## _wp1377

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---

### I. Introduction — research question and approach
- Research question: Are monetary policy shocks effective in influencing bank lending in Mauritania?
- Identification innovation:
  - Use institutional feature: exporters must repatriate at least a fraction of export revenue to the central bank.
  - Treat commercial banks’ deposits at the central bank (reserves) as the most reliable indicator of the monetary policy stance in Mauritania.
  - Under the assumption that export revenue is largely exogenous in the short run, exploit export-driven movements in reserves to identify exogenous monetary policy shocks.
- Main empirical methods:
  - Structural vector autoregressions (VARs).
  - Two-stage least squares panel (2SLS) models.
- Key high-level finding:
  - Mauritanian banks do not react to exogenous monetary policy shocks: bank lending bears no systematic relationship to changes in bank reserves.

### II. Overview of recent developments in bank lending
- Main lending categories:
  - Private-sector credit (includes lending to state-owned enterprises).
  - Government credit.
  - Short-term trade finance.
- Notable quantitative developments:
  - Private-sector credit grew 10 percent year-on-year in 2011.
  - Deposit growth in 2011 was 19 percent.
  - Excess reserves rose from UM3.1 billion in Q1 2011 to UM33.1 billion in the final three months of 2011 (creation of about UM30 billion of free reserves).
- Sectoral and temporal patterns:
  - Private-sector credit and trade finance tracked closely until 2009; after 2009, banks oriented more toward trade finance.
  - Private-sector credit fell as a share of GDP in 2010 and 2011 despite nominal growth.
  - Government credit fell early in the sample (oil exploration episode) and then remained relatively stable, implying a drop relative to GDP.
- Behavioral constraints explaining weak transmission:
  - Banks lend to a narrow range of customers (related parties, state-owned enterprises, civil servants).
  - Banks hold significant liquidity buffers because they cannot be sure of refinancing in domestic money markets.
- Suggested policy implications:
  - Reduce loan concentration ratios.
  - Reform the liquidity management framework to improve monetary policy traction and financial access.

### III. Identifying exogenous shocks in Mauritania’s monetary policy framework
- Assessment of candidate monetary policy indicators:
  - Policy rate:
    - Plays next to no role in banks’ funding costs.
    - Monthly stock of outstanding central bank credit to the banking sector unchanged since 2009.
    - Last change in the policy rate dates to November 2009.
  - T-bill rate:
    - Mostly varies with the Treasury’s domestic financing needs.
    - CBM does not conduct open market operations.
  - Interbank rate:
    - Closely follows the T-bill rate; market is shallow and some large banks avoid it.
    - CBM does not intervene directly.
  - Bank reserves:
    - Unremunerated; free (excess) reserves are reserves above the required 7 percent of resident deposits.
    - If free reserves exceed precautionary thresholds, banks have an incentive to lend.
- Conclusion on indicator:
  - Bank reserves are the only consistent indicator of the monetary policy stance in Mauritania given the weak role of policy, T-bill, and interbank rates.
- CBM operational constraints and external dependence:
  - Small, resource-dependent open economy, closed capital account, managed exchange rate without preannounced path.
  - CBM’s limited ability to sterilize or offset external shocks:
    - One large asset: a nonnegotiable claim on the government equals 57 percent of total assets on average over 2006–11; this claim generates little revenue and cannot be used in liquidity-absorbing operations.
    - CBM refrained from raising the required reserve ratio above the applicable 7 percent to avoid penalizing smaller banks.
  - Empirical episodes:
    - 2010/11 positive terms-of-trade shock: CBM accumulated net foreign assets but did not sterilize, increasing domestic liquidity (free reserves).
    - 2009 negative shock (iron ore price collapse): initial liquidity stimulus was not sustainable; banks’ free reserves drained during the year despite capital account restrictions.
- Identification using fish exports:
  - Rationale:
    - Fish exports are driven by international fish prices and fish captures—factors largely exogenous to the Mauritanian economy.
    - Repatriation requirement for fish exporters creates variation in bank reserves.
  - Exogeneity support:
    - Fish exports accounted for only 16 percent of total good exports over the six years to 2011.
    - Credit to the fish sector stayed flat in nominal terms over the same period; its share in total credit more than halved from 16 percent to 7 percent.
    - Fish prices and captures are unlikely to systematically relate to other domestic demand forces (though fish prices have shown some positive correlation with iron ore prices historically).
  - Empirical strategy:
    - Use fish-export-driven changes in bank reserves as instruments to identify exogenous liquidity shocks and estimate causal effects on bank lending.
  - Institutional detail for identification:
    - Fish exporters were required until 2010 to sell a share of foreign exchange receipts to the central bank; subsequently exporters were asked to domicile receipts at the CBM.
    - Fish export receipts are used with a two-month lag (0.32 at a two-month lag noted in the source) to account for usual payment terms.

