Training course on The Use of Administrative Data for Social Protection Research

Training Course on The Use of Administrative Data for Social Protection Research 

Introduction

The Use of Administrative Data for Social Protection Research is a rapidly growing and transformative area that leverages routinely collected program and government data to generate timely, granular, and comprehensive insights into social protection interventions. Unlike survey data, which can be costly and infrequent, administrative data offers a rich, often real-time, source of information on beneficiary characteristics, program participation, payment delivery, and interactions with other public services. This course moves beyond basic data extraction to equip participants with the advanced theoretical and practical tools necessary to access, manage, analyze, and ethically utilize large-scale administrative datasets for rigorous social protection research, M&E, and policy analysis. It recognizes that unlocking the potential of administrative data is key to building more efficient, responsive, and evidence-based social protection systems.

Training Course on The Use of Administrative Data for Social Protection Research is meticulously designed to equip with the advanced theoretical insights and intensive practical tools necessary to excel in The Use of Administrative Data for Social Protection Research. We will delve into the foundational concepts of administrative data sources and their characteristics, master the intricacies of data access, cleaning, and record linkage, and explore cutting-edge approaches to data analysis, ethical considerations, and data governance. A significant focus will be placed on hands-on application, analyzing real-world complex social protection administrative datasets, and developing tailored research plans that maximize the utility of this invaluable resource. By integrating industry best practices, analyzing complex case studies, and engaging in intensive practical exercises, attendees will develop the strategic acumen to confidently lead and implement research using administrative data, fostering unparalleled data efficiency, analytical depth, and evidence-informed decision-making.

Course Objectives

Upon completion of this course, participants will be able to:

1. Analyze the fundamental concepts of administrative data and its unique characteristics for social protection research.
2. Comprehend the strategic advantages and limitations of using administrative data for M&E and research.
3. Master methodologies for accessing and acquiring administrative data from various sources.
4. Develop expertise in data cleaning, validation, and quality assurance for administrative datasets.
5. Formulate strategies for record linkage and data integration across multiple administrative sources.
6. Understand the critical role of data governance, security, and privacy in administrative data use.
7. Implement robust approaches to descriptive and inferential analysis of administrative data.
8. Explore key strategies for leveraging administrative data for targeting, fraud detection, and program optimization.
9. Apply methodologies for linking administrative data with survey data for richer insights.
10. Understand and address ethical and legal considerations in using sensitive administrative data.
11. Develop preliminary skills in using statistical software for large administrative datasets.
12. Design a comprehensive research plan utilizing administrative data for a social protection topic.
13. Examine global best practices and lessons learned in administrative data use for social protection.

Target Audience

This course is essential for professionals seeking to leverage administrative data for social protection research:

1. Researchers & Academics: Specializing in social policy, economics, and public administration.
2. Data Analysts & Statisticians: Working with large government datasets.
3. M&E Specialists: Seeking to integrate administrative data into evaluations.
4. Government Officials: From ministries of social welfare, finance, planning, and national statistical offices.
5. Social Protection Program Managers: Overseeing program data and operations.
6. IT Professionals: Managing social protection information systems.
7. Development Practitioners: From NGOs and international organizations.
8. Consultants: Providing data analysis and research services.

Course Duration: 10 Days

Course Modules

Module 1: Introduction to Administrative Data for Social Protection

• Define administrative data and its unique characteristics (e.g., routinely collected, large scale, real-time potential).
• Discuss the various sources of administrative data relevant to social protection (e.g., social registries, payment systems, health, education, tax).
• Understand the advantages of administrative data for M&E and research (e.g., cost-effectiveness, coverage, timeliness).
• Explore the limitations of administrative data (e.g., purpose-built, data quality, lack of counterfactual).
• Identify key opportunities for leveraging administrative data in social protection.

Module 2: Accessing and Acquiring Administrative Data

• Master methodologies for navigating data access protocols and legal frameworks.
• Learn about data sharing agreements and Memoranda of Understanding (MoUs).
• Discuss the importance of trust-building and collaboration with data custodians.
• Explore different data acquisition methods (e.g., direct access, data extracts, APIs).
• Address practical challenges in accessing sensitive administrative data.

Module 3: Data Cleaning, Validation, and Quality Assurance 

• Develop expertise in data cleaning and validation specific to administrative datasets.
• Learn techniques for identifying and handling missing values, outliers, and inconsistencies.
• Understand methods for de-duplication and record standardization.
• Discuss the importance of data quality checks and automated validation rules.
• Practice cleaning and validating a sample administrative dataset.

