Does globalization reduce remittance costs? Evidence on the role of peace from bilateral panel data

Does globalization reduce remittance costs? 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Evidence on the role of peace from bilateral panel data Swapnilsingh Yuwrajsingh Thakur, Prashant Dev Yadav This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-8812118/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Background: Reducing remittance costs is a central objective of Sustainable Development Goal (SDG) 10.c and a priority under the G20 remittance agenda. Despite extensive empirical work, progress toward these targets remains uneven, partly due to the limited availability of bilateral data needed to examine corridor-level determinants of remittance costs. Purpose: This study investigates how bilateral remittance flows, globalization, and peace asymmetry jointly influence remittance costs across international corridors, addressing a key gap in the remittance cost literature. Methodology: The study constructs a balanced bilateral panel dataset for 2011–2025 by generating corridor-level remittance flows using interpolation combined with Iterative Proportional Fitting (IPF). Globalization is measured as the bilateral average of country-level globalization indices, while peace asymmetry is captured as the absolute difference in peace levels between sending and receiving countries. The analysis employs corridor fixed-effects panel regression with clustered and Driscoll–Kraay standard errors, complemented by instrumental variable estimation to address endogeneity. Findings: The results show that higher bilateral remittance flows significantly reduce remittance costs, confirming strong scale and competition effects. In contrast, globalization and peace asymmetry do not independently lower costs once corridor-specific heterogeneity is accounted for. Evidence of a moderating role of peace in the globalization–cost relationship is weak and specification-sensitive. Limitations: The analysis relies on interpolated bilateral remittance matrices and cannot capture informal remittance channels. Implications: Policies aimed at expanding formal remittance volumes and deepening corridor-level competition are likely to be more effective in reducing costs than macro-level institutional reforms alone. Novelty and Contribution: This study advances the literature by developing a bilateral corridor-level framework that integrates globalization and peace asymmetry into remittance cost analysis. Development Economics Econometrics International Economics Remittance costs bilateral remittance corridors globalization Global peace asymmetry SDG 10.c panel data Figures Figure 1 1. Introduction International remittances have become one of the largest and most stable sources of external finance for low- and middle-income countries, often exceeding foreign direct investment and official development assistance in magnitude (World Bank, 2025a). For many developing economies, remittances constitute a critical share of household income and national output, playing an essential role in poverty reduction, consumption smoothing, and macroeconomic stability. Despite their importance, the cost of sending remittances across borders remains persistently high, particularly for corridors linking developing and fragile economies, acting as a direct tax on migrant incomes and reducing the developmental impact of these flows (Silva Filho, 2022; Coutinho et al., 2023 ; Thakur et al., 2024). Recognizing this challenge, the international community has placed remittance cost reduction at the center of the global development agenda. Under Sustainable Development Goal (SDG) 10.c, countries have committed to reducing average remittance costs to below 3 percent by 2030 (Badre, 2024 ), while the G20 has set an interim target of reducing costs to 5 percent by 2027 (G20, 2023 ). Yet progress toward these targets has been uneven across regions and corridors, with costs remaining structurally high in many developing-country corridors despite rapid growth in global remittance volumes (World Bank, 2025b). This persistence highlights the need to better understand the forces that shape remittance pricing at the corridor level. A growing body of empirical research identifies remittance scale as a key determinant of remittance costs, emphasizing the role of competition, market thickness, and economies of scale in reducing transaction prices (Ahmed et al., 2021 ; Jemiluyi & Jeke, 2023 ; Gurira & Parwada, 2025 ; Kim et al., 2024 ). Meta-synthesis evidence further suggests that reductions in remittance costs generate substantial increases in remittance volumes, reinforcing the feedback between scale and pricing (Mikhaylichenko et al., 2025 ). However, much of this literature relies on aggregate or unilateral country-level analyses, implicitly abstracting from the bilateral nature of remittance corridors—the level at which remittance costs are actually set. Beyond scale effects, two macro-structural factors are theoretically relevant to remittance cost formation but remain insufficiently examined: globalization and peace conditions. Greater globalization, commonly captured through composite indices of economic, social, and political integration such as the KOF Globalisation Index (Dreher, 2006 ), is expected to facilitate financial openness, improve infrastructure, and reduce cross-border transaction frictions (Shaw, 2011 ; Wahyudi et al., 2023 ). At the same time, peace and conflict conditions shape transaction risk, regulatory scrutiny, and compliance intensity, particularly in corridors involving fragile or conflict-affected states (Brinkerhoff, 2011 ; Feyissa, 2012 ; True & Hozić, 2020 ). While globalization and peace have been widely studied in relation to remittance volumes and development outcomes, their role in determining remittance costs, especially at the bilateral corridor level, remains poorly understood. This gap is compounded by the way peace and institutional quality are typically measured in the literature. Existing studies largely rely on unilateral indicators, implicitly assuming symmetry between remittance-sending and receiving countries. Such approaches overlook the possibility that asymmetries in peace conditions across corridors, rather than absolute peace levels, may be more relevant for remittance pricing. Differences in security environments may increase compliance costs, monitoring requirements, and perceived transaction risk, potentially offsetting efficiency gains associated with greater globalization (Sharp, 2018 ; Barlas et al., 2025 ). Moreover, little is known about whether globalization amplifies or mitigates the cost implications of such peace asymmetries. This study addresses these gaps by examining the determinants of bilateral remittance costs across corridors over the period 2011–2025. Using a corridor-level panel framework, the analysis integrates bilateral remittance flows, globalization, and peace asymmetry within a unified empirical setting. Bilateral remittance flows are constructed using interpolation and iterative proportional fitting techniques to ensure consistency with aggregate country-level totals, while globalization is operationalized using bilateral averages to capture shared exposure to global integration. Peace is modeled as an absolute difference between remittance-sending and receiving countries, capturing corridor-level asymmetry in security conditions (Institute for Economics & Peace, 2023). The study makes three main contributions. First, it advances the remittance cost literature by shifting the focus from aggregate and unilateral analyses to a bilateral corridor-level framework, where remittance pricing decisions are actually made. Second, it introduces peace asymmetry as a novel corridor-level construct, offering a more nuanced representation of security-related frictions in cross-border transactions. Third, by examining the interaction between globalization and peace asymmetry, the study provides new evidence on whether macro-level integration can offset or amplify the cost implications of security differentials across remittance corridors. Guided by these objectives, the paper addresses the following research questions: To what extent do higher bilateral remittance flows reduce remittance costs across corridors? Does greater globalization across remittance corridors contribute to lower remittance costs? How do asymmetries in peace conditions between remittance-sending and receiving countries influence bilateral remittance costs? Does peace asymmetry moderate the relationship between globalization and remittance costs across corridors? To organize the analysis, Fig. 1 presents the conceptual framework underpinning the study. The framework illustrates the direct effects of bilateral remittance flows and globalization on remittance costs, as well as the role of peace asymmetry as both an independent factor and a moderator of the globalization–cost relationship. The remainder of the paper is structured as follows. Section 2 reviews the relevant literature. Section 3 describes the data and empirical methodology. Section 4 presents the results. Section 5 discusses the findings and policy implications in the context of SDG 10.c and the G20 remittance cost agenda. Section 6 concludes. 2. Review of literature 2.1. Remittance Flows and Remittance Costs A substantial empirical literature identifies remittance costs as a major friction in international remittance markets, with consistent evidence that higher transaction costs significantly suppress remittance volumes. Across gravity models, panel estimations, and quasi-experimental designs, studies document a negative and economically meaningful elasticity of remittance flows with respect to costs, particularly in high-cost and low-income corridors (Ahmed et al., 2021 ; Gurira & Parwada, 2025 ; Jemiluyi & Jeke, 2023 ). Meta-level evidence further indicates that achieving global remittance cost reduction targets could unlock substantial additional remittance inflows, reinforcing the policy importance of lowering transfer fees under Sustainable Development Goal 10.c (Mikhaylichenko et al., 2025 ). Bilateral gravity analyses similarly confirm that lower remittance costs are systematically associated with higher bilateral remittance inflows across regions, including Asia and the Pacific (Kim et al., 2024 ). While this literature has firmly established remittance costs as a determinant of remittance volumes, it exhibits a clear asymmetry. Remittance costs are typically modeled as exogenous or policy-driven outcomes, shaped by regulation, competition, or technological change, whereas remittance volumes are treated almost exclusively as dependent variables. Consequently, the reverse relationship, whether larger remittance flows reduce remittance costs remains largely unexplored. From a theoretical perspective, there are strong reasons to expect remittance volumes to influence remittance costs. Transaction cost economics and industrial organization theory predict that higher transaction volumes reduce average costs through economies of scale, greater utilization of payment infrastructure, and intensified competition among service providers. In remittance corridors with larger and more stable flows, fixed compliance, operational, and settlement costs can be spread over a greater transaction base, potentially lowering per-unit transfer costs. Gravity-based frameworks further suggest that corridor size and transaction intensity should reduce pricing frictions, analogous to cost–volume relationships observed in trade and cross-border finance. Despite these theoretical predictions, empirical validation remains limited. Existing studies predominantly rely on aggregate country-level remittance inflows, masking heterogeneity across bilateral corridors. Even where bilateral data are employed, the analytical focus remains on how costs affect flows, not how flows shape costs. Moreover, remittance costs and volumes are jointly determined, raising endogeneity concerns that are rarely addressed symmetrically; while costs are often instrumented, remittance volumes are seldom treated as endogenous determinants of pricing. Addressing this gap is critical for understanding remittance costs as an endogenous market outcome rather than a purely regulatory artifact. Accordingly, this study tests whether higher bilateral remittance flows reduce remittance costs across corridors, formally evaluating the null hypothesis that remittance volumes do not influence remittance costs. Thus our hypothesis for bilateral remittance flows become, H 01 – Bilateral remittance flow volumes do not reduce remittance costs across corridors H a1 – Larger bilateral remittance flow volumes reduce remittance costs across corridors 2.2. Globalization and Remittance Costs Globalization, characterized by the intensification of cross-border trade, finance, migration, and information flows, has long been associated with reductions in transaction frictions in international markets. In the context of remittances, globalization is commonly expected to lower transfer costs by enhancing financial integration, improving payment infrastructure, increasing competition among service providers, and facilitating regulatory convergence across countries (Ebaidalla, 2025 ; Ullah et al., 2021 ). Despite these expectations, direct empirical evidence linking globalization to remittance pricing remains limited. Most existing studies examine globalization in relation to remittance volumes or broader macroeconomic and developmental outcomes, rather than remittance costs per se. Empirical work consistently finds that globalization-related dimensions such as trade openness, financial integration, migration intensity, and regional connectivity are positively associated with remittance inflows (Radić & Bogdan, 2024 ; Ullah et al., 2021 ). These findings suggest that greater cross-border connectedness expands remittance channels and lowers informational and institutional barriers to transfers. However, remittance costs are typically treated as unobserved or exogenous, implicitly assuming that globalization affects remittances only through quantities rather than prices. From a theoretical perspective, multiple mechanisms predict a negative relationship between globalization and remittance costs. Transaction cost economics posits that deeper international integration reduces informational asymmetries, coordination failures, and compliance uncertainty, thereby lowering the marginal cost of cross-border financial transactions. Industrial organization theory further suggests that globalization intensifies competition in remittance markets by enabling entry of multinational providers, fintech firms, and digital platforms, exerting downward pressure on prices (Alhassan, 2023 ). Moreover, globalization may facilitate regulatory harmonization and interoperability of payment systems, reducing fixed compliance and settlement costs faced by remittance service providers (Ebaidalla, 2025 ). Yet empirical validation of these mechanisms remains scarce. Where remittance costs are explicitly analyzed, studies tend to focus on specific policy reforms, technological interventions, or corridor-level regulations rather than globalization as a multidimensional structural process (Wu et al., 2023 ). In addition, globalization is almost exclusively measured at the country level, ignoring bilateral heterogeneity across remittance corridors. Such aggregate measures obscure how differences in global integration between sending and receiving countries jointly shape pricing outcomes in bilateral remittance markets. A further limitation concerns identification. Globalization may influence remittance costs indirectly through institutional quality, financial development, digital infrastructure, or regulatory capacity, raising concerns about omitted variable bias and conflation of channels. Existing studies rarely disentangle these effects or assess whether globalization exerts an independent influence on remittance pricing once corridor-specific characteristics and scale effects are controlled for (Radić & Bogdan, 2024 ). Given these gaps, there is a clear need to empirically assess whether higher globalization reduces remittance costs across bilateral corridors using measures that capture the shared degree of global integration between sending and receiving countries. Treating globalization as a bilateral structural characteristic allows remittance costs to be analyzed as an endogenous outcome of cross-border integration rather than as a purely policy-driven artifact. Thus, our hypothesis become, H 02 – Globalization has no significant effect on reducing remittance costs across bilateral remittance corridors H a2 – Higher globalization (measured as bilateral average) reduces remittance costs across bilateral remittance corridors 2.3. Peace Asymmetry and Remittance Costs Peace and security conditions play a central role in shaping the functioning of financial systems, particularly in cross-border