Dissecting the Economics of Tourism and Its Influencing Variables—Facts on the National Capital City (IKN)

Abstract

The field of tourism economics has consistently attracted big attention from scholars across various countries. Tourism is inherently linked to economic aspects. Concurrently, Indonesia has relocated its Ibu Kota Negara/National Capital City, now named “IKN”, from Jakarta to East Kalimantan. In addition to extensive public infrastructure development, the Indonesian government is also working to revitalize the tourism sector in IKN. To assess the economic feasibility of this sector, an in-depth study is necessary. This research aims to examine labor absorption, tourist visits, and economic growth as indicators of successful tourism economic performance. It also analyzes the variables that influence these indicators, including (1) wages, (2) occupancy rates, (3) room rates, (4) food and beverage facilities, (5) inflation, (6) hotel and lodging taxes, (7) restaurant and eating-house taxes, and (8) investment. The regression testing method employs Ordinary Least Squares (OLS). According to the data analyzed from 2013 to 2024, the authors identified three main points: First, tourist visits and inflation have positive and significant impacts on labor absorption. Second, labor absorption, wages, occupancy rates, economic growth, and investment positively and significantly influence tourist visits. Third, tourist visits, room rates, food and beverage facilities, and inflation have positive and significant effects on economic growth. The implications of this research can be enlightening for regulators and future initiatives. This is particularly important for guiding further empirical investigations and policy planning aimed at accelerating economic development in the tourism sector.

1. Introduction

According to Nurjanana et al. (2025) and Syaban and Appiah-Opoku (2023), the development of the IKN in East Kalimantan, as outlined by the Indonesian government in Law Number 3 of 2022, encompasses several strategic dimensions, including political, economic, social, and environmental. The five primary objectives of the IKN development are as follows: First, to promote equitable development and national economic growth, the relocation of the capital city from Jakarta to Kalimantan aims to alleviate the burden on Java Island and facilitate more balanced economic growth, particularly in Central and Eastern Indonesia. Second, to reduce the strain on Jakarta, as Jakarta currently faces numerous challenges, including congestion, pollution, flooding, and high population density, the transfer of the government center is anticipated to alleviate pressure on the city’s infrastructure and the environment. Third, to create a sustainable city, IKN is designed to be a smart, environmentally friendly, and livable urban area, prioritizing digital technology, renewable energy, and sustainability-oriented urban planning. Fourth, IKN serves as an inclusive and representative symbol of national identity. It is expected to embody the spirit of Indonesian unity and diversity while also representing progress and an inclusive national identity. Fifth, it promotes government efficiency and effectiveness. By conceptualizing spatial planning and infrastructure from the outset, the government aims to enhance the efficiency and integration of bureaucratic processes and public services.

So far, although the development of the IKN has not been fully completed, the essence of this transfer has become a benchmark for the success of the Indonesian government (Negara & Rebecca, 2024). The six transition periods that form the basis for the development of the IKN are as follows: First, in 2017, the government began studying the relocation of the capital city from Jakarta, taking into account the burden on Jakarta and the imbalance of the development between the regions. Second, in 2019, the then-President of Indonesia, Joko Widodo, officially announced the plan to move IKN to East Kalimantan, specifically in the Penajam Paser Utara (PPU) area. Third, during 2020–2021, the government prepared the legal foundation and master plan for the development of the IKN, despite the coronavirus disease 2019 (COVID-19) pandemic slowing down the planning process. Fourth, in 2022, the Dewan Perwakilan Rakyat/House of Representatives (DPR) of the Republic of Indonesia passed Law Number 3 of 2022, concerning the IKN. Fifth, from 2023 to 2024, the physical development phase commenced, which includes the construction of essential infrastructure such as roads, state palaces, ministry offices, and housing for government employees. Sixth, in 2025 and beyond, the government will prioritize early-stage transfers for government employees and state institutions. Development will continue until IKN becomes an independent, sustainable, and fully functional city.

Furthermore, in addition to its main concern of economic equality, the Indonesian government has also prioritized the development of the tourism sector in IKN, aiming to establish it as a sustainable modern ecotourism destination (Cahyoputra & Febrianna, 2024). Various tourist attractions are being developed to draw both domestic and international visitors while supporting the local economy (Fitriadi et al., 2023; R. Rahmawati et al., 2023; Zulfikri et al., 2023). Bellboy (2024) identifies five tourist destination projects being developed in IKN: (1) Taman Safari Nusantara, (2) Glamping and Orchid Center, (3) Miniature Tropical Rainforest, (4) IKN Forest Terrace, and (5) Swissôtel Nusantara and Mall Duty Free Nusantara. Basically, IKN is designed as a sustainable forest city that harmoniously integrates nature, culture, and technology. The development of the tourism sector is expected to create jobs, enhance community income, and showcase East Kalimantan’s biodiversity to the world.

Over the past decade, tourism has significantly driven the economies of many countries (Elgin & Elveren, 2024; Li et al., 2018). In emerging markets, the tourism sector serves as a key driver of economic growth (Khan et al., 2020; Z. Sun et al., 2025). For instance, in Indonesia, the tourism sector makes a substantial contribution to the national economy (Achmad & Wiratmadja, 2024). Data released by the Ministry of Tourism and Creative Economy of Indonesia (2025) indicate that the tourism sector contributed from approximately 4.01% to 4.5% of Indonesia’s Gross Domestic Product (GDP) in 2024, reflecting an increase from 3.9% in 2023. Moreover, the tourism sector played a crucial role in shaping the Gross Regional Domestic Product (GRDP) of East Kalimantan Province in 2024, reaching 10.41%. In comparison, the contribution of the tourism sector to the GRDP of PPU Regency, which serves as the center of IKN, was 14.85% in 2024. This indicates that tourism-driven economic growth in IKN is more pronounced than that at both the provincial and national levels (BPS-Statistics of East Kalimantan Province, 2025; BPS-Statistics of Penajam Paser Utara Regency, 2025).

The tourism sector in IKN has the potential to drive commodity production and related economic activities in an inclusive manner. In addition, the region’s economy is highly dependent on the tourism sector. From a macroeconomic perspective, the development of the tourism industry is intrinsically linked to the tourism economy. Tourism economic affairs encompass the economic aspects related to tourism activities, including how the tourism sector contributes to the economy of a region or country. In general, tourism economics comprises five components: First, revenue generated from tourists, which includes the money spent by both local and foreign visitors on accommodation, food, transportation, souvenirs, entrance tickets, and other related expenses. Second, job creation, as tourism generates employment opportunities across various sectors, such as hotels, restaurants, tour guiding, transportation, and local creative industries. Third, investment and infrastructure development, as the growth of the tourism sector necessitates the enhancement of roads, airports, ports, and other public facilities. Fourth, the contribution to economic growth: The tourism sector often serves as one of the vital pillars of the national or regional economy. Fifth, there are indirect economic impacts, such as increased demand for local products—like handicrafts and specialty foods—which support local farmers and artisans. Therefore, the tourism economy encompasses not only the number of tourists but also the broader effects of tourism on the economy as a whole.

Ideally, development should not be evaluated solely based on a single attribute; rather, it must be balanced with coherent transformation. The intersecting issue of the tourism economy in IKN serves as both an initiative to verify that tourism development yields tangible economic benefits for local communities rather than solely for large investors (Rivandi & Pramono, 2024; Syaban & Appiah-Opoku, 2024). A critical perspective is essential to assess the social, environmental, and economic inequalities that may arise from the industrialization of tourism at the regional level (Hackbarth & de Vries, 2021; Sukmana & Azizah, 2024; Zamfir & Corbos, 2015). This study also functions as a monitoring tool for policy direction, enabling stakeholders in the tourism sector to prepare for the monumental explosion of change. In response to this landscape, the uniqueness of this study lies in its examination of how the relocation of a national capital can positively influence tourism development while simultaneously fostering local prosperity. To illustrate this, historical examples from two countries that have successfully relocated their capital cities and experienced significant impacts on their local tourism sectors are reconstructed. The first example is Kazakhstan. In 1997, Kazakhstan moved its capital from Almaty to Akmolinsk (later renamed Astana, now Nur-Sultan). This relocation aimed to bring the center of the government closer to the northern regions and reduce dependence on Almaty. Although initially controversial, the development of modern infrastructure in the new capital attracted both tourists and investors, thereby contributing to the growth of the local tourism sector (Berdenov et al., 2024). The second example is Poland, which relocated its capital from Kraków to Warsaw in 1596. This move enhanced Warsaw’s status as a political and economic center, further bolstering the city’s appeal to tourists. This suggests that a city’s designation as a national capital can enhance its tourism potential through symbolism and the concentration of administrative and business activities (Przybyła et al., 2019).

