A meta-analysis of the recreational value of fishing

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WORKING PAPER 2022:01

A meta-analysis of the recreational value of fishing

Ing-Marie Gren

, George Marbuah

ECONOMICS

a

Department of Economics, Swedish University of Agricultural Sciences, Box 7013, 750 07, Uppsala, Sweden, e-mail: ing-marie.gren@slu.se

b

Stockholm Environment Institute, Box 24218, 104 51 Stockholm, Sweden. E-mail:

george.marbuah@sei.org

Sveriges lantbruksuniversitet, Institutionen för ekonomi Working Paper Series 2022:01 Swedish University of Agricultural Sciences,

Department of Economics, Uppsala

ISSN 1401-4068 Corresponding author:

ISRN SLU-EKON-WPS-2201-SE Ing-Marie.Gren@slu.se

_______________________________________________________________

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A meta-analysis of the recreational value of fishing

Abstract: This paper makes a meta-analysis of studies estimating willingness-to-pay for fishing

at the global scale. Focus of studies, determinants of the value, and predictive power are analyzed with 208 usable studies. Most of the studies were applied to fishing in the USA and measured fishing value as value-per-day. Variables reflecting contextual, environmental and study specific factors were included as explanatory determinants in different sub-sample regression models. Mixed effect methods were used, and a robust result was that temperature had a significant and positive effect on the fishing value. The estimated predictive power for sub-sample models with values measured as value-per-day for specific fish species (trout and bass) and valuation methods (contingent valuation and travel cost methods) was relatively good when compared with other studies on meta-analysis of environmental values. Predictions of an increase in temperature by 1°C indicated an increase by 2 % in the fishing value.

Keywords: willingness-to-pay, fishing, meta-regression, environmental factors, prosperity, global

JEL codes: Q21, Q51, Q57

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1. Introduction

Fishing is as old as mankind and has been an important food resource for humans and a source of income for commercial fishery. Fishing also provides other values, such as recreational opportunities, which have been important for many individuals. Defining recreational fishing as fishing that does not constitute individual’s basic resource and nutritional need and is subject to market transactions, the number of anglers at the global scale can be twice as large as the number of fishers in commercial fishery (FAO, 2021a). Global estimates of recreational fishers range between 200 million to 700 million (Arlinghaus et al., 2015). However, similar to commercial fishery, recreational fishers can threaten fish population by e.g. selective harvest of

‘trophy fish’, introduction of non-native species, and disturbance of the environment. Hyder et al. (2018) showed that recreational fishery of cod and bass contributes to 27 % of the total removal of these species in marine waters of Europe. Commercial fishery has been much subjected to national and international governmental interventions and regulations in order to prevent large declines in the fish population and to promote sustainable fisheries (OECD, 2020).

Only in a few countries, recreational fishery is also considered when allocating quotas on fishing (e.g. NOAA 2021a).

In general, regulations on fishing are based on the need for conservation of fish stocks, economic development, and social values (e.g. OECD, 2020). While the economic value of commercial fishery can be assessed from existing data on catches and their market prices the lack of such information for recreational fishing is an impediment for assessing its value.

Therefore, a large body of studies have been estimating anglers’ value of fishing in monetary terms since late 1960s (e.g. Johnston et al., 2006; RUVD, 2016). There is a large heterogeneity between these studies with respect to fish species studied, and choices of valuation method and measurement. One of the first studies, Kalter and Gosse (1969), estimated the willingness-to- pay (WTP) for freshwater fishing for one day in New York state. A later study estimated marginal WTP of 1 kg fish in New Zealand (Mkwara et al. 2015).