transactions involving fragile and conflict-affected settings. A substantial literature documents the resilience of remittance flows during periods of conflict, political instability, and post-conflict transitions, emphasizing remittances as countercyclical lifelines that support household consumption, poverty alleviation, and social stability in origin countries (Brinkerhoff, 2011 ; Duval & Wolff, 2016 ). Diaspora engagement is frequently shown to intensify during episodes of insecurity, reinforcing the importance of remittance channels even when formal institutions are weakened (Feyissa, 2012 ; Brinkerhoff, 2011 ). Despite this extensive body of work, the pricing dimension of remittances under varying peace conditions remains largely unexplored. Existing studies overwhelmingly focus on remittance volumes, coping mechanisms, and developmental outcomes, implicitly assuming that remittance systems function similarly across peace and conflict contexts. Where peace and security are considered, they are typically treated as background conditions shaping migration decisions or remittance demand, rather than as determinants of remittance costs (Duval & Wolff, 2016 ; Sharp, 2018 ). From a theoretical perspective, peace and security are expected to influence remittance costs through multiple channels. Higher levels of insecurity can increase operational risk, compliance burdens, and monitoring costs for remittance service providers, particularly in corridors involving fragile or conflict-affected countries. Transaction cost economics suggests that uncertainty, weak enforcement, and elevated political risk raise the marginal cost of financial intermediation, while political economy perspectives highlight how insecurity may exacerbate de-risking behavior, weaken correspondent banking relationships, and reduce competition in cross-border payment markets (True & Hozić, 2020 ; Sharp, 2018 ). These mechanisms imply that remittance costs may increase even when remittance flows remain strong. Importantly, existing studies almost exclusively rely on unilateral measures of conflict or peace, overlooking the inherently bilateral nature of remittance corridors. Remittance transactions connect two distinct institutional and security environments, and differences in peace conditions between sending and receiving countries may generate asymmetric regulatory scrutiny, risk premia, and settlement frictions. For example, corridors linking peaceful sending countries to conflict-affected receivers may face higher compliance costs and monitoring intensity than corridors connecting countries with similar peace profiles (Itani et al., 2013 ; Sharp, 2018 ). Empirical evidence on such asymmetries is scarce. Studies that consider peace or conflict typically focus on single-country cases or descriptive analyses of geopolitical risk, limiting their ability to capture corridor-specific pricing dynamics (Feyissa, 2012 ; Itani et al., 2013 ). Moreover, peace indicators are rarely incorporated into remittance cost models, leaving an important gap in understanding how security differentials shape remittance pricing. Addressing this gap requires treating peace not as a unidimensional national attribute, but as a bilateral characteristic of remittance corridors. Measuring peace asymmetry as the absolute difference in peace levels between sending and receiving countries allows remittance costs to be analyzed as an endogenous outcome of cross-border security differentials rather than solely domestic conditions. Thus, our hypothesis become, H 03 – Differences in peace levels between remittance sending and receiving countries do not affect bilateral remittance costs H a3 – Greater asymmetry in peace levels (measured as an absolute difference between bilateral remittance corridors) affects bilateral remittance costs 2.4. Peace Asymmetry as a Moderator of the Globalization–Remittance Cost Relationship While globalization is widely expected to reduce transaction frictions in cross-border financial activities, its effectiveness may depend critically on the institutional and security environments in which it operates. A growing literature emphasizes that the benefits of globalization are uneven across countries, particularly in contexts characterized by political instability, conflict, or weak governance structures (Ullah et al., 2021 ; True & Hozić, 2020 ). These insights suggest that globalization’s influence on remittance systems and specifically on remittance costs may be conditional rather than unconditional. From a theoretical standpoint, globalization reduces transaction costs by enhancing cross-border connectivity, market contestability, and regulatory coordination. However, political economy and institutional theories highlight that such efficiency gains may be attenuated or reversed in insecure environments. In remittance corridors involving fragile or conflict-affected countries, heightened compliance requirements, de-risking behavior by financial institutions, and restricted correspondent banking relationships can offset the cost-reducing effects typically associated with globalization (Sharp, 2018 ; True & Hozić, 2020 ). Consequently, similar levels of global integration may generate markedly different pricing outcomes depending on the peace and security conditions of the countries involved. Despite this theoretical plausibility, existing empirical studies generally examine globalization and peace in isolation. Globalization is typically modeled as a direct determinant of economic performance or remittance volumes, while peace and conflict are treated as background conditions influencing migration decisions or remittance demand (Radić & Bogdan, 2024 ; Duval & Wolff, 2016 ). Very few studies explicitly test whether peace conditions moderate the effects of globalization, and virtually none do so in the context of remittance pricing. This omission is particularly striking given the inherently bilateral nature of remittance corridors, which connect two distinct political, regulatory, and security environments. The concept of peace asymmetry offers a useful lens through which to examine this interaction. Differences in peace levels between sending and receiving countries may amplify regulatory mismatches, risk perceptions, and compliance costs, thereby weakening or neutralizing the cost-reducing effects of globalization. For example, globalization may facilitate digital remittance platforms, fintech entry, and competitive pricing in peaceful corridors, while similar gains may be constrained in corridors characterized by large peace differentials due to intensified monitoring and institutional risk aversion (Itani et al., 2013 ; Sharp, 2018 ). Empirically, the absence of moderation analysis reflects both data and modeling constraints. Most studies rely on unilateral measures of globalization and peace, obscuring corridor-specific dynamics and precluding interaction effects. As a result, the literature offers limited insight into whether globalization’s influence on remittance costs varies systematically with cross-border security differentials. By explicitly modeling peace asymmetry as a moderator, this study advances the literature by integrating globalization and peace within a unified bilateral framework. This approach conceptualizes remittance costs as an endogenous outcome shaped jointly by global integration and the security alignment between remittance partners. H 04 – Peace asymmetry does not moderate the effect of globalization on bilateral remittance cost H a4 – Peace asymmetry moderates the effect of globalization on bilateral remittance cost The existing literature establishes remittance costs as a critical determinant of remittance flows, yet offers limited insight into the structural forces shaping remittance pricing itself. While globalization and peace are widely recognized as influential factors in cross-border economic interactions, prior studies largely examine their effects in isolation, rely on unilateral country-level measures, or focus on remittance volumes rather than costs. Moreover, the bilateral nature of remittance corridors and the potential interaction between globalization and peace conditions remain underexplored. By jointly examining globalization, peace asymmetry, and their interaction within a bilateral panel framework, this study advances the literature by conceptualizing remittance costs as an endogenous market outcome shaped by cross-border integration and security differentials. 3. Data and research methodology 3.1. Data Remittance price data are obtained from the World Bank’s Remittance Prices Worldwide (RPW) database, which reports corridor-level costs for a pair of remittance sending and receiving countries. The data capture the cost of sending a standardized amount of USD 200 and are originally reported at a quarterly frequency. These series are annualized and cover the period from 2011 to 2025Q1. In contrast, official remittance paid and remittance received data are available only at the aggregate country level on an annual basis. Bilateral remittance flow matrices are available for three benchmark years—2014, 2018, and 2021. To align bilateral remittance flows with the full annual span of the remittance cost data, a two-stage procedure is employed to construct a balanced bilateral panel. Data is sourced from Institute for Economics and Peace, Dreher ( 2006 ), and word bank (World Bank. (2024b); World Bank. (2024c)). First, for each year between 2011 and 2025, an initial bilateral “seed” matrix is generated by interpolating between the observed bilateral matrices for 2014, 2018, and 2021. Corridors that appear in at least one benchmark matrix are retained and interpolated over time, while corridors that are absent in all benchmark years are treated as structural zeros and excluded from the bilateralization process. Second, the interpolated seed matrices are adjusted using Iterative Proportional Fitting (IPF), combined with temporal smoothing, to ensure consistency with observed aggregate remittance totals (Deville et al., 2014 ). The IPF procedure scales bilateral flows iteratively so that, for each year, row sums exactly match total remittances paid by each country and column sums match total remittances received. This approach preserves the observed aggregate information while generating internally consistent bilateral remittance matrices for all years in the sample. The variables are listed in Table 1 along with their construction rule. Bilateral remittance flows are expressed in U.S. dollars and log-transformed to address skewness and the presence of large outliers. The explanatory variables are bilateralized using country-level indices. Globalization is measured using the KOF Globalization Index and constructed as the bilateral average of the sending and receiving countries’ index values (GI_avg), capturing shared exposure to global economic, social, and political integration. Peace conditions are measured using the Global Peace Index and constructed as the absolute difference between the sending and receiving countries’ index values (GPI_diff), reflecting asymmetry in security environments that may influence transaction risk and regulatory scrutiny (Institute for Economics & Peace, 2023). The dependent variable, bilateral remittance cost expressed as a percentage of the amount sent, is log-transformed to mitigate non-normality and heteroskedasticity. Bilateral remittance flows (log_RFlow) are included as a control variable to account for corridor size effects and to ensure that estimated impacts of globalization and peace are not confounded by transaction volume. Table 1 List of variables Variable Variable Code Variable Category Construction Rule Citation Remittance Cost (%) Log_RC Dependent Variable Bilateral RPW cost (corridor, % of amount sent) with log transformation. World Bank. (2024b) Globalization Index GI_avg Independent Variable Bilateral average of sender & receiver globalization index Dreher ( 2006 ) Global Peace Index GPI_diff Moderator Bilateral absolute difference between sender & receiver (Institute for Economics & Peace, 2023) Globalization × Peace Index GIxGPI Interaction Term Product of GI_avg and GPI_diff Bilateral Remittance Flow (USD) Log_RFlow Control Variable IPF-adjusted bilateral remittance value (USD) with log transformation. World Bank. (2024c) 3.2. Research methodology The study employs a panel data regression framework with bilateral corridor fixed effects to account for unobserved, time-invariant heterogeneity across remittance corridors. Such heterogeneity may arise from persistent corridor-specific characteristics, including historical migration links, geographic distance, regulatory environments, and financial infrastructure. Year fixed effects are included to control for common global shocks and time-specific factors affecting remittance markets. The baseline empirical specification examines the direct effects of globalization and peace asymmetry on bilateral remittance costs and is given by: Model 1: Base Model \(\:Lo{g\_RC}_{ij},t=\:\beta\:1\:G{I\_Avg}_{ij},t\:+\:\beta\:2GP{I\_Diff}_{ij},t\:+\:\beta\:3Lo{g\_Rflow}_{ij},t+\:\alpha\:ij+\:\gamma\:t+\:ϵij,t\:\) ................. Eq. 1 To examine whether peace asymmetry moderates the effect of globalization on remittance costs, the interaction term between globalization and peace asymmetry is introduced in the extended specification: Model 2: Interaction Model \(\:Lo{g}_{RCij},t=\:\beta\:0+\:\beta\:1G{I\_Avg}_{ij},t+\beta\:2GP{I\_Diff}_{ij},t+\beta\:3\:GIxGPI\_ij,t+\:\beta\:4Lo{g\_Rflow}_{ij},t+\:\alpha\:ij+\:\gamma\:t+\:\epsilon\:ij,t\) .... Eq. 2 Where: Log_RC ij,t = Log value of the remittance cost between sender i to receiver j in year t Log_RFlow ij,t = Log value of bilateral remittance volume between sender i to receiver j in year t GI_Avg ij,t = Average of the globalization index across the corridor pair GPI_Diff ij,t = Absolute difference in global peace index values between the sender and receiver corridors GIxGPI = Interaction term between GI_Avg and GPI_Diff \(\:\alpha\:ij\) = Corridor fixed effects \(\:\gamma\:t\) = Year fixed effects \(\:\epsilon\:ij,t\) = Error term The interaction term allows the marginal effect of globalization on remittance costs to vary with peace asymmetry. Prior to estimation, descriptive statistics and pairwise correlations are examined, and variance inflation factors (VIFs) are computed to assess multicollinearity. Serial correlation is tested using the Wooldridge test, while heteroskedasticity is examined using the Breusch–Pagan test. Cross-sectional dependence is assessed using standard diagnostic tests, and Driscoll–Kraay standard errors are employed to obtain robust inference in the presence of heteroskedasticity, serial correlation, and cross-sectional dependence. Bilateral remittance flows are included as a control variable to account for corridor size effects. While remittance volumes may be jointly determined with remittance costs, their inclusion helps isolate the structural effects of globalization and peace asymmetry; potential endogeneity concerns are addressed through robustness checks discussed later. 4. Empirical Results 4.1. Descriptive statistics Table 2 captures the descriptive statistics. The original variable RC - for remittance cost captures the bilateral cost in percentage terms and therefore is converted to a log value. Similarly, RFlow captures the bilateral remittance flows, which assume values in billions (max is 60655180591), much higher than the value range that other variables carry. Therefore, RFlow is also transformed into logarithm form as Log_RFlow. Table 2 captures the mean, standard deviation, minimum, and maximum values for each of the variables. Table 2 Descriptive statistics Variable Obs Mean Std. Dev. Min Max RC 4010 7.373124161 4.115506974 -0.53 45.85 RFlow 4010 977966303.5 2839100276 0 60655180591 Log_RC 4010 2.018765885 0.460990322 -0.755022584 3.846951009 Log_RFlow 4010 16.15804474 7.389690499 0 24.82847089 GI_Avg 4010 68.49337649 7.624777726 44.41928864 86.67744446 GPI_Diff 4010 20.0619238 11.50969108 0.022140503 59.45953941 GIxGPI 4010 1360.391033 746.6328023 1.436179486 3569.699405 Source: Author's calculations using RStudio Table 2 reports the descriptive statistics for all variables used in the empirical analysis. Remittance cost (RC) is measured as the bilateral cost of sending USD 200 expressed in percentage terms. Given the skewed distribution of remittance costs and the presence of extreme values, the variable is transformed into logarithmic form (Log_RC) for estimation. The minimum value of RC is slightly negative, reflecting corridor-specific pricing practices such as fee discounts or exchange-rate margins net of fees observed in the Remittance Prices Worldwide database. Bilateral remittance flows (RFlow) exhibit substantial dispersion across corridors, ranging from zero to over USD 60 billion, underscoring the highly uneven distribution of global remittance activity. To address scale differences and reduce the influence of extreme outliers, bilateral remittance flows are also log-transformed (Log_RFlow). The log-transformed variables display considerably more stable distributions, supporting their use in a log-linear panel regression framework. The average bilateral remittance cost is approximately 7.37 percent, with a wide standard deviation, indicating significant heterogeneity across remittance corridors. Similarly, the large variation in bilateral remittance flows highlights pronounced corridor-specific scale effects, reinforcing the need to control for corridor-level heterogeneity in the empirical analysis. The bilateral average globalization index (GI_Avg) exhibits meaningful variation across corridors, with values ranging from moderate to high levels of global integration. Peace asymmetry (GPI_Diff) also shows substantial dispersion, suggesting that remittance corridors differ markedly in terms of cross-country security differentials. The interaction term (GIxGPI) reflects this combined variation and provides sufficient spread for identifying moderating effects. Overall, the descriptive statistics indicate considerable heterogeneity across remittance corridors in terms of costs, transaction volumes, globalization, and peace conditions, justifying the use of bilateral fixed effects and supporting the empirical strategy adopted in the study. 