Measuring Economic Resilience of Tourist Villages Overtime: An Analysis of Temporal Variations of Pre and Post the Covid-19 Pandemic

Nafiah Ariyani1, Akhmad Fauzi2, Ade Suherlan3

1Department of Management, Faculty of Economics and Business, Sahid University, Jakarta, Indonesia

2 Department of Resources and Environmental Economics, Faculty of Economics and Management, IPB University, Bogor, Indonesia

3 Department of Tourism, School of Social Sciences, University of Western Sydney, Australia

Corresponding Author: nafiah_ariyani@usahid.ac.id

Received: September 2023 | Revised: November 2023 | Accepted: December 2023

ABSTRACT

Tourist village plays an important role in rural development in Indonesia. Nevertheless, tourist village is also prone to external shocks such as national and global economic volatilities and recent public health events of the Covid-19 pandemic. This study attempts to analyze a temporal variations of tourist village economic resilience from pandemic shock in 24 tourist village destinations covering the period of 2019-2022 in Indonesia. A synthetic composite index of the Adjusted Mazziotta-Pareto Index (AMPI) was used to measure resilience, followed by clustering analysis to determine the typology of the resilience. The resilience index was composed of capacity and performance dimension related to resilience. The results show that most villages were severely affected in the first year of Covid-19, yet they recovered afterward, as indicated by positive differences in the AMPI index before and after Covid-19. This result shows that tourist villages in Indonesia have a tendency of strong capacity and performance to recover from the pandemic shock. The economic components of the capacity and performance were able to readjust after the pandemic indicating that these components are relatively adaptable to the shocks. The indicator that has the most significant influence on the typology of resilience in the performance dimension is the number of visitors. Meanwhile, the Development Village Index (DVI) indicator is the most significant influence on the capacity dimension.

Keywords: tourist village; economic resilience; Adjusted Mazziotta-Pareto Index (AMPI); temporal

variations; pandemic Covid-19

JEL classification: Z320, R580

How to Cite: Ariyani, N., Fauzi, A., Suherlan, A. (2023). Measuring Economic Resilience of Tourist Villages Overtime: An Analysis of Temporal Variations of Pre and Post the Covid-19 Pandemic, 24(1), 233-255. doi:https://doi.org/10.23917/jep.v24i2.23036

DOI: https://doi.org/10.23917/jep.v24i2.23036

INTRODUCTION

Rural tourism is becoming a worldwide trend due to its ability to encourage and provide economic, social, and environmental benefits. Amghani M. et al. (2016); Bayrak (2022); Jamini & Dehghani (2022); Shi et al. (2022); Liu et al. (2023); Huang et al. (2023); Stepanova et al. (2023), stated rural tourism is a critical dimension and new kinetic energy in revitalizing the development of rural regions. Rural tourism is a vector of sustainable

development capable of generating employment and income creation, combating rural exodus, becoming a socio-economic networking proposal, saving and enhancing cultural and natural heritage, and improving the quality of life for residents (Rodrigues et al., 2021). Shin et al., (2017) stated rural tourism is one of the most paramount factors in driving economic growth in rural areas and provides many direct benefits to residents. Rural tourism is considered a breakthrough in overcoming numerous problems in rural areas and encouraging the sustainable development of rural communities (Neumeier & Pollermann, 2014; Lv et al., 2021).

In Indonesia, rural tourism plays an essential role in providing employment, community empowerment, and strengthening entrepreneurship for local workers (Herawati, 2014) while helping preserve culture (Fatimah, 2015; Latif, 2018). From time to time, rural tourism in Indonesia has grown steadily, marked by the emergence of rural tourism destinations spread across various regions. Since 2021 by the Coordinating Ministry for Economic Affairs, rural tourism has been institutionalized as tourist villages and has been determined to be the direction for rural tourism development. This provision aims to encourage increased economic growth and peoples welfare, eradicate poverty, overcome unemployment, preserve nature and the environment, and promote rural culture (Ariyani et al., 2022) The number of tourist villages until 2019 has reached 1,831 (Ariyani & Fauzi, 2023).

