Dwi Haryanto*
| Abdul Ghafar Ismail | Jaka Sriyana | Achmad Tohirin
© 2026 The authors. This article is published by IIETA and is licensed under the CC BY 4.0 license (http://creativecommons.org/licenses/by/4.0/).
OPEN ACCESS
Islamic finance is widely promoted as a development-oriented financial system that can foster economic growth and alleviate poverty. However, empirical evidence across Organisation of Islamic Cooperation (OIC) countries remains mixed, particularly regarding whether the expansion of Islamic financial assets translates into inclusive and growth-enhancing outcomes. This study aims to re-examine the impact of Islamic finance on economic growth and poverty in OIC countries by explicitly distinguishing between Islamic financial assets, Islamic financial inclusion, and overall financial development, while accounting for institutional quality and macroeconomic conditions. The analysis uses a balanced panel dataset of 40 OIC member countries covering the period 2013–2023, compiled from SESRIC, the World Bank, Transparency International, and other international sources. To address endogeneity, unobserved heterogeneity, and dynamic persistence in growth and poverty, the study employs dynamic panel estimators, namely First-Difference Generalized Method of Moments (FD-GMM) and System GMM (SYS-GMM). Model validity is assessed using Hansen and Arellano Bond tests, with carefully restricted and collapsed instruments. The results reveal a nuanced relationship between Islamic finance and development outcomes. Islamic financial assets exhibit a negative, statistically significant effect on economic growth and are positively associated with poverty intensity, suggesting that debt-based asset expansion does not foster inclusive development. In contrast, Islamic financial inclusion significantly reduces poverty, while trade openness and foreign direct investment support economic growth. Institutional quality and human development further condition these effects. These findings suggest that Islamic finance contributes to development not only through asset growth but also through inclusive, access-oriented, and productivity-enhancing mechanisms. Policy efforts in OIC countries should therefore shift from expanding Islamic financial assets toward strengthening Islamic financial inclusion and institutional quality to achieve sustainable growth and poverty reduction.
Islamic finance, economic growth, poverty, System Generalized Method of Moments, institutional quality, legal origin, Organisation of Islamic Cooperation countries
Inclusive and sustainable economic growth, along with poverty alleviation, remains a priority for most developing countries, especially those within the Organisation of Islamic Cooperation (OIC) [1]. Islamic finance, grounded in Sharia principles that prohibit riba (usury), gharar (excessive uncertainty), and maysir (gambling), emphasizes equity-based, profit-and-loss-sharing to promote real-sector investment and value-driven development [2, 3]. While its global expansion has been linked to greater financial stability, productive investment, and more equitable wealth distribution [4-6], outcomes remain uneven. Despite high rankings in the global Islamic economy index (GIEI) and Islamic financial development index (IFDI), countries such as Malaysia, Saudi Arabia, Pakistan, and Oman still face persistent poverty, with the gap most evident in Nigeria and Pakistan.
Empirical evidence on Islamic finance’s direct impact on growth and poverty is scarce and mixed, with some studies reporting negative effects [7-9]. Measurement challenges stem from limited data, difficulty isolating its effects, and institutional diversity across OIC states [10]. Given the complexity of growth and poverty dynamics, analyses require broad controls to ensure unbiased estimates [11].
Macroeconomic conditions such as trade openness [12], inflation [13], government spending [14], and FDI inflows [15] shape development trajectories. Demographic factors, including the human development index (HDI) [16, 17] and labor force quality [18], are equally influential, while shocks such as COVID-19 exacerbate vulnerabilities [19]. Institutional quality, as captured by governance measures such as the corruption perceptions index (CPI) [20], legal traditions [21], and broader political and regulatory stability [22], is essential for sustained growth and poverty reduction.
This study empirically assesses the impact of Islamic finance on economic growth and poverty in OIC countries, controlling for these macroeconomic, demographic, and institutional variables, to provide robust evidence and policy insights for optimizing Islamic finance as a tool for social welfare.
The discussion of the study will be divided into 6 sections. Section 1 discusses the introduction; Section 2 presents the theoretical underpinnings; Section 3 presents the estimation method; Section 5 presents the discussion; and Section 6 presents the conclusion.
Islamic finance constitutes a comprehensive paradigm rooted in divine revelation (Qur’an), prophetic tradition (Sunnah), and Islamic jurisprudence (fiqh), supported by scholarly consensus (Ijma), analogical reasoning (Qiyas), and independent legal reasoning (Ijtihad) [23-25]. Operating through profit-and-loss-sharing mechanisms such as musharakah and mudarabah, it ensures equitable risk distribution, mandates asset-backed transactions, and prohibits exploitative practices and the financing of unlawful activities [26-28].
The finance growth nexus is well documented: developed financial systems mobilize capital, diversify risk, strengthen governance, and spur innovation [29, 30]. However, excessive expansion of the financial sector can impede growth by diverting resources from productive uses and increasing systemic risk [31]. Financial inclusion especially for SMEs and low-income households enhances participation, reduces inequality, and promotes development [32].
Growth generally reduces poverty [33], with the World Bank estimating that GDP growth is linked to measurable declines in extreme poverty [34]. However, impacts vary with inequality, institutional quality, sectoral composition, and redistribution policies. Islamic finance's asset-backed intermediation, risk-sharing, and mandatory wealth redistribution hold potential for equitable growth, though empirical evidence across OIC countries is mixed [26, 35].