### Empirical results — methods and main findings
- Methods:
  - Structural VARs (bank-by-bank) for six banks over January 2006 to October 2011.
  - 2SLS panel models with bank fixed effects (quarterly averages derived from monthly data).
  - Fish exports to the EU used as an instrument for bank reserves.
- Main empirical findings:
  - The estimated impulse response functions of the VARs are flat: monetary policy shocks do not lead to a significant response in bank lending.
  - Estimated coefficients on bank reserves in the panel 2SLS models are never statistically different from zero.
  - Granger-causality tests (pooled Fisher-test, meta-analysed via the Fisher statistic):
    - Private-sector credit: Fish exports: 0.45; Reserves: 0.20; Fish exports & reserves: 0.17.
    - Government credit: Fish exports: 0.00***; Reserves: 0.08*; Fish exports & reserves: 0.01***.
    - (Duplicate label in source): Fish exports: 0.76; Reserves: 0.33; Fish exports & reserves: 0.70.
    - Note: * p<0.10, ** p<0.05, *** p<0.01.
    - Interpretation: Granger tests fail to detect a significant relationship between reserves and (i) private-sector credit and (ii) short-term trade credit. Fish exports and reserves jointly Granger-cause government credit, but this result appears driven by one bank and may be a statistical artifact.
  - Structural VAR identification:
    - Fish exports treated as the most exogenous variable; reserves may respond contemporaneously to fish exports and credit; credit does not react contemporaneously to fish exports.
    - Parsimonious two-variable VARs (reserves and credit) produce identical results: bank lending does not display a significant reaction to changes in reserves, regardless of ordering.
  - 2SLS panel estimates (benchmark):
    - Point estimates of the reserves coefficient range from -0.1 to 0.2 and are never statistically significant, not even jointly.
    - OLS estimates are slightly larger (sometimes changing sign) but generally not significant.
  - Identification diagnostics:
    - Angrist and Pischke (first-stage chi-squared Wald) rejects under-identification for each endogenous regressor in reported cases (examples: Angrist-Pischke chi-squared statistic of Reserves: 7.10 p-value: 0.01; 9.40 p-value: 0.00).
    - Kleibergen-Paap LM statistic examples: 3.64 (p-value: 0.06); 3.67 (p-value: 0.06); 2.61 (p-value: 0.11).
    - Identification statistics improve when using excess reserves (at the cost of losing observations with negative values).
  - Robustness checks (adding capital, first-differencing, alternative instruments) do not materially change the conclusion: exogenous changes in reserves are not associated with subsequent changes in bank lending.
- Select table coefficient examples (preserved exactly as reported):
  - Table 2 (Private-sector Credit Using Reserves): Reserves coefficients (various columns): 0.00; 0.34; -0.07; 0.24; -0.08. ∆ Capital: 0.26** (t-statistic (2.69)).
  - Table 5 (Private-sector Credit Using Free Reserves): ∆ Free Reserves: 0.00; -0.02**; 0.03; 0.04; 0.00 with t-statistic for -0.02** reported as (-3.97). ∆ Capital: 0.29** (t-statistic (3.14)).
  - (Additional table entries and diagnostics appear in Tables 2–7 as reported in the source.)

### Data and sample characteristics
- Sample and banks:
  - Panel of six Mauritanian banks (January 2006 to October 2011).
  - The six banks represent over 75 percent of total assets of the Mauritanian banking system.
  - Combined private-credit to GDP ratio averages 14 percent over the sample period.
  - All six banks are privately owned; five belong to larger commercial conglomerates and one is a subsidiary of a large European banking group.
- Summary statistics (preserved exactly as reported in the source):
  - Bank-specific:
    - Log (private-sector credit) 407 16.61 1.07 7.4 17.7
    - Log (government credit) 404 15.50 0.91 2.2 17.2
    - Log (short-term trade finance) 406 16.80 0.71 2.5 17.9
    - Log (reserves) 408 14.40 0.91 0.3 16.4
  - Common:
    - Log (fish exports) 70 14.90 0.31 4.1 15.7
  - (Table 1: Summary Statistics of Main Variables)

