## _wp0975

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---

### I. Introduction and Motivation
- International commitment in 2000 to achieve eight Millennium Development Goals (MDGs) by 2015 required substantial scaling-up of official grants to low-income countries, particularly in Sub-Saharan Africa (SSA).
- Question addressed: will large resource transfers (grants and remittances) be associated with movements in the equilibrium real exchange rate (ERER)?
  - Real exchange rate appreciation → decreased international competitiveness and resource movement from tradable to non-tradable sector (Dutch disease).
  - Real exchange rate depreciation → increased international competitiveness and resource movement from non-tradable to tradable sector.
- Distinction emphasized between the level of the real exchange rate and deviations from its equilibrium; deviations can cause misallocation and, if overvalued, reserve losses and balance of payments stress.

### II. Theoretical Framing and Remittances
- Historical debate: Ohlin (1929) (prices fully adjust to new steady-state) versus Keynes (1929) (non-tradable prices/wages may not adjust fully/quickly).
- Remittances to SSA have more than doubled since 2001; remittances may affect growth and ERER similarly to grants.

### III. Empirical Focus, Data, and Methodology
- Primary objective: estimate long-run relationship between grants and remittances and the equilibrium real exchange rate for 36 SSA countries (data span: 1980-2006 in most cases).
- Main estimator: pooled mean group (PMG) estimator of Pesaran et al. (1999) — ARDL-based estimator constraining long-run coefficients to be homogeneous across groups while allowing heterogeneous short-run dynamics.
- Key model elements:
  - Dependent variable: RER (real effective exchange rate, CPI-based).
  - Fundamentals (X) restricted to no more than four or five variables per specification.
  - Deterministic regressors D include intercepts and time trends.
- Unit root and cointegration testing:
  - Panel unit root tests (Levin, Lin, and Chu (2002); Breitung (2000); Im, Pesaran, and Shin (2003); Hadri (2000)) broadly indicate non-stationarity (variables can be considered I(1)), though some variables may be stationary.
  - Pedroni (2000) panel cointegration tests (Panel ADF, Panel PP, Group ADF, Group PP) confirm a cointegrating vector in most cases.
- Robustness checks: Fully Modified OLS (Pedroni (2000)) and Panel Dynamic OLS (Mark and Sul (2003)) gave generally similar but less significant results.

### IV. Grant-Spending Measures Constructed
- Standard measures: ODA (grants as percent of GDP), REM (remittances as percent of GDP).
- Two alternative grant-spending measures:
  - ODA1: ODA1 ≡ Grants/GDP × φ where φ is ∆(G-T)/∆Grants (truncated at zero and unity). Interpreted as the share of grants spent by government as reflected by the widening of the fiscal deficit (does not distinguish tradables vs non-tradables).
  - ODA2: ODA2 ≡ ((∆(G-T)+∆NFA)/∆Grants-1) × Grants/GDP where ((∆(G-T)+∆NFA)/∆Grants-1) is truncated at zero and unity. Intended to capture grant spending channeled toward non-tradables (captures spending on non-tradables that affects ∆NFA).

### V. Estimation Results (selected)
- Baseline (specifications I and IV in Table 1):
  - ODA coefficient = -0.256 (t-statistic (-9.329)). Interpretation: a one percentage point increase in grants to GDP is associated with a quarter percent depreciation in the real exchange rate.
  - REM coefficient = -0.044 (t-statistic (-1.461)); not statistically significant.
- Specification II (including ODA1):
  - ODA1 coefficient = -0.003 (t-statistic (-1.780)); negative but statistically insignificant.
- Specification III (including ODA2):
  - ODA2 coefficient = -0.008 (t-statistic (-3.868)); negative and statistically significant.
  - Text interpretation: "The coefficient on ODA2 is estimated to be -0.008. This result implies that a 1 percent increase in grant spending in the non-tradable sector is associated with a 0.8 percent depreciation of the equilibrium real exchange rate."
  - Note: ODA1 and ODA2 can take value zero and are not expressed in logarithms; coefficients are semi-elasticities.
- Error-correction and dynamics (specification III):
  - EC = -0.244 (t-statistic (-4.123)).
  - Corresponding half-life of a RER deviation from long-run equilibrium ≈ two and a half years.
  - Implication example: for each 1 percent of GDP decline in grant spending on non-tradables, the equilibrium real exchange rate would appreciate by 0.8 percent; half of that disequilibrium would dissipate within two and a half years.
- Sample sizes (Table 1):
  - Sample period: 1980-2006.
  - Observations by specification: 783, 756, 729, 405.
  - Countries by specification: 29, 28, 27, 15.
- Aggregate contribution (Table 2 summary):
  - Average annual equilibrium RER depreciation in SSA, 1980-2006: 1.8 percent.
  - GDP (relative productivity), OPN (openness), and TOT (terms of trade) together explain more than 90 percent of this depreciation.
  - ODA2 explains about 7 percent of the depreciation change.

