Next Article in Journal
Efficiency Decomposition Analysis of the Marine Ship Industry Chain Based on Three-Stage Super-Efficiency SBM Model—Evidence from Chinese A-Share-Listed Companies
Previous Article in Journal
Development and Assessment of Augmented Reality Technology for Using in an Equipment Maintenance and Diagnostic System
 
 
Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
Journals
Sustainability
Volume 14
Issue 19
10.3390/su141912156
sustainability-logo
Submit to this Journal Review for this Journal Propose a Special Issue
► Article Menu

Article Menu

share Share announcement Help format_quote Cite question_answer Discuss in SciProfiles thumb_up
...
Endorse textsms
...
Comment
first_page
settings
Order Article Reprints
Font Type:
Arial Georgia Verdana
Font Size:
Aa Aa Aa
Line Spacing:
Column Width:
Background:
Article

Examining the Relationship between Tourism and Gender Equality: Evidence from Asia

by
Yan Zhang
1,
Shenglan Xu
2,* and
Jiekuan Zhang
3
1
School of Tourism Management, Guilin Tourism University, Guilin 541006, China
2
College of Tourism and Urban-Rural Planning, Chengdu University of Technology, Chengdu 610000, China
3
Asean Tourism Research Centre of China Tourism Academy, Guilin Tourism University, Guilin 541006, China
*
Author to whom correspondence should be addressed.
Sustainability 2022, 14(19), 12156; https://doi.org/10.3390/su141912156
Submission received: 27 August 2022 / Revised: 21 September 2022 / Accepted: 22 September 2022 / Published: 26 September 2022
Browse Figures
Versions Notes

Abstract

:
This study analyzes the relationship between tourism and gender equality using panel vector autoregression and vector error correction models estimated by the generalized method of moment. This methodology is based on panel data from 36 Asian countries between 2006 and 2019. The other three variables including the economy, employment, and education are also analyzed. The results show that there is a long-run equilibrium relationship between tourism and gender equality. In the short run, tourism is not the Granger cause for gender equality; however, tourism significantly contributes to gender equality in all regions in the long run. Furthermore, a heterogeneous causality between tourism and gender equality is found across the regions. Tourism’s positive effect on gender equality does not depend entirely on economic empowerment. All the economy, education and employment mostly contribute to realizing gender equality, but this contribution is not decisive. The findings and policy implications are finally discussed.

1. Introduction

Gender equality is one of the key evaluation indicators of global and regional sustainable development [1]. As an important global economic sector, tourism’s association with gender issues has always been a focus of the scientific community [2,3,4]. The evidence of the positive impact of tourism on gender equality has been provided in numerous micro cases [5,6,7,8,9]. These studies focus on the impact of tourism on the empowerment of women’s economy, employment, and education. The results show that tourism plays an important role in promoting women’s status and thus narrows the gender gap. It was also reported that, relative to the broader economy, tourism employs more women (54% versus 39%); women in tourism earn 14.7% less than men, and this figure is 16.8% in the general economy; and 23.7% of tourism ministers are women, but only 20.7% of the general government ones (World Bank Open Data, available at: https://data.worldbank.org (accessed on 21 September 2019)). Therefore, the role of tourism in promoting gender equality has again captured more academic attention since the United Nations included gender equality in the 17 Sustainable Development Goals (SDGs) in 2015 [10]. However, we note that these studies basically rely on sociological methodologies. This dominant paradigm of sociology makes it difficult to quantify the effect of tourism on gender equality.
The fieldwork methods from the sociological perspective dominate this subfield. Few macro studies have been conducted at the national or international level. In addition, case studies are often difficult to replicate in other regions, which makes it difficult to identify common features of the nexus of tourism and gender equality. Notably, the effect of tourism on gender equality is not always positive. As argued by Lenao and Basupi and Nguyen, the marginalization of women is equally noteworthy in tourism [7,11]. Therefore, we need a new tool for quantifying the relationship between tourism and gender equality. However, current sociological methods seem impossible to tell us the answer to this quantification. Given this background, we adopt a novel econometric method to investigate the relationship between tourism and gender equality. Moreover, given the significant effect of the economy, employment, and education on gender equality (see Section 2), we simultaneously concern these three variables. Consequently, this study explores the relationship among tourism, economy, employment, education, and gender equality.
In contrary to prior empirical studies, our article contributes to the literature in three ways. First and foremost, we do our research from an economic perspective rather than a traditional sociological one. The fixed sociological characteristics have made this classic and important tourism field less innovative [1,2]. We expect the new perspective to lead to new findings and inspire more novel research. Second, our data is derived from official statistics or reports rather than from the traditional primary survey. This study depends on the panel data of five variables: tourism, economy, education, employment, and gender equality. The advantage of doing so is that our research framework can be replicated, thus free from the subjectivity and uncertainty of case studies. In addition, macro research based on panel data helps find common conclusions in different regions.
Third, methodologically, we employ the panel vector autoregression (PVAR) approach to explain the impact of tourism shock on gender equality. Since Sims introduced the VAR model into economic analysis [12], the VAR model has been one of the most popular methods for analyzing multiple related variables. Generally speaking, if there are fewer economic variables in the VAR model, satisfactory parameters can be obtained through the ordinary least square (OLS) or maximum likelihood estimation method. However, if there are more variables (such as the five variables involved in our study), the panel fixed effect will make the results from the VAR model inconsistent and bias due to more parameter estimation. In this context, we build an advanced augmented PVAR model to improve the model’s robustness. Concretely, we employ Arellano–Bond’s generalized method of moment (GMM) estimator to estimate the PVAR model. The GMM estimation method defines the criterion function as the correlation function of the instrumental variable and disturbance term and minimizes it to obtain a robust parameter estimation [13].
We organize this paper as follows. Section 2 presents a literature review. In Section 3, we introduce the methodology and data collection. Section 4 reports the empirical results, including cointegration tests, impulse response functions, and short and long-run Granger causality tests. Section 5 discusses the results and underlines the policy implications. The final section concludes.