Despite the importance in practice of recreational fishing and the large body of literature

estimating WTP for fishing, there exists no systematic review of these studies at the global

scale, but only for fishing values in the USA (Johnston et al. 2006) and in the Arctic region

(Gren and Marbuah, 2021). The purpose of this study is to make a systematic review of all

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studies for an explorative assessment of the determinants of the magnitude of the fishing value and estimation of predictive power of the regression models. To this end, the study uses meta- regression analysis (MRA) which was first suggested by Glass (1976). It is a tool for a systematic extraction, quantification and synthesis of results from existing studies, which has been much used for assessing environmental values (e.g. Nelson and Kennedy, 2009). Since heterogeneity in the measurement of the dependent variable, fishing value in the present study, is a common problem in many MRA studies, different regression models with specification based on fish species, valuation method or measurements are estimated and compared.

Following Gren and Marbuah (2021), climate factors are considered as potential determinants of the fishing value, which has not been included in other MRA studies of environmental values.

The main contribution of this study is the MRA at a global scale, which has not been made before. The study then extends the literature of MRA on the value of fishing, which has been made by only two published studies. Johnston et al. (2006) focused on one value measurement, value of a marginal fish, and included only studies applied to Canada and USA. Gren and Marbuah (2021) considered several fish species, valuation methods and measurements but included only studies on the Arctic region. The present study is organized as follows. Section 2 describes the data retrieval, the econometric approach and estimations are presented in Section 3, and predictions are made in Section 4. The results are discussed and conclusions are drawn in Section 5.

2. Description of data

Data on WTP for fishing is obtained by a systematic review of studies, which includes choice of studies to be included in the analysis, and specification of dependent and independent variables in the regression analysis.

2.1 Collection of studies

Source study identification was obtained by three different methods; i) various combinations of

the keywords fishing OR fish AND value OR willingness to pay in different data bases of studies,

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authors known to have undertaken non-market valuation of fishing, and iii) application of the snowball method. The search was made during the period from winter 2020 to summer 2021.

Several databases were used including Google, Google Scholar, REPEC/Ideas, ResearchGate, Scopus, Semantic Scholar, and Web of Science. The Web of Science and Scopus websites provided studies published in journals. The other databases contained data on studies in the

‘grey literature’, which include reports from non-academic institutions, and reports and working papers from academic institutions that are not published in journals. RUVD (2016) was an important data base which includes studies on fishing recreation values in USA and Canada during 1958 and 2015. The snowball method was useful for identifying many studies from references within or to a specific study, in particular to Johnston et al. (2006) which is the only review studies of fishing values.

In addition to data on fishing value, there were three types of requirement for including a study in the analysis. In order to account for study specific characteristics there is a need for information on region, fish species subject to valuation, valuation method, and valuation measurement. In total, 221 studies with a total of 1495 observations were found with this information.

After controlling for overlapping information and duplicates, 209 studies with 1116 observations were used for the analyses (see list of studies in Table B1). The average of 5.3 observations per study is close to the average of 4.9 in environmental economics meta- regression studies reported by Nelson and Kennedy (2009). A few studies estimated WTP for different fish species and years in all states in the USA, where one study could include 75 observations (Table B1 in Appendix B).

2.2 Dependent and independent variables

The estimated fishing value reflect an angler’s WTP, which constitutes the dependent variable

in this study. The WTP reflects the consumer surplus, i.e. the value of angling in excess of the

costs, and it is measured in year 2018 USD. The conversion to year 2018 values was based on

country-specific consumer price indices (World Bank, 2021c), and converted into USD using

the average exchange rate for 2018 (The Swedish Riksbank, 2021).

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The independent variables are classified into three different categories; context, climate, and study characteristics. The context related variables include income, population density in the state or country, year of the study, and application region. For a normal good, income shows a positive impact on valuation of environmental goods. However, due to lack of data on income in all studies, gross regional product per capita is used as a measure of income in the study region. Since studies reported fishing values for different states and provinces in USA and Canada, data on these variables are obtained at that spatial level. For USA total state income is obtained from BEA (2020) and BEA (2004), and for Canada in Statistics Canada (2021). Data for the other countries, which includes Norway, Sweden, Ireland, UK, Germany, Chile, Australia, and New Zealand, are on a national basis which was found in World Bank (2021a).