### VI. Country-level Indices and Oil Exporters
- Equilibrium RER and disequilibrium indices constructed from specification III with country-specific intercepts; disequilibrium series reflect historical events (1994 CFA franc devaluation; Ethiopia famine and regime change; Rwanda civil war; Tanzania liberalization and 1986 devaluation).
- Large oil producers (Angola, Equatorial Guinea, Gabon, Nigeria) show increasing undervaluation in 2005-08 because equilibrium RER appreciated with rising real oil prices but actual RER did not follow.
- PMG estimates for oil exporters (Table A4, Max 1 lag):
  - TOT: 0.223 (2.953)
  - OPN: -0.135 (-1.723)
  - GDP: 0.171 (2.446)
  - EC: -0.593 (-3.932)
  - HM: 2.700 (0.440)
  - Country-specific long-run elasticities include Angola: 1.138 (4.060); Gabon: 0.488 (3.670); Nigeria: 1.162 (1.709).
  - Observations: 196; Countries: 7.

### VII. Key Findings
- The portion of transfers spent on non-tradables (captured by ODA2) matters most for the equilibrium real exchange rate.
- Main coefficient results:
  - ODA = -0.256 (t-statistic (-9.329)).
  - ODA2 = -0.008 (t-statistic (-3.868)).
  - REM = -0.044 (t-statistic (-1.461), not significant).
- Interpretation:
  - Transfers channeled to productivity-enhancing or supply-easing investments in the non-tradable sector are associated with a depreciation of the equilibrium real exchange rate.
  - ODA2 yields a larger and more significant impact on equilibrium RER than ODA1 (general grant spending).
- Aggregate magnitudes:
  - Average annual equilibrium RER depreciation in SSA, 1980-2006: 1.8 percent.
  - ODA2 explains about 7 percent of that depreciation.
  - Error-correction EC ≈ -0.244 → half-life ≈ two and a half years.

### VIII. Policy-Relevant Implications
- Scaling-up of grants (and remittances) to achieve MDGs is unlikely, on average, to lead to an appreciation of the RER in SSA if transfers are used to boost productivity or ease supply constraints in non-tradables.
- The end of a scaling-up could lead to a small but significant overvaluation of the RER if wages in the non-tradable sector do not adjust and governments use unsustainable fiscal policy to resist adjustment.
- Given the half-life of about two and a half years, any RER disequilibrium from a significant decline in grants is unlikely, on average, to be a major medium-term problem for SSA countries.
- Countries that spend grant inflows on productivity-enhancing non-tradables should prepare for an adjustment when grant inflows decline (possible equilibrium RER appreciation and rising non-tradable prices); sticky prices or fiscal resistance could produce temporary disequilibrium and resource misallocation.

### IX. Caveats and Alternative Explanations
- Reverse causality: countries with depreciated RERs may receive larger grant inflows; authors argue this would likely correlate all grant measures, not only ODA2.
- Excess capacity in the non-tradable sector: additional grant spending could raise capacity utilization without increasing non-tradable prices, producing no upward pressure on RER (relevant in post-conflict contexts).
- Pooling assumption: PMG relies on long-run homogeneity across countries; if long-run RER generation differs substantially across countries, country-specific analyses may differ.
- Data limitations prevented estimation of a multiple-equation system to better identify reverse causality.

### X. Recommendations for Future Research
- Conduct case studies to test robustness of pooled results, especially where long-run RER generation processes may differ across countries.
- As historical data availability improves, estimate multiple-equation systems to better identify reverse causality between aid and the real exchange rate.
- Focus on estimating the impact of aid and remittances on the real exchange rate in a single equation where feasible.

*Source: _wp0975 - References (excerpt).*

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

### References

### I. Introduction and Motivation
- The international community committed in 2000 to achieving eight Millennium Development Goals (MDGs) by 2015.
- Achievement of the MDGs requires a substantial scaling-up of official grants to low-income countries, particularly in Sub-Saharan Africa (SSA), to finance increased public expenditures.
- Some scaling-up has already occurred, with more expected as the target date approaches (see Figure 1: The Scaling-Up of Grants).
- Large resource transfers raise the question: will such transfers be associated with movements in the equilibrium real exchange rate (ERER) in recipient economies?
  - A real exchange rate appreciation implies decreased international competitiveness and a movement of resources from the tradable to the non-tradable sector (Dutch disease).
  - A real exchange rate depreciation implies increased international competitiveness and a movement of resources from the non-tradable to the tradable sector.
- It is important to distinguish between the level of the real exchange rate and its deviation from the underlying equilibrium level; deviations can cause misallocation of resources and, in the case of overvaluation, loss of reserves and potential balance of payments crisis.

