Silviase/jgov_v1.4_full_ann huggingface dataset:
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	1.55	1.61	1.81	1.88
(0.99)	(0.87)	(0.87)	(0.88)	(0.91)
Jan.-Feb.	1.86	1.89	1.96	2.16	2.23
(0.87)	(0.74)	(0.76)	(0.79)	(0.83)
Dec.-Feb.	1.63	1.66	1.73	1.94	2.00
(0.79)	(0.67)	(0.68)	(0.71)	(0.73)
Nov.-Feb.	1.80	1.83	1.90	2.10	2.17
(0.80)	(0.68)	(0.68)	(0.70)	(0.73)
Oct.-Feb.	1.82	1.85	1.92	2.12	2.19
(0.80)	(0.67)	(0.67)	(0.70)	(0.73)
Clothing Sales Relative to Average Monthly Sales in:
Feb.	1.97	1.61	1.22	1.81	1.68
(1.21)	(1.07)	(1.06)	(1.05)	(1.06)
Jan.-Feb.	2.47	2.11	1.72	2.31	2.18
(1.05)	(0.85)	(0.82)	(0.83)	(0.84)
Dec.-Feb.	1.88	1.52	1.13	1.71	1.58
(1.00)	(0.77)	(0.72)	(0.71)	(0.71)
Nov.-Feb.	1.84	1.48	1.09	1.68	1.55
(1.02)	(0.78)	(0.71)	(0.69)	(0.70)
Oct.-Feb.	2.05	1.69	1.30	1.89	1.76
(1.02)	(0.78)	(0.72)	(0.70)	(0.70)

Notes: The unit of observation is a prefecture (47 prefectures in total). The dependent variable is the log of the ratio of the sum of retail sales in a prefecture after March to the average monthly retail sales before March. The main independent variable is the ratio of the total number of the eligible people in the prefecture x 20,000 yen to monthly regional income in the previous year. Regressions also include indicator variables for each prefecture and indicator variables for year.
Figure 1: Timing of Receipt and Expenditure of Coupon (EPA Survey)

Panel A: Households with Children <15

Panel B: Households with Elderly Members
Figure 2: Time Between Receipt and Redemption of Coupon

Source: Management and Coordination Agency (2000)
Figure 3: Difference in Difference Estimates of Elasticity of Consumption to Shopping Coupons

See text and notes to Table 4 for additional details.
Figure 4: Cross-Sectional Differences in Seasonal Patterns of Consumption in 2000 (relative to 1990-1998)
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 \times 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 does 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.
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, $ (x_{1}^{(1)}, x_{2}^{(1)}, \ldots, x_{m}^{(1)}) $ and $ (x_{1}^{(2)}, x_{2}^{(2)}, \ldots, x_{n}^{(2)}) $ , to compare, we construct two empirical (cumulative) distribution functions as

$$ F_{m}^{(1)}(x)=\sum_{i=1}^{m}\frac{I(x_{i}^{(1)}\leq x)}{m}\quad\text{and}\quad F_{n}^{(2)}(x)=\sum_{i=1}^{n}\frac{I(x_{i}^{(2)}\leq x)}{n}, $$

where $ I(x_{i}^{(k)} \leq x) $ is a function such that $ I(x_{i}^{(k)} \leq x) = \begin{cases} 1 & x_{i}^{(k)} \leq x \\ 0 & x_{i}^{(k)} > x \end{cases} $ . In this setting, the

K-S statistic for the case of two samples is defined as
$$ d_{_{m,n}}=\max_{x}\Big\{\left|F_{_{m}}^{(1)}\left(x\right)-F_{_{n}}^{(2)}\left(x\right)\right|\Big\}. $$

