Silviase/jgov_v1.4_full_ann huggingface dataset:
and the 1965 school cohort dummy is positive and statistically significant. This suggests that those who were born in March 1966 were seriously held back compared with those who were born in April 1965. This evidence, again, is consistent with the relative age effect hypothesis. We should, however, keep in mind that those females who were born in 1966 were stigmatized, and this stigma might have directly affected their educational outcomes.

Overall, the evidence from the Fire Horse (Hinoeuma) is consistent with the relative age effect hypothesis as an explanation for why April-born people tend to have more education than March-born people.

## 6 Conclusion

This paper examined whether those who are older in the same school cohort do better than their younger counterparts in terms of educational attainment and labor market outcomes using a Japanese labor force survey that records birth months. The phenomena that older children in a school cohort take advantage of their physical and mental maturity is called the relative age effect in the developmental psychology literature, and it is widely confirmed in education performance and sports.

This paper exploits the feature of the Japanese school system that defines the school-entering timing by the child’s age on March 31. Those who are born in March enter primary schools at the beginning of age 6, while those who are born in April start at the end of age 6 or almost at age 7. Thus those who are born in March are presumably handicapped compared with
those who are born in April. Exploiting this feature, we can identify the relative age effect by examining the discontinuity of educational attainment and labor market outcomes between March-born and April-born individuals. Japan is the ideal country for examining the relative age effect because the law requires a uniform 9 years of compulsory education irrespective of birth month. Accordingly, variations in educational attainment across birth months are generated purely by the relative age effect.

The analysis results indicate that April-born males had 0.14 year more education than March-born males, whereas April-born females had 0.07 year more education than March-born females. The initial advantage of April-born over March-born children in primary school persists and eventually develops into a difference in eventual educational attainment. However, this difference in educational attainment does not turn into a difference in annual income. An additional year of education induced by the relative age effect is not rewarded in the labor market. Thus the relative age effect washes out in the labor market.

Two hypotheses are explored to explain the difference in educational attainment between April-born and March-born individuals. One hypothesis is the absolute age effect, which claims that absolute age at school entry determines an individual's performance in school. In contrast, the relative age hypothesis claims that the age at school entry relative to the ages of other students determines school performance. The analysis that exploits the large variation in the distribution of birth months within a school cohort
is consistent with the relative age effect hypothesis.

The strong evidence for the relative age effect in terms of the years of education calls for further studies that examine the effect of birth month on educational outcomes. The results so far suggest a need for a policy adjustment that can soften the disadvantage of those who are younger in the same school cohort, particularly at the early stage of primary education. At least, letting teachers know the disadvantage of relatively younger children in a class might lead them to give such children extra attention.

The use of school tracking during the early stage without giving careful consideration to the relative age effect will exacerbate the situation. The Japanese public school system usually starts tracking in high school, but some local governments have initiated tracking during junior high school in the last several years. Extra attention should be paid to the relative age effects at the time of students' admission.

## References

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Joshua Angrist and Alan B. Krueger. Does compulsory schooling affect schooling and earnings? Quarterly Journal of Economics, 106(4):979–1014, 1991.
Joshua Angrist, Guido Imbens, and Donald Rubin. Identification of causal effect using instrumental variables. Journal of American Statistical Association, 91(434):444–455, 1996.

Mark G. Borg and Joseph M. Falzon. Birth date and sex effects on the scholastic attainment of primary schoolchildren: A cross-sectional study. British Educational Research Journal, 21(1):61–74, 1995.

Ashlesha Datar. Does delaying kindergarten entrance give children a head start? Economics of Education Review forthcoming, 2005.

Ad Dudink. Birth date and sporting success. Nature, 369:592, 1994.

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Peter Fredriksson and Bjorn Ockert. Is early learning really more productive? the effect of school starting age on school and labor market performance. IZA DP No. 1659, 2005.

Werner F. Helsen, Janet L. Starkes, and Jan Van Winckel. Effect of a change in selection year on success in male soccer players. American Journal of Human Biology, 12(6):729–735, 2000.