However, the Covid-19 outbreak, which was

first detected in Wuhan, China, at the end of December 2019 (Rahmayani et al., 2021), has had a significant impact on rural tourism and tourist villages in Indonesia. The Covid-19 pandemic is the largest in history and its impact is evenly distributed throughout the world, both developing and developed countries, none of which were immune from the shock (Retnasih & Herdianti, 2023). However, from mid-2019 to the end of 2021, rural tourism experienced a drastic decline as a result of the Covid-19 pandemic (Sasongko et al., 2022). Tourist numbers dropped, several tourist villages closed, managers income decreased, traders around tourist destinations lost their livelihoods, and rising unemployment occurred in tourist villages (Damanik et al., 2022). Data from the Central Statistics Agency during 2019-2023 shows a decrease in the percentage of domestic tourists visiting tourist villages as seen in Figure

1.   During the Covid-19 pandemic, 3,539 people who are operational officers from 70 villages have lost their jobs because the tourist villages closed their services (Raharjana & Anshori, 2022).

Figure 1. Percentage of Domestic Tourists Visiting Rural Tourism

Source: BPS, 2019-2022

Tourism is a sector that is very vulnerable to various shocks or disruptions (Gallego & Font, 2019). The vulnerability of the tourism sector is mainly caused by the easily damaged structure and function and the inability of the tourism system to adapt to disturbances quickly (Qin & Chen, 2022). The Covid-19 pandemic has been the biggest shock for tourism throughout the world (Gssling et al., 2020) and tourism is the sector most affected by this outbreak (Henseler et al., 2022; Marco-Lajara et al., 2022).

Nevertheless, it is important to note that not all tourist villages in Indonesia have been negatively affected. While some tourist villages had to temporarily close their operations during the pandemic, others continued to operate partially, and certain villages even saw development opportunities (Damanik et al., 2022). Wibowo & Hariadi (2022) stated the Covid-19 pandemic had a positive impact and created new opportunities in rural tourism by implementing the nature tourism concept.

Therefore, building resilience in tourism is a crucial factor and a new solution to managing sustainability (Higgins-Desbiolles, 2020; Feng et al., 2021; Hu et al., 2021; Ohe, 2022). Planning- based resilience is an effective alternative for developing sustainable tourism (Hall et al., 2017; Lew, 2014). Specifically, resilience is crucial to rural tourism sustainability and an essential variable in assisting the tourism sector in recovering from the effects of a pandemic (Pocinho et al., 2022).

Giacometti & Teras (2019) stated that although there is no consensus on a single definition of resilience. resilience is identified as the systems capacity to adapt toward challenges that threaten system function (Folke et al., 2010; Southwick et al., 2014). Proag (2014) Hosseini et al. (2016); and Heslinga et al. (2020) stated that resilience describes the ability of a system to work well when under pressure or the ability of a system to absorb and recover from the impact of disruptive events without fundamental changes in the function or structure of the system. Meanwhile, Rgibeau & Rockett (2013) defined resilience as the ability of an economy, society, organization, or individual to recover effectively from an unexpected shock.

Since the 21^st century, resilience has become increasingly widely accepted as a basic framework for understanding the world system in dealing with various (anthropogenic) disaster contexts, including its application in tourism (Cochrane, 2010). Resilience was first used in the field of tourism in the 90s (OHare & Barret, 1994), and gradually, studies of resilience expanded to rural tourism, including community resilience in rural tourism (Lew et al., 2016); tourism projects in rural land development (Shi et al., 2022); the impact of the tourism industry on the overall resilience capacity of a region (Ibanescu et al., 2022); rural tourism in Japan during the new normal (Ohe, 2022), and the impact of Covid-19 on rural area resilience (Yu et al., 2023).

