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The association between adolescent height and student school satisfaction: Recent evidence from Catalonia
Economics and Human Biology 11 (2013) 197–200
Contents lists available at SciVerse ScienceDirect
Economics and Human Biology journal homepage: http://www.elsevier.com/locate/ehb
The association between adolescent height and student school satisfaction: Recent evidence from Catalonia Toni Mora * Universitat Internacional de Catalunya & IEB, Spain
A R T I C L E I N F O
A B S T R A C T
Article history: Received 24 February 2011 Received in revised form 8 March 2012 Accepted 8 March 2012 Available online 27 September 2012
We examine the relationship between high-school students’ height and their self-reported school satisfaction. This relationship is explored on the basis of a survey conducted in 2008 among some 2200 Catalan (Spanish) students. We find a negative association between height and school satisfaction: an association apparently correlated with the students’ maturity, which in turn influences the degree of their disenchantment with the educational system. A 10 cm increase in height decreased the probability of falling into the ‘‘very satisfied’’ category by 9.8%. ß 2012 Elsevier B.V. All rights reserved.
Keywords: Height School satisfaction Adolescence
1. Introduction Three benefits can be derived from the satisfaction expressed by high-school students with their school: first, a high degree of satisfaction is closely related to academic achievement (Guay et al., 2003), since satisfied students are less likely to drop out and likely to achieve higher marks than are dissatisfied ones; second, there is a direct correlation between such satisfaction and the adoption of healthy behavior patterns and enhanced quality of life (Samdal et al., 1998); and third, happiness in childhood leads, as a rule, to economic and emotional well-being and other positive outcomes in adult life (Heckman et al., 2006). Moreover, there is considerable evidence that height is positively correlated with an individual’s general sense of live satisfaction (Deaton and Arora, 2009) and that it offers a range of personal advantages (Herpin, 2005). In order to chart the route to these benefits as it traverses
* Correspondence address: School of Economics and Social Sciences, Universitat Internacional de Catalunya, Immaculada, 22, 08017 Barcelona, Spain. Tel.: +34 932541800x4511; fax: +34 932541850. E-mail address: [email protected]. 1570-677X/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ehb.2012.03.006
adolescence, we examine the issue of whether anthropometric measures are associated with self-reported school satisfaction among high-school students. The physical appearance of adolescents undergoes constant changes, which inevitably affect their behavior, thus also their satisfaction with their school. A priori, height is expected to have two mutually opposed effects on high-school satisfaction. One of them is positive, a consequence of the fact that those students who express the greatest satisfaction with their body image also score more positively in general and that students of above-average height are less likely than others to be the butt of bullying or other forms of discrimination on the part of their classmates. Indeed, physical appearance during an individual’s high-school years has been shown to be a determinant of who becomes a leader as an adult (Dhuey and Lipscomb, 2008), and has been found to correlate negatively with the presentation of relatively few symptoms of depression (Rees et al., 2009). The other expected effect is negative; since status of maturity, pubertal timing and height are very closely related, it might be the case that early maturers find the constraints of schooling disturbing and that could have a negative influence on their school satisfaction.
198
T. Mora / Economics and Human Biology 11 (2013) 197–200
We classify high-school students as pseudomature, mature, or immature. Pseudomature students are taller than those in the other two categories (Galambos and Tilton-Weaver, 2000). The onset of puberty affects dating and lifestyle behavior (Sunder, 2006), and since pseudomature students tend to adopt adult behaviors, they also tend to feel disappointed or impatient with the schooling process.
state of mind were considerably less significant (0.15), a finding that suggests that our measure is much more closely related to feelings regarding school than it is to students’ self-esteem. Nevertheless, we performed several sensitivity analyses throughout the remainder of the empirical analysis. In order to accommodate ordinality in the econometric analysis, satisfaction domains were regressed with an Ordered Logit model.
