Silviase/jgov_v1.4_full_ann huggingface dataset:
## References

Doi, Takero. (2001) “Chochiku Ritu Kansuu ni Motoduku Yobiteki Chochiku Kasetsu no Kenshou” (A Test of the Precautionary Savings Hypothesis Based on the Savings Rate Function), ESRI Discussion Paper Series No.1.

Dynan, Karen. (1993) “How Prudence Are Consumers?”, Journal of Political Economy, vol.101, no 6, pp.1104-1113.

Economic Planning Agency (1999) the. Annual report on the Japanese Economy (Nihon Keizai no Genkyo) for 1999.

Hori, Masahiro, Hsieh, Chang-Tai, Murata, Keiko and Shimizutani, Satoshi (2002) "Did the Shopping Coupon Program Stimulate Consumption? Evidence from Japanese Micro Data", ESRI Discussion Paper Series No.12.

Hori, Masahiro and Shimizutani, Satoshi. (2002) “Consumer Response to the 1998 Tax Cut: Which of Tax Cut or Coupon Program is More Effective?” Economic and Social Research Institute, Cabinet Office, Japanese Government.

Ishihara, Hidehiko. (2001) “Life-Cycle/Permanent Income Hypothesis and Precautionary Saving”, ESRI Discussion Paper Series No.2.

Kimball, Miles. (1990) “Precautionary Saving in the Small and in the Large.” Econometrica 58, pp53-73.

Merrigan, Philip and Normandin, Michel. (1996) “Precautionary Saving Motives: An Assessment From UK Time Series of Cross-Sections.” Economic Journal 106, pp1193-1208.

Ministry of Public Management, Home Affairs, Posts, and Telecommunications. Annual report on the Family Income and Expenditure Survey, various years.

Muto, Hiromichi. (1999) Syohi Fukyo no Keizaigaku (in Japanese), Nihon-Keizai-Shinbun-Sya.

Shimizutani, Satoshi. (2002) “Consumption and Tax Policy in Japan during the 1990s: Evidence from Household Data” University of Michigan, mimeo.

Zeldes, Stephen. (1989) “Consumption and Liquidity Constraints: An Empirical Investigation.” Journal of Political Economy, vol.97, no 2
Table 1: Summary Statistics

Mean	S.D.
1995 sample (N=748)
Annual consumption growth	0.02	0.23
Annual consumption (1995)	5,262,949	2,283,478
Annual consumption (1996)	5,212,726	2,295,875
Annual income (pre-tax) (1995)	6,367,219	2,758,212
Annual income (pre-tax) (1996)	6,469,773	2,955,917
Number of family members	3.37	1.17
Number of employees	1.13	0.62
Age of household head	46.8	13.2
Assets/Annual Income	1.85	2.50
1996 sample (N=749)
Annual consumption growth	0.00	0.23
Annual consumption (1996)	5,442,215	2,765,408
Annual consumption (1997)	5,229,028	2,703,237
Annual income (pre-tax) (1996)	6,626,008	3,039,277
Annual income (pre-tax) (1997)	6,686,662	3,158,300
Number of family members	3.31	1.19
Number of employees	1.06	0.64
Age of household head	48.8	13.9
Assets/Annual Income	1.94	2.35
1997 sample (N=751)
Annual consumption growth	0.01	0.24
Annual consumption (1997)	5,333,610	2,360,254
Annual consumption (1998)	5,163,809	2,213,831
Annual income (pre-tax) (1997)	6,467,137	3,003,720
Annual income (pre-tax) (1998)	6,472,250	3,172,710
Number of family members	3.32	1.23
Number of employees	1.09	0.68
Age of household head	48.0	14.4
Assets/Annual Income	1.88	2.31
1998 sample (N=678)
Annual consumption growth	0.01	0.25
Annual consumption (1995)	5,686,167	2,582,999
Annual consumption (1996)	5,610,219	2,497,051
Annual income (pre-tax) (1995)	6,585,280	3,062,134
Annual income (pre-tax) (1996)	6,450,959	3,055,439
Number of family members	3.31	1.14
Number of employees	1.08	0.63
Age of household head	49.2	15.0
Assets/Annual Income	1.93	2.41
Table 2 (1): Estimation of the Coefficient of Prudence

$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $
1995-1996	-1.14 (0.95)	-2.28 (1.80)	0.02
1996-1997	-0.25 (1.05)	-0.50 (2.10)	0.02
1997-1998	0.60 (0.71)	1.20 (1.42)	0.04
1998-1999	2.03 (1.01)	4.06 (2.01)	0.02