### Interpretation and proposed explanations for ineffectiveness
- Overarching conclusion:
  - Monetary policy (as measured by export-related changes in reserves) did not systematically affect bank lending in Mauritania in 2006–11; the bank-lending channel of monetary transmission was ineffective over the sample period.
- Explanatory considerations:
  - Banking sector shallowness alone is not an obvious sufficient explanation:
    - Mauritania’s private-sector credit/GDP ratio was 29 percent in 2009 (including provisions; net private-sector credit/GDP was 21 percent), 11 percentage points lower than the global median across all countries.
    - Expected level of private-sector credit (controlling for structural characteristics) was 20.4 percent in 2009, implying a positive gap of about 8.6 percent.
  - Two proximate constraints emphasized and within policy purview:
    1. High loan concentration
       - At end-2010, the 20 biggest loans accounted for 30 percent of all outstanding loans in Mauritania.
       - Historical lending focus: parties affiliated with banks’ parent companies, the government, and state-owned enterprises.
       - Large informal sector and limited bank relationships; banks may rationally avoid lending to informality due to low risk-adjusted returns.
       - Endogeneity of effective demand: banks’ risk management capabilities and effort to search for new lending opportunities matter (the “lazy banks” notion).
       - Regulatory responses already taken: tighter definitions of related-party and single-party exposures (from February 2012 companies in same mother group treated as a single entity for exposure limits); obligation for banks to invest in appropriate risk management systems.
    2. Liquidity management framework and refinancing constraints
       - An average of 80 percent of banks’ liabilities was in the form of unremunerated sight deposits over the sample period.
       - Interbank market remained shallow; banks are wary of lending to each other given limited transparency and judicial uncertainty over collateral enforcement.
       - Central bank refinancing facility rarely used; banks perceive discretionary access.
       - Consequence: incentives to accumulate excess liquidity rather than extend longer-term lending, so liquidity injections do not translate into more lending.
- Caveats:
  - Easing some constraints while others still bind may not change outcomes; there may be other prerequisites for monetary policy effectiveness (for example, emergence of credit-intensive industries and broader country-specific structural reforms).
  - The two highlighted constraints are not proven to be the sole causes; explanations are partly anecdotal and not quantified in this analysis.
  - Monetary contractions may be more effective than monetary expansions in this environment, but the focus here is on making expansions more effective.

### Policy considerations and recommendations
- On addressing loan concentration:
  - Continue regulatory measures tightening definitions of related-party and single-party exposures so cumulative exposure to a single group must fall within regulatory limits.
  - Require banks to invest in appropriate risk management systems to build capacity for assessing lending opportunities to new clients.
- On improving liquidity management and refinancing:
  - Tighten transparency requirements: for example, require banks to publish audited financial statements (including notes and management letter) on banks’ websites to improve counterparties’ ability to assess bank health.
  - Make access to the central bank refinance window fully automatic to remove perceived discretionary access and encourage use of temporary liquidity surpluses for customer lending.
  - Review collateral requirements: consider widening acceptable collateral to include high-quality customer loans because supply of T-bills has not grown in tandem with the wider economy; widening acceptable collateral would ease banks’ worries about refinancing and make these loan categories more likely to attract injected liquidity.

### Conclusions and external validity
- Main takeaway:
  - Using export-related repatriation-induced reserve changes as an exogenous identification strategy, the paper finds no evidence that these exogenous monetary shocks systematically affected bank lending (private-sector credit, government credit, or trade finance) in Mauritania during 2006–11.
- Conditions for applicability of identification strategy:
  - (i) the exchange rate exhibits little short-term volatility;
  - (ii) bank reserves are an appropriate indicator of the monetary stance; and
  - (iii) repatriation requirements for export receipts are in place, establishing a positive link between exports and reserves.
- Recommendation for further work:
  - Replicating the analysis in other countries that meet the three conditions would help better pinpoint the causes of monetary policy ineffectiveness in low-income countries; country policy constraints will vary and empirical results across countries would inform reform priorities.

*Source: _wp1377 - 0.32 at a two-month lag to account for the (PDF chapter/section).*

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

### _wp1377 - References .............................................................................................................

### I. Introduction — research question and approach
- Research question: Are monetary policy shocks effective in influencing bank lending in Mauritania?
- Identification innovation:
  - Use institutional feature: exporters must repatriate at least a fraction of export revenue to the central bank.
  - Treat commercial banks’ deposits at the central bank (reserves) as the most reliable indicator of the monetary policy stance in Mauritania.
  - Under the assumption that export revenue is largely exogenous in the short run, exploit export-driven movements in reserves to identify exogenous monetary policy shocks.
- Main empirical methods:
  - Structural vector autoregressions (VARs).
  - Two-stage least squares panel (2SLS) models.
- Key high-level finding:
  - Mauritanian banks do not react to exogenous monetary policy shocks: bank lending bears no systematic relationship to changes in bank reserves.

### II. Overview of recent developments in bank lending
- Three main forms of bank lending:
  - Private-sector credit (includes lending to state-owned enterprises).
  - Government credit.
  - Short-term trade finance.
- Notable quantitative developments and dynamics:
  - Private-sector credit grew 10 percent year-on-year in 2011.
  - Deposit growth in 2011 was 19 percent.
  - Excess reserves rose from UM3.1 billion in Q1 2011 to UM33.1 billion in the final three months of 2011 (creation of about UM30 billion of free reserves).
- Sectoral and temporal patterns:
  - Private-sector credit and trade finance tracked closely until 2009; after 2009, banks oriented more toward trade finance.
  - Private-sector credit fell as a share of GDP in 2010 and 2011 despite nominal growth.
  - Government credit fell early in the sample (oil exploration episode) and then remained relatively stable, implying a drop relative to GDP.
- Behavioral constraints explaining weak transmission:
  - Banks lend to a narrow range of customers (related parties, state-owned enterprises, civil servants).
  - Banks hold significant liquidity buffers because they cannot be sure of refinancing in domestic money markets.
- Policy implications suggested:
  - Reduce loan concentration ratios.
  - Reform the liquidity management framework to improve monetary policy traction and financial access.

