Silviase/jgov_v1.4_full_ann huggingface dataset:
insignificant across the models and the deviation value in Model III also became

insignificant. The most important finding to note is that Model III showed that school

quality did not matter after all. As already described, we treated the deviation value for

high schools as a proxy of the prior attainment. However, this variable may in part reflect

school quality, because students who have been accepted by higher-ranking high schools

are more likely to pursue higher education.

We then restricted our sample to only MZ twins (Tables 3-3 and 3-4). MZ twins are produced in the same pregnancy when a single zygote splits by chance to result in two separate embryos. The two are, thus, genetically identical, which hence enables us to hypothesize that a pair of MZ twin share not only the same family and neighborhood environments but also genetic endowments. The fixed-effect estimate restricted to MZ twins must be more accurate than the estimate for the entire sample because it can control unobserved ability that may be determined by genetic makeups.

As seen in Table 3-3, Model I showed that a lower student–teacher ratio and a lower placement rate were associated with higher student achievements. Model II indicated that the deviation value and shadow education were the crucial determinants. Model III told a
similar story to Model I and Model II, but the placement rate became insignificant.

Considering the twins-fixed effect estimates in Table 3-4, we found no significant

association of student achievement with any variable—not only school quality but also

prior attainment and individual activity during attendance at high school. Looking at the

coefficients for prior attainment and shadow education, the magnitude of fixed-effect

estimates is smaller than the OLS estimates. This drop in magnitude is consistent with the

idea that more able individuals—as captured by a higher unobserved heterogeneity—are

more likely to go to higher-ranking schools and receive shadow education. Regardless of

whether the deviation value of the high school reflects the school quality, after controlling

for unobserved characteristics, the effect of prior attainment is indistinguishable from

zero.

We then repeated the estimates with the restricted sample of DZ twins. Because DZ twins are produced when two eggs are fertilized to form two embryos in the uterus at the same time, DZ twins are not genetically identical. They are, thus, regarded as ordinary siblings of the same age who are raised by the same parent(s) in the same family environments. This setting enables us to understand how genetic makeups play a role in explaining student achievements: if the results for MZ and DZ twins were the same, we could argue that genetic endowments do not matter.
Table 3-5 shows the OLS results for DZ twins. Similar to the results for the entire sample and MZ twins, a lower proportion of students who work after graduation was associated with higher student achievement. In addition, a higher ratio of students to teachers raised student achievements, which is a counterintuitive result, but the coefficient became insignificant when we applied the fixed-effects model (Model III in Table 3-6). In Models II and III, prior attainment was positively associated with student achievements. A more important finding was that school quality and individual characteristics variables were significantly associated with student achievements even in twins-fixed effect estimates (Table 3-6), unlike in the case of MZ twins. Indeed, a higher proportion of students who work after graduation was associated with decreased student achievements in Model III. These results contrast with those for MZ twins reported in Table 3-4, which showed no significant effect of any school or individual variable on student achievements.

The difference between the results for MZ and DZ twins indicates that even if the family environments are the same, different genetic makeups can confound the impact of school quality as well as that of individual characteristics on student achievement. This, in turn, underscores the importance of genetic makeup as a determinant of student achievements, at least at the level of college enrollment, although we should be cautious.
in interpreting these results given the small sample size of the dataset in the current study.

## Effects on Labor Market Outcomes

Much previous literature has pointed out that even though the school quality does not matter for academic achievements, it does matter for longer-run educational outcomes, such as earnings (see Card & Krueger (1996) for a comprehensive review). To investigate this point, we ran a separate regression with logarithm of annual earnings as the dependent variable, instead of academic achievements. Our measure of income is the natural logarithm of annual before-tax wage earned during the fiscal year 2009. The response category in the twin survey ranged from 1 (= no income or less than 0.5 million JPY) through 16 (= more than 15 million JPY). We set the minimum (1 = no income and less than 0.5 million JPY) to zero and the maximum (16 = more than 15 million JPY) to 15 million JPY. We then took the median value for categories between 2 (= 0.5 million to 0.99 million JPY) and 15 (= 10 million to 14.99 million JPY). In the wage equation, we included the control variables deemed to affect productivity, and hence, earnings: marital status (1 = married), hours worked per day, and number of years in current employment.

