Shanlax International Journal of Economics

Leisure Expenditure in Mumbai: Income and Household Influence

2026-03-02T12:56:17+00:00

This study looks at the social and economic factors that affect how much money urban families in Mumbai spend on leisure activities in 2025. Leisure spending includes money spent on restaurants, fun activities, and other things that aren’t necessary. This definition comes from data from 777 urban households. We use descriptive statistics to show what the households are like, and then we use an Ordinary Least Squares (OLS) regression model to find the most important factors that affect how much people spend on leisure. The results show that adjusted total income, type of job, and larger household size all have a statistically significant and positive effect on how much people spend on leisure. On the other hand, age, gender, and level of education don’t seem to have a big effect. These results indicate that in a major metropolitan area such as Mumbai, discretionary spending is primarily influenced by income levels and household composition rather than demographic factors. The research provides empirical evidence to the body of work on urban consumption behavior and offers valuable insights for policymakers focused on urban planning, welfare design, and enhancements in urban quality of life. This study looks at the social and economic factors that will affect how much people in Mumbai’s cities spend on leisure activities in 2025. It looks at data from 777 households to see how family size, income, and type of job affect how much people spend on eating out, going out, and traveling. They use basic statistical methods and regression analysis. The results show that people with higher incomes, certain jobs, and bigger families spend more on leisure time. Age, gender, and education don’t matter as much. Future studies can look at how spending on leisure activities has changed over time and compare urban and rural areas.

Macroeconomic Effects of Debt Servicing, Foreign Reserves, and Investment on Nepal’s Growth Trajectory

2026-03-02T12:56:17+00:00

This study investigates the impact of macroeconomic variables such as debt servicing, foreign reserves, and gross fixed capital formation (GFCF) on Nepal’s economic growth from the fiscal year 1993/94 to 2023/24. It is based on the Solow-Swan Growth Model, Debt Overhang Hypothesis, and Keynesian investment theory. The ARDL test and ECM as a theoretical background have been used to assess the long- and short-run dynamics. The empirical results show that there is no significant long-run effect of debt servicing on economic growth, which raises concerns about the efficiency of the use of borrowed funds. While foreign reserves help for the short-term stability of the economy, in the long run, they do not show any lasting effect, suggesting an unproductive allocation of funds. GFCF shows a negative long-run impact on GDP, which points to inefficiency, misallocation, or even reduced private investment. Moreover, a positive error correction term throws doubt into the capability of the economy to back at equilibrium without policies because of its instability. Overall, Nepal’s economic issue is not the lack of resources, but their allocation, demanding better governance, strategic use of reserves, and quality investment. However, it has some limitations (such as relying on macroeconomic data, omitting other potential economic growth determinants, and considering political and governance variables are considers, etc.).Therefore, future research should consider institutional variables and capture the political economy dimension of growth in the South Asian economies context to provide valuable insights.

User Issues and Challenges of Telecom Services in Sivagangai District

2026-03-14T12:11:01+00:00

Purpose: This study examines the major issues and challenges faced by telecom service users in Sivagangai District, Tamil Nadu, with particular focus on network reliability, billing transparency, and customer service efficiency. Methodology: The study adopts a descriptive research design using both primary and secondary data. Primary data were collected from 100 telecom users through a structured questionnaire. Statistical tools such as Factor Analysis were employed to identify key dimensions of user challenges. The Kaiser-Meyer-Olkin (KMO) test and Bartlett’s Test of Sphericity were applied to assess sampling adequacy and correlation strength. Results: The KMO value of 0.588 and significant Bartlett’s test (χ² = 428.376, p < 0.05) confirmed the suitability of factor analysis. The rotated component matrix revealed three major factors: (1) Network-related issues (coverage, call drops, slow internet), (2) Billing and transparency issues (hidden charges, service disruptions), and (3) Customer-related concerns (data privacy, inflexible plans). Network coverage (.917) and data privacy (.915) emerged as the most significant concerns.Conclusion: The findings indicate that technical reliability, transparent billing practices, and customer-centric service improvements are critical for enhancing user satisfaction in rural telecom markets. Strengthening infrastructure, improving service transparency, and offering flexible plans are essential to bridge the rural telecom service gap.