Does Crime Affect Inbound Tourism? Evidence From a Sub-national Perspective in Japan

Abstract

Inbound tourism is welcomed by many governments due to its numerous benefits for the destination country. Besides, crime is often considered a factor that negatively affects tourism; but empirical evidence does not universally support this perspective. Therefore, this study investigates the impact of crime on inbound tourism using Japan as a case study. For further investigating the spatial externalities of inbound tourism from a sub-national perspective, we employ spatial panel data models. The findings indicate that the total crime rate does not have a significant impact on inbound tourism, while the violent crime rate exhibits a significant negative effect. This suggests that the selection of crime variables is important. Additionally, the significant spillover effects underscore the importance of regional cooperation.

Keywords

crime inbound tourism Japan spillover effect sub-national tourism

Introduction

Tourism is a rapidly growing industry around the world. Figure 1 shows the growth rates of global economy gross domestic product (GDP) and global travel & tourism (T&T) GDP. It can be found that T&T GDP maintains the high growth rate and even surpassed GDP during 2011 to 2019. Tourism can generate income and tax earnings, create new jobs, attract investment, and alleviate ﬁnancial deﬁcit. Actually, tourism revenue mainly comes from domestic tourism and inbound tourism. Compared with domestic tourism, inbound tourism can also increase exchange reserves. For those countries struggling with economic recession due to the insufﬁcient domestic demand, inbound tourism is more important. Thus, many countries make policies to attract inbound tourists in order to attain economic recovery. Japan is a representative one of those countries. Japanese government sets up lots of policies to stimulate inbound tourism. For example, it relaxes visa policy for residents from many countries such as China, Indonesia and Philippines¹. Another example is that the duty-free products for overseas tourists have been expanded from a small range of commodities such as home appliances and clothing to all daily necessities since October 1, 2014 (MLIT, 2014). Driven by the favorable policy environment, Japan’s tourism has received a fast growing in recent years, while there is still much potential for its development. In 2019, the total contribution of T&T to Japanese GDP was 7%, lower than the 9.8% level of Northeast Asia and the 10.3% level of the world (WTTC, 2020). To further develop the Japanese tourism industry to catch up with the world level, enhancing the competitiveness of Japanese tourism and attracting more inbound tourists are essential. From this point of view, this paper explores the inﬂuencing factors on inbound tourism in Japan to provide suitable policies for sustainable tourism developments.

There have existed much research on discussing the determinants of inbound tourism (Y. Liu et al., 2018; Saha et al., 2017), including economic factors, political factors, marketing factors and so on. However, crime as a type of insecurity, which threatens the safety of inbound tourists, rarely appears in previous research (Altindag, 2014; Santana-Gallego & Fourie, 2022). Compared to other types of insecurity, such as earthquake, tsunami, terrorist attack and war, criminal activity is relatively easy to control. If the high crime rate reduces the number of inbound tourists signiﬁcantly, controlling the crime rate can be a useful way of destination management to enhance tourism competitiveness and attraction. While there are some studies discussing the impact of crime on inbound tourism (Altindag, 2014; Santana-Gallego & Fourie, 2022), their conclusions are not consistent. Hence, the inﬂuence of crime rate is considered as a determinant of inbound tourism in this paper to investigate the impact of crime on inbound tourism and analyze the reasons for the inconsistent results.

Meanwhile, studying the inbound tourism in a regional (sub-national) perspective is necessary, since taking Japan as whole to analyze the determinants of inbound tourism may mask regional differences. On the one hand, due to the heterogeneous tourism demand across space, diverse spatial patterns may exist in different regions of the same country (Y. Yang & Wong, 2013). On the other hand, different regions of Japan have different scene types, like mountains and seaside, which may attract different tourists. Hence, it is more appropriate to consider the sub-national analysis. Besides, since the tourism industry depends on regional impacts (Capone & Boix, 2008), then the sub-national analysis can also help regional governments make policies to better develop tourism.

It has been well known that the spillover effects of tourism industry exist. The spatial externalities of tourism development can spill across geographic boundaries (Y. Yang & Fik, 2014), especially from the sub-national perspective. There are several reasons for the spillover effects of sub-national tourism. First, tourists can easily travel to multiple destinations in the same country, since there are few traveling barriers across regional administrative borders. Second, there are usually few restrictions on labor movement among regions. Finally, it is easy for joint promotion between regions. Furthermore, the spillover effects of crime events between regions have been demonstrated in numerous studies, such as those conducted by Kakamu et al. (2008) and Andrés-Rosales et al. (2017). Hence, this paper also considers the spatial agglomeration of sub-national inbound tourism and uses the spatial panel data models to explore the influence of crime on inbound tourism.

Overall, taking Japan as a case study, this paper uses the spatial panel data models to study the impacts of crime on inbound tourism and the corresponding spillover effects from a sub-national perspective. Based on these results, we provide some tourism policy implication for Japanese government. For this purpose, the rest of this paper is organized as follows. Section 2 provides a literature background; the methodology of this study is introduced in Section 3; Section 4 describes the dataset; the model results are presented and discussed in Section 5; and Section 6 concludes this paper.

