An Estimation of Intra-City Fiscal Transfers in Japan: Stealth Fiscal Transfers

Shunsuke Sekiguchi

School of Economics, Tokyo International University, Kawagoe, Japan.

DOI: 10.4236/cus.2023.113021                

Abstract

This study aims to investigate intracity fiscal transfers from the city planning areas (CPAs) to outside the city planning areas (non-CPAs) that are not recorded on the municipal account settlement cards and to estimate the existence and amount of these transfers. The current location optimization plan in Japan attempts to realize compact cities by defining residential zones and urban function zones and by providing preferential tax treatment. Nevertheless, the location optimization plan does not cover non-CPAs, which means that the location optimization plan does not function for non-CPAs, and this is considered a social issue. Non-CPAs have low population density, and based on previous studies, the fiscal efficiency of non-CPAs is considered low. Intracity fiscal transfers are probably made from CPAs to non-CPAs, just as so-called intergovernmental fiscal transfers have a horizontal fiscal adjustment function. In this study, we refer to this intracity fiscal transfer as stealth fiscal transfer (SFT). To estimate the SFT, we estimated the average expenditure function using five-year data from FY1990 to FY2010 and we simulated the SFT for FY2005 and FY2010 using the estimated average expenditure function and mesh data. By estimating SFT, the existence and amount of SFT will be revealed, and as a policy implication, an academic basis for expanding the scope of the location optimization plan will be derived. The simulation results showed that CPAs, the payers of SFT, were estimated to have paid a national weighted average of ￥19,218 per capita in FY2005 and ￥18,360 per capita in FY2010. Conversely, non-CPA, the recipient of SFT, was estimated to have received a national weighted average of ￥164,017 per capita in FY2005 and ￥171,360 per capita in FY2010. The total value of SFT in Japan was estimated to be ￥1,104,830,463,745 in FY2005 and ￥1,062,534,779,839 in FY2010. Although the location optimization plan is a plan for CPAs, the policy implication derived from this study is that in the future, the location optimization plan is expected to cover not only CPAs but also the entire area within a municipality, including non-CPAs.

Keywords

Compact City, Stealth Fiscal Transfers, Intergovernmental Fiscal Transfers, Intra-City Fiscal Transfers, Average Cost, Municipalities’ Efficiency, Japan

1. Introduction

There is a long history of attempts to improve the efficiency of municipal finances, with accumulated research and practical applications in Japan and abroad. One example is the focus on the size of a municipality, i.e., its population. The optimal city size theory and the minimum efficient city size theory are representatives of such studies, and overseas discussions include Hirsch (1959, 1965) , Walzer (1972) , Oates (1972) , Mirrlees (1972) , Dixit (1973) , Richardson (1973) , and Bodkin and Conklin (1971) . Sekiguchi (2019) Although there are some differences, like those that derive the optimal scale for the entire city or those that specialize in the optimal scale of a particular publicly provided good or service, they generally analyze the optimal scale from the perspective of benefit maximization or cost minimization.

In Japan, many studies discussed the optimal city size from the viewpoint of cost minimization, including Nakai (1988) , Yokomichi and Okino (1996) , Yoshimura (1999) , Nishikawa (2002) , and Hayashi (2002, 2003) . Hayashi (2002) , in his study on the optimal city scale in Japan, uses the minimum efficient scale (MES) theory to construct a theoretical model and conduct an empirical analysis. Although the size of cities in the MES varies depending on the time of analysis, Nakai (1988) estimated 128,000 people, Yokomichi and Okino (1996) estimated 90,000 to 200,000 people, Yoshimura (1999) estimated 210,000 to 270,000 people, Nishikawa (2002) estimated 170,000 people, Hayashi (2002) estimated 310,000 to 460,000 people, and Hayashi (2003) estimated at 200,000 - 270,000 people, respectively. Based on the academic evidence accumulated through these studies, the number of municipalities in Japan has decreased from 3234 in 1997 to 1727 in 2010, about half the number of municipalities in Japan because of the so-called Heisei no Dai Gappei, a nationwide merger of municipalities.

