## 1. Dimensions and Indicators of Financial Inclusion

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### Introduction and background
- Remittance inflows to low and middle-income countries (LMICs) reached 491 USD billion in 2018.
- Remittances are the second most important source of foreign finance for these countries, ranking second to FDI inflows since 1996, ahead of official aid and portfolio inflows.
- For low-income countries (LICs), remittance inflows have surpassed FDIs since 2015.
- Literature links remittances to growth, poverty reduction, education, labor supply, health, entrepreneurship, and financial sector development.
- Financial inclusion levels (percent with bank accounts) by region:
  - East Asia: 55 percent
  - Latin America: 39 percent
  - Eastern Europe: 35 percent
  - South East Asia: 33 percent
  - Sub Saharan Africa: 25 percent

### Methodology and data
- Empirical framework:
  - Baseline dynamic panel with system GMM.
  - Baseline equation: FI_i,t = α + β Remit_i,t + δ X_i,t + μ_i + λ_t + ε_i,t
  - Dynamic specification: FI_i,t = α FI_i,t-1 + β Remit_i,t + δ X_i,t + μ_i + λ_t + ε_i,t
  - Estimation uses system GMM (Arellano and Bover; Blundell and Bond); tests include AR(2) and Hansen overidentifying restrictions.
- Sample: panel of 187 countries over 2004–15.
- Financial inclusion (FI) measures:
  - Access: Branches = log(number of commercial bank branches per 100,000 adults), ATMs = number of ATMs per 100,000 adults.
  - Usage: Deposits = log(deposit accounts with commercial banks per 1,000 adults), Borrowers = log(number of borrowers at commercial banks per 1,000 adults).
  - Composite: Financial Inclusion Index (FII) constructed using weighted normalization and coefficient-of-variation weights; Borrowers excluded from FII construction in some computations because of data availability.
- Remittances: measured as remittances inflows to GDP (percent of GDP), World Development Indicators (2004–15 averages).
- Controls X include: LGDPPC (log GDP per capita, 2010 US $), inflation (annual % change CPI), Trade = (X+M)/GDP, and kaopen (Chinn and Ito capital account openness index).

### Main empirical findings
- Baseline (linear) GMM, full sample:
  - Remittances coefficient positive but statistically insignificant for Branches (0.0007) and ATMs (+0.0032); Deposits and Borrowers positive but insignificant; FII insignificant.
  - Financial development (FD = total credit to private sector % of GDP) positive and significant (+0.4930, significant at 1%).
  - Inflation negative and significant; Trade positive and significant; kaopen generally positive but often insignificant.
- Subsample by income (linear):
  - LMICs: remittances coefficients very low and insignificant for Branches (+0.0003), Deposits (+0.0001), Borrowers (+0.001); FII positive but insignificant.
  - HICs: remittances coefficients mostly negative or insignificant (example: Branches -0.0036).
- Nonlinear (quadratic) specification (Remit and Remit^2):
  - Reveals a U-shaped (convex) relationship between remittances and financial inclusion for the full sample and many subsamples.
  - Full-sample derived thresholds (Remit* = β / (2γ)) vary by FI proxy between 11.3 percent and 23.6 percent of GDP.

### Estimated thresholds and selected coefficients
- Table 6 (GMM estimates of the “U” relationship, 2004–15) thresholds:
  - Branches threshold: 23.6%
  - ATMs threshold: 20.8%
  - Deposits threshold: 11.3%
  - Borrowers threshold: 22.4%
  - FII threshold: 12.2%
- Table 4 (Whole sample GMM, 2004–15) examples:
  - Remit coefficient on FD (column 6): 0.0031*** (t-statistic 2.85)
  - LGDPPC coefficients:
    - ATMs: 0.0799*** (t-statistic 3.35)
    - Deposits: 0.1231** (t-statistic 2.34)
  - Inflation (Inf) significant negative examples:
    - Branches: -0.0259*** (t-statistic -3.00)
    - Borrowers: -0.0735*** (t-statistic -6.66)
- Table 7 (U-relationship by income level), LMIC thresholds:
  - Branches: 15.7%
  - ATMs: 21.3%
  - Deposits: 13.3%
  - Borrowers: 22.0%
  - FII: 13.0%

### Bank balance-sheet patterns in LMICs by remittance intensity (Table 8)
- Time, saving and foreign currency deposits (% GDP), Demand deposits (% GDP), Total Deposits (% GDP), and Total credit (% GDP) at remittance cutoffs:
  - Remit ≤ 1%: Time/saving/foreign currency deposits 52.63; Demand deposits 56.84; Total Deposits 110.10; Total credit 31.51
  - Remit ≤ 5%: Time/saving/... 35.67; Demand 31.49; Total Deposits 66.77; Total credit 28.77
  - Remit ≤ 10%: Time/saving/... 33.55; Demand 27.61; Total Deposits 61.05; Total credit 29.98
  - Remit ≤ 11%: 33.28; 27.29; 60.46; 29.86
  - Remit ≤ 12%: 33.01; 26.94; 59.83; 29.76
  - Remit ≤ 13%: 33.07; 26.64; 59.61; 29.94
  - Remit ≤ 14%: 33.13; 26.47; 59.53; 29.95
  - Remit ≤ 15%: 33.38; 26.30; 59.66; 30.13
  - Remit ≤ 20%: 35.91; 25.35; 61.29; 30.97
  - Remit ≤ 25%: 37.15; 24.79; 61.96; 31.20
  - Remit ≤ 30%: 37.28; 24.54; 61.83; 31.26
- Interpretation: Time, saving and foreign currency deposits decline with remittances up to about 12 percent; starting at 13 percent household deposit behavior shifts toward longer-maturity deposits, enabling banks to lend more — consistent with the U-shaped relationship and remittance threshold effect.