### II. Historical and Theoretical Framing
- The debate recalls Ohlin (1929) versus Keynes (1929):
  - Ohlin (1929): relative prices, including the real exchange rate, can fully adjust following resource transfers, bringing the economy to a new steady-state equilibrium with full employment.
  - Keynes (1929): prices (including wages) in the non-tradable sector may not adjust fully/quickly, so the resulting real exchange rate may differ significantly from that required to maintain full employment.
- The potential for disequilibrium in the real exchange rate is especially relevant for scaling-up aid to achieve MDGs and for the eventual scaling-down if transfers dry up.

### III. Remittances as an Additional Dimension
- Remittances to SSA countries have more than doubled since 2001, partly reflecting new banking rules and regulations on money laundering in advanced economies after September 11, 2001 (see Figure 2: The Scaling-up of Remittances).
- A large increase in remittances may affect growth performance in SSA countries in ways similar to grants.

### IV. Empirical Focus and Hypothesis
- The paper examines the relationship between resource inflows (grants and remittances) and the equilibrium real exchange rate.
- Key argument: it is not the international transfer per se that matters; rather, the use of the transfer in the non-tradable sector determines its effect on the ERER.
  - If grants and remittances are used to increase demand in the non-tradable sector → expect ERER appreciation.
  - If grants and remittances are used to ease supply constraints or increase productivity in the non-tradable sector → transfers may be associated with ERER depreciation.
- Section II of the paper develops this argument in detail (referenced in the source text).

### V. Data, Methodology, and Scope (as described)
- Section III presents data and methodology used to estimate the relationship between grants and remittances and the real exchange rate for 36 SSA countries.
- The empirical approach follows closely previous work by Chudik and Mongardini (2007).
- Panel estimation results are presented in Section IV; Section V draws policy implications (sections referenced in the source text).

### VI. Figures, Tables, and Appendix Contents (as listed)
- Tables listed:
  - Table 1. Pooled Mean Group Estimates - Grants and Remittances
  - Table 2. Contribution of Fundamentals to Changes in ERER, 1980-2006
- Figures listed:
  - Figure 1. The Scaling-up of Grants: Net Official Development Assistance to Sub-Saharan Africa (Billions of 2000 US$)
  - Figure 2. The Scaling-up of Remittances to Sub-Saharan Africa (Billions of 2000 US$)
- Appendix Tables listed:
  - A1. Data Description and Sources
  - A2. Unit Root Tests
  - A3. Pooled Mean Group Estimates-Fundamentals
  - A4. Pooled Mean Group Estimates for Oil Exporters
- Appendix Figures listed:
  - A1. Real Effective Exchange Rates and Equilibrium Real Exchange Rates, Non-oil Countries, 1980-2006
  - A2. Real Exchange Rate Disequilibria, Non-oil Countries, 1980-2006 (in percent)
  - A3. Equilibrium Real Exchange Rates and Real Exchange Rates Oil Countries
  - A4. Real Exchange Rate Misalignments, Oil Countries (in percent)

*Source: _wp0975 - References (excerpt).*

### conclusions.

### conclusions.

### Recent literature
- Empirical findings are mixed on whether transfers (grants and remittances) appreciate or depreciate the equilibrium real exchange rate (RER).
- Evidence cited:
  - Rajan and Subramanian (2005): grant inflows associated with overvaluation and declines in labor-intensive/tradable manufacturing share; private-to-private transfers (remittances) do not show same adverse effects.
  - Elbadawi et al. (2008): foreign aid leads to appreciation of the equilibrium real exchange rate in a panel of 39 conflict and 44 nonconflict countries; aid has little impact on post-conflict overvaluation.
  - Saadi-Sedik and Petri (2006): grants and remittances in Jordan positively related to equilibrium RER, grants larger impact.
  - Lane and Milesi-Ferretti (2004): countries with net external liabilities have more depreciated RER; grant-based inflows associated with appreciation; main transmission via relative price of non-tradables.
  - Berg et al. (2007), Li and Rowe (2007), Ogun (1995), Sackey (2001), Cerra et al. (2008), Lee, Haacker, and Singh (2008): evidence in various samples/cases of little or opposite aid-related Dutch disease, including depreciation or negative correlation between remittances and RER.
- Key conceptual point: only the portion of transfers spent on non-tradables (or that eases non-tradable supply constraints) should affect the equilibrium RER. Transfers can be saved or spent on tradables or non-tradables; spending that increases non-tradable demand tends to appreciate the RER, while spending that eases non-tradable supply constraints tends to depreciate the RER.
- This study introduces two alternative measures of grant spending to better capture the portion spent in the non-tradable sector and compares them to standard measures (grants and remittances as percent of GDP).

