Is Transparency Good For You? by Rachel Glennerster, Yongseok Shin - PowerPoint PPT Presentation

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Is Transparency Good For You? by Rachel Glennerster, Yongseok Shin

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  1. IMF Annual Research Conference November 4-5, 2004 Is Transparency Good For You? by Rachel Glennerster, Yongseok Shin Discussed by: Campbell R. Harvey Duke University National Bureau of Economic Research

  2. Motivation Popular Opinion: • Emerging markets financial crisis caused or made worse by lack of transparency Purpose of paper: • Does increased transparency reduce sovereign spread (which presumably implies a lower probability of crisis)?

  3. Measures of Transparency Measures: • Probability of crisis: country credit spreads • Transparency: • IMF Article IV publication • SDDS adoption • ROSC adoption

  4. Overview of Findings Results: • Increased transparency  credit spreads more than 300bp in regression (1) • Larger drop in spreads for: • Initially less transparent countries • Countries with smaller debt markets

  5. Theory Why does transparency affect borrowing costs? • Two models of asymmetric info: • 1) Moral hazard (behavior of debtor) • 2) Signaling (nature of random shock)

  6. Theory Moral Hazard • Transparency increases => reduces asymmetric information => less difference between informed and uninformed traders increases liquidity and reduces borrowing cost decreases (always) Signaling • Good shock => voluntary transparency => borrowing cost decreases

  7. News Results News Effect of IMF publication • Higher volatility of daily spreads associated with IMF publication • IMF publication => more informed markets • But IMF publication also has information about economic outlook. When non-borrowing countries are examined, volatility also increases

  8. Details Data: • Country Credit Spread = EMBI portfolio yield – US zero coupon yield • Number of countries: 23 emerging markets • Sample period: 1 January 1999 – 30 June 2002

  9. Estimation Panel estimation: (p.14) • ln(spread)it = i + t qt + 1 Pubit + 2 ROSCit + 3 SDDSit + 4 Pubit *ROSCit + 5 Pubit * SDDSit + 6 SDDSit * ROSCit + it • Country effects • Time effects • Transparency dummies • Pub, ROSC, SDDS • Interaction terms • Marginal benefit of transparency – increasing/decreasing? • Std Errors – corrected with Newey West

  10. Estimation Panel estimation: (p.14) • Other variables: • Kauffman et al’s rule of law, Transparency International’s Corruption Perception Index • Size of debt market • Macro variables (inflation, current account/GDP, fiscal balance/GDP) • PIN (Public Info Notice)

  11. Estimation Panel estimation: (p.14) • Model (6) • 16 variables, 23 country effects, 14 time effects • 322 observations

  12. Endogeneity Problem Adopting the IMF disclosure is a government decision. The decision could be strategic and related to growth opportunities in the economy. • Hence, any relation between spreads and publishing could be spuriously induced as a result of severe endogeneity

  13. Endogeneity Problem Author’s approach: “Any endogeneity bias is corrected for in two ways..” • Exclude program countries • 2SLS

  14. Endogeneity Problem 2SLS First stage: (i) Run OLS regression of Pub dummy (e.g.) on: average time between Article IV discussions, interacted with regional dummies, GDP per capita in 1998, size of debt market, rule of law, voice, corruption, squares of some of the variables, regional dummies

  15. Endogeneity Problem 2SLS First stage: (ii) Regression (1) has 322 observations, 40 variables and 85% R-square. (iii) Full 2SLS similar to OLS

  16. Endogeneity Problem 2SLS issues • Fitted values lie outside the 0-1 range • Is it really the case that the 45 variables are uncorrelated with spreads? “…the precise timing of compliance depends more on the time since the country committed to meet the specifications of the SDDS than on concurrent events.” • With enough variables in the first, we would get a 100% R-square. It is no surprise that the results are similar.

  17. Endogeneity Problem

  18. Endogeneity Problem What to do? • Problem very similar to Bekaert, Harvey and Lundblad, “Does Financial Liberalization Spur Growth” forthcoming JFE. • Here regressions of economic growth are run on a liberalization dummy – suffers from same type of endogeneity bias.

  19. Endogeneity Problem Develop a measure of exogenous growth opportunities • Assume that a country’s growth opportunities are related to its industrial mix • Assume that global price to earnings (PE) ratios contain information about growth opportunities

  20. Endogeneity Problem Develop a measure of exogenous growth opportunities • Create growth opportunities measure by weighting global PE ratios by a particular country’s industrial weights • This variable strongly predicts economic growth – but is exogenous. • This variable has both cross-sectional and time-series variation

  21. Endogeneity Problem Develop a measure of exogenous growth opportunities • Suggest adding to the regression to control for growth opportunities • For more details, see Bekaert, Harvey, Lundblad, Siegel, 2004, “Growth Opportunities and Market Integration”

  22. Specification Issues • Much of the significance comes from the interaction of the transparency indicators (which is not reported in the paper) • Much of the explanatory power comes from the country fixed effects (which is not reported in the paper). The time effects are not important.

  23. Specification Issues 3. What happens when other measures of country risk are included in the regression?

  24. Specification Issues

  25. Specification Issues Difficult to see changes in the data. Here is the “pub” variable. Spread change

  26. Specification Issues Spread change

  27. Conclusions • I believe the story (transparency leads to lower spreads) • There are some issues that need to be cleaned up