Silviase/jgov_v1.4_full_ann huggingface dataset:
only when it unexpectedly changes lifetime income. Although the impact of the year-end adjustment on lifetime income is negligible, it was a surprise, so we would expect consumption responses, based upon previous studies on small income shocks.

Estimated coefficients on the adjustment terms, which are reported in the middle rows of Table 5, indicate positive contemporaneous impacts on expenditures. Though they are not necessarily statistically significant $ ^{9} $ , the size of the elasticity estimates on the contemporaneous end-of-year adjustment is all larger (two to five times) than those on the bonus. Recalling the fact that the estimated elasticity of consumption to bonus payments reported above were smaller than those of previous studies looking at small income changes, the observed difference between the two variations, i.e. the bonus vs. the year-end adjustment, seem to suggest that Japanese households overreact not only to relatively small irregular income changes but also to large and highly predictable changes, though the degree of overreaction to the latter is far smaller. In this sense, we might be able to say that the behavior of Japanese households is consistent with the Hsieh and Browning-Collado conjecture in a looser sense.

### 5 -c. Excess Sensitivity and Liquidity Constraints

What explains the excess sensitivity of Japanese household consumption to even large anticipated income variations? Since Japan has a much less developed personal credit market than the United States, liquidity constraints seem to be a promising explanation. To examine the role of liquidity constraints, we test the impact of the bonus on households with different levels of assets. $ ^{10} $ The FIES does not collect information on assets, but the Family Savings Survey (henceforth, FSS) collects data on financial assets data on December 31 in every year from the same households surveyed in the FIES who entered the sample in August, September and October. $ ^{11} $ We can therefore create a matched dataset from the FIES and FSS to measure the impact of bonus incomes across families with different level of financial assets. Since we need data covering both December and March, the only panel we can use is the October-March panel.

Using the matched FIES-FSS panels from October to March, Table 6 presents the estimated impact of bonus payments on consumption seasonality for our different groups of households. The first two columns present the estimates on households with asset-income ratios above and below 1.0, the next two columns present estimates for households with the ratios above and below 1/2, and the last two columns with the ratio above and below the mean ratio. $ ^{12} $ Though the estimated coefficients on the contemporaneous bonus terms are always a slightly
higher for households that are likely to be liquidity constrained, the coefficients for the less constrained households are still positive and significant. The difference between the two types of households is much larger for the coefficients on the year-end adjustment. Therefore, while Japan's less developed personal credit market might have worked to strengthen the excess sensitivity of consumption to (the small and unexpected) tax refunds, it does not account for the overreaction of consumption to (the large and highly predictable) bonus payments.

### 6. Macroeconomic Evidence

This section examines the impact of the bonus system on seasonal patterns of Japanese aggregate consumption over years. If the bonus payments are affecting consumption seasonality, the recent decline in the ratio of bonus income to total compensation due to the continued economic stagnation should have reduced the overall seasonality of consumption. We exploit this inference to confirm our excess sensitivity finding. Since micro data, especially those from a diary, are generally liable to noise, confirmation of our prior findings with macro data provides an instructive supplement.

Our earnings data are compiled from the published tabulations from the Monthly Labor Survey conducted by Ministry of Health, Labor and Welfare (MHLW). Subtracting “contractual cash earnings” from “total cash earnings” $ ^{13} $ , we can obtain a series that proxies for average monthly bonus payments. As a proxy for aggregate monthly consumption, we use the index of retail trade and department store sales from the Current Survey of Commerce conducted by Ministry of Economy, Trade and Industry (METI). We also examine chain store sales from the Japan Chain Stores Association. $ ^{14} $