### Heterogeneity by financial sector characteristics and regions
- Financial development (Table 9):
  - Countries split by median CPY (median = 26.8 percent) into LFDCs and MFDCs.
  - Non-monotonic relationship more evident in MFDCs; example HFDCs, Branches: Remit -0.0071* (t-statistic -1.98), Remit^2 0.0001* (t-statistic 1.69).
  - HFDCs, FII: Remit -0.0364*** (t-statistic -3.06), Remit^2 0.0013* (t-statistic 3.06).
- Financial stability (Table 10):
  - Countries split by Z-score median = 9.62 into LFSCs and MFSCs.
  - U-shaped relationship tends to be confirmed in MFSCs; examples:
    - MFSCs, ATMs: Remit -0.0119** (t-statistic -2.53); Remit^2 0.0002* (t-statistic 2.20)
    - MFSCs, Deposits: Remit -0.0227** (t-statistic -2.27); Remit^2 0.0005* (t-statistic 1.93)
- Banking sector efficiency (Table 11):
  - Countries split by interest rate spread median = 6.53 into LEFS and MEFS.
  - Nonlinear relationship stronger and significant in MEFS; examples:
    - MEFSs, Branches: Remit -0.0263** (t-statistic -2.44); Remit^2 0.0012** (t-statistic 2.54)
    - MEFSs, Deposits: Remit -0.0194** (t-statistic -2.05); Remit^2 0.0004* (t-statistic 1.89)
  - Thresholds lower in MEFS (example thresholds: Branches Remit* = 10.9 percent; Borrowers Remit* = 14.3 percent in MEFS in summary discussion).
- Regional contribution:
  - South Asia (SA) and East Asia & Pacific (EAP) are important contributors to the non-linear relationship; removing these regions reduces evidence of nonlinearity.
  - Latin America & Caribbean (LAC) shows no consistent pattern; Sub-Saharan Africa (SSA) appears not to strongly influence the non-linear relationship.
- Fragile states (pooled OLS due to data limitations):
  - Using IMF and World Bank fragile-state definitions, pooled OLS indicates remittances have an overall positive effect on financial inclusion for fragile states across most FI indicators.
  - Borrowers proxy shows negative sign with IMF approach and insignificant effect with World Bank approach, suggesting banking weaknesses may limit conversion of deposits into loans in some fragile states.

### Interpretations and mechanisms
- U-shaped intuition:
  - At low remittance-to-GDP levels, remittances are mainly used for consumption by liquidity-constrained households and may substitute for formal credit (disintermediation), yielding little or negative effects on FI.
  - Beyond a threshold remittances are more likely to be saved via formal channels (time/savings/foreign-currency deposits), increasing loanable funds and prompting banks to expand services and reach — enhancing financial inclusion.
- Threshold heterogeneity:
  - Threshold levels vary by FI proxy, country income level, financial development, financial stability, banking sector efficiency, and region.
  - Efficient, stable, and more financially developed systems exhibit U-shaped patterns at lower remittance thresholds.

### Descriptive statistics and correlations (selected)
- Table 2 highlights:
  - Branches: Obs. 2108; Mean 19.54; Std. Dev 26.69; Min 0.12; Max 289.83
  - ATMs: Obs. 1947; Mean 41.40; Std. Dev 44.32; Min 0; Max 290.14
  - Deposits: Obs. 1205; Mean 1110.86; Std. Dev 1195.67; Min 1.25; Max 12.420.85
  - Borrowers: Obs. 973; Mean 178.00; Std. Dev 210.70; Min 0.01; Max 1156.04
  - FII: Obs. 1107; Mean 0,10; Std. Dev 0.09; Min 0; Max 0.69
  - Remittances: Obs. 2060; Mean 4.71; Std. Dev 6.85; Min 0.00; Max 49.28
  - Log GDP per capita: Obs. 2197; Mean 8.45; Std. Dev 1.49; Min 5.32; Max 11.60
  - Inflation: Obs. 2077; Mean 0.18; Std. Dev 5.36; Min -0.35; Max 244.11
  - Trade: Obs. 2121; Mean 0.93; Std. Dev 0.54; Min 0.00; Max 4.55
  - Kaopen: Obs. 1877; Mean 4.27; Std. Dev 1.60; Min -1.89; Max 2.38
- Table 3 (Correlation matrix) selected entries:
  - Branches–ATMs: 0.52
  - ATMs–Deposits: 0.69
  - Deposits–Borrowers: 0.75
  - FI Index correlations: Branches 0.77; ATMs 0.88; Deposits 0.85; Borrowers 0.73
  - Remittances correlations with financial variables small/negative: Remittances–Branches -0.01; Remittances–ATMs -0.17; Remittances–Deposits -0.03; Remittances–Borrowers -0.13