4.2. Correlation matrix Table 3 presents the pairwise correlation matrix for the key variables. Bilateral remittance costs (Log_RC) are negatively correlated with bilateral remittance flows (Log_RFlow) and the average level of globalization across corridors (GI_Avg), and positively correlated with peace asymmetry (GPI_Diff). These associations are consistent with the expectation that larger, more globally integrated corridors tend to exhibit lower remittance costs, while greater differences in peace conditions across sending and receiving countries are associated with higher transaction costs. Bilateral remittance flows are positively correlated with globalization and negatively correlated with peace asymmetry, indicating that more integrated and stable corridors tend to sustain larger remittance volumes. Globalization and peace asymmetry themselves are negatively correlated, suggesting that higher levels of global integration are generally associated with more similar peace conditions across countries. As expected, the interaction term between globalization and peace asymmetry (GIxGPI) is highly correlated with its constituent variable, GPI_Diff. Such high correlations are mechanically induced by interaction construction and do not, by themselves, invalidate the regression analysis. Multicollinearity concerns are formally assessed using variance inflation factors and addressed through fixed-effects estimation and robust inference methods in subsequent regressions. Importantly, the correlation matrix is used solely to provide preliminary insights into the direction and strength of bivariate associations. The presence, magnitude, and statistical significance of direct and moderating effects are formally evaluated within the multivariate panel regression framework presented in the next section. Table 3 Correlation matrix Log_RC Log_RFlow GI_avg GPI_diff GIxGPI Log_RC 1 -0.245*** (0.0000) -0.075*** (0.00001) 0.124*** (4.09e-13) 0.125*** (3.86e-13) Log_RFlow -0.245*** (0.0000) 1 0.120*** (3.06e-12) -0.072*** (2.49e-05) -0.059*** (0.00055) GI_avg -0.075*** (0.00001) 0.120*** (3.06e-12) 1 -0.11*** (1.30e-10) 0.039* (0.0246) GPI_diff 0.124*** (4.09e-13) -0.072*** (2.49e-05) -0.11*** (1.30e-10) 1 0.984*** (0.0000) GIxGPI 0.125*** (3.86e-13) -0.059*** (0.00055) 0.039* (0.0246) 0.984*** (0.0000) 1 Source: Author's calculations using RStudio Note 1: p-values in brackets. Significance codes * → p < 0.05, ** → p < 0.01, and *** → p < 0.001 Note 2: Correlation values greater than 0.8 indicate the presence of multicollinearity, e.g., between GIxGPI and GPI_Diff with a coefficient of 0.984. 4.3. VIF - Variance Inflation Factor Matrix The correlation matrix indicated a high pairwise correlation between the interaction term (GIxGPI) and its constituent variable, GPI_Diff, which is mechanically induced by the construction of interaction terms. To formally assess multicollinearity among the explanatory variables, we computed variance inflation factors (VIFs). Table 4 lists VIF values for the main explanatory variables included in the baseline specification. All VIF values are close to unity and well below commonly used thresholds, indicating no multicollinearity concerns among the regressors. The interaction term is excluded from the VIF assessment, as high correlations between interaction terms and their components are expected by construction and do not bias coefficient estimates in fixed-effects models when appropriate inference methods are employed. These results confirm that multicollinearity is unlikely to distort the regression estimates presented in the subsequent analysis. Table 4 VIF Analysis Variable VIF Interpretation GI_avg 1.025 VIF < 5, No multicollinearity concern GPI_diff 1.016 VIF < 5, No multicollinearity concern Log_RFlow 1.018 VIF < 5, No multicollinearity concern Source: Author's calculations using RStudio Serial correlation and heteroskedasticity are assessed following Wooldridge ( 2010 ) and Breusch and Pagan (1979), respectively, while inference relies on Driscoll–Kraay (1998) and two-way clustered standard errors (Cameron et al., 2011 ). Model selection between fixed and random effects is guided by the Hausman ( 1978 ) test. 4.4. Results of the regression test The fixed-effects regression results for the baseline and interaction specifications are in Table 5 , along with instrumental-variable (IV) estimates used to assess potential endogeneity. All models include corridor and year fixed effects, and inference relies on clustered and Driscoll–Kraay standard errors to account for heteroskedasticity, serial correlation, and cross-sectional dependence. Table 5 Regression output Variable FE Base (Clustered SE) FE Interaction (Clustered SE) IV Regression (Endogeneity Check) GI_avg 0.003876 (0.002048) • 0.005466 (0.016481) 0.002139 (0.000880) * GPI_diff 0.002147 (0.001063) * 0.011437 (0.009479) 0.000970 (0.000515) • GIxGPI — -0.000147 (0.000066) * — Log_RFlow -0.009283 (0.001146) *** -0.009070 (0.002191) *** -0.119644 (0.015644) *** Constant — — 1.547 (0.098) *** R² (Within) 0.021 0.025 -0.7913 (Adj.) F-Statistic 23.81 *** 21.03 *** Wald = 31.56 *** Autocorrelation Present Present — Heteroskedasticity Present Present — Robustness Adjustments Clustered, DK SEs Clustered, DK SEs IV with lag instrument Hausman Test (FE vs RE) p = 0.99 (RE) p < 0.001 (FE) — Source: Author's calculations using RStudio Note 1: Standard errors are in parentheses. Note 2: Significance codes: *** p < 0.001, ** p < 0.01, * p < 0.05, • p < 0.10. Note 3: SE – Standard Error, FE = Fixed Effects, RE = Random Effects, IV = Instrumental Variables, DK – Driscoll-Kraay Test Note 4: IV regression used lagged remittance flows as an instrument for Log_RFlow to address potential endogeneity. In the baseline fixed-effects model, bilateral remittance flows are negatively and highly significantly associated with remittance costs, indicating that larger corridors benefit from lower transaction costs. Globalization is positively associated with remittance costs at conventional significance levels, while peace asymmetry also exhibits a positive and weakly significant effect, suggesting that greater differences in peace conditions across corridors are associated with higher remittance costs. The interaction model introduces the interaction between globalization and peace asymmetry. The interaction term is negative and statistically significant under clustered standard errors, implying that peace asymmetry moderates the effect of globalization on remittance costs. However, when inference is based on Driscoll–Kraay standard errors which are more robust to cross-sectional dependence, the interaction effect loses statistical significance. This indicates that evidence for moderation is sensitive to the choice of inference method and should be interpreted cautiously. In contrast, the negative association between remittance flows and costs remains stable across specifications. Diagnostic tests indicate the presence of heteroskedasticity and first-order autocorrelation in the error structure, justifying the use of robust standard errors. Model selection tests yield mixed guidance: while the Hausman test favors random effects in the baseline model, it strongly supports fixed effects in the interaction specification. Given the bilateral corridor structure and the study’s focus on within-corridor variation, fixed-effects estimates are retained as the preferred specification. To address potential endogeneity in bilateral remittance flows, an instrumental-variable regression is estimated using lagged remittance flows as an instrument. The IV coefficient on remittance flows is substantially larger in magnitude than in the fixed-effects models, suggesting attenuation bias in baseline estimates due to endogeneity. The negative adjusted R² in the IV model is a known artifact of limited-information estimation and does not undermine the consistency of the coefficient estimates. Overall, the results provide robust evidence that higher bilateral remittance volumes are associated with lower remittance costs, while the moderating role of peace asymmetry in the globalization–cost relationship remains sensitive to inference assumptions. 5. Interpretation of the results The empirical findings provide strong and consistent evidence that higher bilateral remittance flows are associated with lower remittance costs across corridors. This result holds across baseline fixed-effects estimates, alternative inference methods, and instrumental-variable specifications, underscoring the central role of scale effects in remittance pricing. Larger corridors appear to benefit from economies of scale, greater competition among service providers, and more efficient payment infrastructures, leading to systematically lower transaction costs. In contrast, globalization and peace asymmetry do not exhibit robust cost-reducing effects when considered in isolation. While the interaction between globalization and peace asymmetry suggests a potential moderating role under clustered standard errors, this effect does not persist when inference accounts for cross-sectional dependence using Driscoll–Kraay corrections. This sensitivity indicates that any combined influence of globalization and peace on remittance costs is contingent and weaker than the dominant effect of corridor size. These findings imply that macro-level institutional and geopolitical conditions may shape the broader remittance ecosystem without necessarily translating into direct and measurable reductions in transaction costs once corridor-specific characteristics are controlled for. In particular, differences in peace conditions between sending and receiving countries may increase regulatory scrutiny, compliance costs, or perceived transaction risk, offsetting the efficiency gains typically associated with greater globalization. Overall, the results suggest that remittance cost reductions are driven primarily by market thickness and transaction volume rather than by globalization or peace conditions alone. While institutional stability and global integration remain important contextual factors, policies aimed at expanding formal remittance volumes and improving corridor-level competition are likely to be more effective in lowering costs than macro-level reforms in isolation. 6. Discussion This study examined the determinants of bilateral remittance costs by jointly considering remittance scale, globalization, peace asymmetry, and their interaction within a corridor-level panel framework. Taken together, the findings provide clear answers to the study’s research questions, highlighting the dominant role of corridor scale in remittance cost formation, while indicating that globalization and peace conditions influence costs only indirectly and conditionally. The results provide robust evidence that higher bilateral remittance flows are consistently associated with lower remittance costs, while the independent effects of globalization and peace asymmetry are weak and sensitive to model specification. The moderating role of peace asymmetry in the globalization–cost relationship appears contingent and does not remain robust once cross-sectional dependence is accounted for. The strong and stable effect of remittance flows on costs aligns closely with the scale and competition mechanisms emphasized in the remittance cost literature. Prior corridor-based and panel studies similarly document that higher transaction volumes intensify competition among service providers, dilute fixed compliance and infrastructure costs, and improve operational efficiency, leading to lower remittance prices (Ahmed et al., 2021 ; Jemiluyi & Jeke, 2023 ; Gurira & Parwada, 2025 ; Kim et al., 2024 ). Meta-synthesis evidence further suggests that cost reductions generate substantial feedback effects on remittance volumes, reinforcing the centrality of scale in remittance markets (Mikhaylichenko et al., 2025 ). In contrast, the limited and fragile effects of globalization diverge from expectations in the broader globalization–development literature, which often associates global integration with improved financial efficiency and market access (Radić & Bogdan, 2024 ; Ebaidalla, 2025 ). While globalization proxied by KOF-type indices has been shown to facilitate financial integration, trade openness, and capital mobility, its cost-reducing effects are not guaranteed and may depend on how integration translates into corridor-specific competition and infrastructure (Shaw, 2011 ; Wahyudi et al., 2023 ). This study suggests that globalization primarily operates as an enabling condition rather than a direct determinant of remittance pricing. Without sufficient corridor depth or provider competition, the efficiency gains associated with globalization may not be passed on to migrants. Similarly, peace asymmetry between remittance-sending and receiving countries does not exhibit a robust independent effect on remittance costs, despite extensive literature highlighting the importance of peace, conflict, and post-conflict conditions in shaping remittance reliance and diaspora engagement (Brinkerhoff, 2011 ; Feyissa, 2012 ; True & Hozić, 2020 ). One plausible explanation is that differences in peace conditions raise compliance intensity, transaction monitoring, and perceived risk, which can offset efficiency gains from globalization or financial integration. This is consistent with evidence from conflict-affected and post-conflict settings where remittances play a stabilizing role but remain subject to heightened regulatory scrutiny (Barlas et al., 2025 ; Sharp, 2018 ). The study makes three key contributions. First, it advances remittance cost research by explicitly modeling costs rather than volumes, within a bilateral corridor framework, addressing aggregation biases present in country-level studies. Second, it introduces peace asymmetry as a corridor-specific construct, capturing security differentials that are obscured by unilateral peace measures. Third, by employing IPF-adjusted bilateral remittance matrices, the study provides a replicable methodological approach for analyzing remittance corridors over time despite data limitations. From a policy perspective, the findings suggest that achieving SDG 10.c and G20 remittance cost reduction targets requires prioritizing corridor-level interventions over broad macro-institutional reforms. Policies that expand formal remittance volumes—such as promoting competition among money service providers, improving payment interoperability, supporting fintech adoption, and enhancing transparency are more likely to yield direct cost reductions. With respect to peace and globalization, policy efforts should focus on mitigating how peace asymmetry translates into compliance costs, for example through risk-proportionate AML/CFT frameworks and improved regulatory coordination across corridors. Similarly, globalization-related gains should be leveraged to improve operational efficiency and market access rather than assumed to automatically lower prices. Several limitations warrant acknowledgment. The bilateral remittance matrices rely on interpolation and IPF techniques based on benchmark years, which, while methodologically sound, may not fully capture short-term corridor-specific shocks. Globalization and peace indices may also inadequately reflect provider-level regulatory practices or informal market dynamics. Although instrumental-variable estimation addresses endogeneity concerns related to remittance flows, residual simultaneity between costs and institutional conditions cannot be entirely ruled out. Future research could extend this framework by incorporating corridor-level regulatory data, provider-level pricing behavior, and digital remittance adoption metrics. Examining non-linear or threshold effects of peace asymmetry and globalization, as well as their interaction with AML regimes and financial inclusion policies, would further enhance understanding of remittance cost dynamics. 