The spread of coronavirus disease, 2019, has affected several countries in the world including Asian countries. The occurrences of COVID infections are uneven across countries and the same is determined by socioeconomic situations prevailing in the countries besides the preparedness and management. The paper is an attempt to empirically examine the socioeconomic determinants of the occurrence of COVID in Asian countries considering the data as of June 18, 2020, for 42 Asian countries. A multiple regression analysis in a cross‐sectional framework is specified and ordinary least square (OLS) technique with heteroscedasticity corrected robust standard error is employed to obtain regression coefficients. Explanatory variables that are highly collinear have been dropped from the analysis. The findings of the study show a positive significant association of per capita gross national income and net migration with the incidence of total COVID‐19 cases and daily new cases. The size of net migration emerged to be a potential factor and positive in determining the total and new cases of COVID. Social capital as measured by voters' turnout ratio (VTR) in order to indicate the people's participation is found to be significant and negative for daily new cases per million population. People's participation has played a very important role in checking the incidence of COVID cases and its spread. In alternate models, countries having high incidence of poverty are also having higher cases of COVID. Though the countries having higher percentage of aged populations are more prone to be affected by the spread of virus, but the sign of the coefficient of this variable for Asian country is not in the expected line. Previous year health expenditure and diabetic prevalence rate are not significant in the analysis. Therefore, people‐centric plan and making people more participatory and responsive in adhering to the social distancing norms in public and workplace and adopting preventive measures need to be focused on COVID management strategies. The countries having larger net migration and poverty ratio need to evolve comprehensive and inclusive strategies for testing, tracing, and massive awareness for sanitary practices, social distancing, and following government regulation for management of COVID‐19, besides appropriate food security measures and free provision of sanitary kits for vulnerable section.
The study analyses the relationship between formal agricultural credit and agricultural productivity in India. Secondary data have been collected from various sources for the selected states of Andhra Pradesh, Assam, Bihar, Gujarat, Haryana, Himachal Pradesh, Karnataka, Kerala, Madhya Pradesh, Maharashtra, Punjab, Rajasthan, Odisha, Tamil Nadu, Uttar Pradesh and West Bengal for the time period 1990–1991 to 2017–2018. Fixed effect model is used to perform the state‐level panel data analysis to establish the relationship between the agricultural credit and agricultural productivity. In addition to this, the study also focuses on analysing the effectiveness of Doubling of agricultural credit policy. The findings from the analysis show that direct agricultural credit and doubling of agricultural credit policy has a positive impact on productivity, whereas the indirect credit has a significant negative impact on productivity. In order to increase agricultural productivity, policies should focus on providing direct credit at a larger scale.
This article empirically examines the existence of inter-sectoral growth linkages among the key sectors of the Indian economy at the state level. The examination evaluates the impact of the non-agricultural sectors of the states and that of the rest of the states on agricultural output of a particular state. An annual panel data set for 15 general category states have been taken for the period 1980–1981 to 2012–2013. Panel cointegration and fully modified ordinary least square methods have been used to study the existence of a long-run equilibrium relationship between sectors. The results suggest that there is a long-run equilibrium relationship among three sectors of the economy in the Indian states. The evaluation indicates that the industrial sector contributes positively in complementing the growth of agriculture, but the service sector advancement affects agricultural growth negatively. However, services having some direct reference to agriculture such as transport, storage and communication (TSC), trade, hotel and restaurant (THR) and banking and insurance (BI) have positive linkage with agriculture. The state specific econometric evaluation of the agricultural output varies relatively across different states, for example, in Kerala, the impact of rest of the industries and services leaves a positive significance; whereas, the study foresees the negative impact of industry and services in the states such as Bihar, Madhya Pradesh, Orissa and Rajasthan. In order to neutralize the negative linkages of service sector on agriculture, policies for promoting pro-agricultural services such as crop and agricultural insurance, agricultural loans, facilities for agricultural warehouse, marketing services, weather communication, transport services and provision of technical support to farm activities are important. Such initiatives can help agricultural sector grow along in the simultaneous development of sectors propelling growth of the economy at a faster rate.
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