Although the concept of resilience has developed in various fields, there is no measurement method agreement generally that can become a reference in policies for strengthening the resilience of rural tourism. This study aims to analyze the economic resilience of tourist villages in Indonesia in the face of the Covid-19 pandemic, determine the typology and economic resilience factors of tourist villages. The problem that will be answered in this research is: what is the level of resilience of tourist villages in Indonesia in facing the Covid-19 pandemic? What is the typology of tourist village resilience, and how do various variables influence the economic resilience of tourist villages? The research results will become a model for measuring the resilience of tourist villages and become a reference for developing resilient and sustainable tourist villages.

RESEARCH METHODS

1.1     Study Area and Data Source

The study was conducted in 24 tourist villages representing tourist villages located in Central Java, East Java, West Java, Special Region of Yogyakarta, and West Nusa Tenggara. The selection of these villages was based on several factors: the availability of data, they were suspected of being affected by the Covid-19 shock, and their presence was assessed as having impacted the economy and social the rural society. Table 1 provides an overview of the profiles of the 24 villages and their main tourist attractions.

Table 1. Profile of 24 Villages as Research Object

Source: https://jadesta.kemenparekraf.go.id/

This study uses secondary data in the form of documents of tourist village management. The data collection process uses the documentation observation method. To assess the resilience of these villages in facing the Covid-19 shock, data was collected from 2019 (before the Covid-19 pandemic) to 2022 (during and after the Covid-19 pandemic).

1.2     Analysis Method

This study aims to assess the resilience of tourist villages over time, specifically before and after the Covid-19 pandemic. By incorporating a time dimension and utilizing a composite index, this approach provides a unique perspective on the interplay between normalization, aggregation, and temporal factors (Bacchini et al., 2020). While previous research by (Frigerio et al., 2018) has explored the concept of vulnerability in relation to resilience, this study does not include spatial correlation analysis. This study focuses on examining the temporal variations in resilience levels across 24 tourism villages, dividing the study into three distinct periods: pre-crisis (2019- 2020), crisis (2020-2021), and post-crisis (2021- 2022). This stage follows the concept of crisis management, which consists of three steps: pre- crisis, crisis, and post-crisis (Coombs, 2023)

To measure economic resilience of tourist village is used a synthetic indicator known as the Adjusted Mazziotta-Pareto Index (AMPI) was developed by Mazziotta & Pareto (2016). AMPI is a composite index that allows data comparison across units and time, resulting in compatibility with the spatiotemporal approach. The choice of the AMPI method also corresponds to the characteristics of a tourism system that is complex, dynamic, and associated with many variables (Baggio, 2020; Lv et al., 2021) so that it cannot be measured by a single indicator (Mazziotta & Pareto, 2013; Mazziotta & Pareto, 2017; Scaccabarozzi et al., 2022).

The AMPI method started with normalizing data or indicators using the following formula:

where is the matrix of n rows containing unit analysis and m columns containing indicators, and and are the goalspots for indicator j. Such normalization is a refinement of the MPI method to appreciate absolute changes over time (Mazziotta & Pareto, 2104). The range of normalization is varied between 70 and 130. If we denote and as the mean and standard deviation of normalized value of unit, respectively, the generalized form of AMPI is given by the following equation:

Where represents the coefficient of variation of the unit i. The sign indicates whether the phenomenon to be measured is maximized (the higher, the better) or minimized (the lower, the better).

As stated earlier, the AMPI needs a goal spot to facilitate interpreting the results. A reference point of 100, which is the average of indicators in a given year, is used. The AMPI value higher or lower than this reference point indicates whether the unit being analyzed is progressing or regressing. In this case, it indicates whether the units are more resilient or vice versa. The procedure to set the goal spot is the following:

where

where and represents the maximum and the minimum of indicator across all periods and the reference value of indicator j (Mazziotta & Pareto, 2017).

