## _wp0494 - References

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### Introduction and research question
- Research question: Do elections and political opportunism influence policymakers’ choice of nominal anchor (exchange rate versus monetary aggregate) in inflation stabilization programs?
- Key behavioral hypothesis: ERBS produces an initial consumption boom and later recession; MBS produces an initial consumption bust and later recovery. Opportunistic policymakers may choose ERBS before elections and MBS after elections.
- Empirical sample: 34 full-fledged stabilization episodes examined.

### Money-based versus Exchange-rate-based Stabilization (stylized facts)
- Formal distinction:
  - ERBS uses the exchange rate as the nominal anchor.
  - MBS uses a monetary aggregate (e.g., M1 or monetary base).
- Empirical regularities (Calvo and Végh (1999) summary):
  - ERBS:
    - Slow convergence of the inflation rate to the rate of devaluation.
    - Initial increase in real GDP and private consumption followed by a later contraction.
    - Real appreciation of the domestic currency.
    - Deterioration of the trade balance and current account deficit.
    - Ambiguous impact on domestic real interest rates.
  - MBS:
    - Slow convergence of the inflation rate to the rate of growth of the money supply.
    - Initial contraction in economic activity.
    - Real appreciation of the domestic currency.
    - No definite response of the trade balance and the current account.
    - Initial increase in domestic real interest rates.
- Theoretical interpretation: Differences are primarily timing of costs—“recession-now-versus-recession-later” hypothesis.
- Literature caveats: Alternative explanations include endogenous shocks (Echenique and Forteza (1997)) or initial-condition dependence (Gould (2001)); the paper advances political opportunism as a rationale for observed consumption cycles.

### Political opportunism and macroeconomic variables
- Political-economy precedents:
  - Election cycles affect fiscal deficits, inflation, and real exchange rates.
  - Voter models: retrospective/myopic voting (Nordhaus (1975)) and signaling of competence under rational expectations (Rogoff (1990)).
- Both voter-behavior traditions can generate consumption booms prior to elections; empirical verification is the paper’s aim.

### Empirical motivation and anecdotes
- Notable anecdotal examples:
  - Brazil 1994 Real Plan (ERBS): vote shares shifted—Cardoso from 17% (June) to 54% (October results); Lula from 39% to 27% over same period (table reproduced in text).
  - Mexican ERBS launched Dec-1987 preceded July-1988 election win.
  - Austral (Argentina, Jun-85) and Cruzado (Brazil, Feb-86) as ERBS launched before elections; Bonex (Argentina, Dec-89) and Collor (Brazil, Mar-90) as MBS launched soon after elections.
- Observed pattern: MBS programs often launched after elections; ERBS programs often launched before elections, though many ERBS occur after elections as well.
- Sample listing highlights (from Table 4): 34 episodes including Turkey Jan-80 (MBS), Israel Dec-80 (ERBS), Bolivia Nov-82 (ERBS), Iceland May-83 (ERBS), Argentina Austral Jun-85 (ERBS), Brazil Cruzado Feb-86 (ERBS), Mexico Dec-87 (ERBS), Argentina BONEX Dec-89 (MBS), Brazil Collor Mar-90 (MBS), Argentina Convertibility Apr-91 (ERBS), Brazil Real Plan Jul-94 (ERBS), Turkey Jan-00 (ERBS).

### Data sources and sample construction
- Stabilization episodes defined using an “episodic” approach to capture full-fledged announced stabilization packages.
- Sample: 34 episodes (Table 4).
- Election data: Lijphart Elections Archive (University of California, San Diego, 1980).
- Economic data: IFS and central banks.
- Political fragmentation index: Beck et al. (1998) Database of Political Institutions.
- Key sample choice: Focus on post-1980 democratic-era programs to reduce biases from nondemocratic regimes.

### Econometric model and main regressors
- Dependent variable Yi: 1 if ERBS, 0 if MBS. Probit specification P(Yi=1)=Φ(Xi′β).
- Main regressors:
  - X1: months to next election (often logged: ln(X1i)).
  - X2: international reserves (Reserves/M3).
  - X3: openness (exports + imports over GDP, measured one month before program).
  - X4: political fragmentation index (dummy: 1 = fragmented, 0 = cohesive).
  - X5: quarterly GDP growth rate (two-quarter lag from program start).
- Identification assumptions:
  - Decision to stabilize assumed to precede choice of nominal anchor.
  - Distance to next election treated as exogenous.
  - Robust (Huber-White) standard errors used to address heteroskedasticity.
- Theoretical expected signs:
  - β1 (ln months to next election): negative.
  - β2 (reserves): positive.
  - β3 (openness): positive.
  - β4 (fragmentation): positive.
  - β5 (growth): negative.