Module 4: Record Linkage and Data Integration

• Formulate strategies for record linkage across multiple administrative sources.
• Differentiate between deterministic, probabilistic, and fuzzy matching techniques.
• Learn about the role of unique identifiers (e.g., national ID numbers) in linkage.
• Discuss the challenges of linking data with varying levels of granularity.
• Explore methods for integrating linked datasets for comprehensive analysis.

Module 5: Data Governance, Security, and Privacy

• Understand the critical role of data governance in administrative data use.
• Learn about data security protocols: encryption, access control, audit trails.
• Discuss principles of data privacy, confidentiality, and anonymization.
• Explore legal and ethical frameworks for using sensitive administrative data.
• Develop protocols for responsible data handling and storage.

Module 6: Descriptive and Inferential Analysis of Administrative Data

• Implement robust approaches to descriptive analysis of administrative data.
• Learn to calculate key indicators: coverage, timeliness of payments, targeting accuracy.
• Discuss inferential analysis techniques applicable to large datasets.
• Explore methods for analyzing trends, patterns, and anomalies in administrative data.
• Practice conducting descriptive and basic inferential analysis.

Module 7: Leveraging Administrative Data for Program Optimization

• Explore key strategies for using administrative data for program design and improvement.
• Discuss its role in optimizing targeting and beneficiary selection.
• Learn about fraud detection and error identification using data analytics.
• Understand how administrative data can inform payment delivery mechanisms.
• Analyze case studies of administrative data driving program efficiency.

Module 8: Linking Administrative Data with Survey Data

• Apply methodologies for linking administrative data with survey data for richer insights.
• Understand the complementary nature of these two data sources.
• Discuss the advantages of linked data for causal inference and impact evaluation.
• Learn about methods for reconciling differences between linked datasets.
• Explore case studies where linked data provided novel insights.

Module 9: Ethical and Legal Considerations in Practice

• Understand and address complex ethical and legal dilemmas in using administrative data.
• Discuss issues of function creep, surveillance, and potential discrimination.
• Learn about data minimization and purpose limitation principles.
• Explore the role of data protection authorities and oversight bodies.
• Develop ethical review protocols for administrative data research.

Module 10: Statistical Software for Large Administrative Datasets

• Develop preliminary skills in using statistical software for large administrative datasets.
• Gain hands-on experience with data manipulation and analysis in Stata, R, or Python.
• Learn about efficient coding practices for handling big administrative data.
• Discuss strategies for managing computational resources.
• Practice analyzing a large administrative dataset using chosen software.

Module 11: Challenges and Solutions in Administrative Data Use

• Understand common technical, institutional, and political challenges.
• Discuss issues of data quality, fragmentation, and lack of standardization.
• Explore strategies for building capacity and fostering collaboration.
• Learn about incremental approaches to administrative data utilization.
• Analyze lessons learned from successful administrative data initiatives.

Module 12: Practical Application and Capstone Project

• Design a comprehensive research plan utilizing administrative data for a social protection topic.
• Develop a strategy for data access, cleaning, linkage, and analysis.
• Present the research plan and discuss its potential contributions.
• Collaborate on a group project to analyze a simulated administrative dataset.
• Discuss the policy implications of findings derived from administrative data.

 

Training Methodology

• Interactive Workshops: Facilitated discussions, group exercises, and problem-solving activities.
• Case Studies: Real-world examples to illustrate successful community-based surveillance practices.
• Role-Playing and Simulations: Practice engaging communities in surveillance activities.
• Expert Presentations: Insights from experienced public health professionals and community leaders.
• Group Projects: Collaborative development of community surveillance plans.
• Action Planning: Development of personalized action plans for implementing community-based surveillance.
• Digital Tools and Resources: Utilization of online platforms for collaboration and learning.
• Peer-to-Peer Learning: Sharing experiences and insights on community engagement.
• Post-Training Support: Access to online forums, mentorship, and continued learning resources.

 

Register as a group from 3 participants for a Discount

Send us an email: info@datastatresearch.org or call +254724527104

Certification

Upon successful completion of this training, participants will be issued with a globally recognized certificate.

Tailor-Made Course

We also offer tailor-made courses based on your needs.

Key Notes

• Participants must be conversant in English.
• Upon completion of training, participants will receive an Authorized Training Certificate.
• The course duration is flexible and can be modified to fit any number of days.
• Course fee includes facilitation, training materials, 2 coffee breaks, buffet lunch, and a Certificate upon successful completion.
• One-year post-training support, consultation, and coaching provided after the course.
• Payment should be made at least a week before the training commencement to DATASTAT CONSULTANCY LTD account, as indicated in the invoice, to enable