The effect of population density can be negative if there is congestion (

), and/or associated with pollution of streams and water affecting fish populations. Another aspect is that it can reflect urbanization which, in turn, is expected to reduce the interest for recreational fishing due to, e.g. distance to suitable waters (Arlinghaus et al., 2015). Population data in each state in the USA is found in CDC (2021) and US Bureau of Census (2020), and for provinces in Canada in Statistics Canada (2020). Data for other countries is obtained from World Bank (2021b).

Regarding year of study, the included studies covers of period of 57 years over which there has been demographic changes, urbanization and changes in preferences which can affect the WTP and is therefore included as an explanatory variable. As shown by Arlinghaus et al (2015) the participation rate of anglers in relation to the total population has been reduced in many industrialized countries. Most studies are applied to USA, and the other studies are divided into applications in Europe and Other countries,

Climate factors may act directly on angler’s preferences for fishing by affecting pleasure and

costs of fishing and indirectly by impacts on the fish population size and composition. An

increase in fish populations would then have a positive effect on the value of fishing when the

catches matter, and vice versa. Two climate variables are included in this study; temperature

(

C) and precipitation (mm), data of which are found in the publicly available database

(WorldClim, 2020). This study used a spatial resolution of 10 minutes (~340 km

), which was

sufficient to capture the data needed for each study’s waterbody location. For each spatial

resolution, average long term (1970-2000) data on temperature and precipitation was extracted

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and matched to the geographic specific location or site of each study’s waterbody based on its longitude and latitude based on NOAA (2021b). The climate variables for each study was then obtained by geoprocessing of the two datasets u

The study characteristics include the fish species valued in the study, the value measurement method, the unit of value measurement, and publication outlet. All these factors are treated as dummy variables. Regarding choice of fish species, relatively many studies estimated values of trout, salmon, and bass. Several studies do not report fish species, but instead freshwater and salt water. A few studies refer only to fishing in general or to several fish species without distinction denoted ‘other fish’. In this study, dummy variable are introduced for all six categories.

With respect to value estimation method, the methods for obtaining estimates of non-market values of is usually divided into revealed and stated preference methods and previous MRA studies have shown that choice of method affect the results (e.g. Johnston et al., 2006). Revealed preference methods are based on behaviour in indirect markets, which can be related to changes in fishing conditions, and stated preference methods on surveys to elicits respondents’ WTP for environmental changes. The travel cost method (TCM) is one of the most applied revealed preference methods and links unpriced public goods to a priced market good, and include measurement of only so-called use values. Stated preference methods also consider non-use values, such as existence values, and include contingent valuation methods (CVM) and choice experiments (CE).

Regarding unit of value measurement, the studies applied different approaches where the value per day is most common. Other measurements are value per trip, per season, or per fish. Some studies use other measurements, such as marginal value per kg fish (Mwkara, 2015) or use a mix of unspecified measurements, which are classified as ‘other measurements’. A dummy variable is introduced for studies published in scientific journals with independent referee system, or in other publication outlets. A test is also made of publication bias, which is described in Section 4.

The data is thus divided into 28 explanatory variables, and associated descriptive statistics for

all variables are displayed in Table 1.

8 Table 1: Descriptive statistics, N=1116

Mean St dev Minimum Maximum

Fishing value, constant

2018 USD

302.27 791.27 0.01 11414.40

GRP/capita, 1000 constant

2018 USD

45.02 15.97 6.41 100.41

Year of study

1993 10.72 1960 2017

Population/km², thousand

42.64 60.545 0.07 445.07

Temperature, ̊ C

9.34 6.30 -8.11 25.63

Precipitation, cm

7.64 7.01 3.92 31.26

Region:

USA 0.88 0 1

Europe 0.06

Other countries 0.06 0 1

Fish:

Trout 0.27 0 1

Salmon 0.07 0 1

Bass 0.22

Saltwater 0.09

Fresh water 0.13

Other fish 0.24

Publication:

Journal 0.39 0 1

Non-journal 0.61 0 1

Valuation method:

TCM 0.42 0 1

CVM 0.46

CE 0.05

Hedonic 0.05 0 1

Other method 0.02

Value measurement:

Day 0.85 0 1

Trip 0.04

Season 0.06

Fish 0.02

Other measure 0.03 0 1

The mean estimate of the fishing value is 302 USD, but the variation is large, between 0.01 and

11414 USD. The low fishing value refers to the marginal value of 1 kg trout in New Zealand

(Mwkara, 2015), and the high values of fishing are found for salmon in the Alaska for a fishing

season (Carson et al. 2009). The studies have been made during a period of almost 60 years,

where Kalter and Gosse (1969) was the earliest study and it projected recreational values of

fishing for the New York state in 1960.

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A majority of the observations, 88%, is applied to fishing in the USA, and the others to Europe (Norway, Sweden, Finland, Iceland, Denmark, England, Ireland, Germany, France) and other countries (Australia, New Zealand, Chile, Canada) in equal proportions. Trout and bass were the most commonly fish species subject to valuation. CVM and TCM accounted for 46 and 42

%, respectively, of the valuation method choices. Value per day is by far the most commonly used fishing value measurement, which accounts for 85 % of all observations.

However, Table 1 reveals a high heterogeneity in the measurement of fishing value, which makes it difficult to compare studies. On the other hand, inclusion of many studies in the regression analysis increases the sample size which improves the statistical performance.

Therefore different sub-sample models are examined depending on study characteristics, which includes choice of value object, value measurement and method. Both mean and precision differ between these sub-samples when estimated for fish species (Table 2). Since the number of observations using fishing value-per-day as value measurement is large, further division of the subsamples are made where only value-per-day measurements are considered

Table 2: Fishing value mean, std., min and max for different sub-samples with all observations and with observations only for value-per-day measurements

N Mean St dev Minimum Maximum

All;

Trout 296 178.37 413.03 0.01 4529.52

Bass 223 190.25 377.35 0.63 4163.01

Salmon 74 874.60 1591.88 2.43 8591.00

CVM 517 178.64 332.80 0.77 4163.01

TCM 464 434.76 1066.61 0.01 11414.40

Day 952 205.25 438.86 0.28 7674.32

Day measurements;

Day and trout 266 138.66 199.82 8.26 1724.55

Day and bass 219 174.46 268.23 0.63 1431.79

Day and salmon 35 394.31 856.39 2.83 4711.60

Day and CVM 479 181.78 290.79 1.72 1844.99

Day and TCM 383 234.31 434.55 0.28 4711.60

When divided into one level sub-samples, the mean values differ between the cases, but the

dispersal is relatively similar when relating it to the mean. The coefficient of variation (standard

deviation divided by the mean) is approximately 2 in all cases when all observations are

included. This dispersion is slightly reduced with the two level sub-samples where the value

measurement is the same for all cases, but value object and method differ.

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3. Econometric specification and results

The data set includes two levels with studies at the top level and observations at the bottom level. In such a data set there is a risk of within and between study correlations, and a mixed effect model is defined which accounts for the existence of correlation in observations within and between studies (e.g. Gelman and Hill, 2007). The method is much used in meta-regression analysis (e.g. Nelson and Kennedy, 2009; Hedges et al., 2010). Study-specific effects may impact the intercept and the estimated coefficients of the independent variables. Tests were made using maximum likelihood estimator with random effects on only the intercept and with impacts on the intercept and on the slope of GRP/capita with assumption of independence in the covariation. The results showed best statistical fit when random effects are included in the intercept and the slope.

Different functional forms of the regression equation were tested with different combinations of logarithms of the dependent variables and the continuous explanatory variables (income, population density, temperature and precipitation). The best statistical fit as measured by AIC, BIC, and log-likelihood was found for the logarithm of fishing value, GRP/capita and population density and linear specification of temperature and precipitation. The following regression equation was then estimated:

, 0 1 i,j 2 3 , 4 , 5, , 0. 1, ,

lnV

= α α + lnGRPC + α ln

+ α

+ α

+ ∑

α

+ λ

+ λ

+ ε

⁽¹⁾

where V

is the value of observation i in study j, GRPC

is the GRP/capita, POP is population density, Temp is temperature, Prec is precipitation, and X

, is a vector of dummy variables on study characteristics where h=1,…,m characteristics. The term λ

shows the random effect at the study level in the intercept and λ

in the coefficient of lnGRPC

. The term ε

is the stochastic error term at the individual level.

