Exploring the nexus between employment and economic contribution: A study of the travel and tourism industry in the context of COVID-19

Vašaničová, Petra; Bartók, Katarína Article Exploring the nexus between employment and economic contribution: A study of the travel and tourism industry in the context of COVID-19 Economies Provided in Cooperation with: MDPI – Multidisciplinary Digital Publishing Institute, Basel Suggested Citation: Vašaničová, Petra; Bartók, Katarína (2024) : Exploring the nexus between employment and economic contribution: A study of the travel and tourism industry in the context of COVID-19, Economies, ISSN 2227-7099, MDPI, Basel, Vol. 12, Iss. 6, pp. 1-20, https://doi.org/10.3390/economies12060136 This Version is available at: https://hdl.handle.net/10419/329062 Standard-Nutzungsbedingungen: Die Dokumente auf EconStor dürfen zu eigenen wissenschaftlichen Zwecken und zum Privatgebrauch gespeichert und kopiert werden. Sie dürfen die Dokumente nicht für öffentliche oder kommerzielle Zwecke vervielfältigen, öffentlich ausstellen, öffentlich zugänglich machen, vertreiben oder anderweitig nutzen. Sofern die Verfasser die Dokumente unter Open-Content-Lizenzen (insbesondere CC-Lizenzen) zur Verfügung gestellt haben sollten, gelten abweichend von diesen Nutzungsbedingungen die in der dort genannten Lizenz gewährten Nutzungsrechte. Terms of use: Documents in EconStor may be saved and copied for your personal and scholarly purposes. You are not to copy documents for public or commercial purposes, to exhibit the documents publicly, to make them publicly available on the internet, or to distribute or otherwise use the documents in public. If the documents have been made available under an Open Content Licence (especially Creative Commons Licences), you may exercise further usage rights as specified in the indicated licence. https://creativecommons.org/licenses/by/4.0/ Citation: Vašaniˇcová, Petra, and Katarína Bartók. 2024. Exploring the Nexus between Employment and Economic Contribution: A Study of the Travel and Tourism Industry in the Context of COVID-19. Economies 12: 136. https://doi.org/10.3390/ economies12060136 Academic Editor: Angeliki N. Menegaki Received: 30 April 2024 Revised: 23 May 2024 Accepted: 27 May 2024 Published: 30 May 2024 Copyright: © 2024 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/). economies Article Exploring the Nexus between Employment and Economic Contribution: A Study of the Travel and Tourism Industry in the Context of COVID-19 Petra Vašaniˇcová* and Katarína Bartók Department of Finance, Accounting and Mathematical Methods, Faculty of Management and Business, University of Presov, 080 01 Presov, Slovakia *Correspondence: [email protected] Abstract: The travel and tourism industry plays a crucial role in economies around the world. The impact of the COVID-19 pandemic on the tourism industry has been very pronounced. This paper aims to study the relationship between the country’s T&T industry Share of Employment (TTEMPL) and the country’s T&T industry Share of Gross Domestic Product (TTGDP). This study is specific because we do not focus on the development of indicators over time; instead, we propose the models for 117 countries using the quantile regression (QR) while comparing models in the context of COVID-19 (between 2019 and 2021). The results of the QR determined that individual percentiles of the TTGDP are more affected by the TTEMPL than other percentiles of the TTGDP, which is then reflected in the changes in regression coefficients. In addition, we compare analyzed indicators among countries according to region and income group. The study reveals that the tourism downturn caused by COVID-19 has adverse effects on the TTEMPL and the TTGDP. In addition, the results show that the impact of COVID-19 on the tourism industry appears to be varied among countries, regions, and income groups. Keywords: employment; GDP; travel and tourism; industry; COVID-19 1. Introduction The travel and tourism industry serves as a primary contributor to employment, government income, and foreign currency earnings for the economy. Tourism activities are significantly dependent on external factors, rendering the industry exceptionally susceptible to impacts such as terrorist attacks, climate change, natural disasters, economic downturns, and pandemics (Duro et al. 2021;Monterrubio 2022). The COVID-19 pandemic served as a crucial instance of a crisis capable of overturning entire socioeconomic systems worldwide (Jencova et al. 2021;Yepez and Leimgruber 2024). Since the beginning of the COVID-19 crisis, the impact of the pandemic on the tourism industry has been very pronounced. The tourism sector experienced one of the first, and probably most severe, shocks to be caused by the international spread of COVID-19 (Mariolis et al. 2021). During the health crisis and economic downturn caused by COVID-19 , the tourism and hospitality sectors have been severely affected, primarily due to various interconnected factors such as travel restrictions, nationwide lockdowns, business shutdowns, and the consequent effects on lives and livelihoods (Peterson and DiPietro 2021; Šenkováet al. 2021). Measures to contain the virus in terms of lockdowns, travel restrictions, border closures, and mobility constraints have significantly affected the economic structure of many countries, especially those which have tourist-dependent economies (Tandrayen-Ragoobur et al. 2022). Countries whose tourism sectors contribute a high share of GDP are facing considerable economic impact, as the tourism sector plays a vital role in driving economic progress (Henseler et al. 2022). The occasions connected with the crisis Economies 2024,12, 136. https://doi.org/10.3390/economies12060136 https://www.mdpi.com/journal/economies Economies 2024,12, 136 2 of 20 led to significant declines in both revenue and employment opportunities on a global scale (Seabra and Bhatt 2022). Therefore, it is crucial to assess the economic influence of tourism and its contribution to the GDP. Consequently, policy briefs, industry reports, and scientific articles frequently commence by presenting statistics regarding the travel and tourism share of GDP (Figini and Patuelli 2022). Moreover, in addition to its significant role in enhancing the overall welfare of contemporary societies, employment stands as a crucial phenomenon and a topic that cannot be overlooked in macroeconomic scrutiny. The existence of the relationship between employment and GDP has been verified in many studies (Burggraeve et al. 2015;Klinger and Weber 2020). The relationship between these indicators is frequently investigated using time series data (Ghosh 2009;Klinger and Weber 2020;Scarlett 2021). However, the connection between these specific indicators within the field of tourism is rarely explored. This paper innovatively contributes to the extant literature on the nexus of economic growth (GDP) and employment in the tourism industry by employing a quantile regression (QR) model for 117 countries while comparing models in the context of COVID-19 . Specifically, this paper aims to study the relationship between the country’s T&T industry Share of Employment (TTEMPL) and the country’s T&T industry Share of GDP (TTGDP). Only a few studies use both indicators (Bulin 