Not many studies have explored the economic affairs of tourism and the factors that influence it within IKN. Explicitly, this exploration is closely tied to various elements that can determine the progress of the tourism economy. It is noted that a comprehensive review of the economic model of tourism, as presented in various scientific literature works, has not yet been conducted. First, the relationships among the influences of tourist arrivals, hotel accommodations, hotel costs, restaurant facilities, inflation, taxes, investment, and economic growth on labor are linear. On this point, Dogru et al. (2024), Dorta-González and González-Betancor (2021), Huseynli (2024), Kalantzi et al. (2016), Mulet-Forteza et al. (2024), P. C. Nguyen et al. (2025), Wang and Tziamalis (2023), and Zhao et al. (2023) mentioned that there is positive correlations among the increases in tourist numbers, wage levels, economic growth, occupancy rates, room rates, revenue in the food and beverage subsector, inflation within the tourism sector, tax distribution in the hotel and restaurant industry, and the realization of private investment in job creation. Second, there are linear relationships among labor costs, hotel accommodation and rental expenses, restaurant facilities, inflation, taxes, investment, and economic growth in relation to tourist arrivals. Aburumman et al. (2018), Dorta-González and González-Betancor (2021), Cárdenas-García et al. (2024), Chen (2011), Elders (2025), Firgo (2025), Khalid et al. (2019), and A. Rahmawati et al. (2024) have explained that factors such as wage income, restaurant service taxes, lodging demand, investment in the tourism sector, hotel occupancy rates, fluctuations in hotel room rates, food and beverage service quality, inflation crises, and economic growth performance serve as significant drivers of increased tourist arrivals. Third, there are linear relationships among tourist arrivals, hotel accommodations, rental costs, restaurant facilities, inflation, taxes, investment, and labor in relation to economic growth. Aida et al. (2024), Azizurrohman et al. (2021), Kurniawati and Fathoni (2023), İlban and Liceli (2022), Philander and Roe (2013), Samila et al. (2025), Sarkhanov and Baghirov (2023), Simorangkir et al. (2024), and Xiong et al. (2022) have indicated that the length of stay, influenced by tourist arrivals, hotel occupancy rates, the number of workers, labor costs, the food and beverage industry, the Consumer Price Index (CPI), restaurant and hotel taxes, and the total investment, generates spillover effects on economic growth performance within the tourism sector. The research synthesis incorporates variables that impact labor absorption, tourist visits, and economic growth.

For this reason, the authors aim to investigate the economic dynamics of tourism in IKN according to the influencing variables. The research variables focus on the scale of the tourism economy, including (1) labor absorption, (2) tourist visits, (3) wages, (4) occupancy rates, (5) room rates, (6) food and beverage facilities, (7) inflation, (8) hotel and lodging taxes, (9) restaurant and eating-house taxes, (10) investment, and (11) economic growth. The variables in the tourism economy are categorized into three analytical models. The first model elaborates the relationships between tourist visits, wages, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, investment, and economic growth in relation to labor absorption. The second model calibrates the relationships between labor absorption, wages, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, investment, and economic growth concerning tourist visits. The third model identifies the relationships between labor absorption, tourist visits, wages, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, and investment in relation to economic growth. This research is expected to contribute both theoretically and practically. First, it can enrich academic insights for similar studies by incorporating relevant variables in the analysis of tourism economics. Second, the findings can assist the government in formulating and implementing policies related to the tourism sector.

2. Materials and Methodology

2.1. Approach and Data

This research employs a quantitative approach, utilizing numerical and statistical data to empirically test hypotheses and draw conclusions regarding the estimated variables. The data used in this research are secondary, sourced from two government agencies: BPS–Statistics of East Kalimantan Province and BPS–Statistics of Penajam Paser Utara Regency. Data collection was conducted through extraction and tabulation methods. Operationally, the research data are of the annual type. The research specifically focuses on PPU Regency as the core area of the IKN, with the analysis covering the period from 2013 to 2024.

2.2. Constructed Variables

This study attempts to identify the variables associated with the tourism economy. In particular, three causal models are proposed as follows: first, the effects of tourist visits, wages, economic growth, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, and investment on labor absorption; second, the effects of investment, inflation, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant and eating-house taxes, economic growth, and wages on tourist visits; third, the impacts of tourist visits, wages, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant and eating-house taxes, inflation, and investment on economic growth.

Table 1. Research variables.

Variable Name	Data	Conceptual Definition	Parameters
Labor absorption	Tingkat Partisipasi Angkatan Kerja/Labor Force Participation Rate (TPAK) in the tourism sector	Percentage of the labor force in the tourism sector, which encompasses accommodation, food and beverage services, tourist transportation, travel agencies, and cultural and recreational activities, as compared to the total working-age population (aged 15 years and older)	Percentage (%)
Tourist visits	Number of tourist visits	Average number of domestic and foreign tourists entering and visiting destinations and recreations	Tourists (person)
Wages	Upah Minimum Kabupaten/District Minimum Wage (UMK)	The minimum wage standard officially set by the district government that must be paid by employers to workers or laborers as the lowest monthly wage	Indonesian Rupiah (IDR)
Occupancy rates	Number of hotel (non-star) and lodging guests	The number of tourists who fill and rent rooms from either hotels (non-star) or lodgings, calculated per year	Tourists (person
Room rates	Average stay rate per room	Average rental rate per year set by (non-star) hotels and lodgings	Indonesian Rupiah (IDR)
Food and beverage facilities	Restaurants, cafes, and eateries	Number of restaurants, cafes, and eateries	Business unit
Inflation	Inflation rate	Percentage of the average increase in the prices of goods and services in the tourism sector per year, based on the CPI of the nearest region (in this case, Balikpapan City) as a reference, representation, and proxy	Percentage (%)
Hotel and lodging taxes	Hotel and lodging tax revenue	Realization of local tax revenue from hotels (non-star) and lodgings	Indonesian Rupiah (IDR)
Restaurant and eating-house taxes	Restaurant and eatery receipts	Realization of local tax revenue from restaurants and eateries	Indonesian Rupiah (IDR)
Investment	Investment credit in the tourism sector	Investment loans disbursed by public and private banks to the tourism sector	Indonesian Rupiah (IDR million)
Economic growth	GRDP growth in the tourism sector	Economic growth at constant prices in the tourism sector	Percentage (%)

Notes: PPU Regency does not yet have a star hotel.

, below, presents the research variables.

This study categorizes variables into two types: independent variables and dependent variables. In the first model, the independent variables include tourist visits, wages, economic growth, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, and restaurant and eating-house taxes, with labor absorption serving as the dependent variable. In the second model, the independent variables consist of investment, inflation, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant and eating-house taxes, economic growth, and wages, while tourist visits are the dependent variable. Finally, in the third model, the independent variables are represented by tourist visits, wages, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant taxes, inflation, and investment, with economic growth as the dependent variable.

2.3. Analytical Tools and Econometric Models

The research analysis tool employs OLS. This technique was adopted in linear regression to predict the relationships among the three models mentioned in the previous subsections. Subsequently, the secondary data were processed using Statistical Package for the Social Sciences (SPSS) version 22 The fundamental equations for the three research models are written as follows:

$${LA}_{it}=f_1({TVs}_{it},{Wgs}_{it},{EG}_{it},{ORs}_{it},{RRs}_{it},{FBF}_{it},{Inf}_{it},{HLT}_{it},{REHTs}_{it},{Inv}_{it})$$

(1)

$${TVs}_{it}=f_2({Inv}_{it},{Inf}_{it},{ORs}_{it},{RRs}_{it},{FBF}_{it},{LA}_{it},{HLT}_{it},{REHTs}_{it},{EG}_{it},{Wgs}_{it})$$

(2)

$${EG}_{it}=f_3({TVs}_{it},{Wgs}_{it},{ORs}_{it},{RRs}_{it},{FBF}_{it},{LA}_{it},{HLT}_{it},{REHTs}_{it},{Inf}_{it},{Inv}_{it})$$

(3)

From each of the function equations above, an econometric model has been derived as follows:

$$\begin{array}{l}{LA}_{it}=\alpha +{\beta }_1\left({TVs}_{it}\right)+{\beta }_2{(Wgs}_{it})+{\beta }_3\left({EG}_{it}\right)+{\beta }_4\left({ORs}_{it}\right)+{\beta }_5{(RRs}_{it})\\ +{\beta }_6\left({FBF}_{it}\right)+{\beta }_7\left({Inf}_{it}\right)+{{\beta }_8(HLT}_{it})+{\beta }_9\left({REHTs}_{it}\right)\\ +{\beta }_{10}\left({Inv}_{it}\right)+{\mu }_{it}\end{array}$$

(4)

$$\begin{array}{l}{TVs}_{it}=\alpha +{\beta }_1\left({Inv}_{it}\right)+{\beta }_2{(Inf}_{it})+{\beta }_3({ORs}_{it})+{\beta }_4({RRs}_{it})+{\beta }_5({FBF}_{it})\\ +{{\beta }_6(LA}_{it})+{{\beta }_7(HLT}_{it})+{\beta }_8\left({REHTs}_{it}\right)+{\beta }_9\left({EG}_{it}\right)\\ +{{\beta }_{10}(Wgs}_{it})+{\mu }_{it}\end{array}$$

(5)

$$\begin{array}{l}{EG}_{it}=\alpha +{\beta }_1\left({TVs}_{it}\right)+{\beta }_2\left({Wgs}_{it}\right)+{\beta }_3\left({ORs}_{it}\right)+{{\beta }_4(RRs}_{it})+{\beta }_5\left({FBF}_{it}\right)\\ +{\beta }_6\left({LA}_{it}\right)+{{\beta }_7(HLT}_{it})+{\beta }_8{(REHTs}_{it})+{\beta }_9\left({Inf}_{it}\right)\\ +{\beta }_{10}\left({Inv}_{it}\right)+{\mu }_{it}\end{array}$$

(6)

where f = function equation, _it = entities observed at a certain time, α = constant, β = regression coefficient, µ = random residuals, LA = labor absorption, TVs = tourist visits, Wgs = wages, ORs = occupancy rates, RRs = room rates, FBF = food and beverage facilities, Inf = inflation, HLT = hotel and lodging taxes, REHTs = restaurant and eating-house taxes, Inv = investment, and EG = economic growth.