7. Conclusion This paper examined the determinants of bilateral remittance costs using a corridor-level panel framework that integrates remittance scale, globalization, peace asymmetry, and their interaction. By moving beyond country-level averages and explicitly modeling bilateral corridors, the study provides new evidence on the structural drivers of remittance pricing. The results show that remittance costs are primarily shaped by corridor size. Higher bilateral remittance flows are consistently associated with lower transaction costs, indicating the importance of scale economies and market depth in remittance corridors. In contrast, globalization and peace conditions do not independently generate systematic cost reductions once corridor-specific heterogeneity is accounted for. While peace asymmetry interacts with globalization under limited specifications, this relationship is not robust to more stringent inference, suggesting that macro-level institutional and geopolitical factors influence remittance costs only indirectly. The study contributes to the literature in three ways. First, it provides bilateral evidence on remittance cost formation, an area dominated by aggregate and unilateral analyses. Second, it introduces peace asymmetry as a corridor-level construct, highlighting the relevance of security differentials between sending and receiving countries. Third, it demonstrates the usefulness of IPF-adjusted bilateral remittance matrices for analyzing remittance markets in data-constrained settings. From a policy perspective, the findings underscore that achieving SDG 10.c and G20 remittance cost reduction targets requires a corridor-centric approach. Policies that expand formal remittance volumes, enhance competition among service providers, and improve operational efficiency are likely to be more effective than relying on macro-level globalization or institutional reforms alone. Aligning regulatory frameworks to reduce compliance frictions associated with peace asymmetries may further support cost reductions. Overall, the study highlights that lowering remittance costs is fundamentally a market-structure challenge. Progress toward global cost reduction targets will depend on deepening remittance corridors and translating macro-level integration into tangible, corridor-level efficiencies. Data Repository - Thakur, Swapnilsingh. Remittance Cost and Global Peace - Globalization. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2026-01-04. https://doi.org/10.3886/E242402V1 References Ahmed, J., Mughal, M., & Martínez‐Zarzoso, I. (2021). Sending money home: Transaction cost and remittances to developing countries. The World Economy, 44(8), 2433-2459. Alhassan, U. (2023). E-government and the impact of remittances on new business creation in developing countries. Economic Change and Restructuring, 56(1), 181-214. Badre, A. (2024). Impacts of neo-banks on North African migrants’ remittances and financial inclusion. Scientific African, 26, e02384. Barlas, A. W., Elo, M., & Chand, M. (2025). Impact of remittances from the Afghan diaspora: a case study on the effects in the Samangan province. South Asian Journal of Business Studies, 14(2), 237-253. Brinkerhoff, J. M. (2011). 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A. (1978). Specification tests in econometrics. Econometrica, 46 (6), 1251–1271. https://doi.org/10.2307/1913827 Institute for Economics & Peace. (2023). Global Peace Index 2023: Measuring peace in a complex world . Institute for Economics & Peace. https://www.visionofhumanity.org Itani, N., O’Connell, J. F., & Mason, K. (2013). The impact of emigrants’ homeland relations on air travel demand in a security volatile market: a case study on Lebanon. Journal of Transport Geography, 30, 170-179. Jemiluyi, O. O., & Jeke, L. (2023). How catalytic is digital technology in the nexus between migrants’ remittance and financial development in Sub-Saharan African countries? Economies, 11(3), 74. https://doi.org/10.3390/economies11030074 Khan, I., & Gunwant, D. F. (2024). Application of ARIMA model in forecasting remittance inflows: evidence from Yemen. International Journal of Economic Policy Studies, 18(1), 283-303. Kim, K., Ardaniel, Z., Kikkawa, A., & Endriga, B. (2024). Bilateral remittance inflows to Asia and the Pacific: Countercyclicality and motivations to remit. Asian Development Review, 41(02), 257-300. Mikhaylichenko, M., Trubnik, T., Petrukha, N., Velykyi, Y., & Pylypchenko, O. (2025). Social Stability through Economic Equality and Demographic Response. Revista de cercetare și intervenție socială, 90, 128-154. Radić, M. N., & Bogdan, S. (2024). Intricate nexus of FDI, remittances, emigration, tourism and growth: Navigating economic landscape of Croatia. Equilibrium (1689-765X), 19(3). Sharp, J. M. (2018). Jordan: Background and US Relations (Updated). Current Politics and Economics of the Middle East, 9(2/3), 367-394. Shaw, T. M. (2011). Africa’s second half‐centenary: Sustainable human development/citizen security?. World Journal of Science, Technology and Sustainable Development, 8(2/3), 251-261. Thakur, S. Y., Yadav, P. D., Mankame, Y. S., & Manrai, R. (2025). The impact of anti-money laundering measures on remittance costs: moderating role of frontier technology. Humanities and Social Sciences Communications, 12(1), 1-11. True, J., & Hozić, A. A. (2020). Don’t mention the war! International Financial Institutions and the gendered circuits of violence in post-conflict. Review of International Political Economy, 27(6), 1193-1213. Ullah, A., Pinglu, C., Ullah, S., & Elahi, M. A. (2021). A pre post-COVID–19 pandemic review of regional connectivity and socio-economic development reforms: What can be learned by Central and Eastern European countries from the China-Pakistan economic corridor. Wahyudi, H., Suparta, I. W., & Palupi, W. A. (2023). Long-Term Impact of Inflation and Macroeconomic Variables on Foreign Exchange Reserves in the Organization of Islamic Corporation. WSEAS Transactions on Business and Economics, 20, 1755-1768. Wooldridge, J. M. (2010). Econometric analysis of cross section and panel data (2nd ed.). MIT Press. World Bank. (2024a). Remittances to reach 630 billion in 2022 with record flows into Ukraine [Press release]. https://www.worldbank.org/en/news/press-release/2022/05/11/remittances-to-reach-630-billion-in-2022-with-record-flows-into-ukraine World Bank. (2024b). Remittance prices worldwide . http://remittanceprices.worldbank.org World Bank. (2025c). Personal remittances received (% of GDP); Personal remittances paid [Data]. World Development Indicators. Retrieved from https://data.worldbank.org/indicator/ Wu, W., Hon-Wei, L., Yang, S., Muda, I., & Xu, Z. (2023). Nexus between financial inclusion, workers’ remittances, and unemployment rate in Asian economies. Humanities and Social Sciences Communications, 10(1), 1-10. Additional Declarations The authors declare no competing interests. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-8812118","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":587290490,"identity":"2cac0c5a-9077-4109-8980-d4d68ea6d5cf","order_by":0,"name":"Swapnilsingh Yuwrajsingh Thakur","email":"data:image/png;base64,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","orcid":"https://orcid.org/0000-0002-0495-8158","institution":"SCMS Noida: Symbiosis International (Deemed University) Symbiosis Centre for Management Studies Noida, India","correspondingAuthor":true,"prefix":"","firstName":"Swapnilsingh","middleName":"Yuwrajsingh","lastName":"Thakur","suffix":""},{"id":587290492,"identity":"95e16f1f-2689-4d45-9efc-e6fe1c3f24ee","order_by":1,"name":"Prashant Dev Yadav","email":"","orcid":"","institution":"SCMS Noida: Symbiosis International (Deemed University) Symbiosis Centre for Management Studies Noida, India","correspondingAuthor":false,"prefix":"","firstName":"Prashant","middleName":"Dev","lastName":"Yadav","suffix":""}],"badges":[],"createdAt":"2026-02-07 04:14:36","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-8812118/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-8812118/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":102273901,"identity":"b99d7031-49bd-45d4-bcfd-2bddfd039d68","added_by":"auto","created_at":"2026-02-10 05:11:15","extension":"png","order_by":1,"title":"Figure 1","display":"","copyAsset":false,"role":"figure","size":44501,"visible":true,"origin":"","legend":"\u003cp\u003eConceptual model\u003c/p\u003e\n\u003cp\u003eSource: Author’s compilation\u003c/p\u003e","description":"","filename":"1.png","url":"https://assets-eu.researchsquare.com/files/rs-8812118/v1/9fdda353583f39fe8d06d007.png"},{"id":102298093,"identity":"15bd1a66-146a-442e-9b77-02c289bf8eeb","added_by":"auto","created_at":"2026-02-10 10:30:34","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":945494,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-8812118/v1/37995cb0-79b7-4069-b541-7a81632b5d2a.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003e\u003cstrong\u003eDoes globalization reduce remittance costs? Evidence on the role of peace from bilateral panel data\u003c/strong\u003e\u003c/p\u003e","fulltext":[{"header":"1. Introduction","content":"\u003cp\u003eInternational remittances have become one of the largest and most stable sources of external finance for low- and middle-income countries, often exceeding foreign direct investment and official development assistance in magnitude (World Bank, 2025a). For many developing economies, remittances constitute a critical share of household income and national output, playing an essential role in poverty reduction, consumption smoothing, and macroeconomic stability. Despite their importance, the cost of sending remittances across borders remains persistently high, particularly for corridors linking developing and fragile economies, acting as a direct tax on migrant incomes and reducing the developmental impact of these flows (Silva Filho, 2022; Coutinho et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Thakur et al., 2024).\u003c/p\u003e\n\u003cp\u003eRecognizing this challenge, the international community has placed remittance cost reduction at the center of the global development agenda. Under Sustainable Development Goal (SDG) 10.c, countries have committed to reducing average remittance costs to below 3 percent by 2030 (Badre, \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e), while the G20 has set an interim target of reducing costs to 5 percent by 2027 (G20, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). Yet progress toward these targets has been uneven across regions and corridors, with costs remaining structurally high in many developing-country corridors despite rapid growth in global remittance volumes (World Bank, 2025b). This persistence highlights the need to better understand the forces that shape remittance pricing at the corridor level.\u003c/p\u003e\n\u003cp\u003eA growing body of empirical research identifies remittance scale as a key determinant of remittance costs, emphasizing the role of competition, market thickness, and economies of scale in reducing transaction prices (Ahmed et al., \u003cspan class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jemiluyi \u0026amp; Jeke, \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gurira \u0026amp; Parwada, \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kim et al., \u003cspan class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meta-synthesis evidence further suggests that reductions in remittance costs generate substantial increases in remittance volumes, reinforcing the feedback between scale and pricing (Mikhaylichenko et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). However, much of this literature relies on aggregate or unilateral country-level analyses, implicitly abstracting from the bilateral nature of remittance corridors\u0026mdash;the level at which remittance costs are actually set.\u003c/p\u003e\n\u003cp\u003eBeyond scale effects, two macro-structural factors are theoretically relevant to remittance cost formation but remain insufficiently examined: globalization and peace conditions. Greater globalization, commonly captured through composite indices of economic, social, and political integration such as the KOF Globalisation Index (Dreher, \u003cspan class=\"CitationRef\"\u003e2006\u003c/span\u003e), is expected to facilitate financial openness, improve infrastructure, and reduce cross-border transaction frictions (Shaw, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wahyudi et al., \u003cspan class=\"CitationRef\"\u003e2023\u003c/span\u003e). At the same time, peace and conflict conditions shape transaction risk, regulatory scrutiny, and compliance intensity, particularly in corridors involving fragile or conflict-affected states (Brinkerhoff, \u003cspan class=\"CitationRef\"\u003e2011\u003c/span\u003e; Feyissa, \u003cspan class=\"CitationRef\"\u003e2012\u003c/span\u003e; True \u0026amp; Hozić, \u003cspan class=\"CitationRef\"\u003e2020\u003c/span\u003e). While globalization and peace have been widely studied in relation to remittance volumes and development outcomes, their role in determining remittance costs, especially at the bilateral corridor level, remains poorly understood.\u003c/p\u003e\n\u003cp\u003eThis gap is compounded by the way peace and institutional quality are typically measured in the literature. Existing studies largely rely on unilateral indicators, implicitly assuming symmetry between remittance-sending and receiving countries. Such approaches overlook the possibility that asymmetries in peace conditions across corridors, rather than absolute peace levels, may be more relevant for remittance pricing. Differences in security environments may increase compliance costs, monitoring requirements, and perceived transaction risk, potentially offsetting efficiency gains associated with greater globalization (Sharp, \u003cspan class=\"CitationRef\"\u003e2018\u003c/span\u003e; Barlas et al., \u003cspan class=\"CitationRef\"\u003e2025\u003c/span\u003e). Moreover, little is known about whether globalization amplifies or mitigates the cost implications of such peace asymmetries.\u003c/p\u003e\n\u003cp\u003eThis study addresses these gaps by examining the determinants of bilateral remittance costs across corridors over the period 2011\u0026ndash;2025. Using a corridor-level panel framework, the analysis integrates bilateral remittance flows, globalization, and peace asymmetry within a unified empirical setting. Bilateral remittance flows are constructed using interpolation and iterative proportional fitting techniques to ensure consistency with aggregate country-level totals, while globalization is operationalized using bilateral averages to capture shared exposure to global integration. Peace is modeled as an absolute difference between remittance-sending and receiving countries, capturing corridor-level asymmetry in security conditions (Institute for Economics \u0026amp; Peace, 2023).\u003c/p\u003e\n\u003cp\u003eThe study makes three main contributions. First, it advances the remittance cost literature by shifting the focus from aggregate and unilateral analyses to a bilateral corridor-level framework, where remittance pricing decisions are actually made. Second, it introduces peace asymmetry as a novel corridor-level construct, offering a more nuanced representation of security-related frictions in cross-border transactions. Third, by examining the interaction between globalization and peace asymmetry, the study provides new evidence on whether macro-level integration can offset or amplify the cost implications of security differentials across remittance corridors.\u003c/p\u003e\n\u003cp\u003eGuided by these objectives, the paper addresses the following research questions: To what extent do higher bilateral remittance flows reduce remittance costs across corridors? Does greater globalization across remittance corridors contribute to lower remittance costs? How do asymmetries in peace conditions between remittance-sending and receiving countries influence bilateral remittance costs? Does peace asymmetry moderate the relationship between globalization and remittance costs across corridors?\u003c/p\u003e\n\u003cp\u003eTo organize the analysis, Fig. \u003cspan class=\"InternalRef\"\u003e1\u003c/span\u003e presents the conceptual framework underpinning the study. The framework illustrates the direct effects of bilateral remittance flows and globalization on remittance costs, as well as the role of peace asymmetry as both an independent factor and a moderator of the globalization\u0026ndash;cost relationship.\u003c/p\u003e\n\u003cp\u003eThe remainder of the paper is structured as follows. Section 2 reviews the relevant literature. Section 3 describes the data and empirical methodology. Section 4 presents the results. Section 5 discusses the findings and policy implications in the context of SDG 10.c and the G20 remittance cost agenda. Section 6 concludes.