## Introduction

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### Research question and motivation
- Research question: Do central banks tend to implement FX sales, leaning against depreciation, during electoral periods? If yes, under what conditions are these electoral cycles in FXI policy more prevalent, and is there any mitigating factor?
- Motivation:
  - De facto central bank independence is challenging because central bank governors are often politically appointed and monetary policy affects aggregate economic performance for which politicians may be held electorally accountable.
  - Recent evidence finds political pressures even on central banks with high de jure independence scores (Binder, 2021; Bianchi et al., 2023).
  - Politicians may manipulate the exchange rate for electoral purposes because depreciation reduces national purchasing power and is politically unpopular (Klein and Marion 1997; Frieden, Ghezzi, and Stein 2001; Broz and Frieden, 2008).
- Contribution:
  - Investigates political economy factors influencing Foreign Exchange Intervention (FXI) policies using monthly data for 28 countries (17 EMs and 11 AEs) over 2000–2019.
  - Estimates an FX policy reaction function that accounts for the size of reserves and provides policy reforms to mitigate electoral cycles in FXI.

### Data, measurement, and sample
- Sample and primary data sources:
  - Cross-country monthly data covering 28 countries (17 emerging markets and 11 Advanced Economies) over the period 2000 - 2019.
  - Sample includes 140 elections.
  - Foreign Exchange Intervention (FXI) source: Adler et al. (2021); defined as “any transaction that changes the central bank’s foreign-currency position”.
  - Election dates source: Data of Political Institutions (DPI, 2020).
- Primary and alternative FX measures:
  - Primary measure: monthly FX sales expressed in percent of yearly GDP.
  - Alternatives used in robustness: FX sale dummy; scaling FX sales by reserves and by imports.
- Controls and reaction-function variables:
  - Volatility of USD/local currency (standard deviation over calendar year) from IFS.
  - Monthly change in log bilateral exchange rate (USD/local currency).
  - Interest rate differential: monthly difference between country policy rate and US policy rate (BIS).
  - Size of reserves: monthly IFS data calculated as share of yearly GDP; alternative scaling in months of imports.
  - Political variables: Boix-Miller-Rosato democracy indicator; frequency of irregular central bank turnover (Dreher et al.); central bank transparency indices (Dincer et al., overall 0–15 and subindices 0–3); government popularity proxy: change in ICRG government stability index (0–12).

### Stylized facts and empirical strategy
- Stylized facts:
  - Cumulative FX sales tend to be larger in the 12-month pre-electoral period compared to the 12-month post-electoral period, driven by emerging market economies (EMs).
  - Exchange rate considerations matter more in EMs due to their influence on demand and inflation expectations.
- Three-step empirical approach:
  1. Baseline fixed-effects estimates of FX_sales_cmy on Pre−elec_cmy (1 = twelve-month run-up to election, 0 = 12 months post-election), including μ_c, μ_y, μ_my; robustness with controls and six-month window; probit for dummy dependent variable.
  2. Interaction specification: Pre−elec_cmy * Political (competitiveness, irregular turnover, transparency) to test heterogeneity; hypotheses: competitiveness and political pressure amplify (θ>0), transparency mitigates (θ<0).
  3. Political gains test: dynamic panel for Govpop_cy on lagged FX_sales_cy−1 to test whether FX sales increase government popularity (δ>0).

### Main empirical findings
- Baseline and probability results:
  - FX sales are about 22 percent larger in pre-electoral period compared to post-electoral period, in particular in the sub-sample of EMs. (Note: marginal effect obtained as (exp(β) −1) ×100 percent.)
  - Probability of an FX sale:
    - About 3 percent higher in pre-electoral period in full sample (column 4).
    - About 5.5 percent higher in EMs.
    - No statistically significant effect in AEs.
- Selected coefficient magnitudes (controlling for FX reaction function arguments):
  - Pre-Election coefficients (selected):
    - Full sample: 0.090 (standard error 0.060)
    - EMs: 0.188** (standard error 0.084)
    - AEs: -0.052 (standard error 0.095)
  - Exchange rate volatility coefficient example: 9.308*** (standard error 3.194) in some specifications.
  - Log reserves (% GDP) (t-1) example: 0.352*** (standard error 0.109) in some specifications.
- Heterogeneity and mechanisms:
  - Electoral cycle in FX policy mainly a phenomenon in EMs with competitive elections (Competitive election mean 0.908; Std. dev. 0.289).
  - Stronger pre-electoral FX sales where frequency of irregular CB governor turnover is higher (Irregularity mean 1.489; Std. dev. 1.508).
  - Central bank transparency generally mitigates electorally motivated FX sales; aggregated index effect not always statistically significant but disaggregated dimensions (economic, policy, operational) show mitigation.
- Political gains from FX sales:
  - FX sales associated with subsequent increases in government popularity (δ>0) across dynamic panel, GMM, and fixed-effects estimators; interpreted with caution due to potential endogeneity.

