metadata: authors: 0: author: Hanusch,Marek
1: author: Vaaler,Paul Martin
count: World
docna: 0: docna: Credit rating agencies in emerging
            democracies : Guardians of fiscal discipline ?
docty: Policy Research Working Paper
subtopic: Macro-Fiscal Policy,Consumption,Fiscal & Monetary Policy,Economic Adjustment and Lending,Public Sector Economics,Inflation,Employment and Unemployment,Public Finance Decentralization and
            Poverty Reduction,Inequality
teratopic: Private Sector Development,Governance,Finance and Financial Sector Development
abstracts: cdata!: Credit rating agencies have drawn
            criticism for failing to anticipate and deter root causes of
            the 2008-2009 financial crisis in the United States.
            However, this paper presents evidence that credit rating
            agencies regularly anticipate and deter governments in
            emerging democracies from opportunistic borrowing and
            potential financial crises related to elections and the
            political budget cycle behavior they encourage. The paper
            considers a sample of 18 such countries holding 32
            presidential elections from 1989 to 2004. The analysis shows
            that credit rating agencies induced greater fiscal
            discipline during election periods when governments had
            incentives to borrow opportunistically for short-term
            electoral gain. Countries with higher credit rating agency
            sovereign ratings borrowed less than lower-rated countries
            in election periods, but borrowed more in non-election
            periods. Credit rating agencies promoted fiscal discipline
            during increasingly frequent election periods in emerging democracies.
repnme: repnme: Credit rating agencies in emerging
            democracies : Guardians of fiscal discipline ?
display_title: Credit rating agencies in emerging
            democracies : Guardians of fiscal discipline
listing_relative_url: /research/2013/03/17408765/credit-rating-agencies-emerging-democracies-guardians-fiscal-discipline
topicv3: Fiscal Stimulus and Fiscal Rules m1326652 1073,Poverty m1326687 695,Inflation m1326709 0870F4727ACED1CF8525815C00471347,Macroeconomic and Structural Policies
            m1327380 439,Unemployment m1504051 2747
publishtoextweb_dt: 2013-03-01T00:00:00Z
historic_topic: Private Sector Development,Governance,Finance and Financial Sector Development
no_of_pages: 13
author_keys: 0: author_key: 1045860
1: author_key: 1322143
model_extractions: WPS6379 Policy Research Working Paper 6379 Guardians of Fiscal Discipline?

_Marek Hanusch Paul M. Vaaler_ The World Bank Africa Region Poverty Reduction and Economic Management Department March 2013
Policy Research Working Paper 6379

# **Abstract**

Credit rating agencies have drawn criticism for failing to anticipate and deter root causes of the 2008–2009 financial crisis in the United States. However, this paper presents evidence that credit rating agencies regularly anticipate and deter governments in emerging democracies from opportunistic borrowing and potential financial crises related to elections and the political budget cycle behavior they encourage. The paper considers a sample of 18 such countries holding 32 presidential elections from 1989 to 2004. The analysis shows that credit rating agencies induced greater fiscal discipline during election periods when governments had incentives to borrow opportunistically for short-term electoral gain. Countries with higher credit rating agency sovereign ratings borrowed less than lower-rated countries in election periods, but borrowed more in non-election periods. Credit rating agencies promoted fiscal discipline during increasingly frequent election periods in emerging democracies.

This paper is a product of the Poverty Reduction and Economic Management Department, Africa Region. It is part of a larger effort by the World Bank to provide open access to its research and make a contribution to development policy discussions around the world. Policy Research Working Papers are also posted on the Web at http://econ.worldbank.org. The author may be contacted at mhanusch@worldbank.org.

_The Policy Research Working Paper Series disseminates the findings of work in progress to encourage the exchange of ideas about development issues. An objective of the series is to get the findings out quickly, even if the presentations are less than fully polished. The papers carry the names of the authors and should be cited accordingly. The findings, interpretations, and conclusions expressed in this paper are entirely those of the authors. They do not necessarily represent the views of the International Bank for Reconstruction and Development/World Bank and its affiliated organizations, or those of the Executive Directors of the World Bank or the governments they represent._ Produced by the Research Support Team
# Credit rating agencies in emerging democracies: Guardians of fiscal disci line? <u>p</u>

Marek Hanusch<sup>a</sup> _The World Bank Washington, DC 20433, USA_ Paul M. Vaaler<sup>b</sup> _Carlson School of Management, University of Minnesota, Minneapolis, MN 55455, USA_ _JEL classifications_ : F30, F34, G24, H63, O10, O23 _Keywords_ : credit rating agencies, fiscal policy, political budget cycles _Sector Board:_ EPOL.

