## 0.164 and 0.3 percent in GDP growth volatility, respectively in different sample periods and

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---

### III. AN INITIAL LOOK AT THE DATA
- Remittances represent an important source of foreign currency for several developing countries.
- As a share of GDP:
  - Tajikistan: "slightly above 40 percent of its GDP on average over 2010-2015".
  - Followed by Kyrgyz Republic, Liberia, Nepal and Comoros.
- Sample medians and thresholds:
  - Vertical red line at 3.6 percent of GDP in Figure 1 (remittances-to-GDP median).
  - Vertical red line at $2.9 billion in Figure 2 (90th percentile of remittances in absolute US$).
- In absolute values:
  - Nigeria: "slightly above $20 billion a year on average over 2010-2015", followed by Bangladesh, Vietnam and Sri Lanka.
- Data sources used:
  - Calculations based on IMF WEO and WB Migration and Remittances databases.

### Geographic concentration of remittance origins (HHI)
- Measurement:
  - Herfindahl–Hirschman Index (HHI): sum of the squares of the shares of remittance sending countries to all countries, range between 0 and 10,000 points, normalized to an index between 0 and 1. Higher HHI = higher concentration.
- Empirical patterns:
  - High concentration in several developing countries, mostly in the African region with remittances originating mostly from South Africa and France.
  - Montenegro: highest concentration in the period, with remittances mostly from Serbia.
  - Lesotho and Madagascar: remittances almost entirely originating from South Africa and France, respectively.
- Likely drivers of concentration:
  - Geographic proximity; formal colonial ties; shared languages.
- Distributional threshold:
  - Figure 3 vertical red line at around 0.4 HHI index (sample’s top HHI quartile).

### Remittances and macroeconomic volatility — descriptive comparisons
- Definition and measurement:
  - "Large remittances" defined as remittances-to-GDP exceeding 1 percent (De et al (2016), World Bank (2015), IMF (2005)).
  - Volatility measured using a rolling 10-year window of the standard deviation of external and real sector variables (higher values indicate higher volatility).
- Key comparative statistics (developing countries, average 2010-2015):
  - Median volatility of real GDP growth:
    - 0.29 in developing countries with large remittances (Figure 4 panel A).
    - 0.34 in developing countries without large remittances (Figure 4 panel B).
  - Volatility of exchange rate movements (median):
    - 0.07 in countries with large remittances (Figure 5 panel A).
    - 0.12 in countries without large remittances (Figure 5 panel B).
  - Volatility of imports growth (median):
    - 16.07 in countries with large remittances.
    - 21.5 in countries without large remittances.
- Noted summary statement:
  - "Large remittances seem to be associated with lower economic volatilities."
- Contextual numeric points mentioned:
  - "0.164 and 0.3 percent in GDP growth volatility, respectively in different sample periods and countries, and after controlling for other factors as identified in the literature."
  - "0.12 compared to median of 0.15 in countries without large remittances)."
- Figures referenced:
  - Figure 1: Remittances-to-GDP, average 2010-2015.
  - Figure 2: Remittances, in US Billions, average 2010-2015.
  - Figure 3: Herfindahl index: Remittances Concentration, average 2010-2015.
  - Figure 4 and Figure 5: volatility comparisons (panels A and B as noted).

### Method, data and identification
- Sample:
  - Balanced panel of 72 developing countries over 2010-2015 (and extended sample 1990-2015 cited for some figures).
  - 385 country-year observations noted; about 23 percent of sample country-year observations lie below the 1 percent remittances-to-GDP definition.
- Dependent variables:
  - Volatilities of: exchange rate changes (nominal LCU/USD), imports of G&S growth, government revenue changes, and real GDP growth.
- Independent variables:
  - rem_dummy1 (1 if remittances-to-GDP ≥ 1 percent), HHI (remittance concentration), rem_dummy1*hhi interaction, and control vector X including CPIA, initial GDP, trade openness, L.reer, L.doil_price, L.world_growth, and L.source_vol.
- Estimation:
  - Static panel FE and RE models. Regressors lagged and expressed in logs where appropriate. Diagnostics: F-test, Breusch-Pagan LM, Hausman. Dynamic panel methods not used due to very short T.
- HHI construction:
  - From annual bilateral remittance data (World Bank Migration and Remittances database) over 2010-2015.