The estimation results are summarized in Table 3-7, which employed twins-fixed effect models. The results of analysis restricted to MZ twins show that the student–teacher ratio was negatively associated with subsequent earnings in later life, which is
consistent with the evidence reported in Dearden et al (2002), while the remaining school

quality variables were not statistically significant. However, the results of analysis

restricted to DZ twins show that no school quality variables were statistically significant

at a 5% level. The conclusion drawn from these analyses is that we found evidence of

some effects of the student–teacher ratio. It is consistent with the results demonstrated by

Dearden (2002), which is that the secondary student–teacher ratio has a stronger impact at

an older age. This result lends some supports to findings in the previous literature that

school quality matters for outcomes measured later in life. Furthermore, in this context,

the differences in genetic makeup between twin pairs did not affect the results.

## Conclusion

Does school quality really matter in Japan even after controlling for family and genetic endowments? Based on our empirical analysis, the answer to this specific causal question is “no” for academic achievements, but “yes” for labor market outcomes. There has been a growing consensus in the literature that school quality is positively associated with student achievements. This conventional view, however, began to be questioned when we realized that, in Japan, parent(s) are playing a more crucial role in financing children’s educational investments relative to public expenditure.

This study employed a large set of twin data combined with official statistics, the
School Basic Survey and a series of deviation values and attempted to estimate the effect of school quality at high schools, holding unobserved family characteristics constant. The School Basic Survey collected by the Ministry of Education, Culture, Sports, Science and Technology contains a wide variety of school characteristics, such as student–teacher ratios and the number of students enrolled. A dependent variable defined as student achievements was the deviation value of the college/university from which a respondent graduated, which represents the ranking or relative competitiveness of each educational institution across the country. We then estimated the effect of school resources controlling for observed individual demographic factors and unobserved family components.

The results drawn from the sample restricted to identical twins suggested no significant association of student achievement with any variable—not only school resources but also prior attainment and individual activity during attendance at high school. This result suggests that student achievements may be largely determined by unobserved family environments and genetic makeups. Surprisingly, the result from the sample restricted to non-identical twins indicated that some school quality variables were significantly associated with student achievements, unlike the case of identical twins. This means that different genetic makeups can confound the impact of school quality as well as that of individual characteristics on student achievement. Moreover, we found
evidence of some effects of student–teacher ratio on earnings. This result lends some support to earlier findings that school quality matters for outcomes measured later in life.

Furthermore, in this context, the differences in genetic makeup between twin pairs did not affect the results. The messages of this result may be that genetic makeup has some effects when we consider children’s educational outcomes, although we should be cautious in interpreting these results given the small sample size of the dataset in the current study.
(Source) Ministry of Education, Culture, Sports, Science and Technology, “Statistics for Children’s Educational Expenditures” (2010)

Table 1: Household Educational Expenditures on Schools and Shadow Education (Thousand JPY)

Elementary Sch	Junior High Sch	High Sch
Public	Private	Public	Private	Public	Private
Educational expenditure	304	1,465	460	1,279	393	923
Within School	55	835	132	990	238	685
Outside School	207	584	293	279	156	238
Shadow Education	86	296	229	181	125	188
% of total expenditure	(28%)	(20%)	(50%)	(14%)	(31%)	(13%)

(Note) The expenditure on school meals is included in the total educational expenditures for elementary and junior high schools.
Table 2: Descriptive Statistics