2. Literature Review

The 20th century saw profound changes in traditional notions of gender. In the new century, gender equality in particular has become a global trend and is even considered politically correct in most major countries around the world [14]. Moreover, gender equality is listed among both the Millennium Development Goals (MDGs) and SDGs. Nevertheless, deep-rooted patriarchal culture and different levels of social and economic development make gender equality vary greatly in different regions [15,16]. There are still some key factors significantly affecting the realization of gender equality, including work, income and rights [17], education, employment, and political participation [18], income [19,20], education [21], and various material and social resources, such as income, rights, and employment [22]. Likewise, The Global Gender Gap Report 2006 introduced by the World Economic Forum summarized these factors as economic participation and opportunity, educational attainment, health and survival and political empowerment [23]. Therefore, the economy, education, employment, and political rights could largely explain the gender gap.
Once the idea of sustainable development was put forward, it was closely related to tourism. Nowadays, that tourism should and could contribute to global and regional sustainable development is a fundamental consensus. Consequently, tourism is duty-bound to promote gender equality. Conversely, without gender equality, there is no sustainable tourism as well [24,25]. Therefore, as mentioned above, extensive published literature focusing on tourism and gender equality has constituted a hot spot in sociology. Wilkinson and Pratiwi early indicated that increased income is the biggest change for women in rural tourism development [26]. Afterward, economic empowerment has always been the major concern of scholars in the field of tourism and gender equality [8,24,27,28]. Along with women’s economic empowerment, much attention has been paid to the increase in women’s employment opportunities since the increase in women’s tourism income is usually achieved through employment. Nyaruwata and Nyaruwata and Uduji et al. argued that tourism is the main industry for women’s employment [9,29]. Similarly, Gentry and Rinaldi and Salerno concluded that tourism is an important contributor to helping women create new jobs [30,31]. This is also well reflected in the Global Report on Women in Tourism that finds that women account for the majority of tourism employment worldwide and tourism provides a smaller gender pay gap than the broader economy [32] (pp. 8–9).
Few studies have focused on education and gender equality in tourism. However, education for women is vital for their empowerment and requires sustaining institutional and budgetary support [21]. On the one hand, UNWTO indicated that a lack of education or formal training could jeopardize women’s active participation in tourism [32] (pp. 13). On the other hand, tourism needs more female employees with high service skills and knowledge levels. Therefore, more opportunities for education in tourism are provided for women and make them more independent [33]. Furthermore, a large number of female senior managers and even leaders exist in tourism [34,35].
It is thus rationally concluded that to a large extent, tourism promotes gender equality through empowering women’s economy, education, and employment. We also notice that The Global Gender Gap Report 2006 pointed out the importance of political participation in gender equality [23]. This, however, has not captured enough attention in tourism studies. It may be difficult to explore women’s political empowerment from the perspective of tourism. Nevertheless, the proportion of female officials in tourism administrations is higher than that in the whole government [32] (pp. 65–66), which partly shows that tourism can realize the political empowerment of women as well.
Although previous studies have shown that tourism promotes gender equality, the obstacles to this process are still diverse and gender discrimination is still widespread [36]. For example, Harvey et al. early found the inequality between the benefits of women and men [37]. Oliver and Sard found that gender discrimination may be the main reason for the gender wage gap in the hospitality sector [38]. Alrwajfah et al. indicated that obstacles still exist in women’s employment in the Jordanian tourism sector, leading them to be less optimistic about the development of the tourism economy [39]. Similarly, in Islamic society, conservative social traditions and customs significantly affect women’s current and future employment in tourism [40,41]. This reveals that traditional gender culture weakens the positive effect of tourism on women’s economy, employment, and education, which is also substantiated [42]. Moreover, gender discrimination against women persists even among top-level managers [4,34].
To sum up, gender equality seems to be an important outcome of tourism. In the development of tourism, the rise of women’s status has been widely acknowledged. However, sexism still exists significantly in tourism. It should be noted that these conclusions are mainly based on micro case studies. However, prior studies have failed to investigate the nexus of tourism and gender equality from large macro-scales such as national and even international. The primary reason for this gap is that previous studies on tourism and gender equality have been fixed in the paradigm of sociology [2,3]. The fieldwork approach dominates this subfield and is challenging to use in macro research at the national or international level. Additionally, case studies are difficult to replicate in other regions, thus making it difficult to find common problems of tourism and gender equality. Given this background, some scholars quantified the influence of tourism on gender equality [1,11] and explored the configuration of tourism’s influence on the gender gap [43] at the national level, thus expanding the research scope of such an important field of tourism and gender. However, the dynamic relationship between tourism and gender equality and related variables remains unknown. Therefore, unlike prior research, we attempt to explore the equilibrium and causal relationship between tourism and gender equality at also the national level by applying economic techniques.
As was mentioned in the introduction, the research method is the GMM-PVAR model, in which tourism and gender equality are regarded as two economic variables. We simultaneously introduce the economy, education, and employment into the GMM-PVAR model based on the above literature review. However, we do not take into account political rights and cultural variables due to limited data availability.

3. Methods and Materials

3.1. GMM-PVAR Model

As a result of the lags of the dependent variables in the classic PVAR model, the fixed effects correlate with the independent variables. Therefore, using the standard mean-differencing operation to remove the fixed effects may cause the deviation and inconsistency of the estimated coefficients. In order to obtain consistent and valid estimates in this case, we apply the forward mean-differencing and keep the orthogonality between lagged independent variables and transformed variables. Consequently, we employ the system GMM method to estimate these coefficients [44]. The system GMM is an extension of the earlier difference GMM [45] and uses the lags of both the variables and the difference variable as the instrumental variables, which can effectively avoid the shortcomings of the weak instrumental variables in difference GMM. In this study, the PVAR and panel vector error correction (PVEC) models are estimated by performing a robust system GMM technique according to Ouyang and Li and Zhang and Zhang [46,47].

3.2. Empirical Region and Data Collection

Our empirical area is Asia, where there are world-famous tourist destinations such as Thailand and the Maldives, developed countries such as Japan and Israel, developing countries such as China and India, some super-high oil-rich countries such as Qatar and Saudi Arabia, and the Philippines, with its high gender equality index, and Yemen and Jordan, which rank last in the world. Asia shows remarkable diversity in tourism, economy, and gender equality. Therefore, studying Asia is very representative globally, which not only helps to understand the relationship between tourism, economy, education, employment, and gender equality within Asia but also has a very high reference value for other regions.
Referring to the existing research, the proxies for tourism and economy are international inbound tourists [48,49] and gross domestic product (GDP) per capita [50,51], respectively. Education is represented by the government expenditure on education and measured as the percentage of government expenditure. Considering that tourism-related industries are mainly services, employment is measured as the percentage of female employment in services. Since no measurement of gender equality can be found in the published tourism literature, we use the gender gap index to represent gender equality in this study, which is reported by The Global Gender Gap Report published annually by the World Economic Forum that quantifies the gender gap index for each country.
All the data on tourism, economy, education, and employment are collected from the World Bank open data (http://data.worldbank.org/, accessed on 10 March 2022). The data on gender equality are derived from The Global Gender Gap Report. Since the first Report was published in 2006, considering the balance of the panel data, all data are from 2006. It is noteworthy that since some data are missing seriously, Laos, East Timor, North Korea, Bhutan, Syria, Palestine, Iraq, The United Arab Emirates, Afghanistan, Turkmenistan, and Uzbekistan are excluded from our study. Finally, we collect 36 Asian countries’ panel data on tourism, economy, education, employment, and gender equality over the period 2006–2019. Using data from 2020 and beyond may distort the link between tourism and gender equality due to the impact of the COVID-19 pandemic; therefore, we have data up to 2019. Based on these data, we examine the relationship among these five analyzed variables. To further explore the heterogeneity of different regional relationships, we divide Asia into three regions [1]: East and Southeast Asia, including 12 countries (Brunei, Cambodia, China, Indonesia, Japan, Korea, Malaysia, Mongolia, Philippines, Singapore, Thailand, Vietnam); South Asia, including 6 countries (Bangladesh, India, Maldives, Nepal, Pakistan, Sri Lanka); and West and Central Asia, including 18 countries (Azerbaijan, Armenia, Bahrain, Cyprus, Georgia, Iran, Israel, Jordan, Kazakhstan, Kuwait, Kyrgyzstan, Lebanon, Oman, Qatar, Saudi Arabia, Tajikistan, Turkey, Yemen).
Table 1 reports the descriptive statistics for the whole of Asia. From 2006 to 2019, Qatar had the highest GDP per capita in 2012, while Nepal had the smallest in 2006; China had the largest number of inbound arrivals in 2019, while Bangladesh had the smallest in 2015; Jordan had the biggest proportion of education expenditure in 2018 while Brunei had the smallest in 2006; Saudi Arabia had the largest proportion of female employment in services in 2008, while Nepal had the smallest in 2006; the Philippines had the largest gender equality in 2018, while Yemen had the smallest in 2007. Given the vast difference in the magnitude of different variables, we take the natural logarithm of tourism and GDP per capita.