### Key empirical findings — volatility of external variables (exchange rate; imports growth)
- Exchange rate volatility (nominal LCU/USD; higher values indicate depreciation):
  - rem_dummy1 coefficients: -0.09*** (eq.1), -0.09*** (eq.2), -0.10*** (eq.3).
  - hhi coefficients: -0.156** (eq.1), -0.162** (eq.2), -0.155** (eq.3).
  - rem_dummy1hhi interaction coefficients: 0.167** (eq.1), 0.178** (eq.2), 0.182** (eq.3).
  - L.reer: 18.524*** (eq.2), 18.806*** (eq.3), 18.943*** (eq.3 second mention).
  - exr_regime dummy: 0.042*** (eq.1), 0.042*** (eq.2), 0.045*** (eq.3).
  - Observations: 355 (eqs.1-2), 335 (eq.3). R-squared: 0.126 (eq.1), 0.139 (eq.2), 0.143 (eq.3).
  - Methods: Panel RE reported for eqs.1-3. Diagnostics: F-test 3.51***, 3.55***, 3.49***; Breusch-Pagan LM 101.3***, 70.47***, 62.14***; Hausman test 9.72, 10.54, 13.03.
  - Interpretation:
    - Large remittances alone associated with lower exchange rate volatility (negative and significant rem_dummy1).
    - Positive and significant rem_dummy1hhi implies that when large remittances come from concentrated sources (high HHI), they can aggravate exchange rate volatility.
  - Robustness:
    - Results robust to adding L.doil_price, L.world_growth, L.source_vol, and exchange-rate-regime dummy.
- Imports of G&S growth volatility:
  - rem_dummy1 coefficients: -2.729*** (eq.4), -2.516** (eq.5), -2.641** (eq.6).
  - hhi coefficients: -4.771** (eq.4), -4.392** (eq.5), -3.731* (eq.6).
  - rem_dummy1hhi interaction coefficients: 5.543** (eq.4), 4.798** (eq.5), 5.133** (eq.6).
  - L.openness: 1.860 (eq.4), 3.177*** (eq.5), 2.800** (eq.6).
  - Observations: 355 (eqs.4-5), 331 (eq.6). R-squared: 0.597 (eq.4), 0.603 (eq.5), 0.617 (eq.6).
  - Methods: Panel FE (eq.4,6) and Panel RE (eq.5). Diagnostics: large and highly significant F-tests and Breusch-Pagan LM.
  - Interpretation:
    - Large remittances by themselves stabilize imports volatility (negative rem_dummy1).
    - Positive rem_dummy1hhi indicates high remittance concentration can reverse the stabilizing effect and aggravate imports volatility.
    - Higher trade openness associated with higher import growth volatility.

### Key empirical findings — volatility of real variables (government revenue; real GDP growth)
- Government revenue volatility:
  - rem_dummy1 coefficients: -0.022** (eq.7), -0.024** (eq.8), -0.019* (eq.9).
  - hhi coefficients: -0.027 (eq.7), -0.031 (eq.8), -0.035* (eq.9).
  - rem_dummy1hhi interaction coefficients: 0.050** (eq.7), 0.053** (eq.8), 0.037* (eq.9).
  - L.lcpia: -0.18*** (eq.7), -0.14*** (eq.8), -0.109** (eq.9).
  - L.world_growth: -0.53*** (eq.8) where reported.
  - Observations: 355 (eqs.7-8), 305 (eq.9). R-squared: 0.179 (eq.7), 0.191 (eq.8), 0.070 (eq.9).
  - Methods: Panel RE (eq.7), Panel FE (eq.8-9). Diagnostics: F-test and Breusch-Pagan LM highly significant; Hausman tests reported.
  - Interpretation:
    - Large remittances tend to lower revenue volatility (negative rem_dummy1).
    - Positive rem_dummy1hhi implies that when remittances are both large and highly concentrated, revenue volatility can be aggravated.
    - Stronger institutions (higher CPIA) and higher global growth associated with lower revenue volatility.
- Real GDP growth volatility:
  - rem_dummy1 coefficients: -0.012** (eq.10), -0.013** (eq.11), -0.012** (eq.12).
  - hhi coefficients: -0.018 (eq.10), -0.017 (eq.11), -0.018 (eq.12) — statistically insignificant.
  - rem_dummy1hhi interaction coefficients: 0.012 (eq.10), 0.012 (eq.11), 0.010 (eq.12) — statistically insignificant.
  - L.lcpia: -0.050*** (eq.10), -0.05*** (eq.11), -0.045** (eq.12).
  - L.world_growth: -0.227** (eq.11), -0.216** (eq.12) where reported.
  - Observations: 355 (eqs.10-11), 340 (eq.12). R-squared: 0.061 (reported).
  - Methods: Panel RE (eqs.10-12). Diagnostics: F-test and Breusch-Pagan LM highly significant; Hausman tests reported.
  - Interpretation:
    - Large remittances are stabilizing for real GDP growth volatility (negative and significant rem_dummy1).
    - Remittance concentration and its interaction with large remittances are statistically insignificant for growth volatility.
    - Stronger institutions (higher CPIA) associated with lower growth volatility.