Estimates of regression equation (1) were made for different sub-samples, and USA and Journal

were used as the dummy reference variables for all cases. This implies a minimum of 10

explanatory variables, which differs depending on sub-sample. The number of observations

also differs between the sub-sample and too few observations in relation to explanatory

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variables may give good statistical fit but poor predictive ability (e.g. Rosenberg and Loomis 2001). In general 10 observations per predictor, which is regarded sufficient although larger number of observations are advised (Katz, 2006). Regressions were therefore made with all sub-samples presented in Table 2 except for salmon because of the relatively low number of observations (<5 per predictor). When estimates were made for trout, the additional reference variables are Day and CVM, for CVM observations Day and Trout, and for DAY they are Trout and CVM.

Tests did not show any concern for multicollinearity, with an average VIF (Variance Inflation Factors) ranging between 1.57 and 3.52 for the different sub-samples (e.g. O’Brien, 2007). A Breusch-Pagan test revealed problems with heteroscedasticity, and robust standard errors were therefore estimated.

Tests were also made for the existence of publication bias. In general, it is regarded that significant results are more likely to be published than non-significant results, and the existence of publication bias would include a variable of the standard error of each observation (e.g.

Nelson and Kennedy 2009). Such data is not available for most of the studies included in the study, which is a common problem for MRA studies. As shown by Stanley and Rosenberger (2009) the square root of the inverse of the sample size can be used as a satisfactory measure of precision in the estimates. Following Vedogbeton and Johnston (2020) tests of publication bias were made for all models with this proxy variable as an independent variable, and use of weighted least square (WLS) with sample size as weight. The proxy variable was not significant in any model.

Regression results for the five different cases with sufficient number of observations are presented in Table 3.

,

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Table 3: Regression results of mixed effect models with lnalue as dependent variable for different sub-samples (standard errors in parentheses)

Trout Bass CVM TCM Day

Constant -37.24 (28.71) 71.749*(38.82) -16.367(34.57) 22.381(20.953) 4.745(19.519) Lngrpc 0.183 (0.252) 0.080(0.223) -0.022(0.239) 0.844**(0.386) 0.335**(0.162) Lnpop -0.062*(0.032) 0.038(0.221) -0.008(0.039) 0.126(0.086) -0.114(0.096) Temperature ^0.029***(0.010) 0.033***(0.011) 0.020**(0.008) 0.017(0.020) 0.006(0.012) Precipitation -0.024 (0.018) -0.018(0.014) -0.012(0.018) -0.012(0.016) -0.011(0.093) Year 0.020 (0.014) -0.034*(0.019) 0.010(0.017) -0.014(0.011) -0.002(0.010) Europe 3.396***(1.30) -1.425(0.976) -1.055(0.645) -1.612(1.052) Other

country 0.921(1.231) -2.528***(0.70) -0.972*(0.510) -0.031(0.0597) -2.034***(0.44) Nonjournal -0.124(0.290) -1.151*(0.589) -0.220(0.438) 0.106(0.243) -0.063(0.209) Salmon ^1.070***(0.29) 0.173(0.382) 1.121***(0.402)

Bass -0.218***(0.07) -0.138(0.420) 0.046(0.184)

Other fish 0.439(0.571) 0.049(0.281) 0.166(0.302)

Fresh water 0.600(0.569) -0.639**(0.32) 0.050(0.381) Salt water 0.769*(0.409) -0.415(0.318) 0.261(0.256) Trip -0.239(1.354) 0.795***0.16) 2.161**(0.903)

Season 3.352***(0.35) 3.201***(0.34)

