Silviase/jgov_v1.4_full_ann huggingface dataset:
null of $ \beta_{1}=0 $ can be rejected with the alternative of $ \beta<0 $ , the noise view holds: the revisions are predictable and should be eliminated beforehand, i.e. they are 'noise'. Otherwise, they are not predictable and therefore contain new information, i.e. the news view holds. Also note unbiasedness and efficiency tests are applied in a similar manner to Section 4.

Table 6 shows the noise view holds in most cases. In addition, both unbiasedness and efficiency hypothesis are rejected. This result is likely to pose an interesting question of how forecasters interpret revisions and utilize them when updating their forecasts: do they really believe in the incoming data and change their projections, or do they discount the changes and stick to their previous projections? Clearly, further studies are awaited.

## (Table 6 Noise or News)

### 5.2 Subjective Distribution

Kawagoe (2007b) analyzed distribution of individual forecasts and their corresponding actual values every month during FY 2004 and 2005 and found the actual real GDP growth rates tend to be ‘outliers’ from forecast distribution: all the forecasters tend to make same (rather significant) mistake. Remember the micro-homogeneity hypothesis holds across individual forecasters.

The result has motivated us to undertake two projects: to show uncertainty around the consensus forecast, and to encourage non-specialists to join forecasting, thereby making information sets used for forecasting more heterogeneous. This subsection deals with the first, and the next does the second.

It is well known that dispersion measures, such as standard deviation, of individual forecasts do not represent their overall uncertainty (Zarnowitz and Lambros, 1987). Kawagoe (2007b) showed actual values far from individual forecasts could be well justified once their overall uncertainty is considered.

Let $ k(y_{t} - f_{it}; h) $ be uncertainty forecaster i attach to his point forecast, $ f_{it}^{11} $ . k stand for the stochastic density function called the kernel function, which assumes normal distribution with standard deviation of h, which represents uncertainty attached to point forecast and also controls smoothness of the overall distribution. The Kernel estimator of realized value is distribution over n forecasters as follows,

$$ \begin{array}{r c l}{\hat{g}(y_{t})}&{=}&{\displaystyle\frac{1}{n}\sum_{i=1}^{n}k(y_{t}-f_{i t};h).}\\ \end{array} $$

A non-parametric method sets h equal to $ h^{*}=0.24\% $ , which minimizes MISE (Mean Integrated Squared Error), $ E\{\int[\hat{g}(y)-g(y)]^{2}dy\} $ , and is given below $ ^{12} $ ,

$$ h^{*}\quad=\quad\left(\frac{4}{3n}\right)^{1/5}\bar{\sigma} $$

$$ \bar{\sigma}\quad\equiv\quad m e d i a n(\mid f_{i t}-m e d i a n(f_{i t})\mid)/0.6745. $$
An alternative way is to plug an equation standard error of an AR model of $ y_{t} $ adjusted for forecast horizon, $ h^{**} = 1.28\% $ , into h, in Eq.(7).

The two methods result in distributions shown by solid and dotted lines, respectively, in Figure 4. The figure suggests the realised value seems unexpectedly low in terms of the solid line distribution, but may be a well expected event in terms of the dotted one. The large difference between the two distributions can explain why so many real values seem outliers far from clustering individual forecasts. The point is that the uncertainty attached to point forecasts are large, compared to distance among individual forecasts.

## (Figure 4 Uncertainty around Individual Forecasts and Their Actual Values)

The result implies it is necessary to show explicitly uncertainty around the consensus: otherwise, users may underestimate its uncertainty. Studying FRB Philadelphia’s SPF and BOE’s external forecaster survey in the Inflation Report, the ESPF Steering Committee, which includes two of the authors as its members, decided to ask forecasters to submit their subjective distribution of real GDP growth rate and CPI inflation rate. The results have been available since June 2008.

### 5.3 Non-specialist Forecasts

A simple way to overcome forecast homogeneity is to ask the same question to other people, while, in practice, it may be difficult to do so to people on the street! If arguments of ‘Wisdom of Crowds’ (Surowiecki, 2004) holds, a well designed survey of general public could beat experts. But each survey has its own defects. As for inflation expectations, a casual look at Figure 5, which plots inflation expectations in the next 12 months calculated from the Monthly Consumer Confidence Survey, and their corresponding outcomes, may conclude they are biased, especially in a deflation period, and heavily affected by recent developments they face.

