Silviase/jgov_v1.4_full_ann huggingface dataset:
triple and single track routes we chose as samples demand may meet or exceed supply. Regarding this point, we did a further investigation: we tried to reveal whether the average load factor in triple track routes mean the congestion level or not. In judging whether congestion occurs, we selected a criteria of 75% level of load factor above which it is difficult for passengers to reserve the seat. Then we verified the null hypothesis that the annual round-trip average load factor of triple track routes is 75% (See Table 11). For the purpose of the comparison with the case of the triple track routes, we also dealt with the double track routes.

According to the results, as regards both triple and double track routes, the null hypothesis cannot be rejected except in 1992, the slump year. So congestion may occur, or be on the verge of occurrence in both types of routes, but it is clear that the value of standard deviation of double track routes is larger than that of triple track routes. This may mean that a variety of routes are included in double track routes and some of them cause the destruction of the causal chain that the higher concentration leads to the higher load factor. So we classified the double track routes into two groups: (1) Haneda or Itami are at least included in the origin or destination, (2) Nagoya is included in either of the endpoint, and verified the null hypothesis that there is no significant difference between two groups (See Table 12).

Table 12. The two-tailed test of the null hypothesis that there is no significant difference between two groups

load factor ('89-'92)	standard deviation	sample number	t-value
Group (1)	70.57	5.73	59	3.18 (significant 1% level)
Group (2)	66.02	5.32	19

The findings are that the average load factor of group (2), i.e., the routes either of which endpoint is at least Nagoya, is significantly lower than that of category (1), and that we can reject the null hypothesis that the average load factor of category (2) is 75% at 10% level (t-value is 1.70). So in the category (2) type double track routes, excess capacity may exist. The reasons behind these results are that the market size is relatively smaller in these routes, and that routes such as Nagoya/Sendai and Nagoya/Nagasaki, (of which the number of passengers is less than 400,000, the benchmark above which double track is authorized), have been included for some reason. $ ^{18} $

### 6.3 Summary of Findings

In this section we revealed that the traffic may be congested or be on the point of congestion in the triple track routes, in the double track routes the endpoint of which is Haneda or Itami Airport, (in which the trunk routes are also included), and in single track routes where the
entry was newly authorized between 1989-1992. With regard to these routes the authorization of new entry has been unable to solve the problem of excess demand. An expansion of airport capacities is required to relieve the traffic.

However, in some of the double track routes excess capacity may exist. This is thought to be brought about by the re-allocation of the routes for the purpose of adjusting the vested rights among airlines or by some intention which has little to do with the viewpoint of economic regulation. In these routes the rationalization of supply is desired.

## 7 The Regulatory Change and Cost Structure

In this section we will investigate how the regulatory change affected the cost structure of three Japanese airlines, JAL, ANA, and JAS.

### 7.1 Models and Regression Results

As already mentioned, Japanese airlines were authorized to enter new domestic and international routes after the regulatory change in 1985. For example, the average stage length of ANA and JAS became longer because they were authorized to enter both international routes and domestic routes of longer distance, and to exit the shorter haul local routes and hand them to their subsidiaries. These regulatory changes caused them to employ wider-bodied airplanes, which lower the operating cost per passenger or output. On the other hand, JAL entered domestic local routes that were comparatively shorter but denser. This may have led to higher cost per output.

In order to measure the effect of these entries and exits on airlines' cost, we constructed two kinds of cost functions of the Cobb & Douglas type. The variables will be shown in Table 13.