### Robustness and diagnostics
- Robustness checks:
  - Adding global shocks (L.doil_price and L.world_growth) does not change main signs or significance of rem_dummy1, hhi, rem_dummy1hhi, and CPIA.
  - Controlling for source-country volatility (L.source_vol) leaves main coefficients intact.
  - Using exchange-rate-regime dummy (flexible de facto regimes defined as yearly nominal changes of ± 2 percent = 1, fixed = 0): results unchanged.
  - Alternative large-remittance cutoffs and exchange rate definitions tested (10 percent of GDP, 20 percent of exports; nominal versus end-of-period) reported as robustness checks.
- Diagnostics reported across regressions:
  - F-test (pool OLS), Breusch-Pagan LM, Hausman tests; specification and method choices (FE vs RE) documented and used according to diagnostics.

### Overall conclusions and policy implications
- Main empirical conclusion:
  - Large remittances can be stabilizing on average across external and real-sector volatilities (exchange rate, imports growth, government revenues, real GDP growth).
  - High geographic concentration of remittance sources (high HHI) can aggravate economic volatility when combined with large remittance inflows (positive and significant rem_dummy1*hhi in several equations).
- Role of institutions:
  - Stronger institutions (higher CPIA) are generally associated with lower external and real-sector volatilities and help avoid the “remittances trap.” Strong institutions can mitigate Dutch-disease effects, improve incentives to work and lower the cost of doing business.
- Policy recommendations:
  - Diversify the source countries of remittances to reduce vulnerability to shocks concentrated in a few remittance-sending countries.
  - Replicate policy initiatives that facilitate communication networks with emigrants and promote trade and investment opportunities to diversify remittance sources (illustrated by Mitra et al (2016) for MENA).
  - Reduce financial costs of sending remittances (including via mobile money); evidence cited that remittance-sending costs in 2017 were about 25 percent higher in SSA than in the rest of the world (Arizala et al (2018)).
    - Example costs in 2017: Nigeria ≈ 8 percent average cost; Tajikistan ≈ 4 percent average cost.
    - Example: sending remittances from Malaysia to Indonesia ≈ 6 percent versus from Saudi Arabia ≈ 4 percent.
- Trade-offs:
  - Concentration may sometimes yield benefits (e.g., geographically-concentrated diasporas increasing venture funding access as in Vaaler (2013)), but empirical evidence here emphasizes stabilizing role of larger, diversified remittance inflows and destabilizing potential of concentrated sources.

*Source: Calculations and analysis based on IMF WEO, WB WDI, and WB Migration and Remittances databases, as presented in the supplied PDF content.*

### 0.164 and 0.3 percent in GDP growth volatility, respectively in different sample periods and

### wpiea2020015-print-pdf - 0.164 and 0.3 percent in GDP growth volatility, respectively in different sample periods and

### III. AN INITIAL LOOK AT THE DATA
- Remittances represent an important source of foreign currency for several developing countries.
- As a share of GDP:
  - Tajikistan has the highest share; "slightly above 40 percent of its GDP on average over 2010-2015".
  - Followed by Kyrgyz Republic, Liberia, Nepal and Comoros.
- Sample medians and thresholds shown in figures:
  - Vertical red line at 3.6 percent of GDP in Figure 1 (remittances-to-GDP median).
  - Vertical red line at $2.9 billion in Figure 2 (90th percentile of remittances in absolute US$).
- In absolute values:
  - Nigeria reported the highest remittances; "slightly above $20 billion a year on average over 2010-2015", followed by Bangladesh, Vietnam and Sri Lanka.
- Data sources used:
  - Calculations based on IMF WEO and WB Migration and Remittances databases.

### Geographic concentration of remittance origins (HHI)
- Methodology and index:
  - Geographic concentration measured with the Herfindahl–Hirschman Index (HHI).
  - HHI defined as the sum of the squares of the shares of remittance sending countries to all countries, producing a range between 0 and 10,000 points, which is then normalized to become an index between 0 and 1.
  - A higher HHI indicates higher concentration.
- Empirical patterns:
  - Concentration appears to be high in several developing countries, mostly in the African region with remittances originating mostly from South Africa and France.
  - Montenegro has the highest concentration during the period under consideration, with remittances mostly from Serbia.
  - Lesotho and Madagascar show remittances almost entirely originating from South Africa and France, respectively.
- Likely drivers of concentration (as suggested in source):
  - Geographic proximity.
  - Formal colonial ties.
  - Shared languages.
  - References cited in the source: Arizala et al 2018; Gonzalez-Garcia et al 2016; Barajas et al 2010; Lueth and Ruiz-Arranz 2006.
- Distributional threshold shown:
  - Figure 3 vertical red line at around 0.4 HHI index (sample’s top HHI quartile).

