Silviase/jgov_v1.4_full_ann huggingface dataset:
We have not been able to separate network effects from brand effects in either the hedonic price model or NL model. We may partly overcome this problem if we can obtain data for products by firms other than Microsoft and Justsystem, and if we conduct analyses with dummies representing Microsoft products.

Another defect with the methodologies used in our present study is that both hedonic price and NL models test the relationship between the equilibrium user base and price, and neither separates demand side effects from supply side effects, when network effects only operate on the demand side.

### 7.3 Strategic Pricing

Apart from the data and methodological limitations described above, the possibility may be raised that one reason why we observed little or no network effects in the beginning of our sample period is that Microsoft continued to set low prices strategically. However, as figure 2 indicates, Microsoft had obtained a high market share by this time, and it would be necessary to investigate whether Microsoft really did have an incentive to engage in this pricing strategy.

### 7.4 Switching Costs

Switching costs may be yet another reason why little or no network effects showed up towards the beginning of our sample period. When compatibility between different software groups is imperfect, a user who has accumulated documents written on word-processing software will incur switching costs when she decides to switch to a product of a different software
group. If she cannot read her new software documents written on her old software, she will incur disutility directly. Converting file format involves time and effort, and figures, tables and styles planted on a document often cannot be converted accurately. These are all sources of switching costs.

Ichitaro had a large share of the word-processing software market until the mid-1990s, so it is conceivable that the average switching costs involved in a switch from Ichitaro to Word was larger than those involved in a switch in the opposite direction.

It can be easily shown that, in general, if the switching cost involved in a switch from format A to B is larger than that involved in a switch in the opposite direction, the price of A is set at a higher level than that of B, other things being equal.

Thus, the results from cases 5 and 6 in table 3 may be interpreted as follows. On the one hand, network effects were indeed at work in 1998-1999, and this had the effect that Word would be priced higher than Ichitaro. On the other hand, in this period the switching costs involved in switching from Word to Ichitaro were higher than those involved in a switch in the opposite direction, and this had the effect that Ichitaro would be priced higher. These two effects acting together largely cancelled each other out, so the estimated coefficient of the user base variable was weak or not statistically significant. In 1999-2000, Word had enjoyed a high market share for some time, and the average switching costs involved in switching from Ichitaro to Word were no longer larger than those involved in a change in the opposite direction, and the network effects appear without hindrance by asymmetric switching costs in estimated results.

Switching costs may also be used to provide one explanation for the
difference between the results from our hedonic price and NL analyses regarding whether the adoption of key product features would have allowed Justsystem to overtake Microsoft's share. In the hedonic price analysis, the difference in the sizes of the user bases is reflected in the estimates only through network effects. In contrast, in the NL analysis the difference in the sizes of the user bases is reflected in the estimates through both network effects and switching costs. The presence of switching costs gives rise to the tendency for shares to be sticky, giving rise to a large estimated coefficient for the network effect variable, which in fact reflects both network effects and switching costs.

### 8. How was Microsoft able to Overturn Ichitaro's High Market Share?

Despite the fact that both network effects and switching costs tend to cause lock-in, Microsoft was able to topple Justsystem’s dominance in the word-processing software market around 1997.

When there is large and asymmetric technological progress, this kind of change in places is possible even in the presence of network effects and switching costs. However, as we will see in section 9, there has unlikely been large and asymmetric technological progress in word-processing software since 1995. There are at least three reasons why Microsoft was able to trade places with Justsystem despite this.

First, it is claimed that Microsoft required PC manufacturers to pre-install Word in all PCs with pre-installed Excel (Microsoft). $ ^{19} $ Microsoft
had already achieved dominance in the spreadsheet software, so network effects in the spreadsheet market meant that a new buyer of spreadsheet software was more likely to choose Excel over other alternatives. If Word was bundled with Excel and pre-installed on PCs, network effects in the spreadsheet market meant that a PC buyer who also was considering buying business software was likely to buy a PC with Word and Excel both pre-installed, and this helped the widespread adoption of Word.

