Transparency Index

Data Owner	Addul Riza ([e-mail]), 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

United Nations Development Program (UNDP)Publish What You Fund

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.