The first one is the average cost function where the input price variables were excluded. Before the estimation of the cost function, we investigated the partial correlation matrix to check whether multicollinearity existed (See Table 14). Taking into account the results in Table 14, we constructed the average cost function in the following manner: (1) as the permit of entry and exit is the most remarkable point in the regulatory change, we included the network variable NET in the average cost function. (2) we then chose to include those variables which had little correlation with the variable NET. $ ^{19} $ The reason for selecting average cost function of this type is to keep as numerous degree of freedom as possible, as well as to avoid multicollinearity. The regulatory change dummy DRC (1 for 1986-1992) and the firm dummy variables (ANA ; 1 for ANA, and JAS ; 1 for JAS) were introduced in each intercept and all the coefficients, but those which were not significant even at 20% level were excluded (See Table 15).
Table 13 Variables of cost function

Variables	Definition
TC	total operating cost deflated by RPI index
AC	average operating cost (=TC/ATK)
EQ	the average annual number of seats per flight
FRQ	the total departure in the year
DIST	the average annual distance per flight
NET	the total number of entrance and withdrawal to/from routes since '79
PAX	the total passenger in the year
ATK	the total available ton kilo in the year
LP	Labor price (labor cost/the number of employees) deflated by RPI index.
FP	Fuel price (fuel cost/ATK)
CP	Capital cost deflated by RPI index (the sum of depreciation cost, maintenance cost, cost of using airport facilities, insurance cost, divided by the number of airplane really owned)

Note: All the data were obtained from Koku Toukei Yoran, published annually by Nihon Koku Kyokai.

Table 14 Partial correlation coefficients between independent variables of average cost Function(N=42)

EQ	FRQ	DIST	NET	PAX	ATK
EQ	1
FRQ	0.202	1
DIST	0.615 $ ^{*} $	-0.722 $ ^{*} $	1
NET	0.299	-0.704 $ ^{*} $	-0.841 $ ^{*} $	1
PAX	0.792 $ ^{*} $	0.277	-0.406	-0.117	1
ATK	-0.343	0.578 $ ^{*} $	0.915 $ ^{*} $	0.874 $ ^{*} $	0.339	1

Note: $ * $ : significant at 0.1% level

The findings are that (1) the economies of network rationalization were substantial in all the equations and especially so with regard to ANA and JAS, (2) the economies of traffic did not exist (see 1), (3) the economy of aircraft size is observed with regard to ANA and JAS, and especially strengthened after regulatory change, but in the case of JAL, (4) the economy of distance had not existed, but was improved after the regulatory change (See 4).
Table 15 Regression results of the Cobb=Douglas average cost function

INTERCEPT	LnPAX	LnNET	LnEQ	LnDIST	adjR²
1	3.796+0.136ANA(9.70a)(2.54b)+1.248JAS(8.88a)	0.242(2.55b)	-0.123-0.152JAS(5.36a)(5.47a)-0.022DRC(2.94a)	0.967
2	2.859+2.208ANA(3.36a)(2.54b)+1.720JAS(5.62a)	-0.096-0.216JAS(3.72a)(4.89a)	0.387-0.399JAS(2.14b)(2.28b)-0.020DRC(2.79a)	0.967
3	-1.720+7.246ANA(0.89)(3.58a)+6.429JAS+0.533DRC(3.49a)	(2.80a)	-0.091-0.181JAS(3.19a)(2.88a)	0.922-1.373ANA(3.39a)(3.60a)-0.972JAS(2.43b)	0.384-0.146DRC(1.61d)(3.28a)	0.973

Note (1) t-statistics are in parentheses : $ a=1 \%, b=5\%, c=10\%, d=20\% $

(2) estimated by OLS, using annually aggregated firm data of '79-92, N=42

The other cost function is the total cost function that included the input price variables AP, FP, and LP, in which multicollinearity was not taken into account. The empirical model of log linear form which includes the dummy variables is as follows;