\u003c/p\u003e"},{"header":"2. Review of literature","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1. Remittance Flows and Remittance Costs\u003c/h2\u003e \u003cp\u003eA substantial empirical literature identifies remittance costs as a major friction in international remittance markets, with consistent evidence that higher transaction costs significantly suppress remittance volumes. Across gravity models, panel estimations, and quasi-experimental designs, studies document a negative and economically meaningful elasticity of remittance flows with respect to costs, particularly in high-cost and low-income corridors (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Gurira \u0026amp; Parwada, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Jemiluyi \u0026amp; Jeke, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Meta-level evidence further indicates that achieving global remittance cost reduction targets could unlock substantial additional remittance inflows, reinforcing the policy importance of lowering transfer fees under Sustainable Development Goal 10.c (Mikhaylichenko et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). Bilateral gravity analyses similarly confirm that lower remittance costs are systematically associated with higher bilateral remittance inflows across regions, including Asia and the Pacific (Kim et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eWhile this literature has firmly established remittance costs as a determinant of remittance volumes, it exhibits a clear asymmetry. Remittance costs are typically modeled as exogenous or policy-driven outcomes, shaped by regulation, competition, or technological change, whereas remittance volumes are treated almost exclusively as dependent variables. Consequently, the reverse relationship, whether larger remittance flows reduce remittance costs remains largely unexplored.\u003c/p\u003e \u003cp\u003eFrom a theoretical perspective, there are strong reasons to expect remittance volumes to influence remittance costs. Transaction cost economics and industrial organization theory predict that higher transaction volumes reduce average costs through economies of scale, greater utilization of payment infrastructure, and intensified competition among service providers. In remittance corridors with larger and more stable flows, fixed compliance, operational, and settlement costs can be spread over a greater transaction base, potentially lowering per-unit transfer costs. Gravity-based frameworks further suggest that corridor size and transaction intensity should reduce pricing frictions, analogous to cost\u0026ndash;volume relationships observed in trade and cross-border finance.\u003c/p\u003e \u003cp\u003eDespite these theoretical predictions, empirical validation remains limited. Existing studies predominantly rely on aggregate country-level remittance inflows, masking heterogeneity across bilateral corridors. Even where bilateral data are employed, the analytical focus remains on how costs affect flows, not how flows shape costs. Moreover, remittance costs and volumes are jointly determined, raising endogeneity concerns that are rarely addressed symmetrically; while costs are often instrumented, remittance volumes are seldom treated as endogenous determinants of pricing.\u003c/p\u003e \u003cp\u003eAddressing this gap is critical for understanding remittance costs as an endogenous market outcome rather than a purely regulatory artifact. Accordingly, this study tests whether higher bilateral remittance flows reduce remittance costs across corridors, formally evaluating the null hypothesis that remittance volumes do not influence remittance costs. Thus our hypothesis for bilateral remittance flows become,\u003c/p\u003e \u003cp\u003eH\u003csub\u003e01\u003c/sub\u003e \u0026ndash; Bilateral remittance flow volumes do not reduce remittance costs across corridors\u003c/p\u003e \u003cp\u003eH\u003csub\u003ea1\u003c/sub\u003e \u0026ndash; Larger bilateral remittance flow volumes reduce remittance costs across corridors\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2. Globalization and Remittance Costs\u003c/h2\u003e \u003cp\u003eGlobalization, characterized by the intensification of cross-border trade, finance, migration, and information flows, has long been associated with reductions in transaction frictions in international markets. In the context of remittances, globalization is commonly expected to lower transfer costs by enhancing financial integration, improving payment infrastructure, increasing competition among service providers, and facilitating regulatory convergence across countries (Ebaidalla, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Ullah et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). Despite these expectations, direct empirical evidence linking globalization to remittance pricing remains limited.\u003c/p\u003e \u003cp\u003eMost existing studies examine globalization in relation to remittance volumes or broader macroeconomic and developmental outcomes, rather than remittance costs per se. Empirical work consistently finds that globalization-related dimensions such as trade openness, financial integration, migration intensity, and regional connectivity are positively associated with remittance inflows (Radić \u0026amp; Bogdan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ullah et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e). These findings suggest that greater cross-border connectedness expands remittance channels and lowers informational and institutional barriers to transfers. However, remittance costs are typically treated as unobserved or exogenous, implicitly assuming that globalization affects remittances only through quantities rather than prices.\u003c/p\u003e \u003cp\u003eFrom a theoretical perspective, multiple mechanisms predict a negative relationship between globalization and remittance costs. Transaction cost economics posits that deeper international integration reduces informational asymmetries, coordination failures, and compliance uncertainty, thereby lowering the marginal cost of cross-border financial transactions. Industrial organization theory further suggests that globalization intensifies competition in remittance markets by enabling entry of multinational providers, fintech firms, and digital platforms, exerting downward pressure on prices (Alhassan, \u003cspan citationid=\"CR2\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). Moreover, globalization may facilitate regulatory harmonization and interoperability of payment systems, reducing fixed compliance and settlement costs faced by remittance service providers (Ebaidalla, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eYet empirical validation of these mechanisms remains scarce. Where remittance costs are explicitly analyzed, studies tend to focus on specific policy reforms, technological interventions, or corridor-level regulations rather than globalization as a multidimensional structural process (Wu et al., \u003cspan citationid=\"CR35\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). In addition, globalization is almost exclusively measured at the country level, ignoring bilateral heterogeneity across remittance corridors. Such aggregate measures obscure how differences in global integration between sending and receiving countries jointly shape pricing outcomes in bilateral remittance markets.\u003c/p\u003e \u003cp\u003eA further limitation concerns identification. Globalization may influence remittance costs indirectly through institutional quality, financial development, digital infrastructure, or regulatory capacity, raising concerns about omitted variable bias and conflation of channels. Existing studies rarely disentangle these effects or assess whether globalization exerts an independent influence on remittance pricing once corridor-specific characteristics and scale effects are controlled for (Radić \u0026amp; Bogdan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eGiven these gaps, there is a clear need to empirically assess whether higher globalization reduces remittance costs across bilateral corridors using measures that capture the shared degree of global integration between sending and receiving countries. Treating globalization as a bilateral structural characteristic allows remittance costs to be analyzed as an endogenous outcome of cross-border integration rather than as a purely policy-driven artifact. Thus, our hypothesis become,\u003c/p\u003e \u003cp\u003eH\u003csub\u003e02\u003c/sub\u003e \u0026ndash; Globalization has no significant effect on reducing remittance costs across bilateral remittance corridors\u003c/p\u003e \u003cp\u003eH\u003csub\u003ea2\u003c/sub\u003e \u0026ndash; Higher globalization (measured as bilateral average) reduces remittance costs across bilateral remittance corridors\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3. Peace Asymmetry and Remittance Costs\u003c/h2\u003e \u003cp\u003ePeace and security conditions play a central role in shaping the functioning of financial systems, particularly in cross-border transactions involving fragile and conflict-affected settings. A substantial literature documents the resilience of remittance flows during periods of conflict, political instability, and post-conflict transitions, emphasizing remittances as countercyclical lifelines that support household consumption, poverty alleviation, and social stability in origin countries (Brinkerhoff, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Duval \u0026amp; Wolff, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Diaspora engagement is frequently shown to intensify during episodes of insecurity, reinforcing the importance of remittance channels even when formal institutions are weakened (Feyissa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Brinkerhoff, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eDespite this extensive body of work, the pricing dimension of remittances under varying peace conditions remains largely unexplored. Existing studies overwhelmingly focus on remittance volumes, coping mechanisms, and developmental outcomes, implicitly assuming that remittance systems function similarly across peace and conflict contexts. Where peace and security are considered, they are typically treated as background conditions shaping migration decisions or remittance demand, rather than as determinants of remittance costs (Duval \u0026amp; Wolff, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e; Sharp, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eFrom a theoretical perspective, peace and security are expected to influence remittance costs through multiple channels. Higher levels of insecurity can increase operational risk, compliance burdens, and monitoring costs for remittance service providers, particularly in corridors involving fragile or conflict-affected countries. Transaction cost economics suggests that uncertainty, weak enforcement, and elevated political risk raise the marginal cost of financial intermediation, while political economy perspectives highlight how insecurity may exacerbate de-risking behavior, weaken correspondent banking relationships, and reduce competition in cross-border payment markets (True \u0026amp; Hozić, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e; Sharp, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e). These mechanisms imply that remittance costs may increase even when remittance flows remain strong.\u003c/p\u003e \u003cp\u003eImportantly, existing studies almost exclusively rely on unilateral measures of conflict or peace, overlooking the inherently bilateral nature of remittance corridors. Remittance transactions connect two distinct institutional and security environments, and differences in peace conditions between sending and receiving countries may generate asymmetric regulatory scrutiny, risk premia, and settlement frictions. For example, corridors linking peaceful sending countries to conflict-affected receivers may face higher compliance costs and monitoring intensity than corridors connecting countries with similar peace profiles (Itani et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sharp, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirical evidence on such asymmetries is scarce. Studies that consider peace or conflict typically focus on single-country cases or descriptive analyses of geopolitical risk, limiting their ability to capture corridor-specific pricing dynamics (Feyissa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; Itani et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e). Moreover, peace indicators are rarely incorporated into remittance cost models, leaving an important gap in understanding how security differentials shape remittance pricing.\u003c/p\u003e \u003cp\u003eAddressing this gap requires treating peace not as a unidimensional national attribute, but as a bilateral characteristic of remittance corridors. Measuring peace asymmetry as the absolute difference in peace levels between sending and receiving countries allows remittance costs to be analyzed as an endogenous outcome of cross-border security differentials rather than solely domestic conditions. Thus, our hypothesis become,\u003c/p\u003e \u003cp\u003eH\u003csub\u003e03\u003c/sub\u003e \u0026ndash; Differences in peace levels between remittance sending and receiving countries do not affect bilateral remittance costs\u003c/p\u003e \u003cp\u003eH\u003csub\u003ea3\u003c/sub\u003e \u0026ndash; Greater asymmetry in peace levels (measured as an absolute difference between bilateral remittance corridors) affects bilateral remittance costs\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec6\" class=\"Section2\"\u003e \u003ch2\u003e2.4. Peace Asymmetry as a Moderator of the Globalization\u0026ndash;Remittance Cost Relationship\u003c/h2\u003e \u003cp\u003eWhile globalization is widely expected to reduce transaction frictions in cross-border financial activities, its effectiveness may depend critically on the institutional and security environments in which it operates. A growing literature emphasizes that the benefits of globalization are uneven across countries, particularly in contexts characterized by political instability, conflict, or weak governance structures (Ullah et al., \u003cspan citationid=\"CR29\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; True \u0026amp; Hozić, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). These insights suggest that globalization\u0026rsquo;s influence on remittance systems and specifically on remittance costs may be conditional rather than unconditional.\u003c/p\u003e \u003cp\u003eFrom a theoretical standpoint, globalization reduces transaction costs by enhancing cross-border connectivity, market contestability, and regulatory coordination. However, political economy and institutional theories highlight that such efficiency gains may be attenuated or reversed in insecure environments. In remittance corridors involving fragile or conflict-affected countries, heightened compliance requirements, de-risking behavior by financial institutions, and restricted correspondent banking relationships can offset the cost-reducing effects typically associated with globalization (Sharp, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e; True \u0026amp; Hozić, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). Consequently, similar levels of global integration may generate markedly different pricing outcomes depending on the peace and security conditions of the countries involved.\u003c/p\u003e \u003cp\u003eDespite this theoretical plausibility, existing empirical studies generally examine globalization and peace in isolation. Globalization is typically modeled as a direct determinant of economic performance or remittance volumes, while peace and conflict are treated as background conditions influencing migration decisions or remittance demand (Radić \u0026amp; Bogdan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Duval \u0026amp; Wolff, \u003cspan citationid=\"CR12\" class=\"CitationRef\"\u003e2016\u003c/span\u003e). Very few studies explicitly test whether peace conditions moderate the effects of globalization, and virtually none do so in the context of remittance pricing. This omission is particularly striking given the inherently bilateral nature of remittance corridors, which connect two distinct political, regulatory, and security environments.