ids-data-creation.qmd

# The Journey of Debt Data - From Reporting to Release

Understanding the process by which the International Debt Statistics (IDS) are created is crucial for interpreting and using the data effectively. This chapter provides a detailed look at the journey of debt data from initial reporting to final publication.

## The Debtor Reporting System (DRS)

The foundation of the IDS is the World Bank's Debtor Reporting System (DRS), established in 1951.

### Key Features of the DRS:

-   Mandatory reporting requirement for all countries borrowing from the World Bank (IBRD or IDA)
-   Loan-by-loan reporting for public and publicly guaranteed debt
-   Aggregate reporting for private non-guaranteed debt
-   Quarterly reporting of new loan commitments
-   Annual reporting of debt stocks and flows

### Types of Data Collected:

-   Loan terms (interest rates, maturity, grace period)
-   Creditor information
-   Currency of repayment
-   Disbursements
-   Principal and interest payments
-   Debt restructuring information

## Data Submission Process

Countries typically follow these steps to submit data:

1.  Data compilation by national debt management offices
2.  Verification and approval by relevant government authorities
3.  Submission to the World Bank, usually via electronic templates

Frequency: New commitments are reported quarterly, while stocks and flows are reported annually.

## Data Validation and Quality Control

Upon receipt, World Bank staff perform several checks:

1.  Consistency checks: Ensuring data aligns with previous submissions
2.  Cross-validation: Comparing with data from other sources (e.g., creditor reports, IMF data)
3.  Follow-up: Querying countries about discrepancies or unusual patterns
4.  Historical reconciliation: Ensuring consistency of time series data

## Data Enrichment

The World Bank enhances the raw DRS data in several ways:

1.  Adding data on IMF lending and SDR allocations
2.  Incorporating short-term debt data from other sources (e.g., BIS, QEDS)
3.  Estimating missing data where necessary
4.  Calculating derived indicators (e.g., debt ratios)

## Integration with Other Datasets

To provide a comprehensive picture, IDS data is integrated with:

-   World Bank national accounts data (for GNI, GDP)
-   IMF Balance of Payments data (for exports, imports)
-   World Bank Global Economic Monitor (for reserves data)

## Aggregation and Analysis

The World Bank team then:

1.  Produces country-level aggregates
2.  Calculates regional and income group aggregates
3.  Analyzes trends and patterns in the data
4.  Prepares analytical text and visualizations for the IDS report

## Review and Verification

Before publication, the data undergoes several rounds of review:

1.  Internal review by World Bank debt statisticians
2.  Cross-departmental review within the World Bank
3.  Review by country authorities, who have the opportunity to comment on their data

## Publication and Dissemination

The final steps in the process are:

1.  Preparation of the annual IDS report
2.  Updating of the online IDS database
3.  Creation of data visualizations and other supplementary materials
4.  Official release, typically in October each year

## Ongoing Updates

Even after publication, the process continues:

-   Data revisions are incorporated as countries provide updated information
-   The online database is refreshed periodically (typically December and April)

## Challenges in the Process

Several challenges can affect the data creation process:

1.  Reporting delays or incomplete submissions from countries
2.  Differences in accounting practices across countries
3.  Difficulties in capturing all forms of debt, especially newer instruments
4.  Balancing timeliness with comprehensiveness and accuracy

## Recent Improvements

The World Bank has made several enhancements to the process in recent years:

1.  Increased emphasis on capturing previously unreported debts
2.  Improved coverage of debt to non-traditional creditors (e.g., China)
3.  Enhanced data on the terms of lending (e.g., collateralization)
4.  Greater integration with other debt databases (e.g., QEDS)

Understanding this data creation process is crucial for users of the IDS. It helps in appreciating the strengths of the data, such as its comprehensive coverage and rigorous validation. At the same time, it highlights potential limitations, such as the reliance on self-reporting and the challenges in capturing all forms of debt. This knowledge allows for more informed and nuanced use of the IDS in research and policy analysis.