$$ \begin{aligned}Ln\big(TC\big)&=a_{1}+\sum_{\alpha=1}^{2}a_{2\alpha}\big(DF_{\alpha}\big)+a_{3}\big(DRC\big)+\Bigg\{a_{4}+\sum_{\alpha=1}^{2}a_{5\alpha}\big(DF_{\alpha}\big)+a_{6}\big(DRC\big)\Bigg\}Ln\big(DIST\big)&\\&+\Bigg\{a_{7}+\sum_{\alpha=1}^{2}a_{8\alpha}\big(DF_{\alpha}\big)+a_{9}\big(DRC\big)\Bigg\}Ln\big(NET\big)+\Bigg\{a_{10}+\sum_{\alpha=1}^{2}a_{11\alpha}\big(DF_{\alpha}\big)+a_{12}\big(DRC\big)\Bigg\}Ln\big(FRQ\big)\\ &\\&+\Bigg\{a_{13}+\sum_{\alpha=1}^{2}a_{14\alpha}\big(DF_{\alpha}\big)+a_{15}\big(DRC\big)\Bigg\}Ln\big(EQ\big)+\Bigg\{a_{16}+\sum_{\alpha=1}^{2}a_{17\alpha}\big(DF_{\alpha}\big)+a_{18}\big(DRC\big)\Bigg\}Ln\big(LF\big)\\ &\\&+\Bigg\{a_{19}+\sum_{\alpha=1}^{2}a_{20\alpha}\big(DF_{\alpha}\big)+a_{21}\big(DRC\big)\Bigg\}Ln\big(LP\big)+\Bigg\{a_{22}+\sum_{\alpha=1}^{2}a_{23\alpha}\big(DF_{\alpha}\big)+a_{24}\big(DRC\big)\Bigg\}Ln\big(FP\big)\\ &\\&+\Bigg\{a_{25}+\sum_{\alpha=1}^{2}a_{26\alpha}\big(DF_{\alpha}\big)+a_{27}\big(DRC\big)\Bigg\}Ln\big(CP\big)+\upmu\\ \end{aligned} $$

$$ \begin{aligned}&a_{19}=1-a_{22}-a_{25}\\&a_{19}+a_{22\alpha}=1-\left(a_{22}+a_{23\alpha}\right)-\left(a_{25}+a_{26\alpha}\right)\\&a_{19}+a_{21}=1-\left(a_{22}+a_{24}\right)-\left(a_{25}+a_{27}\right)\\ \end{aligned} $$

where (1) the variable of output was divided into DIST, FRQ, and EQ, (2) $ \mu $ is the error term, and (3) DF $ \alpha $ is the firm dummy (DF1 represents ANA, and DF2 : JAS).

The procedure of estimation is that (1) we estimated with the dummy variable DRC and DF $ \alpha $ included in the intercept and each coefficient, (2) then excluded both independent variables and the dummy variables insignificant, even at the 20% level, in order to keep the degree of
freedom and improve the statistics. $ ^{20} $ And the parameter constraint that “total cost is homogeneous to input price” was added in the equation in the upper row, while no constraint was added in the other (See Table 16).

Table 16 Total cost function of Cobb=Douglas form

INTERCEPT	LnDIST	LnNET	LnFRQ	LnEQ	LnLF	LnLP	LnFP	LnCP	Statistics
-2.472-4.340DRC(1.25)(4.20a)	0.471(2.46b)	0.050+0.125DRC(1.68c)(1.62d)	0.514(2.09b)+0.285DRC(3.72a)	-0.162DRC(2.27b)+0.805ANA(2.95a)+0.909JAS(5.94a)	0.708DRC(3.86a)	0.388(2.82a)	0.206(3.67a)	0.406(3.39a)	adjR²=0.9991
-3.737ANA(3.01a)	0.471(2.46b)	-0.097ANA(2.11b)	0.514(2.09b)+0.285DRC(3.72a)	0.708DRC(3.86a)	0.388(2.82a)	0.206(3.67a)	0.406(3.39a)	0.406(3.39a)	SE=0.017
-4.684JAS(7.64a)	-0.060JAS(1.47d)
0.359-4.184DRC(0.31)(4.09a)	+0.586ANA(4.40a)	0.032+0.083DRC(1.56d)(1.71d)	0.316(2.04c)+0.196DRC(3.45a)+0.797ANA(2.37b)	-0.109DRC(2.33b)+0.721ANA(3.98a)+0.386JAS(1.92c)	-0.306(1.98c)+0.752DRC(3.87a)	0.214(6.50a)	0.338(6.67a)	adjR²=0.9997
-9.792ANA(5.49a)	+0.842JAS(3.29a)	-0.119ANA(3.59a)	0.316(2.04c)+0.196DRC(3.45a)+0.797ANA(2.37b)	-0.109DRC(2.33b)+0.721ANA(3.98a)+0.386JAS(1.92c)	-0.306(1.98c)+0.752DRC(3.87a)	0.214(6.50a)	0.338(6.67a)	SE=0.013
-5.945JAS(10.05a)	-0.051JAS(1.40d)

