034)
✓ ✓
✓ ✓ ✓
✓ ✓ ✓ ✓
14,380 0.146 0.145
14,380 0.267 0.214
14,250 0.268 0.212
Demographic Family School FE (9th grade) State FE (College) Observations R2 Adjusted-R2
15,330 0.002 0.002
Average dep var
0.577
15,400 0.001 0.001
15,310 0.002 0.002
15,310 0.002 0.002
This table shows how sexual orientation and gender identity are associated with the probability of attending college at the time of the last interview (February 2016). Reported coefficient from Linear Probability models. Same controls, fixed effects, and standard errors as Table 4. Source: HSLS:09. * p < 0.10, ** p < 0.05, *** p < 0.01. 4
Economics of Education Review 73 (2019) 101933
D. Sansone
Table 6 Expectations, school identity, and discrimination. Student expectation
Sexual orientation Non-hetero
Hetero
Gender identity Trans
M/F
Confident HS graduation (9th) Expect to complete BA (9th) Expect to complete BA (11th) Expect to complete BA (2016) Aspire to complete BA (11th) Think parents aspire BA (2016)
0.821 0.591 0.659 0.604 0.808 0.811
0.868 0.626 0.682 0.663 0.799 0.826
−0.047*** −0.035*** −0.023* −0.059*** 0.009 −0.015
0.762 0.617 0.617 0.604 0.732 0.787
0.865 0.621 0.680 0.657 0.800 0.824
−0.103*** −0.004 −0.063** −0.053* −0.068** −0.037
Parental expectations Aspire to complete BA (9th) Expect to complete BA (9th) Expect to complete BA (11th)
0.962 0.766 0.677
0.951 0.762 0.710
0.011 0.004 −0.033
0.960 0.749 0.639
0.952 0.762 0.707
0.008 −0.013 −0.068
School identity School belonging (9th) School engagement (9th) School motivation (11th)
−0.072 0.067 0.017
0.131 0.120 0.100
−0.203*** −0.053* −0.083***
−0.157 −0.036 −0.030
0.114 0.116 0.095
−0.271*** −0.152** −0.125**
Discrimination Education discrimination Work discrimination
0.179 0.279
0.112 0.160
0.067*** 0.119***
0.274 0.434
0.117 0.168
0.157*** 0.266***
Observations
1530
13,800
260
15,080
Unweighted statistics. The number of observations refers to “Education discrimination.” BA stands for “Bachelor's degree.” * p < 0.10,
⁎⁎
p < 0.05,
⁎⁎⁎
p < 0.01.
the Online Appendix). This result is particularly relevant when discussing attrition. Among the 15,330 students analyzed in Table 4 Column 1, 92.3% of them had completed high school. Among the 2010 students not included in the sample, but for whom high school graduation information is available, the completion rate is 86%. Therefore, the final sample analyzed in Table 4 seems to contain relatively higher-achieving students. This is a limitation of the study, although Online Appendix Table C5 suggests that this attrition may not vary between LGBT and non-LGBT individuals: there is no reason to believe that the high school dropouts for whom there is no information on sexual orientation are more or less likely to be LGBT. To further support this claim, one could include the aforementioned 2010 individuals in the analysis. The sexual orientation indicator is missing for these students either because they did not answer this specific question (540 respondents, as previously considered in Section 4.2 and Table C2 in the Online Appendix), or because the question was not administered (1470 individuals). Indeed, an abbreviated survey was sent to individuals who had not completed the fulllength survey in order to reduce non-response rates. The robustness of the estimates in Tables 4 and 5 can then be tested by randomly assigning a value for the sexual orientation indicator to a varying percentage of these individuals. As shown in Tables C6–C7 in the Online Appendix, non-heterosexual individuals remain less likely to graduate from high school or to attend college even when including all the 2010 non-respondents in the analysis and counting between 1% and 15% of them as non-heterosexuals.
