Silviase/jgov_v1.4_full_ann huggingface dataset:
$$ \Delta USAGE_{ij}=\frac{USAGE_{i}-\overline{USAGE}_{j}}{100} $$

$$ \Delta HISTORY_{ij}=\frac{HISTORY_{i}-\overline{HISTORY}_{j}}{100} $$

where $ USAGE_{j} $ is the average value of usage intensity (hours per week) and $ HISTORY_{j} $ denotes the average usage history (months) of each access mode j.

These variables may be related to the difference in the preferences for the Internet between a household and the average subscriber with regard to each access mode. Table 6 presents the mean values of these variables. While the diagonal elements are considerably small because of the variable definitions, the off-diagonal elements depict complicated patterns. The off-diagonal elements are the sources of the various substitution patterns that are not subject to the IIA property.

= Table 6 =

#### 4.3. Estimation Results

##### 4.3.1. Demand Estimation

Table 7 presents the estimation results of the Internet access demand. The variables that are included in the first model are price, traffic speed, an NTT-user dummy, and interaction terms between a CATV-user dummy and the three prefecture dummies of Toyama, Fukui, and Mie, where CATV penetration rates are distinctively high. The estimated coefficients of price and speed have expected signs: the price coefficient is negative and the speed coefficient is positive. Further, both coefficients are statistically significant at the 1% level. In other words, the marginal utility of both income and traffic speed is positive.

Subsequently, in Model 2, we include the cross terms between price and the several demographic variables, i.e., a dummy for single female households, household size, dummy for a household with a student, dummy for rented housing, and the municipal population density of each residential area. Similarly, we also add the cross terms between speed and these demographic variables.

The estimation result in Model 2 suggests that the marginal utility of income is significantly affected by the following three demographic variables: the single female household dummy, household size, and the dummy for households with students. On the other hand, the marginal utility of traffic speed depends on household size and the municipal population density.

Further, we include the interaction effect variables (i.e., usage intensity and usage
(history) in Model 3. As shown in Equation (1), we insert the two variables independently and the cross terms between the variables and both price and speed concurrently. The estimation result in Model 3 reveals that the coefficients of the usage intensity variable and these cross terms are statistically significant. A higher intensity of Internet usage would result in a lower marginal utility of income and a higher marginal utility of speed. We use the estimates of the third model in the following analyses.

$$ =Table7= $$

##### 4.3.2. Elasticities

Table 8 presents the estimates of the price elasticity and traffic speed semi-elasticity matrices that are calculated by using Equations (7) and (8). Cell entries $ (i,j) $ , where i indexes the rows and j indexes the columns in the price elasticity matrix in the upper panel of Table 8, provide the percent change in the share of access mode j with respect to a one percent change in price. Similarly, the traffic speed semi-elasticity in the lower panel of Table 8 denotes the percent change in the share of access mode j with respect to a 1 Mbps change in traffic speed of i. Note that the corresponding cell entries $ (1,2) $ and $ (2,1) $ in Table 8 are always zero since we do not allow substitution between FTTH for detached housing and complex housing.

Concerning price elasticities, all estimates of the own-price elasticity are greater than unity, for example, -3.48 of FTTH for detached housing, -2.88 of FTTH for complex housing, -3.16 of high-speed ADSL services, and -2.68 of CATV $ ^{11} $ .

Cross-price elasticities of FTTH with respect to the other access modes are smaller than those of the other access modes. For example, the first row of the upper panel of Table 8 shows that the cross-price elasticity of high-speed ADSL with respect to FTTH is 0.339, whereas the third row of the panel shows that cross-price elasticity of FTTH with respect to high-speed ADSL is 0.235. This implies that the cross-price elasticities between FTTH and ADSL are asymmetric. On the other hand, the estimates of traffic speed semi-elasticities are considerably small. In other words, in comparison with the cross-price effect, traffic speed does not significantly affect the demand substitution between access modes.

= Table 8 =

### 5. Simulations of the FTTH Coverage Area Expansion
#### 5.1. Simulations for Covered and Noncovered Households

We simulate the effect of the coverage area expansion on consumers' switching to FTTH by using the estimated demand system. The coverage area expansion does not automatically result in the increase of FTTH subscribers, because the consumers' heterogeneity affects the extent of switching between access modes.

