Journal of International Fisheries
vol.20，2021▶Article ◀

Economic Value Analysis of Naka River Recreational Ayu Fishing Using theTravel Cost Method: Truncated Negative Binomial Analyses
Tsutom MIYATA† and Yutaka YOSHIDA＊（Japan Fisheries Research and Education Agency／＊Tochigi Prefecture）
E-mail：†tmiyata@affrc.go.jp【abstract】
Policies for promoting Tochigi Prefecture’s recreational ayu fishing industry are
considered very important in the inland fisheries sector. The prefecture has only used
total licensing fees to assess the economic value of recreational ayu fishing for the
making policies regarding economic matters. Accurate assessment of the economic scaleof the fishing industry by the prefecture has important implications for implementing
measures for the development of fisheries. To analyze economic value, we distributed
survey questionnaires among Naka River ayu anglers in Tochigi Prefecture andreceived 455 valid responses. We applied truncated negative binomial regression(TNBR) and the individual travel cost method (ITCM) to analyze recreational ayu
fishing. The results showed that the coefficients of interaction variables with travel costand respondents’ residential regions were statistically significant. Furthermore, thecoefficients of respondents’ years of fishing experience and their loyalty to the NakaRiver were also significant in the models. The annual benefit (consumer surplus) ofrecreational ayu fishing was 1,368 million yen for all anglers, 640 million yen foranglers living in Tochigi, 405 million yen for anglers living in prefectures adjacent toTochigi, and 323 million yen for those residing outside these prefectures (Akita, Iwate,
Miyagi, Chiba Saitama, Tokyo, Kanagawa, Yamanashi, and Nagano). The results
clarified that the benefit for anglers living in Tochigi was several times larger than the
total licensing fees. Thus, the prefecture will be able to expand the scale of projects inthe prefectural master plan of fishery development or in a future master plan.
【keywords】
baseline survey before implementing fishery policy, evaluation of non-market goods,
inland fisheries, regional developments

1．Introduction-1-

Tsutom MIYATA and Yutaka YOSHIDA

Inland Japanese fisheries mainly target the following species for stock enhancement:
chum salmon Oncorhynchus keta; rainbow trout Oncorhynchus mykiss; red-spottedmasu salmon Oncorhynchus masou ishikawae, cherry trout Oncorhynchus masou

masou, whitespotted char Salvelinus leucomaenis; ayu Plecoglossus altivelis
altiveautlis; carp Cyprinus carpio; crucian carp Carassius spp.; freshwater eel Anguillajaponica; and Japanese smelt Hypomesus nipponensis. These species are released as
juvenile fishes or eggs for propagation based on inland fishery rights. Excluding
artificial eggs and shellfish, the total number released for each species in 2013
amounted to 304 million chum salmon, the highest number; 115 million of ayu, thesecond highest; and 12 million cherry trout, the third highest (1). Catching chum salmon
in a river is prohibited according to fishery law in Japan. Therefore, the number of
released juvenile ayu has tended to be the largest among these species in Japan’s inland
fisheries. The total number of released juvenile ayu was 185 million in 2003, 137 million
in 2008, and 115 million in 2013 (1). Thus, the number of released ayu has been
decreasing. Ayu catch amount has also been dropping from 84 hundred tons in 2003 to
34 hundred tons in 2008 and then to 23 hundred tons in 2013 (2).
To clarify the object of our research, we first illustrate the current situation ofrecreational ayu fishing. With respect to annual and fixed-term licenses for ayu fishing,
302 thousand were issued in 2008; this number dropped to 240 thousand in 2013, a
decrease of 21% compared to the 2008 number (1). The number of licenses issued in eachprefecture in 2013, ordered from the highest to lowest, was 30 thousand in Kanagawa,23 thousand in Gifu, 18 thousand in Shizuoka, 17 thousand in Tochigi, and 12 thousandin Toyama (1). We chose to conduct our survey of recreational ayu fishing in Japan’s
fourth largest prefecture, Tochigi.

The development of recreational ayu fishing is a major policy issue in Tochigi
Prefecture (3). In Articles 4 and 5 of the National Law on the Promotion of Inland
Fisheries (National Law No. 103 of June 2014), it is specified that the national
government and prefectures implement policies for the promotion of inland fisheries.
Cost-effectiveness is an important factor in determining the implementation of aprefectural political measure (Hirano and Taniguchi (2010)). The prefecture has onlyused total licensing fees to assess the economic value (effectiveness) of recreational ayu
fishing for making policies regarding economic matters. Thus, one of the priorities ofthe prefecture has been to shed further light on the economic value of the ayu fishingindustry, including its potential value. Accurate assessment of the economic scale of thefishing industry by prefecture has important implications for implementing measures-2-

Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses
for fishery development. Additionally, the prefecture can understand the baseline
economic value of fishing to clarify how to create value in fishing promotion projects
under the policies of Tochigi’s master plan for fishery development.
Many previous studies on economic value have used the travel cost method (TCM) toassess recreational fishing abroad (McConnell (1979), Vaughan and Russell (1982),
Samples and Bishop (1985), Ward and Loomis (1986), Shrestha et al. (2002), Bergstrom

et al. (2004), Prayaga et al. (2010), Grilli et al. (2017)). These have increased since the1980s, when TCM research was developed. The TCM was invented by Hotelling (1947).
The aforementioned studies estimated economic value using regression. An advantageof the individual travel cost method (ITCM) is that not only is it able to estimate an
economic value for recreational fishing, but also it can also analyze the effects of
respondents’ demographic information on economic value (Shrestha et al. (2002),
Bergstrom et al. (2004), Prayaga et al. (2010), Grilli et al. (2017)).
Several previous studies has focused on the economic value of Japan’s recreational
fisheries and fishing (Tamaki (1999), Nakahara (2002), Nakahara and Lou (2002),
Tamaki (2007), Miyata (2010)). Recently, the number of papers on the economic valueof inland Japanese fisheries has increased (Ueshima et al. (2018), Suzuki and Suzuki(2018), Yoshiyama et al. (2018)). This recent increase may imply growing interest in
this field of research. However, these previous studies on the economic value of Japan’srecreational fisheries and fishing activities employ the TCM using four arithmetic
operations or the zone travel cost method (ZTCM) using simple regression, excluding
the effects of respondents’ demographic information.
In Article 5, the first chapter, National Law on the Promotion of Inland Fisheries,
clearly states that the roles for the promotion of inland fisheries are to be shared
between the national government and a prefecture. Tochigi Prefecture expects toincrease the benefit of anglers living in the prefecture by implementing the promotion
measures of fisheries via Tochigi’s master plan; therefore, it makes sense to separate
the anglers living in the prefecture from those living in other prefectures when
conducting analysis using the TCM. Furthermore, there are economic relations between
Tochigi and its adjacent prefectures as well as somewhat weak relations between
Tochigi and outside prefectures beyond the adjacent prefectures. Additionally, Tochigi’s
fishery promotion projects are expected to differ between the anglers living in Tochigi
and those in each other region. For example, these projects are designed to improve
and/or expand ayu angling lectures for Tochigi residents, fresh fish products as asouvenir to anglers living in adjacent prefectures, and angler lodges for anglers living-3-

Tsutom MIYATA and Yutaka YOSHIDA

in outside prefectures. It also makes sense to separate the anglers living in Tochigi,
those living in the adjacent prefectures, and those living in outside prefectures when
conducting analysis using TCM.
In our study, we employ more modern methods: truncated negative binomial
regression (TNBR), which is based on econometrics theory, to estimate the travel cost
while including demographic variables.

2. Materials and methods
The Naka River Fisheries Cooperative Association of the Tochigi Prefecture
federation supported our survey, distributing the ITCM questionnaires to Naka Riverayu anglers from the 1st of June to the end of November 2017.
Half of the total ITCM questionnaires were placed at ayu decoy shops (which sell
adult ayu as decoys, fishing license, fishing gear, etc.) around the Naka River. The otherquestionnaires were handed out to anglers by the staff and observers of four fisherycooperative associations under the Tochigi Prefecture Naka River fisheries Cooperative
Federation at major fishing points, as shown in Figure 1, and recreational ayu fishing
event sites during the same period. Almost all ayu anglers visit ayu decoy shops suchas fishing shops before they begin fishing; thus, we covered five major ayu decoy shops
for the survey. Recreational ayu fishing is a unique type of fishing in that ayu exhibit
territorial behavior and tend to attack interlopers; therefore, anglers deploy decoy ayu
as interlopers near the target ayu. Thus, almost all anglers buy lively ayu at the shops
each time they fish.
In total, 2,500 questionnaires were distributed to ayu anglers at the shops, the points
and the sites. The incentive for respondents to complete and return the questionnaires
was entry into a random drawing, the prize for which was a gift of local food. A total of
584 responses were received by postal mail, and 68 questionnaires were duplicatessubmitted by the same respondents (the same names and addresses); ultimately, thevalid number of responses for the regression analysis was 455, accounting for allquestionnaires in which the respondents fully responded to all items.
We employed the ITCM as the travel cost method. The trip distance of a respondent
was calculated with the starting point as the city hall in the town where the respondent
lived. The ayu fishing destination was the first fishing point of the day, where was 11
points along the mainstream and tributaries of the Naka River. The red points in Figure-4-Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses

Sosasa River

Coordinate:37.035402,140.097534

Nasushiobara City

Kuro River

Coordinate:36.952363,140.095170

Coordinate:37.082086,140.157453

Ootawara City
Coordinate:36.838452,
140.129259

Houki River

Coordinate:36.842041,139.944095

Mumo River
Coordinate:36.837270,
140.194144

Nakagawa Town

Uchi River

Coordinate:36.754699,140.141472

Coordinate:36.778439,139.940451

Ara River

Coordinate:36.722460,139.991821

Nasukarasuyama
City Coordinate:36.646465,
140.161091

Motegi Town
Coordinate:36.576251,140.215020

10km

Figure 1

First fishing point of the day

1 represent the first fishing points that mark the middle points of each tributary or thecenter of the mainstream.
The Naka River is more than 5 km away from other rivers, but it is possible to reachthose rivers from tributaries of the Naka River within a few kilometers. Therefore, we
assumed that there would be an equal number of anglers who would move from the
Naka River to other rivers and who would come to the Naka River from other rivers on
the same day. Therefore, we did not consider this potential transfer situation in themodel of this study.
Respondents provided their address, mode of transportation to the fishing point,
highway information, and income. We estimated the trip distance using Google Maps.
The travel cost for a respondent i is calculated with the following formula (1). We asked
the respondents what type of license they obtained, since different licenses havedifferent fees. Additionally, the index “i” of dependent variables in formulas (1) and (2)were taken from data collected via the questionnaires.
TCi=((transDi×fuel /effic)×disti+expressi)×0.85+pubi+timei×oppoi+liceDi×licei(1)

oppoi=(incomei/average annual labor time) ×1/3

(2)

-5-

Tsutom MIYATA and Yutaka YOSHIDA

where transD is the dummy for transportation; fuel is the unit price of gasoline(130yen/l); effic is the fuel economy (11.59km/L) (Kudoh et al. (2008)); dist is the roundtrip distance; express is the round-trip expressway fees; the factor of 0.85 is the inverse
average headcount in a car (1.17 heads/car) (Kubota (2017)); pub is the round-trip public
transportation fees; time is the round-trip time, excluding fishing time; oppo is theopportunity cost of the trip; liceD is the dummy for license type; lice is the value of a
one-time license (the annual license fee divided by fishing days in one year); income is
the respondent’s annual income; average annual labor time is 1,781 hours per year (4);and the factor of 1/3 is the opportunity cost of recreation (Cesario (1976)).
For our models, the utility of consumer i from the general framework of utility
maximization is represented by formula (3) (Shrestha et al. (2002)):
max ui (yi , zi|gi ) subject to TCi yi + q i zi = incomei
yi ,zi(3)where yi is the total number of fishing trips in a year, z i is the consumption of othergoods (vector), TCi is the travel cost of one trip, q i is the price of other goods (vector),
TCi yi is the total travel cost in a year, and q i zi is the total other goods cost per
year. TCi yi + q i zi is constrained by incomei , and yi is influenced by gi (vector) as ademographic condition.
Shaw (1988) indicated that ITCM data had problems as (a) non-negative integers, (b)truncation, and (c) endogenous stratification. Therefore, we employed truncatednegative binomial regression for (a) and (b), and we excluded questionnaires from
duplicate respondents to address endogenous stratification for (c), through which we
could identify names and addresses of respondents to send the incentive (local food) towinners.
Then, if consumer i accepts ϵ the total number of fishing trips, the expected yi canbe defined by formula (4). In addition, angler non-appearance is truncated because thisis an onsite survey wherein the possibility of yi on the condition of y>0 is formulated
as a truncated negative binomial distribution of formula (5) (Shrestha et al. (2002)).
The Poisson regression model is indicated by previous studies to have a defect interms of overdispersion (Lindén and Mäntyniemi (2011)). The negative binomial
regression model compensates for the defect of the Poisson regression model by adding
α, which reflects unobserved heterogeneity. Moreover, yi is the total number ofrecreational ayu fishing trips per year, xi is TCi and the vector of gi , and γ() is a
gamma function.
ϵi = E(yi |xi) = exp (bxi )
-6-

(4)

Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses
α−1yiγ(yi + α−1 )
α−1
ϵi
(
)
(
)
γ(α−1 )𝛾(𝑦𝑖 + 1) α−1 + ϵi
α−1 + ϵiPr(yi = k|yi > 0, xi ) =−1/α
1 − (1 + αϵi )(5)Then, the log likelihood functions of formula (5) are the following functions in formula(6):
1) log(1 + α2 exp(bx i ))α2
1
1
+ log γ (yi + 2 ) − log(𝛾(𝑦𝑖 + 1)) − logγ ( 2 )ααlog L(b, α) = ∑ yi log(α2 exp(bxi)) − (yi +(6)where α in formula (6) is estimated by maximizing the functions.
The Naka River is famous as a recreational ayu fishing river and as a site for the
migration of wild ayu in Japan (Kitada (1999)). Some ayu anglers living in outside
prefectures seek the wild ayu of the Naka River. According to one of the largest fishingtackle retail company in Japan, the Naka River is the most famous river for ayu fishingin Kitakanto (Ibaraki, Tochigi, and Gunma Prefectures) (5).
Some ayu anglers come to the Naka River from afar because of the different opening
days for the recreational ayu fishing season. Opening days in the Tohoku region(Fukushima, Miyagi, Iwate, Yamagata, Akita, and Aomori Prefectures) and in theprefectures of the Koushin region (Nagano and Yamanashi Prefectures) areapproximately one month later than the opening day for the Naka River, when is June
1st. The opening days of the recreational ayu fishing season are based on river rules (6).
In addition, a month's delay can make a big difference in growth because the species isan annual fish (fish with a lifespan of one year). Consequently, some anglers living inthese regions travel to the Naka River to be able to fish ayu earlier in the season before
the opening days in regions in which they live and to fish ayu larger than those in therivers closer to their residences. Thus, the utility of these anglers differs from that ofanglers living in Tochigi.
In addition, some anglers living in metropolitan regions go to the Naka River to seeka good catch. There are metropolises in Chiba, Saitama, Tokyo, and Kanagawa in asouthward direction from Tochigi. Clear rivers for fishing ayu in the region are limited,while the number of ayu anglers living in the region is sizable. Therefore, the catch ofrecreational ayu fishing in nearby rivers is generally poor. The utility of these anglers
thus also differs from that of anglers living in Tochigi.
Against this background, it is necessary for a model to include interactional variables
between travel cost and anglers’ residences. However, the sampling numbers of ayu
anglers from adjacent and outside prefectures were not adequate to allow for these-7-

Tsutom MIYATA and Yutaka YOSHIDA

analyses (Table 1). Consequently, we constructed a function with interactionalvariables between travel cost and residence in an adjacent prefecture seeking wild ayu
or residence in an outside prefecture seeking early ayu and/or good catch and/or wildayu (Figure 2). The function is shown by formula (7):xi = (δ + ε × adjacencei + θ × outsidei ) TCi + μgi(7)where adjacence is the interaction dummy for prefectures adjacent to Tochigi; outside
is the interaction dummy for prefectures outside the adjacent prefectures; δ, ε, and θ
are coefficients; and gi (vector) represents demographic variables for respondent i.
Additionally, the effect of income in formula (3) is included in the opportunity cost ofTC.
The average benefit (consumer surplus) per trip is calculated as follows: - 1/δ foranglers living in Tochigi, -1/(δ+ ε) for anglers living in adjacent prefectures; and -1/(δ+ θ)
for anglers living in outside prefectures, respectively.
Previous studies have indicated a problem with heteroskedasticity of regression using
cross-sectional data (White (1980)). Therefore, we employ a robust standard error
bootstrapped by 1000 replications. All estimations were calculated using STATA,
version 16.

Table 1

Respondents' residences
Freq.

Percent

Location*1

Prefecture

△

Iwate

10

2.20

△

Miyagi

3

0.66

△

Akita

2

0.44

〇

Fukushima

15

3.30

〇

Ibaraki

41

9.01

◎

Tochigi

306

67.25

〇

Gunma

20

4.40

〇

Saitama

29

6.37

△

Chiba

12

2.64

△

Tokyo

13

2.86

△

Kanagawa

1

0.22

△

Yamanashi

1

0.22

△

Nagano

2

0.44

Total

455

100

Note: *1; ◎ is the target research area; 〇 is adjacent to theresearch area; △ is outside of the adjacent areas.
-8-Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses

Figure 2

Residence prefecture of respondents

Note: the gray-colored prefectures are
adjacent to the research prefecture,
and

the

dark

gray-colored

prefectures are outside of theadjacent ones; this map excludes
theislandsofnorthernandsouthern Japan.

3. Results
3-1. Results of descriptive statistical analysis
There were 306 respondents living in Tochigi (Table 1) and 15-41 respondents livingin each of the prefectures adjacent to Tochigi. Respondents living in outside prefectures
came from Iwate, Akita, and Miyagi in northern Japan and from Chiba, Tokyo,
Kanagawa, Yamanashi, and Nagano in the Minamikanto and Koushin regions. There
were 1-13 respondents living in each of these outside prefectures (Table 1).
The demographic data of all respondents were as follows: males accounted for 99% of
respondents; the average age was 60; the average income was 4.20 million yen/year; thepercentage of lodgers was 5.05%; the average number of years of recreational fishingexperience was 5.54; the average number of recreational ayu fishing trips per year was
20.23, and the percentage of loyalty to the Naka River was 56.70%. Loyalty wasassessed with a question asking respondents if they would continue fishing in the NakaRiver regardless of fishing results (Table 2). Transportation to the fishing point on the-9-Tsutom MIYATA and Yutaka YOSHIDA

Table 2

Respondents’ characteristics
Total

Male
Age*1
Income*2
LodgerYears of fishing experience
Naka River ayu fishing trips per year
Loyalty to the Naka River*3

455
454
455
455
455
455
455

Freq.