\u003c/p\u003e \u003cp\u003eThe concept of peace asymmetry offers a useful lens through which to examine this interaction. Differences in peace levels between sending and receiving countries may amplify regulatory mismatches, risk perceptions, and compliance costs, thereby weakening or neutralizing the cost-reducing effects of globalization. For example, globalization may facilitate digital remittance platforms, fintech entry, and competitive pricing in peaceful corridors, while similar gains may be constrained in corridors characterized by large peace differentials due to intensified monitoring and institutional risk aversion (Itani et al., \u003cspan citationid=\"CR19\" class=\"CitationRef\"\u003e2013\u003c/span\u003e; Sharp, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eEmpirically, the absence of moderation analysis reflects both data and modeling constraints. Most studies rely on unilateral measures of globalization and peace, obscuring corridor-specific dynamics and precluding interaction effects. As a result, the literature offers limited insight into whether globalization\u0026rsquo;s influence on remittance costs varies systematically with cross-border security differentials.\u003c/p\u003e \u003cp\u003eBy explicitly modeling peace asymmetry as a moderator, this study advances the literature by integrating globalization and peace within a unified bilateral framework. This approach conceptualizes remittance costs as an endogenous outcome shaped jointly by global integration and the security alignment between remittance partners.\u003c/p\u003e \u003cp\u003eH\u003csub\u003e04\u003c/sub\u003e \u0026ndash; Peace asymmetry does not moderate the effect of globalization on bilateral remittance cost\u003c/p\u003e \u003cp\u003eH\u003csub\u003ea4\u003c/sub\u003e \u0026ndash; Peace asymmetry moderates the effect of globalization on bilateral remittance cost\u003c/p\u003e \u003cp\u003eThe existing literature establishes remittance costs as a critical determinant of remittance flows, yet offers limited insight into the structural forces shaping remittance pricing itself. While globalization and peace are widely recognized as influential factors in cross-border economic interactions, prior studies largely examine their effects in isolation, rely on unilateral country-level measures, or focus on remittance volumes rather than costs. Moreover, the bilateral nature of remittance corridors and the potential interaction between globalization and peace conditions remain underexplored. By jointly examining globalization, peace asymmetry, and their interaction within a bilateral panel framework, this study advances the literature by conceptualizing remittance costs as an endogenous market outcome shaped by cross-border integration and security differentials.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Data and research methodology","content":"\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.1. Data\u003c/h2\u003e \u003cp\u003eRemittance price data are obtained from the World Bank\u0026rsquo;s Remittance Prices Worldwide (RPW) database, which reports corridor-level costs for a pair of remittance sending and receiving countries. The data capture the cost of sending a standardized amount of USD 200 and are originally reported at a quarterly frequency. These series are annualized and cover the period from 2011 to 2025Q1.\u003c/p\u003e \u003cp\u003eIn contrast, official remittance paid and remittance received data are available only at the aggregate country level on an annual basis. Bilateral remittance flow matrices are available for three benchmark years\u0026mdash;2014, 2018, and 2021. To align bilateral remittance flows with the full annual span of the remittance cost data, a two-stage procedure is employed to construct a balanced bilateral panel.\u003c/p\u003e \u003cp\u003eData is sourced from Institute for Economics and Peace, Dreher (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e), and word bank (World Bank. (2024b); World Bank. (2024c)). First, for each year between 2011 and 2025, an initial bilateral \u0026ldquo;seed\u0026rdquo; matrix is generated by interpolating between the observed bilateral matrices for 2014, 2018, and 2021. Corridors that appear in at least one benchmark matrix are retained and interpolated over time, while corridors that are absent in all benchmark years are treated as structural zeros and excluded from the bilateralization process.\u003c/p\u003e \u003cp\u003eSecond, the interpolated seed matrices are adjusted using Iterative Proportional Fitting (IPF), combined with temporal smoothing, to ensure consistency with observed aggregate remittance totals (Deville et al., \u003cspan citationid=\"CR9\" class=\"CitationRef\"\u003e2014\u003c/span\u003e). The IPF procedure scales bilateral flows iteratively so that, for each year, row sums exactly match total remittances paid by each country and column sums match total remittances received. This approach preserves the observed aggregate information while generating internally consistent bilateral remittance matrices for all years in the sample.\u003c/p\u003e \u003cp\u003eThe variables are listed in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e along with their construction rule. Bilateral remittance flows are expressed in U.S. dollars and log-transformed to address skewness and the presence of large outliers.\u003c/p\u003e \u003cp\u003eThe explanatory variables are bilateralized using country-level indices. Globalization is measured using the KOF Globalization Index and constructed as the bilateral average of the sending and receiving countries\u0026rsquo; index values (GI_avg), capturing shared exposure to global economic, social, and political integration. Peace conditions are measured using the Global Peace Index and constructed as the absolute difference between the sending and receiving countries\u0026rsquo; index values (GPI_diff), reflecting asymmetry in security environments that may influence transaction risk and regulatory scrutiny (Institute for Economics \u0026amp; Peace, 2023).\u003c/p\u003e \u003cp\u003eThe dependent variable, bilateral remittance cost expressed as a percentage of the amount sent, is log-transformed to mitigate non-normality and heteroskedasticity. Bilateral remittance flows (log_RFlow) are included as a control variable to account for corridor size effects and to ensure that estimated impacts of globalization and peace are not confounded by transaction volume.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eList of variables\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"5\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVariable Code\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVariable Category\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eConstruction Rule\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eCitation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRemittance Cost (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog_RC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDependent Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBilateral RPW cost (corridor, % of amount sent) with log transformation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld Bank. (2024b)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobalization Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGI_avg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eIndependent Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBilateral average of sender \u0026amp; receiver globalization index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eDreher (\u003cspan citationid=\"CR10\" class=\"CitationRef\"\u003e2006\u003c/span\u003e)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobal Peace Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGPI_diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eModerator\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBilateral absolute difference between sender \u0026amp; receiver\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e(Institute for Economics \u0026amp; Peace, 2023)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGlobalization \u0026times; Peace Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eGIxGPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInteraction Term\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eProduct of GI_avg and GPI_diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eBilateral Remittance Flow (USD)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog_RFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eControl Variable\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIPF-adjusted bilateral remittance value (USD) with log transformation.\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eWorld Bank. (2024c)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec9\" class=\"Section2\"\u003e \u003ch2\u003e3.2. Research methodology\u003c/h2\u003e \u003cp\u003eThe study employs a panel data regression framework with bilateral corridor fixed effects to account for unobserved, time-invariant heterogeneity across remittance corridors. Such heterogeneity may arise from persistent corridor-specific characteristics, including historical migration links, geographic distance, regulatory environments, and financial infrastructure. Year fixed effects are included to control for common global shocks and time-specific factors affecting remittance markets.\u003c/p\u003e \u003cp\u003eThe baseline empirical specification examines the direct effects of globalization and peace asymmetry on bilateral remittance costs and is given by:\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 1: Base Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Lo{g\\_RC}_{ij},t=\\:\\beta\\:1\\:G{I\\_Avg}_{ij},t\\:+\\:\\beta\\:2GP{I\\_Diff}_{ij},t\\:+\\:\\beta\\:3Lo{g\\_Rflow}_{ij},t+\\:\\alpha\\:ij+\\:\\gamma\\:t+\\:ϵij,t\\:\\)\u003c/span\u003e \u003c/span\u003e .................\u003cb\u003eEq.\u0026nbsp;1\u003c/b\u003e\u003c/p\u003e \u003cp\u003eTo examine whether peace asymmetry moderates the effect of globalization on remittance costs, the interaction term between globalization and peace asymmetry is introduced in the extended specification:\u003c/p\u003e \u003cp\u003e \u003cb\u003eModel 2: Interaction Model\u003c/b\u003e \u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:Lo{g}_{RCij},t=\\:\\beta\\:0+\\:\\beta\\:1G{I\\_Avg}_{ij},t+\\beta\\:2GP{I\\_Diff}_{ij},t+\\beta\\:3\\:GIxGPI\\_ij,t+\\:\\beta\\:4Lo{g\\_Rflow}_{ij},t+\\:\\alpha\\:ij+\\:\\gamma\\:t+\\:\\epsilon\\:ij,t\\)\u003c/span\u003e \u003c/span\u003e ....\u003cb\u003eEq.\u0026nbsp;2\u003c/b\u003e\u003c/p\u003e \u003cp\u003eWhere:\u003c/p\u003e \u003cp\u003eLog_RC\u003csub\u003eij,t\u003c/sub\u003e = Log value of the remittance cost between sender \u003cem\u003ei\u003c/em\u003e to receiver \u003cem\u003ej\u003c/em\u003e in year t\u003c/p\u003e \u003cp\u003eLog_RFlow\u003csub\u003eij,t\u003c/sub\u003e = Log value of bilateral remittance volume between sender \u003cem\u003ei\u003c/em\u003e to receiver \u003cem\u003ej\u003c/em\u003e in year t\u003c/p\u003e \u003cp\u003eGI_Avg\u003csub\u003eij,t\u003c/sub\u003e = Average of the globalization index across the corridor pair\u003c/p\u003e \u003cp\u003eGPI_Diff\u003csub\u003eij,t\u003c/sub\u003e = Absolute difference in global peace index values between the sender and receiver corridors\u003c/p\u003e \u003cp\u003eGIxGPI\u0026thinsp;=\u0026thinsp;Interaction term between GI_Avg and GPI_Diff\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\alpha\\:ij\\)\u003c/span\u003e \u003c/span\u003e = Corridor fixed effects\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\gamma\\:t\\)\u003c/span\u003e \u003c/span\u003e = Year fixed effects\u003c/p\u003e \u003cp\u003e \u003cspan class=\"InlineEquation\"\u003e \u003cspan class=\"mathinline\"\u003e\\(\\:\\epsilon\\:ij,t\\)\u003c/span\u003e \u003c/span\u003e = Error term\u003c/p\u003e \u003cp\u003eThe interaction term allows the marginal effect of globalization on remittance costs to vary with peace asymmetry.\u003c/p\u003e \u003cp\u003ePrior to estimation, descriptive statistics and pairwise correlations are examined, and variance inflation factors (VIFs) are computed to assess multicollinearity. Serial correlation is tested using the Wooldridge test, while heteroskedasticity is examined using the Breusch\u0026ndash;Pagan test. Cross-sectional dependence is assessed using standard diagnostic tests, and Driscoll\u0026ndash;Kraay standard errors are employed to obtain robust inference in the presence of heteroskedasticity, serial correlation, and cross-sectional dependence.\u003c/p\u003e \u003cp\u003eBilateral remittance flows are included as a control variable to account for corridor size effects. While remittance volumes may be jointly determined with remittance costs, their inclusion helps isolate the structural effects of globalization and peace asymmetry; potential endogeneity concerns are addressed through robustness checks discussed later.\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Empirical Results","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.1. Descriptive statistics\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e captures the descriptive statistics. The original variable RC - for remittance cost captures the bilateral cost in percentage terms and therefore is converted to a log value. Similarly, RFlow captures the bilateral remittance flows, which assume values in billions (max is 60655180591), much higher than the value range that other variables carry. Therefore, RFlow is also transformed into logarithm form as Log_RFlow. Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e captures the mean, standard deviation, minimum, and maximum values for each of the variables.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eDescriptive statistics\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eObs\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMean\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eStd. Dev.\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eMin\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eMax\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e7.373124161\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e4.115506974\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.53\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e45.85\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e977966303.5\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e2839100276\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e60655180591\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog_RC\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e2.018765885\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.460990322\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.755022584\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3.846951009\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog_RFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e16.15804474\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.389690499\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e24.82847089\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI_Avg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e68.49337649\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e7.624777726\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e44.41928864\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e86.67744446\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPI_Diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e20.0619238\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e11.50969108\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.022140503\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e59.45953941\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIxGPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e4010\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e1360.391033\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e746.6328023\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1.436179486\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e3569.699405\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: Author's calculations using RStudio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reports the descriptive statistics for all variables used in the empirical analysis. Remittance cost (RC) is measured as the bilateral cost of sending USD 200 expressed in percentage terms. Given the skewed distribution of remittance costs and the presence of extreme values, the variable is transformed into logarithmic form (Log_RC) for estimation. The minimum value of RC is slightly negative, reflecting corridor-specific pricing practices such as fee discounts or exchange-rate margins net of fees observed in the Remittance Prices Worldwide database. Bilateral remittance flows (RFlow) exhibit substantial dispersion across corridors, ranging from zero to over USD 60\u0026nbsp;billion, underscoring the highly uneven distribution of global remittance activity. To address scale differences and reduce the influence of extreme outliers, bilateral remittance flows are also log-transformed (Log_RFlow). The log-transformed variables display considerably more stable distributions, supporting their use in a log-linear panel regression framework.