Silviase/jgov_v1.4_full_ann huggingface dataset:
biased because not all the households received and spent their coupons during these months (which is not the case for the March-June or March-July estimates). We can adjust for this bias using information on the total fraction of coupons distributed in each month (see figure 1). For example, we know that close to 80 percent of the coupons were spent in March (see figure 1, panel A). The “attenuated” MPC in March can therefore be calculated as $ a_{1} \times (C/Y) \times (1/0.8) $ . Using this information, the “attenuated” MPC on semi-durables is 0.24 in March, 0.14 in March-April, and falls to 0.1 in March-June. While most households quickly redeemed their coupons, the coupons largely substituted for what they would have spent on anyway. In addition, most households spent less on semi-durables in subsequent months.

These findings are confirmed by similar regressions, but that use a different classification of consumption into food, apparel, transportation, entertainment, personal care, and reading materials. $ ^{22} $ These coefficient estimates are plotted in figure 3, along with their respective 95 percent confidence intervals. As can be seen, these estimates indicate that the coupons had some effect on apparel and entertainment. These items are likely to be for the children’s benefit. Because families received the coupons because of their children, they may have faced psychological pressure to spend the coupons on items for their children. In addition, the impact appears to be smaller as we consider consumption of these items over longer time periods.

So far, we have analyzed the effect of the coupons after they were received and spent by consumers. However, since the program was announced in November of 1998 and widely publicized, a forward-looking household may have already adjusted its consumption even before the coupons were distributed in March. As stipulated by the canonical model of consumption, consumption should only change upon news of unexpected income changes, and should not react to anticipated income changes. To
assess this possibility, we employ a variant of equation (1) to estimate the change in consumption after the coupon program was announced in November. $ ^{23} $ These results, presented in Table 5, provide no evidence that consumption responded to news of the coupon program. All the estimated coefficients are small and statistically insignificant.

To summarize, a comparison of the cross-sectional distribution of seasonal patterns of consumption in 1999 with the pattern in previous years indicates that the coupons had a small effect on consumption of semi-durables, but no effect on other categories of consumption. However, these estimates may be biased if the cross-sectional distribution of consumption changed in 1999 for reasons unrelated to the coupon program. To check for this, we report estimates of the cross-sectional differences in the seasonal patterns of consumption of semi-durables in 2000, compared to the cross-sectional differences in previous years (excluding the 1999 panel). These estimates, shown in figure 4, suggest that the cross-sectional differences in consumption that we see in 1999 are not due to some change in that year. Instead, Figure 4 indicates that the small increase in semi-durables consumption in 1999 was only for that year, and returned to "normal" in 2000.

Finally, to examine the impact of liquidity constraints, we examine the impact of the coupon program for households with different levels of assets. $ ^{24} $ To do this, we obviously need data both on consumption and on assets. 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. $ ^{25} $ We can therefore
create a matched dataset from the FIES and FSS to measure the impact of the coupons across families with different levels of financial assets. Since we need data covering the period before and after March, the only panel we can use is the October-March panel. The “after” period is only the month of March, the period in which the coupon effect is most apparently observed.

Using the matched FIES-FSS panels from October-March, we present the estimated impact of the coupons in March for our different groups of households (Table 6). The first two columns present the estimates on households with asset-income ratios above and below and mean, the next two columns present estimates for households with ratios above and below 1, and the last two columns for those with asset-income ratios above and below 1/2. $ ^{26} $ As can be seen, the estimated impact is always much higher for households that we deem likely to be liquidity constrained. For example, the estimated elasticity of consumption of semi-durables is 6.83 for households with asset-income ratios less than 1, while the coefficient estimate for households with asset-income ratios above than 1 is statistically insignificant from zero.

In sum, the shopping coupons program appears to have had a small effect on consumption of semi-durables, with larger effect in the initial months the coupons were distributed, and with larger impacts among families that are more likely to be liquidity constrained. However, it's important to bear in mind the limitations of these estimates. First, the coupons only accounted for a small fraction of a household's monthly income: the value of coupons received by a typical family was only 78 percent of the family's monthly income (see Table 1). Therefore, to the extent that the change in monthly income due to the coupons is measured with error, this will impart a downward bias to the estimated impact of the coupons. Second, the FIES micro-data only allows us to
analyze the response of household with children because we can not precisely identify elderly people who received the coupons. Although the response of families with children to the coupons was relatively small, it could be the case that the impact of the coupons was much larger among the elderly people. To address these limitations, the next section of the paper turns to an analysis of the impact of the shopping coupons with aggregated regional data on retail sales.

