Search

We analyzed the relationship between tourism destination competitiveness (TDC) and economic performance using the Travel and Tourism Development Index (TTDI) and various tourism metrics. Employing a Structural Equation Model (SEM) and the latest available 2021 data, we focused on factors such as connectivity, infrastructure, demand drivers, sustainability, and the enabling environment. The findings indicated a positive correlation between these factors and the pillars of destination competitiveness. Key elements such as connectivity, infrastructure, demand drivers, sustainability, and enabling environment significantly influence TDC in Asia. Our research demonstrates that these indicators strongly affect various aspects of performance in Asian countries, including tourism arrivals, revenue, and job creation. We found a negative correlation between certain aspects of destination performance and tourism growth, suggesting that highly competitive destinations may experience lower growth rates than less competitive ones, thereby affecting the overall development of the sector. This highlights the necessity of specific strategies to leverage the positive influence of competitive tourist destinations on economic dynamics and job creation within a destination. This study highlights the crucial role of core resources and infrastructure in enhancing tourism performance and competitiveness, and advocates targeted efforts for sustainable and resilient sector growth. These findings offer valuable insights into the relationship between competitiveness and performance in the Asian tourism industry. INSET: c. [ABSTRACT FROM AUTHOR]

Although the effect differs among occupations, this study indicated that aside from socioeconomic variables, household technology relates to the labor force participation of married women in Indonesia. While the link between household technology and women's labor force participation was analyzed, the research on the impact of household technology on women's occupational choices in the labor market (e.g., white-collar, pink-collar, bluecollar jobs, agricultural) and full-time homemakers was not the focus. Primary data were obtained from the Indonesia Demographic and Health Survey 2017. The data involved a usable sample of 32,559 married women aged 15-49. Using the multinomial logistic model, the study finds evidence that household technology primarily supports married women in pink-collar jobs. Thus, information and communication technology facilitates women in white-collar jobs. However, these elements are negatively linked to women's labor force participation in blue-collar and agriculture. Additionally, the study reveals that the lack of household technology influences inactive married women in the labor market (homemakers). [ABSTRACT FROM AUTHOR]

This study aims to determine the chronicity of poverty in Indonesia using the equally distributed equivalent (EDE) poverty gap method, tracking Indonesian households in 2007 and 2014. The results indicate that the largest component of poverty among households was chronic (77%). Compared to transient poverty, chronic poverty is more common across individuals. Unlike previous studies, we used data at the district level to measure the poverty line, discovering that the cost of poverty inequality is lower than often reported. This decision is because disaggregated data (poverty line) can show a more realistic poverty threshold. Using a quantile regression approach, we found evidence that age, gender, employment status, and education were significant factors of chronicity. Other significant factors of chronic poverty include large household size, having no access to services (finance, electricity, information, and mobility), and having limited or no assets. Casual employment in the agricultural sector and living in rural areas increase the probability of poverty, although not necessarily chronic. Poverty alleviation programs, therefore, need to target the right causes as exposure to poverty varies across households. [ABSTRACT FROM AUTHOR]

This study uses an autoregressive distributed lag (ARDL) model to investigate the role of incomes, relative price competitiveness, and substitution prices in tourism demand from Indonesia‘s six largest countries of origin from 2007Q1 to 2019Q4. Income level, competitive prices, and substitution prices significantly impact the demand for tourism in Indonesia. Malaysia, Singapore, Australia, Japan, and India are income elastic, signaling that tourism is a luxury good, but China (normal good). Malaysia and China are price elastic while Japan, India, Singapore, and Australia are less affected by changes in relative prices. Substitute prices may drive tourist to other destinations if the change in prices is large. [ABSTRACT FROM AUTHOR]