## Improving Sovereign Financing Conditions Through Data Transparency

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### Introduction: background, definitions, and approach
- Transparency reduces uncertainty about economic developments, enhances accountability and credibility of policies, and helps make markets more efficient.
- Increased transparency through data dissemination is expected to lower risk premiums when countries access financial markets.
- Focus: relationship between increased transparency through data dissemination and the sovereign bond spreads of emerging and frontier markets.

- IMF Data Standards timeline and definitions:
  - Special Data Dissemination Standard (SDDS) established in 1996 to guide countries with or seeking access to international capital markets.
  - General Data Dissemination System (GDDS) launched in 1997 to guide development of statistical systems; initially focused on disclosure of statistical frameworks, metadata, and development plans rather than data dissemination.
  - SDDS Plus established in 2012 as the third and highest tier, aimed at economies with systemically important financial sectors.
  - e-GDDS (enhanced GDDS) launched in 2015: key element is publication of data (not just metadata) through a National Summary Data Page (NSDP).
  - Paper focuses on economies that have enhanced data dissemination by joining the SDDS, GDDS prior to 2015, and those that have launched NSDPs under the e-GDDS.

- Methods and empirical approach:
  - Event studies and local projection models to study dynamic response of sovereign spreads to joining data dissemination standards.
  - Comparison across tiers (SDDS, GDDS, e-GDDS/NSDP) and investigation of institutional environment effects.

- Key introductory findings:
  - Sovereign borrowers benefit from better conditions to access financing in global capital markets when they join the IMF data dissemination standards.
  - Significant reduction in sovereign spreads can be observed in quarters following subscription to the SDDS or implementation of the e-GDDS by publishing macroeconomic and financial data through the NSDP.
  - Publishing data through NSDPs (e-GDDS) is studied for the first time and adds to benefits from joining the GDDS.
  - Local projections: subscribing to the SDDS and initial participation in the GDDS show larger benefits compared to smaller but still significant benefits from publishing data through NSDPs.
  - Interaction with governance indicators shows countries with relatively weaker governance tend to benefit the most from implementing the e-GDDS or subscribing to the SDDS.

### Empirical analysis: data, event studies, and local projections
- Overview and sample:
  - Objective: assess whether adoption of IMF data dissemination standards relates to declines in sovereign borrowing costs.
  - Empirical approaches: event analysis (short windows), panel event-study models (Freyaldenhoven and others, 2021), and panel local projection methods (Jordà, 2005).
  - Sample: Unbalanced panel covering December 1993 to March 2020.

- Data and variables:
  - Transparency reform indicators: three dummies for subscribing to the SDDS; joining the GDDS; implementing the e-GDDS by publishing through the NSDP (dummies = 1 from day/quarter of join, zero before).
  - Dependent variable: J.P. Morgan EMBIG spread index (includes sovereign and quasi-sovereign bonds).
  - Coverage: 30 SDDS subscribers (that remained during sample); 29 GDDS participants, 21 of which have launched NSDPs.
  - Global controls: Effective Federal Funds Rate; VIX index.
  - Country variables: quarterly macroeconomic variables (only inflation sufficient for some specifications); Fitch sovereign credit ratings (Q3 1994 to end of sample); governance indicators (Worldwide Governance Indicators composite and government effectiveness; ICRG quality of bureaucracy).

- Event Studies (short-window analysis):
  - Paired t-tests comparing mean log sovereign spreads in 30-, 60-, and 90-day windows before and after adoption:
    - 30 days:
      - SDDS: Test statistic -1.37; p-value 0.15
      - GDDS: Test statistic -1.23; p-value 0.17
      - NSDP: Test statistic -0.88; p-value 0.26
    - 60 days:
      - SDDS: Test statistic -1.82; p-value 0.08
      - GDDS: Test statistic -1.11; p-value 0.20
      - NSDP: Test statistic -1.67; p-value 0.10
    - 90 days:
      - SDDS: Test statistic -1.87; p-value 0.06
      - GDDS: Test statistic -1.18; p-value 0.18
      - NSDP: Test statistic -1.96; p-value 0.06
  - Interpretation:
    - All test statistics negative (mean spreads fall after joining).
    - Reductions statistically significant for SDDS and (e-)GDDS in several cases using 60- and 90-day windows.

