Abstract

Objectives: To critically evaluate direct benefit transfer mission in India, in terms of Aadhaar Card linkages, seeded bank accounts and in light of global experience. The paper also takes an account of the inflationary pressures that unconditional cash transfers may bring in to the system.

Methods: This is more of a descriptive study. Literature related to the study is critically approached and a few facts and figure that are officially released by direct benefit transfer mission of India has been used for the better understanding for the period 2013-2017.

Findings: From the analysis it is evident that by the time the Direct Benefit Transfer (DBT) system has been introduced in 2013, 5.68 crore beneficiaries were not holding an Aadhaar and the number became 8.36 crore in 2017. In 2017 only 29.01 % of fund transfer has been done through Aadhaar seeded bank accounts. Therefore even though Direct Benefit Transfer system is an innovative and efficient system, India was not prepared to take it up. So serious efforts of government is required in the grass ischolar_main level by providing Aadhaar and Aadhaar bridge payment assistance to the common people.

Application: Continuous evaluation is required to ensure that no beneficiaries have denied their services in name of Aadhaar bridge payments. They must be given enough time and support considering India’s depth and breadth in size and its poor literacy level among poor people. A study in the future when more data is available about the implementation of direct benefit transfer, can tell more systematically about the success and failures of the programme in India.

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Abstract

Objectives: To study ERPT - a case for India; to examine the vulnerability of various macroeconomic variables with a unit difference in exchange rate considering the liberalization policies adopted in the economy in 1991.

Methods: The study proposes various time series econometric tools like Johansen co-integration, Vector error correction mechanism (VECM), "impulse response function" and "variance decomposition" in order to test the objectives empirically. The proposed empirical analysis will be based on secondary data. Variables considered for this analysis are exchange rate, inflation, money supply, forex reserves, output and international oil price. And the sample period lies between 1993 April-2015 December.

Findings: From the co-integration analysis, a long-run relationship between the variables under consideration is found. Further from the error correction table, it has been noticed that all the variables are "mean reverting" and the "speed of adjustment parameter" reveals that forex reserves and prices are likely to be correcting the error more quickly. Further, by utilizing the "impulse response function" it has been observed that a shock to exchange rate pulls down forex reserves drastically; whereas it pushes prices on to the positive region. However, money supply did not respond to the changes in exchange rate much. In addition from the impulse response analysis, it is noticed that shocks in crude oil prices reduces the foreign exchange reserves substantially. Further, the "variance decomposition" analysis advocates that the forecast error variance of exchange rate has largely been explained by oil and forex reserves and prices in the later quarters. From this it is evident that financial sector variables are not in fact fully insulated from the supply side shocks. The forecast error variance of forex reserves is mostly explained by exchange rate and prices. This finding corroborates the earlier empirical evidence obtained from the ECM analysis. However, the "variance decomposition" of prices reveals that its forecast error variance is by and large explained by oil prices, which shows that prices are indeed more vulnerable to the supply side shocks.

Applications: This study enquires into the relevant concern that "is ERPT a case in India?" After conducting a thorough empirical examination, it has been found that the answer to this question is "yes". The fluctuations in exchange rate do have some impact on some of the important macroeconomic variables such as forex reserves and price levels. Therefore, it requires a serious attention of macroeconomic experts and monetary authority of India to redesign exchange rate management policy such a way that monetary instruments of monetary policy will do its best without getting neutralised by inflation and changes in foreign exchange reserves.

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Abstract

Objectives: To identify effectiveness of Fertilizer Subsidy in the agriculture production considering agricultural credit statistics for India.

Methods: Annual time series data is collected for India from 1970-71 to 2016-17 from EPWRF database. Variables such as Agricultural GDP, Agricultural Credit and Fertilizer subsidy are collected for the analysis. Time series properties of the variables are checked using Augmented-Dickey-Fuller unit ischolar_main test and results were confirmed with Phillips & Perron Unit Root Test. Johansen co-integration test were used to check for any long-run co-integrating vector among the variables. Further vector error correction model and impulse response function was used to explain short-run dynamics among the variable.

Findings: Unit ischolar_main tests confirm presence of unit ischolar_main among the variables at level and all variables are stationary at first difference. Johansen co-integration test identified at most one co-integrating vector among the variables. After normalizing agricultural GDP, the co-integrating relationship suggests that agricultural credit is positively contributing to agricultural GDP and fertilizer subsidy is negatively contributing to agricultural GDP which means an increase in agricultural credit will increase agricultural GDP where an increase in fertilizer subsidy will decrease the agricultural GDP. The negative relationship between fertilizer subsidy and agricultural GDP is because of the high leakage in the delivery system of fertilizer subsidy. Speed of adjustment parameter suggests that agricultural credit corrects the short-run equilibrium more quickly than fertilizer subsidy. Impulse response function suggests that a standard deviation shock to fertilizer will not reflect in agricultural production for long (less than one year) but a standard deviation shock to agricultural credit may affect the agricultural production severely. Impulses of credit shock will reflect in production for four to five years.

Applications: Attempts by government to withdraw agricultural subsidy and establish cash transfers and more focus on agriculture credits will improve agricultural production in the long-run.

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