Visual inspection of the data (Figure 4) seems to support our prior. As shown in the upper half of the figure, the ratio of bonus to average monthly total earnings visibly declined in the late 1990s, and the seasonality of the sales also declined in that period. $ ^{15} $ We examine the data more rigorously by running the following time-series regression:

$$ \ln\left(\frac{C_{t}}{C_{t-1}}\right)=\sum_{n=0}^{3}\alpha_{n}\left(\frac{Bonus_{t-n}}{MonthlyIncome_{\tau}}\right)+Z_{h,t}^{^{\prime}}\cdot\gamma+Month_{t}^{^{\prime}}\cdot\omega+\varepsilon_{t} $$

where t refers to the month. The dependent variable is the log of the ratio of sales series in a given month t relative to those in the previous month. The main independent variables are

$ \frac{Bonus_{t-n}}{MonthlyIncome_{\tau}} $ , which are the values of bonus proxy divided by average monthly total
earnings. Additional variables are nominal interest rate, CPI inflation rate, and monthly dummies. We used lagged variables as instruments to avoid the possible simultaneity.

Coefficients estimates on the bonus terms are reported in Table 7. The impact of the contemporaneous bonus is positive with marginally significant 0.128 elasticity in the retail sales regression. Contemporaneous elasticity measured in the department store and chain store regressions are both much larger and significant. Though the estimated aggregate elasticity above are slightly larger than that from the micro-based estimates, the MPC derived from those figures are at most in the order of the second decimal place $ ^{16} $ , not much different from that of the micro estimates. Thus our micro-data finding that the bonus payments (large and well-anticipated income variations) affect Japanese consumption seasonality is confirmed by aggregate data from different sources.

### 7. Conclusion

We exploit a notable institutional feature of the Japanese economy, the bonus system, to examine whether households in Japan react to predictable and large changes in household income within the year. We find evidence that the bonus-oriented changes in income affect the seasonal pattern of consumption expenditures in Japan, despite the fact that Japanese bonus payments are large and fairly predictable.

Our empirical findings appear to contradict earlier studies on different countries, particularly Browning and Collado (2001) and Hsieh (2002). There are several possible reasons for the observed difference. First, possibility is that one or the other set of results is wrong due to some (as yet undetected) contaminations; however, it is unlikely because the amplitude of income swings is enough large to be identified. Institutional differences among the countries under study may explain the observed difference. In particular, the fact that personal credit markets are much less developed in Japan, seems to be a promising explanation. However, our Euler equation estimates for households with different asset levels indicate that liquidity constraints cannot account for the overreaction of consumption to the bonus payments. Other possible explanation is differences in preference or culture. Estimated coefficients of prudence for Japanese households are much higher than those for the U.S. households (see Part II of this Discussion Paper), and Japanese people are often believed to be relatively risk-averse by international standard. A stronger precautionary savings motive and buffer-stock savings might be one explanation of Japanese excess sensitivity, though any possible explanation requires more theoretical and empirical investigation.
The evidence presented here against consumption smoothing has far-reaching implications for debates on the effect of fiscal policy on aggregate consumption and demand. If people are bounded-rational as assumed by Hsieh (2002) and Browning and Collado (2001), tax and fiscal policy measures have an effect only when the policy change is small (so it is costly for people to understand precisely how their incomes are affected). Yet our findings indicate that in Japan large and anticipated policies could be effective. We should not expect too much of larger policies, however, considering the small MPC estimates of less than 0.1 and the transitory nature of the effects.
## REFERENCES

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Campbell, John Y., and Mankiw, N. Gregory. (1989) “Consumption, Income, and Interest Rates: Reinterpreting the Time Series Evidence.” NBER Macroeconomics Annual 4: 185-216.

Hsieh, Chang-Tai. (2001) “Do Consumers React to Anticipated Income Changes? Evidence from the Alaska Permanent Fund,” Princeton University mimeo.

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, Economic and Social Research Institute, Cabinet Office, Tokyo, Japan.

Hori, Masahiro and Shimizutani, Satoshi. (2002) “Micro Data Studies on Japanese Tax Policy and Consumption in the 1990s,” Economic and Social Research Institute, Cabinet Office, Japan, mimeo.

