## wpiea2023111-print-pdf — Introduction and section III concludes

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### Overview and research question
- Focus: Do dollar exchange rate volatility have implications for productivity growth in EMs? If yes, what are the channels?
- Emphasis on uncertainty regarding the dollar exchange rate (volatility) rather than depreciation or appreciation alone.
- Builds on Aghion et al (2009): exchange rate volatility induced by both real and financial aggregate shocks affects growth performance of credit-constrained firms, particularly when wages are rigid to exchange rate movements.

### Theoretical background (key mechanisms)
- Wage rigidity mechanism (Aghion et al, 2009): with fixed local-currency wages, appreciations reduce firm earnings, lowering borrowing capacity and innovation; depreciations have opposite effects but are not fully offset due to credit constraints.
- Balance sheet effects: exchange rate fluctuations affect firm leverage, especially foreign currency denominated debt; appreciations increase net worth of firms with local-currency assets, allowing more borrowing, and depreciations have the opposite effect.
- Dominant currency paradigm: central role of the USD in international transactions; BIS (2014) estimate: around 80 percent of bank trade credit is denominated in US dollars.
- Trade and financial channels both matter; volatility can affect EMs through trade exposure to the US, dollar invoicing, financial development, wage-setting institutions, and capital account openness.

### Data and empirical scope
- Firm-level data: Orbis (Bureau van Dijk); sample: 16 Emerging Markets (EMs) over 1998-2019; unbalanced panel including listed and unlisted non-financial firms.
- Main productivity measure: firm-level Total Factor Productivity (TFP) from Díez et al (2021), estimated using Ackerberg et al (2015) gross output approach with cost of goods sold and tangible fixed assets as inputs.
- Alternative productivity measure: labor productivity (real value added per employee).
- Investment rate = capital expenditure / lagged total assets (Capital expenditure = change in tangible fixed assets + depreciation and amortization).
- Liquidity measures: Current ratio = current assets / current liabilities; Quick ratio = cash and cash equivalent / current liabilities.
- Other firm variables: firm age, total assets, leverage = total debt / EBITDA, investment irreversibility = tangible fixed assets / lagged total assets.
- Country-level dollar exchange rate volatility measured two ways:
  - Standard deviation of the log difference of monthly bilateral USD exchange rate (local currency units per USD) (yearly volatility).
  - GARCH (1,1) univariate model on monthly first differences; average standard deviation over the year.
  - Correlation between the two measures: 0.94.
- Controls: World Uncertainty Index, output gap (IMF WEO), trade openness, CPI inflation, log real GDP per capita (WDI), Chinn-Ito capital account openness, domestic credit to private sector (% of GDP), stock market capitalization (% of GDP), USD invoicing (share of exports and imports invoiced in USD), bilateral trade shares with the US, Foreign Exchange Intervention (FXI, annual sum expressed as percent of yearly GDP), share of employees covered by collective agreements (ILO), financial crises (Nguyen et al, 2022).
- Time-series variables: VIX index (CBOE); US monetary policy shocks (Nakamura and Steinsson, 2018), aggregated monthly to annual following Holm et al (2021); series updated with Acosta (2022) and Acosta and Saia (2020).
- Empirical framework: local projection framework (Jordá, 2005) with firm fixed effects and country-sector fixed effects; instrumental variable approach instruments country-level dollar exchange rate volatility by exogenous US monetary policy shocks and their interaction with country average dollar invoicing in imports.