## (Figure 5 Expected CPI Inflation rate: the Monthly Consumer Confidence Survey)

Iiduka and Kawagoe (2009) tried to collect information from economists, but not specialists of forecasting. They distributed a simplified version of ESPF's questionnaire to members of the Japan Association of Business Cycle Studies (JABCS hereafter), and compared the responses with the ESPF. While two-thirds of them turned out to have some forecast experiences $ ^{13} $ , we will call them 'non-specialists'.

Non-specialists’ forecasts are usually different from specialists’(Table 6): cumulative distributions are significantly different between the two, except for 2 cases, according to Kolmogorov-Smirnov test. Interestingly, the specialists can not always win: JABCS beats ESPF no less than 5 times out of 17 matches.

(Table 7 Comparison between specialists’ and non-specialists’ forecasts)
ESPF is better at (quarterly) CPI inflation forecast than JABCS, probably because quarterly CPI figures are directly inferred from incoming monthly data $ {}^{14} $ , to which specialists are naturally likely to devote more resources than non-specialists. Advantages for specialists are less obvious in forecasting the real GDP growth rate because there are no direct links between monthly indicators and the quarterly GDP estimate, and much uncertainty remains. Thus, some information held by non-specialist, but not by specialists, may improve the performance of the consensus when there is a great deal of uncertainty, if it is properly combined with the information of specialists.

This is just a single. Our colleagues are now undertaking another, setting up a game called ‘Challenge economists!’ on a website $ {}^{15} $ , with the co-operation of Prediction Inc., in which participants bet by token and increase their wealth when beating the ESPF consensus. Although there were only a few participants at the start, there are now more than 60. Some preliminary results will be available in the near future.

### 5.4 Recent Developments since the Lehman Shock

An advantage of the ESPF is its monthly release, especially in terms of uncertainty of the consensus. This is very valuable in the current rapidly changing situation following the Lehman shock in September, 2008.

The consensus forecast of 2008Q4 growth rate (s.a.a.r.) was 1.1 per cent in August just before the shock, but then revised downwards to a negative rate, -0.6 per cent, in November, and further to -5.1 and -10.6 per cent in January and February, 2009, respectively. Then actual figure turned out to be -12.7 per cent.

The deterioration of the prospects of forecasters is extraordinary, but point forecasts could be well documented in other sources. Our clear strength is shown in monthly developments of mean probabilities of FY growth rate (Figure 6). Given rapid deterioration in economic conditions, they provide the government as well as private sector with valuable information of possible size of uncertainty. Individual behaviours are also interesting, but such dynamics hidden in the mean probabilities remain to be explored.

## (Figure 6 Changes in subjective distributions)

## 6 Conclusion

It has been four years since the launch of the ESPF. Now the consensus of the ESPF plays a role of anchoring economic prospects. This paper has reviewed the major results of the three year research programme funded by the ESRI. The consensus forecast performs well, compared to individual forecasters, which is consistent with US experience. The consensus forecast of real GDP growth rate is found to satisfy the average form of rational expectations hypothesis defined in Pesaran and Weale (2006) in short forecast horizon. However, as is well known, the results have misleading implications.
for individual forecasts: their unbiasedness and efficiency are rejected in most of the individual regressions.

There are still many areas for further research. Data revisions are too large to be ignored in the case of real GDP growth rate. It remains to be seen how they affect the updating of individual forecasts. Another interesting topic is forecast uncertainty. Dispersion of individual forecasts may lead to underestimation of uncertainty of the consensus forecast. This introduced a new question to ask forecasters for their subjective distribution of their point forecasts in June last year. This also motivated new research of how different forecast specialists and non-specialists are from each other in terms of economic predictions. Monthly update of subjective distribution of growth rate should be quite useful for analysis of how economic prospects are changed in the face of current turbulent situations. We hope the ESPF data will be widely used and shed a new light on expectations formation.
## References

Ban, Kanemi (2009) “Testing the Rational Expectations Hypothesis Using a Survey of Professional Forecasters in Japan.” a paper presented at ‘Expectation Formation’ part of Tokyo meeting for the ESRI International Collaboration Projects 2008 ‘Macroeconomic Perspectives in Population Decline’ held on March 9, 2009.

