## 1. Sample Composition and Data Sources

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---

### Sample composition
- Monthly remittance dataset compiled for a sample of 52 countries.
- Income classification within the sample: 6 high-income countries, 35 middle-income countries, and 11 low-income countries.
- Monthly bilateral (corridor) remittance flows available for 16 recipient countries, totaling 410 corridors.
- Time coverage: January 2018 through December 2020.
- The 52 countries in the sample accounted for 45 percent of worldwide remittances in 2019.

### Data definitions and sources
- Remittances definition follows the World Bank: sum of personal transfers and compensation of employees as compiled in national balance of payments data collected by the IMF, supplemented by national central banks and statistical institutes.
- Personal transfers: all current transfers in cash or in kind between resident and nonresident individuals, regardless of sender income or household relationship (WDI, 2020).
- Compensation of employees: income of cross-border, seasonal, and other short-term workers employed where they are nonresident, or residents employed by nonresident entities.
- The study covers formal transfers only; informal transfers, in-kind remittances, and hawala-type transactions are excluded.

### Data processing and coverage notes
- Source documents: detailed balance of payments and statistical notes published by national central banks and statistical institutes.
- Currency conversion: where necessary, converted into US dollars using the monthly average USD/local currency exchange rate from the IMF's International Financial Statistics (IFS) database or the relevant central banks.
- Practical adjustments: where countries reported workers' remittances instead of personal transfers, workers' remittances were used as a proxy; some countries did not report compensation of employees but these flows are noted as significantly smaller relative to personal transfers.
- Rationale: monthly high-frequency data were compiled to capture fast-changing dynamics during the COVID-19 pandemic and to enable corridor-level analysis not available in annual World Bank data.

### Stylized facts, hypotheses, and analytic focus
- Key stylized observation: many countries saw sustained increases in remittances during the pandemic, especially after May 2020.
- Three main hypotheses tested in the paper:
  - (i) Resilience in remittances driven by surge in COVID-19 cases in home countries as migrants remit more to cushion impacts.
  - (ii) Resilience reflects shift from informal to formal remittance channels triggered by border closures and travel restrictions.
  - (iii) Resilience due to increases supported by unprecedented fiscal stimulus in richer host countries.
- Main empirical approaches and variables highlighted:
  - Use of a novel monthly remittance dataset and corridor data to test drivers.
  - Local projection models à la Jordà (2005) to estimate dynamic responses.
  - Controls include proxies for economic activity, infection rates, stringency of containment measures, international air traffic (flight arrivals), and size of fiscal stimulus in host economies.
- Principal findings summarized in the introduction:
  - Remittances responded positively to COVID-19 infection rates in home countries, after controlling for economic activity, underscoring remittances' role as an automatic stabilizer.
  - Higher COVID-19 infection rates in host countries slowed remittance flows (corridor-level evidence).
  - More stringent virus containment measures in home countries appear to have dampened remittance flows, conditional on activity and infection rates.
  - Travel restrictions (measured by reductions in flight arrivals) boosted formal remittances, though the impact is short-lived.
  - Size of fiscal stimulus in host economies has positive spillover effects on remittances to migrants’ home countries through favorable impacts on host economic activity.

### Cross-country outcomes and key statistics (2020)
- Sample coverage: 52 countries.
- Number of countries with an increase in inward remittances in 2020: 39.
- Notable country outcomes:
  - Bhutan: almost threefold surge in remittances (partially driven by Bhutanese migrants returning home with their savings).
  - Comoros: positive surprise in remittance growth linked to migrants’ altruism amid the pandemic.
  - Gambia: exceptionally high remittances from official channels driven in part by reduction in private transfers through informal channels (which migrated to formal channels), remittances from the Gambian diaspora in response to COVID-19, and an improved remittance data recording system.
  - Bulgaria: 59 percent decline in inward remittances in 2020 (mainly reflecting deterioration of economic activity in the euro area).
  - Lebanon: remittances in nominal terms shrank by 20.1 percent in 2020; remittances-to-GDP ratio increased by 9.2 percent of GDP as the economy is estimated to have contracted by more than the decline in remittances (figures for Lebanon should be interpreted with caution due to unusually high uncertainty related to multiple exchange rate practices).
- Remittance-to-GDP ratio movements:
  - Bulgaria: drop in remittances-to-GDP ratio was mild, about 1 percentage point of GDP despite a 59 percent drop in remittances.
  - Bhutan: large increase in remittances translates into only a 2-percentage point increase in remittance-to-GDP ratio.
- Aggregate pattern: V-shaped intra-year recovery in remittances in 2020 (median year-on-year growth of cumulative remittances fell sharply early in the pandemic, bottomed out in May 2020, then recovered to finish the year above December 2019 levels).
- Fiscal context: total COVID-19-related fiscal measures in 2020 amounted to 9.14 percent of GDP in advanced economies and 5 percent of GDP for developing economies (IMF, 2021b).

