Silviase/jgov_v1.4_full_ann huggingface dataset:
To examine this possibility, Figure 14 displays the changes by firm size and by sex. We see a declining trend for each firm size category across birth cohorts. For male workers, the trend is almost the same for different firm sizes, while both the level and speed of decline are more obvious with the increase in firm size for female workers.

To conclude, the mean tenure years of male workers drop continuously for all birth cohorts born after the WWII regardless of age, industry, or firm size. Declining long-term employment is a ubiquitous trend among male Japanese workers. In contrast, the trends of female workers are somewhat heterogeneous.

### 6. Positive Job Hopping?

The literature finds that job changes are associated with wage growth in several countries, such as the US and Germany (Topel (1991), Topel and Ward (1991), Shörnberg (2007)). Considering these findings, declining long-term employment does not necessarily imply worsening labor-market conditions if it is resulting from an increase of positive job hopping among Japanese workers. To examine if this is the case, we rely on the Survey on Employment Trends (Koyo Doko Chosa) implemented by the Ministry of Health, Labor and Welfare. The survey covers about 16,000 randomly sampled establishments and asks for the number of terminated employment contracts and the major reason for each of them. The survey asks employers to distribute questionnaires to new entrants and then collect them. This new entrant's survey asks the change of wage before and after the job change. We do not have access to the micro data of this survey for this project but aggregate statistics are publicly available.

Figure 16 indicates the time series of the fraction of the incidence of involuntary employment termination among all terminations of employment. The fraction of involuntary separation is calculated as the number of “separations due to the end of contract” and “separations due to managerial reasons” divided by the total number of separations. It is difficult to classify “mandatory retirement” as a voluntary or involuntary separation, so we dropped this category from both the numerator and denominator. The fraction of involuntary separation increased from 10% in 1990 to 20% in 2000, and then declined to 15% afterward. This time series indicates that the decline of long-term employment is not a consequence of happy job hopping becoming more prevalent in the Japanese labor market.

The survey also asks new entrants to firms about their experience of wage change before and after the job change by five categories until 1997 (decrease by 30% or more, decrease between 10 and 30%, decrease or increase within 10%, increase between 10 and 30%, increase 30% or more) and seven categories in 1998 and afterward (decrease
by 30% or more, decrease between 10 and 30%, decrease less than 10%, unchanged, increase less than 10%, increase between 10 and 30%, increase 30% or more). Figure 17 draws the time series of the composition of wage change associated with job change. While there is a gap in time series because of the change of the questionnaire format, the composition of wage changes are largely unchanged during the sample period. If there is any change, the fraction of workers who experienced a 30% or more wage decrease had slightly increased, and the fraction of workers who experienced a wage increase between 10 and 30% had decreased. Again, this time-series does not seem to suggest that declining long-term employment is not a byproduct of the positive change of Japanese labor market that allows its workers enjoy more opportunities for happy job hopping.

### 7. Concluding Remarks

This study examined the trends of long-term employment in Japan based on two sets of independent government micro data, the Employment Status Survey (ESS, Shugyo Kozo Kihon Chosa) and the Basic Survey of Wage Structure (BSWS, Chingin Kozo Kihon Chosa).

The analysis based on the two samples renders different pictures of the trends of job stability among male workers. Both ESS and BSWS result indicate a secular decline of job stability for male workers, but the pace of decline is estimated to be much faster in the ESS than in the BSWS. One major factor for this trend is the increase in the proportion of nonpermanent-regular workers, who generally have higher turnover than permanent-regular workers. The relative fraction of nonpermanent-regular workers greatly increased among birth cohorts after 1970. The relative tenure for male permanent-regular workers has a gradual declining trend for birth cohorts after 1950. As a background for such decline, we can point out factors such as (i) an increase in the proportion of workers employed at small-sized and/or newly established firms, and (ii) an increase in the proportion of workers employed in industry sectors such as the service industry, whose turnover is relatively greater than that of the other sectors. Empirical assessments of these possibilities, however, point to the fact that declining long-term employment is ubiquitous across all firm sizes of all industries.