Table 2(2): Estimation of the Coefficient of Prudence by Employment

Head of Household is not employed	Head of Household is employed
$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $	$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $
1995-1996	-1.19 (13.0)	-2.39 (26.0)	0.00 N=104	1.01 (0.97)	2.02 (1.94)	0.02 N=644
1996-1997	-2.84 (1.62)	-5.68 (3.24)	0.07 N=131	1.99 (1.40)	3.99 (2.80)	0.01 N=618
1997-1998	-0.57 (0.93)	-1.14 (1.86)	0.12 N=139	0.26 (0.82)	0.52 (1.64)	0.04 N=612
1998-1999	-0.35 (1.40)	-0.70 (2.80)	0.07 N=108	2.37 (1.06)	4.73 (2.11)	0.03 N=570

Notes: Standard errors in parenthesis. The dependent variable is growth rate of consumption in each household. $ \alpha_{1} $ is the coefficient on the main independent variable, squared consumption growth rate, which is instrumented by number of family size, number of employees, change in number of family size, change in number of employees, firm size of head of household (if employed), occupation of head of household (if employed), dummy variable for employed head of household and liquidity condition defined as share of total financial assets to pre-tax annual income.
Table 3: Estimation of the Coefficient of Prudence by Industry

Manufacturing	Non-Manufacturing
$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $	$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $
1995-1996	-0.88 (1.92)	-1.77 (3.84)	0.02 N=224	1.13 (1.20)	2.27 (2.40)	0.03 N=308
1996-1997	2.26 (1.10)	4.52 (2.20)	0.06 N=237	-0.12 (1.42)	-0.23 (2.84)	0.03 N=259
1997-1998	-1.09 (1.00)	-2.18 (2.00)	0.07 N=227	0.46 (1.10)	0.92 (2.20)	0.04 N=273
1998-1999	1.42 (1.11)	2.84 (2.22)	0.06 N=196	1.72 (1.36)	3.44 (2.72)	0.03 N=243

Notes: Standard errors in parenthesis. The dependent variable is growth rate of consumption in each household. $ \alpha_{1} $ is the coefficient on the main independent variable, squared consumption growth rate, which is instrumented by number of family size, number of employees, change in number of family size, change in number of employees, firm size of head of household (if employed), occupation of head of household (if employed), dummy variable for employed head of household and liquidity condition defined as share of total financial assets to pre-tax annual income.

Table 4: Estimation of the Coefficient of Prudence by Age

Head of Household	<50	>50
$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $	$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $
1995-1996	0.94 (1.00)	1.87 (1.99)	0.03 N=486	-0.48 (1.63)	-0.96 (3.27)	0.02 N=262
1996-1997	2.59 (1.87)	5.18 (3.74)	0.01 N=436	-0.45 (1.31)	-0.90 (2.62)	0.03 N=313
1997-1998	0.81 (0.85)	1.72 (1.70)	0.03 N=458	0.72 (0.89)	1.45 (1.79)	0.06 N=293
1998-1999	1.42 (0.85)	2.84 (1.70)	0.05 N=386	1.53 (1.27)	3.06 (2.54)	0.03 N=292

Head of Household	<40	>40
$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $	$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $
1995-1996	3.03 (1.68)	6.06 (3.75)	0.03 N=252	-1.26 (1.09)	-2.53 (2.18)	0.03 N=496
1996-1997	2.69 (2.13)	5.38 (4.26)	0.02 N=203	-0.55 (1.11)	-1.09 (2.22)	0.02 N=546
1997-1998	-0.56 (1.19)	-1.11 (2.38)	0.04 N=248	0.19 (0.67)	0.38 (1.34)	0.06 N=503
1998-1999	3.49 (1.49)	6.98 (2.97)	0.05 N=211	1.25 (0.93)	2.50 (1.86)	0.04 N=467

Notes: Standard errors in parenthesis. The dependent variable is growth rate of consumption in each household. $ \alpha_{1} $ is the coefficient on the main independent variable, squared consumption growth rate, which is instrumented by number of family size, number of employees, change in number of family size, change in number of employees, firm size of head of household (if employed), occupation of head of household (if employed), dummy variable for employed head of household and liquidity condition defined as share of total financial assets to pre-tax annual income.
Table 5: Estimation of the Coefficient of Prudence by Liquidity Condition

(LIQ=Total financial assets/pre-tax annual income)