2. Data and econometric methodology
Si ¼ a þ z0i d þ ei Pr½Si ¼ j ¼ Fða j z0i dÞ Fða j1 z0i dÞ
The data for this analysis are drawn from a sample of secondary-school students in Catalonia, a region with one of the highest GDPs per capita in Spain. The sampling took place between February and June 2008. Secondary-school mathematics teachers in Catalonia were invited to participate in the survey and to help with data collection. We restricted the sample to two specific age cohorts, both of them consisting of students who were enrolled in compulsory secondary-education programs and who were under the age of 17 (that is, they had not repeated grades). This restriction enabled us to consider individual satisfaction scores while using very close reference levels and thereby to avoid the disenchantment effect attributable to an increase in chronological age, rather than to maturity. The final sample contained information on more than 2200 students at 70 high schools. The questionnaire was supplied either on-line or in paper format.1 It consisted of six blocks of questions: personal data (including selfreported anthropometric data); educational characteristics; the teaching process; parental background information; conscientiousness and motivation questions; and lifestyle conditions. Math is compulsory for all students in the school years under study here.2 The students’ responses regarding school satisfaction3 indicated that 14.7% were dissatisfied with their school (6.3% very dissatisfied and 8.4% dissatisfied) and 44.7% were satisfied, while 10.9% described themselves as very satisfied. The remaining students reported being neither dissatisfied nor satisfied (44.7%). It might be argued that this measure partly captures life satisfaction rather than simply proxying satisfaction with the school, because personality traits jointly determine both, and because of the importance of school in overall life satisfaction. For this reason we computed correlations between our measure and other dimensions of satisfaction and found a close correlation (0.45) between student school satisfaction and both teacher evaluation and family satisfaction scores. However, correlations with students’ health status and
1 No substantive differences between the two kinds of data collection were observed. 2 Those who were absent on the day in question were considered nonresponders. The fact that none of the students had access to the questionnaire prior to responding enabled us to avoid any attrition effects on account of disqualification, as well, but students had the right not to respond to all of the questions. 3 It should be noted that the students were asked to rate their degree of satisfaction not only with their school but also with specific aspects of their school experience, such as classroom climate and classmates’ effort.
(1)
where Si, i = 1, . . ., N, denote the dependent ordered variable, zi is the k-vector of explanatory variables, d is a kvector of unknown parameters and aj corresponds to the threshold parameters and ei represents the independently distributed random error term being logistically distributed. F refers to the cumulative distribution function of ei. The variable ranges from 1 (completely unsatisfied) to 5 (completely satisfied). The results presented in the paper are obtained by computing robust clustered standard errors at the classroom level. Finally, we tested for the presence of non-linearities in the relationship with height: that is, the model explored whether, once a threshold value for height has been achieved, no further changes in school satisfaction are obtained. However, this phenomenon was not corroborated in our regression. Students’ satisfaction with school may be regarded as being also dependent on personal characteristics such as sociability and social features of family, peer and school environments (Samdal et al., 1998). Hence, as controls, we used a long list of covariates (zk,i) including adolescent characteristics (age, gender and immigrant status), family characteristics (family type, difference in years between the mother and the adolescent, mother’s education, change of residence and type of school and parental health status), and we accounted for social class (percentage of mothers with university education; percentage of female students and percentage of immigrants, class size and having the same peers as during the last academic year). Throughout the empirical analysis, we controlled for the fixed effects of neighborhood residence and high-school location. Finally, we controlled for demographic differences such as immigrant status. Table 1 summarizes the covariates. We included personality traits as a proxy for individual traits in the sense of ‘‘skill at school’’ rather than that of a specific ‘‘knowledge ability,’’ which would be more closely related to high-school grades. Panel data can be controlled for by including individual effects, but since the present sample is a cross-section, we need to control for students’ psychological traits. In this regard, Boyce (2010) has argued that in a panel-data approach personality accounts for greater individual heterogeneity than do fixed effects. We therefore treated all conscientiousness items as a single factor. To measure variability in the resulting dimension, we quantified Cronbach’s alpha reliability (0.74), which confirmed that the ratio was satisfactory. The Kaiser–Meyer–Olkin measure of sampling adequacy indicated that multivariate analysis provides excellent results (the factor accounted for 95% of the overall