The selection of the three OLS models presented above is based on prior research that emphasizes the impacts of various factors, including wages, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, and investment on labor absorption, tourist visits, and economic growth (e.g., Aburumman et al., 2018; Aida et al., 2024; Azizurrohman et al., 2021; Cárdenas-García et al., 2024; Chen, 2011; Dogru et al., 2024; Dorta-González & González-Betancor, 2021; Elders, 2025; Firgo, 2025; Huseynli, 2024; İlban & Liceli, 2022; Kalantzi et al., 2016; Khalid et al., 2019; Kurniawati & Fathoni, 2023; Mulet-Forteza et al., 2024; P. C. Nguyen et al., 2025; Philander & Roe, 2013; A. Rahmawati et al., 2024; Samila et al., 2025; Sarkhanov & Baghirov, 2023; Simorangkir et al., 2024; Xiong et al., 2022; Wang & Tziamalis, 2023; Zhao et al., 2023). The formal hypothesis sequence is structured according to the following three models:

• First model

• Null hypothesis (H₀): Tourist visits, wages, economic growth, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, and investment significantly influence labor absorption;
• Alternative hypothesis (H_a): Tourist visits, wages, economic growth, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, and investment have insignificant influences on labor absorption.

• Second model

• Null hypothesis (H₀): Investment, inflation, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant and eating-house taxes, economic growth, and wages significantly influence tourist visits;
• Alternative hypothesis (H_a): Investment, inflation, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant and eating-house taxes, economic growth, and wages have insignificant influences on tourist visits.

• Third model

• Null hypothesis (H₀): Tourist visits, wages, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant and eating-house taxes, inflation, and investment significantly influence economic growth;
• Alternative hypothesis (H_a): Tourist visits, wages, occupancy rates, room rates, food and beverage facilities, labor absorption, hotel and lodging taxes, restaurant and eating-house taxes, inflation, and investment have insignificant influences economic growth.

3. Results

3.1. Descriptive Statistics

Mathematically, each variable possesses distinct parameters, resulting in variations in the values of the descriptive statistics. However, some variables share the same parameters. Labor absorption, inflation, and economic growth are all expressed as percentages. In contrast, tourist visits and occupancy rates are measured in terms of individuals. Wages, room rates, hotel and lodging taxes, as well as restaurant and eating-house taxes, are denominated in IDR. This differs slightly from the previous four variables, as investment is quantified in billions of IDRs. Then, food and beverage facilities are described by business units.

In

Table 2. Results of descriptive statistics.

	Minimum	Maximum	Mean	Std. Error
Labor absorption	14.14	20.18	17.62	0.49
Tourist visits	18,692	236,953	76,688.17	22,001.99
Wages	1,903,262	3,715,817	2,885,245.75	175,413.76
Occupancy rates	8083	52,502	17,739.75	4225.5
Room rates	225,000	400,000	293,750	14,798.2
Food and beverage facilities	43	91	68.33	4.53
Inflation	0.65	8.56	3.94	0.68
Hotel and lodging taxes	105,652,857	790,407,664	223,393,939.58	56,400,522.99
Restaurant and eating-house taxes	1,653,106,658	5,114,157,408	2,695,859,227.25	276,753,825.52
Investment	246,251	843,181	486,865.58	49,868.18
Economic growth	–3.47	14.85	6.4	1.49
Obs.	132	132	132	132

, four items of descriptive statistics are summarized: (1) minimum, (2) maximum, (3) mean, and (4) standard error. The descriptive statistical results for the variable groups with the same parameters are organized based on the SPSS output. Notably, the descriptive statistics emphasize the mean value, as it reflects the central tendency of all the observed data. First, the mean labor absorption rate is 17.62%, the mean inflation rate is 3.94%, and the mean economic growth rate is 6.4%. Second, tourist visits have a mean of 76,688.17 tourists, while occupancy rates have a mean of 17,739.75 tourists. Third, the mean wage reached IDR 2,885,245.75, the mean room rate was IDR 293,750, the mean hotel and lodging taxes amounted to IDR 223,393,939.58, and the mean restaurant and eating-house taxes reached IDR 2,695,859,227.25. The mean investment totaled IDR 486,865.58 million. Fourth, food and beverage facilities have a mean of 68.33 business units.

Broadly speaking, Table 2, above, indicates that variables with varying mean values exhibit different statistical patterns from 2013 to 2024. The labor absorption data reveal that only approximately 17.62% of the total working-age population is employed or actively seeking employment in the tourism sector. This means that out of every one hundred individuals of working age, around 18 are directly engaged in the economic activities of the tourism industry, including both those currently employed and those looking for work in this field. The mean figure of 17.62% also implies that a relatively small portion of the productive-age workforce is either interested in or absorbed by the tourism sector. Consequently, this sector has not yet emerged as the dominant force in the employment structure of the IKN. On average, approximately 76,688.17 tourists visited the region over the past 12 periods. For one local tourist destination, the visitation capacity is relatively high, driven by the effectiveness of tourism promotion strategies, accessibility, and the attractiveness of the destination in stimulating tourist visits.

Wages averaged IDR 2,885,245.75, representing the arithmetic mean of the UMK across all the sectors, including tourism. In summary, the policy for determining the UMK is based on the cost of living required to meet basic monthly needs throughout the year. Regions with higher UMKs may attract workers; however, this can pose a burden for employers in the tourism sector. The average occupancy rate of 17,739.75 tourists per year suggests that each hotel and lodging unit accommodates approximately 17,739.75 tourists annually. This statistic can serve as a benchmark for evaluating the performance of locations, especially in tourism within IKN, regarding the provision of hotels and inns. Occupancy performance is relatively high, as facilities such as hotels and inns typically have a limited number of rooms. This observation aligns with the mean room rate of IDR 293,750. The room rate data reflect the average rental price per room over the course of a year, as established by hotel and lodging services. The room rate data indicate tenant turnover and are considered as high due to the limited number of units available in hotel and lodging establishments. In addition to room rates, food and beverage facilities are important considerations for tourists. Notably, the average number of food and beverage facilities is 68.33 business units. This figure is significant, given the area’s demographics, population, and the purchasing power of the residents surrounding IKN. The presence of these facilities presents promising prospects and is strategically advantageous amid the ongoing development of tourism.

Locally and nationally, an inflation rate of below 5% is generally regarded as moderate (Glawe & Wagner, 2024). The average inflation rate in IKN is 3.94%, indicating that it remains under control and reflects a balance between economic growth and price stability. A moderate inflation rate suggests that people’s purchasing power is not significantly diminished, provided that incomes also increase over time. Furthermore, moderate inflation plays a crucial role in the macroeconomy, as long as it is not accompanied by extreme fluctuations. As the hotel and lodging services industry, along with food and beverage facilities, experiences positive growth, it significantly impacts the local revenue. One of the key contributions is the imposition of taxes. In practice, local tax revenues generated from hotel and lodging taxes, as well as restaurant and eating-house taxes, have shown promising results, with average revenues reaching IDR 223,393,939.58 and IDR 2,695,859,227.25, respectively. These tax types are crucial for the sustainability of the tourism economy. A robust monetary and fiscal ecosystem can foster synergy in this sector. The influx of investment, averaging IDR 486.865.58 billion from 2013 to 2024, has brought about substantial changes. The flow of investment through bank credit schemes serves as an alternative government policy aimed at revitalizing tourism economic development.