### 5. Impact of Coupons Across Prefectures

This section examines the impact of the shopping coupons on seasonal patterns of consumption between different prefectures in Japan. $ ^{27} $ As previously discussed, the coupons had to be spent within each local region (city, village, or town). Since there are no cities, towns, or villages that overlap more than one prefecture, we can compare the change in consumption in a prefecture where a large number of people received the coupons relative to a prefecture where the coupons were distributed to a smaller number of people. Again, since there are reasons for why the seasonal pattern of consumption in a prefecture with a larger number of children and elderly people may differ from that in a prefecture where a smaller number of people were eligible for the coupons, it is important to control for this by using the seasonal patterns of consumption across different prefectures in previous years.

Our main data are compiled from the published tabulations from the Current Survey of Commerce conducted by MITI. $ ^{28} $ We combine the data on monthly retail sales in a prefecture with the administrative data on the total number of coupons distributed in each prefecture. The main advantage of this data is that we are now capturing the
response of coupons distributed to the elderly, as well as those distributed to households with children. We will work with two measures of retail sales: total retail sales and sales of apparel and clothing. We compile data from 1990 to 1999, so we have eight years of data to control for seasonal patterns of consumption across prefectures.

The basic specification we estimate is similar to that in equation 1:

$$ \ln\left(\frac{S_{i,t\ (After\ March)}}{S_{i,t\ (Before\ March)}}\right)=a_{1}\cdot\left(\frac{Coupon_{i,t}}{MonthlyIncome_{i,t}}\right)+Z_{i}^{^{\prime}}\cdot a_{2}+Year_{t}^{^{\prime}}\cdot a_{3} $$

where i indexes prefectures (47 prefectures in total), t refers to year, $ Z_{i} $ represents a vector of indicator variables for prefecture, and $ Year_{t} $ is a vector of indicator variables for year. The main dependent variable is now the ratio of the sum of retail sales in prefecture i after March $ (S_{i,t} $ (After March)) to the average monthly retail sales prior to March $ ((S_{i,t} $ (Before March)), and the main independent variable is the total coupon income in a prefecture (computed as the total number of coupons distributed in a prefecture) relative to the average monthly income in the prefecture in the previous year. $ ^{29} $ Clearly, for years other than 1999, $ Coupon_{i,t} $ is set to zero.

As before, we begin with estimates of cross-sectional differences in the seasonal pattern of consumption in 1999. To do this, we employ a variant of equation (2) in which we exclude indicator variables for prefectures and year. The point estimates indicate that in the Spring of 1999, aggregate retail sales increased by more in a prefecture where a larger fraction of the prefecture’s population was eligible for the coupons relative to a prefecture where a smaller fraction of people were affected by the coupon program. However, we cannot attribute this entirely to the coupon program, since there are reasons to believe that seasonal patterns of consumption may be different between prefectures with more children and elderly people and prefectures where a smaller number of people were eligible for the coupons.
Table 8 thus turns to estimates that compare differences in the seasonal pattern of retail trade across prefectures in 1999 to patterns in 1990-1998. The first row in Table 8 presents the estimated impact of the coupons on retail sales in the months after March relative to sales in February. The estimated elasticities range between 1.5 and 2, and are marginally significant. Since sales by the stores covered by this survey account for roughly 5 percent of GDP, an elasticity of 2 implies a marginal propensity to consume of 0.1, which is remarkably consistent with the estimates obtained earlier from the FIES. To confirm that the results do not depend on the denominator used for the dependent variable, we also estimate the impact of the coupons on sales after March relative to average sales in December-February (row 3), November-February (row 4), and October-February (row 5). These results are broadly similar to those in the first two rows. Finally, panel B presents estimates of retail sales of clothing and apparel. Once more, the estimated elasticities are positive and marginally significant in most cases, which suggests that the shopping coupons had a small effect on consumption.
### 6. Conclusion

This paper investigates the effects of a unique experiment in fiscal policy undertaken by the Japanese Government in the spring of 1999. We use two sources of data to estimate the impact of the program on consumption. The results using consumption micro-data demonstrate that the coupon program stimulated consumption of semi-durables right after the coupons were distributed, particularly consumption of apparel and services. The MPC on these semi-durables is estimated to be 0.2-0.3 in the first month, but the estimated elasticity (and the MPC) declines to 0.1 as we consider the consumption of households over longer time periods. The results using regional variation in the impact of the program also suggest that the MPC of the coupons was roughly 0.1.

