Quantifying the ‘Yellow Card Policy’ Effect: An Intervention Analysis of Chinese Tourist Migration to South Korea amidst the COVID-19 Pandemic

Abstract

The focal point of this investigation is to unravel the intricate consequences of the ‘Yellow Card Policy’ enacted by South Korea on the inflow of Chinese tourists, set against the backdrop of the tumultuous global disruption instigated by the COVID-19 pandemic. Leveraging a dataset delineating monthly periods from January 2020 to May 2023 and invoking the intervention analysis method for empirical evaluation, we excavate a series of salient findings. We discern that South Korea’s “Yellow Card Policy” casts a significant negative shadow over the magnitude of Chinese tourist arrivals, with a particularly profound impact on the long-term outlook. Simultaneously, our investigation illuminates a discernible negative correlation between South Korea’s inflation rate and the influx of Chinese tourists, thereby underlining the critical influence of domestic economic health on international tourism trajectories. Conversely, we observe a distinct positive association between the China–South Korea exchange rate and the influx of Chinese tourists. This insinuates that an advantageous exchange rate can serve as a compelling economic catalyst, stimulating tourism demand by making the host country more financially appealing to potential tourists. In essence, this array of findings paints a multifaceted tableau of how policy maneuvers, economic landscapes, and global health upheavals converge to sculpt the contours of international tourism.

1. Introduction

In the contemporary global landscape, international tourism serves as a vital catalyst for economic growth and cultural exchange, playing a crucial role in fostering sustainable development [1,2]. However, the intricate balance of this global phenomenon is susceptible to disruption by various complex factors, including geopolitical tensions, economic fluctuations, and, most recently, global health crises. The COVID-19 pandemic has had a profound and lasting impact on international travel [3,4], reshaping the trajectory of the tourism industry and highlighting the need for sustainable practices to navigate uncertain times. South Korea, known for its rich cultural heritage and thriving tourism sector, has experienced far-reaching consequences from the pandemic [5,6]. The influx of Chinese tourists, a significant market segment, has been significantly disrupted, exacerbating the challenges faced by the country’s tourism industry [7,8]. In addition to the pandemic’s effects, South Korea’s introduction of the “Yellow Card Policy” has added an additional layer of complexity. This policy, which has drawn criticism for perceived biases and discrimination, particularly in relation to Chinese tourism, underscores the importance of considering sustainability in policy decisions. Against this backdrop, our research aims to comprehensively examine the implications of the “Yellow Card Policy” on Chinese tourist arrivals in South Korea during the COVID-19 pandemic within the context of sustainability. By delving into this topic, our study seeks to significantly contribute to the existing literature and offer informed perspectives to policymakers and stakeholders. The investigation provides valuable insights into the challenges faced by the tourism industry in maintaining sustainability amidst an unprecedented global crisis.

To substantiate the prominence of China as the foremost origin of foreign tourists for South Korea and underscore the pivotal role of Chinese tourists in the sustainability of South Korea’s tourism industry, an analysis of the Korea Tourism Statistics Bureau data from January 2020 to May 2023 has been conducted, yielding insightful findings displayed in Figure 1. This graphical representation presents a comprehensive overview of the influx of tourists from major foreign countries into South Korea.

The findings illustrated in Figure 1 unequivocally indicate that China holds the foremost position as the primary source of foreign tourists entering South Korea throughout the designated sample period, thus establishing a significant context for exploring the influence of the yellow card policy on Chinese tourist influx during the COVID-19 pandemic. This empirical evidence underscores the importance of considering sustainability in the analysis of this paper, as sustainable tourism practices aim to ensure the long-term viability and resilience of destinations while minimizing negative impacts. The exploration of the yellow card policy’s impact on Chinese tourist arrivals aligns with the sustainable tourism framework by elucidating this issue. By examining the interplay between the yellow card policy and the Chinese tourist influx during the COVID-19 pandemic, this study contributes to the understanding of sustainable tourism development in South Korea. In doing so, it provides insights for policymakers to implement sustainable strategies that can foster the recovery and long-term sustainability of South Korea’s tourism industry.

In light of the comprehensive background analysis, the fundamental ambition of this research is to illuminate the multifaceted consequences of South Korea’s ‘Yellow Card Policy’ on the ingress of Chinese tourists during the highly disruptive epoch of the COVID-19 pandemic. We rigorously scrutinize an extensive assembly of monthly data sets running from January 2020 through May 2023, applying a sophisticated intervention analysis methodology to yield several noteworthy inferences. Our analysis unequivocally illustrates that South Korea’s initiation of the ‘Yellow Card Policy’ wields a potent and adverse influence on the quantum of Chinese tourist arrivals. This insight bears substantial connotations, emphasizing how political maneuvers can inadvertently sway global travel patterns, particularly during a time marked by global vulnerability precipitated by the ongoing pandemic. Additionally, our investigation identifies a negative interdependence between the inflation rate in South Korea and the influx of Chinese tourists. This finding aligns with fundamental economic principles, asserting that as the expense associated with goods and services in South Korea escalates, it exerts a deterring influence on Chinese tourists due to the consequent erosion of their purchasing power. This, in turn, underscores the crucial role macroeconomic conditions play in influencing tourist patterns and predilections. Conversely, our research reveals a positive symbiosis between the exchange rate between China and South Korea and the surge of Chinese tourists. This suggests a significant role for advantageous exchange rates in stimulating tourism demand, as they can potentially render the destination country more economically attractive to prospective travelers. Together, these findings deliver a profound understanding of the intricate mechanics that govern Chinese tourist flow into South Korea amid the COVID-19 pandemic. This study underscores the complex synchronization of policy, economic indicators, and global health phenomena and their collective impact on international tourism.