An analysis of two surveys indicates similar trends of average tenure among female workers. The analysis of ESS data indicates a continuous decrease of average tenure after the 1960-born cohort. Results based on both surveys indicate the relative fraction of nonpermanent regular workers has an increasing trend through birth cohorts. Among permanent-regular workers, the relative tenure declined after the 1970 cohort from both
survey results. Among nonpermanent-regular workers, the ESS analysis indicates a secular decline of job stability, while the BSWS shows a slight increase of job stability.

In sum, an analysis based on the ESS indicates a secular decline of job stability, but an analysis based on the BWSs indicates a decline of long-term employment to a relatively smaller extent. The difference of the results could be well explained by the sampling structure of the two surveys. The BWSs's sampling procedure arguably drops workers whose job stability has declined. The discrepancy between the two surveys is to some degree inevitable, because the ESS is designed to represent the employment status of the Japanese population, while the BWSs is designed to represent the wage of workers by workers' attributes. The distribution of conditioning variables in the BWSs is not designed to be nationally representative.

It is worth noting again that the declining trend of long-term employment is a secular trend among the cohorts born after the WWII and not an episodic event attributable to macroeconomic stagnation after the early 1990s in Japan. This secular decline of long-term employment practices partly takes the form of an increase in the fraction of nonpermanent-regular workers. Thus, the mechanism behind the increase of the fraction of nonpermanent-regular workers, which has attracted recent attention from policy makers, should be investigated in the context of this secular trend.

As a way to understand the reasons behind the secular decline of long-term employment practices, a comparison of the Japanese experience with the US experience is useful. Applying Farber's (2007) method to ESS, which is a household survey comparable to US Current Population Survey, renders a similar degree of secular decline of job stability found by Farber (2007) based on the CPS. Among US male workers, age-specific average tenure of the 1970 cohort is about 20 percent shorter than that of the 1945 cohort. The corresponding figure for Japanese male workers is about the same. Countries on both sides of the Pacific share the same degree of declining job stability despite numerous differences in labor-market institutions. This finding suggests that a fundamental change in the global economic environment, such as technological progress or deepening international dependence, drives the decline of long-term employment practices. Unpacking the factor behind a common decline of long-term employment practices in both Japan and the US is left for future investigation.
## Reference

Ariga, Kenn, Gorgio Brunello and Yasushi Ohkusa (2000) Japanese Internal Labor Market in Transition. Cambridge: Cambridge University Press.

Chuma, Hiroyuki (1998) “Is Japan's Long-Term Employment System Changing?” In Isao Ohashi and Toshiaki Tachibanaki eds., Internal Labour Markets, Incentives and Employment. London: Macmillan.

Farber, Henry (2007) "Labor Market Adjustment to Globalization: Long-Term Employment in the United States and Japan," Working Paper No. 519, Princeton University Industrial Relations Section.

Hamaaki, Junya, Masahiro Hori, Saeko Maeda, and Keiko Murata (2010) “Is the Japanese Employment System Degenerating? Evidence from the Basic Survey on Wage Structure,” ESRI Discussion Paper Series No.232.

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Mincer, Jacob and Yoshio Higuchi (1988) "Wage Structures and Labor Turnover in the United States and Japan," Journal of the Japanese and International Economies, Vol. 2, No. 2, pp. 97-133.

Kato, Takao (2001) “The End of Lifetime Employment in Japan?: Evidence from National Surveys and Field Research,” Journal of the Japanese and International Economies, Vol. 15, No. 4, pp. 489-514.

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Moriguchi, Chiaki and Hiroshi Ono (2006) "Japanese Lifetime Employment: A Century's Perspective," in Institutional Change in Japan, edited by Magnus Blomstrom and Sumner La Croix, New York: Routledge.
Neumark, David (2000) On the job: Is Long-term Employment a Thing of the Past? New York: Russell Sage Foundation.

Ono, Hiroshi (2010) “Lifetime Employment in Japan: Concepts and Measurements,” Journal of the Japanese and International Economies, Vol. 24, No. 1, pp.1-27.

Shimizutani, Satoshi and Izumi Yokoyama (2009) "Japan's Long-Term Employment Practice Survived? Developments Since the 1990s," Industrial and Labor Relations Review, vol. 62, No. 3, pp. 313-326.