Second, Microsoft was able to develop and sell the Windows 95-compatible version of Word promptly, but it took Justsystem a long time to develop a Windows 95-compatible version of Ichitaro. It is often reported that this fact, coupled with the fact that many PCs with Windows 95 pre-installed also had Word pre-installed, meant that Word diffused rapidly as Windows 95 PC sales grew rapidly.

Third, Microsoft strategically lowered the price of Word around 1996. As explained briefly in section 2, by lowering the prices of Word substantially Microsoft was able to create a price differential in excess of network effect disadvantage, and this was also a possible reason Microsoft was able to trade places with Justsystem.

### 9. Effects of Weakened Competition

When network effects or switching costs are present, fierce competition tends to take place in the beginning to attract customers, while competition tends to be weak once there is a large difference in market shares. As we have seen in section 2, Word had acquired a large section of the market by
1998 and has slowly but steadily increased its share since then, suggesting that competition has indeed become weak.

Weakening of competition arising from a firm’s dominance has two harmful consequences, namely, high prices and slow technological progress. In terms of prices, our base model shows the year dummies have negative coefficients that increase in absolute terms with time. So prices have not stayed high, although the rate of price decrease has slowed.

To obtain an indication of how much technological progress took place, we conducted a questionnaire survey in December 2002, asking users to evaluate how much technological progress was made with the arrival of new versions of the software. We surveyed IT personnel in universities and large firms. 1179 firms, Internet service providers (ISPs) and universities were approached, and we obtained 771 responses (65.4% response rate). The list of firms comprises 649 firms listed on the first section of the Tokyo Stock Exchange whose non-consolidated total assets is no less than 100 billion yen (excluding banks and insurance firms), and all 207 member banks and firms of the Japanese Bankers Association, the Life Insurance Association of Japan and the General Insurance Association of Japan. The list of ISPs comprises 50 members of the New Media Development Association with no less than 10,000 subscribers and 51 randomly selected members of the Japan Internet Providers Association that have nation-wide operations. The list of universities comprises all 99 national universities and all 123 members of the Japan Association of Private Colleges and Universities. We asked respondents to reply in the capacity as personal PC users.

A number of new versions of both Word and Ichitaro were introduced. We asked respondents to evaluate each new version in terms of functional
improvements in percentage over the previous version from the viewpoint of an ordinary user rather than as a technical specialist. We asked them not to comment on versions they had not used. The replies for two consecutive versions are summarised in figure 4 for Word and figure 5 for Ichitaro. The introduction dates for the new versions are stated in brackets.

We can see from these figures that functional improvements with the introduction of a new version have become smaller for both Word and Ichitaro. Of course, there is a problem involved in whether a user who responded to the survey in late 2002 or early 2003 could really remember and compare two consecutive versions from 1993 as well as she could evaluate versions that were around in 2002. Theoretically also, the lower size of innovation per upgrade may have been caused by the maturity of the product and not by weakened competition. Thus, we cannot conclusively claim that the figures show slowed technological progress as a result of one firm's dominance. However, the figures are suggestive of this possibility, and further investigations on the speed of technological progress and its determinants are warranted when designing policy prescriptions.

### 10. Conclusions

This paper tested whether network effects were at work in the Japanese market for word-processing software in the period between 1998 and 2000 (or 2001) using both hedonic price and NL models. The presence of network effects was verified in the full three-year regression for 1998-2000 (and in the full four-year regression for 1998-2001 using lagged values of the network effect variable). Regressions for two consecutive years showed that network effects, as measured by the positive effect of the size of the user base.
on the product price or on the probability of the software group being chosen, were verified for the latter part of the sample period but not in the beginning. We discussed data and methodological limitations, as well as the effect of switching costs and the possibility that Microsoft continued to set strategically low prices as possible causes for our results.

The paper also discussed possible reasons why Microsoft was able to overturn Ichitaro's dominance despite the tendency for lock-in in markets characterized by network effects and switching costs. It also suggested, using results from a questionnaire survey, the possibility that technological progress slowed after Microsoft achieved dominance.