Bonham, Carl S. and Richard H. Cohen (2001) “To Aggregate, Pool, or Neither: Testing the Rational-Expectations Hypothesis Using Survey Data.” Journal of Business and Economic Statistics. Vol. 19. pp. 278–290.

Bowman, Adrian W. and Adelchi Azzalini (1997) Applied Smoothing Techniques for Data Analysis: the Kernel Approach with S-Plus Illustrations. Oxford, UK: Oxford University Press.

Clemen, Robret T. (1989) “Combining Forecasts: A Review and Annotated Bibliography.” International Journal of Forecasting. Vol. 5. pp. 559–583.

Croushore, Dean (1993) “Introducing: The Survey of Professional Forecasters.” FRB Philadelphia Business Review. pp. 3–13. Nov/Dec.

Croushore, Dean (2009) “Philadelphia Fed Forecasting Surveys: Their Value for Research.”a paper presented at International Workshop on Expectation Formations (sponsored by ESRI and FRB Philadelphia) on February, 27 2009.

Diebold, Francis X. and Jose A. Lopez (1996) “Forecast Evaluation and Combination.” In G. S. Maddala and C. R. Rao. eds. Handbook of Statistics vol.14. Amsterdam, Netherlands: North-Holland. pp. 241–268.

Faust, Jon, John H. Rogers, and Jonathan H. Wright (2005) “News and Noise in G-7 GDP Announcements.” Journal of Money, Credit and Banking. Vol. 37. pp. 403–419.

Fingleski, Stephen and Paul Watchel (1983) “Rational Expectations, Informational Efficiency, and Tests Using Survey Data: A Reply.” Review of Economics and Statistics. Vol. 65. pp. 529–531.

Iiduka, Nobuo and Masaaki Kawagoe (2009) “How different Are Non-specialists’ Forecasts from Specialists’?: Part I.”. mimeo. (in Japanese).

Kawagoe, Masaaki (2007a) “How Is Economic Growth Rate Revised Afterwards?: Real-time Data Analysis of Real GDP Growth Rate.” Quarterly Journal of National Accounts of Japan, No.134. Economic and Social Research Institute, Cabinet Office, Japanese Government (in Japanese).

Kawagoe, Masaaki (2007b) “Is the Consensus Forecast Just the Average?: Re-examining an Evaluation of the ESP Forecast.”.ESRI Discussion Paper Series No.180, Economic and Social Research Institute, Cabinet Office, Japanese Government (in Japanese).
Kawagoe, Masaaki (2008) “A Technical Note on Evaluation of Participants in FY 2007 ESP Forecast.”.available on the Economic Planning Association HP (http://www.epa.or.jp/esp/fcst/fcst.html). (in Japanese).

Mankiw, N. Gregory and Matthew D. Shapiro (1986) “News or Noise: An Analysis of GNP Revisions.” Survey of Current Business. Vol. May. pp. 20–25.

Mankiw, N. Gregory, David E. Runkle, and Matthew D. Shapiro (1984) “Are Preliminary Announcements of the Money Stock Rational Forecasts?” Journal of Monetary Economics. Vol. 14. pp. 15–27.

Pesaran, M. Hashem and Martin Weale (2006) “Survey Expectations.” In Graham Elliott, Clive W. J. Granger, and Allan Timmermann. eds. Handbook of Economic Forecasting. Amsterdam, Netherlands: North-Holland. pp. 715–776.

Silverman, Bernard W. (1986) Density Estimation for Statistics and Data Analysis. London, UK: Chapman and Hall.

Surowiecki, James (2004) The Wisdom of Crowds: Why the Many Are Smarter Than the Few. London, UK: Abacus.