### Data and empirical methodology
- Primary interest: long-run relationship between grants and remittances and the equilibrium real exchange rate.
- Main estimator: pooled mean group (PMG) estimator of Pesaran et al. (1999) (an ARDL-based estimator constraining long-run coefficients to be homogeneous across groups while allowing heterogeneous short-run dynamics).
- Justification for PMG: long-run RER movements expected to be generated by a similar process across countries; short-run dynamics allowed to vary by country.
- General reduced-form equation (∆ notation and lag structure shown in source) where:
  - RER is the real exchange rate.
  - X is vector of fundamental determinants including grants and remittances.
  - D includes deterministic regressors (intercepts and time trends).
  - i and t are country and time subscripts.
- Data span: 1980-2006 in most cases.
- Fundamentals (X) restricted to no more than four or five variables per specification because of limited time span.
- Unit root and panel cointegration testing:
  - Levin, Lin, and Chu (2002) test and other panel unit root tests (Breitung (2000), Im, Pesaran, and Shin (2003), Hadri (2000)) broadly indicate non-stationarity (variables can be considered I(1)), though some variables may be stationary.
  - Panel cointegration tests following Pedroni (2000): Panel ADF, Panel PP, Group ADF, Group PP. All four tests confirm a cointegrating vector in most cases; at least one test confirms cointegration in all cases.
  - Cointegration permits consistent long-run coefficient estimation despite potential endogeneity between RER and fundamentals.
- Robustness checks: Fully Modified OLS (Pedroni (2000)) and Panel Dynamic OLS (Mark and Sul (2003)) used; results were generally similar but less significant.
- Exploratory regressions (updating Chudik and Mongardini (2007)) find TOT (terms of trade), OPN (trade openness), GDP (relative productivity), GOV (government spending), and DS (debt service) significant determinants of equilibrium RER in SSA.
- Lane and Milesi-Ferretti net external position included and confirms that higher net external liabilities associate with more depreciated RER.
- Two alternative grant-spending measures constructed:
  - ODA1 (following Berg et al. (2007)): the share of grants spent by government as reflected by the widening of the fiscal deficit (net of grants) that accompanies an incremental increase in grants, φ, multiplied by grants as percent of GDP: ODA1 ≡ Grants/GDP × φ. Interpreted as the amount of grants used to finance expenditure increases or tax reductions; does not distinguish tradables vs non-tradables.
  - ODA2: intended to capture grant spending funneled toward non-tradables by using changes in net foreign assets (∆NFA). Formula combines φ and ω (proportion of grant spending on non-tradables) to isolate grants spent on non-tradables and normalizes by GDP. ODA2 is defined so as to capture spending on non-tradables that affects ∆NFA.