### Remittances and macroeconomic volatility
- Prior findings cited:
  - Bouoiyour et al (2016) find that remittances in Morocco can smooth growth volatility, while FDI flows can aggravate it — empirical support for counter-cyclicality of remittances versus other FX flows.
  - The source references the importance of financial development in how remittances affect macroeconomic volatility (Adeniyi et al 2019; Ahamada and Coulibaly 2011).
- Definition and measurement in this analysis:
  - "Large remittances" defined following De et al (2016), World Bank (2015) and IMF (2005) as remittances-to-GDP exceeding 1 percent.
  - Volatility measured using a rolling 10-year window of the standard deviation of external and real sector variables (higher values indicate higher volatility).
- Key comparative statistics (developing countries, average 2010-2015):
  - Median volatility of real GDP growth:
    - 0.29 in developing countries with large remittances (Figure 4 panel A).
    - 0.34 in developing countries without large remittances (Figure 4 panel B).
  - Volatility of exchange rate movements (median):
    - 0.07 in countries with large remittances (Figure 5 panel A).
    - 0.12 in countries without large remittances (Figure 5 panel B).
  - Volatility of imports growth (median):
    - 16.07 in countries with large remittances.
    - 21.5 in countries without large remittances.
- Other notes from the source:
  - "Large remittances seem to be associated with lower economic volatilities."
  - The source uses rolling-window standard deviations of external and real-sector variables to support these comparisons.

### Contextual numeric points mentioned
- "0.164 and 0.3 percent in GDP growth volatility, respectively in different sample periods and countries, and after controlling for other factors as identified in the literature." (phrasing preserved from source)
- Figures and panels referenced:
  - Figure 1: Remittances-to-GDP, average 2010-2015.
  - Figure 2: Remittances, in US Billions, average 2010-2015.
  - Figure 3: Herfindahl index: Remittances Concentration, average 2010-2015.
  - Figure 4 and Figure 5: volatility comparisons (panels A and B as noted above).

*Source: IMF working paper content (figures and text excerpts as provided).*

### 0.12 compared to median of 0.15 in countries without large remittances).

### wpiea2020015-print-pdf - 0.12 compared to median of 0.15 in countries without large remittances).

### Method, data and identification
- Sample: balanced panel of 72 developing countries over 2010-2015 (and extended sample 1990-2015 cited for some figures).
- Dependent variables: volatilities of four types — exchange rate changes (nominal LCU/USD), imports of G&S growth, government revenue changes, and real GDP growth.
- Independent variables: remittances (large-remittance dummy), remittance concentration (Herfindahl–Hirschman Index, HHI), interaction rem_dummy1*hhi, and control vector X including quality of institutions (CPIA), initial GDP, trade openness, real effective exchange rate (L.reer), global shocks (L.doil_price, L.world_growth), and source-country volatility (L.source_vol).
- Large remittance dummy: rem_dummy1 = 1 if remittances-to-GDP ≥ 1 percent (IMF (2005), World Bank (2015), De et al (2016)); about 23 percent of sample country-year observations lie below this 1 percent definition in the 385 country-year observations noted.
- Remittance concentration: HHI constructed from annual bilateral remittance data (World Bank Migration and Remittances database) over 2010-2015.
- Estimation: static panel FE and RE models. Regressors lagged and expressed in logs where appropriate. Diagnostics reported (F-test, Breusch-Pagan LM, Hausman test). Dynamic panel methods not used due to very short T.