Based on the percentage, an average economic growth rate of 6.4% can enhance people’s welfare, especially in the tourism sector. In medium-developing countries, an economic growth rate exceeding 5% is considered as relatively high (Fialho & Van Bergeijk, 2017; Kohli et al., 2012). The current economic performance of the tourism sector in IKN highlights that government policies for managing the tourism industry are both compatible.

3.2. Normality Test of the Data

Before proceeding to the further analysis phase, the normality of the data was checked. To determine whether the data used in OLS are free from error terms, the Kolmogorov–Smirnov (K-S) test was employed. The normality assumption in OLS pertains to the residuals, which are the differences between the observed and predicted values. The K-S test, as a general statistical tool, is particularly sensitive and most appropriate for large sample sizes (n > 50). The procedure for assessing data normality is as follows: If the p-value is greater than 0.05 or equal to 0.200, the data are considered as normally distributed; conversely, if the p-value is less than 0.05, the data are deemed to be not normally distributed.

Table 3. Summary of the K-S test.

	Statistic (Sig.)	95% CI (Lower Bound)	95% CI (Upper Bound)
Labor absorption	0.195 * (0.200)	16.529	18.706
Tourist visits	0.288 (0.007)	28,262.122	125,114.211
Wages	0.201 (0.194)	2,499,162.666	3,271,328.834
Occupancy rates	0.386 (0.000)	8439.485	27,040.015
Room rates	0.201 (0.193)	261,179.38	326,320.62
Food and beverage facilities	0.188 * (0.200)	58.359	78.307
Inflation	0.219 (0.118)	2.457	5.433
Hotel and lodging taxes	0.321 (0.001)	99,257,225.466	347,530,653.7
Restaurant and eating-house taxes	0.243 (0.049)	2,086,728,164.282	3,304,990,290.218
Investment	0.180 * (0.200)	377,106.468	596,624.699
Economic growth	0.170 * (0.200)	3.132	9.679
Obs.	132	132	132

Notes: CI = confidence interval, (*) this is a lower bound of the true significance.

validates the data that deviate significantly from the normal distribution and vice versa. Visually, it is evident that four variables do not meet the normality criteria (p < 0.05): tourist visits, occupancy rates, hotel and lodging taxes, and restaurant and eating-house taxes. The data for these four variables are not normally distributed, indicating that the assumption of residual normality is also not satisfied. In contrast, the data for the other variables—labor absorption, wages, room rates, food and beverage facilities, inflation, investment, and economic growth—exhibit probabilities that exceed the threshold for normality (p > 0.05 and p = 0.200). Therefore, it can be concluded that the data for these seven variables follow a normal distribution, and the assumption of residual normality is met.

3.3. Correlation Analysis

Correlation analysis is primarily focused on examining the relationship between two variables. The Pearson correlation coefficient (r) and the associated probability value (p) are the key instruments used to determine whether the relationship between the two variables is positive and significant or otherwise. A positive correlation value (+) indicates a unidirectional relationship between the variables, while a negative correlation value (–) suggests an inverse relationship. There are two thresholds for assessing significance in this analysis: 5% and 1%. If the probability value is less than 5% (p < 0.05) or less than 1% (p < 0.01), it is concluded that there is a significant relationship between the two variables. Conversely, if the probability value exceeds 5% (p > 0.05) or 1% (p > 0.01), it is inferred that the relationship between the two variables is not significant.

Based on both 5% and 1% probability levels, four classifications of relationships were identified: (1) a positive and significant two-way relationship between the two variables, (2) a negative and significant two-way relationship between the two variables, (3) a positive and insignificant two-way relationship between the two variables, and (4) a negative and insignificant two-way relationship between the two variables. In this study, the results of the correlation analysis highlighted both a positive and significant two-way relationship and a negative and significant two-way relationship between the two variables. At the 5% probability level, six positive and significant two-way relationships were evident: tourist visits to wages (r = 0.695; p = 0.012), tourist visits to food and beverage facilities (r = 0.702; p = 0.011), wages to occupancy rates (r = 0.644; p = 0.024), wages to hotel and lodging taxes (r = 0.649; p = 0.022), occupancy rates to food and beverage facilities (r = 0.643; p = 0.024), and food and beverage facilities to hotel and lodging taxes (r = 0.659; p = 0.020).

Through a significance level of 1%, it is demonstrated that there are twenty-two positive and significant two-way relationships. These include tourist visits to occupancy rates (r = 0.970; p = 0.000), tourist visits to room rates (r = 0.795; p = 0.002), tourist visits to hotel and lodging taxes (r = 0.887; p = 0.000), tourist visits to restaurant and eating-house taxes (r = 0.884; p = 0.000), tourist visits to investment (r = 0.829; p = 0.001), wages to room rates (r = 0.873; p = 0.000), wages to food and beverage facilities (r = 0.951; p = 0.000), wages to restaurant and eating-house taxes (r = 0.807; p = 0.002), wages to investment (r = 0.872; p = 0.000), occupancy rates to room rates (r = 0.789; p = 0.002), occupancy rates to hotel and lodging taxes (r = 0.823; p = 0.001), occupancy rates to restaurant and eating-house taxes (r = 0.845; p = 0.001), occupancy rates to investment (r = 0.743; p = 0.006), room rates to food and beverage facilities (r = 0.884; p = 0.000), room rates to hotel and lodging taxes (r = 0.838; p = 0.001), room rates to restaurant and eating-house taxes (r = 0.938; p = 0.000), room rates to investment (r = 0.882; p = 0.000), food and beverage facilities to restaurant and eating-house taxes (r = 0.803; p = 0.002), food and beverage facilities to investment (r = 0.894; p = 0.000), hotel and lodging taxes to restaurant and eating-house taxes (r = 0.941; p = 0.000), hotel and lodging taxes to investment (r = 0.834; p = 0.001), and restaurant and eating-house taxes to investment (r = 0.885; p = 0.000). On the other hand, at the same probability level, two negative and significant two-way relationships were identified. According to

Table 4. Results of the correlation analysis.

	1	2	3	4	5	6	7	8	9	10	11
Labor absorption	1	0.101 (0.754)	0.289 (0.363)	–0.010 (0.976)	0.071 (0.825)	0.257 (0.420)	–0.187 (0.561)	0.062 (0.848)	0.019 (0.954)	0.362 (0.248)	–0.472 (0.121)
Tourist visits	0.101 (0.754)	1	0.695 * (0.012)	0.970 ** (0.000)	0.795 ** (0.002)	0.702 * (0.011)	–0.362 (0.247)	0.887 ** (0.000)	0.884 ** (0.000)	0.829 ** (0.001)	0.318 (0.314)
Wages	0.289 (0.363)	0.695 * (0.012)	1	0.644 * (0.024)	0.873 ** (0.000)	0.951 ** (0.000)	–0.801 ** (0.002)	0.649 * (0.022)	0.807 ** (0.002)	0.872 ** (0.000)	–0.197 (0.539)
Occupancy rates	–0.010 (0.976)	0.970 ** (0.000)	0.644 * (0.024)	1	0.789 ** (0.002)	0.643 * (0.024)	–0.259 (0.415)	0.823 ** (0.001)	0.845 ** (0.001)	0.743 ** (0.006)	0.320 (0.310)
Room rates	0.071 (0.825)	0.795 ** (0.002)	0.873 ** (0.000)	0.789 ** (0.002)	1	0.884 ** (0.000)	–0.505 (0.094)	0.838 ** (0.001)	0.938 ** (0.000)	0.882 ** (0.000)	0.208 (0.516)
Food and beverage facilities	0.257 (0.420)	0.702 * (0.011)	0.951 ** (0.000)	0.643 * (0.024)	0.884 ** (0.000)	1	–0.737 ** (0.006)	0.659 * (0.020)	0.803 ** (0.002)	0.894 ** (0.000)	–0.002 (0.946)
Inflation	–0.187 (0.561)	–0.362 (0.247)	–0.801 ** (0.002)	–0.259 (0.415)	–0.505 (0.094)	–0.737 ** (0.006)	1	–0.309 (0.328)	–0.483 (0.112)	–0.571 (0.053)	0.394 (0.205)
Hotel and lodging taxes	0.062 (0.848)	0.887 ** (0.000)	0.649 * 0(0.022)	0.823 ** (0.001)	0.838 ** (0.001)	0.659 * (0.020)	–0.309 (0.328)	1	0.941 ** (0.000)	0.834 ** (0.001)	0.429 (0.164)
Restaurant and eating-house taxes	0.0019 (0.954)	0.884 ** (0.000)	0.807 ** (0.002)	0.845 ** (0.001)	0.938 ** (0.000)	0.803 ** (0.002)	–0.483 (0.112)	0.941 ** (0.000)	1	0.885 ** (0.000)	0.339 (0.281)
Investment	0.362 (0.248)	0.829 ** (0.001)	0.872 ** (0.000)	0.743 ** (0.006)	0.882 ** (0.000)	0.894 ** (0.000)	–0.571 (0.053)	0.834 ** (0.001)	0.885 ** (0.000)	1	0.038 (0.906)
Economic growth	–0.472 (0.121)	0.318 (0.314)	–0.197 (0.539)	0.320 (0.310)	0.208 (0.516)	–0.022 (0.946)	0.394 (0.205)	0.429 (0.164)	0.339 (0.281)	0.038 (0.906)	1
Obs.	132	132	132	132	132	132	132	132	132	132	132

Notes: (*) correlation is significant at the 0.05 level, and (**) correlation is significant at the 0.01 level.