We conduct simulations for households in municipalities where FTTH is either available or unavailable. As of June 2005, among the 3,246 households of the present dataset, 2,699 households were in an FTTH coverage area, whereas 547 households were not. Specifically, we first simulate the switches between access modes if FTTH becomes available to the noncovered households. We extrapolate the regional average price for each region (Hokkaido, Tohoku, Kanto, Tokai, Hokuriku, Kansai, Chugoku, Shikoku, or Kyushu) into the hypothetical price for the noncovered households. In addition, we assume that the traffic speed of FTTH is 100 Mbps. $ ^{12} $

Table 9 presents the simulation results for the noncovered households. Column (1) is the number of households for every access mode in our dataset, and column (2) denotes the corresponding share. Column (3) presents the estimated share distribution based on the demand system (status quo), and column (4) shows the simulated share distribution if FTTH becomes available to the noncovered households.

A comparison between columns (2) and (3) suggests that there are some discrepancies between the actual and estimated choice probabilities. For example, the share of high-speed ADSL is overestimated and that of CATV is underestimated. Therefore, we use the estimated choice probabilities in column (3) as a benchmark for comparison with the simulated results in column (4).

According to column (4), the coverage area expansion causes only 10.76% of the noncovered households to switch to FTTH. The new FTTH subscribers are primarily previous ADSL and ISDN subscribers; the ADSL share falls from 54.82% to 49.77% and the ISDN share, from 30.48% to 26.45%. Thus, more than three quarters of the households remain narrow-band users.

The estimated share of FTTH for the covered households is 15.23%, whereas the corresponding simulated share of the noncovered households is 10.76%. Hence, the simulated switching probability of the noncovered households is much smaller than that of the covered households.

Moreover, the switch from narrow-band access modes to FTTH for complex housing
for noncovered households is 2.48%, which is much smaller than the figure (8.22%) for covered households. One of the possible reasons for the low rate of switching among the noncovered households could be that complex housing is not prevalent in the noncovered municipalities in Japan.

$$ =Table9= $$

#### 5.2. Household Characteristics and Coverage Area Expansion

Household characteristics may affect the extent of switching between access modes. Therefore, we conduct simulations for seven subsamples, divided based on the following household characteristics: (i) single female household, (ii) household size, (iii) households with students, (iv) rented housing, (v) city size, (vi) Internet usage history, and (vii) Internet usage intensity.

Table 10 presents the simulation results. Columns (1) presents the choice probability of FTTH in status quo. Apparently, it is 0% for a noncovered household, as is shown in column (1). On the other hand, column (2) provides the simulated choice probability of FTTH.

The simulation results indicate that the most important household characteristics are the presence of a student as a household member and usage intensity. Households with a student are much more likely to switch to FTTH than those without a student. For example, if FTTH became available to noncovered households, only 9.33% of them without a student would switch to FTTH; however, 17.83% of households with students would switch to FTTH under similar conditions. Similarly, with regard to usage intensity, only 6.97% of nonheavy users switch to FTTH; on the other hand, 14.04% of heavy users switch to FTTH. We can confirm a virtually similar tendency for covered household.

$$ =Table10= $$

#### 5.3. Welfare Changes due to Coverage Area Expansion

Finally, we compute the welfare changes induced by the coverage area expansion. Given the linear income effect of the conditional logit, the change in the consumer surplus for household i can be estimated as follows:

$$ \Delta C S_{i}=-\frac{\ln(\sum_{j}\exp V_{ij}^{1})-\ln(\sum_{j}\exp V_{ij}^{0})}{a_{i}} $$

where superscript 1 means that FTTH is available, and superscript 0 denotes that FTTH is unavailable. Table 11 presents the welfare changes due to FTTH coverage area expansion based
on our simulation results. The median increase in the consumer surplus due to the coverage area expansion is 91.57 yen for the noncovered households (per capita per month), while the same value for the covered households is 144.64 yen. This difference in the willingness-to-pay can be attributed to the preference heterogeneity: noncovered households do not consider the availability of the FTTH to be as valuable as the covered households do. In other words, the simulation results indicate that inertia to the existing access modes is considerably persistent under the current price–speed structure.