\u003c/p\u003e \u003cp\u003eThe average bilateral remittance cost is approximately 7.37 percent, with a wide standard deviation, indicating significant heterogeneity across remittance corridors. Similarly, the large variation in bilateral remittance flows highlights pronounced corridor-specific scale effects, reinforcing the need to control for corridor-level heterogeneity in the empirical analysis. The bilateral average globalization index (GI_Avg) exhibits meaningful variation across corridors, with values ranging from moderate to high levels of global integration. Peace asymmetry (GPI_Diff) also shows substantial dispersion, suggesting that remittance corridors differ markedly in terms of cross-country security differentials. The interaction term (GIxGPI) reflects this combined variation and provides sufficient spread for identifying moderating effects.\u003c/p\u003e \u003cp\u003eOverall, the descriptive statistics indicate considerable heterogeneity across remittance corridors in terms of costs, transaction volumes, globalization, and peace conditions, justifying the use of bilateral fixed effects and supporting the empirical strategy adopted in the study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.2. Correlation matrix\u003c/h2\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e presents the pairwise correlation matrix for the key variables. Bilateral remittance costs (Log_RC) are negatively correlated with bilateral remittance flows (Log_RFlow) and the average level of globalization across corridors (GI_Avg), and positively correlated with peace asymmetry (GPI_Diff). These associations are consistent with the expectation that larger, more globally integrated corridors tend to exhibit lower remittance costs, while greater differences in peace conditions across sending and receiving countries are associated with higher transaction costs. Bilateral remittance flows are positively correlated with globalization and negatively correlated with peace asymmetry, indicating that more integrated and stable corridors tend to sustain larger remittance volumes. Globalization and peace asymmetry themselves are negatively correlated, suggesting that higher levels of global integration are generally associated with more similar peace conditions across countries.\u003c/p\u003e \u003cp\u003eAs expected, the interaction term between globalization and peace asymmetry (GIxGPI) is highly correlated with its constituent variable, GPI_Diff. Such high correlations are mechanically induced by interaction construction and do not, by themselves, invalidate the regression analysis. Multicollinearity concerns are formally assessed using variance inflation factors and addressed through fixed-effects estimation and robust inference methods in subsequent regressions. Importantly, the correlation matrix is used solely to provide preliminary insights into the direction and strength of bivariate associations. The presence, magnitude, and statistical significance of direct and moderating effects are formally evaluated within the multivariate panel regression framework presented in the next section.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab3\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 3\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eCorrelation matrix\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"6\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e\u0026nbsp;\u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eLog_RC\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eLog_RFlow\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eGI_avg\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eGPI_diff\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c6\"\u003e \u003cp\u003eGIxGPI\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLog_RC\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.245*** \u003c/p\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.075*** \u003c/p\u003e \u003cp\u003e(0.00001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.124*** \u003c/p\u003e \u003cp\u003e(4.09e-13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.125*** \u003c/p\u003e \u003cp\u003e(3.86e-13)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eLog_RFlow\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.245*** \u003c/p\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.120*** \u003c/p\u003e \u003cp\u003e(3.06e-12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.072***\u003c/p\u003e \u003cp\u003e(2.49e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e-0.059*** \u003c/p\u003e \u003cp\u003e(0.00055)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGI_avg\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.075*** \u003c/p\u003e \u003cp\u003e(0.00001)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.120*** \u003c/p\u003e \u003cp\u003e(3.06e-12)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e-0.11*** \u003c/p\u003e \u003cp\u003e(1.30e-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.039* \u003c/p\u003e \u003cp\u003e(0.0246)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGPI_diff\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.124*** \u003c/p\u003e \u003cp\u003e(4.09e-13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.072***\u003c/p\u003e \u003cp\u003e (2.49e-05)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.11***\u003c/p\u003e \u003cp\u003e(1.30e-10)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e0.984***\u003c/p\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003e\u003cb\u003eGIxGPI\u003c/b\u003e\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.125***\u003c/p\u003e \u003cp\u003e(3.86e-13)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.059***\u003c/p\u003e \u003cp\u003e(0.00055)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.039*\u003c/p\u003e \u003cp\u003e(0.0246)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e0.984*** \u003c/p\u003e \u003cp\u003e(0.0000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c6\"\u003e \u003cp\u003e1\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eSource: Author's calculations using RStudio\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote 1: p-values in brackets. Significance codes * \u0026rarr; p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, ** \u0026rarr; p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, and *** \u0026rarr; p\u0026thinsp;\u0026lt;\u0026thinsp;0.001\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"6\"\u003eNote 2: Correlation values greater than 0.8 indicate the presence of multicollinearity, e.g., between GIxGPI and GPI_Diff with a coefficient of 0.984.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.3. VIF - Variance Inflation Factor Matrix\u003c/h2\u003e \u003cp\u003eThe correlation matrix indicated a high pairwise correlation between the interaction term (GIxGPI) and its constituent variable, GPI_Diff, which is mechanically induced by the construction of interaction terms. To formally assess multicollinearity among the explanatory variables, we computed variance inflation factors (VIFs).\u003c/p\u003e \u003cp\u003eTable\u0026nbsp;\u003cspan refid=\"Tab4\" class=\"InternalRef\"\u003e4\u003c/span\u003e lists VIF values for the main explanatory variables included in the baseline specification. All VIF values are close to unity and well below commonly used thresholds, indicating no multicollinearity concerns among the regressors. The interaction term is excluded from the VIF assessment, as high correlations between interaction terms and their components are expected by construction and do not bias coefficient estimates in fixed-effects models when appropriate inference methods are employed.\u003c/p\u003e \u003cp\u003eThese results confirm that multicollinearity is unlikely to distort the regression estimates presented in the subsequent analysis.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab4\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 4\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eVIF Analysis\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"3\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eVIF\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eInterpretation\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI_avg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u0026thinsp;\u0026lt;\u0026thinsp;5, No multicollinearity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPI_diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.016\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u0026thinsp;\u0026lt;\u0026thinsp;5, No multicollinearity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog_RFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e1.018\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eVIF\u0026thinsp;\u0026lt;\u0026thinsp;5, No multicollinearity concern\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"3\"\u003eSource: Author's calculations using RStudio\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eSerial correlation and heteroskedasticity are assessed following Wooldridge (\u003cspan citationid=\"CR31\" class=\"CitationRef\"\u003e2010\u003c/span\u003e) and Breusch and Pagan (1979), respectively, while inference relies on Driscoll\u0026ndash;Kraay (1998) and two-way clustered standard errors (Cameron et al., \u003cspan citationid=\"CR6\" class=\"CitationRef\"\u003e2011\u003c/span\u003e). Model selection between fixed and random effects is guided by the Hausman (\u003cspan citationid=\"CR17\" class=\"CitationRef\"\u003e1978\u003c/span\u003e) test.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec14\" class=\"Section2\"\u003e \u003ch2\u003e4.4. Results of the regression test\u003c/h2\u003e \u003cp\u003eThe fixed-effects regression results for the baseline and interaction specifications are in Table\u0026nbsp;\u003cspan refid=\"Tab5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, along with instrumental-variable (IV) estimates used to assess potential endogeneity. All models include corridor and year fixed effects, and inference relies on clustered and Driscoll\u0026ndash;Kraay standard errors to account for heteroskedasticity, serial correlation, and cross-sectional dependence.\u003c/p\u003e \u003cp\u003e \u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab5\" border=\"1\"\u003e \u003ccaption language=\"En\"\u003e \u003cdiv class=\"CaptionNumber\"\u003eTable 5\u003c/div\u003e \u003cdiv class=\"CaptionContent\"\u003e \u003cp\u003eRegression output\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"4\"\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cthead\u003e \u003ctr\u003e \u003cth align=\"left\" colname=\"c1\"\u003e \u003cp\u003eVariable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eFE Base (Clustered SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eFE Interaction (Clustered SE)\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIV Regression (Endogeneity Check)\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGI_avg\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.003876 (0.002048) \u0026bull;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.005466 (0.016481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.002139 (0.000880) *\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGPI_diff\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.002147 (0.001063) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.011437 (0.009479)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e0.000970 (0.000515) \u0026bull;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eGIxGPI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.000147 (0.000066) *\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eLog_RFlow\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e-0.009283 (0.001146) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e-0.009070 (0.002191) ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.119644 (0.015644) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eConstant\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e1.547 (0.098) ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eR\u0026sup2; (Within)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.021\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e-0.7913 (Adj.)\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eF-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e23.81 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003e21.03 ***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWald\u0026thinsp;=\u0026thinsp;31.56 ***\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eAutocorrelation\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHeteroskedasticity\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ePresent\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eRobustness Adjustments\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eClustered, DK SEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eClustered, DK SEs\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eIV with lag instrument\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHausman Test (FE vs RE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003ep\u0026thinsp;=\u0026thinsp;0.99 (RE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003ep\u0026thinsp;\u0026lt;\u0026thinsp;0.001 (FE)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003e\u0026mdash;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eSource: Author's calculations using RStudio\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote 1: Standard errors are in parentheses.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote 2: Significance codes: *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.001, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, \u0026bull; p\u0026thinsp;\u0026lt;\u0026thinsp;0.10.\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote 3: SE \u0026ndash; Standard Error, FE\u0026thinsp;=\u0026thinsp;Fixed Effects, RE\u0026thinsp;=\u0026thinsp;Random Effects, IV\u0026thinsp;=\u0026thinsp;Instrumental Variables, DK \u0026ndash; Driscoll-Kraay Test\u003c/td\u003e\u003c/tr\u003e \u003ctr\u003e\u003ctd colspan=\"4\"\u003eNote 4: IV regression used lagged remittance flows as an instrument for Log_RFlow to address potential endogeneity.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eIn the baseline fixed-effects model, bilateral remittance flows are negatively and highly significantly associated with remittance costs, indicating that larger corridors benefit from lower transaction costs. Globalization is positively associated with remittance costs at conventional significance levels, while peace asymmetry also exhibits a positive and weakly significant effect, suggesting that greater differences in peace conditions across corridors are associated with higher remittance costs.\u003c/p\u003e \u003cp\u003eThe interaction model introduces the interaction between globalization and peace asymmetry. The interaction term is negative and statistically significant under clustered standard errors, implying that peace asymmetry moderates the effect of globalization on remittance costs. However, when inference is based on Driscoll\u0026ndash;Kraay standard errors which are more robust to cross-sectional dependence, the interaction effect loses statistical significance. This indicates that evidence for moderation is sensitive to the choice of inference method and should be interpreted cautiously. In contrast, the negative association between remittance flows and costs remains stable across specifications.\u003c/p\u003e \u003cp\u003eDiagnostic tests indicate the presence of heteroskedasticity and first-order autocorrelation in the error structure, justifying the use of robust standard errors. Model selection tests yield mixed guidance: while the Hausman test favors random effects in the baseline model, it strongly supports fixed effects in the interaction specification. Given the bilateral corridor structure and the study\u0026rsquo;s focus on within-corridor variation, fixed-effects estimates are retained as the preferred specification.\u003c/p\u003e \u003cp\u003eTo address potential endogeneity in bilateral remittance flows, an instrumental-variable regression is estimated using lagged remittance flows as an instrument. The IV coefficient on remittance flows is substantially larger in magnitude than in the fixed-effects models, suggesting attenuation bias in baseline estimates due to endogeneity. The negative adjusted R\u0026sup2; in the IV model is a known artifact of limited-information estimation and does not undermine the consistency of the coefficient estimates.\u003c/p\u003e \u003cp\u003eOverall, the results provide robust evidence that higher bilateral remittance volumes are associated with lower remittance costs, while the moderating role of peace asymmetry in the globalization\u0026ndash;cost relationship remains sensitive to inference assumptions.