, these were wages to inflation (r = −0.801; p = 0.002) and food and beverage facilities to inflation (r = −0.737; p = 0.006).

The analytical interpretations of the correlation results at the 5% probability level highlight six key points: First, the significant positive Pearson correlation between tourist visits and wages suggests that an increase in tourist visits may be associated with a rise in wages, driven by heightened labor demand in the tourism sector. Conversely, higher wages can attract more tourists, as they often indicate improved service quality and infrastructure. Second, the significant positive Pearson correlation between tourist visits and food and beverage facilities indicates that an increase in the number of tourists may stimulate the growth of food and beverage facilities to meet their consumption needs. Conversely, the presence of more such facilities can also draw additional tourists by offering convenience and a diverse array of culinary options. Third, the significant positive Pearson correlation between wages and occupancy rates suggests that higher wages can lead to increased occupancy rates, reflecting enhanced purchasing power and service quality within the hospitality industry. Conversely, higher occupancy rates can result in increased wages due to greater labor demand in the tourism sector, particularly in accommodation. Fourth, the significant positive Pearson correlation between wages and hotel and lodging tax indicates that higher wages may be associated with increased tax revenue from hotels, likely due to a more active and expanding tourism sector. This suggests that higher tax revenue from hotels may reflect a well-developed hospitality industry capable of offering higher wages to its employees. Fifth, the significant positive Pearson correlation between occupancy rates and food and beverage facilities implies that high occupancy rates can stimulate the growth of food and beverage facilities to meet the needs of guests. The presence of numerous food and beverage facilities enhances the appeal of accommodations, thereby contributing to increased occupancy rates. Sixth, the significant positive Pearson correlation between food and beverage facilities and hotel and lodging tax indicates that an increase in food and beverage facilities can enhance tourist activity, thereby contributing to higher hotel and lodging tax revenue. The substantial tax revenue generated from hotels may reflect a well-developed tourist area, which, in turn, fosters the growth of food and beverage facilities to cater to tourists.

The statistical interpretation of the correlation results at a 1% probability level reveals several noteworthy insights. The significant positive Pearson correlation between tourist visits and occupancy rates indicates that an increase in the number of tourists is generally associated with a rise in occupancy rates. Conversely, high occupancy rates may also signify a substantial volume of tourist visits. Additionally, the significant positive Pearson correlation between tourist visits and room rates suggests that an increase in tourist numbers tends to drive up room rates, while elevated room rates may reflect heightened tourist demand. Even further, the significant positive Pearson correlation between tourist visits and hotel and lodging tax revenue implies that an increase in tourist visits typically leads to higher hotel tax revenues, while increased tax revenues may also indicate robust tourist activity in IKN. The positive significant Pearson correlation between tourist visits and restaurant and eating-house taxes indicates that an increase in tourist numbers is associated with a rise in tax revenue from these venues. Additionally, higher tax revenues may reflect increased tourist activity. Similarly, the significant positive Pearson correlation between tourist visits and investment suggests that a rise in tourist visits correlates with increased investment; conversely, greater investment can stimulate growth in tourist numbers. The significant positive Pearson correlation between wages and room rates indicates that increases in wages are associated with higher room rates, while elevated room rates may reflect the industry’s capacity to offer higher wages. The significant positive Pearson correlation between wages and food and beverage facilities suggests that rising wages may promote the expansion of food and beverage facilities, while the proliferation of such facilities may also generate additional jobs that contribute to higher average wages. The significant positive Pearson correlation between wages and restaurant and eating-house taxes indicates that an increase in wages may lead to higher consumption in restaurants, subsequently boosting tax revenues, while the elevated tax levels reflect robust economic activity that supports higher wages. The significant positive Pearson correlation between wages and investment suggests that rising wages can attract more investment, as they are indicative of a growing economy. Conversely, increased investment can also generate jobs, which, in turn, drive up wages.

The significant positive Pearson correlation between occupancy rates and room rates indicates that higher occupancy rates tend to drive up room rates. Conversely, elevated room rates may also reflect strong demand and stable occupancy levels. Also, the significant positive Pearson correlation between occupancy rates and hotel and lodging tax suggests that increased occupancy rates contribute to higher tax revenues, while elevated tax rates may also indicate high occupancy and visitor activities in IKN. Also, the significant positive Pearson correlation between occupancy rates and restaurant and eating-house tax revenues implies that higher occupancy rates can lead to increased consumption in restaurants, thereby boosting tax revenues. In turn, high tax revenues may reflect tourism activity that enhances accommodation occupancy. Lastly, the significant positive Pearson correlation between occupancy rates and investment indicates that high occupancy rates can stimulate increased investment in the tourism and hospitality sector. In turn, substantial investments can enhance the quality and capacity of accommodations, further increasing occupancy rates.

Furthermore, the significant positive Pearson correlation between room rates and food and beverage facilities indicates that higher room rates may be associated with areas that offer numerous dining options, thereby enhancing the value of accommodations. Additionally, the presence of such facilities can attract tourists, which, in turn, allow for elevated room rates. The significant positive Pearson correlation between room rates and hotel and lodging tax implies that increased room rates can lead to higher tax revenues from hotels. Conversely, elevated tax rates may also signify a high-demand area that supports increased room rates. Similarly, the significant positive Pearson correlation between room rates and restaurant and eating-house taxes suggests that higher room rates may be indicative of regions with a vibrant culinary scene that generates substantial restaurant tax revenues. Moreover, high tax rates can reflect the appeal of the destination, enabling hotels to charge higher room rates. The significant positive Pearson correlation between room rates and investment indicates that elevated room rates can attract more investment in the hospitality sector, while substantial investments can enhance service quality, thereby justifying higher room rates.

Quantitatively, the significant positive Pearson correlation between food and beverage facilities and restaurant and eating-house taxes indicates that an increase in food and beverage facilities contributes to higher restaurant tax revenues. Conversely, elevated tax revenues suggest robust culinary business activities. Additionally, the significant positive Pearson correlation between food and beverage facilities and investment implies that the expansion of these facilities is associated with increased investment in the tourism sector, while substantial investments further stimulate the development of additional establishments. The significant positive Pearson correlation between hotel and lodging tax and restaurant and eating-house taxes demonstrates that the rise in tax revenue from hotels corresponds with an increase in tax revenue from restaurants, reflecting heightened tourist activity that drives consumption in both accommodation and the culinary sectors. The significant positive Pearson correlation between hotel and lodging tax and investment indicates that an increase in tax revenue from hotels reflects the growth of the tourism sector, which, in turn, encourages further investment. Conversely, higher investment can enhance the capacity and quality of accommodations, thereby increasing tax revenues. The significant positive Pearson correlation between restaurant and eating-house taxes and investment suggests that rising tax revenues from restaurants is indicative of growth in the culinary sector, which attracts additional investment. Increased investment fosters the expansion of food and beverage businesses, leading to higher tax revenues.

While the majority of the correlation values indicate positive and significant connections, there are two causalities that contradict these findings. First, the negative significant Pearson correlation between wages and inflation suggests that an increase in wages is associated with a decrease in inflation. Also, low inflation can foster stability in the tourism economy, which, in turn, supports wage growth. Second, the negative significant Pearson correlation between food and beverage facilities and inflation indicates that a higher number of food and beverage facilities is associated with lower inflation. Furthermore, low inflation can promote growth in the tourism sector by enhancing price stability and strengthening purchasing power.

3.4. Regression Testing

Regression tests were conducted via OLS to evaluate the influences among the model variables. Through these regression tests, the partial relationships, simultaneous relationships, and the strengths of these relationships can be traced (see

Table 5. Regression results with OLS.