include these individuals in the non-heterosexual sub-sample. Despite this strong assumption, it is reassuring to note that the estimated coefficients of sexual orientation remain negative and statistically significant when analyzing the probabilities of completing high school and attending college with this expanded sample size (Table C2 in the Online Appendix). The magnitude of the coefficients in Online Appendix Table C2 are even larger than those in Tables 4 and 5. The stability of the estimated coefficient of the sexual orientation indicator in case of omitted variables can be further tested by following Oster (2019). The key assumption in this case is that the bias due to unobservable components is correlated with the observed controls. This hypothesis is plausible in this application since the unobserved propensity to disclose one's sexual orientation is likely to be correlated with the included individual and household controls. Oster's method and suggested calibration implies that the unobservables would need to be 2.4 times as important as the observables to push the relationship between sexual orientation and high school graduation (Column 7 Table 4) to 0, well above the heuristic threshold of 1 . A close value (1.84) is obtained when testing the robustness of the relationship between sexual orientation and the probability of attending college (Column 7 Table 5). 4.3. Non-response rates Due to some respondents participating in the survey but not answering certain questions, the full set of controls is not available for all individuals. Consequently, one may wonder whether the changes across specifications in the magnitude of the coefficients in Tables 4 and 5 are due to the additional covariates being added, or to the underlying shifts in sample size. Tables C3–C4 in the Online Appendix replicate the main Tables 4 and 5 by estimating all models using the same core analytic sample, i.e. the one in Column 7 Table 4. When comparing Tables C3–C4 with Tables 4 and 5, the coefficients associated with the sexual orientation indicator remain negative, statistically significant, and with similar magnitudes. Item non-response rates might also vary by sexual orientation or gender identity. Nevertheless, among those who participated in the last follow-up survey, few did not reply to the questions about their educational attainment. In particular, the non-response rates do not differ systematically between LGBT and non-LGBT individuals (Table C5 in
4.4. Additional extensions and sensitivity analyses Sexual minorities have lower educational achievements also when looking at additional high school outcomes (Table C8 in the Online Appendix). As already shown in Table 4, non-heterosexual students are less likely to graduate from high school (replicated in Column 1). This finding holds even when counting GED recipients as high school graduates (Column 2). Furthermore, non-heterosexual students are more likely to dropout from high school at least once (Column 3), have lower GPA (Column 4), and accumulate a lower number of credits while in school (Column 5). However, these students have on average slightly higher SAT/ACT (Column 6) and PSAT scores (Column 7) than their 5
Economics of Education Review 73 (2019) 101933
D. Sansone
heterosexual peers. As in the main Tables 4 and 5, the coefficient associated with the gender identity indicator is imprecisely estimated in most specifications. For the sake of completeness, Online Appendix Table C9 replicates Online Appendix Table C8 by estimating weighted instead of unweighted regressions. For most outcomes, the conclusions do not change. There is still evidence that non-heterosexual students are less likely to graduate and have lower performance in terms of GPA and number of credits. In contrast instead with Online Appendix Table C8, the weighted gaps in SAT/ACT and PSAT scores are no longer statistically significant. In order to test the robustness of the main estimates on high school graduation and college attendance to different functional form assumptions, Online Appendix Tables C10–C11 reports the marginal effects from Logit models rather than Linear Probability models. The coefficients associated with the sexual orientation indicator remain negative, statistically significant, and with magnitude similar to those in Tables 4 and 5.