### Key descriptive statistics (selected, exact values)
- Log (TFP): Obs 349,303; Mean 1.859; Std. dev. 0.796; Min 0.58; Max 3.05
- Log Labor Productivity: Obs 127,395; Mean 9.620; Std. dev. 0.961; Min 7.70; Max 11.17
- Log (Total Assets): Obs 349,303; Mean 13.685; Std. dev. 2.283; Min 9.54; Max 17.28
- Leverage Ratio: Obs 349,303; Mean 1.088; Std. dev. 1.965; Min 0; Max 7.30
- Age: Obs 349,303; Mean 11.488; Std. dev. 6.971; Min 2; Max 25
- Investment Ratio: Obs 348,090; Mean 0.118; Std. dev. 0.173; Min 0.002; Max 0.76
- Current Ratio: Obs 348,394; Mean 3.269; Std. dev. 4.833; Min 0.306; Max 23.945
- Quick Ratio: Obs 340,912; Mean 1.010; Std. dev. 1.842; Min 0.002; Max 7.333
- Investment Irreversibility (tangible fixed asset / total asset): Obs 349,303; Mean 0.301; Std. dev. 0.285; Min 0; Max 0.941
- Dollar Exchange Rate volatility: Obs 225; Mean 0.023; Std. dev. 0.014; Min 0.001; Max 0.079
- Dollar Exchange Rate volatility - GARCH (1,1): Obs 225; Mean 0.030; Std. dev. 0.009; Min 0.021; Max 0.082
- FXI (percent of GDP): Obs 223; Mean 1.309; Std. dev. 3.349; Min -8.021; Max 11.020
- Dollar Invoicing (percent of Exports): Obs 194; Mean 56.025; Std. dev. 31.137; Min 9.470; Max 99.530
- Collective Bargaining Coverage (percent): Obs 128; Mean 17.891; Std. dev. 16.214; Min 0.4; Max 97.1

### Empirical strategy
- Baseline: local projections (Jordá, 2005) estimating firm-level impulse responses of TFP and investment to country-level dollar exchange rate volatility; controls include firm-level controls (log total assets, leverage, age), country-level controls, VIX, firm fixed effects (μ_f), and country-sector fixed effects (μ_cs).
- Identification: year fixed effects not included to preserve cross-sectional variation in country-level volatility; VIX captures common time-varying shock.
- Heterogeneity:
  - Semi-parametric group approach: countries split by median of characteristic Z_c (high vs low).
  - Interaction specification: θ_h + δ_h × Z_c,t−1 to estimate conditional impacts.
  - Firm-level heterogeneity: firms classified by lagged X_f,t−1 into low (<25th percentile) and high (>75th percentile) groups for liquidity, leverage, irreversibility, age.
- Instrumental variables: instrument dollar exchange rate volatility with US monetary policy shocks and interaction with country average dollar invoicing.

### Baseline empirical findings
- Bivariate correlation: Average TFP growth and lagged dollar exchange rate volatility: correlation = -1.02.
- Local projection baseline impulse responses (for a 0.01 standard deviation in dollar exchange rate volatility):
  - TFP: one-year delayed reduction in TFP of about 0.3 percent.
  - Investment: contemporaneous reduction in investment of 0.5 percent.
- Interpretation: consistent with a liquidity/financial-friction channel (Aghion et al, 2009): exchange rate volatility reduces current investment, which lowers future productivity; effects are on average short-lived.
- Instrumental variable estimates (US monetary policy shocks interacted with dollar invoicing): qualitatively consistent with baseline and show larger estimated impacts.

### Cross-country heterogeneities (amplifiers and mitigators)
- Amplifiers of the negative effect on productivity:
  - Low level of financial development (consistent with financial constraint channel).
  - High proportion of workers covered by collective bargaining agreements (suggestive role for wage rigidities).
  - High trade exposure to the US.
  - High prevalence of dollar invoicing.
  - Greater capital account openness.
- Mitigator:
  - Foreign Exchange Interventions (FXI) that lean against excessive exchange rate volatility can mitigate the negative impact on productivity.
- Specific results:
  - Persistent negative effect on productivity in countries with low financial development; quicker recovery in high financial development countries.
  - Larger and more persistent impact in countries with high collective bargaining coverage; no negative statistically significant effect in countries with low collective bargaining coverage.
  - Impact about twice the baseline effect in countries with high share of exports to the US and high share of exports invoiced in USD.
  - Countries with open capital accounts experience disproportionately larger and more persistent negative productivity effects; FXI can mitigate the impact.

### Firm-level heterogeneities and channels
- Liquidity channel:
  - Firms with high liquidity needs are disproportionately affected by dollar exchange rate volatility.
  - Investment declines contemporaneously mainly for firms with low liquidity (current ratio below 25th percentile); high-liquidity firms are less affected.
- Balance sheet channel:
  - Highly leveraged firms (leverage above 75th percentile) are more affected in investment and productivity responses.
- Firm age:
  - Young firms (below 25th percentile: < 6 years) delay investment in response to volatility; older firms (above 75th percentile: > 16 years) recover more quickly.
- Real-options (investment irreversibility) channel:
  - Firms with high investment irreversibility (tangible fixed assets / lagged total assets above 75th percentile) experience large and persistent negative impact on investment.
  - No negative impact when investment is defined focusing on intangible assets.
- Overall mechanism: evidence supports a financial-friction channel—low-liquidity, highly leveraged, young firms, and firms with highly irreversible (tangible) investment are most vulnerable.