  - Daily-panel event-study regressions (window M = G = 90 days):
    - Specification: log daily EMBIG sovereign spread index; country and time fixed effects; controls including Fed Funds rate, VIX, country trends, day-of-week and date-in-month indicators.
    - Sample sizes and coverage:
      - SDDS: about 140,000 daily observations; 30 countries. Estimates show clear post-subscription negative changes; many significant at 90 percent confidence.
      - GDDS: about 79,000 observations; 28 countries. Results show little change in pattern.
      - e-GDDS/NSDP: about 46,000 observations; 18 countries. Almost all post-launch estimates negative; some statistically different from zero at 90 percent confidence.
    - No pre-event trend detected in any case.

- Local projection methods (quarterly cumulative responses, horizons h = 0 to h = 3):
  - Specification variations:
    - Model 1: country and time fixed effects, seasonal dummies; βSh, βGh, βNh capture cumulative responses for SDDS, GDDS, e-GDDS/NSDP.
    - Model 2: adds lags of sovereign spreads, lagged and contemporary VIX and Fed Funds rate, lagged domestic variables Xt−j.
    - Model 3: Model 2 + Fitch sovereign credit ratings.
  - Identification supported by event studies and robustness checks (heteroskedasticity-robust standard errors clustered at country level).

  - Main quantitative findings (preserve exact reported results):
    - Model 1:
      - SDDS: reduction in spreads index identifiable during first and second quarters after subscription; reduction reaches 10 percent after one year.
      - GDDS: significant decrease of 7 percent in spreads index only two quarters after joining.
      - NSDP (e-GDDS publication): reductions between 6 and 8 percent and statistically significant during the year following implementation.
    - Model 2:
      - SDDS: reaches a 12-percent reduction in spreads index after one year.
      - GDDS: contributes to reduction of about 6 percent (statistical significance not consistent across horizons).
      - NSDP: reductions between 5 and 8 percent in spreads index, statistically significant during the year following launch.
    - Model 3:
      - Confirms findings of Models 1 and 2: significant reduction after SDDS subscription and after e-GDDS/NSDP implementation.

- Comparisons with prior literature:
  - Cady (2005): ~20 percent primary market decline after SDDS subscription.
  - Cady and Pellechio (2006): 19 percent decline for SDDS and 9 percent for GDDS.
  - Choi and Hashimoto (2018): overall 13 percent reduction combining standards (22 percent for SDDS alone; 11 percent for GDDS not statistically significant).

- Role of institutional environment (interaction with governance indicators):
  - Empirical approach: modify Model 3 with interaction terms and smooth transition function I i,t = 1/(1 + exp(−γ z i,t)) with γ = 3, where z i,t is standardized distance of governance indicator from sample mean.
  - Interpretation of I i,t:
    - Low governance values → I i,t closer to 1 (estimates for low-governance βShL, βGhL, βNhL).
    - High governance values → I i,t closer to 0 (estimates for high-governance βShH, βGhH, βNhH).
  - Governance indicators: composite governance index; government effectiveness (WGI); quality of bureaucracy (ICRG).

  - Key heterogeneous results (one-year cumulative, h = 4 in Table 4):
    - SDDS:
      - Countries with relatively weaker institutions: estimated declines in spreads between 30–40 percent in the year after subscription.
      - Countries at the top of governance distribution: do not show declines in spreads after SDDS subscription.
    - GDDS:
      - Countries at both top and bottom of governance distribution benefit with decreases in spreads ranging from 14 to 18 percent.
    - e-GDDS / NSDP:
      - Countries with lower governance scores benefit with statistically significant cumulative reductions in spreads index between 7 and 10 percent.