less willing to identify as LGBT in the past. Clearly, more data and research is needed on individuals who identify as LGBT at different points in their lives.2 5.2. Expectations, school identity, and discrimination In order to shed light on the experiences of LGBT students in high school, and to explore potential factors driving their low educational achievements, Table 6 compare LGBT students with their peers in terms of educational expectations, school identity, and experienced discrimination. LGBT students have lower expectations. Fewer LGBT than non-LGBT students are very sure about graduating from high school. Similarly, these students are less likely to think that they will complete a Bachelor's degree. These gaps can be found when comparing students’ responses in 9th grade, 11th grade, and in the last follow-up interview. On the other hand, while fewer non-heterosexual students expect to achieve a tertiary educational level, there is no significant difference between heterosexual and non-heterosexual students when asking them how far in school they would like to go if there were no barriers. Parental expectations are not different between LGBT and non-LGBT students. LGBT students are not less likely to believe that their parents would like them to complete a Bachelor's degree if there were no barriers. Parents (or guardians) of LGBT students also provide similar answers to their peers when asked in the baseline interviews how far they would like their 9th graders to go if there were no barriers, and how far in school they think their offspring would actually get. As mentioned in the previous section, marginalization of LGBT students might affect their sense of school identity. Indeed, LGBT 9th graders have a lower sense of school belonging: they score lower on average in a comprehensive index measuring whether students feel safe at school, are proud of their school, have an adult in school they feel comfortable talking with about their problems, do not believe that school is a waste of time, and think that getting good grades is important. Moreover, LGBT students have lower levels of school engagement, as measured using different indicators such as whether respondents go to class late or do not complete their homework. Such indices of school identity do not improve over time. LGBT 11th graders have lower motivation: their in-school behavior is worse than their peers. These findings are in line with previous literature showing that certain lesbian, gay, and bisexual adolescents can be at a greater risk for substance abuse (Goldbach, Tanner-Smith, Bagwell, & Dunlap, 2014; Russell, Driscoll, & Truong, 2002). Last but not least, LGBT students are much more likely to have suffered from discrimination. A substantial fraction of non-heterosexual and trans students feel that discrimination or unfair treatment due to their personal characteristics has limited their educational and work opportunities. In particular, 43% of trans students in the sample have experienced discrimination in the workplace, compared with less than 17% among non-trans respondents. In conclusion, while there is no evidence that parents have different expectations and aspirations for LGBT and non-LGBT students, nonheterosexual and trans students have lower educational expectations, lower levels of school identity, and are severely impacted by discrimination. Similar conclusions are reached when comparing weighted instead of unweighted statistics (Table C12 in the Online Appendix).
5. Discussion on potential mechanisms and limitations 5.1. Sexual orientation and gender identity is reported only once One limitation of the HSLS:09 is that information on sexual orientation and gender identity has been collected only in the last followup survey. Individuals who identify as LGBT in high school might have different experiences, personal and family characteristics than those who identify as LGBT in college or later in adulthood, thus resulting in different educational outcomes. Whether individuals who identify as LGBT earlier in life are more or less likely to have lower educational level is not clear ex-ante. Indeed, individuals who start to identify as LGBT in college may not experience the strong stigma often associated in high school with their sexual and gender identity, or they might have had the time to develop the skills to cope and confront such stigma and biases (Ueno, Roach, & PeñaTalamantes, 2013). The stress associated with the coming out or transition process might prevent LGBT high school students from focusing on their education. In addition, LGBT students might be marginalized while in high school. This process could lead them to become detached from their teachers and coursework (Poteat & Espelage, 2007; Rostosky, Owens, Zimmerman, & Ellen, 2003), and could reduce their sense of school belonging (Akerlof & Kranton, 2002). At the same time, these LGBT students may become resilient, develop their grittiness (Duckworth, Peterson, Matthews, & Kelly, 2007), and thrive once they move to a more welcoming environment such as a college campus. Furthermore, individuals who identify as LGBT only later in life might still suffer while in high school because of stigma, heteronormativity, implicit and explicit biases. Previous studies have found a positive association between educational attainment and the age at which individuals identify as LGBT (Barrett, Pollack, & Tilden, 2002; Pearson & Wilkinson, 2017), thus supporting the hypothesis that individuals who identify as LGBT earlier in life are more likely to struggle in school. The HSLS:09 sample includes individuals who started to identify as LGBT either in high school or in college. Since the first group seems to have been more penalized in terms of educational achievements, it is plausible that the educational gaps found in Tables 4 and 5 would have been even larger if the analysis had focused only on students who started to identify as LGBT in high school. It is possible that additional – highly educated – respondents might identify as LGBT later in life. These progressive variations in the LGBT sample might help explaining why LGBT students appear to underperform in school surveys such as the HSLS:09, while adults in same-sex couples have higher educational achievements in general datasets such as the American Community Survey. Alternatively, as discussed in the Introduction, there is evidence suggesting that low-educated adults were
2 An additional factor explaining the differences in educational levels among LGBT individuals reported in the HSLS:09 and in previous studies could be that most statistics on LGBT adults are computed from data on same-sex couples in the Census or the American Community Survey. Since college graduates are more likely to be married (Fry and Cohn, 2011), omitting single individuals might inflate the estimated average educational level of LGBT adults. Nevertheless, similar educational patterns have also been highlighted in a few studies analyzing (smaller) datasets including sexual minority individuals without a partner (Black et al., 2007; Carpenter and Gates, 2008).
