Silviase/jgov_v1.4_full_ann huggingface dataset:
Table 7

Euler equation estimates on aggregate sales data from 1990-2001

Retail Sales	Department Store Sales	Chain Store Sales
Bonus/Av.Income(t)	0.128	0.533	0.363
(0.071)	(0.113)	(0.087)
Bonus/Av.Income(t-1)	-0.228	-0.499	-0.236
(0.089)	(0.156)	(0.120)
Bonus/Av.Income(t-2)	0.039	0.241	-0.013
(0.074)	(0.132)	(0.102)
Bonus/Av.Income(t-3)	0.082	0.209	-0.001
(0.152)	(0.272)	(0.210)
Std. Error of regression	0.021	0.039	0.030
Adjusted R-squared	0.975	0.980	0.960
Durbin-Watson	2.470	2.785	2.750
Number of observations	141	144	144

Notes:

Dependent variables are $ \log(\text{Sales current month}/\text{Sales previous month}) $

Standard errors in parentheses.

Regressions include nominal interest rate, monthly dummies, and monthly rate of CPI change.

All regressions are IV using lagged variables as their instruments.
Figure 1-1

No-Bonus Group (Nov., Dec., Jan., Feb., March, April and May)
Figure 1-2

December Bonus Group (Nov., Dec., Jan., Feb., March, April and May)

December Bonus Group (December and January)

December Bonus Group (March and April)

Monthly Growth of Household Head Income
Figure 1-3

December&March Bonus Group (Nov.,Dec.,Jan.,Feb.,March,April and May)

December&March Bonus Group (December and January)
Figure 2

Seasonal Pattern of Household Head Income by Group [from November to December]
Figure 3-1 Seasonal Pattern of Consumption (Semi-Durables)
Figure 3-2 Seasonal Pattern of Consumption (Non-Durables)
Figure 3-3 Seasonal Pattern of Consumption (Services)
Figure 3-4 Seasonal Pattern of Consumption (Total)
Figure 4 Pattern of Bonus Payment and Aggregate Consumption Seasonality
Appendix: Summer Bonus Observations Table A

Effects of the Bonus Payments on the Seasonal Pattern of Consumption [January-June, February-July, and March-August Panels]

Semi-Durable Consumption	Non-Durable Consumption	Service Consumption	Total Consumption
Coef.	Std.Err.	Coef.	Std.Err.	Coef.	Std.Err.	Coef.	Std.Err.
June Bonus Dummy	March	-0.007	(0.044)	0.002	(0.008)	0.007	(0.025)	0.009	(0.015)
April	0.019	(0.037)	-0.007	(0.007)	0.019	(0.021)	-0.005	(0.013)
May	0.005	(0.037)	-0.001	(0.007)	-0.054	(0.021)	-0.017	(0.013)
June	0.193	(0.037)	0.014	(0.007)	0.096	(0.021)	0.089	(0.013)
July	-0.100	(0.047)	0.003	(0.009)	0.002	(0.026)	-0.012	(0.016)
August	-0.284	(0.068)	-0.050	(0.013)	-0.019	(0.038)	-0.076	(0.024)
July Bonus Dummy	March	0.036	(0.054)	0.000	(0.010)	-0.044	(0.031)	-0.025	(0.019)
April	0.029	(0.040)	-0.007	(0.008)	0.008	(0.023)	0.008	(0.014)
May	-0.067	(0.040)	0.002	(0.008)	-0.009	(0.023)	-0.003	(0.014)
June	0.016	(0.040)	-0.016	(0.008)	-0.020	(0.023)	-0.017	(0.014)
July	0.268	(0.046)	0.039	(0.009)	0.137	(0.026)	0.122	(0.016)
August	-0.223	(0.066)	-0.021	(0.013)	-0.103	(0.037)	-0.109	(0.023)
March & June Bonus Dummy	March	0.221	(0.050)	0.022	(0.010)	0.129	(0.028)	0.105	(0.018)
April	-0.043	(0.042)	-0.003	(0.008)	-0.050	(0.024)	-0.033	(0.015)
May	-0.111	(0.042)	-0.018	(0.008)	-0.035	(0.024)	-0.030	(0.015)
June	0.090	(0.043)	0.008	(0.008)	0.065	(0.024)	0.065	(0.015)
July	0.078	(0.055)	0.029	(0.010)	0.064	(0.031)	0.053	(0.019)
August	-0.254	(0.079)	-0.009	(0.015)	-0.080	(0.045)	-0.103	(0.028)
March & July Bonus Dummy	March	0.195	(0.140)	0.014	(0.027)	0.217	(0.079)	0.120	(0.049)
April	0.039	(0.100)	0.034	(0.019)	-0.118	(0.057)	-0.031	(0.035)
May	-0.137	(0.101)	-0.030	(0.019)	0.053	(0.057)	-0.041	(0.036)
June	-0.116	(0.102)	-0.025	(0.020)	-0.094	(0.058)	-0.043	(0.036)
July	0.258	(0.104)	0.046	(0.020)	0.166	(0.059)	0.157	(0.037)
August	-0.057	(0.147)	-0.034	(0.028)	-0.039	(0.084)	-0.022	(0.052)
(a) All June Dummies	F(6,38829) =	8.3	F(6,40325) =	3.27	F(6,40282) =	4.86	F(6,40325) =	10.09
(b) All July Dummies	F(6,38829) =	8.23	F(6,40325) =	4.65	F(6,40282) =	6.54	F(6,40325) =	14.07
(c) All June&March Dummies	F(6,38829) =	7.37	F(6,40325) =	3.18	F(6,40282) =	7.00	F(6,40325) =	14.04
(d) All July&March Dummies	F(6,38829) =	1.92	F(6,40325) =	2.36	F(6,40282) =	3.92	F(6,40325) =	4.65
(a)+(b)+(c)+(d)	F(24,38829) =	6.53	F(24,40325) =	3.23	F(24,40282) =	5.76	F(24,40325) =	10.45
Number of observations	38860	40356	40313	40356
Adj. R-squared	0.030	0.046	0.014	0.036
Root MSE	1.289	0.251	0.739	0.461