### Estimation results
- Baseline specifications (specifications I and IV in Table 1): regressions including grants (ODA) and remittances (REM) as percent of GDP show:
  - ODA coefficient = -0.256 (t-statistic in parentheses: (-9.329)). Interpretation: a one percentage point increase in grants to GDP is associated with a quarter percent depreciation in the real exchange rate.
  - REM coefficient = -0.044 (t-statistic (-1.461)); coefficient not statistically significant.
- Specification II (including ODA1):
  - ODA1 coefficient = -0.003 (t-statistic (-1.780)); negative but statistically insignificant.
  - Interpretation: ODA1 may not capture the key dimension (non-tradable absorption) and thus shows little long-run effect on RER.
- Specification III (including ODA2):
  - ODA2 coefficient = -0.008 (t-statistic (-3.868)); negative and statistically significant.
  - Reported interpretation in text: "The coefficient on ODA2 is estimated to be -0.008. This result implies that a 1 percent increase in grant spending in the non-tradable sector is associated with a 0.8 percent depreciation of the equilibrium real exchange rate."
    - Note on interpretation: because ODA1 and ODA2 can take a value of zero, they are not expressed in logarithms; their coefficients should be interpreted as semi-elasticities rather than elasticities.
- Comparative findings:
  - ODA2 (non-tradable spending) yields a larger and more significant impact on equilibrium RER than ODA1 (general grant spending).
  - Table 2 results: On average, SSA countries experienced a 1.8 percent annual depreciation in their equilibrium real exchange rates between 1980 and 2006. More than 90 percent of this depreciation is explained by changes in GDP (relative productivity), openness (OPN), and terms of trade (TOT). The ODA2 measure explains about 7 percent of the depreciation change (text: "the most powerful contribution for grants is found for the ODA2 measure, which explains about 7 percent of the depreciation change").
- Error-correction and dynamics:
  - Error-correction term (EC) in specification III = -0.244 (t-statistic (-4.123) for spec III as in table).
  - Corresponding half-life of a RER deviation from long-run equilibrium ≈ two and a half years.
  - Implication: for each 1 percent of GDP decline in grant spending on non-tradables, the equilibrium real exchange rate would appreciate by 0.8 percent; given the half-life, the initial disequilibrium would decline to 0.4 percent within two and a half years.
- Sample, observations, and countries (from Table 1):
  - Sample period reported: 1980-2006.
  - Observations by specification: 783, 756, 729, 405.
  - Countries by specification: 29, 28, 27, 15.
- Cointegration and diagnostic statistics (selected):
  - Panel PP, Panel ADF, Group PP, Group ADF test statistics and p-values reported in Table 1; most specifications show evidence consistent with cointegration (p-values reported with each test in Table 1).
  - Hausman (HM) test p-values reported in Table 1 for each specification.
- Country-level indices and disequilibrium:
  - Equilibrium RER and disequilibrium indices constructed from specification III coefficients with country-specific intercepts (figures in appendix referenced).
  - Disequilibrium series reflect known historical events (e.g., 1994 CFA franc devaluation; Ethiopia famine and regime change; Rwanda civil war; Tanzania liberalization and 1986 devaluation).
  - Large oil producers (Angola, Equatorial Guinea, Gabon, Nigeria) show increasing undervaluation in 2005-08 because equilibrium RER appreciated with rising real oil prices but actual RER did not follow.

### Key findings and policy-relevant implications
- Main findings:
  - It is the portion of transfers spent on non-tradables (captured by ODA2) that matters most for the equilibrium real exchange rate.
  - An increase in transfers (grants and remittances) that is channeled to productivity-enhancing or supply-easing investments in the non-tradable sector is associated with a depreciation of the equilibrium real exchange rate.
  - ODA2 coefficient: -0.008 (statistically significant). ODA coefficient: -0.256. REM coefficient: -0.044 (not significant).
  - Average annual equilibrium RER depreciation in SSA, 1980-2006: 1.8 percent.
  - ODA2 explains about 7 percent of that depreciation.
  - Estimated half-life of RER deviations ≈ two and a half years (error-correction EC ≈ -0.244).
- Policy implications:
  - Scaling-up of grants (and remittances) to achieve the MDGs is unlikely, on average, to lead to an appreciation of the RER in SSA if transfers are used to boost productivity or ease supply constraints in non-tradables.
  - The end of a scaling-up could lead to a small but significant overvaluation of the RER if wages in the non-tradable sector do not adjust and governments use unsustainable fiscal policy to resist adjustment.
  - Given the half-life of about two and a half years, any RER disequilibrium from a significant decline in grants is unlikely, on average, to be a major medium-term problem for SSA countries.
  - Countries that spend grant inflows on productivity-enhancing non-tradables should prepare for an adjustment when grant inflows decline (possible equilibrium RER appreciation and rising non-tradable prices); sticky prices or fiscal resistance could produce temporary disequilibrium and resource misallocation.
- Caveats and possible alternative explanations:
  - Reverse causality: countries with depreciated RERs (e.g., post-crisis) may receive larger grant inflows; authors argue this would likely correlate all grant measures, not only ODA2.
  - Excess capacity in non-tradable sector: additional grant spending could raise capacity utilization without increasing non-tradable prices, producing no upward pressure on RER; especially relevant in post-conflict contexts.
  - Pooling assumption: PMG relies on long-run homogeneity across countries; if long-run RER generation differs substantially across countries, case studies and country-specific analyses may yield different results.
  - Data limitations prevented estimation of a multiple-equation system to better identify reverse causality; future work should explore case studies and multiple-equation approaches as data availability improves.