LIQ<1.0	LIQ>1.0
$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $	$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $
1995-1996	-0.30 (1.01)	-0.60 (2.01)	0.03 N=353	-1.40 (2.17)	-2.80 (4.34)	0.01 N=395
1996-1997	-2.27 (1.68)	-4.54 (3.37)	0.02 N=314	2.75 (1.92)	5.50 (3.84)	0.01 N=435
1997-1998	-1.68 (0.95)	-3.57 (1.90)	0.04 N=348	0.31 (0.79)	0.61 (1.58)	0.06 N=403
1998-1999	1.29 (1.31)	2.58 (2.61)	0.03 N=298	1.54 (1.05)	3.08 (3.10)	0.04 N=380

LIQ<0.5	LIQ>0.5
$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $	$ \alpha_{1} $	Implied $ \rho $	First-stage $ R^{2} $
1995-1996	0.08 (1.19)	0.16 (2.38)	0.06 N=167	-1.03 (1.03)	-2.06 (2.06)	0.01 N=581
1996-1997	-2.29 (1.37)	-4.58 (2.74)	0.07 N=118	0.55 (1.14)	1.10 (2.28)	0.02 N=631
1997-1998	-0.50 (1.10)	-1.10 (2.20)	0.06 N=145	-0.14 (0.68)	-0.28 (1.36)	0.05 N=606
1998-1999	-3.16 (1.98)	-6.31 (3.95)	0.03 N=140	1.71 (0.99)	3.41 (1.98)	0.03 N=538

Notes: Standard errors in parenthesis. The dependent variable is growth rate of consumption in each household. $ \alpha_{1} $ is the coefficient on the main independent variable, squared consumption growth rate, which is instrumented by number of family size, number of employees, change in number of family size, change in number of employees, firm size of head of household (if employed), occupation of head of household (if employed), dummy variable for employed head of household and liquidity condition defined as share of total financial assets to pre-tax annual income.
Income Variability and Consumption: A Full Consumption Insurance Test Using Japanese Micro Data in the 1990s

## Abstract:

This paper explores the relationship between income variability and consumption in the 1990s in Japan. Using micro-level data from the Family Income and Expenditure Survey, this study tests the full consumption insurance hypothesis and quantifies the sources of insurance. The consumption full insurance hypothesis is strongly rejected for both total consumption and consumption by type of goods, which implies that Japanese households fail to smooth consumption. However, the deviation from full insurance is small: there is only a 1 yen to 6 yen change in total consumption per 100 yen change in head of household income, which is comparable to previous studies in the U.S. The main source of insurance to compensate for changes in head of household income is tax payment and the change in tax paid is 12-13 yen in response to a 100 yen change in head of household income. Other sources such as spouse's income, social security transfers and savings a play smaller role in compensating for changes in head of household income. These findings hold even after controlling for different liquidity constraints.
### 1. Introduction

The purpose of this paper is to address quantitatively the relationship between income and consumption in the 1990s in Japan, the historically low growth decade that has come to be called the “Lost Decade”. In the 1990s the Japanese economy suffered from a dramatic increase in the unemployment rate and weak growth in wages, both of which have brought large negative shocks to household consumption. Nevertheless, surprisingly, there is little quantitative research assessing how much income variability has adversely affected consumption, how the composition of consumption was adjusted, or how the negative effects have been mitigated by savings or government transfers. However, all of these questions are clearly important and hold an interest for both academics and policy makers.

This paper addresses these issues by testing the “full consumption insurance hypothesis” investigating how Japanese consumers succeeded or failed in smoothing consumption under variable income shocks in the 1990s. Thus, this paper contributes to the empirical literature on how the level and composition (across goods) of consumption adjusts to income shocks and on the sources of consumption insurance. This paper follows the spirit and methodology of Mace (1991) and Cochrane (1991), who offered basic methodology to address the consumption insurance in macroeconomic literature, as well as Dynarski and Gruber (1997), who investigated the completeness and the sources of smoothing of idiosyncratic earnings variation in the U.S., based on the substantial increase in earnings variation in the U.S. between 1970 and 1991.

Like their approach, this paper quantitatively evaluates income variability and consumption during a long recession. Moreover, this paper investigates sources of consumption insurance to evaluate policy implications. We take advantage of micro-level consumer data in this paper: the Family Income and Expenditure Survey (henceforth, FIES) and the Family Savings Survey (henceforth, FSS), both of which are compiled by the Japanese government. The FIES is a monthly consumption survey and consists of itemized income and expenditures as well as household characteristics. A household is surveyed for six months, so the data can be constructed into six months panel. The FSS collects information on financial assets from the households which enter the FIES sample in August, September and October.