As a direct consequence of the results presented within this scholarly inquiry, we have augmented our collective understanding in the following five key dimensions: (1) This research enriches the ever-expanding literature by probing the intricate relationship between policy enforcement—specifically, South Korea’s “Yellow Card Policy”—and global tourism trends, with a focus on the ingress of Chinese tourists. This research diverges from prior studies such as those conducted by Li et al. [9], Zhai and Shi [10], and Butu et al. [11], which predominantly spotlight consumer behavior and economic considerations. Instead, our research magnifies the consequential influence of policy decisions on tourism trajectories, particularly amidst global health crises. (2) Our scholarly endeavor further embellishes the field by empirically underscoring the inverse correlation between South Korea’s inflation rate and the arrival of Chinese tourists. This inference amplifies the macroeconomic investigation of tourism by Eugenio-Martin et al. [12], thereby corroborating the pivotal role of inflation as an influencer of international tourism demand. (3) By bridging an existing knowledge gap in the corpus of tourism literature, our study illuminates the influential role of exchange rates in shaping tourism patterns. We identify a positive relationship between the China–South Korea exchange rate and Chinese tourist inflows, thereby enhancing and refining the existing body of research, including Smeral’s [13] study, and accentuating the fact that attractive exchange rates can significantly stimulate tourism demand. (4) Methodologically, this study adopts an intervention analysis approach to dissect the effects of the ‘Yellow Card Policy’, which renders a more precise and nuanced understanding than conventional regression models. This methodological progression harmonizes with the advancements in tourism research brought forth by luminaries such as Kulendran and Witt [14]. (5) Lastly, this research presents an all-encompassing comprehension of Chinese tourist movements into South Korea, contextualized within the COVID-19 pandemic, by considering a multifaceted array of influences ranging from policy decisions to economic indicators. This holistic approach extends the insightful work of Gössling et al. [15], who advocate for a more exhaustive understanding of tourism phenomena amidst global health crises.

The subsequent sections of this scholarly investigation are organized in the following sequential manner: Section 2 embarks on a comprehensive exploration of the existing literature, delving into related studies and drawing relevant correlations with our research objectives. Section 3 introduces and elucidates the variables and models harnessed in our empirical examination. In Section 4, we unfold our primary findings and engage in a discursive analysis of their implications and correlations. Lastly, Section 5 encapsulates the essence of our research, presenting our conclusive inferences and their prospective ramifications in the domain of study.

2. Literature Review

To establish a robust academic underpinning for the discourse presented in this article, it is imperative to conduct a comprehensive review of the existing scholarly corpus. Emergencies and policy implementations, exemplified by the conceptual ‘Yellow Card Policy,’ undoubtedly reverberate throughout various economic sectors, with international tourism experiencing profound ramifications. The scholarly discourse surrounding policy modifications and crisis scenarios, underpinned by the theoretical construct of the ‘Yellow Card Policy’ and its subsequent implications for international inbound tourism, manifests as a broad and multifaceted inquiry.

This scholarly investigation encompasses a diverse spectrum of research subjects, ranging from individual travelers to expansive tourism enterprises and occasionally even encompassing entire nations. These multifaceted research subjects served as a testament to the intricate and interdependent nature of the tourism sector, thereby highlighting the intricate repercussions of policies and emergencies (Chen et al. [16]; Khan et al. [17]). Multiple studies using rigorous and well-grounded methods, like those by Neuburger and Egger [18] and Zenker and Kock [19], have clearly shown that crises or strict policy actions always cause a big drop in international tourists coming to visit. There are many underlying factors that could explain this trend, but the main ones are the decline in traveler confidence, the implementation of strict travel restrictions, and the increased costs and logistical challenges associated with travel [20]. Prideaux et al. [21] provided empirical validation for this argument, emphasizing how shifts in tourists’ perceptions of risk and safety following a crisis significantly influenced their travel decisions. Echoing this dynamic of evolving traveler behavior in response to crises, Ritchie and Jiang [22] underscored the pivotal role of perceived risk in shaping tourism demand. Crandall [23] further elucidated how the extensive repercussions of emergencies and policy implementations could permeate the entire tourism value chain, exacerbating travel expenses and complicating travel arrangements. The burgeoning body of evidence unequivocally demonstrated that emergencies and stringent policies imposed a substantial burden on inbound international tourism, inciting a ripple effect that reverberated through the diverse layers of the tourism ecosystem (Moosa and Khatatbeh [22]; Plzáková and Smeral [24]; Seyfi et al. [25]).

