Silviase/jgov_v1.4_full_ann huggingface dataset:
Chart6 Production, Real Wage and Unemployment Rate in Japan and the U.S.A (1) Japan

(2)U.S.A.

(Source) Japn: Ministry of General Affairs, Ministry of Welfare and Labor

U.S.: Department of Commerce, Department of Laobr
Chart 7 Phillips Curve

CPI increase rate to the same month of the previous

(Source) Ministry of General Affairs "Labor Force Survey," "Consumer Price Index," (Note) Effects of Consumption tax on CPI are excluded.
Chart 8 Newly Introduced Variables (1) Real Wage and Maginal Productivity of Labor

## (1) Excess Debt(All Industries)
Table 9 Unit Root Test

(Level)

Variables	Trend + Constant	Constant	None
No. of Lags	1	2	3	4	1	2	3	4	1	2	3	4
NIPUB	-8.89 ***	-2.02	1.16	-1.10	-5.47 ***	-1.66	-1.22	-1.27	0.14	0.33	1.54	0.20
CALL	-3.97 **	-2.69	-2.61	-2.52	-3.43 **	-2.05	-1.41	-1.09	-3.94 ***	-2.58 **	-1.77 *	-1.48
M2CD	-0.62	-1.13	-1.32	-1.96	-2.91 *	-3.15 **	-2.80 *	-2.33	2.60	1.91	1.40	0.73
EXCHANGE	-1.54	-1.20	-1.89	-1.97	-1.52	-1.36	-1.38	-1.36	-1.02	-1.07	-0.75	-0.72
PGDP	-0.98	-0.49	1.56	1.24	-4.43 ***	-2.93 **	-4.97 ***	-0.88	2.18	1.27	2.67	-0.27
RW-MPL	-1.55	-1.46	-1.45	-1.72	-1.64	-1.51	-1.42	-1.68	-1.65 *	-1.51	-1.42	-1.68 *
EXPORT	-4.04 **	-4.01 **	-2.88	-6.55 ***	-0.86	-0.86	-0.86	-0.48	2.92	3.01	4.00	1.84
GDP	-5.45 ***	-2.03	1.11	-0.76	-1.93	-1.65	-4.54 ***	-2.13	1.12	2.65	9.61	2.36
DEBTS	-2.99	-2.32	-2.51	-5.64 ***	-3.13 **	-3.00 **	-2.68 *	-6.12 ***	1.03	1.25	0.86	1.56

(Fist Difference)

Variables	Trend + Constant	Constant	None
No. of Lags	1	2	3	4	1	2	3	4	1	2	3	4
NIPUB	-24.25 ***	-31.65 ***	-3.11	-3.60 **	-24.33 ***	-30.86 ***	-3.01 **	-3.46 **	-24.45 ***	-30.55 ***	-3.04 ***	-3.46 ***
CALL	-6.36 ***	-4.66 ***	-4.41 ***	-3.64 **	-6.30 ***	-4.71 ***	-4.50 ***	-3.75 ***	-6.04 ***	-4.60 ***	-4.42 ***	-3.76 ***
M2CD	-3.94 **	-3.18 *	-2.12	-2.50	-2.58	-1.99	-1.26	-1.73	-1.61	-1.36	-1.11	-1.43
EXCHANGE	-6.74 ***	-4.17 ***	-3.73 **	-3.72 **	-6.70 ***	-4.18 ***	-3.74 ***	-3.64 ***	-6.63 ***	-4.14 ***	-3.70 ***	-3.55 ***
PGDP	-8.07 ***	-18.79 ***	-3.68 **	-2.67	-6.90 ***	-11.45 ***	-2.80 **	-1.68	-6.76 ***	-10.74 ***	-3.22 ***	-2.07 **
RW-MPL	-7.81 ***	-6.21 ***	-4.25 ***	-3.51 **	-7.85 ***	-6.24 ***	-4.27 ***	-3.52 ***	-7.89 ***	-6.28 ***	-4.30 ***	-3.55 ***
EXPORT	-7.84 ***	-8.55 ***	-2.76	-4.00 **	-7.88 ***	-8.60 ***	-2.87 **	-4.14 ***	-6.95 ***	-6.99 ***	-2.16 **	-3.15 ***
GDP	-17.94 ***	-37.98 ***	-3.79 **	-3.08	-17.88 ***	-32.98 ***	-3.17 **	-2.51	-17.08 ***	-21.70 ***	-2.01 **	-1.58
DEBTS	-10.15 ***	-5.69 ***	-7.04 ***	-5.30 ***	-9.77 ***	-5.43 ***	-5.90 ***	-4.94 ***	-9.56 ***	-5.31 ***	-5.44 ***	-4.83 ***