Timmermann, Allan (2006) “Forecast Combinations.” In Graham Elliott, Clive W. J. Granger, and Allan Timmermann. eds. Handbook of Economic Forecasting. Amsterdam, Netherlands: North-Holland. pp. 135–196.

Tosetto, E. (2006) “Revisions in Quarterly GDP of OECD Countries: An Update.”.presented at OECD National Account Working Party.

Wand, M. P. and M. C. Jones (1995) Kernel Smoothing. London, UK: Chapman and Hall.

Zarnowitz, Victor and Louis A. Lambros (1987) “Consensus and Uncertainty in Economic Prediction.” Journal of Political Economy. Vol. 95. No. 3. pp. 591–621.

Zarnowitz, Victor (1985) “Rational Expectations and Macroeconomic Forecasts.” Journal of Business and Economic Statistics. Vol. 3.
Table 1 Comparison of the ESPF with other forecast surveys

Name	conducted by	the number of participants	Questions asked # of Qs, forecast period annuan/quarterly	Frequency	launched in	anonymity	subjective distribution	long-term forecasts	other questions
Y/N	Questions about	Y/N	Questions about
Japan
ESP Forecast (1)	Economic Planning Association	36	16, FY 2007 and 2008; 3, up to 2009Q1	monthly	May, 2004	Y	Y	annual real GDP and CPI growth rates	n.a.	n.a.	monetary policy changes, recession (or recovery)
USA
Blue Chip Economic Indicators (1)	Randell Moore (editor) Aspen Publisher	53	15, CY 2007 and 2008; 12, up to 2008Q4	monthly	1976	N (4)	n.a.	n.a.	biannually (March and October)	15 items for each of the coming 5 years, and average of 6 to 10 years	five to six questions are asked every time.
Survey of Professional Forecasters (2)	FRB Philadelphia (ASA/NBER by 1990)	49	22, CY 2007 and 2008; 22, 2008Q1.	quarterly	1968Q4	Y	Y	probabilities of negative growth rate, real GDP growth rate, and 3 inflation rates measured by GDP deflator, CPI and PCE.	once a year (Jan.) except for CPI and PCE inflation rates surveyed every month	5 year average for CPI and PCE inflation rate, and 10 year average for 7 items
NABE Outlook Survey (2)	National Association of Business	47	27, CY 2007 and 2008; 5, up to 2008Q4.	quarterly	1965	Y	n.a.	n.a.	n.a.	n.a.	recession probability, oil prices, etc.
Livingston Survey (3)	FRB Philadelphia (Mr. Livingston by 1989)	39	16, CY 2007 and 2008; 16, 06Q4-07Q2, 07Q2-07Q4 and 07Q4-08Q2.	biannually (June and December)	1946	Y	n.a.	n.a.	n.a.	n.a.
UK
Inflation Report (2)	Bank of England	22	4, 2008Q1 and 2009Q1.	quarterly	1993Q1 (5)	Y	Y	real GDP growth rate and RPIX inflation rate for 2009Q1 and 2010Q1.	n.a.	n.a.

Note

(1) As of January, 2007, in principle.

(2) As of 2007Q1, in principle.

(3) As of June, 2007, in principle.

(4) Forecast institutions' names are disclosed for annual forecasts.

(5) RPIX inflation only. Real GDP growth rate in 1997Q3, and interest rate and exchange rate in 1999Q3.
Figure 1 Forecasts and Actual Values of Fiscal Year Growth Rate
Figure 2 Forecasts and Actual Values of Quarterly Growth Rate
Figure 3 Forecasts and Actual Values of Quarterly CPI inflation Rate
Table 2 Performance of the Consensus Forecast

