ABSTRACT
Tracking and analyzing the labour market dynamics at regular, frequent intervals is critical. However, this was not possible for India, a large emerging economy with a significant population undergoing demographic transition, due to a paucity of data. We use the new dataset Centre for Monitoring Indian Economy (CMIE)-Consumer Pyramids Household Survey (CPHS) and use a panel to create Labour Flow Charts and Transition Matrices for India from January 2019 to December 2021. To the best of our knowledge, this is the first time these were created for India. We then use that to look at the impact of Covid-19 on the Indian labour market. We not only look at transitions between employment, unemployment and out of labour force, but also across types of employment-full-time and part-time. The rich data also allows us to consider heterogeneity in the labour market and look at the differential impact of the pandemic across different education groups and gender. From the labour flow charts and transition probabilities, we find that while all groups have been impacted, the magnitude of the impact is different across groups. The recovery is also uneven, and the extent depends on education levels. Further, we do an event study analysis to examine the likelihood of getting a full-time job across different educational and gender groups. Men, on average, enjoy a higher likelihood of getting a full-time job than women. The likelihood coefficients also go up with increasing educational qualifications. Looking at skill heterogeneity, while the likelihood of getting a full-time job either goes down for most groups during the pandemic or the change is minuscule, strikingly it goes up for those with no education, for both men and women. The likelihood coefficients remain elevated for men even after the restrictions are removed, and that for women reverts to the level seen before the pandemic. Finally, this paper provides a way to continuously monitor the dynamics of the labour market as data is released in the regular intervals in the future, which would be of great value for researchers and policymakers alike.
ABSTRACT
In the age of global climate change, land use and land cover mapping help us to understand the vital modifications taking place in our environment. LULC mapping assumes great significance in planning, management of resources and keeping track of various programmes at different levels. The data acquired from the land use and land cover investigations are vital for policy formulation and sustainable development of our towns, cities and villages and also to track the disorganized growth of urban areas. Tourism is a tool for economic development in many developing countries of the world. The unplanned tourism growth has led to many ecological problems. This study makes an earnest effort to examine the LULC change using the transition model in the Bardez taluka, which is a well-known global tourist destination in Goa, India. The study has been investigated by using satellite imageries and GIS technologies have been used to analyse the changes occurring in LULC patterns for the years 1991, 2001 and 2021. The result indicates that the area under the built-up class has increased substantially by 11.12 sq. km. as a result of the rise in commercialization, tourism growth and tourism-related activities. Bardez taluka is known for some of the most breath-taking beaches in the world. During 2019-20, just before Covid-19, about 25, 33,234 domestic and 2, 74,840 foreign tourists visited the enchanting beaches of Bardez taluka. Land use classes such as residential, commercial and services, industrial, transportation and utilities also witnessed the growth in their land use and land cover classes whereas classes like agricultural land, coconut plantation, cashew plantation, barren land, DM and FDM forest land, open scrub and fairly dense scrub witnessed a negative change in their class values. © 2023, World Research Association. All rights reserved.
ABSTRACT
BACKGROUND: Since pneumonia caused by coronavirus disease 2019 (COVID-19) broke out in Wuhan, Hubei province, China, tremendous infected cases has risen all over the world attributed to its high transmissibility. We aimed to mathematically forecast the inflection point (IFP) of new cases in South Korea, Italy, and Iran, utilizing the transcendental model from China. METHODS: Data from reports released by the National Health Commission of the People's Republic of China (Dec 31, 2019 to Mar 5, 2020) and the World Health Organization (Jan 20, 2020 to Mar 5, 2020) were extracted as the training set and the data from Mar 6 to 9 as the validation set. New close contacts, newly confirmed cases, cumulative confirmed cases, non-severe cases, severe cases, critical cases, cured cases, and death were collected and analyzed. We analyzed the data above through the State Transition Matrix model. RESULTS: The optimistic scenario (non-Hubei model, daily increment rate of - 3.87%), the cautiously optimistic scenario (Hubei model, daily increment rate of - 2.20%), and the relatively pessimistic scenario (adjustment, daily increment rate of - 1.50%) were inferred and modeling from data in China. The IFP of time in South Korea would be Mar 6 to 12, Italy Mar 10 to 24, and Iran Mar 10 to 24. The numbers of cumulative confirmed patients will reach approximately 20 k in South Korea, 209 k in Italy, and 226 k in Iran under fitting scenarios, respectively. However, with the adoption of different diagnosis criteria, the variation of new cases could impose various influences in the predictive model. If that happens, the IFP of increment will be earlier than predicted above. CONCLUSION: The end of the pandemic is still inapproachable, and the number of confirmed cases is still escalating. With the augment of data, the world epidemic trend could be further predicted, and it is imperative to consummate the assignment of global medical resources to curb the development of COVID-19.