Notes:
Euler equation estimates: Response of Consumption to Bonus Income [January-June, February-July, and March-August Panels]

Semi-Durable Consumption	Non-Durable Consumption	Service Consumption	Total Consumption
Bonus/Inc.(t)	0.135	0.016	0.073	0.065
(0.016)	(0.003)	(0.009)	(0.006)
Bonus/Inc.(t-1)	-0.064	-0.004	-0.009	-0.020
(0.019)	(0.004)	(0.011)	(0.007)
Bonus/Inc.(t-2)	-0.128	-0.020	-0.006	-0.034
(0.030)	(0.006)	(0.017)	(0.011)
Bonus/Inc.(t-3)	-0.270	-0.023	-0.081	-0.070
(0.099)	(0.019)	(0.056)	(0.035)
Sum(Bonus Coef.0,3)	-0.327	-0.030	-0.023	-0.058
(0.102)	(0.020)	(0.058)	(0.036)
Number of Observations	38860	40356	40313	40356
Adj. R-squared	0.029	0.0461	0.0127	0.0351
Root MSE	1.2891	0.25107	0.73964	0.4609

Notes:

Dependent variable are $ \log(\text{C current month}/\text{C previous month}) $

Standard errors in parenthesis.

All regressions are OLS and include a quadratic in age, the number of family members, and monthly dummies.
# Are Japanese Consumers Prudent in the Late 1990s? Evidence From Japanese Micro Data

## Abstract:

This paper explores empirically whether Japanese consumers are prudent in the 1990s, a decade with historically low growth rate. Using the methodology of Dynan (1993), this study uses micro-level data from the Family Income and Expenditure Survey and Family Savings Survey to estimate the coefficient of prudence of Japanese households in the second half of the 1990s. The estimates indicate that the coefficient of prudence is positive and statistically significant in 1998-1999 with an implied coefficient of prudence of 4, much higher than those estimated for U.S. households. The estimates are higher for workers' households and for younger households whose head of household age is under 40. There is not much difference between manufacturing and non-manufacturing or households with different liquidity conditions.
### 1. Introduction

The question of what accounts for the weak consumption in Japan in the 1990s is surely an urgent research topic and, at the same time, a key to remedies for how to recover the Japanese consumption under increasing uncertainty. A decrease in permanent income or an increase in unemployment might be candidates for explaining the weak trend in consumption but there is another explanation which has not been properly explored: an increase in precautionary saving. In face of increased uncertainty, consumers are inclined to increase precautionary saving which might have dampened consumption.

This paper empirically explores the prudence of Japanese consumers' in the 1990s, a decade with historically low growth rate. The basic technique used to measure prudence follows Dynan (1993). She used the Euler equation to estimate directly the coefficient of prudence proposed by Kimball (1990) and found a small precautionary motive, “too small to be consistent with widely accepted beliefs about risk aversion”.