Scholarly investigations in this domain frequently encompass a broad chronological spectrum. Blake and Sinclair [26] and Ritchie et al. [27] commenced with the antecedents leading to the emergency or policy initiation, traversed through the initiation and evolution of the crisis or policy implementation, and often extended into the subsequent recuperative phase. This comprehensive temporal perspective facilitated a holistic understanding of the unfolding and progression of impacts, as well as the regenerative patterns that manifest post-crisis. Within this research domain, an array of methodological frameworks is employed, creating a diverse landscape of techniques. This methodological diversity ranges from comparative case studies (Hall [28]; Ioannides and Apostolopoulos [29]) to intricate econometric modeling (Henderson [30]; António and Rita [31]; Wang et al. [32]). Moreover, emerging methodologies like big data analytics are embraced for real-time insight extraction (Flaherty et al. [33]; Chen [34]; Benítez et al. [35]). Such a range of different research methods shows how complicated the subject being studied really is, and how important it is to have a wide range of analytical tools that can help us understand it all.

The deductions derived from these scholarly endeavors consistently underscore the adverse effects on inbound tourism that accompany the emergence of crises or the enforcement of restrictive policies (Rodríguez-Antón and Alonso-Almeida [36]; Cakar [37]; Khalid et al. [38]; Yacoub and ElHajjar [39]). Nevertheless, it is imperative to acknowledge that the magnitude and duration of these repercussions are highly heterogeneous. They hinge upon a multitude of determining factors, encompassing the nature of the emergency or policy, the remedial measures enacted by the destination, and the prevailing global economic backdrop [40]. These findings served to underscore the intricate interplay between policy, the environment, and the tourism sector, offering valuable insights into their consequential implications for effective crisis management and the reinvigoration of the tourism industry. As such, an in-depth comprehension of this complex nexus becomes paramount in the formulation of efficacious strategies aimed at mitigating detrimental impacts and catalyzing recovery in the aftermath of crises (Guo et al. [41]; Adedoyin et al. [42]; Frey and Briviba [43]; Sharpley [44]).

The COVID-19 pandemic has introduced unprecedented dynamics into the already complex relationship between exchange rates, inflation, and inbound tourism, sparking a contentious discourse among scholars. While some, like Ndhlovu and Dube [45] and Zaman et al. [46], argue that a weakened domestic currency, often associated with the economic repercussions of the pandemic, could make a destination more affordable for international tourists and potentially stimulate tourism recovery, others, including Chang et al. [47], Saif et al. [48], and Wójcik and Ioannou [49], contend that the uncertainty and financial instability brought about by the pandemic-induced exchange rate fluctuations may actually deter travel. Furthermore, the impact of inflation during the pandemic is a subject of ongoing debate. Research by Allan et al. [50], Rasul et al. [51], and Bürgisser and Di Carlo [52] suggested that high inflation rates, exacerbated by supply chain disruptions and increased operational costs for the tourism industry, might lead to increased travel expenses, potentially suppressing inbound tourism. However, the extent to which this translates into reduced tourism remains a matter of dispute, with some studies like Santos et al. [53], Elavarasan et al. [54], and Sun et al. [55] proposing that travelers may prioritize safety over cost considerations during the pandemic, attenuating the influence of inflation. In summary, the interplay of exchange rates and inflation on inbound tourism amid the COVID-19 crisis remains a contentious issue, with scholars presenting divergent viewpoints that call for further examination and discussion.

An examination of the pertinent academic literature has enabled the discernment of critical research imperatives for investigation within this study’s framework. The advent of the unparalleled COVID-19 pandemic has wrought far-reaching and multifaceted consequences for South Korea’s vibrant and dynamic tourism sector. This scholarly article is dedicated to an exhaustive exploration of the implications stemming from the contentious ‘Yellow Card Policy’ implemented by the South Korean government regarding the influx of Chinese tourists into South Korea amidst the complex and evolving backdrop of the pandemic. These results will be carefully compared to other research that has already been conducted. The goal of this study is to make important contributions by finding new ideas and information that could greatly improve the current academic conversation. The smooth mixing of empirical evidence and theoretical frameworks is expected to shed light on the complicated and multifaceted dynamics underlying the complicated interplay between government policies, the unprecedented pandemic crisis, and the complicated flow of Chinese tourists into South Korea’s beautiful realm, thereby propelling the steady growth of the body of knowledge.

3. Variable Description and Model Specification

3.1. Variable Description

Given the central focus of this study—examining the implications of the ‘Yellow Card Policy’ during the COVID-19 pandemic on the volume of Chinese tourists entering South Korea—the count of these tourists is treated as the dependent variable within our analytical framework. The rationale for this choice is rooted in several economic and tourism-specific factors. Economically, Chinese tourists represent a significant contributor to South Korea’s tourism revenue, making their travel patterns vital to understanding economic trends. Additionally, changes in the volume of these tourists can have ripple effects on local businesses and employment, thereby influencing the broader South Korean economy. From a tourism perspective, Chinese tourists have consistently formed a substantial proportion of inbound tourism in South Korea. Understanding the factors affecting their travel behavior, such as the ‘Yellow Card Policy’, can guide strategic decision-making in the tourism industry, shaping policies to optimize inbound tourist flow and enhance the overall visitor experience.