### Key regression results and statistical highlights
- Baseline findings: Distance to next election and political fragmentation are statistically significant predictors (often at 1% level); reserves and openness also statistically significant (often at 5%).
- Representative coefficient estimates (preserve reported values):
  - Table 5 variant (1):
    - log(Months to Next Elections) coefficient = -2.26 *** (robust SE 0.72).
    - Reserves coefficient = 16.312 ** (robust SE 7.61).
    - Openness coefficient = 3.374 ** (robust SE 1.58).
    - Fragmentation coefficient = 2.43 *** (robust SE 0.93).
    - Constant = 2.815 (robust SE 2.27).
  - Table 6 (months-from-past-elections): log(Months from Past Elections) example coefficient = 0.942 * (robust SE 0.51) in variant (1).
  - Table 7 variant (1) (normalized distance): log(Months to Next Elections / Cycle) = -4.391 *** (robust SE 1.66); Reserves = 27.039 *** (robust SE 9.59); Openness = 2.987 * (robust SE 1.70); Fragmentation = 3.099 ** (robust SE 1.40).
  - Joint model (Table 9): log(Months to Next Elections) = -2.091 *** (robust SE 0.68); log(Months from Past Elections) positive but often less significant due to collinearity.
- Model fit:
  - Pseudo R2 examples: 0.6266, 0.6708, 0.7048 across specifications.
- Robustness: Results robust to alternative distance normalizations, inclusion of both distance measures, and interaction terms.

### Marginal effects and predictive illustrations
- Predicted probability patterns (benchmarked to sample means):
  - Probability of adopting ERBS rises as distance to next elections shrinks.
  - Example predicted range: probability of ERBS ranges from 76 percent to 100 percent as distance to next elections varies across sample extreme values (Figure 5).
- Interaction examples (textual numeric illustrations preserved):
  - Interaction with reserves (Figure 6):
    - With reserves covering 10 percent of M3, three policymakers launching stabilization at different times had ERBS probabilities of 45 percent (three years before elections), 78 percent (two years before), and 99 percent (one year before).
    - For reserves ≥ 40 percent of M3, ERBS probability close to 1 regardless of electoral timing.
  - Interaction with openness (Figure 7):
    - For minimum openness (less than 5 percent), ERBS probability can be as low as 30 percent when elections are far away.
    - For maximum openness (sample max), ERBS probability = 1 regardless of time to next election.
  - Interaction with political fragmentation (Figure 8):
    - For large distances to next elections, high fragmentation substantially increases ERBS probability relative to low fragmentation; differences can reach as much as 75 percentage points.
- Interaction regression (Table 10) reported coefficients:
  - log(Months to Next Elections) = -3.689 *** (robust SE 0.88).
  - log(Months to Next Elections) * Reserves = 5.217 ** (robust SE 2.62).
  - log(Months to Next Elections) * Openness = 0.925 * (robust SE 0.51).
  - log(Months to Next Elections) * Pol. Fragmentation = 0.787 *** (robust SE 0.23).
  - Model Pseudo R2 = 0.6403; Prob > Chi2 = 0.0013.

### Extensions and additional findings
- Country dummies for repeated failed ERBS:
  - Dummies show negative coefficients in reported variants:
    - Dummy for Argentina = -3.652 (robust SE 2.35, not significant at conventional levels in reported variant).
    - Dummy for Brazil = -2.131 ** (robust SE 0.99).
  - Interpretation: successive ERBS failures can reduce credibility and make policymakers prefer MBS.
- Contagion/covariance across countries:
  - Temporal clustering of programs (1985–1994) complicates isolation of contagion effects; year dummies could be used but sample concentration limits inference.
  - Any positive covariance would reinforce the political-opportunism interpretation.
- Electoral success of opportunistic stabilization:
  - Anecdotal evidence: some ERBS (e.g., Cruzado Plan) helped incumbents or their parties electorally despite later collapse; failed stabilization attempts prior to elections often punished incumbents.