4. Results

4.1. Data Stationary Test

Stationary data are the premise of building a PVAR model. In terms of the stationary test, we first conduct the cross-section dependence test for all variables. The results in Table 2 show that the null hypothesis of no cross-section dependence is rejected at the 1% significance level. This implies that all the data series are cross-section dependent.
Then we conducted a panel unit root test for all data series. The traditional first generation panel unit root test assumes cross-sectional independence between variables, which is subjective and unreasonable. However, as shown in Table 2, all variables are cross-section dependent. Therefore, the first generation panel unit root tests such as the Levin–Lin–Chu (LLC) test, Breitung test, Fisher test, and Im–Pesaran–Shin (IPS) test may lead to significant biases [52]. On this account, we use the second generation panel unit root test to overcome the cross-sectional correlation problem confirmed above by introducing heterogeneous shocks in multiple unobservable factor models in order to significantly enhance the reliability of our findings. Table 3 presents the results of panel unit roots tests.
Columns 1 and 2 show the IPS panel unit root test for each series. The results show that only lnt, lnGDPpc, and employment have a unit root. However, as indicated in Table 2, all variables show cross-sectional dependence. Therefore, we further perform Pesaran’s CIPS test for panel unit roots [53]. The new test results are shown in columns 3 and 4 in Table 3. The results clearly show that all data series have a unit root on this condition. However, the first difference of the panel data series is stationary at the 5% significance level. Consequently, we can perform the cointegration test.

4.2. Cointegration Test

This section examines the cointegration relationship (i.e., the long-run equilibrium relationship) amongst tourism, economy, education, employment, and gender equality. Table 4 presents the results for cointegration tests including the Pedroni test and the Kao test. For the Pedroni tests, four of the seven p values corresponding to the panel and group test statistics are smaller than 0.05, indicating that at the 5% or less significance level, the five analyzed variables are panel cointegrated. The results for the Kao test also confirm that there is a co-integration relationship among these five analyzed variables at the 5% significance level. Conclusively, there is a long-run equilibrium relationship amongst Asian tourism, economy, employment, education, and gender equality.

4.3. Impulse Response Analysis

Here we present the impulse response curves for the whole Asian sample and three subsamples as shown in Figure 1, Figure 2, Figure 3 and Figure 4. The vertical axis represents the deviation percentage, and the horizontal axis represents the lag time of the shock. Impulse response function describes the unit impact of one variable on other variables in the system and provides information such as positive and negative direction of impact, adjustment lag period, and stabilization process. The orthogonalization of the VAR residuals is conducive to segregating the response of the economy, education, employment, and gender equality to a shock on tourism. We synthesize our investigation into three categories: the impulse response functions of gender equality to tourism shock; the impulse response functions of the economy, education, and employment to tourism shock; the impulse response functions of gender equality to the economy, education, and employment shocks.
Figure 1 illustrates the Asian impulse response functions that imply the dynamic relationship among tourism, economy, education, employment, and gender equality. Figure 1 also shows that responding to a positive shock to tourism, the gender equality originally declines and afterward rises marginally and steadies in the long run. Additionally, the increase in tourism leads to an initial increase and subsequent slight decrease in GDP that steadies in the long run. In response to a positive shock to tourism, education fluctuates marginally and steadies in the long run. Moreover, Figure 1 displays that a positive shock to tourism in Asia exerts a significantly positive impact on employment. The results in Figure 1 simultaneously imply that a positive shock to the economy produces a positive impact on gender equality, a positive shock to education produces a positive-negative volatile impact on gender equality, and a positive shock to employment produces a slight negative impact on gender equality.
In the East and Southeast Asian countries, Figure 2 shows that in the case of a positive shock to tourism, gender equality originally declines and later sharply increases and steadies in the long run. The increase in tourism leads to an initial increase and subsequent decrease in GDP that steadies in the long run. The impact of a positive shock to tourism on education fluctuates between positive and negative and steadies in the long run. Differently, employment reacts from negatively to positively to a positive shock to tourism and then reaches its steady state. Figure 2 also clearly demonstrates that among the economy, education and employment, the increase in education has the smallest impact on gender equality. Surprisingly, the economic growth basically negatively affects gender equality. Responding to the increase in employment, gender equality initially declines and then increases and steadies in the long run.
Figure 3 displays the impulse response functions of South Asia, implying the dynamic relationship among the tourism, economy, education, employment and gender equality. The results show that a positive shock to tourism has a slight negative impact on gender equality at the beginning and then positively affects gender equality. The impact steadies in the long run. The economy initially responds positively to a positive shock to tourism in South Asia and later responds negatively and steadies in the long run. The increase in tourism has a bit effect on education, while it significantly increases employment. In South Asia, similar to tourism, economic growth also initially negatively and then positively affects gender equality and steadies in the long run. The impact of education and employment on gender equality fluctuates between negative and positive and then reaches a steady state.
Figure 4 shows the impulse response functions of West and Central Asia. The results demonstrate that a positive shock to tourism initially increases gender equality and later decreases and steadies in the long run. Moreover, in general, the increase in tourism in West and Central Asia exerts a slight positive effect on economic growth, education, and employment. We find that both employment and education have a bit impact on gender equality. Additionally, a positive shock to the economy has an initial negative and later positive effect on gender equality and steadies in the long run.

4.4. Granger Causality Tests

To gain a better understanding of the relationship between tourism, economy, education, employment, and gender equality, we further test the Granger causality between these analyzed variables for the whole sample and three subsamples. We employ a 1 lag GMM-PVEC model to test short-run causality and a 2 lag GMM-PVAR model to test long-run causality. The results for the Granger-causality tests are reported in Table 5 and Table 6. We also summarize our analyses into three categories according with the impulse response analyses above.