= Table 11 =

### 6. Concluding Remarks

The present paper conducted simulations regarding the accessibility to FTTH. The simulation results indicated that more than three quarters of the households remain narrow-band users despite the FTTH coverage area expansion and that the median increase in the consumer surplus due to coverage expansion is marginal. These results suggest that, under the current price–speed structure, inertia to the existing access modes, ADSL in particular, is considerably persistent in Japan. For example, around 50% (26%) of the households continue to use ADSL (ISDN). A low-speed access mode may be sufficient for some households whose Internet usage is not heavy. Hence, government policy should focus not only on the expansion of the FTTH coverage area, but also on the enhancement of quality and variety of software associated with FTTH.

Limitations of the present study are as follows. First, our simulation analyses depended specifically on the demand side assumptions, and we do not explicitly incorporate the supply side factors such as the strategic behaviors among carriers. Second, we did not explicitly consider the government regulations on access. Unbundling regulation and access price would drastically change the evolution of the broadband access market. Third, the basic data on price and speed were separately collected and merged into the InfoCom survey. Hence, it may be different from the actual price and speed for each household. Finally, the demand estimation of the present study depends only on the cross-sectional variations of the households. It is very likely that the broadband access demand dynamically changes, and therefore, we should be sufficiently careful when drawing definite conclusions from the demand estimations.
## References

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## Appendix A: The Japanese Broadband Market

According to OECD (2006), with regard to the penetration rate of broadband access technologies such as DSL, cables, fiber/LAN, and others, Japan ranked fourteen among the OECD member countries in December 2006 (See Figure 4).

= Figure 4 =

Japan was number one with respect to fiber optics, and the number of FTTH subscribers in Japan was larger than the total number of broadband subscribers in 23 of the 30 OECD countries.

Appendix B: Service Areas of the Broadband Access Services and Demographic Variables

Japanese broadband access services were unavailable throughout the nation. Thus, it is important to incorporate the information about the available access modes for each sample household into our analysis. First, we obtained the list of municipalities where NTT East/West supplied their FTTH (“B FLETS”) and ADSL (“FLETS ADSL”: 47 Mbps, 40 Mbps, 24 Mbps, 12 Mbps, 8 Mbps, and 1.5 Mbps) services in June 2005, from the following websites:

(i) NTT FLETS Introduction Square (http://ntt-ocn.parfait.ne.jp/): “B FLETS” (only for the east area) and “FLETS ADSL”

(ii) NTT FLETS Window (http://www.ntt-flets.jp/)：“B FLETS”

(iii) NTTT West, FLETS (http://flets-w.com/): “FLETS ADSL” (only for the west area)

The web-search was conducted on September 13, 2005. On the other hand, for CATV, we employed the data from Satemaga B.I. (2005) and constructed eight dummy variables that represent the respective access modes that may or may not be available in each municipality.

Second, the municipal JIS codes and the ZIP codes were merged to the microdata, using the ZIP code as a key variable. Finally, we constructed the availability index function for our choice alternatives as follows: (i) FTTH for detached housing “B FLETS,” (ii) FTTH for complex housing “B FLETS,” (iv) ADSL (M) “FLETS ADSL” (12/8 Mbps), (v) ADSL (L)
“FLETS ADSL” (1.5 Mbps), and (vi) CATV (Satemaga B.I.); ISDN and Dial-up services are assumed to be available to everyone.

In addition, to computing the density within the residential area for each household, the information about the population and habitable area of each municipality was obtained from “Municipalities in Statistics” in MIC (2005b) and was merged into the database, using the JIS code.