(Note) *** stands for 1%, ** 5%, *10% level of significance.
Table 10 Explanation of Valibles

Names of	Definitions	Sources
NIPUB	GROSS DOMESTIC FIXED CAPITAL FORMATION-PUBLIC SECTORS	ANNUAL REPORT ON NATIONAL ACCOUNTS
CALL	CALL RATES-COLLATERALIZED OVERNIGHT, CALL RATES-UNCOLLATERALIZED	FINANCE AND ECONOMIC STATISTICS MONTHLY
M2CD	MONEY SUPPLY AVERAGE OUTSTANDING-M2+CD	FINANCE AND ECONOMIC STATISTICS MONTHLY
EXCHANGE	EXCHANGE RATES-YEN PER US DOLLAR(SPOT, MIDDLE, MONTHLY AVERAGE)	FINANCE AND ECONOMIC STATISTICS MONTHLY
PGDP	GROSS DOMESTIC PRODUCT(OR GROSS DOMESTIC EXPENDITURE)(DEFRATOR)1995	ANNUAL REPORT ON NATIONAL ACCOUNTS
Real Wage - Marginal Produitivity of Labor	See Appendix
EXPORT	EXPORTS OF GOODS & SERVICES(AT CONSTANT PRICES)	ANNUAL REPORT ON NATIONAL ACCOUNTS
EXCESS DEBT	(1)-( (2)+(3)-(4) )+(5)+(6)-( (7)×0.5+(6) )×20 Data is seasonally adjusted. (1) CORPORATE CURRENT LIABILITIES-SHORT.TERM DEBT (2) CORP.CURRENT ASSETS-NOTES RECEIVABLE.ACCOUNTS RECEIVABLE (3) CORPORATE CURRENT INVENTORIES (4) CORPORATE CURRENT LIABILITIES-NOTES PAYABLE,ACCOUNTS PAYABLE (5) CORPORATE FIXED LIABILITIES-LONG.TERM DEBT-ALL INDUSTRIES (6) CORPORATE FIXED LIABILITIES-CORPORATE BONDS-ALL INDUSTRIES (7) CORPORATE CURRENT PROFIT-ALL INDUSTRIES (8) DEPRECIATION EXPENSES	QUARTERLY REPORTS OF INCORPORATED ENTERPRISES QUARTERLY REPORTS OF INCORPORATED ENTERPRISES QUARTERLY REPORTS OF INCORPORATED ENTERPRISES QUARTERLY REPORTS OF INCORPORATED ENTERPRISES QUARTERLY REPORTS OF INCORPORATED ENTERPRISES
GDP	GROSS DOMESTIC PRODUCT(OR GROSS DOMESTIC EXPEND.) (AT CONSTANT)	ANNUAL REPORT ON NATIONAL ACCOUNTS
Chart 11 Granger Causality
Chart 12 Impulse Response
Table 13 Accumulated Response of Real GDP to One Percent Shock of Each Variable

NIPUB	CALL	M2CD	EXCHANGE	GDP Deflator	RW-MPL	EXPORT	EXCESS DEBTS	Real GDP
0.14	-0.46	1.37	0.06	0.05	-0.28	0.28	0.00	0.49

Table 14 Accumulated Response of Real GDP to One Percent Shock of Each Variable - In Case of Placing Excess Debt in First Order -

