Brief Background

Uttar Pradesh, the most populous state in India, has a substantial welfare landscape, encompassing over 180 state and central Direct Benefit Transfer (DBT) schemes with a budget exceeding 80,000 crore INR, impacting more than 17 crore beneficiaries. These schemes cover critical areas such as food security, education, health and nutrition, and livelihood support. Despite these efforts, there are significant challenges faced in scheme application and benefit delivery, some of which include low awareness among citizens, insufficient information on eligibility, and locating beneficiaries, particularly the most vulnerable. To tackle these challenges, the Government of Uttar Pradesh launched the Family ID initiative in July 2022. This program aims to establish a high-confidence database encompassing all families in the state to ensure proactive and efficient welfare delivery. By leveraging different departments, the initiative strives to identify and reach out to every eligible beneficiary proactively. Given that eligibility criteria such as caste, income, and domicile are determined at the family level, a comprehensive family-level database is crucial. This allows the government to accurately identify and enroll eligible beneficiaries. The Family ID, a unique 12-digit identification number, integrates various departmental databases with the existing Ration Card database. This integration ensures precise and comprehensive identification of eligible beneficiaries. The initiative is led by the Planning Department, with technical support from the IT & Electronics Department through Centre for e-Governance (CeG) and the State e-Governance Mission Team (SeMT). The Family ID initiative seeks to transition from individual to family-level eligibility determination, recognizing the importance of a holistic approach in welfare distribution. A family, as defined by this initiative, includes the primary earner, their spouse, parents, dependent siblings, children, and other dependents. Currently, the initiative covers approximately 3.6 crore families and 15 crore Aadhaar-validated Ration Card holders, with the Ration Card number serving as the Family ID. A dedicated portal facilitates the enrollment of non-ration card holders, ensuring inclusivity and comprehensive coverage. The initiative's objectives include the proactive delivery of schemes and services, effective welfare saturation, and the elimination of ineligible, duplicate, and ghost beneficiaries. The Family ID database serves as a central repository, providing a detailed record of family attributes crucial for determining eligibility and extending benefits. By leveraging high-confidence databases and integrating with departmental systems, the initiative enhances the accuracy and efficiency of benefit delivery. Key insights from the Family ID database reveal substantial improvements in the identification and inclusion of eligible beneficiaries across various schemes. Notably, significant numbers of previously unregistered pensioners and students have been integrated into existing welfare programs. The initiative has also streamlined the issuance of caste and other essential certificates, reducing processing times and enhancing accessibility. This paper explores the why Family ID was required and the initiative's implementation strategies, challenges, impacts, and learnings. Through advanced data integration and proactive service delivery, the Family ID initiative aims to set a new standard for transparent, efficient, and inclusive welfare distribution in Uttar Pradesh.

Benefits

·       Easy access of government benefits to beneficiaries

·       Priorities actual beneficiaries

·       Evidence based planning

·       Elimination of fake/bogus beneficiaries will result in huge savings to govt. budget.

·       360 degree view of Beneficiaries (Family Tree Structure)

·       Suo-Moto Service Delivery

·       Provision of Family Passbook

·       Entitlement Index for each family

·       Self-service Business Intelligence (BI) & Visualization

·       Advanced analytics capability like machine learning

·       Historical data retention for better prediction/insight