Analysis of network effects using richer data, including those on integrated software and software pre-installed on PCs, would be an obvious direction for future research, as would be direct modelling of switching costs.
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Figure 1 Size of the Japanese word-processing software market

Source: Japan Personal Computer Software Association

Note: Volume data available only since 1992.
Figure 2 Market shares of the major word-processing software by producer in Japan

Source: Business Computer News (1994-1996), Nikkei Market Access (IT Basic Survey) (1997-2000)
Figure 3 List prices of major word-processing software products in Japan

Source: Justsystem, Microsoft, and Nikkei Newspapers
Figure 4 Users' evaluation of function improvement of Word

Note: Time of sales is in parenthesis.
Figure 5 Users' evaluation of function improvement of Ichitaro

Note: Time of sales is in parenthesis.
Table1 Sample distribution by software group

Word (Microsoft)	Ichitaro (Justsystem)	Total
1998	4	9	13
1999	10	13	23
2000	9	15	24
2001	8	8	16
Total	31	45	76
Table 2 Definition of variables

variable	definition	notes
price	AVG_RPRICE	Average real retail price of each software product (yen)	Average nominal retail price is divided by quarterly GDP deflators under 93SNA standard
network effects	SHARE	User base share of each software group (one when all PC users use products falling under this group)
SHARE_L1	One-year lagged value of user base share of each software group (one when all PC users use products falling under this group)
LIB	Logarithm of user base size of each software group
LIB_L1	One-year lagged value of logarithm of user base size of each software group
functions and options (dummy variables)	D_MULTIPLECLIPS	Multiple clippings (where the clipboard can store more than one clippings)
D_WORKINGWINDOW	Working window (where an auxiliary window appears by the side of the text window that helps editing)	Knowledge Window in Ichitaro
D_WORKSHEET	Worksheet (where a multiple of documents including ones made on other software can be saved as a single file on the word-processing software)
D_JP	Has a Kana-Kanji transformation software component	MS-IME in Word, and ATOK in Ichitaro
D_VOICE	Has a Voice recognition software unit and a microphone
D_LITE	Lite version	Ichitaro Lite
target market (dummy variables)	D_UPGRADE	Upgrade discount
D_SPOFFER_JS	Discount for users of products by the same firm
D_CUPGRADE	Discount for users of products by the same or competitor firm
year (dummy variables)	D_99	1999 data
D_00	2000 data
D_01	2001 data
sale share	sj	Market share in sales (of individual products)
so	Market share of those who made no purchase
sj/g	Within-group share in sales (of individual products)

Note: Dummies take the value one when the data fits the description.
Table 3 Results of hedonic price estimates