6
Economics of Education Review 73 (2019) 101933
D. Sansone
6. Conclusions
families. Journal of Economic Perspectives, 21(2), 53–70. Carpenter, C. S. (2007). Revisiting the income penalty for behaviorally gay men: Evidence from NHANES III. Labour Economics, 14(1), 25–34. Carpenter, C. S. (2009). Sexual orientation and outcomes in college. Economics of Education Review, 28(6), 693–703. Carpenter, C. S., & Gates, G. J. (2008). Gay and lesbian partnership: Evidence from California. Demography, 45(3), 573–590. Coffman, K. B., Coffman, L. C., & Ericson, K. M. M. (2017). The Size of the LGBT population and the magnitude of antigay sentiment are substantially underestimated. Management Science, 63(10), 3168–3186. Drydakis, N. (2009). Sexual orientation discrimination in the labour market. Labour Economics, 16(4), 364–372. Duckworth, A. L., Peterson, C., Matthews, M. D., & Kelly, D. R. (2007). Grit: Perseverance and passion for long-term goals. Journal of Personality and Social Psychology, 92(6), 1087–1101. Duprey, M. A., Pratt, D. J., Jewell, D. M., Cominole, M. B., Fritch, L. B., Ritchie, E. A., et al. (2018). High school longitudinal study of 2009 (HSLS: 09) base-year to second follow-up, Vol. 2018-140 Washington, D.C. Flores, A. R., & Barclay, S. (2016). Backlash, consensus, legitimacy, or polarization: The effect of same-sex marriage policy on mass attitudes. Political Research Quarterly, 69(1), 43–56. Fry, R., & Cohn, D. (2011). Prevalence and growth of cohabitation. In P. Taylor, R. Fry, D. Cohn, W. Wang, G. Velasco, & D. Dockterman (Eds.). Living together: The economics of cohabitation (pp. 7–10). Washington, D.C: Pew Social & Demographic Trends. Geijtenbeek, L., & Plug, E. (2018). Is there a penalty for registered women? Is there a premium for registered men? Evidence from a sample of transsexual workers. European Economic Review, 109(October), 334–347. Genicot, G., & Ray, D. (2017). Aspirations and Inequality. Econometrica, 85(2), 489–519. Goldbach, J. T., Tanner-Smith, E. E., Bagwell, M., & Dunlap, S. (2014). Minority stress and substance use in sexual minority adolescents: A meta-analysis. Prevention Science, 15(3), 350–363. Heckman, J. J., Humphries, J. E., & Mader, N. S. (2011). The GED. In E. A. Hanushek, S. Machin, & L. Woessmann (Vol. Eds.), Handbooks in economics: Economics of education: 3, (pp. 423–484). North-Holland. Kenny, C., & Patel, D. (2017). Norms and reform: Legalizing homosexuality improves attitudes. CGD Working Paper465. Kreitzer, R. J., Hamilton, A. J., & Tolbert, C. J. (2014). Does policy adoption change opinions on minority rights? The effects of legalizing same-sex marriage. Political Research Quarterly, 67(4), 795–808. Murnane, R. J. (2013). U.S. high school graduation rates: Patterns and explanations. Journal of Economic Literature, 51(2), 370–422. Newport, F. (2018). In U.S., estimate of LGBT population rises to 4.5%. Gallup May(22). Oster, E. (2019). Unobservable selection and coefficient stability: Theory and evidence. Journal of Business and Economic Statistics, 37(2), 187–204. Papageorge, N. W., Gershenson, S., & Kang, K. (2019). Teacher expectations matter. Review of Economics and Statistics Accepted https://doi.org/10.1162/rest_a_00838. Pearson, J., & Wilkinson, L. (2017). Same-sex sexuality and educational attainment: The pathway to college. Journal of Homosexuality, 64(4), 538–576. Plug, E., & Berkhout, P. (2004). Effects of sexual preferences on earnings in the Netherlands. Journal of Population Economics, 17(1), 117–131. Poteat, P. V., & Espelage, D. L. (2007). Predicting psychosocial consequences of homophobic victimization in middle school students. Journal of Early Adolescence, 27(2), 175–191. Rostosky, S. S., Owens, G. P., Zimmerman, R. S., & Ellen, D. B. R. (2003). Associations