2014; Radovanov et al. 2020); thus, we aim to fill this research gap. 2. Literature Review Studies on the tourism–growth nexus provide insights into the existence of a long-term and/or short-term relationship between tourism and economic growth. They also examine the adjustment mechanisms that restore equilibrium following a disruption in tourism and analyze how these relationships behave after shocks or regime changes (Ahmad et al. 2020). Ahmad et al. (2020) performed a systematic literature review of the tourism–growth nexus and focused mainly on the causality nexus. The studies analyzed utilized time series data. From an economic perspective, the focus was on the country’s overall economic indicators, e.g., real GDP (Dogan et al. 2017;Dogru and Bulut 2018), GDP per capita (Paramati et al. 2017), and industrial production (Antonakakis et al. 2015). From a tourism perspective, studies usually dealt with international tourist arrivals (Antonakakis et al. 2015;Dogan et al. 2017), international tourism receipts (Dogru and Bulut 2018), and international tourism receipts per capita (Tang and Tan 2015;Paramati et al. 2017). The studies analyzed by Ahmad et al. (2020) have not yet included events related to the COVID-19 pandemic. Our study differs in three respects. First, it analyzes cross-sectional data instead of time series. Second, we analyze cross-sectional data for 2019 (before COVID-19 ) and 2021 (during COVID-19 ). Third, we consider the economic indicators directly related to tourism— TTGDP and TTEMPL. It is now clear that the pandemic has had an unprecedented impact on the tourism industry worldwide. The discussion about the impacts of the spread of the disease on national economies continues. As stated by Škare et al. (2021), at their time of writing, several empirical studies had evaluated the impact of the pandemic outbreak on the tourism industry. Nowadays, there are already significantly more studies on this topic. Existing studies usually deal with impacts in a particular country, e.g., Greece (Mariolis et al. 2021), Spain (Moreno-Luna et al. 2021), Portugal (de Fátima Brilhante and Rocha 2023), Australia (Pham et al. 2021;Munawar et al. 2021;Solarin et al. 2024), Ethiopia (Bogale et al. 2020), Tanzania (Henseler et al. 2022), Mauritius (Tandrayen-Ragoobur et al. 2022), Sri Lanka (Wickramasinghe and Naranpanawa 2023), Guangdong Province, China (Wu et al. 2022), Macao (Lim and To 2022); or in a particular region, e.g., Latin America and the Caribbean (Mulder 2020), Indonesia (Sun et al. 2021;Pham and Nugroho 2022), Germany and Spain (Rodousakis and Soklis 2022), Spanish provinces (Duro et al. 2021), Andalusia (Cardenete et al. 2022), Europe (Pasieka et al. 2022), European regions (Curtale et al. 2023), the Central and Eastern European region (Nagaj and Žuromskait ˙ e 2021), Europe, the USA, and China (Islam and Fatema 2020), the Chengdu-Chongqing region in China (Ding et al. 2024). Economies 2024,12, 136 3 of 20 The tourism industry serves as a significant generator of foreign exchange revenues, playing a pivotal role in driving economic growth through various channels, as evidenced by numerous studies (Ramlall 2024). Before the pandemic, tourism development was utilized to generate significant advantages for the macro-level economy, such as enhancing foreign receipts, stimulating service exports, and playing a crucial part in driving growth in the domestic economy through tourism-led initiatives (Sun et al. 2022;Brida et al. 2016). The tourism industry constitutes a vital source of income and employment (Kavya Lekshmi and Mallick 2022;Navarro-Chávez et al. 2023;Sánchez López 2024). Furthermore, tourism has a positive impact on currency circulation, employment rates, balance of payment, and investment in the development of necessary infrastructure, all of which facilitate the execution of tourism-related activities. Additionally, it contributes to bolstering the state budget by increasing government expenditure via public services and government revenue through the collection of both direct and indirect taxes (Tabash et al. 2023). Tourism, being reliant on labor, offers numerous opportunities for both skilled and unskilled workers (Sun et al. 2022). Expanding employment opportunities is the most important effect of economic growth (Koži´c and Sever 2022). In tourism, employment demand depends on the number of tourist arrivals, assuming that there is a positive relationship (Walmsley 2017). Tourism is seen as a significant driver of economic development in many developing countries, particularly for tackling poverty. It stands out as a major sector in these countries, as it is capable of fostering economic growth and enhancing social well-being at a regional level (Kavya Lekshmi and Mallick 2022;Monterrubio 2022). Moreover, given tourism’s intersectoral connections, it can be seen as a potential catalyst for development in regions conducive to tourism activities (Sánchez López 2024). Economic vulnerability and job instability in tourism have led to the inevitable onset of economic troubles (Sun et al. 2022). Islam (2021) noted that employment was expected to correspond with the economy’s output level, meaning that as output decreases due to the pandemic, there is a potential for decreased employment and an increase in the unemployment rate. Sun et al. (2022) evaluate how reduced international tourism consumption affects tourism employment. The impact of tourism on economic activity fluctuates based on the income levels and institutional characteristics of the host countries (Tang and Tan 2017;Borrego-Domínguez et al. 2022). Unfortunately, typically, standard economic impact analyses present findings through aggregate data, often lacking essential details to identify the countries that are most susceptible to economic vulnerability. 3. Methodology We study the relationship between the country’s TTEMPL and the country’s TTGDP. Both indicators express the economic importance of tourism in a specific region or country. The TTEMPL shows how many people are employed in industries related to tourism. A higher TTEMPL means that tourism provides a significant number of jobs. The TTGDP shows how much tourism contributes to overall economic activity. A higher TTGDP means that tourism is a significant source of income and has a large economic impact. The link between these two indicators arises because industries that contribute to GDP through tourism also create jobs. For example, hotels, restaurants, and travel agencies need employees to provide services to tourists. Therefore, the economic contribution (GDP) and employment in tourism interact and often show similar trends. A high TTGDP often correlates with a high TTEMPL, as a greater economic contribution usually means more job opportunities are created. Considering the aim of the paper, this study is specific because we do not focus on the development of indicators over time (time series), but we propose models for 117 countries considering conditional quantiles of the dependent variable. Therefore, we use QR. For comparison, we present results obtained by the ordinary least square (OLS) method. Economies 2024,12, 136 4 of 20 A linear regression model determines parameters that minimize the sum of squared errors. When the residuals are normally distributed, the