Fiscal Year	forecast for FY figure	forecast for quarterly figure	overall evaluation
real GDP growth rate	nominal GDP growth rate	real GDP growth rate (s.a.a.r.)	CPI y-o-y growth rate	unemployment rate (s.a.)
2004 H	ranking	35	—	26	34	35	36
T-score	60.63	—	52.47	62.15	63.75	59.75
consensus	10	—	8	16	11	5
T-score	44.57	—	41.70	44.53	43.71	43.63
L	14	—	27	17	15	15
T-score	45.23	—	55.63	46.19	45.26	48.05
2005 H	ranking	6	—	2	6	27	5
T-score	41.60	—	38.76	40.47	51.69	43.13
consensus	16	—	14	6	13	8
T-score	46.92	—	45.15	42.10	44.80	44.74
L	34	—	37	35	31	35
T-score	56.32	—	66.81	66.82	55.37	61.33
2006 H	ranking	33	31	23	31	28	30
T-score	68.71	66.45	53.83	66.37	55.99	62.27
consensus	17	15	8	7	4	6
T-score	46.59	47.62	42.08	41.91	38.03	43.26
L	1	1	32	17	30	13
T-score	29.35	33.41	62.17	46.78	59.47	46.24
2007 H	ranking	33	32	1	3	25	21
T-score	65.47	64.86	30.07	37.04	62.82	52.05
consensus	8	12	11	13	6	9
T-score	43.85	45.48	45.17	47.47	41.04	44.60
L	1	1	29	29	13	17
T-score	34.62	29.56	71.29	68.32	46.04	49.97

Note: H: average of 8 highest forecasts.

L: average of 8 lowest forecasts.

T-score: score based on normal distribution with mean of 50 and std. of 10.

(source) performance evaluation results published by Economic Planning Association in Sep., 2006, Oct., 2007 and Sep., 2008.
Table 3 Tests of Individual Forecasts

j month before	unbiasedness	efficiency
# of rejections	pooled data	# of rejections	pooled data
F test	P value	F test	P value
j = 0	0	26.64	0.00	1	35.97	0.00
j = 1	4	60.56	0.00	0	36.11	0.00
j = 2	8	93.64	0.00	2	59.59	0.00
j = 0 to 2	23	146.04	0.00	31	110.10	0.00
j = 3 to 5	17	111.45	0.00	31	142.10	0.00
j = 6 to 8	20	177.88	0.00	22	113.13	0.00
j = 9 to 11	18	145.68	0.00	33	293.14	0.00
j = 12 to 14	33	1486.79	0.00	33	884.04	0.00

(source) Ban (2009)

Table 4 Tests of the Consensus Forecasts

j month	unbiasedness	efficiency
before	F test	P value	F test	P value
j = 0	0.79	0.47	1.62	0.23
j = 1	0.83	0.45	0.92	0.52
j = 2	1.82	0.20	0.85	0.56
j = 0 to 2	1.30	0.28	3.18	0.01
j = 3 to 5	0.10	0.90	3.66	0.00
j = 6 to 8	1.03	0.37	3.44	0.01
j = 9 to 11	0.25	0.78	6.28	0.00
j = 12 to 14	18.25	0.00	14.60	0.00

(source) Ban (2009)
Table 5 Revisions to the First Estimates of Real GDP Growth Rate

$ R^{21}_{t} $	$ R^{F1}_{t} $	reference
$ y^{1E}_{t} $	$ y^{2E}_{t} $	$ y^{FE}_{t} $
Mean	0.275	0.054	2.483	2.758	2.537
STD	0.883	2.873	2.156	2.029	2.549
MAE	0.708	2.144
RMSE	0.900	2.788

(source) Kawagoe (2007a)

Table 6 Noise or News

dependent variable	const	y1E t	other variables	R2	adj-R2
est.	std	est.	std
R21t	0.625	0.303 *	-0.141	0.059 **	0.119	0.060
0.937	0.320 **	-0.152	0.055 **	seasonal dummy	0.440	0.253
2.030	0.878 **	-0.171	0.074 **	a dummy and financial variables	0.500	0.333
RFIt	1.765	0.956 *	-0.152	0.055 **	0.267	0.060
1.616	1.778	-0.604	0.345	seasonal dummy	0.267	0.079
4.904	2.654 *	-0.892	0.294 **	a dummy and financial variables	0.449	0.322

(Note) $ ^{*} $ : significant at 10 per cent; $ ^{**} $ : significant at 5 per cent.