Guido W Imbens and Joshua D Angrist. Identification and estimation of local average treatment effects. Econometrica, 62(2):467–475, 1994.
Kanae Kaku and Scott Matsumoto. Influence of a folk superstition on fertility of Japanese in California and Hawaii, 1966. American Journal of Public Health, 65(2):170–174, 1975.

Satomi Kurosu. Sex ratio and the years of the fire horse: Cultural and regional experiences in Japan. In Fauve-Chamoux and Soelvi Sogner, editors, Socio-economic Consequences of Sex-rations in Historical Perspective, pages 77–90. University of Bocconi, 1994.

Jungmin Lee. Marriage, female labor supply, and asian zodiacs. Economics Letters, 87:427–432, 2005.

Lars Lien, Kristian Tambs, Brit Oppedal, Sonja Heyerdahl, and Espen Bjertness. Is relatively young age within a school year a risk factor for mental health problems and poor school performance? a population-based cross-sectional study of adolescents in Oslo, Norway. BMC Public Health, 5(102), 2005.

Roy P. Martin, Patricia Foels, Greg Clanton, and Kathryn Moon. Season of birth is related to child retention rates, achievement, and rate of diagnosis of specific l.d. Journal of Learning Disabilities, 37(4):309–317, July 2004.

Angus H Thompson, Roger H Barnsley, and Ronald J Dyck. A new factor in youth suicide: The relative age effect. Canadian Journal of Psychiatry, 44:82–85, 1999.
D. Thompson. Season of birth and success in the secondary school. Educational Research, 14:56–60, 1971.
Table 1: Discontinuity by Birth Month, Males 25-60 in 2002

(1)	(2)	(3)	(4)	(5)
Dep. Var.	Years of Education	Employed	Log(Income)	Married	Father's Education
May	-0.01	-0.00	0.01	-0.00	-0.06
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
June	-0.01	0.00	0.01	0.00	-0.02
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
July	-0.01	0.01	0.01	-0.00	0.00
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
August	0.05	0.00	0.02	-0.00	0.07
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
September	-0.03	0.00	0.02	0.00	0.09
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
October	-0.05	0.00	0.01	0.00	-0.02
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
November	-0.08	0.00	0.01	0.01	0.07
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
December	-0.08	-0.00	0.00	0.00	0.04
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
January	-0.10	-0.00	0.00	0.01	0.05
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
February	-0.12	-0.01	0.01	0.00	0.02
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
March	-0.14	-0.01	-0.01	0.01	0.05
(0.02)	(0.00)	(0.01)	(0.00)	(0.04)
Constant	11.46	0.90	5.64	0.70	9.85
(0.04)	(0.01)	(0.02)	(0.01)	(0.20)
Observations	259756	259756	237542	259756	58188
R-squared	0.06	0.17	0.07	0.02	0.09

Note: Heteroskedasticity robust standard errors are in parentheses. All specifications include school cohort dummy variables. Father’s education is available given that the father co-resides.
Table 2: Discontinuity by Birth Month, Females 25-60 in 2002

(1)	(2)	(3)	(4)	(5)
Dep. Var.	Years of Education	Employed	Log(Income)	Married	Father's Education
May	0.00	-0.00	-0.02	-0.00	-0.03
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
June	-0.02	-0.00	-0.00	0.00	0.04
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
July	-0.02	-0.00	-0.01	-0.01	0.04
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
August	-0.00	-0.01	-0.00	-0.00	0.11
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
September	-0.00	-0.00	-0.00	-0.01	0.05
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
October	-0.03	0.00	-0.00	0.01	0.02
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
November	-0.04	-0.00	-0.01	0.00	0.04
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
December	-0.07	-0.01	-0.02	0.01	0.09
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
January	-0.03	-0.01	-0.01	0.01	0.11
(0.02)	(0.00)	(0.01)	(0.00)	(0.06)
February	-0.06	-0.00	-0.02	0.01	0.07
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
March	-0.07	-0.01	-0.02	0.01	0.15
(0.02)	(0.00)	(0.01)	(0.00)	(0.07)
Constant	10.96	0.81	4.55	0.44	10.82
(0.03)	(0.01)	(0.02)	(0.01)	(0.47)
Observations	267838	267838	177533	267838	28574
R-squared	0.14	0.11	0.02	0.02	0.06

Note: The same note applies as in Table 1.
Table 3: The effect of birth month on the years of education and annual income.