OLS estimator is the best linear unbiased estimate. Inference is made on the conditional mean and is used to determine whether to accept or reject the null hypothesis, which states that there is no relationship between the predictor xand the outcome variable y. In QR, parameters are identified for each quantile by minimizing the sum of absolute residuals. This method does not assume a specific distribution for the error terms, giving it nonparametric characteristics and making it robust to outliers. Unlike the linear regression model, which has a single set of parameters, the QR model produces a different set of parameters for each quantile. It allows for inferences at various quantiles, and statistical tests at each quantile can determine whether to accept or reject the null hypothesis (Li 2015). We also visually compare linear regression lines concerning regions or income groups of analyzed countries. To enrich the study, this paper also compares the TTEMPL and the TTGDP among regions and income groups and examines differences in these indicators before and during COVID-19 . The investigation, together with geographic and economic aspects, will allow for a better understanding of this complex relationship. 3.1. Data The data were collected via a database of the Travel and Tourism Development Index 2021, which was published by the World Economic Forum in May 2022 (Uppink Calderwood and Soshkin 2022). Specifically, the Tourism Satellite Account Research of the World Travel & Tourism Council is the source of both analyzed indicators the TTEMPL (% of total employment) and the TTGDP (% of total GDP). We compare data and results for 2019 (before COVID-19 ) and 2021 (during COVID-19 ). We chose this period because we drew the data from the Travel and Tourism Development Index 2021 database, in which available data are only for these two years. Data are available for 117 countries, which are further divided into two categories, namely region (Asia–Pacific, Europe and Eurasia, Middle East and North Africa, SubSaharan Africa, the Americas) and income group (high, upper-middle, lower-middle, and low-income economies). These categories are also derived from the database known as the Travel and Tourism Development Index. The countries are listed according to region and income group in Appendix A(Tables A1 and A2). Tables 1and 2present descriptive statistics of the TTEMPL according to region and income group. Figures 1and 2visualize boxplots of the TTEMPL according to region and income group. Table 1. Descriptive statistics of the TTEMPL according to region. Asia–Pacific (20) Europe and Eurasia (43) Middle East and North Africa (12) Sub-Saharan Africa (21) The Americas (21) 2019 2021 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.80 4.22 4.63 4.17 4.96 3.51 3.91 2.31 3.96 2.74 S.D. 3.02 3.17 2.84 2.31 2.14 1.21 3.37 2.00 2.00 1.37 Min 1.12 0.73 0.82 1.05 1.14 1.45 0.93 0.69 1.04 0.93 Q1 3.13 1.74 2.37 2.34 3.90 2.74 1.91 1.27 2.55 1.87 Median 4.57 3.18 3.61 3.76 5.41 3.97 3.09 1.82 3.52 2.62 Q3 5.24 5.27 6.45 6.16 6.80 4.40 4.21 2.66 4.58 3.06 Max 14.63 11.45 12.72 9.29 7.44 4.89 16.42 9.09 9.06 6.39 Source: own processing using R. Economies 2024,12, 136 5 of 20 Table 2. Descriptive statistics of the TTEMPL according to income group. High (45) Low (6) Lower-Middle (33) Upper-Middle (33) 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.96 4.24 3.46 1.88 4.49 2.98 3.87 3.38 S.D. 2.67 2.06 2.28 0.86 3.34 2.48 2.25 2.40 Min 1.12 1.05 1.14 0.83 0.93 0.70 0.82 0.69 Q1 3.25 2.60 1.76 1.33 2.86 1.55 2.25 1.85 Median 4.60 4.07 2.83 1.69 3.55 2.24 3.21 2.74 Q3 5.90 5.62 5.02 2.48 5.07 3.33 5.19 4.14 Max 12.72 9.29 6.80 3.09 16.42 11.45 8.58 10.75 Source: own processing using R. We see (in Figure 1and Table 1) the highest median of the TTEMPL in the Middle East and North Africa region in both periods (5.41% in 2019 and 3.97% in 2021). The lowest median of the TTEMPL is in the Sub-Saharan Africa region (3.09% in 2019 and 1.82% in 2021). Moreover, we see some outliers. In 2019, they correspond to Cambodia (14.46%) and New Zealand (9.30%) in the Asia–Pacific region; Greece (12.72%) in the Europe and Eurasia region; Cape Verde (16.42%) in the Sub-Saharan Africa region; Uruguay (9.06%) and Mexico (8.57%) in The Americas region. In 2021, the outliers are the Philippines (11.45%) and Thailand (10.75%) in the Asia–Pacific region, Cape Verde (9.09%) and Mauritius (6.20%) in the Sub-Saharan Africa region, and Uruguay (6.39%) and Mexico (5.45%) in the Americas region. According to the income group, we see (in Figure 2and Table 2) the highest median of the TTEMPL in the high-income economies (4.60% in 2019 and 4.07% in 2021) and the lowest median in the low-income economies (2.83% in 2019 and 1.69% in 2021). Considering outliers, in 2019, the outliers are Greece (12.72%), Malta (11.35%), and Croatia (10.04%) in the high-income countries; Cape Verde (16.42%) and Cambodia (14.63%) in lower-middleincome countries. In 2021, the outliers are the Philippines (11.45%), Cape Verde (9.09%), Cambodia (8.44%), and Mauritius (6.20%) in lower-middle-income countries, and Thailand (10.75%), Georgia (8.58%), and Malaysia (7.78%) in upper-middle-income countries. Economies 2024, 12, x FOR PEER REVIEW 5 of 21 Table 1. Descriptive statistics of the TTEMPL according to region. Asia–Pacific (20) Europe and Eurasia (43) Middle East and North Africa (12) Sub-Saharan Africa (21) The Americas (21) 2019 2021 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.80 4.22 4.63 4.17 4.96 3.51 3.91 2.31 3.96 2.74 S.D. 3.02 3.17 2.84 2.31 2.14 1.21 3.37 2.00 2.00 1.37 Min 1.12 0.73 0.82 1.05 1.14 1.45 0.93 0.69 1.04 0.93 Q1 3.13 1.74 2.37 2.34 3.90 2.74 1.91 1.27 2.55 1.87 Median 4.57 3.18 3.61 3.76 5.41 3.97 3.09 1.82 3.52 2.62 Q3 5.24 5.27 6.45 6.16 6.80 4.40 4.21 2.66 4.58 3.06 Max 14.63 11.45 12.72 9.29 7.44 4.89 16.42 9.09 9.06 6.39 Source: own processing using R. Figure 1. Boxplots of the TTEMPL according to region. Source: own processing using R. According to the income group, we see (in Figure 2 and Table 2) the highest median of the TTEMPL in the high-income economies (4.60% in 2019 and 4.07% in 2021) and the lowest median in the low-income economies (2.83% in 2019 and 1.69% in 2021). Considering outliers, in 2019, the outliers are Greece (12.72%), Malta (11.35%), and Croatia (10.04%) in the high-income countries; Cape Verde (16.42%) and Cambodia (14.63%) in lower-middle-income countries. In 2021, the outliers are the Philippines (11.45%), Cape Verde (9.09%), Cambodia (8.44%), and Mauritius (6.20%) in lower-middle-income countries, and Thailand (10.75%), Georgia (8.58%), and Malaysia (7.78%) in upper-middle-income countries. Table 2. Descriptive statistics of the TTEMPL according to income group. High (45) Low (6) Lower-Middle (33) Upper-Middle (33) 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.96 4.24 3.46 1.88 4.49 2.98 3.87 3.38 S.D. 2.67 2.06 2.28 0.86 3.34 2.48 2.25 2.40 Min 1.12 1.05 1.14 0.83 0.93 0.70 0.82 0.69 Q1 3.25 2.60 1.76 1.33 2.86 1.55 2.25 1.85 Median 4.60 4.07 2.83 1.69 3.55 2.24 3.21 2.74 Q3 5.90 5.62 5.02 2.48 5.07 3.33 5.19 4.14 Max 12.72 9.29 6.80 3.09 16.42 11.45 8.58 10.75 Source: own processing using R. Figure 1. Boxplots of the TTEMPL according to region. Source: own processing using R. Economies 2024,12, 136 6 of 20 