Schönberg, Uta (2007) “Wage Growth due to Human Capital Accumulation and Job Search: A Comparison between the United States and Germany,” Industrial and Labor Relations Review, Vol. 60, No. 4, pp. 562-586.

Topel, Robert H. (1991) "Specific Capital, Mobility, and Wages: Wages Rise with Job Seniority," Journal of Political Economy, Vol. 99, No. 1, pp. 145-76.

Topel, Robert H. and Michael Ward (1991) "Job Mobility and the Careers of Young Men," Quarterly Journal of Economics, Vol. 107, No.2, pp. 439-79
Table 1: Distribution of Age, Tenure and Permanent-Regular Work Status by Birth Cohort in ESS and BSWS

ESS	BSWS
Birth Years	Age	Tenure	Permanent Regular	Age	Tenure	Permanent Regular
1944-1949	39.96	14.43	0.96	47.76	19.09	0.99
(0.02)	(0.03)	(0.0008)	(0.01)	(0.03)	(0.0001)
1950-1959	33.16	9.39	0.96	43.07	15.48	0.99
(0.02)	(0.02)	(0.0007)	(0.01)	(0.02)	(0.0002)
1960-1969	27.68	5.35	0.95	34.46	9.52	0.98
(0.01)	(0.02)	(0.001)	(0.01)	(0.02)	(0.0002)
1970-1981	29.76	6.23	0.89	29.52	5.96	0.96
(0.02)	(0.02)	(0.001)	(0.01)	(0.01)	(0.0005)
Total	34.55	10.43	0.96	38.22	12.15	0.98
(0.01)	(0.02)	(0.0006)	(0.01)	(0.01)	(0.0001)

Female
ESS	BSWS
Birth Years	Age	Tenure	Permanent Regular	Age	Tenure	Permanent Regular
1944-1949	40.79	6.82	0.51	48.01	9.22	0.64
(0.02)	(0.03)	(0.003)	(0.01)	(0.02)	(0.002)
1950-1959	33.83	5.54	0.59	44.33	8.31	0.61
(0.02)	(0.02)	(0.002)	(0.02)	(0.02)	(0.002)
1960-1969	27.37	4.24	0.75	34.86	6.31	0.73
(0.02)	(0.02)	(0.003)	(0.02)	(0.01)	(0.001)
1970-1981	29.46	4.65	0.59	29.13	4.76	0.80
(0.02)	(0.02)	(0.002)	(0.01)	(0.01)	(0.001)
Total	35.19	5.77	0.59	38.60	7.04	0.69
(0.02)	(0.02)	(0.002)	(0.02)	(0.01)	(0.001)

Note: Means are reported. Standard errors of means are reported in parenthesis. Sampling weights are used to calculate all the statistics.
Table 2: Distribution of Years of Education by Birth Cohort, BSWS Male

ESS	BSWS
years of education	years of education
Birth Years	9-11	12	13-15	16-	9-11	9-11	12	13-15
1944-1949	23.38	49.75	3.79	23.08	22.47	52.14	3.49	21.90
1950-1959	14.43	49.35	5.85	30.37	11.13	51.60	5.69	31.59
1960-1969	6.95	48.93	10.15	33.97	3.89	48.27	9.22	38.61
1970-1981	6.26	42.09	17.73	35.09	3.08	44.83	13.80	38.29
Total	16.22	49.42	5.89	28.48	8.82	49.26	8.11	33.81
Female
ESS	BSWS
years of education	years of education
Birth Years	9-11	12	13-15	16-	9-11	9-11	12	13-15
1944-1949	26.28	61.01	9.39	3.32	23.31	63.71	9.81	3.17
1950-1959	13.36	61.60	18.19	6.85	10.57	63.32	19.64	6.47
1960-1969	3.74	51.01	32.84	12.41	2.06	52.44	31.87	13.63
1970-1981	3.51	36.74	39.78	19.97	1.14	37.38	39.21	22.27
Total	16.33	59.34	17.72	6.60	7.13	53.18	27.29	12.40

