Devinit-尼泊尔LNOB评估：Tulsipur市的景观数据（英）-2023.6-24页_211kb

The LNOB Nepal assessment evaluates data on poverty and inequality in Tulsipur municipality, conducted as part of a leave-no-one-behind framework to support local decision-making. This summary highlights key findings from the report.

Introduction
The assessment applies a systematic methodology to review existing data, identify data gaps, and make recommendations. It was conducted in collaboration with local partners like Backward Society Education (BASE) and involved desk reviews, interviews, and workshops. The goal is to improve data for reducing poverty and inequality.

Part 1: Data Inventory
Nine data systems (5 administrative, 1 survey, 5 mixed-method) were identified, providing data on employment, education, health, and social protections. Gender disaggregation is complete but missing for geography (ward levels), age, ethnicity, and disability. Data collection frequency varies, with some systems updating in real-time, while others are annual or infrequent. Most data is not publicly accessible, and metadata is incomplete.

Part 2: Data Use
Data usage in Tulsipur is low due to limited data literacy, poor data quality, and a lack of sharing culture. Some departments use data for specific purposes, but broader evidence-based decision-making is rare. Most of the data in this part focuses on hindering effective use.

Part 3: Data Ecosystem Foundations
Strengths include an integrated data management system (IDMS), but weaknesses involve insufficient staffing for data management, weak cross-departmental coordination, and inadequate awareness of data protection laws. Legislation mandates data collection, but policies are ad-hoc, and budgets for data activities are very low (0.14% of total municipal spending).

Part 4: Recommendations
To strengthen the data ecosystem:

• Digitize and enhance frequency of data collection for ethnicity, disability, and other missing disaggregations.
• Improve staff training in data analysis, metadata creation, and IT skills.
• Increase transparency through open data sharing via IDMS, with privacy safeguards.
• Allocate more budget to data infrastructure and cross-departmental coordination.

Annex Highlights
Detailed tables outline data systems, collection processes, user roles, and municipal data responsibilities. For instance, the Local Government Operations Act (2017) requires data for poverty alleviation.

Notes Overview
Key points include the absence of unofficial data, software lag in data collection, and concerns about data validity and consultation in existing reports.