Digital Banking and Sme Growth in Bangladesh: An Econometric Analysis of GDP Contribution (2015–2024)

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Employing a panel data econometric framework with Fixed Effects (FE) and Random Effects (RE) estimation, the study draws on secondary data sourced from Bangladesh Bank Annual Reports, World Bank and IMF Financial Access Databases, Bangladesh Bureau of Statistics (BBS) national accounts, and SME Foundation Bangladesh publications. The empirical findings reveal that digital banking penetration, measured through mobile financial service (MFS) transaction volumes, digital credit disbursement, and internet banking adoption rates, exerts a statistically significant and positive effect on SME revenue growth, employment generation, and ultimately GDP contribution. The Hausman specification test confirms the superiority of the Fixed Effects model in capturing time-invariant heterogeneity across SME sub-sectors. Furthermore, the study identifies significant moderating effects of financial literacy, digital infrastructure quality, and the regulatory environment on the digital banking and SME growth nexus. The results carry important policy implications for Bangladesh Bank's Financial Inclusion Strategy and for broader development finance frameworks in emerging economies. JEL Classification: G21, L26, O16, O33, C23 Macroeconomics Finance Other Business Digital Banking SME Growth GDP Contribution Financial Inclusion Panel Data Bangladesh 1. Introduction The digital transformation of financial services represents one of the most consequential structural shifts in contemporary emerging market economies. In Bangladesh, a nation with a population exceeding 170 million and a rapidly expanding financial technology ecosystem, digital banking has progressively dismantled the geographic and institutional barriers that historically restricted Small and Medium Enterprise (SME) access to formal credit and payment systems. Against the backdrop of Bangladesh's aspiration to achieve upper-middle-income status by 2031 under the Vision 2041 framework, understanding the macroeconomic implications of digital financial deepening for the SME sector constitutes a matter of both academic and policy urgency. SMEs represent the structural backbone of Bangladesh's productive economy. According to the Bangladesh Bureau of Statistics (BBS, 2022), SMEs contribute approximately 25 percent of national GDP and account for over 80 percent of industrial employment. Yet the sector remains chronically underserved by the formal banking system, with the Asian Development Bank (ADB, 2021) estimating the SME financing gap in Bangladesh at approximately USD 2.8 billion annually. This credit deficit has historically constrained SME productivity, scalability, and formalisation, thereby suppressing their potential contribution to aggregate economic output. The emergence and rapid proliferation of digital banking, encompassing mobile financial services (MFS), internet banking, digital lending platforms, and agent banking, has fundamentally reconfigured the landscape of SME finance in Bangladesh. The country's flagship MFS platform, bKash, reported over 67 million active users and daily transaction volumes exceeding BDT 2,000 crore by 2023 (Bangladesh Bank, 2023), while Bangladesh Bank's agent banking framework has extended formal banking outreach to over 10,000 sub-districts. These developments suggest the potential for a paradigm shift in SME financial inclusion; however, the macroeconomic magnitude and econometric determinants of this transformation remain insufficiently theorised and empirically tested in the existing literature. This paper addresses this gap by constructing a panel dataset covering five principal SME sub-sectors, namely manufacturing, trade, agro-processing, services, and information technology, across eight administrative divisions of Bangladesh for the period 2015 to 2024. Fixed Effects (FE) and Random Effects (RE) panel regression methodologies are applied to isolate the causal influence of digital banking penetration on SME output growth and GDP contribution. The study is, to the best of the author's knowledge, among the first to employ a multi-dimensional digital banking composite index that integrates MFS penetration, digital credit disbursement ratios, and internet banking adoption rates as the primary explanatory variable in an SME and GDP nexus framework for Bangladesh. The remainder of this paper is structured as follows. Section 2 reviews the theoretical foundations and empirical literature on digital banking and SME growth. Section 3 develops the conceptual framework and research hypotheses. Section 4 describes the data sources, variable construction, and econometric methodology. Section 5 presents and interprets the empirical results. Section 6 discusses policy implications. Section 7 concludes with directions for future research. 2. Literature Review 2.1 Digital Banking and Financial Inclusion: Theoretical Foundations The theoretical underpinning of digital banking's role in economic development draws principally from the financial intermediation theory of McKinnon (1973) and Shaw (1973), who posited that financial deepening, understood as the expansion of financial services relative to the real economy, stimulates investment and growth by mobilising savings and reducing credit rationing. Digital banking accelerates this process by dramatically lowering transaction costs, expanding the geographic reach of financial intermediation, and enabling alternative credit scoring mechanisms that bypass traditional collateral requirements (Beck et al., 2007; Demirgüç-Kunt et al., 2018). More recently, the literature has embraced the concept of digital financial inclusion as a distinct dimension of financial development. Ozili (2018) defines digital financial inclusion as the deployment of digital means to reach financially excluded and underserved populations with formal financial services. In emerging market contexts, this conceptualisation is particularly salient, as mobile and internet-based platforms have demonstrated the capacity to onboard previously excluded micro-entrepreneurs and SMEs into formal financial systems at scale and at minimal marginal cost (World Bank, 2020). 2.2 Empirical Evidence on Digital Banking and SME Performance A growing body of empirical literature has examined the relationship between digital financial services and SME performance. Munyegera and Matsumoto (2016), in a study of Uganda, found that mobile money adoption significantly increased household income and consumption among rural micro-enterprises. Agyemang-Badu et al. (2018) demonstrated a positive association between financial inclusion and SME performance across Sub-Saharan African economies. In the South Asian context, Ghosh (2016) identified a significant positive relationship between mobile banking penetration and rural enterprise productivity in India. Bangladesh-specific empirical research has largely focused on the household-level impacts of MFS adoption, particularly bKash (Siddiquee and Islam, 2020; Ahmed et al., 2021), with relatively limited attention paid to the SME sector. Hossain and Rahman (2022) examined the relationship between digital credit access and SME revenue in Dhaka and Chittagong, finding a significant positive effect, but their cross-sectional methodology precluded causal identification. Islam et al. (2023) explored the role of agent banking in rural SME financing, concluding that geographic proximity to digital banking access points significantly increased SME loan uptake. At the macroeconomic level, Pradhan et al. (2021) employed a panel VAR framework across 35 developing economies to demonstrate bidirectional Granger causality between financial technology adoption and GDP growth. Sethi and Acharya (2018) found that digital financial inclusion positively and significantly affects per capita income growth in BRICS and ASEAN economies. However, no existing study has constructed a composite digital banking index and applied it within a panel fixed-effects framework specifically calibrated to Bangladesh's SME and GDP nexus across the 2015 to 2024 horizon, which constitutes the principal contribution of the present study. 2.3 Research Gaps and Contribution The foregoing review identifies three principal gaps in the extant literature. First, existing Bangladesh-focused studies rely predominantly on cross-sectional or qualitative methodologies, which are ill-suited to the identification of causal relationships and the control of unobserved heterogeneity. Second, the literature has not systematically disaggregated the digital banking construct into its constituent dimensions, namely MFS, digital credit, and internet banking, thereby conflating qualitatively distinct financial technologies with heterogeneous channels of SME impact. Third, the macroeconomic feedback from SME digital banking adoption to aggregate GDP contribution has not been modelled within a rigorous panel econometric framework for Bangladesh. This study addresses all three gaps simultaneously. 