	Model 1	Model 2	Model 3
Constant	3.003 (0.205)	–2.669 (0.228)	2.809 (0.218)
Labor absorption		14.092 * (0.045)	–13.430 * (0.047)
Tourist visits	14.092 * (0.045)		30.277 * (0.021)
Wages	–9.847 (0.064)	17.268 * (0.037)	–26.452 * (0.024)
Occupancy rates	–14.527 * (0.044)	139.842 ** (0.005)	–29.716 * (0.021)
Room rates	10.790 (0.059)	–28.866 * (0.022)	21.269 * (0.030)
Food and beverage facilities	11.029 (0.058)	–16.156 * (0.039)	23.815 * (0.027)
Inflation	14.944 * (0.043)	–15.331 * (0.041)	14.195 * (0.045)
Hotel and lodging taxes	–5.455 (0.115)	6.132 (0.103)	–5.938 (0.106)
Restaurant and eating-house taxes	3.375 (0.183)	–3.483 (0.178)	3.691 (0.168)
Investment	–11.045 (0.057)	30.277 * (0.021)	–23.508 * (0.027)
Economic growth	–13.430 * (0.047)	25.582 * (0.025)
F (sig.)	57.579 * (0.021)	58.884 ** (0.000)	43.788 * (0.023)
R2	0.998	1	0.927
Adjusted R2	0.981	0.999	0.911
Std. error of the estimate	0.237	0.232	0.289
Obs.	132	132	132

Notes: (*) probability is significant at the 0.05 level, and (**) probability is significant at the 0.01 level.

). First, the partial relationships are assessed using two measurement tools: the t-statistic and the probability value. The t-statistic is compared to the critical value from the t-table, while the probability value is compared to the predetermined significance levels. For the three regression models tested, the critical values from the t-table were set at 1.657 for a 5% significance level and at 2.358 for a 1% significance level. The two assumptions of the proposed hypothesis are as follows: (1) If the t-statistic is greater than the critical value from the t-table and the probability value is less than the established criteria (p < 0.05 or p < 0.01) then there is a positive and significant partial relationship; (2) if the t-statistic is less than the critical value from the t-table and the probability value is greater than the established criteria (p > 0.05 or p > 0.01) then there is a positive and insignificant relationship, or even a negative and insignificant partial relationship. Second, simultaneous relationships are assessed using the F-statistic and probability values. The F-statistic is compared to the F-table values, where the F-table value at a probability level of 5% is 1.91 and at a probability level of 1% is 2.47. In a manner similar to the partial test, if the F-statistic exceeds the critical value from the F-table and the p-value is below the established criteria (p < 0.05 or p < 0.01), it can be concluded that there are significant positive relationships among the variables. Third, the strength of the relationship in the regression model is measured using the coefficient of determination (R²). The characteristics of the R² value are divided into five classes: (1) R² = 0 indicates that the model does not explain any variation in the dependent variable; (2) 0 < R² < 0.5 suggests that the model is weak in explaining the relationship; (3) 0.5 ≤ R² < 0.75 indicates that the model is fairly effective at explaining most of the variation; (4) 0.75 ≤ R² < 1 signifies that the model is very close to explaining nearly all the variation; and (5) R² = 1 denotes that the model perfectly accounts for all the variation in the dependent variable.

In the first model, two variables were found to have a positive and significant partial effect on labor absorption: tourist visits (t = 14.092; p = 0.045) and inflation (t = 14.944; p = 0.043). Oppositely, two other variables exhibited a negative and partially significant effect on labor absorption: occupancy rates (t = −14.527; p = 0.044) and economic growth (t = −13.430; p = 0.047). All the independent variables collectively exert a positive and significant effect on labor absorption, as evidenced by the F-statistic being greater than the critical F-value (57.579 > 1.91), with a probability value of less than 5% (0.021 < 0.05). An R² value of 0.998 indicates that the first model explains a very high proportion of the variance in labor absorption, with the remaining 0.002 representing other variations not accounted for by the independent variables.

The partial relationships in the second model underline that four variables exert a positive and significant influence on tourist visits at a 5% significance level. These variables are labor absorption (t = 14.092; p = 0.045), wages (t = 17.268; p = 0.037), investment (t = 30.277; p = 0.021), and economic growth (t = 25.582; p = 0.025). Conversely, three variables demonstrate a negative and significant effect on tourist visits: room rates (t = −28.866; p = 0.022), food and beverage facilities (t = −16.156; p = 0.039), and inflation (t = −15.331; p = 0.041). Next, one variable shows a positive and significant effect on tourist visits at the 1% significance level: occupancy rates (t = 139.842; p = 0.005). The F-statistic exceeds the critical value (58.884 > 2.47), with a probability value of less than 1% (0.000 < 0.01), indicating that all the independent variables in the second model have a positive and significant effect on tourist visits simultaneously. An R² value of 1 suggests that the second model perfectly explains all the variations in the tourist visits variable.

In the third model, the partial relationship, simultaneous relationship, and strength of the relationship are assessed using a significance level of 5%. The regression results indicate that four variables have a positive and significant influence on economic growth: tourist visits (t = 30.277; p = 0.021), room rates (t = 21.269; p = 0.030), food and beverage facilities (t = 23.815; p = 0.027), and inflation (t = 14.195; p = 0.045). Four other variables exhibit a negative and significant influence on economic growth: labor absorption (t = −13.430; p = 0.047), wages (t = −26.452; p = 0.024), occupancy rates (t = −29.716; p = 0.021), and investment (t = −23.508; p = 0.027). The F-statistic for the third model is 43.788, which exceeds the critical value from the F-table (43.788 > 1.91), with a probability value of below 5% (0.023 < 0.05). This indicates that all the independent variables collectively have a positive and significant effect on economic growth. An R² value of 0.911 suggests a very strong relationship in explaining economic growth, while the remaining 0.089 represents variation not accounted for by the third model.

4. Discussion

The premise of this research is to project labor absorption, tourist visits, and economic growth by examining a combination of several variables that have not been extensively discussed in previous studies. In the context of global tourism, it has been displayed that tourist visits and inflation can enhance labor absorption (Athari et al., 2020; Baral & Rijal, 2022; Sánchez López, 2019; Y.-Y. Sun et al., 2022). Oppositely, labor absorption, standard wages, investment, and economic growth are considered as being capable of boosting tourist visits (Apriyanti et al., 2024; Erzsebet, 2024; Modi, 2024; Rasool et al., 2021; Shu et al., 2022; Yang et al., 2025). Additionally, factors such as tourist visits, hotel rental fees, the food and beverage industry, and inflation can stimulate economic growth (Anggraeni, 2017; Kalenjuk Pivarski et al., 2024; Kurniawati & Fathoni, 2023; Sánchez López, 2024; Z. Sun et al., 2025; Yamin et al., 2020). In light of the tourism economy phenomenon in IKN, several previous studies have highlighted the significance of the aforementioned variables in influencing labor absorption, tourist visits, and economic growth. Nevertheless, other studies present findings that contradict this research. The contrasting perspectives—both positive and negative—can enrich the overall understanding of the topic.

Figure 1 displays the first model, which indicates that factors such as tourist visits, room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes contribute to increased labor absorption. Conversely, wages, occupancy rates, hotel and lodging taxes, investment, and economic growth tend to decrease labor absorption. J. Sharma and Mitra (2020) confirmed a positive asymmetric relationship between tourist arrivals and tourism employment in Sri Lanka. Nonetheless, Mhlanga (2020) challenges the empirical argument regarding the positive interaction between room rates and labor absorption, demonstrating that the adjustment of room rates through the presence of the Airbnb platform in South Africa was ineffective and negatively impacted long-term self-employment. In 24 Organisation for Economic Co-operation and Development (OECD) countries, the food and beverage subsector exhibits lower revenue productivity per tourist arrival compared to those of other subsectors, such as the sports and recreation industry, as well as travel agencies and other reservation services. Additionally, due to its limited capacity, the food and beverage subsector’s ability to absorb labor is also diminished (Dorta-González & González-Betancor, 2021). Macroeconomic factors, such as inflation, can significantly influence labor absorption. In their study, Rahmatullah and Marseto (2024) demonstrated that inflation positively affects labor absorption in East Nusa Tenggara Province, Indonesia. This study also highlights notable differences in the impact of restaurant and eating-house taxes on labor absorption. Mahangila and Anderson (2017) examined the burden of tax administration in the tourism sector, including restaurants, in Zanzibar, Tanzania. The complexity and uncertainty of tax regulations, such as income tax and value-added tax, can impose significant burdens on businesses and subsequently affect labor absorption in this sector.

An increase in wages within the tourism sector can structurally weaken labor absorption in IKN. Philander and Roe (2013) clarify that moderate wage setting can enhance labor competitiveness in the tourism industry, while excessive wage growth may diminish competitiveness in 40 countries. There is no denying that wages play crucial roles in factors such as occupancy rates, hotel and lodging taxes, investment, and economic growth, all of which contribute to expanding labor absorption. Yet empirical evidence indicates a negative influence. Litvin (2020) navigates data from the hospitality industry in the United States, focusing on the accommodation sector’s relationship to employment. Although this sector’s growth is impressively faster than that of the general economy, its performance is highly volatile, particularly in stimulating hotel occupancy rates, which remain a primary driver of employment in the tourism sector. The impact of lodging taxes on hotel performance in the United States has been well-documented. Lodging taxes can positively influence hotel occupancy and revenue, which, in turn, stimulate employment in the tourism sector (A. Sharma et al., 2022). This finding contrasts with the situations in Vietnam, China, India, Indonesia, Brazil, Mexico, Russia, and Turkey (the E-7 countries), where tourism development generates positive returns for labor in both the short and long term, with the most significant contributions stemming from foreign investment (Hoang, 2023; Zhao et al., 2023). Sustainable tourism has the potential to enhance national income and generate jobs, particularly in tourism-dependent regions of Pakistan (Manzoor et al., 2019). Furthermore, the diversification of tourism markets and activities can boost the aggregate GDP and create employment opportunities in Australia (Solarin et al., 2023).