### Robustness checks and identification
- Alternative volatility measure: GARCH (1,1) yields very similar results to baseline but with larger and more persistent impacts.
- Instrumental variable approach: US monetary policy shocks interacted with country average USD invoicing produce similar but larger effects, supporting causal interpretation.
- Other robustness tests: alternative measures of financial development (domestic credit to private sector), different bilateral trade and dollar invoicing measures (imports), different liquidity measure (quick ratio), manufacturing subsample, alternative productivity measure (labor productivity), investment defined on intangible assets, IV estimates for investment and TFP — results remain robust.
- Separation from exchange rate first moment: controlling for change in bilateral exchange rate (appreciation/depreciation) shows dollar exchange rate volatility has a larger and more persistent effect compared to change in exchange rate level.

### Conclusion and policy implications
- Main findings:
  - Dollar exchange rate volatility has a delayed and short-lived negative effect on productivity growth in EMs.
  - Larger adverse impacts in EMs with low financial development, high dollar invoicing, high bilateral trade with the US, and open capital accounts.
  - FXI can dampen the adverse impact on firm productivity.
  - Firm-level evidence points to financial-friction and real-options channels: low liquidity, high leverage, young firms, and firms with highly irreversible (tangible) investment are most affected.
  - Results robust to multiple checks, including an instrumental variable strategy using US monetary policy shocks.
- Policy implications (precise implications as stated):
  - Prolonged periods of dollar exchange rate volatility could drag on productivity growth in EMs and thereby on overall growth.
  - Both the level and the second moment (uncertainty) of the dollar exchange rate matter for EMs.
  - Findings do not imply EMs would be better off with fixed or managed exchange rate regimes; fixed/managed regimes have other drawbacks (loss of monetary policy autonomy, inability to use exchange rate as shock absorber).
  - Policy recommendations indicated by the evidence:
    - Enhance financial development to reduce vulnerabilities.
    - Reduce financial vulnerabilities at firm and system levels.
    - Use policy interventions to smooth excessive volatility (e.g., FXI as a mitigating tool).
    - Develop hedging instruments to help firms manage dollar exchange rate volatility.

*Source: wpiea2023111-print-pdf - Introduction and section III concludes*

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

### Introduction

### Overview and research question
- Focus: Do dollar exchange rate volatility have implications for productivity growth in EMs? If yes, what are the channels?
- Emphasis on uncertainty regarding the dollar exchange rate (volatility) rather than depreciation or appreciation alone.
- The analysis builds on the theoretical framework developed by Aghion et al (2009) where exchange rate volatility induced by both real and financial aggregate shocks affects growth performance of credit-constrained firms, particularly when wages are rigid to exchange rate movements.

### Theoretical background (key mechanisms)
- Wage rigidity mechanism (Aghion et al, 2009): with fixed local-currency wages, appreciations reduce firm earnings, lowering borrowing capacity and innovation; depreciations have opposite effects but are not fully offset due to credit constraints.
- Balance sheet effects: exchange rate fluctuations affect firm leverage, especially foreign currency denominated debt; appreciations increase net worth of firms with local-currency assets, allowing more borrowing, and depreciations have the opposite effect (see Kalemli-Ozcan et al, 2021).
- Dominant currency paradigm: central role of the USD in international transactions (Gopinath et al, 2020); Bank for International Settlements (2014) estimates that globally, around 80 percent of bank trade credit is denominated in US dollars.
- Trade and financial channels both matter; volatility can affect EMs through trade exposure to the US, dollar invoicing, financial development, wage-setting institutions, and capital account openness.

### Data and empirical scope
- Firm level data source: Orbis.
- Sample: 16 Emerging Markets (EMs) over the period 1998-2019.
- Empirical framework: local projection framework (Jordá, 2005), controlling for financial crises and measures of uncertainty, exploiting cross-country and firm-level heterogeneities.
- Complementary identification: instrumental variable approach instrumenting country-level dollar exchange rate volatility by exogenous US monetary policy shocks and their interaction with country’s average level of dollar invoicing in imports.