### Recommendations for future research
- Conduct case studies to test robustness of pooled results, especially where long-run RER generation processes may differ across countries.
- As historical data availability improves, estimate multiple-equation systems to better identify reverse causality between aid and the real exchange rate.
- Focus on estimating the impact of aid and remittances on the real exchange rate in a single equation where feasible.

*Source: _wp0975 - conclusions.*

### REFERENCES

### _wp0975 - REFERENCES

### Bibliographic References (selected entries)
- Acosta, Pablo A., Emmanuela K.K. Lartey, and Federico S. Mandelman. 2007. “Remittances and the Dutch Disease,” Working Paper No. 2007-8 (Atlanta: Federal Reserve Bank of Atlanta).
- Berg, Andrew, et al.. 2007. “The Macroeconomics of Scaling Up Aid: Lessons from Recent Experience,” IMF Occasional Paper No. 253 (Washington: International Monetary Fund).
- Bourdet, Yves, and Hans Falck, 2006, “Emigrants’ Remittances and Dutch Disease in Cape Verde,” Working Paper No. 11 (Kristianstad, Sweden: Kristianstad University College).
- Breitung, Jörg. 2000. “The Local Power of Some Unit Root Tests for Panel Data,” Advances in Econometrics, Vol. 15 (Amsterdam: JAI Press), p. 161-78.
- Cerra, Valerie, Serpil Tekin, and Stephen J. Turnovsky. 2008. “Foreign Aid and Real Exchange Rate Adjustments in a Financially Constrained Dependent Economy,” forthcoming.
- Chami, Ralph, Adolfo Barajas, et al.. 2008. “Macroeconomic Consequences of Remittances,” IMF Occasional Paper No. 259. (Washington: International Monetary Fund).
- Choi, I.. 2001. “Unit Root Tests for Panel Data,” Journal of International Money and Finance, Vol. 20: 249-72.
- Chudik, Alexander and Joannes Mongardini. 2007. “In Search of Equilibrium: Estimating Equilibrium Real Exchange Rates in Sub-Saharan African Countries,” IMF Working Paper No. 07/90 (Washington: International Monetary Fund).
- Collier, Paul. 2007. “Aid ‘Dependency’: A Critique,” Journal of African Economies, Vol. 8, no. 4, pp. 528-545.
- Elbadawi, Ibrahim A., Linda Kaltani, and Klaus Schmidt-Hebbel. 2008. “Foreign Aid, the Real Exchange Rate, and Economic Growth in the Aftermath of Civil Wars,” The World Bank Economic Review.
- Gupta, Sanjeev, Robert Powell, and Yongzheng Yang. 2006. “Macroeconomic Challenges of Scaling Up Aid to Africa, A Checklist for Practitioners,” Washington DC: International Monetary Fund.
- Hadri, Kaddour. 2000. “Testing for Stationarity in Heterogeneous Panel Data,” Econometric Journal, Vol. 3, 148-61.
- Hausman, J.. 1978. “Specification Tests in Econometrics,” Econometrica, Vol. 46, pp.1, 251-71.
- Im, K. S., M. H. Pesaran, and Y. Shin. 2003. “Testing for Unit Roots in Heterogeneous Panels,” Journal of Econometrics, Vol. 115, 53-74.
- Keynes, John Maynard. 1929. “The German Transfer Problem,” “The Reparation Problem: A Discussion. II. A Rejoinder,” “ Views on the Transfer Problem. III. A Reply,” Economic Journal39 (March 1929, 1–7, (June 1929), 172–178, (September 1929), 404–408.
- Lane, Philip and Gian Maria Milesi-Ferretti. 2004. “The Transfer Problem Revisited: Net Foreign Assets and Real Exchange Rates,” The Review of Economics and Statistics, Vol. 86, No. 4 (November 2004).
- Lane, Philip and Gian Maria Milesi-Ferretti. 2001. “The External Wealth of Nations: Measures of Foreign Assets and Liabilities for Industrial and Developing Countries,” Journal of International Economics, Vol. 55 (2001) 263-294.
- Lane, Philip and Gian Maria Milesi-Ferretti. 2006. “The External Wealth of Nations Mark II: Revised and Extended Estimates of External Assets and Liabilities, 1970-2004,” IMF Working Paper, 06/69 (March).
- Lee, Kyung-woo, Markus Haacker, and Raju Singh. 2008. “Determinants and Macroeconomic Impact of Remittances in Sub-Saharan Africa,” IMF Working Paper, forthcoming.
- Levin, A., C. F. Lin, and C. Chu. 2002. “Unit Root Tests in Panel Data: Asymptotic and Finite-Sample Properties,” Journal of Econometrics, Vol. 108, 1-24.
- Li, Y. and F. Rowe. 2007. “Aid Inflows and the Real Effective Exchange Rate in Tanzania,” World Bank Policy Research Working Paper, No. 4456.
- Mark, Nelson C., and Donggyu Sul. 2003. “Cointegration Vector Estimation by Panel DOLS and Long-Run Money Demand,” Oxford Bulletin of Economics and Statistics, Vol. 65, No. 5, pp. 655-80.
- Ogun, O.. 1995. “Real Exchange Rate Movements and Export Growth: Nigeria, 1960 – 1999”, African Economic Research Consortium Research Paper.
- Ohlin, Bertil. 1929. “The Reparation Problem: A Discussion. I. Transfer Difficulties, Real and Imagined”, “Mr. Keynes’s views on the Transfer Problem. II. A Rejoinder,” Economic Journal 39 (June 1929), 172–182 (September 1929), 400–404.
- Pedroni, Peter. 2000. “Fully Modified OLS for Heterogeneous Cointegrated Panels,” Advances in Econometrics, Vol. 15, 93-130.
- Pesaran, M Hashem, Yongcheol Shin, and Richard J Smith. 1999. “Pooled Mean Group Estimation Of Dynamic Heterogeneous Panels,” Journal of the American Statistical Association, Vol. 94, 621-34.
- Rajan, Raghuram, and Arvind Subramanian. 2005. “What Undermines Aid’s Impact on Growth?” NBER Working Paper 11657. October.
- Saadi-Sedik, T. and M. Petri. 2006. “To Smooth or Not to Smooth—The Impact of Grants and Remittances on the Equilibrium Real Exchange Rate in Jordan,” International Monetary Fund Working Paper No. WP/06/257, Washington, DC, November 2006.
- Sackey. Harry A.. 2001. “External Aid Inflows and the Real Exchange Rate in Ghana,” AERC Research Paper 110, AERC, Nairobi, U. of Manitoba.
- Sachs, Jeffrey D. 2005. “The End of Poverty: Economic Possibilities for Our Time,” The Penguin Press (New York, USA).