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Ishikawa, Tsuneo and Ueda, Kazuo. (1984) “The Bonus Payment System and Japanese Personal Savings,” in Masahiko Aoki, ed., The Economic Analysis of the Japanese Firm. Amsterdam: North-Holland, 133-92.

Parker, Jonathan A. (1999) “The Reaction of Household Consumption to Predictable Changes in Social Security Taxes.” American Economic Review 89 (September) 959-73.

Romer, David. (2001) Advanced Macroeconomics Second edition McGraw-Hill International Edition.

Shapiro, Matthew D. and Slemrod, Joel. (1995) “Consumption Response to the Timing of Income: Evidence from a Change in Tax Withholding.” American Economic Review 85 (March): 274-83.

Shea, John. (1995) “Union Contracts and the Life-Cycle/Permanent-Income Hypothesis.” American Economic Review 85 (March): 186-200.

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

Souleles, Nicholas S. (1999) “The Response of Household Consumption to Income Tax Refunds.” American Economic Review 89 (September): 947-58.

Wilcox, David W. (1989) “Social Security Benefits, Consumption Expenditure, and the Life Cycle Hypothesis.” Journal of Political Economy 97 (April): 288-304.
Table 1-1 Ratio of Bonus Receivers (Observations from 1990-1999, Workers' Households)

December	Private	Public Services	Total
Construction	Manufacturing & Mining	Services
Non-office workers	1-299	0.48	0.67	0.60	0.59
300-	(692)	(895)	(1548)	(3135)
Government	0.79	0.81	0.78	0.80
(66)	(624)	(587)	(1277)
0.79	0.93	0.82
(105)	(27)	(132)
Office workers	Private	1-299	0.67	0.71	0.71	0.71
300-	(649)	(853)	(3218)	(4720)
0.79	0.82	0.82	0.82
(364)	(1563)	(3041)	(4968)
Public	Government	1.00	0.89	0.91	0.90
(3)	(1484)	(1973)	(3460)
Total	0.63	0.76	0.76	0.91	0.76
(1771)	(3938)	(9983)	(2000)	(17692)

June	Private	Public Services	Total
Construction	Manufacturing & Mining	Services
Non-office workers	1-299	0.05	0.10	0.10	0.09
300-	(623)	(723)	(1322)	(2668)
Government	0.41	0.36	0.28	0.32
(86)	(610)	(614)	(1310)
0.63	0.87	0.67
(116)	(25)	(141)
Office workers	Private	1-299	0.12	0.16	0.24	0.21
300-	(553)	(751)	(2954)	(4258)
0.38	0.44	0.48	0.46
(336)	(1563)	(2933)	(4832)
Public	Government	0.77	0.69	0.71	0.70
(4)	(1394)	(1906)	(3304)
Total	0.16	0.30	0.37	0.71	0.38
(1598)	(3651)	(9333)	(1931)	(16513)

March	Private	Public Services	Total
Construction	Manufacturing & Mining	Services
Non-office workers	1-299	0.01	0.01	0.02	0.02
300-	(745)	(876)	(1614)	(3235)
Government	0.06	0.02	0.04	0.03
(71)	(685)	(644)	(1400)
0.46	0.60	0.49
(109)	(28)	(137)
Office workers	Private	1-299	0.02	0.01	0.09	0.07
300-	(661)	(891)	(3340)	(4892)
0.05	0.03	0.09	0.06
(354)	(1636)	(3130)	(5120)
Public	Government	0.77	0.68	0.72	0.71
(4)	(1533)	(2090)	(3627)
Total	0.02	0.02	0.17	0.72	0.18
(1831)	(4092)	(10370)	(2118)	(18411)