(1)	(2)	(3)	(4)	(5)	(6)
Male	Male	Male	Female	Female	Female
Dep. Var.	1(Educ≥12)	1(Educ≥14)	1(Educ≥16)	1(Educ≥12)	1(Educ≥14)	1(Educ≥16)
May	-0.00	0.00	-0.00	0.00	0.00	0.00
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
June	0.00	-0.00	-0.01	-0.00	-0.00	0.00
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
July	-0.00	0.00	-0.00	-0.00	-0.00	-0.00
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
August	0.00	0.01	0.01	-0.00	0.00	0.00
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
September	-0.01	-0.00	-0.00	-0.00	0.00	-0.00
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
October	-0.01	-0.00	-0.00	-0.00	-0.00	-0.00
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
November	-0.01	-0.01	-0.01	-0.01	-0.01	-0.00
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
December	-0.01	-0.01	-0.01	-0.01	-0.01	-0.01
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
January	-0.01	-0.02	-0.02	0.00	-0.01	-0.01
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
February	-0.02	-0.02	-0.01	-0.01	-0.01	-0.01
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
March	-0.02	-0.02	-0.02	-0.01	-0.01	-0.01
(0.00)	(0.00)	(0.00)	(0.00)	(0.00)	(0.00)
Constant	0.61	0.17	0.15	0.57	0.10	0.03
(0.01)	(0.01)	(0.01)	(0.01)	(0.01)	(0.00)
Observations	259756	259756	259756	267838	267838	267838
R-squared	0.07	0.04	0.02	0.11	0.09	0.02

Note: Heteroskedasticity robust standard errors are in parentheses. All specifications include school cohort dummy variables.
Table 4: Implied return to education, Male sample, March and April born only

(1)	(2)	(3)	(4)
Dep. Var.	Log(Income)	Years of Education	Log(Income)	Log(Income)
Est. Method	OLS	OLS	OLS	IV
Education	0.09	-	-	0.04
(0.00)	(0.05)
April	-	0.14	0.01	-
(0.02)	(0.01)
Observations	41956	45893	41956	41956
R-squared	0.16	0.07	0.07	0.13

Note: All specifications include a constant and school cohort dummy variables. Heteroskedasticity robust standard errors are in parentheses.

Table 5: Implied return to education, Female sample, March and April born only

(1)	(2)	(3)	(4)
Dep. Var.	Log(Income)	Years of Education	Log(Income)	Log(Income)
Est. Method	OLS	OLS	OLS	IV
Education	0.12	-	-	0.19
(0.00)	(0.12)
April	-	0.07	0.02	-
(0.02)	(0.01)
Observations	31711	47746	31711	31711
R-squared	0.07	0.15	0.02	0.05

Note: The same note applies as in Table 4.
Table 6: Implied return to education, Male sample, February-May born only

(1)	(2)	(3)	(4)
Dep. Var.	Log(Income)	Years of Education	Log(Income)	Log(Income)
Est. Method	OLS	OLS	OLS	IV
Education	0.09	-	-	0.03
(0.00)	(0.04)
April-May	-	0.12	0.00	-
(0.02)	(0.00)
Observations	82006	89770	82006	82006
R-squared	0.16	0.06	0.07	0.12

Note: The same note applies as in Table 4.

Table 7: Implied return to education, Female sample, February-May born only

(1)	(2)	(3)	(4)
Dep. Var.	Log(Income)	Years of Education	Log(Income)	Log(Income)
Est. Method	OLS	OLS	OLS	IV
Education	0.12	-	-	0.10
(0.00)	(0.10)
April-May	-	0.07	0.01	-
(0.01)	(0.01)
Observations	61446	92726	61446	61446
R-squared	0.07	0.15	0.02	0.07

Note: The same note applies as in Table 4.
Table 8: Controlling Father’s Education, Male sample, March and April born only

(1)	(2)	(3)	(4)
Dep. Var.	Log(Income)	Years of Education	Log(Income)	Log(Income)
Est. Method	OLS	OLS	OLS	IV
Education	0.08	-	-	0.05
(0.00)	(0.07)
April	-	0.19	0.01	-
(0.04)	(0.01)
Observations	9414	10464	9414	9414
R-squared	0.14	0.16	0.09	0.13

Note: All specifications include a constant, school cohort dummy and father’s education category dummy variables. Heteroskedasticity robust standard errors are in parentheses.