Comparing this statistic with critical values for $ m \times n $ samples, i.e., $ c_{0}(m, n) $ , from the statistical table for the K-S Test, we can test $ H_{0} $ : $ F_{m}^{(1)}(x) = F_{n}^{(2)}(x) $ versus $ H_{1} $ : $ F_{m}^{(1)}(x) \neq F_{n}^{(2)}(x) $ , and reject the null hypothesis if $ d_{m,n} \geq c_{0}(m, n) $ . And in case of large samples, i.e., $ m + n \to \infty $ , we can calculate the p-value for the given observation under null hypothesis, resorting to the following approximation. $ ^{33} $

$$ \mathrm{P r}\left(\sqrt{\frac{m n}{m+n}}\times d_{m,n}\geq z\right)\approx2\sum_{i=1}^{\infty}\left(-1\right)^{i-1}\exp(-2i^{2}z^{2}) $$

### 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 patterns 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 $ F_{2433}^{(o)}(x) $ to denote the functions for the group without children, and $ F_{2440}^{(w)}(x) $ for the group with children, we can define the difference between the two distribution functions.
as $ F_{2433}^{(o)}(x) - F_{2440}^{(w)}(x) $ . The test statistic that we are calculating is $ d_{24332440} = \left| F_{2433}^{(o)}(x^{*}) - F_{2440}^{(w)}(x^{*}) \right| $ , where $ x^{*} $ denote the value of x that maximizes the function $ \left| F_{2433}^{(o)}(x) - F_{2440}^{(w)}(x) \right| $ . In our example,

$$ d_{_{2433,2440}}=\left|F_{_{2433}}^{(o)}(x^{*})-F_{_{2440}}^{(w)}(x^{*})\right|=0.238. $$

Though the critical values for these large samples are not reported in the standard tables for the K-S statistic, approximation formula suggests that

$$ \Pr\big(d_{2433,2440}\geq0.238\big)=\Pr\Bigg(\sqrt{\frac{2433\times2440}{2433+2440}}\times d_{2433,2440}\geq\sqrt{\frac{2433\times2440}{2433+2440}}\times0.238\Bigg) $$

$$ \approx2\sum_{i=1}^{\infty}\left(-1\right)^{i-1}\exp(-2i^{2}\times\left\{\sqrt{\frac{2433\times2440}{4873}}\times0.238\right\}^{2})=0.000. $$

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.
## 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)}_{\mathrm{Zn}}(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)}_{\mathrm{Zn}}(x) = d^{(O)}_{\mathrm{Zn}}(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 $ x_{i} $ is defined as

$$ (1-p)\times d^{(O)}{}_{Zm}(x_{i})+p\times d^{(C)}{}_{Zm}(x_{i})=(1-p)\times d^{(O)}{}_{Zm}(x_{i})+p\times d^{(O)}{}_{Zm}(x_{i}-aZ), $$

where p denotes (unknown) ratio of the coupon receipt. Here, if we knew functional form of the density functions $ d^{(O)}_{Zn}(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

$$ L=\prod_{i=1}^{m}\left\{(1-p)\times d^{(O)}\mathrm{z}_{m}\left(x_{i}\right)+p\times d^{(O)}\mathrm{z}_{m}\left(x_{i}-a Z\right)\right\}. $$

$$ \hat{p}_{_{ML}}=\underset{p}{\max}L $$

In actual calculation, we constructed the empirical distributions without coupons $ (D^{(O)}_{\mathrm{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)}_{\mathrm{Zm}}(x) $ by differentiation $ d^{(O)}_{\mathrm{Zm}}(x) = (D^{(O)}_{\mathrm{Zm}}(x + h) - D^{(O)}_{\mathrm{Zm}}(x))/h $ , where h denotes a certain small interval. Then we calculated both $ O(x_i) = d^{(O)}_{\mathrm{Zm}}(x_i) $ and $ C(x_i) = d^{(C)}_{\mathrm{Zm}}(x_i) = d^{(O)}_{\mathrm{Zm}}(x_i - aZ) $ for each individual gift observation $ x_i $ , and rearranged the households in the order of the ratio $ R(x_i) = C(x_i)/O(x_i) $ . Our strategy was to regard the top $ \hat{p}_{ML} \times 100 $ percent of household in the rearranged data received the shopping coupons amount to the number of children under $ 15 \times 20{,}000 $ yen. Following this procedure, we can identify (guess statistically) the timing and amounts of the coupon delivery to each household. $ ^{34} $
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 \times 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")