T. Mora / Economics and Human Biology 11 (2013) 197–200 Table 1 Sample descriptives.
199
3. Empirical results
Student’s height (in meters) Student’s weight (in kilograms) Conscientiousness factor Age Last compulsory academic year Being female Being immigrant Parents’ marital status (not married) Age differential with mother (years) Changed residence within last 3 years Father’s poor health Mother’s poor health Mother – education unknown Mother – up to primary education Mother – higher education Private and half-private school Share of female classmates
1.68 58.84 0.60 15.48 0.54 0.52 0.09 0.22 28.61 0.14 0.15 0.13 0.14 0.11 0.30 0.56 0.52
(0.09) (10.54) (0.13) (0.65) (0.50) (0.50) (0.28) (0.41) (4.61) (0.35) (0.36) (0.34) (0.34) (0.32) (0.46) (0.50) (0.14)
3.46 3.83 4.18 4.37 3.82
(1.01) (0.98) (0.93) (0.70) (0.89)
Satisfaction dimensions With their school Classroom climate satisfaction Household environment Self-reported health degree Student’s state of mind
Our findings regarding the association between height and students’ school satisfaction scores are displayed in Table 2. School fixed effects are controlled for in columns 2–5. Fixed effects altered the estimates once this type of heterogeneity was taken into consideration. The conscientiousness covariate when school fixed effects is added is accounted for in column 3 but not in column 2. Here, however, no differences in the results were observed, although a slight improvement was recorded in the explanatory power when the factor was included. In column 4 we display the sensitivity of our results to the inclusion of average anthropometric measures at the classroom level, thereby accounting for unobserved heterogeneity at this aggregate level. Although the association with weight is stable across all regressions, the height effect follows a smooth downward trend. These estimates lead us to conclude that – when one has controlled for self-reported weight, personality traits, average classroom anthropometric characteristics and school fixed effects – height is negatively correlated with school satisfaction in this sample. Specifically, for a unit change in height (measured in meters), with all other variables constant, the odds are around 0.4. Given that this explanation is quite difficult to understand, we computed other coefficients. In terms of elasticities, the estimated coefficient was 0.63 for the ‘‘satisfied’’ group and 1.65 for the ‘‘very satisfied’’ one. That is, a 10 cm increase in
Average values and standard deviations in parentheses.
variability). Subsequently, we re-scaled the factor predictions to [0–1], since individuals’ opinions regarding themselves should not have a negative value, while 1 represents complete self-confidence.
Table 2 Determinants of student’s school satisfaction: odd-ratios coefficients. (2)
(1) Student’s height Student’s BMI
**
0.34 1.03*
(3) ***
0.40 1.03*
(4) ***
0.46 1.03**
Average classroom height Average classroom BMI
0.45 1.03**
0.36*** 1.03*
1.21 1.06
Conscientiousness factor
12.24***
Age Compulsory academic year Being female Being immigrant Parents not married Age differential with mother Changed residence Father’s poor health Mother’s poor health Mother – education unknown Mother – up to primary education Mother – higher education Private and half-private school Share of female classmates
1.02 0.97 1.45*** 1.76*** 1.00 1.02** 0.74*** 0.86 0.67*** 0.95 0.90 0.95 1.05 0.39***
School fixed effects N Pseudo R2 Likelihood-ratio proportionality
(5) **
14.43***
14.44***
8.47***
0.91 0.94 1.57*** 1.70*** 0.96 1.02* 0.74** 0.80* 0.58*** 0.92 0.87 1.01 0.54* 0.29**
0.96 0.87 1.53*** 1.71*** 0.98 1.02* 0.76** 0.84 0.60*** 0.97 0.89 0.96 0.54* 0.28**
0.96 0.86 1.52*** 1.69*** 0.98 1.02* 0.76** 0.84 0.60*** 0.97 0.89 0.96 0.50* 0.29**
0.99
No
Yes
Yes
Yes
FEclass
2239 0.026
2239 0.048
2239 0.059
2239 0.059
2203 0.0465
1.59*** 1.65*** 0.87 1.02* 0.83 0.83 0.67*** 1.00 0.94 0.96
65.84
Note: We obtained very similar results when including school location fixed effects and residence fixed effects. Adjusted robust standard errors clustered at the classroom level were computed. Conditional Logit (column 5) model considers classroom fixed effects instead of school fixed effects. * Significance at 10% level. ** Significance at 5% level. *** Significance at 1% level.