### Key numeric findings (preserved exactly)
- Remittance inflows to LMICs: 491 USD billion in 2018.
- Bank-account ownership by region: East Asia 55 percent; Latin America 39 percent; Eastern Europe 35 percent; South East Asia 33 percent; Sub Saharan Africa 25 percent.
- Full-sample quadratic-model thresholds vary between 11.3 percent and 23.6 percent.
- LMIC-specific thresholds: Branches 15.7 percent; ATMs 21.3 percent; Deposits 13.28 percent; Borrowers 22 percent; FII 13.0 percent.
- Example remittance-to-GDP recipients (2004–18 averages): Tajikistan 35.0 percent; Bermuda 31.6 percent; Tonga 28.2 percent; Kyrgyz Republic 22.9 percent.
- Median CPY (credit to private sector % of GDP) = 26.8 percent.
- Z-score median = 9.62.
- Interest rate spread median = 6.53.

### Policy-relevant implications
- Remittances can support financial inclusion, but positive effects materialize only beyond country- and context-specific remittance thresholds.
- Strengthening banking sector efficiency reduces the remittance threshold at which positive inclusion effects arise; policy actions that improve banking efficiency (narrow spreads, better intermediation) can amplify remittances’ inclusion benefits.
- Enhancing financial stability and financial development (higher CPY) supports conversion of remittance flows into inclusive financial services at lower remittance levels.
- Policies to encourage formal channeling of remittances (reducing transaction costs, improving deposit products targeted to remittance recipients) may accelerate the shift from consumption/demand deposits toward time/saving deposits and credit expansion.
- Targeted regional and country approaches matter given heterogeneity (example prominence of SA and EAP; fragility, financial development, stability, bank efficiency differences).

*Source: wpiea2020066-print-pdf — https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020066-print-pdf.pdf*

### 1. Dimensions and Indicators of Financial Inclusion _______________________________23

### 1. Dimensions and Indicators of Financial Inclusion

### Major sections and headings (as listed)
- 2. Descriptive Statistics ______________________________________________________23
- 3.Correlation Matrix _______________________________________________________24
- 3
- 4. Remittances and Financial Inclusion, GMM Dynamic Panel Estimates for the Whole Sample: 2004–15 _______________________________________________________25
- 5. Remittances and Financial inclusion, GMM Dynamic Panel Estimates: 2004–15 ______26
- 6. Remittances and Financial Inclusion, GMM Dynamic Panel Estimates of the _________27
- 7. Remittances and Financial Inclusion According to Income Level, GMM Dynamic Panel Estimates of the ___________________________________________________28
- 8. Deposits and Credits in Bank Balance Sheets of LMICS __________________________29
- Remittances and Financial Inclusion, the Role of Financial Stability, GMM Dynamic Panel Estimates: 2004–2015 ___________________________________________________30
- Remittances and Financial Inclusion, the Role of Financial Stability, GMM Dynamic Panel Estimates: 2004–2015 (Continued) _________________________________________31
- Remittances and Financial Inclusion in LIMCs, the Role of Financial Sector Efficiency— _32

### Appendices and supplementary items
- 12. Variable Definitions _____________________________________________________33
- Appendices 
- Appendices _______________________________________________________________23
- References

*Source: wpiea2020066-print-pdf - 1. Dimensions and Indicators of Financial Inclusion — https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020066-print-pdf.pdf*

### References ________________________________________________________________37

### References — wpiea2020066-print-pdf

### Introduction and background
- Remittance inflows to low and middle-income countries (LMICs) reached 491 USD billion in 2018.
- Remittances are the second most important source of foreign finance for these countries, ranking second to FDI inflows since 1996, ahead of official aid and portfolio inflows.
- For low-income countries (LICs), remittance inflows have surpassed FDIs since 2015.
- Literature highlights remittances’ links to growth, poverty reduction, education, labor supply, health, entrepreneurship, and financial sector development.
- Financial inclusion remains low in many regions (percent with bank accounts): 55 percent in East Asia, 39 percent in Latin America, 35 percent in Eastern Europe, 33 percent in South East Asia, and 25 percent in Sub Saharan Africa.

### Methodology and data
- Empirical model: dynamic panel with system GMM  
  - Baseline equation: FI_i,t = α + β Remit_i,t + δ X_i,t + μ_i + λ_t + ε_i,t  
  - Dynamic specification includes lagged FI: FI_i,t = α FI_i,t-1 + β Remit_i,t + δ X_i,t + μ_i + λ_t + ε_i,t
- Sample: panel of 187 countries over 2004–15.
- Financial inclusion (FI) measures (access and usage dimensions):  
  - Access: Branches = log(number of commercial bank branches per 100,000 adults), ATMs = number of ATMs per 100,000 adults.  
  - Usage: Deposits = log(deposit accounts with commercial banks per 1,000 adults), Borrowers = log(number of borrowers at commercial banks per 1,000 adults).  
  - Composite: Financial Inclusion Index (FII) constructed using weighted normalization and coefficient-of-variation weights (formulas (2)–(5)); Borrowers excluded from FII construction in some computations because of data availability.
- Remittances measured as remittances inflows to GDP (percent of GDP), World Development Indicators (2004–15 averages).
- Controls X include: LGDPPC (log GDP per capita, 2010 US $), inflation (annual % change CPI), Trade = (X+M)/GDP, and kaopen (Chinn and Ito capital account openness index).
- Estimation uses system GMM (Arellano and Bover; Blundell and Bond); tests include AR(2) and Hansen overidentifying restrictions.