Table 9: Controlling Father’s Education, Female sample, March and April born only

(1)	(2)	(3)	(4)
Dep. Var.	Log(Income)	Years of Education	Log(Income)	Log(Income)
Est. Method	OLS	OLS	OLS	IV
Education	0.11	-	-	0.19
(0.01)	(0.12)
April	-	0.17	0.03	-
(0.04)	(0.02)
Observations	4105	5093	4105	4105
R-squared	0.09	0.20	0.03	0.05

Note: The same note applies as in Table 8.
Table 10: Stable Results over Time; Male Sample

(1)	(2)	(3)	(4)
Sample Period	Log(Income)	Years of Education	Log(Income)	Log(Income)
Estimation Method	OLS	OLS	OLS	IV
1945-1954	Years of education	of	0.10	0.03
April	(0.00)	(0.07)
Observations	13420	14668	13420	13420	13420
R-squared	0.11	0.02	0.00
1955-1964	Years of education	of	0.10	0.05
April	(0.00)	(0.11)
Observations	11453	12147	11453	11453	11453
R-squared	0.12	0.00	0.01
1965-1974	Years of education	of	0.07	-	-	0.39
April	(0.00)	(0.26)
Observations	-	0.10	0.02	-
Observations	10673	11546	10673	10673	10673
R-squared	0.11	0.00	0.04

Note: All specifications include a constant, and school cohort dummy variables. Heteroskedasticity robust standard errors are in parentheses.
Table 11: Stable Results over Time; Female Sample

(1)	(2)	(3)	(4)
Sample Period	Log(Income)	Years of Education	Log(Income)	Log(Income)
Estimation Method	OLS	OLS	OLS	IV
1945-1954	Years of education	of	0.10	0.33
April	(0.00)	(0.21)
Observations	10523	15224	10523	10523	10523
R-squared	0.04	0.03	0.00
1955-1964	Years of education	of	0.12	0.21
April	(0.01)	(0.27)
Observations	9112	12702	9112	9112	9112
R-squared	0.05	0.01	0.00	0.02
1965-1974	Years of education	of	0.14	0.31
April	(0.01)	(0.24)
Observations	7318	12043	7318	7318
R-squared	0.10	0.01	0.02

Note: The same note applies as in Table 10.
Table 12: Evidence for the relative age effect rather than the absolute age effect: Evidence from the small birth cohort of 1966 due to the Fire-Horse year (Hinoeuma). Sample: Born between 1960 April and 1970 March

(1)	(2)	(3)	(4)
Sample	Male	Male	Female	Female
Dependent Variables	Years of Education	Log(Income)	Years of Education	Log(Income)
April	0.18	0.02	0.07	0.03
(0.04)	(0.01)	(0.03)	(0.02)
April	-0.02	-0.02	0.32	0.10
*1965	April	-	1966	(0.14)	(0.04)	(0.07)
March
April	-0.22	-0.03	0.01
*1966	April	-	1967	(0.14)	(0.04)	(0.07)
March
Observations	11386	10640	12064
R-squared	0.00	0.02	0.00

Note: Due to the superstition based on the Chinese Zodiac, “Hinoeuma,” the size of the birth cohort of 1966 is 25 percent smaller than adjacent cohort. Accordingly, there are smaller numbers of January-March born among the 1965 school cohort (1965 April – 1966 March) and larger numbers of January-March born among the 1966 school cohort (1966 April – 1967 March).
Figure 1: Japanese School System

Note: Primary school and junior high school are compulsory. Major tracking starts from age 15.
Figure 2: Number of Observations Sample: Male 25-60 in 2002

Sample: Female 25-60 in 2002
Figure 3: Years of Education Sample: Male 25-60 in 2002