Other value -4.011***(1.281) 1.270(0.942) 0.977(1.606) -3.148***(0.35)

Fish 1.505***(0.371) -1.383*(0.773) 0.173/0.399)

TCM -0.157(0.185) -0.581(0.723) -0.031(0.197)

CE -1.629*(0.894) -0.712(0.737) -0.057(0.350)

Hedonic -1.241***(0.329)

Random effect parameters

λ

λ

ε

Model statistics

N ^{296 223 517 464 943}

Studies ^{58 34 68 126 166}

AIC/N 2.927 1.964 2.098 3.181 2.678

BIC/N 3.152 2.177 2.262 3.368 2.781

McFadden’s

R^{2 0.04 0.09 0.06 0.04 0.05}

Significance: ***p<0.01, **p<0.05, *p<0.10

The models show statistically satisfactory results with statistically improved results compared with models without covariates. The Bass model indicates the best fit with the lowest values of the observations adjusted information criteria and the highest McFadden pseudo R

. Each model also contains statistically significant independent variables.

Results common to all models are the positive effect of temperature, and negative of

is not significant in any model. The coefficients are interpreted as a change in percent in the

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fishing value from a change by one unit in the variable. For example, the coefficient of 0.029 of temperature in the Trout model shows that the fishing value increases by 2.9 % when the temperature increases by 1°C.

When comparing the two models of value objects, i.e. Trout and Bass, the positive effect of

that the Bass model includes no observations in Europe and only Other value measurements in addition to values per day. The comparison of models with different valuation methods, i.e.

CVM and TCM, reveals similarities with respect to the effects of fishing values in Europe and

measured per Trip. It can also be noticed that the fishing value increases for Salmon compared with Trout in both models. This effect is also positive and significant in the Day model, which, similar to the Trout and Bass models, also shows negative effects of valuation method other than the CVM.

Most of these results remain when only observations measuring fishing value-per-day are

included in the data set (Table 4).

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Table 4: Regression results of mixed effect models with lnalue as dependent variable for different sub-samples with value-per-day measurements (standard errors in parentheses).

Trout Bass CVM TCM

Constant -38.768(28.301) 37.710(42.989) -18.542(38.306) 18.655(22.507) Lngrpc 0.337(0.227) 0.067(0.216) 0.267(0.202) 1.146***(0.387) Lnpop -0.074***(0.027) 0.033(0.028) -0.046*(0.027) 0.209**(0.093) Temperature 0.023***(0.008) 0.033***(0.011) 0.019**(0.007) 0.001(0.018) Precipitation -0.008(0.012) -0.016(0.012) 0.004(0.012) -0.011(0.012) Year 0.020(0.014) -0.017(0.021) 0.010(0.019( -0.013(0.012)

Europe -0.082(2.143) -1.688***(0.425)

Other country -0.632**(0.257) -0.995**(0.479) -1.357**(0.606) Nonjournal -0.167(0.261) -0.657(0.695) -0.003(0.432) 0.126(0.232)

Salmon 1.243***(0.171) 0.975(0.627)

Bass -0.226***(0.073) -0.245(0.430)

Other fish 0.374(0.706) -0.263(0.251)

Fresh water 0.611(0.558) -0.628**(0.319)

Salt water 0.783(0.433) -0.429(0.320)

TCM -0.168*(0.095) -0.084(0.809)

CE -0.490(0.456) 0.252(0.777)

Hedonic Random effect parameters

λ

λ

ε

Model statistics

N ^{266 215 477 383}

Studies ^{51 31 58 114}

AIC/N 1.88 1.94 1.90 2.79

BIC/N 2.06 2.13 2.05 2.96

McFadden’s

R^{2 0.05 0.08 0.07 0.05}

Significance: ***p<0.01, **p<0.05, *p<0.10

The statistical performance is slightly improved compared with all value measurements as shown by the observation adjusted AIC and BIC. Temperature has a positive effect in all models, and is significant in three models. The impact of Lngrpc is positive in all models and that of the dummy variable for Other countries is negative in all models. It can be noted that there are no value-per-day observations for Europe for the Trout and Bass models.