### Baseline empirical findings
- Dollar exchange rate volatility shocks:
  - Contemporaneously reduce investment (lead to a delay in investment).
  - Reduce productivity with a one-year lag.
  - The productivity-reducing effect is short-lived.
- Instrumental variable estimates (using US monetary policy shocks interacted with dollar invoicing) show qualitatively consistent but larger impacts compared to baseline local projections.

### Cross-country heterogeneities (amplifiers and mitigators)
- Amplifiers of the negative effect on productivity:
  - Low level of financial development (consistent with financial constraint channel).
  - High proportion of workers covered by collective bargaining agreements (suggestive role for wage rigidities).
  - High trade exposure to the US.
  - High prevalence of dollar invoicing.
  - Greater capital account openness.
- Mitigator:
  - Foreign Exchange Interventions that lean against excessive exchange rate volatility can mitigate the negative impact on productivity.

### Firm-level heterogeneities and channels
- Liquidity channel:
  - Firms with high liquidity needs are disproportionately affected by dollar exchange rate volatility (consistent with Aghion et al, 2009 and cross-country financial development results).
- Balance sheet channel:
  - Highly leveraged firms (weak balance sheets) are more affected, consistent with higher financial constraints for new investments (see Myers,1977; Li et al, 2020).
- Firm age:
  - Younger firms, more subject to financial constraints (see Cloyne et al, 2023), are more vulnerable to dollar exchange rate uncertainty.
- Real-options (investment irreversibility) channel:
  - Firms with highly irreversible investment see larger and more persistent delays in investment following a dollar exchange rate volatility shock (consistent with Bertola and Caballero, 1994; Bloom, 2009; Husted et al., 2020).

### Identification and robustness
- Baseline: local projections controlling for crises and uncertainty measures, exploiting heterogeneity across countries and firms.
- Instrumental variable strategy: instrument country-level dollar exchange rate volatility by exogenous US monetary policy shocks and their interaction with country average dollar invoicing in imports to allow heterogeneous responses across countries and to draw on the literature on US monetary policy spillovers (e.g., Rey, 2016; Miranda-Agrippino and Rey, 2020).
- IV results: qualitatively consistent with baseline and show larger estimated impacts.

### Contributions relative to prior literature
- First to investigate the impact of dollar exchange rate volatility on productivity growth in EMs using firm-level data.
- Advances Aghion et al (2009) by:
  - Focusing on dollar exchange rate volatility rather than effective exchange rate.
  - Testing additional cross-country mediators: dollar invoicing, dependence on trade with the US, collective bargaining coverage, capital account openness, and foreign exchange interventions.
  - Explicitly testing the liquidity channel using firm-level data.
  - Examining short-term volatility (rather than long-term 5-year volatility).
  - Exploiting US monetary policy shocks as an exogenous source of variation in dollar exchange rate volatility.
- Connects to literature on US monetary policy spillovers and dominant currency invoicing (e.g., Gopinath et al, 2020; Boz et al, 2022; Berthou et al, 2022) and to studies on financial channels of exchange rate fluctuations (e.g., Avdjiev et al, 2019; Kalemli-Ozcan et al, 2021).