The main conclusion of this paper is that the full consumption insurance hypothesis is strongly rejected for both aggregate consumption and consumption by type of goods, which implies that Japanese households fail to smooth consumption. However, the deviation from full insurance is substantially small: there is only a 1 yen to 6 yen in change in total consumption per 100 yen change in head of household income, which is comparable to the U.S. case. The main source of insurance to compensate for changes in head of household income is tax payment and the change in tax paid is 12-13 yen in response to change of 100 yen in head of household income. Other sources such as spouse's
income, social security transfers and savings play a smaller role in compensating for changes in head of household income in Japan. These findings hold even after controlling for liquidity constraints.

This paper is organized as follows. Section 2 briefly describes the related literature, focusing on Mace and Cochrane's research as well as Dynarski and Gruber's. Section 3 gives a description of the micro-level data set used in this paper. Section 4 turns to a test of the full consumption insurance hypothesis and Section 5 evaluates sources of insurance. Section 6 investigates these tests across households with different liquidity conditions. The final section concludes and discusses the policy implications of the findings.

### 2. Literature survey

This paper follows the spirit and methodology of Mace (1991) and Cochrane (1991) as well as Dynarski and Gruber (1997). Mace and Cochrane (henceforth, Mace/Cochrane method), which were published simultaneously, offered the basic framework to address the consumption insurance in macroeconomic literature. The basic idea is that change in individual consumption is determined by changes in aggregate consumption rather than by changes in idiosyncratic risk such an individual income or employment status. More concretely, their basic approach is to regress change in consumption in a household on the average consumption across households and idiosyncratic shock such as the household's income and to test the coefficient on the average consumption is a unity and that on idiosyncratic shock is zero if consumption insurance holds. Mace concluded that the results are mixed while Cochrane insisted that “[F]ull insurance is rejected for long illness and involuntary job loss but not rejected for spells of unemployment, loss of work due to strike and an involuntary move”.

Based on these consumption insurance literatures, Dynarski and Gruber (henceforth, Dynarski/Gruber method) assessed “the completeness and the sources of smoothing of idiosyncratic earnings variation”. They investigated the completeness and the sources of smoothing of idiosyncratic earnings variation, based on the fact that earning variation in the U.S. had increased substantially between 1970 and 1991. They assess how much families respond to various income shocks, what types of goods are affected by what types of income shocks and how families smooth consumption by offsetting income flows by government transfers, tax, or savings.

This research quantitatively investigates the relationship between income and consumption by testing full the consumption insurance hypothesis. As stated above, the full consumption insurance hypothesis is based upon the idea that if households pool risk, the growth rate of the marginal utility of consumption across households is equalized. That is, “the growth of consumption will depend only on the growth rate of aggregate resources and changes in household’s preference” and “will not depend on changes in household resources that are uncorrelated with shifts in preferences” (Dynarski and
hypothesis but also the sources of smoothing, which leads to policy implications. Fourth, this paper considers the heterogeneity of individual households including liquidity constraints in order to evaluate how differently a household’s consumption responds to change in head’s earnings under different liquidity constraints.

### 3. Data

The main data set used in this paper is micro-level data from the Family Income and Expenditure Survey (FIES) from 1990 to 2000 $ ^{1} $ . To improve the reliability of the estimates, a household is dropped from the sample using the following criteria. First, any households in which the head of household is self-employed were deleted. Second, households in which the reported age of the head of household decreases or increases by more than one year during 6 months or in which the household's tenancy status changes from owner to renter (or from renter to owner) are also excluded. Third, a household is excluded if the number of family members is greater than 10. Fourth, the sample is confined to the households in which the head of household is under the age of sixty. By focusing on these households, endogeneity between change in consumption and change in income is mitigated.

In what follows, we will investigate the full consumption insurance hypothesis using total consumption and consumption by types of goods; durables, semi-durables, non-durables and services $ ^{2} $ . Summary statistics are reported in Table 1.

### 4. Tests of the Full Consumption Insurance Hypothesis

This paper adopts two types of specification to test the full consumption insurance hypothesis; Dynarski/Gruber method and Mace/Cochrane method.