### Key empirical findings — volatility of external variables (nominal exchange rate; imports growth)
- Exchange rate volatility (nominal LCU/USD; higher values indicate depreciation):
  - rem_dummy1 coefficients: -0.09*** (eq.1), -0.09*** (eq.2), -0.10*** (eq.3).
  - hhi coefficients: -0.156** (eq.1), -0.162** (eq.2), -0.155** (eq.3).
  - rem_dummy1hhi interaction coefficients: 0.167** (eq.1), 0.178** (eq.2), 0.182** (eq.3).
  - L.reer (lagged real effective exchange rate) listed where applicable: 18.524*** (eq.2), 18.806*** (eq.3), 18.943*** (eq.3 second mention).
  - exr_regime dummy: 0.042*** (eq.1), 0.042*** (eq.2), 0.045*** (eq.3).
  - Observations: 355 (eqs.1-2), 335 (eq.3). R-squared: 0.126 (eq.1), 0.139 (eq.2), 0.143 (eq.3).
  - Methods: Panel RE reported for eqs.1-3. Diagnostics: F-test (pool OLS) 3.51***, 3.55***, 3.49***; Breusch-Pagan LM 101.3***, 70.47***, 62.14***; Hausman test 9.72, 10.54, 13.03.
  - Interpretation: large remittances by themselves are associated with lower exchange rate volatility (negative and significant rem_dummy1). However, the positive and significant interaction rem_dummy1hhi implies that when large remittances come from concentrated sources (high HHI), they can aggravate exchange rate volatility despite the stabilizing effect of large remittances alone.
  - Robustness: results robust to adding global shocks (L.doil_price, L.world_growth) and source-country exchange rate volatility (L.source_vol), and to an exchange-rate-regime dummy.

- Imports of G&S growth volatility:
  - rem_dummy1 coefficients: -2.729*** (eq.4), -2.516** (eq.5), -2.641** (eq.6).
  - hhi coefficients: -4.771** (eq.4), -4.392** (eq.5), -3.731* (eq.6).
  - rem_dummy1hhi interaction coefficients: 5.543** (eq.4), 4.798** (eq.5), 5.133** (eq.6).
  - L.openness: 1.860 (eq.4), 3.177*** (eq.5), 2.800** (eq.6).
  - L.reer: reported in eqs.5-6 where applicable.
  - Observations: 355 (eqs.4-5), 331 (eq.6). R-squared: 0.597 (eq.4), 0.603 (eq.5), 0.617 (eq.6).
  - Methods: Panel FE (eq.4,6) and Panel RE (eq.5). Diagnostics: large and highly significant F-tests and Breusch-Pagan LM.
  - Interpretation: large remittances by themselves are stabilizing for imports volatility (negative rem_dummy1). But the positive and significant rem_dummy1hhi interaction indicates that high remittance concentration can reverse this stabilizing effect and aggravate imports volatility. Higher trade openness is associated with higher import growth volatility.

### Key empirical findings — volatility of real variables (government revenue; real GDP growth)
- Government revenue volatility:
  - rem_dummy1 coefficients: -0.022** (eq.7), -0.024** (eq.8), -0.019* (eq.9).
  - hhi coefficients: -0.027 (eq.7), -0.031 (eq.8), -0.035* (eq.9).
  - rem_dummy1hhi interaction coefficients: 0.050** (eq.7), 0.053** (eq.8), 0.037* (eq.9).
  - L.lcpia (lagged CPIA): -0.18*** (eq.7), -0.14*** (eq.8), -0.109** (eq.9).
  - L.world_growth: -0.53*** (eq.8) reported; negative and significant where applicable.
  - Observations: 355 (eqs.7-8), 305 (eq.9). R-squared: 0.179 (eq.7), 0.191 (eq.8), 0.070 (eq.9).
  - Methods: Panel RE (eq.7), Panel FE (eq.8-9). Diagnostics: F-test and Breusch-Pagan LM highly significant; Hausman tests reported.
  - Interpretation: large remittances tend to lower revenue volatility (negative rem_dummy1). However, the positive rem_dummy1hhi implies that when remittances are both large and highly concentrated, revenue volatility can be aggravated. Stronger institutions (higher CPIA) and higher global growth are associated with lower revenue volatility. Results robust to global shocks and source-country volatility.

- Real GDP growth volatility:
  - rem_dummy1 coefficients: -0.012** (eq.10), -0.013** (eq.11), -0.012** (eq.12).
  - hhi coefficients: -0.018 (eq.10), -0.017 (eq.11), -0.018 (eq.12) — statistically insignificant.
  - rem_dummy1hhi interaction coefficients: 0.012 (eq.10), 0.012 (eq.11), 0.010 (eq.12) — statistically insignificant.
  - L.lcpia (lagged CPIA): -0.050*** (eq.10), -0.05*** (eq.11), -0.045** (eq.12).
  - L.world_growth: -0.227** (eq.11), -0.216** (eq.12) where reported.
  - Observations: 355 (eqs.10-11), 340 (eq.12). R-squared: 0.061 (reported).
  - Methods: Panel RE (eqs.10-12). Diagnostics: F-test and Breusch-Pagan LM highly significant; Hausman tests reported.
  - Interpretation: large remittances are stabilizing for real GDP growth volatility (negative and significant rem_dummy1). Remittance concentration and its interaction with large remittances are statistically insignificant for growth volatility. Stronger institutions (higher CPIA) associated with lower growth volatility. Results robust to global shocks and source-country volatility.