### Empirical strategy and model specifics
- Focus period: Jan-2020 through Dec-2020.
- Main dependent variable: ∆ln(Rem) — year-on-year cumulative change in remittances for a given month since Jan 2020 (e.g., for June 2020, change in first six months vs same period in 2019, in percent).
- Key explanatory variables:
  - Covid: total COVID-19 cases per million population in the remittance-receiving country.
  - X: controls including total COVID-19 cases per million population in remittance-sending country; NO2 emissions per head (proxy for economic activity) in sending and receiving countries; US dollar/local currency exchange rate (average).
- Lags and horizons:
  - Number of lags, n, limited at 3.
  - Forecast horizon, h, constrained to 4- 5 months by the time dimension of the data.
- Functional forms and estimation:
  - All variables in logarithmic form unless otherwise indicated.
  - To deal with zero values, ln(1+x) is used.
  - Estimation method: local projection approach (LP) developed by Jordà (2005).
- Identification and controls:
  - Model includes time dummy (v), country-specific effect (u), cluster-robust errors at country level.
  - Interaction terms introduced for policy stringency (Oxford stringency index: values between 0 and 100).
  - For corridor analysis, migrant stock matrix (World Bank 2017) used to weight sending-country variables; corridor-specific fixed effects used and error term clustered at corridor level.

### Main empirical findings
- Remittances respond positively to home-country COVID-19 infection rates:
  - A 10 percent rise in COVID-19 cases per million population leads to a 0.3 percentage point increase in remittances on a cumulative basis after 5 months.
  - Interpretation: supports altruism/insurance motive; remittances acted as shock absorbers for vulnerable households beyond what home- and host-country economic impacts would imply.
- Role of host-country economic activity controls:
  - Controlling for NO2 emissions in host countries reduces the estimated positive response, indicating that reduced economic activity in host countries exerts downward pressure that partially offsets migrants’ increased transfers.
- Containment measures and stringency:
  - Oxford stringency index percentiles used in interaction analysis: 10th percentile = 28; 90th percentile = 87.
  - Interaction results: more pronounced drop in remittances a month after a COVID-19 shock in countries with stricter containment measures, though difference not statistically significant in that conditional IRF.
  - Unconditional response of remittances to stricter containment measures in the home country is clearly negative and statistically significant after controlling for COVID-19 infection rate.
- Air travel and the informal channel hypothesis:
  - Introducing year-on-year monthly change in arrival flights shows air travel restrictions have a positive and significant impact on formal remittance flows.
  - Illustrative magnitude: a complete shutdown of passenger air traffic (a 100 percent drop) would lead to an increase in formal remittances inflows by about 10 percentage points within the first two months, after which the impact phases out gradually.
- Corridor-level evidence (16 receiving countries; 410 corridors):
  - Corridor-based LP confirms positive reaction of remittances to home-country COVID-19 shocks.
  - Magnitude and timing differ: remittances rise within the first month to reach a peak of about 0.4 percentage point increase following a 10 percent surge in total COVID-19 cases per million, then decline somewhat before rebounding (post-initial changes not statistically significant).
  - Remittances decline with COVID-19 shocks in the host country; stringency in host economies is negatively associated with remittances to the home economy.

### Robustness and additional tests
- Using remittance-to-GDP ratio as dependent variable yields similar results, confirming altruism/insurance hypothesis.
- Using new COVID-19 cases per million population instead of total cases does not qualitatively alter main findings.
- Tests for asymmetry in response to positive versus negative changes in COVID-19 infections:
  - Positive change in infection rate leads to an increase in remittance flows; a decline in infection rate reduces remittances.
  - Difference between coefficients is not statistically significant across horizons—suggesting short-term response rather than structural increase.
- Tests on informal channel hypothesis and flight arrivals indicate air travel restrictions plausibly shifted informal flows into formal statistics.