Literature Review

Inbound Tourism in Sub-national Levels

Due to the importance of inbound tourism in economic development, the studies in inbound tourism especially the studies in the determinants of international tourism have always attracted the attention of stakeholders and scholars (Martins et al., 2017; Pham et al., 2017; Viljoen et al., 2019). With the development of modeling tools and the improvement of access to data, panel data analysis is one of the most frequently used tools to analyze the determinants of inbound tourism. De Vita (2014) used a panel data of 27 countries over a period 1980 to 2011 to explore the effect of exchange rate regimes on international tourism ﬂows. Habibi (2017) investigated the determinants of international tourist ﬂows to Malaysia based on the annual panel data set including the number of arrivals from 33 countries during the period 2000 to 2012 and the possible explanatory variables. Y. Liu et al. (2018) examined Thailand’s inbound tourism statistics spanning from 1996 to 2015, emphasizing the change and differentiation of its inbound tourism sources from the ASEAN countries. Gozgor et al. (2019) studied the impacts of the effectiveness of the legal system and protection of the property right on inbound tourism using a panel data of 152 countries over the period 1995 to 2015. Through panel analysis, these paper studies two kinds of groups: origins (Habibi, 2017; Y. Liu et al., 2018) and destinations (De Vita, 2014; Gozgor et al., 2019). For policy makers, the studies on these two kinds of groups can provide different policy implications. Based on the analysis for data of origins, the tourism demand of different origins can be explored, and the government can formulate different policies for different origins; while data of destinations provide hints about the improvement of economic and social circumstances in supply side to increase the international tourist arrivals and revenues. This study aims at providing policy suggestions for government to promote inbound tourism in a perspective of destinations.

Based on the above studies, it can also be found that they all focus on the comparisons of inbound tourism in the national level. Actually, the comparative analysis in the sub-national or regional levels are relatively scarce in the studies on inbound tourism. In recent years, due to the availability of regional data and the development of precision tourism marketing, the regional or sub-national tourism has aroused the attention of scholars (Centinaio et al., 2023; Deng et al., 2017; Y. Yang & Fik, 2014). But to our knowledge, only Y. Yang and Fik (2014) and Deng et al. (2017) addressed research regarding the impact of determinants on inbound tourism. Y. Yang and Fik (2014) constructed a regional tourism growth model based on a panel data of 342 Chinese prefectural-level cities over a period of 2002 to 2010. Deng et al. (2017) discussed the impact of air pollution on inbound tourism based on a panel data of 31 provinces in China from 2001 to 2013.

Due to the less restriction on tourist movement and the easier collaboration among regions, spillover effects, which are widely known and accepted in economic activities, of tourism in sub-national levels have received more attention (Bo et al., 2017; Carrascal Incera et al., 2015; Tian et al., 2022). Most of empirical results reveal the existence of positive effects in tourism development, such as Y. Yang and Fik (2014) and Comerio et al. (2020). Because there are often similar attractions especially natural and cultural attractions in a cluster, negative spillover effects, also called competition effects could be also observed in empirical studies, such as Romão and Saito (2017) and J. Liu et al. (2017). The opposite results in different cases reveal that the importance of special analysis for understanding the interaction of inbound tourism within each group of destinations of interest.

The Impact of Crime on Inbound Tourism

Ever since Gunn (1973) firstly found that the increasing number of criminal incidents negatively affects the willingness of tourists visit the destination, the scholars have shown significant interest in exploring the impact of crime on tourism (Ajagunna, 2006; Alleyne & Boxill, 2003). The recent research of this topic includes Rauf et al. (2022) and Vakhitova et al. (2023). However, it is noteworthy that studies investigating crime as a determinant of inbound tourism remain relatively scarce (Moyo & Ziramba, 2013).

Tourism, being an environmentally sensitive industry, places a premium on safety and security (George, 2003; Zou & Meng, 2020; Zou & Yu, 2022). These considerations hold particular significance for travelers, especially those venturing abroad (Fourie et al., 2020; Lepp & Gibson, 2003). Crime, a social issue with the potential to jeopardize people’s safety (Barton et al., 2017; Vakhitova et al., 2023; Zhao et al., 2010), should not be overlooked in studies concerning the determinants of inbound tourism.

Furthermore, crime seems to exert a negative influence on inbound tourism, but not all empirical findings substantiate this perspective. Altindag (2014) observed that violent crime adversely affects international tourism based on a panel data analysis of European countries. Fourie et al. (2020) discovered that the crime rate, measured as the number of homicides per 100,000 inhabitants, had a significantly negative impact on inbound tourism in the host country. In contrast, the empirical research by Andrés-Rosales et al. (2017) presented that total crime does not have a significant impact on tourism GDP. The divergent findings emphasize the importance of conducting additional research to delve deeper into the influence of crime on inbound tourism and to analyze the underlying factors contributing to these inconsistent results.

In terms of methodology, various modeling tools have been employed to investigate the impact of crime on tourism. These include time series analyses (Ahad et al., 2022; Rauf et al., 2022), panel data modeling (Altindag, 2014; Moyo & Ziramba, 2013), and the gravity model (Fourie et al., 2020; Lepp & Gibson, 2003). Nonetheless, the consideration of the spatial impact of crime on tourism has been lacking. In reality, insecurity can impact not only the host destination but also its neighboring regions. Empirical evidence supporting these spillover effects from criminal activities can be found in studies such as Kakamu et al. (2008) and Andrés-Rosales et al. (2017). Additionally, spatial panel models have gained popularity in describing spillover effects in tourism (Y. R. Kim et al., 2022; Park et al., 2023; Y. Yang & Wong, 2012). Hence, the utilization of spatial panel data modeling becomes essential when studying the influence of crime on tourism.