Economies 2024, 12, x FOR PEER REVIEW 6 of 21 Figure 2. Boxplots of the TTEMPL according to income group. Source: own processing using R. Tables 3 and 4 present descriptive statistics of the TTGDP according to region and income group. Figures 3 and 4 visualize boxplots of the TTGDP according to region and income group. We see (in Figure 3 and Table 3) the highest median of the TTGDP in the Middle East and North Africa region in both periods (5.36% in 2019 and 2.13% in 2021). The lowest median of the TTGDP is in the Sub-Saharan Africa region (3.35% in 2019 and 1.39% in 2021). Moreover, we see some outliers. In the Asia–Pacific region, they correspond to Cambodia (14.46%) and the Philippines (12.38%) in 2019 and the Philippines (8.22%) in 2021. In the Europe and Eurasia region, the outliers are Croatia (10.93%), Montenegro (10.38%) in 2019, Albania (4.54%), and Croatia (4.46%) in 2021. In the Sub-Saharan Africa region, the highest TTGDP was in Cape Verde (18.39%) in 2019 and Cape Verde (5.00%), Mauritius (3.96%), and Namibia (3.33%) in 2021. In the Americas region, the outliers are Uruguay (9.12%) in 2019 and Uruguay (5.02%), and Mexico (4.51%) in 2021. Table 3. Descriptive statistics of the TTGDP according to region. Asia–Pacific (20) Europe and Eurasia (43) Middle East and North Africa (12) Sub-Saharan Africa (21) The Americas (21) 2019 2021 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.90 2.04 4.18 1.90 4.96 1.99 4.35 1.74 4.04 1.92 S.D. 3.49 1.81 2.71 1.01 2.27 0.79 3.66 1.18 1.97 1.09 Min 0.92 0.59 0.96 0.39 1.64 0.68 1.28 0.57 1.34 0.76 Q1 2.82 1.04 2.29 1.20 3.09 1.35 2.34 0.98 2.74 1.09 Median 3.76 1.52 3.44 1.68 5.36 2.13 3.35 1.39 3.71 1.85 Q3 5.78 2.55 5.52 2.45 6.41 2.57 4.73 1.90 5.29 2.26 Max 14.46 8.22 10.93 4.54 8.33 3.21 18.39 5.00 9.13 5.02 Source: own processing using R. Figure 2. Boxplots of the TTEMPL according to income group. Source: own processing using R. Tables 3and 4present descriptive statistics of the TTGDP according to region and income group. Figures 3and 4visualize boxplots of the TTGDP according to region and income group. Table 3. Descriptive statistics of the TTGDP according to region. Asia–Pacific (20) Europe and Eurasia (43) Middle East and North Africa (12) Sub-Saharan Africa (21) The Americas (21) 2019 2021 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.90 2.04 4.18 1.90 4.96 1.99 4.35 1.74 4.04 1.92 S.D. 3.49 1.81 2.71 1.01 2.27 0.79 3.66 1.18 1.97 1.09 Min 0.92 0.59 0.96 0.39 1.64 0.68 1.28 0.57 1.34 0.76 Q1 2.82 1.04 2.29 1.20 3.09 1.35 2.34 0.98 2.74 1.09 Median 3.76 1.52 3.44 1.68 5.36 2.13 3.35 1.39 3.71 1.85 Q3 5.78 2.55 5.52 2.45 6.41 2.57 4.73 1.90 5.29 2.26 Max 14.46 8.22 10.93 4.54 8.33 3.21 18.39 5.00 9.13 5.02 Source: own processing using R. Table 4. Descriptive statistics of the TTGDP according to income group. High (45) Low (6) Lower-Middle (33) Upper-Middle (33) 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.06 1.96 4.11 1.64 5.04 2.08 4.24 1.71 S.D. 2.32 1.00 2.23 0.92 3.76 1.57 2.62 1.05 Min 0.92 0.39 1.94 0.66 1.28 0.59 0.96 0.57 Q1 2.43 1.29 2.38 1.08 3.02 1.18 2.71 1.00 Median 3.30 1.68 3.42 1.49 3.73 1.63 3.68 1.34 Q3 5.48 2.55 5.94 1.87 5.74 2.52 5.24 1.95 Max 10.93 5.02 7.08 3.26 18.39 8.22 10.38 4.54 Source: own processing using R. We see (in Figure 3and Table 3) the highest median of the TTGDP in the Middle East and North Africa region in both periods (5.36% in 2019 and 2.13% in 2021). The lowest median of the TTGDP is in the Sub-Saharan Africa region (3.35% in 2019 and 1.39% in 2021). Moreover, we see some outliers. In the Asia–Pacific region, they correspond to Cambodia (14.46%) and the Philippines (12.38%) in 2019 and the Philippines (8.22%) in 2021. In the Europe and Eurasia region, the outliers are Croatia (10.93%), Montenegro (10.38%) in 2019, Economies 2024,12, 136 7 of 20 Albania (4.54%), and Croatia (4.46%) in 2021. In the Sub-Saharan Africa region, the highest TTGDP was in Cape Verde (18.39%) in 2019 and Cape Verde (5.00%), Mauritius (3.96%), and Namibia (3.33%) in 2021. In the Americas region, the outliers are Uruguay (9.12%) in 2019 and Uruguay (5.02%), and Mexico (4.51%) in 2021. According to the income group, we see (in Figure 4and Table 4) that the median of the TTGDP is variable in time. In terms of outliers, in high-income economies, they correspond to Croatia (10.93%) in 2019, Uruguay (5.02%), and Croatia (4.46%) in 2021. In low-income economies, we see only one outlier in 2021, in Lesotho (3.26%). In lowermiddle-income economies, outliers correspond to Cape Verde (18.39%), Cambodia (14.46%), and the Philippines (12.38%) in 2019, and the Philippines (8.22%), Cape Verde (5.00%), and Cambodia (4.62%) in 2021. In upper-middle-income economies, the outliers are Montenegro (10.38%), Georgia (10.09%), and Thailand (6.62%) in 2019, and Albania (4.54%), Mexico (4.54%), and Thailand (3.70%) in 2021. Economies 2024, 12, x FOR PEER REVIEW 7 of 21 Figure 3. Boxplots of the TTGDP according to region. Source: own processing using R. According to the income group, we see (in Figure 4 and Table 4) that the median of the TTGDP is variable in time. In terms of outliers, in high-income economies, they correspond to Croatia (10.93%) in 2019, Uruguay (5.02%), and Croatia (4.46%) in 2021. In lowincome economies, we see only one outlier in 2021, in Lesotho (3.26%). In lower-middleincome economies, outliers correspond to Cape Verde (18.39%), Cambodia (14.46%), and the Philippines (12.38%) in 2019, and the Philippines (8.22%), Cape Verde (5.00%), and Cambodia (4.62%) in 2021. In upper-middle-income economies, the outliers are Montenegro (10.38%), Georgia (10.09%), and Thailand (6.62%) in 2019, and Albania (4.54%), Mexico (4.54%), and Thailand (3.70%) in 2021. Table 4. Descriptive statistics of the TTGDP according to income group. High (45) Low (6) Lower-Middle (33) Upper-Middle (33) 2019 2021 2019 2021 2019 2021 2019 2021 Mean 4.06 1.96 4.11 1.64 5.04 2.08 4.24 1.71 S.D. 2.32 1.00 2.23 0.92 3.76 1.57 2.62 1.05 Min 0.92 0.39 1.94 0.66 1.28 0.59 0.96 0.57 Q1 2.43 1.29 2.38 1.08 3.02 1.18 2.71 1.00 Median 3.30 1.68 3.42 1.49 3.73 1.63 3.68 1.34 Q3 5.48 2.55 5.94 1.87 5.74 2.52 5.24 1.95 Max 10.93 5.02 7.08 3.26 18.39 8.22 10.38 4.54 Source: own processing using R. Figure 3. Boxplots of the TTGDP according to region. Source: own processing using R. Economies 2024, 12, x FOR PEER REVIEW 8 of 21 Figure 4. Boxplots of the TTGDP according to income group. Source: own processing using R. 3.2. Quantile Regresiion To meet the research aim, we use QR. The description is based on that provided by Kalina and Vidnerová (2019, p. 25), Vašaničová and Jenčová (2022, p. 387), and Vašaničová and Miškufová (2023, p. 416). In the standard linear regression model ββ β ε =+ ++ + = 011 ... , 1,..., iipipi YXXin , (1) the regression τ-quantile for τ ∈(0,1) is defined as a (regression) line with parameters obtained as () τ ρ = ∈ −  1 argmin p nT ii i b YXb , (2) where () =1,..., T ii ip XX X denotes the i-th observation and τ ρ (defined in Koenker (2005) as loss function) is considered in the form () () τ ρτ =−<∈  10,xx x x , (3) with indicator function denoted by 1. Alternatively, τ ρ may be formulated as () () τ τ ρτ ≥  =−<   if 0, 1f0. xx xxix (4) If we assume that the quantile τ of the conditional distribution of the dependent variable Yi is a linear