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T. Mora / Economics and Human Biology 11 (2013) 197–200
height (around 6%) decreased the probability of falling into the ‘‘satisfied’’ category by 3.8% and into the ‘‘very satisfied’’ category by 9.8%. All of these coefficients indicate a statistically significant correlation with height. The results hold even when we include categorical variables denoting physical characteristics in terms of BMI (underweight, overweight and obese relative to the base category of normal weight). None of these dummy variables representing physical appearance proved to be statistically significant. Next, we tested the parallel regression assumption,4 and found that it was not rejected (x2 = 67.17; pvalue = 0.17). However, because the inclusion of neighborhood and school fixed effects – that is, the treatment of data as a panel set – may bias these results, we decided to run a conditional logit model grouping by classroom (column 5). Once again we found that the levels of students’ school satisfaction were negatively correlated with height.5 4. Conclusion We investigated, by means of a survey of 2200 highschool students, the correlation between adolescent height and the degree of school satisfaction. Students differ in terms of physical appearance and therefore in terms of acceptance levels as well. We found a negative correlation between adolescent height and school satisfaction.6 A 10 cm increase in height decreased the probability of falling into the ‘‘satisfied’’ category by 3.8% and into the ‘‘very satisfied’’ category by 9.8%. We infer that this is a consequence of the close relationship between stage of maturity, pubertal timing and height. Given the high dropout rates in Catalan high schools,
4 The assumption of proportional odds in the Ordered Logit corresponds to the idea of parallel regressions for each outcome. 5 Our results were robust to the use of alternative econometric procedures (generalized Ordered Logit, SURE, Frontier and structural models) and accounted for the students’ other satisfaction scores. 6 However, although we have accounted for conscientiousness (which constitutes what might be seen as an individual fixed effect), school fixed effects and average classroom features, unobservable factors may bias our results. To examine the robustness of the results, we ran estimations accounting for several dissimilar satisfaction scores (including household environment and state of mind), which attested to the reliability of students’ answers to the questionnaire. Likewise, our results were robust irrespective of the alternative econometric procedures used.
height data could be useful to school organizations as a means of anticipating which of their students intend to drop out. Acknowledgements The author gratefully acknowledges the financial support of 09SGR102 (Generalitat of Catalonia) and thanks Ada Ferrer-i-Carbonell and the editor John Komlos for their helpful comments. The usual disclaimer applies. References Boyce, C.J., 2010. Understanding fixed effects in human well-being. Journal of Economic Psychology 31 (1), 1–16. Deaton, A., Arora, R., 2009. Life at the top: the benefits of height. Economics and Human Biology 7 (2), 133–136. Dhuey, E., Lipscomb, S., 2008. What makes a leader? Relative age and high school leadership. Economics of Education Review 27, 173–183. Galambos, N.L., Tilton-Weaver, L.C., 2000. Adolescents’ psychosocial maturity, problem behaviour, and subjective age: in search of the adultoid. Applied Developmental Science 4 (4), 178–192. Guay, F., Marsh, H., Boivin, M., 2003. Academic self-concept and academic achievement: developmental perspectives on their causal ordering. Journal of Educational Psychology 95 (1), 124–136. Heckman, J.J., Stixrud, J., Urzua, S., 2006. The effects of cognitive and noncognitive abilities on labour market outcomes and social behavior. Journal of Labor Economics 24 (3), 411–480. Herpin, N., 2005. Love, careers, and heights in France, 2001. Economics and Human Biology 3 (3), 420–449. Rees, D.I., Sabia, J.J., Argys, L.M., 2009. A head above the rest: Height and adolescent psychological well-being. Economics and Human Biology 7 (2), 217–228. Samdal, O., Nutbeam, D., Wold, B., Kannas, L., 1998. Achieving health and educational goals through schools – a study of the importance of the school climate and the students’ satisfaction with school. Health Education Research, Theory & Practice 13 (3), 383–397. Sunder, M., 2006. Physical stature and intelligence as predictors of the timing of baby Boomer’s very first dates. Journal of Biosocial Science 38, 821–833.