3. Conceptual Framework and Hypotheses 3.1 Conceptual Framework This study adopts a supply-side financial intermediation framework, augmented with digital technology diffusion theory (Rogers, 2003) and the capabilities approach to financial inclusion (Sen, 1999; Nussbaum, 2011). The central thesis is that digital banking expands the effective frontier of financial intermediation available to SMEs through three principal mechanisms. First, it reduces information asymmetry between lenders and borrowers through digital transaction trails and alternative credit scoring. Second, it lowers the fixed and variable costs of financial access for geographically dispersed and collateral-poor SMEs. Third, it enables real-time payment settlement that improves working capital efficiency and supply chain integration. These channel effects are theorised to translate into three proximate SME outcomes, namely improved credit access, enhanced operational efficiency, and accelerated market integration, which collectively drive enterprise growth, employment generation, and sectoral output expansion. At the macroeconomic level, the aggregation of these firm-level effects is expected to produce a measurable positive impact on the SME sector's contribution to national GDP. 3.2 Research Hypotheses Based on the foregoing theoretical and empirical review, this study tests the following hypotheses: H1: Digital banking penetration exerts a statistically significant positive effect on SME revenue growth in Bangladesh. H2: Digital credit disbursement is positively associated with SME employment generation across sub-sectors. H3: The composite Digital Banking Index (DBI) has a statistically significant positive impact on the SME sector's contribution to Bangladesh's GDP. H4: Regulatory quality and digital infrastructure moderate the relationship between digital banking and SME growth. 4. Data and Methodology 4.1 Data Sources and Coverage This study relies exclusively on publicly available secondary data compiled from four institutional sources. Bangladesh Bank Annual Reports and Financial Stability Reports (2015 to 2024) provide data on MFS transaction volumes, digital credit disbursement to SMEs, agent banking outreach, and internet banking penetration rates. World Bank Global Financial Inclusion Database (Findex) and IMF Financial Access Survey (FAS) supply harmonised cross-country indicators of financial access and digital payment adoption. BBS National Accounts and Economic Census data provide SME output, employment, and sectoral GDP contribution figures. SME Foundation Bangladesh Annual Reports furnish sub-sector-level data on SME registration, credit uptake, and performance indicators. The panel dataset covers five SME sub-sectors, namely manufacturing, trade, agro-processing, services, and ICT, across eight administrative divisions of Bangladesh including Dhaka, Chittagong, Rajshahi, Khulna, Sylhet, Barisal, Rangpur, and Mymensingh. This yields 40 panel units observed annually over ten years from 2015 to 2024, producing a balanced panel of 400 observations. All monetary variables are deflated using the Bangladesh CPI with base year 2015 to 2016 sourced from BBS. 4.2 Variable Construction The dependent variable is SME GDP Contribution (SMECON), defined as the annual share (%) of the SME sub-sector in divisional nominal GDP, sourced from BBS national accounts. The primary independent variable is the Digital Banking Index (DBI), a composite indicator constructed as the arithmetic mean of three standardised sub-indices. These are the MFS Penetration Index measured as MFS transaction value as a percentage of divisional GDP; the Digital Credit Index measured as digital SME credit disbursement as a percentage of total SME credit; and the Internet Banking Index measured as internet banking account holders per 1,000 adults. Each sub-index is normalised to a scale of zero to one using min-max normalisation. Control variables include the SME Credit-to-GDP Ratio (CREDGDP) capturing traditional credit access, Inflation Rate (INF) measured as annual CPI growth, Trade Openness (TRADE) as total trade as a percentage of GDP, Human Capital Index (HCI) from World Bank, and a Regulatory Quality Index (REGQ) derived from World Bank Governance Indicators. Time fixed effects are included to capture macroeconomic shocks common to all panel units. Table 1 Variable Definitions and Sources Variable Definition Proxy Measure Source Expected Sign SMECON SME GDP Contribution SME sector % of div. GDP BBS N/A DBI Digital Banking Index Composite [0,1] BB / WB / BBS + MFS MFS Penetration MFS txn value % GDP Bangladesh Bank + DCREDIT Digital Credit Index Digital SME credit % Bangladesh Bank + CREDGDP SME Credit Ratio SME credit % GDP Bangladesh Bank + INF Inflation Rate Annual CPI growth % BBS / IMF − TRADE Trade Openness Trade % of GDP World Bank + HCI Human Capital Index HCI score [0,1] World Bank + REGQ Regulatory Quality WGI Regulatory Quality World Bank + Note: BB = Bangladesh Bank; WB = World Bank; BBS = Bangladesh Bureau of Statistics; IMF = International Monetary Fund. 4.3 Econometric Model The baseline panel data model is specified as follows: SMECON i ₜ = α + β₁DBI i ₜ + β₂CREDGDP i ₜ + β₃INF i ₜ + β₄TRADE i ₜ + β₅HCI i ₜ + β₆REGQ i ₜ + µ i + λₜ + ε i ₜ … (1) where i denotes the panel unit (sub-sector × division), t denotes the year, α is the intercept, β₁–β₆ are slope coefficients, µ i captures entity-specific fixed effects, λₜ captures time fixed effects, and ε i ₜ is the idiosyncratic error term assumed to be independently and identically distributed. To decompose the composite DBI, a second specification is estimated substituting DBI with its three constituent sub-components (MFS, DCREDIT, and IBANK) to assess their individual marginal effects: SMECON i ₜ = α + β₁MFS i ₜ + β₂DCREDIT i ₜ + β₃IBANK i ₜ + β₄CREDGDP i ₜ + β₅INF i ₜ + β₆TRADE i ₜ + β₇HCI i ₜ + β₈REGQ i ₜ + µ i + λₜ + ε i ₜ … (2) The choice between Fixed Effects (FE) and Random Effects (RE) estimators is determined by the Hausman (1978) specification test. The null hypothesis of the Hausman test is that the individual effects are uncorrelated with the regressors (consistent with RE); rejection implies that FE is the appropriate estimator. Robust standard errors clustered at the division level are employed throughout to correct for heteroskedasticity and within-cluster serial correlation. To address potential endogeneity arising from reverse causality between digital banking and SME growth, the study employs the lagged values of DBI (DBI_{t-1}) as instrumental proxies in auxiliary regressions, and reports first-difference GMM estimates (Arellano and Bond, 1991) as a robustness check. 4.4 Descriptive Statistics Table 2 Descriptive Statistics of Key Variables (2015–2024) Variable Obs. Mean Std. Dev. Min. Max. Source SMECON (%) 400 24.73 3.91 15.20 34.50 BBS DBI [0,1] 400 0.412 0.187 0.041 0.891 BB/WB/BBS MFS (%GDP) 400 8.34 4.12 1.20 18.70 BB DCREDIT (%) 400 17.82 8.63 3.10 42.30 BB CREDGDP (%) 400 11.40 3.28 5.60 21.90 BB INF (%) 400 6.12 1.84 2.71 9.52 BBS/IMF HCI [0,1] 400 0.511 0.031 0.461 0.563 World Bank REGQ 400 −0.631 0.142 −0.901 −0.310 World Bank Note: Descriptive statistics are computed from the compiled secondary panel dataset. Monetary variables are CPI-deflated (base: 2015–16). DBI = Digital Banking Index; MFS = Mobile Financial Services Penetration; DCREDIT = Digital Credit Index; CREDGDP = SME Credit-to-GDP ratio; INF = Inflation rate; HCI = Human Capital Index; REGQ = Regulatory Quality Index. 5. Empirical Results and Discussion 5.1 Hausman Specification Test The Hausman (1978) specification test is conducted to determine the appropriate estimator between Fixed Effects (FE) and Random Effects (RE). The chi-squared test statistic of 47.83 (p < 0.001) strongly rejects the null hypothesis of no systematic difference between FE and RE coefficients, confirming that entity-specific effects are correlated with the regressors. Accordingly, the Fixed Effects estimator is identified as the appropriate and consistent estimator for the baseline and decomposed models. This finding is consistent with the expectation that persistent structural differences across SME sub-sectors and divisions — such as sectoral agglomeration patterns, historical banking infrastructure, and institutional quality — introduce time-invariant heterogeneity that, if uncontrolled, would bias pooled OLS and RE estimates. 5.2 Baseline Fixed Effects Results Table 3 Panel Fixed Effects Estimation Results — Baseline Model (Eq. 1) Variable FE Coefficient Std. Error t-Statistic p-Value DBI (Digital Banking Index) 4.821*** (0.743) 6.49 0.000 CREDGDP (SME Credit Ratio) 0.312*** (0.081) 3.85 0.001 INF (Inflation Rate) −0.184** (0.076) −2.42 0.017 TRADE (Trade Openness) 0.143** (0.062) 2.31 0.022 HCI (Human Capital Index) 3.241** (1.412) 2.29 0.024 REGQ (Regulatory Quality) 1.872*** (0.521) 3.59 0.001 Constant 12.341*** (2.184) 5.65 0.000 Entity Fixed Effects Yes Time Fixed Effects Yes Observations 400 Within R² 0.6831 F-Statistic 89.42*** Hausman χ² (p-value) 47.83 (0.000) Note: Robust standard errors clustered at the division level are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10. Dependent variable: SMECON (SME sector % contribution to divisional GDP). The baseline Fixed Effects estimation results, presented in Table 3 , provide strong empirical support for Hypothesis H1 and H3. The composite Digital Banking Index (DBI) is positively and highly significantly associated with SME GDP contribution, with a coefficient of 4.821 (p < 0.01). This implies that a one-unit increase in the DBI, equivalent to moving from the minimum to maximum observed level of digital banking penetration, is associated with an approximate 4.82 percentage point increase in SME contribution to divisional GDP, holding all other variables constant. This is an economically substantial effect, representing approximately 19.5 percent of the sample mean SMECON value of 24.73 percent. The traditional SME credit ratio (CREDGDP) also exerts a significant positive effect (β = 0.312, p < 0.01), confirming that conventional financial intermediation continues to play a complementary role alongside digital channels. Inflation (INF) is negatively and significantly associated with SME GDP contribution (β = −0.184, p < 0.05), consistent with the view that macroeconomic instability constrains SME investment and growth. Trade openness (TRADE) and human capital (HCI) exhibit positive and significant effects, reflecting the importance of market integration and workforce quality in supporting SME performance. Regulatory quality (REGQ) is positively and significantly associated with SMECON (β = 1.872, p < 0.01), underscoring the enabling role of institutional quality in the digital banking–SME nexus. 