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ESRI Discussion Paper Series No.12
Did the Shopping Coupon Program Stimulate Consumption?
Evidence from Japanese Micro Data
by
Masahiro Hori, Chang-Tai Hsieh, Keiko Murata, and Satoshi Shimizutani
April 2002
Economic and Social Research Institute Cabinet Office Tokyo, Japan
i
Did the Shopping Coupon Program Stimulate Consumption?
Evidence from Japanese Micro Data*
by
Masahiro Hori, Chang-Tai Hsieh, Keiko Murata, and Satoshi Shimizutani+
April 2002
Main Text : Did the Shopping Coupon Program Stimulate Consumption?
……………………………. 1.
Appendix : Shopping Coupons in the Family Income Expenditure Survey.
…………………………… 34.
Please address correspondence to:
Chang-Tai Hsieh, Princeton University, Princeton, NJ, USA [email protected]
Satoshi Shimizutani, Cabinet Office, Tokyo, Japan [email protected]
Masahiro Hori, ESRI, Cabinet Office, Tokyo, Japan [email protected]
* This paper is a part of our on-going study in the ESRI on Japanese consumption and macro policies in the 1990s. We are grateful to the Ministry of Public Management, Home Affairs, Posts, and Telecommunications for providing the micro-data from the FIES. We thank Alan Blinder, Angus Deaton, Gary Saxonhouse, Matthew Shapiro, Lars Svensson, Katherine Terrell, David Weinstein, and ESRI colleagues for their helpful comments. The views expressed in this paper are personal and do not necessarily represent those of the Economic and Social Research Institute or of the Cabinet Office (Japanese Government). + Hsieh and Shimizutani developed the research design and Shimizutani conducted the econometric work of the main text, and Hori is responsible for the appendix.
2
Abstract:
In March, 1999, the Japanese Government handed out “shopping coupons” worth 20,000
yen (about 200 dollars) to families for every child under the age of 15 and to roughly half
of Japan’s elderly population. In total, 25 percent of Japan’s population receive d the
coupons. The coupons expired after six months and had to be spent within the recipient’s
local community. We use variation in the impact of the program across families with
different numbers of children and variation across prefectures to identify its impact on
consumption. We control for "normal" seasonal patterns of consumption across families
and prefectures by comparing the seasonal variation in consumption in the spring of 1999
with that in previous years when the coupons were not distributed. Our two sets of
difference-in-difference estimates suggest that the MPC out of the coupons ranged
between 0.2-0.3 in the first month the coupons were distributed. However, families that
received coupons spent less in subsequent months, so the MPC falls to 0.1 when we
measure the change in consumption over the next 3 or 4 months.
3
1. Introduction
The continued stagnation of the Japanese economy, despite the massive
expansionary fiscal policies undertaken by the Japanese authorities over the last few
years, is surely one of the major dilemmas of macroeconomic policy makers around the
world today. For those who believe that fiscal policy is effective in stimulating aggregate
demand, the Japanese experience poses a major challenge. The fiscal expansion
undertaken by Japan ha s been of unprecedented magnitude , yet the Japanese economy
remains mired in recession. Many people have suggested that factors such as the stance
of monetary policy and the condition of the banking sector are responsible for Japan’s
continued stagnation. Thus, it is possible that fiscal policy may have been helpful, but its
expansionary effect is simply overwhelmed by factors beyond the control of the fiscal
authorities. However, an alternative explanation is that in the current state of the
Japanese economy, expansionary fiscal policy is simply useless – that in the state of
uncertainty that the Japanese economy finds itself in, households will simply choose to
save rather than to spend any additional disposable income it receives.
Our objective in this paper is to examine the consumption response to an unusual
experiment in Japanese fiscal policy. In the spring of 1999, the Japanese government
implemented the “regional promotion coupons program," perhaps better known as the
“shopping coupon program." This program provided coupons worth 20,000 yen (about
200 dollars) that could be used for most purchases to families for every child under the
age of 15 and to roughly half of Japan’s elderly population (people over the age of 65).
What was unusual about the program was that the coupons expired after six months if
they were not used and that they had to be spent within a household’s local community
(city, village, or town). The number of people eligible for the coupons amounted to
approximately 25 percent of the total population in Japan and the total amount distributed
was approximately 620 billion yen (about 6 billion US dollars).
4
To measure the impact of the shopping coupons on consumption, we use two
identification strategies that are based on two independent data sources. Our first
identification strategy uses micro-data on household consumption and exploits the fact
that the coupons were provided to families depending on the number of children under
the age of 15. This data does not allow us to reliably identify the elderly people who
received the coupons, so we can only measure the impact of the coupons distributed to
children. To measure the impact of the program, we measure the impact of the program
on consumption by comparing the seasonal variation in consumption between families
with different numbers of children (under the age of 15) in the Spring of the 1999 with
the cross-sectional distribution in other years (1990-1998) when the coupons were not
distributed. This difference in difference approach controls for the fact that the “normal”
seasonal pattern of consumption will differ across families with different numbers of
children.
We implement this difference-in-difference estimation procedure on micro-data
from successive six -month panels of the Family Income and Expenditure Survey (FIES),
a nationally representative household consumption survey, from 1990 to 2000. Our
estimates suggest that the coupons had a small effect on expenditures on semi-durables
(primarily clothes) immediately after the coupons were distributed. However, the
households that received more coupons spent less on semi-durables in the subsequent
months, so the net effect on consumption was smaller than what the additional
expenditure in the first 1 or 2 months the coupons were distributed would indicate.
Therefore, while our estimates suggest that the marginal propensity to consume from the
coupon ranged from 0.2 to 0.3 in the month the coupons were distributed, but the MPC
falls to 0.1 when we measure the change in consumption for three or four additional
months.
Our second identification strategy exploits the fact that the coupons had to be
spent within each household’s local community. Using data on monthly retail sales in
5
each prefecture, we compare the change in regional retail sales in a prefecture where a
larger fraction of the population received the shopping coupons to that in a prefecture
where a smaller fraction of households received the coupons in 1999, and compare this
difference to the cross-prefecture difference in the seasonal patterns of expenditures in
previous years.1 The advantage of this strategy relative to our estimates based on micro-
data from the household consumption survey is that it captures the response of
consumption of the elderly people to the shopping coupons, as well as that of households
with children. Our estimates indicate that the coupon program had net positive effect on
consumption and the estimated impact on consumption is roughly the same magnitude as
that indicated by the micro consumption data, on the order of a MPC of 0.1.