Average/percent

451

99.03%
60.00 years old
4.20 million yen
5.05%
5.54 years
20.23 times
56.70%

23

Note: *1 Answer choices were divided into 10 age groups; *2 Answer choices were:
under 3 million, 3-5, 5-7, 7-9, 9-11, 11-13, 13-15, 15-17, and over 17 million,
and the income of respondent was defined as the average of each income groupand 1.5 million when it was under 3 million and 19 million when it was over
17 million; *3 The respondents answered that they would continue ayu fishingin the Naka River even if they could not catch a satisfactory number of ayu.

Naka River was by car (96.04%), by train (1.10%), or by another mode including by footand bicycle (2.86%).
As mentioned above, the average income per respondent was 4.20 million yen under
the conditions of 99% male and average 60 years old (Table 2). The average income ofJapanese males in the age range of 55-59 years old according to the national statistics
was between 4.12 and 4.24 million yen (7). The average income of respondents was
similar to the values of these statistics. The average number of Naka River ayu fishingtrips per year was 20.2 times for all respondents, 23.5 times for respondents living in
Tochigi, 14.2 times for respondents living in adjacent prefectures, and 11.9 times forrespondents living in outside prefectures.
3-2. Results of regression analysis
For the results of TNBR, the Akaike information criterion (AIC) and Bayesian
information criterion (BIC) of models with/without interaction variables for TC(formula (7)) were 3,374 and 3,402, respectively, for the model with the adjacence andoutside variables; 3,415 and 3,440, respectively, for the model with the adjacencevariable; 3,392 and 3,417, respectively, for the model with outside variable; and 3,421
and 3,442, respectively, for the model without adjacence and outside variables.
Therefore, we selected the model with adjacence and outside variables.
The coefficient of TC was estimated to be -0.000144 (Table 3), and the average benefit
for respondents living in Tochigi was estimated to be 6,944 yen/trip (5,650-9,091- 10 -

Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses
yen/trip; 95% confidence intervals). The average benefit for respondents living inprefectures adjacent to Tochigi was estimated to be 12,821 yen/trip (9,512-19,656
yen/trip; 95% confidence intervals), and the average benefit for respondents living in
outside prefectures was estimated to be 24,390 yen/trip (18,142-37,202 yen/trip; 95%
confidence intervals). The average benefit for all respondents was 10,078 yen/trip as
estimated from the coefficients of TNBR.
The coefficient of fishing experience was estimated to be 0.084. This indicates thatthe number of fishing trips increased 0.14 trips/year with an increase of one year offishing experience, which is a marginal effect under a condition of other coefficients
fixed at means (8). The coefficient of loyalty was estimated to be 0.153. This indicatesthat the number of trips for respondents answering yes to the loyalty question is 2.8
trips/year more than the number of respondents who answered no, which is also a
marginal effect under a condition of other coefficients fixed at means.
The alpha in Table 3 is α reflected unobserved heterogeneity in formula (5), and theLR test rejected the null hypothesis, alpha=0.
To address endogenous stratification, the above analyses excluded questionnaires
from duplicate respondents. To make assurance double sure, we followed themethodology of Grilli et al. (2017), and conducted regression analysis using thegeneralized negative binomial model with endogenous stratification corrected fortruncation. The results were almost the same as those in Table 3 (9), in which thecoefficients as well as AIC and BIC were similar. Therefore, we concluded that
endogenous stratification was addressed by excluding questionnaires from duplicate
respondents.

4. Discussion
The utility of anglers from adjacent and outside prefectures differed from that ofanglers living in Tochigi, since in the regressions, the coefficients of interaction for
estimating benefits were significant and different (Table 3). This is the originality of
our study, as previous studies (described in the introduction) do not discuss this.
The cumulative number of ayu anglers coming to the Naka River in 2017 was 137
thousand (10). Thus, the estimated number of anglers from each residence group was asfollows: 92 thousand from Tochigi, 32 thousand from adjacent prefectures, and 13
thousand from outside prefectures (Table 4). These were estimated from the 137
thousand cumulative ayu anglers multiplied by the quotient of the number of anglers- 11 -

Tsutom MIYATA and Yutaka YOSHIDA

Table 3Results of truncated negative binomial regression

No. of obs.
455
Replications
1,000
Wald chi2(5)115Prob>chi2 0.000
Log pseudolikelihood-1680
Pseudo R2
0.053
AIC
3,374
BIC
3,402
LR test of alpha=0: chibar2(01) = 1,625
Prob >= chibar2 =
0.000
*1
Coef.
[95% Conf. Interval]
TC-0.000144
Interaction variables for TC
Adjacence
0.000066
Outside0.000103
Experience
0.083667
Loyalty
0.153350
Constant
2.870018
lnalpha-1.251685
alpha
0.286022***-0.000177-0.000110***

0.000035
0.000071
0.033417
0.038716
2.574931-1.421074
0.241455

0.000096
0.000135
0.133916
0.267983

***
***
**
***
***
***

3.165104-1.082297
0.338816

Note: *1 Significance levels: *** 1 %, ** 5 %, and *10%; based on thebootstrapped standard error.