Measuring resilience using the AMPI method goes through several stages, starting with selecting indicators for tourism village resilience. At this stage, because there is no unanimous consensus in terms of both definition and measurement (Stancǩovᠠ & Meleck, 2018; Martin, 2012; Mutra et al., 2020), the determination of economic resilience indicators in the study follows the following principles: must involve various dimensions/variables that are considered proxies of a phenomenon so that appropriate action can be taken (Proag, 2014) or part of it, may react adversely during the occurrence of a hazardous event. This concept of vulnerability implies a measure of risk associated with the physical, social and economic aspects and implications resulting from the systems ability to cope with the resulting event. Concepts of resilience take two broad forms, namely (1; the selection of non-neutral variables; each may represent a different aspect of resilience in reacting to shocks, depending on the territorial context (Compagnucci & Urso, 2021).

In this study, economic resilience tourist village indicators are compiled from two main components: capacity and performance dimension. Capacity relates to resources that are part of the tourist village system, which is measured through indicators: (1) capacity building (cbdg), (2) employees (emp): (3) Village Development Index (VDI). Meanwhile, performance is related to the results of the work of the tourist village during and after disturbances, which is measured through indicators: (1) tourist (trs) (2) income (inc), (3) cost (cst). Indicators of tourist village economic resilience are listed in Table 2.

Table 2. Resilience Indicators of Rural Tourism

In accordance with the AMPI stages, data on resilience indicators from 24 tourist villages is normalized first. Data normalization results are shown in Table 3-5.

Table 3. Normalized AMPI Indicator in 2019

Source: AMPI Analysis*) variables symbol

Table 4. Normalized AMPI Indicator in 2020

Source: AMPI Analysis*) variable symbol

Table 5. Normalized AMPI Indicator in 2021

Source: AMPI Analysis

*) variable symbol

Table 6. Normalized AMPI Indicator in 2022

Source: AMPI Analysis *) variable symbol

Futhermore, a typology of tourist village resilience will be presented in the 2019-2022, 2020-202, and 2019-2021 periods to describe the resilience characteristics of each tourist village. Determining the resilience typology of tourist villages uses the resilience trend matrix developed by Compagnucci & Urso (2021), which classifies resilience typologies into eight types (Table 7). This resilience trend matrix helps investigate how the use of different indicators will affect resilience measures and helps explore whether certain indicators are more appropriate for assessing the resilience of tourist village.

Table 7. Resilience Trends Schema

The resilience trend metric will analyze and compare the conditions of each village based on sequentialn variations of the resilience index (∆) during the 2019-2022 period, calculated as the geometric value of the resilience index through the following equation:

In equation [1] we consider the ratio between number of capacity building activities (Σcbdg) at the end of the period (t+k) on the value the capacity building activities (t), we raise the result to the power of one divided by the period length (k) and we subtract one from the subsequent result. In equations [2], [3] to equation [6] according to the number of variables, we perform the same calculations using the variables number of tourists (Σ trs), total income (Σ inc), number of employees (Σ emp), and total costs (Σ cost) and the value of the Village Development Index (VDI)2.

RESULTS AND DISCUSSION

4.1         Economic Resilience Tourism Village Index

Table 8 shows that one year after the pandemic, all tourist villages suffered from a lower performance, as indicated by decreases in their AMPI scores. The delta (Δ) score from 2019- 2020 showed changes in the resilience index of 24 tourist villages in 2019-2020. This period was the most critical period to hit the tourism village. All 24 villages studied experienced a decreased resilience index (negative delta AMPI). The impact, however, affected villages differently. Some experienced a slight reduction in their resilience score, while others were significantly affected.