Sample: Female 25-60 in 2002
Figure 4: Log Earnings

Sample: Male 25-60 in 2002

Sample: Female 25-60 in 2002
Figure 5: Employment Rate Sample: Male 25-60 in 2002

Sample: Female 25-60 in 2002
Figure 6: Marriage Rate

Sample: Male 25-60 in 2002

Sample: Female 25-60 in 2002
Figure 7: Father’s years of education conditional on co-residence with own father Sample: Male 25-60 in 2002

Sample: Female 25-60 in 2002
Figure 8: The 1966 fertility rate drop due to the Fire-Horse (Hinoeuma) year. Sample: Male

Sample: Female
# 昭和50年度政府関係機関補正予算(機第1号)

(昭和50年度政府関係機関補正予算参照書添付)

第 76 回 国 会 ( 臨 時 会 ) 提 出
## 総目録

## 昭和50年度政府関係機関補正予算(機第1号)

予算総則補正

甲号收入支出予算補正

(添付)

昭和50年度政府関係機関補正予算(機第1号)参照書

昭和50年度政府関係機関

収入支出予算補正予定計算書等
# 昭和50年度政府関係機関補正予算(機第1号)目録

予算総則補正
1

甲号収入支出予算補正
3

日本専売公社
3

日本国有鉄道
3

日本電信電話公社
4

住宅金融公庫
5

農林漁業金融公庫
5

環境衛生金融公庫
6
(添付)

## 昭和50年度政府関係機関補正予算(機第1号)参照書目録

79
昭和50年度政府関係機関補正予算(機第1号)
# 昭和50年度政府関係機関補正予算

# 予算総則補正

第１条 次に掲げる各政府関係機関の昭和５０年度収入支出予算補正は、「甲号収入支出予算補正」に掲げるとおりとする。

日 本 専 壳 公 社

日本国有鉄道

日 本 電 信 電 話 公 社

住宅金融公庫

農 林 漁 業 金 融 公 庫

環境衛生金融公庫

第2条 昭和50年度政府関係機関予算総則第4条に定める日本専売公社の長期借入金の限度額「343,000,000千円」を「380,500,000千円」に改める。

第3条 昭和50年度政府関係機関予算総則第8条第1項に定める日本専売公社がその職員に対して支給する基準内給与の額「66,521,858 千円」を「69,181,490

千円」に、基準外給与の額「44,221,318 千円」を「46,229,022 千円」に、給与の総額「110,743,176 千円」を「115,410,512 千円」に改める。

第4条 昭和50年度政府関係機関予算総則第13条第1項の日本国有鉄道の借入金等の限度額の表中

長期借入金及び鉄道債券

長期借入金、政府引受債及び政府保証債

ロ イ以外の鉄道債券

941,600,000千円

438,300,000

を

長期借入金及び鉄道債券

長期借入金、政府引受債及び政府保証債

ロイ以外の鉄道債券

に改める。

1,077,000,000 千円

467,700,000
第 5 条 昭和 50 年度政府関係機関予算総則第 17 条第 1 項に定める日本国有鉄道がその職員に対して支給する基準内給与の額「766,120,269 千円」を「797,686,334 千円」に、基準外給与の額「453,808,442 千円」を「474,267,610 千円」に、給与の総額「1,219,928,711 千円」を「1,271,953,944 千円」に改める。

第 6 条 昭和 50 年度政府関係機関予算総則第 22 条第 1 項の日本電信電話公社の借入金等の限度額の表

債
券
等	限
度
額
電信電話債券及び長期借入金
イ
政府引受債	38,000,000千円
ロ
「電信電話設備の拡充のための暫定措置に関する法律」第2条から第8条までの規定による引受け又は受益者の引受けにより発行するもの	475,000,000
ハ
イ及びロ以外のもの	585,900,000
一時借入金	495,000,000

債
券
等	限
度
額
電信電話債労及び長期借入金
イ
政府引受債
口「電信電話設備の拡充のための暫定措置に関する法律」第2条から第8条までの規定による引受け又は受益者の引受けにより発行するもの
ハイ及び口以外のもの	48,000,000千円
475,000,000
631,700,000
一時借入金	521,000,000