Table 4

Annual benefit for ayu fishing at the Naka River
Cumulative number of
ayu anglers (thousand)Tochigi
Adjacence
Outside
Total

92
32
13
137

67%
23%
10%
100%

benefit(million yen)

640
405
323
1,368

47%
30%
24%
100%

Note: The total number of anglers as 137 thousand was takenfrom Sakai (2018) (10).

from our survey in each residence group.
The benefit of each residence group was estimated by TNBR as follows: 640 million
yen for residents from Tochigi, 405 million yen for residents from adjacent prefectures,and 323 million yen for residents from the outside prefectures (Table 4). The total
benefit of these anglers was estimated to be 1,368 million yen (Table 4).
The total licensing fees for the Naka River was recently calculated as 80-100 million
yen/year (Kubota (2017)). The total benefit, as mentioned above, was 1,368 million yen,
14-17 times higher than the total licensing fees. If Tochigi Prefecture were to focus on
prefectural inhabitants, the benefit of anglers living in Tochigi, at 640 million yen, was- 12 -

Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses
6.4-8 times higher than the total licensing fees.
As mentioned above, the prefecture has only used total licensing fees to assess the
economic value of recreational ayu fishing for policymaking. Tochigi’s master plan forfishery development (2016) enumerates improvement of the sales channels of licensetickets, promotion of recreational fishing, increasing the number of juvenile ayu
released, etc. Thus, the prefecture will be able to expand a scale of projects in the masterplan or in future master plans by up to 6.4 or possibly even 8 times. Moreover, theprefecture will request governmental subsidies several times larger than the current
subsidy scale because the Ayu anglers living outside Tochigi are provided the utility
from recreational ayu fishing in the Naka River.
This model is based on the following assumption. When recreational ayu fishing in
the Naka River is not available, the anglers will move to other rivers to fish ayu or otherfish species, the number of anglers in the rivers will increase, and the rivers will become
crowded, making it difficult to catch enough ayu or other species. Consequently, theutility of the anglers will be reduced. In this model, the reduced utility is the same as
the utility of ayu fishing anglers in the Nakagawa River.
In the questionnaire, we also asked about alternative relations between the NakaRiver and other rivers. Of the respondents, 56.7% answered that they would continue
ayu fishing in the Naka River even if they could not catch a satisfactory number of ayu.
A total of 16.0% of the respondents answered that they would move to other rivers in
Tochigi if they could not catch a satisfactory catch, while 22.6% answered that theywould move to other rivers in other prefectures if they could not catch a satisfactory
catch, and 4.6% gave other answers. It is recommended that Tochigi Prefecture adjust
the estimated benefits to take these results into account and that the adjustment be
decided based on the Tochigi fishing policy and the national fishing policy, in which thepromotion of inland fisheries is to be shared between the national government and aprefecture (National Law on the Promotion of Inland Fisheries).
The reputational damage from radioactive contamination after the Great East Japan
Earthquake might be still remained, when we conducted this survey in 2017. However,
the annual number of anglers recovered by 2014, and the number since 2014 has been
decreasing at a rate similar to that before the earthquake (11). Kubota points out thatthe decline of ayu anglers is caused by aging of the anglers and a significant decreasein newcomers (Kubota (2017)). Thus, countermeasures against the decline of anglers
and aging are also important for the policy.
Previous studies on the travel cost method of recreational ayu fishing in Japan have
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Tsutom MIYATA and Yutaka YOSHIDA