### Robustness checks and descriptive statistics
- Robustness:
  - Alternative scalings (Log FX sales % Imports; Log FX sales % Reserves): Pre-Election coefficients positive and significant for Full and EMs.
  - Published-data subsample: Pre-Election coefficient example 0.271*** (std. err. 0.068) (Table A5).
  - Alternative definitions of reserves (months of exports/imports), six-month pre-election window, and exclusion of Denmark produce similar EM-driven patterns.
- Selected summary statistics (preserved exactly):
  - Log FX sales (%GDP): Obs. 3,327; Mean 1.364; Std. dev. 1.734; Min 0; Max 6.517
  - FX sales (Dummy): Obs. 3,327; Mean 0.450; Std. dev. 0.498; Min 0; Max 1
  - Log FX purchase (%GDP): Obs. 3,327; Mean 1.750; Std. dev. 1.846; Min 0; Max 6.921
  - ∆log (US/LCU): Obs. 3,327; Mean 0.000; Std. dev. 0.006; Min -0.072; Max 0.037
  - Interest rate differential with US: Obs. 3,249; Mean 3.908; Std. dev. 7.067; Min -5; Max 81.139
  - Log reserves (% GDP): Obs. 3,327; Mean 2.673; Std. dev. 0.671; Min 0.894; Max 4.619
  - Volatility of USD/LCU: Obs. 392; Mean 0.023; Std. dev. 0.014; Min 0; Max 0.135
  - MP Transparency Index: Obs. 392; Mean 9.320; Std. dev. 2.855; Min 2; Max 14.5
  - Economic Transparency Index: Obs. 392; Mean 1.630; Std. dev. 0.853; Min 0; Max 3
  - Policy Transparency Index: Obs. 392; Mean 1.735; Std. dev. 0.924; Min 0; Max 3
  - Operational Transparency Index: Obs. 392; Mean 1.741; Std. dev. 0.620; Min 0; Max 3
  - Government Popularity: Obs. 532; Mean -0.128; Std. dev. 1.257; Min -4.5; Max 6

### Policy implications and recommendations
- Core policy implications:
  - Politically motivated FX sales can deplete reserves and hamper central banks’ ability to respond to large shocks, creating a divergence between political incentives and social-welfare-maximizing macroeconomic management.
- Recommended reforms:
  - Strengthen monetary policy transparency to curb electorally driven FX sales, with emphasis on:
    - Economic transparency: disclose the economic data used for forecasts and decision evaluation.
    - Policy transparency: disclose policy decisions, explanations/rationale, and forward guidance.
    - Operational transparency: disclose challenges affecting policy implementation and execution, including disturbances that affect monetary policy transmission.
  - Use the IMF’s Central Bank Transparency Code (IMF, 2020) as a mapping and guide for reforms.
  - Consider rule-based approaches for FXI to reduce discretion during electoral periods, examples include:
    - Predetermined ranges or thresholds for exchange rate depreciations to make FXI policy more transparent.
    - Rules guided by the Fund’s Integrated Policy Framework - Principles for Use of Foreign Exchange Intervention; risk-based triggers (e.g., value at risk approaches) as in Lafarguette and Veyrune (2021).
- Broader considerations:
  - Transparency and rules can mitigate political interference and better align central bank decisions with social welfare.
  - Policymakers should account for intertemporal trade-offs from depleting reserves to boost near-term household purchasing power.