function of the vector of independent variables (Xi), then we can write the quantile conditional regression as (Kováč 2013): ττ βε ⋅ =+ + = 0, 1,..., iii YinβX, (5) A specific feature of QR is that the estimated coefficients of the independent variables, τ β, can be significantly different in various quantiles, which may indicate a heterogeneous conditional distribution of the dependent variable (Cupák et al. 2016). The advantage of QR is that it is the most suitable tool for modeling heteroscedastic data (Kalina and Vidnerová 2019, p. 25; Koenker 2005). To meet the aim of this paper, the model for the OLS is as follows: ββ ε =+ + = 01 , 1,..., iii TTGDP TTEMPL i n , (6) Figure 4. Boxplots of the TTGDP according to income group. Source: own processing using R. 3.2. Quantile Regresiion To meet the research aim, we use QR. The description is based on that provided by Kalina and Vidnerová(2019, p. 25), Vašaniˇcováand Jenˇcová(2022, p. 387), and Vašaniˇcová and Miškufová(2023, p. 416). In the standard linear regression model Yi=β0+β1Xi1+. . . +βpXip +εi,i=1, . . . , n, (1) Economies 2024,12, 136 8 of 20 the regression τ -quantile for τ∈( 0,1 ) is defined as a (regression) line with parameters obtained as argmin b∈Rp n ∑ i=1 ρτYi−XT ib, (2) where Xi=Xi1, . . . , XipT denotes the i-th observation and ρτ (defined in Koenker (2005) as loss function) is considered in the form ρτ(x)=x(τ−1[x<0]),x∈R, (3) with indicator function denoted by 1. Alternatively, ρτmay be formulated as ρτ(x)=τxif x≥0, (τ−1)xif x<0. (4) If we assume that the quantile τ of the conditional distribution of the dependent variable Y i is a linear function of the vector of independent variables (X i ), then we can write the quantile conditional regression as (Kováˇc 2013): Yi=β0+βτXi·+εiτ,i=1, . . . , n, (5) A specific feature of QR is that the estimated coefficients of the independent variables, βτ , can be significantly different in various quantiles, which may indicate a heterogeneous conditional distribution of the dependent variable (Cupák et al. 2016). The advantage of QR is that it is the most suitable tool for modeling heteroscedastic data (Kalina and Vidnerová 2019, p. 25; Koenker 2005). To meet the aim of this paper, the model for the OLS is as follows: TTGDPi=β0+β1TTEMPLi+εi,i=1, . . . , n(6) while for QR, we consider the model according to (5) and the sequence of estimated coefficients is from τ = 0.05 to τ = 0.95 by 0.05. We test the presence of heteroscedasticity by the Breusch–Pagan test. If the residuals are heteroskedastic in the regression model, we use a paired bootstrap to compute p-values. To estimate the regression parameters of the QR model, we use the RStudio and the quantreg package, which was created as described by Koenker (2005) and Koenker et al. (2017). To test whether the slope coefficients of the models are identical, we use ANOVA and the anova.rq package. 4. Results Figures 5and 6depict scatterplots that point to the relationship between the TTGDP and the TTEMPL in 2019 and 2021. Countries (dots) are color-coded by region (Figure 5) and income group (Figure 6). Our aim is not to point out the coefficients of the individual regression lines, but only to show that the regression lines (with 95% confidence intervals marked by region or income group) differ across groups. In all cases, the relationship is positive. Countries with low levels of the TTEMPL also have low levels of the TTGDP and vice versa. We see that the slopes of the regression lines vary across the years analyzed. Table 5presents the estimates of QR and OLS models for 2019 and 2021. The results of the ANOVA test detected that QR estimates significantly differ across quantiles. The regression model parameter estimates obtained using OLS were statistically significant, and the model explained up to 76.43% (in 2019) and 62.21% (in 2021) of the variability of the TTGDP. However, we indicated the presence of heteroskedasticity, which we confirmed through the Breuch–Pagan test (in 2019, BP = 23.670, p= 0.0000; in 2021, BP = 32.508, p= 0.0000). Therefore, the use of quantile regression is justified. The results of QR show that the TTEMPL is statistically significant at each quantile level. Moreover, the coefficients differ between the models for 2019 and 2021. However, in both cases, as the quantile grows, the coefficient grows. Economies 2024,12, 136 15 of 20 sector should begin to rebound once the health risks and travel impediments diminish. To efficiently address and alleviate the social repercussions of COVID-19 , robust and credible evidence is essential to inform policy interventions. In light of these findings, it is evident that further research could provide more comprehensive insights into the evolving dynamics of the travel and tourism industry. Examining the impact of other variables can further enhance our understanding of the tourism sector’s dynamics. Expanding the scope of research to include more diverse geographical and economic contexts will also contribute to a more comprehensive understanding of tourism’s economic impact. If this study is repeated after some time when more data are available, the study will yield more detailed results. While this study provides valuable insights into the relationship between the TTEMPL and the TTGDP, several limitations should be acknowledged. The study relies on data from 117 countries, but the data quality may vary significantly across different regions. Some countries may lack comprehensive or up-to-date information, which can affect the accuracy of the analysis. While the study examines the impact of COVID-19 , it does not deeply explore the long-term effects of the pandemic on the tourism industry. Future research could extend the analysis to cover post-pandemic recovery phases. The impact of global events (e.g., international travel restrictions) versus local factors (e.g., domestic tourism policies) is not distinctly analyzed. A more detailed examination of these influences could offer better policy recommendations. By acknowledging these limitations, future research can address these gaps, leading to a more comprehensive understanding of the dynamics between the TTEMPL and the TTGDP. Author Contributions: Conceptualization, P.V. and K.B.; methodology, P.V.; software, P.V.; validation, P.V.; formal analysis, P.V. and K.B.; investigation, P.V.; resources, P.V.; data curation, P.V.; writing—original draft preparation, P.V.; writing—review and editing, P.V. and K.B.; visualization, P.V.; supervision, P.V.; project administration, P.V.; funding acquisition, K.B. and P.V. All authors have read and agreed to the published version of the manuscript. Funding: This research was funded by the Cultural and Educational Grant Agency of the Ministry of Education, Science, Research, and Sport of the Slovak Republic, grant No. 001PU-4/2022–KEGA. Informed Consent Statement: Not applicable. Data Availability Statement: For requests concerning the data, please contact the corresponding author. Conflicts of Interest: The authors declare no conflicts of interest. Appendix A Table A1. Countries according to region. Asia–Pacific Europe and Eurasia Middle East and North Africa Sub-Saharan Africa The Americas AUS LKA ALB ESP ISL NLD ARE AGO MWI ARG MEX BGD MNG ARM EST ITA POL BHR BEN NAM BOL NIC HKG MYS AUT FIN KAZ PRT EGY BWA