We are of two minds about the broader lessons that can be drawn from this unusual experiment in fiscal policy. On the one hand, basic economic theory predicts that the marginal propensity to consume out of a temporary increase in income should be roughly equal to the inverse of the interest rate. Therefore, a marginal propensity to consume of 0.1 is exactly the response one expects from rational utility-maximizing households facing a real interest rate of 10 percent. $ ^{30} $ On the other hand, there is large body of evidence that people do not respond to income changes in the manner predicted by our canonical models of consumption, at least not when the income changes are small and irregular. For example, Blinder (1981) and Poterba (1988) find that a temporary income tax cut in 1975 had significant effects on consumption. To take another example, Shapiro and Slemrod (1995) present survey evidence that a significant number of consumers would have responded to temporary increases in after-tax income due to changes in income tax withholding rates during the 1992 presidential election. We don’t have a good explanation why our results appear to be at odds with this large body of
evidence from the US, particularly given the fact that consumers were forced to spend the coupons and that the coupons accounted for a relatively small share of a typical household's income. A fruitful topic for further research is to examine whether our result is specific to this episode, or whether consumers in Japan have responded in a similar manner to other fiscal policy changes in the 1990s.
## References

Blinder, Alan. “Temporary Income Taxes and Consumer Spending,” Journal of Political Economy 89 (1), February 1981, pp. 26-53.

Browning, Martin and Dolores Collado. “The Response of Expenditures to Anticipated Income Changes: Panel Data Estimates,” American Economic Review, June 2001.

Cabinet Office. Annual Report on Prefectural Account, 2001.

Economic Planning Agency. “Chiiki-Shinkou-Ken No Shouhi Kanki Koka Nado Nituite” (On the consumption stimulation effect by the “Regional Promotion Coupons”) 1999.

Management and Coordination Agency. “Kakei Chousa kara mita Chiiki-Shinkou -Ken no Riyou Zyoukyou” (On how coupons were used from the FIES survey), Kakei chousa sankou siryou No.64, 2000.

___ Annual report on the Family Income and Expenditure Survey, various years.

Ministry of Economy and Trade. Census of Commerce, 2000.

Yearbook of the Current Survey of Commerce. various years.

Hayashi, Fumio. “Testing the Life Cycle-Permanent Income Hypothesis on Japanese Monthly Panel Data,” Keizai Kenkyu 101, 1986, pp. 1-23.

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

Ministry of Home Affairs. “Chiiki-Shinkou-ken Koufu Zigyou Q&A” (Questions and Answers on the Regional Promotion Coupons Program). 1998

Parker, Jonathan. “The Reaction of Household Consumption to Predictable Changes in Payroll Tax Rates.” American Economic Review, September 1999, 89(4), pp. 959-973.

Poterba, James. “Are Consumers Forward Looking? Evidence from Fiscal Experiments,” American Economic Review (Papers and Proceedings), 78(2), May 1988, pp. 413-18.

Shapiro, Matthew, and Slemrod, Joel. “Consumer Response to the Timing of Income: Evidence from a Change in Tax Withholding,” American Economic Review, 85(1), March 1995, pp. 274-283.
Shea, John. “Union Contracts and the Life-Cycle/Permanent-Income Hypothesis.” American Economic Review, March 1995, 85(1), pp. 186-200

Souleles, Nicholas. “The Response of Household Consumption to Income Tax Refunds.” American Economic Review, September 1999, 89(4), pp. 947-958

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

January-June Panel (N=6,329)	Mean	S. D.
Consumption:
Durables	17,560	114,588
Semi-durables	34,223	46,331
Non-durables	106,040	38,860
Services	108,506	126,330
Amount of Coupon (all households) $ ^{1} $	18,568	20,435
Amount of Coupon (households that received coupons) $ ^{1} $	35,367	14,182
Monthly income $ ^{2} $	595,119	262,130
Age (household head)	43.8	9.9
Number of children under 15	1.0	1.0
Number of other family members	2.5	0.7
February-June Panel (N=6,413)
Consumption:
Durables	20,710	136,797
Semi-durables	34,303	51,378
Non-durables	107,654	39,082
Services	110,368	139,174
Amount of Coupon (all households) $ ^{1} $	18,294	20,097
Amount of Coupon (households that received coupons) $ ^{1} $	34,103	14,618
Monthly Income $ ^{2} $	586,945	263,655
Age (household head)	43.9	10.0
Number of children under 15	1.0	1.0
Number of other family members	2.5	0.8

$ ^{1} $ Only for 1999 Panel.