Japanese Tourism

The rapid development of tourism industry in Japan and the importance of tourism in Japanese economy makes Japanese tourism catch the attention from the scholars during recent years. Uzama (2009) combined the information in newspaper, magazines, academic articles, internet and the Japan National Tourism Organization to provide marketing suggestions to promote inbound tourism. Henderson (2017) discussed the factors, such as national conditions, government policies, attractions, accessibility and destination marketing, which contributed the uptrend of Japan’s international tourist arrivals. J. Kim et al. (2016) studied the influence of Abenomics, the Japanese economic policy, on South Koreans’ travel to Japan. Their findings underscore the significance of governmental economic policies in stimulating international tourism demand. Tang (2021) investigates the inherent correlation between trade facilitation and the efficiency of inbound tourism in Japan using data from 2011 to 2019.

Recently, the studies on the determinants of Japanese tourism in sub-national perspective have attracted attention. Romão and Saito (2017) discussed the impact of several explanatory variables, including level of qualiﬁcation of the work force, specialization in tourism, share of foreigners within the regional overnight stays and night spent in regional accommodation establishments per habitant, on the regional GDP per habitant in the tourism sector for the case of Japanese Prefectures. Comerio et al. (2020) pinpointed the inﬂuence of the determinants, involving Gross Prefectural Production, Shinkansen stops, museums, conferences and hotel, on the number of overnight stays. It can be easily found that both of these papers focused on the whole tourism industry rather than inbound tourism. But inbound tourism is a special component of tourism, since it can promote international exchanges and increase foreign exchange earnings in addition to the beneﬁts of domestic tourism. Romão and Saito (2017) also concluded that international tourism plays a main role determining the regional tourism performance based on the case study of Japanese Prefectures. Hence, research in the determinants of inbound tourism in Japan, which is the main focus in this paper, can be beneﬁcial to propose targeted policies for international tourists and then is essential for the regional tourism development in Japan.

Summary

According to the above discussions, it can be concluded that our paper advances this ﬁeld of research in two ways. First, the impact of crime on inbound tourism in sub-national level has been analyzed by the spatial panel data for the ﬁrst time. Second, this work offers a contribution for the analysis about the impact of the determinants on Japanese inbound tourism in sub-national perspective, since the empirical application of inbound tourism in a sub-national perspective is rather scarce and there is no discussion for the Japanese case. These innovations allow us to understand the inﬂuencing factors of inbound tourism in Japan, as well as their spillover effect among regions, which will be useful for designing more appropriate policies to attract inbound tourists to Japan and promote the Japanese economy.

Methodology

Spatial econometric modeling is a basic tool for empirical study in this paper. Hence, this section introduces spatial related concepts, models and statistical analysis procedure.

Specification of Spatial Weight Matrix

In constructing a spatial econometric model, the ﬁrst step is to specify the spatial weight matrix. The spatial weight matrix reﬂects the spatial dependence and speciﬁes the connections of regions. There are different methods to deﬁne the spatial weight matrix. This paper uses $K$ -nearest neighbors spatial weight matrix, which is proposed by Kelley Pace and Barry (1997). Set $W = (w_{i j}), i, j = 1, \dots, N$ with $N$ being the number of spatial units as the notation of spatial weight matrix. Then the element of this matrix can be expressed as

\begin{matrix} w_{i j} = {\begin{cases} 1, j district belongs to the K - nearest & neighbors of the i district, \\ 0, & otherwis e, \end{cases} \end{matrix}

where the distance between two districts are the Euclidean distance between two center coordinates. In this paper, $K$ is set as 4, which is the same as Kelley Pace and Barry (1997). Based on this matrix, spatial autocorrelation can be tested, and spatial econometric model can be built.

Spatial Autocorrelation Test

Testing the spatial autocorrelation is for determining whether the spatial econometric model should be used. Moran’s I statistics, proposed by Moran (1950), is most frequently used to test the global spatial autocorrelation. It is formulated as

\begin{matrix} I = \frac{N}{\sum_{i = 1}^{N} \sum_{j = 1}^{N} w_{i j}} \times \frac{\sum_{i = 1}^{N} \sum_{j = 1}^{N} w_{i j} (x_{i} - \bar{x}) (x_{j} - \bar{x})}{\sum_{i = 1}^{N} {(x_{i} - \bar{x})}^{2}}, \end{matrix}

where $x_{i}, i = 1, \dots, N$ is the sample of target variable and $\bar{x}$ is the sample mean. Specially, when the spatial weight matrix $W$ is row-standardized, Moran’s I index takes value between $- 1$ and $1$ . The index with the value closer to $1$ (or $- 1$ ) represents a stronger positive (or negative) spatial autocorrelation, while the zero value means that there is no spatial autocorrelation. As the number of spatial units $N$ tends to infinity, the standardized value of Moran’s I statistics asymptotically follows the standard normal distribution (Cliff & Ord, 1970). Based on this asymptotic property, spatial autocorrelation based on Moran’s I statistics can be tested. If spatial autocorrelation exists, then a spatial econometric model should be used.

Spatial Panel Data Model

In this paper, spatial panel data model is used to deal with the multidimensional data containing observations with different units over time, where the units have spatial interactions. An obvious characteristic of spatial panel data model distinguished from traditional spatial econometrical model is the inclusion of unobservable individual effect and (or) unobservable time effect, where these effects can be ﬁxed or random. In the spatial panel data model, a general formulation of the error term $ϵ_{i t}$ can be written as

\begin{matrix} \begin{matrix} ϵ_{i t} = μ_{i} + γ_{t} + u_{i t}, \end{matrix} \end{matrix}

(1)

where $i (i = 1, \dots, N)$ denotes the spatial unit, $t (t = 1, \dots, T)$ denotes the time, $u_{i t}$ are residual errors, which will be set differently in different spatial panel models; $μ_{i} = 0$ for the model with time effect only, $γ_{t} = 0$ for the model with individual effect only. In the following, the spatial econometric models are introduced in the framework of panel data version, which means that the error term $ϵ_{i t}$ is set by Equation 1 in these models.