The ‘Yellow Card Policy’ enacted by the South Korean government, specifically targeted at Chinese tourists entering South Korea, has sparked considerable discontent and perceptions of discrimination among this visitor demographic. This adverse sentiment appears to have served as a deterrent, discouraging prospective Chinese tourists from selecting South Korea as their travel destination. In light of these dynamics, it becomes imperative to delve into the repercussions of the ‘Yellow Card Policy’. As such, this study positions the ‘Yellow Card Policy’ as the principal explanatory variable. The objective is to quantitatively discern its impact, thereby generating nuanced insights into the policy’s wider ramifications on inbound tourism, cross-cultural relationships, and potentially the broader economic landscape.

To reinforce the robustness and stability of our estimation results, this study incorporates a set of key control variables into the research framework. Drawing on the insights from the works of Durani et al. [56], Rastogi and Kanoujiya [57], and Canbay et al. [58], we have incorporated inflation into our analytical structure. This inclusion reflects the recognition of inflation’s potential influence on travel decisions and tourism patterns. Moreover, guided by the research of Li and Pu [59], and Tai et al. [60], the exchange rate is integrated into our research framework. The exchange rate is a crucial economic parameter that can affect tourism as it influences the relative cost of travel between countries. This study relies on data obtained from two sources, namely Korean Tourism Statistics and Statistics Korea. In pursuit of enhanced clarity and comprehension of the variables employed within this study, we present

Table 1. Results of variable description.

Variable	Form	Definition
Volume of Chinese tourists entering South Korea	tourism	Volume of Chinese tourists entering South Korea in log
Yellow card policy	policy	Before January 2023, policy = 0; After January 2023, policy = 1
South Korean inflation rate	inflation	Inflation rate
Sino-Korean exchange rate	rate	Sino-Korean exchange rate in log

. This table outlines the definitions and forms of the variables, providing a straightforward reference for their roles and operationalization within our analysis.

3.2. Model Specification

Amid the ongoing COVID-19 pandemic, the South Korean government has made substantial efforts to mitigate the introduction of foreign viral imports. A strategy has been the implementation of the ‘Yellow Card Policy’, specifically targeted at Chinese travelers arriving at South Korean international airports. This novel policy, albeit devised with public health considerations in mind, has triggered profound unease and perceived discrimination among Chinese tourists. Further compounding the issue, it has also elicited heightened scrutiny and concern from the Chinese government. This policy shift has substantially influenced South Korea’s tourism industry. Building on the foundational works of Min et al. [61], Wu et al. [62], and Dolnicar [63], this paper will utilize an intervention analysis approach to construct a comprehensive model that expounds upon the following:

$${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}={\mathrm{a}}_0+{\mathrm{a}}_1{\sum }_{\mathrm{i}=1}^{\mathrm{n}}{\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}-\mathrm{i}}+{\mathrm{a}}_2{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}+{\mathrm{a}}_3{\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{t}}+{\mathrm{a}}_4{\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{t}}.$$

(1)

In Equation (1), we define ${\mathrm{a}}_0$ as a constant variable. ${\mathrm{a}}_1$, on the other hand, is the auto-regressive coefficient associated with ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}$. The coefficient ${\mathrm{a}}_2$ is employed to measure the direct or shock influence of the ‘Yellow Card Policy’ on the influx of Chinese tourists to South Korea. The statistical significance of ${\mathrm{a}}_2$ can be ascertained through the standard t-statistic value. In a scenario where ${\mathrm{a}}_2$ holds a negative value and is statistically distinguishable from zero, it would substantiate the hypothesis that the ‘Yellow Card Policy’ has adversely affected the volume of Chinese tourists visiting South Korea. The coefficients ${\mathrm{a}}_3$ and ${\mathrm{a}}_4$ encapsulate the impacts of both South Korean inflation and the Sino-Korean exchange rate, respectively, on the Chinese tourist count in South Korea. Lastly, ${\mathsf{ϵ}}_{\mathrm{t}}$ is employed as a placeholder for the white noise component.

The enduring influence of the aforementioned intervention, denoted as $\frac{{\mathrm{a}}_0}{1-{\mathrm{a}}_1}$, is determined by the difference between the emergent long-term mean $\frac{{\mathrm{a}}_0+{\mathrm{a}}_2}{1-{\mathrm{a}}_1}$ and the initial mean $\frac{{\mathrm{a}}_0}{1-{\mathrm{a}}_1}$. The temporal impact of this intervention on variable ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}$ is effectively assessed using the impulse response function, a robust tool for analyzing dynamic systems. By adopting the lag operator, a crucial mathematical tool in the realm of time-series analysis, we are enabled to reformulate Equation (1). The revamped version provides a fresh perspective on the interrelationships of the variables and allows for an augmented understanding of the causal mechanisms at play. The following section will present this revised equation:

$$(1-{\mathrm{a}}_1{\mathrm{L}}^{\mathrm{i}}){\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}={\mathrm{a}}_0+{\mathrm{a}}_2{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}+{\mathrm{a}}_3{\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{t}}+{\mathrm{a}}_4{\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{t}}+{\mathsf{ϵ}}_{\mathrm{t}}.$$

(2)