Note : N=42, estimated by OLS, and t-statistics are in parentheses.

Findings : Looking at the coefficient of $ \ln(\text{NET}) $ , we can recognize that the entry and exit after the regulatory change deteriorated the cost efficiency. This is different from the result in Table 15. But in addition to this result, if we take it into consideration that the t-value of DRC is unstable, we can conclude that the cost inefficiency of JAL that entered the shorter haul routes represents the whole effect and made the sign of DRC positive. And one reason for the unsuitability of t-value of DRC is that some domestic routes of ANA and JAS had already been rationalized before the regulatory change. Especially with regard to ANA, which was more successful in entering longer haul routes than JAS, it can be said that the rationalization of these routes had led to slightly lowering the total cost before the regulatory change if we exclude the effect of unstable dummy variable DRC. So at least with regard to ANA, it would be natural to think that the economies caused by the entry into the international markets and exit from domestic local markets arose around or after the regulatory change.

Looking at the coefficient of $ \ln(\text{FRQ}) $ , the regulatory change accelerated the cost inefficiency. But if we look at this result from the opposite viewpoint, we may say the reason why this phenomenon arose is that, as regards ANA and JAS, cost efficiency was achieved by the
rationalization of frequency caused by the exit from domestic short-haul routes and entry into international routes. With regard to ANA, this inclination can be clearly observed in the equation in the lower row.

Next we look at the coefficient of $ \ln(\text{EQ}) $ . As the sign of the coefficient of DRC is negative, it is said that the regulatory change improved the economies of aircraft size. However, it is also observed that the coefficient of JAL is unstable (so not shown). JAL had already employed the jumbo (B747 series) by the time of the regulatory change, so the economy of aircraft size was comparatively obscure. As regards the coefficients of input price variables, the values of labor price and fuel price are a little greater than the results of Caves et al. [1984] (labor price: 0.356, fuel price: 0.166, material-capital: 0.478) and Gillen et al. [1985] (0.330, 0.177, 0.500). $ ^{21} $ It may be inappropriate to compare our empirical results with theirs because we introduced a different form of the cost function, but our results probably reflect the higher labor cost structure inherent in the Japanese airline industry.

### 7.2 The Summary of Findings

Summing up the results of Table 15 and Table 16, we can see that not only the cost structure of Japanese airlines but the effects of regulatory change on them are different from one another. Regarding cost structure, ANA may have benefited most by the regulatory change that authorized the company to enter the long-haul international markets and exit from some domestic shorter-haul ones. This can be observed in the regression coefficients of either network or aircraft size. Similar results were obtained in the case of JAS, but JAS probably did not benefit as much as ANA.

On the other hand, it is not clear whether JAL benefited by the regulatory change. Authorization for JAL to enter the domestic denser routes may have brought more revenue to the company, but worsened the cost efficiency, because it made the average stage length shorter with average aircraft size constant or larger. So we have to say that these noncommittal policies to JAL caused to obscure its operational performance.

