Transparency
Data Owner | Addul Riza (abdul.riza@undp.org), BPPS Effectiveness |
Availability in Data Warehouse | Not Available |
Data Refresh Rate | Yearly |
Accountability Weighted Scoring | 30% |
Transparency Index
This is the global indicator.
Introduction to Indicator
Calculation of Scoring.
Publish What You Fund Score = Indicator Score
The traffic light indicator methodology for this indicator is:
Green = 80+
Yellow = 60+
Red = <60
This is aligned with the Very Good / Good / Fair scoring at Publish What You Fund
Transparency Dashboard Performance.
This is a draft and under testing
This is the indicator for Country & Bureau levels.
Available Data
Downloading the dataset from the UNDP Data Warehouse:
UNDP_PROJECTS data - List of all unique projects in UNDP
Columns in UNDP_PROJECTS data
hq_co: Headquarters (HQ), Country Office (CO), or RC.
bureau: The name of the specialized unit or division responsible.
rollup_ou: Organizational unit's code.
rollup_ou_description: Organizational unit's name.
PROJECT_ID: Project identifier.
PROJECT_NUMBER: Project number.
PROJECT_NAME: Name of the project.
PROJECT_DESCRIPTION: Description of the project.
ATLAS_AWARD_NUMBER: Atlas award number.
ATLAS_AWARD_DESCIPTION: Description of the Atlas award.
BUSINESS_UNIT: Business unit identifier.
ORGANIZATION: Organization involved in the project.
DEPARTMENT: Department associated with the project.
START_DATE: Start date of the project.
COMPLETION_DATE: Completion date of the project.
CLOSED_DATE: Date when the project was closed.
PROJECT_TYPE: Type of project.
PROJECT_TYPE_DESCRIPTION: Description of the project type.
PROJECT_STATUS: Status of the project.
PROJECT_MANAGER: Project manager's name.
PROJECT_MANAGER_EMAIL: Email address of the project manager.
IMPLEMENTING_PARTNER: Implementing partner code.
IMPLEMENTING_PARTNER_DESCRIPTION: Description of the implementing partner.
IMPLEMENTATION_MODALITY: Implementation modality code.
IMPLEMENTATION_MODALITY_DESCRIPTION: Description of the implementation modality.
PROJECT_ORIG_TEMPLATE: Original template of the project.
PROGRAMME_FUNDING_FLAG: Flag indicating program funding.
GEF_GCF_PROJECT_FLAG: Flag indicating GEF/GCF project.
Other_References_Value: Other references value.
Calculation:
1. Define the rollup_ou_description to filter:
Filters the DataFrame df_UNDP_PROJECTS by selecting only the rows where the value in the 'rollup_ou' column matches the value stored in the variable target_rollup_ou (note: which is 'ALB' in this case as an example).
2. Calculates the count of projects for each unique project status in the dataframe if count>0.
The statuses are:
Financially Closed
On Going
Operationally Closed
Submit for Operational Close
Submitted for Financial close
3. Visualization - Grouping projects by project status and counting the number of projects in each status.
4. Filter data by specified project statuses: Select rows from the dataframe where the 'PROJECT_STATUS' column matches the specified status values: 'On Going', 'Operationally Closed', and 'Submit for Operational Close'.
Then remaining visualizations are done using this filtered dataframe.
5. Visualization: creates a horizontal bar chart showing the number of projects by project type, with the bars colored in sky blue except for the bars representing projects with null Project_Type, which are colored in red.
Grouping projects by project type and counting the number of projects in each type - First, count the occurrences of each unique project type in the 'PROJECT_TYPE' column. Then resets the index and renames the columns to 'Project_Type' and 'Count' respectively to prepare the data for visualization.
Then, filters the dataframe to select rows where the 'PROJECT_TYPE' column is null and counting projects with null Project_Type. After that, adding the count of projects with null Project_Type to the dataframe. Then created a bar chart including the rest of the project types.
6. Visualization: Counting the number of projects with and without descriptions - counts the number of non-null values in the 'PROJECT_DESCRIPTION' column and then calculates the number of projects without descriptions by subtracting the count of projects with descriptions from the total number of projects. Then created a bar chart and annotated each bar with its respective count.
7. Visualization: Counting the number of projects with and without PROJECT_TYPE - calculates the number of non-null values in the 'PROJECT_TYPE' column and then calculates the number of projects without a 'PROJECT_TYPE' by subtracting the count of projects with 'PROJECT_TYPE' from the total number of projects. Then created a bar chart and annotated each bar with its respective count.
8. Visualization: creates a horizontal bar chart, annotates each bar with its respective count - Computes various statistics about projects on such as:
Total number of projects
Projects with less than 10 characters in project name
Projects without description
Projects without implementing partner
Projects with description less than 80 characters
Transparency Scoring for CO - Methodology:
Step 1: Make sure having a filtered dataframe (rollup_ou = [mention CO], also, PROJECT_STATUS'].isin(['On Going', 'Operationally Closed', 'Submit for Operational Close').
Step 2: Penalty Factors for More Severe Penalization (1 to 5): Define penalty factors for each indicator to adjust the severity of penalization -
Penalty factor for projects with names shorter than 10 characters = 1
Penalty factor for projects without a description = 2
Penalty factor for projects without an implementing partner = 4
Penalty factor for projects with descriptions shorter than 80 characters = 3
Penalty factor for projects without a project type = 5
Step 3: Count of All Projects : Calculate the total number of projects for normalization purposes.
Step 4: count the occurrences of projects in each noncompliance case:
projects with names shorter than 10 characters.
projects without a description.
projects without an implementing partner.
projects with descriptions shorter than 80 characters.
projects without a project type
Step 5: Determine total number of projects for each indicator.
Step 6: Scores Calculation:
Calculation of Percentages: Calculate the percentage of non-compliant projects relative to the total number of projects for each indicator.
Penalty Factor Application: Apply the penalty factors to the percentages of non-compliant projects instead of raw counts. This adjustment ensures that the severity of penalization is proportionate to the extent of non-compliance relative to the total project count.
Scoring Calculation: Calculate the scores based on the adjusted percentages of non-compliant projects multiplied by 20. This scaling operation ensures that the scores fall within a range of 0 to 20.
Finally, individual scores for each indicator, along with the total score out of 100 for the CO, are displayed. Note: With each indicator having a maximum of 20 points and 5 indicators, the maximum possible total score would be 100 points.
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