## **____________**

a: mhanusch@worldbank.org b: vaal0001@umn.edu 1
# **Introduction**

At first glance, credit rating agency (CRA) assessments of a borrower’s capability and willingness to meet financial obligations may seem like little more than opinions released by private companies. But as White (2010) and others (e.g _._ , Sinclair, 2005) have noted, industry conventions, national regulations and international agreements have elevated assessments of individual, corporate, municipal and sovereign creditworthiness by CRAs like Moody’s Investors Service (Moody’s), Standard and Poor’s Financial Services (S&P) and Fitch Ratings (Fitch) from mere opinion to guiding principles, if not binding rules, on lending and investment decisions taken by banks, pension funds, finance ministries and central banks. Recognizing this, the US government increased regulation on CRAs in the past decade, first through the 2006 Agency Reform Act, and then through the 2010 Dodd-Frank Act. Passage of Dodd-Frank followed a financial crisis in the US with roots in subprime mortgages, often incorrectly classified by CRAs as safe investments.

Such failures are important to highlight, but so, too, are apparent CRA successes in disciplining borrowers to avert potential financial crises. One such instance may be CRA sovereign ratings (ratings) and government borrowing during election periods in what Brender and Drazen (2006) call “new democracies” of the developing world and we call “emerging democracies” to include developing countries with democratic governments having no recent precedents or reformed democratic governments following periods of non-democratic rule by the military or a single dominant party. Political budget cycle models since Nordhaus (1974) have analyzed government incentives to borrow opportunistically to enhance their prospects during election periods. Brender and Drazen (2005) and Shi and Svensson (2006) document the significance of such political budget cycle behavior in emerging democracies, which historically have also been more vulnerable to financial crises. If CRAs prompt fiscal discipline, then their ratings should curb government borrowing during election periods.

We find evidence consistent with such CRA effectiveness in econometric analyses of government borrowing and ratings for 18 emerging democracies holding 32 presidential elections during 1989-2004. Governments with higher (lower) ratings borrow less (more) during 2
election periods with opposite trends in non-election periods. Our findings are important not only because they highlight CRA effectiveness in transnational fiscal oversight. Our results turn the tables on political budget cycle research that has largely treated CRAs as passive observers and ratings as a mere responses to election-period fiscal manipulations (Block and Vaaler, 2004). Our study positions political budget cycle research in line with other work demonstrating how private often foreign-domiciled financial actors prompt governments to build solid credit histories, perhaps under threat of future credit freezes (e.g., Eaton and Gersovitz, 1981). Governments in emerging democracies have incentives to build that history during elections. Forbearance from opportunistic borrowing to stay in office is part of a larger relationship with CRAs that also facilitates more borrowing in non-election periods.

# **2. Empirical Methodology**

## **2.1. Model Specification and Tests**

To test our proposition that ratings curb opportunistic borrowing by governments during election periods, we define the following statistical model: In (1), the dependent variable, _Budget Balance_ , is the central government budget balance as a percentage of GDP for country _i_ in year _t_ . A negative value of _Budget Balance_ represents a fiscal deficit which the government finances by issuing debt or other means. We regress _Budget Balance_ on: an intercept ( α ); a one-year lagged dependent variable (LDV), _Budget Balanceit-1_ ( β ); lead, current-year and lagged values of country _i_ ’s average rating on January 1 of year _t_ , 3
_Ratingit+1_ , _Ratingit_ and _Ratingit-1_ ( γ _’_ );<sup>1</sup> lead, current-year and lagged 0-1 dummies for an election in country _i_ in year _t_ , _Electionit+1_ , _Electionit_ and _Electionit-1_ ( δ _’_ ); three interaction terms combining ratings with election dummies, _Rating*Electionit+1_ , _Rating*Electionit_ and _Rating*Electionit-1_ ( ρ _’_ ); six additional lagged controls for country _i_ in year _t_ unemployment rate ( _Unemploymentit-1_ ), inflation rate divided by 100 ( _Inflationit-1_ ), foreign reserves in months of imports ( _Reservesit-1_ ), currency crisis indicator, that is, a 0-1 dummy indicating a decrease in the value of the local currency by more than 10% against the US dollar ( _Curr Crisisit-1_ ), the average value of civil liberties and political rights ( _Rightsit-1_ ), and the stability of the policy environment based on the number of individuals with power in government to veto policy changes ( _Checksit-1_ ) ( φ _’_ ) ; country _i_ dummies ( μ ) ; year _t_ dummies ( θ ) ; and an error term ( ε ).