## 8 Summary and Conclusions

The air transport market in Japan was strictly regulated until 1986. In that year, the government changed its air transport policy to promote competition in the market. This competition policy, however, was far from the deregulation policies adopted in other developed countries, especially in the United States. The characteristic feature of Japanese competition policy is that the competition is controlled through the administrative process. It is true that an airport capacity shortage exists and that this prevents free entry into the market, but the government does not want to revise the law and to clarify entry conditions. It is not clear how effectively the air transport markets work. Comparing fares in Japan with those in the States, we can conclude that
the consumers in Japan hardly benefited from the new policy. We think that the government should urgently adopt a more effective competition policy.

In section 5 to 7, we investigated the effects of the policy change on the airlines' behavior, supply and demand conditions in markets and airlines' cost structure by econometric analysis. As noted above, the policy change brought about “controlled competition,” in which price competition is effectively ruled out. In this case, the possibility exists that airlines engage in service competition such as frequency and in seat-per-flight share competition. Our analysis shows that JAL and JAS, which expanded their rout network by the new policy, were able to obtain a large share by increasing the frequency and seat-per-flight share. From the experience in the States, especially during the era under the Civil Aeronautics Board's regulation, it is said that service competition of this type leads to over-capacity. But in Japan, airport capacity constraints are so severe that such effects were not recognized, with the exception of some routes. Finally, we can conclude that by the relaxation of entry regulations, carriers that started long-haul operations benefited in improving their cost structure.

## APPENDIX

In this appendix we will investigate in what kind of markets the fares are set higher or lower, and how these fares will change in case fares be set approximately at the average cost level, and clarify what kind of consumer will benefit or lose by the revision of the way of setting fares.

Regarding the fares of the U.S. domestic markets under CAB's regulation, as was already mentioned, fares were set higher (lower) in longer-haul (shorter-haul) markets judged by the fact that the longer the stage length was, the lower the average load factor. However, the similar phenomena are not observed in the Japanese case, i.e., there is no correlation between distance and load factor (see Figure 9). Although the Japanese domestic air fares are regulated, the way of setting fares (or the demand character) is quite different from what was observed in USA. The following part is to investigate the way of setting air fares in the Japanese domestic market.

To begin with, we will construct the fare determination function as follows:

$ FARE = f(+DIST, +PAX, +LFsd) $

‘FARE’ is the normal round-trip fare filed in the time table. ‘DIST(=the stage length of a market)’ is the variable that represents the operational cost at a market, and both ‘PAX(the total number of passengers in a year)’ and ‘LFsd(the standard deviation of the monthly-average load factors in a year)’ are the variables that represent the mark-up ratio. It is expected that the larger the number of passenger is, and the larger the value of standard deviation of the monthly-average load factors is, the higher the mark up ratio. $ ^{22} $
The estimated result of the fare determination function of log-linear form is shown in table 17. The sample number is 164, and they are obtained from the cross-section data of 1991.

Table 17. Estimated results of the fare determination function (by OLS)

Dependent Variable	INTERCEPT	Ln(DIST)	Ln(LFsd)	Ln(PAX)	adjR²	SE
Ln(FARE)	1.083(8.18a)	0.740(38.82a)	0.055(1.86c)	-0.028(3.16a)	0.926	0.137

Note : t-statistics are in parenthesis ; level of significance ; $ a=1\% $ , $ c=10\% $ .

Except for $ \mathrm{Ln(PAX)} $ , the signs of the coefficients meet the expected conditions. The negative sign of the coefficient of $ \mathrm{Ln(PAX)} $ means that the regulator (MoT) refrains from setting “too high” fares at larger markets.

Then comparing the obtained estimated fares (i.e., close to average cost based fares) with the real ones filed in the time table (i.e., residual between estimated value and real one), we will specify the markets where the fares are set higher or lower. If the estimated fare of a market is lower than the real one, we judge that the passengers of the market lose the mutual consumer surplus. Those which are classified as “high fare markets” are, for example, short haul markets in Hokkaido (where the standard deviation of the monthly average load factor is large) or long haul markets without any substitutional (competitive) mode (e.g., Shin Kansen). Speaking of the former type of markets, the real fares are 20-30% higher than the estimated ones. On the other hand, those which can be classified as “lower fare markets” are the ones to/from isolated islands. With regard to these markets, the real fares are 40-50% lower than the estimated ones.