July	Private	Public Services	Total
Construction	Manufacturing & Mining	Services
Non-office workers	1-299	0.18	0.40	0.31	0.30
300-Government	(636)	(723)	(1303)	(2662)
0.32	0.38	0.40	0.39
(80)	(601)	(609)	(1290)
0.11	0.09	0.11
(109)	(22)	(131)
Office workers	Private	1-299	0.35	0.42	0.35	0.36
300-	(536)	(747)	(2959)	(4242)
0.31	0.32	0.27	0.29
(330)	(1584)	(2909)	(4823)
Public	Government	0.00	0.16	0.15	0.15
(0)	(1370)	(1875)	(3245)
Total	0.27	0.37	0.29	0.15	0.29
(1582)	(3655)	(9259)	(1897)	(16393)
Table 1-2 Ratio of Bonus Income to the Monthly Household Head Regular Income (Average of Bonus Receivers)

All Observations from 1990 to 1999	After 1997 Observations
Private	Public Services	Private	Public Services
Construction	Manufacturing &amp;	Services	Construction	Manufacturing &amp;	Services
December	Non-office workers	1-299	1.28	1.52	1.35	1.02	1.34	1.30
Government	300-2.15	2.05	2.03	1.90	1.95	1.84
Office workers	Private	1-299	1.83	1.91	1.97	1.63	1.84	1.88
300-	2.62	2.50	2.48	2.11	2.37	2.35
Public	Government	2.84	2.43	2.34	2.43	2.27
March	Non-office workers	1-299	0.38	0.85	0.63	0.17	0.93	0.71
Government	300-0.39	0.66	0.64	0.00	0.55	0.87
Office workers	Private	1-299	1.05	0.92	0.68	1.80	0.56	0.58
300-	0.78	0.92	0.69	0.63	0.92	0.65
Public	Government	0.41	0.49	0.48	0.51	0.50
June	Non-office workers	1-299	1.80	1.60	1.50	1.73	1.53	1.51
Government	300-2.06	2.00	1.94	2.13	2.18	1.94
Office workers	Private	1-299	2.12	2.00	2.00	1.51	2.02	2.02
300-	2.72	2.52	2.35	2.01	2.44	2.32
Public	Government	2.14	2.02	2.01	2.06	2.01
July	Non-office workers	1-299	1.32	1.48	1.32	1.19	1.39	1.25
Government	300-1.68	1.99	1.56	1.50	1.87	1.41
Office workers	Private	1-299	1.73	1.78	1.65	1.48	1.71	1.53
300-	2.22	2.40	2.05	1.69	2.35	1.97
Public	Government	1.89	1.96	1.88	2.03
Table 2

Summary Statistics: FIES Panels (October-March, November-April, and December-May) (1990-1999)