The second model examines the relationships among various factors, including labor absorption, wages, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, investment, and economic growth, and their impacts on tourist visits. As illustrated in Figure 2, labor absorption, wages, occupancy rates, hotel and lodging taxes, investment, and economic growth positively influence tourist visits. Conversely, Figure 2 also confirms that room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes negatively impact tourist visits. There are notable similarities and differences between these results and those found in the reviewed literature. For instance, tourism demand positively influences employment levels in 13 Mediterranean countries (Yılancı & Kırca, 2024). This contrasts with the research conducted by Walmsley et al. (2022), which suggests that overtourism can lead to reductions in both real and nominal wages, exacerbate the disparity between local and migrant workers, and deteriorate working conditions in many places. Amenities provided by tourism managers, such as affordable occupancy rates, can significantly influence the volume of tourist arrivals. The distribution of hotel occupancy rates in Turkey pertains to both international and domestic tourist arrivals. Distinct spatial patterns exist between international and domestic tourists in their accommodation choices (Aktaş et al., 2017).

The implementation of tourist taxes, such as those levied on hotels and lodging, can enhance sustainability within the tourism sector. For instance, tax revenue generated from the tourism industry can significantly influence the number of tourist arrivals in Andalusia, Spain (Durán-Román et al., 2021). Capital investment in tourism infrastructure—including hotels, transportation, restaurants, communication networks, and recreational facilities—positively impacts international tourist trade flows to Association of Southeast Asian Nations (ASEAN) and small islands (Fauzel, 2021; Q. H. Nguyen, 2021; Nonthapot, 2018). A robust economy is also likely to yield favorable outcomes for tourist visits. Research conducted by Enilov and Wang (2021) and Rasool et al. (2021) indicates that in Brazil, Rusia, India, China, and South Africa (BRICS) as well as in 23 developed and developing nations, there exists a time-varying, bidirectional causal relationship between economic growth and inbound tourism. This relationship suggests that tourism can serve as a catalyst for future economic growth.

The negative impacts of room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes on tourist visits are a subject of empirical debate. Y.-X. Lin et al. (2021) found that the relationship between hotel sales revenue and international tourist arrivals is an “inverted U”. With extensive promotional schemes, the hotel industry across 31 provinces and cities in China can moderate the effects of tariff premiums on tourist arrivals and hotel performance. The dynamic pricing strategies employed by the hotel industry, such as room rates, are highly responsive to fluctuations in tourist arrivals. Mitra (2020) demonstrated that room rates do not have a long-term relationship with tourist arrivals; rather, price adjustments are primarily influenced by short-term considerations in Oslo, Norway. In addition to room rates, geographical factors and affordable amenities can significantly impact tourists’ decisions to visit. From a gastronomic perspective, the roles of food and beverage facilities in attracting and retaining tourists in Jeonju, South Korea, were examined by Carpio et al. (2021). With its rich local culinary experiences, Jeonju City serves as a major attraction that enhances tourist satisfaction and the intention to revisit. In the tourism industry of Gdańsk, Poland, the growth of the food and beverage service ecosystem plays a crucial role in supporting local tourism (Pilis et al., 2022). The volume of tourist visits is influenced by inflation, as noted by Barnet (1975), Khalid et al. (2019), and Meo et al. (2018). In the long run, inflation has a significant negative impact on tourism demand in Pakistan. The inflation crisis has led to a decline in international tourism demand across the Americas, Asia, and Europe. Meanwhile, in Brazil, Taiwan, and Hawaii, rising inflation rates can diminish tourists’ purchasing power and influence their destination preferences. As a fiscal element, the tax rate on restaurants should significantly affect tourist arrivals. However, in Europe, the nominal and marginal increases in value-added tax on restaurant services have not substantially impacted the flow of tourists (Kristjánsdóttir, 2021). In Germany, changes in the value-added tax on restaurant services have had a causal effect on price elasticity and tax burden, resulting in a further decline in demand for tourist services (Firgo, 2025).

The scenario presented in the third model analyzes the relationships between labor absorption, tourist visits, wages, occupancy rates, room rates, food and beverage facilities, inflation, hotel and lodging taxes, restaurant and eating-house taxes, and investment in relation to economic growth. Specifically, Figure 3 explains that tourist visits, room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes positively influence economic growth. On the other hand, labor absorption, wages, occupancy rates, hotel and lodging taxes, and investment tend to hinder economic growth. This model reveals both similarities and differences when compared to those in related studies.

The frequency of visitor arrivals in the tourism sector has been a significant driver of economic growth for countries in Northern Europe, particularly before the global economic crisis of 2007–2010 (Pérez-Rodríguez et al., 2021). Comprehensive tourism development enables countries with open tourism policies to reap greater economic benefits. According to Z. Sun et al. (2025), the tourism sector, supported by optimal institutional quality, positively influences economic growth in 182 countries. Inbound tourism exhibits a bidirectional causality with economic growth over the long term in ASEAN countries (Indriani, 2022). Across both developed and emerging markets, the expansion of tourism, as evidenced by the mobility of tourists, significantly influences hotel room rates based on seasonal demand. The determination of these rates is affected by various factors, including location, tourists’ purchasing power, service quality, destination appeal, and available amenities. Collectively, these elements contribute to the economic performance of the tourism sector at both national and regional levels (Arora & Mathur, 2020; Chatibura, 2025; Chen, 2010; Costa & Costa, 2024; Y.-X. Lin et al., 2021). At the global scale, restaurants play a crucial role in providing authentic culinary experiences, thereby enhancing gastronomy tourism and contributing to economic growth (Bertan, 2020). In countries such as Thailand and Turkey, the growth of tourism is closely linked to gastronomy tourism, which encompasses businesses in the food and beverage sector. This connection significantly contributes to the economic growth of the tourism industry (İlban & Liceli, 2022; Piboonrungroj et al., 2023). At the local level, culinary tourism, which includes food and beverage establishments, can prolong tourists’ stays and enhance economic revenue in Amhara State, Ethiopia (Wondirad et al., 2021). Uncontrolled inflation can hinder tourism revenue derived from the influx of tourists. Uula et al. (2024) demonstrate that inflation positively influences the economic growth of the tourism sector in 53 countries from the Organization of Islamic Cooperation (OIC). Conversely, Raifu and Afolabi (2024) and Sadeghi et al. (2024) found that inflation negatively impacts economic growth in the tourism industries of Iran and Nigeria. High taxes can diminish destination attractiveness, influence tourist decisions and choices, reduce competitiveness, and lower producer revenues in a competitive tourism market. In many countries, the substantial tax burden on restaurants can adversely affect the economic growth of the tourism sector (Descals-Tormo & Ruiz-Tamarit, 2022).

The research compiled from various papers proves that labor absorption, wages, occupancy rates, hotel and lodging taxes, and investment can significantly influence economic growth, either positively or negatively. First, employment in the tourism sector has a positive impact on economic growth in Sri Lanka (Madhumini, 2024). In contrast, the relationship between employment in the tourism sector and economic growth in OECD countries is non-linear and is influenced by the level of specialization within each country’s tourism sector (Vuković et al., 2023). Moreover, the decline in the tourism sectors in 117 countries due to the COVID-19 pandemic has negatively affected employment opportunities, ultimately leading to a decrease in economic growth (Vašaničová & Bartók, 2024). Second, the decomposition of workers’ income into an integral part of the sectoral wage share in the tourism industry explicitly affects GDP performance in China (Shu et al., 2022). Also, variations in workers’ wages across different income levels and competitiveness can amplify positive impacts for the tourism sector in 203 countries (W. L. Lin, 2024). Third, tourism indicators, such as the average length of stay for tourists, positively influence Indonesia’s economic growth (Rahmayani et al., 2022). The expansion of the foreign tourist market in Taiwan accounts for a significant portion of the variation in tourist demand for occupancy rates, highlighting the importance of hotel performance (Chen, 2010). Fourth, a relationship exists between hotel taxes and local revenue in California, the United States of America (USA), where the impact on the overall economic growth is immediate, as it is influenced by various internal and external factors (Swenson, 2021). The success or failure of tourism tax implementation depends on the local market conditions, including the industry structure and demand elasticity, which play a crucial role in determining the effect of the tax ratio on a country’s tourism economy (Sheng, 2017). Fifth, the magnitude of the economic growth in tourism serves as an indicator of whether investments in the sector are functioning optimally or not. For example, in Morocco, the influx of Foreign Direct Investment (FDI) into the tourism sector has adversely affected economic growth, whereas FDI in non-tourism sectors has positively contributed to economic growth (El Menyari, 2021). By contrast, the long-term economic growth rates in Bangladesh and Estonia are positively influenced by FDI flows (Sadekin, 2025; Sokhanvar & Jenkins, 2022).