In order to classify the markets more strictly, we will introduce the hierarchical cluster analysis. Using the same variables already introduced (i.e., DIST, PAX, LFsd, which can be regarded as market structure variables) and the same dataset, we will classify 160 markets into 5 clusters by Ward method that is most frequently used. Then multiplying the residual (converted into ¥) of each market by the total annual passengers of the market, we intend to obtain the total gain or loss of consumer surplus after the current method of pricing (i.e., price discrimination) is abolished and the method approximate to the average cost pricing is achieved.

The results of cluster analysis is shown in table 18.
Table 18. Results of cluster analysis

Cluster.1	Cluster.2	Cluster.3	Cluster.4	Cluster.5
the attribute of the markets	long haul & very large (double or triple track)	long-medium haul & large(single or double track)	long haul & medium (single track, including a few double track)	short haul from/to/in Hokkaido and Kyusyu district	short haul other than the markets in Cluster.4
the name (or the type) of markets	Most of the Trunk Lines Tokyo-Kanazawa Tokyo-Hiroshima Tokyo-Kyusyu district Osaka-Kagoshima Nagoya-Sapporo etc.	Tokyo-Kushiro Fukuoka-Sapporo Nagoya-Fukuoka Tokyo-Akita Osaka-Sendai Tokyo-Takamatsu Osaka-Kochi Tokyo-Toyama etc.	Osaka-Aomori Fukuoka-Sendai Tokyo-Obihiro Nagoya-Matsuyama Nagoya-Nagasaki Okayama-Sapporo etc.	Sapporo-Kushiro Sapporo-Aomori Kochi-Fukuoka Kochi-Miyazaki Miyazaki-Nagasaki Sapporo-Wakkanai etc.	Osaka-Takamatsu Tokyo-Izuoshima Osaka-Yonago Osaka-Izumo Osaka-Tottori Osaka-Tokushima etc.
total gain(+) or loss(-) of consumer surplus (1991 Million yen)	+65,797.4	+2,748.5	+789.7	+1,865.8	-1,309.8

Note : It depends considerably on author's subjective judgment to determine how many clusters are introduced. In this case, “the error sum of square” is 0.0296, at which level 160 data are classified into 5 clusters.

Judging by the results, we can point out

(a) that the nominal total gain of consumer surplus is about 70,000 million yen,

(b) that in most of the markets the consumer surplus will increase,

(c) that from the viewpoint of the expected change of consumer surplus, shorter haul markets can be classified into 2 types.

With regard to Cluster. 4, we can classify them into more detailed 3 clusters. One is (4-1) the markets to/from/in Hokkaido, another is (4-2) to/from/in Kyusyu, and the other is (4-3) to/from/between isolated islands. And after the re-calculation, we got the following results: as to (4-1) type markets, the gain of consumer surplus will amount to 1,550.1 million yen, and (4-2) type 629.0 million yen, whereas as to (4-3) the loss of consumer surplus will be about 313.3 million yen.

In addition, regardless of the size or type of the markets, the loss of consumer surplus is recognized as to the markets to/from Naha, which will amount to 11,041.6 million yen, while the consumer surplus of Cluster.1 will increase to be 72,152.4, Cluster.2 to be 3,945, and Cluster.3 to be 3,879.8 million yen, excluding the effect of the markets to/from Naha. Taking into consideration this result and the result of (4-3) type cluster, the fares to/from/in Okinawa prefecture are set lower, partly because of the lower income level, or partly because of the higher ratio of tourists the price elasticity of whom is thought to be larger.

However, even though we take it into consideration that the discount air tickets have already been available in the Japanese domestic markets, we can expect the consumer surplus to increase in most of the markets after the abolishment of the price discrimination.