*Source: wpiea2023111-print-pdf - Introduction*

### section III concludes.

### wpiea2023111-print-pdf - section III concludes

### I. Data
- Sample: 16 Emerging Markets (EMs) over the period 1998 - 2019; unbalanced panel of firms across countries.
- Firm-level data
  - Source: Orbis (Bureau van Dijk); includes both listed and unlisted non-financial firms.
  - Main productivity measure: firm-level Total Factor Productivity (TFP) from Díez et al (2021), estimated using Ackerberg et al (2015) gross output approach with cost of goods sold and tangible fixed assets as inputs.
  - Alternative productivity measure for robustness: labor productivity (real value added per employee).
  - Investment rate: ratio of capital expenditure over lagged total assets. (Capital expenditure = change in tangible fixed assets + depreciation and amortization.)
  - Liquidity measures: Current ratio = current assets / current liabilities; Quick ratio = cash and cash equivalent / current liabilities.
  - Other firm variables: firm age, total assets, leverage = total debt / EBITDA, investment irreversibility = tangible fixed assets / lagged total assets.
  - Data treatment: firm-level data winsorized at 5 percent.
- Country-level data
  - Main variable: dollar exchange rate volatility measured two ways:
    - Standard deviation of the log difference of monthly bilateral USD exchange rate (local currency units per USD) from IMF International Finance Statistics (yearly volatility).
    - GARCH (1,1) univariate model on monthly first differences; average standard deviation over the year.
    - Correlation between the two measures: 0.94.
  - Controls: World Uncertainty Index, output gap (IMF WEO), trade openness (share of total trade in GDP), CPI inflation, log real GDP per capita (WDI), Chinn-Ito capital account openness, domestic credit to private sector (% of GDP), stock market capitalization (% of GDP), USD invoicing (share of exports and imports invoiced in USD), bilateral trade shares with the US, Foreign Exchange Intervention (FXI, annual sum expressed as percent of yearly GDP), share of employees covered by collective agreements (ILO), financial crises (Nguyen et al, 2022).
- Time-series variables
  - VIX index (CBOE).
  - US monetary policy shocks (Nakamura and Steinsson, 2018), aggregated monthly to annual following Holm et al (2021); original series 1998-1999 updated with Acosta (2022) and Acosta and Saia (2020).

- Key descriptive statistics (selected, exact values from Table A1)
  - Log (TFP): Obs 349,303; Mean 1.859; Std. dev. 0.796; Min 0.58; Max 3.05
  - Log Labor Productivity: Obs 127,395; Mean 9.620; Std. dev. 0.961; Min 7.70; Max 11.17
  - Log (Total Assets): Obs 349,303; Mean 13.685; Std. dev. 2.283; Min 9.54; Max 17.28
  - Leverage Ratio: Obs 349,303; Mean 1.088; Std. dev. 1.965; Min 0; Max 7.30
  - Age: Obs 349,303; Mean 11.488; Std. dev. 6.971; Min 2; Max 25
  - Investment Ratio: Obs 348,090; Mean 0.118; Std. dev. 0.173; Min 0.002; Max 0.76
  - Current Ratio: Obs 348,394; Mean 3.269; Std. dev. 4.833; Min 0.306; Max 23.945
  - Quick Ratio: Obs 340,912; Mean 1.010; Std. dev. 1.842; Min 0.002; Max 7.333
  - Investment Irreversibility (tangible fixed asset / total asset): Obs 349,303; Mean 0.301; Std. dev. 0.285; Min 0; Max 0.941
  - Dollar Exchange Rate volatility: Obs 225; Mean 0.023; Std. dev. 0.014; Min 0.001; Max 0.079
  - Dollar Exchange Rate volatility - GARCH (1,1): Obs 225; Mean 0.030; Std. dev. 0.009; Min 0.021; Max 0.082
  - FXI (percent of GDP): Obs 223; Mean 1.309; Std. dev. 3.349; Min -8.021; Max 11.020
  - Dollar Invoicing (percent of Exports): Obs 194; Mean 56.025; Std. dev. 31.137; Min 9.470; Max 99.530
  - Collective Bargaining Coverage (percent): Obs 128; Mean 17.891; Std. dev. 16.214; Min 0.4; Max 97.1

### II. Empirical strategy
- Estimation approach
  - Local projection approach (Jordá, 2005) to estimate firm-level impulse responses of TFP and investment to country-level dollar exchange rate volatility.
  - Baseline specification (Equation (1)) regresses firm log TFP growth on bilateral dollar exchange rate volatility (σ_ct^ER), firm-level controls (log total assets, leverage, age), country-level controls (CPI inflation, output gap, log real GDP per capita, trade openness, country-specific uncertainty, financial crises), VIX, firm fixed effects (μ_f), and country-sector fixed effects (μ_cs).
  - Identification note: year fixed effects are not included to preserve cross-sectional variation in country-level volatility; VIX captures common time-varying shock.
- Heterogeneity and channels
  - Semi-parametric group approach (Equation (2)) using indicator T[Z_c ∈ J] where countries are split by whether a country characteristic Z_c is above/below the sample median (high vs low groups).
  - Interaction specification (Equation (3)) to estimate conditional impact: θ_h + δ_h × Z_c,t−1 (e.g., capital account openness, FXI).
  - Firm-level heterogeneity for investment response (Equation (4)) classifies firms by lagged X_f,t−1 into low (<25th percentile) and high (>75th percentile) groups for liquidity, leverage, irreversibility, age.
- Instrumental variables
  - Instrument dollar exchange rate volatility with US monetary policy shocks and interaction with country average dollar invoicing to exploit exogenous variation and allow heterogeneous responses.