In addition to its relevance for Japanese macroeconomic policy, and to Japanese
fiscal policy in particular, this paper contributes to the large body of literature on the
empir ical relevance of the life-cycle/permanent income hypothesis of consumption. Our
work in this paper follows the recent body of work that exploits quasi-natural
experiments to identify households that are subject to anticipated income changes.2 What
is unique about the episode we exploit is that the households were, in effect, forced to
spend the anticipated income change. As in all schemes in which households are forced
to spend (or save) a particular amount (such as in many social security programs), the
central question is the extent to which households simply substitute for what they are
forced to do.
This paper is organized as follows. The next section briefly describes the
shopping coupon program. We then turn to a description of the data we use to analyze
the program. The next section turns to estimates of the program across families with
different numbers of children. The next section uses regional sales data to analyze the
impact of the program across prefectures. The final section concludes.
1 There are 47 prefectures in Japan. 2 See Browning and Collado (2001), Hsieh (2001), Shea (1995), Parker (1999), and Souleles (1999).
6
2. The “Shopping Coupon” Program
The shopping coupons program was adopted as part of the “Emergency economics
measures” approved by the Japanese government on November 16, 1998. Under the
program, a coupon worth 20,000 yen per eligible person was given to two groups of
people. First, coupons were provided to households with a child under the age of 15 (one
coupon for every eligible child), regardless of the household’s income. Second, coupons
were also distributed to two broad groups of elderly people : (1) those who received state-
funded pensions and; (2) those over the age of 65 who did not pay taxes in either 1998 or
1999. 3 More than half of the elderly over the age of 65 fell into one of these two
categories. More than 31 million people qualified for these coupons, which is slightly
more than 25 percent of Japan’s population. Of this number, 11.6 million were elderly
(roughly 56 percent of all people over the age of 65). The total amount distributed was
621.4 billion yen, which represented roughly 0.12 percent of Japan’s GDP.
The shopping coupons were funded by the Japanese central government, but were
distributed by local governments.4 The coupons had to be spent in the recipient’s local
community (city, town, or village), although the local government had the authority to
widen the area, and sometimes did in rural regions. The coupons were not transferable
and change was not provided for purchases smaller than 1,000 yen. 5 The coupons could
be spent on most consumption goods and services, although most local governments
restricted their use to private consumption items. 6 Finally, the coupons expired in
September 1999 if they had not been used by then.
Most local governments began to distribute the coupons in early March. Based on
3 Specifically, recipients of old age welfare pensions, basic disability pensions, basic bereaved pensions, mother and baby pensions, bereaved child pensions, child family allowances , disabled child welfare allowances were eligible for the pensions. In addition, a person who is eligible for these categories and with a child under the age of 15 can get the sum of the amounts. 4 There were 3,252 local governments (cities, town, or villages) in Japan on January, 1999. 5 The coupons were distributed as 20 coupons worth 1,000 yen each. 6 The coupons could not be used for lottery tickets, stamps, taxes, utilities, or debt payments. Store owners who received the coupons could redeem them by depositing them in a bank just like a check.
7
the household registration data that they kept, local governments were able to determine
the precise amount every family with children was eligible for. Because of this,
households with children received the coupons automatically. However, since only about
half of the elderly people were eligible for the coupons, this group of people had to file an
application to receive the coupons. By the end of June, the coupons had been distributed
to 30.968 million people, which is 97 percent of the estimated number of eligible people
(31.922 million). 7 By April 20, merchants had redeemed almost 40 percent of total
number of coupons distributed under the program. This figure increased to 79.5% by
June 30 and finally reached 99.6% (618.961 million yen out of 621.402 million yen).
In addition to the administrative data, there are two surveys of coupon recipients that
provide more information on when the coupons were received and how they were spent.
First, the Economic Planning Agency (henceforth, EPA) conducted a surve y in July 1999
of 9,000 people who had received these coupons.8 This survey shows that 74.4% of
households with children under 15 got their coupons in March, followed by another
16.8% in April (figure 1, panel A). Because the elderly had to file an application to
receive the coupons , it took more time for the coupons to be distributed to these
households. Nonetheless, by the end of April, almost 90 percent of the eligible elderly
had received the coupons (figure 1, panel B). A second survey by the Management and
Cooperation Agency (henceforth, MCA) provides similar information. 9 This survey
indicates that 81.8% of households with children under 15 received their coupons within
the first week after the local governments began to distribute them.
These surveys also show that many households spent their coupons relatively soon
after they were received, despite the fact that the coupons were effective for six months.
7 The numbers cited in this paragraph are based on administrative data provided by the Ministry of Home Affairs, the central government agency responsible for administering the program. 8 The sample size of households with children under 15 was 5,400. The EPA is now called the Cabinet Office. 9 The MCA is now called the Ministry of Public Management, Home Affairs, Posts and Telecommunications.
8
The EPA survey indicates that by April, almost 70 percent of households with children
had already spent their coupons (figure 1, panel A). The MCA survey provides
corroborating evidence that most households spent their coupons rather quickly. This
survey indicates that 11.8% of the coupons were redeemed on the same day they were
distributed, and that at the end of four weeks, 80% of the coupons had been spent (figure
2).
In sum, the administrative data indicates that most of the intended beneficiaries of
the coupons actually received them. Moreover, the two surveys suggest that the vast
majority of eligible households received their coupons in March and April, and that they
spent the coupons very quickly after they received them. Obviously, the central question
is whether the coupons were spent on items that the households would have purchased
anyway.
3. Data
Our first source of data is the micro-level data from the Family Income and
Expenditure Survey (FIES) from 1990 to 2000. 10 This survey provides detailed
information on income and expenditures for individual households as well as on the
characteristics of these households. The monthly consumption data is compiled from a
diary, which is collected twice a month. The survey covers approximately 8,000
households, which are representative at the national level. Single households and
households employed in the agriculture or fishery industries are not surveyed. 11 Each
household is surveyed for six months and one–sixth of the households are replaced by
new households every month, making it possible to construct six-month panels.
10 The FIES is the Japanese Government’s main source of information on aggregate consumption. Hayashi (1986) also used the FIES for his work on the effect of durables on serial correlation in consumption patterns. 11 The FIES began to cover agriculture or fishery households in July 1999.
9
To improve the reliability of our estimates, we make the following cuts. First, we
delete households in which the household head is self-employed. We restrict our analysis
to households with wage earners for two reasons. First, monthly information on income
is available only for these households. In addition, the monthly pattern of income for
wage earners is very different than that of self-employed households due to the bonus
system. Second, we also exclude a household if the reported age of the household head
decreases or increases by more than one year during 6 months or if the household's
tenancy status changes from owner to renter (or from renter to owner), because these
changes are likely to be due to large shocks that may also have large effects on the
household's consumption. Third, we exclude all households with a person over 65 or
more of age, so our focus is on consumption of households with children under the age of
15. We do this because we are not able to reliably identify elderly people who received a
coupon in the FIES. 12 Fourth, 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 smaller households that are the norm in
Japan.13 Fifth, we excluded a household if the absolute change in consumption between
successive months exceeds the mean by more than three standard deviations. Finally, we
also exclude a household if there is a change in the family size between successive
months. After these cuts, we end up with a sample of roughly 600 households in each
six-month panel.