The _Election_ terms should be negatively related to _Budget Balance_ ( δ _’_ < 0) indicating that the occurrence of elections prompts governments to borrow (and spend) opportunistically to garner voter support. The _Rating*Election_ interaction terms should be positively related to _Budget Balance_ ( ρ _’_ > 0) indicating that election-period borrowing is diminished with higher _Rating_ and the fiscal discipline such CRA assessment entails. These test statistics are central to our research proposition that ratings curb political budget cycle behavior. _Ratings_ terms capture non-election period effects on _Budget Balances_ and should exhibit a negative sign indicating that higher ratings facilitate more borrowing in non-election periods ( δ _’_ < 0). Lead, current-year and lagged effects let us probe for effects on _Budget Balance_ across election and non-election periods broadly defined.

> 1 Ratings are letter-based ordinal-level assessments of the capability and willingness of borrowers to meet their financial obligations. We convert this CRA scale to a 17-level numerical scale: AAA = 16 (highest rating/most creditworthy), AA+ = 15...B- = 1, C or lower = 0 (lowest rating/least creditworthy) (Block and Vaaler, 2004).

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# **2.2. Data and Sampling**

Data for variables in (1) come from different sources. _Budget Balance_ data are from the World Bank. Time series end in 2004 when change in fiscal accounting methods and reporting was completed at the International Monetary Fund. Data for _Elections_ are from the International Federation of Electoral Systems and from Block and Vaaler (2004). Election years are adjusted for government fiscal years consistent with Hanusch and Keefer (2011). _Ratings_ data are from _Bloomberg_ , and are measured annually as an average of up to three ratings published by Moody’s, S&P and or Fitch (or CRAs merged into Fitch in the 1990s) on January 1. These raters dominate the industry, including sovereign rating segments, with the broadest country coverage, the most analysts and the largest market shares of ratings for individual bond issuances (White, 2010). Their ratings tend to move together with financial market players following them as a group. Data for other controls include the World Bank’s World Development Indicators ( _Employment, Inflation, Reserves, Curr Crisis_ ) and Database of Political Institutions (DPI) (Beck et al _._ , 2004) ( _Checks_ ), and Freedom House ( _Rights_ ).

We sample from 18 prominent emerging democracies including: Argentina, Bolivia, Brazil, Bulgaria, Chile, Colombia, Ecuador, Indonesia, Mexico, Paraguay, Peru, Philippines, Poland, Russia, South Africa, South Korea, Uruguay, and Venezuela. They uniquely meet three sampling requirements. Starting in different years from 1989 and the fall of the Berlin Wall to 1998 and the fall of the Suharto regime in Indonesia, these countries have: 1) competitive presidential executive electoral systems that are new (e.g., Russia) or recently reformed (e.g., Brazil); 2) ratings from Moody’s, S&P and or Fitch; and 3) comparable budget balance data for years when rated. Sampling from these 18 countries yields 141 country-year observations from 1989-2004 with as few as three (Ecuador) and as many as 14 (Brazil) observations per country. The election year dummy, _Electionit_ , equals one for 32 of these 141 country-year observations.<sup>2</sup>

> 2 Classification as a presidential system is based on the DPI’s “SYSTEM” variable.  A country must score a 0 or 1, meaning that it has a non-ceremonial chief executive elected directly by voters or specialized electors (0), or have a non-ceremonial chief executives chosen by the legislature but with substantial powers to avoid recall by that same

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## **2.3. Estimation Strategy**

We have a dynamic panel model with unit and time effects. We initially estimate (1) with fixed effects, but acknowledge significant (downward) bias in LDV estimates. We correct for this bias using a general method of moments (GMM) estimator that is also well-suited for persistent dependent variables (Arellano and Bond, 1991; Arellano and Bover, 1995; Blundell and Bond, 1998). This system GMM estimator generates plausibly exogenous instruments in lagged levels and differences in levels of the LDV and other right-hand side terms in (1) that may be endogenously determined (e.g., _Ratings_ ). We note the number of instruments generated and diagnostic tests commonly used to verify that GMM assumptions are met.