4. Predictions

The estimated regression functions can be useful for predictions of impacts on the fishing WTP

of changes in the explanatory variables, such as temperature which had a positive and

significant effect on the fishing value in several models. This can be of relevance for many

regions when considering the expected increase in temperature by 2 within the next 20 years

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(IPCC 2021). The results in this paper indicate that this will increase anglers’ fishing value. In principle, this can occur directly on the angler’s utility by, e.g. reduced cost of fishing, and indirectly by increased fish populations.

In order to evaluate the model’s ability to predict effects of such changes their predictive power is estimated by within-sample forecasts of the mean, standard deviation, and 95 % confidence intervals. Mean percentage errors (MPE) are estimated which shows the average deviation in the predicted values compared with the source data. Smeared estimated values account for bias from the non-zero mean error distribution for logged-dependent variable models (Woolridge, 2013). The predicted value i for characteristic h, Valuepred

ln , / 2

, ^{Vi h}

*

Valuesti h

=

where ε

is the error term.

Since the models measuring value per day in Table 4 show the best statistical fit, predictions are made for these models (Table 5).

Table 5: Predicted mean, standard deviation, lower and upper 95 % confidence interval, and MPE for sub-sample models with value-per-day measurement

Predicted mean, USD/day (% of the sample mean)

Std. 95 % confidence interval, USD/day;

Lower Upper

MPE, %

Trout

137 (99 %) 175 116 158 39

Bass

176 (101 %) 240 143 208 46

CVM

178 (98 %) 253 155 201 46

TCM

199 (85 %) 235 175 222 65

All models except the TCM model predict means within 2 % deviation from the sample mean, and the MPE is relatively low for these models. Similar estimates of meta-analyses of fishing values have not been made, but Rosenberger (2015) showed that the average MPE across all function transfer models of environmental values in the literature was 65%.

Since Temperature is significant in three models, predictions at different degrees are made for these models. For each of the model, the marginal effect of Temperature is determined as:

𝜕𝜕𝑉𝑉_ℎ

𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕𝜕

= 𝛼𝛼

∗ 𝑉𝑉

(2)

where h represents the Trout, Bass and CVM model, and α

is the estimated coefficient for

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increase in fishing values of an increase in temperature by 1°C ranges between 3.2 and 5.8 USD/day for the different cases (Figure 1).

Figure 1: Increase in fishing value from an increase with 1°C for Trout, Bass and CVM models in the reference case with all countries and separately for USA and Other countries

The regression estimates for Trout and CVM show that fishing values in Other countries is lower than in USA. Marginal values for Trout and CVM were therefore calculated for these two regions (Figure 1). The marginal values for Other countries are less than half of that for

The average temperature in the data set is 9.34 °C. The impact of the temperature level is illustrated for the three different models for increases in the temperature up to 2°C.

Figure 2: Per cent increases in average fishing value per day at different temperatures for different sub-samples.

At the most, the fishing value per day increases by approximately 7 % when the temperature increases by 2°C. This occurs for the Bass mode which is explained by the higher coefficient in the regressions than for the other cases.

0,00 1,00 2,00 3,00 4,00 5,00 6,00 7,00

Trout Bass CVM

USD/day

Reference USA Other countries

0 2 4 6 8

0 0,5 1 1,5 2

Per cent

°C increase

Trout Bass CVM

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5. Discussion and conclusions

The main purpose of this study was fourfold; to examine patterns in studies of willingness-to- pay for fishing, to quantify determinants of the WTP, to estimate the predictive power of the regression models, and to illustrate the impact on WTP from changes in robust parameter estimates. To this end, MRA was used with a total of 208 studies on valuation of fishing during the period 1960 to 2017. Findings from the explorative analysis included large concentration of studies in the USA, which accounted for 88% of all observations, and the common measurement of the value as per angler and day (85%). With regard to value object, 27 % and 22 % of the observations were obtained from studies applied to trout and bass fishing, respectively, and CVM was the most common valuation method. The average value-per-day varied between 138 USD and 379 USD, with the lowest for trout and highest for salmon.