Table 8. Comparison of Tourism Village Resilience Index Prior to During the Covid-19 Pandemic

Source: AMPI Analysis

Table 9. Comparison of Tourism Village Resilience Index After the Covid-19 Pandemic

Tourist AMPI

Source: AMPI Analysis

During 2019-2020, the Pentingsari tourist village experienced the most significant plunge in the resilience index compared with other villages. The governments travel ban policy resulted in a degradation dramatically in the number of tourist arrivals. Therefore, the tourist village, offering rural and agricultural cultural attractions, closed its services rather than bearing costs disproportionate to its income. However, several tourist villages remain open despite the number of visitors and their income decreasing sharply. They kept their activities to maintain their status of tourist village by engaging in other activities such as training their staff or maintaining facilities.

Compared with 2019-2021 and 2020-2021, they have depicted extreme differences in the tourist village resilience index. From 2020 to 2021 (one year after the Covid-19 pandemic), almost all villages, except Tambaksari village, showed a remarkable recovery evidenced by positive changes in their AMPI scores (delta AMPI positive). These conditions indicate that during this period, the tourist village has adapted to the shocks caused by Covid-19. The resilience index generally increases because tourist villages implemented health protocols in the tourism sector (Cleanliness Healthy Safety Environment or CHSE Protocol) and conducted training related to services during the new normal period, and several villages modified tourist destinations in digital formats by offering travel packages. Digital tourism was developed to target visitors who could not visit in person or were still afraid of catching Covid-19. In addition, the recovery was also supported by the government policy that gradually reopened tourism activities. As a result of this policy, the number of visitors gradually increased.

As seen from Table 8, during 2020-2021 (Δ2), the most significant increase in the resilience index occurred in Karangrejo village. Karangrejo village is a community-based tourist village offering rural and agricultural cultural attractions. With full support from the community, especially in providing lodging facilities and implementing the CHSE protocol to ensure visitors health during their tours, this village raised the number of visitors, followed by increases in other resilience indicators.

Table 9 compares AMPI resilience scores before, during, and after Covid-19 (i.e., from 2019 to 2022, using 2022 as a goal spot). As can be seen from Table 9, the overall AMPI scores showed significant climbs toward 2022, indicating a strong recovery trend from the shock. The most significant rise in AMPI scores occurred in 2021- 2022, when all villages experienced an increase in the index, reaching double digits. This condition illustrated that tourist villages have adapted to the Covid-19 shock and can be fully recovered. One of the villages that showed strong resilience, indicated by the highest positive value of delta (Δ), was Karangrejo. One of the reasons for the success of Karangrejo village was its ability to build partnerships with several parties, especially with State-Owned Enterprises, by forming the Village Economic Center (known locally as Balkondes). This is in accordance with research Fafurida (2017) Public and private partnerships can increase the economic growth of the tourism sector.

In addition, the Tourism Awareness Group

(known as Pokdarwis) has played a pivotal role in strengthening resilience to the Covid-19 shock. The collaboration of the two institutions is influential in developing creativity and encouraging visitor arrival. Karangrejo also pointed out a high level of community involvement in providing homestays and other supporting facilities that have been adapted to health protocols, which have increased the performance of this tourist village, both in terms of the number of visitors and income. This condition is evidence of the successful implementation of community- based tourism, which has successfully dealt with external shocks. The success of the Karangrejo tourist village has earned it an award from the Indonesian government as a sustainable tourist village.

4.2        Typology of Economic Resilience Tourist Village Index

The results of the AMPI analysis (Table 8-9) were used to determine and analyze the typology of tourist village resilience. Futhermore, based on the trend resilience scheme (Table 7), tourist villages are grouped based on their resilience trend (Table 10). Table 10 shows that at the start of the pandemic (2019-2020), all villages showed a negative AMPI index. However, in 2020-2021, 12 villages (50%) could adjust so that their AMPI index values were positive. These conditions continue, so all villages showed positive resilience in 2021-2022.