$ ^{2} $ Pretax annual income/12.

Note: Unit of observation is a household. Consumption refers to average monthly consumption in current yen.
Table 2: Summary Statistics (Survey of Commerce)

Mean	S.D.
Retail Sales
October	38,516	59,258
November	38,388	59,633
December	57,749	89,460
January	39,186	55,086
February	32,132	47,781
March	40,467	62,178
April	37,577	56,109
May	37,617	56,516
June	37,175	57,453
July	45,787	70,766
Clothing Sales
October	16,476	26,751
November	15,892	25,299
December	20,945	32,653
January	16,901	24,733
February	11,665	17,804
March	16,724	26,592
April	15,031	23,539
May	15,180	23,910
June	14,593	23,065
July	16,793	26,483
Average Monthly Income	688,630	778,073
Total Coupons/Monthly Income $ ^{1} $	0.0225	0.0051
Retail Sales/Monthly Income	0.05099	0.01312

$ ^{1} $ total number of eligible people in a prefecture x 20,000 yen/average

monthly regional income in 1998.

Note: The unit of observation is a prefecture (there are a total of 47 prefectures).
Table 3: Impact of Shopping Coupons on Seasonal Consumption Patterns, 1999 Cross-Section.

March	March-April	March-May	March-June	March-July
January-June Panel (N=497)
Durables	8.18	2.49	1.99	-0.43
(4.91)	(4.50)	(3.91)	(3.72)
Semi-durables	6.21	4.59	3.38	2.75
(1.35)	(1.04)	(0.96)	(0.91)
Non-durables	0.20	0.14	0.13	0.07
(0.21)	(0.17)	(0.17)	(0.17)
Services	-0.12	-0.09	-0.26	-0.30
(0.65)	(0.54)	(0.49)	(0.47)
February-July Panel (N=517)
Durables	-0.67	2.89	3.91	-0.63	2.10
(6.91)	(5.81)	(5.62)	(5.36)	(5.14)
Semi-durables	4.07	4.59	3.79	2.83	2.05
(1.50)	(1.35)	(1.24)	(1.20)	(1.19)
Non-durables	0.36	0.41	0.27	0.26	0.23
(0.23)	(0.20)	(0.20)	(0.20)	(0.20)
Services	-0.85	-1.16	-0.70	-0.63	-0.75
(0.72)	(0.65)	(0.60)	(0.57)	(0.58)

Notes: Standard errors in parenthesis. The dependent variable is the log of the ratio of the sum of consumption after March to the average monthly consumption before March. The estimated elasticity to the shopping coupons is the coefficient on coupons (computed as number of children x 20,000 yen) relative to monthly pre-tax income in the previous year. All regressions also include a quadratic in the age of the household head
Table 4: Impact of Shopping Coupons on Seasonal Consumption Patterns, Difference in Difference Estimates.

March	March-April	March-May	March-June	March-July
January-June Panel (N=5,855)
Durables	5.84	2.96	2.31	0.28
(4.44)	(3.89)	(3.39)	(3.25)
Semi-durables	3.34	2.41	1.75	1.15
(1.16)	(0.96)	(0.87)	(0.84)
Non-durables	0.24	0.20	0.15	0.10
(0.19)	(0.16)	(0.16)	(0.15)
Services	0.84	0.72	0.45	0.15
(0.57)	(0.49)	(0.45)	(0.43)
February-July Panel (N=5,945)
Durables	-0.54	3.87	3.28	-1.24	1.31
(6.60)	(5.41)	(5.07)	(4.72)	(4.59)
Semi-durables	2.68	2.93	2.56	2.01	1.63
(1.33)	(1.17)	(1.11)	(1.08)	(1.06)
Non-durables	0.18	0.21	0.20	0.12	0.11
(0.23)	(0.20)	(0.19)	(0.19)	(0.19)
Services	0.09	-0.22	-0.04	0.19	-0.17
(0.65)	(0.58)	(0.55)	(0.54)	(0.53)

Notes: Standard errors in parenthesis. Data are panels from 1990 to 1999. The dependent variable is the log of the ratio of the sum of consumption after March to the average monthly consumption before March. The estimated elasticity to the shopping coupons is the coefficient on coupons (computed as number of children x 20,000 yen) relative to monthly pre-tax income in the previous year. All regressions also include a quadratic in the age of the household head, the number of other family members, and indicator variables for year
Table 5: Impact of Announcement on Seasonal Consumption Patterns

Nov.	Nov.-Dec.	Nov.-Jan.	Nov.-Feb.
June-November Panel (N=6,338)
Semi-durables	-0.30
(0.88)
Non-durables	-0.15
(0.17)
Services	-0.80
(0.45)
July-December Panel (N=6,319)
Semi-durables	-0.34	-0.18
(1.04)	(0.78)
Non-durables	-0.08	-0.17
(0.20)	(0.17)
Services	0.30	0.30
(0.54)	(0.48)
August-January Panel (N=6,277)
Semi-durables	0.40	0.08	0.33
(0.96)	(0.78)	(0.71)
Non-durables	0.01	-0.15	-0.20
(0.19)	(0.16)	(0.15)
Services	0.60	0.47	0.41
(0.50)	(0.45)	(0.41)
September-February Panel (N=6,364)
Semi-durables	-0.62	-1.16	-0.67	-0.65
(0.92)	(0.75)	(0.70)	(0.68)
Non-durables	0.11	-0.03	-0.07	-0.08
(0.18)	(0.16)	(0.15)	(0.14)
Services	-0.29	-0.46	-0.37	-0.28
(0.50)	(0.46)	(0.43)	(0.42)

Note: Regressions are difference in difference estimates on successive panels from 1990 to 1999. The dependent variable is the log of the ratio of the sum of consumption after November to the average monthly consumption before November, and the main independent variable is the value of coupons relative to monthly pre-tax income in the previous year. See notes to Table 4 for additional details.
Table 6: Impact of Coupons by Asset Levels

A/Y > mean	A/Y < mean.	A/Y > 1	A/Y < 1	A/Y > 1/2	A/Y < 1/2
Semi-durable	-3.12 (2.50)	5.73 (1.45)	0.38 (1.73)	6.83 (1.79)	1.88 (1.49)	7.23 (2.30)
N	1441	2744	2144	2033	3329	854

Note: Regressions are difference in difference estimates on matched panels of the FIES and FSS from 1990 to 1999. Dependent variable is the log of the ratio of consumption of semi-durables in March to the average monthly consumption before March. A/Y is the ratio of gross assets to annual pre-tax income. See notes to Table 4 for additional details.
Table 7: Impact of Shopping Coupons on Seasonal Retail Sales across Prefectures, 1999 Cross-Section.

March	March-April	March-May	March-June	March-July
Retail Sales Relative to Average Monthly Sales in:
Feb.	2.31	2.89	2.89	2.58	2.16
(1.69)	(1.22)	(1.24)	(1.22)	(1.34)
Jan.-Feb.	1.61	2.19	2.18	1.88	1.45
(2.01)	(1.51)	(1.55)	(1.53)	(1.62)
Dec.-Feb.	0.17	0.75	0.74	0.44	0.01
(1.48)	(1.00)	(1.00)	(1.05)	(1.01)
Nov.-Feb.	1.00	1.58	1.58	1.28	0.85
(1.40)	(0.96)	(0.99)	(1.04)	(1.02)
Oct.-Feb.	1.41	1.99	1.99	1.68	1.26
(1.26)	(0.87)	(0.90)	(0.97)	(0.96)
Clothing Sales Relative to Average Monthly Sales in:
Feb.	-0.07	0.90	0.86	1.15	0.40
(2.40)	(1.76)	(1.60)	(1.54)	(1.59)
Jan.-Feb.	-0.14	0.82	0.78	1.07	0.32
(2.50)	(1.79)	(1.56)	(1.55)	(1.54)
Dec.-Feb.	-1.91	-0.94	-0.98	-0.69	-1.45
(2.23)	(1.44)	(1.14)	(1.11)	(1.06)
Nov.-Feb.	-1.18	-0.22	-0.26	0.03	-0.72
(2.06)	(1.28)	(1.04)	(1.00)	(0.97)
Oct.-Feb.	-0.09	0.88	0.84	1.13	0.38
(1.87)	(1.11)	(0.90)	(0.89)	(0.88)

Notes: The unit of observation is a prefecture (47 prefectures in total). The dependent variable is the log of the ratio of the sum of retail sales in a prefecture after March to the average monthly retail sales before March. The main independent variable is the ratio of the total number of the eligible people in the prefecture x 20,000 yen to monthly regional income in the previous year.