In Hong Kong, parental preference for babies born in the Chinese Zodiac dragon year causes spikes in fertility. The larger number of “dragon babies” born in 1988 resulted in a schooling cohort which was 5% larger on average every school-year. Using an innovative identification strategy that avoids selection bias, I find that dragon cohort students increase their time spent studying math by an average of 0.26 hours per week (a 9% increase relative to the mean). These effort responses are strongest for girls and for students whose parents do not have post-secondary education. Being in the dragon cohort also results in higher math scores. These empirical findings are consistent with competitive behavior changes of dragon cohort members responding to the presence of additional students. I cannot, however, rule out other possible mechanisms, such as cooperation, peer quality, and educational investments, acting in conjunction to improve academic outcomes. This paper is the first to document the test score and effort impacts of such zodiac cohorts; its findings highlight the importance of cultural forces in determining population changes, and their potential to influence education and other societal outcomes.
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A similar double cohort occurred in Hong Kong in 2012 due to its transition from a 3- to 4-year university program. The timing of this change does not affect this paper’s analysis, which uses data collected prior.
Basten and Verropoulou (2013) argue that this increase was the result of families avoiding the restrictive fertility policies in mainland China, as well as the relaxation of travel restrictions for mainland Chinese into Hong Kong around that time. The subsequent drastic decrease in births after 2012 can be attributed to a new ban on mainland-Chinese mothers giving birth in Hong Kong hospitals instituted that year.
They do not want to gamble with the astrological fate of their child.
I use the measure “Total outbound internationally mobile tertiary students studying abroad, all countries, both sexes” (UIS.OE.56.40510) in the World Bank’s Education Statistics series. This is defined as the number of “students who have crossed a national or territorial border for the purpose of education and are now enrolled outside their country of origin” for tertiary education.
For a survey, see Konrad (2009).
In the corresponding grade 9 PISA 2006 non-dragon cohort comparison group, students born outside of these two months are similarly excluded to make the cells of the difference-in-differences analysis symmetric. I include students born in February because only birth month, and not exact birth date, is observed. I argue this is reasonable since a majority of days in February 1988 were non-dragon baby birth-dates, and the inherently different parents who are intentionally targeting their childbirths seem to “play it safe” by not aiming for a date close to the lunar new year cutoff, as Fig. 2 suggests. In Appendix A, I conduct robustness checks that exclude February-born students; the estimates from these checks are very similar.
Occupational status is based on the International Socioeconomic Index (ISEI). Education levels are categorized using definitions in the International Standard Classification of Education (ISCED). The number of books variable is a categorical variable with the following categories: 0–10 books, 11–25 books, 26–100 books, 101–200 books, 201–500 books, and more than 500 books.
The categories were no time, less than 2 h a week, 2 or more but less than 4 h a week, 4 or more but less than 6 h a week, and 6 or more hours a week.
This procedure is described in Appendix C. There, I also repeat the regression analyses in this section using an interval regression procedure which accounts for the category nature of the unmapped outcome variable; the results are robust to the procedure used.
This is because grade 10 in 2003 is the untreated non-dragon cohort comparison group. The fact that the normalization is carried out separately across PISA rounds does not matter because year fixed effects are included in the regressions. The means and standard deviations calculated in Table 2 are not exactly zero and one respectively in the grade 10 columns for two reasons. First, the normalization procedure makes use of weights provided by the OECD, while the summary statistics are unweighted. Second, the normalization is done over all observations with non-missing test scores, whereas the summary statistics are calculated over the sample of analysis, which exclude observations with missing covariates. For the normalization procedure, plausible value raw scores reported by PISA are averaged across each student observation.
The PISA recruits students born within a specific 1-year period at the time of the survey. In PISA 2003, this group encompassed students born between March 1987 and February 1988. In PISA 2006, because of survey timing, this group encompassed students born between February 1990 and January 1991. Moreover, in 2003, PISA surveyed the “average” student in June 2003, while in 2006, PISA surveyed the “average” student in May 2006. This difference in survey timing interacts with the fact that recruited students had birth-months that did not align across the two PISA years, resulting in the observed difference-in-differences in age. To address potential concerns about differences in age and birth-month profiles across the two PISA years, I conduct robustness analyses in Appendix A.
Some parameters in this and subsequent regression equations will be omitted because of perfect collinearity with a base category. However, I include them in the equations nonetheless for expositional simplicity.
Since the PISA does not follow the same schools across years, the dataset comprises two separate cross sections of students in different schools. The clustered standard errors account for correlations within each school within a particular PISA round/year. Clustering at the school-by-year level in this way is equivalent to clustering at the school level.
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I would like to thank Alex Mas, Cecilia Rouse, Harvey Rosen, Henry Farber, Nicholas Lawson, Quynh Nguyen, and the seminar and workshop participants at the Industrial Relations Section and the Public Finance Working Group at Princeton University for invaluable insights and comments. I am grateful to anonymous referees for their helpful feedback and guidance.
Conflict of interest
The author declares that there is no conflict of interest.
The views expressed are those of the author and do not necessarily reflect those of the Federal Trade Commission or any individual Commissioner.