### Main empirical findings
- Baseline (linear) GMM results for full sample:
  - Remittances coefficient positive but statistically insignificant for Branches (0.0007), ATMs (+0.0032), Deposits and Borrowers (positive but insignificant), and FII (insignificant).
  - Financial development (FD = total credit to private sector % of GDP) shows positive and significant coefficient (+0.4930, significant at 1%).
  - Inflation negative and significant; Trade positive and significant; kaopen generally positive but often insignificant.
- Subsample by income (LMICs vs HICs) using linear specification:
  - LMICs: remittances coefficients remain very low and insignificant for Branches (+0.0003), Deposits (+0.0001), Borrowers (+0.001), FII positive but insignificant.
  - HICs: remittances coefficients mostly negative or insignificant (e.g., Branches -0.0036).
- Nonlinear (quadratic) specification (Remit and Remit^2 included) reveals a U-shaped relationship between remittances and financial inclusion:
  - For full sample (Table 6): Remit coefficient negative and Remit^2 coefficient positive (Remit^2 significant at various levels), indicating convex (U-shaped) relationship.
  - Derived thresholds (Remit* = β / (2γ)) from full-sample estimates vary by FI proxy between 11.3 percent and 23.6 percent of GDP.
- LMIC-specific nonlinear results (Table 7):
  - Branches: Remit negative and Remit^2 positive and significant at 10% for LMICs; threshold = 15.7 percent.
  - ATMs: Remit^2 positive and significant at 95% for LMICs; threshold = 21.3 percent.
  - Deposits: threshold = 13.28 percent for LMICs.
  - Borrowers: threshold = 22 percent for LMICs.
  - FII: threshold = 13.0 percent for LMICs.
- Bank balance-sheet corroboration (Table 8 following Barajas et al. 2018):
  - Time, saving and foreign currency deposits (% GDP) decline with remittances up to 12 percent; starting at 13 percent (Remit ≤ 13%), household deposit behavior shifts from demand deposits toward longer-maturity deposits, enabling banks to lend more — consistent with the U-shaped relationship and the remittance threshold effect.
- Financial development heterogeneity (Table 9):
  - LMICs split into less financially developed countries (LFDCs) and more financially developed (MFDCs) using median CPY (median = 26.8%).
  - Non-monotonic relationship more evident in MFDCs for several FI proxies; coefficients for Remit and Remit^2 significant with expected signs for some usage/access proxies.
- Financial stability heterogeneity (Table 10):
  - LMICs split by Z-score median (median = 9.62) into LFSCs and MFSCs.
  - U-shaped relationship tends to be confirmed in more financially stable countries (MFSCs): Remit negative and Remit^2 positive and significant at 10%/5% levels.
- Banking sector efficiency heterogeneity (Table 11):
  - LMICs split by interest rate spread median (median = 6.53) into less efficient (LEFS) and more efficient (MEFS) banking sectors.
  - Nonlinear relationship is stronger and significant in LMICs with more efficient banking sectors (MEFS); in LEFS many coefficients are insignificant.
  - Thresholds lower in MEFS (e.g., Remit* = 10.9 percent with Branches; Remit* = 14.3 percent with Borrowers), consistent with efficient banks converting remittance deposits into services and loans at lower remittance levels.
- Regional analysis (Figures 4–8):
  - Removing regions one at a time indicates South Asia (SA) and East Asia & Pacific (EAP) are important contributors to the non-linear relationship; red bars (differences in Remit^2 coefficients) positive and blue bars (differences in Remit coefficients) negative for SA and EAP across many FI proxies.
  - Latin America & Caribbean (LAC) shows no consistent pattern; Sub-Saharan Africa (SSA) appears not to strongly influence the non-linear relationship.
- Fragile states (pooled OLS due to data limitations; Figure 9):
  - Using IMF and World Bank fragile-state definitions, pooled OLS indicates remittances have an overall positive effect on financial inclusion for fragile states across most FI indicators.
  - Borrowers proxy shows negative sign with IMF approach and insignificant effect with World Bank approach; interpretation: banking weaknesses may limit transformation of deposits into loans in some fragile states.