4.4.1. Short-Run Granger Causality

In the short run, for the whole Asian sample, tourism is not the Granger cause for economic growth (p = 0.4524) but not vice versa (p = 0.0686). The null hypothesis that tourism is not the Granger cause for education (p = 0.0000) and employment (p = 0.0176) is rejected at the 1% significance level. The null hypothesis that education is not the Granger cause for tourism is rejected at the 1% significance level (p = 0.0000), while the null hypothesis that employment is not the Granger cause for tourism is accepted (p = 0.1047). In the short run, tourism is not the Granger cause for gender equality (p = 0.7869) and vice versa (p = 0.6225). Additionally, there are no Granger causalities between the economy and gender equality, employment, and gender equality. However, we find the unidirectional Granger causality running from education to gender equality (p = 0.0750).
Specific to the east and southeast Asian countries, in the short run, Table 5 demonstrates that there is no Granger causality between tourism and economy (p = 0.4028 and p = 0.9978). There is a unidirectional Granger causality running from tourism to employment (p = 0.0621). Education unidirectionally Granger causes tourism (p = 0.0070). Different from the whole sample, the results show that there is a bidirectional Granger causality between tourism and gender equality (p = 0.0235 and p = 0.0387). In the East and Southeast Asian countries, Table 5 indicates the bidirectional Granger causality between employment and gender equality (p = 0.0000 and p = 0.0522) and the unidirectional Granger causalities running from gender equality to the economy (p = 0.0542) and education (p = 0.0042).
In the South Asian countries, the results in Table 5 demonstrate that tourism unidirectionally Granger causes the economy (p = 0.0366), education (p = 0.0890), employment (p = 0.0018), and gender equality (p = 0.0075). There are no Granger causalities between the economy and gender equality, education and gender equality, employment and gender equality. In the west and central Asian countries, tourism is the Granger cause for education (p = 0.0000), and vice versa (p = 0.0000). Tourism is the Granger cause for employment (p = 0.0790), but not vice versa (p = 0.1214). There is no Granger causality between tourism and gender equality. Also, we find no Granger causality between the economy or education or employment and gender equality

4.4.2. Long-Run Granger Causality

Table 6 reports the long-run causality tests for the whole Asian sample and three sub-regions. Table 6 shows that in Asia, tourism is the Granger cause for gender equality at the 1% significance level (p = 0.0097). Also, tourism unidirectionally Granger causes the economy, education and employment (p = 0.0265, p = 0.0206 and p = 0.0399). The economy, education, and employment are the Granger causes for gender equality at the 5%, 10%, and 10% significance level, respectively. We find no bidirectional Granger causality between tourism, economy, education, employment, and gender equality.
In the east and southeast Asian countries, there is a long-run bidirectional Granger causality between tourism and gender equality (p = 0.0491 and p = 0.0468). Also, tourism unidirectionally Granger causes education at the 10% significance level (p = 0.0515). Both the economy and education in East and Southeast Asia are not the Granger causes for gender equality. On the contrary, employment unidirectionally Granger causes gender equality (p = 0.0082), and vice versa (p = 0.0238).
In South Asia, there is a long-run unidirectional Granger causality running from tourism to gender equality at the 1% significance level (p = 0.0071). Tourism also unidirectionally Granger causes employment at the 5% significance level (p = 0.0299). Moreover, the economy unidirectionally Granger causes tourism at the 10% significance level in the long run (p = 0.0694). There exists a long-run unidirectional Granger causality running from the economy to gender equality in South Asia. In the west and central Asian countries, Table 6 indicates that tourism is still the Granger cause for gender equality at the 1% significance level (p = 0.0044). In addition, employment unidirectionally Granger causes gender equality at the 10% significance level (p = 0.0847). However, there is no Granger causality running from both the economy and education to gender equality.

5. Discussion

Our results clearly demonstrate a long-run equilibrium relationship amongst tourism, economy, employment, education, and gender equality for both the whole sample and three subsamples. Although both tourism and gender equality series are not stationary, their linear combination becomes stationary. This further suggests that in the long run, tourism and gender equality possess a common drift. Gender equality may be deviated from equilibrium by some random disturbance in the short term; however, this deviation will return to equilibrium over time through the channels of tourism and the other three variables. This new finding confirms the long-run intrinsic relationship between tourism and gender equality, which provides a theoretical basis for achieving gender equality through developing tourism.
Our results also demonstrate that tourism contributes to realizing gender equality. This conclusion is consistent with the studies of Zhang and Zhang [1], Ferguson [5], Gil Arroyo et al. [6], and Nguyen [11]; however, we found something new. For example, the impulse response results show that the impact of tourism on gender equality shifts between positive and negative in different periods and will reach a steady state in the long run. Overall, tourism is the Granger cause for gender equality for both the whole Asian sample and three subsamples. This implies that developing tourism could narrow the gender gap. As indicated in extensive case studies, tourism positively affects gender equality through the economy, employment, and education. Our research confirms the role of tourism in promoting these three factors.
Tourism involves a wide variety of positions that have different entry requirements. In general, the entry threshold for tourism employment is relatively low, which is conducive to attracting a large number of female laborers who are less competitive than men. In other words, the employment opportunities for women in tourism are greater than those in the broader economy. Therefore, tourism provides more jobs for women and in turn increases their income and enhances their status in families and society. Similarly, as argued by Moswete and Lacey [33], tourism also provides more opportunities for women’s education. This is more about vocational skills than schooling, which makes women more versatile in tourism services. All in all, with the development of tourism, the economy, employment, and education opportunities for women have increased. In this context, traditional dominant patriarchal gender discourse and practices are gradually being challenged.
According to the impulse response results, it seems difficult to judge the direction of the impact of the economy, education, and employment on gender equality as the impulse response curve fluctuates significantly. By contrast, the results for Granger causality tests clearly show that these three variables significantly contribute to the realization of gender equality in the long run but have opposite results in the short run. The same is valid for tourism. In the short run, tourism cannot lead to gender equality either. Hence, developing tourism, economy and education as well as increasing women’s employment cannot lead to the short-run improvement of women’s status.
We also found heterogeneous impulse response functions and Granger causalities between tourism and gender equality across the regions. In East and Southeast Asia, the results indicate that the impact of a positive shock to tourism on gender equality fluctuates the most. The Granger causalities also indicate that tourism causes gender equality at the least significance level in the long run. That is, compared with other regions, tourism has the weakest positive effect on gender equality in East and Southeast Asia. On the contrary, tourism in South Asia leads most to gender equality. In West and Central Asia, tourism basically shows a positive impact on gender equality.
Surprisingly, the economy does not contribute to gender equality in East and Southeast Asia and West and Central Asia. East and Southeast Asia are the world’s leading tourist destinations, with China, Japan, and Thailand, which makes great achievements in inbound tourism. Additionally, the major developed countries in Asia are concentrated in this region, such as South Korea, Japan, and Singapore. However, according to The Global Gender Gap Report, these countries with higher economic achievements score lower on the gender equality index. In contrast, the Philippines has long ranked first in Asia and the top 10 in the world in terms of the gender equality index. As a result, the economy does not explain the gender gap in East and Southeast Asia. At this point, the culture may be more persuasive.
It is well known that the Confucian culture and Islamic culture dominate East and Southeast Asia. In this cultural atmosphere, the idea of men being superior to women is deeply rooted. Even in highly developed countries such as Japan, the status of women remains low, resulting in that Japan’s gender equality has long ranked outside the top 100 worldwide. The same is true in West and Central Asia, where popular Islamic culture makes it difficult to distribute economic development achievements to women, thus leading to a lower index of gender equality. Exceptionally, we find a high index of gender equality in the Philippines, which may be related to its more popular Christian culture. This finding is actually not new and is similar to the studies of Alrwajfah et al. [39] and Uduji et al. [40], who found that conservative social traditions and customs hinder the narrowing of the gender gap. This further confirms the findings of Kabeer [15], who concluded that the impact of economic growth on gender equality is regionally heterogeneous.
We noticed that compared to the economy, tourism contributes more to gender equality. This implies that the effect of tourism on gender equality is reflected in not only the material resources such as the economy and employment but also the social resources such as culture to a large extent. Tourism differs greatly from the other economic industries in that tourism does not produce “cold” and “inanimate” consumer goods but provides “warm” and “living” services face to face. Furthermore, tourism activities are mainly characterized by person-to-person communication. This determines the significant cultural exchange characteristics of the production and consumption of tourism products. Furthermore, tourism in this study is measured as the number of inbound tourist arrivals. The interaction of the extremely different cultures between tourism destinations and tourist sources is significantly conducive to promoting women’s status. Given this background, tourism is more powerful than any other economic tool for promoting gender equality. Therefore, developing tourism, especially inbound tourism, plays a vital role in achieving gender equality in destinations.
Based on these findings and the discussion above, we underline some important policy implications on tourism and gender equality. First, in Asia, we should increase support for developing tourism, especially inbound tourism, and vigorously strengthen marketing for tourist source markets with high gender equality index (mainly referring to European and North American markets), thereby changing the traditional gender perceptions through the cultural function of tourism. Second, in tourism, investment in women’s service skills, training, and education should be increased to guarantee women’s access to finance, markets, technology, and information. In this way, women can be retained and given more opportunities for promotion and decision-making. Third, we should balance the wages of men and women and implement the rules and regulations of equal pay for equal work in tourism, thus propagandizing and establishing the corporate culture of gender equality. Fourth, we should provide an environment conducive to women’s employment and entrepreneurship in tourism, which includes laws, policies, and welfare, so that women can be gradually freed from the constraints of their families and conservative patriarchal culture.

