Abstract
This paper analyzes the Indonesian Family Life Survey to estimate the relationship between height and happiness in a developing country, Indonesia. This paper finds that tall men and women are happier than their short counterparts and that the magnitude of the relationship is large. More important, a parsimonious set of channels is identified to substantially explain the relationship between height and happiness: education and earnings for men, and education and relative position of earnings for women. However, for men but not for women, height still exhibits a non-negligible relationship with happiness even after controlling for an extensive array of covariates.
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Notes
The irrational choice in the first set of questions is the choice of Rp. 1 million today over Rp. 1 million in 1 year. A similar answer is considered irrational for the second set of questions.
Some health measures are restricted as follows to remove unreasonable values: 120 cm ≤ height ≤ 200 cm; 30 kg ≤ weight ≤ 150 kg; 10 kg/m2 ≤ BMI ≤ 45 kg/m2; 0.4L ≤ lung capacity ≤ 6L; 1 g/dL ≤ hemoglobin level ≤ 30 g/dL; 70 mmHg ≤ systolic ≤ 300 mmHg; 50 mmHg ≤ astolic ≤ 200 mmHg. Hypertension is defined by a systolic pressure equal to or greater than 140 mmHg or diastolic pressure equal to or greater than 90 mmHg.
Entering the number of symptoms of depression produces qualitatively the same results (not shown).
The marginal effects of lung capacity, hemoglobin level, and hypertension are not listed because they are neither statistically nor economically significant.
A small number of paid employees and self-employed workers reported very great earnings. For this paper, earnings over the 99th percentile value of earnings in the raw data for each type of workers (Rp. 5 million for paid employees and Rp. 9 million for self-employed workers) are replaced by the 99th percentile value of earning. Results are qualitatively the same without the adjustment or the observations.
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Acknowledgments
I am grateful to Antonella Fave, Stephanie Rossouw, and two anonymous referees for helpful comments.
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Appendices
Appendix A
1.1 Possibility of Using Instrumental Variables (IVs)
One way to tackle endogeneity is to use IVs. However, the literature on the height premium illustrates difficulty and possible hazards in instrumenting height. Schultz (2002) used four sets of instrumental variables (IVs) for Ghana, Brazil, and the US and argued that, depending on the IVs used, the height estimates were several times (possibly as much as 20 times) larger than the corresponding OLS estimates. The finding that IV estimates were greater than OLS estimates is not surprising. The surprising point is the size of the differences. The literature on the return to education has generated many plausible IVs, so it is worth comparing the differences between OLS and IV estimates in this literature. In Card’s (2001) survey, the effect of education on earnings with IVs is at most three times as great as that without IVs. Even a difference of three times is the exception rather than the rule. Schultz explained the large differences between the IV and OLS estimates by arguing that a large portion of height (at least 90 %, according to him) was determined by seemingly random genetic factors. He believed that the OLS estimates contained these substantial random effects, which artificially reduced the height premium.
However, the overidentifying restrictions are not testable in general (Parente and Santos Silva 2012), and using implausible IVs introduces an additional problem rather than addressing the original problem. Because of such difficulty and hazards, recent influential studies on the height premium regard height as a proxy for childhood conditions and do not attempt to instrument height (Persico et al. 2004; Case and Paxson 2008; Lundborg et al. 2014); the section on conceptual framework is consistent with this idea, and therefore, we do not employ IVs. In preliminary analyses, we considered instrumenting own height with parental height. However, parental height is provided in IFLS4 only for respondents who resided with their parents, and we suspected a sample selection bias for this subgroup. Thus, we did not use this IV.
1.2 Twin Fixed Effects
Böckerman and Vainiomäki (2013) used Finnish twin data in estimating the height premium to account for unobserved differences. Unfortunately, this option is not available to us. In addition, although they could remove unobserved differences possibly to a large extent, the question of why the twin pair exhibited a height difference in the first place remains. If the factors driving the height difference were related to the wage difference, their height premium would be biased. For the same reason, this concern cannot be addressed by instrumenting the height difference in 1975 with that in 1981, as they did. Even if these methodological issues are negligible, their estimates concern only twins not the general population; thus, the issue of generalizability remains.
1.3 Testing the Proportional Odds Assumption
One crucial assumption for an ordered probit is that β 1 and β 2 are the same for each value of y referred to as the proportional odds assumption. This assumption is testable. For illustration purposes, we perform this test with the covariates identical to those in Column 1 of Table 6 for men and in Column 1 of Table 7 for women, i.e., with the parsimonious set of covariates for each gender. Specifically, we take the assumption, which is equivalent to a standard ordered probit, and then relax the assumption for height; the first model is nested in the second model. Then, we run a likelihood ratio test. For both genders, the test fails to reject the null hypothesis that the assumption is violated.
If the null hypothesis were violated, one alternative could be a generalized ordered probit, which relaxes the assumption and makes the model more robust. However, Brant (1990) explained that the rejection of the null hypothesis could result from three sources: misspecification of the latent regression, heteroscedasticity of e, and misspecification of the distributional form for the latent variable. As a result, the rejection does not automatically favor a generalized ordered probit as an alternative. He warned that it might be best to view the alternative not as a scientifically meaningful model but as a directional alternative helpful in validating the standard model. Even if a generalized ordered probit were favored, among others, the issue of interpretation of results from the model remains. The model is not even ordinal, meaning that rearrangement of the categories would hardly affect the fit. Thus, the spirit of continuous y * is lost.
In addition, as the typical tradeoff between robustness and efficiency in econometrics suggests, the generalized model is less efficient than the standard model. If the less efficient model is used, β 1 and β 2 would lose statistical significance more often than if the more efficient model is used. Recall that one of our interests is to find whether a small set of covariates could substantially make β 1 and β 2 become small and possibly lose statistical significance and, if so, to identify such a set. If the model is less efficient, it is more likely that a small set of covariates emerge although it may not be the case if the more efficient model is used. It is more conservative to employ the more efficient model; if we identify such a small set even after using the more efficient model, this only reinforces our argument. As a result, the standard model is used in this paper. Furthermore, this model facilitates comparisons with other studies, which typically use the standard model probably for the above concerns.
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Sohn, K. Height and Happiness in a Developing Country. J Happiness Stud 17, 1–23 (2016). https://doi.org/10.1007/s10902-014-9566-8
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DOI: https://doi.org/10.1007/s10902-014-9566-8