### Robustness and diagnostics
- Robustness checks include:
  - Adding global shocks: L.doil_price and L.world_growth do not change main signs or significance of rem_dummy1, hhi, rem_dummy1hhi, and CPIA.
  - Controlling for source-country volatility (L.source_vol) in the respective dependent-variable equations: results on main coefficients remain.
  - Using exchange-rate-regime dummy (flexible de facto regimes defined as yearly nominal changes of ± 2 percent = 1, fixed = 0): results unchanged.
  - Alternative large-remittance cutoffs and exchange rate definitions tested (10 percent of GDP, 20 percent of exports; nominal versus end-of-period) reported as robustness checks.

### Overall conclusions and policy implications
- Main empirical conclusion:
  - Large remittances can be stabilizing on average across external and real-sector volatilities (exchange rate, imports growth, government revenues, real GDP growth).
  - However, high geographic concentration of remittance sources (high HHI) can aggravate economic volatility when combined with large remittance inflows (positive and significant interaction rem_dummy1*hhi in several equations).
- Role of institutions:
  - Stronger institutions (higher CPIA) are generally associated with lower external and real-sector volatilities and help avoid the “remittances trap.” Strong institutions can mitigate Dutch-disease effects and improve incentives to work and lower the cost of doing business.
- Policy recommendations and implications highlighted:
  - Diversify the source countries of remittances to reduce vulnerability to shocks concentrated in a few remittance-sending countries.
  - Replicate policy initiatives that facilitate communication networks with emigrants and promote trade and investment opportunities to diversify remittance sources (illustrated by Mitra et al (2016) for MENA).
  - Reduce financial costs of sending remittances (including via mobile money); evidence cited that remittance-sending costs in 2017 were about 25 percent higher in SSA than in the rest of the world (Arizala et al (2018)). Example costs in 2017: Nigeria ≈ 8 percent average cost; Tajikistan ≈ 4 percent average cost. A noted example: sending remittances from Malaysia to Indonesia ≈ 6 percent versus from Saudi Arabia ≈ 4 percent.
- Trade-offs:
  - While concentration may sometimes yield benefits (e.g., geographically-concentrated diasporas increasing venture funding access as in Vaaler (2013)), the empirical evidence here emphasizes the stabilizing role of larger, diversified remittance inflows and the destabilizing potential of concentrated sources.

*Source: Calculations and analysis based on IMF WEO, WB WDI, and WB Migration and Remittances databases, as presented in the supplied PDF content.*

### REFERENCES

### REFERENCES

### Remittances and macroeconomic effects
- Abdih, Yasser, Adolfo Barajas, Ralph Chami, and Christian Ebeke (2012) “Remittances Channel and Fiscal Impact in the Middle East, North Africa, and Central Asia”, IMF Working Paper no. 12/104. Washington: International Monetary Fund.
- Abdih, Yasser, Ralph Chami, Jihad Dagher, and Peter Montiel (2012) “Remittances and Institutions: Are Remittances a Curse?”, World Development 40 (4): pp. 657-666.
- Abdih, Yasser, Ralph Chami, Michael Gapen, and Amine Mati (2009) “Fiscal Sustainability in Remittance-Dependent Economies”, IMF Working Paper no. 09/190. Washington: International Monetary Fund.
- Barajas, Adolfo, Ralph Chami, Christian H Ebeke, and Sampawende J Tapsoba (2012) “Workers’ Remittances: An Overlooked Channel of International Business Cycle Transmission?”, IMF Working Paper no. 12/251. Washington: International Monetary Fund.
- Barajas, Adolfo, Ralph Chami, Connel Fullenkamp, and Anjali Garg (2010) “The Global Financial Crisis and Workers’ Remittances to Africa: What’s the Damage?”, IMF Working Paper no. 10/24. Washington: International Monetary Fund.
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- Chami, Ralph, Connel Fullenkamp, and Samir Jahjah (2003) “Are Immigrant Flows a Source of Capital for Development?”, IMF Working Paper no. 03/189. Washington, D.C.: International Monetary Fund.
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- Jidoud, Ahmat (2015) “Remittances and Macroeconomic Volatility in African Countries”, IMF Working Paper no. 15/49. Washington, D.C.: International Monetary Fund.
- Ncube, Mthuli and Zuzana Brixiova (2013) “Remittances and their Macroeconomic Impact: Evidence from Africa”, AfDB Working Paper no. 188. Tunisia: African Development Bank.
- World Bank (2006a) “Economic Implications of Remittances and Migration”, WB Global Economic Prospects. Washington, D.C.: The World Bank.
- World Bank (2006b) “The Development Impact of Workers’ Remittances in Latin America, Vol. 2: Detailed Findings, Report No. 37026. Washington, D.C.: The World Bank.
- World Bank (2015) “Can Remittances Help Promote Consumption Stability?”, Chapter 4 in WB Global Economic Prospects. Washington, D.C.: The World Bank.