Oct.-March, Nov.-April, and Dec.-May	December	March
Full Sample	Non-Bonus Group	December Bonus Group	December Bonus Group	Full Sample	Non-Bonus Group	December Bonus Group	December & March Bonus Group	Full Sample	Non-Bonus Group	December Bonus Group	December & March Bonus Group
Number of Households x Months	46929	10731	28027	8171	7853	1792	4697	1364	7785	1784	4642	1359
Consumption	281504	243415	283772	323744	357244	294114	362571	421839	290207	252160	289650	342051
(217830)	(196660)	(216058)	(240657)	(249279)	(230138)	(242079)	(277426)	(244083)	(237218)	(245623)	(238394)
Durables	19673	12933	19681	28495	32091	20042	32969	44899	21048	16751	20269	29349
(125529)	(91108)	(128260)	(151789)	(148833)	(126342)	(146594)	(179550)	(132395)	(124647)	(135522)	(131187)
Semi-Durables	37150	29064	37235	47474	54591	38477	55833	71486	38754	29394	38003	53607
(48633)	(42931)	(47974)	(55471)	(62642)	(51587)	(63170)	(68759)	(53487)	(44739)	(52353)	(63763)
Non-Durables	113354	105161	113595	123287	145631	132465	146053	161474	113257	104917	113218	124339
(45365)	(45837)	(44124)	(46862)	(58529)	(57689)	(57064)	(60522)	(41418)	(41001)	(40317)	(43088)
Services	111328	96256	113261	124488	124931	103129	127716	143980	117147	101099	118160	134756
(129528)	(129676)	(128925)	(129547)	(134566)	(132381)	(134865)	(132697)	(155591)	(159119)	(160454)	(129931)
Monthly Income	554578	506742	553392	621471	554310	506685	553329	620256	554673	506395	553572	621811
(232818)	(251423)	(228611)	(203860)	(232536)	(251470)	(228511)	(202897)	(232906)	(251248)	(228595)	(204663)
Regular Income	386363	341514	388005	439633	386497	351501	386638	431988	387407	340269	389588	441836
(172808)	(212746)	(158469)	(143012)	(178055)	(222030)	(164063)	(146438)	(173819)	(211812)	(159651)	(145837)
Bonus Income	120974	0	141378	209864	682355	0	843600	1023563	40933	0	0	234482
(352761)	(0)	(388318)	(411043)	(592423)	(0)	(554445)	(380072)	(114063)	(0)	(0)	(170756)
End of Year Adjustment (Estimated)	3199	2595	3253	3805	19191	15630	19483	22849	0	0	0	0
(39055)	(40234)	(41388)	(27540)	(94047)	(97718)	(99724)	(64204)	(0)	(0)	(0)	(0)
Age	42.93	45.87	41.94	42.45	42.91	45.87	41.93	42.42	42.95	45.89	41.96	42.45
(10.64)	(12.12)	(10.13)	(9.51)	(10.62)	(12.10)	(10.11)	(9.51)	(10.66)	(12.14)	(10.15)	(9.53)
Family Size	3.49	3.27	3.55	3.56	3.49	3.26	3.56	3.56	3.49	3.28	3.55	3.57
(1.15)	(1.19)	(1.14)	(1.11)	(1.15)	(1.18)	(1.14)	(1.11)	(1.15)	(1.19)	(1.14)	(1.11)
Dwelling (owner=1)	0.53	0.56	0.51	0.54	0.53	0.56	0.51	0.54	0.53	0.56	0.51	0.53
(0.50)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)	(0.50)
Table 3

Effects of the Bonus Payments on the Seasonal Pattern of Consumption [October-March, November-April, and December-May Panels)

Semi-Durable Consumption	Non-Durable Consumption	Service Consumption	Total Consumption
Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.	Coef.	Std. Err.
December Bonus Dummy	November	-0.054	(0.069)	0.014	(0.014)	0.024	(0.036)	-0.009	(0.023)
December	0.218	(0.048)	0.030	(0.010)	0.077	(0.025)	0.097	(0.016)
January	-0.068	(0.038)	-0.015	(0.008)	-0.074	(0.020)	-0.058	(0.013)
February	-0.185	(0.039)	-0.007	(0.008)	0.039	(0.020)	-0.014	(0.013)
March	0.093	(0.039)	-0.010	(0.008)	-0.033	(0.020)	-0.005	(0.013)
April	-0.081	(0.046)	-0.014	(0.009)	0.022	(0.024)	-0.007	(0.015)
December & March Bonus Dummy	November	0.037	(0.087)	0.035	(0.018)	0.022	(0.046)	0.004	(0.029)
December	0.294	(0.061)	0.085	(0.012)	0.184	(0.033)	0.163	(0.021)
January	-0.124	(0.049)	-0.045	(0.010)	-0.139	(0.026)	-0.105	(0.017)
February	-0.185	(0.050)	-0.013	(0.010)	0.054	(0.026)	-0.011	(0.017)
March	0.264	(0.050)	0.016	(0.010)	0.062	(0.026)	0.072	(0.017)
April	-0.143	(0.060)	-0.002	(0.012)	-0.018	(0.032)	-0.035	(0.020)
(a) All Dec. Dummies (b) All Dec&March Dummies (a)+(b)	F(6,35230) = F(6,35230) = F(12,35230)	9.400	F(6,36530) = F(6,36530) = F(12,36530)	3.1900	F(6,36493) = F(6,36493) = F(12,36493)	5.0500	F(6,36530) = F(6,36530) = F(12,36530)	9.830	20.720
12.950	12.6900	11.6900	F(6,36530) = F(12,36530) =	11.840
Number of observations	35249	36549	36512	36549
Adj R-squared	0.075	0.349	0.026	0.153
Root MSE	1.345	0.277	0.728	0.462

Notes:

Dependent Variables are $ \log(C\ t/C\ t-1) $ , and all Cs are in real terms.