To focus on the consumption response of households in the spring of 1999 when the
coupons were distributed, we form two panels of households for each year. For each year
(1990-2000), we form panels from January to June and from February to July. As
previously discussed, most of the coupons were distributed and spent by June. Since our
12 There is no explicit data on the amount of coupons a household received in the FIES micro-data. According to officials responsible for the survey, the coupons were supposed to be classified as “gifts”, but it is not clear whether these instructions were followed by all the enumerators. 13 The number of household removed from our sample due to this criteria is very small.
10
focus is to compare the change in consumption after the coupons were distributed in
March of 1999, the January-June and February-July panels are the only ones that us to
measure the change in consumption after the coupons were spent. Finally, we aggregate
the different items into four consumption categor ies: durables, semi-durables, non-
durables, and services.14 The summary statistics of our two panels can be seen in Table 1.
Our second dataset is from the published tabulations of the Current Survey of
Commerce conducted by the Ministry of International Trade and Industry (MITI). 15
This is a census of every retail establishment or supermarket with more than 50
employees.16 Using these data, we compile monthly estimates of retail trade in 47
prefectures in Japan from 1990 to 1999, and combine this data with administrative data
on the number of coupons distributed in each prefecture. The summary statistics of this
second dataset can be seen in Table 2.
14 This classification follows that used by the Ministry of Public Management, Home Affairs, Posts, and Telecommunications in the published tabulations of this survey (“Annual report on the Family Income and Expenditure Survey”). Durables include household durables, automobiles, communication equipment, and recreational durable goods. Semi-durables include clothing, footwear, sporting goods, video games, computer hardware and software, and books. Non-durables include food (except eating out), fuel, light, and water charges, medicines, films, plants and gardening goods , and tobacco. Services include eating out, rents for housing, medical expenses, public transportation, communication (except communication equipment), education (except school textbooks and reference books), recreational services and personal care services. 15 MITI is now called the Ministry of Economy, Trade, and Industry (METI). 16 Stores with fewer than 50 employees are not surveyed. In 1999, 3,185 retail stores were covered by the Survey of Commerce. According to the Census of Commerce (which covered all retail stores, including those with fewer than 50 employees) conducted in June 1999, stores with more than 50 employees account for 15.8 percent of total retail sales in Japan (“Census of Commerce,” MITI). Total retail sales are roughly 30 percent of GDP.
11
4. Impact of Coupons across Families
We begin by using the six-month panels from the FIES to estimate whether
consumption changes when the coupons were distributed. As discussed, each family
received a coupon depending on the number of children under the age of 15. We can
therefore compare whether consumption of families with a large number of children
under the age of 15 in March to July of 1999 increases by more than families with a
smaller number of children, relative to the cross-sectional difference in consumption
patterns of similar households in other years (when the coupons were obviously not
distributed).
Our basic empirical specification is the following linear Euler equation:
(1) 3/
2/
,,
,1
)(,
)(,ln aYearaZIncomeMonthly
Coupona
C
Ctth
th
th
MarchBeforeth
MarchAfterth ⋅+⋅+
⋅=
where h indexes households and t refers to the year. The dependent variable is the log of
the ratio of the sum of consumption in household h in year t in the period after March [Ch, t (After March)] to the average monthly consumption before March [Ch, t (Before March) ].
The dependent variable is thus the cumulative change in consumption after March
relative to average monthly consumption prior to March. The main independent variable
is th
th
IncomeMonthly
Coupon
,
, , which is the total value of coupons received by a family relative to
the household’s monthly income.17 The total value of coupons is calculated as the
number of children under the age of 15 x 20,000 yen for households in the 1999 panels.18
For the other years (1990-1998), the coupon amount is obviously set to zero. Finally, Zh,t
is a vector of household characteristics (a quadratic in the age of the household head, the
number of children under the age of 15, and the number of other family members), and
Yeart is a vector of indicator variables for year. The coefficient of interest is a1, which
measures the elasticity of consumption to income due to the coupon program.
17 Monthly income is calculated as the previous year’s pretax income divided by 12. 18 As previously discussed, the administrative data indicates that almost 100% of the eligible households received the coupons.
12
Since the total amount of coupons received by a family is completely determined
by the number of children under the age of 15, equation (1) essentially tests whether the
change in consumption after March is greater among families with more children (under
the age of 15) relative to families with a smaller number of children, controlling for the
cross-sectional differences between similar families in other years when the coupons
were not distributed. Our dataset consists of six-month panels from 1990 to 1999, which
means that we have nine years to control for cross-sectional differences in seasonal
consumption patterns. To compare the impact of the coupons at different points in time,
we run up to five regressions with each one of our panels. Specifically, for the January-
June panel, we estimate four regressions with the following dependent variables: change
in consumption in March, in March and April, in March through May, and in March
through June, all relative to average monthly consumption in January and February.
Similarly, for the February-July panel, we estimate the same four regressions (but this
time relative to consumption in February), and one additional regression that measures
the change in consumption in March through July relative to February. The main
adva ntage of measuring the change in consumption in March through June (or March
through July) is that we know that almost all the coupons were spent over this time period.
In contrast, the estimates in the earlier months (say March or March-April) are
downwardly biased, since some of the households did not receive and redeem their
coupons during this time period.19
As a benchmark, we begin by measuring the cross-sectional distribution of
changes in consumption in the spring of 1999. To do this, we employ a version of
equation (1) that only uses the 1999 panel, and that omits the indicator variables for year
and controls for family size. Since the amount of the coupon is completely determined 19 However, this bias is not large. As previously mentioned, 70-80 percent of the coupons were spent by April. In addition, we would ideally liked to have panels that span the entire period the coupons could have been spent (March-September). However, since we only have six-month panels, if we were to use the panel that covers all these months, we would not have any consumption data prior to the coupon program. However, we know that close to 100 percent of the coupons were spent by June.
13
by the number of children under the age of 15 in the household, these estimates measure
whether the consumption of households with a larger number of children under the age of
15 increased by more than households with a smaller number of children. As can be seen
(Table 3), there is a large difference in the change in consumption between these
households, particularly for semi-durables. However, it is clear that we cannot attribute
this cross-sectional difference to the impact of the coupons, because there are clearly
many reasons to expect the seasonal pattern of consumption to differ across families with
different numbers of children. For example, April is the beginning of the school semester
in Japan, and families with children will obviously spend more on clothes and other
school items in the months of March and April.