### III. Identifying exogenous shocks in Mauritania’s monetary policy framework
- Choice of monetary policy indicator — assessment of four candidates:
  - Policy rate:
    - Plays next to no role in banks’ funding costs.
    - Monthly stock of outstanding central bank credit to the banking sector unchanged since 2009.
    - Last change in the policy rate dates to November 2009.
  - T-bill rate:
    - Mostly varies with the Treasury’s domestic financing needs.
    - CBM does not conduct open market operations.
  - Interbank rate:
    - Closely follows the T-bill rate; market is shallow and some large banks avoid it.
    - CBM does not intervene directly.
  - Bank reserves:
    - Unremunerated; free (excess) reserves are reserves above the required 7 percent of resident deposits.
    - If free reserves exceed precautionary thresholds, banks have an incentive to lend.
- Conclusion on indicator:
  - Bank reserves are the only consistent indicator of the monetary policy stance in Mauritania given the weak role of policy, T-bill, and interbank rates.
- External dependence of reserves and CBM operational constraints:
  - Mauritania: small, resource-dependent open economy, closed capital account, managed exchange rate without preannounced path.
  - CBM’s limited ability to sterilize or offset external shocks:
    - One large asset: a nonnegotiable claim on the government equals 57 percent of total assets on average over 2006–11; this claim generates little revenue and cannot be used in liquidity-absorbing operations.
    - CBM refrained from raising the required reserve ratio above the applicable 7 percent to avoid penalizing smaller banks.
  - Empirical episode examples:
    - 2010/11 positive terms-of-trade shock (foreign exchange inflows): CBM accumulated net foreign assets but did not sterilize because of limited financial resources, increasing domestic liquidity (free reserves).
    - 2009 negative shock (iron ore price collapse): initial liquidity stimulus (rationing FX, reducing T-bill issuance, raising government spending) was not sustainable; banks’ free reserves drained during the year despite capital account restrictions.
- Identification of exogenous monetary shocks using fish exports:
  - Rationale:
    - Fish exports are driven by international fish prices and fish captures—factors largely exogenous to the Mauritanian economy.
    - Fish exports explain a significant fraction of variation in bank reserves because of a repatriation requirement for fish exporters.
  - Exogeneity conditions argued:
    - Fish exports must not directly influence bank lending, nor affect unobserved third factors that drive bank lending, nor be affected by those third factors.
    - Supporting facts:
      - Fish exports accounted for only 16 percent of total good exports over the six years to 2011.
      - Credit to the fish sector stayed flat in nominal terms over the same period, with its share in total credit more than halving from 16 percent to 7 percent.
      - Fish prices and captures are unlikely to systematically relate to other domestic demand forces (though fish prices have shown some positive correlation with iron ore prices historically).
- Empirical strategy preview:
  - Use fish-export-driven changes in bank reserves as instruments to identify exogenous liquidity shocks and estimate the causal effect of these liquidity shocks on bank lending.

*Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp1377.pdf*

### 0.32 at a two-month lag to account for the

### _wp1377 - 0.32 at a two-month lag to account for the

### Empirical results — summary of methods and main findings
- Methods used:
  - Structural VARs (bank-by-bank) for six banks over January 2006 to October 2011.
  - 2SLS panel models with bank fixed effects (quarterly averages derived from monthly data).
  - Fish exports to the EU used as an instrument for bank reserves (repatriation rule creates exogenous variation).
- Main empirical findings:
  - The estimated impulse response functions of the VARs are flat: monetary policy shocks do not lead to a significant response in bank lending.
  - Estimated coefficients on bank reserves in the panel 2SLS models are never statistically different from zero.
  - Granger-causality tests (meta-analysed via the Fisher statistic) fail to detect a significant relationship between reserves and (i) private-sector credit and (ii) short-term trade credit. Fish exports and reserves jointly Granger-cause government credit, but this result appears driven by one bank and may be a statistical artifact.
  - Structural VAR identification: fish exports are treated as the most exogenous variable; reserves may respond contemporaneously to fish exports and credit; credit does not react contemporaneously to fish exports.
  - Parsimonious two-variable VARs (reserves and credit) produce identical results: bank lending does not display a significant reaction to changes in reserves, regardless of ordering.
  - 2SLS panel estimates (benchmark) produce point estimates of the reserves coefficient ranging from -0.1 to 0.2 and are never statistically significant, not even jointly. OLS estimates are slightly larger (sometimes changing sign) but generally not significant.
  - Identification diagnostics:
    - Angrist and Pischke (first-stage chi-squared Wald) rejects under-identification for each endogenous regressor.
    - Kleibergen-Paap LM statistic rejects joint under-identification at significance levels just over 5 percent.
    - Identification statistics improve when using excess reserves (at the cost of losing observations with negative values).
  - Robustness checks (adding capital, first-differencing, alternative instruments) do not materially change the conclusion: exogenous changes in reserves are not associated with subsequent changes in bank lending.

