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Transparency

Data OwnerAddul Riza ([email protected]), BPPS Effectiveness
Availability in Data WarehouseNot Available
Data Refresh RateYearly
Accountability Weighted Scoring30%

Transparency Index

This is the global indicator.

Introduction to Indicator

{% embed url="https://www.publishwhatyoufund.org/the-index/2022/united-nations-development-program-undp/" %}

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

{% hint style="info" %} This is aligned with the Very Good / Good / Fair scoring at Publish What You Fund {% endhint %}

Transparency Dashboard Performance.

{% hint style="info" %} This is a draft and under testing {% endhint %}

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.