All regressions are OLS and include (ordinary) monthly dummies and the nominal interest rates as additional regressors.
Table 4

Euler equation estimates: Response of Consumption to Bonus Income [October-March, November-April and December-May Panels]

Semi-Durable Consumption	Non-Durable Consumption	Service Consumption	Total Consumption
Bonus/Inc.(t)	0.128	0.016	0.059	0.064
(0.023)	(0.005)	(0.013)	(0.008)
Bonus/Inc.(t-1)	-0.053	0.001	-0.051	-0.038
(0.019)	(0.004)	(0.010)	(0.007)
Bonus/Inc.(t-2)	-0.082	-0.012	0.019	-0.015
(0.020)	(0.004)	(0.010)	(0.007)
Bonus/Inc.(t-3)	0.052	-0.002	-0.004	0.008
(0.020)	(0.004)	(0.011)	(0.007)
Sum(Bonus Coef.0,3)	0.045	0.003	0.023	0.019
(0.042)	(0.008)	(0.022)	(0.014)
Number of observations	35249	36549	36512	36549
Adj R-squared	0.075	0.348	0.025	0.152
Root MSE	1.345	0.277	0.729	0.463

Notes:

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

Standard errors in parentheses.

All regressions are OLS and include a quadratic in age, the number of family members, and monthly dummies

Table 5

Euler equation estimates: Response of Consumption to Bonus and End-of-Year Tax [October-March, November-April and December-May Panels]

Semi-Durable Consumption	Non-Durable Consumption	Service Consumption	Total Consumption
Bonus/Inc.(t)	0.128	0.016	0.059	0.064
(0.023)	(0.005)	(0.013)	(0.008)
Bonus/Inc.(t-1)	-0.053	0.001	-0.050	-0.039
(0.019)	(0.004)	(0.010)	(0.007)
Bonus/Inc.(t-2)	-0.082	-0.012	0.018	-0.015
(0.020)	(0.004)	(0.010)	(0.007)
Bonus/Inc.(t-3)	0.053	-0.002	-0.004	0.008
(0.020)	(0.004)	(0.011)	(0.007)
End-of-Year Adj./Inc.(t)	0.317	0.028	0.304	0.109
(0.288)	(0.059)	(0.155)	(0.098)
End-of-Year Adj./Inc.(t-1)	0.031	0.047	-0.210	0.039
(0.278)	(0.057)	(0.150)	(0.095)
End-of-Year Adj./Inc.(t-2)	-0.411	-0.061	0.177	-0.051
(0.283)	(0.057)	(0.151)	(0.096)
End-of-Year Adj./Inc.(t-3)	-0.038	-0.014	-0.152	-0.110
(0.284)	(0.058)	(0.151)	(0.096)
Sum(Bonus Coef.0,3)	0.046	0.003	0.023	0.019
(0.042)	(0.008)	(0.022)	(0.014)
Sum(EOY adj. Coef.0,3)	-0.101	0.001	0.119	-0.013
(0.569)	(0.116)	(0.305)	(0.193)
Number of observations	35193	36493	36456	36493
Adj R-squared	0.075	0.348	0.025	0.152
Root MSE	1.345	0.277	0.729	0.462

Notes:

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

Standard errors in parentheses.