NGA BRA PAN CHN NPL AZE FRA KGZ ROU ISR CIV RWA CAN PER IDN NZL BEL GBR LTU SRB JOR CMR SEN COL PRY IND PAK BGR GEO LUX SVK KWT CPV SLE CRI SLV JPN PHL BIH GRC LVA SVN LBN GHA TCD DOM TTO KHM SGP CYP HRV MDA SWE MAR KEN TZA ECU URY KOR THA CZE HUN MKD TJK QAT LSO ZAF GTM USA LAO VNM DEU CHE MLT TUR SAU MLI ZMB HND VEN DNK IRL MNE TUN MUS CHL YEM Source: own processing. Note: countries’ codes are according to Alpha-3. Economies 2024,12, 136 16 of 20 Table A2. Countries according to income group. High Low Lower-Middle Upper-Middle ARE FIN ISR POL GHA BEN KGZ NPL AGO DOM MLI AUS FRA ITA PRT LSO BGD KHM PAK ALB ECU MNE AUT GBR JPN QAT RWA BOL LAO PHL ARG GEO MYS BEL GRC KOR SAU SEN BWA LKA SLV ARM GTM PAN BHR HKG KWT SGP SLE CIV MAR TCD AZE CHN PER CAN HRV LTU SVK YEM CPV MNG TJK BGR JOR PRY CYP HUN LUX SVN EGY MUS TUN BIH KAZ ROU CZE CHE LVA SWE HND MWI TZA BRA LBN SRB DEU CHL MLT TTO IDN NAM VEN CMR MDA THA DNK IRL NLD URY IND NGA VNM COL MEX TUR ESP ISL NZL USA KEN NIC ZMB CRI MKD ZAF EST Source: own processing. Note: countries’ codes are according to Alpha-3. Appendix B Table A3. Differences in the TTEMPL and the TTGDP (2021–2019). Country ∆ TTEMPL ∆ TTGDP Country ∆ TTEMPL ∆ TTGDP Country ∆ TTEMPL ∆ TTGDP Country ∆ TTEMPL ∆ TTGDP CPV −7.34 −13.40 MWI −1.34 −2.21 LTU −0.80 −0.68 PRY −0.11 −0.58 GRC −6.35 −5.43 SWE −1.32 −0.89 BRA −0.75 −1.28 ZMB −0.10 −1.49 KHM −6.18 −9.85 ARG −1.30 −1.37 CAN −0.72 −1.03 SVK −0.07 −1.31 TUN −4.48 −4.84 AZE −1.25 −2.68 IDN −0.70 −0.97 DNK −0.05 −0.69 MAR −3.72 −5.55 PAK −1.24 −1.62 ECU −0.68 −1.82 FIN −0.02 −0.75 LSO −3.71 −3.82 TTO −1.23 −1.52 CHN −0.65 −1.71 ITA 0.00 −2.63 LKA −3.49 −4.24 ALB −1.22 −4.26 ESP −0.64 −3.21 GEO 0.00 −6.63 MEX −3.12 −3.54 MUS −1.22 −3.63 COL −0.62 −1.15 KOR 0.03 −0.26 NZL −3.11 −2.97 QAT −1.17 −0.64 EGY −0.62 −3.68 BEL 0.05 −1.01 MLT −2.94 −4.03 NIC −1.17 −3.02 GHA −0.62 −1.35 MKD 0.11 −0.76 JOR −2.86 −3.84 HRV −1.17 −6.47 VEN −0.60 −1.19 BGR 0.12 −1.84 RWA −2.78 −4.52 BGD −1.16 −1.59 SRB −0.49 −1.47 LVA 0.15 −1.85 URY −2.67 −4.11 GTM −1.13 −1.85 AGO −0.48 −0.78 SAU 0.15 −1.47 AUT −2.62 −4.01 ROU −1.10 −0.77 BIH −0.47 −1.50 MNE 0.17 −7.91 TZA −2.61 −2.70 HUN −1.10 −1.51 AUS −0.44 −1.46 SGP 0.27 −2.43 CYP −2.51 −5.73 BHR −1.07 −3.45 CHL −0.43 −1.29 YEM 0.30 −1.13 LBN −2.33 −5.86 ZAF −1.05 −1.47 DEU −0.42 −1.53 IRL 0.31 −1.55 HND −2.13 −3.25 BOL −1.01 −1.65 FRA −0.40 −1.92 ARM 0.42 −2.65 CRI −2.13 −3.12 HKG −1.01 −3.84 MLI −0.35 −2.50 TUR 0.62 −2.57 PAN −2.03 −3.92 SLV −1.01 −2.58 ISR −0.33 −0.96 JPN 0.76 −0.85 BWA −1.97 −3.34 EST −1.01 −1.61 KWT −0.32 −1.25 SVN 0.82 −0.76 CIV −1.92 −2.85 USA −0.99 −1.01 BEN −0.30 −1.25 KGZ 1.91 0.55 NAM −1.87 0.11 KAZ −0.97 −1.08 LUX −0.28 −0.79 MDA 2.44 0.18 SEN −1.82 −2.71 TJK −0.97 −1.71 CHE −0.26 −1.01 MYS 3.14 −1.87 IND −1.74 −1.97 ARE −0.94 −3.05 GBR −0.25 −2.39 NLD 3.71 0.70 LAO −1.56 −2.74 SLE −0.88 −1.28 TCD −0.23 −0.63 THA 4.29 −5.91 MNG −1.54 −2.69 PER −0.87 −2.04 VNM −0.22 −3.60 PHL 4.38 −4.16 KEN −1.43 −2.11 NGA −0.87 −1.17 CZE −0.21 −0.99 ISL −1.42 −5.83 CMR −0.85 −1.76 POL −0.15 −1.06 NPL −1.34 −2.44 DOM −0.83 −3.15 PRT −0.13 −3.88 Source: own processing. Note: a higher drop is indicated by a deeper red color. Economies 2024,12, 136 17 of 20 References Ahmad, Nisar, Angeliki N. Menegaki, and Saeed Al-Muharrami. 2020. Systematic literature review of tourism growth nexus: An overview of the literature and a content analysis of 100 most influential papers. Journal of Economic Surveys 34: 1068–110. [CrossRef] Akamavi, Raphaël K., Fahad Ibrahim, and Raymond Swaray. 2023. Tourism and troubles: Effects of security threats on the global travel and tourism industry performance. Journal of Travel Research 62: 1755–800. [CrossRef] Antonakakis, Nikolaos, Mina Dragouni, and George Filis. 2015. How strong is the linkage between tourism and economic growth in Europe? Economic Modelling 44: 142–55. [CrossRef] Bariši´c, Patrik, and Tibor Kovaˇc. 2022. The effectiveness of the fiscal policy response to COVID-19 through the lens of short and long run labor market effects of COVID-19 measures. Public Sector Economics 46: 43–81. [CrossRef] Bogale, Mekonnen, Shimekit Kelkay, and Wubishet Mengesha. 2020. COVID-19 pandemic and tourism sector in Ethiopia. Horn of African Journal of Business and Economics 1: 1–9. Borrego-Domínguez, Susana, Fernanda Isla-Castillo, and Mercedes Rodríguez-Fernández. 2022. Determinants of Tourism Demand in Spain: A European Perspective from 2000–2020. Economies 10: 276. [CrossRef] Brida, Juan Gabriel, Isabel Cortes-Jimenez, and Manuela Pulina. 2016. Has the tourism-led growth hypothesis been validated? A literature review. Current Issues in Tourism 19: 394–430. [CrossRef] Bulin, Daniel. 2014. EU Travel and Tourism Industry-A Cluster Analysis of Impact and Competitiveness. Global Economic Observer 2: 150–62. Burggraeve, Koen, Grégory de Walque, and Helene Zimmer. 2015. The relationship between economic growth and employment. Economic Review 1: 32–52. Bussink, Henri, Tobias Vervliet, and Bas Ter Weel. 2022. The short-term effect of the COVID-19 crisis on employment probabilities of labour-market entrants in the Netherlands. De Economist 170: 279–303. [CrossRef] Cardenete, Manuel Alejandro, María del Carmen Delgado, and Paula Villegas. 2022. Impact assessment of COVID-19 on the tourism sector in Andalusia: An economic approach. Current Issues in Tourism 25: 2029–35. [CrossRef] Costanzo, Antonella, and Marta Desimoni. 2017. Beyond the mean estimate: A quantile regression analysis of inequalities in educational outcomes using INVALSI survey data. Large-Scale Assessments in Education 5: 1–25. [CrossRef] Cupák, Andrej, Ján Pokrivˇcák, and Martin Rizov. 2016. Diverzita spotreby potravín na Slovensku. PolitickáEkonomie 64: 608–26. [CrossRef] Curtale, Riccardo, Filipe Batista e Silva, Paola Proietti, and Ricardo Barranco. 2023. Impact of COVID-19 on tourism demand in European regions-An analysis of the factors affecting loss in number of guest nights. Annals of Tourism Research Empirical Insights 4: 100112. [CrossRef] de Fátima Brilhante, Maria, and Maria Luísa Rocha. 2023. COVID-19 pre-pandemic tourism forecasts and post-pandemic signs of recovery assessment for Portugal. Research in Globalization 7: 100167. [CrossRef] Ding, Chenhao, Xin Gao, and Zhiyang Xie. 2024. Analysing the differential impact of the COVID-19 pandemic on the resilience of the tourism economy: A case study of the Chengdu-Chongqing urban agglomeration in China. International Journal of Disaster Risk Reduction 102: 104255. [CrossRef] Dogan, Eyup, Fahri Seker, and Serap Bulbul. 2017. Investigating the impacts of energy consumption, real GDP, tourism and trade on CO 2 emissions by accounting for cross-sectional dependence: A panel study of OECD countries. Current Issues in Tourism 20: 1701–19. [CrossRef] Dogru, Tarik, and Ercan Sirakaya-Turk. 2017. Engines of tourism’s growth: An examination of efficacy of shift-share regression analysis in South Carolina. Tourism Management 58: 205–14. [CrossRef] Dogru, Tarik, and Umit Bulut. 2018. Is tourism an engine for economic recovery? Theory and empirical evidence. Tourism Management 67: 425–34. [CrossRef] Duro, Juan Antonio, Alejandro Perez-Laborda, Judith Turrion-Prats, and Melchor Fernández-Fernández. 2021. COVID-19 and tourism vulnerability. Tourism Management Perspectives 38: 100819. [CrossRef] European Commission. 