### Interpretations and mechanisms
- Intuition for U-shaped relationship:
  - At low remittance-to-GDP levels, remittances mainly used for consumption by liquidity-constrained households and may substitute for formal credit (disintermediation), yielding little or negative effects on FI.
  - Beyond a threshold remittances are more likely to be saved via formal channels (time/savings/foreign-currency deposits), increasing loanable funds and prompting banks to expand services and reach — enhancing financial inclusion.
- Threshold heterogeneity:
  - Threshold levels vary by FI proxy, country income level, financial development, financial stability, banking sector efficiency, and region.
  - Efficient, stable, and more financially developed systems exhibit U-shaped patterns at lower remittance thresholds.

### Key numeric findings (preserved exactly)
- Remittance inflows to LMICs: 491 USD billion in 2018.
- Bank-account ownership by region: East Asia 55 percent; Latin America 39 percent; Eastern Europe 35 percent; South East Asia 33 percent; Sub Saharan Africa 25 percent.
- Full-sample quadratic-model thresholds vary between 11.3 percent and 23.6 percent (by FI proxy).
- LMIC-specific thresholds: Branches 15.7 percent; ATMs 21.3 percent; Deposits 13.28 percent; Borrowers 22 percent; FII 13.0 percent.
- Example remittance-to-GDP recipients (2004–18 averages): Tajikistan 35.0 percent; Bermuda 31.6 percent; Tonga 28.2 percent; Kyrgyz Republic 22.9 percent.
- Median CPY (credit to private sector % of GDP) = 26.8 percent.
- Z-score median = 9.62.
- Interest rate spread median = 6.53.

### Policy-relevant implications (based on paper’s evidence)
- Remittances can support financial inclusion, but positive effects materialize only beyond country- and context-specific remittance thresholds.
- Strengthening banking sector efficiency reduces the remittance threshold at which positive inclusion effects arise; policy actions that improve banking efficiency (narrow spreads, better intermediation) can amplify remittances’ inclusion benefits.
- Enhancing financial stability and financial development (higher CPY) also supports the conversion of remittance flows into inclusive financial services at lower remittance levels.
- Policies to encourage formal channeling of remittances (reducing transaction costs, improving deposit products targeted to remittance recipients) may accelerate the shift from consumption/demand deposits toward time/saving deposits and credit expansion.
- Regional and country heterogeneity matters: targeted approaches recognizing regional patterns (e.g., SA and EAP prominence) and country features (fragility, financial development, stability, bank efficiency) are important when designing policies to leverage remittances for financial inclusion.

*Source: wpiea2020066-print-pdf — https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020066-print-pdf.pdf*

### 12.28 percent in LMICs with more sable, efficient and developed financial sectors. When

### wpiea2020066-print-pdf - 12.28 percent in LMICs with more sable, efficient and developed financial sectors. When

### Main empirical finding: nonlinearity of remittances' effect on financial inclusion
- Remittances have a U-shaped (nonlinear) relationship with financial inclusion: at low levels remittances substitute for formal financial services; at high levels they complement and promote formal financial inclusion.
- The nonlinear effect is robust to different proxies for financial inclusion and across regional distributions, with the effect most significant in East and South Asia and Pacific regions.
- For fragile states, despite data limitations, the positive impact of remittances on financial inclusion is confirmed for the most part.

### Estimated thresholds and key coefficients (selected results)
- Table 6 (GMM estimates of the “U” relationship, 2004–15) reports thresholds for remittances:
  - Branches threshold: 23.6%
  - ATMs threshold: 20.8%
  - Deposits threshold: 11.3%
  - Borrowers threshold: 22.4%
  - FII threshold: 12.2%
- Table 4 (Whole sample GMM, 2004–15):
  - Remit coefficient on FD (column 6): 0.0031*** (t-statistic 2.85)
  - LGDPPC coefficients vary across specifications; examples:
    - ATMs: 0.0799*** (t-statistic 3.35)
    - Deposits: 0.1231** (t-statistic 2.34)
  - Inflation (Inf) significant negative effects in several specifications (examples):
    - Branches: -0.0259*** (t-statistic -3.00)
    - Borrowers: -0.0735*** (t-statistic -6.66)
- Table 7 (U-relationship by income level) reports subgroup thresholds (examples):
  - Branches, LMICs threshold: 15.7%
  - ATMs, LMICs threshold: 21.3%
  - Deposits, LMICs threshold: 13.3%
  - Borrowers, LMICs threshold: 22.0%
  - FII, LMICs threshold: 13.0%

### Bank balance-sheet patterns in LMICs by remittance intensity (Table 8)
- Time, saving and foreign currency deposits (% GDP) and demand deposits (% GDP) for LMICs at remittance cutoffs:
  - Remit ≤ 1%: Time/saving/foreign currency deposits 52.63; Demand deposits 56.84; Total Deposits 110.10; Total credit 31.51
  - Remit ≤ 5%: Time/saving/... 35.67; Demand 31.49; Total Deposits 66.77; Total credit 28.77
  - Remit ≤ 10%: Time/saving/... 33.55; Demand 27.61; Total Deposits 61.05; Total credit 29.98
  - Remit ≤ 11%: 33.28; 27.29; 60.46; 29.86
  - Remit ≤ 12%: 33.01; 26.94; 59.83; 29.76
  - Remit ≤ 13%: 33.07; 26.64; 59.61; 29.94
  - Remit ≤ 14%: 33.13; 26.47; 59.53; 29.95
  - Remit ≤ 15%: 33.38; 26.30; 59.66; 30.13
  - Remit ≤ 20%: 35.91; 25.35; 61.29; 30.97
  - Remit ≤ 25%: 37.15; 24.79; 61.96; 31.20
  - Remit ≤ 30%: 37.28; 24.54; 61.83; 31.26