6. Concluding Remarks

This study investigates the endogenous relationships among tourism, economy, education, employment, and gender equality by using the GMM-PVAR technique based on panel data from 36 Asian countries over the period 2006–2019. The results show the long-run equilibrium relationship among tourism, gender equality, economy, education, and employment. Furthermore, developing tourism is conducive to narrowing the gender gap, which however differs significantly across different Asian regions. The positive impact of tourism on the economy, education, and employment also varies significantly across East and Southeast Asia, South Asia, and West and Central Asia. All the economy, education, and employment mostly contribute to realizing gender equality, but such a contribution is not decisive. Tourism promotes gender equality more than the economy, education, and employment. Relative to the economic function, the cultural role of tourism is more beneficial to the realization of gender equality to some extent.
This article provides new economics insights into the subfield of tourism and gender equality and contributes significantly to addressing the research gap focusing only on sociological ones indicated by Figueroa-Domecq et al. [2]. Furthermore, we provide a novel econometric analytical framework to explore the cointegration, dynamic, and causal relationships between tourism and gender equality, which can be applied to various regions beyond Asian countries. In conclusion, the study assigns an interdisciplinary character to the field of tourism and gender equality.
Despite the robust results obtained using the GMM-PVAR approach, one limitation of this study lies in that our conclusions may not be applicable in individual regions due to the special socio-economic characteristics of a particular country. Future studies could explore the relationship between the five analyzed variables based on national time series data. Besides, as explained in the article, the culture variable may play an important role in gender equality, which however is not explored in the current study due to limitations in data availability. Thus, quantifying the impact of culture on the relationship between tourism and gender equality appears to be also interesting and important in the future. Finally, gender equality in this study in nature is a comprehensive indicator and has many components; however, we do not explore the effect of tourism on each component. Future studies thus could be conducted by extending the proxies for gender equality by introducing more segmented indicators, thereby leading to a more in-depth understanding of the impact of tourism on gender equality.

Author Contributions

Conceptualization, S.X.; Data curation, Y.Z.; Funding acquisition, Y.Z. and J.Z.; Methodology, J.Z.; Writing—original draft, Y.Z.; Writing—review & editing, S.X. and J.Z. All authors have read and agreed to the published version of the manuscript.

Funding

This research was funded by [Improvement Project of Young and Middle-aged Teachers’ Research Ability in Guangxi’s Colleges] grant number [2020KY22018] and [National Natural Science Foundation of China] grant number [71764027]. And The APC was funded by National Natural Science Foundation of China] grant number [71764027].

Institutional Review Board Statement

Not applicable.

Informed Consent Statement

Not applicable.

Data Availability Statement

The data that support the findings of this study are available on request from the corresponding author.

Acknowledgments

This work was supported by the Improvement Project of Young and Middle-aged Teachers’ Research Ability in Guangxi’s Colleges under Grant 2020KY22018 and National Natural Science Foundation of China under Grant 71764027.

Conflicts of Interest

The authors declare no conflict of interest.