### Remittances, volatility, and growth dynamics
- Adeniyi, Oluwatosin, Kazeem Ajide, and Ibrahim D. Raheem (2019) “Remittances and output growth volatility in developing countries: Does financial development dampen or magnify the effects?”, Empirical Economics 56 (3): pp. 865–882.
- Ahamada, I., and D. Coulibaly (2011) “How Does Financial Development Influence the Impact of Remittances on Growth Volatility?”, Economic Modeling 28: pp. 2748–2760.
- Ahmed, B. and I. Martínez-Zarzoso (2016) “Blessing or Curse: The Stabilizing Role of Remittances Compared with Other Financial Flows”, Journal of South Asian Development 11 1: pp. 38-66.
- Ajide, Kazeem, Ibrahim Raheem and Oluwatosin Adeniyi (2015) “Output Growth Volatility, Remittances and Institutions”, International Journal of Development Issues 14 (3): pp. 190-203.
- Bettin, Giulia, Andrea F. Presbitero, and Nikola Spatafora (2014) “Remittances and Vulnerability in Developing Countries”, IMF Working Paper no. 14/13. Washington, D.C.: International Monetary Fund.
- Bouoiyour, Jamal, Refk Selmi, and Amal Miftah (2016) “What Mitigates Economic Growth Volatility in Morocco? Remittances or FDI”, Journal of Economic Integration 31 (1): pp. 65-102.
- Bugamelli M., and F. Paterno (2009b) “Output Growth Volatility and Remittances”, Economica 78 (311): pp. 480–500.
- Chami, Ralph, Dalia Hakura, and Peter J. Montiel (2012) “Do Worker Remittances Reduce Output Volatility in Developing Countries?”, Journal of Globalization and Development 13 (1): pp. 1-23.
- Craigwell, Roland, Mahalia Jackman, and Winston Moore (2010) "Economic volatility and remittances", International Journal of Development Issues 9 (1): pp.25-42.
- De, Supriyo, Ergys Islamaj M. Ayhan Kose S. Reza Yousefi (2016) “Remittances over the Business Cycle: Theory and Evidence”, KNOMAD Working Paper 11. Global Knowledge Partnership on Migration and Development.
- Frankel, J. (2011) “Are Bilateral Remittances Counter-Cyclical?”, Open Economy Review 22 (1): pp. 1–16.
- Dridi, Jemma, Tunc Gursoy, Hector Perez-Saiz and Mounir Bari (2019) “The Impact of Remittances on Economic Activity: The Importance of Sectoral Linkages”, IMF Working Paper no. 19/175. Washington: International Monetary Fund.
- Tansel, Aysıt and Pınar Yaşar (2010) “Macroeconomic Impact of Remittances on Output Growth: Evidence from Turkey”, IZA Discussion Paper no. 5376. Germany: The Institute for the Study of Labor.

### Remittances, institutions, diasporas, and investment
- Abdih, Yasser, Ralph Chami, Jihad Dagher, and Peter Montiel (2012) “Remittances and Institutions: Are Remittances a Curse?”, World Development 40 (4): pp. 657-666.
- Chami, Ralph, Ekkehard Ernst, Connel Fullenkamp, and Anne Oeking (2018) “Is There a Remittance Trap?”, IMF Finance & Development 55 (3): pp. 44-47.
- Vaaler, P. M. (2013) “Diaspora Concentration and the Venture Investment Impact of Remittances”, Journal of International Management 19 (2013): pp. 26–46.
- Rapoport, H. and F. Docquier (2006) “The Economics of Migrants’ Remittances”, In S. Kolm and J. Mercier Ythier (eds.), Handbook on the Economics of Giving, Altruism and Reciprocity, vol. 2, Elsevier, pp. 1135–1198.
- Lueth, Erik, and Marta Ruiz-Arranz (2006) “A Gravity Model of Workers’ Remittances,” IMF Working Paper no. 06/290. Washington, D.C.: International Monetary Fund.
- Chami, Ralph, Connel Fullenkamp, and Samir Jahjah (2003) “Are Immigrant Flows a Source of Capital for Development?”, IMF Working Paper no. 03/189. Washington, D.C.: International Monetary Fund.

