Settlement speed, digital channels, and the cost of remittances in the world economy

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Type of the article: Research Article

Abstract
The cost of sending remittances remains above the 3% target of Sustainable Development Goal 10.c.1 even as digital technologies reshape cross-border payments. This study asks which dimension of the digital transformation of remittance markets is associated with lower costs and whether the association survives provider identity. Using the World Bank Remittance Prices Worldwide database (202,851 quotations, 372 corridors, 2016–2025), the study estimates fixed-effects models at the quotation and corridor-year levels with corridor × quarter and provider fixed effects. Three findings emerge. At the market level, corridors shifting toward instant settlement record lower costs (b = −1.12 percentage points, so a 10-point higher instant share corresponds to about 0.11 points), an association that runs through incumbent cash prices, survives stable provider sets, and is concentrated after 2022. At the quotation level, the instant discount (−0.44) reflects provider composition. Money transfer operators supply 94% of instant quotations at half the mean price of banks; within providers, speed carries a premium that eroded from 1.5 points in 2016 to −0.9 in 2025. What providers price lower is digital delivery (−0.9); mobile money is cheapest throughout (−2.85). The discount holds across developing destinations and reverses in high-income ones. Ukraine, Armenia, and Kazakhstan illustrate these margins at different adoption stages, where each point saved supports household resilience, economic security, and human capital. The results support faster end-to-end settlement, complemented by provider presence in low-income corridors, as the margins associated with lower remittance costs.

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    • Figure 1. Mean remittance cost and instant-settlement share across corridors, 2016–2025
    • Figure 2. Estimated instant-settlement coefficient by year, 2016–2025
    • Table 1. Variable definitions
    • Table 2. Descriptive statistics
    • Table 3. Quotation-level cost regressions under alternative fixed effects
    • Table 4. Corridor-year cost regressions (corridor and year fixed effects)
    • Table 5. Heterogeneity by destination income group (modal classification)
    • Table 6. Cost decomposition into transaction fee and exchange-rate margin (quotation level)
    • Table A1. Economies in the sample by income group and role
    • Table B1. Quotation-level correlations
    • Table B2. Corridor-year correlations
    • Table C1. Robustness of the instant-settlement association
    • Table D1. Coverage of the illustrative receiving economies by corridor and sub-period
    • Table E1. Within-cell variation of the service indicators
    • Table F1. Processing and estimation workflow
    • Conceptualization
      Yerkezhan Moldakenova, Taliat Bielialov, Vladyslav Kutsenko, Grigor Nazaryan, Maryna Salun, Ainur Imanaliyeva, Grigor Hayrapetyan
    • Project administration
      Yerkezhan Moldakenova, Ainur Imanaliyeva
    • Supervision
      Yerkezhan Moldakenova
    • Validation
      Yerkezhan Moldakenova, Grigor Nazaryan, Maryna Salun, Grigor Hayrapetyan
    • Visualization
      Yerkezhan Moldakenova, Ainur Imanaliyeva, Grigor Hayrapetyan
    • Writing – original draft
      Yerkezhan Moldakenova, Taliat Bielialov, Vladyslav Kutsenko, Grigor Nazaryan, Maryna Salun, Ainur Imanaliyeva, Grigor Hayrapetyan
    • Writing – review & editing
      Yerkezhan Moldakenova, Taliat Bielialov, Vladyslav Kutsenko, Grigor Nazaryan, Maryna Salun
    • Data curation
      Taliat Bielialov, Vladyslav Kutsenko, Ainur Imanaliyeva
    • Formal Analysis
      Taliat Bielialov, Vladyslav Kutsenko, Grigor Nazaryan, Maryna Salun, Grigor Hayrapetyan
    • Software
      Taliat Bielialov, Vladyslav Kutsenko
    • Investigation
      Vladyslav Kutsenko, Grigor Nazaryan, Maryna Salun, Ainur Imanaliyeva, Grigor Hayrapetyan
    • Resources
      Grigor Nazaryan, Maryna Salun