References

  1. Zhang, J.K.; Zhang, Y. Tourism and gender equality: An Asian perspective. Ann. Tour. Res. 2020, 85, 103067. [Google Scholar] [CrossRef]
  2. Figueroa-Domecq, C.; Pritchard, A.; Segovia-Pérez, M.; Morgan, N.; Villacé-Molinero, T. Tourism gender research: A critical accounting. Ann. Tour. Res. 2015, 52, 87–103. [Google Scholar] [CrossRef]
  3. Cohen, S.A.; Cohen, E. New directions in the sociology of tourism. Curr. Issues Tour. 2019, 22, 153–172. [Google Scholar] [CrossRef]
  4. Dashper, K.; Turner, J.; Wengel, Y. Gendering knowledge in tourism: Gender (in) equality initiatives in the tourism academy. J. Sustain. Tour. 2022, 30, 1621–1638. [Google Scholar] [CrossRef]
  5. Ferguson, L. Promoting gender equality and empowering women? Tourism and the third Millennium Development Goal. Curr. Issues Tour. 2011, 14, 235–249. [Google Scholar] [CrossRef]
  6. Gil Arroyo, C.; Barbieri, C.; Sotomayor, S.; Knollenberg, W. Cultivating Women’s Empowerment through Agritourism: Evidence from Andean Communities. Sustainability 2019, 11, 3058. [Google Scholar] [CrossRef]
  7. Lenao, M.; Basupi, B. Ecotourism development and female empowerment in Botswana: A review. Tour. Manag. Perspect. 2016, 18, 51–58. [Google Scholar] [CrossRef]
  8. Hao, F.; Zhang, S.; Xiao, H. A return to innocence: Guimi tourism and women empowerment. Int. J. Tour. Res. 2021, 23, 971–983. [Google Scholar] [CrossRef]
  9. Uduji, J.I.; Okolo-Obasi, E.N.; Onodugo, V.A.; Nnabuko, J.O.; Adedibu, B.A. Corporate social responsibility and the role of rural women in strengthening agriculture-tourism linkages in Nigeria’s oil producing communities. J. Touism Cult. Chang. 2021, 19, 754–780. [Google Scholar] [CrossRef]
  10. Boluk, K.; Cavaliere, C.T.; Higgins-Desbiolles, F. Critical thinking to realize sustainability in tourism systems: Reflecting on the 2030 sustainable development goals. J. Sustain. Tour. 2017, 25, 1201–1204. [Google Scholar] [CrossRef]
  11. Nguyen, C.P. Tourism and gender (in) equality: Global evidence. Tour. Manag. Perspect. 2022, 41, 100933. [Google Scholar] [CrossRef]
  12. Sims, C.A. Macroeconomics and reality. Econom. J. Econom. Soc. 1980, 48, 1–48. [Google Scholar] [CrossRef]
  13. Biagi, B.; Brandano, M.G.; Lambiri, D. Does Tourism Affect House Prices? Evidence from Italy. Growth Chang. 2015, 46, 501–528. [Google Scholar] [CrossRef]
  14. Nhamo, G.; Muchuru, S.; Nhamo, S. Women’s needs in new global sustainable development policy agendas. Sustain. Dev. 2018, 26, 544–552. [Google Scholar] [CrossRef]
  15. Kabeer, N. Gender equality, economic growth, and women’s agency: The “endless variety” and “monotonous similarity” of patriarchal constraints. Fem. Econ. 2016, 22, 295–321. [Google Scholar] [CrossRef]
  16. Seguino, S. Global trends in gender equality. J. Afr. Dev. 2016, 18, 9–30. [Google Scholar] [CrossRef]
  17. Uduji, J.I.; Okoloobasi, E.N.; Asongu, S.A. Corporate social responsibility and the role of rural women in sustainable agricultural development in sub-Saharan Africa: Evidence from the Niger Delta in Nigeria. Sustain. Dev. 2019, 27, 692–703. [Google Scholar] [CrossRef]
  18. Yadav, A.K.; Sahni, B.; Jena, P.K. Education, employment, economic status and empowerment: Implications for maternal health care services utilization in India. J. Public Aff. 2021, 21, e2259. [Google Scholar] [CrossRef]
  19. Pepin, J.R. Beliefs about money in families: Balancing unity, autonomy, and gender equality. J. Marriage Fam. 2019, 81, 361–379. [Google Scholar] [CrossRef]
  20. Seguino, S. Macroeconomic policy tools to finance gender equality. Dev. Policy Rev. 2019, 37, 504–525. [Google Scholar] [CrossRef]
  21. North, A.; Longlands, H. Gender, Poverty and Educational Equality. In The SAGE Handbook of Inclusion and Diversity in Education; SAGE Publications Ltd.: London, UK, 2019; p. 103. [Google Scholar]
  22. Falk, A.; Hermle, J. Relationship of gender differences in preferences to economic development and gender equality. Science 2018, 362, eaas9899. [Google Scholar] [CrossRef]
  23. World Economic Forum. The Global Gender Gap Report. 2006, pp. 3–5. Available online: https://www3.weforum.org/docs/WEF_GenderGap_Report_2006.pdf (accessed on 3 August 2022).
  24. Alarcón, D.M.; Cole, S. No sustainability for tourism without gender equality. J. Sustain. Tour. 2019, 27, 903–919. [Google Scholar] [CrossRef]
  25. Abou-Shouk, M.A.; Mannaa, M.T.; Elbaz, A.M. Women’s empowerment and tourism development: A cross-country study. Tour. Manag. Perspect. 2021, 37, 100782. [Google Scholar] [CrossRef]
  26. Wilkinson, P.F.; Pratiwi, W. Gender and tourism in an Indonesian village. Ann. Tour. Res. 1995, 22, 283–299. [Google Scholar] [CrossRef]
  27. Acharya, B.P.; Halpenny, E.A. Homestays as an alternative tourism product for sustainable community development: A case study of women-managed tourism product in rural Nepal. Tour. Plan. Dev. 2013, 10, 367–387. [Google Scholar] [CrossRef]
  28. Tucker, H.; Boonabaana, B. A critical analysis of tourism, gender and poverty reduction. J. Sustain. Tour. 2012, 20, 437–455. [Google Scholar] [CrossRef]
  29. Nyaruwata, S.; Nyaruwata, L.T. Gender equity and executive management in tourism: Challenges in the Southern African Development Community (SADC) region. Afr. J. Bus. Manag. 2013, 7, 2059. [Google Scholar]
  30. Gentry, K.M. Belizean women and tourism work: Opportunity or impediment? Ann. Tour. Res. 2007, 34, 477–496. [Google Scholar] [CrossRef]
  31. Rinaldi, A.; Salerno, I. The tourism gender gap and its potential impact on the development of the emerging countries. Qual. Quant. 2020, 54, 1465–1477. [Google Scholar] [CrossRef]
  32. UNWTO (United Nations World Tourism Organization). Global Report on Women in Tourism, 2nd ed.; UNWTO: Madrid, Spain; UN Women: New York, NY, USA, 2019. [Google Scholar]
  33. Moswete, N.; Lacey, G. “Women cannot lead”: Empowering women through cultural tourism in Botswana. J. Sustain. Tour. 2015, 23, 600–617. [Google Scholar] [CrossRef]