Contents lists available at SciVerse ScienceDirect
Economics and Human Biology journal homepage: http://www.elsevier.com/locate/ehb
The association between adolescent height and student school satisfaction: Recent evidence from Catalonia Toni Mora * Universitat Internacional de Catalunya & IEB, Spain
A R T I C L E I N F O
A B S T R A C T
Article history: Received 24 February 2011 Received in revised form 8 March 2012 Accepted 8 March 2012 Available online 27 September 2012
We examine the relationship between high-school students’ height and their self-reported school satisfaction. This relationship is explored on the basis of a survey conducted in 2008 among some 2200 Catalan (Spanish) students. We find a negative association between height and school satisfaction: an association apparently correlated with the students’ maturity, which in turn influences the degree of their disenchantment with the educational system. A 10 cm increase in height decreased the probability of falling into the ‘‘very satisfied’’ category by 9.8%. ß 2012 Elsevier B.V. All rights reserved.
Keywords: Height School satisfaction Adolescence
1. Introduction Three benefits can be derived from the satisfaction expressed by high-school students with their school: first, a high degree of satisfaction is closely related to academic achievement (Guay et al., 2003), since satisfied students are less likely to drop out and likely to achieve higher marks than are dissatisfied ones; second, there is a direct correlation between such satisfaction and the adoption of healthy behavior patterns and enhanced quality of life (Samdal et al., 1998); and third, happiness in childhood leads, as a rule, to economic and emotional well-being and other positive outcomes in adult life (Heckman et al., 2006). Moreover, there is considerable evidence that height is positively correlated with an individual’s general sense of live satisfaction (Deaton and Arora, 2009) and that it offers a range of personal advantages (Herpin, 2005). In order to chart the route to these benefits as it traverses
* Correspondence address: School of Economics and Social Sciences, Universitat Internacional de Catalunya, Immaculada, 22, 08017 Barcelona, Spain. Tel.: +34 932541800x4511; fax: +34 932541850. E-mail address: [email protected]. 1570-677X/$ – see front matter ß 2012 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.ehb.2012.03.006
adolescence, we examine the issue of whether anthropometric measures are associated with self-reported school satisfaction among high-school students. The physical appearance of adolescents undergoes constant changes, which inevitably affect their behavior, thus also their satisfaction with their school. A priori, height is expected to have two mutually opposed effects on high-school satisfaction. One of them is positive, a consequence of the fact that those students who express the greatest satisfaction with their body image also score more positively in general and that students of above-average height are less likely than others to be the butt of bullying or other forms of discrimination on the part of their classmates. Indeed, physical appearance during an individual’s high-school years has been shown to be a determinant of who becomes a leader as an adult (Dhuey and Lipscomb, 2008), and has been found to correlate negatively with the presentation of relatively few symptoms of depression (Rees et al., 2009). The other expected effect is negative; since status of maturity, pubertal timing and height are very closely related, it might be the case that early maturers find the constraints of schooling disturbing and that could have a negative influence on their school satisfaction.
198
T. Mora / Economics and Human Biology 11 (2013) 197–200
We classify high-school students as pseudomature, mature, or immature. Pseudomature students are taller than those in the other two categories (Galambos and Tilton-Weaver, 2000). The onset of puberty affects dating and lifestyle behavior (Sunder, 2006), and since pseudomature students tend to adopt adult behaviors, they also tend to feel disappointed or impatient with the schooling process.
state of mind were considerably less significant (0.15), a finding that suggests that our measure is much more closely related to feelings regarding school than it is to students’ self-esteem. Nevertheless, we performed several sensitivity analyses throughout the remainder of the empirical analysis. In order to accommodate ordinality in the econometric analysis, satisfaction domains were regressed with an Ordered Logit model.