(source) Kawagoe (2007a)
Figure 4 Uncertainty around Individual Forecasts and Their Actual Value
Figure 5 Expected CPI inflation Rate: the Monthly Consumer Confidence Survey

(Source) Iidulka and Kawagoe (2009)
Table 7 Comparison between Specialists’ and Non-specialists’ Forecasts

average	standard deviation	CDF
ESPF	JABCS	test	ESPF	JABCS	test
real GDP growth rate
2007Q4(3.7)	Dec. 2007	1.02	1.19	1.00	1.46
Jan. 2008	1.01	1.03	0.92	1.59
Feb. 2009	1.36	0.89	*	0.60	1.45	**
2008Q1(3.3)	Dec. 2007	2.05	0.93	**	1.04	1.15	**
Jan. 2008	2.10	0.99	**	0.96	1.10	**
Feb. 2009	1.65	0.70	**	0.84	0.94	**
FY 2008(n.a.)	Dec. 2007	1.98	1.35	**	0.33	0.78	**
Jan. 2008	1.92	1.49	**	0.32	0.73	**
Feb. 2009	1.74	1.23	**	0.35	0.77	**
CPI inflation rate
2007Q4(0.5)	Dec. 2007	0.16	0.07	**	0.11	0.23	**
Jan. 2008	0.27	0.16	**	0.11	0.24	**
2008Q1(1.0)	Dec. 2007	0.37	0.19	**	0.14	0.36	**
Jan. 2008	0.48	0.29	**	0.14	0.30	**
Feb. 2009	0.76	0.75	0.21	0.35
FY 2008(n.a.)	Dec. 2007	0.37	0.32	0.18	0.47	**
Jan. 2008	0.39	0.40	0.13	0.48	**
Feb. 2009	0.51	0.79	**	0.13	0.57	**

Note: *: significant at 10 per cent; **: significant at 5 per cent.

Figures in parentheses are actual values.

(source) Iiduka and Kawagoe (2009)
## Figure 6 Changes in subjective distributions

(source) ESPF

Mean Probabilities of CPI y-o-y growth rate in FY 2009
ESRI Discussion Paper Series No.140

# フリーターの増加と労働所得格差の拡大

by

太田 清

May 2005

內閣府経済社会総合研究所

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan
# フリーターの増加と労働所得格差の拡大 $ ^{1} $

太田清 $ ^{2} $
# フリーターの増加と労働所得格差の拡大（要旨） 内閣府経済社会総合研究所 太田清

## 1 問題意識

日本は長い間、個人間の経済的格差に関し、平等な社会であると言われてきた。1990年代後半頃から、日本でも所得格差は拡大しているとの見方も出てきたが、家計所得や賃金統計などに見られる格差の拡大の多くは、人口の高齢化に伴う見かけ上の拡大にすぎないことが指摘されてきた。しかし、90年代の特に後半以降に起こった労働市場の変化の中で、最近では、所得格差は拡大しているのではないだろうか。本稿では労働市場の変化をもとらえた統計で、そのことを検証した。

## 2 分析手法等

所得格差や賃金格差に関する実証研究では、これまで非正規雇用者までカバーされていなかったが、本稿では非正規雇用をもカバーする統計を用いた。男性の労働所得（賃金など）について、ジニ係数等で個人間の不平等度（格差の大きさ）を計測した。人口の年齢構成の変化が格差の動向に見かけ上の影響を与えてしまうことを考慮し、年齢別に不平等度を算出した（年齢構成要因のコントロール）。特に若年層に分析の焦点を置いた。さらに、正規雇用者、非正規雇用者に分け、非正規の増加が全体としての格差の拡大にどのように影響しているかを計測した（対数分散等による要因分解）。

## 3 分析結果のポイントとインプリケーション

1990 年代後半から最近にかけて、個人間の労働所得格差が拡大していることがわかった。いずれの年齢層でも格差は拡大しているが、特に若年層でその拡大テンポが速い。この若年層内における格差の拡大は、フリーター化など非正規雇用の増大の影響が大きい。