been rare, excepting the paper of Suzuki and Suzuki in 2018. Thus, we compare ourresults with those of that study. The previous study neglected the number of passengers
in a car as well as the mode of transportation, and it estimated the ZTCM. Furthermore,
the study did not consider the opportunity cost for recreation but instead used acommon labor cost (2,406 yen/hour). Thus, it may have overestimated, even in thepresence of guidelines for economic evaluation in non-market reports to avoid
overestimation (Arrow et al. (1993)). Therefore, we used the one-third common labor
cost as in formula (2) for curbing an overestimation of benefit using the TCM (Cesario(1976)). However, the benefit per trip in the previous study was estimated at 8,711
yen/trip (2011) to 10,158 yen/trip (2012). Our result was 10,078 yen/trip. That is, theestimated benefits were similar. Therefore, it was difficult to explain the comparative
results of the ITCM in this study and the ZTCM in the previous study for recreational
ayu fishing and regarding the overestimation of the TCM.
Further study is needed to clarify differences among the utilities of ayu anglers living
not in adjacent and outside prefectures but in the Tohoku and Koushin regions (regionswith late opening days) and the Minamikanto region (metropolises) by increasing thenumber of anglers sampled.
Notes(1) Ministry of Agriculture, Forestry and Fisheries (2013) The 2013 Census of Fisheries.
(2) Annual Statistical Report of the Fisheries and Aquaculture Production: http://www.maff.go.jp/j/tokei/ kouhyou/kaimen_gyosei/, “Accessed 14 April 2020”.
(3) Tochigi’s Master Plan of Fisheries Development: http://www.pref.tochigi.lg.jp/g02/suisa
n/suisansinkokeikaku1.html, “Accessed 14 April 2020”.
(4) Ministry of Health, Labour and Welfare (2017) Monthly Labour Survey.
(5) One of the largest fishing tackle retail company in Japan, “Johshuya,” said that the
Naka River was truly a kingdom for recreational ayu fishing in Kitakanto (Johshuya
news

on

27

April

2019:

http://www.johshuya.co.jp/news/detail.php?no=156854“Accessed 14 September 2020”.
(6) List of opening days: http://www.naisuimen.or.jp/fising/ayu.html, National Federationof Inland Fisheries Cooperative Association, “Accessed 18 April 2020”.
(7) Ministry of Health, Labour and Welfare (2017) Basic Survey on Wage Structure.
(8) The choices were 1.under 1 year, 2. 2-5 years, 3. 6-10 years, 4. 11-20 years, 5. 21-30
years, 6. 31-50 years, and 7. over 50 years, and the fishing experience year of
respondent was defined as the average of each year group, 0.5 years when it was under- 14 -

Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses
1 year, and 55 years when it was over 50 years. The marginal effect estimated byordinary least squares was 0.14 (Figure (8)).

Figure (8)Regression for estimating marginal effect of fishing experience year
Note: Trips per year by each choice are
estimated under a condition of othercoefficients fixed at means.

(9) Table (9) shows the results of generalized negative binomial regression withendogenous stratification corrected for truncation.

Table (9)Results of regression of “Generalized Negative Binomial with
Endogenous Stratification”

AIC

3,374
Coef.

TC
-0.000152
Interaction variables for TC
Adjacence
0.000070
Outside0.000109
Experience
0.087433
Loyalty0.160231
Constant
2.415180
lnalpha
-0.711364
alpha
0.490974

BIC

3,403
[95% Conf. Interval]*1***-0.000187-0.000116***

0.000037
0.000074
0.034685
0.033317
2.085126-0.956859
0.384097

0.0001020.000144
0.1401800.287145
2.745233-0.465869
0.627590

***
***
**
***
***
***

Note: *1 Significance levels: *** 1 %, ** 5 %, and *10%; based on
the bootstrapped standard error- 15 -

Tsutom MIYATA and Yutaka YOSHIDA(10) Sakai T. (2018) “Survey on catch amount of Ayu in Naka river,” Annual research
report of Tochigi prefecture fisheries experience station.
(11) Sakai T. (2019) “Survey on catch amount of Ayu in Naka river,” Annual research
report of Tochigi prefecture fisheries experience station.

References[1] Arrow K., Solow R., Portney PR., Leamer EE., Radner R. and Schuman H. (1993)“Report of the NOAA panel on contingent valuation,” Federal Register, 58(10), 46014614.
[2] Bergstrom JC., Dorfman JH. and Loomis JB. (2004) “Estuary management andrecreational fishing benefits,” Coastal Manage , 32(4),417-432.
[3] Cesario FJ. (1976) “Value of time in recreation benefit studies,” Land Economics, 52(1),32-41.
[4] Grilli G., Landgraf G., Curtis J. and Hynes S. (2017) “A travel cost method estimationof demand for two destination salmon rivers in Ireland,” ESRI Working Paper.
[5] Hirano M. and Taniguchi K. (2010) “A study on the priority of public policy in local
government,” Okayama economics review, 42(3), 13-44 (in Japanese).
[6] Hotelling H. (1947) “The economics of public recreation,” The Prewitt Report.
[7] Kitada S. (1999) “A review of sampling techniques in fisheries science,” Japanese