# **3. Results**

## **3.1. Linear Trend Line Results**

Figure 1 partitions the sample into election and non-election periods, plots current-year _Budget Balance_ against lagged _Ratings_ and fits linear trends to each sub-sample. The graph illustrates our research proposition. In non-election periods, there is a negative relationship between ratings budget balances. Higher-rated countries borrow more. But in election periods, the trend is reversed. Negative budget balances diminish and turn to slight budget surpluses for the highestrated countries. These contrasting patterns persist when re-fitting trend lines at higher and lower rating values. The patterns are consistent with a trade-off governments may make between forbearance from opportunistic borrowing during elections and better access to credit for other non-opportunistic, welfare enhancing purposes.

legislature (1). Classification of electoral system competitiveness is based on the DPI’s “EIEC” variable. A country must score 7 on a 1-7 scale (7 = most competitive). A country’s first election in the 1989-2004 period must be within four elections of their first EIEC score of 7 consistent with notions of the new or recently reformed nature of its democracy. Additional detail on data sources and sampling strategies, including years when specific countries enter the sample, is available from the authors.

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<!-- Start of picture text --> 10.00 Non-Election Period<br>Trend Line Pre-, Current- or Post-<br>Election Year Trend Line<br>0.00<br>-10.00 Pre-, Current- or Post-<br>Election Year Observation<br>Non-Election Period<br>Observation<br>-20.00<br>-30.00<br>0 5 10 15<br>Average Sovereign Rating (Moody's, S&P, Fitch)<br>Budget Balance (% of GDP)<br><!-- End of picture text -->

**Figure 1.** _Results from analyses of linear trends for budget balances during election and nonelection periods, 1989-2004_

# **3.2. Regression Results**

Regression results in Table 1 confirm trends observed in Figure 1. GMM estimation of current year election effects ( _Electionit)_ in Columns 3-9 is consistently negative and statistically significant, again documenting the link between election periods and fiscal expansion through government borrowing. But the interaction of this election dummy with current-year rating ( _Rating*Electionit_ ) is positive and significant, indicating that governments with higher ratings borrow relatively less in election years –fiscal discipline in the face of opportunistic borrowing pressure. The current-year rating ( _Ratingit_ ) is negative and significant, indicating that countries 7
with higher ratings borrow relatively more in non-election years –better access to credit given fiscal discipline in election years. We also observe evidence of lagged effects consistent with the notion of continued fiscal discipline in the year _after_ an election ( _Rating*Electionit-1_ ) but magnitudes and statistical significance levels are lower. These regression results prove robust to the inclusion of additional country controls, which enter step-wise to limit instrument count. Overall, the evidence points to a relationship between CRAs and governments in emerging democracies where higher ratings imply government alternation between fiscal discipline and relatively less borrowing in election and immediate post-election years, followed by relatively more borrowing in non-election years.

# **4. Conclusion**

CRAs drew substantial and arguably justifiable criticism for failing to assess risk accurately and deter borrowing that helped bring on the US financial crisis of 2008-2009. But CRAs also matter outside the US where opportunistic election-period borrowing is a regular and predictable tendency that can also lead to financial crisis. CRAs appear to help blunt that tendency. To the extent that fiscal smoothing is good development policy, then CRAs play a positive role in emerging democracies around the world as private guardians of public fiscal discipline.

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**Table 1.** _Descriptive statistics, and fixed effects and dynamic panel system GMM regression results.  Dependant variable: Annual budget balance as a percentage of GDP, 1989-2004_<sup>_a_</sup>