If the accumulation of research on MES is taken as an ex-ante evaluation study of municipal mergers, the following studies can be cited as ex-post evaluation studies of whether mergers of municipalities contributed to efficiency gains, including expenditure reduction: Uemura and Sumi (2003) , Takemoto et al. (2004) , Hayashi (2013) , Nakazawa (2014) , and Hirota and Yunoue (2016) . Uemura and Sumi (2003) estimated that mergers could reduce expenditures by up to 0.7 trillion yen in Japan as a whole. Takemoto et al. (2004) did not estimate the scale of reduction but estimated that economies of scale would work and expenditures could be reduced. Hayashi (2013) also estimated that a certain level of expenditure reduction is expected. Conversely, Nakazawa (2014) and Hirota and Yunoue (2016) estimated that there would not necessarily be an efficiency of expenditure reduction, and it is difficult to say that a consistent evaluation has been obtained as an ex-post evaluation.

Another study on the possibility of distorting the efficiency of municipalities is the inefficiency of intercity fiscal transfers or so-called soft budget constraints due to intergovernmental fiscal transfers. In Japan, studies on the allocation of local allocation tax (LAT) grants subsidies, include Kuroda (1986) , Kornai (1986) , Sato (2002) , Yamashita et al. (2002) , Kornai et al. (2003) , Miyazaki (2004) , and Otsuka and Goto (2014) . Kuroda (1986) highlighted the institutional problems of LAT grants, Kornai (1986) was the first to point out the concept of soft budget problem, and Sato (2002) and Kornai et al. (2003) argued that fiscal transfers from central government to local governments induce unproductive public goods supply. Yamashita et al. (2002) clarified these possibilities through theoretical and empirical analysis. Miyazaki (2004) also estimated the possibility that intergovernmental fiscal transfers cause the softening of local government budget constraints using a stochastic frontier model. Otsuka and Goto (2014) estimated the loss of efficiency in total expenditure caused by the soft budget problem and found it to be approximately 23% of LAT grants. Previous studies up to this point can be viewed as studies of city size focusing on population size. In terms of improving municipal efficiency, it is important to pursue economies of scale through mergers and to solve institutional problems with intergovernmental fiscal transfers, but it will be necessary to focus not only on the size of the city as a whole but also on the city structure, including population density.

The first report of the Council for Social Infrastructure in Japan (2006) focused on the city structure, considering the existing city structure to be disorderly and diffuse, and argued for the realization of an intensive city structure, or compact city, as a review of this structure. The concept of the compact city has been incorporated into Japan’s major policies, including the Second Report of the Council for Social Infrastructure Development (Council for Social Infrastructure in Japan, 2007) and the Basic Policies for Economic and Fiscal Management and Reform. Studies on the relationship between city structure and municipal finance include Carruthers and Ulfarsson (2003, 2008) , Hortas-Rico and Solé-Ollé (2010) , Kawasaki (2009) , Morimoto (2011) , Sekiguchi (2012) , Wada and Ohno (2013) , Kutsuzawa (2016) , and others on the efficiency of compact cities. Of these, Kawasaki (2009) and Sekiguchi (2012) used population density as an index to measure city structure, whereas Kawasaki (2009) and Sekiguchi (2012) estimated the optimal compactness from the perspective of cost minimization and fiscal surplus maximization, respectively. Kutsuzawa (2016) measured the city structure using the concept of standardized standard distance and estimated the contribution of city compactness to municipal finances. Morimoto (2011) evaluated city compactness positively from both fiscal and environmental perspectives by using Utsunomiya City, Tochigi Prefecture, as a case study. Wada and Ohno (2013) focused on the area and evaluated the fiscal impact of urban compact using Nagaoka City, Niigata Prefecture, as a case study.

Various indicators measure city structure, for example, population density, area, and distance, and although there remains room for debate as to what indicator should be used to measure, the common indicator is population density. Wada and Ohno (2013) , who used the area as an indicator, considered areas with high population density to be aggregation destinations, and Kutsuzawa (2016) , who used distance to be an indicator, also used population density in the calculation of standardized standard distances. Conversely, in the relationship between city structure and municipal finance, the concept of city centrality can be considered, but the location of the center must also be taken into account, and the center in terms of economic activity and that in terms of administrative planning are not necessarily the same. Additionally, from the perspective of economies of scale, which is significant for efficiency, having a certain degree of population density may be more important for the efficiency of municipal finances than the concept of a city center. Hence, in this paper, the population density will be used as an indicator to measure city structure.