EXCESS DEBTS	NIPUB	CALL	M2CD	EXCHANGE	GDP Deflator	RW-MPL	EXPORT	Real GDP
0.00	0.14	-0.35	1.36	0.06	0.01	-0.26	0.28	0.49
Chart 15 Change of the Rate of JGB to Total Asset of Japanese Banks 1997 - 2001

(Source) National Bankers Association

(Note)Data of major marged banks are averages of the former banks.
# 平成26年度 食品安全関係予算案の概要

平成 25 年 12 月

厚生労働省食品安全部

※他省庁、他局計上分を含む

1 輸入食品の安全確保対策などの推進 9,171百万円(9,941百万円)

・輸入食品の監視体制などの強化

1,600百万円

輸入食品が増加する中で、検疫所のモニタリング検査について、食品群ごとの輸入量、違反率等に基づき必要な検体数を適切に処理できるよう、精度管理の向上、民間の検査機関の活用など検査体制の充実を図る。

また、輸入食品の問題事案の発生防止及びその早期解決を図るため、輸出国の食品安全対策の実施状況について、計画的に調査・評価を行うとともに、対日輸出食品の衛生管理の実態調査、二国間協議など輸入食品の安全対策を推進する。

## 2 食品安全分野における輸出促進対策の推進（一部新規）

27百万円（8百万円）

食品の輸出促進に向けて、輸出先国が求める衛生管理基準に対応するとともに、国内の食品関係事業者の衛生水準の向上を図るため、食品関係事業者へのHACCP（※）の導入を支援する指定普及機関の創設などHACCPの普及を図る。

(※) HACCP (Hazard Analysis Critical Control Point)：微生物による汚染、金属の混入などの危害を予測した上で、危害の防止につながる特に重要な工程を継続的に監視・記録する工程管理のシステム。

## 3 残留農薬などの安全確保対策の推進 863百万円（924百万円）

## （１）残留農薬などの基準設定手続の迅速化

707百万円

ポジティブリスト制度（※1）の導入の際に設定した農薬などの暫定基準について、迅速に見直しを行うとともに、食品添加物について、国際汎用添加物（※2）等の迅速な指定や安全性確保の取組を更に強化する。

※1 ポジティブリスト制度：食品中に残留する農薬などについて、残留基準を設定し、基準を超えて食品中に残留する場合、その食品の販売等を禁止するもの。

※2 国際汎用添加物：国際的に安全性が確認され、欧米で広く使用が認められており、国が主体的に指定に向けた検討を進めるとされた45品目（平成25年12月時点で37品目は指定済み。）。
## （２）健康食品の安全確保対策の推進

いわゆる健康食品による健康被害を未然に防ぐため、食品成分について安全性試験や分析調査を行うとともに、被害発生時の迅速かつ適切な対応を図る。

## （3） 食品用容器包装などの安全確保対策の推進 80百万円

食品用容器包装などに用いられる化学物質の規制について、容器包装から食品への溶出試験の実施等により具体的なデータの蓄積を行い、欧米等で導入されているポジティブリスト化に向けた制度の検討を進める。

また、近年、利用が拡大し、食品用途にも応用されつつあるナノマテリアル（※）について、溶出試験の実施等により具体的データの蓄積を行い、リスク管理手法の検討を進める。

(※)ナノマテリアル: 大きさが100ナノメートル以下の小さな物質(ナノとは1ミリの100万分の1)。

## （4） 食品污染物質に係る安全確保対策の推進 51 百万円

食品中の汚染物質対策について、重金属、かび毒などの汚染実態や摂取量の調査などを行い、基準の設定や見直しなどの安全性確保の取組を進める。

## 4 食中毒対策の推進 48百万円（67百万円）

近年の大規模・広域化した食中毒事件の被害拡大防止のため、菌株収集等による原因究明調査を行うとともに、自治体等による疫学調査が迅速に行われるよう担当官を現地に派遣するなど、食中毒対策を推進する。