  - Interpretation:
    - Consistent pattern: countries with relatively weaker governance tend to benefit most from increased transparency via data dissemination.
    - Possible mechanism: subscription to standards and NSDP publication serve as stronger signals of improved transparency where governance is weak.

- Key numeric summaries (selected and preserved exactly):
  - Sample period: December 1993–March 2020.
  - Paired t-test results (mean log sovereign spreads changes):
    - 30-day windows: SDDS test statistic -1.37 (p 0.15); GDDS -1.23 (p 0.17); NSDP -0.88 (p 0.26).
    - 60-day windows: SDDS -1.82 (p 0.08); GDDS -1.11 (p 0.20); NSDP -1.67 (p 0.10).
    - 90-day windows: SDDS -1.87 (p 0.06); GDDS -1.18 (p 0.18); NSDP -1.96 (p 0.06).
  - Daily-panel event-study sample sizes:
    - SDDS: ~140,000 observations; 30 countries.
    - GDDS: ~79,000 observations; 28 countries.
    - e-GDDS/NSDP: ~46,000 observations; 18 countries.
  - Local projection quantitative results (selected):
    - Model 1: SDDS → 10 percent reduction in spreads index after one year; GDDS → 7 percent decrease at two quarters; NSDP → 6–8 percent reductions during year following.
    - Model 2: SDDS → 12-percent reduction after one year; GDDS → ~6 percent reduction; NSDP → 5–8 percent reductions during year following.
    - Model 3: confirms SDDS and NSDP significant reductions when including Fitch ratings.
  - Institutional-environment heterogeneous effects (one-year cumulative):
    - SDDS: weaker-institution countries → 30–40 percent decline; top-governance countries → no decline.
    - GDDS: low- and high-governance countries → 14–18 percent declines.
    - e-GDDS (NSDP): lower-governance countries → 7–10 percent decline.

### Conclusions: policy implications and recommendations
- Main conclusions:
  - Providing essential macroeconomic data through IMF Data Standards Initiatives can reduce uncertainty, lower risk premia, reduce sovereign spreads, and improve countries’ financing conditions.
  - Event studies and local projection models identify declines in sovereign spreads after joining data dissemination standards, with the most robust effects for SDDS subscription.
  - Publication of data under the enhanced-GDDS (NSDP) is associated with additional reductions in sovereign spreads and is studied here for the first time.

- Quantitative policy takeaways (preserve exact reported ranges):
  - Subscribing to the SDDS: between 10 percent and 14 percent reduction in the year after subscription across models.
  - Participating in the GDDS: associated with reductions in spreads, though smaller and less statistically robust than SDDS effects.
  - Initiating NSDP data dissemination under the enhanced-GDDS: additional reductions in spreads ranging from 6 to 4 percent beyond GDDS participation.
  - Targeting data dissemination improvements may produce larger financing benefits in countries with relatively weaker governance.

- Summary numeric table excerpts reported in conclusions (preserved as presented):
  - Governance
    - -31.2
    - ***
    - 9.4
    - -18.1
    - ***
    - -5.2
    - -9.9
    - ***
    - 5.5
  - Government efficiency
    - -41.1
    - ***
    - 9.6
    - -13.6
    - *
    - -15.8
    - **
    - -8.8
    - ***
    - 4.4
  - Quality of bureaucracy
    - -30.3
    - ***
    - 5.4
    - -13.6
    - *
    - -15.5
    - **
    - -6.9
    - **
    - 2.3

- Overall policy implication:
  - Strengthening public data dissemination—especially via SDDS subscription and via NSDP publication under the e-GDDS—can materially improve sovereign financing conditions, with the largest measurable benefits accruing to countries with relatively weaker governance.

*Source: IMF Working Paper — Improving Sovereign Financing Conditions Through Data Transparency (WP/22/230).*

### 1. Introduction ........................................................................................................