Although studies on city structure evaluate the compact city as a desired city structure, Japan’s Urban Revitalization Special Measures Law was amended in 2014 to introduce a location optimization planning system. According to the guidebook for preparing the location optimization plan (Ministry of Land, Infrastructure, Transport and Tourism, 2022) , the location optimization plan is an advanced version of the Municipal Master Plan and is intended to realize compact cities by defining residential zones and urban function zones and by providing tax incentives.

Conversely, according to the 12th edition of the Operational Guidelines for City Planning (2022), “The area of the location optimization plan must be within the city planning area, but from the perspective of looking at the entire city, it is fundamental that the entire city planning area is subject to the area of the Location optimization plan. Additionally, when there are multiple city planning areas (CPAs) within a municipality, it is fundamental to prepare a location optimization plan for all CPAs”, it should be noted that the location optimization plan is a plan limited to the scope of the CPA.

Additionally, according to the guidelines, the location optimization plan allows for establishing a residential adjustment zone outside of the residential zone when it is necessary to control residential development, and a site management zone when the number of vacant lots is increasing and proper management of these lots is necessary. However, non-CPAs are outside the scope of the location optimization plan, and the fact that the location optimization plan does not work for non-CPAs is deemed a social issue.

This would not be a social issue if the entire area of a municipality were designated to be CPAs, but in the case of a rural city, only a portion of the area is designated to be a CPA. If urban downsizing is considered to increase population density through the consolidation of city structures, it should not only be limited to CPAs but should also include non-CPAs. Non-CPAs have low population densities, and based on previous studies, the fiscal efficiency of these areas is low. A study focusing on differences in efficiency allocation within the same city was conducted by Sekiguchi and Nagase (2019) , who highlighted the possibility of intracity fiscal transfers from areas with high population density and high efficiency to areas with low population density and low efficiency within the same city. This intracity fiscal transfer can be considered an academic issue that has not been highlighted in previous studies.

Social issue: the location optimization plan does not cover non-CPAs.

Academic issue: most previous studies do not consider the possibility of intracity fiscal transfers.

Based on the social and academic issues, this study aims to estimate the amount of invisible fiscal transfers from CPAs to non-CPAs, which we call stealth fiscal transfers (SFTs). This estimation will reveal how much SFT is paid per capita by CPAs and how much SFT is received per capita by non-CPAs, thus revealing the existence and amount of intracity fiscal transfers, which is an academic issue. Additionally, if the existence and amount of SFTs are revealed, policy implications can be derived to expand the scope of the location optimization plan and contribute to solving social issues.

The structure of the rest of this paper is as follows: The simulation model of total expenditure per capita, followed by simulations using the estimated parameters in Section 2. The simulation results are tabulated for each prefecture in Section 3. Finally, one summary and future issues are presented in Section 4.

2. Simulation

2.1. Estimation Model

First, we specify the following model by decomposing the average cost, which is the total expenditure per capita in the municipality i in fiscal year t, into $M_{it}$ , the population density as a city structure into $D_{it}$ , and other factors of type j, which cannot be expressed by the city structure index, into ${\gamma }_{itj}$ for example, population aging rate and types of municipalities as dummy variables of the ordinance-designated city, core city, special city. The types of municipalities are mainly based on population requirements and the affairs transferred from the prefectures (see Table 1 for details).

$M_{it}=D_{it}^{\alpha }{\gamma }_{itj}^{{\beta }_j}$ (1)

Here, we transform the logarithm of Equation (1) and specify it as follows to perform panel data analysis.

$\ln M_{it}=\alpha \ln D_{it}+{\displaystyle {\sum }_{j=1}{\beta }_j{\gamma }_{itj}}+{\beta }_0+{\epsilon }_{it}$ (2)

Note that ${\beta }_0$ is the constant term and ${\epsilon }_{it}$ is the error term that satisfies the usual assumptions.

To obtain the average cost of a municipality, we divide its total expenditure by its population, but the total expenditure is strongly affected by natural disasters,

Table 1. Types of municipalities.

Reference: Prepared by the authors based on the Ministry of Internal Affairs and Communications website (confirmed on April 14, 2022). https://www.soumu.go.jp/main_content/000799385.pdf.

including torrential rain disasters. In this study, unbalanced panel data for FY1990 for 1737 municipalities, FY1995 for 1740 municipalities, FY2000 for 1741 municipalities, FY2005 for 1741 municipalities, and FY2010 for 1739 municipalities, which correspond to the years in which the census was conducted, are used, but using data for each year directly is problematic because it reflects large single-year fluctuations. Therefore, as shown in Table 2, the total expenditure for each fiscal year is the geometric mean (GM) of the total expenditures, including the total expenditures of the fiscal years before and after the fiscal year in which the data are used. When using actual values, the arithmetic mean (AM) is generally used, but the GM is used because the GM is equal to or less than the AM, and the GM can reduce fluctuations in a single fiscal year compared to AM.