Responsible editor: Junsen Zhang
Appendix A: Robustness checks excluding february-born students
This appendix details robustness checks using an analysis sample which excludes February-born students. Using this restricted sample addresses two concerns: (1) that the February-born group in the dragon cohort treatment cell contains both dragon babies and non-dragon babies (see footnote 11) and (2) that there is a misalignment in birth-months between PISA 2003 (March to February birth-months) and PISA 2006 (February to January birth-months) interacting with delayed survey timing (see footnote 16).
In particular, excluding February-born students fixes the misalignment in birth-months between the two PISA years; now, students in each PISA round are born from March to January. This means that despite the survey being conducted later by a month in 2003, there is no peculiar interaction with misaligned birth-months anymore, and the differences in age will difference out between the two PISA years.
This is confirmed by the summary statistics in Table 5, which presents the means and standard deviations of age (in months) of the four difference-in-differences cells, as well as tests for differences, over the sample excluding February-born students. Basically, this is identical to the statistics in the 7th row of Table 2, except for the new sample exclusion.
The calculations show that after removing February-born students from the sample, there is exactly a 1-month difference between 9th graders in the two PISA years, accounting for the misaligned birth-month profile in 2003. There is also no statistically significant difference in the differences between grades and PISA years. This suggests that it is the presence of February-born students in the full sample, and how their birth-months interact with the delay in surveying, that is causing the original difference-in-differences of − 0.70 in Table 2.
Even though it seems like the February-born students are making the treatment and control groups not exactly comparable, this should not matter in the regressions controlling for an age quadratic function, which would account for any differences in age between the groups. If regression estimates using the full sample versus the restricted sample excluding February-born students are similar, then that would suggest that age differences have been adequately accounted for, and including February-born students into the analysis sample is not an issue.
Furthermore, if the estimates from these two regressions are similar, it also suggests that including some dragon babies born in late February is not an issue from a birth-month targeting perspective.
To test this, Tables 6 and 7 report results using this sample excluding February-born students. In each table, row (A) shows the original OLS estimates for ease of comparison, while row (B) shows corresponding estimates using the restricted sample. Comparing the two sets of estimates, we see that they are very similar, although the latter are less statistically significant because of the reduced sample size. This gives me confidence that (1) the presence of February-born students in the full sample did not materially affect the findings and that (2) the inclusion of quadratic covariates in age was adequate in accounting for any remaining differences with respect to age between the treatment and control groups. This is also consistent with the evidence in Fig. 2 which shows that parents selecting into the dragon year are not doing so in these initial months of the dragon year.
Appendix B: Robustness checks excluding control variables
This appendix details robustness checks showing that the original estimates are robust to the inclusion/exclusion of control variables (sex, birth country, age and its square, highest parental occupational status, each parent’s education level, number of books at home). These robustness specifications nonetheless include the full set of difference-in-differences terms and school fixed effects. The estimates are shown in row (C) of Tables 6 and 7 respectively.
Compared to the original OLS estimates in row (A), the estimates from these specifications without controls remain essentially unchanged. In spite of the exclusion of student control variables, the estimates that were statistically significant before remain as such. (In a host of similar robustness checks not reported here, I run regressions that exclude the control variables successively. With each combination of controls, similar results persist.) These findings show that the main results are robust to specification changes, and suggest that explanatory power of the dependent variables is coming primarily from the difference-in-differences dragon cohort interaction term, as opposed to other observable characteristics, such as those found to be dissimilar in Table 2. If the difference-in-differences estimates were biased by non-comparable comparison groups, then one would expect significant shifts in point estimates when the controls were included/excluded, which is not the case.
Overall, the results from these robustness checks suggest that the difference-in-differences analysis is not affected by the slight differences in observable characteristics, and that the other non-dragon cohort students in other grades and PISA years serve as valid counterfactuals.
Appendix C: Mapping procedure and interval regressions
The mapping procedure is as follows. First, integer hours in 2003 are mapped into the 2006 categories. Next, an integer number of hours is assigned to each category. For “No time,” 0 is assigned. For ranged categories with two endpoints, the midpoint of the endpoints is assigned. (For example, “2 or more but less than 4 h...” is assigned 3 h) For the “6 or more hours...” category, 8 h is assigned, because in the 2003 data, this is the median number of hours conditional on reporting 6 or more hours.
To assess whether the mapping procedure used to generate the continuous dependent variable hoursisy influences the results, I repeat the effort regressions using an interval regression procedure, where the dependent variable hoursisy is treated as the categories defined in the PISA survey. The interval regression technique is a generalization of the Tobit method, and accounts for censoring of the data within intervals defined by ranged categories of the dependent variable.
Row (D) of Table 6 presents these estimates for the full sample and restricted sub-samples. Compared to the original OLS estimates (row (A)), they are very similar, showing that the findings are robust to mapping hours categories into exact number of hours.
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Lau, Y. The dragon cohort of Hong Kong: traditional beliefs, demographics, and education. J Popul Econ 32, 219–246 (2019). https://doi.org/10.1007/s00148-018-0711-2
- Chinese zodiac
- Dragon year
- Schooling cohort
- Birth rate