  34. Carvalho, I.; Costa, C.; Lykke, N.; Torres, A. Beyond the glass ceiling: Gendering tourism management. Ann. Tour. Res. 2019, 75, 79–91. [Google Scholar] [CrossRef]
  35. Litwin, A.; Ngan, H.F.B.; Atembe, R. Attitudes towards female managers in Austrian and Macau tourism industry. J. Hosp. Tour. Manag. 2019, 39, 1–8. [Google Scholar] [CrossRef]
  36. Je, J.S.; Khoo, C.; Yang, E.C.L. Gender issues in tourism organisations: Insights from a two-phased pragmatic systematic literature review. J. Sustain. Tour. 2022, 30, 1658–1681. [Google Scholar] [CrossRef]
  37. Harvey, M.J.; Hunt, J.; Harris, C.C., Jr. Gender and community tourism dependence level. Ann. Tour. Res. 1995, 22, 349–366. [Google Scholar] [CrossRef]
  38. Oliver, X.; Sard, M. Gender wage gap in hospitality. J. Hosp. Tour. Res. 2021, 45, 345–372. [Google Scholar] [CrossRef]
  39. Alrwajfah, M.M.; Almeida-García, F.; Cortés-Macías, R. Females’ perspectives on tourism’s impact and their employment in the sector: The case of Petra, Jordan. Tour. Manag. 2020, 78, 104069. [Google Scholar] [CrossRef]
  40. Alsawafi, A.M. Exploring the challenges and perceptions of Al Rustaq College of Applied Sciences students towards Omani women’s empowerment in the tourism sector. Tour. Manag. Perspect. 2016, 20, 246–250. [Google Scholar] [CrossRef]
  41. Uduji, J.I.; Okolo-Obasi, E.N.; Asongu, S.A. Sustaining cultural tourism through higher female participation in Nigeria: The role of corporate social responsibility in oil host communities. Int. J. Tour. Res. 2020, 22, 120–143. [Google Scholar] [CrossRef]
  42. Scheyvens, R.; Hughes, E. Can Tourism help to “end poverty in all its forms everywhere”? The challenge of tourism addressing SDG1. J. Sustain. Tour. 2019, 27, 1061–1079. [Google Scholar] [CrossRef]
  43. Zhang, J.; Zhang, Y. A qualitative comparative analysis of tourism and gender equality in emerging economies. J. Hosp. Tour. Manag. 2021, 46, 284–292. [Google Scholar] [CrossRef]
  44. Arellano, M.; Bover, O. Another look at the instrumental variable estimation of error-components models. J. Econom. 1995, 68, 29–51. [Google Scholar] [CrossRef] [Green Version]
  45. Arellano, M.; Bond, S. Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. Rev. Econ. Stud. 1991, 58, 277–297. [Google Scholar] [CrossRef]
  46. Ouyang, Y.; Li, P. On the nexus of financial development, economic growth, and energy consumption in China: New perspective from a GMM panel VAR approach. Energy Econ. 2018, 71, 238–252. [Google Scholar] [CrossRef]
  47. Zhang, J.K.; Zhang, Y. The relationship between China’s income inequality and transport infrastructure, economic growth, and carbon emissions. Growth Chang. 2021, 52, 243–264. [Google Scholar] [CrossRef]
  48. Roudi, S.; Arasli, H.; Akadiri, S.S. New insights into an old issue–examining the influence of tourism on economic growth: Evidence from selected small island developing states. Curr. Issues Tour. 2019, 22, 1280–1300. [Google Scholar] [CrossRef]
  49. Zhang, J.; Zhang, Y. Tourism and low-carbon performance: An fsQCA approach. Asia Pac. J. Tour. Res. 2021, 26, 626–639. [Google Scholar] [CrossRef]
  50. Balli, E.; Sigeze, C.; Manga, M.; Birdir, S.; Birdir, K. The relationship between tourism, CO2 emissions and economic growth: A case of Mediterranean countries. Asia Pac. J. Tour. Res. 2019, 24, 219–232. [Google Scholar] [CrossRef]
  51. Lee, J.W.; Brahmasrene, T. Investigating the influence of tourism on economic growth and carbon emissions: Evidence from panel analysis of the European Union. Tour. Manag. 2013, 38, 69–76. [Google Scholar] [CrossRef]
  52. Palm, F.C.; Smeekes, S.; Urbain, J.P. Cross-sectional dependence robust block bootstrap panel unit root tests. J. Econom. 2011, 163, 85–104. [Google Scholar] [CrossRef]
  53. Pesaran, M.H. A simple panel unit root test in the presence of cross-section dependence. J. Appl. Econom. 2007, 22, 265–312. [Google Scholar] [CrossRef] [Green Version]
Sustainability 14 12156 g001 550
Figure 1. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Figure 1. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Sustainability 14 12156 g001
Sustainability 14 12156 g002 550
Figure 2. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in East and Southeast Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Figure 2. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in East and Southeast Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Sustainability 14 12156 g002
Sustainability 14 12156 g003 550
Figure 3. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in South Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Figure 3. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in South Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Sustainability 14 12156 g003
Sustainability 14 12156 g004 550
Figure 4. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in West and Central Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Figure 4. Impulse responses for 2 lag VAR (according to Schwarz information criterion) of tourism, lnGDPpc, education, employment, and gender in West and Central Asia. Solid lines denote impulse response curves and dotted lines denote 95% confidence interval bands generated by Monte Carlo simulation with 1000 repetitions.
Sustainability 14 12156 g004
Table
Table 1. Descriptive statistics.
Table 1. Descriptive statistics.
Tourism (Number of Arrivals)GDPpc (Current US Dollars)Education (% of Government Expenditure)Employment (% of Female Employment in Services)Gender Equality
Mean7,693,16613,619.9213.869259.96390.6507
Median2,989,5005574.52813.370058.04400.6582
Maximum61,173,55985,076.1528.526698.6690.799
Minimum104,416.7346.94533.845110.7510.451
Std. Dev.11,696,94217,105.514.241725.67910.0545
Observations504504504504504
Table
Table 2. Cross-section dependence tests.
Table 2. Cross-section dependence tests.
LntLnGDPpcEducationEmploymentGender Equality
Breusch-Pagan LM4919.201 *** 4565.246 *** 4371.415 *** 6138.536 *** 2971.595 ***