|Descriptive Statistics and|(1)|(2)|(3)|(4)|(5)|(6)|(7)|(8)|(9)|
|---|---|---|---|---|---|---|---|---|---|
|<br>Estimators→<br>_Variables_↓|Means<br>(Std. Dev.)|Fixed<br>Effects|System<br>GMM|System<br>GMM|System<br>GMM|System<br>GMM|System<br>GMM|System<br>GMM|System<br>GMM|
|||-0.392|2.584†|3.266†|1.378|4.885*|2.167|1.859|2.053|
|Constant||(1.443)|(1.524)|(1.855)|(1.442)|(2.397)|(1.600)|(2.154)|(2.166)|
||-2.061|0.703**|0.912**|0.916**|0.896**|0.813**|0.925**|0.895**|0.910**|
|_Budget Balanceit-1_|(4.314)|(0.158)|(0.144)|(0.136)|(0.155)|(0.156)|(0.156)|(0.147)|(0.142)|
||4.937|1.244|0.973†|0.930†|1.079*|1.047*|1.013*|0.988*|0.969*|
|_Ratingit+1_|(3.094)|(0.795)|(0.512)|(0.487)|(0.499)|(0.445)|(0.511)|(0.509)|(0.498)|
||4.989|-1.344*|-1.645**|-1.624**|-1.634**|-1.348**|-1.630**|-1.634**|-1.644**|
|_Ratingit_|(3.018)|(0.674)|(0.634)|(0.639)|(0.658)|(0.478)|(0.617)|(0.621)|(0.631)|
||5.099|0.184|0.011|-0.014|0.177|-0.291|0.012|-0.011|0.023|
|_Ratingit-1_|(2.925)|(0.260)|(0.306)|(0.277)|(0.257)|(0.319)|(0.281)|(0.280)|(0.301)|
||0.241|-0.230|-0.261|-0.158|-0.461|0.040|-0.228|-0.342|-0.265|
|_Electionit+1_|(0.429)|(0.976)|(1.085)|(0.877)|(0.843)|(0.678)|(1.103)|(1.181)|(1.028)|
||0.227|-1.308|-2.536*|-2.293*|-2.614*|-1.737†|-2.712**|-2.654*|-2.552*|
|_Electionit_|(0.420)|(1.302)|(1.313)|(1.126)|(1.307)|(0.993)|(1.025)|(1.156)|(1.060)|
||0.206|-0.041|-1.085|-0.928|-1.117|-0.971|-1.022|-1.063|-1.089|
|_Electionit-1_|(0.406)|(1.135)|(0.998)|(0.888)|(0.976)|(0.953)|(0.994)|(1.056)|(0.961)|
||1.154|0.009|0.090|0.073|0.093|0.013|0.087|0.098|0.093|
|_Rating*Electio_nit+1|(2.539)|(0.169)|(0.164)|(0.140)|(0.127)|(0.102)|(0.163)|(0.178)|(0.154)|
||1.094|0.251|0.535*|0.495*|0.503*|0.382*|0.562*|0.543*|0.538**|
|_Rating*Electionit_|(2.516)|(0.230)|(0.226)|(0.225)|(0.251)|(0.196)|(0.218)|(0.230)|(0.218)|
||1.041|0.067|0.297†|0.274†|0.277†|0.263†|0.288†|0.299†|0.298†|
|_Rating*Electionit-1_|(2.479)|(0.168)|(0.158)|(0.157)|(0.167)|(0.154)|(0.156)|(0.161)|(0.158)|
|_Unemploymentit-1_|9.912<br>(5.696)|||-0.042<br>(0.076)||||||
|_Inflationit-1_|1.014<br>(4.129)||||-0.032<br>(0.065)|||||
||5.164|||||-0.540*||||
|_Reservesit-1_|(2.358)|||||(0.233)||||
|_Curr Crisisit-1_|0.191<br>(0.395)||||||0.810<br>(0.893)|||
|_Rightsit-1_|-2.770<br>(1.073)|||||||-0.271<br>(0.518)||
||3567||||||||0136|
|_Checksit-1_|.<br>(1.306)||||||||.<br>(0.531)|
|_Country/Year Dummies_||Yes|Yes|Yes|Yes|Yes|Yes|Yes|Yes|
|Instruments Generated|||71|86|86|86|72|72|72|
|Hansenχ<sup>_2_</sup>Test|||0.00|0.00|0.00|0.00|0.00|0.00|0.00|
|Arellano-Bond AR(2)_Z_Test|||-1.52|-1.49|-1.59|-1.63†|-1.57|-1.52|-1.56|
|_N_|141|141|141|141|141|141|141|141|141|
|Waldχ<sup>2</sup>(_R_<sup>_2_</sup>)||(0.749)|162.59**|207.56**|2077.83**|8406.03**|47.42**|658.10**|302..99**|

> aSample means and standard deviations are reported in Column 1. Coefficient estimates and robust standard errors are reported in Columns 2-9. Lagged _Budget Balance_ , all _Election_ , all _Rating_ , _Unemploymen_ t, _Inflation_ and _Reserves_ terms are treated as endogenous while _Curr Crisis_ , _Rights_ and _Checks_ are treated as exogenous in system GMM estimations in Columns 3-9. We do not report but obtain consistent results when treating _Curr Crisis_ , _Rights_ and _Checks_ terms as endogenous.  Significance: † p < 0.10, * p < 0.05, ** p < 0.01.

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# **Acknowledgements**

Paul M. Vaaler is grateful to Mari Sako and Chris Flegg of the University of Oxford’s Saïd Business School and to the late Mark Janes of the University of Oxford’s Bodleian Library for time and resources helpful to this research during his visiting professorship in 2012. This paper benefitted from comments, criticisms and suggestions from Aseem Kaul, Robert Kudrle, and other workshop participants at the Freeman Center for International Economic Policy at the University of Minnesota’s Humphrey School of Public Affairs. The opinions and findings here are those of the authors and do not represent the views of the World Bank or its directors. All errors are ours.

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