Table 2. Adjustment for fluctuations in a single fiscal year.

Let us restate here the purpose of this study. This study aims to reveal the existence and amount of SFT. In this estimation, simulation is performed based on statistical information (population by five-year age group and area data) that the so-called 1 km² mesh data provided by the Ministry of Land, Infrastructure, Transport and Tourism of Japan possesses, which places restrictions on the variables that can be used in Equation (2). In other words, Equation (2) is estimated by using only those variables that apply to both the statistical information compiled and publicly available for each municipality and the 1 km² mesh data. With this restriction, the following variables are used in this study.

Due to the statistical limitations of the 1 km² mesh data, the estimation model for this study is specified as follows:

$\ln M_{it}=\alpha \ln D_{it}+{\beta }_A{A}_{it}+{\beta }_S{S}_{it}+{\beta }_C{C}_{it}+{\beta }_T{T}_{it}+{\beta }_0+{\epsilon }_{it}$ (3)

where $A_{it}$ is the aging rate of municipality i in the year t and $S_{it}$ is a dummy variable, whose value is 1 if the municipality was an ordinance-designated city as of April 1 of year t and 0 otherwise. $C_{it}$ is a dummy variable, whose value is 1 if the municipality was a core city as of April 1 of year t and 0 otherwise. $T_{it}$ is a dummy variable, whose value is 1 if the city was a special case city as of April 1 of year t and 0 otherwise. The sign condition in Equation (3) is expected to be $\alpha <0$ because, in accordance with previous studies, economies of scale are expected to operate as $D_{it}$ , an indicator of the increase in city structure and an expected decrease in the average cost. ${\beta }_A>0$ is expected because social security-related costs are expected to increase with a higher aging rate. Additionally, ${\beta }_S>0,{\beta }_C>0,{\beta }_T>0$ for these cities and ${\beta }_S>{\beta }_C>{\beta }_T$ , respectively, because of the transfer of authority from the prefectures compared with the other municipalities. The data sources and remarks are shown in Table 3 and the descriptive statistics in Table 4, and the scatter plot between average cost and population density is shown in Figure 1. According to Figure 1, it is possible to specify the model as a quadratic function, but due to multicollinearity issues and the complexity of interpretation, for the sake of simplicity of discussion, the parameter estimation in this study is performed using a linear equation.

Note: Kitakami City, Iwate Prefecture; Miyake Village, Tokyo; Kofu City, Yamanashi Prefecture; Fuji-Kawaguchiko Town, Yamanashi Prefecture; Hamamatsu City, Shizuoka Prefecture; Shimabara City, Nagasaki Prefecture; Minami-Shimabara City, Nagasaki Prefecture are missing FY1990, FY2000, FY2010, FY2010, FY1990, FY1990, and FY1990 respectively.

Table 3. Data sources and/or remarks.

Note: Municipal data is prepared by municipalities as of March 31, 2020.

Table 4. Simulation descriptive statistics.

Table 5 indicates the estimation results based on Equation (3). Regarding the validation of the fixed-effects model and the random-effects model, the results of the Hausman test yielded 60.92, the χ-square value, indicating the adoption of the fixed-effects model.

The coefficient of population density, an indicator of city structure, in the fixed and random-effects models is negative and significant indicating results that are consistent with those of previous studies including Kawasaki (2009) , Sekiguchi (2012) and Sekiguchi and Nagase (2019) . In other words, the results indicate that as population density increases, the average cost decreases, and an

Figure 1. Scatter plots of population density and average expenditure.

Table 5. Estimation results.

Note: ***, **, *, and + represent significance levels of 1%, 5%, 10%, and 15%, respectively.

increase by 1% in population density results in a decrease by 0.075% in the average cost. The results are also positively significant for the aging rate and satisfy the sign condition.

The results for the ordinance-designated city dummy and the core city dummy are positive and significant, although the significance level is low, and the sign condition is satisfied, with the ordinance-designated city dummy outperforming the core city dummy. The result was consistent with prior expectations. Conversely, the result for the special case city dummy was negatively significant.