### Open questions and further research
- If central banks receive broader mandates and use more instruments, will they face greater political pressure?
- Need for more and better-quality data to further understand political economy drivers and to design reforms that shield central banks from political pressures.

*Italic: Content summarized from “Introduction” (wpiea2026130-source-pdf).*

### Introduction ...........................................................................................................

### wpiea2026130-source-pdf - Introduction

### Introduction: research question and motivation
- Research question: Do central banks tend to implement FX sales, leaning against depreciation, during electoral periods? If yes, under what conditions are these electoral cycles in FXI policy more prevalent, and is there any mitigating factor?
- Motivation:
  - De facto central bank independence is challenging because central bank governors are often politically appointed and monetary policy affects aggregate economic performance for which politicians may be held electorally accountable.
  - Recent evidence finds political pressures even on central banks with high de jure independence scores (Binder, 2021; Bianchi et al., 2023).
  - Politicians may manipulate the exchange rate for electoral purposes because depreciation reduces national purchasing power and is politically unpopular (Klein and Marion 1997; Frieden, Ghezzi, and Stein 2001; Broz and Frieden, 2008).
- Contribution:
  - Investigates political economy factors influencing Foreign Exchange Intervention (FXI) policies using monthly data for 28 countries (17 EMs and 11 AEs) over 2000–2019.
  - Estimates an FX policy reaction function that accounts for the size of reserves and provides policy reforms to mitigate electoral cycles in FXI.

### Key empirical findings (stylized facts and main results)
- Data and sample:
  - Cross-country monthly data covering 28 countries (17 emerging markets and 11 Advanced Economies) over the period 2000 - 2019.
  - Sample includes 140 elections.
- Main empirical results:
  - Evidence of larger and more frequent FX sales in pre-electoral periods compared to post-election periods.
  - The result is driven by emerging markets (EMs) with competitive elections and where political pressures on central bank governors are plausibly more prevalent.
  - Suggestive evidence that FX sales are followed by an increase in government popularity, consistent with a political business cycle in FX policy.
  - Transparency mitigates the magnitude of electorally motivated FX sales.
- Robustness and alternatives:
  - FXI data from Adler et al. (2021) defined as “any transaction that changes the central bank’s foreign-currency position”; monthly series on FX sales expressed in percent of yearly GDP used as primary dependent variable.
  - Alternative measures used in robustness checks include:
    - A dummy variable equal to 1 if there was an FX sale in a given month and 0 otherwise.
    - Scaling FX sales by reserves and by imports (see Table A4 in Appendix).
  - Subset robustness: checks using the subset with published FX data (see Table A5 in Appendix).
- Limitations noted:
  - Adler et al. (2021) proxy focuses on central bank operations and may face cross-country definitional differences.
  - Investigation of central bank independence indices yielded inconclusive results (discussed further in the paper).

### Data description and measurement
- Foreign Exchange Intervention (FXI):
  - Source: Adler et al. (2021).
  - Definition: “any transaction that changes the central bank’s foreign-currency position”.
  - Advantages: focuses on active central bank transactions and adjusts for valuation changes and returns on investment induced changes in reserves; data available from 2000.
  - Primary measure: monthly FX sales expressed in percent of yearly GDP.
  - Alternative measures: FX sale dummy; scaling by reserves and imports in robustness checks.
- Election dates:
  - Source: Data of Political Institutions (DPI, 2020).
  - Included elections: legislative elections for parliamentary systems and executive elections for presidential systems (consistent with political budget cycle literature).
  - Sample includes 140 elections.
- Controls and contextual variables:
  - Exchange rate volatility controlled for in some specifications.
  - Size of reserves included as a determinant of FX sales in the empirical setting.
  - Rationale: countries with sufficient foreign reserves could use FXI to withstand non-fundamental depreciation in the short term; exchange-rate related uncertainty higher during election periods in EMs.