### Data Description and Sources (Table A1)
- RER: Logarithm of the real effective exchange rate (CPI based). Source: IMF EER facility and WEO.
- TOT: Logarithm of the terms of trade for goods. Source: WEO.
- OPN: Logarithm of exports plus imports of goods as a percent of GDP. Source: WEO.
- GDP: Logarithm of real GDP per capita relative to weighted average of trading partners. Source: WEO.
- GOV: Logarithm of government consumption as a percent of GDP relative to trading partners. Source: WEO.
- DS: Total debt service as a percent of exports. Source: WEO.
- NFA: Net external position as a percent of GDP. Source: External Wealth of Nations Dataset as described in Lane and Milesi-Ferretti (2007).
- ODA: Logarithm of net Official Development Assistance as a percent of GDP. Source: OECD.
- ODA1: ∆(G-T)/∆Grants multiplied by Grants as a percent of GDP where G is government expenditure and T is revenue (minus grants). ∆(G-T)/∆Grants is truncated at zero and unity. Source: OECD, IMF African Department Database, WEO, and Authors' calculations.
- ODA2: ((∆(G-T)+∆NFA)/∆Grants-1) multiplied by Grants as a percent of GDP where G is government expenditure and T is revenue (minus grants). ((∆(G-T)+∆NFA)/∆Grants-1) is truncated at zero and unity. Source: OECD, IMF African Department Database, WEO, and Authors' calculations.
- REM: Logarithm of remittances received as a percent of GDP. Source: Lee, Haacker, and Singh (forthcoming), IFS, WDI, and IMF country desk data.

Note: WEO is the World Economic Outlook. OECD is the Organization for Economic Co-operation and Development. IFS is International Financial Statistics/ WDI is World Development Indicators.

### Unit Root Tests (Table A2)
- LLC p-value reported for levels and first differences (LLC represents the Levin, Lin, and Chu t-stat). All specifications include an intercept and deterministic time trend.
- Level and First Difference results (variable — Level LLC p-value — First Difference LLC p-value):
  - RER — 1.0030.842 — -9.8730.000
  - TOT — -0.0600.476 — -11.7510.000
  - OPN — -1.0280.152 — -10.3240.000
  - GDP — -0.3070.380 — -7.7130.000
  - GOV — -1.3030.096 — -10.2770.000
  - DS — -0.6020.274 — -6.9160.000
  - NFA — 3.2671.000 — -8.1360.000
  - ODA — -0.9630.168 — -8.7390.000
  - ODA1 — -10.3030.000 — -21.5340.000
  - ODA2 — -6.9230.000 — -11.4360.000
  - REM — -0.7490.227 — -6.7590.000