Capitalizing on the framework established by Equation (2), we proceed to illustrate the impulse response function. This representation serves as a robust analytical tool, offering valuable insights into the dynamic relationship among the variables of interest, particularly under the influence of shocks or disturbances. In the following segment, we present this function, providing a detailed examination of the variable interactions in our model:

$$\begin{gathered} {\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}=\frac{{\mathrm{a}}_0}{(1-{\mathrm{a}}_1{\mathrm{L}}^{\mathrm{i}})}+{\mathrm{a}}_2{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{a}}_1^{\mathrm{i}}{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}+{\mathrm{a}}_3{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{a}}_1^{\mathrm{i}}{\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}}_{\mathrm{t}}+ \\ {\mathrm{a}}_4{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{a}}_1^{\mathrm{i}}{\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}}_{\mathrm{t}}+{\sum }_{\mathrm{i}=0}^{\mathrm{n}}{\mathrm{a}}_1^{\mathrm{i}}{\mathsf{ϵ}}_{\mathrm{t}}. \end{gathered}$$

(3)

In Equation (3), we have the impulse response function, an integral tool for elucidating the dynamic relationships in our model. The distinct novelty of the intervention variable lies in their capacity to shed light on the impact of the influx of Chinese tourists into South Korea. In an effort to portray the influence of the “Yellow Card Policy” on the volume of Chinese tourists visiting South Korea, we operate under the assumption that for a given period $\mathrm{t}$, the magnitude of the coefficient ${\mathrm{a}}_2$ encapsulates the policy’s effect. In order to generate a sustainable impulse response, two primary conditions must be acknowledged: (1) $\frac{\mathrm{d}{\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}}{\mathrm{d}{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}-\mathrm{i}}}=\frac{\mathrm{d}{\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}+\mathrm{i}}}{\mathrm{d}{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}}$, and (2) for all $\mathrm{i}$ where $\mathrm{i}$ is greater than zero, ${\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}+\mathrm{i}}={\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}$ = 1. With these conditions met, ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}$ in Equation (3) is differentiated with respect to ${\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}-1}$, followed by a correction for Phase 1, ultimately yielding the derivation of Equation (4).

$$\frac{\mathrm{d}{\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}+1}}{\mathrm{d}{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}}={\mathrm{a}}_0+{\mathrm{a}}_2{\mathrm{a}}_1.$$

(4)

In Equation (4), the initial term, denoted as ${\mathrm{a}}_0$, embodies the immediate influence of ${\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}+1}$ on ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}+1}$, signifying the contemporaneous effect within the same time period. Subsequently, the second term, represented as ${\mathrm{a}}_2{\mathrm{a}}_1$, denotes the compound influence arising from the interaction between the effect of ${\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}$ on ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}$ (=${\mathrm{a}}_0$) and that of ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}}$ on ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}+1}$ (=${\mathrm{a}}_1$). This methodology, when sequentially extended, provides a comprehensive framework to articulate the entire impulse response function. In doing so, it allows us to capture the dynamic interactions and trace the evolution of impacts over time, thereby offering a nuanced understanding of the system’s response to shocks or disturbances.

$$\frac{\mathrm{d}{\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{\mathrm{t}+\mathrm{i}}}{\mathrm{d}{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}}}={\mathrm{a}}_2(1+{\mathrm{a}}_1+{\mathrm{a}}_1^2+...{\left({\mathrm{a}}_1\right)}^{\mathrm{i}},\mathrm{f}\mathrm{o}\mathrm{r}{\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}+1}={\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}}_{\mathrm{t}+2}...=1.$$

(5)

Upon approaching the limit $\mathrm{i}$, we can reaffirm the long-term impact as denoted by $\frac{{\mathrm{a}}_0}{1-{\mathrm{a}}_1}$. Assuming $0<{\mathrm{a}}_1<1$, we observe that the absolute magnitude of the influence functions as an ascending function of $\mathrm{i}$. In the event $-1<{\mathrm{a}}_1<0$, we find that the ‘Yellow Card Policy’ exhibits an inhibitory impact on the influx of Chinese tourists to South Korea, suggesting a complex and non-linear relationship. Following the initial surge signified by ${\mathrm{a}}_2$, the continual value representing the count of Chinese arrivals to South Korea exhibits oscillations around the long-term level $\frac{{\mathrm{a}}_0}{1-{\mathrm{a}}_1}$. This pattern suggests the possibility of transitory shocks and adjustments before the system stabilizes at long-term equilibrium, offering further insights into the dynamic and complex impacts of policy measures on tourism flows.

4. Results and Discussion

4.1. Basic Statistical Test

Prior to commencing empirical analysis, it is imperative to ascertain the fundamental statistical characteristics inherent in the variables of interest. This preliminary exploration includes the calculation and examination of key statistical parameters, notably measures such as the mean (representing central tendency), maximum and minimum values (denoting the data’s range), and the standard deviation (quantifying data dispersion). The outcomes of this statistical inquiry are presented in

Table 2. Results of variable description.

Variable and Statistics	Tourism	Policy	Inflation	Tate
Mean	4.267	0.122	0.271	2.255
Maximum	5.683	1.000	0.800	2.304
Minimum	3.595	0.000	−0.600	2.219
standard deviation	0.407	0.331	0.318	0.023

.