In spite of the importance for both policy makers and academic researchers, there is little research that estimates precautionary motives in Japan. One exception is the Economic Planning Agency (1999), which used micro-level data from the Japanese Panel Surveys of Consumption compiled by the Institute for Research on Household Economics, but the sample size and object of that study were limited. This paper uses micro-level data from the Family Income and Expenditure Survey (henceforth, FIES) and the Family Savings Survey (henceforth, FSS), 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 is an annual savings survey and contains information on income and financial assets and liabilities as well as household's characteristics. Since those two surveys overlap the same sample, combining these two survey makes it possible to construct the annual change in consumption as discussed below.

The main conclusion of this paper is that the coefficient of prudence is positive and statistically significant in 1998 with the large magnitude of 4, much higher than those in the U.S. The estimated coefficients of prudence are higher for workers' households and in younger households whose heads of households age is under 40. There is not much difference between manufacturing and non-manufacturing or households with varying liquidity conditions.

This paper is organized as follows. Section 2 briefly describes Dynan’s approach. Section 3 gives a description of the micro-level data set used in this paper. Section 4 turns to estimate the coefficients of prudence and discusses differences in estimated coefficients of prudence in different types of households characterized by employment status, industry, occupation and liquidity constraints. The final section concludes.
### 2. Dynan (1993) and related research

This section briefly describes the technique used in Dynan (1993) and how it may be applied to the Japanese data. For further theoretical and empirical studies on precautionary saving, refer to Doi (2001) and Ishihara (2001) that are published in ESRI discussion paper series.

Dynan (1993) derives a tractable specification that can be used to directly measure precautionary motives from the Euler equation using a second-order Taylor approximation. The basic idea comes from Kimball (1990), who derived the coefficient of relative prudence defined as $ \rho = -C_{it}(U''/U'') $ where $ C_{it} $ is consumption by household i in time t and U is the household's utility function. This relation holds when we assume the constant relative risk aversion (CRRA) utility function (and does not hold for the Hall (1978) type quadratic utility for which $ U'''' = 0 $ ).

Assume households maximize the following discounted utility function $ ^{1} $ .

$$ \max E_{t}\Biggl[\sum_{j=0}^{T-t}\left(1+\delta\right)^{-j}U(C_{i,t+j})\Biggr], $$

subject to a budget constraint:

$$ A_{i,t+j+1}=(1+r_{i})A_{i,t+j}+Y_{i,t+j}-C_{i,t+j}\quad(A_{it}\mathrm{given and}A_{i,T+I}=0) $$

where $ E_{t} $ refers to the expectation operator; $ \delta $ represents the time preference rate; T is the time of death; $ Y_{it} $ is labor income of household i in time t; $ A_{it} $ is nonhuman wealth of household i in time t; $ r_{i} $ is the after-tax real interest rate.

The following first-order condition is obtained using the Keynes-Ramsey rule,

$$ \left(\frac{1+r_{i}}{1+\mathfrak{p}}\right)E_{t}\big[U^{\prime}(C_{i.t+1})\big]=U^{\prime}(C_{i t})\;. $$

Applying a second order Taylor approximation yields the following relation:

$$ E_{t}\left[\frac{C_{i,t+1}-C_{it}}{C_{it}}\right]=\frac{1}{\xi}\left(\frac{r_{i}-\delta}{1+r_{i}}\right)+\frac{\mathsf{\rho}}{2}E_{t}\left[\left(\frac{C_{i,t+1}-C_{it}}{C_{it}}\right)^{2}\right] $$

where $ \xi = -C_{it}(U''/U') $ is the coefficient of relative risk aversion and $ \rho = -C_{it}(U'''/U'') $ is the coefficient of relative prudence.

The empirical specification can be rewritten as

$$ G C_{i}=\frac{1}{\xi}\Bigg(\frac{r_{i}-\delta}{1+r_{i}}\Bigg)+\frac{\uprho}{2}G C_{i}^{2}+\upvarepsilon_{i} $$
where $ (GC)_{i} $ is consumption growth of household i in period t and $ \varepsilon_{i} $ is the error term. Since the error term is correlated with $ (GC^{2})_{i} $ , two stage least squares (TSLS) estimation is adopted to obtain consistent estimates of the coefficient on $ (GC^{2})_{i} $ .

Dynan points out that under the CRRA utility, $ U(C)_{i} = (1 - \gamma)^{-1} C^{1 - \gamma} $ implying $ \rho = 1 + \gamma $ . Usually $ \gamma $ is chosen to be one to four and thus $ \rho $ ranges from two to five. Dynan's estimates are much smaller: the highest estimate of $ \rho $ is 0.312 with a large standard error and the null hypothesis that $ \rho $ is zero cannot be rejected, which seems to contradict with other researches. Moreover, she discusses that the presence of liquidity-constrained households or self-selection of households into risky environments cannot explain this “puzzle” fully.