1997,1998 and 2000
January	February	March	April	May	June
# of children	0	Mean	8210	5508	10051	6321	5713	6687
S.D.	62766	71720	75240	42613	74765	91222
Nobs	5612	5578	5604	5528	5543	3866
1	Mean	15864	5813	15294	9262	5086	3193
S.D.	55871	36807	63806	40631	28358	18302
Nobs	2584	2580	2536	2544	2547	1652
2	Mean	19330	6299	16632	10143	4522	3029
S.D.	37401	33926	63787	41783	18931	21711
Nobs	2921	2921	2907	2936	2882	1918
3	Mean	20912	6641	16702	8313	4090	2591
S.D.	45649	31918	47795	26263	18515	11755
Nobs	820	809	836	880	905	596

1999 (the year of "shopping coupon" program)
January	February	March	April	May	June
# of children	0	Mean	8825	4126	8334	8538	5369	4837
S.D.	56467	45313	61481	104151	61943	62620
Nobs	1806	1748	1742	1753	1770	1757
1	Mean	13665	4655	25798	8313	6119	2453
S.D.	38054	21959	45156	22183	23816	13270
Nobs	858	898	898	881	886	908
2	Mean	20607	8703	40662	14095	5764	3658
S.D.	41568	41872	42034	39660	23342	21640
Nobs	1044	1035	1013	1002	999	968
3	Mean	20959	10924	63275	23599	8914	4278
S.D.	41191	28528	53989	76312	61036	19446
Nobs	274	275	318	317	295	292

Note: Samples are from wage earner observations in the FIES.
Table A-2 Stability of "Gift" distribution: with children vs. without children.

1997
Janu.	Feb.	March	April	May	June
No. Obs. without children	2433	2416	2427	2394	2412	2443
No. Obs. with children	2440	2455	2465	2489	2520	2488
d	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

1998
Janu.	Feb.	March	April	May	June
No. Obs. without children	2380	2339	2433	2448	2451	2494
No. Obs. with children	2511	2496	2477	2466	2408	2387
d	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

1999
Janu.	Feb.	March	April	May	June
No. Obs. without children	2353	2329	2351	2352	2353	2346
No. Obs. with children	2484	2507	2499	2467	2468	2461
d	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

2000
Janu.	Feb.	March	April	May
No. Obs. without children	2353	2339	2327	2282	2288
No. Obs. with children	2381	2386	2356	2390	2387
d	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 observations in the FIES.

Kolmogorov-Smirnov test with large samples.
Table A-3 Stability of "Gift" distribution: between months to see seasonal variations

(1) Monthly observation without children.

1997	Feb. 2416	March 2427	April 2394	May 2412	June 2443
Jan.	2433	0.065	0.025	0.042	0.047	0.058
0.000	0.443	0.028	0.010	0.000
Feb.	2416	0.048	0.037	0.029	0.009
0.007	0.070	0.255	1.000
March	2427	0.023	0.029	0.039
0.542	0.276	0.046
April	2394	0.012	0.030
0.995	0.226
May	2412	0.022
0.587

1998	Feb. 2399	March 2433	April 2448	May 2451	June 2494
0.070	0.012	0.035	0.039	0.054
Jan.	2380	0.000	0.858	0.103	0.054	0.002
Feb.	2399	0.057	0.045	0.042	0.019
0.001	0.016	0.030	0.775
March	2433	0.022	0.030	0.040
0.580	0.227	0.040
April	2448	0.008	0.027
1.000	0.310
May	2451	0.024
0.493

1999	Feb. 2329	March 2351	April 2352	May 2353	June 2346
Jan.	2353	0.083	0.015	0.046	0.068	0.080
0.000	0.947	0.013	0.000	0.000
Feb.	2329	0.077	0.045	0.017	0.008
0.000	0.019	0.907	1.000
March	2351	0.040	0.061	0.070
0.043	0.000	0.000
April	2352	0.034	0.039
0.140	0.054
May	2353	0.013
0.992

2000	Feb.	March	April	May
2339	2327	2282	2288
Jan.	2353	0.057	0.040	0.034	0.046
0.001	0.044	0.134	0.014
Feb.	2339	0.020	0.032	0.012
0.749	0.183	0.996
March	2327	0.014	0.013
0.981	0.990
April	2282	0.024
0.529

(2) Monthly observation with children.