As gleaned from the insights presented in Table 2, it becomes apparent that the influx of Chinese tourists into South Korea exhibits marked fluctuations. This pattern is intricately aligned with the dynamic realities characterizing both China and South Korea. These fluctuations are primarily attributed to the mutable nature of immigration policies, which remain highly responsive to the evolving landscape of the ongoing epidemic. Furthermore, a notable observation pertains to the pronounced variability within the yellow card policy variable, a phenomenon stemming from the inherently contentious nature of this policy framework. In tandem, South Korea’s inflation rates manifest a degree of volatility, a reflection of the intricate adjustments undertaken by the South Korean government to mitigate the compounded repercussions of the pandemic and the yellow card policy. Interestingly, relative exchange rate stability emerges as a distinctive feature, largely underpinned by South Korea’s currency pegging arrangement with the United States dollar, which imparts a degree of resilience to external currency fluctuations. This examination underscores the multifaceted interplay of variables that shaped the economic and policy landscape during this critical period.

The foundation for our statistical analysis and intervention analysis hinges on the stability of the variables under consideration. The existence of a unit root within a variable implies its instability, consequently leading to inaccuracies in our intervention analysis estimates. More critically, the employment of unstable variables with unit roots for intervention analysis can induce spurious regression, a statistical phenomenon that appears to confirm a valid relationship when there is, in fact, no economic basis for it. Such a condition can be avoided by conducting a unit root test. Drawing on seminal literature, such as Dickey and Fuller [64] for the ADF (augmented Dickey–Fuller) test and Phillips and Perron [65] for the PP (Phillips–Perron) test, we leverage these well-established methodologies for unit root testing to confirm the robustness of our variables. These test procedures are recognized for their effectiveness in time-series analysis to detect unit roots, thus affirming the stability of variables. The findings from the application of these tests are succinctly presented in

Table 3. Results of unit root test.

Panel A: ADF Test and PP Test
Variable and Method	ADF Test	PP Test
	I(0)	I(0)
tourism	−5.576 ***	−3.888 **
inflation	−4.247 ***	−4.293 ***
rate	−5.733 ***	−5.841 ***
Panel B: Zivot-Andrews unit root test
Variable	I(0)	Break-dummy
tourism	−5.845 ***	March 2023
inflation	−4.956 ***	January 2021
rate	−7.025 ***	December 2022

Note: ** 5% significant level; *** 10% significant level.

.

Upon analyzing the outcomes presented in Panel A of Table 3, we can infer that each variable significantly rejects the null hypothesis (i.e., the hypothesis asserting the presence of a unit root). Consequently, this indicates the inherent stability of the variables incorporated into our study. Moreover, to mitigate potential issues arising from the discontinuity of variables, which could yield invalid test results in Panel A, we employ the Zivot–Andrews unit root test to reassess the stability of the variables used in this study. As demonstrated in Panel B, the analysis reveals that the variables exhibit stability without any discernible break points. Consequently, based on this comprehensive evaluation, it can be determined that the variables employed in this research possess stationarity characteristics. This methodological approach ensures the robustness and reliability of our findings, facilitating accurate inferences and supporting the validity of subsequent analyses conducted in this paper. Following the revelation from the unit root tests, we transition towards a correlation analysis, a step instrumental in unveiling hidden patterns and potential trends within these variables. A pronounced correlation between two variables could suggest shared underlying determinants or possibly a lag-induced influence of one variable upon another. Identifying and decoding such correlations enables us to generate more accurate forecasts pertaining to the future influx of Chinese tourists into South Korea. Correlation analysis offers invaluable insights to policymakers, allowing them to formulate appropriate strategies in response to shifts in Chinese visitor numbers to South Korea. Furthermore, an awareness of existing correlations among variables facilitates model selection and validation processes. By acknowledging these interdependencies, we can fit data more effectively into selected models, thereby enhancing the accuracy of our predictions. The consequential outcomes of our correlation analysis are summarized in

Table 4. Results of correlation test.

Variable	Tourism	Inflation	Rate
tourism	1.000 (----)
inflation	−0.417 *** (−2.869)	1.000 (----)
rate	0.874 *** (11.227)	0.209 (1.340)	1.000 (----)

Note: T-statistical value shown in parentheses; *** 10% significant level.

.

The results presented in Table 4 reveal a compelling inverse correlation between the number of Chinese visitors to South Korea and the host country’s inflation rate. This negative association implies that higher inflation rates in South Korea could function as a deterrent for Chinese tourists. Economically, this is understandable, as elevated inflation rates would increase the cost of tourist activities in South Korea, potentially making it less appealing to Chinese travelers. In a contrasting dynamic, there exists a significant positive correlation between the influx of Chinese tourists and the exchange rate between China and South Korea. This suggests that a more favorable exchange rate—where the Chinese Yuan holds stronger purchasing power against the South Korean Won—could potentially incentivize Chinese tourists to visit South Korea due to increased financial viability. Intriguingly, the correlation between South Korea’s inflation rate and the Sino-Korean exchange rate does not reach statistical significance. This insubstantial correlation underscores the absence of multicollinearity, a statistical phenomenon in which two or more predictor variables in a multiple regression model are highly correlated. In the present context, this means that the South Korean inflation rate and the Sino-Korean exchange rate act independently of one another in their influence on the number of Chinese tourists visiting South Korea. As a result, the likelihood of inflated or misleading estimates resulting from multicollinearity is minimized, thereby bolstering the validity and reliability of our model.