Based on the trend of resistance variation during 2019-2020; 2020-2021; 2021-2022, as

many as 50% of the total villages (12 tourist villages) are included in the turnaround category (- - +), meaning that at the beginning and during the pandemic they were not able to survive, but then recovered after the pandemic ended. Meanwhile, 12 other villages were shaken by the pandemic in the initial period (2019-2020) and soon recovered in the following period (2020-2021; 2021-2022), so they are classified as villages with a positive jolt typology (- + +). If seen per region, the typology trend of the tourist village typology is more diverse (Table 11). In Central Java, five villages (50% of the tourist villages analyzed lead to a turnaround typology, while five other villages are on a positive jolt typology. In East Java, all observed villages (2 villages) lead to a turnaround typology. In Yogyakarta (DIY), conditions are more diverse; one village (16.67%) leads to a systemic declining typology, three villages (50%) lead to a turnaround typology, and three villages (50%) lead to a positive jolt typology. Meanwhile, in West Java, there is one village (50%) towards a turnaround, and one village (50%) toward a positive jolt. In West Nusa Tenggara (NTB), one village (25%) leads to a turnaround typology, and three villages (75%) lead to a positive jolt.

An analysis per indicator is carried out to find out the indicators that determine the resilience of tourist villages. This analysis will find out how different indicators different effects the resilience of tourist villages. The analysis uses the data in Tables 3-6, and based on the resilience trend scheme in Table 7, the analysis results are shown in Tables 12 and 13.

Table 12 shows that if the tourist village resilience typology is based on indicators of the number of visitors, then there are seventeen villages (70.8%) leading to the standard resilience typology (+ - +), and seven villages (29.2%) leading to the resilience typology (+ + +). If the resilience typology is based on total income indicators, there are three villages (16%) lead to the standard resilience typology (+ - +), sixteen tourist villages (66.6%) lead to turnaround typology (- - +), three villages village (12.5%) leads to a typology of resistance (+ + +), one tourist village (4.16%) leads to a typology of systemic decline (- - -), and one village (4.16%) leads to positive jolt typology (- + +). Furthermore, if resilience is based on cost indicators, there are eight tourist villages (33.3%) that lead to a turnaround typology (- - +), fifteen villages (62.5%) lead to a resistance typology (+ + +), one village (4.16%) leads to the standard resilience typology, and one village (4.16%) leads to the positive jolt typology (- + +). This analysis shows that in the dimensions of resilience performance, the indicator that has the greatest influence on the typology of resilience in tourist villages is the number of visitors. Meanwhile, cost is an indicator that has the least influence on the resilience of a tourist village.

Table 13 presents a typology of tourist village resilience based on capacity dimensions. If the resilience typology is based on the number of employee indicators, there are five tourism villages (20.8%) that lead to a turnaround typology (- - +), fourteen villages (58.3%) lead to a resistance typology (+ + +), three villages (12.5%) leads to a standard resilience typology (+ - +), two villages (0.08%) leads to a positive jolt typology (- + + . If resilienc is based on the Development Village Index (DVI) indicator, there are fiveteen villages (62,5%) leading to a systemic declining typology (- - -). four villages (16,6%) leading to a counter-cyclical typology, (- + -), three tourist villages (12.5%) lead to a typology of lagged shocks (+ + -), two tourist villages (0.08%) lead to a typology of turnaround (- - +). Furthermore, if the resilience typology is based on capacity-building indicators, eleven villages (45.837%) lead to a turnaround typology (- - +), five tourist villages (16.6%) lead to a standard resilience typology (+ - +), nine tourist villages (16.6%) leads to the resistance typology (+ + +). This analysis shows that in the dimensions of resilience capacity, Development Village Index (DVI) indicator has the greatest influence on the typology of resilience in tourist villages.

Table 10. Resilience Trends per Region Prior to During the Covid-19 Pandemic

Source: AMPI Analysis

Table 11. Resilience Trends per Region

Source: AMPI Analysis

Table 12. Trends and Typology of Economic Resilience of Tourist Villages per Region on Performace Dimension During the Covid-19 Pandemic