### Interpretation and policy implications
- Remittances provided a critical complementary social safety net in 2020, particularly in developing countries where fiscal support was constrained relative to advanced economies.
- The positive association between home-country COVID-19 infection rates and remittances supports the view that migrants increased transfers out of altruism or insurance motives, cushioning shocks for vulnerable households.
- Shifts from informal to formal channels (due to border closures and air travel disruptions) likely improved capture of flows in official statistics, amplifying recorded remittance resilience.
- However, the resilience may be temporary:
  - Migrants could be frontloading remittances by drawing on savings, affecting ability to sustain the trend.
  - If the pandemic impact becomes protracted, remittances could decline.
- Policy relevance:
  - In the short run, supporting formal channels and improving remittance data recording can enhance visibility of private transfers that act as safety nets.
  - Consideration of the interaction between containment measures and remittance channels is important for anticipating disruptions to informal transfer mechanisms.

### Conclusions

### Summary of objectives and approach
- Investigated whether the deep global recession brought about by the COVID-19 pandemic caused a sharp decline in remittances.
- Explored competing hypotheses on drivers of remittances in the context of the pandemic.
- Built a unique intra-year monthly dataset and used corridor-level data and impulse response functions to observe remittance dynamics.

### Key empirical findings
- After an initial fall, remittances acted as an automatic stabilizer during the pandemic.
- Remittances rose in most countries in the sample, many of which are developing economies.
- Main drivers and channels identified:
  - Urgent need for migrants to provide assistance to families (altruism or insurance motive) outweighed downward pressure from recession and containment measures in host economies.
  - Shift from informal remittance channels to formal channels contributed to observed increases.
  - Fiscal stimulus in advanced economies supported remittances, mainly through its positive impact on growth in host economies.

### Fiscal stimulus and remittance patterns (corridor evidence)
- Constructed a dummy variable equal to 1 if the size of COVID-19-related fiscal measures in remittance-sending countries is above the sample median, 0 otherwise.
- Interaction of that dummy with the COVID-19 infection rate in the home economy used to test conditional effects on remittance inflows.
- Main empirical results:
  - Figure 11 evidence suggests fiscal stimulus measures have a positive effect on remittance flows, but the effect tends to decline over time.
  - The difference in remittance response across corridors becomes statistically non-significant after controlling for the incidence of COVID-19 and NO2 emissions in the host economy.
  - Interpretation: the primary channel for fiscal stimulus supporting remittances was cushioning adverse economic and health impacts in host economies (i.e., supporting growth), rather than direct transfers to migrants.
- Measurement robustness:
  - Addressed potential measurement issues (announced vs implemented measures) by using the change in the government spending ratio to GDP in 2020 relative to pre-COVID level; results support the hypothesis that larger fiscal responses enabled migrants to send more money home.
- Caveat noted in the analysis:
  - Direct support to households that was part of fiscal stimulus may not have reached many migrants (especially undocumented migrants), so migrants may have benefited mainly indirectly through broader economic support that saved jobs.

### Policy implications
- Understanding remittance behavior during the pandemic informs:
  - Options to address large external financing needs stemming from the global crisis.
  - Appropriate fiscal, monetary, and financial sector policies in response to remittance flows.
  - Assessment of poverty impacts and design of policies to support low-income households that rely on remittances.
- Policy priorities highlighted:
  - Recognize remittances’ countercyclical and stabilizing role when designing macroeconomic and social policies.
  - Address the persistently high cost of remittances to facilitate flows to many countries.

### Durability and outlook
- While evidence shows increases in remittances in most countries so far, some increases are attributable to temporary factors.
- Future dynamics depend on how the pandemic is brought under control and the subsequent impetus to economic activity in various countries.