2023. 2023 Country Report—Greece. Available online: https://economy-finance.ec.europa.eu/system/files/ 2023-05/EL_SWD_2023_608_en.pdf (accessed on 21 May 2024). Figini, Paolo, and Roberto Patuelli. 2022. Estimating the economic impact of tourism in the European Union: Review and computation. Journal of Travel Research 61: 1409–23. [CrossRef] Furio, Maria Cecilia G., Rowena V. Lumandas, Florinda G. Vigonte, and Marmelo V. Abante. 2023. Exploring the Complexities of the Philippine Economy: An In-Depth Analysis of Its Challenges during COVID-19 Pandemic. Available online: https: //papers.ssrn.com/sol3/papers.cfm?abstract_id=4441323 (accessed on 21 May 2024). Ghosh, Sajal. 2009. Electricity supply, employment and real GDP in India: Evidence from cointegration and Granger-causality tests. Energy Policy 37: 2926–29. [CrossRef] Grant, Lewis R. 2024. Lessons Learned from the Kyrgyz Republic’s Public Health Response to COVID-19 .Health Security 22. [CrossRef] Henseler, Martin, Helene Maisonnave, and Asiya Maskaeva. 2022. Economic impacts of COVID-19 on the tourism sector in Tanzania. Annals of Tourism Research Empirical Insights 3: 100042. [CrossRef] Economies 2024,12, 136 18 of 20 Hyslop, Dean, David C. Maré, and Shannon Minehan. 2023. COVID-19 Wage Subsidy: Outcome Evaluation. Wellington: Motu Economic and Public Policy Research Trust. Available online: https://www.msd.govt.nz/documents/about-msd-and-our-work/ publications-resources/statistics/covid-19/wage-subsidy-evaluation-reports/wage-subsidy-scheme-outcomes-evaluation. pdf (accessed on 21 May 2024). ILO Brief. 2021. COVID-19 and Employment in the Tourism Sector in the Asia–Pacific Region. Available online: https://www.ilo.org/ resource/brief/covid-19-and-employment-tourism-sector-asia-pacific-region (accessed on 21 May 2024). Islam, Anisul M. 2021. Impact of COVID-19 pandemic on global output, employment and prices: An assessment. Transnational Corporations Review 13: 189–201. [CrossRef] Islam, Mohammad Monirul, and Farha Fatema. 2020. COVID-19 and sustainable tourism: Macroeconomic effect and policy comparison among Europe, the USA and China. Asian Business Review 10: 53–60. [CrossRef] Jencova, Sylvia, Igor Petruska, and Marta Lukacova. 2021. Relationship between ROA and total indebtedness by threshold regression model. Montenegrin Journal of Economics 17: 37–46. [CrossRef] Julius, Evelina, Samuel Nuugulu, and Lukas Julius. 2022. Forecasting the Economic Impacts of COVID-19 : A Case for the Namibian Economy. In COVID-19 and a World of Ad Hoc Geographies. Edited by Stanley D. Brunn and Donna Gilbreath. Cham: Springer International Publishing, vol. 1, pp. 1445–66. [CrossRef] Kalina, Jan, and Petra Vidnerová. 2019. Implicitly weighted robust estimation of quantiles in linear regression. In Conference Proceedings of the 37th International Conference on Mathematical Methods in Economics 2019. Paper presented at the 37th International Conference on Mathematical Methods in Economics 2019, ˇ CeskéBudˇejovice, Czech Republic, September 11–13.ˇ CeskéBudejovice: University of South Bohemia. Kavya Lekshmi, R. S., and Hrushikesh Mallick. 2022. Contribution of international tourism to economic growth of Kerala: A subnationallevel analysis in India. Journal of Policy Research in Tourism, Leisure and Events 14: 165–82. [CrossRef] Klinger, Sabine, and Enzo Weber. 2020. GDP-employment decoupling in Germany. Structural Change and Economic Dynamics 52: 82–98. [CrossRef] Koenker, Roger. 2005. Quantile Regression. New York: Cambridge University Press. Koenker, Roger, Victor Chernozhukov, Xuming He, and Limin Peng. 2017. Handbook of Quantile Regression. Boca Ranton: CCR Press. Kováˇc, Štefan. 2013. Vybranéfaktory predlženosti podnikov v podmienkach SR. Forum Statisticum Slovacum 7: 79–85. Koži´c, Ivan, and Ivan Sever. 2022. Can tourism activity stabilize cyclical employment? Annals of Tourism Research Empirical Insights 3: 100043. [CrossRef] Lee, Seojin. 2015. Research note: Quality of government and tourism destination competitiveness. Tourism Economics 21: 881–88. [CrossRef] Li, Mingxiang. 2015. Moving beyond the linear regression model: Advantages of the quantile regression model. Journal of Management 41: 71–98. [CrossRef] Lim, Weng Marc, and Wai-Ming To. 2022. The economic impact of a global pandemic on the tourism economy: The case of COVID-19 and Macao’s destination-and gambling-dependent economy. Current Issues in Tourism 25: 1258–69. [CrossRef] Manzoor, Faiza, Longbao Wei, Muhammad Asif, Muhammad Zia ul Haq, and Hafiz ur Rehman. 2019. The contribution of sustainable tourism to economic growth and employment in Pakistan. International Journal of Environmental Research and Public Health 16: 3785. [CrossRef] Mariolis, Theodore, Nikolaos Rodousakis, and George Soklis. 2021. The COVID-19 multiplier effects of tourism on the Greek economy. Tourism Economics 27: 1848–55. [CrossRef] Martín-Critikián, Davina, JoséRodríguez-Terceño, Juan Enrique Gonzálvez-Vallés, and Mónica Viñarás-Abad. 2021. Tourism advertising in times of crisis: The case of Spain and COVID-19. Administrative Sciences 11: 101. [CrossRef] Miyawaki, Atsushi, Takahiro Tabuchi, Yasutake Tomata, and Yusuke Tsugawa. 2021. Association between participation in the government subsidy programme for domestic travel and symptoms indicative of COVID-19 infection in Japan: Cross-sectional study. BMJ Open 11: e049069. [CrossRef] [PubMed] Monterrubio, Carlos. 2022. The informal tourism economy, COVID-19 and socioeconomic vulnerability in Mexico. Journal of Policy Research in Tourism, Leisure and Events 14: 20–34. [CrossRef] Moreno-Luna, Libertad, Rafael Robina-Ramírez, Marcelo Sánchez-Oro Sánchez, and JoséCastro-Serrano. 2021. Tourism and sustainability in times of COVID-19 : The case of Spain. International Journal of Environmental Research and Public Health 18: 1859. [CrossRef] Mulder, Nanno. 2020. The impact of the COVID-19 pandemic on the tourism sector in Latin America and the Caribbean, and options for a sustainable and resilient recovery. In International Trade Series 157. Santiago: Economic Commission for Latin America and the Caribbean. Munawar, Hafiz Suliman, Sara Imran Khan, Fahim Ullah, Abbas Z. Kouzani, and M. A. Parvez Mahmud. 2021. Effects of COVID-19 on the Australian economy: Insights into the mobility and unemployment rates in education and tourism sectors. Sustainability 13: 11300. [CrossRef] Nagaj, Rafał, and Brigita Žuromskait ˙ e. 2021. Tourism in the Era of COVID-19 and Its Impact on the Environment. Energies 14: 2000. [CrossRef] Economies 2024,12, 136 19 of 20 Navarro-Chávez, César Lenin, Francisco Javier Ayvar-Campos, and Celeste Camacho-Cortez. 2023. Tourism, Economic Growth, and Environmental Pollution in APEC Economies, 1995–2020: An Econometric Analysis of the Kuznets Hypothesis. Economies 11: 264. [CrossRef] Paramati, Sudharshan Reddy, Md Samsul Alam, and Ching-Fu Chen. 2017. The effects of tourism on economic growth and CO 2 emissions: A comparison between developed and developing economies. Journal of Travel Research 56: 712–24. [CrossRef] Pasieka, Stanislava, Oleksandr Kirdan, Oksana Braslavska, Inna Kosmidailo, Olga Oliinyk, Inna Povorozniuk, and Maryna Drobotova. 