### Heterogeneity by financial sector characteristics (selected findings)
- Role of financial development (Table 9):
  - In LFDCs vs HFDCs (low vs high financial development) the Remit and Remit^2 coefficients vary; examples:
    - HFDCs, Branches: Remit -0.0071* (t-statistic -1.98), Remit^2 0.0001* (t-statistic 1.69)
    - HFDCs, FII: Remit -0.0364*** (t-statistic -3.06), Remit^2 0.0013* (t-statistic 3.06)
- Role of financial stability (Table 10):
  - MFSCs (more financially stable countries) show different Remit and Remit^2 patterns than LFSCs; examples:
    - MFSCs, ATMs: Remit -0.0119** (t-statistic -2.53), Remit^2 0.0002* (t-statistic 2.20)
    - MFSCs, Deposits: Remit -0.0227** (t-statistic -2.27), Remit^2 0.0005* (t-statistic 1.93)
- Role of financial sector efficiency (Table 11):
  - MEFSs (more efficient financial sectors) show statistically significant Remit^2 coefficients in several specifications; examples:
    - MEFSs, Branches: Remit -0.0263** (t-statistic -2.44); Remit^2 0.0012** (t-statistic 2.54)
    - MEFSs, Deposits: Remit -0.0194** (t-statistic -2.05); Remit^2 0.0004* (t-statistic 1.89)

### Descriptive statistics and correlations (highlights)
- Table 2 (Descriptive statistics):
  - Branches: Obs. 2108; Mean 19.54; Std. Dev 26.69; Min 0.12; Max 289.83
  - ATMs: Obs. 1947; Mean 41.40; Std. Dev 44.32; Min 0; Max 290.14
  - Deposits: Obs. 1205; Mean 1110.86; Std. Dev 1195.67; Min 1.25; Max 12.420.85
  - Borrowers: Obs. 973; Mean 178.00; Std. Dev 210.70; Min 0.01; Max 1156.04
  - FII: Obs. 1107; Mean 0,10; Std. Dev 0.09; Min 0; Max 0.69
  - Remittances: Obs. 2060; Mean 4.71; Std. Dev 6.85; Min 0.00; Max 49.28
  - Log GDP per capita: Obs. 2197; Mean 8.45; Std. Dev 1.49; Min 5.32; Max 11.60
  - Inflation: Obs. 2077; Mean 0.18; Std. Dev 5.36; Min -0.35; Max 244.11
  - Trade: Obs. 2121; Mean 0.93; Std. Dev 0.54; Min 0.00; Max 4.55
  - Kaopen: Obs. 1877; Mean 4.27; Std. Dev 1.60; Min -1.89; Max 2.38
- Table 3 (Correlation matrix) selected entries:
  - Branches–ATMs: 0.52
  - ATMs–Deposits: 0.69
  - Deposits–Borrowers: 0.75
  - FI Index correlations: Branches 0.77; ATMs 0.88; Deposits 0.85; Borrowers 0.73
  - Remittances correlations with financial variables are small/negative in the matrix: e.g., Remittances–Branches -0.01; Remittances–ATMs -0.17; Remittances–Deposits -0.03; Remittances–Borrowers -0.13

### Policy prescriptions
- Strengthen macroeconomic stability to support the complementarity between remittances and formal financial services.
- Provide regulatory reforms to reduce the cost of formal intermediation.
- Remove distortions in credit markets, including informational asymmetries (for example, establish credit rating bureaus).
- Promote a more competitive banking system to lower costs and expand access.
- Promote financial education among households to encourage use of formal financial services and conversion of remittance flows into bank savings and time deposits.

*Source: wpiea2020066-print-pdf - 12.28 percent in LMICs with more sable, efficient and developed financial sectors. When.*