### Conclusions and policy implications
- Core empirical conclusion: Election timing matters for choice of nominal anchor—ERBS are more likely closer to forthcoming elections; MBS are more likely after elections and far from future elections.
- Political and economic determinants:
  - Larger international reserves, higher openness, and greater political fragmentation increase both the probability of ERBS and the degree to which election timing influences that choice.
- Normative implication: Strengthening political and economic institutions that constrain opportunistic policymaking could reduce election-driven manipulation of stabilization strategies and potentially improve policy quality in developing countries.
- Contribution: Provides empirical support for the “recession-now-versus-recession-later” rationale and documents robust political-opportunism effects in choice of nominal anchor across a sample of 34 stabilization episodes.

*Source: _wp0494 - References (content unit provided).*

### 1.         Quarterly GDP Growth Rates (Argentina 1980–2000)  ..............................................10

### 1.         Quarterly GDP Growth Rates (Argentina 1980–2000)  ..............................................10

### Figures (listed in the content unit)
- 1.         Quarterly GDP Growth Rates (Argentina 1980–2000)  ..............................................10
- 2.         Quarterly GDP Growth Rates (Brazil 1980–2000) .....................................................11
- 3.         Distribution of ERBS—MBS by Months to the Next Elections  ............................... 16
- 4.         Distribution of ERBS—MBS by Months from Past Elections  ...................................17
- 5.         Predicted Probabilities with Varying Distance (Months to Next Elections)  ..............25
- 6.         Predicted Probabilities with Varying Reserves ...........................................................26
- 7.         Predicted Probabilities with Varying Distance and Openness ....................................27
- 8.         Predicted Probabilities with Varying Distance and Political Fragmentation ..............28

### Tables (listed in the content unit)
- 1.         Empirical Regularities of Stabilization Programs in Chronic Inflation Countries  .......6
- 2.         Real Plan – Voting Intentions  .......................................................................................9
- 3.         Stabilization Plans and Timing of Elections  ...............................................................12
- 4.         The Sample of Stabilization Programs  .......................................................................13
- 5.         Regression Variants using Absolute Distance to Next Elections  ...............................21
- 6.         Regression Variants using Absolute Distance from Past Elections ............................22
- 7.         Regression Variants using Nomalized Distance to Next Elections  ............................23
- 8.         Regression Variants using Absolute Distance from Past Elections ............................23
- 9. Regression Variants using Distance to next Elections and 
                Distance from Past Elections  ..................................................................................24
- 10.       Regression Model using Interactions with Distance ...................................................28
- 11.       Regression Model with Country Dummies ................................................................ 30

*Source: _wp0494 - 1.         Quarterly GDP Growth Rates (Argentina 1980–2000)  ..............................................10*

### References  ............................................................................................................

### _wp0494 - References

### Introduction
- Research question: Do elections and political opportunism influence policymakers’ choice of nominal anchor (exchange rate versus monetary aggregate) in inflation stabilization programs?
- Key behavioral hypothesis: Exchange-rate-based stabilization (ERBS) produces an initial consumption boom and later recession; money-based stabilization (MBS) produces an initial consumption bust and later recovery. Opportunistic policymakers may choose ERBS before elections and MBS after elections.
- Empirical sample: 34 full-fledged stabilization episodes examined.

### Money-based versus Exchange-rate-based Stabilization (stylized facts)
- Formal distinction: ERBS uses the exchange rate as the nominal anchor; MBS uses a monetary aggregate (e.g., M1 or monetary base).
- Empirical regularities (from Calvo and Végh (1999) summary in text):
  - ERBS: Slow convergence of the inflation rate to the rate of devaluation; initial increase in real GDP and private consumption followed by a later contraction; real appreciation of the domestic currency; deterioration of the trade balance and current account deficit; ambiguous impact on domestic real interest rates.
  - MBS: Slow convergence of the inflation rate to the rate of growth of the money supply; initial contraction in economic activity; real appreciation of the domestic currency; no definite response of the trade balance and the current account; initial increase in domestic real interest rates.
- Theoretical interpretation: In infinite-horizon models where present-value consumption is equal across anchors, differences are primarily timing of costs—“recession-now-versus-recession-later” hypothesis.
- Literature caveats: Alternative explanations include endogenous shocks (Echenique and Forteza (1997)) or initial-condition dependence (Gould (2001)). The paper argues political opportunism provides a rationale for observed consumption cycles.