Total (541 pairs)	MZ Twins (361 pairs)	DZ Twins (180 pairs)
Mean	STDV	Mean	STDV	Mean	STDV
Dependent variables
Deviation value at college	52.26	9.43	52.55	9.44	51.68	9.37
$ \log(\text{wage}) $	6.21	0.70	6.21	0.69	6.20	0.71
School quality
(1) A type of high school (1=public)	0.71	0.46	0.72	0.45	0.68	0.47
(2) The total number of students enrolled (/100)	9.31	3.31	9.34	3.25	9.25	3.42
(3) The ratio of students to administrative staff (/100)	2.03	2.72	1.96	2.62	2.17	2.90
(4) The proportion of students who find work after graduation	0.05	0.11	0.05	0.11	0.05	0.10
(5) The ratio of students to teachers (/100)	0.39	1.21	0.35	0.90	0.45	1.66
(6) The proportion of teachers with long-term absence	0.01	0.02	0.01	0.02	0.01	0.02
Other individual characteristics
Gender (female=1)	0.38	0.49	0.34	0.47	0.47	0.50
Age (if 20-29, reference)	0.15	0.36	0.14	0.35	0.17	0.37
Age (if 30-39)	0.36	0.48	0.35	0.48	0.37	0.48
Age (if 40-49)	0.31	0.46	0.34	0.48	0.24	0.43
Age (if 50-59)	0.18	0.39	0.16	0.37	0.17	0.42
Deviation value at high school	59.25	10.40	59.93	10.26	57.89	10.57
Club activity (1=participated in sports club activities at least once)	0.34	0.47	0.36	0.48	0.30	0.46
Shadow education (1=participated more than three months)	0.33	0.47	0.34	0.47	0.33	0.47

(Source) Authors’ calculations from School Basic Survey and Kanjuku’s Deviation Values in Junior High and High School Nationwide
Table 3-1: Empirical Results (OLS, Entire Sample)

Dependent Variable: Deviation value at College	OLS
Model I	Model II	Model III
School quality
(1) A type of high school (public=1)	-0.835	-0.657
(0.695)	(0.632)
(2) The total number of students enrolled (/100)	0.108	0.072
(0.097)	(0.095)
(3) The ratio of students to administrative staff (/100)	0.039	-0.058
(0.103)	(0.095)
(4) The proportion of students who work after graduation	-15.916***	0.663
(2.820)	(3.585)
(5) The ratio of students to teachers (/100)	0.185	0.102
(0.282)	(0.224)
(6) The proportion of teachers with long-term absence	-16.403	-5.299
(10.757)	(10.351)
Individual Characteristics
Gender (female=1)	-3.316***	-2.664***	-2.656***
(0.596)	(0.555)	(0.565)
Age (=30-39)	0.91	0.746	0.727
(0.801)	(0.765)	(0.762)
Age(=40-49)	-0.449	-0.911	-0.915
(0.848)	(0.800)	(0.796)
Age(=50-59)	0.306	0.155	0.147
(0.930)	(0.892)	(0.898)
Deviation value at high school	0.322***	0.318***
(0.027)	(0.033)
Club sports activity	0.253	0.325
(0.548)	(0.553)
Shadow education	2.574***	2.483***
(0.562)	(0.565)
Constant	53.699***	33.257***	33.424***
(1.501)	(1.757)	(2.426)
R-Squared	0.083	0.182	0.185
Observations	1,082	1,082	1,082
(# of twin pairs)	(541)	(541)	(541)

** represent 1% and 5% significance level, respectively.

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors. *** and

(Source) Authors' calculations
Table 3-2: Empirical Results (Twin-Fixed Effects, Entire Sample)

Dependent Variable: Deviation value at College	Twin-Fixed Effects
Model I	Model II	Model III
School quality
(1) A type of high school (public=1)	1.488	1.227
(0.885)	(0.911)
(2) The total number of students enrolled (/100)	0.102	0.081
(0.083)	(0.078)
(3) The ratio of students to administrative staff (/100)	-0.189	-0.191
(0.154)	(0.151)
(4) The proportion of students who work after graduation	-9.394	-5.495
(4.239)	(4.574)
(5) The ratio of students to teachers (/100)	0.067	0.026
(0.215)	(0.214)
(6) The proportion of teachers with long-term absence	12.46	12.918
(11.613)	(11.524)
Individual Characteristics
Gender (female=1)	-4.704***	-4.371***	-4.364***
(1.005)	(1.027)	(1.036)
Deviation value at high school	0.136**	0.096
(0.045)	(0.048)
Club sports activity	0.311	0.183
(0.731)	(0.745)
Shadow education	0.404	0.243
(1.225)	(1.264)
Constant	52.700***	45.605***	46.944***
(1.216)	(2.893)	(3.327)
R-Squared	0.077	0.072	0.084
Observations	1,082	1,082	1,082
(# of twin pairs)	(541)	(541)	(541)