All regressions are OLS and include a quadratic in age, the number of family members, and monthly dummies.
Table 6

Elasticity of Total Consumption to Income Changes by Asset Levels [dependent variable: $ \ln(\text{Ct}/\text{Ct}-1) $ ]

A/Y>1	A/Y<1	A/Y>0.5	A/Y<0.5	A/Y>mean	A/Y<mean
Bonus Income
Bonus Coef.(t)	0.061	0.069	0.061	0.075	0.047	0.074
S.E.	(0.018)	(0.019)	(0.015)	(0.031)	(0.025)	(0.015)
Bonus Coef.(t-1)	-0.018	-0.048	-0.021	-0.064	0.000	-0.051
S.E.	(0.019)	(0.020)	(0.015)	(0.031)	(0.026)	(0.016)
Bonus Coef.(t-2)	-0.007	-0.023	-0.018	0.005	0.005	-0.019
S.E.	(0.019)	(0.020)	(0.015)	(0.031)	(0.026)	(0.016)
Bonus Coef.(t-3)	0.003	0.005	0.009	-0.030	0.007	0.002
S.E.	(0.019)	(0.020)	(0.015)	(0.031)	(0.026)	(0.016)
Sum(Bonus Coef.0,3)	0.039	0.004	0.030	-0.014	0.059	0.006
S.E.	(0.038)	(0.040)	(0.031)	(0.062)	(0.053)	(0.032)
Number of observations	5199	4122	7589	1732	2965	6356
Adj R-squared	0.147	0.157	0.154	0.132	0.144	0.158
Root MSE	0.485	0.421	0.466	0.419	0.515	0.428
Bonus & End-of-Year Tax Adjustment
Bonus Coef.(t)	0.063	0.069	0.063	0.074	0.051	0.075
S.E.	(0.018)	(0.019)	(0.015)	(0.031)	(0.025)	(0.015)
Bonus Coef.(t-1)	-0.020	-0.049	-0.024	-0.064	-0.005	-0.052
S.E.	(0.019)	(0.020)	(0.015)	(0.031)	(0.026)	(0.016)
Bonus Coef.(t-2)	-0.006	-0.020	-0.017	0.007	0.007	-0.019
S.E.	(0.019)	(0.020)	(0.015)	(0.031)	(0.026)	(0.016)
Bonus Coef.(t-3)	0.003	0.006	0.009	-0.031	0.006	0.002
S.E.	(0.019)	(0.020)	(0.015)	(0.031)	(0.026)	(0.016)
Adjustment Coef.(t)	-0.025	0.221	0.074	0.245	-0.011	0.146
S.E.	(0.261)	(0.210)	(0.187)	(0.369)	(0.404)	(0.177)
Adjustment Coef.(t-1)	0.270	0.203	0.264	0.213	0.058	0.264
S.E.	(0.261)	(0.210)	(0.187)	(0.366)	(0.405)	(0.177)
Adjustment Coef.(t-2)	-0.057	-0.506	-0.238	-0.750	0.007	-0.374
S.E.	(0.261)	(0.210)	(0.187)	(0.365)	(0.405)	(0.177)
Adjustment Coef.(t-3)	0.047	-0.110	-0.085	0.193	-0.443	0.068
S.E.	(0.263)	(0.211)	(0.188)	(0.369)	(0.410)	(0.177)
Sum(Bonus Coef.0,3)	0.040	0.005	0.031	-0.014	0.060	0.006
S.E.	(0.038)	(0.040)	(0.031)	(0.062)	(0.053)	(0.032)
Sum(Adj. Coef.0,3)	0.234	-0.192	0.016	-0.099	-0.389	0.104
S.E.	(0.530)	(0.423)	(0.379)	(0.743)	(0.824)	(0.358)
Number of observations	5179	4114	7565	1728	2945	6348
Adj R-squared	0.147	0.157	0.154	0.132	0.143	0.158
Root MSE	0.484	0.421	0.465	0.420	0.513	0.428

Sample is matched (October-March) panels of the FIES and FSS from 1990 to 1999. Specifications are the same as those of the previous Euler equation regressions. A/Y is the ratio of gross assets to annual pre-tax income.

Notes: