Silviase/jgov_v1.4_full_ann huggingface dataset:
items that households would have purchased in the absence of the coupons. We turn to this next.

### 3. Data

Our first source of data is the household level data from the Family Income and Expenditure Survey (FIES) from 1990 to 1999. The FIES provides detailed information on the demographic characteristics, income, and expenditures for a nationally representative sample of 8,000 households each month. The FIES is the Japanese Government’s main source of information on aggregate consumption and Hayashi (1986) utilized micro-level data. The monthly consumption data is compiled from a diary collected twice a month. Single-person households and households employed in the agriculture or fishery were not surveyed before July 1999. Each household is surveyed for six months before being replaced. Since one in six households is replaced each month, we can follow a panel of 1,300 households over six months.

To improve the reliability of our estimates, we excluded the following households from our sample. First, we dropped households with self-employed household heads because we do not have monthly income information for these households. Second, we dropped households where the reported age of the household head changed by more than one year during the six-month period; where the household's tenancy status changed from owner to renter or vice-versa; or where there was a change in family size between successive months. Third, because the FIES does not allow us to reliably identify elderly people who received a coupon, we exclude all households with a person over the
age of 65. $ ^{26} $ Fourth, a household was excluded if the number of family members was greater than ten because the consumption patterns of large extended households are likely to be significantly different from those of smaller households that are the norm in Japan, though the number of the large households in the sample is very small. Fifth, we confined our sample to households that did not attrite before the sixth interview. Finally, we excluded a household if the change in consumption (in absolute value) between successive months exceeded the average consumption change in our sample by more than three standard deviations. After these adjustments, the size of each six month panel drops from 1,300 to 600 households.

We focus on the five panels spanning March through July because most of the coupon recipients received and spent their coupons in March, April, or May of 1999. Specifically, these are the households we observe from October to March, November to April, December to May, January to June, and February to July in each year. We focus on total spending on nondurables and three categories of nondurables: semi-durables, strictly non-durables, and services. $ ^{27} $ The summary statistics are shown in Appendix Table 1.

Our second data source is the Current Survey of Commerce, a monthly survey of wholesale and retail establishments. $ ^{28} $ We use aggregate monthly sales by large-scale retailers with 50 o more employees in each of the 47 prefectures in Japan from 1990 to
1999 from the published tabulation of this survey. Finally, we obtained the number of coupons distributed in each prefecture from the Ministry of Home Affairs. The summary statistics of this second dataset can be seen in Appendix Table 2.

### 4. Impact of Coupons on the Consumption of Families with Children

We begin by using the six-month panels from the FIES to estimate the impact of the shopping coupons on household consumption. Each family received one coupon for every child under the age of 15. The coupon program thus increased the income of families with a large number of children by more than that of families with a smaller number of children. This is the variation we exploit.

We estimate the following model on our main sample (the five overlapping panels from 1990 through 1999).

$$ \log\left(\frac{C_{h,t}}{C_{h,february}}\right)=a_{1}\cdot\left(\frac{20,000\cdot Children_{h}\cdot I_{1999}}{Income_{h}}\right)+Z_{h}^{^{\prime}}\cdot a_{2}+Year_{h}^{^{\prime}}\cdot a_{3}+\varepsilon_{h,t} $$

Here, h indexes households and t indexes the month. The dependent variable is the log of consumption in month t relative to consumption in February. The key independent variable is the ratio of the value of the coupons to the household's monthly income in the previous year $ (Income_{h}) $ , where the value of the coupons is measured as the product of 20,000 yen, the number of children under the age of 15 $ (Children_{h}) $ , and an indicator variable for observations in 1999 $ (I_{1999}) $ . The other independent variables are a vector of controls at the household level denoted by $ Z_{t} $ (number of children under the age of 15, number of other family members, and a quadratic in the age of the household head).
and indicator variables for year (denoted by $ Year_{h} $ ).

For the household we observe in 1999, the variation in the key independent variable is driven by variation in the number of children relative to income. For households from 1990 to 1998, the value of the coupons is set to zero. Since we include controls for year and the number of children, the coefficient on $ \frac{20,000 \cdot Children_{h} \cdot I_{1999}}{Income_{h}} $ measures whether families with a high children to income ratio increased their spending by more than families with a lower children to income ratio in the year the coupons were distributed (1999) relative to previous years (1990-1989).

Table 1 presents estimates of $ a_{1} $ . Panel A presents the estimates using all five overlapping panels. Each column measures the change in consumption in each month starting in March relative to February. We note that the sample becomes smaller as we measure the response over additional months; we estimate the change in consumption in March from all five panels, but we drop households that are interviewed in October through March when we estimate the change in consumption in April. At the extreme, the change in consumption in July is based only on the February-July panel.

Table 1 provides little evidence that the coupons led to increased spending on non-durables or on services, nor does it provide evidence of an effect on total spending (first row). There is, however, evidence that the coupons did increase spending on semi-durables in March (when the coupons were initially distributed). The estimate indicates that a one percent increase in monthly income due to the coupons is associated with a marginally significant 1.3 percent increase in spending on semi-durables in March. The estimated effect of the coupons on consumption in subsequent months are less
precise because the samples get smaller, but there is generally little evidence that the coupons had a long lasting effect on spending. For example, a one percent increase in income due to the coupons is associated with a statistically insignificant 0.23 percent decline in July.

The estimated consumption response in March and April 1999 to the coupons is likely to be downward biased because not all the households received and spent their coupons during these months. We can use information on the percentage of coupons distributed in each month to adjust for this bias. For example, we know that 27 percent of households with children spent their coupons in March and another 45 percent in April (Figure 1, Panel A). The “attenuation-corrected” marginal propensity to consume (MPC) on semi-durables in March can be calculated as $ a_{1} $ x (C/Y) x $ (1/0.27)=0.26. $ ^{29} Taking the point estimates of $ a_{1} $ for subsequent months, we get an “attenuation-corrected” MPC of 0.09 for April, -0.02 for May, -0.03 for June, and -0.01 for July.

Panel B restricts the sample to the panel that we follow over all five months (February through July). The standard errors are now larger, but the point estimates in the first three columns are also larger. The estimates of $ \alpha_{1} $ imply an attenuation-corrected MPC for March of 0.52 (roughly twice as large as the MPC estimated from all five panels), 0.24 in April, and 0.09 in May.

These estimates based on the February-July panel suggest that the coupons might have had a positive effect on spending even after the coupons were spent (most of the coupons were redeemed in March and April). We now directly measure the total change in spending beginning in March. Table 2 shows the coefficient on coupon
income from equation (1) where we use the log of average monthly consumption between March and the corresponding month (relative to consumption in February) as the dependent variable. Here, there is evidence of a sizable response. For example, average spending on semi-durables in March through July increased by 1.49 percent for one percent increase in income. Since most of the coupons had already been spent by July, this implies the MPC on semi-durables over the five month period from March through July was 0.41. $ ^{30} $ This estimate thus suggests that there was no reversal in spending in June and July after the coupons had been used.

We now probe the sensitivity of our estimates. First, the variation in coupon income we use in Tables 1 and 2 is driven by the variation in the number of children (under 15) relative to monthly income. Table 3 presents estimates where the key independent variable is now the product of an indicator variable for 1999 and the number of children under 15 (but otherwise everything is the same as in equation (1)). The variation is now entirely driven by the number of children and not by variation in (non-coupon) income. In Panel A, the estimated effect on spending on semi-durables in March and April are generally positive, but drops to zero in subsequent months. In Panel B, where we once again restrict the sample to the February-July panel, the effect on spending on semi-durables is positive in March, April, and May and drops to zero in June and July.

Second, our identifying assumption is that the coupon program is the only shock in 1999 that had a differential effect on families with more or less children under the age of

15. However, it is possible that there were other shocks that also had a heterogeneous
effect on families depending on the number of children. If this were the case, we would mistakenly attribute the heterogeneous response of consumption in 1999 to the coupons (Mariger and Shaw, 1993). We cannot completely rule out this possibility, but we can test whether the consumption of households with family members greater than 15 changes by more than that of households with a smaller number of members greater than 15. The idea is that household members greater than 15 did not qualify for the coupons, so we would not expect to see a response if the coupon program was the only shock in 1999. This appears to be what we find. Specifically, when we include a variable for the hypothetical coupon income for household members greater than 15 in equation (1), the point estimates of the coefficient on the hypothetical coupon income are always zero. $ ^{31} $

Table 4 probes for evidence of heterogeneity in the response to the coupons across poor and rich families. We classify families into poor and rich using information on financial assets of individual households. The FIES does not collect information on assets, but the Family Savings Survey (henceforth, FSS) collects data on financial assets on December 31 in every year from the same households surveyed in the FIES who entered the sample in August, September, or October. We can therefore create a matched data set from the FIES and the 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. We can thus only measure the effect of the coupons on spending in March.

Table 4 presents the estimated impact of the coupons on spending in March using two
classifications of rich and poor. Panel A classifies families based on their asset-income ratios, and Panel B classifies families based on their asset-consumption ratios. $ ^{32} $ For each sample, we then estimated the response of consumption in March using the specification in equation (1). In every case, we find that the effect of coupon income on spending is always higher than that of less assets relative to their income or to their consumption. However, the standard errors with these limited samples are such that we can generally not reject the null hypothesis of equal coefficients.