### Selected sample and data notes (from annexes)
- Built on central bank and national statistics corridor data (Annex 1 lists multiple country data sources).
- Appendix 2 (data availability) indicates varied availability of "Personal transfers", "Workers' remittances", and "Compensation of employees" across countries.
- Appendix 2 summary statistics:
  - Change in the cumulative remittances: 524 observations; Mean -0.002; Standard deviation 0.821; Minimum -4.161; Maximum 1.135
  - Change in the cumulative remittances to GDP: 524 observations; Mean 0.005; Standard deviation 0.802; Minimum -4.182; Maximum 1.135
  - New COVID-19 cases per million population: 488 observations; Mean 1,246; Standard deviation 3,011; Minimum 0; Maximum 25,557
  - Total COVID-19 cases per million population: 480 observations; Mean 3,943; Standard deviation 7,868; Minimum 0; Maximum 57,009
  - Change in the NO2 emissions per head: 554 observations; Mean 0.005; Standard deviation 0.197; Minimum -2.399; Maximum 1.416
  - Change in the USD/LCU exchange rate: 521 observations; Mean 0.001; Standard deviation 0.024; Minimum -0.083; Maximum 0.158
  - Stringency index: 456 observations; Mean 59.493; Standard deviation 22.162; Minimum 0; Maximum 100.000
  - International flight arrivals: 576 observations; Mean 3,358; Standard deviation 8,022; Minimum 0; Maximum 59,392
- Appendix 3 correlation matrix example entries:
  - Correlation between "Change in the cumulative remittances" and "Change in the cumulative remittances to GDP": 0.9981* (P-value (0.000))
  - Stringency index correlations: with "Change in the cumulative remittances" 0.2302* (P-value (0.000)); with "Change in the cumulative remittances to GDP" 0.2265* (P-value (0.000))

*Source: wpiea2021186-print-pdf*

### 1. Sample Composition and Data Sources _______________________________________26

### 1. Sample Composition and Data Sources

### Sample composition
- Monthly remittance dataset compiled for a sample of 52 countries.
- Income classification within the sample: 6 high-income countries, 35 middle-income countries, and 11 low-income countries.
- Monthly bilateral (corridor) remittance flows available for 16 recipient countries, totaling 410 corridors.
- Time coverage: January 2018 through December 2020.
- The 52 countries in the sample accounted for 45 percent of worldwide remittances in 2019.

### Data definitions and sources
- Remittances definition follows the World Bank: sum of personal transfers and compensation of employees as compiled in national balance of payments data collected by the IMF, supplemented by national central banks and statistical institutes.
- Personal transfers: all current transfers in cash or in kind between resident and nonresident individuals, regardless of sender income or household relationship (WDI, 2020).
- Compensation of employees: income of cross-border, seasonal, and other short-term workers employed where they are nonresident, or residents employed by nonresident entities.
- The study covers formal transfers only; informal transfers, in-kind remittances, and hawala-type transactions are excluded.

### Data processing and coverage notes
- Source documents: detailed balance of payments and statistical notes published by national central banks and statistical institutes.
- Currency conversion: where necessary, converted into US dollars using the monthly average USD/local currency exchange rate from the IMF's International Financial Statistics (IFS) database or the relevant central banks.
- Practical adjustments: where countries reported workers' remittances instead of personal transfers, workers' remittances were used as a proxy; some countries did not report compensation of employees but these flows are noted as significantly smaller relative to personal transfers.
- Rationale: monthly high-frequency data were compiled to capture fast-changing dynamics during the COVID-19 pandemic and to enable corridor-level analysis not available in annual World Bank data.

### Stylized facts, hypotheses, and analytic focus
- Key stylized observation: many countries saw sustained increases in remittances during the pandemic, especially after May 2020.
- Three main hypotheses tested in the paper:
  - (i) Resilience in remittances driven by surge in COVID-19 cases in home countries as migrants remit more to cushion impacts.
  - (ii) Resilience reflects shift from informal to formal remittance channels triggered by border closures and travel restrictions.
  - (iii) Resilience due to increases supported by unprecedented fiscal stimulus in richer host countries.
- Main empirical approaches and variables highlighted:
  - Use of a novel monthly remittance dataset and corridor data to test drivers.
  - Local projection models à la Jordà (2005) to estimate dynamic responses.
  - Controls include proxies for economic activity, infection rates, stringency of containment measures, international air traffic (flight arrivals), and size of fiscal stimulus in host economies.
- Principal findings summarized in the introduction:
  - Remittances responded positively to COVID-19 infection rates in home countries, after controlling for economic activity, underscoring remittances' role as an automatic stabilizer.
  - Higher COVID-19 infection rates in host countries slowed remittance flows (corridor-level evidence).
  - More stringent virus containment measures in home countries appear to have dampened remittance flows, conditional on activity and infection rates.
  - Travel restrictions (measured by reductions in flight arrivals) boosted formal remittances, though the impact is short-lived.
  - Size of fiscal stimulus in host economies has positive spillover effects on remittances to migrants’ home countries through favorable impacts on host economic activity.

