Modelling dynamics of institutional credit to agriculture in India

Not AvailableCredit is considered as one of the most important and basic input in agricultural production process. The prime source of agricultural credit in India has drastically shifted from non-institutional (money lenders) to institutional source in the last five decades due to various policy initiatives of Government of India. Grass root level analysis of the dynamic helps in further policy framework. Hence in this study based on district wise average outstanding agricultural credit by scheduled commercial banks (SCBs) for the TE ending 2017-18, three districts from each state indicating high, medium and low exposure categories is selected using clustering technique. For these study districts outstanding agricultural credit by SCBs was extracted (1976-2017) and analysed. From the Bai-Perron test years viz., 1983, 1990, 1997, 2004 and 2011 are identified to be most common structural breaks in the time series data of each district owing to various policy reforms in the field of agricultural finance. Based on these breaks the time series further subdivided into six phases viz., phase-I (1976-1982), phase-II (1983-1989), phase-III (1990-1996), phase-IV (1997-2003), phase-V (2004-2010) and phase-VI (2011-2017). Phase-wise CAGR was calculated for all the districts and Garrett ranking technique is employed for further ranking of phases across six regions of the country. Phase-I is identified as the phase with high rate of growth in agricultural advances in selected districts across all regions except southern where it is ranked second. The policy initiatives of that period i.e. setting of priority sector lending targets and establishment of Regional Rural Banks have played crucial role in this growth phenomenon of agricultural advances. Further recent policies like doubling agricultural package and ground level credit policies have also played crucial role in the growth of agricultural advances at grass root level in all regions except eastern and north-eastern regions. Whereas in the eastern and north-eastern region districts the growth in initial phases was relatively better than in the recent phases indicating the effectiveness of initial policy measures in those regions. Institutional credit to agriculture is influenced by various drivers. Hence factors like number of scheduled commercial bank branches, share of GIA in GSA, share of AUC in GSA and annual rainfall are regressed on district wise outstanding agricultural credit by SCBs. To explore the variability panel dataset was created with the above mentioned variables and the impact of these important drivers on institutional credit to agriculture is quantified at different levels (region level, credit exposure category wise and at national level) by employing panel data regression technique. The consistency and suitability of fixed effect model over random effect model is highlighted by Hausman test. Number of operating branches in the district is one of the important variables with positive influence indicates the institutional credit to agriculture is found to be more responsive for branch expansion especially in Andhra Pradesh, Karnataka, Chhattisgarh, Tamil Nadu and Paducherry. In this study, an attempt was made to evaluate the performance of models like ARIMA, ARIMAX and ARIMA intervention on district level agricultural credit series. In the ARIMAX model number of SCB branches in the district is used as explanatory variable and in the ARIMA intervention model year 2004 is used as intervention point. District wise best model was identified and forecasted the institutional credit supply to agriculture at district level for the next five years. We have also made an attempt to estimate the direct credit requirement for agriculture of the district under certain assumptions. Short term and term credit requirement of the district is arrived separately by using the district level data on area under crops, scale of finance and unit cost. Term credit requirement of southern region districts like Guntur and Belgaum is relatively high and in districts of north eastern region viz, West Tripura and Papumpure it is very low. Hence there is need for counterproductive policy of first estimation of agricultural credit requirements depending on crop patterns and later meeting the requirements through effective policies.Not Availabl

Genome-wide analyses identify a role for SLC17A4 and AADAT in thyroid hormone regulation.

Thyroid dysfunction is an important public health problem, which affects 10% of the general population and increases the risk of cardiovascular morbidity and mortality. Many aspects of thyroid hormone regulation have only partly been elucidated, including its transport, metabolism, and genetic determinants. Here we report a large meta-analysis of genome-wide association studies for thyroid function and dysfunction, testing 8 million genetic variants in up to 72,167 individuals. One-hundred-and-nine independent genetic variants are associated with these traits. A genetic risk score, calculated to assess their combined effects on clinical end points, shows significant associations with increased risk of both overt (Graves' disease) and subclinical thyroid disease, as well as clinical complications. By functional follow-up on selected signals, we identify a novel thyroid hormone transporter (SLC17A4) and a metabolizing enzyme (AADAT). Together, these results provide new knowledge about thyroid hormone physiology and disease, opening new possibilities for therapeutic targets