### 1. Introduction

### Background and motivation
- Transparency reduces uncertainty about economic developments, enhances accountability and credibility of policies, and helps make markets more efficient.
- Increased transparency through data dissemination is expected to lower risk premiums when countries access financial markets.
- This paper focuses on the relationship between increased transparency through data dissemination and the sovereign bond spreads of emerging and frontier markets.

### IMF Data Standards timeline and definitions
- The Special Data Dissemination Standard (SDDS) was established by the IMF Executive Board in 1996 and was designed to guide countries that have or might seek access to international capital markets in the dissemination of data.
- The General Data Dissemination System (GDDS) was launched in 1997 to guide countries in the development of sound statistical systems in support of eventual dissemination of data. Initially, the GDDS focused on the disclosure of statistical frameworks, description of data (metadata), and plans for development rather than data dissemination.
- The SDDS Plus, the third and highest tier of the framework, was established in 2012, building on the SDDS and aimed at economies with systemically important financial sectors.
- In 2015, the GDDS was enhanced to improve countries’ incentives to publish data. The key element of the enhanced-GDDS (e-GDDS), which superseded the GDDS, is that countries, following standardized dissemination practices, publish data, and not just metadata, through a National Summary Data Page (NSDP), facilitating public access to timely, key macroeconomic and financial data.
- This paper focuses on economies that have enhanced data dissemination by joining the SDDS, GDDS prior to 2015, and those that have launched the NSDPs under the e-GDDS.

### Methods and empirical approach
- The paper revisits previous findings on transparency and sovereign spreads using new methods.
- Empirical methods include event studies and local projection models to study the dynamic response of sovereign spreads to joining the data dissemination standards.
- The analysis compares empirically the effects of joining different tiers of the data dissemination standards and investigates the impact of the institutional environment on the transparency–sovereign spread relationship.

### Key findings
- Sovereign borrowers benefit from better conditions to access financing in global capital markets when they join the IMF data dissemination standards.
- A significant reduction in sovereign spreads can be observed in the quarters following subscription to the SDDS or implementation of the e-GDDS by publishing macroeconomic and financial data through the NSDP.
- The effect of publishing data through NSDPs (e-GDDS) is studied for the first time in this paper and is in addition to a favorable impact on spreads from joining the GDDS in the first place.
- Local projections allow comparison across tiers: subscribing to the SDDS and initial participation in the GDDS show larger benefits compared to smaller but still significant benefits from publishing data through NSDPs.

### Role of institutional environment
- Interacting the data standards participation variable with indicators of governance shows that countries with relatively weaker governance tend to benefit the most from implementing the e-GDDS or subscribing to the SDDS.
- The larger reductions in sovereign spreads for weaker-governance countries may reflect a signaling effect from countries’ efforts to improve transparency when implementing the Data Standards Initiatives.

### Organization of the paper
- Section 2 explains the approach, describes the data used, and presents results from event studies and local projection models, including the role of governance and institutional environment.
- Section 3 offers the conclusions.

*IMF Working Paper — Improving Sovereign Financing Conditions Through Data Transparency (Introduction).*

### 2. Empirical Analysis

### 2. Empirical Analysis

### Overview
- Objective: Assess whether strengthening transparency via countries’ adoption of the IMF data dissemination standards is related to declines in sovereign borrowing costs by providing market participants with more timely data on key economic and financial developments.
- Empirical approaches:
  - Event analysis (short windows around adoption dates) to detect changes in sovereign spreads.
  - Panel event-study models (Freyaldenhoven and others, 2021) to estimate dynamic relationships while controlling for determinants of sovereign spreads.
  - Local projection methods (Jordà, 2005) in a panel setting to estimate dynamic cumulative responses of sovereign spreads following adoption.
- Sample: Unbalanced panel covering December 1993 to March 2020 (end of sample set to avoid COVID-19 pandemic effects).