2. Data and econometric methodology
Si ¼ a þ z0i d þ ei Pr½Si ¼ j ¼ Fða j z0i dÞ Fða j1 z0i dÞ
The data for this analysis are drawn from a sample of secondary-school students in Catalonia, a region with one of the highest GDPs per capita in Spain. The sampling took place between February and June 2008. Secondary-school mathematics teachers in Catalonia were invited to participate in the survey and to help with data collection. We restricted the sample to two specific age cohorts, both of them consisting of students who were enrolled in compulsory secondary-education programs and who were under the age of 17 (that is, they had not repeated grades). This restriction enabled us to consider individual satisfaction scores while using very close reference levels and thereby to avoid the disenchantment effect attributable to an increase in chronological age, rather than to maturity. The final sample contained information on more than 2200 students at 70 high schools. The questionnaire was supplied either on-line or in paper format.1 It consisted of six blocks of questions: personal data (including selfreported anthropometric data); educational characteristics; the teaching process; parental background information; conscientiousness and motivation questions; and lifestyle conditions. Math is compulsory for all students in the school years under study here.2 The students’ responses regarding school satisfaction3 indicated that 14.7% were dissatisfied with their school (6.3% very dissatisfied and 8.4% dissatisfied) and 44.7% were satisfied, while 10.9% described themselves as very satisfied. The remaining students reported being neither dissatisfied nor satisfied (44.7%). It might be argued that this measure partly captures life satisfaction rather than simply proxying satisfaction with the school, because personality traits jointly determine both, and because of the importance of school in overall life satisfaction. For this reason we computed correlations between our measure and other dimensions of satisfaction and found a close correlation (0.45) between student school satisfaction and both teacher evaluation and family satisfaction scores. However, correlations with students’ health status and
1 No substantive differences between the two kinds of data collection were observed. 2 Those who were absent on the day in question were considered nonresponders. The fact that none of the students had access to the questionnaire prior to responding enabled us to avoid any attrition effects on account of disqualification, as well, but students had the right not to respond to all of the questions. 3 It should be noted that the students were asked to rate their degree of satisfaction not only with their school but also with specific aspects of their school experience, such as classroom climate and classmates’ effort.
(1)
where Si, i = 1, . . ., N, denote the dependent ordered variable, zi is the k-vector of explanatory variables, d is a kvector of unknown parameters and aj corresponds to the threshold parameters and ei represents the independently distributed random error term being logistically distributed. F refers to the cumulative distribution function of ei. The variable ranges from 1 (completely unsatisfied) to 5 (completely satisfied). The results presented in the paper are obtained by computing robust clustered standard errors at the classroom level. Finally, we tested for the presence of non-linearities in the relationship with height: that is, the model explored whether, once a threshold value for height has been achieved, no further changes in school satisfaction are obtained. However, this phenomenon was not corroborated in our regression. Students’ satisfaction with school may be regarded as being also dependent on personal characteristics such as sociability and social features of family, peer and school environments (Samdal et al., 1998). Hence, as controls, we used a long list of covariates (zk,i) including adolescent characteristics (age, gender and immigrant status), family characteristics (family type, difference in years between the mother and the adolescent, mother’s education, change of residence and type of school and parental health status), and we accounted for social class (percentage of mothers with university education; percentage of female students and percentage of immigrants, class size and having the same peers as during the last academic year). Throughout the empirical analysis, we controlled for the fixed effects of neighborhood residence and high-school location. Finally, we controlled for demographic differences such as immigrant status. Table 1 summarizes the covariates. We included personality traits as a proxy for individual traits in the sense of ‘‘skill at school’’ rather than that of a specific ‘‘knowledge ability,’’ which would be more closely related to high-school grades. Panel data can be controlled for by including individual effects, but since the present sample is a cross-section, we need to control for students’ psychological traits. In this regard, Boyce (2010) has argued that in a panel-data approach personality accounts for greater individual heterogeneity than do fixed effects. We therefore treated all conscientiousness items as a single factor. To measure variability in the resulting dimension, we quantified Cronbach’s alpha reliability (0.74), which confirmed that the ratio was satisfactory. The Kaiser–Meyer–Olkin measure of sampling adequacy indicated that multivariate analysis provides excellent results (the factor accounted for 95% of the overall