Spatial lag model (SLM) and spatial error model (SEM) are two commonly used spatial econometric models. The SLM involves the spatial lag term, which is the weighted average of neighboring values of a location (Kelejian & Prucha, 1998). The SLM formulation is

\begin{matrix} y_{i t} = ρ \sum_{j = 1}^{N} w_{i j} y_{i t} + {X^{'}}_{i t} β + ϵ_{i t}, \end{matrix}

where $y_{i t}$ denotes an observation of the dependent variable for spatial unit $i$ at time $t$ , $ρ$ is the spatial autocorrelation coefﬁcient, $X_{i t}$ is a $q \times 1$ vector containing $q$ independent variables, β is a $q \times 1$ vector containing $q$ coefﬁcients; $u_{i t}$ in Equation 1 are set to be independent identically distributed (i.i.d.) from normal distribution. Different from the SLM, the SEM includes a spatially autocorrelated disturbance, and the model is

y_{i t} = X_{i t}^{'} β + ϵ_{i t}, u_{i t} = λ \sum_{j = 1}^{N} w_{i j} u_{i t} + v_{i t},

where $λ$ is the spatial autocorrelation coefﬁcient and $v_{i t}$ follows i.i.d. normal distribution. A more generalized model is spatial autoregressive combined (SAC) model, which contains both a spatially lagged-dependent variable and a spatially autocorrelated disturbance. Thus, the SAC can be regarded as a combination of SAR and SEM, and is formulated as

y_{i t} = ρ \sum_{j = 1}^{N} w_{i j} y_{i t} + {X^{'}}_{i t} β + ϵ_{i t}, u_{i t} = λ \sum_{j = 1}^{N} w_{i j} u_{i t} + v_{i t} .

Modeling Procedure

Modeling spatial panel data involves a series of testing and estimation. Given an observed sample data, the modeling procedure is listed in the following.

Test the spatial autocorrelation. If Moran’s I statistics is signiﬁcant, then building a spatial panel data model is reasonable.

Test the individual ﬁxed effect and time ﬁxed effect of the panel data, respectively. These two effects can be tested by using the likelihood ratio (LR) test according to Baltagi (2021) and Elhorst (2012). If both the individual ﬁxed effect and time ﬁxed effect are signiﬁcant, the two-way (spatial and time) ﬁxed effects model can be used. On the other hand, if one of the effects is insigniﬁcant, the random effect of the error term should be tested, where the Hausman-type speciﬁcation test or the Lagrangian multiplier test can be used; more details see Lee and Yu (2012).

Choose an appropriate spatial model. Based on the effects determined in step 2, the formulation of spatial model should be tested, which can be implemented by robust Lagrange Multiplier (LM) test. Robust LM test, proposed by Elhorst (2014), includes LM-lag test and LM-error test. If the LM-lag test is signiﬁcant while the LM-error test is insigniﬁcant, the SLM is used; if the LM-error test is signiﬁcant while LM-lag test is insigniﬁcant, the SEM is used. If both tests are signiﬁcant, this paper uses the SAC model since the testing results reﬂects that the spatial effect exists in both the lagged dependent variables and error terms.

Estimate the parameters. Under the mild assumptions, the maximum likelihood estimator (MLE) has consistency and asymptotic efﬁciency. Thus, MLE is the most frequently used estimator in the spatial econometrics and this paper also uses this estimator.

Data Description

In this study, the panel data of 47 prefectures in Japan, which is consistent with the Japanese bureaucratic administration division, from 2007 to 2016 is used. This paper uses the number of foreign guest nights to measure inbound tourism, which is also known as foreign tourist overnight stays. And the crime rate per 1000, that is, the number of crimes divided by the population and multiplied by 1000 is a crime-related index used and is denoted as crime. Besides, this paper takes into account several control variables, including economic growth, tourism infrastructure and environmental conditions.

Since economic growth leads to development of infrastructure, education and safety progresses (Sokhanvar et al., 2018), economic expansion has contribution to tourism growth, which is also supported by main empirical studies, such as Oh (2005) and Ahiawodzi (2013). Inﬂation-adjusted per capital income (millions of yen), denoted as PCI, is a proxy for economic development, and the estimated coefﬁcient of this variable is expected to be positive.

Tourism accommodation is usually used to reﬂect the condition of tourism infrastructure in destination regions (Ghaderi et al., 2017). In this paper, tourism infrastructure value is measured by the number of hotels. Since the sufﬁciency of infrastructure may facilitates the arrival of tourists (C.-H. Yang et al., 2010) and higher accommodation capacity can obviously take on more tourists, then the number of hotels is expected as a positive inﬂuencing factor.

Due to the occurrence of the Fukushima nuclear disaster in 2011, environmental issues have become a major focal point for tourists visiting Japan. Therefore, this paper considers environmental factors among the control variables. While the harm of nuclear pollution to the soil is more direct, complete data on soil composition and land degradation are not available. Additionally, air pollution poses a risk to international tourism, and empirical evidence, such as Zhang et al. (2019) and Y. Yang et al. (2022), supports the claim that air pollution negatively influences inbound tourism. Hence, this paper chooses air pollution as an alternative indicator. And this paper selects four major pollutants: carbon monoxide (CO), nitrogen dioxide (NO₂), sulfur dioxide (SO₂) and suspended particulate matter (SPM), standardizes each of four pollutants and averages these standardized values to obtain a composite measure of air pollution, denoted as airpollu. The coefﬁcients of air pollution are expected to be negative.