Appendix C: Service Area Maps of Other Broadband Access Modes

= Figure 5 to 11 =

= Table 12 =

Appendix D: Estimates of the Elasticities Based on Other Models
Figure 1: Growth of the Broadband Access Subscribers in Japan

Note: The original data source of this figure is MIC (2006c). For more details, please refer to the text.
Figure 2: Market Share of Each Carrier in the ADSL and FTTH Services (September 2005)

FTTH for Complex Housing

FTTH in Total

FTTH for Detached Housing

Note: The original data source for the subscriber base is MIC (2006a). For more details, please refer to the text.
Figure 3: Service Areas of FTTH, Provided by NTT

Note: The municipalities where NTT East/West provided “B FLETS” services in June 2005 are marked in black. This information was obtained from the NTT East/West website. The map image has been produced by using the GeoLinkXL software.
Table 1: Service Coverage of Each Broadband Access Line by Population

FTTH	ADSL (47 M)	ADSL (40 M)	ADSL (24 M)	ADSL (12 M)	ADSL (8 M)	ADSL (1.5 M)	CATV
Hokkaido	72.55%	76.94%	80.97%	80.77%	71.81%	92.51%	83.12%	55.66%
Tohoku	63.23%	83.74%	89.43%	81.25%	78.40%	89.43%	86.18%	41.45%
Kanto	86.92%	97.58%	97.34%	96.37%	96.85%	97.58%	97.09%	84.50%
Tokai	83.53%	95.88%	96.47%	92.94%	90.59%	96.47%	96.47%	87.06%
Hokuriku	75.64%	99.22%	98.91%	94.06%	69.37%	89.26%	96.90%	77.76%
Kansai	93.30%	95.69%	91.39%	90.91%	88.04%	96.17%	83.73%	81.82%
Chugoku	82.17%	95.64%	81.14%	85.51%	90.39%	92.35%	91.46%	77.80%
Shikoku	69.75%	89.82%	82.03%	78.60%	82.11%	87.79%	89.93%	72.66%
Kyushu	80.41%	67.23%	49.63%	76.35%	87.16%	92.57%	92.57%	60.79%
Total	82.68%	90.55%	87.40%	88.98%	88.98%	94.49%	92.13%	75.20%

Note: Information about the FTTH and ADSL services was obtained from the NTT East/West website and that about CATV, from Satemaga B.I. (2005). For more details, please refer to the text.
Table 2: Probit Estimates of the Municipal Availability of FTTH

Estimates	Robust S.E	z	p-value	[95% Conf. Interval]
Ratio of People Aged over 65	-17.506	1.594	-10.980	0.000	-20.630	-14.382
No. of Students per Household	6.215	1.606	3.870	0.000	3.068	9.362
Family Size per Household	-0.196	0.200	-0.980	0.327	-0.587	0.196
Ratio of Single-Person Households	2.575	1.459	1.770	0.078	-0.284	5.435
Ratio of Women	30.993	4.246	7.300	0.000	22.672	39.314
House Ownership Rate	-0.785	0.631	-1.240	0.213	-2.021	0.452
Household Density (in 1,000 Households)	0.864	0.237	3.650	0.000	0.399	1.328
Constant	-12.517	2.455	-5.100	0.000	-17.329	-7.705
No. of Observations	2,362
Log Pseudolikelihood	-871.635

Note: The probit regression results. The sample was the municipalities. The dependent variable is the FTTH availability dummy, which is 1 if FTTH was available in June 2005 and 0 otherwise. Explanatory variables are the ratio of people aged over 65, number of students per household, family size per household, ratio of single person households, ratio of women, house ownership rate, and household density. For more details, please refer to the text.
Table 3: User Profile, Internet Usage, and Product Characteristics