### Data
- Transparency reform indicators:
  - Three dummy variables: subscribing to the SDDS; joining the GDDS; implementing the e-GDDS by publishing data through the NSDP. Dummies take value 1 from the day (or quarter) of joining, zero before.
  - Timeline: Adoption started mid-1990s; large wave in SDDS subscriptions; SDDS Plus from 2015; GDDS first phase 1997, second phase May 2000; surge in e-GDDS NSDP launches after 2015.
- Indicator of financing conditions:
  - Dependent variable: J.P. Morgan EMBIG spread index (includes sovereign and quasi-sovereign bonds).
  - Coverage: available for 30 countries that subscribed to the SDDS (and remained during sample) and 29 GDDS participants, 21 of which have launched NSDPs.
- Global variables:
  - Effective Federal Funds Rate (reference rate) — available for entire sample.
  - VIX index (one-month-ahead indicator of volatility).
- Country variables:
  - Quarterly macroeconomic variables considered but only inflation has sufficient observations (used in subsection 2.3).
  - Fitch sovereign credit ratings: unbalanced sample from Q3 1994 to end of sample.
  - Governance/institution indicators: Worldwide Governance Indicators (composite and government effectiveness) and ICRG quality of bureaucracy.

### Event Studies (short-window analysis)
- Paired t-tests compare mean log sovereign spreads using windows of 30, 60, and 90 days before and after adoption.
- Table 1 (paired t-test statistics and p-values):
  - 30 days:
    - SDDS: Test statistic -1.37; p-value 0.15
    - GDDS: Test statistic -1.23; p-value 0.17
    - NSDP: Test statistic -0.88; p-value 0.26
  - 60 days:
    - SDDS: Test statistic -1.82; p-value 0.08
    - GDDS: Test statistic -1.11; p-value 0.20
    - NSDP: Test statistic -1.67; p-value 0.10
  - 90 days:
    - SDDS: Test statistic -1.87; p-value 0.06
    - GDDS: Test statistic -1.18; p-value 0.18
    - NSDP: Test statistic -1.96; p-value 0.06
- Interpretation:
  - All test statistics are negative (mean spreads fall after joining).
  - Reductions are statistically significant for SDDS and (e-)GDDS using the 60- and 90-day windows in several cases.
- Panel daily event-study regressions (equation (1)):
  - Left-hand-side: log of daily EMBIG sovereign spread index (Ei,t).
  - Controls: country and time fixed effects, country-specific controls, Fed Funds rate and VIX (daily), country trends, day-of-week and date-in-month indicators.
  - Window: M = G = 90 days (90 days before and after adoption).
  - Daily-panel estimation sample sizes and results:
    - SDDS: about 140,000 daily panel observations covering 30 countries. Estimates show clear change after subscription; mostly negative post-subscription estimates; many statistically significant at 90 percent confidence.
    - GDDS: about 79,000 observations covering 28 countries. Results show little change in pattern (consistent with GDDS focus on metadata/statistical plans prior to 2015).
    - e-GDDS / NSDP launch: about 46,000 observations covering 18 countries. Almost all post-launch estimates negative; some statistically different from zero at 90 percent confidence. Fed Funds rate could be excluded without changing results. No pre-event trend detected in any case.

- Event-study summary:
  - Simple event studies show reduction in mean sovereign spreads after joining standards.
  - Panel event-study models show clear significant decline after SDDS subscription; some evidence of negative correlations for e-GDDS but less statistical significance.

### Local Projection Methods (quarterly cumulative responses)
- Method: Local projection model (equation (2)) estimating cumulative change in natural log of EMBIG index at horizons h = 0 to h = 3 (quarter of joining up to one year).
- Specification details:
  - First row: country and time fixed effects, seasonal dummies; estimates βSh, βGh, βNh capture cumulative responses for SDDS, GDDS, e-GDDS/NSDP.
  - Second row: adds lags of sovereign spreads, lagged and contemporary VIX and Fed Funds rate, and lagged domestic variables Xt−j.
  - Third row: adds future realizations (leads) of global and domestic variables to obtain consistent estimation.
  - Estimation uses heteroskedasticity-robust standard errors clustered at country level.
  - Identification assumption: adoption is an unexpected shock uncorrelated with other developments (supported by event studies and Choi and Hashimoto (2018)).