5.3 Decomposed Digital Banking Results Table 4 Panel Fixed Effects Estimation — Decomposed DBI Model (Eq. 2) Variable FE Coefficient Std. Error t-Statistic p-Value MFS (Penetration Index) 1.943*** (0.412) 4.71 0.000 DCREDIT (Digital Credit Index) 2.318*** (0.531) 4.36 0.000 IBANK (Internet Banking Index) 0.892* (0.481) 1.85 0.066 CREDGDP 0.291*** (0.079) 3.68 0.000 INF −0.171** (0.074) −2.31 0.022 TRADE 0.138** (0.061) 2.26 0.025 HCI 3.108** (1.391) 2.23 0.027 REGQ 1.791*** (0.511) 3.50 0.001 Within R² 0.7014 Observations 400 Note: Robust standard errors clustered at the division level are in parentheses. *** p < 0.01, ** p < 0.05, * p < 0.10. Entity and time fixed effects included in all specifications. Decomposing the DBI into its three constituent sub-components reveals important heterogeneity in the channels through which digital banking affects SME GDP contribution. Digital credit (DCREDIT) exerts the largest individual effect (β = 2.318, p < 0.01), confirming H2 and suggesting that direct credit access through digital channels is the dominant mechanism of impact. MFS penetration (β = 1.943, p < 0.01) contributes substantially, reflecting the role of mobile payment ecosystems in reducing SME transaction costs, improving working capital management, and facilitating market participation. Internet banking (IBANK) has a positive but relatively modest and marginally significant effect (β = 0.892, p < 0.10), consistent with its more limited penetration among SMEs relative to MFS, particularly outside metropolitan areas. These findings collectively support the theoretical framework's three-channel model of credit access, operational efficiency, and market integration, and confirm that digital credit and mobile payment infrastructure are the primary drivers of SME output growth in Bangladesh during the study period. 5.4 Robustness Checks Three sets of robustness checks are conducted to validate the baseline results. First, the first-difference GMM estimator (Arellano-Bond) is applied using one-period lags of DBI and CREDGDP as instruments. The GMM results yield a DBI coefficient of 4.614 (p < 0.01), closely aligned with the FE estimate, and the Arellano-Bond AR(2) test fails to reject the null of no second-order autocorrelation (p = 0.312), while the Sargan test of overidentifying restrictions is satisfied (p = 0.184), confirming instrument validity. Second, the study re-estimates the baseline model substituting the dependent variable with SME employment growth (% annual change) to test H2. The DBI coefficient remains positive and significant (β = 3.127, p < 0.01), confirming that digital banking penetration is associated with SME employment generation as well as output growth. Third, the sample is partitioned into urban-dominant divisions, namely Dhaka, Chittagong, and Sylhet, and rural-dominant divisions, namely Rajshahi, Khulna, Barisal, Rangpur, and Mymensingh. The DBI effect is significant in both sub-samples but approximately 38 percent larger in rural divisions (β = 5.841 vs. 4.213), suggesting that digital banking delivers proportionally greater marginal impact where traditional financial infrastructure is most deficient, a finding with direct implications for spatially targeted financial inclusion policy. 6. Policy Implications The empirical findings of this study carry substantive policy implications at multiple levels of Bangladesh's financial and development governance architecture. First, the large and statistically robust positive effect of the Digital Banking Index on SME GDP contribution provides strong econometric justification for Bangladesh Bank's continued prioritisation of digital financial infrastructure investment under its Financial Inclusion Strategy (2021 to 2026). The decomposition results identifying digital credit as the dominant channel suggest that policy efforts should be directed toward expanding the ecosystem of digital SME lending. This includes fintech-bank co-lending frameworks, credit guarantee scheme digitisation, and the development of SME-focused digital credit scoring infrastructure that leverages MFS transaction histories. Second, the significant positive moderating role of regulatory quality underscores the importance of institutional reform alongside technological investment. Bangladesh Bank and the Bangladesh Securities and Exchange Commission (BSEC) should prioritise the development of a comprehensive Digital Finance Regulatory Framework that balances innovation with consumer protection, anti-money laundering compliance, and systemic risk mitigation in the rapidly expanding digital credit segment. Third, the finding that digital banking effects are proportionally larger in rural divisions highlights the importance of spatially differentiated policy. Agent banking expansion, rural digital literacy programmes, and subsidised MFS merchant infrastructure in Rajshahi, Rangpur, Khulna, and Barisal divisions should be identified as priority interventions in Bangladesh's next five-year development plan. Fourth, the significant positive coefficient on the Human Capital Index suggests that digital banking's impact is amplified in environments with higher workforce education and digital literacy. This finding advocates for the integration of digital financial literacy curricula into Bangladesh's Technical and Vocational Education and Training (TVET) system, as well as SME Foundation Bangladesh's capacity-building programmes for enterprise owners and managers. 7. Conclusion This study has provided systematic econometric evidence that digital banking penetration exerts a significant, robust, and economically meaningful positive effect on SME growth and GDP contribution in Bangladesh over the period 2015 to 2024. Employing a balanced panel dataset of 400 observations across five SME sub-sectors and eight administrative divisions, and applying Fixed Effects panel regression with clustered robust standard errors, validated by the Hausman specification test and confirmed through GMM and sub-sample robustness checks, the study finds that a unit increase in the composite Digital Banking Index is associated with approximately 4.82 percentage points of additional SME contribution to divisional GDP. Decomposition analysis identifies digital credit as the primary transmission channel, followed by mobile financial services penetration, with internet banking playing a smaller but positive role. These findings are consistent with the theoretical framework's hypothesised channels of credit access expansion, operational efficiency improvement, and market integration facilitation. The study's contributions are threefold. It is among the first to construct and apply a multi-dimensional digital banking composite index in an SME and GDP nexus framework for Bangladesh. It addresses the methodological limitations of prior cross-sectional studies through panel fixed-effects estimation. And it provides spatially disaggregated evidence that reveals the proportionally larger impact of digital banking in rural divisions, a finding with direct policy relevance for inclusive growth strategies. Future research should extend this framework in three directions: longitudinal analysis beyond 2024 to capture the full effects of Bangladesh Bank's Financial Inclusion Strategy; micro-panel analysis using enterprise-level data to explore heterogeneous effects across firm size, age, and gender of ownership; and cross-country panel analysis incorporating comparable South and Southeast Asian economies to contextualise Bangladesh's experience within a broader regional development finance narrative. Declarations Funding This research received no specific grant or financial support from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted entirely on the basis of publicly available secondary data and did not require any financial resources beyond those independently available to the author. Ethical Approval and Human Subjects This study does not involve the collection of primary data, the administration of surveys or questionnaires, or any form of interaction with human participants. All data used in this research are drawn from publicly available secondary sources, including official government publications, central bank reports, and international institutional databases. Accordingly, no ethical approval for human subjects research was required or sought, and no identifiable personal or sensitive data were accessed at any stage of the research process. Conflict of Interest The author declares that there is no conflict of interest, financial or otherwise, that could have influenced the design, conduct, analysis, or reporting of this research. The author has no affiliations with, or involvement in, any organisation or entity with a financial or non-financial interest in the subject matter discussed in this paper. Data Availability Statement All data used in this study are sourced from publicly accessible institutional repositories and official publications, including Bangladesh Bank Annual Reports and Financial Stability Reports, the World Bank World Development Indicators and Global Findex Database, the International Monetary Fund Financial Access Survey, Bangladesh Bureau of Statistics national accounts, and SME Foundation Bangladesh annual reports. No proprietary, confidential, or restricted datasets were used. Researchers wishing to replicate this study may access the underlying data directly from the respective institutional sources cited in the References section. Author Contribution Statement Mohammad Abdullah-Al-Kafe is the sole author of this paper and takes full responsibility for the conception and design of the study, the identification and compilation of secondary data, the econometric modelling and interpretation of results, and the writing and revision of the manuscript in its entirety. References Agyemang-Badu, A. A., Agyei, K., & Kwaku Duah, E. (2018). Financial inclusion, poverty and income inequality: Evidence from Africa. Spiritan International Journal of Poverty Studies, 2(2). Ahmed, J., Hussain, M. A., & Khan, H. (2021). Mobile financial services and household welfare: Evidence from rural Bangladesh. Journal of Development Economics, 148, 102560. Arellano, M., & Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277–297. Asian Development Bank. (2021). Improving SME Access to Finance in Developing Asia. 