1997	Feb. 2455	March 2465	April 2489	May 2520	June 2488
Jan.	2440	0.243	0.132	0.192	0.238	0.273
0.000	0.000	0.000	0.000	0.000
Feb.	2455	0.115	0.065	0.020	0.037
0.000	0.000	0.687	0.072
March	2465	0.065	0.110	0.143
0.000	0.000	0.000
April	2489	0.052	0.095
0.002	0.000
May	2520	0.046
0.010

1998	Feb. 2496	March 2477	April 2466	May 2408	June 2387
Jan.	2511	0.230	0.125	0.160	0.210	0.264
0.000	0.000	0.000	0.000	0.000
Feb.	2496	0.121	0.096	0.035	0.040
0.000	0.000	0.108	0.044
March	2477	0.042	0.096	0.145
0.024	0.000	0.000
April	2466	0.062	0.120
0.000	0.000
May	2408	0.062
0.000

Note.

Year	Com.Yr. 4	Com.Yr. 5	Com.Yr. 6
No.Obs.	No.Obs.	No.Obs.
Base Month 1	No.Obs.	d	d	d
Base Month 2	No.Obs.	p-value	p-value	p-value
Base Month 3	No.Obs.	p-value	p-value
p-value

1999	Feb. 2507	March 2499	April 2467	May 2468	June 2461
Jan.	2484	0.196	0.445	0.084	0.189	0.252
0.000	0.000	0.000	0.000	0.000
Feb.	2507	0.605	0.135	0.035	0.060
0.000	0.000	0.099	0.000
March	2499	0.495	0.620	0.662
0.000	0.000	0.000
April	2467	0.129	0.192
0.000	0.000
May	2468	0.088
0.000

2000	Feb. 2386	March 2356	April 2390	May 2387
Jan.	2381	0.225	0.120	0.142	0.205
0.000	0.000	0.000	0.000
Feb.	2386	0.110	0.098	0.031
0.000	0.000	0.203
March	2356	0.036	0.087
0.097	0.000
April	2390	0.074
0.000

Kolmogorov-Smirnov test with large samples.

$$ d(m,n)=\max\left\{\left|Fm(x)-Gn(x)\right|\right\} $$

$$ Pr(aqn(n=1)(n+1))P(n,n)\geq2\sum(-1)^{n}(n-1)\exp(-2n^{2}n^{2}) $$

Samples are from wage earner observations in the FIES.
Table A-4 Stability of "Gift" distribution: among years to see the impact of the coupon program.

Monthly observation among years (samples with children)

January	1998	1999	2000
2511	2484	2381
1997	2440	0.027	0.033	0.043
0.317	0.145	0.021
1998	2511	0.011	0.024
0.998	0.471
1999	2484	0.022
0.580

February	1998	1999	2000
2496	2507	2386
1997	2455	0.017	0.032	0.022
0.859	0.148	0.578
1998	2496	0.047	0.015
0.008	0.954
1999	2507	0.049
0.006

March	1998	1999	2000
2477	2499	2356
1997	2465	0.016	0.529	0.025
0.900	0.000	0.450
1998	2477	0.542	0.014
0.000	0.967
1999	2499	0.552
0.000

April	1998	1999	2000
2466	2467	2390
1997	2489	0.021	0.102	0.023
0.634	0.000	0.521
1998	2466	0.092	0.009
0.000	1.000
1999	2467	0.088
0.000

May	1998	1999	2000
2408	2468	2387
1997	2520	0.007	0.045	0.012
1.000	0.013	0.994
1998	2408	0.044	0.014
0.018	0.971
1999	2387	0.053
0.002

June	1998	1999
2387	2461
1997	2488	0.019	0.007
0.783	1.000
1998	2387	0.018
0.806

Note.