Overall, Figure 1, Figure 2 and Figure 3 summarize the path relationships in the first, second, and third models, respectively. These three models are derived from OLS regression calculations based on beta coefficients (β) and standard errors. The recapitulation of the path relationships across all the models is presented in

Table 6. Recapitulation of path relationships in all the models.

	Model 1	Model 2	Model 3
Constant	4.278 (1.424)	–11,848.671 (4325.504)	2.824 (1.006)
Labor absorption		0.061 (193.577)	–0.221 (0.049)
Tourist visits	16.224 (0.000)		3.595 (0.000)
Wages	–7.240 (0.000)	0.447 (0.003)	–1.608 (0.000)
Occupancy rates	–12.918 (0.000)	0.796 (0.030)	–2.862 (0.000)
Room rates	6.600 (0.000)	–0.407 (0.021)	1.464 (0.000)
Food and beverage facilities	4.535 (0.045)	–0.279 (83.972)	1.006 (0.014)
Inflation	1.964 (0.096)	–0.121 (256.377)	0.434 (0.067)
Hotel and lodging taxes	–1.589 (0.000)	0.098 (0.000)	–0.352 (0.000)
Restaurant and eating-house taxes	1.259 (0.000)	–0.080 (0.000)	0.288 (0.000)
Investment	–5.533 (0.000)	0.341 (0.006)	–1.228 (0.000)
Economic growth	–4.509 (0.112)	0.278 (135.763)
Obs.	132	132	132

. The analytical interpretation and implications of the relationships among the three models are explained below.

In the first model, increases in wages, occupancy rates, hotel and lodging taxes, investment, and economic growth lead to a reduction in labor absorption. This scenario is not ideal, as it demonstrates that disparities in wages, occupancy rates, hotel and lodging taxes, investment, and economic growth fail to provide inclusive benefits for labor absorption. Interestingly, factors such as tourist visits, room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes contribute to the creation of decent employment opportunities.

In the second model, increases in room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes actually lead to a decrease in tourist visits. This suggests that policies regarding room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes do not have a direct influence on tourists, thereby affecting their arrival. In contrast, factors such as labor absorption, wages, occupancy rates, hotel and lodging taxes, investment, and economic growth can significantly impact tourist visits. Collectively, labor absorption, wages, occupancy rates, hotel and lodging taxes, investment, and sustainable economic growth are essential for attracting tourists and ultimately enhancing visitation levels.

The third model underscores that increases in tourist visits, room rates, food and beverage facilities, inflation, and restaurant and eating-house taxes can foster enhanced economic growth. The interplay of these factors can create a positive spillover effect within the tourism sector. Conversely, labor absorption, wages, occupancy rates, hotel and lodging taxes, and limited investment can hinder economic growth. This negative relationship arises because labor absorption, wages, occupancy rates, hotel and lodging taxes, and restricted investment do not benefit all groups equally. In other words, these five variables do not uniformly contribute to the growth of the tourism economy.

The root causes of high inflation in IKN include price instability at tourist destinations, local supply chain issues, tourism distribution patterns, uncontrolled seasonal demand, market competition, and the effects of digitalization and information transparency. To address the six polemics related to inflation, stakeholders in the tourism sector need to implement the following improvements: (1) Ensure price transparency for accommodation, food, and transportation to prevent unreasonable price spikes during the holiday season, and standardize rates or establish maximum price limits for tourism services (e.g., guide fees and vehicle rentals) through partnerships with business operators. (2) Promote the use of local products, such as agricultural goods, handicrafts, and food, to stabilize supply and control prices while reducing dependence on imported goods. This can be achieved through partnerships between tourism stakeholders and local farmers and artisans for direct supply, thereby cutting long distribution chains. (3) Develop alternative destinations outside of high-concentration areas to prevent the burden from being concentrated in a single region, which can lead to spikes in rental prices and basic necessities. This approach should focus on community-based sustainable tourism to distribute income and mitigate inflationary pressures more evenly. (4) Manage seasonal demand by implementing a scheduling mechanism for tourist visits outside of peak seasons, such as offering low-season travel incentives. This will help to distribute demand more evenly and prevent seasonal inflation. (5) Provide training and access to financing for small and medium-sized enterprises in the tourism sector to maintain competitive pricing and alleviate the burden of high operating costs. (6) Leverage digital platforms to facilitate price comparisons for tourists and discourage unreasonable pricing practices.

5. Conclusions

The motivation of this study is to address the questions and refine the models related to labor absorption, tourist visits, and economic growth, as well as the variables that influence these factors. The regression results obtained using OLS reveal three essential conclusions: First, tourist visits and inflation have positive and significant impacts on labor absorption, while occupancy rates and economic growth exert negative and significant influences on labor absorption. Second, labor absorption, wages, investment, and economic growth positively and significantly affect tourist visits, whereas room rates, food and beverage facilities, and inflation negatively and significantly impact tourist visits. Third, tourist visits, room rates, food and beverage facilities, and inflation positively and significantly influence economic growth, whereas wages, occupancy rates, and investment negatively and significantly affect economic growth. Overall, inflation is the variable that exerts the most dominant effect on labor absorption (in the first model), while occupancy rates have the most significant impact on tourist visits (in the second model), and tourist visits are the variable that has a dominant influence on economic growth (in the third model). All the regression models reveal that both hotel and lodging taxes, as well as restaurant and eating-house taxes, consistently demonstrate insignificant effects.

Four recommendations based on the results of this study can lead to concrete improvements. The first recommendation is that the inflation rate can trigger an increase in labor absorption. Given the strategic position of the IKN region, which is relevant to the trend of tourism development, stakeholders in the sector—including the government, industry associations, tourism managers, and local communities—should work to mitigate inflation, particularly that which arises from service commodities and seasonal consumption.

The second and third recommendations address the relationship between occupancy rates and tourist visits, as well as the connection between tourist visits and economic growth systematically. The establishment of the IKN has significantly impacted the economic prospects of the tourism sector. Numerous tourism-related businesses are constructing non-star hotels and lodgings. Occupancy rates are influenced by the number of tourist visits, despite the limited quality of hotel and lodging facilities. As tourist visits increase, the demand for accommodations, such as hotels and lodgings, grows, subsequently affecting economic growth. In terms of quantity, the frequency of tourist visits directly impacts economic growth within the tourism sector. Given that occupancy rates are linked to tourist visits and tourist visits are also correlated with economic growth, this information can guide stakeholders in restructuring the tourism industry by strategically allocating investments and financing for the development of hotels and lodgings. The fourth recommendation pertains to the hotel and lodging taxes as well as the restaurant and eating-house taxes, which, in reality, have negligible effects on labor absorption, tourist visits, and economic growth. Both taxes play crucial roles in sustaining the tourism industry in IKN. In practice, the government acts as a regulator, supervisor, and tax collector, redistributing local revenue from these taxes to finance both physical and non-physical infrastructures. Through a comprehensive funding program, the government can enhance the potential of the tourism sector and its supporting attributes. The revenue-reporting system for these two taxes must be documented transparently and professionally, allowing the public and consumers—specifically tourists—to monitor it in real time. IKN possesses abundant resources in the tourism sector; therefore, it is essential for the government to empower and support the skills and competencies of the workforce in this sector to effectively address competitive challenges.

These research findings offer a fresh perspective for understanding the dynamics of the tourism economy in a holistic manner. Future studies with similar themes are encouraged to address existing gaps by incorporating additional variables that may influence labor absorption, tourist visits, and economic growth. Also, subsequent research can replicate the current model framework with modifications and alternative terminology. There were difficulties in developing the analytical model. First, the data source is limited in duration, as the extracted and tested period is contemporary. Second, the literature pertaining to the phenomenon under discussion is relatively sparse. Both restricted data access and limited literature support can lead to anomalies in the analysis results. The further deepening of the current study could inspire research centered on cluster analysis models utilizing macro-scale observations. These macro-scale observations can be applied to regional economics to enhance planning within the tourism sector. This study aims to achieve a comprehensive understanding of the indicators that determine the success of tourism within a geographical area referred to as IKN. Substantively, the novelty of this research lies in its methodological refinement, which investigates labor absorption, tourist visits, and economic growth, along with the variables influencing these factors, in the context of tourism—elements that have not been addressed in previous studies. Furthermore, the innovations derived from empirical results facilitate a deeper examination of the gaps at the scale of the analyzed data and in the methods applied. The novelty of this study also paves the way for the advancement of the scientific literature, particularly by integrating additional variables related to tourism economic performance that remain unexplored.