The reason why the sign condition was not met is that, although the special cities have originally received some of the powers that the core cities have, for example, the acceptance of notifications related to environmental preservation, designation of designated areas, and permission, and recommendations related to city planning, which require less administrative burden, economies of scale may have worked to reduce costs.

2.2. Simulation Procedure

The parameters estimated in the previous section will now be used to simulate the scale of the SFT. In the simulation, we estimate the total expenditure for each mesh using the population, area, and aging rate and dummy variables of the core city, government-designated city, and special case city that are included in the 1 km² mesh data.

The 1 km² mesh data are not 1 km² in the strict sense due to map distortion. To correct this distortion, this study used the Universal Transverse Mercator coordinate system¹ to divide Japan into six zones, from 51 to 56, and obtained the area with a scale factor accuracy of 0.9996. Table 6 shows the area of the simulation target aggregated to prefectural units.

Procedure

1) We focused on municipalities that have both CPA and non-CPA because we estimate the SFT from CPA to non-CPA in the simulation (see Table 6 and Table 7).

2) The CPA and non-CPA were fixed to the most recent FY2018 at the time of the study.

3) Calculate the total expenditure per capita for each mesh using the parameters of the estimation model and multiply by the mesh population to calculate the total expenditure per mesh.

4) The total expenditure calculated for each mesh is divided into CPA and non-CPA within a municipality and aggregated.

5) Calculate total expenditure per capita by CPA and non-CPA for each municipality.

6) Estimate the total expenditure per capita for each municipality, and the difference between this and expenditure per capita by CPA and expenditure per capita by non-CPA is per capita SFT paid by CPA and per capita SFT received by non-CPA, respectively.

7) Aggregate the estimation by municipalities to prefectural units.

Table 6. Areas to be simulated.

Unit: Square killometer. Note: There was no difference in area between FY2005 and FY2010.

Table 7. Municipalities covered in this study.

Note: In the simulation, we included municipalities with both CPA and non-CPA. Note: The following municipalities or prefectures are excluded from the list because they do not publish CPA maps. Ebina City, Kanagawa Prefecture; Kanazawa City, Ishikawa Prefecture; all of Aichi Prefecture; Minami-Ise Town, Mie Prefecture; Hirao Town, Yamaguchi Prefecture; Nagasaki City, Nagasaki Prefecture; Sasebo City, Nagasaki Prefecture; Minami-Shimabara City, Nagasaki Prefecture; Soo City, Kagoshima Prefecture; Yushimizu Town, Kagoshima Prefecture; Nakatane Town, Kagoshima Prefecture.

3. Simulation Results

Under the above procedure, SFTs for each of the >1700 municipalities were tabulated by prefecture and are shown in Table 8. The simulation results showed that CPAs, the payers of SFT, were estimated to have paid a national weighted average of ¥19,218 per capita in FY2005 and ¥18,360 per capita in FY2010. Conversely, non-CPA, the recipient of SFT, was estimated to have received a national weighted average of ¥164,017 per capita in FY2005 and ¥171,360 per capita in FY2010. The total value of SFT in Japan was estimated to be ¥1,104,830,463,745 in FY2005 and ¥1,062,534,779,839 in FY2010.

When the PPMCC² between the amount on the payment side of the SFT and

Table 8. Simulation results.

Unit: Japanese Yen.

the ratio of CPA to the municipal area was calculated, the PPMCC was −0.714 (p = 0.000) in FY2005 and −0.720 (p = 0.000) in FY2010, indicating a significant negative correlation. In other words, the larger the ratio of CPA to the municipal area, the more the population shares the burden, resulting in a smaller amount on the payer side of the SFT. Conversely, the PPMCC between the amount of SFT received and the non-CPA ratio was −0.082 (p = 0.591) in FY2005 and −0.129 (p = 0.397) in FY2010, which was negative but not significant. In other words, the larger the ratio of non-CPA to the municipal area, the larger and more diluted the population receiving SFT (see Figure 2 and Table 9).