among sexual attraction status, school belonging, and alcohol and marijuana use in rural high school students. Journal of Adolescence, 26(6), 741–751. Russell, S. T., Driscoll, A. K., & Truong, N. (2002). Adolescent same-sex romantic attractions and relationships: Implications for substance use and abuse. American Journal of Public Health, 92(2), 198–202. Sansone, D. (2018). Pink work: Same-sex marriage. Employment and Discrimination ArXivWorking Paper, 1807.06698. Tankard, M. E., & Paluck, E. L. (2017). The effect of a Supreme Court decision regarding gay marriage on social norms and personal attitudes. Psychological Science, 28(9), 1334–1344. Ueno, K., Roach, T. A., & Peña-Talamantes, A. E. (2013). The dynamic association between same-sex contact and educational attainment. Advances in Life Course Research, 18(2), 127–140.
The paper uses data from a recent cohort of U.S. students to show that non-heterosexual and trans individuals have lower educational outcomes both in high school and college. Individual, family, school and state characteristics cannot fully explain these differences. These results emphasize the vulnerability of LGBT students, and the need to take this sub-population into account when designing educational policies. In addition, given the pivotal role played by subjective expectations and aspirations (Beaman, Duflo, Pande, & Topalova, 2012; Genicot & Ray, 2017; Papageorge, Gershenson, & Kang, 2019), the fact that LGBT students are less likely to believe that they can graduate from high school and complete a Bachelor's degree is particularly worrisome. Similarly, the lower sense of school belonging and engagement is striking given the importance of school identity highlighted by Akerlof and Kranton (2002). Finally, there is ample evidence that sexual minorities experience discrimination from employers, consumers and coworkers (Aksoy, Carpenter, Frank, & Huffman, 2019; Carpenter, 2007; Drydakis, 2009; Plug & Berkhout, 2004). This paper provides suggestive evidence that discrimination might also limit educational opportunities for LGBT students. Future research is needed to conciliate the findings in this paper with the higher educational levels among adults in same-sex couples estimated in previous studies. Longitudinal datasets in which the sexual orientation and gender identity is recorded more than once during a respondent's lifetime would be particularly useful to understand the educational and work career trajectories of LGBT individuals, and the effect of identifying as LGBT earlier in life. In this context, the HSLS:09 follow-up data collection scheduled in 2025 will provide valuable information on the transition of LGBT individuals into adulthood. Declaration of Competing Interest None. Supplementary materials Supplementary material associated with this article can be found, in the online version, at doi:10.1016/j.econedurev.2019.101933. References Akerlof, G. A., & Kranton, R. E. (2002). Identity and schooling: Some lession from the economics of education. Journal of Economic Literature, 40(4), 1167–1201. Aksoy, C. G., Carpenter, C. S., Frank, J., & Huffman, M. L. (2019). Gay glass ceilings : Sexual orientation and workplace authority in the UK. Journal of Economic Behavior & Organization, 159(March), 167–180. Barrett, D. C., Pollack, L. M., & Tilden, M. L. (2002). Teenage sexual orientation, adult openness, and status attainment in gay males. Sociological Perspectives, 45(2), 163–182. Beaman, L., Duflo, E., Pande, R., & Topalova, P. (2012). Female leadership raises aspirations and educational attainment for girls: A policy experiment in India. Science Magazine, 335, 582–586. Black, D. A., Sanders, S. G., & Taylor, L. J. (2007). The economics of lesbian and gay
7