### III. Baseline results
- Bivariate correlation
  - Average TFP growth and lagged dollar exchange rate volatility: correlation = -1.02.
- Local projection baseline impulse responses (for a 0.01 standard deviation in dollar exchange rate volatility)
  - TFP: one-year delayed reduction in TFP of about 0.3 percent.
  - Investment: contemporaneous reduction in investment of 0.5 percent.
- Interpretation
  - Results consistent with a liquidity/financial-friction channel (Aghion et al, 2009): exchange rate volatility reduces current investment, which lowers future productivity.
  - Effects are on average short-lived.

### IV. Exploring channels — cross-country heterogeneity
- Financial development
  - Dollar exchange rate volatility has a persistent negative effect on productivity growth in countries with low financial development; countries with high financial development recover more quickly.
- Collective bargaining coverage (proxy for wage rigidity)
  - Impact is larger and more persistent in countries with high collective bargaining coverage; no negative statistically significant effect in countries with low collective bargaining coverage.
- Trade channel and dollar invoicing
  - Impact is larger (about twice the baseline effect) in countries with high share of exports to the US and high share of exports invoiced in USD.
- Capital account openness and FXI
  - Countries with open capital account experience disproportionately larger and more persistent negative productivity effects.
  - FXI can mitigate the impact of exchange rate volatility on firm productivity.
- Mechanism summary
  - Both trade and financial-friction channels are relevant; dollar invoicing amplifies pass-through and vulnerability; FXI and financial development can moderate adverse effects.

### V. Firm-level heterogeneity (channels at firm level)
- Liquidity
  - Investment declines contemporaneously mainly for firms with low liquidity (current ratio below 25th percentile); high-liquidity firms are less affected.
- Leverage
  - Highly leveraged firms (leverage above 75th percentile) are disproportionately affected in investment and productivity responses.
- Investment irreversibility (`real options’ channel)
  - Firms with high investment irreversibility (tangible fixed assets / lagged total assets above 75th percentile) experience large and persistent negative impact on investment.
  - No negative impact when defining investment focusing on intangible assets.
- Age as proxy for financial constraint
  - Young firms (below 25th percentile: < 6 years) delay investment in response to volatility; older firms (above 75th percentile: > 16 years) recover more quickly.
- Overall mechanism
  - Evidence supports financial-friction channel: low-liquidity, highly leveraged, young firms, and firms with highly irreversible (tangible) investment are most vulnerable to dollar exchange rate volatility.

### VI. Robustness checks
- Alternative volatility measure
  - Using GARCH (1,1) measure yields very similar results to baseline but with larger and more persistent impacts.
- Instrumental variable approach
  - Instrumenting with US monetary policy shocks and its interaction with country average USD invoicing produces similar but larger effects, supporting causal interpretation.
- Other robustness tests (summarized)
  - Alternative measures of financial development (domestic credit to private sector), different measures of bilateral trade and dollar invoicing (imports), different liquidity measure (quick ratio), focus on manufacturing subsample, alternative productivity measure (labor productivity), investment defined on intangible assets, IV estimates for investment and TFP — results remain robust and consistent with baseline patterns.
- Separation from exchange rate first moment
  - Controlling for change in bilateral exchange rate (appreciation/depreciation) shows dollar exchange rate volatility has a larger and more persistent effect compared to the effect of changes in the exchange rate level.

### VII. Conclusion and policy implications
- Main findings
  - Dollar exchange rate volatility has a delayed and short-lived negative effect on productivity growth in EMs.
  - Cross-country heterogeneity: larger adverse impacts in EMs with low financial development, high dollar invoicing, high bilateral trade with the US, and open capital accounts.
  - FXI can dampen the adverse impact on firm productivity.
  - Firm-level evidence points to financial-friction and real-options channels: low liquidity, high leverage, young firms, and firms with highly irreversible (tangible) investment are most affected.
  - Results are robust to multiple checks, including an instrumental variable strategy using US monetary policy shocks.
- Policy implications (precise implications as stated)
  - Prolonged periods of dollar exchange rate volatility could drag on productivity growth in EMs and thereby on overall growth.
  - Both the level and the second moment (uncertainty) of the dollar exchange rate matter for EMs.
  - Findings do not imply EMs would be better off with fixed or managed exchange rate regimes; fixed/managed regimes have other drawbacks (loss of monetary policy autonomy, inability to use exchange rate as shock absorber).
  - Policy recommendations indicated by the evidence:
    - Enhance financial development to reduce vulnerabilities.
    - Reduce financial vulnerabilities at firm and system levels.
    - Use policy interventions to smooth excessive volatility (e.g., FXI as a mitigating tool).
    - Develop hedging instruments to help firms manage dollar exchange rate volatility.