T. Mora / Economics and Human Biology 11 (2013) 197–200 Table 1 Sample descriptives.
199
3. Empirical results
Student’s height (in meters) Student’s weight (in kilograms) Conscientiousness factor Age Last compulsory academic year Being female Being immigrant Parents’ marital status (not married) Age differential with mother (years) Changed residence within last 3 years Father’s poor health Mother’s poor health Mother – education unknown Mother – up to primary education Mother – higher education Private and half-private school Share of female classmates
1.68 58.84 0.60 15.48 0.54 0.52 0.09 0.22 28.61 0.14 0.15 0.13 0.14 0.11 0.30 0.56 0.52
(0.09) (10.54) (0.13) (0.65) (0.50) (0.50) (0.28) (0.41) (4.61) (0.35) (0.36) (0.34) (0.34) (0.32) (0.46) (0.50) (0.14)
3.46 3.83 4.18 4.37 3.82
(1.01) (0.98) (0.93) (0.70) (0.89)
Satisfaction dimensions With their school Classroom climate satisfaction Household environment Self-reported health degree Student’s state of mind
Our findings regarding the association between height and students’ school satisfaction scores are displayed in Table 2. School fixed effects are controlled for in columns 2–5. Fixed effects altered the estimates once this type of heterogeneity was taken into consideration. The conscientiousness covariate when school fixed effects is added is accounted for in column 3 but not in column 2. Here, however, no differences in the results were observed, although a slight improvement was recorded in the explanatory power when the factor was included. In column 4 we display the sensitivity of our results to the inclusion of average anthropometric measures at the classroom level, thereby accounting for unobserved heterogeneity at this aggregate level. Although the association with weight is stable across all regressions, the height effect follows a smooth downward trend. These estimates lead us to conclude that – when one has controlled for self-reported weight, personality traits, average classroom anthropometric characteristics and school fixed effects – height is negatively correlated with school satisfaction in this sample. Specifically, for a unit change in height (measured in meters), with all other variables constant, the odds are around 0.4. Given that this explanation is quite difficult to understand, we computed other coefficients. In terms of elasticities, the estimated coefficient was 0.63 for the ‘‘satisfied’’ group and 1.65 for the ‘‘very satisfied’’ one. That is, a 10 cm increase in
Average values and standard deviations in parentheses.
variability). Subsequently, we re-scaled the factor predictions to [0–1], since individuals’ opinions regarding themselves should not have a negative value, while 1 represents complete self-confidence.
Table 2 Determinants of student’s school satisfaction: odd-ratios coefficients. (2)
(1) Student’s height Student’s BMI
**
0.34 1.03*
(3) ***
0.40 1.03*
(4) ***
0.46 1.03**
Average classroom height Average classroom BMI
0.45 1.03**
0.36*** 1.03*
1.21 1.06
Conscientiousness factor
12.24***
Age Compulsory academic year Being female Being immigrant Parents not married Age differential with mother Changed residence Father’s poor health Mother’s poor health Mother – education unknown Mother – up to primary education Mother – higher education Private and half-private school Share of female classmates
1.02 0.97 1.45*** 1.76*** 1.00 1.02** 0.74*** 0.86 0.67*** 0.95 0.90 0.95 1.05 0.39***
School fixed effects N Pseudo R2 Likelihood-ratio proportionality
(5) **
14.43***
14.44***
8.47***
0.91 0.94 1.57*** 1.70*** 0.96 1.02* 0.74** 0.80* 0.58*** 0.92 0.87 1.01 0.54* 0.29**
0.96 0.87 1.53*** 1.71*** 0.98 1.02* 0.76** 0.84 0.60*** 0.97 0.89 0.96 0.54* 0.28**
0.96 0.86 1.52*** 1.69*** 0.98 1.02* 0.76** 0.84 0.60*** 0.97 0.89 0.96 0.50* 0.29**
0.99
No
Yes
Yes
Yes
FEclass
2239 0.026
2239 0.048
2239 0.059
2239 0.059
2203 0.0465
1.59*** 1.65*** 0.87 1.02* 0.83 0.83 0.67*** 1.00 0.94 0.96
65.84
Note: We obtained very similar results when including school location fixed effects and residence fixed effects. Adjusted robust standard errors clustered at the classroom level were computed. Conditional Logit (column 5) model considers classroom fixed effects instead of school fixed effects. * Significance at 10% level. ** Significance at 5% level. *** Significance at 1% level.