### Political opportunism and macroeconomic variables
- Political-economy precedents: Election cycles have been shown to affect fiscal deficits, inflation, and real exchange rates. Models of opportunism rely on:
  - Retrospective/myopic voting (Nordhaus (1975)) or
  - Signaling of competence under rational expectations (Rogoff (1990)).
- Both voter-behavior traditions can generate consumption booms prior to elections; the paper remains agnostic and aims for empirical verification.

### Political opportunism and inflation stabilization (empirical motivation)
- Anecdotal evidence:
  - Brazil 1994 Real Plan (ERBS) — vote shares shifted in favor of the candidate who launched the plan: Cardoso from 17% (June) to 54% (October results); Lula from 39% to 27% over same period (table reproduced in text).
  - Mexican ERBS launched Dec-1987 preceded July-1988 election win.
  - Examples: Austral (Argentina, Jun-85) and Cruzado (Brazil, Feb-86) as ERBS launched before elections; Bonex (Argentina, Dec-89) and Collor (Brazil, Mar-90) as MBS launched soon after elections.
- Table 4 sample listing: 34 stabilization programs from 1980 onward; sample includes Turkey 1980 (Jan-80, MBS), Israel Aridor I (Dec-80, ERBS), Bolivia Nov-82 (ERBS), Iceland May-83 (ERBS), Argentina Austral I (Jun-85, ERBS), Brazil Cruzado (Feb-86, ERBS), Mexico 1987 (Dec-87, ERBS), Argentina BONEX (Dec-89, MBS), Brazil Collor (Mar-90, MBS), Argentina Convertibility (Apr-91, ERBS), Brazil Real Plan (Jul-94, ERBS), Turkey 2000 (Jan-00, ERBS), among others.
- Observed pattern: MBS programs often launched after elections; ERBS programs often launched before elections, though many ERBS occur after elections as well.

### Data sources and sample construction
- Stabilization episodes defined using the “episodic” approach (case-study-based) to capture full-fledged announced stabilization packages.
- Sample: 34 episodes (Table 4). Election data from Lijphart Elections Archive (University of California, San Diego, 1980). Economic data from IFS and central banks. Political fragmentation index from Beck et al. (1998) Database of Political Institutions.
- Key sample choices: Focus on post-1980 democratic-era programs to reduce biases from nondemocratic regimes.

### Econometric model and main regressors
- Dependent variable Yi: 1 if ERBS, 0 if MBS. Probit specification P(Yi=1)=Φ(Xi′β).
- Main regressors:
  - X1: months to next election (often logged in specifications: ln(X1i) )
  - X2: international reserves (Reserves/M3)
  - X3: openness (exports + imports over GDP, measured one month before program)
  - X4: political fragmentation index (dummy: 1 = fragmented, 0 = cohesive)
  - X5: quarterly GDP growth rate (two-quarter lag from program start)
- Identification assumptions: Decision to stabilize is assumed to precede choice of nominal anchor; distance to next election treated as exogenous. Robust (Huber-White) standard errors used to address heteroskedasticity.
- Theoretical expected signs:
  - β1 (ln months to next election): negative (closer elections → higher prob. of ERBS).
  - β2 (reserves): positive (higher reserves → higher prob. of ERBS).
  - β3 (openness): positive.
  - β4 (fragmentation): positive (fragmentation raises ERBS probability).
  - β5 (growth): negative (recessions → higher ERBS probability).