第7条 昭和50年度政府関係機関予算総則第26条第1項に定める日本電信電話公社がその職員に対して支給する基準内給与の額「470,490,340千円」を「482,254,489千円」に、基準外給与の額「320,963,217千円」を「328,474,702千円」に、給与の総額「791,453,557千円」を「810,729,191千円」に改める。
甲号 收入支出予算補正
政府関係機関	項	補 正 額
追 加 額(千円)	修正減少額(千円)	差 引 額(千円)
日本専売公社収入	たばこ事業収入	0	△	205,659,000	△	205,659,000
塩事業収入	0	△	6,793,950	△	6,793,950
収入補正額	0	△	212,452,950	△	212,452,950
支出	給与其他諸費	5,758,202	0	5,758,202
たばこ事業費	13,833,772	△	18,962,421	△	5,128,649
塩事業費	1,890,425	△	7,990,900	△	6,100,475
固定資產取得費	3,008,453	0	3,008,453
予備費	0	△	3,500,000	△	3,500,000
支出補正額	24,490,852	△	30,453,321	△	5,962,469
日本国有鉄道損益勘定収入	運輸収入	40,286,958	△	88,130,276	△	47,843,318
雑収入	0	△	5,464,703	△	5,464,703
助成金受入	427,333	△	549,925	△	122,592
資本勘定より受入	119,362,900	0	119,362,900
収入補正額	160,077,191	△	94,144,904	65,932,287
支出	給与其他諸費	57,148,995	0	57,148,995
営業費	695,127	0	695,127
4 甲号 收入支出予算補正

政府関係機関	項	補 賴	正 額
追 加 額(千円)	修正減少額(千円)	差 引 額(千円)
資 本 勘 定	保 守 費	6,300,062	0	6,300,062
利 子 及 債 務 取 报 諸 費	1,788,103	0	1,788,103
支 出 補 正 額	65,932,287	0	65,932,287
収 入	鉄 道 債 券 及 借 入 金	164,800,000	0	164,800,000
支 出	損 益 勘 定 へ 線 入	119,362,900	0	119,362,900
工 事 勘 定 へ 線 入	45,437,100	0	45,437,100
支 出 補 正 額	164,800,000	0	164,800,000
工 事 勘 定	資 本 勘 定 へ リ 受 入	45,437,100	0	45,437,100
収 入	東 北 新 幹 線 等 建 設 費	45,000,000	0	45,000,000
建 設 関 連 利 子	437,100	0	437,100
支 出 補 正 額	45,437,100	0	45,437,100
日本電信電話公社	給 与 其 他 諸 費	21,776,281	0	21,776,281
損 益 勘 定	営 業 費	2,803,460	0	2,803,460
利 子 及 債 務 取 报 諸 費	1,220,259	0	1,220,259
資 本 勘 定 へ 線 入	0	△	25,800,000	△	25,800,000
支 出 補 正 額	25,800,000	△	25,800,000	0
資 本 勘 定	損 益 勘 定 へ リ 受 入	0	△	25,800,000	△	25,800,000
収 入
甲号 收入支出予算補正 5