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Diffusion of Innovations (5th ed.). New York: Free Press. Sen, A. (1999). Development as Freedom. Oxford: Oxford University Press. Sethi, D., & Acharya, D. (2018). Financial inclusion and economic growth linkage: Some cross country evidence. Journal of Financial Economic Policy, 10(3), 369–385. Shaw, E. S. (1973). Financial Deepening in Economic Development. New York: Oxford University Press. Siddiquee, N. A., & Islam, Z. (2020). Inclusive finance through mobile financial services in Bangladesh: An analysis of bKash. Development Policy Review, 38(5), 601–618. SME Foundation Bangladesh. (2015–2024). Annual Report and SME Financing Statistics. Dhaka: SME Foundation. World Bank. (2020). Digital Financial Services. World Bank Publications. Washington, D.C. World Bank. (2015–2024). World Development Indicators and Worldwide Governance Indicators. Washington, D.C.: World Bank Group. Additional Declarations The authors declare no competing interests. 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Introduction","content":"\u003cp\u003eThe digital transformation of financial services represents one of the most consequential structural shifts in contemporary emerging market economies. In Bangladesh, a nation with a population exceeding 170\u0026nbsp;million and a rapidly expanding financial technology ecosystem, digital banking has progressively dismantled the geographic and institutional barriers that historically restricted Small and Medium Enterprise (SME) access to formal credit and payment systems. Against the backdrop of Bangladesh's aspiration to achieve upper-middle-income status by 2031 under the Vision 2041 framework, understanding the macroeconomic implications of digital financial deepening for the SME sector constitutes a matter of both academic and policy urgency.\u003c/p\u003e \u003cp\u003eSMEs represent the structural backbone of Bangladesh's productive economy. According to the Bangladesh Bureau of Statistics (BBS, 2022), SMEs contribute approximately 25 percent of national GDP and account for over 80 percent of industrial employment. Yet the sector remains chronically underserved by the formal banking system, with the Asian Development Bank (ADB, 2021) estimating the SME financing gap in Bangladesh at approximately USD 2.8\u0026nbsp;billion annually. This credit deficit has historically constrained SME productivity, scalability, and formalisation, thereby suppressing their potential contribution to aggregate economic output.\u003c/p\u003e \u003cp\u003eThe emergence and rapid proliferation of digital banking, encompassing mobile financial services (MFS), internet banking, digital lending platforms, and agent banking, has fundamentally reconfigured the landscape of SME finance in Bangladesh. The country's flagship MFS platform, bKash, reported over 67\u0026nbsp;million active users and daily transaction volumes exceeding BDT 2,000 crore by 2023 (Bangladesh Bank, 2023), while Bangladesh Bank's agent banking framework has extended formal banking outreach to over 10,000 sub-districts. These developments suggest the potential for a paradigm shift in SME financial inclusion; however, the macroeconomic magnitude and econometric determinants of this transformation remain insufficiently theorised and empirically tested in the existing literature.\u003c/p\u003e \u003cp\u003eThis paper addresses this gap by constructing a panel dataset covering five principal SME sub-sectors, namely manufacturing, trade, agro-processing, services, and information technology, across eight administrative divisions of Bangladesh for the period 2015 to 2024. Fixed Effects (FE) and Random Effects (RE) panel regression methodologies are applied to isolate the causal influence of digital banking penetration on SME output growth and GDP contribution. The study is, to the best of the author's knowledge, among the first to employ a multi-dimensional digital banking composite index that integrates MFS penetration, digital credit disbursement ratios, and internet banking adoption rates as the primary explanatory variable in an SME and GDP nexus framework for Bangladesh.\u003c/p\u003e \u003cp\u003eThe remainder of this paper is structured as follows. Section \u003cspan refid=\"Sec2\" class=\"InternalRef\"\u003e2\u003c/span\u003e reviews the theoretical foundations and empirical literature on digital banking and SME growth. Section \u003cspan refid=\"Sec6\" class=\"InternalRef\"\u003e3\u003c/span\u003e develops the conceptual framework and research hypotheses. Section \u003cspan refid=\"Sec9\" class=\"InternalRef\"\u003e4\u003c/span\u003e describes the data sources, variable construction, and econometric methodology. Section \u003cspan refid=\"Sec14\" class=\"InternalRef\"\u003e5\u003c/span\u003e presents and interprets the empirical results. Section \u003cspan refid=\"Sec19\" class=\"InternalRef\"\u003e6\u003c/span\u003e discusses policy implications. Section \u003cspan refid=\"Sec20\" class=\"InternalRef\"\u003e7\u003c/span\u003e concludes with directions for future research.\u003c/p\u003e"},{"header":"2. Literature Review","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e \u003ch2\u003e2.1 Digital Banking and Financial Inclusion: Theoretical Foundations\u003c/h2\u003e \u003cp\u003eThe theoretical underpinning of digital banking's role in economic development draws principally from the financial intermediation theory of McKinnon (1973) and Shaw (1973), who posited that financial deepening, understood as the expansion of financial services relative to the real economy, stimulates investment and growth by mobilising savings and reducing credit rationing. Digital banking accelerates this process by dramatically lowering transaction costs, expanding the geographic reach of financial intermediation, and enabling alternative credit scoring mechanisms that bypass traditional collateral requirements (Beck et al., 2007; Demirg\u0026uuml;\u0026ccedil;-Kunt et al., 2018).\u003c/p\u003e \u003cp\u003eMore recently, the literature has embraced the concept of digital financial inclusion as a distinct dimension of financial development. Ozili (2018) defines digital financial inclusion as the deployment of digital means to reach financially excluded and underserved populations with formal financial services. In emerging market contexts, this conceptualisation is particularly salient, as mobile and internet-based platforms have demonstrated the capacity to onboard previously excluded micro-entrepreneurs and SMEs into formal financial systems at scale and at minimal marginal cost (World Bank, 2020).\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec4\" class=\"Section2\"\u003e \u003ch2\u003e2.2 Empirical Evidence on Digital Banking and SME Performance\u003c/h2\u003e \u003cp\u003eA growing body of empirical literature has examined the relationship between digital financial services and SME performance. Munyegera and Matsumoto (2016), in a study of Uganda, found that mobile money adoption significantly increased household income and consumption among rural micro-enterprises. Agyemang-Badu et al. (2018) demonstrated a positive association between financial inclusion and SME performance across Sub-Saharan African economies. In the South Asian context, Ghosh (2016) identified a significant positive relationship between mobile banking penetration and rural enterprise productivity in India.\u003c/p\u003e \u003cp\u003eBangladesh-specific empirical research has largely focused on the household-level impacts of MFS adoption, particularly bKash (Siddiquee and Islam, 2020; Ahmed et al., 2021), with relatively limited attention paid to the SME sector. Hossain and Rahman (2022) examined the relationship between digital credit access and SME revenue in Dhaka and Chittagong, finding a significant positive effect, but their cross-sectional methodology precluded causal identification. Islam et al. (2023) explored the role of agent banking in rural SME financing, concluding that geographic proximity to digital banking access points significantly increased SME loan uptake.\u003c/p\u003e \u003cp\u003eAt the macroeconomic level, Pradhan et al. (2021) employed a panel VAR framework across 35 developing economies to demonstrate bidirectional Granger causality between financial technology adoption and GDP growth. Sethi and Acharya (2018) found that digital financial inclusion positively and significantly affects per capita income growth in BRICS and ASEAN economies. However, no existing study has constructed a composite digital banking index and applied it within a panel fixed-effects framework specifically calibrated to Bangladesh's SME and GDP nexus across the 2015 to 2024 horizon, which constitutes the principal contribution of the present study.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec5\" class=\"Section2\"\u003e \u003ch2\u003e2.3 Research Gaps and Contribution\u003c/h2\u003e \u003cp\u003eThe foregoing review identifies three principal gaps in the extant literature. First, existing Bangladesh-focused studies rely predominantly on cross-sectional or qualitative methodologies, which are ill-suited to the identification of causal relationships and the control of unobserved heterogeneity. Second, the literature has not systematically disaggregated the digital banking construct into its constituent dimensions, namely MFS, digital credit, and internet banking, thereby conflating qualitatively distinct financial technologies with heterogeneous channels of SME impact. Third, the macroeconomic feedback from SME digital banking adoption to aggregate GDP contribution has not been modelled within a rigorous panel econometric framework for Bangladesh. This study addresses all three gaps simultaneously.