200
T. Mora / Economics and Human Biology 11 (2013) 197–200
height (around 6%) decreased the probability of falling into the ‘‘satisfied’’ category by 3.8% and into the ‘‘very satisfied’’ category by 9.8%. All of these coefficients indicate a statistically significant correlation with height. The results hold even when we include categorical variables denoting physical characteristics in terms of BMI (underweight, overweight and obese relative to the base category of normal weight). None of these dummy variables representing physical appearance proved to be statistically significant. Next, we tested the parallel regression assumption,4 and found that it was not rejected (x2 = 67.17; pvalue = 0.17). However, because the inclusion of neighborhood and school fixed effects – that is, the treatment of data as a panel set – may bias these results, we decided to run a conditional logit model grouping by classroom (column 5). Once again we found that the levels of students’ school satisfaction were negatively correlated with height.5 4. Conclusion We investigated, by means of a survey of 2200 highschool students, the correlation between adolescent height and the degree of school satisfaction. Students differ in terms of physical appearance and therefore in terms of acceptance levels as well. We found a negative correlation between adolescent height and school satisfaction.6 A 10 cm increase in height decreased the probability of falling into the ‘‘satisfied’’ category by 3.8% and into the ‘‘very satisfied’’ category by 9.8%. We infer that this is a consequence of the close relationship between stage of maturity, pubertal timing and height. Given the high dropout rates in Catalan high schools,
4 The assumption of proportional odds in the Ordered Logit corresponds to the idea of parallel regressions for each outcome. 5 Our results were robust to the use of alternative econometric procedures (generalized Ordered Logit, SURE, Frontier and structural models) and accounted for the students’ other satisfaction scores. 6 However, although we have accounted for conscientiousness (which constitutes what might be seen as an individual fixed effect), school fixed effects and average classroom features, unobservable factors may bias our results. To examine the robustness of the results, we ran estimations accounting for several dissimilar satisfaction scores (including household environment and state of mind), which attested to the reliability of students’ answers to the questionnaire. Likewise, our results were robust irrespective of the alternative econometric procedures used.
height data could be useful to school organizations as a means of anticipating which of their students intend to drop out. Acknowledgements The author gratefully acknowledges the financial support of 09SGR102 (Generalitat of Catalonia) and thanks Ada Ferrer-i-Carbonell and the editor John Komlos for their helpful comments. The usual disclaimer applies. References Boyce, C.J., 2010. Understanding fixed effects in human well-being. Journal of Economic Psychology 31 (1), 1–16. Deaton, A., Arora, R., 2009. Life at the top: the benefits of height. Economics and Human Biology 7 (2), 133–136. Dhuey, E., Lipscomb, S., 2008. What makes a leader? Relative age and high school leadership. Economics of Education Review 27, 173–183. Galambos, N.L., Tilton-Weaver, L.C., 2000. Adolescents’ psychosocial maturity, problem behaviour, and subjective age: in search of the adultoid. Applied Developmental Science 4 (4), 178–192. Guay, F., Marsh, H., Boivin, M., 2003. Academic self-concept and academic achievement: developmental perspectives on their causal ordering. Journal of Educational Psychology 95 (1), 124–136. Heckman, J.J., Stixrud, J., Urzua, S., 2006. The effects of cognitive and noncognitive abilities on labour market outcomes and social behavior. Journal of Labor Economics 24 (3), 411–480. Herpin, N., 2005. Love, careers, and heights in France, 2001. Economics and Human Biology 3 (3), 420–449. Rees, D.I., Sabia, J.J., Argys, L.M., 2009. A head above the rest: Height and adolescent psychological well-being. Economics and Human Biology 7 (2), 217–228. Samdal, O., Nutbeam, D., Wold, B., Kannas, L., 1998. Achieving health and educational goals through schools – a study of the importance of the school climate and the students’ satisfaction with school. Health Education Research, Theory & Practice 13 (3), 383–397. Sunder, M., 2006. Physical stature and intelligence as predictors of the timing of baby Boomer’s very first dates. Journal of Biosocial Science 38, 821–833.