### Pooled Mean Group Estimates — Fundamentals (Table A3)
- Sample/Specification headings: VV, VII, V (periods and samples listed as 1980-2007, 1980-2007, 1980-2006).
- Selected coefficient estimates and statistics (each entry shows coefficient followed by t-statistic in parentheses where provided):
  - TOT: 0.0770.1310.102 (t-stats: (2.701)(3.381)(2.459))
  - OPN: -0.471 -0.389 -0.282 (t-stats: (-11.232)(-6.396)(-4.489))
  - GDP: 1.1151.1641.315 (t-stats: (18.768)(13.449)(17.44))
  - GOV: 0.1150.136 (t-stats: (2.378)(2.221))
  - DS: -0.044-0.108 (t-stats: (-2.515)(-4.634))
  - NFA: 0.002 (t-stat: (3.200))
  - EC: -0.280-0.257-0.365 (t-stats: (-4.358)(-3.410)(-3.587))
  - HM (Hausman test p-values): 7.64010.5701.840 (p-values: (0.110)(0.060)(0.870))
  - Panel PP: -2.004-1.956-2.061 (p-values: (0.054)(0.059)(0.048))
  - Panel ADF: -2.512-2.195-2.137 (p-values: (0.017)(0.036)(0.041))
  - Group PP: -2.332-2.587-1.964 (p-values: (0.026)(0.014)(0.058))
  - Group ADF: -2.843-2.837-1.974 (p-values: (0.007)(0.007)(0.057))
  - Observations: 784504432 (presented as three numbers corresponding to columns)
  - Countries: 281816 (three entries corresponding to columns)

Note: Numbers in parentheses are t-statistics with the exceptions of HM and cointegrations tests which are p-values. EC refers to the error correction term and HM refers to the Hausman test. All specifications include a maximum of one lag.

### Pooled Mean Group Estimates for Oil Exporters (Table A4)
- Specification: Max 1 lag. Dependent Variable: RER.
- Coefficients and t-statistics (where presented):
  - TOT: 0.223 (2.953)
  - OPN: -0.135 (-1.723)
  - GDP: 0.171 (2.446)
  - EC: -0.593 (-3.932)
  - HM: 2.700 (0.440)
- Country-specific long-run elasticities / country dummies and t-statistics (selected entries):
  - Angola: 1.138 (4.060)
  - Cameroon: -0.040 (-0.796)
  - Congo, Republic of: -0.056 (-0.775)
  - Cote d'Ivoire: 0.034 (0.305)
  - Equatorial Guinea: 1.528 (0.660)
  - Gabon: 0.488 (3.670)
  - Nigeria: 1.162 (1.709)
- Observations: 196
- Countries: 7

Note: Numbers in parentheses are t-statistics with the exceptions of HM and cointegrations tests which are p-values. EC refers to the error correction term and HM refers to the Hausman test. All specifications include a maximum of one lag.

### Figures and Visual Material (figure titles and notes)
- Figure A1. Real Effective Exchange Rates and Equilibrium Real Exchange Rates, Non-oil Countries, 1980-2006.
  - Country panels include Benin, Burkina Faso, Burundi, Cape Verde, Central African Republic, Chad, Democratic Republic of Congo, Ethiopia, The Gambia, Ghana, Guinea, Madagascar, Malawi, Mali, Mauritius, Rwanda, Senegal, Seychelles, Sierra Leone, Swaziland, Tanzania, Togo, Uganda, Zambia.
  - Plotted series: RER and ERER (1980-2006).
- Figure A2. Real Exchange Rate Disequilibria, Non-oil Countries, 1980-2006 (in percent).
  - Country panels and percent-scale disequilibria for the same non-oil country set (1980-2006).
- Figure A3. Equilibrium Real Exchange Rates and Real Exchange Rates, Oil Countries.
  - Country panels include Angola, Cameroon, Republic of Congo, Cote d'Ivoire, Equatorial Guinea, Gabon, Nigeria.
  - Note: ERER calculated on the basis of Table A4.
- Figure A4. Real Exchange Rate Misalignments, Oil Countries (in percent).
  - Misalignments calculated based on the results of Table A4; country panels as in Figure A3.

*Source: _wp0975 - REFERENCES*

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_Source: https://www.imf.org/-/media/websites/imf/imported-full-text-pdf/external/pubs/ft/wp/2009/_wp0975.pdf_