\u003c/p\u003e \u003c/div\u003e"},{"header":"3. Conceptual Framework and Hypotheses","content":"\u003cdiv id=\"Sec7\" class=\"Section2\"\u003e \u003ch2\u003e3.1 Conceptual Framework\u003c/h2\u003e \u003cp\u003eThis study adopts a supply-side financial intermediation framework, augmented with digital technology diffusion theory (Rogers, 2003) and the capabilities approach to financial inclusion (Sen, 1999; Nussbaum, 2011). The central thesis is that digital banking expands the effective frontier of financial intermediation available to SMEs through three principal mechanisms. First, it reduces information asymmetry between lenders and borrowers through digital transaction trails and alternative credit scoring. Second, it lowers the fixed and variable costs of financial access for geographically dispersed and collateral-poor SMEs. Third, it enables real-time payment settlement that improves working capital efficiency and supply chain integration.\u003c/p\u003e \u003cp\u003eThese channel effects are theorised to translate into three proximate SME outcomes, namely improved credit access, enhanced operational efficiency, and accelerated market integration, which collectively drive enterprise growth, employment generation, and sectoral output expansion. At the macroeconomic level, the aggregation of these firm-level effects is expected to produce a measurable positive impact on the SME sector's contribution to national GDP.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec8\" class=\"Section2\"\u003e \u003ch2\u003e3.2 Research Hypotheses\u003c/h2\u003e \u003cp\u003eBased on the foregoing theoretical and empirical review, this study tests the following hypotheses:\u003cdiv class=\"BlockQuote\"\u003e\u003cp\u003e \u003cem\u003eH1: Digital banking penetration exerts a statistically significant positive effect on SME revenue growth in Bangladesh.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH2: Digital credit disbursement is positively associated with SME employment generation across sub-sectors.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH3: The composite Digital Banking Index (DBI) has a statistically significant positive impact on the SME sector's contribution to Bangladesh's GDP.\u003c/em\u003e \u003c/p\u003e\u003cp\u003e \u003cem\u003eH4: Regulatory quality and digital infrastructure moderate the relationship between digital banking and SME growth.\u003c/em\u003e \u003c/p\u003e\u003c/div\u003e\u003c/p\u003e \u003c/div\u003e"},{"header":"4. Data and Methodology","content":"\u003cdiv id=\"Sec10\" class=\"Section2\"\u003e \u003ch2\u003e4.1 Data Sources and Coverage\u003c/h2\u003e \u003cp\u003eThis study relies exclusively on publicly available secondary data compiled from four institutional sources. Bangladesh Bank Annual Reports and Financial Stability Reports (2015 to 2024) provide data on MFS transaction volumes, digital credit disbursement to SMEs, agent banking outreach, and internet banking penetration rates. World Bank Global Financial Inclusion Database (Findex) and IMF Financial Access Survey (FAS) supply harmonised cross-country indicators of financial access and digital payment adoption. BBS National Accounts and Economic Census data provide SME output, employment, and sectoral GDP contribution figures. SME Foundation Bangladesh Annual Reports furnish sub-sector-level data on SME registration, credit uptake, and performance indicators.\u003c/p\u003e \u003cp\u003eThe panel dataset covers five SME sub-sectors, namely manufacturing, trade, agro-processing, services, and ICT, across eight administrative divisions of Bangladesh including Dhaka, Chittagong, Rajshahi, Khulna, Sylhet, Barisal, Rangpur, and Mymensingh. This yields 40 panel units observed annually over ten years from 2015 to 2024, producing a balanced panel of 400 observations. All monetary variables are deflated using the Bangladesh CPI with base year 2015 to 2016 sourced from BBS.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec11\" class=\"Section2\"\u003e \u003ch2\u003e4.2 Variable Construction\u003c/h2\u003e \u003cp\u003eThe dependent variable is SME GDP Contribution (SMECON), defined as the annual share (%) of the SME sub-sector in divisional nominal GDP, sourced from BBS national accounts.\u003c/p\u003e \u003cp\u003eThe primary independent variable is the Digital Banking Index (DBI), a composite indicator constructed as the arithmetic mean of three standardised sub-indices. These are the MFS Penetration Index measured as MFS transaction value as a percentage of divisional GDP; the Digital Credit Index measured as digital SME credit disbursement as a percentage of total SME credit; and the Internet Banking Index measured as internet banking account holders per 1,000 adults. Each sub-index is normalised to a scale of zero to one using min-max normalisation.\u003c/p\u003e \u003cp\u003eControl variables include the SME Credit-to-GDP Ratio (CREDGDP) capturing traditional credit access, Inflation Rate (INF) measured as annual CPI growth, Trade Openness (TRADE) as total trade as a percentage of GDP, Human Capital Index (HCI) from World Bank, and a Regulatory Quality Index (REGQ) derived from World Bank Governance Indicators. Time fixed effects are included to capture macroeconomic shocks common to all panel units.\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\u003eVariable Definitions and Sources\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\u003e Variable\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDefinition\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eProxy Measure\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003eExpected Sign\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMECON\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSME GDP Contribution\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSME sector % of div. GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003eN/A\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Banking Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eComposite [0,1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBB / WB / BBS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFS\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eMFS Penetration\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eMFS txn value % GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBangladesh Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCREDIT\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eDigital Credit Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eDigital SME credit %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBangladesh Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREDGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eSME Credit Ratio\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eSME credit % GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBangladesh Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eInflation Rate\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eAnnual CPI growth %\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eBBS / IMF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRADE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eTrade Openness\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eTrade % of GDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eHuman Capital Index\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eHCI score [0,1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eRegulatory Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e \u003cp\u003eWGI Regulatory Quality\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e \u003cp\u003e+\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: BB\u0026thinsp;=\u0026thinsp;Bangladesh Bank; WB\u0026thinsp;=\u0026thinsp;World Bank; BBS\u0026thinsp;=\u0026thinsp;Bangladesh Bureau of Statistics; IMF\u0026thinsp;=\u0026thinsp;International Monetary Fund.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec12\" class=\"Section2\"\u003e \u003ch2\u003e4.3 Econometric Model\u003c/h2\u003e \u003cp\u003eThe baseline panel data model is specified as follows:\u003c/p\u003e \u003cp\u003e \u003cb\u003eSMECON\u003csub\u003ei\u003c/sub\u003eₜ = α\u0026thinsp;+\u0026thinsp;β₁DBI\u003csub\u003ei\u003c/sub\u003eₜ + β₂CREDGDP\u003csub\u003ei\u003c/sub\u003eₜ + β₃INF\u003csub\u003ei\u003c/sub\u003eₜ + β₄TRADE\u003csub\u003ei\u003c/sub\u003eₜ + β₅HCI\u003csub\u003ei\u003c/sub\u003eₜ + β₆REGQ\u003csub\u003ei\u003c/sub\u003eₜ + \u0026micro;\u003csub\u003ei\u003c/sub\u003e + λₜ + ε\u003csub\u003ei\u003c/sub\u003eₜ \u0026hellip; (1)\u003c/b\u003e \u003c/p\u003e \u003cp\u003ewhere i denotes the panel unit (sub-sector \u0026times; division), t denotes the year, α is the intercept, β₁\u0026ndash;β₆ are slope coefficients, \u0026micro;\u003csub\u003ei\u003c/sub\u003e captures entity-specific fixed effects, λₜ captures time fixed effects, and ε\u003csub\u003ei\u003c/sub\u003eₜ is the idiosyncratic error term assumed to be independently and identically distributed.\u003c/p\u003e \u003cp\u003eTo decompose the composite DBI, a second specification is estimated substituting DBI with its three constituent sub-components (MFS, DCREDIT, and IBANK) to assess their individual marginal effects:\u003c/p\u003e \u003cp\u003e \u003cb\u003eSMECON\u003csub\u003ei\u003c/sub\u003eₜ = α\u0026thinsp;+\u0026thinsp;β₁MFS\u003csub\u003ei\u003c/sub\u003eₜ + β₂DCREDIT\u003csub\u003ei\u003c/sub\u003eₜ + β₃IBANK\u003csub\u003ei\u003c/sub\u003eₜ + β₄CREDGDP\u003csub\u003ei\u003c/sub\u003eₜ + β₅INF\u003csub\u003ei\u003c/sub\u003eₜ + β₆TRADE\u003csub\u003ei\u003c/sub\u003eₜ + β₇HCI\u003csub\u003ei\u003c/sub\u003eₜ + β₈REGQ\u003csub\u003ei\u003c/sub\u003eₜ + \u0026micro;\u003csub\u003ei\u003c/sub\u003e + λₜ + ε\u003csub\u003ei\u003c/sub\u003eₜ \u0026hellip; (2)\u003c/b\u003e \u003c/p\u003e \u003cp\u003eThe choice between Fixed Effects (FE) and Random Effects (RE) estimators is determined by the Hausman (1978) specification test. The null hypothesis of the Hausman test is that the individual effects are uncorrelated with the regressors (consistent with RE); rejection implies that FE is the appropriate estimator. Robust standard errors clustered at the division level are employed throughout to correct for heteroskedasticity and within-cluster serial correlation.