Note: Sampling weights are used.
Table 3: Industry and Firm Size Distribution in ESS and BSWS

Male	1987	1989	2007
ESS	BWS	ESS	BWS
(Industry)	Share	Tenure	Share	Tenure	Share	Tenure	Share	Tenure
Agriculture	0.39	7.99	0	-	0.96	10.90	0	-
Forestry	0.10	7.97	0	-	0.11	8.89	0	-
Fishery	0.64	12.82	0	-	0.29	16.46	0	-
Mining	0.29	9.67	0.11	10.93	0.12	14.75	0.76	12.64
Construction	15.47	10.68	1.41	11.34	14.39	13.78	3.84	12.99
Manufacturing	29.86	11.18	45.70	11.07	29.34	13.96	17.72	12.86
Public Utilities	0.84	13.56	2.62	11.48	0.95	20.29	2.99	15.85
Communication & Transportation	11.09	10.66	13.34	12.24	13.13	12.25	8.67	13.03
Wholesale & Retail	21.77	9.58	12.30	11.09	16.53	13.03	18.63	12.72
Finance, Insurance, Real Estate	4.45	10.84	5.99	10.91	3.60	15.08	17.95	12.85
Service	15.10	9.07	18.52	11.01	20.58	11.40	29.44	12.82
Total	100	10.37	100	11.22	100	13.10	100	12.93

Female	1987	1989	2007
ESS	BWS	ESS	BWS
(Industry)	Share	Tenure	Share	Tenure	Share	Tenure	Share	Tenure
Agriculture	0.57	4.29	0	-	1.48	11.52	0	-
Forestry	0.04	7.13	0	-	0.02	8.03	0	-
Fishery	0.14	6.90	0	-	0.13	14.57	0	-
Mining	0.07	8.95	0.12	6.90	0.02	11.69	0.82	7.60
Construction	4.02	4.41	1.34	7.10	3.71	9.91	3.51	7.41
Manufacturing	31.11	5.91	45.52	6.40	18.40	9.20	17.53	7.57
Public Utilities	0.20	8.72	2.49	6.87	0.15	12.71	1.85	8.48
Communication & Transportation	2.52	8.39	9.83	7.20	4.88	6.90	7.93	7.60
Wholesale & Retail	29.20	4.91	12.71	6.51	23.47	7.13	19.53	7.33
Finance, Insurance, Real Estate	6.51	5.56	6.71	6.49	5.35	8.46	19.13	7.30
Service	25.62	6.57	20.29	6.52	42.39	7.77	29.70	7.30
Total	100	5.85	100	6.55	100	8.03	100	7.41
Table 3. Continued

Male	1987	1989	2007
ESS	BWS	ESS	BWS
(Firm size)	Share	Tenure	Share	Tenure	Share	Tenure	Share	Tenure
1-4	7.57	9.81	0	-	8.00	13.06	0	-
5-9	10.01	9.33	4.11	7.68	8.87	11.98	3.41	9.20
10-29	16.26	8.88	13.09	7.38	15.08	11.05	11.68	9.18
30-99	17.06	9.09	17.66	8.90	16.92	11.12	17.27	10.60
100-299	13.90	10.04	14.50	10.44	14.51	12.41	17.48	12.21
300-499	5.40	10.65	6.15	11.48	6.41	13.40	7.15	13.19
500-999	6.19	11.25	7.44	12.20	7.16	14.44	8.73	14.00
1000-	23.61	12.85	37.05	14.15	23.04	16.13	34.29	15.78
Total	100	10.37	100	11.22	100	13.07	100	12.93

Female	1987	1989	2007
ESS	BWS	ESS	BWS
(Firm size)	Share	Tenure	Share	Tenure	Share	Tenure	Share	Tenure
1-4	11.97	5.47	0	-	9.36	10.75	0.00	-
5-9	12.80	5.63	6.46	5.79	10.11	8.34	4.61	6.95
10-29	19.09	5.38	18.80	5.05	16.52	7.29	14.05	6.55
30-99	18.51	5.33	22.27	5.58	18.11	7.10	17.63	6.84
100-299	14.16	6.05	15.70	6.31	15.16	7.79	16.11	7.29
300-499	4.49	6.60	6.06	7.35	5.81	8.41	6.11	7.81
500-999	4.33	6.80	6.34	7.59	6.26	8.36	7.67	7.92
1000-	14.65	6.93	24.37	8.47	18.66	7.73	33.83	7.99
Total	100	5.84	100	6.55	100	7.98	100	7.41