### Policy implications and recommendations
- Transparency as a mitigating factor:
  - Improving monetary policy transparency could curb electorally driven FX sales.
  - Suggested dimensions of transparency to strengthen:
    - Economic transparency: disclose the economic data used for forecasts and decision evaluation.
    - Policy transparency: disclose policy decisions, explanations/rationale, and forward guidance.
    - Operational transparency: disclose challenges affecting policy implementation and execution, including disturbances that affect monetary policy transmission.
  - The IMF’s Central Bank’s Transparency Code (IMF, 2020) can help map and steer reforms toward international best practices.
- Rule-based approaches for FXI:
  - Consider FX policy intervention rules to curb pressures to draw down reserves during electoral periods.
  - Examples:
    - Setting predetermined ranges or thresholds for exchange rate depreciations to make FXI policies more transparent.
    - Rules guided by the Fund’s Integrated Policy Framework - Principles for Use of Foreign Exchange Intervention, including clear triggers for policy actions.
  - Reference to related rule proposals: numerical fiscal rules mitigate political budget cycles (Eklou and Joanis, 2019); FXI rules based on value at risk triggers (Lafarguette and Veyrune, 2021).
- Broader implication:
  - Politically motivated FX sales could hamper central banks’ ability to respond effectively to large shocks if reserves are depleted to temporarily boost voters’ purchasing power.

### Relation to literature
- Builds on literature showing politicians may manipulate exchange rates for electoral purposes (Klein and Marion 1997; Frieden, Ghezzi, and Stein 2001; Broz and Frieden, 2008; Jäger, 2016).
- Extends Jäger (2016) by estimating an FX policy reaction function that accounts for reserves and by proposing policy reforms.
- Contributes to literature documenting political pressures on central banks (Binder, 2021; Bianchi et al., 2023; Goncharov et al., 2023; Ioannidou et al., 2023) by showing transparency reforms can mitigate political interference in FX sales.
- Links to Integrated Policy Framework literature on use cases for FXI (destabilizing premia from FX market frictions; countering financial stability risks from FX mismatches; preventing de-anchoring of inflation expectations).

### Organization of the paper (structure)
- Section I: Data and Stylized Facts.
  - Subsection A: Data.
  - Subsection B: Stylized Facts.
- Section II: The Political Economy of Foreign Exchange Intervention: Empirical Analysis.
  - Subsection A: Empirical Strategy.
  - Subsection B: Results and Policy Discussions.
- Section III: Conclusion.
- Appendix and References follow.

_Italic: Content summarized from “Introduction” (wpiea2026130-source-pdf)._

### Appendix for more details).

### References

### References

### Foreign exchange intervention and reserves
- Adler, G., Lisack, N., and Mano, R. C. (2019). Unveiling the effects of foreign exchange intervention: A panel approach. Emerging Markets Review, 40, 100620.
- Adler, G., Chang, K. S., Mano, R., and Shao, Y. (2021). Foreign exchange intervention: A dataset of public data and proxies. International Monetary Fund.
- Daude, C., Yeyati, E. L., and Nagengast, A. J. (2016). On the effectiveness of exchange rate interventions in emerging markets. Journal of International Money and Finance, 64, 239-261.
- Kim, S. J., and Sheen, J. (2002). The determinants of foreign exchange intervention by central banks: evidence from Australia. Journal of International money and Finance, 21(5), 619-649.
- Lafarguette, R., and Veyrune, M. R. M. (2021). Foreign Exchange Intervention Rules for Central Banks: A Risk-based Framework. International Monetary Fund.
- Jäger, K. (2016). The role of regime type in the political economy of foreign reserve accumulation. European Journal of Political Economy, 44, 79-96.