政府關係機関	項	補 補 正 縮 縮
追加 額(千円)	修正 減 少 額(千円)	差 引 額(千円)
支 建 設 勘 定 収 入 出	電信電話債券及借入金 収 入 補 正 額 建 設 勘 定 へ 線 入 資 本 勘 定 より 受 入 電 信 電 話 施 設 費 局 舍 建 設 費 総 係 費 支 出 補 正 額	55,800,000	0	25,800,000	55,800,000
30,000,000	0	30,000,000
30,000,000	0	30,000,000
24,182,983	0	24,182,983
5,564,000	0	5,564,000
253,017	0	253,017
30,000,000	0	30,000,000
政府関係機関	款	補 補 正 縮
追加 額(千円)	修正 減 少 額(千円)	差 引 額(千円)
住宅金融公庫 収 入	事業益金 雑 収 入	事業益金 一般会计より受入 雑 収 入 収 入 補 正 額 事業 損 金 予 備 費 支 出 補 正 額	4,177,692	0	4,177,692
4,177,692	0	4,177,692
2,289,226	0	2,289,226
2,285,000	0	2,285,000
4,226	0	4,226
6,466,918	0	6,466,918
7,904,761	△	30,141	7,874,620
0	△	550,000	△	550,000
7,904,761	△	580,141	7,324,620
農林漁業金融公庫
政府關係機關	款項	項目	補 補 修正 額
追加額(千円)	修正減少額(千円)	差引額(千円)
收入	事業益金	258,082	0	258,082
雑收入	事業益金	258,082	0	258,082
一般会计より受入	0	△	8,114,171	△	8,114,171
收入補正額	0	△	8,114,171	△	8,114,171
支出	事業損金	258,082	△	8,114,171	△	7,856,089
予備費	360,936	△	336,793	△	24,143
支出補正額	0	△	200,000	△	200,000
環境衛生金融公庫	360,936	△	536,793	△	175,857
收入	事業益金	168,949	0	168,949
事業益金	168,949	0	168,949
雑收入	一般会计より受入	0	△	971,619	△	971,619
收入補正額	0	△	971,619	△	971,619
支出	事業損金	168,949	△	971,619	△	802,670
予備費	306,956	△	51,165	△	255,791
支出補正額	0	△	10,000	△	10,000
306,956	△	61,165	△	245,791
(添 付)

昭和50年度政府関係機関補正予算(機第1号)参照書
昭和50年度1010日本専売公社甲号 收入支出予算補正予定計算書
区分	昭和50年度成立予算額(千円)	補正予定	改昭和50年度予定額(千円)
追加額(千円)	修正減少額(千円)	差引額(千円)
1 収入2 支出	1,567,300,5321,187,367,686	024,490,852	△212,452,950△30,453,321	△212,452,950△5,962,469	1,354,847,5821,181,405,217
[收入支出予算補正予定額内訳]
収入項別区分並びに各項の見積の事由及び計算の基礎
項	補正予定	見積の事由及び計算の基礎
追加額(千円)	修正減少額(千円)	差引額(千円)
01 たばこ事業収入02 塩事業収入	00△205,659,000△6,793,950	△205,659,000△6,793,950	最近までの販売実績等を勘案し、製造たばこ定価改定の実施期日を昭和50年11月1日と見込んだことに伴う製造たばこ売払代の修正減少ソーダ用塩売払見込数量の減少等に伴う塩売払代の修正減少
支 出
事 项 别 内 訳
項	事 項	補 正 予 定	說
追 加 額(千円)	修正減少額(千円)	差 引 額(千円)	明
01 給与其他諸費	仲裁裁定の実施等に伴う給与改善に必要な経費	5,758,202	0	5,758,202	日本専売公社職員の給与に関する仲裁裁定の実施等に伴う給与改善
02 たばこ事業費	葉たばこの調達に必要な経費の増加	8,460,048	0	8,460,048	「たばこ専売法」第5条第1項の規定による内国葉たばこ購入費の増額
製造たばこの製造に必要な経費	5,373,724	18,962,421	13,588,697	1 製造たばこの輸入数量の増加等に伴う製造たばこ購入費の増額
03 塩 事 業 費	塩の購入加工に必要な経費	1,890,425	7,990,900	6,100,475	1 「塩専売法」第5条第1項の規定による国内塩購入費の増額
05 固定資産取得費	固定資産取得に必要な経費	3,008,453	0	3,008,453	2 ソーダ用塩の輸入数量の減少等に伴う塩購入費の修正減少
09 予 備 費	予 備 費 の 減 少	0	3,500,000	3,500,000	支出追加の財源に充てるための修正減少
収入支出予算補正予定額科目別表
科目	昭和50年度成立予算額(千円)	補正予定	改昭和50年度予定額(千円)
追 加 額(千円)	修正減少額(千円)	差 引 額(千円)
収入
01 た ば こ 事 業 収 入	1,497,376,237	0	205,659,000	205,659,000	1,291,717,237
02 塩 事 業 収 入	68,292,506	0	6,793,950	6,793,950	61,498,556
科目	昭和50年度成立予算額(千円)	補正予定	改昭和50年度予定額(千円)
追加額(千円)	修正減少額(千円)	差引額(千円)
03 共 通 収 入	1,631,789	0	0	0	1,631,789
収 入 合 计	1,567,300,532	0	△ 212,452,950	△ 212,452,950	1,354,847,582
支 出
01 給 与 其 他 諸 費	133,506,427	5,758,202	0	5,758,202	139,264,629
02 た ば こ 事 業 費	900,480,260	13,833,772	△ 18,962,421	△ 5,128,649	895,351,611
03 塩 事 業 費	84,317,535	1,890,425	△ 7,990,900	△ 6,100,475	78,217,060
04 共 通 費	10,603,112	0	0	0	10,603,112
07 支 払 利 子	12,000,000	0	0	0	12,000,000
05 固 定 資 產 取 得 費	42,047,352	3,008,453	0	3,008,453	45,055,805
08 出 資	413,000	0	0	0	413,000
09 予 備 費	4,000,000	0	△ 3,500,000	△ 3,500,000	500,000
支 出 合 计	1,187,367,686	24,490,852	△ 30,453,321	△ 5,962,469	1,181,405,217
# 昭 和 50 年 度 日本 専 壳 公 社