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors and clustering at the family level. *** and ** represent 1% and 5% significance level, respectively. (Source) Authors' calculations
Table 3-3: Empirical Results (OLS, MZ Twins)

Dependent Variable: Deviation value at College	OLS
Model I	Model II	Model III
School quality
(1) A type of high school (public=1)	-2.073	-1.328
(0.864)	(0.798)
(2) The total number of students enrolled	0.106	0.101
(0.119)	(0.118)
(3) The ratio of students to administrative staff	-0.009	-0.072
(0.131)	(0.123)
(4) The proportion of students who work after graduation	-12.958***	2.848
(3.430)	(4.690)
(5) The ratio of students to teachers	-0.769***	-0.627**
(0.218)	(0.232)
(6) The proportion of teachers with long-term absence	-25.514	-17.017
(13.796)	(12.563)
Individual Characteristics
Gender (female=1)	-2.693***	-1.942**	-2.028**
(0.751)	(0.691)	(0.707)
Age (=30-39)	1.798	2.058	2.003
(1.014)	(0.949)	(0.943)
Age(=40-49)	-0.315	0.116	0.017
(1.079)	(0.999)	(0.993)
Age(=50-59)	-0.479	0.446	0.138
(1.186)	(1.135)	(1.139)
Deviation value at high school	0.303***	0.301***
(0.036)	(0.046)
Club sports activity	0.109	0.187
(0.675)	(0.677)
Shadow education	2.782***	2.564***
(0.706)	(0.700)
Constant	54.761***	33.220***	33.984***
(1.865)	(2.391)	(3.350)
R-Squared	0.083	0.162	0.175
Observations	722	722	722
(# of twin pairs)	(361)	(361)	(361)

** represent 1% and 5% significance level, respectively.

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors. *** and

(Source) Authors' calculations
Table 3-4: Empirical Results (Twin-Fixed Effects, MZ Twins)

Dependent Variable: Deviation value at College	Twin-Fixed Effects
Model I	Model II	Model III
School quality
(1) A type of high school (public=1)	0.038	-0.233
(1.159)	(1.218)
(2) The total number of students enrolled	0.068	0.081
(0.112)	(0.105)
(3) The ratio of students to administrative staff	-0.040	-0.078
(0.191)	(0.191)
(4) The proportion of students who work after graduation	-4.377	-1.117
(5.631)	(5.551)
(5) The ratio of students to teachers	-0.178	-0.181
(0.261)	(0.284)
(6) The proportion of teachers with long-term absence	5.645	5.442
(14.353)	(13.438)
Individual Characteristics
Deviation value at high school	0.099	0.096
(0.063)	(0.066)
Club sports activity	-0.303	-0.341
(0.874)	(0.877)
Shadow education	-1.873	-1.901
(1.873)	(1.883)
Constant	52.174***	47.391***	47.142***
(1.279)	(3.838)	(4.293)
R-Squared	0.005	0.014	0.016
Observations	722	722	722
(# of twin pairs)	(361)	(361)	(361)

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors and clustering at the family level. *** and ** represent 1% and 5% significance level, respectively. (Source) Authors' calculations
Table 3-5: Empirical Results (OLS, DZ Twins)

Dependent Variable: Deviation value at College	OLS
Model I	Model II	Model III
School quality
(1) A type of high school (public=1)	1.194	0.659
(1.124)	(1.039)
(2) The total number of students enrolled	0.109	0.030
(0.166)	(0.172)
(3) The ratio of students to administrative staff	0.055	-0.062
(0.168)	(0.155)
(4) The proportion of students who work after graduation	-26.067***	-9.000
(4.208)	(4.734)
(5) The ratio of students to teachers	0.841***	0.626***
(0.158)	(0.168)
(6) The proportion of teachers with long-term absence	13.516	23.995
(16.871)	(15.631)
Individual Characteristics
Gender (female=1)	-4.501***	-3.858***	-3.933***
(0.976)	(0.919)	(0.944)
Age (=30-39)	-0.508	-1.937	-1.536
(1.296)	(1.289)	(1.291)
Age(=40-49)	-0.548	-2.748	-2.398
(1.429)	(1.346)	(1.367)
Age(=50-59)	1.194	-0.804	-0.307
(1.502)	(1.457)	(1.491)
Deviation value at high school	0.372***	0.330***
(0.040)	(0.050)
Club sports activity	0.447	0.165
(0.947)	(0.973)
Shadow education	1.756	1.343
(0.960)	(0.974)
Constant	52.580***	32.807***	34.390***
(2.505)	(2.582)	(3.641)
R-Squared	0.172	0.246	0.268
Observations	360	360	360
(# of twin pairs)	(180)	(180)	(180)