Note: Share (%), Tenure (years, average)
Figure 1: Mean Tenure for Japan and the US

Note: Employment Status Survey 2007 and Basic Survey of Wage Structure 2005-2008 are used for Japan. Current Population Survey January Supplement 2004, 2006 and 2008 are used for the US.
Figure 2A: Mean Tenure by birth cohort, Employment Status Survey

Figure 2B: Mean tenure by birth cohort, Basic Survey of Wage Structure
Figure 3: Relative Years of Job Tenure

Note: Age adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007 and BSWS 1988-2008

Figure 4: Relative Years of Job Tenure, Male and Female, Education Adjusted, ESS

Note: Age and education adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007
Figure 5: Percentage Change of Permanent Regular Workers

Note: Age adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007 and BSWS 1988-2008

Figure 6: Job Tenure among Permanent Regular Workers

Note: Age adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007 and BSWS 1988-2008
Figure 7: Job Tenure among Non-Permanent Regular Workers

Note: Age adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007 and BSWS 1988-2008

Figure 8: Residual Distribution, Male
Figure 9: Residual Distribution, Female

## Log Tenure Residual

Figure 10: Weight Distribution
Figure 11: The Effect of Sampling Difference between ESS and BSWS, Permanent Regular Workers

Figure 12: The Effect of Sampling Difference between ESS and BSWS, Non-Permanent Regular Workers

Note: Age adjusted
Figure 13: Changes of mean tenure by age groups

Note: Age adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007

Figure 14: Changes of mean tenure by industries

Note: Age adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007
Figure 15: Changes of mean tenure by firm sizes

Percent Change from 1944 Cohort: By Firm Size

Note: Age adjusted, Source: ESS 1982, 1987, 1992, 1997, 2002, 2007
Figure 16: Fraction of involuntary job separation

Source: Survey of Employment Trends

Sample: All individuals

Note: Percentage of involuntary separation is defined as (Separation due to the end of contract + separation due to managerial reasons - separation due to mandatory retirement) / (total separation - separation due to mandatory retirement).
Figure 17: Composition of wage change through job change

Source: Survey of Employment Trends

Sample: All individuals

Note: Until 1997, the wage change between -10% and 10% was a single choice, whereas this category consists of "wage decrease up to 10%," "unchanged" and "wage increase up to 10%" in 1998 and afterward. The change of this questionnaire explains the gap between 1997 and 1998.
ESRI Discussion Paper Series No.223