Journal of Biometrics ,20(1), 1-29 (in Japanese with English abstract).
[8] Kubota H. (2017) “Understanding of the actual situation of wild Ayu in recreational
fishing,” Research report regarding environment conservation and recreational fishingdevelopment in inland water, Japan Fisheries Research and Education Agency, 64-74(in Japanese).
[9] Kudoh Y., Matsuhashi K., Kondo Y., Kobayashi S., Moriguchi Y. and Yagita. H (2008)“Statistical analysis on transition of actual fuel consumption by improvement ofJapanese 10・15 mode fuel consumption,” Journal of the Japan Institute of Energy , 87,
930-937 (in Japanese with English abstract).
[10] Lindén A. and Mäntyniemi S. (2011) “Using the negative binomial distribution to model
overdispersion in ecological count data,” Ecology, 92(7), 1414-1421.
[11] McConnell KE. (1979) “Values of marine recreational fishing: Measurement and impact
of measurement,” American Journal of Agricultural Economics, 61(5), 921-925.
[12] Miyata T. (2010) “Analysis of contribution to local economy by recreational fishing using
TCM and CVM,” In: National Association for the Promotion of Productive Sea (eds),

Book of national workshop for stock enhancement, National Association for the- 16 -

Economic Value Analysis of Naka River Recreational Ayu Fishing Using the Travel
Cost Method: Truncated Negative Binomial Analyses
Promotion of Productive Sea, 1-12 (in Japanese).
[13] Nakahara N. (2002) “Economic evaluation for facility of public sea fishing: Case studyof sea fishing park in Kagoshima City,” Journal of regional fisheries economy , 42(2),119-131 (in Japanese).
[14] Nakahara N. and Lou X. (2002) “Social evaluation of Experience fisheries andexperience fisheries with interaction between urban and rural area: Case study of
tourism service using Triangular set net by Tomonoura fisheries cooperative
association in Tokushima,” Journal of the north Japan fisheries economics, 30, 85-100(in Japanese).
[15] Prayaga P., Rolfe J. and Stoeckl N. (2010) “The value of recreational fishing in the Great
Barrier Reef, Australia: A pooled revealed preference and contingent behaviour model,”Marine Policy, 34(2), 244-251.
[16] Samples KC. and Bishop RC. (1985) “Estimating the value of variations in anglers'
success rates: An application of the multiple-site travel cost method,” Marine Resource

Economics, 2(1), 55-74.
[17] Shaw D. (1988) “On-site samples' regression: Problems of non-negative integers,
truncation, and endogenous stratification,” Journal of Econometrics, 37(2), 211-223.
[18] Shrestha RK., Seidl AF. and Moraes AS. (2002) “Value of recreational fishing in theBrazilian Pantanal: A travel cost analysis using count data models,” Ecological

Economics, 42(1-2), 289-299.
[19] Suzuki K. and Suzuki Y. (2018) “Recreational value of Ayu fishing at the Okitsu River
in Shizuoka Prefecture, evaluated using the travel cost method,” Nippon Suisan

Gakkaishi, 84(6), 1034-1043 (in Japanese with English abstract).
[20] Tamaki Y. (1999) “Evaluation of the amenity of the sailing trawl fishery in Lakes
Kasumigaura and Kitaura,” Japanese journal of fisheries economics , 44(1), 51-73 (inJapanese with English abstract).
[21] Tamaki Y. (2007) “Case study of evaluation for multiple functions of inland fisheries
and fishing villages in Japan,” Journal of the north Japan fisheries economics, 35, 5173 (in Japanese).
[22] Ueshima G., Hoshikawa H., Matsuzawa S., Yamamoto S. and Sawamoto Y. (2018)“Present condition and economic ripple effect of recreational smelt fishing in Misuzu
Lake estimated by a questionnaire survey of anglers,” Nippon Suisan Gakkaishi , 84(4),711-719 (in Japanese with English abstract).
[23] Vaughan WJ. and Russell CS. (1982) “Valuing a fishing day: An application of asystematic varying parameter model,” Land Economics, 58(4), 450-463.

- 17 -

Tsutom MIYATA and Yutaka YOSHIDA[24] Ward FA. and Loomis JB. (1986) “The travel cost demand model as an environmental
policy assessment tool: A review of literature,” Western Journal of Agricultural

Economics, 11(2), 164-178.
[25] White H. (1980) “A heteroskedasticity-consistent covariance matrix estimator and a
direct test for heteroskedasticity,” Econometrica, 48(4), 817-838.
[26] Yoshiyama T., Tsuboi J. and Matsuishi T. (2018) “Consumption activities andexpenditure of anglers targeting endangered fishes in Hokkaido Lakes, Japan,” Nippon

Suisan Gakkaishi, 84(5), 858-871 (in Japanese with English abstract).
[Acknowledgements] This study was partially funded by the Tokyo Fisheries Promotion
Foundation’s “Survey on environment conservation and recreational fishing development ininland water.” The study was also supported by Dr. Hitoshi Kubota (Tochigi Prefecture), Dr.
Tomoyuki Nakamura (the Japan Fisheries Research and Education Agency), theAssociation for Promotion of the Naka River Ayu Kaido, and the Federation of the Tochigi
Prefecture Naka River Fisheries Cooperative Association. We would very much like toextend our appreciation to these persons and organizations. Finally, we sincerely appreciate
the two reviewers.

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