*Source: wpiea2023111-print-pdf - section III concludes.*

### References

### References

### Monetary policy, spillovers, and identification
- Acosta, M., and Saia, J. (2020). Estimating the effects of monetary policy via high frequency factors. Unpublished working paper. Columbia University.  
- Acosta, M. (2022). The perceived causes of monetary policy surprises. Published Manuscript.  
- Cavallino, P. (2019). Capital flows and foreign exchange intervention. American Economic Journal: Macroeconomics, 11(2), 127-70.  
- Cloyne, J., C. Ferreira, M. Froemel, and P. Surico. (2023). “Monetary Policy, Corporate Finance and Investment.” Journal of the European Economic Association.  
- Di Giovanni, J., and Rogers, J. H. (2022). The Impact of US Monetary Policy on Foreign Firms. FRB of New York Staff Report, (1039).  
- Holm, M. B., Paul, P., and Tischbirek, A. (2021). The transmission of monetary policy under the microscope. Journal of Political Economy, 129(10), 2861-2904.  
- Miranda-Agrippino, S., and Rey, H. (2020). US monetary policy and the global financial cycle. The Review of Economic Studies, 87(6), 2754-2776.  
- Nakamura, E., and Steinsson, J. (2018). High-frequency identification of monetary non-neutrality: the information effect. The Quarterly Journal of Economics, 133(3), 1283-1330.  
- Rey, H. (2016). International channels of transmission of monetary policy and the Mundellian trilemma. IMF Economic Review, 64(1), 6-35.  
- Shousha, S. (2019). The dollar and emerging market economies: Financial vulnerabilities meet the international trade system. International Finance Discussion Paper, (1258).

### Exchange rates, foreign exchange intervention, and invoicing currency
- Adler, G., Chang, K. S., Mano, R., and Shao, Y. (2021). Foreign exchange intervention: A dataset of public data and proxies. International Monetary Fund.  
- Aghion, P., Bacchetta, P., Ranciere, R., and Rogoff, K. (2009). Exchange rate volatility and productivity growth: The role of financial development. Journal of monetary economics, 56(4), 494-513.  
- Berthou, A., Horny, G., and Mésonnier, J. S. (2022). The real effects of invoicing exports in dollars. Journal of International Economics, 135, 103569.  
- Boz, E., Casas, C., Georgiadis, G., Gopinath, G., Le Mezo, H., Mehl, A., and Nguyen, T. (2022). Patterns of invoicing currency in global trade: New evidence. Journal of International Economics, 136, 103604.  
- 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.  
- Gopinath, G., Boz, E., Casas, C., Díez, F. J., Gourinchas, P. O., and Plagborg-Møller, M. (2020). Dominant currency paradigm. American Economic Review, 110(3), 677-719.  
- Kalemli-Ozcan, S., Liu, X., and Shim, I. (2021). Exchange rate fluctuations and firm leverage. IMF Economic Review, 69(1), 90-121.  
- Hsu, P. H., Taylor, M. P., Wang, Z., and Xu, Q. (2022). Currency volatility and global technological innovation. Journal of International Economics, 137, 103607.  
- Cavallino, P. (2019). Capital flows and foreign exchange intervention. American Economic Journal: Macroeconomics, 11(2), 127-70. (also relevant to monetary transmission and intervention)