To our knowledge, there is no research that estimates the coefficient of relative prudence for Japanese households except Economic Planning Agency (1999). Of course, one of the reasons is unavailability of micro-level Japanese consumption data $ ^{2} $ . Employing the above-discussed specification, Economic Planning Agency (1999) uses micro-level data of Japanese Panel Surveys of Consumption compiled by the Institute of Household Economy and reports that the estimate of $ \rho $ for the period between 1994 and 1998 is 0.877, but statistically insignificant; and that for 1998 is marginally significant at 1.72. $ ^{3} $

### 3. Data

The data set used in this paper is micro-level data from the Family Income and Expenditure Survey (FIES) and the Family Saving Survey (FSS) from 1995 to 1999. The sample of households overlaps in both surveys and can be matched.

The FIES is a monthly consumption data compiled from a diary, which is collected twice a month and contains detailed information on income and expenditures for individual households as well as on the characteristics of these households. A household is surveyed over six months. The households that enter into the sample of the FIES in August, September and October are also surveyed as the FSS samples both at the end of December in the year and in the next year. The FSS contains detailed information on financial assets and liabilities for individual households in both stock and flow basis. A household surveyed first in the year t FSS is matched with that surveyed again in the year $ t+1 $ FSS. Then a household in the year t FSS is matched with that in the FIES of the same year. As a result, a household's information is available on monthly income and expenditure from the FIES of August (of year t) to January (of year $ t+1 $ ), September to February, or October to March, and on financial assets.
and liabilities as of December from the year t and year $ t+1 $ FSS. $ ^{4} $

There is no explicit data on annual consumption in either survey, but it is possible to derive annual consumption by using the following relationship.

$$ A_{i,t+j+1}=(1+r_{i})A_{i,t+j}+(YG_{i,t+j}-TAX_{i,t+j})-C_{i,t+j} $$

where $ A_{it} $ is nonhuman wealth; $ YG_{it} $ is gross labor income; $ r_{i} $ is the real after-tax interest rate; $ TAX_{it} $ is tax payment and $ C_{it} $ is consumption. In all variables, i refers to a household. The variables of nonhuman wealth and gross labor income are explicitly available in the FSS. Unfortunately, the data on real assets is not available from the FSS, and thus it is necessary to assume that real assets value was constant while a household was surveyed. In addition to this simplifying assumption, we have to estimate tax payments correctly to derive annual consumption. Although the FSS does not have data on tax payment, the FIES has detailed information, though only for over six months, on household demographics and monthly tax payments of national income tax, local inhabitant tax and other tax. So we use this half-year information from FIES to recover annual tax payments as discussed below.

The FIES contains income data of total household members, the head of household and spouse respectively, and three different tax payments: national income tax, local inhabitant tax and other tax. However, the amount of tax payment is available only for the total household and is not disaggregated. In order to precisely estimate the amount of tax liabilities for individual household members, we dropped some of household observations from our sample using the following criteria. A household is dropped if there are more than three persons with a job or if there are two persons with a job and spouse's income is greater than 1.03 million yen (this would qualify the spouse for a tax exemption $ ^{5} $ ). Moreover, a household is dropped if the head of household is self-employed, since that makes it impossible to calculate the precise amount of tax liabilities. As a result, the sample contains worker's households whose head is only a taxpayer in a household and households whose income comes from pension benefits. After these adjustments to the data set, total tax payments for each household are obtained as the sum of national income tax, local inhabitant tax and other tax.

National income tax liabilities are calculated based upon the gross annual income in each year as reported in the FSS. First, non-taxable income is subtracted. $ ^{6} $ Then exemption of salary income or exemption of official pension receipt is calculated. The total amount of
exemption is calculated as the sum of these exemptions and other items $ ^{7} $ . The base for individual households is obtained by subtracting these exemptions from taxable income and then actual tax liabilities are calculated by applying tax rates to this tax base.

Inhabitant’s tax is calculated based upon annual salary in the previous year $ ^{8} $ . Because the FIES collected at year t lacks information from year t-2, the inhabitant’s tax amount had to be determined by information beyond that available in the FIES. In this paper, the growth rate of annual wage data from the Basic Survey of Wage Structure $ ^{9} $ by industry, age, sex and firm size are matched with individual household head to estimate wages.

The total tax reduction calculated for each year is based upon these national income and local inhabitant tax payments. The amount of tax cut other than these income tax payments is assumed to be unchanged throughout the cycle of two years.