Regarding the second question raised in the study of determinants of the WTP, 9 variables reflecting contextual and environmental conditions and 16 variables describing study characteristics were included. Different sub-sample models were specified to reduce the heterogeneity in the estimated WTP. A common result to all models was that the environmental variable measured as temperature showed a robust result with a positive and significant effect in most models. Income was in general positive, but seldom significant. The fishing value was increased when salmon was targeted and decreased when other valuation methods than CVM were used. The predictive power of all models was satisfactory when comparing the MPE with other MRA studies on environmental values, and it was particularly good for the model with value-per-day for trout fishing. Because of the robust result on the impact of temperature on the fishing value, simulations of temperature increases were made with the models with the best predictive power. The results showed that the effect of an increase in the average temperature by 1°C on the value-per-day ranges between 3.2 and 5.7 USD depending on sub-sample model.

The results can be compared with the only MRA study on environmental values which includes

environmental factors as explanatory variables (Gren and Marbuah, 2021), and with a MRA of

fishing values (Johnston et al. 2006). Gren and Marbuah (2021) made a MRA of fishing values

in the Arctic region with the specific purpose of calculating impacts of climate change. Similar

to the present study, it was found that temperature increases the value. Another result was that

precipitation had a negative effect. Income had a positive effect, which is a common result for

18

the value of any good; when income increases demand for a normal good increases. This was also found by Johnston et al. (2006) in the MRA of the value of a marginal catch in the USA.

Similar to the present study, Johnston et al. (2006) obtained significant effects of valuation method and design. They also found significant effects of angler characteristics, such as age, which could not be made in the present study because of lack of data from all included studies.

The result of a positive effect on the fishing value of temperature increases can be explained by impacts on the fish populations. An increase in fish population will, according to economic theory, raise the value of fishing because of large catches. There is large body of literature indicating that fish species move northwards with climate warming (e.g. Rijnsdorp et al. 2009;

Huang et al, 2021). Rijnsdorp et al. (2009) found shifts in the abundance and distribution of Northeast Atlantic fish species with increases in Lusitanian species, and a reallocation of boreal species with increase in the northern region and decreases in the southern parts. In a review of 1187 studies on the relation between fish growth and temperature Huang et al. (2021) showed that most studies found a negative impact on fish growth, but they also found that only a small part of the fish species (<1% of global fishes) have been analyzed and that there is a lack of studies on species in freshwater systems.

However, the regression estimates of the determinants of the fishing value excluded several variables because of lack of data, such as anglers’ characteristics with respect to age and motives for fishing. The positive effect of temperature on the fishing value can be explained by impacts on fish population composition and sizes, but also by other motives for fishing.

Birdsong et al. (2021) showed in a meta-analysis of 23 studies on fishing motives that catch related factors (size of the caught fish, harvest) was one of three important determinants of anglers’ satisfaction with the fishing. Other factors were access to fishing sites and crowding, which were not considered in the present study.

Nevertheless, the results seem reasonable based on the partial comparison with other relevant

studies and pointed out potentially value enhancing effects of temperature. This raises the

question whether the results can be transferred to other countries than the source regions, which

have been discussed very much in the literature (e.g. Johnston et al. 2015). If so, the estimated

values per day and marginal impacts of temperature changes could be calculated for entire

countries and regions . For example, the number of fishing days in marine waters in the USA

amounts to 68 million (Hyder et al., 2018). The average value-per-day in the USA amounts to

19

195 USD (Table A1), which implies a total value of 13.20 billion USD The estimated total effect of an increase by 1°C would be 0.25 billion USD with a marginal value of 3.71 USD/day (Figure 1). However, these results rest on incomplete understanding of anglers’ motives for fishing and mechanism regarding temperature effects on fish populations, which require further research.

Appendix A: Tables A1-A2

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