### Data and sample characteristics
- Sample and banks:
  - Panel of six Mauritanian banks (January 2006 to October 2011).
  - The six banks represent over 75 percent of total assets of the Mauritanian banking system.
  - Combined private-credit to GDP ratio averages 14 percent over the sample period.
  - All six banks are privately owned; five belong to larger commercial conglomerates and one is a subsidiary of a large European banking group.
- Institutional detail used for identification:
  - Fish exporters were required until 2010 to sell a share of foreign exchange receipts to the central bank; subsequently exporters were asked to domicile receipts at the CBM.
  - Fish export receipts are used with a two-month lag (0.32 at a two-month lag noted in the source) to account for usual payment terms in export transactions.
- Summary statistics (preserved exactly as reported):
  - VariablesObservationsMean
    Standard 
    deviationMin.Max.
  - Bank-specific
    - Log (private-sector credit)40716.61.07.417.7
    - Log (government credit)40415.50.912.217.2
    - Log (short-term trade finance)40616.80.712.517.9
    - Log (reserves)40814.40.910.316.4
  - Common
    - Log (fish exports)7014.90.314.115.7
  - (Table 1: Summary Statistics of Main Variables)

### Interpretation and proposed explanations for ineffectiveness
- Overarching conclusion:
  - Monetary policy (as measured by export-related changes in reserves) did not systematically affect bank lending in Mauritania in 2006–11; the bank-lending channel of monetary transmission was ineffective over the sample period.
- Possible explanations highlighted:
  - Banking sector shallowness alone is not an obvious sufficient explanation:
    - Mauritania’s private-sector credit/GDP ratio was 29 percent in 2009 (including provisions; net private-sector credit/GDP was 21 percent), 11 percentage points lower than the global median across all countries.
    - Expected level of private-sector credit (controlling for structural characteristics) was 20.4 percent in 2009, implying a positive gap of about 8.6 percent.
  - Two proximate constraints emphasized and within policy purview:
    1. High loan concentration
       - At end-2010, the 20 biggest loans accounted for 30 percent of all outstanding loans in Mauritania.
       - Historical lending focus: parties affiliated with banks’ parent companies, the government, and state-owned enterprises.
       - Large informal sector and limited bank relationships; banks may rationally avoid lending to informality due to low risk-adjusted returns.
       - Endogeneity of effective demand: banks’ risk management capabilities and effort to search for new lending opportunities matter (the “lazy banks” notion).
       - Regulatory responses already taken: tighter definitions of related-party and single-party exposures (from February 2012 companies in same mother group treated as a single entity for exposure limits); obligation for banks to invest in appropriate risk management systems.
    2. Liquidity management framework and refinancing constraints
       - An average of 80 percent of banks’ liabilities was in the form of unremunerated sight deposits over the sample period.
       - Interbank market remained shallow; banks are wary of lending to each other given limited transparency and judicial uncertainty over collateral enforcement.
       - Central bank refinancing facility rarely used; banks perceive discretionary access.
       - Consequence: incentives to accumulate excess liquidity rather than extend longer-term lending, so liquidity injections do not translate into more lending.

### Policy considerations and recommendations
- On addressing loan concentration:
  - Continue regulatory measures tightening definitions of related-party and single-party exposures so cumulative exposure to a single group must fall within regulatory limits.
  - Require banks to invest in appropriate risk management systems to build capacity for assessing lending opportunities to new clients.
- On improving liquidity management and refinancing:
  - Tighten transparency requirements: for example, require banks to publish audited financial statements (including notes and management letter) on banks’ websites to improve counterparties’ ability to assess bank health.
  - Make access to the central bank refinance window fully automatic to remove perceived discretionary access and encourage use of temporary liquidity surpluses for customer lending.
  - Review collateral requirements: consider widening acceptable collateral to include high-quality customer loans because supply of T-bills has not grown in tandem with the wider economy; widening acceptable collateral would ease banks’ worries about refinancing and make these loan categories more likely to attract injected liquidity.
- Caveats and broader context:
  - Easing some constraints while others still bind may not change outcomes; there may be other prerequisites for monetary policy effectiveness (for example, emergence of credit-intensive industries and broader country-specific structural reforms).
  - The paper’s two highlighted constraints are not proven to be the sole causes; explanations are partly anecdotal and not quantified in this analysis.
  - Monetary contractions may be more effective than monetary expansions in this environment (theoretical and practical reasons discussed), but the focus of policy advice here is on making expansions more effective.

### Conclusions and external validity
- Main takeaway:
  - Using export-related repatriation-induced reserve changes as an exogenous identification strategy, the paper finds no evidence that these exogenous monetary shocks systematically affected bank lending (private-sector credit, government credit, or trade finance) in Mauritania during 2006–11.
- Conditions under which the identification strategy is appropriate (as noted in the source):
  - (i) the exchange rate exhibits little short-term volatility;
  - (ii) bank reserves are an appropriate indicator of the monetary stance; and
  - (iii) repatriation requirements for export receipts are in place, establishing a positive link between exports and reserves.
- Recommendation for further work:
  - Replicating the analysis in other countries that meet the three conditions would help better pinpoint the causes of monetary policy ineffectiveness in low-income countries; country policy constraints will vary and empirical results across countries would inform reform priorities.