### References

### References (wpiea2020066-print-pdf)

### Remittances, poverty, human capital, and household outcomes
- Acosta, P., Pablo, F., and Humberto, L. (2007). The Impact of Remittances on Poverty and Human Capital: Evidence from Latin American Household Surveys. World Bank Policy Research Working Paper, WP 4247.
- Adams, R. (2004). Remittances and Poverty in Guatemala. World Bank Policy Research Paper, WP 3418.
- Adams, R. (2006). Remittances and Poverty in Ghana. World Bank Policy Research Paper, WP 3838.
- Adams, R., and Cuecuecha, A. (2010). Remittances, Household Expenditure and Investment in Guatemala. World Development, 38: 1626-1641.
- Amrosius, C., and Cuecuecha, A. (2016). Remittances and the Use of Formal and Informal Financial Services. World Development, 77: 80-98.
- Amuedo-Dorantes, C., and Pozo, S. (2006). Remittance Receipt and Business Ownership in the Dominican Republic. The World Economy, 29 (7): 939-956.
- Bredl, S. (2011). Migration, Remittances, and Educational Outcomes: The Case of Haiti. International Journal of Educational Development, 31: 162-168.
- Calero, C., Bedi, A. S., and Sparrow, R. (2009). Remittances, Liquidity Constraints and Human Capital Investments in Ecuador. World Development, 37: 1143–1154.
- Carelo, C., Bedi, A., and Sparrow, R. (2009). Remittances, Liquidity, Constraints, and Human Capital Investments in Ecuador. World Development, 37:1143-1154.
- De, P., and Ratha, D. (2012). Impact of Remittances on Household Income, Asset and Human Capital: Evidence from Sri Lanka. Migration and Development, 1 (1): 163-179.
- Yang, D. (2008). International Migration, Remittances, and Household Investment: Evidence from Philippines Migrants' Exchange Rate Shocks. The Economics Journal, 118: 591-630.
- Taylor, E. J., & Wyatt, T. J. (1996). The Shadow Value of Migrant Remittances, Income and Inequality in a Household-Farm Economy. Journal of Development Studies, 32(6): 899–912.
- Kim, N. (2007). The Impact of Remittances on Labor Supply: The Case of Jamaica. World Bank Policy Research Working Paper 4120.
- Rodriguez, E., & Tiongson, E. (2001). Temporary Migration Overseas and Household Labor Supply: Evidence from Urban Philippines. International Migration Review, 35 (3): 709-725.

### Remittances, financial development, and financial inclusion
- Aga, G. A., and Martínez Pería, M. S. (2014, July). International Remittances and Financial Inclusion in Sub-Saharan Africa. Policy Research Working Paper #6991, p. 32 pages.
- Aggarwal, R., Demirgüç-Kunt, A., and Martínez-Pería, M. (2011). Do Remittances Promote Financial Development? Journal of Development Economics, 96 (2): 255-64.
- Anzoategui, D., Demirgüç-Kunt, A., and Martínez Pería, M. (2014). Remittances and Financial Inclusion: Evidence from El Salvador. World Development, 54, 338-349.
- Beck, T., Demirgüç-Kunt, A., and Martínez-Pería, M. (2007). Reaching Out: Access to and Use of Banking Services Across Countries. Journal of Financial Economics, 85(1): 234-66.
- Demirgüç-Kunt, A., Cordova, E. L., Martinez-Peria, M., and Woodruff, C. (2011). Remittances and Banking Sector Breadth and Depth: Evidence from Mexico. Journal of Development Economics, 95 (2): 229-41.
- Giuliano, P., and Ruiz Arranz, M. (2005). Remittances, Financial Development, and Growth. IMF Working Papers, 05/234.
- Giuliano, P., and Ruiz-Arranz, M. (2009). Remittances. Financial Development, and Growth. Journal of Development Economics, 90: 114-152.
- Martínez-Pería, M. S., Mascaró, S. Y., and Moizeszowicz, F. (2008). Do Remittances Affect Recipient Countries' Financial Development? In P. Fajnzylber, and J. H. Lopez, Remittances and Development: Lessons from Latin America (pp. 171-215). Washington, DC: World Bank.
- Mundaca, G. (2008). Remittances, Financial Markets Development, and Economic Growth: The Case of Latin America and the Caribbean. Review of Development Economics, 13: 288-303.
- Gupta, S., Pattillo, C. A., and Wagh, S. (2009). Effect of Remittances on Poverty and Financial Development in Sub-Saharan Africa. World Development, 37 (1): 104-15.
- Chowdhury, M. B. (2011). Remittances Flow and Financial Development in Bangladesh. Economic Modeling, 28 (6): 2600-08.
- Cooray, A. (2012). Migrant Remittances, Financial Sector Development and the Government Ownership of Banks: Evidence from a Group of non-OECD Economies. Journal of International Financial Markets, Institutions and Money, 22 (4): 936-57.
- Inoue, T. (2011). Financial Inclusion and Poverty Alleviation in India: An Empirical Analysis Using State-wise Data. In S. Hirashima, H. Oda, and Y. Tsujita, Inclusiveness in India: A Strategy for Growth and Equality (pp. 88-108). Basingstoke, UK and New York: Palgrave Macmillan.
- Inoue, T., and Hamori, S. (2012). How has Financial Deepening Affected Poverty Reduction in India? Empirical Analysis Using State-level Panel Data. Applied Financial Economics, 22 (5): 395-408.
- Inoue, T., and Homori, S. (2016). Do Workers' Remittances Promote Access to Finance? Evidence from Asia-Pacific Developing Countries. Emerging Markets Finance and Trade, 52: 765-774.
- Amrosius, C., and Cuecuecha, A. (2016). Remittances and the Use of Formal and Informal Financial Services. World Development, 77: 80-98.
- Sahay, R., Cihak, M., P., N., Barajas, A., Mitra, S., Kyobe, A., and Yousefi, S. R. (2015, September). Financial Inclusion: Can It Meet Multiple Macroeconomic Goals? IMF Stuff Discussion Note, SDN/15/17.
- Wang, X., & Guan, J. (2017). Financial Inclusion: Measurement, Spatial Effects and influencing Factors. Applied Economics, 49:18, 1751-1762.
- Tu, C. A., Phi, N. T. M., Tuan, L. Q., Yoshino, N., Sarker, T., and Taghizadeh-Hesary, F. (2019). Remittance Inflows, Financial Inclusion, and Economic Development: An Empirical Analysis of the World Sample. ADBI Working Paper Series # 1000, Asian Development Bank Institute.
- Beck, T., Demirgüç-Kunt, A., and Martínez-Pería, M. (2007). Reaching Out: Access to and Use of Banking Services Across Countries. Journal of Financial Economics, 85(1): 234-66.