To address this problem, we turn to estimates that compare the cross-sectional
differences in consumption in 1999 with that in other years. These difference-in-
difference estimates, shown in Table 4, indicate that the coupons appeared to have only
had an effect on expenditures on semi-durables. The estimates of a1 for durables, non-
durables, and services are invariably unstable and statistically insignificant. In contrast,
the estimates of a1 for semi-durables are always positive and in most cases, statistically
significant. Nonetheless, the estimated effect is small. For example, the elasticity of
consumption of semi-durables from March through July (for the February-July panel) is
1.63, which suggests that the marginal propensity to consume of the coupons was roughly
0.1. 20
It is interesting to note that the estimates of a1 for semi-durables decline as we
move to the right across a row in Table 4. The estimated elasticity of consumption of
semi-durables is 3.34 in March (relative to average monthly consumption before the
coupons were distributed), 2.41 in March-April, and falls to 1.15 in March-June. 21 In
addition, bear in mind that the estimates in the earlier months are likely to be downwardly
20 MPC=a 1 x (C/Y). Table 1 indicates that semi-durables/income=0.058, so MPC=1.63 x 0.058 = 0.095. 21 Here, we obviously refer to the January-June panel.
14
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 a1 x (C/Y) x (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
22 This classification follows Parker (1999).
15
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 23 The main difference being that the dependent variable is the change in consumption after November relative to average consumption before November. The amount of coupon each household expected to receive is calculated from the number of children under 15 in November. The coupon program was proposed by Komeito (one of the three parties in the coalition government) on Oct. 6, 1998, without specifying a precise amount or whom would be eligible for them. According to the Nikkei newspaper, Komeito reached an agreement with the Liberal Democratic Party over the coupon program on Nov. 9, 1998. 24 Zeldes (1989) used a similar procedure to identify families which were potentially liquidity constrained. 25 Specifically, the FSS collects data on savings deposits, life insurance, trusts, and securities. There is no
16
create a matched da taset 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 7-8 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
information on land or housing assets. There is also no information on liabilities, so we only have a measure of a family’s gross assets. 26 Asset/Income refers to the ratio of total gross financial assets to annual pre-tax income.
17
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
27 The average population of a prefecture was 2.682 million on March 31, 2000. 28 The tabulations from this survey are published in annual issues of “Yearbook of the Current Survey of Commerce” published by MITI.
18
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:
(2) 3/
2/
,
,1
)(,
)(,ln aYearaZIncomeMonthly
Coupona
S
Sti
ti
ti
MarchBeforeti
MarchAfterti ⋅+⋅+
⋅=
where i indexes prefectures (47 prefectures in total), t refers to year, Zi represents a vector
of indicator variables for prefecture, and Yeart 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 (Si, t (After March)) to the average monthly retail sales prior to March ((Si, t (Befire
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, Couponi,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 can not attribute this entirely to the coupon program, since there are reason
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.
29 Monthly income in a prefecture is defined as GDP of a prefecture/12. The data on prefectural GDP is from the publication "Annual Report on Prefectural Accounts" published by the Cabinet Office (2001).
19
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 sa les 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.
20
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 demonstrates 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 dur ing the 1992 presidential election. We don’t
have a good explanation why our results appear to be at odds with this large body of
30 This is not completely correct. Basic consumption theory also predicts that consumption should rise on the announcement of the anticipated income change, not when the income change takes place. As we’ve shown in the paper, this prediction is not supported by the evidence.
21
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.
22
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 Hypothes is 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.
23
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)
Mean S. D.January-June Panel (N=6,329) Consumption: Durables 17,560 114,588 Semi-durables 34,223 46,331 Non-durables 106,040 38,860 Services 108,506 126,330Amount of Coupon (all households)1 18,568 20,435Amount of Coupon (households thatreceived coupons)1 35,367 14,182Monthly income2 595,119 262,130Age (household head) 43.8 9.9Number of children under 15 1.0 1.0Number 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 110,368 139,174Amount of Coupon (all households)1 18,294 20,097Amount of Coupon (households thatreceived coupons)1 34,103 14,618Monthly Income2 586,945 263,655Age (household head) 43.9 10.0Number of children under 15 1.0 1.0Number 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 688,630 778,073Total Coupons/Monthly Income1 0.0225 0.0051Retail Sales/Monthly Income 0.05099 0.01312
1 total number of eligible people in a prefecture x 20,000 yen/averagemonthly regional income in 1998.Note: The unit of observation is a prefecture (there are a total of 47 prefectures).The data are from 1990 to 1999. Monthly income and retail sales are in million yen.
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
(4.91)2.49
(4.50)1.99
(3.91)-0.43(3.72)
Semi-durables 6.21(1.35)
4.59(1.04)
3.38(0.96)
2.75(0.91)
Non-durables 0.20(0.21)
0.14(0.17)
0.13(0.17)
0.07(0.17)
Services -0.12(0.65)
-0.09(0.54)
-0.26(0.49)
-0.30(0.47)
February-July Panel (N=517) Durables -0.67
(6.91)2.89
(5.81)3.91
(5.62)-0.63(5.36)
2.10(5.14)
Semi-durables 4.07(1.50)
4.59(1.35)
3.79(1.24)
2.83(1.20)
2.05(1.19)
Non-durables 0.36(0.23)
0.41(0.20)
0.27(0.20)
0.26(0.20)
0.23(0.20)
Services -0.85(0.72)
-1.16(0.65)
-0.70(0.60)
-0.63(0.57)
-0.75(0.58)