Case 1	Case 2	Case 3	Basic model	Case 5	Case 6
Period	98-00	98-99	99-00	98-00	98-99	99-00
Dependent var.	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE
Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.
C	-35441.5	-1.69597 *	28146.0	.537349	-61773.3	-2.77740 ***	-22833.0	-1.27853	23835.6	.496000	-42327.8	-2.37359 **
LIB	2930.91	2.20326 **	-1166.59	-.348089	4302.51	3.10402 ***	2098.48	1.87290 *	-882.942	-.288366	3071.85	2.79031 ***
D_MULTIPLECLIPS	7057.67	7.50780 ***	5554.06	3.61100 ***	8789.41	8.60119 ***	7265.46	7.84920 ***	5539.48	3.67200 ***	9179.03	9.19245 ***
D_WORKSHEET	1478.83	1.15160	-463.879	-.231389	1993.43	1.42825
D_JP	1354.73	1.29801	1377.16	.815950	1773.24	1.81280 *	1462.82	1.40262	1336.68	.811157	1838.30	1.85546 *
D_LITE	2455.63	1.92642 *	-229.000	-.112038	5173.67	3.64255 ***	1879.42	1.59774	-74.9895	-.039530	4268.70	3.31185 ***
D_VOICE	6651.44	4.09196 ***	6736.43	2.84749 ***	6818.37	3.67695 ***	6957.71	4.32448 ***	6638.67	2.90539 ***	7580.93	4.21020 ***
D_UPGRADE	-8528.19	-9.55487 ***	-7921.57	-5.81180 ***	-8700.50	-9.55276 ***	-8496.01	-9.49195 ***	-7918.83	-5.91874 ***	-8647.84	-9.37194 ***
D_SPOFFER_JS	-2147.66	-1.03121	-3766.21	-1.34238	-1294.91	-.519044	-3142.39	-1.65269	-3512.36	-1.38566	-3225.85	-1.51735
D_CUPGRADE	-4768.47	-4.48604 ***	-4513.95	-2.88404 ***	-5423.00	-4.40980 ***	-4569.12	-4.34223 ***	-4556.05	-2.98565 ***	-4977.47	-4.12678 ***
D_99	-3067.65	-2.91336 ***	-1262.27	-.802038	-2522.28	-2.67311 ***	-1442.94	-1.07572
D_00	-4039.75	-3.49965 ***	-1130.83	-1.61745	-3320.12	-3.40951 ***	-934.024	-1.34393
No. observations	60	36	47	60	36	47
R²	.843913	.818859	.876237	.839600	.818471	.869224
Adjusted R²	.808143	.746403	.841858	.806865	.755635	.837414
F statistic	23.5927	11.3014	25.4879	25.6486	13.0254	27.3252

$ ^{*} $ Significant at 10% level

** Significant at 5% level

*** Significant at 1% level
Table 4 Robustness check

Method of est.	WLS	OLS	WLS
Dependent var.	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE
Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.
C	-22048.1	-3.26702 ***	7247.64	3.33840 ***	6196.56	6.44281 ***
LIB	1977.75	4.66729 ***
SHARE	4708.15	1.83486 *	4560.05	4.87048 ***
D_MULTIPLECLIPS	5025.62	12.3532 ***	7289.52	7.80276 ***	5066.50	12.5756 ***
D_JP	3452.52	5.11424 ***	1460.35	1.39447	5.22759 ***
D_LITE	-271.653	-.470169	1.60623	-.374197
D_VOICE	6908.46	14.9973 ***	6968.21	4.32217 ***	6927.28	15.2332 ***
D_UPGRADE	-9215.30	-37.8058 ***	-8488.53	-9.47179 ***	-9205.82	-38.2584 ***
D_SPOFFER_JS	-3830.33	-6.34686 ***	-3131.66	-1.64486	-6.44595 ***
D_CUPGRADE	-5570.36	-18.7011 ***	-4566.51	-4.33150 ***	-5558.08	-18.9030 ***
D_99	-635.196	-2.57786 **	-2284.70	-2.48907 **	-381.431	-1.66880
D_00	-699.758	-2.32016 **	-2659.02	-2.86644 ***	-79.0719	-.280441
No. observations	60	60	60
R²	.984315	.839168	.984733
Adjusted R²	.981114	.806345	.981617
F statistic	307.495	25.5666	316.049

$ ^{*} $ Significant at 10% level

** Significant at 5% level

*** Significant at 1% level
Table 5 Results of hedonic price estimates (with the size of the user base as the network effect variable)