** represent 1% and 5% significance level, respectively.

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors. *** and

(Source) Authors' calculations
Table 3-6: Empirical Results (Twin-Fixed Effects, DZ Twins)

Dependent Variable: Deviation value at College	Twin-Fixed Effects
Model I	Model II	Model III
School quality
(1) A type of high school (public=1)	3.287**	2.821
(1.203)	(1.312)
(2) The total number of students enrolled	0.209	0.129
(0.114)	(0.129)
(3) The ratio of students to administrative staff	-0.449	-0.425
(0.261)	(0.257)
(4) The proportion of students who work after graduation	-15.991***	-13.166**
(4.425)	(4.782)
(5) The ratio of students to teachers	0.539**	0.408
(0.181)	(0.218)
(6) The proportion of teachers with long-term absence	25.593	26.427
(19.421)	(19.416)
Individual Characteristics
Gender (female=1)	-4.418***	-3.996***	-4.090***
(1.015)	(1.056)	(1.109)
Deviation value at high school	0.170**	0.066
(0.061)	(0.074)
Club sports activity	1.337	0.759
(1.226)	(1.334)
Shadow education	2.350	1.812
(1.612)	(1.664)
Constant	50.755***	42.524***	46.847***
(1.699)	(3.924)	(4.901)
R-Squared	0.203	0.159	0.212
Observations	360	360	360
(# of twin pairs)	(180)	(180)	(180)

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors and clustering at the family level. *** and ** represent 1% and 5% significance level, respectively. (Source) Authors' calculations
Table 3-7: Empirical Results (Twin-Fixed Effects)

Dependent Variable: $ \log(\text{wage}) $	Twin-Fixed Effects
ALL	MZ	DZ
School quality
(1) A type of high school (public=1)	-0.016	-0.114	0.127
(0.061)	(0.065)	(0.122)
(2) The total number of students enrolled	0.002	-0.008	0.019
(0.005)	(0.006)	(0.009)
(3) The ratio of students to administrative staff	0.000	-0.002	-0.006
(0.009)	(0.009)	(0.025)
(4) The proportion of students who work after graduation	-0.256	-0.354	0.312
(0.231)	(0.253)	(0.520)
(5) The ratio of students to teachers	0.015	-0.032**	0.045
(0.017)	(0.012)	(0.019)
(6) The proportion of teachers with long-term absence	0.132	0.698	-0.627
(0.700)	(0.736)	(1.823)
Individual Characteristics
Gender (female=1)	-0.409***	-0.313**
(0.117)	(0.115)
Deviation value at high school	0.002	0.002	0.004
(0.002)	(0.003)	(0.004)
Club sports activity	-0.057	-0.126	0.036
(0.050)	(0.059)	(0.098)
Shadow education	-0.012	-0.043	-0.094
(0.073)	(0.086)	(0.138)
Marital status (married=1)	0.004	0.023	0.022
(0.047)	(0.061)	(0.088)
Years of the current employment	0.017***	0.012**	0.025
(0.005)	(0.005)	(0.011)
Working hours a day	0.081***	0.060**	0.110**
(0.019)	(0.020)	(0.035)
Constant	5.258***	5.619***	4.436***
(0.264)	(0.277)	(0.516)
R-Squared	0.303	0.200	0.451
Observations	876	596	280
(# of twin pairs)	(438)	(298)	(140)

(Note) The standard errors in parentheses represent heteroskedasticity-robust standard errors and clustering at the family level. *** and ** represent 1% and 5% significance level, respectively. (Source) Authors' calculations
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