## 5 食品に関する情報提供や意見交換(リスクコミュニケーション)の推進 9百万円（9百万円）

食品安全に対する消費者の意識の高まりなどに対応するため、食品安全基本法や食品衛生法に基づき、消費者等への積極的な情報提供や双方向の意見交換を行う。

## 6 食品の安全の確保に資する研究の推進

706百万円（882百万円）

食中毒の予防や食品中の化学物質への基準設定等の課題について、科学的根拠に基づく調査研究を進める。
## 7 カネミ油症患者に対する支援策の実施（一部再掲）

636百万円（626百万円）

カネミ油症患者に対する総合的な支援策の一環として、ダイオキシン類の直接の経口摂取による健康被害という特殊性を踏まえ、油症患者の健康実態調査を実施し、健康調査支援金（一人当たり19万円）を支給するとともに、研究・検診・相談事業を推進する。

## 8 食品中の放射性物質対策の推進（復興庁計上）

226百万円（333百万円）

食品中の放射性物質の安全対策を推進するため、食品の汚染状況や摂取状況を調査し、基準値を継続的に検証するとともに、流通段階で買上調査を実施するなどの取組を行い、放射性物質による国民の食品に対する不安を解消する。

また、各自治体が食品中の放射性物質の検査を円滑に実施できるよう、検査機器の整備に対する補助を行うほか、食品中の放射性物質に関する調査研究を進める。
平成26年度 食品安全関係予算案総括表

事項	平成25年度当初予算額(A)	平成26年度予算案(B)	対前年度増△減額(B)-(A)	対前年度比率(B)/(A)
1 輸入食品の安全確保対策などの強化	百万円	百万円	百万円
9,941 >	9,171 >	△770 >	92.3%
9,917	9,149	△768	92.3%
(1)輸入食品の監視体制などの強化	2,864 >	1,600 >	△1,264 >	55.9%
2,864	1,600	△1,264	55.9%
(2)その他の食品安全対策等	7,077 >	7,571 >	494 >	107.0%
7,053	7,549	496	107.0%
2 食品安全分野における輸出促進対策の推進	8 >	27 >	20 >	351.5%
8	27	20	351.5%
3 残留農薬などの安全確保対策の推進	924 >	863 >	△61 >	93.4%
924	863	△61	93.4%
(1)残留農薬などの基準設定手続きの迅速化	765 >	707 >	△58 >	92.5%
765	707	△58	92.5%
(2)健康食品の安全確保対策の推進	24	25	1	102.6%
(3)食品用容器包装等の安全確保対策の推進	85	80	△5	93.9%
(4)食品污染物質の安全確保対策の推進	50	51	1	102.3%
4 食中毒対策の推進	67 >	48 >	△19 >	71.5%
66	47	△19	71.0%
5 食品安全に関する情報提供や意見交換の推進	9 >	9 >	0 >	100.1%
9	9	0	100.5%
6 食品安全の確保に資する研究の推進	882 >	706 >	△176 >	80.0%
0	0	0	-
7 カネミ油症患者に対する支援策の実施	626 >	636 >	10 >	101.6%
426	427	1	100.3%
合計(一般会計)	12,256 >	11,250 >	△1,005 >	91.8%
5,442 >	6,015 >	573 >	110.5%
11,349	10,521	△827	92.7%

<東日本大震災復興特別会計>

8 食品中の放射性物質対策の推進	333	226	△ 107	68.0%

注 ①. 計数は、それぞれ四捨五入しているので、端数において合計と一致しない場合がある。

②. 上段＜＞は他局計上分を含む。

③. 1(2)の下段の数字は検疫所の人件費を含んでいる。(合計欄の[ ]が検疫所の人件費のみの額。)

④. 7の上段の数字には重複となる経費(6の研究費の一部)を含む。
## Section 1 Traffic Accidents in 2013

The number of fatalities from traffic accidents in 2013 was 4,373. While that number has been steadily dropping since 2001, the reduction has recently shrunk due to the increase in the population of the elderly whose fatality rate is higher compared to other age groups, and from 2010 the fatality rate shifted to an upswing for the first time in 3 years.