Period	98-01	98-00	99-01	98-99	99-00	00-01
Dependent var.	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE	AVG_RPRICE
Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.
C	-36261.8	-1.99010	*	-40670.0	-1.26018	-54547.0	-2.89958	***	32446.1	.582567	-84801.8	-2.57233	**	-59261.1
LIB_L1	2991.39	2.53698	**	3334.01	1.58782	3897.25	3.27542	***	-1466.29	-.403449	5853.96	2.79648	***	4116.50
D_MULTIPLECLIPS	7079.44	8.33664	***	7317.67	7.53612	***	8671.27	9.23360	***	5465.11	3.71502	***	9502.55	9.08712
D_JP	2125.23	2.46043	**	1370.61	1.29394	2361.87	2.80109	***	1261.68	.761737	1909.36	1.90739	*	2523.13
D_LITE	2067.67	1.89876	*	1953.79	1.54344	4136.53	3.40394	***	-209.552	-.111962	4809.91	3.44342	***	4053.16
D_VOICE	7015.66	4.87528	***	6972.84	4.27880	***	7542.06	4.74688	***	6601.74	2.89704	***	7757.69	4.28701
D_UPGRADE	-8709.52	-11.0613	***	-8427.03	-9.35011	***	-8816.32	-10.7767	***	-7909.76	-5.92647	***	-8615.46	-9.36667
D_SPOFFER_JS	-3172.44	-1.76330	*	-3091.84	-1.61071	-3168.36	-1.57149	***	-3526.41	-1.39305	-3129.28	-1.47267	***	-2071.19
D_CUPGRADE	-4191.19	-4.43261	***	-4581.06	-4.30245	***	-4204.80	-3.96323	***	-4575.34	-3.02552	**	-4945.73	-4.09725
D_99	-2858.76	-2.88880	***	-3215.31	-2.72203	***	-1141.11	-.669058	-1815.03	-2.33204	***	-3939.06
D_00	-4028.99	-3.68627	***	-4502.95	-3.11449	***	-1413.24	-1.88273	*
D_01	-3406.85	-2.55097	**	-1135.60	-1.17871	291.572	.339648
No. observations	76	60	63	36	47	40
R²	.826930	.836529	.845547	.819024	.869325	.860135
Adjusted R²	.797184	.803167	.815844	.756378	.837539	.818176
F statistic	27.7994	25.0747	28.4672	13.0739	27.3494	20.4992

$ ^{*} $ Significant at 10% level

** Significant at 5% level

*** Significant at 1% level
Table 6 Results of NL estimates

Case 1	Case 2	Case 3	Case 4
Period	98-00	98-00	98-99	99-00
Sample	All	Volume over 2	Volume over 2	Volume over 2
Dependent var.	log(sj)-log(s0)	log(sj)-log(s0)	log(sj)-log(s0)	log(sj)-log(s0)
Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.	Est. Coeff.	T stats.
C	-33.9253	-20.2340 ***	-37.1437	-14.1159 ***	-17.1445	-14.9531 ***	-37.6988	-12.7573 ***
log(sj/g)	.960343	50.5392 ***	.955370	26.9535 ***	1.00827	145.761 ***	.945992	22.1601 ***
AVG_RPRICE	.00000230308	.042072	-.000129623	-2.34138 **	.0000106050	1.01395	-.000107016	-2.22826 **
LIB	2.20652	16.8381 ***	2.47868	13.6227 ***	1.13333	14.7757 ***	2.46872	12.9332 ***
D_MULTIPLECLIPS	.266614	.688499	1.19938	2.95418 ***	-.126433	-1.57278	1.16122	2.65450 ***
D_JP	.035038	.330477	.471185	2.49294 **	-.032991	-.743505	.415195	2.35388 **
D_LITE	.322258	2.59607 ***	.472275	3.04272 ***	-.067330	-1.82127 *	.545242	2.64215 ***
D_VOICE	.094256	.241447	.883513	2.29430 **	-.085222	-1.07261	.751997	2.12320 **
D_UPGRADE	.044611	.094292	-1.18627	-2.37506 **	.081522	.897834	-1.04280	-2.29850 **
D_SPOFFER_JS	-.096722	-.471964	-.538438	-1.73259 *	.055591	1.00249	-.546773	-1.32474
D_CUPGRADE	.051864	.200800	-.715460	-2.13153 **	.038162	.653389	-.593311	-1.89182 *
D_99	-.384216	-2.66370 ***	-.424692	-3.42603 ***	.00197146	.071308	-1.28479	-11.6721 ***
D_00	-1.51004	-8.25211 ***	-1.71906	-10.1725 ***
No. observations	60	41	26	31
R²	.997817	.995102	.999890	.995926
Adjusted R²	.997259	.993003	.999804	.993567

$ ^{*} $ Significant at 10% level

** Significant at 5% level

*** Significant at 1% level