4.2. The Ramifications of the ‘Yellow Card Policy’ on Chinese Tourists Inflow into South Korea in the Context of the COVID-19 Pandemic

Due to the enactment of the ‘Yellow Card Policy’ by the South Korean government, a sentiment of bias and discrimination has been triggered amongst Chinese travelers. This, in turn, has significantly diminished their zeal to visit South Korea. As such, the objective of this segment is to scrutinize the repercussions of South Korea’s ‘Yellow Card Policy’ on the influx of Chinese tourists into the country. The policy’s implications hold particular relevance, given their potential to dissuade a substantial segment of tourists and, hence, impact the nation’s tourism industry at large. By deploying appropriate methodologies, this section aims to discern the quantifiable effect of the policy, thus helping to better comprehend its wider socio-economic consequences. The findings of this examination, encapsulated in

Table 5. Results of the effect of the “Yellow Card Policy” on Chinese tourists Inflow into South Korea in the context of the COVID-19 pandemic.

Variable	Model (1)
${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{-1}$	0.552 *** (7.130)
$\mathrm{p}\mathrm{o}\mathrm{l}\mathrm{i}\mathrm{c}\mathrm{y}$	−0.349 *** (−4.016)
$\mathrm{i}\mathrm{n}\mathrm{f}\mathrm{l}\mathrm{a}\mathrm{t}\mathrm{i}\mathrm{o}\mathrm{n}$	−0.061 *** (−7.026)
$\mathrm{r}\mathrm{a}\mathrm{t}\mathrm{e}$	0.698 ** (2.167)
$\mathrm{c}$	−4.256 (−1.550)
${\mathrm{R}}^2$	0.782
$\mathrm{F}-\mathrm{statistic}\mathrm{value}$	35.943 ***
$\mathrm{AIC}$	${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{-5}(\mathrm{A}\mathrm{I}\mathrm{C}=-6.758)$; ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{-4}(\mathrm{A}\mathrm{I}\mathrm{C}=-7.847)$; ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{-3}(\mathrm{A}\mathrm{I}\mathrm{C}=-6.334)$; ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{-2}(\mathrm{A}\mathrm{I}\mathrm{C}=-4.193)$; ${\mathrm{t}\mathrm{o}\mathrm{u}\mathrm{r}\mathrm{i}\mathrm{s}\mathrm{m}}_{-1}(\mathrm{A}\mathrm{I}\mathrm{C}=-8.011)$
$\mathrm{Long}-\mathrm{run}\mathrm{effect}$	−0.799
Heteroskedasticity Test-ARCH	0.887
Breusch-Godfrey Serial Correlation LM Test	0.174

Note: ** 5% significant level; *** 10% significant level; T-statistical value shown in parentheses; c constant; long-run effect $\frac{{\mathrm{c}}_0}{1-{\mathrm{a}}_1}$.

, offer crucial insights into the tourism dynamics altered by the policy. This analysis is not only vital from a policy perspective, but also substantially contributes to the broader discourse surrounding the interplay between public health policies and international tourism.

As delineated in Table 5, the Akaike Information Criterion (AIC) test demonstrates a lag of one period in the number of Chinese tourists entering South Korea. As such, to align with these findings, this study adopts a model that incorporates a one-stage lag in the estimation of Chinese tourist arrivals. implementation of the ‘Yellow Card Policy’ by the South Korean government, a significant decrease was observed in the influx of Chinese tourists. Notably, the long-term impact of the policy, measured at −0.799, far exceeds its short-term implications. This suggests that the policy has created a persistent deterrent for Chinese tourists, potentially harming the South Korean tourism industry in a prolonged manner. Simultaneously, an inflation increase in South Korea of 1% led to a decrease of 0.061% in the number of Chinese tourists. This supports the economic theory of purchasing power parity, suggesting that as domestic prices increase, international tourists might perceive the destination as less affordable. Conversely, a rise of 1% in the China–South Korea exchange rate correlates with an increase of 0.689% in Chinese arrivals. This underlines the importance of favorable exchange rates in promoting international tourism, as tourists from China can receive more value from their money when visiting South Korea. Heteroskedasticity and autocorrelation tests, specifically the ARCH and Breusch–Godfrey Serial Correlation LM Test, indicate no heteroscedasticity or autocorrelation in the model residuals, reinforcing the reliability and validity of the study’s findings. These outcomes echo the results found in several authoritative pieces of literature. For instance, the impact of exchange rate variations on tourism demand has been extensively documented in studies such as Ulucak et al. [66], Dogru et al. [67], and Işık et al. [68]. Similarly, the influence of inflation on tourism demand has been explored in studies such as Murshed et al. [69], Emili et al. [70], and Khalid et al. [71]. Our findings, however, reveal a greater magnitude of impact, suggesting that the specific context of the ‘Yellow Card Policy’ and the target demographic may have heightened these effects. Policy implications drawn from this analysis suggest that in devising policies for managing public health crises, it is crucial to consider their potential impact on tourism demand. This consideration is particularly vital for countries like South Korea, where tourism represents a significant portion of the economy. Moreover, economic factors such as inflation and exchange rates should be carefully managed to enhance their appeal to international tourists. This study extends the existing literature by quantifying the specific impact of the ‘Yellow Card Policy’ on Chinese tourist arrivals in South Korea, a topic previously unexplored. As such, it provides both policy and academic contributions to the discourse on the intersection of public health policy and tourism.