*Source: _wp1377 - 0.32 at a two-month lag to account for the (PDF chapter/section).*

### REFERENCES

### _wp1377 - REFERENCES

### References (selected)
- Agénor, Pierre-Richard and Robert P. Flood, 1994, “Macroeconomic Policy, Speculative Attacks and Balance of Payments Crises,” In Handbook of International Macroeconomics, edited by Frederick van der Ploeg, Oxford: Blackwell.
- Agénor, Pierre-Richard, Joshua Aizenman, and Alexander W. Hoffmaister, 2004, “The Credit Crunch in East Asia: What Can Bank Excess Liquid Assets Tell Us?” Journal of International Money and Finance, Vol. 23, pp. 27–49.
- Al Hussainy, Ed, and others, 2011, “FinStats 2011: A Ready-to-Use tool to Benchmark Financial Sector Access Across Countries Over Time,” (Washington: World Bank).
- Amisano, Gianni, and Carlo Giannini, 1997, Topics in Structural VAR Econometrics, New York: Springer, 2nd edition.
- Angrist, Joshua D. and Jörn-Steffen Pischke, 2009, Mostly Harmless Econometrics: An Empiricist's Companion (Princeton: Princeton University Press).
- Baldini, Alfredo, and others, 2012, “Monetary Policy in Low Income Countries in the Face f the Global Crisis: The Case of Zambia,” IMF Working Paper 12/94 (Washington: International Monetary Fund).
- Becerra, Oscar, Eduardo Cavallo, and Carlos Scartascini, 2012, “The politics of financial development: The role of interest groups and government capabilities”, Journal of Banking & Finance, Vol. 36, pp. 626–43.
- Bernanke, Ben S. and Alan S. Blinder, 1992, “The Federal Funds Rate and the Channels of Monetary Transmission.” American Economic Review, 82(4), pp. 901–21.
- Bernanke, Ben and Ilian Mihov, 1998, “Measuring Monetary Policy,” The Quarterly Journal of Economics, Vol. 113(3), pp. 869–902.
- Christiano, Lawrence J and Martin Eichenbaum, 1992, "Liquidity Effects and the Monetary Transmission Mechanism," American Economic Review, Vol. 82(2), pages 346–53.
- Christiano, Lawrence J., Martin Eichenbaum, and Charles L. Evans, 1999, "Monetary policy shocks: What have we learned and to what end?,” Handbook of Macroeconomics, edited by J. B. Taylor and M. Woodford, Vol. 1(2), pp. 65–148.
- Chinn, Menzie D. and Hiro Ito, 2006, "What Matters for Financial Development? Capital Controls, Institutions, and Interactions," Journal of Development Economics, Vol. 81(1), pp. 163–192.
- Davoodi, Hamid, Shiv Dixit, and Gabor Pinter, 2013, “Monetary Transmission Mechanism in the East African Community: An Empirical Investigation,” IMF Working Paper 13/39 (Washington: International Monetary Fund).
- Fisher, R. A., 1932, Statistical Methods for Research Workers, Edinburgh: Oliver and Boyd, 4th edition.
- Flood, Robert P. and Peter M. Garber, 1984, “Collapsing Exchange-Rate Regimes: Some Linear Examples,” Journal of International Economics, Vol. 17(1–2), pages 1–13.
- Guiso, Luigi, Paola Sapienza, and Luigi Zingales, 2004, “The Role of Social Capital in Financial Development,” American Economic Review, 94(3): 526–556.
- Hauner, David, 2009, “Public Debt and Financial Development,” Journal of Development Economics, Vol. 88, pp. 171–83.
- Im, Kyung So, M. Hashem Pesaran, and Yongcheol Shin, 2003, "Testing for Unit Roots in Heterogeneous Panels," Journal of Econometrics, Vol. 115(1), pp. 53–74.
- Khemraj, Tarron, 2010, “What does Excess Bank Liquidity say about the Loan Market in Less Developed Countries?” Oxford Economic Papers, Vol. 62(1), pp. 86–103.
- Kleibergen, Frank and Richard Paap, “Generalized Reduced Rank Tests Using the Singular Value Decomposition,” Journal of Econometrics, Vol. 133, pp. 97–126.
- Krugman, Paul, 1979, “A Model of Balance-of-Payments Crises,” Journal of Money, Credit and Banking, Vol. 11(3), pages 311–25.
- La Porta, Rafael, Florencino Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny, 1997, “Legal Determinants of External Finance,” Journal of Finance, Vol. 52, pp. 1131–1150.
- La Porta, Rafael, Florencino Lopez-de-Silanes, Andrei Shleifer, and Robert W. Vishny, 1998, “Law and Finance,” Journal of Political Economy, Vol. 106, pp. 1113–55.
- Levin, Andrew, Chien-Fu Lin, and Chia-Shang Chu, 2002, “Unit Root Tests in Panel Data: Asymptotic and Finite Sample Properties,” Journal of Econometrics, Vol. 108, 1–24.
- Levine, Ross, Norman V. Loayza, and Thorsten Beck, 2000, “Financial intermediation and economic growth. Causality and Causes,” Journal of Monetary Economics, Vol. 46, pp. 31–77.
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### Granger Causality Tests (Table 1)
- Explanatory variables: p-value of null hypothesis that explanatory variables do not help forecast credit.
- Pooled Fisher-test for private-sector credit:
  - Fish exports: 0.45
  - Reserves: 0.20
  - Fish exports & reserves: 0.17
- Pooled Fisher-test for government credit:
  - Fish exports: 0.00***
  - Reserves: 0.08*
  - Fish exports & reserves: 0.01***
- Pooled Fisher-test for government credit (duplicate label in source):
  - Fish exports: 0.76
  - Reserves: 0.33
  - Fish exports & reserves: 0.70
- Note: * p<0.10, ** p<0.05, *** p<0.01.