1 FTTH for Detached Housing	2 FTTH for Complex Housing	3 High-speed ADSL	4 Medium-speed ADSL	5 Low-speed ADSL	6 CATV	7 ISDN	8 Dial-up
Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.	Mean	S.D.
I. Household Profile
Income	599.16	288.56	561.21	238.82	529.64	279.12	483.03	239.01	474.00	234.86	557.76	266.99	475.97	243.47	498.62	229.47
Single Female Household *	0.00%	1.03%	1.49%	1.37%	1.72%	0.63%	1.43%	2.98%
No. of Family Members	3.59	1.19	2.93	1.28	2.94	1.46	3.09	1.39	3.12	1.36	3.31	1.46	3.24	1.45	2.94	1.44
Living with Students *	13.66%	6.51%	14.92%	14.29%	16.18%	15.55%	12.82%	6.09%
Rent Housing *	1.23%	8.63%	20.40%	19.54%	17.84%	7.50%	11.39%	13.47%
Density	0.40	0.36	0.69	0.44	0.51	0.50	0.38	0.45	0.44	0.46	0.46	0.47	0.30	0.40	0.45	0.43
II. Internet Usage
Intensity (Hrs/week)	22.79	15.70	22.39	15.14	21.37	15.58	19.31	14.98	17.99	14.57	22.05	15.04	17.52	14.51	11.56	12.32
History (Months)	58.93	13.82	58.44	15.00	55.00	17.53	53.72	17.28	54.90	16.28	57.23	14.31	57.96	13.20	58.61	13.36
Shopping *	97.48%	96.41%	96.17%	93.47%	92.21%	94.35%	93.39%	87.22%
Auctions *	61.34%	58.30%	62.10%	54.81%	57.89%	57.88%	51.25%	36.34%
Music *	7.14%	7.62%	7.66%	5.63%	5.89%	5.18%	3.64%	3.01%
Games *	26.47%	22.87%	22.38%	22.50%	15.79%	19.76%	15.03%	4.76%
Movies *	68.07%	63.68%	64.92%	60.44%	52.42%	53.88%	28.93%	26.32%
Price	6,376.26	761.24	3,870.99	622.74	4,321.42	418.41	4,110.88	553.43	4,008.62	827.71	4,545.94	950.34	4,521.92	539.77	2,500.00	0.00
Speed	100.00	0.00	100.00	0.00	36.38	11.42	10.12	1.99	1.55	0.46	12.73	10.62	0.06	0.00	0.06	0.00
No. of Observations	238	223	496	551	475	425	439	399
Share	7.33%	6.87%	15.28%	16.97%	14.63%	13.09%	13.52%	12.29%

Note: * denotes that the variable is a dummy variable. The total sample size was 3,246. Income was evaluated as the median value of each class of the original questionnaire, and it is yearly in 10 thousand JPY. This is same for the Internet usage per week in hours and the Internet history in months. The prices are the sum of the monthly base charges for the access line and by the ISP, and have been evaluated in JPY. The download traffic speed was evaluated in Mbps. For more details, please refer to the text.
Table 4: Availability of Each Broadband Access Mode in the InfoCom Survey

FTTH	ADSL (47 M)	ADSL (40 M)	ADSL (24 M)	ADSL (12 M)	ADSL (8 M)	ADSL (1.5 M)	CATV
Region
Hokkaido	67.66%	73.13%	77.11%	79.60%	70.15%	94.53%	80.60%	58.21%
Tohoku	65.84%	85.77%	93.59%	88.26%	83.99%	95.37%	92.88%	41.99%
Kanto	86.70%	98.04%	97.05%	96.61%	96.07%	97.68%	97.68%	83.30%
Tokai	88.51%	98.78%	97.31%	98.04%	92.91%	97.80%	98.04%	87.78%
Hokuriku	48.74%	81.51%	89.92%	63.87%	59.66%	89.92%	89.08%	74.79%
Kansai	95.17%	96.01%	94.96%	94.54%	90.13%	97.90%	97.48%	96.01%
Chugoku	83.04%	95.09%	81.25%	86.16%	92.86%	94.20%	92.41%	71.43%
Shikoku	70.00%	95.00%	83.00%	79.00%	85.00%	95.00%	97.00%	68.00%
Kyushu	87.97%	79.11%	65.51%	82.28%	91.14%	95.57%	95.89%	60.76%
City Size
Large Cities	99.92%	99.85%	98.84%	99.92%	99.54%	99.92%	99.92%	96.91%
Other Cities	89.22%	94.66%	92.33%	94.05%	95.26%	99.05%	98.71%	73.36%
Towns and Villages	46.71%	77.22%	73.67%	71.27%	65.70%	87.22%	82.91%	48.86%
Total	83.15%	92.48%	90.39%	90.85%	89.77%	96.52%	95.35%	76.80%

Note: These figures denote the service coverage ratio by population. For FTTH and ADSL, the information about the municipalities where NTT East/West provided FTTH and ADSL (47 Mbps, 40 Mbps, 24 Mbps, 12 Mbps, 8 Mbps, and 1.5 Mbps) services until June 2005 was obtained from the NTT East/West website. On the other hand, for CATV, we used the data from Satemaga B.I. (2005). For more details, please refer to the text.