## 改職員給与総額算定表

区 分	た ば こ 事 業
(千円)	塩 事 業
(千円)	合 計
(千円)	昭和50年度成立予算額
(千円)	差 引 追 加 額
(千円)
基 準 内 給 与	67,533,187	1,648,303	69,181,490	66,521,858	2,659,632
基 本 給	65,379,461	1,595,736	66,975,197	64,429,888	2,545,309
扶 養 手 当	864,528	21,101	885,629	822,475	63,154
調 整 手 当	1,289,198	31,466	1,320,664	1,269,495	51,169
基 準 外 給 与	45,127,580	1,101,442	46,229,022	44,221,318	2,007,704
通 勤 手 当	1,660,416	40,526	1,700,942	1,651,465	49,477
特 殊 勤 務 手 当	1,554,510	37,697	1,582,207	1,527,153	55,054
宿 日 直 手 当	458,913	11,201	470,114	470,114	0
期 末 手 当	22,587,574	551,302	23,138,876	22,211,027	927,849
奨 励 手 当	5,643,728	137,748	5,781,476	5,549,706	231,770
寒 冷 地 手 当	704,501	17,195	721,696	695,363	26,333
住 居 手 当	471,160	11,500	482,660	478,478	4,182
超 過 勤 務 手 当	11,845,057	289,106	12,134,163	11,429,788	704,375
休 職 者 給 与	211,721	5,167	216,888	208,224	8,664
合 計	112,660,767	2,749,745	115,410,512	110,743,176	4,667,336
# 日 本 専 壳 公 社

# 昭 和 50 年 度 補 正 事 業 計 画

1 補正たばこ事業

(1) 補正葉たばこの購入高

種類	昭和50年度成立予算額
(千円)	補正予定額
(千円)	改昭和50年度予定額
(千円)	備
數
量	考
価(円)	耕作面積
内国葉
在来種	59,573,369	3,813,208	63,386,577	千kg
45,995	1kg当
余
1,378	ha
17,357
黄色種	122,834,753	4,182,137	127,016,890	95,540	1,329	34,361
パレ一種	23,934,689	468,429	24,403,118	20,784	1,174	7,055
在来種(葉のし代)	37,056	△	3,726	33,330	(165)	202
小計	206,379,867	8,460,048	214,839,915	162,319	58,773
外国葉	115,287,779	0	115,287,779	99,692	1,156
合計	321,667,646	8,460,048	330,127,694	262,011
(2)補正製造たばこの製造高及び購入高
種類	製造及び購入本数	備
考
昭和50年度	補正予定	改昭和50年度
国内生産
フィルター付	百万本
265,090	百万本
0	百万本
265,090
両切	百万本
8,210	百万本
0	百万本
8,210
口付	百万本
150	百万本
0	百万本
150
刻	千kg
350	千kg
0	千kg
350
パイプ	千kg
95	千kg
0	千kg
95