### Key regression results (high-level)
- Baseline findings: Distance to next election and political fragmentation are statistically significant predictors (often at 1% level); reserves and openness also statistically significant (often at 5%).
- Representative coefficient highlights (from reported tables):
  - Example (Table 5, variant (1)): log(Months to Next Elections) coefficient = -2.26 *** (robust SE 0.72). Reserves coefficient = 16.312 ** (robust SE 7.61). Openness coefficient = 3.374 ** (robust SE 1.58). Fragmentation coefficient = 2.43 *** (robust SE 0.93). Constant = 2.815 (robust SE 2.27).
  - Using months-from-past-elections (Table 6): log(Months from Past Elections) coefficients positive (e.g., 0.942 * with robust SE 0.51 in variant (1)).
  - Normalized distance specifications (Tables 7 and 8) produce consistent signs and statistical significance (e.g., Table 7 variant (1) log(Months to Next Elections / Cycle) = -4.391 *** (robust SE 1.66); Reserves = 27.039 *** (robust SE 9.59); Openness = 2.987 * (robust SE 1.70); Fragmentation = 3.099 ** (robust SE 1.40)).
  - Joint model including both distances (Table 9): log(Months to Next Elections) remains negative and significant in many variants (e.g., -2.091 *** with robust SE 0.68); log(Months from Past Elections) positive but often less statistically significant due to collinearity.
- Model fit and robustness:
  - Pseudo R2 values vary by specification (examples: 0.6266, 0.6708, 0.7048 in different specifications).
  - Results robust to alternative distance normalizations, inclusion of both distance measures, and interaction terms.

### Marginal effects and predictive illustrations (benchmarked to sample means)
- Predicted probability patterns (benchmark model):
  - Probability of adopting ERBS rises as distance to next elections shrinks.
  - Example predicted range: probability of ERBS ranges from 76 percent to 100 percent as distance to next elections varies across sample extreme values (Figure 5).
- Interaction effects:
  - Interaction with reserves (Figure 6): with reserves covering 10 percent of M3 as example, three policymakers launching stabilization at different times had ERBS probabilities of 45 percent (three years before elections), 78 percent (two years before), and 99 percent (one year before) in the case where reserves cover 10 percent of M3 (text example). For very low reserves, distance to election greatly changes ERBS probability; for reserves ≥ 40 percent of M3, ERBS probability close to 1 regardless of electoral timing.
  - Interaction with openness (Figure 7): for minimum openness (less than 5 percent), ERBS probability can be as low as 30 percent when elections are far away; for maximum openness (sample max), ERBS probability = 1 regardless of time to next election.
  - Interaction with political fragmentation (Figure 8): for large distances to next elections, high fragmentation substantially increases ERBS probability relative to low fragmentation; differences can reach as much as 75 percentage points.
- Interaction regression (Table 10) summary:
  - log(Months to Next Elections) = -3.689 *** (robust SE 0.88).
  - log(Months to Next Elections) * Reserves = 5.217 ** (robust SE 2.62).
  - log(Months to Next Elections) * Openness = 0.925 * (robust SE 0.51).
  - log(Months to Next Elections) * Pol. Fragmentation = 0.787 *** (robust SE 0.23).
  - Model Pseudo R2 = 0.6403; Prob > Chi2 = 0.0013.

### Extensions and additional findings
- Country dummies for repeated failed ERBS attempts:
  - Dummies for countries with multiple failed ERBS show negative coefficients (e.g., Dummy for Argentina = -3.652 (robust SE 2.35, not significant at conventional levels in reported variant), Dummy for Brazil = -2.131 ** (robust SE 0.99)), suggesting repeated ERBS failure reduces likelihood of future ERBS in those countries.
  - Interpretation: successive ERBS failures can reduce credibility and make policymakers prefer MBS.
- Contagion/covariance across countries:
  - Temporal clustering of programs (1985–1994) complicates isolate contagion effects; year dummies could be used but sample concentration limits inference. Any positive covariance would reinforce the political-opportunism interpretation.
- Electoral success of opportunistic stabilization:
  - Anecdotal evidence: some ERBS (e.g., Cruzado Plan) helped incumbents or their parties electorally despite later collapse; failed stabilization attempts prior to elections often punished incumbents.

### Conclusions and policy implications
- Core empirical conclusion: Election timing matters for choice of nominal anchor in inflation stabilization—ERBS are more likely closer to forthcoming elections; MBS are more likely after elections and far from future elections.
- Political and economic determinants:
  - Larger international reserves, higher openness, and greater political fragmentation increase both the probability of ERBS and the degree to which election timing influences that choice.
- Normative implication: Strengthening political and economic institutions that constrain opportunistic policymaking could reduce election-driven manipulation of stabilization strategies and potentially improve policy quality in developing countries.
- Contribution: Provides empirical support for the “recession-now-versus-recession-later” rationale and documents robust political-opportunism effects in choice of nominal anchor across a sample of 34 stabilization episodes.

*Source: _wp0494 - References (content unit provided).*

### References

### _wp0494 - References

### References list

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