### Macroeconomic consequences, monetary policy, and remittances
- Barajas, A., Chami, R., Cosimano, T., Fullenkamp, C., Gapen, M., and Montiel, P. (2008). Macroeconomic Consequences of Remittances. IMF Occasional Paper 259.
- Barajas, A., Chami, R., Fullenkamp, C., Gapen, M., and Montiel, P. (2009). Do Workers’ Remittances Promote Economic Growth? IMF Working Papers 09/153.
- Barajas, A., Chami, R., Ebeke, C., and Oeking, A. (2018). What's Different About Monetary Policy Transmission in Remittance-Dependent Countries? Journal of Development Economics, pp. 134 (September): 272-288.
- Chami, R., Fullenkamp, C., and Jahjah, S. (2005). Are Immigrant Remittance Flows a Source of Capital for Development? IMF Staff Papers, 52(1): 55-81.
- Chami, R., Hakkura, D., and Montiel, P. (2009, April). Remittances: An Automatic Output Stabilizer? IMF Working Papers 09/91.
- Chami, R., Ekkehard, E., Fullenkamp, C. and Oeking, A. (2018). Are Remittances Good for Labor Markets in LICs, MICs, and Fragile States? Evidence from Cross-Country Data. IMF Working Papers 18/102.
- Mishra, P., Montiel, P., and Spilimbergo, A. (2012). Monetary Transmission in Low-Income Countries: Effectiveness and Policy Implications. IMF Economic Review, International Monetary Fund, 60(2): 270-302.
- Summers, L. (2000). International Financial Crises: Causes, Prevention and Cures. American Economic Review, 90(2), 1-16.

### Financial openness, capital account, and financial development literature
- Chinn, M., and Ito, H. (2002). Capital Account Liberalization, Institutions, and Economic Growth: Cross-Country Evidence. NBER Working Papers.
- Chinn, M., and Ito, H. (2006). What Matters for Financial Development? Capital Controls, Institutions and Interactions. Journal of Development Economics, 81(1), 163-192.
- Chinn, M., and Ito, H. (2011). The Chinn-Ito Index- a De Jure Measure of Financial Openness. Retrieved from http://web.pdx.edu/~ito/Chinn-Ito_website.htm.
- Klein, M., and Olivei, J. (2008). Capital Account Liberalization, Financial Depth and Economic Growth. Journal of International Money and Finance, 27, 861-875.
- De Gregorio, J., and Guidotti, P. (1995). Financial Development and Economic Growth. World Development, 23(3), 433–448.
- Eichengreen, B., Gullapalli, R., and Panizza, U. (2011). Capital Account Liberalization, Financial Development and Industry Growth: A Synthetic View. Journal of International Money and Finance, 30(6), 1090–1106.
- Arcand, J., Berkes, E., and Panizza, U. (2012). Too Much Finance? IMF Working Papers 12/161.

### Firm growth, methodology, and data sources
- Demirgüç-Kunt, A., and Maksimovic, V. (1996). Financial Constraints, Uses of Funds, and Firm Growth: An International Comparison. World Bank Policy Research Paper, 1671, 34. Washington, DC: World Bank.
- Arellano, M., and Bover, O. (1995). Another Look at Instrumental Variables Estimation of Error-Component Models. Journal of Econometrics, 68, 29–51.
- Blundell, R., and Bond, S. (1998). Initial Conditions and Moment Restrictions in Dynamic Panel-Data. Journal of Econometrics, 115–143.
- Blundell, R., Dearden, L., Goodman, A., and Reed, H. (2000). The Returns to Higher Education in Britain: Evidence From a British Cohort. Journal of Econometrics, 110: F82–F99.
- Burgess, R., and Pande, R. (2005). Do Rural Banks Matter? Evidence from the Indian Social Banking Experiment. American Economic Review, 95 (3): 780-95.
- Ashraf, N., Aycinena, D., Martinez, C., and Yang, D. (2015). Savings in Transnational Households: A Field Experiment Among Migrants From El Salvador. Review of Economics and Statistics, 97(2): 332–351.

### Institutional and policy references, datasets
- IMF, (2017), Building Fiscal Capacity in Fragile State. Washington DC: International Monetary Fund.
- World Bank. (2016). World Development Indicators. Washington, DC: World Bank.
- World Bank. (2018). World Development Indicators. Washington, DC: World Bank.

*References as listed in wpiea2020066-print-pdf - References*

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_Source: https://www.imf.org/-/media/files/publications/wp/2020/english/wpiea2020066-print-pdf.pdf_