The Dynarski/Gruber method is simply to estimate the ability of individual households to smooth variation in the head of household earnings, that is,

$$ (1)\Delta C_{t}=\boldsymbol{\alpha}+\boldsymbol{\beta}\Delta Y_{t}+\mathbf{X}_{h}^{^{\prime}}\boldsymbol{\delta}+\boldsymbol{\varepsilon}_{t} $$
where $ \Delta C_{t} $ is change in consumption for household h, $ \Delta Y_{t} $ is change in the head of household income for household h and $ X_{h} $ is a vector of family characteristics and indicator variable for each year and month $ ^{3} $ . Family characteristics include change in the number of family members. When the full consumption insurance hypothesis holds, the coefficient on the change in head of household income should be zero. To correct the seasonality, regressions will be run on the pooled panels and panels beginning in different months respectively.

On the other hand, the Mace/Cochrane method is

$$ (2)\Delta C_{t}=\boldsymbol{\alpha}+\boldsymbol{\beta}_{1}\Delta C^{a}{}_{t}+\boldsymbol{\beta}_{2}\Delta Y_{t}+\mathbf{X}_{h}{}^{^{\prime}}\boldsymbol{\delta}+\boldsymbol{\varepsilon}_{t} $$

where the notations are same as in the specification (1) except $ \Delta C^{a} $ is the average consumption across individual households (see Mace (1991) for the derivation of this specification). If the aggregate risk was insured, that is, the coefficient on $ \Delta C^{a} $ is zero, specification (2) and (1) would be merged.

We will estimate equation (1) both in levels and in logs by ordinary least squares. As a benchmark, we estimate (1) in levels for total consumption and consumption by type of goods. The results, reported in Table 2, show that all coefficients on change in head of household income are positive and significant. This indicates that the full consumption insurance hypothesis is strongly rejected for both total consumption and more disaggregated consumption $ ^{4} $ . However, the deviation from the full insurance hypothesis is small: there is only a 1 yen to 6 yen change in total consumption per 100 yen change in head of household income. By types of goods and services, the changes in consumption observed for a 100 yen change in head of household income are more or less 1 yen for durables, non-durables and semi-durables. The change is somewhat larger for services but still small, at 1 or 2 yen. These findings are comparable with previous findings using U.S. data. U.S. PSID data revealed that each dollar rise in head of household earnings brought about only a 1 cent change in food consumption or a 1.7 cent change in food and housing consumption. Reported coefficients for U.S. households were even smaller using Consumer Expenditure Survey data $ ^{5} $ .

Table 3 reports the results of the same specification estimated in logs. The dependent variable is the change in the logarithm of consumption in individual households and the main independent variable is the change in the logarithm of the head of household earnings. In this case, the coefficient
on change rate in income in the right hand side indicates the elasticity of household consumption with respect to head of household income. The results show that all coefficients except some cases in durables are positive and significant as in Table 2. The estimated elasticity of consumption in a household with respect to head of household income is about 0.1 for total consumption, which is fairly small. The elasticity is larger for semi-durables, 0.1-0.2, about the same for services, 0.1, and smaller for non-durables, 0.02-0.04. The elasticity for durables is generally higher than other types of goods and services though they are not precisely estimated in some cases and the magnitudes vary across different panels.

In general, these findings are also true for the Mace/Cochrane method indicated in Table 4 $ ^{6} $ . Although the full insurance hypothesis is rejected for the total consumption, the deviation from it is so small: there is only a 1 yen to 6 yen change in total consumption per 100 yen change in head of household income. The coefficients on the idiosyncratic risk in Table 4 are almost same with those in Table 2. It should be noted that the full insurance hypothesis is not rejected for services consumption in some cases (see Table 5 for the results based on the log specification).

It might be surprising that the deviation from the full consumption insurance hypothesis is very small because the rapid increase in unemployment and weak growth in wages are usually considered important factors in explaining Japan’s low growth in the 1990s. In other words, negative income shocks might have had larger effect on consumption than positive income shocks. To detect possible asymmetry of consumption responses to changes in income, the sample is divided according to whether the head of household income increases or decreases. Contrary to expectations, these results, reported in Table 6 and Table 7 for the Dynarski/Gruber method, and Table 8 and Table 9 for the Mace/Cochrane method, show that the response of consumption to an increase or decrease in head of household income is small and not substantially different from those reported above. Upon closer examination, we see that the coefficients on the change in income are larger for income increases than income decreases and that they are not significantly different from zero in the latter case. This means that consumption increases when head of household income increases but that it does not necessarily decrease when head of household income decreases. This might seem somewhat puzzling if one considers the weak consumption in the 1990s. That fact that the full consumption insurance hypothesis holds in some cases when head of household income decreases might be accounted for by downward rigidity in consumption in short run. This finding is clearer in the Mace/Cochrane method showed in