### Regional studies and spillovers
- Arizala, Francisco, Matthieu Bellon, Margaux MacDonald, Montfort Mlachila, and Mustafa Yenice (2018) “Regional Spillovers in Sub-Saharan Africa: Exploring Different Channels”, IMF Spillover Notes no. 18/01. Washington, D.C.: International Monetary Fund.
- Gonzalez-Garcia, Jesus, Ermal Hitaj, Montfort Mlachila, Arina Viseth, and Mustafa Yenice (2016) “Sub-Saharan African Migration: Patterns and Spillovers”, IMF Spillover Notes no. 9. Washington, D.C.: International Monetary Fund.
- Ben Naceur, Sami, Amr S. Hosny, and Gregory Hadjian (2019) "How to De-dollarize Financial Systems in the Caucasus and Central Asia?", Empirical Economics, 56 (6): pp. 1979-1999.
- Mitra, Pritha, Amr Hosny, Gohar Minasyan, Mark Fischer, and Gohar Abajyan (2016) “Avoiding the New Mediocre: Raising Long-Term Growth in the Middle East and Central Asia”, Middle East and Central Asia Department Paper no.16/01. Washington, D.C.: International Monetary Fund.

### Methodology, econometrics, and policy frameworks
- Baltagi, Badi H. (2008) “Econometric Analysis of Panel Data”, John Wiley & Sons Ltd.
- Breusch, T. S., and A. R. Pagan (1980) “The Lagrange Multiplier Test and its Applications to Model Specification in Econometrics”, Review of Economic Studies 47: pp.239-253.
- Hausman, J. A. (1978) “Specification Tests in Econometrics”, Econometrica 46 (6): pp.1251-1271.
- Swamy, P. A. V. B. and S. S. Arora (1972) “The Exact Finite Sample Properties of the Estimators of Coefficients in the Error Components Regression Models”, Econometrica 40 (2): pp. 261-275.
- Jack, W. and T. Suri (2014) “Risk Sharing and Transactions Costs: Evidence from Kenya’s Mobile Money Revolution”, American Economic Review 104 (1): pp. 183-223.
- Hosny, Amr S., Narayan K. Kishor and Mohsen Bahmani-Oskooee (2015) "Understanding the Dynamics of the Macroeconomic Trilemma", International Review of Applied Economics 29 (1): pp. 32-64.
- Hosny, Amr Sadek (2011) “Does GAFTA Defy Gravity?”, Middle Eastern Finance and Economics 10 (2011): pp. 146-157.
- Hyder, A., J. Behrman, and H. Kohler (2015) “Negative Economic Shocks and Child Schooling: Evidence from Rural Malawi”, Development Southern Africa 32 (4): pp. 458-476.
- Frankel, J. (2011) “Are Bilateral Remittances Counter-Cyclical?”, Open Economy Review 22 (1): pp. 1–16.
- Cooray, A. and D. Mallik (2013) “International Business Cycles and Remittance Flows”, The B.E. Journal of Macroeconomics, 13 (1): pp. 1–33.
- Amuedo-Dorantes, C. and Pozo, S. (2004) “Workers’ Remittances and the Real Exchange Rate: A Paradox of Gifts,” World Development 32 (8): pp.1407-1417.

### Policy guidance and institutional references
- IMF (2005) “Two Current Issues Facing Developing Countries”, Chapter 2 in World Economic Outlook, October 2005. Washington, D.C.: International Monetary Fund.
- IMF (2013) “Staff Guidance Note on the Application of the Joint Bank-Fund Debt Sustainability Framework for Low-Income Countries”, IMF Board Paper. Washington, D.C.: International Monetary Fund.
- Chami, Ralph, Thomas F. Cosimano, and Michael T. Gapen (2006) “Beware of Emigrants Bearing Gifts: Optimal Fiscal and Monetary Policy in the Presence of Remittances,” IMF Working Paper no. 06/61. Washington: International Monetary Fund.
- Barajas, Adolfo, Ralph Chami, Connel Fullenkamp, and Anjali Garg (2010) “The Global Financial Crisis and Workers’ Remittances to Africa: What’s the Damage?”, IMF Working Paper no. 10/24. Washington: International Monetary Fund.

*Source: wpiea2020015-print-pdf - REFERENCES*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020015-print-pdf.pdf_