*Source: wpiea2021186-print-pdf - 1. Sample Composition and Data Sources _______________________________________26*

### 2020. Defying the odds at the time when the COVID-19 pandemic unfolded, formal remittances

### 2020. Defying the odds at the time when the COVID-19 pandemic unfolded, formal remittances surprisingly rose in most of the countries in the sample, including developing economies.

### Cross-country outcomes and key statistics
- Sample coverage: 52 countries.
- Number of countries with an increase in inward remittances in 2020: 39.
- Notable country outcomes:
  - Bhutan: almost threefold surge in remittances (partially driven by Bhutanese migrants returning home with their savings).
  - Comoros: positive surprise in remittance growth linked to migrants’ altruism amid the pandemic.
  - Gambia: exceptionally high remittances from official channels driven in part by reduction in private transfers through informal channels (which migrated to formal channels), remittances from the Gambian diaspora in response to COVID-19, and an improved remittance data recording system.
  - Bulgaria: 59 percent decline in inward remittances in 2020 (mainly reflecting deterioration of economic activity in the euro area).
  - Lebanon: remittances in nominal terms shrank by 20.1 percent in 2020; remittances-to-GDP ratio increased by 9.2 percent of GDP as the economy is estimated to have contracted by more than the decline in remittances (figures for Lebanon should be interpreted with caution due to unusually high uncertainty related to multiple exchange rate practices).
- Remittance-to-GDP ratio movements:
  - Bulgaria: drop in remittances-to-GDP ratio was mild, about 1 percentage point of GDP despite a 59 percent drop in remittances.
  - Bhutan: large increase in remittances translates into only a 2-percentage point increase in remittance-to-GDP ratio.
- Aggregate pattern: V-shaped intra-year recovery in remittances in 2020 (median year-on-year growth of cumulative remittances fell sharply early in the pandemic, bottomed out in May 2020, then recovered to finish the year above December 2019 levels).
- Fiscal context: total COVID-19-related fiscal measures in 2020 amounted to 9.14 percent of GDP in advanced economies and 5 percent of GDP for developing economies (IMF, 2021b).

### Empirical strategy and model specifics
- Focus period: Jan-2020 through Dec-2020.
- Main dependent variable: ∆ln(Rem) — year-on-year cumulative change in remittances for a given month since Jan 2020 (e.g., for June 2020, change in first six months vs same period in 2019, in percent).
- Key explanatory variables:
  - Covid: total COVID-19 cases per million population in the remittance-receiving country.
  - X: controls including total COVID-19 cases per million population in remittance-sending country; NO2 emissions per head (proxy for economic activity) in sending and receiving countries; US dollar/local currency exchange rate (average).
- Lags and horizons:
  - Number of lags, n, limited at 3.
  - Forecast horizon, h, constrained to 4- 5 months by the time dimension of the data.
- Functional forms and estimation:
  - All variables in logarithmic form unless otherwise indicated.
  - To deal with zero values, ln(1+x) is used.
  - Estimation method: local projection approach (LP) developed by Jordà (2005).
- Identification and controls:
  - Model includes time dummy (v), country-specific effect (u), cluster-robust errors at country level.
  - Interaction terms introduced for policy stringency (Oxford stringency index: values between 0 and 100).
  - For corridor analysis, migrant stock matrix (World Bank 2017) used to weight sending-country variables; corridor-specific fixed effects used and error term clustered at corridor level.