### Table summaries: Estimation setups, samples, and diagnostic statistics
- Common notes across Tables 2–7:
  - Observations are three-month averages over the period 2006–11.
  - Cluster-robust t statistics in parentheses.
  - Tables present OLS and 2SLS estimates of the impact of (log) reserves or free reserves on the (log) of different credit aggregates.
  - The 2SLS estimates use the one period-lag of fish exports to the EU as instrument for reserves.
  - Columns (1)–(5) specify the model in levels and include bank fixed-effects and a linear time trend.
  - Columns (6)–(10) specify the model in first differences.
  - All specifications include a dummy for the six months following the 2008 coup d'etat.
  - Significance notation: * p<0.10, ** p<0.05, *** p<0.01.

- Table 2: Private-sector Credit Using Reserves
  - Selected coefficient and test values (as reported in the table):
    - Reserves coefficients (various columns): 0.00; 0.34; -0.07; 0.24; -0.08
    - Reserves (-1): 0.19; 0.18; 0.18
    - ∆ Reserves: 0.02; 0.04; 0.04; 0.07; -0.03
    - ∆ Reserves (-1): 0.02; 0.19; 0.17
    - ∆ Capital: 0.26** (t-statistic (2.69))
    - Observations: 134; 140; 129; 135; 128; 134; 123; 129; 129; 123
    - R-squared: 0.52; 0.37; 0.49; 0.36; 0.02; 0.01; -0.27; 0.00; 0.49; -0.25
    - Kleibergen-Paap LM statistic: 3.64; 3.67; 2.61; 3.36; 3.63; 2.90
      - p-value: 0.06; 0.06; 0.11; 0.07; 0.06; 0.09
    - Angrist-Pischke chi-squared statistic of Reserves: 7.10; 9.40; (others blank)
      - p-value: 0.01; 0.00
    - Angrist-Pischke chi-squared statistic of Reserves (-1): 4.26; 4.45
      - p-value: 0.04; 0.03
    - Angrist-Pischke chi-squared statistic of ∆ Reserves: 3.47; 7.48; 2.17
      - p-value: 0.06; 0.01; 0.14
    - Angrist-Pischke chi-squared statistic of ∆ Reserves (-1): 8.95; 8.59
      - p-value: 0.00; 0.00

- Table 3: Government Credit Using Reserves
  - Selected coefficient and test values:
    - Reserves coefficients: 0.14; 0.23**; -0.05; -0.10; -0.05
    - Reserves (-1): 0.05; 0.14; 0.14
    - ∆ Reserves: 0.05; 0.04; -0.27; -0.57; -0.18
    - ∆ Reserves (-1): 0.04; 0.53; 0.56
    - ∆ Capital: -0.36
    - Observations: 134; 140; 129; 135; 128; 134; 123; 129; 129; 123
    - R-squared: 0.07; 0.09; 0.05; -0.04; 0.01; 0.01; -0.85; -0.73; 0.05; -0.76
    - Kleibergen-Paap LM statistic and p-values: same numeric sequence as Table 2 (3.64; 3.67; 2.61; 3.36; 3.63; 2.90 with p-values 0.06; 0.06; 0.11; 0.07; 0.06; 0.09)
    - Angrist-Pischke statistics mirror Table 2 entries for Reserves and lags, with same p-values.

- Table 4: Short-term Trade Finance Credit Using Reserves
  - Selected coefficient and test values:
    - Reserves coefficients: -0.01; 0.21; -0.09; 0.20; -0.16
    - Reserves (-1): -0.02; 0.14; 0.14
    - ∆ Reserves: 0.05; 0.08; 0.06; 0.29; 0.01
    - ∆ Reserves (-1): 0.02; -0.14; -0.15
    - Capital: 0.53
    - ∆ Capital: 0.23
    - Observations and R-squared values: Observations same sequence as prior tables; R-squared: 0.45; 0.33; 0.41; 0.32; 0.03; 0.01; -0.35; -0.06; 0.44; -0.35
    - Kleibergen-Paap and Angrist-Pischke entries as in earlier tables (same numeric values and p-values).