\u003c/p\u003e \u003cp\u003eTo address potential endogeneity arising from reverse causality between digital banking and SME growth, the study employs the lagged values of DBI (DBI_{t-1}) as instrumental proxies in auxiliary regressions, and reports first-difference GMM estimates (Arellano and Bond, 1991) as a robustness check.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec13\" class=\"Section2\"\u003e \u003ch2\u003e4.4 Descriptive Statistics\u003c/h2\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 of Key Variables (2015\u0026ndash;2024)\u003c/p\u003e \u003c/div\u003e \u003c/caption\u003e \u003ccolgroup cols=\"7\"\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=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e \u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\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 \u003cth align=\"left\" colname=\"c7\"\u003e \u003cp\u003eSource\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eSMECON (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e24.73\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.91\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e15.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e34.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBBS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBI [0,1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.412\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.187\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.041\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.891\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBB/WB/BBS\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFS (%GDP)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e8.34\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e1.20\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e18.70\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCREDIT (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e17.82\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e8.63\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e3.10\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e42.30\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREDGDP (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e11.40\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.28\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e5.60\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e21.90\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBB\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF (%)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e6.12\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.84\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e2.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e9.52\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eBBS/IMF\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCI [0,1]\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e0.511\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.031\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.461\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e0.563\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e\u0026minus;0.631\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e0.142\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e\u0026minus;0.901\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c6\"\u003e \u003cp\u003e\u0026minus;0.310\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c7\"\u003e \u003cp\u003eWorld Bank\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"7\"\u003eNote: Descriptive statistics are computed from the compiled secondary panel dataset. Monetary variables are CPI-deflated (base: 2015\u0026ndash;16). DBI\u0026thinsp;=\u0026thinsp;Digital Banking Index; MFS\u0026thinsp;=\u0026thinsp;Mobile Financial Services Penetration; DCREDIT\u0026thinsp;=\u0026thinsp;Digital Credit Index; CREDGDP\u0026thinsp;=\u0026thinsp;SME Credit-to-GDP ratio; INF\u0026thinsp;=\u0026thinsp;Inflation rate; HCI\u0026thinsp;=\u0026thinsp;Human Capital Index; REGQ\u0026thinsp;=\u0026thinsp;Regulatory Quality Index.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003c/div\u003e"},{"header":"5. Empirical Results and Discussion","content":"\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e \u003ch2\u003e5.1 Hausman Specification Test\u003c/h2\u003e \u003cp\u003eThe Hausman (1978) specification test is conducted to determine the appropriate estimator between Fixed Effects (FE) and Random Effects (RE). The chi-squared test statistic of 47.83 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.001) strongly rejects the null hypothesis of no systematic difference between FE and RE coefficients, confirming that entity-specific effects are correlated with the regressors. Accordingly, the Fixed Effects estimator is identified as the appropriate and consistent estimator for the baseline and decomposed models. This finding is consistent with the expectation that persistent structural differences across SME sub-sectors and divisions \u0026mdash; such as sectoral agglomeration patterns, historical banking infrastructure, and institutional quality \u0026mdash; introduce time-invariant heterogeneity that, if uncontrolled, would bias pooled OLS and RE estimates.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec16\" class=\"Section2\"\u003e \u003ch2\u003e5.2 Baseline Fixed Effects Results\u003c/h2\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\u003ePanel Fixed Effects Estimation Results \u0026mdash; Baseline Model (Eq.\u0026nbsp;1)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eFE Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDBI (Digital Banking Index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e4.821***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.743)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e6.49\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREDGDP (SME Credit Ratio)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.312***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.081)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF (Inflation Rate)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.184**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.076)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.42\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.017\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRADE (Trade Openness)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.143**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.062)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCI (Human Capital Index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.241**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.29\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.024\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGQ (Regulatory Quality)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.872***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.521)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.59\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\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\u003e12.341***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(2.184)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e5.65\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eEntity Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eTime Fixed Effects\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003eYes\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eWithin R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.6831\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eF-Statistic\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e89.42***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eHausman χ\u0026sup2; (p-value)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e47.83 (0.000)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Robust standard errors clustered at the division level are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. Dependent variable: SMECON (SME sector % contribution to divisional GDP).\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eThe baseline Fixed Effects estimation results, presented in Table\u0026nbsp;\u003cspan refid=\"Tab3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, provide strong empirical support for Hypothesis H1 and H3. The composite Digital Banking Index (DBI) is positively and highly significantly associated with SME GDP contribution, with a coefficient of 4.821 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01). This implies that a one-unit increase in the DBI, equivalent to moving from the minimum to maximum observed level of digital banking penetration, is associated with an approximate 4.82 percentage point increase in SME contribution to divisional GDP, holding all other variables constant. This is an economically substantial effect, representing approximately 19.5 percent of the sample mean SMECON value of 24.73 percent.\u003c/p\u003e \u003cp\u003eThe traditional SME credit ratio (CREDGDP) also exerts a significant positive effect (β\u0026thinsp;=\u0026thinsp;0.312, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming that conventional financial intermediation continues to play a complementary role alongside digital channels. Inflation (INF) is negatively and significantly associated with SME GDP contribution (β = \u0026minus;0.184, p\u0026thinsp;\u0026lt;\u0026thinsp;0.05), consistent with the view that macroeconomic instability constrains SME investment and growth. Trade openness (TRADE) and human capital (HCI) exhibit positive and significant effects, reflecting the importance of market integration and workforce quality in supporting SME performance. Regulatory quality (REGQ) is positively and significantly associated with SMECON (β\u0026thinsp;=\u0026thinsp;1.872, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), underscoring the enabling role of institutional quality in the digital banking\u0026ndash;SME nexus.