In addition, of the elderly fatalities in traffic accidents, about 60% occurred while walking or riding a bicycle. Police are cooperating with related agencies and other organizations to implement measures such as individual guidance through home visits and enlightenment activities and traffic safety education in facilities such as hospitals and care centers.

Trends in the numbers of traffic accidents, fatalities, injuries, licensed drivers, owned vehicles, kilometers driven (1966–2013)

Note: Figures for Owned Vehicles and Km Driven are derived from MLIT documents.

## Section 2 Boosting Traffic Safety Awareness 1 Police Initiative to Eradicate Drunken Driving

The number of traffic accidents due to drunken driving in 2013 fell for the thirteenth consecutive year to 4,335. As well as actively informing the public about the dangers of drunken driving and the facts about accidents caused by drunken driving, the police are promoting safety education through participation, hands-on experience and practice using driving simulators and other equipment.

## 2 Initiatives Aimed at Traffic Safety for the Elderly

The number of the elderly fatalities in traffic accidents in 2013 increased to 2,303 for the first time in 12 years. The elderly account for more than half of overall fatalities in traffic accidents.

Number of elderly fatalities by circumstances when traffic accident occurred (2013)
# Intergenerational Transfers and Asset Inequality in Japan: Empirical Evidence from New Survey Data

Junya Hamaaki, Masahiro Hori, and Keiko Murata

March 2012

Economic and Social Research Institute

Cabinet Office

Tokyo, Japan

The views expressed in “ESRI Discussion Papers” are those of the authors and not those of the Economic and Social Research Institute, the Cabinet Office, or the Government of Japan.
# Intergenerational Transfers and Asset Inequality in Japan: Empirical Evidence from New Survey Data

Junya Hamaaki, Masahiro Hori, and Keiko Murata $ ^{†} $

## Abstract

This paper tries to quantitatively examine the impact of intergenerational transfers on asset inequality among Japanese households. For that purpose, we estimate an intergenerational asset transfer function with various control variables, using a unique micro dataset taken from the “Household Survey on Family Relationships, Employment, Retirement Payments, and Intergenerational Transfers of Assets and Education,” conducted by the Economic and Social Research Institute, Cabinet Office, Government of Japan. Employing three different models – a Tobit model, an interval regression model, and an ordered probit model – to ensure that our results are independent of the specific econometric approach used, we examine whether asset transfers received are correlated with households’ financial strength. We find that higher income households are likely to receive larger asset transfers. However, the contribution of intergenerational transfers to asset inequality appears to be quantitatively limited when measuring financial strength in terms of households’ life cycle wealth.

JEL Classification: D12, D91, E21

Key words: Intergenerational transfers; Asset inequality; Japan.
### 1. Introduction

Growing economic inequality is a major concern in advanced economies around the world. Japan is no exception, as illustrated by the term kakusa shakai, or “social gap society,” which has gained wide currency in recent years. Of course, economic inequality has many causes, but the intergenerational transfer of assets is likely one of them. Especially in a country like Japan, characterized by low economic growth and declining fertility, inherited assets are bound to make up an increasing share of household assets overall. This means that if the amount of asset transfers is positively correlated with the financial strength of heirs, intergenerational asset transfers are likely to perpetuate or even increase asset inequality across households and hence the economic gap between wealthier and less well off households.