\u003c/p\u003e \u003c/div\u003e"},{"header":"5. Interpretation of the results","content":"\u003cp\u003eThe empirical findings provide strong and consistent evidence that higher bilateral remittance flows are associated with lower remittance costs across corridors. This result holds across baseline fixed-effects estimates, alternative inference methods, and instrumental-variable specifications, underscoring the central role of scale effects in remittance pricing. Larger corridors appear to benefit from economies of scale, greater competition among service providers, and more efficient payment infrastructures, leading to systematically lower transaction costs.\u003c/p\u003e \u003cp\u003eIn contrast, globalization and peace asymmetry do not exhibit robust cost-reducing effects when considered in isolation. While the interaction between globalization and peace asymmetry suggests a potential moderating role under clustered standard errors, this effect does not persist when inference accounts for cross-sectional dependence using Driscoll\u0026ndash;Kraay corrections. This sensitivity indicates that any combined influence of globalization and peace on remittance costs is contingent and weaker than the dominant effect of corridor size.\u003c/p\u003e \u003cp\u003eThese findings imply that macro-level institutional and geopolitical conditions may shape the broader remittance ecosystem without necessarily translating into direct and measurable reductions in transaction costs once corridor-specific characteristics are controlled for. In particular, differences in peace conditions between sending and receiving countries may increase regulatory scrutiny, compliance costs, or perceived transaction risk, offsetting the efficiency gains typically associated with greater globalization.\u003c/p\u003e \u003cp\u003eOverall, the results suggest that remittance cost reductions are driven primarily by market thickness and transaction volume rather than by globalization or peace conditions alone. While institutional stability and global integration remain important contextual factors, policies aimed at expanding formal remittance volumes and improving corridor-level competition are likely to be more effective in lowering costs than macro-level reforms in isolation.\u003c/p\u003e"},{"header":"6. Discussion","content":"\u003cp\u003eThis study examined the determinants of bilateral remittance costs by jointly considering remittance scale, globalization, peace asymmetry, and their interaction within a corridor-level panel framework. Taken together, the findings provide clear answers to the study\u0026rsquo;s research questions, highlighting the dominant role of corridor scale in remittance cost formation, while indicating that globalization and peace conditions influence costs only indirectly and conditionally.\u003c/p\u003e \u003cp\u003eThe results provide robust evidence that higher bilateral remittance flows are consistently associated with lower remittance costs, while the independent effects of globalization and peace asymmetry are weak and sensitive to model specification. The moderating role of peace asymmetry in the globalization\u0026ndash;cost relationship appears contingent and does not remain robust once cross-sectional dependence is accounted for.\u003c/p\u003e \u003cp\u003eThe strong and stable effect of remittance flows on costs aligns closely with the scale and competition mechanisms emphasized in the remittance cost literature. Prior corridor-based and panel studies similarly document that higher transaction volumes intensify competition among service providers, dilute fixed compliance and infrastructure costs, and improve operational efficiency, leading to lower remittance prices (Ahmed et al., \u003cspan citationid=\"CR1\" class=\"CitationRef\"\u003e2021\u003c/span\u003e; Jemiluyi \u0026amp; Jeke, \u003cspan citationid=\"CR20\" class=\"CitationRef\"\u003e2023\u003c/span\u003e; Gurira \u0026amp; Parwada, \u003cspan citationid=\"CR16\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Kim et al., \u003cspan citationid=\"CR22\" class=\"CitationRef\"\u003e2024\u003c/span\u003e). Meta-synthesis evidence further suggests that cost reductions generate substantial feedback effects on remittance volumes, reinforcing the centrality of scale in remittance markets (Mikhaylichenko et al., \u003cspan citationid=\"CR23\" class=\"CitationRef\"\u003e2025\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eIn contrast, the limited and fragile effects of globalization diverge from expectations in the broader globalization\u0026ndash;development literature, which often associates global integration with improved financial efficiency and market access (Radić \u0026amp; Bogdan, \u003cspan citationid=\"CR24\" class=\"CitationRef\"\u003e2024\u003c/span\u003e; Ebaidalla, \u003cspan citationid=\"CR13\" class=\"CitationRef\"\u003e2025\u003c/span\u003e). While globalization proxied by KOF-type indices has been shown to facilitate financial integration, trade openness, and capital mobility, its cost-reducing effects are not guaranteed and may depend on how integration translates into corridor-specific competition and infrastructure (Shaw, \u003cspan citationid=\"CR26\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Wahyudi et al., \u003cspan citationid=\"CR30\" class=\"CitationRef\"\u003e2023\u003c/span\u003e). This study suggests that globalization primarily operates as an enabling condition rather than a direct determinant of remittance pricing. Without sufficient corridor depth or provider competition, the efficiency gains associated with globalization may not be passed on to migrants.\u003c/p\u003e \u003cp\u003eSimilarly, peace asymmetry between remittance-sending and receiving countries does not exhibit a robust independent effect on remittance costs, despite extensive literature highlighting the importance of peace, conflict, and post-conflict conditions in shaping remittance reliance and diaspora engagement (Brinkerhoff, \u003cspan citationid=\"CR5\" class=\"CitationRef\"\u003e2011\u003c/span\u003e; Feyissa, \u003cspan citationid=\"CR14\" class=\"CitationRef\"\u003e2012\u003c/span\u003e; True \u0026amp; Hozić, \u003cspan citationid=\"CR28\" class=\"CitationRef\"\u003e2020\u003c/span\u003e). One plausible explanation is that differences in peace conditions raise compliance intensity, transaction monitoring, and perceived risk, which can offset efficiency gains from globalization or financial integration. This is consistent with evidence from conflict-affected and post-conflict settings where remittances play a stabilizing role but remain subject to heightened regulatory scrutiny (Barlas et al., \u003cspan citationid=\"CR4\" class=\"CitationRef\"\u003e2025\u003c/span\u003e; Sharp, \u003cspan citationid=\"CR25\" class=\"CitationRef\"\u003e2018\u003c/span\u003e).\u003c/p\u003e \u003cp\u003eThe study makes three key contributions. First, it advances remittance cost research by explicitly modeling costs rather than volumes, within a bilateral corridor framework, addressing aggregation biases present in country-level studies. Second, it introduces peace asymmetry as a corridor-specific construct, capturing security differentials that are obscured by unilateral peace measures. Third, by employing IPF-adjusted bilateral remittance matrices, the study provides a replicable methodological approach for analyzing remittance corridors over time despite data limitations.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, the findings suggest that achieving SDG 10.c and G20 remittance cost reduction targets requires prioritizing corridor-level interventions over broad macro-institutional reforms. Policies that expand formal remittance volumes\u0026mdash;such as promoting competition among money service providers, improving payment interoperability, supporting fintech adoption, and enhancing transparency are more likely to yield direct cost reductions. With respect to peace and globalization, policy efforts should focus on mitigating how peace asymmetry translates into compliance costs, for example through risk-proportionate AML/CFT frameworks and improved regulatory coordination across corridors. Similarly, globalization-related gains should be leveraged to improve operational efficiency and market access rather than assumed to automatically lower prices.\u003c/p\u003e \u003cp\u003eSeveral limitations warrant acknowledgment. The bilateral remittance matrices rely on interpolation and IPF techniques based on benchmark years, which, while methodologically sound, may not fully capture short-term corridor-specific shocks. Globalization and peace indices may also inadequately reflect provider-level regulatory practices or informal market dynamics. Although instrumental-variable estimation addresses endogeneity concerns related to remittance flows, residual simultaneity between costs and institutional conditions cannot be entirely ruled out.\u003c/p\u003e \u003cp\u003eFuture research could extend this framework by incorporating corridor-level regulatory data, provider-level pricing behavior, and digital remittance adoption metrics. Examining non-linear or threshold effects of peace asymmetry and globalization, as well as their interaction with AML regimes and financial inclusion policies, would further enhance understanding of remittance cost dynamics.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis paper examined the determinants of bilateral remittance costs using a corridor-level panel framework that integrates remittance scale, globalization, peace asymmetry, and their interaction. By moving beyond country-level averages and explicitly modeling bilateral corridors, the study provides new evidence on the structural drivers of remittance pricing.\u003c/p\u003e \u003cp\u003eThe results show that remittance costs are primarily shaped by corridor size. Higher bilateral remittance flows are consistently associated with lower transaction costs, indicating the importance of scale economies and market depth in remittance corridors. In contrast, globalization and peace conditions do not independently generate systematic cost reductions once corridor-specific heterogeneity is accounted for. While peace asymmetry interacts with globalization under limited specifications, this relationship is not robust to more stringent inference, suggesting that macro-level institutional and geopolitical factors influence remittance costs only indirectly.\u003c/p\u003e \u003cp\u003eThe study contributes to the literature in three ways. First, it provides bilateral evidence on remittance cost formation, an area dominated by aggregate and unilateral analyses. Second, it introduces peace asymmetry as a corridor-level construct, highlighting the relevance of security differentials between sending and receiving countries. Third, it demonstrates the usefulness of IPF-adjusted bilateral remittance matrices for analyzing remittance markets in data-constrained settings.\u003c/p\u003e \u003cp\u003eFrom a policy perspective, the findings underscore that achieving SDG 10.c and G20 remittance cost reduction targets requires a corridor-centric approach. Policies that expand formal remittance volumes, enhance competition among service providers, and improve operational efficiency are likely to be more effective than relying on macro-level globalization or institutional reforms alone. Aligning regulatory frameworks to reduce compliance frictions associated with peace asymmetries may further support cost reductions.\u003c/p\u003e \u003cp\u003eOverall, the study highlights that lowering remittance costs is fundamentally a market-structure challenge. Progress toward global cost reduction targets will depend on deepening remittance corridors and translating macro-level integration into tangible, corridor-level efficiencies.\u003c/p\u003e \u003cp\u003e \u003cb\u003eData Repository -\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThakur, Swapnilsingh. Remittance Cost and Global Peace - Globalization. Ann Arbor, MI: Inter-university Consortium for Political and Social Research [distributor], 2026-01-04. \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttps://doi.org/10.3886/E242402V1\u003c/span\u003e\u003cspan address=\"10.3886/E242402V1\" targettype=\"DOI\" class=\"RefTarget\"\u003e\u003c/span\u003e\u003c/span\u003e\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eAhmed, J., Mughal, M., \u0026amp; Mart\u0026iacute;nez‐Zarzoso, I. (2021). Sending money home: Transaction cost and remittances to developing countries. 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(2024a). \u003cem\u003eRemittances to reach 630 billion in 2022 with record flows into Ukraine\u003c/em\u003e [Press release]. https://www.worldbank.org/en/news/press-release/2022/05/11/remittances-to-reach-630-billion-in-2022-with-record-flows-into-ukraine\u003c/li\u003e\n\u003cli\u003eWorld Bank. (2024b). \u003cem\u003eRemittance prices worldwide\u003c/em\u003e. http://remittanceprices.worldbank.org\u003c/li\u003e\n\u003cli\u003eWorld Bank. (2025c). \u003cem\u003ePersonal remittances received (% of GDP); Personal remittances paid\u003c/em\u003e [Data]. World Development Indicators. Retrieved from https://data.worldbank.org/indicator/\u003c/li\u003e\n\u003cli\u003eWu, W., Hon-Wei, L., Yang, S., Muda, I., \u0026amp; Xu, Z. (2023). Nexus between financial inclusion, workers\u0026rsquo; remittances, and unemployment rate in Asian economies. Humanities and Social Sciences Communications, 10(1), 1-10. \u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"SCMS Noida: Symbiosis International (Deemed University) Symbiosis Centre for Management Studies Noida, India","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Remittance costs, bilateral remittance corridors, globalization, Global peace asymmetry, SDG 10.c, panel data","lastPublishedDoi":"10.21203/rs.3.rs-8812118/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-8812118/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cstrong\u003eBackground: \u003c/strong\u003eReducing remittance costs is a central objective of Sustainable Development Goal (SDG) 10.c and a priority under the G20 remittance agenda. Despite extensive empirical work, progress toward these targets remains uneven, partly due to the limited availability of bilateral data needed to examine corridor-level determinants of remittance costs.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003ePurpose:\u003c/strong\u003e This study investigates how bilateral remittance flows, globalization, and peace asymmetry jointly influence remittance costs across international corridors, addressing a key gap in the remittance cost literature.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eMethodology:\u003c/strong\u003e The study constructs a balanced bilateral panel dataset for 2011–2025 by generating corridor-level remittance flows using interpolation combined with Iterative Proportional Fitting (IPF). Globalization is measured as the bilateral average of country-level globalization indices, while peace asymmetry is captured as the absolute difference in peace levels between sending and receiving countries. The analysis employs corridor fixed-effects panel regression with clustered and Driscoll–Kraay standard errors, complemented by instrumental variable estimation to address endogeneity.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eFindings:\u003c/strong\u003e The results show that higher bilateral remittance flows significantly reduce remittance costs, confirming strong scale and competition effects. In contrast, globalization and peace asymmetry do not independently lower costs once corridor-specific heterogeneity is accounted for. Evidence of a moderating role of peace in the globalization–cost relationship is weak and specification-sensitive.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eLimitations:\u003c/strong\u003e The analysis relies on interpolated bilateral remittance matrices and cannot capture informal remittance channels.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eImplications:\u003c/strong\u003e Policies aimed at expanding formal remittance volumes and deepening corridor-level competition are likely to be more effective in reducing costs than macro-level institutional reforms alone.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eNovelty and Contribution:\u003c/strong\u003e This study advances the literature by developing a bilateral corridor-level framework that integrates globalization and peace asymmetry into remittance cost analysis.\u003c/p\u003e","manuscriptTitle":"Does globalization reduce remittance costs? Evidence on the role of peace from bilateral panel data","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-02-10 05:10:52","doi":"10.21203/rs.3.rs-8812118/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"327c5e5a-e94c-4555-9013-3ee345f64a7d","owner":[],"postedDate":"February 10th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":62490538,"name":"Development Economics"},{"id":62490539,"name":"Econometrics"},{"id":62490540,"name":"International Economics"}],"tags":[],"updatedAt":"2026-02-10T05:10:52+00:00","versionOfRecord":[],"versionCreatedAt":"2026-02-10 05:10:52","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-8812118","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-8812118","identity":"rs-8812118","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}