Since the number of foreign guest nights and the number of hotels is discrete data, the logarithm of these two variables, denoted as lnguestn and lnhotel, are used in the following empirical model. The data associated with air pollution is obtained from http://www.nies.go.jp/igreen/td_down.html, and the remaining data is downloaded from https://dashboard.e-stat.go.jp/en/dataSearch, where the main statistical data is provided by the Japan government.

Given the data of 47 prefectures with a time period of 10 years, there are 470 records in total. Table 1 presents the statistical description of the ﬁve variables mentioned above. The first and second column lists the variables and the corresponding notations respectively, and the last four columns present the means, standard deviations (s.d.), maximum (max) and minimum (min) values of the variables.

Table 1.

Descriptive Statistics of Variables (2007–2016).

Variable	Notation	Mean	s.d.	Max	Min
Tourist stays	lnguestn	12.174	1.525	16.709	9.242
Crime	crime	9.490	3.648	24.471	2.918
Economic development	PCI	2.741	0.506	5.894	1.931
Tourism infrastructure	lnhotel	5.091	0.694	6.542	3.526
Air pollution	airpollu	0	0.805	3.639	-1.192

Results and Findings

Statistical Results

The modeling procedure is implemented in Matlab R2021b. Using the center position coordinates of 47 prefectures to calculate distances, spatial weight matrix with row standardization can be determined. Then the Moran’s I indexes of the number of foreign guest nights and its inﬂuencing factors are calculated. Table 2 presents the indexes and their signiﬁcance levels. The results show that most of Moran’s I indexes are positive and signiﬁcant at the 10% level or above, which means that these variables have spatial autocorrelation. Specially, all the Moran’s I index of the number of foreign guest nights and crime are signiﬁcant at the 5% level or above, which also supports that there are agglomeration effects of tourism industry and crime events. Hence, the spatial econometric modeling can be used to avoid biased estimates when the relationship between tourism industry and its determinants are explored.

Table 2.

Testing Spatial Autocorrelation by Moran’s I Index.

Year	lnguestn	crime	PCI	lnhotel	airpollu
2007	0.186**	0.433***	0.307***	0.111*	0.396***
2008	0.171**	0.443***	0.332***	0.109*	0.396***
2009	0.191***	0.408***	0.290***	0.111*	0.409***
2010	0.177**	0.415***	0.302***	0.102*	0.395***
2011	0.186**	0.397***	0.294***	0.097*	0.405***
2012	0.207***	0.404***	0.279***	0.099*	0.422***
2013	0.184**	0.440***	0.279***	0.099*	0.423***
2014	0.178**	0.369***	0.279***	0.096*	0.432***
2015	0.209***	0.353***	0.283***	0.095*	0.399***
2016	0.158**	0.366***	0.287***	0.091	−0.047

***,

**, and * denote the signiﬁcant levels at 1%, 5%, and 10%, respectively.

To determine the model formulation, several tests have been conducted. Table 3 presents the hypothesis testing results of ﬁxed effects in both spatial and time dimension. Since both individual and time ﬁxed effects are signiﬁcant, a two-way ﬁxed effects model is used. Then the robust LM-lag test and the robust LM-error test are implemented, and the results are presented in Table 4. It can be found that both robust LM-lag test and robust LM-error test have signiﬁcant results, and hence the SAC model is used in the empirical study. From the results in Table 5, it can be found that both the AIC and BIC values of SAC are smaller than those of SLM and SEM, which also indicates that the SAC model is preferable.

Table 3.

Testing Results of Individual Fixed Effect and Time Fixed Effect.

	Spatial ﬁxed effect	Time ﬁxed effect
LR statistics (p-value)	1,357.203 (.000)	317.967 (.000)

Table 4.

Robust LM Test Results.

	Robust LM-lag test	Robust LM-error test
Statistics (p-value)	30.295 (.000)	17.87 (.000)

Table 5.

Estimates of Spatial Panel Models.

Variable	SLM	SEM	SAC	SAC (spatial ﬁxed)
crime	−0.027 (.063)	−0.028 (.077)	−0.017 (.110)	−0.016 (.001)
PCI	−0.561 (.001)	−0.566 (.001)	−0.399 (.002)	0.232 (.000)
lnhotel	0.446 (0.072)	0.378 (.136)	0.515 (.010)	0.425 (.017)
airpollu	−0.137 (.002)	−0.129 (.005)	−0.110 (.003)	−0.041 (.192)
$ρ$	0.319 (.000)		0.662 (.000)	0.862 (.000)
$λ$		0.301 (.000)	−0.733 (.000)	−0.931 (.000)
AIC	88.045	95.590	69.331	128.645
BIC	345.515	353.060	326.801	348.740

Note. p-values are reported in parentheses.

The fourth column in Table 5 lists the parameter estimation results of the SAC model. Most estimators are signiﬁcant at 5% level except the coefﬁcient of crime with p-Value of .110. Besides, most coefﬁcients have the same directions as expected except the coefﬁcient of PCI. The coefﬁcient estimator of lnhotel is .515, which reﬂects that a 1% increase in the number of hotels will contribute to a 0.515% increase in foreign tourist overnight stays. Air pollution has a coefﬁcient of −.110, which reveals that one unit increase in the air pollution index will lead to an 0.110% decrease of foreign tourist overnight stays.

In the following, we will concentrate on examining the influence of crime on inbound tourism. Additionally, we will elucidate the unexpected outcomes of PCI estimators and engage in a discussion regarding the spillover effect, which constitutes one of the primary concerns in spatial econometrics.