- Main model results (summarized findings from Models 1–3):
  - Model 1 (basic fixed effects specification):
    - SDDS: reduction in spreads index identifiable during first and second quarters after subscription; reduction reaches 10 percent after one year.
    - GDDS: significant decrease of 7 percent in spreads index only two quarters after joining.
    - NSDP (e-GDDS publication): reductions between 6 and 8 percent and statistically significant during the year following implementation.
  - Model 2 (adds lags/leads of spreads, VIX, Fed Funds, lagged inflation):
    - SDDS: reaches a 12-percent reduction in spreads index after one year.
    - GDDS: contributes to reduction of about 6 percent (statistical significance not consistent across horizons).
    - NSDP: reductions between 5 and 8 percent in spreads index, statistically significant during the year following launch.
  - Model 3 (Model 2 + Fitch sovereign credit ratings):
    - Confirms findings of Models 1 and 2: significant reduction after SDDS subscription and after e-GDDS/NSDP implementation.
    - Robustness supports assumption that joining standards is a valid unexpected shock.

- Comparisons and prior literature:
  - Findings align with earlier studies: Cady (2005) ~20 percent primary market decline after SDDS subscription; Cady and Pellechio (2006) found 19 percent decline for SDDS and 9 percent for GDDS; Choi and Hashimoto (2018) found overall 13 percent reduction combining standards (22 percent for SDDS alone; 11 percent for GDDS not statistically significant).

- Visual cumulative-change results:
  - Figures (Model 1–3) illustrate cumulative percent changes by quarter after joining each standard; significance denoted by *, **, *** at 0.10, 0.05, 0.01 levels.

### Role of Institutional Environment
- Question: Do the effects of joining data dissemination standards on sovereign spreads depend on governance/institutional quality?
- Empirical approach:
  - Model 3 is modified with interaction terms and a smooth transition function I i,t = 1/(1 + exp(−γ z i,t)) with γ = 3, where z i,t is standardized distance of governance indicator from sample mean.
  - Low governance values → I i,t closer to 1 (identifies low-governance estimates βShL, βGhL, βNhL).
  - High governance values → I i,t closer to 0 (identifies high-governance estimates βShH, βGhH, βNhH).
  - Governance indicators used: composite governance index; government effectiveness (WGI); quality of bureaucracy (ICRG).
- Key results (cumulative changes in spreads one year after joining; h = 4 in Table 4):
  - SDDS:
    - Countries with relatively weaker institutions (lower governance) show estimated declines in spreads between 30–40 percent in the year after subscription.
    - Countries at the top of the governance distribution do not show declines in spreads after SDDS subscription.
  - GDDS:
    - Countries at both top and bottom of governance distribution benefit from decreases in spreads ranging from 14 to 18 percent.
  - e-GDDS / NSDP:
    - Countries with lower governance scores benefit from implementing e-GDDS with statistically significant cumulative reductions in spreads index between 7 and 10 percent.
- Interpretation:
  - Consistent pattern across three governance indicators: countries with relatively weaker governance tend to benefit most from increased transparency via data dissemination.
  - Possible explanation: joining the standards is a stronger signal of improved transparency where governance is weak; NSDP publication under enhanced-GDDS provides additional credible information in such contexts.