Notes: Standard errors in parenthesis. The dependent variable is the log of the ratio of the sum of consumptionafter March to the average monthly consumption before March. The estimated elasticity to the shopping coupons isthe coefficient on coupons (computed as number of children x 20,000 yen) relative to monthly pre-tax income in theprevious 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
(4.44)2.96
(3.89)2.31
(3.39)0.28
(3.25) Semi-durables 3.34
(1.16)2.41
(0.96)1.75
(0.87)1.15
(0.84) Non-durables 0.24
(0.19)0.20
(0.16)0.15
(0.16)0.10
(0.15) Services 0.84
(0.57)0.72
(0.49)0.45
(0.45)0.15
(0.43)
February-July Panel (N=5,945) Durables -0.54
(6.60)3.87
(5.41)3.28
(5.07)-1.24(4.72)
1.31(4.59)
Semi-durables 2.68(1.33)
2.93(1.17)
2.56(1.11)
2.01(1.08)
1.63(1.06)
Non-durables 0.18(0.23)
0.21(0.20)
0.20(0.19)
0.12(0.19)
0.11(0.19)
Services 0.09(0.65)
-0.22(0.58)
-0.04(0.55)
0.19(0.54)
-0.17(0.53)
Notes: Standard errors in parenthesis. Data are panels from 1990 to 1999. The dependent variable is the log of theratio of the sum of consumption after March to the average monthly consumption before March. The estimatedelasticity 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 thehousehold 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
(1.04)-0.18(0.78)
Non-durables -0.08(0.20)
-0.17(0.17)
Services 0.30(0.54)
0.30(0.48)
August-January Panel (N=6,277) Semi-durables 0.40
(0.96)0.08
(0.78)0.33
(0.71) Non-durables 0.01
(0.19)-0.15(0.16)
-0.20(0.15)
Services 0.60(0.50)
0.47(0.45)
0.41(0.41)
September-February Panel (N=6,364)) Semi-durables -0.62
(0.92)-1.16(0.75)
-0.67(0.70)
-0.65(0.68)
Non-durables 0.11(0.18)
-0.03(0.16)
-0.07(0.15)
-0.08(0.14)
Services -0.29(0.50)
-0.46(0.46)
-0.37(0.43)
-0.28(0.42)
Note: Regressions are difference in difference estimates on successive panels from 1990 to 1999. The dependentvariable is the log of the ratio of the sum of consumption after November to the average monthly consumptionbefore November, and the main independent variable is the value of coupons relative to monthly pre-tax income inthe 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-durables
-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. Dependentvariable 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
(1.69)2.89
(1.22)2.89
(1.24)2.58
(1.22)2.16
(1.34) Jan.-Feb. 1.61
(2.01)2.19
(1.51)2.18
(1.55)1.88
(1.53)1.45
(1.62) Dec.-Feb. 0.17
(1.48)0.75
(1.00)0.74
(1.00)0.44
(1.05)0.01
(1.01) Nov.-Feb. 1.00
(1.40)1.58
(0.96)1.58
(0.99)1.28
(1.04)0.85
(1.02) Oct.-Feb. 1.41
(1.26)1.99
(0.87)1.99
(0.90)1.68
(0.97)1.26
(0.96)
Clothing Sales Relative to Average Monthly Sales in: Feb. -0.07
(2.40)0.90
(1.76)0.86
(1.60)1.15
(1.54)0.40
(1.59) Jan.-Feb. -0.14
(2.50)0.82
(1.79)0.78
(1.56)1.07
(1.55)0.32
(1.54) Dec.-Feb. -1.91
(2.23)-0.94(1.44)
-0.98(1.14)
-0.69(1.11)
-1.45(1.06)
Nov.-Feb. -1.18(2.06)
-0.22(1.28)
-0.26(1.04)
0.03(1.00)
-0.72(0.97)
Oct.-Feb. -0.09(1.87)
0.88(1.11)
0.84(0.90)
1.13(0.89)
0.38(0.88)
Notes: The unit of observation is a prefecture (47 prefectures in total). The dependent variable is the log of the ratioof the sum of retail sales in a prefecture after March to the average monthly retail sales before March. The mainindependent variable is the ratio of the total number of the eligible people in the prefecture x 20,000 yen to monthlyregional income in the previous year.
Table 8: Impact of Shopping Coupons on Seasonal Retail Sales across Prefectures,Difference in Difference Estimates.
March March-April March-May March-June March-July
Retail Sales Relative to Average Monthly Sales in: Feb. 1.51
(0.99)1.55
(0.87)1.61
(0.87)1.81
(0.88)1.88
(0.91) Jan.-Feb. 1.86
(0.87)1.89
(0.74)1.96
(0.76)2.16
(0.79)2.23
(0.83) Dec.-Feb. 1.63
(0.79)1.66
(0.67)1.73
(0.68)1.94
(0.71)2.00
(0.73) Nov.-Feb. 1.80
(0.80)1.83
(0.68)1.90
(0.68)2.10
(0.70)2.17
(0.73) Oct.-Feb. 1.82
(0.80)1.85
(0.67)1.92
(0.67)2.12
(0.70)2.19
(0.73)
Clothing Sales Relative to Average Monthly Sales in: Feb. 1.97
(1.21)1.61
(1.07)1.22
(1.06)1.81
(1.05)1.68
(1.06) Jan.-Feb. 2.47
(1.05)2.11
(0.85)1.72
(0.82)2.31
(0.83)2.18
(0.84) Dec.-Feb. 1.88
(1.00)1.52
(0.77)1.13
(0.72)1.71
(0.71)1.58
(0.71) Nov.-Feb. 1.84
(1.02)1.48
(0.78)1.09
(0.71)1.68
(0.69)1.55
(0.70) Oct.-Feb. 2.05
(1.02)1.69
(0.78)1.30
(0.72)1.89
(0.70)1.76
(0.70)
Notes: The unit of observation is a prefecture (47 prefectures in total). The dependent variable is the log of the ratioof the sum of retail sales in a prefecture after March to the average monthly retail sales before March. The mainindependent variable is the ratio of the total number of the eligible people in the prefecture x 20,000 yen to monthlyregional income in the previous year. Regressions also include indicator variables for each prefecture and indicatorvariables for year.
Figure 1: Timing of Receipt and Expenditure of Coupon (EPA Survey)
Panel B: Households with Elderly Members
0
10
20
30
40
50
60
70
80
March April May June
% in
eac
h m
on
th
receipt of couponredemption of coupon
Panel A: Households with Children <15
0
10
20
30
40
50
60
70
80
March April May June
% in
eac
h m
on
th
receipt of couponredemption of coupon
Source: Management and Coordination Agency (2000)
Figure 2: Time Between Receipt and Redemption of Coupon
0
5
10
15
20
25
30
35
40
45
1 Week 2 Weeks 3 Weeks 4 Weeks > 5 Weeks
Figure 3: Difference in Difference Estimates of Elasticity of Consumption to Shopping Coupons
See text and notes to Table 4 for additional details.
A. Food
-0.6-0.4-0.2
00.20.40.60.8
March April May June
B. Apparel
-2-1012345
March April May June
C. Transportation
-8
-6
-4
-2
0
2
March April May June
D. Entertainment
-1
0
1
2
3
4
5
March April May June
E. Personal Care
-4
-3
-2
-1
0
1
2
March April May June
F. Reading
-2
-1
0
1
2
3
March April May June
Figure 4: Cross-Sectional Differences in Seasonal Patterns of Consumption in 2000(relative to 1990-1998)
-3
-2
-1
0
1
2
3
March April May June
34
Appendix: Shopping coupons in the Family Income Expenditure Survey*
1 Introduction In the main text of this discussion paper, we examined the consumption response to the
“shopping coupon” program, using the variations observed in the Family Income and
Expenditure Survey (FIES) and the Current Survey of Commerce. The results suggest that the
program stimulated consumption of semi-durables immediately after the coupon distribution,
but the effect declines over longer time periods. The merits of our study are that it takes
advantage of the unusual quasi-natural experiments of the coupon program, and it is a study on
Japanese consumption micro-data with few precedents.