2022. The Economic Role of Tourism in European Countries’ Sustainable Development. Management Theory and Studies for Rural Business and Infrastructure Development 44: 323–37. [CrossRef] Peterson, Ryan R., and Robin B. DiPietro. 2021. Exploring the impact of the COVID-19 pandemic on the perceptions and sentiments of tourism employees: Evidence from a small island tourism economy in the Caribbean. International Hospitality Review 35: 156–70. [CrossRef] Pham, Tien, and Anda Nugroho. 2022. Tourism-induced poverty impacts of COVID-19 in Indonesia. Annals of Tourism Research Empirical Insights 3: 100069. [CrossRef] Pham, Tien Duc, Larry Dwyer, Jen-Je Su, and Tramy Ngo. 2021. COVID-19 impacts of inbound tourism on Australian economy. Annals of Tourism Research 88: 103179. [CrossRef] [PubMed] Radovanov, Boris, Branislav Dudic, Michal Gregus, Aleksandra Marcikic Horvat, and Vincent Karovic. 2020. Using a two-stage DEA model to measure tourism potentials of EU countries and Western Balkan countries: An approach to sustainable development. Sustainability 12: 4903. [CrossRef] Ramlall, Indranarain. 2024. Modeling the impact of COVID-19 on the tourism sector in Mauritius: A dynamic stochastic general equilibrium analysis. Annals of Tourism Research Empirical Insights 5: 100130. [CrossRef] Rodousakis, Nikolaos, and George Soklis. 2022. The COVID-19 multiplier effects of tourism on the German and Spanish economies. Evolutionary and Institutional Economics Review 19: 497–510. [CrossRef] Sánchez López, Fernando. 2024. Tourism and Economic Misery: Theory and Empirical Evidence from Mexico. Economies 12: 88. [CrossRef] Scarlett, Hubert G. 2021. Tourism recovery and the economic impact: A panel assessment. Research in Globalization 3: 100044. [CrossRef] Schubert, Stefan Franz. 2021. COVID-19 : Economic consequences for a small tourism dependent economy. Revista Brasileira de Pesquisa em Turismo 15: 2297. [CrossRef] Seabra, Cláudia, and Ketan Bhatt. 2022. Tourism sustainability and COVID-19 pandemic: Is there a positive side? Sustainability 14: 8723. [CrossRef] ¸Sengel, Ümit, Merve I¸skın, Mustafa Çevrimkaya, and Gökhan Genç. 2023. Fiscal and monetary policies supporting the tourism industry during COVID-19. Journal of Hospitality and Tourism Insights 6: 1485–501. [CrossRef] Šenková, Anna, Martina Košíková, Daniela Matušíková, Kristína Šambronská, Ivana KravˇcákováVozárová, and Rastislav Kotuliˇc. 2021. Time series modeling analysis of the development and impact of the COVID-19 pandemic on spa tourism in Slovakia. Sustainability 13: 11476. [CrossRef] Škare, Marinko, Domingo Riberio Soriano, and Małgorzata Porada-Rocho´n. 2021. Impact of COVID-19 on the travel and tourism industry. Technological Forecasting and Social Change 163: 120469. [CrossRef] [PubMed] Solarin, Sakiru Adebola, Gloria Claudio-Quiroga, and Luis A. Gil-Alana. 2024. Persistence in Australian tourism employment industries. Current Issues in Tourism 27: 754–67. [CrossRef] Sultana, Sharmin. 2016. Economic contribution of tourism industry in Bangladesh. Journal of Tourism, Hospitality and Sports 22: 45–54. Sun, Ya-Yen, Lintje Sie, Futu Faturay, Ilmiawan Auwalin, and Jie Wang. 2021. Who are vulnerable in a tourism crisis? A tourism employment vulnerability analysis for the COVID-19 management. Journal of Hospitality and Tourism Management 49: 304–8. [CrossRef] Sun, Ya-Yen, Mengyu Li, Manfred Lenzen, Arunima Malik, and Francesco Pomponi. 2022. Tourism, job vulnerability and income inequality during the COVID-19 pandemic: A global perspective. Annals of Tourism Research Empirical Insights 3: 100046. [CrossRef] Tabash, Mosab I., Suhaib Anagreh, Balal Haider Subhani, Mamdouh Abdulaziz Saleh Al-Faryan, and Krzysztof Drachal. 2023. Tourism, remittances, and foreign investment as determinants of economic growth: Empirical evidence from selected Asian economies. Economies 11: 54. [CrossRef] Tandrayen-Ragoobur, Verena, Neha Devi Tengur, and Sheereen Fauzel. 2022. COVID-19 and Mauritius’ tourism industry: An island perspective. Journal of Policy Research in Tourism, Leisure and Events 1: 1–17. [CrossRef] Tang, Chor Foon, and Eu Chye Tan. 2015. Does tourism effectively stimulate Malaysia’s economic growth? Tourism Management 46: 158–63. [CrossRef] Tang, Chor Foon, and Eu Chye Tan. 2017. Tourism-Led Growth Hypothesis: A New Global Evidence. Cornell Hospitality Quarterly 59: 304–11. [CrossRef] Tangkitvanich, Poum. 2021. The paradox of Thailand’s success in controlling COVID-19 .Asian Economic Papers 20: 175–99. [CrossRef] Turismo de Portugal. 2023. Government Materialises a Funding Line with 50 Million Euros for Investments in Sustainability. Available online: https://business.turismodeportugal.pt/en/noticias/Pages/turismo-de-portugal-cria-linha-credito-investimentossustentabilidade.aspx (accessed on 21 May 2024). Economies 2024,12, 136 20 of 20 Uppink Calderwood, Lauren, and Maksim Soshkin. 2022. Travel & Tourism Development Index 2021: Rebuilding for a Sustainable and Resilient Future. Geneva: World Economic Forum. Vašaniˇcová, Petra, and Marta Miškufová. 2023. Portfolio Cash Flow on Peer-to-Peer (P2P) Lending Platform: The Quantile Regression Approach. In Conference Proceedings of the 41th International Conference on Mathematical Methods in Economics 2023. Paper presented at the 41th International Conference on Mathematical Methods in Economics 2023, Prague, Czech Republic, September 13–15. Prague: Prague University of Economics and Business. Vašaniˇcová, Petra, and Sylvia Jenˇcová. 2022. Determinants of International Tourism Inbound Receipts: The Quantile Regression Approach. In Conference Proceedings of the 40th International Conference on Mathematical Methods in Economics 2022. Paper presented at the 40th International Conference on Mathematical Methods in Economics 2022, Jihlava, Czech Republic, September 7–9. Jihlava: College of Polytechnics Jihlava. Vasanicova, Petra, Sylvia Jencova, Beata Gavurova, and Radovan Bacik. 2021. Cultural and Natural Resources as Determinants of Travel and Tourism Competitivenes. Transformations in Business & Economics 20: 300–16. Vernekar, Shradha. 2015. The Growth of Travel and Tourism: An Overview. Journal of Commerce and Management Thought 6: 547–57. [CrossRef] Walmsley, Andreas. 2017. Overtourism and underemployment: A modern labour market dilemma. Paper presented at the 13th International Conference on Responsible Tourism in Destinations, Reykjavik, Iceland, September 29–30; Reykjavik: Icelandic Tourism Research Centre. Wickramasinghe, Kanchana, and Athula Naranpanawa. 2023. Tourism and COVID-19 : An economy-wide assessment. Journal of Hospitality and Tourism Management 55: 131–38. [CrossRef] Wu, Doris Chenguang, Chenyu Cao, Wei Liu, and Jason Li Chen. 2022. Impact of domestic tourism on economy under COVID-19 : The perspective of tourism satellite accounts. Annals of Tourism Research Empirical Insights 3: 100055. [CrossRef] Xiong, Yu, and Xiaohan Tang. 2023. Tourism during health disasters: Exploring the role of health system quality, transport infrastructure, and environmental expenditures in the revival of the global tourism industry. PLoS ONE 18: e0290252. [CrossRef] [PubMed] Yepez, Carlos, and Walter Leimgruber. 2024. The evolving landscape of tourism, travel, and global trade since the COVID-19 pandemic. Research in Globalization, 100207, in press. [CrossRef] Disclaimer/Publisher’s Note: The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.