Devinit-尼泊尔LNOB评估：Simta市的数据景观（英）-2023.6-22页_206kb

Summary of LNOB Assessment Nepal: Data Landscaping in Simta Municipality, June 2023

Overview

This report evaluates Simta Municipality's data ecosystem for poverty and inequality, assessing data inventory, use, and foundational elements to support evidence-informed decision-making. Conducted in 2022 and 2023, the assessment used a data landscaping approach, identifying gaps and providing recommendations. Key themes include data accessibility, disaggregation, and infrastructure limitations.

Data Inventory

Nine data systems were identified, including five administrative, two surveys, and two mixed-methods sources, covering demographics, social protection, education, health, and disaster risk reduction. Common issues include:

• Inadequate disaggregation for ethnicity (absent in seven systems) and disability (collected in only one system), limiting intersectional analysis.
• Variable data frequency, with systems like HMIS updated monthly while others, such as the IEMIS, are infrequent (e.g., annually), hindering reliable evidence.
• Metadata incomplete for four systems, and open data limited to one source (DRR portal summary statistics).
• Discrepancies in disaster data, e.g., conflicting figures across sources, undermining trust.
• No identified unofficial poverty and inequality data sources.

Data Use

Current data supports policies, such as social security distribution and health planning, but its potential is underutilized due to:

• Barriers including low interest in evidence-based decisions, lack of digitization, poor accessibility, distrust in data quality, and insufficient data literacy.
• Recommendations suggest training staff and promoting data advocacy.

Data Ecosystem Foundations

The ecosystem is supported by policies and infrastructure but faces challenges:

• Governance: Simta has a Data Management Policy but it is not widely known or implemented.
• ICT: Poor electrification (only ward six fully electrified) and weak internet connectivity reduce benefits of digitization.
• Coordination: No formal cross-departmental data-sharing protocols; the Department of Planning, Monitoring and Data exists only on paper.
• Budget: No specific funding allocated for data activities, except for a small portion towards IEMIS training.

Recommendations

• Data Sources: Collect ethnicity and disability data digitally, improve education data frequency, train ward-level staff, complete metadata for all systems, and expand open data availability.
• Data Use: Enhance basic data analysis skills, target data dissemination, and promote evidence-informed decision-making through workshops.
• Data Governance: Mainstream policies into daily operations, ensure full electrification and internet connectivity, establish the data department, and increase budget allocation.