- Table 5: Private-sector Credit Using Free Reserves
  - Selected coefficient and test values:
    - Free Reserves coefficients: 0.01; 0.10; -0.05; 0.07; -0.02
    - Free Reserves (-1): 0.06; 0.02; 0.01
    - ∆ Free Reserves: 0.00; -0.02**; 0.03; 0.04; 0.00
      - t-statistic for ∆ Free Reserves in column with -0.02**: (-3.97)
    - ∆ Free Reserves (-1): -0.01; 0.07; 0.06
    - ∆ Capital: 0.29** (t-statistic (3.14))
    - Observations: 87; 108; 82; 98; 72; 87; 67; 82; 82; 67
    - R-squared: 0.46; 0.28; 0.41; 0.27; 0.01; 0.01; -0.10; -0.02; 0.42; -0.04
    - Kleibergen-Paap LM statistic: 4.22; 3.64; 2.83; 2.69; 4.39; 2.70
      - p-value: 0.04; 0.16; 0.09; 0.10; 0.04; 0.10
    - Angrist-Pischke chi-squared statistic of Reserves: 10.52; 7.93; (others blank)
      - p-value: 0.00; 0.02
    - Angrist-Pischke chi-squared statistic of Reserves (-1): 8.86; 9.79
      - p-value: 0.00; 0.00
    - Angrist-Pischke chi-squared statistic of ∆ Reserves: 4.26; 5.46
      - p-value: 0.04; 0.02
    - Angrist-Pischke chi-squared statistic of ∆ Reserves (-1): --; 5.30; -4.94
      - p-value: --; 0.02; -0.03

- Table 6: Government Credit Using Free Reserves
  - Selected coefficient and test values:
    - Free Reserves / Reserves coefficients: -0.01; 0.03; -0.09; -0.05; -0.07
    - Reserves (-1): 0.13; 0.04; 0.04
    - ∆ Reserves: -0.11; -0.13*; -0.21; -0.27; -0.23
      - t-statistics include (-1.96) and (-2.20) for first two entries of ∆ Reserves
    - ∆ Reserves (-1): 0.14; 0.13; 0.12
    - Observations: 87; 108; 82; 98; 72; 87; 67; 82; 82; 67
    - R-squared: 0.09; 0.03; 0.06; 0.08; 0.19; 0.09; 0.13; -0.01; 0.07; 0.12
    - Kleibergen-Paap and Angrist-Pischke entries mirror Table 5 sequences.

- Table 7: Short-term Trade Finance Credit Using Free Reserves
  - Selected coefficient and test values:
    - Reserves / Free Reserves coefficients: 0.06; 0.09; 0.08; 0.13; -0.03
    - Reserves (-1): 0.00; 0.06*; 0.09
      - t-statistic for Reserves (-1) where marked *: (2.27)
    - ∆ Reserves: 0.03; -0.00; 0.06; 0.14; 0.05
    - ∆ Reserves (-1): -0.00; -0.02; -0.02
    - Capital: 0.52
    - ∆ Capital: 0.10
    - Observations and R-squared: Observations same sequence as Tables 5–6; R-squared: 0.43; 0.29; 0.42; 0.29; 0.03; 0.00; 0.02; -0.06; 0.44; 0.04
    - Kleibergen-Paap and Angrist-Pischke test entries include:
      - Angrist-Pischke chi-squared statistic of Reserves: 10.52; 7.93; (others blank)
        - p-value: 0.00; 0.02
      - Angrist-Pischke chi-squared statistic of Reserves (-1): 8.86; 9.79
        - p-value: 0.00; 0.00
      - Angrist-Pischke chi-squared statistic of ∆ Reserves: 4.26; 5.46
        - p-value: 0.04; 0.02
      - Angrist-Pischke chi-squared statistic of ∆ Reserves (-1): --; 5.30; -4.94
        - p-value: --; 0.02; -0.03

### Figures (structural impulse response functions)
- Figure 1. Private-sector Credit: Structural Impulse Response Functions
  - Panels by bank (Bank_1 through Bank_6) show:
    - fish exports -> reserves
    - fish exports -> prvt-sct. credit
    - 95% CI and structural irf plotted over Month (0, 5, 10, 15)
  - Y-axis tick labels shown as -1, -.5, 0, .5, 1.

- Figure 2. Government Credit: Structural Impulse Response Functions
  - Panels by bank (Bank_1 through Bank_6) show:
    - fish exports -> gvt. credit
    - fish exports -> reserves
    - 95% CI and structural irf plotted over Month (0, 5, 10, 15)
  - Y-axis tick labels shown as -1, -.5, 0, .5, 1.

- Figure 3. Short-term Trade Finance Credit: Structural Impulse Response Functions
  - Panels by bank (Bank_1 through Bank_6) show:
    - fish exp. -> s.t. trd. fin. credit
    - fish exp. -> reserves (repeated label in source)
    - 95% CI and structural irf plotted over Month (0, 5, 10, 15)
  - Y-axis tick labels shown as -.5, 0, .5, 1.

*Source: _wp1377 - REFERENCES (IMF PDF).*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2013/_wp1377.pdf_