data scattered across multiple sources are preferable to partial records even if they are in a single publication.

Reflecting these priorities, this section focuses on a sub-index that aggregates five core indicators that describe each country’s data availability, and excludes: data format (indicator 1), periodicity (indicator 5), and time lag (indicator 6). Indicator 9 on other debt statistics/contingent liabilities is also removed as it does not apply to all countries (e.g., a country may not have guarantees or arrears).

The index is therefore obtained as a simple average of the following sub-components of the heatmap that measure: instrument and sectoral coverage of debt statistics, availability of financial terms on new loans, and publication of DMS and ABP.

The index is standardized to have mean zero and unit variance in the sample of IDA countries to facilitate the interpretation of the results. Relying on cross sectional data of 74 IDA countries, the role of different factors on debt transparency is estimated using the following equation: where Yij denotes the transparency level for country i in subregion j .<sup>11</sup> The matrix Xij includes the following set of determinants:

- _Type of debt recording and management system (DRMS)_ . Debt Management Offices (DMOs) in LIDCs uses either off-the shelf recording systems, developed by the Commonwealth Secretariat (CS-DRMS/Meridian) or UNCTAD (DMFAS), or idiosyncratic procedures (mostly Excel-based).

- _Portfolio Composition_ : presence of collateralized debt or Eurobonds in the public debt portfolio.

- _External scrutiny_ : availability of a rating from one of three major rating agencies (Fitch, Moody’s, Standard & Poor’s).

- _Debt management capacity_ , proxied by the share of college graduates among DMO staff (source: WB 2020 survey)

- _DM Legal framework:_ presence of legal requirements to produce debt statistics, strategy and/or annual borrowing plan (source: WB 2020 survey)

The matrix Zij adds a set of controls including categorical variables that capture the country’s income level, debt sustainability risk as rated by WB/IMF’s LIC-DSA (low, moderate, high, or in debt distress), status of fragile and conflict-afflicted country as defined by the WB,<sup>12</sup> and participation in the Highly Indebted Poor Countries (HIPC) initiative. Even though country fixed effects to address endogeneity could not be added, sub regional fixed effects ( δj ) have been included to partially deal with this issue.<sup>13</sup> Table 1 presents the results from estimating the linear model in equation (1). The model in column 1 shows that using a standard DRMS significantly contributes to improvements in debt transparency. Comparing an off-the-shelf DRMS to a non-standardized system has shown that both CS-DRMS/Meridian and DMFAS facilitate production of debt statistics, the former being more robust. LIDCs benefit from

> 11 Regional patterns are relevant since debt disclosure requirements could be enforced by regional institutions (e.g. Central Bank of West African States or Eastern Caribbean Central Bank).

> 12 <u>https://www.worldbank.org/en/topic/fragilityconflictviolence/brief/harmonized-list-of-fragile-situations</u> 13 Subregions are defined as follows: Central America and Caribbean (8), Eastern Europe and Central Asia (4), East Asia and Pacific (18), South Asia (6); Sub-Saharan Africa non-CEMAC (23); Sub-Saharan Africa CEMAC (4), West Africa non-WAEMU (9); West-Africa WAEMU (7).