Pesaran scaled LM120.835 *** 110.863 *** 107.177 *** 155.185 *** 65.967 ***
Bias-corrected scaled LM119.450 *** 109.478 *** 105.827 *** 153.801 *** 64.582 ***
Pesaran CD55.586 *** 54.892 *** 49.722 *** 39.203 *** 26.919 ***
Note: *** indicates the 1% significance level.
Table
Table 3. Results for panel unit root tests.
Table 3. Results for panel unit root tests.
VariablesIPSCIPS
LevelFirst DifferenceLevelFirst Difference
Lnt−2.276 −3.169 *** 2.847 −3.802 ***
lnGDPpc−2.854 −7.104 *** 4.642 −7.814 ***
Education−4.920 * −8.988 *** −2.601 −7.820 ***
Employment−2.004 −4.028 *** 3.285 −4.488 ***
Gender equality−4.868 * −6.209 *** 3.601 −2.111 ***
Notes: * and *** denotes the 10% and 1% significance level, respectively.
Table
Table 4. Panel cointegration tests for lnt, lnGDPpc, education, employment, and gender equality.
Table 4. Panel cointegration tests for lnt, lnGDPpc, education, employment, and gender equality.
Pedroni Test
StatisticProb.
Panel v-Statistic (Weighted)−2.4832230.9935
Panel rho-Statistic (Weighted)2.6637590.9961
Panel PP-Statistic (Weighted)−5.9524430.0000
Panel ADF-Statistic (Weighted)−1.6636730.0481
Group rho-Statistic5.1630991.0000
Group PP-Statistic−8.8247780.0000
Group ADF-Statistic−1.4649070.0415
Kao Test
ADF−1.3152730.0442
Table
Table 5. Results for short-run Granger causality tests.
Table 5. Results for short-run Granger causality tests.
AsiaEast and Southeast AsiaSouth AsiaWest and Central Asia
Dependent VariableExcludedProb.Dependent VariableExcludedProb.Dependent VariableExcludedProb.Dependent VariableExcludedProb.
D(Tourism)D(lnGDPpc)0.0686D(Tourism)D(lnGDPpc)0.4028D(Tourism)D(lnGDPpc)0.1667D(Tourism)D(lnGDPpc)0.7873
D(Education)0.0000D(Education)0.0070D(Education)0.1830D(Education)0.0000
D(Employment)0.1047D(Employment)0.0682D(Employment)0.1722D(Employment)0.1214
D(Gender)0.6225D(Gender)0.0235D(Gender)0.2068D(Gender)0.3536
All0.0000All0.0347All0.0406All0.0000
D(lnGDPpc)D(Tourism)0.4524D(lnGDPpc)D(Tourism)0.9978D(lnGDPpc)D(Tourism)0.0366D(lnGDPpc)D(Tourism)0.4690
D(Education)0.0761D(Education)0.0476D(Education)0.3567D(Education)0.4754
D(Employment)0.3284D(Employment)0.7185D(Employment)0.5182D(Employment)0.4990
D(Gender)0.8105D(Gender)0.0543D(Gender)0.1291D(Gender)0.1600
All0.3136All0.0976All0.1916All0.4086
D(Education)D(Tourism)0.0000D(Education)D(Tourism)0.3004D(Education)D(Tourism)0.0890D(Education)D(Tourism)0.0000
D(lnGDPpc)0.6568D(lnGDPpc)0.2053D(lnGDPpc)0.2384D(lnGDPpc)0.2478
D(Employment)0.3257D(Employment)0.0291D(Employment)0.5446D(Employment)0.5996
D(Gender)0.6121D(Gender)0.0042D(Gender)0.7245D(Gender)0.3564
All0.0000All0.0000All0.3456All0.0001
D(Employment)D(Tourism)0.0176D(Employment)D(Tourism)0.0621D(Employment)D(Tourism)0.0018D(Employment)D(Tourism)0.0790
D(lnGDPpc)0.0117D(lnGDPpc)0.0018D(lnGDPpc)0.8149D(lnGDPpc)0.6552
D(Education)0.0732D(Education)0.0387D(Education)0.7909D(Education)0.0285
D(Gender)0.7553D(Gender)0.0000D(Gender)0.4286D(Gender)0.4152
All0.0193All0.0000All0.6809All0.2697
D(Gender)D(Tourism)0.7869D(Gender)D(Tourism)0.0387D(Gender)D(Tourism)0.0075D(Gender)D(Tourism)0.4043
D(lnGDPpc)0.5455D(lnGDPpc)0.1888D(lnGDPpc)0.5943D(lnGDPpc)0.2035
D(Education)0.0750D(Education)0.1885D(Education)0.1133D(Education)0.1001
D(Employment)0.4904D(Employment)0.0522D(Employment)0.8083D(Employment)0.2857
All0.6387All0.0065All0.4753All0.2649
Table
Table 6. Results for long-run Granger causality tests.
Table 6. Results for long-run Granger causality tests.
AsiaEast and Southeast AsiaSouth AsiaWest and Central Asia
Dependent VariableExcludedProb.Dependent VariableExcludedProb.Dependent VariableExcludedProb.Dependent VariableExcludedProb.
TourismlnGDPpc0.7869TourismlnGDPpc0.0007TourismlnGDPpc 0.0694TourismlnGDPpc0.4551
Education0.0972Education0.1100Education 0.6766Education0.3113
Employment0.4179Employment0.5383Employment 0.2517Employment0.6354
Gender0.1065Gender0.0491Gender 0.3018Gender0.1144
All0.0254All0.0168All 0.0287All0.0827
lnGDPpcTourism0.0265lnGDPpcTourism0.4831lnGDPpcTourism 0.2244lnGDPpcTourism0.9401
Education0.7739Education0.1375Education 0.6046Education0.7472
Employment0.0858Employment0.0432Employment 0.8776Employment0.7616
Gender0.9763Gender0.1889Gender 0.1298Gender0.2683
All0.5075All0.0276All 0.1714All0.5432
EducationTourism0.0206EducationTourism0.0515EducationTourism 0.9980EducationTourism0.6614
lnGDPpc0.6192lnGDPpc0.3083lnGDPpc 0.9507lnGDPpc0.8245
Employment0.2443Employment0.2076Employment 0.4042Employment0.5066
Gender0.7756Gender0.5470Gender 0.2494Gender0.5532
All0.7756All0.2334All 0.7856All0.8549
EmploymentTourism0.0399EmploymentTourism0.0975EmploymentTourism 0.0299EmploymentTourism0.8225
lnGDPpc0.2817lnGDPpc0.0338lnGDPpc 0.0311lnGDPpc0.7900
Education0.5062Education0.0155Education 0.9277Education0.0422
Gender0.9586Gender0.0238Gender 0.3483Gender0.3173
All0.5483All0.0096All 0.0483All0.4226
GenderTourism0.0097GenderTourism0.0468GenderTourism 0.0071GenderTourism0.0044
lnGDPpc0.0448lnGDPpc0.9904lnGDPpc 0.0498lnGDPpc0.1713
Education0.0762Education0.9348Education 0.1898Education0.1776
Employment0.0676Employment0.0082Employment 0.5756Employment0.0847
All0.0706All0.0413All 0.6071All0.5563
Publisher’s Note: MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

Share and Cite

MDPI and ACS Style

Zhang, Y.; Xu, S.; Zhang, J. Examining the Relationship between Tourism and Gender Equality: Evidence from Asia. Sustainability 2022, 14, 12156. https://doi.org/10.3390/su141912156

AMA Style

Zhang Y, Xu S, Zhang J. Examining the Relationship between Tourism and Gender Equality: Evidence from Asia. Sustainability. 2022; 14(19):12156. https://doi.org/10.3390/su141912156

Chicago/Turabian Style

Zhang, Yan, Shenglan Xu, and Jiekuan Zhang. 2022. "Examining the Relationship between Tourism and Gender Equality: Evidence from Asia" Sustainability 14, no. 19: 12156. https://doi.org/10.3390/su141912156

Note that from the first issue of 2016, this journal uses article numbers instead of page numbers. See further details here.

Article Metrics

Back to TopTop