DAC諸国のODA支出とGDPなどの経済指標との

相関・因果関係に関する分析：

GDPはどこまでODA支出を説明できるか

大村昌弘

September 2009

內閣府経済社会総合研究所

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
# DAC諸国のODA支出とGDPなどの経済指標との

# 相関・因果関係に関する分析：

# GDPはどこまでODA支出を説明できるか

大村昌弘 $ ^{*} $

內閣府経済社会総合研究所

2009年9月
## 要旨

ODAに関する国際目標が、もっぱらドナー国のODA支出とGDPとの関係に注目していることの妥当性に関して過去のデータに基づいて検証した。クロスセクション分析では、各種説明変数の中でGDPの説明力が際立って高く、租税負担率、経常収支、失業率、公的債務が次ぐ。ODAとGDPとの単回帰分析では、GDPの回帰係数が、1990年代初頭以降1997年まで下降し、その後安定、2002年以降持ち直した趨勢が明らかとなった。こうした傾向は、1990年代前半において国際援助理念が模索期にあったこと、また、2000年代に入りモンテレー合意に象徴されるように各国の援助コミットメントが再確認されたことと平仄が合っている。パネルデータ分析においては、GDPのみを説明変数とするモデルの説明力が一番高く、GDP、公的債務、財政収支、経常収支の4変数を用いるモデルがそれに次ぐ。1変数モデルにおいて、GDPの回帰係数は、0.2077であり、DAC諸国のGDPが100億ドル増加すると、ODA支出が2077万ドル増加することになる。GDP以外では、公的債務、財政収支、経常収支についてその順序で説明力が認められた。時系列分析では、重回帰における説明変数の中で、GDPが最も多くのケースで採用され、租税負担率、経常収支、失業率、公的債務が次ぐ。DAC等において、GDPとODA支出との関係に注目し、国際目標を作成したり、その比率をモニターすることには妥当性があると言えよう。しかし、GDP以外の経済指標（パネルデータ分析によれば、公的債務、財政収支、経常収支、回帰分析によれば、租税負担率、経常収支、失業率、公的債務）にも比較的強い説明力が認められるので、これら指標の動向にもしかるべく留意することが適当である。
# Analysis on correlations and causal relations between ODA disbursement and economic indicators including GDP among DAC countries: How much can GDP explain ODA disbursement?

Masahiro OMURA

(Senior Research Fellow, Economic and Social Research Institute, Cabinet Office)

## Abstract

ODA disbursements of donor countries have been examined and compared at international fora including DAC. Mostly attention has been paid to ODA/GDP ratio. International target of 0.7% ODA/GDP ratio has been maintained since the 1970s. The premise is that GDP determines ODA. Other economic indicators such as fiscal and international balances might explain ODA disbursement to some extent. So far attention has been centered on GDP. This paper analyzes relations between ODA disbursement and economic indicators including GDP and shows how much GDP can explain ODA. It uses cross-section, time series and panel data analyses.

Cross-section analysis examined the data between 1991 and 2006 covering the period after the end of the Cold War. ODA disbursement was regressed against eight variables including GDP, GDP growth rate, fiscal balance, government debt, current account balance, international reserves, total tax rate, and unemployment rate. Backward elimination method was employed to identify more relevant variables – GDP, tax rate, current account balance, unemployment rate and government debt in that order. Simple linear regression between ODA and GDP reveals a downward trend in the coefficient of GDP between 1992 and 1997 and an upward trend between 2002 and 2005. These trends respectively coincide with uncertainty about aid motives in the early 1990s and renewed motivation in the early 2000s exemplified by the Monterrey consensus.

Panel data analysis was conducted using four variables including GDP, government debt, fiscal balance and current account balance after an elimination of four less relevant variables in an eight variable model. A single variable model with GDP was found to have slightly higher overall R-squared than a four variable model. The coefficient of GDP was 0.2077 in the former, indicating ODA increase of 20.77 million dollars was realized by GDP increase of 10 billion dollars by the DAC countries. In the four models examined the coefficients of GDP were found to have the highest t-scores. The government debt, fiscal balance and current account balance were also found to
have an explanatory power in that order.

In the time series analysis, ODA was regressed against GDP together with the first time lag of ODA. The result showed that ten DAC member countries had a long term dynamic multiplier of more than 0.7 per cent. A multi-variable regression was conducted with logarithm conversion as well as with the backward elimination. The variable most heavily selected was GDP followed by tax rate, current account balance, unemployment rate and government debt in that order. The serial correlation was mostly not confirmed. Impulse reaction analyses were conducted based on VAR models between real ODA and real GDP using differences to eliminate a unit root. In most cases the accumulative effect of GDP to ODA was positive, and that of ODA to GDP was the same in many cases, though Granger causality was not confirmed in many cases. ECM analysis was conducted in the cases of two countries which were found to have cointegration relations.

The results of multi-variable regressions as well as panel data analyses showed that GDP has the strongest explanatory power among economic indicators regarding ODA disbursements. It seems adequate that international fora including the DAC focus their attention to the relations between ODA and GDP and monitor ODA/GDP ratios. It would be appropriate, however, due attention be also paid to other economic indicators (according to panel data analyses, governmental debt, fiscal balance and current account balance, and according to cross-section and time series regression analyses, tax rate, current account balance, unemployment rate and government debt) in the analysis of ODA disbursements as they also have relatively strong explanatory power regarding ODA disbursements.