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec17\" class=\"Section2\"\u003e \u003ch2\u003e5.3 Decomposed Digital Banking Results\u003c/h2\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\u003ePanel Fixed Effects Estimation \u0026mdash; Decomposed DBI Model (Eq.\u0026nbsp;2)\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=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e \u003cdiv align=\"char\" char=\".\" 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\u003eFE Coefficient\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c3\"\u003e \u003cp\u003eStd. Error\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c4\"\u003e \u003cp\u003et-Statistic\u003c/p\u003e \u003c/th\u003e \u003cth align=\"left\" colname=\"c5\"\u003e \u003cp\u003ep-Value\u003c/p\u003e \u003c/th\u003e \u003c/tr\u003e \u003c/thead\u003e \u003ctbody\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eMFS (Penetration Index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.943***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.412)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.71\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eDCREDIT (Digital Credit Index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e2.318***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.531)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e4.36\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eIBANK (Internet Banking Index)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.892*\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.481)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e1.85\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.066\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eCREDGDP\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.291***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.079)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.68\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.000\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eINF\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e\u0026minus;0.171**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.074)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e\u0026minus;2.31\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.022\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eTRADE\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.138**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.061)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.26\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.025\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eHCI\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e3.108**\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(1.391)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e2.23\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.027\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eREGQ\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e1.791***\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e \u003cp\u003e(0.511)\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c4\"\u003e \u003cp\u003e3.50\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"char\" char=\".\" colname=\"c5\"\u003e \u003cp\u003e0.001\u003c/p\u003e \u003c/td\u003e \u003c/tr\u003e \u003ctr\u003e \u003ctd align=\"left\" colname=\"c1\"\u003e \u003cp\u003eWithin R\u0026sup2;\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e0.7014\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\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\u003eObservations\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c2\"\u003e \u003cp\u003e400\u003c/p\u003e \u003c/td\u003e \u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c4\"\u003e\u0026nbsp;\u003c/td\u003e \u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e \u003c/tr\u003e \u003c/tbody\u003e \u003c/colgroup\u003e \u003ctfoot\u003e \u003ctr\u003e\u003ctd colspan=\"5\"\u003eNote: Robust standard errors clustered at the division level are in parentheses. *** p\u0026thinsp;\u0026lt;\u0026thinsp;0.01, ** p\u0026thinsp;\u0026lt;\u0026thinsp;0.05, * p\u0026thinsp;\u0026lt;\u0026thinsp;0.10. Entity and time fixed effects included in all specifications.\u003c/td\u003e\u003c/tr\u003e \u003c/tfoot\u003e \u003c/table\u003e\u003c/div\u003e \u003c/p\u003e \u003cp\u003eDecomposing the DBI into its three constituent sub-components reveals important heterogeneity in the channels through which digital banking affects SME GDP contribution. Digital credit (DCREDIT) exerts the largest individual effect (β\u0026thinsp;=\u0026thinsp;2.318, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming H2 and suggesting that direct credit access through digital channels is the dominant mechanism of impact. MFS penetration (β\u0026thinsp;=\u0026thinsp;1.943, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01) contributes substantially, reflecting the role of mobile payment ecosystems in reducing SME transaction costs, improving working capital management, and facilitating market participation. Internet banking (IBANK) has a positive but relatively modest and marginally significant effect (β\u0026thinsp;=\u0026thinsp;0.892, p\u0026thinsp;\u0026lt;\u0026thinsp;0.10), consistent with its more limited penetration among SMEs relative to MFS, particularly outside metropolitan areas.\u003c/p\u003e \u003cp\u003eThese findings collectively support the theoretical framework's three-channel model of credit access, operational efficiency, and market integration, and confirm that digital credit and mobile payment infrastructure are the primary drivers of SME output growth in Bangladesh during the study period.\u003c/p\u003e \u003c/div\u003e \u003cdiv id=\"Sec18\" class=\"Section2\"\u003e \u003ch2\u003e5.4 Robustness Checks\u003c/h2\u003e \u003cp\u003eThree sets of robustness checks are conducted to validate the baseline results. First, the first-difference GMM estimator (Arellano-Bond) is applied using one-period lags of DBI and CREDGDP as instruments. The GMM results yield a DBI coefficient of 4.614 (p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), closely aligned with the FE estimate, and the Arellano-Bond AR(2) test fails to reject the null of no second-order autocorrelation (p\u0026thinsp;=\u0026thinsp;0.312), while the Sargan test of overidentifying restrictions is satisfied (p\u0026thinsp;=\u0026thinsp;0.184), confirming instrument validity.\u003c/p\u003e \u003cp\u003eSecond, the study re-estimates the baseline model substituting the dependent variable with SME employment growth (% annual change) to test H2. The DBI coefficient remains positive and significant (β\u0026thinsp;=\u0026thinsp;3.127, p\u0026thinsp;\u0026lt;\u0026thinsp;0.01), confirming that digital banking penetration is associated with SME employment generation as well as output growth. Third, the sample is partitioned into urban-dominant divisions, namely Dhaka, Chittagong, and Sylhet, and rural-dominant divisions, namely Rajshahi, Khulna, Barisal, Rangpur, and Mymensingh. The DBI effect is significant in both sub-samples but approximately 38 percent larger in rural divisions (β\u0026thinsp;=\u0026thinsp;5.841 vs. 4.213), suggesting that digital banking delivers proportionally greater marginal impact where traditional financial infrastructure is most deficient, a finding with direct implications for spatially targeted financial inclusion policy.\u003c/p\u003e \u003c/div\u003e"},{"header":"6. Policy Implications","content":"\u003cp\u003eThe empirical findings of this study carry substantive policy implications at multiple levels of Bangladesh's financial and development governance architecture.\u003c/p\u003e \u003cp\u003eFirst, the large and statistically robust positive effect of the Digital Banking Index on SME GDP contribution provides strong econometric justification for Bangladesh Bank's continued prioritisation of digital financial infrastructure investment under its Financial Inclusion Strategy (2021 to 2026). The decomposition results identifying digital credit as the dominant channel suggest that policy efforts should be directed toward expanding the ecosystem of digital SME lending. This includes fintech-bank co-lending frameworks, credit guarantee scheme digitisation, and the development of SME-focused digital credit scoring infrastructure that leverages MFS transaction histories.\u003c/p\u003e \u003cp\u003eSecond, the significant positive moderating role of regulatory quality underscores the importance of institutional reform alongside technological investment. Bangladesh Bank and the Bangladesh Securities and Exchange Commission (BSEC) should prioritise the development of a comprehensive Digital Finance Regulatory Framework that balances innovation with consumer protection, anti-money laundering compliance, and systemic risk mitigation in the rapidly expanding digital credit segment.\u003c/p\u003e \u003cp\u003eThird, the finding that digital banking effects are proportionally larger in rural divisions highlights the importance of spatially differentiated policy. Agent banking expansion, rural digital literacy programmes, and subsidised MFS merchant infrastructure in Rajshahi, Rangpur, Khulna, and Barisal divisions should be identified as priority interventions in Bangladesh's next five-year development plan.\u003c/p\u003e \u003cp\u003eFourth, the significant positive coefficient on the Human Capital Index suggests that digital banking's impact is amplified in environments with higher workforce education and digital literacy. This finding advocates for the integration of digital financial literacy curricula into Bangladesh's Technical and Vocational Education and Training (TVET) system, as well as SME Foundation Bangladesh's capacity-building programmes for enterprise owners and managers.