Crime

The coefficient for crime, with a p-value of .110 in our empirical study, suggests that total crime does not have a statistically significant impact on inbound tourism. This finding is in line with the results reported in Andrés-Rosales et al. (2017). However, Altindag (2014) discovered that violent crime negatively affects international tourism based on a panel dataset of European countries. A potential reason for the different results is the different crime-related variables used. The former one is total crime, while the latter one is violent crime. In fact, tourists have different risk perceptions for different types of crime, hence, different types of crime usually have diverse impact on tourist decision, which is empirically supported by some research. For example, Altindag (2014) had found that violent crimes has negative impacts on inbound tourist while the impact of property crime is insigniﬁcant based on a panel data of 34 European countries with a time period of 1995 to 2003. Another example is that Moyo and Ziramba (2013) investigated the long-run impacts of total crime, car hijacking, illegal ﬁrearms, kidnapping, murder and sexual crime on tourist inﬂows and found only car hijacking, kidnapping, murder and sexual crime have signiﬁcant impact. Hence, using the total crime rate as an inﬂuencing factor may mask the true impact of crime on inbound tourism, which is also the main reason that crime has an insigniﬁcant impact in the SAC model. Therefore, in the following, the impacts of different types of crime on inbound tourism are explored by a step-wise regression method.

The Japan government counts several types of crimes including felonious offenses (denoted as crime_f), violent offenses (denoted as crime_v), larceny offenses (denoted as crime_l), moral offenses (denoted as crime_m) and intellectual offences (denoted as crime_i). To further explore the different impacts of different categories of the crimes on inbound tourists, total crime rate is replaced by the crime rates of these ﬁve types of offenses in the two-way ﬁxed SAC model. In contrast to the approach taken in Moyo and Ziramba (2013), which only included a single crime variable in each regression, we employ stepwise regression to simultaneously consider all potential crime variables and implement variable selection. The results are presented in Table 6. Compared to single crime variable approach, as shown in Table 7, stepwise regression effectively addresses the issue of multicollinearity by avoiding the simultaneous inclusion of multiple highly correlated independent variables, accurately selecting the most appropriate predictors. Specially, the violent crime rate is the only crime-related variable selected in the ﬁnal model using the stepwise regression, while both violent offenses and larceny offenses influence inbound tourism at a significance level of 10% by single crime variable approach. Due to the omission of the relationship between violent crime and larceny crime, the latter approach was incorrectly categorized as a factor influencing inbound tourism.

Table 6.

Estimates by Stepwise Regression.

Variable	Step1	Step2	Step 3	Step 4	Step 5
crime_f	−0.678 (.665)
crime_l	−0.017 (.251)	−0.019 (.157)	−0.018 (.185)	−0.019 (.156)
crime_m	0.459 (.494)	0.453 (.499)	0.494 (.458)
crime_v	−0.403 (.039)	−0.407 (.038)	−0.377 (.048)	−0.355 (.058)	−0.392 (.033)
crime_i	0.129 (.515)	0.133 (.503)
PCI	−0.382 (.003)	−0.376 (.003)	−0.376 (.003)	−0.368 (.003)	−0.374 (.002)
lnhotel	0.481 (.015)	0.480 (.016)	0.484 (.014)	0.484 (.013)	0.486 (.012)
airpollu	−0.116 (.002)	−0.114 (.002)	−0.113 (.002)	−0.111 (.002)	−0.102 (.004)
$ρ$	0.666 (.000)	0.661 (.000)	0.666 (.000)	0.669 (.000)	0.694 (.000)
$λ$	−0.750 (.000)	−0.737 (.000)	−0.747 (.000)	−0.767 (.000)	−0.801 (.000)

Note. p-values are reported in parentheses.

Table 7.

Estimates by Single Crime Variable Approach.

Variable	crime_f	crime_l	crime_m	crime_v	crime_i
crime_*	−1.651 (.251)	−0.023 (.087)	0.395 (.547)	−0.392 (.033)	0.000 (.999)
PCI	−0.427 (.000)	−0.403 (.002)	−0.426 (.001)	−0.375 (.002)	−0.419 (.001)
lnhotel	0.522 (.007)	0.516 (.010)	0.523 (.008)	0.486 (.012)	0.522 (.008)
airpollu	−0.106 (.004)	−0.110 (.003)	−0.110 (.006)	−0.102 (.004)	−0.099 (.007)
$ρ$	0.691 (.000)	0.658 (.000)	0.682 (.000)	0.694 (.000)	0.686 (.000)
$λ$	−0.782 (.000)	−0.724 (.000)	−0.735 (.000)	−0.800 (.000)	−0.754 (.000)

Note. p-Values are reported in parentheses. Each coefficient within the “crime_*” row represents the coefficient of crime variable in the corresponding column.

According to the stepwise regression, it can be found that the violent crime has a negative impact on foreign tourists, which is consistent with the results in Altindag (2014). Furthermore, the smaller AIC value (67.668) and BIC value (325.137) of the final model compared to those of the SAC model with the total crime rate presented in the fourth column of Table 5 also imply the special inﬂuence of violent crime and hence the importance of the study in the impact of different crime categories on inbound tourism.

Economic Development

Usually, the economy growth leads to the development of the infrastructure, which makes the destination more attractive. Hence, the positive impact of economic development on tourist industry is intuitive and has large empirical supports such as Y. Yang and Fik (2014) and Ahiawodzi (2013). However, the negative coefﬁcient of PCI in our empirical study indicates that economic development reduces the number of inbound tourists. This subsection explores the reasons for this surprising result.