### Main empirical findings
- Remittances respond positively to home-country COVID-19 infection rates:
  - A 10 percent rise in COVID-19 cases per million population leads to a 0.3 percentage point increase in remittances on a cumulative basis after 5 months.
  - Interpretation: supports altruism/insurance motive; remittances acted as shock absorbers for vulnerable households beyond what home- and host-country economic impacts would imply.
- Role of host-country economic activity controls:
  - Controlling for NO2 emissions in host countries reduces the estimated positive response, indicating that reduced economic activity in host countries exerts downward pressure that partially offsets migrants’ increased transfers.
- Containment measures and stringency:
  - Oxford stringency index percentiles used in interaction analysis: 10th percentile = 28; 90th percentile = 87.
  - Interaction results: more pronounced drop in remittances a month after a COVID-19 shock in countries with stricter containment measures, though difference not statistically significant in that conditional IRF.
  - Unconditional response of remittances to stricter containment measures in the home country is clearly negative and statistically significant after controlling for COVID-19 infection rate.
- Air travel and the informal channel hypothesis:
  - Introducing year-on-year monthly change in arrival flights shows air travel restrictions have a positive and significant impact on formal remittance flows.
  - Illustrative magnitude: a complete shutdown of passenger air traffic (a 100 percent drop) would lead to an increase in formal remittances inflows by about 10 percentage points within the first two months, after which the impact phases out gradually. (Interpretation: flows may have shifted from informal to formal channels; from recipients’ perspective flows may not necessarily increase, but are better captured in official statistics.)
- Corridor-level evidence (16 receiving countries; 410 corridors):
  - Corridor-based LP confirms positive reaction of remittances to home-country COVID-19 shocks.
  - Magnitude and timing differ: remittances rise within the first month to reach a peak of about 0.4 percentage point increase following a 10 percent surge in total COVID-19 cases per million, then decline somewhat before rebounding (post-initial changes not statistically significant).
  - Remittances decline with COVID-19 shocks in the host country; stringency in host economies is negatively associated with remittances to the home economy.

### Robustness and additional tests
- Using remittance-to-GDP ratio as dependent variable yields similar results, confirming altruism/insurance hypothesis.
- Using new COVID-19 cases per million population instead of total cases does not qualitatively alter main findings.
- Tests for asymmetry in response to positive versus negative changes in COVID-19 infections:
  - Positive change in infection rate leads to an increase in remittance flows; a decline in infection rate reduces remittances.
  - Difference between coefficients is not statistically significant across horizons—suggesting short-term response rather than structural increase.
- Tests on informal channel hypothesis and flight arrivals indicate air travel restrictions plausibly shifted informal flows into formal statistics.

### Interpretation and policy implications
- Remittances provided a critical complementary social safety net in 2020, particularly in developing countries where fiscal support was constrained relative to advanced economies.
- The positive association between home-country COVID-19 infection rates and remittances supports the view that migrants increased transfers out of altruism or insurance motives, cushioning shocks for vulnerable households.
- Shifts from informal to formal channels (due to border closures and air travel disruptions) likely improved capture of flows in official statistics, amplifying recorded remittance resilience.
- However, the resilience may be temporary:
  - Migrants could be frontloading remittances by drawing on savings, affecting ability to sustain the trend.
  - If the pandemic impact becomes protracted, remittances could decline.
- Policy relevance:
  - In the short run, supporting formal channels and improving remittance data recording can enhance visibility of private transfers that act as safety nets.
  - Consideration of the interaction between containment measures and remittance channels is important for anticipating disruptions to informal transfer mechanisms.

*Source: Authors’ calculations and analysis from the provided IMF working paper excerpt.*

### conclusions

### Conclusions (wpiea2021186-print-pdf - conclusions)

### Summary of objectives and approach
- Investigated whether the deep global recession brought about by the COVID-19 pandemic caused a sharp decline in remittances.
- Explored competing hypotheses on drivers of remittances in the context of the pandemic.
- Built a unique intra-year monthly dataset and used corridor-level data and impulse response functions to observe remittance dynamics.

### Key empirical findings
- After an initial fall, remittances acted as an automatic stabilizer during the pandemic.
- Remittances rose in most countries in the sample, many of which are developing economies.
- Main drivers and channels identified:
  - Urgent need for migrants to provide assistance to families (altruism or insurance motive) outweighed downward pressure from recession and containment measures in host economies.
  - Shift from informal remittance channels to formal channels contributed to observed increases.
  - Fiscal stimulus in advanced economies supported remittances, mainly through its positive impact on growth in host economies.