The basic specification in our micro-data study is the linear Euler equation with the
dependent variable of the cumulative change in consumption after March relative to average
monthly consumption prior to March, and with the main independent variable of the number of
children under the age of 15× 20,000 yen relative to the household’s monthly income (see
equation (1) in the main text). We used the cumulative change rather than monthly panels, since
the FIES micro-data dose not explicitly report the timing and amount of coupons a household
receives. As mentioned in the footnote 12 of the main text, the coupons were supposed to be
classified as “gift”, but it is not clear whether the instructions were followed by all the
enumerators.
In this appendix, we present a result of our attempt to identify the (timing and amount of)
coupon delivery to each household using the FIES gift entry. The appendix is organized as
follows. Section 2 gives a description of the FIES gift entry data used to estimate the coupon
delivery. Section 3 introduces the Kolmogorov-Smirnov’s Test to detect the change in the data
distribution, and applies it to the FIES gift data to show that the coupon program certainly
changed the gift distribution. Using the empirical distribution, section 4 presents our
Maximum-Likelihood estimates of the percentage of the eligible households that received the
coupons. The estimated ratio is 76-78% and considerably under-biased; endorsing our anxiety
that it is not clear whether all the enumerators followed the instructions.
2 Data Our source of data here is the gift entry in the micro-data of the FIES from 1997 to 2000. As
described in the main text, the survey covers approximately 8,000 households; the data used in
this appendix covers only about 4,800 samples for every data point (year-month), however,
since we restrict our analysis to wage earner households.
* Masahiro Hori drafted this appendix.
35
The “gift” is a part of non-current income, and is entered when there are customary transfer
incomes such as money given at sympathy, congratulation, or condolences. On the occasion of
the “shopping coupon” program, enumerators of the survey were instructed to classify the
coupons as the “gift”. So, if the instructions were scrupulously followed and each household
entered it correctly, we would not only be able to expect the shift of gift distribution by the
amount of coupon delivery, but also be able to recover the coupon delivery to each household.
The summary statistics of our gift data can be seen in Table A-1. 31 To see the effects of the
coupon program, we calculated the statistics for the years without coupons (1997, 1998 and
2000) and that for the year with coupons (1999) separately. Tables report the basic statistics by
the number of children under 15 and by month to control family structures and seasonal
variations. Large differences between the means, observed especially in the March and April
figures from the samples with children, seem to suggest the presence of coupons in the gift data;
larger standard deviations prevent us from detecting it statistically, however.
3 The effects of “Shopping Coupon” on the gift data distribution
Our final goal in this appendix is to identify the timing and amount of coupon delivery to
each household using the FIES gift entry. However, as a prerequisite of our attempts, we have to
confirm the fact that the coupon delivery is surely reflected in the gift entry of the survey. In this
section, we applied the Kolmogorov-Smirnov’s Test, a nonparametric procedure to determine
whether two sets of data could reasonably have originated from the same probability
distribution, to detect the impact of the coupon program on the gift data.
3.1 Methodology: the Kolmogorov-Smirnov’s Test
The procedure of the Kolmogorov-Smirnov’s (K-S) Test is as follows:32
Given two sets of samples, (x1(1),x2
(1),…,xm(1)) and (x1
(2),x2(2),…,xn
(2)), to compare, we
construct two empirical (cumulative) distribution functions as
∑=
≤=
m
i
im m
xxIxF
1
)1()1( )(
)( and ∑=
≤=
n
i
in n
xxIxF
1
)2()2( )(
)( ,
where )( )( xxI ki ≤ is a function such that
>≤
=≤xxxx
xxIk
i
kik
i )(
)()(
01
)( . In this setting, the
K-S statistic for the case of two samples is defined as
31 The data used here again excludes all households with persons over 65. 32 See Yamauti (1972) for details.
36
{ })()(max )2()1(, xFxFd nmxnm −= .
Compareing this statistic with critical values for m× n samples, i.e., c0(m,n), from the statistical
table for the K-S Test, we can test H0: )()( )2()1( xFxF nm = versus H1: )()( )2()1( xFxF nm ≠ ,
and reject the null hypothesis if ),(0, nmcd nm ≥ . And in case of large samples, i.e., ∞→+ nm ,
we can calculate the p-value for the given observation under null hypothesis, resorting to the
following approximation. 33
)2exp()1(2Pr 22
1
1, zizd
nmmn
i
inm −−≈
≥×
+ ∑∞
=
−
3.2 Detected changes in the gift data distribution
Our main interest here is, of course, whether the test above can statistically detect the
change of the gift distribution at March and April of 1999, when the coupon deliveries were
supposed to have concentrated. Since the gift is usually related with congratulations and
condolences, it is expected to have some seasonality and correlation with the family structures.
So, in the following, we started our analyses by dividing the household observations into two
groups with and without children, and examining whether there are different pattern of gift
distribution between the two groups and among months. If we find some differences, we have to
control those factors in advance.
3.2-(1) An example of calculation
For the sake of instruction, here we present a concrete calculation procedure, using an
example to compare the households without children and those with children as of January 1997
(the result is reported at the top-left in Table A-2).
The number of wage earner household observations in the January 1997 survey is 4,873. We
divide the data into two groups without children (number of observations, m=2433) and with
children (n=2440). Here, we rearrange the households of each group in the order of the gift
amount received, and construct empirical distribution functions for those two groups. If we use
the notation )()(2433 xF o to denote the functions for the group without children, and )()(
2440 xF w
for the group with children, we can define the difference between the two distribution functions
33 See Takeuchi (1989, pp.124).
37
as )()( )(2440
)(2433 xFxF wo − . The test statistic that we are calculating is )()( *)(
2440*)(
24332440,2433 xFxFd wo −= ,
where x* denote the value of x that maximizes the function )()( )(2440
)(2433 xFxF wo − . In our
example,
)()( *)(2440
*)(24332440,2433 xFxFd wo −= =0.238.
Though the critical values for these large samples are not reported in the standard tables for the
K-S statistic, approximation formula suggests that
( )
×
+×
≥×+×
=≥ 238.02440243324402433
2440243324402433
Pr238.0Pr 2440,24332440,2433 dd
)238.04873
244024332exp()1(2
2
2
1
1
××
×−−≈ ∑∞
=
− ii
i =0.000.
So, at least for the samples from January 1997, the gift distribution of the households without
children is statistically different from that of the households with children.
3.2-(2) The results of the K-S Tests
Table A-2s report the results of the K-S Tests to see whether the presence of children affects
the gift distribution in the FIES of each month (from January to June) after 1997. As expected
from Japanese gift customs, such as New Year’s, Children’s Day’s and Commencement presents,
the tests detect the differences in distributions except for results from June samples. Table A-3s
report the test statistic that examined seasonal variations. The results suggest that the seasonality
is prominent in the gift distributions of the households with children.