Month	Com.Yr.	Com.Yr.	Com.Yr.
4	5	6
No.Obs.	No.Obs.	No.Obs.
Base Year 1	No.Obs.	d	d	d
Base Year 2	No.Obs.	p-value	p-value	p-value
Base Year 3	No.Obs.	d	d	d
p-value	p-value	p-value

Samples are from wage earner household observations in the FIES (excluding households with persons over 65)

Kolmogorov-Smirnov test with large samples.

$ d(m,n)=\max\{|Fm(x)-Gn(x)|\} $

$$ Pr(sqrt(m\timesn/(m+n))\timesd(m,n)\geqz)\neq2\sum(-1)^{(i-1)}\timesexp[-2i^2\timesz^2] $$
Table A-5 Results of p estimation by the ML procedure with empirical densities.

# of children
1	2	3
February	0.033	0.035	0.070
March	0.578	0.635	0.608
April	0.112	0.080	0.097
May	0.025	0.005	0.002
June	0.010	0.003	0.000
Total	0.758	0.758	0.777

Note: Samples are from wage earner household observations in the FIES (excluding households with persons over 65)
特別支援学校の高等部を含む。

※高等学校：中等教育学校の後期課程、

## ⑤ 日本語指導が必要な児童生徒に対する施策の実施状況

平成25年度中に日本語指導が必要な児童生徒の受入れに関し、各教育委員会が国（市町村については国及び都道府県）の補助金や委託費を受けずに、独自に予算措置を行うなどして実施した施策。

### 1. 指導体制

1 担当教員（常勤）の配置

2 児童生徒の母語を話せる支援員の派遣

3 1、2 以外の支援員等の派遣

※小・中学校：中等教育学校の前期課程、

特別支援学校の小・中学部を含む。

（1）都道府県数（小・中学校）

（2）市区町村（小·中学校）

（4）都道府県（高等学校）

（3）市区町村（高等学校）

### 2. 研修

1 担当教員の研修

2 支援員の研修

（2）市区町村

3 在籍学級担任・教科担当教員も含めた研修

（1）都道府県

### 3. 受入体制

1 関係機関と連携した協議会等の開催

2 拠点校・センター校の設置

3 日本語を指導する教室等の設置

(民間や地域の団体によるものは含まない。)

（1）都道府県

（2）市区町村

### 4. 情報提供

（2）市区町村

（1）都道府県

1 就学・教委相談窓口の設置

2 就学に関するガイドブック作成・配布

3 外国人児童生徒の保護者に対する就学案内

4 域内の小・中段階の子供の就学状況調査

5 就学前の子供の保護者に対する就学ガイダンス

6 就学前の子供を対象としたプレクラス

7 小・中学生とその保護者に対する就学ガイダンス
### 5. その他

## （1） 都道府県

・在籍している児童生徒の人数に応じて日本語学級を設置し、加配教員を配置している。

・三者面談や学校説明会、入学説明会への通訳支援

· NPO 法人と連携した高校進学ガイダンスの実施

・教育相談やボランティアの派遣、学習教材等の展示・貸出等を行うセンターの設置及びコーディネータの配置

・ウェブページの公開

・日本語指導が必要な児童生徒等の受け入れ状況等に関する調査（毎年実施）

・日本語指導テキスト（多言語）の配付

## （2） 市区町村

・日本語学級の設置や日本語指導者の配置・派遣

・保護者面談時等の通訳の手配・派遣

・日本語指導に必要な教材の提供

・学校文書の翻訳

・就学ガイダンスや国際交流会の開催等
19 内閣府所管

平 成 2 8 年 度 歲 出 概 算 要 求 書

1. 平成 28 年度歳出概算要求額総表 …… 1

2. 平成 28 年度歳出概算要求額明細表 5

（組織）010內閣本府 5