### Uncertainty, economic policy uncertainty, and volatility
- Ahir, H., Bloom, N., and Furceri, D. (2022). The world uncertainty index. National bureau of economic research. (No. w29763)  
- Balcilar, M., Gupta, R., Kyei, C., and Wohar, M. E. (2016). Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Economies Review, 27(2), 229-250.  
- Bartsch, Z. (2019). Economic policy uncertainty and dollar-pound exchange rate return volatility. Journal of International Money and Finance, 98, 102067.  
- Bloom, N. (2009). The impact of uncertainty shocks. Econometrica, 77(3), 623-685.  
- Charles, A., Darné, O., & Tripier, F. (2018). Uncertainty and the Macroeconomy: Evidence from an uncertainty composite indicator. Applied Economics, 50(10), 1093-1107.  
- Husted, L., Rogers, J., and Sun, B. (2020). Monetary policy uncertainty. Journal of Monetary Economics, 115, 20-36.  
- Kido, Y. (2016). On the link between the US economic policy uncertainty and exchange rates. Economics Letters, 144, 49-52.  
- Krol, R. (2014). Economic policy uncertainty and exchange rate volatility. International Finance, 17(2),241-256.  
- Lakdawala, A., Moreland, T., and Schaffer, M. (2021). The international spillover effects of us monetary policy uncertainty. Journal of International Economics, 133, 103525.  
- Balcilar, M., Gupta, R., Kyei, C., and Wohar, M. E. (2016). Does economic policy uncertainty predict exchange rate returns and volatility? Evidence from a nonparametric causality-in-quantiles test. Open Economies Review, 27(2), 229-250. (duplicate listing in source consolidated here)

### Investment, corporate finance, and firm-level responses
- Bertola, G., and Caballero, R. J. (1994). Irreversibility and aggregate investment. The Review of Economic Studies, 61(2), 223-246.  
- Beck, T., Döttling, R., Lambert, T., & van Dijk, M. (2022). Liquidity creation, investment, and growth. Journal of Economic Growth, 1-40.  
- Di Giovanni, J., and Rogers, J. H. (2022). The Impact of US Monetary Policy on Foreign Firms. FRB of New York Staff Report, (1039). (also relevant to monetary policy spillovers)  
- Ebeke, M. C. H., and Eklou, K. M. (2017). The granular origins of macroeconomic fluctuations in Europe. International Monetary Fund.  
- Kalemli-Ozcan, S., Kamil, H., and Villegas-Sanchez, C. (2016). What hinders investment in the aftermath of financial crises: Insolvent firms or illiquid banks? Review of Economics and Statistics, 98(4), 756-769.  
- Li, D., Magud, N. E., and Valencia, F. (2020). Financial shocks and corporate investment in emerging markets. Journal of Money, Credit and Banking, 52(2-3), 613-644.  
- Myers, S. C. (1977). Determinants of corporate borrowing. Journal of financial economics, 5(2), 147-175.  
- Nguyen, T. C., Castro, V., and Wood, J. (2022). A new comprehensive database of financial crises: Identification, frequency, and duration. Economic Modelling, 108, 105770.  
- Kalemli-Ozcan, S., Liu, X., and Shim, I. (2021). Exchange rate fluctuations and firm leverage. IMF Economic Review, 69(1), 90-121. (relevant for firm capital structure responses)

### Methods, measures, and databases
- Ackerberg, D. A., K. Caves, and G. Frazer (2015). “Identification Properties of Recent Production Function Estimators.” Econometrica 83(6):2411-2451.  
- Hansen, P. R., and Lunde, A. (2005). A forecast comparison of volatility models: does anything beat a GARCH (1, 1)? Journal of applied econometrics, 20(7), 873-889.  
- Jordà, Ò. (2005). Estimation and inference of impulse responses by local projections. American Economic Review, 95(1), 161-182.  
- Chinn, M. D., and Ito, H. (2008). A new measure of financial openness. Journal of Comparative Policy Analysis, 10(3), 309-322.  
- Avdjiev, S., Bruno, V., Koch, C., and Shin, H. S. (2019). The dollar exchange rate as a global risk factor: evidence from investment. IMF Economic Review, 67(1), 151-173.  
- 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.  
- Bank for International Settlements, BIS. 2008. “Transmission Mechanisms for Monetary Policy in Emerging Market Economies,” BIS Papers No. 35.  
- Berthou, A., Horny, G., and Mésonnier, J. S. (2022). The real effects of invoicing exports in dollars. Journal of International Economics, 135, 103569. (also contributes empirical evidence on invoicing and trade)

*References list from wpiea2023111-print-pdf - References*

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_Source: https://www.imf.org/-/media/files/publications/wp/2023/english/wpiea2023111-print-pdf.pdf_