Whether asset transfers indeed contribute to asset inequality in Japan has been the subject of a number of studies, which, however, have failed to produce conclusive results. Instead, it appears that the findings of studies using aggregate or macro data substantially depend on the choice of methodology to estimate the amount of wealth transferred. Studies based on the life cycle saving approach, which was first applied by Kotlikoff and Summers (1981), have tended to result in relatively small estimates of the ratio of wealth transferred to total household assets in Japan (e.g., Hayashi, 1986; Dekle, 1989; and Campbell, 1997), while studies employing more direct measures of inherited assets often arrive at large estimates (e.g., Barthold and Itoh, 1992; Asoh, 1998; and Shimono and Ishikawa, 2002). Studies using household-level or other micro data have similarly produced only mixed results. While a considerable number of such studies indicate that asset (as well as educational) transfers have led to an increase in asset inequality (e.g., Noguchi et al., 1989; Noguchi, 1990; Kito et al., 1993; Takayama et al., 1996; Matsuura, 2006), more recent studies by Horioka (2008; 2009) suggest otherwise.

Although there are likely to be several reasons for the conflicting results, the most obvious
explanation is that these studies only provide descriptive – rather than econometric – analyses and therefore do not incorporate potentially important factors such as the level of educational investment and the financial strength of family members of the preceding generation in examining the dynamics of inequality through intergenerational asset transfers.

The purpose of this paper is to address these shortcomings and empirically investigate the impact of intergenerational transfers on asset inequality. We do so by estimating the determinants of the amount of transfers – consisting of gifts and inheritances – using various control variables, based on a unique set of microdata taken from the “Household Survey on Family Relationships, Employment, Retirement Payments, and Intergenerational Transfers of Assets and Education,” conducted in January 2010. Because our data of intergenerational asset transfers is censored from below at 0 yen and from above at 50 million yen, we first apply a Tobit model to our data to obtain an intergenerational asset transfer function. We also apply an interval regression and an ordered probit models to ensure that our results are independent of the choice of econometric model.

Regardless of which econometric model we employ, we were able to obtain reasonable estimates for the intergenerational asset transfer function with similar parameter values. Our results regarding basic household attributes can be summarized as follows. First, most intergenerational transfers appear to occur at the time of death of the heir's parents. Second, the amount of intergenerational transfers is positively correlated to heirs' age. Third, a household will receive a larger amount of asset transfers when its head is/was self-employed or a family employee.

Based on the estimated intergenerational asset transfer functions, we then examine the relationship between asset inequality and intergenerational transfers. The regressions show that households with higher educational attainment are likely to receive larger asset transfers. This
suggests that intergenerational transfers in the form of educational investment and post mortem asset transfers seem not to be substitutes for each other. In addition, we find that the amount of asset transfers received is significantly correlated with heirs' labor earnings, but not with households' life cycle wealth. Therefore, higher income households are likely to receive larger intergenerational transfers. On the other hand, judging from the small increase in asset transfers associated with greater life cycle wealth, the role of intergenerational transfers in widening asset inequality across Japanese households appears to be quantitatively limited.

The remainder of this paper is organized as follows. Section 2 explains our data source and provides descriptive statistics of our sample. Section 3 presents our empirical model, while Section 4 reports our estimation results and discusses their implications. Next, Section 5 shows how the probability of receiving intergenerational transfers and the amount of such transfers are correlated with household characteristics. Finally, Section 6 summarizes our findings and concludes the paper.

### 2. Data description

### 2.1 Data source

The microdata used in this paper are taken from the “Household Survey on Family Relationships, Employment, Retirement Payments, and Intergenerational Transfers of Assets and Education,” conducted from January 12 to 31, 2010 by the Economic and Social Research Institute (ESRI), Cabinet Office, Government of Japan (see Hamaaki et al., 2011, for details of the survey). $ ^{1} $ This survey collects information not only on the basic characteristics of each family member (i.e., the household head, his/her spouse, and their children), but also on other characteristics that are not covered in existing government statistics. The former includes
information on respondents' age, sex, employment status (both past and present), and educational attainment. The latter includes, among other things, information on gifts and inheritances received from parents and on other assets holdings such as financial assets and real estate. Information on gifts and inheritances covers both financial and real assets and it should be noted that instead of asking for the current value of these assets, the survey inquires about their value on the day that respondents received them.