### Key quantitative findings (selected)
- Sample period: December 1993–March 2020.
- Paired t-test results (changes in mean log sovereign spreads):
  - 30-day windows: SDDS test statistic -1.37 (p 0.15); GDDS -1.23 (p 0.17); NSDP -0.88 (p 0.26).
  - 60-day windows: SDDS -1.82 (p 0.08); GDDS -1.11 (p 0.20); NSDP -1.67 (p 0.10).
  - 90-day windows: SDDS -1.87 (p 0.06); GDDS -1.18 (p 0.18); NSDP -1.96 (p 0.06).
- Daily-panel event-study sample sizes and coverage:
  - SDDS: ~140,000 observations; 30 countries.
  - GDDS: ~79,000 observations; 28 countries.
  - e-GDDS/NSDP: ~46,000 observations; 18 countries.
- Local projection quantitative results (selected):
  - Model 1: SDDS → 10 percent reduction in spreads index after one year; GDDS → 7 percent decrease at two quarters; NSDP → 6–8 percent reductions during year following.
  - Model 2: SDDS → 12-percent reduction after one year; GDDS → ~6 percent reduction (less robust); NSDP → 5–8 percent reductions during year following.
  - Model 3: Confirms SDDS and NSDP significant reductions when including Fitch ratings.
- Institutional-environment heterogeneous effects (one-year cumulative):
  - SDDS: weaker-institution countries → 30–40 percent decline; top-governance countries → no decline.
  - GDDS: both low- and high-governance countries → 14–18 percent declines.
  - e-GDDS (NSDP): lower-governance countries → 7–10 percent decline.

*Source: IMF staff calculations, IMF Working Paper — 2. Empirical Analysis.*

### 3. Conclusions

### 3. Conclusions

### Main findings on transparency and sovereign spreads
- Providing essential macroeconomic data to all interested stakeholders can help reduce uncertainty about current economic developments and prospects, which can lower risk premia, reduce sovereign spreads, and improve countries’ financing conditions in international markets.
- This paper studies the relationship between increased transparency when countries implement the IMF Data Standards Initiatives and sovereign spreads.
- Event studies identify a decline in sovereign spreads for the countries that enhance data transparency by joining the data dissemination standards.
  - The tests show declines in the mean sovereign spreads after joining the standards.
  - More full-fledged models of event studies with dynamic effects show a clear pattern of statistically significant negative relationship between sovereign spreads and subscribing to the SDDS.
- Cumulative effects estimated with local projection models suggest that there is a significant negative relationship between increased transparency and sovereign bond spreads, including for the publication of data under the enhanced-GDDS which is studied for the first time in this paper.
- The decrease in sovereign spreads index is more marked when subscribing to the SDDS: between 10 percent and 14 percent across models during the year following the subscription.
- Participating in the GDDS (prior to 2015) is also associated with a reduction in spreads, but the declines tend to be smaller and less robust to identify statistically.
- Starting data dissemination through NSDP under the enhanced-GDDS is also associated with improvements in countries’ financing conditions, with an estimated reduction of spreads ranging from 6 to 4 percent that is additional to the benefits from participating in the GDDS in the first place.
- These favorable effects are showed to manifest more significantly in countries with relatively weaker governance as they would likely set to benefit the most from increasing transparency through data dissemination.

### Quantitative results (as presented)
- Variable
- Governance
- -31.2
- ***
- 9.4
- -18.1
- ***
- -5.2
- -9.9
- ***
- 5.5
- Government efficiency
- -41.1
- ***
- 9.6
- -13.6
- *
- -15.8
- **
- -8.8
- ***
- 4.4
- Quality of bureaucracy
- -30.3
- ***
- 5.4
- -13.6
- *
- -15.5
- **
- -6.9
- **
- 2.3
- β
- NhH
- β
- ShL
- β
- ShH
- β
- GhL
- β
- GhH
- β
- NhL
- SDDS
- GDDS
- NSDP

### Implications for policy and practice
- Subscribing to the SDDS yields the largest and most robust reductions in sovereign spreads (between 10 percent and 14 percent in the year after subscription across models).
- Participation in GDDS yields reductions in spreads, though smaller and less statistically robust than SDDS effects.
- Initiating data dissemination through NSDP under the enhanced-GDDS provides additional reductions in spreads (ranging from 6 to 4 percent) beyond GDDS participation.
- Targeting data dissemination improvements may produce larger financing benefits in countries with relatively weaker governance.

*Improving Sovereign Financing Conditions Through Data Transparency, Working Paper No. WP/22/230.*

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_Source: https://www.imf.org/-/media/files/publications/wp/2022/english/wpiea2022230-print-pdf.pdf_