Finally, since the program was widely anticipated by the time the coupons were distributed in March, a forward-looking household may have already adjusted its spending even before the coupons were distributed in March. News reports indicate that the program was first proposed in early October 1998 with no information on the amount of the coupons or who was to qualify. The final agreement was announced and widely publicized in early November. Although an argument can be made that the program was already anticipated in October 1998, we think that November 1998 is a more plausible date for when the public was informed of the program. We will therefore measure the change in consumption starting in November when the program was announced, but before households began to receive the coupons in March 1999. The specification we estimate is the same as in equation (1), with the only difference being that the dependent variable is now the change in consumption relative to October. The 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.
In sum, the shopping coupons program appears to have had a positive effect on the consumption of semi-durables when the coupons were distributed. We find no evidence of a reversal in spending after the coupons were used. We also find a larger effect among families that were poor and more likely to be liquidity constrained.

### 5. Impact on Spending across Prefectures

This section examines whether consumption increased by more in prefectures where a larger share of the population received shopping coupons. The coupons could be used only within the recipient's local region (city, village, or town). Since prefectures with more children and elderly received more coupons, one way to measure the effect of the coupon program is to compare the change in consumption in a prefecture with a large number of children and elderly people (relative to the population in the prefecture) to a prefecture with fewer children and elderly. Since the seasonal pattern of consumption in a prefecture with a larger number of children and elderly people may differ from that in a prefecture with a smaller number of people eligible for the coupons, it is important to control for this “normal” seasonal pattern. We use the seasonal patterns of consumption across prefectures in previous years (1990-1998) to control for these “normal” seasonal expenditure changes.

Our dependent variable is the monthly retail sales in a prefecture. $ ^{33} $ 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 consumption response to the distribution of coupons to the
elderly as well as to households with children. We 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 nine years of data to control for seasonal patterns of consumption across prefectures.

The basic specification we estimate is similar to equation (1):

$$ \log\left(\frac{S_{i,t}}{S_{i,february}}\right)=b_{1}\cdot\left(\frac{Coupon_{i,t}}{Monthly\quad Income_{i,t}}\right)+Z_{i}^{^{\prime}}\cdot b_{2}+Year_{t}^{^{\prime}}\cdot b_{3}+\varepsilon_{i,t} $$

where i indexes prefectures (47 prefectures in total), t refers to the year, $ Z_{i} $ represents a vector of indicator variables for each prefecture, and $ Year_{t} $ is a vector of indicator variables for each year. The main dependent variable is now the retail sales in a prefecture in a month starting in March, and the main independent variable is the total coupon income in a prefecture (computed as the total value of coupons distributed in a prefecture) relative to the average aggregate monthly GDP in the prefecture in the previous year. $ ^{34} $ For observations prior to 1999, $ Coupon_{i,t} $ is set to zero. The coefficient on $ \frac{Coupon_{i,t}}{Monthly Income_{i,t}} $ measures whether aggregate retail sales increased by more in 1999 in prefectures where the coupons represented a larger increase in aggregate income relative to previous years (1990-1998).

Table 6 presents estimates of $ b_{1} $ from equation (2). The first row in Table 6 presents estimates of $ b_{1} $ in equation (2) over successive months after February. The estimated income elasticity of retail sales is 1.5 for March, 1.8 for April, 1.9 for May, 2.9 for June, and 2.7 for July, and are generally marginally significant. As before, we can
estimate the “attenuation-corrected” marginal propensity to consume using the aggregate data on the fraction of coupons spent in each month. A survey of coupon recipients by the Economic Planning Agency in July 1998 (we presented some of the data from this survey in Figure 1) indicates that 21 percent of the coupons were redeemed in March, 44 percent in April, 24 percent in May, 8 percent in June, and 3 percent in July. Using this information, the “attenuation-corrected” MPC for March is 0.35 $ (1.47 \times 0.05 \times 1/0.21) $ , that for April 0.14, that for May 0.11, that for June 0.15, and that for July 0.14. These estimates are generally larger than the MPC obtained by using the differential impact of the coupon program across families with children (Tables 1 and 2). In addition, perhaps because the sample size does not decline when we measure the longer run response to the coupons, we also find more consistent evidence that the coupons had a positive effect on spending in the longer run.

Finally, the second row of Table 6 presents estimates of the effect of the coupon program on retail sales of clothing and apparel. The estimated effect on spending are generally positive, but the estimates are less precise.

### 6. Conclusion

This paper investigated the effects of an experiment in fiscal policy undertaken by the Japanese Government in the spring of 1999. Under the shopping coupon program, the Japanese government handed out shopping coupons worth 20,000 yen (about 200 dollars) to the parents of families with children under the age of 15 and to roughly half of the elderly population. The coupons had to be used in the recipient's local community and expired if they were not used. The central question with regard to this program is the
extent to which coupon spending substituted for other spending or substituted for spending in the succeeding months.

We use two sources of data to answer this question. The results using household level data demonstrate that the program stimulated consumption of semi-durables when the coupons were distributed. The MPC on semi-durables is 0.3 in March, with little evidence of a reversal in spending after the coupons were used. The results using regional variation in the impact of the program suggest that the MPC in March was of a similar magnitude, and provides stronger evidence that spending did not fall after the coupons had been redeemed.
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