Since it is possible that estimated tax amounts have measurement errors stemming from insufficient information to calculate them, a household is eliminated if its consumption increases or decreases more than 50% in a year. In addition, a household is excluded if the number of family members is greater than 10 because the consumption patterns of large extended households are likely to be significantly different from that of normal size households in Japan. $ ^{10} $ Also, a household 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, because these changes are likely to be due to large shocks that may also have large effects on the household's consumption. Summary statistics are reported in Table 1.

### 4. Estimation of the coefficients of prudence

The specification used to estimate the coefficients of prudence is as follows.

$$ G C_{i}=\boldsymbol{\alpha}_{0}+\boldsymbol{\alpha}_{1}*(G C^{2})_{i}+\boldsymbol{\varepsilon}_{i} $$

where $ (GC)_{i} $ is consumption growth in household i and $ \varepsilon_{i} $ is the error term. $ \alpha_{1} $ is the coefficient on the main independent variable, squared consumption growth rate and is equal to half of the coefficient of prudence $ \rho = -C_{it}(U''/U'') $ . As discussed in the previous section, two-stage least squares (TSLS) estimation is adopted to obtain consistent estimates of the coefficient on $ (GC^{2})_{i} $ , since the error term is correlated with $ (GC^{2})_{i} $ . As instruments, we
used number of family size, number of employees, change in number of family size, change in number of employees, firm and 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. The coefficient on the main variable is expected to be greater than zero.

Table 2(1) reports the results. In 1995-1996 and 1996-1997, the coefficients are negative and not significantly different from zero. The coefficient for 1997-1998 is positive but not significant and the implied coefficient of prudence is estimated to be 1.2, roughly comparable with Economic Planning Agency (1999). Finally, the coefficient for 1998-1999 is positive and significant and the implied coefficient of prudence is estimated to be 4, much larger than that of the US by Dynan (1993).

Table 2(2) divides the sample into two categories; households in which the head of household is employed and households in which the head is not employed. Figures in Table 2(2) indicate that the coefficient is larger for employed households. The coefficient of prudence is estimated to be as high as 4.73 for those households, which implies that employed households became very prudent in 1998-1999. On the contrary, not-employed households, which mostly consist of elderly pensioners, were not prudent, that is the coefficient of prudence was insignificantly negative, in the 1990s.

To identify the factors that affected the coefficient of prudence, the above specification is applied to sub-samples divided by head of household industry, age, and the liquidity condition of the household. Table 3 reports the results by head of household industry. Though the coefficient of prudence for manufacturing is positive and significant in 1996-1997, other coefficients are not significant indicating no systematic difference between industries. Even in 1998-1999 sample, the coefficients are positive for both industries with large standard errors.

Table 4 reports the results by head of household age. If the sample is divided at age of 50, the coefficients for younger households are insignificantly positive; those for the older households turn from insignificant negative to positive. If we divide the sample at age of 40, the difference between the generations becomes clear, and the coefficients for younger households are significantly positive with an exceptional period of 1997-1998.

Table 5 reports the results by different liquidity conditions for individual households. In this table, total financial assets relative to pre-tax annual income is used as a proxy for liquidity condition of each household. Most of coefficients are not significant with large standard errors and there seems to be no systematic pattern.

To sum up, though the data limitation prevents us from precise estimates, the coefficient of prudence seems to be positive and significant for 1998-1999 with large magnitude of 4, which indicates that Japanese households became prudent on that period. The
coefficient is larger for workers' households, and for younger households. However, we could not find much difference, when we divide the household sample by industry (manufacturing and non-manufacturing) or by liquidity condition.

### 5. Conclusion

This paper explores empirically how Japanese consumers become prudent in the 1990s, a decade with historically low growth rate. Using the methodology of Dynan (1993), this study uses micro-level data from the Family Income and Expenditure Survey and the Family Savings Survey to estimate the coefficient of prudence for Japanese households in the second half of the 1990's. The estimates indicate that the coefficients of prudence become positive and statistically significant in 1998-1999 with an implied coefficient of prudence of 4, much higher than those estimated for U.S. households.

The Japanese economy began to decline in the spring of 1997 right after an increase in consumption tax rates. The financial crisis in the fall of 1997 exacerbated the economic downturn and the situation became worse in 1998. Under these conditions, household expectations of future economic conditions become more uncertain, which surfaces in the larger coefficient of prudence. The fact that the coefficient is larger for workers' households suggests that downsizing and restructuring by firms enhancing the uncertainty. Moreover, the larger coefficients for younger generations seem to suggest us that the uncertainty is not only about near future but also about farther future. Building solid social safety net focusing on younger employed households might be useful to mitigate depressed consumption due to an increase in the precautionary saving.