Given the findings from the tests on seasonality and the presence of children, we would like
to report the results of K-S Tests on the samples with children among years after controlling
seasonality (Table A-4s). Our main interest is whether the test can detect the shifts in the gift
distribution of 1999, the year when the coupon program implemented. The tables inarguably
demonstrate that the coupon program shifted the gift distributions as expected, though the
distributions without the program were stable among years. That is, we do not see any changes
in the gift distributions for January and June observations; however, the K-S statistic detects
(with 1% level significance) the peculiarity of distributions for the spring (February to May)
1999, the period when the coupons were supposed to be delivered.
38
4 The Maximum-Likelihood (ML) estimates of the coupon delivery
Founding on the findings in the previous section, we utilize the derived empirical
distributions in our ML estimation to identify the timing and amount of coupon delivery to each
household.
Let d(O)Zm(x) denote the probability density function of the gift distribution (without the
coupons) of month-m for households with Z children. Since the gift distributions are stable
without the coupon program, and the program shifts the distributions by the amount of coupon
delivery, the density function of the month-m distribution with the coupons for the same
households is derived as d(C)Zm(x)= d(O)
Zm(x- aZ), where a is the known amount of the coupon
per children under 15, i.e., 20,000 yen in the 1999 experiment. Therefore, given a household
observation, for which the receipt of the coupons are not certain, the density function for the
observed gift amount xi is defined as
)()()1()()()1( )()()()( aZxdpxdpxdpxdp iZmO
iZmO
iZmC
iZmO −×+×−=×+×− ,
where p denotes (unknown) ratio of the coupon receipt. Here, if we knew functional form of the
density functions d(O)Zm(x), we can estimate the ratio of the coupon receipt (p), by using the gift
entry for each eligible household, to maximize the following likelihood function
{ }∏=
−×+×−=m
iiZm
OiZm
O aZxdpxdpL1
)()( )()()1( .
Lpp
ML maxˆ =
In actual calculation, we constructed the empirical distributions without coupons (D(O)Zm(x))
for six months (m is from January to June) with different number of children (Z=1, 2, or 3),
using the data from 1997-98 and 2000, the years without coupon programs, and derived
empirical density function d(O)Zm(x) by differentiation d(O)
Zm(x)= (D(O)Zm(x+h)- D(O)
Zm(x))/h,
where h denotes a certain small interval. Then we calculated both O(xi)= d(O)Zm(xi) and C(xi)=
d(C)Zm(xi)= d(O)
Zm(xi -aZ) for each individual gift observation xi, and rearranged the households in
the order of the ratio R(xi)=C(xi)/O(xi). Our strategy was to regard the top 100ˆ ×MLp percent
of household in the rearranged data received the shopping coupons amount to the number of
children under 15× 20,000 yen. Following this procedure, we can identify (guess statistically)
the timing and amounts of the coupon delivery to each household. 34
34 One obvious problem of this procedure is that there are some risks to over-detect multiple time receipts of coupons by a household, in case when the household received more than 20,000 yen gift over multiple months. To avoid the problem, we have to derive (empirical) joint probability density functions of the gifts among months; data limitation to construct empirical distributions restricts us to one-dimensional case, however.
39
Table A-5 reports the estimated percentage of the households (by the number of children
under 15) received the coupons each month after February 1999. Estimated timing of the receipt
of coupons seems consistent with the results of the EPA survey mentioned in the main text; the
peak of coupon deliveries came in March, and the ratio declined over months to have virtually
disappeared until June. However, the cumulated percentages of the households estimated to
have received the coupons barely reach 76-78%, regardless of the administrative data that
indicates almost 100 percent of the eligible households received the coupons. Considering the
reliability of the administrative data and the FIES gift data compiled from diaries, we would
guess that three-fourth of the coupon receipts were correctly entered as “gifts” in the FIES;
one-fourth of them were not entered or entered somehow in the different categories, however.
Given the shortcomings in the gift entries and the administrative data that reports almost
100 percent delivery, we finally decided to estimate the Euler equation using the number of
children under the age of 15× 20,000 yen as the proxy for the coupon receipts.
References
Statistics Bureau of the Ministry of Public Management, Home Affairs, Posts and
Telecommunications, Japan, Annual Report on the Family Income and Expenditure Survey,
various issues.
Takeuchi, Kei, Toukei-Gaku Jiten (The Statistics Dictionary), Toyo-Keizai-Shinposya, 1989.
Yamauti, Ziro eds. Statistical Tables and Formulas with Computer Applications JSA-1972,
Japanese Standards Association, 1972.
Table A-1 Summary Statistics (the FIES "gift")
January February March April May JuneMean 8210 5508 10051 6321 5713 6687
0 S.D. 62766 71720 75240 42613 74765 91222Nobs 5612 5578 5604 5528 5543 3866Mean 15864 5813 15294 9262 5086 3193
1 S.D. 55871 36807 63806 40631 28358 18302Nobs 2584 2580 2536 2544 2547 1652Mean 19330 6299 16632 10143 4522 3029
2 S.D. 37401 33926 63787 41783 18931 21711Nobs 2921 2921 2907 2936 2882 1918Mean 20912 6641 16702 8313 4090 2591
3 S.D. 45649 31918 47795 26263 18515 11755Nobs 820 809 836 880 905 596
January February March April May JuneMean 8825 4126 8334 8538 5369 4837
0 S.D. 56467 45313 61481 104151 61943 62620Nobs 1806 1748 1742 1753 1770 1757Mean 13665 4655 25798 8313 6119 2453
1 S.D. 38054 21959 45156 22183 23816 13270Nobs 858 898 898 881 886 908Mean 20607 8703 40662 14095 5764 3658
2 S.D. 41568 41872 42034 39660 23342 21640Nobs 1044 1035 1013 1002 999 968Mean 20959 10924 63275 23599 8914 4278
3 S.D. 41191 28528 53989 76312 61036 19446Nobs 274 275 318 317 295 292
Note: Samples are from wage earner obserbations in the FIES.
1999 (the year of "shopping coupon" program)
# ofchildren
# ofchildren
1997,1998 and 2000
Table A-2 Stability of "Gift" distribution: with children vs. without children.