4. Discussion

Kuroda (1986) , Kornai (1986) , Sato (2002) , Yamashita et al. (2002) , Kornai et al. (2003) , Miyazaki (2004) , Otsuka and Goto (2014) pointed out the soft budget problem. That is, intergovernmental fiscal transfers, or in other words, intercity fiscal transfers may cause municipalities to spend inefficiently. In the context of this study, non-CPAs are less densely populated and therefore less efficient than CPAs; SFTs are intra-city fiscal transfers to these less efficient areas. The ratio of SFT to LAT grants (intergovernmental fiscal transfers) is shown in Table 10, which shows that SFT accounted for a national weighted average of 19.31% in FY2005 and 17.16% in FY2010. According to Otsuka and Goto (2014) , who estimated the loss of efficiency in total expenditure caused by LAT grants, the loss is estimated to be approximately 23% for LAT grants. This suggests that 74% to 84% of the soft budget problem caused by intergovernmental fiscal transfers is likely due to SFT, whose existence is revealed in this study.

Table 9. PPMCC between the scale of SFT and the ratio of CPA.

Table 10. The ratio of SFT to LAT grants.

Unit: Japanese Yen.

Figure 2. Scatter plots of the amount of SFT and ratio to the municipal area.

It is important to note that the above results reveal the existence and amount of SFT, as well as the desirability of expanding the scope of the location optimization plan to cover not only the CPA but also the entire area within the municipality, from the perspective of social issues.

5. Conclusion

Based on the social and academic issues, this study aims to estimate the existence and scale of intracity fiscal transfers from CPA to non-CPA that are not on the municipal account settlement card, which we call SFT. The simulation results showed that CPAs, the payers of SFT, were estimated to have paid a national weighted average of ¥19,218 per capita in FY2005 and ¥18,360 per capita in FY2010. Conversely, non-CPA, the recipient of SFT, was estimated to have received a national weighted average of ¥164,017 per capita in FY2005 and ¥171,360 per capita in FY2010. The total value of SFT in Japan was estimated to be ¥1,104,830,463,745 in FY2005 and ¥1,062,534,779,839 in FY2010. The ratio of SFT to LAT grants was 19.31% in FY2005 and 17.16% in FY2010, and this study found that SFT accounted for 74% to 84% of the loss due to LAT grants as identified by Otsuka and Goto (2014) .

Although the location optimization plan is a plan for CPAs, it is important to note that the policy implication derived from this study is that in the future, the location optimization plan is expected to cover not only CPAs but also the entire area within a municipality, including non-CPAs.

Finally, we would like to address the remaining future issues for this study. The first issue is that although population density was used in this study as an indicator to measure the city structure, the analysis will also incorporate natural conditions, like height above sea level. For the second issue, when estimating the expenditure function, some previous studies, for example, Akai and Sato (2011) and Miyazaki (2020) have conducted panel data analysis in which the variable is whether or not the municipality is allocated LAT grants. In the future, besides natural conditions such as height above sea level, we will conduct an analysis in which the allocation of the local tax is a variable. As a third issue, this study analyzed the social issue that the scope of the location adequacy plan is limited to CPAs. As a unit of the estimation of SFT, SFT from urbanized areas to other areas could be considered, which would be expected to be larger both per capita and in total. We would like to conduct such an analysis in the future.

Funding

This work was supported by the Kayamori Foundation of Informational Science Advancement. Owing to being supported by the foundation, we were able to gather many paid geographic information data and use a geographic information system to analyze them.

NOTES

¹The Geospatial Information Authority of Japan (GSI) describes UTM as follows. “A projection method that represents the spherical Earth on a flat surface, a projection of the spherical Earth onto a cylinder lying horizontally around the Earth’s equatorial plane. When a sphere is projected onto a plane, distortion occurs. The UTM method uses a width of 6 degrees of longitude, which is within the range of least distortion. This projection method is widely used worldwide, including topographic maps by GSI. The UTM cartography divides the area projected on the plane into grids in the longitude and latitude directions. For longitude, the earth is divided into 60 longitude zones every 6 degrees eastward from 180 degrees west longitude. For latitude, the earth is divided into 20 latitude zones every 8 degrees from 80 degrees south latitude to 84 degrees north latitude (the range is 12 degrees from 72 degrees north latitude to 84 degrees north latitude only).” Sources: GSI website (confirmed on April 14, 2022). https://www.gsi.go.jp/chubu/minichishiki10.html.

²PPMCC stands for Pearson Product-Moment Correlation Coefficient.

Conflicts of Interest

The authors declare no conflicts of interest regarding the publication of this paper.

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