To explain the phenomenon, the estimators of time effect $γ_{t}$ in SAC model is studied. Figure 2 presents these estimators in a time series plot, where the horizontal axis and the vertical axis represents the time year and the time effect estimates $γ_{t}$ respectively. Time effect shows an apparently increasing trend especially during 2011 to 2016 although it ﬂuctuates in the ﬁrst few years. The main reason for the decrease in 2008 to 2009 is the collapse of Lehman Brothers and the global ﬁnancial crisis, and the decrease in 2011 is largely due to the happening of a catastrophic earthquake and tsunami in the northeastern Japan in March of that year. The increasing trend in the unobservable time effects presents the prosperous tourism in Japan. Hence, the negative impact of economic development indicates that the development of tourism industry surpasses the economic development. According to the statistics on https://knoema.com/, the average growth of T&T GDP in 2011 to 2018 is 1.9375%, while the average growth of gross national income (GNI) during the same period is only 1.125%, which also supports that tourism is developing at a higher speed. To further verify the statement that the presence of time effect leads to the negative PCI coefﬁcient, the individual ﬁxed effect SAC model with no time effect is conducted and the results are presented in the ﬁfth column of Table 5. The coefﬁcient of PCI in this model becomes positive.

Spillover Effects

Spillover effects refer to the inﬂuence that seemingly unrelated events in one region can have economies of other regions. Since spillover effects can provide the information of regional dependency, it is usually critical in the spatial econometric models. In the SAC model, spillover effects refer to two types of effects. One type of effects is represented by the spatial autocorrelation coefﬁcients including both spatially lagged-dependent coefﬁcient $ρ$ and spatially autocorrelated disturbance $λ$ . The other one is the indirect effects of explanatory variables on dependent variable. Actually, the change of an explanatory variable in a particular unit will not only cause the change of the dependent variable in that unit itself but also bring about the change of the dependent variables in other units. And the effects of these two impacts are called a direct effect and an indirect effect respectively.

Since all the coefficients in the final model obtained through stepwise regression are statistically significant, as indicated in the last column of Table 6, and this model exhibits lower AIC and BIC values compared to the models presented in Table 5, our discussions in this subsection will be centered on this model. This model highlights the presence of both types of spillover effects, reinforcing the notion of spatial agglomeration in inbound tourism in Japan and underscoring the importance of employing spatial econometrics techniques.

According to the last column in Table 6, it can be found that both the spatial autocorrelation coefﬁcients are signiﬁcant but in the opposite directions. The positive value in spatially lagged coefﬁcient $ρ$ reﬂects the spillover effect on inbound tourism over the tourism industry of the neighbors, while the competition effects are presented in the unobservable factors of inbound tourism in neighbors according to the estimator of spatial autocorrelation in spatially autocorrelated error term $λ$ .

Due to the existence of the spatially lagged-dependent variables, the spatial effects of explanatory variables can be decomposed into the direct and indirect effects. The decomposition results are listed in Table 8, where the total effect is the sum of the direct effect and the indirect effect. As shown in this table, the direct effect and the indirect effect have the same direction, and the spillover effect is much larger than the direct effect, reﬂecting that the effect of each variable on inbound tourism is greatly enhanced by the effects of the same variable from neighbors.

Table 8.

Decomposition of Spatial Effects of Explanatory Variables.

Variable	Direct effect	Indirect effect	Total effect
crime_v	−0.462 (.035)	−0.835 (.057)	−1.297 (.045)
PCI	−0.449 (.002)	−0.805 (.005)	−1.254 (.002)
lnhotel	0.573 (.015)	1.031 (.028)	1.604 (.020)
airpollu	−0.121 (.005)	−0.217 (.009)	−0.339 (.006)

Note. p-Values are reported in parentheses.

Policy Implications

Based on the previous results, several important policy implications are provided in this subsection.

First, although the impact of the total crime rate on inbound tourism is insigniﬁcant, the violent crime rate has a signiﬁcantly negative effect. Mitigating the incidence of violent crimes not only contributes to the enhancement of tourism competitiveness but also plays a pivotal role in shaping tourists’ travel experiences. Since tourists’ perceptions of safety during travel significantly influence their future travel decisions, addressing incidents of violent crime, especially those involving tourists, requires careful consideration. Moreover, the negative impact of violent crime on tourism extends beyond immediate safety concerns. Tourists often share their experiences through various platforms, including social media and travel reviews, which can significantly influence the destination’s reputation. This underscores the importance of post-crime treatment and remedies as integral components in managing the aftermath of such incidents. Specially, destination management should formulate effective crisis communication and public relations strategies.

Second, the tourism industry has shown a consistent upward trend in recent years, surpassing the growth rate of the overall economy. The surge in the tourism industry not only reflects its robust growth but also underscores its potential as a key driver for Japan’s economic resurgence. In other words, Japan is well-positioned to set the ambitious goal of transforming into a tourist nation as a strategic move to rebound from a prolonged economic downturn. Positioning Japan as a tourist nation becomes not just a recovery strategy but a proactive approach to capitalize on a thriving sector. By embracing this goal, Japan has the opportunity to diversify its economic portfolio, reduce dependency on traditional industries, and foster sustainable development. Consequently, crafting and implementing targeted policies to attract inbound tourists should be a central focus for the government. These policies could encompass a wide range of initiatives, including infrastructural improvements, cultural enrichment programs, and streamlined visa processes. The ultimate aim is not solely to welcome an increased number of tourists but also to position Japan as a premier destination, thereby cultivating a positive global image and revitalizing the nation’s overall socio-economic landscape.