Janu. Feb. March April May JuneNo.Obs.without children 2433 2416 2427 2394 2412 2443
No.Obs.with children 2440 2455 2465 2489 2520 2488d 0.238 0.062 0.127 0.087 0.052 0.035
p-value 0.000 0.000 0.000 0.000 0.003 0.106
Janu. Feb. March April May JuneNo.Obs.without children 2380 2339 2433 2448 2451 2494
No.Obs.with children 2511 2496 2477 2466 2408 2387d 0.219 0.057 0.120 0.108 0.056 0.016
p-value 0.000 0.001 0.000 0.000 0.001 0.900
Janu. Feb. March April May JuneNo.Obs.without children 2353 2329 2351 2352 2353 2346
No.Obs.with children 2484 2507 2499 2467 2468 2461d 0.199 0.092 0.611 0.180 0.111 0.034
p-value 0.000 0.000 0.000 0.000 0.000 0.116
Janu. Feb. March April MayNo.Obs.without children 2353 2339 2327 2282 2288
No.Obs.with children 2381 2386 2356 2390 2387d 0.203 0.042 0.130 0.110 0.059
p-value 0.000 0.030 0.000 0.000 0.001
Notes: Samples are from wage earner obserbations in the FIES.Kolmogorov-Smirnov test with large samples. d(m,n)=max{|Fm(x)-Gn(x)|}
2000
1998
1999
1997
Table A-3 Stability of "Gift" distribution: between months to see seasonal variations
(1) Monthly obserbation without children.
Feb. March April May June Feb. March April May June2416 2427 2394 2412 2443 2399 2433 2448 2451 2494
0.065 0.025 0.042 0.047 0.058 0.070 0.012 0.035 0.039 0.0540.000 0.443 0.028 0.010 0.000 0.000 0.858 0.103 0.054 0.002
0.048 0.037 0.029 0.009 0.057 0.045 0.042 0.0190.007 0.070 0.255 1.000 0.001 0.016 0.030 0.775
0.023 0.029 0.039 0.022 0.030 0.0400.542 0.276 0.046 0.580 0.227 0.040
0.012 0.030 0.008 0.0270.995 0.226 1.000 0.310
0.022 0.0240.587 0.493
Feb. March April May June Feb. March April May2329 2351 2352 2353 2346 2339 2327 2282 2288
0.083 0.015 0.046 0.068 0.080 0.057 0.040 0.034 0.0460.000 0.947 0.013 0.000 0.000 0.001 0.044 0.134 0.014
0.077 0.045 0.017 0.008 0.020 0.032 0.0120.000 0.019 0.907 1.000 0.749 0.183 0.996
0.040 0.061 0.070 0.014 0.0130.043 0.000 0.000 0.981 0.990
0.034 0.039 0.0240.140 0.054 0.529
0.013 0.992
(2) Monthly obserbation with children.
Feb. March April May June Feb. March April May June Note.2455 2465 2489 2520 2488 2496 2477 2466 2408 2387
0.243 0.132 0.192 0.238 0.273 0.230 0.125 0.160 0.210 0.264 Com.Yr. 4 Com.Yr. 5 Com.Yr. 60.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 No.Obs. No.Obs. No.Obs.
0.115 0.065 0.020 0.037 0.121 0.096 0.035 0.040 d d d0.000 0.000 0.687 0.072 0.000 0.000 0.108 0.044 p-value p-value p-value
0.065 0.110 0.143 0.042 0.096 0.145 d d0.000 0.000 0.000 0.024 0.000 0.000 p-value p-value
0.052 0.095 0.062 0.120 d0.002 0.000 0.000 0.000 p-value
0.046 0.0620.010 0.000
Feb. March April May June Feb. March April May Kolmogorov-Smirnov test with large samples.2507 2499 2467 2468 2461 2386 2356 2390 2387 d(m,n)=max{|Fm(x)-Gn(x)|}
0.196 0.445 0.084 0.189 0.252 0.225 0.120 0.142 0.2050.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000 0.000
0.605 0.135 0.035 0.060 0.110 0.098 0.031 Samples are from wage earner obserbations in the FIES.0.000 0.000 0.099 0.000 0.000 0.000 0.203
0.495 0.620 0.662 0.036 0.0870.000 0.000 0.000 0.097 0.000
0.129 0.192 0.0740.000 0.000 0.000
0.088 0.000
Base Month3
No.Obs.
Year
Base Month1
No.Obs.
Base Month2
No.Obs.
1997 1998
20001999
Jan.
Feb.
March
April
May
Jan.
Jan. 2353
Feb.
March
April
May
2329
2351
2352
2353
Feb.
March
April
May
Jan.
Feb.
March
April
2412
2380
2399
2433
2448
2451
2433
2416
2427
2394
2353
2339
2327
2282
2440
2455
2465
2489
Jan.
Feb.
March
April
May
Jan.
Feb.
March
April
May
Jan.
Feb.
March
April
2484
2507
2499
2467
2468
2511
2496
2477
2466
2408
2381
2386
2356
2390
1997 1998
1999 2000
2520
Jan.
Feb.
March
April
May
Table A-4 Stability of "Gift" distribution: among years to see the impact of the coupon program.
Monthly obserbation among years (samples with children)
1998 1999 2000 1998 1999 2000 1998 1999 20002511 2484 2381 2496 2507 2386 2477 2499 23560.027 0.033 0.043 0.017 0.032 0.022 0.016 0.529 0.0250.317 0.145 0.021 0.859 0.148 0.578 0.900 0.000 0.450
0.011 0.024 0.047 0.015 0.542 0.0140.998 0.471 0.008 0.954 0.000 0.967
0.022 0.049 0.5520.580 0.006 0.000
1998 1999 2000 1998 1999 2000 1998 19992466 2467 2390 2408 2468 2387 2387 24610.021 0.102 0.023 0.007 0.045 0.012 0.019 0.0070.634 0.000 0.521 1.000 0.013 0.994 0.783 1.000
0.092 0.009 0.044 0.014 0.0180.000 1.000 0.018 0.971 0.806
0.088 0.0530.000 0.002
Note.
Com.Yr.4
Com.Yr.5
Com.Yr.6
No.Obs. No.Obs. No.Obs. Samples are from wage earner household obserbations in the FIES (excluding households with persons over 65)d d d
p-value p-value p-value Kolmogorov-Smirnov test with large samples.d d d(m,n)=max{|Fm(x)-Gn(x)|}
p-value p-valued
p-value
Month
Base Year1
No.Obs.
2484
24891997
1998
1999
April
January Feburary March
1997 2440 2455 19971997
Base Year2
No.Obs.
Base Year3
No.Obs.
2499
June
2465
1998 2511 1998 2496 1998 2477
1999
May
1997
2507 1999
2488
2466 2408 1998 23871998
2520 1997
1999 2467 1999 2387
Table A-5 Results of p estimation by the ML procedure with empirical densities.
1 2 3February 0.033 0.035 0.070
March 0.578 0.635 0.608April 0.112 0.080 0.097May 0.025 0.005 0.002June 0.010 0.003 0.000
Total 0.758 0.758 0.777Note: Samples are from wage earner household obserbations in the FIES
(excluding households with persons over 65)
# of children