The survey is based on two-stage stratified random sampling: 4,000 households were randomly extracted from 200 municipal cities that were selected from 28 stratified groups (7 areas×4 city sizes). The survey covers households throughout Japan whose household heads are aged between 20 and 75 years. Households were visited by an enumerator to hand over the questionnaire, and the enumerator returned a few days later to collect it. Respondents received a 500 yen book voucher as remuneration for participating in the survey.

Out of the 4,000 extracted households, 2,302 households responded to the survey (valid response rate: 57.6%). The response rate in urban areas, such as Kanto and Kansai regions, and large cities is relatively low and, moreover, is lower for younger households than for older ones. It should also be noted that the sample is slightly biased toward households whose heads are male and those with more than two members compared with the population as a whole. $ ^{2} $

### 2.2 Descriptive statistics

This section reports the descriptive statistics of the variables used in our empirical analysis. Variables include the following: (1) the value of intergenerational transfers – consisting of gifts and inheritances – received by respondents by the survey date (see below); (2) the age of the
household head and that of his/her spouse; (3) the occupational status of the household head; (4) the educational attainment of the household head and his/her spouse; (5) the number of siblings of the household head and his/her spouse; (6) whether the household head and his/her spouse are the first-born son or daughter in their respective families; (7) whether the household head's and his/her spouse's parents are alive or not; (8) the educational attainment of the household head's and his/her spouse's parents; (9) the household's financial strength; and (10) regional dummy variables. We use (2) to (10) as our independent variable.

The first variable, i.e., the amount of intergenerational transfers received by a household, is used as the dependent variable in our regression. The variable is based on the response to the following question: “Over your lifetime, what is the value of gifts and inheritances bequeathed to your household from your parents and other relatives?” Respondents could choose among the following answers: (i) haven’t received any, (ii) less than 2 million yen, (iii) 2 million to 5 million yen, (iv) 5 million to 10 million yen, (v) 10 million to 20 million yen, (vi) 20 million to 30 million yen, (vii) 30 million to 50 million yen, (viii) more than 50 million yen, and (ix) don’t know. For our regression analysis we drop the last category (“don’t know”) and use (i) to (viii) for our dependent variable.

Table 1 shows the distribution of intergenerational transfers for the following four groups of observation households: (1) all households for which the amount of intergenerational asset transfers is available, and (2) to (4): households for which all explanatory variables for our regression analyses, i.e., specifications (A) to (C) in the following sections, are available. The table indicates that more than 60 percent of households had not received intergenerational transfers by the survey date, regardless of which group of observations is used. This result is in line with previous studies using similar surveys (Noguchi et al., 1989; Kito et al., 1993; Takayama et al., 1996; and Horioka, 2008). The average amount received by households that
did receive gifts and inheritances was about 10 million yen. $ ^{3} $ Given that the average net worth of those households $ ^{4} $ was 32-35 million yen, the transferred assets thus accounted for roughly 30 percent of their outstanding assets.

Next, Table 2 presents the descriptive statistics of the remaining variables. Regarding the household head's occupation, we use a dummy variable that takes a value of one if the household head is/was self-employed or a family employee. As can be seen, for 16 percent of household heads this was the case. Next, looking at educational attainment, which is represented by dummy variables for junior high school graduates and university graduates, meaning that senior high school/junior college/technical college graduates make up the references group, we find that 42 percent of household heads were university graduates, while only 17 percent of spouses were university graduates. This gap probably reflects gender-based differences in the average level of education. Turning to our next set of variables, the number of siblings (including the household head or spouse) is slightly above three both for the household head and for the spouse. Further, we include a dummy variable indicating whether the household head or his/her spouse was the first-born son in their family and the first-born daughter. We find that in more than 60 percent of cases, one of the two was the first-born son and in almost 60 percent of case one was the first-born daughter. Next, turning to the dummies indicating whether the parents were still alive, we find that two-thirds of household heads and 60 percent of spouses had already lost at least one of their parents. Finally, we include one of the following two variables in different equations to represent households' financial strength. The first one is the