5. Conclusions

The primary objective of this investigation is to elucidate the effects of the ‘Yellow Card Policy’ on the inflow of Chinese tourists into South Korea during the unprecedented times of the COVID-19 pandemic. Drawing upon monthly datasets spanning from January 2020 through May 2023 and implementing the nuanced intervention analysis methodology for rigorous empirical examination, we discern certain key trends. Our results clearly demonstrate that the introduction of the ‘Yellow Card Policy’ by South Korea imparts a detrimental influence on the volume of Chinese tourists entering the country. This finding carries significant implications, emphasizing how policy decisions can inadvertently impact international tourist movements, especially in a sensitive global climate marked by the COVID-19 pandemic. Another key finding is the negative relationship between South Korea’s inflation rate and Chinese tourist arrivals. As the cost of goods and services in South Korea escalates, it deters Chinese tourists due to diminished purchasing power. This finding resonates with economic theory and underscores the role of macroeconomic factors in shaping tourist behaviors and preferences. In a contrasting vein, our study also uncovers a positive correlation between the China–South Korea exchange rate and the influx of Chinese tourists. This highlights the pivotal role of favorable exchange rates in bolstering tourism demand, as they can render the destination more economically attractive to potential visitors. Collectively, these findings provide a comprehensive understanding of the dynamics at play regarding Chinese tourist movements into South Korea in the context of the COVID-19 pandemic, underscoring the complex interplay of policy, economic indicators, and global health events.

Drawing from the empirical findings and analytical insights generated from this study, we can delineate several policy implications that can inform effective strategy formulation: (1) The adverse impact of the “Yellow Card Policy” on Chinese tourist arrivals underscores the necessity for a nuanced review of this policy. This review could seek to address perceived biases and ensure equitable treatment, thereby potentially restoring the appeal of South Korea as a destination for Chinese tourists. (2) The negative correlation between South Korea’s inflation rate and the number of Chinese tourists signifies the importance of managing inflation to foster tourism. As inflation can erode purchasing power and deter potential tourists, maintaining price stability could serve as a key strategic lever to attract foreign visitors. (3) The positive relationship between the China–South Korea exchange rate and Chinese tourist arrivals indicates the potential advantages of a favorable exchange rate. Policymakers could explore exchange rate management strategies to make South Korea an economically appealing destination for Chinese tourists. (4) In light of the ‘Yellow Card Policy’ repercussions, South Korea may benefit from renewed and culturally sensitive destination marketing efforts. These initiatives could aim to rebuild positive associations and reaffirm South Korea’s commitment to welcoming Chinese tourists. (5) Lastly, the multifaceted impacts of macroeconomic variables and policy decisions on tourism underscore the importance of sustained engagement with various stakeholders. This includes government agencies, the tourism industry, potential tourists, and the general public. A participatory approach to policy-making can ensure that the perspectives of all stakeholders are considered and incorporated, leading to more effective, inclusive, and well-received policies. In a broader sense, these policy implications underline the necessity for an integrated and balanced approach blending economic management, policy sensitivity, effective marketing, and active stakeholder engagement to foster a conducive environment for international tourism, particularly in the context of global events such as the COVID-19 pandemic.

This study, while providing valuable insights into the dynamics affecting Chinese tourist arrivals in South Korea amidst the COVID-19 pandemic and the implementation of the ‘Yellow Card Policy’, is not without limitations. These limitations could also serve as springboards for future research directions. Here are five notable limitations and corresponding future research pathways: (1) Our study primarily focuses on South Korea as a destination and China as the outbound country. As such, it does not account for global patterns and trends in tourism, which could impact the behaviors and preferences of Chinese tourists. Future research could consider a comparative analysis involving multiple destinations and source countries to capture a broader perspective of the international tourism landscape. (2) The research is conducted amidst the unprecedented global health crisis of COVID-19. The pandemic-specific constraints may not reflect the regular dynamics of tourism between the two nations. Future research could examine the post-pandemic scenario to understand how the dynamics evolve and whether the trends identified in this study persist. (3) Our study uses inflation and exchange rates as key economic indicators, while other factors like GDP growth, unemployment rates, or consumer confidence might also influence tourism flows. Subsequent research could incorporate a wider range of economic indicators to present a more comprehensive picture of the economic determinants of tourism. (4) The impact of the ‘Yellow Card Policy’ is assessed based on aggregated data and does not capture the varied individual responses to the policy. Qualitative research methods, such as interviews or surveys, could be used in future studies to explore individual perceptions and reactions to such policies, providing a more nuanced understanding of their impacts. (5) The study is constrained to a particular time frame (January 2020–May 2023), which may limit its findings to this period. Future research could examine longer-term data to identify whether the impacts identified in this study are short-term phenomena or persist over a more extended period. Collectively, these limitations and future research directions emphasize the need for more nuanced, comprehensive, and longitudinal analyses to advance our understanding of the complex dynamics affecting international tourism, especially in the face of global events and policy changes.