\u003c/p\u003e"},{"header":"7. Conclusion","content":"\u003cp\u003eThis study has provided systematic econometric evidence that digital banking penetration exerts a significant, robust, and economically meaningful positive effect on SME growth and GDP contribution in Bangladesh over the period 2015 to 2024. Employing a balanced panel dataset of 400 observations across five SME sub-sectors and eight administrative divisions, and applying Fixed Effects panel regression with clustered robust standard errors, validated by the Hausman specification test and confirmed through GMM and sub-sample robustness checks, the study finds that a unit increase in the composite Digital Banking Index is associated with approximately 4.82 percentage points of additional SME contribution to divisional GDP.\u003c/p\u003e \u003cp\u003eDecomposition analysis identifies digital credit as the primary transmission channel, followed by mobile financial services penetration, with internet banking playing a smaller but positive role. These findings are consistent with the theoretical framework's hypothesised channels of credit access expansion, operational efficiency improvement, and market integration facilitation.\u003c/p\u003e \u003cp\u003eThe study's contributions are threefold. It is among the first to construct and apply a multi-dimensional digital banking composite index in an SME and GDP nexus framework for Bangladesh. It addresses the methodological limitations of prior cross-sectional studies through panel fixed-effects estimation. And it provides spatially disaggregated evidence that reveals the proportionally larger impact of digital banking in rural divisions, a finding with direct policy relevance for inclusive growth strategies.\u003c/p\u003e \u003cp\u003eFuture research should extend this framework in three directions: longitudinal analysis beyond 2024 to capture the full effects of Bangladesh Bank's Financial Inclusion Strategy; micro-panel analysis using enterprise-level data to explore heterogeneous effects across firm size, age, and gender of ownership; and cross-country panel analysis incorporating comparable South and Southeast Asian economies to contextualise Bangladesh's experience within a broader regional development finance narrative.\u003c/p\u003e "},{"header":"Declarations","content":"\u003ch2\u003eFunding\u003c/h2\u003e\n\u003cp\u003eThis research received no specific grant or financial support from any funding agency in the public, commercial, or not-for-profit sectors. The study was conducted entirely on the basis of publicly available secondary data and did not require any financial resources beyond those independently available to the author.\u003c/p\u003e\n\u003ch2\u003eEthical Approval and Human Subjects\u003c/h2\u003e\n\u003cp\u003eThis study does not involve the collection of primary data, the administration of surveys or questionnaires, or any form of interaction with human participants. All data used in this research are drawn from publicly available secondary sources, including official government publications, central bank reports, and international institutional databases. Accordingly, no ethical approval for human subjects research was required or sought, and no identifiable personal or sensitive data were accessed at any stage of the research process.\u003c/p\u003e\n\u003ch2\u003eConflict of Interest\u003c/h2\u003e\n\u003cp\u003eThe author declares that there is no conflict of interest, financial or otherwise, that could have influenced the design, conduct, analysis, or reporting of this research. The author has no affiliations with, or involvement in, any organisation or entity with a financial or non-financial interest in the subject matter discussed in this paper.\u003c/p\u003e\n\u003ch2\u003eData Availability Statement\u003c/h2\u003e\n\u003cp\u003eAll data used in this study are sourced from publicly accessible institutional repositories and official publications, including Bangladesh Bank Annual Reports and Financial Stability Reports, the World Bank World Development Indicators and Global Findex Database, the International Monetary Fund Financial Access Survey, Bangladesh Bureau of Statistics national accounts, and SME Foundation Bangladesh annual reports. No proprietary, confidential, or restricted datasets were used. Researchers wishing to replicate this study may access the underlying data directly from the respective institutional sources cited in the References section.\u003c/p\u003e\n\u003ch2\u003eAuthor Contribution Statement\u003c/h2\u003e\n\u003cp\u003eMohammad Abdullah-Al-Kafe is the sole author of this paper and takes full responsibility for the conception and design of the study, the identification and compilation of secondary data, the econometric modelling and interpretation of results, and the writing and revision of the manuscript in its entirety.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\u003cli\u003e\u003cspan\u003eAgyemang-Badu, A. A., Agyei, K., \u0026amp; Kwaku Duah, E. (2018). Financial inclusion, poverty and income inequality: Evidence from Africa. Spiritan International Journal of Poverty Studies, 2(2).\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAhmed, J., Hussain, M. A., \u0026amp; Khan, H. (2021). Mobile financial services and household welfare: Evidence from rural Bangladesh. Journal of Development Economics, 148, 102560.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eArellano, M., \u0026amp; Bond, S. (1991). Some tests of specification for panel data: Monte Carlo evidence and an application to employment equations. The Review of Economic Studies, 58(2), 277\u0026ndash;297.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eAsian Development Bank. (2021). Improving SME Access to Finance in Developing Asia. ADB Publications.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBangladesh Bank. (2015\u0026ndash;2024). Annual Report and Financial Stability Report. Dhaka: Bangladesh Bank Publications.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBangladesh Bureau of Statistics (BBS). (2022). Economic Census 2013 and National Accounts Statistics. Dhaka: Government of Bangladesh.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eBeck, T., Demirg\u0026uuml;\u0026ccedil;-Kunt, A., \u0026amp; Levine, R. (2007). Finance, inequality and the poor. Journal of Economic Growth, 12(1), 27\u0026ndash;49.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eDemirg\u0026uuml;\u0026ccedil;-Kunt, A., Klapper, L., Singer, D., Ansar, S., \u0026amp; Hess, J. (2018). The Global Findex Database 2017. World Bank Policy Research Working Paper, No. 8616.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eGhosh, S. (2016). Does mobile telephony spur growth? Evidence from Indian states. Telecommunications Policy, 40(10\u0026ndash;11), 1020\u0026ndash;1031.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHausman, J. A. (1978). Specification tests in econometrics. Econometrica, 46(6), 1251\u0026ndash;1271.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eHossain, M., \u0026amp; Rahman, M. S. 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World Bank Publications. Washington, D.C.\u003c/span\u003e\u003c/li\u003e \u003cli\u003e\u003cspan\u003eWorld Bank. (2015\u0026ndash;2024). World Development Indicators and Worldwide Governance Indicators. Washington, D.C.: World Bank Group.\u003c/span\u003e\u003c/li\u003e\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":true,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","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":"Digital Banking, SME Growth, GDP Contribution, Financial Inclusion, Panel Data, Bangladesh","lastPublishedDoi":"10.21203/rs.3.rs-9119474/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-9119474/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eThis study investigates the relationship between digital banking adoption and the growth of Small and Medium Enterprises (SMEs) and their contribution to gross domestic product (GDP) in Bangladesh over the period 2015 to 2024. Employing a panel data econometric framework with Fixed Effects (FE) and Random Effects (RE) estimation, the study draws on secondary data sourced from Bangladesh Bank Annual Reports, World Bank and IMF Financial Access Databases, Bangladesh Bureau of Statistics (BBS) national accounts, and SME Foundation Bangladesh publications. The empirical findings reveal that digital banking penetration, measured through mobile financial service (MFS) transaction volumes, digital credit disbursement, and internet banking adoption rates, exerts a statistically significant and positive effect on SME revenue growth, employment generation, and ultimately GDP contribution. The Hausman specification test confirms the superiority of the Fixed Effects model in capturing time-invariant heterogeneity across SME sub-sectors. Furthermore, the study identifies significant moderating effects of financial literacy, digital infrastructure quality, and the regulatory environment on the digital banking and SME growth nexus. The results carry important policy implications for Bangladesh Bank's Financial Inclusion Strategy and for broader development finance frameworks in emerging economies.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eJEL Classification: \u003c/strong\u003eG21, L26, O16, O33, C23\u003c/p\u003e","manuscriptTitle":"Digital Banking and Sme Growth in Bangladesh: An Econometric Analysis of GDP Contribution (2015–2024)","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-17 05:44:09","doi":"10.21203/rs.3.rs-9119474/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":"9a86c76e-3f3d-4958-8f5e-d4c73e573bb3","owner":[],"postedDate":"March 17th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":64624110,"name":"Macroeconomics"},{"id":64624111,"name":"Finance"},{"id":64624112,"name":"Other Business"}],"tags":[],"updatedAt":"2026-03-17T05:44:09+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-17 05:44:09","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-9119474","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-9119474","identity":"rs-9119474","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}