Third, spillover effects of both tourism variable and its inﬂuencing factors are all signiﬁcant. Japan government should take advantage of these spillover effects in inbound tourism. Indeed, Japan has undertaken initiatives to promote regional collaboration in tourism, exemplified by the Japan Railways Group, a collaborative effort involving six local companies. This partnership provides foreign tourists with the Japan Rail Pass (JR Pass), offering an economical and convenient means of exploring Japan by rail. However, there remains substantial room for improvement. In terms of a sub-national scope, inbound tourists often opt for multiple destinations within a single trip. The existence of the spillover effects makes establishing a regional tourism block (a collaboration with several adjacent regions) proﬁtable. On one hand, less attractive destinations can benefit from their neighboring regions with more attractions. On the other hand, these less attractive destinations can enhance auxiliary services and infrastructure to contribute to the overall diversity of the entire tourism block. Therefore, establishing a regional tourism block not only increases the appeal of all the regions involved but also serves as an efficient tool to alleviate unbalanced development between regions. A strategic approach for creating the regional tourism block involves clustering destinations based on shared themes such as cultural heritage, natural scenery, or shopping. By establishing different tourism blocks, Japan can offer tourists a diverse and immersive journey. This approach not only maximizes the advantages of spillover effects but also encourages longer stays and increased exploration of different regions.

Furthermore, the collaboration between regions in the realms of crime governance and air pollution control stands out as a multifaceted imperative for the Japanese government in its pursuit of attracting inbound tourists and fostering sustainable tourism practices. Specially, in crime governance, a united approach between regions can not only ensure the safety and well-being of tourists but also create a seamless and secure travel environment. Implementing coordinated strategies for law enforcement, public safety initiatives, and emergency response systems can contribute to an overall positive perception of Japan as a secure destination.

Conclusions

This paper focuses on Japanese tourism as a case study to examine the influence of crime on inbound tourism, an aspect often overlooked in existing literature. In our empirical analysis, we find that the total crime rate does not exert a significant impact on inbound tourism. Given that tourists hold varying risk perceptions for different types of crimes, we contend that utilizing the total crime rate, which aggregates various crime categories, may obscure the genuine influence of crime. Therefore, we employ the stepwise regression method to assess tourists’ varying sensitivity to different crime categories. Our findings reveal that only violent crime significantly and negatively affects Japanese inbound tourist overnight stays. Furthermore, through a review of the literature, we also find that one major reason for the inconsistent conclusions regarding the impact of crime on inbound tourism is the use of different crime variables as independent variables. Therefore, it is evident that the choice of crime variables is highly important, and this paper proves that stepwise regression is an effective tool in addressing this issue.

This paper also delves into additional determinants, such as economic development, tourism infrastructure, and air pollution, to analyze Japanese inbound tourism from a local perspective. To account for spatial dependencies in both tourism and independent variables, we employ the SAC model, a spatial econometric panel model. And the substantial spillover effects in Japanese sub-national tourism further underscore the validity of employing spatial econometric modeling. Furthermore, the indirect effects of all these determinants are considerably larger than their direct effects. Considering the influence of variables, the coefficients for tourism infrastructure and air pollution align with our expectations, but the coefficient for economic development yields an unexpectedly negative outcome. Further analysis reveals that this surprising outcome can be attributed to the influence of time effects, which exhibit a conspicuous upward trend. This upward trajectory in unobservable time effects reflects Japan’s flourishing tourism industry. Consequently, the negative impact of economic development suggests that the growth of the tourism sector outpaces overall economic development.

Based on empirical results, several implications are provided for policy makers. First, tourists have different levels of sensitivity toward types of crime in Japan, and violence offenses against inbound tourists should be carefully dealt with to minimize the loss to the subsequent tourism industry. Second, according to the phenomenon of economic development catching up with inbound tourism development presented in the empirical study, encouraging inbound tourism is important for the development of Japanese economy. Third, the inter-promotion of inbound tourism in neighbor prefectures will essentially contribute to the common development of regional inbound tourism. Finally, cross-regional governance in social problems, such as crime and air-pollution will be useful to develop the regional inbound tourism.

This study uses a spatial panel model to describe the relationship between Japanese inbound tourism and its determinants using data of 47 prefectures during 2007 to 2016. In this model, although the spatial and time effects are distinctive, the impacts of determinants are unchanged across areas and time, which can not fully catch the regional tourism characteristics. In future study, a multi-group model or geographically weighted regression models can be used to describe the spatial variation. Combined this kind of models with the panel model constructed in this paper, both regional and local policies can be provided to maximize the development of inbound tourism.

Footnotes

Declaration of Conflicting Interests

The author(s) declared no potential conflicts of interest with respect to the research, authorship, and/or publication of this article.

Funding

The author(s) disclosed receipt of the following financial support for the research, authorship, and/or publication of this article: This research is supported by the Fujian Province Innovation Strategy Research Project (No. 2021R0065).

ORCID iD

Jing Cheng

Notes

Author Biographies

Jing Cheng is an associate professor of Statistics, School of Statistics, Huaqiao University, Fujian, China. Her research interests include Applied Econometrics, Applied Statistics, and Tourism Economics.

Yida Wu is a graduate student, School of Mathematical Sciences, Xiamen University, Xiamen, Fujian, China. His research interests include Applied Statistics and Economic Statistics.

Kun Chen is a professor of Statistics, School of Statistics, Southwestern University of Finance and Economics, Chengdu, Sichuan, China. His research interests include Applied Statistics, Time Series Analysis and Empirical Likelihood Method.

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