Effectiveness of government schemes: A critical review of most widely used schemes (PMFBY)

Introduction

India is primarily an agrarian economy, with a significant proportion of the population directly or indirectly dependent on agriculture for their livelihood. Despite the increasing contributions of the industrial and service sectors to the national GDP, agriculture continues to play a crucial role in ensuring food security, rural employment, and sustainable economic development. However, Indian agriculture is fundamentally characterized by small and marginal holdings, high dependence on the monsoon, and vulnerability to a wide array of risks including natural calamities like droughts, floods, hailstorms, and pest attacks. In this context, crop insurance schemes assume vital importance in safeguarding farmers from financial distress and ensuring the stability of agricultural incomes. The introduction of the Pradhan Mantri Fasal Bima Yojana (PMFBY) in 2016 marked a significant milestone in India's agricultural risk management framework. Replacing earlier schemes like the National Agricultural Insurance Scheme (NAIS) and Modified National Agricultural Insurance Scheme (MNAIS), PMFBY was launched with the aim of offering comprehensive crop insurance coverage to farmers at substantially subsidized premium rates. Unlike its predecessors, PMFBY sought to address key implementation bottlenecks, improve claim settlement processes, and enhance transparency through technological integration. The scheme emphasized uniform premium rates for farmers-2% for Kharif crops, 1.5% for Rabi crops, and 5% for commercial/horticultural crops-while the remaining premium burden was to be equally shared between the central and state governments. It also sought to integrate remote sensing, smartphones, and drone technology for accurate yield estimation and faster claim processing.

Review of Literature

Kumar and Singh (2020) [7] performed a comparative study between PMFBY and NAIS and concluded that although PMFBY had better design features, it lagged in achieving farmer satisfaction due to inconsistent implementation. The research highlights that innovative design alone is not sufficient; consistent delivery is key to building trust and satisfaction. Deshmukh and Pawar (2020) [5] evaluated PMFBY's performance in Maharashtra and found that although the scheme increased coverage, satisfaction remained low due to delayed claim settlements and lack of coordination between banks and insurance firms. Their study emphasizes the implementation challenges that directly influence farmers' perceptions and trust in crop insurance programs, www.extensionjournal.com offering insights into areas where PMFBY needs policy and operational reforms to enhance satisfaction. Patel et al. (2021) conducted a satisfaction survey of PMFBY beneficiaries in Gujarat and found that timely service, effective grievance redressal, and personal interaction with field officers significantly influenced satisfaction. This study adds a behavioral and servicequality dimension to the evaluation of crop insurance schemes, reinforcing the human factor in policy implementation.

Objectives of the Study

The specific objectives of this study are as follows: 1. To study about the socio-economic profile of the respondent in the study area.

To assess the impact of Pradhan Mantri Fasal Bima

Yojana on reducing agricultural risk. 3. To evaluate farmer satisfaction and awareness levels regarding Pradhan Mantri Fasal Bima Yojana. 4. To assess the key constraints faced by farmers in accessing benefits from the yojana and suggest suitable measures.

Research Methodology 4.1 Study Area and Sampling Design

The study focuses on Darbhanga district of Bihar, chosen purposively due to its significant role in agriculture and a relatively high number of PMFBY beneficiaries. Darbhanga is an important district in the Mithila region of Bihar, which has a predominantly agrarian economy. The district is known for its rice, maize, and sugarcane cultivation, but also includes a considerable amount of land dedicated to crop farming that can benefit from PMFBY. A multistage sampling technique was used for respondent selection. Initially, five villages were chosen based on represent a range of socio-economic conditions, farming practices, and insurance participation rates. From these villages, 120 respondents were randomly selected.

The sample was stratified into five categories based on landholding:

• Marginal Farmers (less than 1 ha of land): 30% (36 respondents) • Small Farmers (1 to 2 ha of land): 25% (30 respondents) • Medium Farmers (2 to 4 ha of land): 20% (24 respondents) • Large Farmers (above 4 ha of land): 25% (30 respondents)

Data Collection

Primary data for the study is collected through structured interviews and questionnaires, which are pre-tested before the main data collection. Secondary data is collected from various relevant sources to provide context and supplement the primary data.

Analytical Tools

• Likert Scale: Applied to opinions, attitudes, or behaviours of the respondents.

Results and Discussion

The present study of socio-economic profile of the respondents in the study area observed and relating through operationalization according to their age, education skill, income. The majority of marginal and small farmers reported a significant or moderate reduction in agricultural risks, with marginal farmers (40%) and small farmers (45.7%) being the most positive. Larger farmers also perceived a positive impact, with 53.3% of medium farmers reporting a significant reduction in risk. A small percentage (6.7%) reported increased risk, especially among marginal farmers. The awareness level of PMFBY is generally high, especially among larger farmers, with 80% of large farmers fully aware. Marginal and small farmers show a mix of full and partial awareness, with 53.3% of marginal farmers being partially aware. More awareness efforts are needed for small and marginal farmers. 42.5% of farmers are satisfied with the PMFBY claims process, with 30% being very satisfied. However, there are still issues, with 12.5% expressing dissatisfaction, especially in the marginal and small farmer categories. These groups are more likely to experience delays or complications in the claims process

Age wise Distribution of farmers

References

1. Bandyopadhyay S, Das A. Role of private insurance companies in PMFBY: A critical analysis. Indian J Agric Econ. 2018;73(3):410-421.
2. Bhagat R, Reddy P. Socio-economic impact of PMFBY on tribal farmers in Odisha. J Rural Dev Manag Stud. 2019;5(2):89-98.
3. Chand R, Raju SS. Progress and problems in agricultural insurance in India. Indian J Agric Econ. 2009;64(2):285-298.
4. Das K, Tripathi A. An evaluation of PMFBY implementation in West Bengal. Indian J Public Adm. 2020;66(1):45-59.
5. Deshmukh S, Pawar R. Performance evaluation of PMFBY in Maharashtra. J Agric Insur Risk Manag. 2020;9(4):121-134.
6. Gulati A, Terway P, Hussain S. Pradhan Mantri Fasal Bima Yojana (PMFBY): An assessment. Indian Council for Research on International Economic Relations (ICRIER) Working Paper No. 352. 2017.
7. Kumar A, Singh D. A comparative study of NAIS and PMFBY with reference to farmer satisfaction. Indian J Agric Res Policy. 2020;9(1):13-25.
8. Kumari R, Singh R. Impact of PMFBY implementation in Bihar. J Agrar Stud. 2018;4(3):101.