In the second paper, "Determinants of Youth Unemployment in India", Karuna Bohini, C Hussain Yaganti and Mini P Thomas examine the demographic, monetary and macroeconomic factors influencing India's rising youth unemployment, using Autoregressive Distributed Lag (ARDL) approach to cointegration for the period 1983-2018. The study has established the existence of long-run cointegration of youth unemployment with youth working-age population, private investment and real interest rates. The results indicate that India's inflation did not exert a statistically significant influence on youth unemployment, while private investment exerted a statistically significant and positive impact. The results also indicate a negative and statistically significant impact of the proportion of youth in the working-age population on India's youth unemployment. The study highlights the facilitation of internships to tackle the unemployment rate among the youth who are positioned at the entry level in the labor market.

In the third paper, "Determinants of Elementary Education Expenditure in Uttar Pradesh: An Empirical Investigation", Sandeep Kumar Tiwari, Pabitra Kumar Jena and Kirtti Ranjan Paltasingh attempt to study the disparities in household expenditure on education up to the elementary level in Uttar Pradesh using Tobit regression model. The study reveals that gender, religion, caste, consumption quintile, type of institution, medium of teaching, head of the household, occupation of the head of the household, and household size significantly determine the expenditure of household on elementary education. The study also reveals that the association of caste and class plays a significant role in determining educational expenditure in rural Uttar Pradesh, and the association of religion and class shows mixed results in both urban and rural sectors of the nation and the state. The authors opine that there is a need to strengthen the management system and ensure the accountability of teachers in government schools.

In the research note, "Predicting Housing Prices: A Comparison of Three Alternative Methodologies", Sanjay Fuloria compares the results of three machine learning algorithms, viz., Decision Tree Regression (DTR), Support Vector Machines (SVMs), and Gradient Boosting Machines (GBMs). These machine learning algorithms are used in estimating housing prices. The results reveal that GBMs are capable of generating price estimations with lower prediction errors than DTR and SVM, based on the dataset used in the study. The results also reveal that machine learning algorithms like DTR, SVMs, and GBMs are promising tools for property researchers to predict housing prices. The author opines that the size of the dataset, the computing power of the equipment, and the amount of time available to wait for the results-all play a role in the algorithm selection process.

The current issue also features an interview with Prof Bandi Kamaiah, former Dean and Senior Faculty Member in the School of Economics, University of Hyderabad, who has attained a high degree of knowledge in academics and research. The candid expression of his thoughts on the research trends across the globe will certainly inspire the research community at large.