### Central bank transparency, independence, and governance
- Aftab, M., and Mehmood, A. (2023). Does Central bank transparency deter the exchange rate volatility? New evidence from Asian emerging markets. Journal of Central Banking Theory and Practice, 12(2), 133-163.
- Bianchi, F., Gómez-Cram, R., Kind, T., and Kung, H. (2023). Threats to central bank independence: High-frequency identification with Twitter. Journal of Monetary Economics, 135, 37-54.
- Binder, C. C. (2021). Political pressure on central banks. Journal of Money, Credit and Banking, 53(4), 715-744.
- Dincer, N., Eichengreen, B., and Geraats, P. (2022). Trends in monetary policy transparency: further updates. International Journal of Central Banking, 18(1), 331-348.
- Dreher, A., Sturm, J. E., and De Haan, J. (2008). Does high inflation cause central bankers to lose their job? Evidence based on a new data set. European Journal of Political Economy, 24(4), 778-787.
- Dreher, A., Sturm, J. E., and De Haan, J. (2010). When is a central bank governor replaced? Evidence based on a new data set. Journal of Macroeconomics, 32(3), 766-781.
- Goncharov, I., Ioannidou, V., and Schmalz, M. C. (2023). (Why) do central banks care about their profits? The Journal of Finance, 78(5), 2991-3045.
- Ioannidou, V, S Kokas, T Lambert, and A Michaelides (2023), “(In)dependent central banks”, CEPR Discussion Paper No. DP17802
- International Monetary Fund. (2020). “The Central Bank Transparency Code.” IMF Policy Paper No. 2020/038. Washington, D.C., International Monetary Fund.
- Romelli, D. (2022). The political economy of reforms in Central Bank design: Evidence from a new dataset. Economic Policy, 37(112), 641-688.
- Vuletin, G., and Zhu, L. (2011). Replacing a “disobedient” central bank governor with a “docile” one: A novel measure of central bank independence and its effect on inflation. Journal of Money, Credit and Banking, 43(6), 1185-1215.

### Political economy, regime type, and political cycles
- Boix, C., Miller, M., and Rosato, S. (2013). A complete data set of political regimes, 1800–2007. Comparative political studies, 46(12), 1523-1554.
- Boix, C., Miller, M., and Rosato, S. (2022). “Boix-Miller-Rosato Dichotomous Coding of Democracy, 1800–2020.” Version 4. Harvard Dataverse.
- Broz, J. L., and Frieden, J. A. (2008). The political economy of exchange rates. In D. A. Wittman and B. R. Weingast (Eds.), The Oxford handbook of political economy. Oxford University Press.
- Cruz, C., Keefer, P., and Scartascini, C. (2021). DPI2020 Database of Political Institutions. Inter-American Development Bank Department of Research and Chief Economist.
- Frieden, J. A., Ghezzi, P., and Stein, E. (2001). Politics and exchange rates: a cross-country approach to Latin America. Pp. 21–63 in The Currency Game: Exchange Rate Politics in Latin America, ed. J. A. Frieden and E. Stein. Baltimore: Johns Hopkins University Press.
- Hall, M. G. (2020). The Global Political Economy of Exchange Rates. In Oxford Research Encyclopedia of International Studies.
- Herrera, H., Ordoñez, G., C. Trebesch, 2020 “Political booms, financial crises” Journal of Political Economy, Vol.128(2), pp. 507-543.
- Sever, C. (2021). Political booms and currency crises. Journal of Macroeconomics, 70, 103373.
- Shi, M., and Svensson, J. (2006). Political budget cycles: Do they differ across countries and why? Journal of public economics, 90(8-9), 1367-1389.
- Ugurlu, E. N., and Razmi, A. (2023). Political economy of real exchange rate levels. Journal of Comparative Economics.

### Foundational and methodological works
- Bank for International Settlements, BIS. 2008. Transmission Mechanisms for Monetary Policy in Emerging Market Economies, BIS Papers No. 35.
- Calvo, G. 1978. On the Time Consistency of Optimal Policy in the Monetary Economy. Econometrica 46 (6): 1411–28
- Kydland, F., and Prescott, E. (1977). Rules Rather than Discretion: The Inconsistency of Optimal Plans. Journal of Political Economy 85 (3): 473–92.
- Klein, M. W., and Marion, N. P. (1997). Explaining the duration of exchange-rate pegs. Journal of Development Economics, 54: 387–404.
- Goncharov, I., Ioannidou, V., and Schmalz, M. C. (2023). (Why) do central banks care about their profits? The Journal of Finance, 78(5), 2991-3045.
- Eklou, K. M., and Joanis, M. (2019). Do Fiscal Rules Cause Fiscal Discipline Over the Electoral Cycle? International Monetary Fund.

*The Political Economy of Foreign Exchange Interventions Working Paper No. WP/2026/130*

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_Source: https://www.imf.org/-/media/files/publications/wp/2026/english/wpiea2026130-source-pdf.pdf_