### Fiscal stimulus and remittance patterns (corridor evidence)
- Constructed a dummy variable equal to 1 if the size of COVID-19-related fiscal measures in remittance-sending countries is above the sample median, 0 otherwise.
- Interaction of that dummy with the COVID-19 infection rate in the home economy used to test conditional effects on remittance inflows.
- Main empirical results:
  - Figure 11 evidence suggests fiscal stimulus measures have a positive effect on remittance flows, but the effect tends to decline over time.
  - The difference in remittance response across corridors becomes statistically non-significant after controlling for the incidence of COVID-19 and NO2 emissions in the host economy.
  - Interpretation: the primary channel for fiscal stimulus supporting remittances was cushioning adverse economic and health impacts in host economies (i.e., supporting growth), rather than direct transfers to migrants.
- Measurement robustness:
  - Addressed potential measurement issues (announced vs implemented measures) by using the change in the government spending ratio to GDP in 2020 relative to pre-COVID level; results support the hypothesis that larger fiscal responses enabled migrants to send more money home.
- Caveat noted in the analysis:
  - Direct support to households that was part of fiscal stimulus may not have reached many migrants (especially undocumented migrants), so migrants may have benefited mainly indirectly through broader economic support that saved jobs.

### Policy implications
- Understanding remittance behavior during the pandemic informs:
  - Options to address large external financing needs stemming from the global crisis.
  - Appropriate fiscal, monetary, and financial sector policies in response to remittance flows.
  - Assessment of poverty impacts and design of policies to support low-income households that rely on remittances.
- Policy priorities highlighted:
  - Recognize remittances’ countercyclical and stabilizing role when designing macroeconomic and social policies.
  - Address the persistently high cost of remittances to facilitate flows to many countries.

### Durability and outlook
- While evidence shows increases in remittances in most countries so far, some increases are attributable to temporary factors.
- Future dynamics depend on how the pandemic is brought under control and the subsequent impetus to economic activity in various countries.

### Selected sample and data notes (from annexes)
- Built on central bank and national statistics corridor data (Annex 1 lists multiple country data sources).
- Appendix 2 (data availability) indicates varied availability of "Personal transfers", "Workers' remittances", and "Compensation of employees" across countries.
- Appendix 2 summary statistics (preserve numbers exactly as presented):
  - Number of observations and distributional statistics:
    - Change in the cumulative remittances: 524 observations; Mean -0.002; Standard deviation 0.821; Minimum -4.161; Maximum 1.135
    - Change in the cumulative remittances to GDP: 524 observations; Mean 0.005; Standard deviation 0.802; Minimum -4.182; Maximum 1.135
    - New COVID-19 cases per million population: 488 observations; Mean 1,246; Standard deviation 3,011; Minimum 0; Maximum 25,557
    - Total COVID-19 cases per million population: 480 observations; Mean 3,943; Standard deviation 7,868; Minimum 0; Maximum 57,009
    - Change in the NO2 emissions per head: 554 observations; Mean 0.005; Standard deviation 0.197; Minimum -2.399; Maximum 1.416
    - Change in the USD/LCU exchange rate: 521 observations; Mean 0.001; Standard deviation 0.024; Minimum -0.083; Maximum 0.158
    - Stringency index: 456 observations; Mean 59.493; Standard deviation 22.162; Minimum 0; Maximum 100.000
    - International flight arrivals: 576 observations; Mean 3,358; Standard deviation 8,022; Minimum 0; Maximum 59,392
- Appendix 3 correlation matrix reports correlations and p-values; example notable entries (preserved exactly):
  - Correlation between "Change in the cumulative remittances" and "Change in the cumulative remittances to GDP": 0.9981* (P-value (0.000))
  - Stringency index correlations: with "Change in the cumulative remittances" 0.2302* (P-value (0.000)); with "Change in the cumulative remittances to GDP" 0.2265* (P-value (0.000))

*Source: wpiea2021186-print-pdf - conclusions*

---


_Source: https://www.imf.org/-/media/files/publications/wp/2021/english/wpiea2021186-print-pdf.pdf_