若年層の間での格差拡大は、日本社会の将来の姿を先取りしたものである可能性もある。本稿の分析結果は、若年者が職業能力（稼得能力）を獲得する機会を十分に持てるようにする政策が極めて重要であることを改めて示している。また、やや技術的な問題に関する含意であるが、経済的格差の拡大等、経済社会の重要な変化をできるだけ早期に察知できるよう、統計の整備が望まれる。
Rise in Labor Income Inequality with increasing numbers of freeters

by

Kiyoshi Ota $ ^{1} $

May, 2005

## Abstract

This paper examines trend in labor income inequality for individuals in Japan with focus on young male. Gini coefficient and other inequality measures are computed for labor income of male, using data from Employment Status Survey which covers not only regular workers but non-regular, temporary workers such as freeter – free+albeiter. Inequality measures computed, by controlling for the impact of changes in age structure of population, provide evidences of rise in inequality in labor income, particularly for the young in recent years. Rise in numbers of non-regular workers has contributed to increasing inequality among young male.
目次
1．はじめに
2．本稿の分析の特徴
2.13つの特徴
2.2日本社会の将来の姿の展望に役立つ指標—特徴1
2.3非正規雇用者を含めた分析－特徴2
2.4年齢要因のコントロール－特徴3
3．先行研究
3.1世帯ベースの所得格差に関する先行研究
3.2労働所得、賃金の格差に関する先行研究
4．使用するデータ
5．個人間の労働所得格差の計測
5.1計測手法
5.2基本的統計量（有業者等の所得分布状況）
5.3不平等尺度の計測結果
5.42種類の追加的なジニ係数の計測
6結論と今後の課題
参考文献
付注1対数分散による変化の要因分解
図表
付表
## フリーターの増加と労働所得格差の拡大

太田清 $ ^{1} $

### 1. はじめに

## (問題意識 - 所得格差は拡大しているのか)

本稿では、個人の経済力、所得の最も大きな源泉である労働所得（賃金など）について、その個人間の格差の動向を分析する。

日本社会は長い間、個人間の経済的格差や富、所得の分配の上では、平等な社会であると言われてきた。これに対して、格差は拡大してきているという見方が、1990年代後半以降、互いに異なった学問分野の研究者などから示されるようになった（橘木(1998)、佐藤(2000)、苅谷(2001））。しかし、家計統計や賃金統計のデータに見られる所得格差の拡大は、単に人口の高齢化（年齢構成の変化）に伴う見かけ上の拡大であって、不平等感を高めるようものではないと指摘されてきた。実際、論争にもなっていた2000年前後に利用可能だった家計所得データでは、世帯主の年齢について、年齢計でみると格差は拡大していたが、年齢別にみると格差の拡大はほとんどみられなかった。

しかし、他方で、その家計統計や賃金統計ではカバーされていない部分も多い。例えば、よく使われる賃金統計は、小規模企業や非正規労働者を含まない。もちろん、失業者やホームレスの増加に伴う実質的な格差の拡大もとらえていない。また、家計の統計も非正規雇用者の動向を把握できない。統計とは別に、多くの人は実感として格差が拡大していると受け止めているのではないかとの指摘もある(橘木(2004))。

1990 年代後半以降、労働力の非正規化が一段と進行し、失業率もかなり上昇した。非正規雇用者の多くは女性であり、主に30歳以上の年齢層である。しかし、特に90年代後半以降になると、経済の停滞が強まる中で、若年労働力の非正規化が加速し、かつ男性若年層にまで非正規化の波が及ぶようになってきた。若者の「フリーター化」である。この間失業率が最も上昇したのも若年層であった。

本稿では、これまで実証研究と違って、非正規雇用等をもカバーする統計により、労働所得格差（賃金等の格差）の動向を最近年まで分析する。個人間の経済的格差において「平等」であるといわれてきた日本において、その傾向に変化はないかを検証する。特に、非正規労働の増加がその傾向を変えていないか、それが影響しているとすれば、どの程度であるかを計測する。

## (本稿の構成)

本稿の構成は以下の通りである。まず、第2章では、本稿の分析の特徴について述べる。第3章では、先行研究を概観する。第4章では、データとして使う「就業構造基本調査」、「労働力調査」などについて説明する。第5章では、男性の個人間の労働所得格差を計測し、その要因分解などを行う。特に若年男性労働力の非正規化が全体的な格差の拡大にど