Studies often find positive links between Islamic finance and growth or poverty alleviation [36, 37], but effects differ by instrument and context. Sukuk can finance long-term projects while showing limited short-term growth effects [38], whereas mudarabah and musharakah strongly support real-sector investment, job creation, and poverty reduction [39, 40]. Outcomes weaken when Islamic products mimic conventional finance [41].
Broader factors also shape these relationships. Islamic financial inclusion correlates positively with growth and negatively with poverty [42], consistent with wider finance–development findings [43, 44].
Trade and FDI often promote growth and poverty alleviation but can raise inequality or prove insignificant in some settings [45-48]. Inflation typically harms growth and people with low incomes [49], though neutral effects are sometimes observed [50]. Post-COVID-19 evidence suggests inequality impacts have intensified [51].
This study uses panel data regression to examine the causal links among Islamic financial development, economic growth, and poverty reduction across 40 member states of the OIC from 2013 to 2023. Following established econometric approaches [52], the analysis uses a balanced dataset compiled from SESRIC, World Bank Development Indicators, World Governance Indicators, Transparency International, and IMF databases.
Islamic finance is measured along three dimensions: Islamic financial assets, the Islamic Financial Inclusion Index, and the IFDI. To enhance robustness and address potential omitted variable bias, the model includes a wide range of macroeconomic, institutional, and demographic controls namely inflation (annual CPI change), trade openness (exports + imports % of GDP), government expenditure (% of GDP), FDI net inflows, labor force participation, HDI, legal origin (common vs. civil law), institutional quality, and the CPI. The detailed data and description of each variable are presented in Table 1.
Table 1. Definition of operational variables
|
Variable |
Sign |
Description |
Source |
|
Islamic Finance Assets |
ASSET |
Total assets of Islamic financial institutions, in million USD |
SESRIC |
|
Financial Development Index |
FINDEV |
Composite index of financial system depth, access, and efficiency |
SESRIC |
|
Islamic Financial Inclusion Index |
FINC |
Index measuring access to Islamic financial services |
SESRIC World Bank |
|
Trade Openness |
TO |
Ratio of total exports and imports to GDP (%) |
SESRIC |
|
Foreign Direct Investment |
FDI |
Net inflows of foreign direct investment, in million USD |
SESRIC |
|
Government Expenditure |
GOVE |
Total government spending as a percentage of GDP |
Country Economy |
|
Inflation |
INFL |
Annual percentage change in the consumer price index |
World Bank |
|
Labour |
LAB |
Economically active population aged 15–64 as a percentage of total population |
SESRIC |
|
Human Development Index |
HDI |
Composite index of health, education, and living standards |
SESRIC |
|
Corruption Perception Index |
CPI |
Index of perceived public sector corruption (0 = very clean; 1 = highly corrupt) |
Transparency International |
|
Legal Origin |
LEGOR |
Legal system classification: Common Law = 1; Civil Law = 0 |
Dartmouth Dataset |
|
COVID-19 Pandemic |
COVID |
Pandemic period indicator: Before COVID-19 = 0; During COVID-19 = 1 |
— |
|
Economic Growth (Dependent) |
ECG |
Annual GDP growth rate in constant PPP prices (%) |
SESRIC |
|
Poverty (Dependent) |
POV |
Reflects the relative severity of poverty in a country, measured on a 0–100 scale, where higher values indicate greater poverty intensity |
Legatum |
|
Institutional Quality |
INSQUA |
Measured using a composite governance indicator capturing regulatory effectiveness, rule of law, and control of corruption |
- |
|
Corruption Perceptions Index |
CPI |
Transparency International measures on a scale from 0 to 100, where higher scores indicate lower perceived corruption. Accordingly, a positive coefficient implies that lower corruption is associated with improved economic outcomes |
- |
Poverty is measured using the Legatum Prosperity Index's poverty intensity score, which ranges from 0 to 100, with higher values indicating greater deprivation. This index captures multidimensional aspects of poverty, including income adequacy, access to basic services, and living conditions, rather than focusing solely on monetary poverty. Unlike World Bank headcount or poverty gap measures, this indicator reflects relative poverty intensity and is particularly suitable for cross-country comparisons where official poverty lines are not harmonized. Nevertheless, results are interpreted cautiously and emphasize relative rather than absolute poverty effects.
3.1 Poverty measurement and robustness considerations
Poverty in this study is measured using the poverty intensity score from the Legatum Prosperity Index, scaled from 0 to 100, where higher values indicate greater poverty intensity. This indicator captures multidimensional aspects of deprivation, including material resources, access to basic services, and living conditions, rather than income poverty alone. The measure is normalized across countries, making it suitable for cross-country comparative analysis where national poverty lines are not fully harmonized.
Unlike standard World Bank poverty indicators such as the poverty headcount ratio and the poverty gap, which are based on absolute income thresholds, the Legatum poverty intensity index reflects relative and multidimensional poverty. As a result, the dependent variable in this study should be interpreted as capturing differences in the severity of poverty across countries rather than absolute poverty incidence. The Legatum index ensures comparability through consistent methodology across countries; however, it does not directly measure extreme income poverty. Accordingly, the results emphasize structural and institutional correlates of poverty intensity, and conclusions are drawn with appropriate caution.
Where available, robustness checks using World Development Indicators (WDI) poverty measures (poverty headcount ratio and poverty gap) were considered. Due to limited and uneven data coverage across OIC countries during the study period, these indicators could not be applied uniformly to all specifications. To maintain sample consistency, the baseline results rely on the Legatum index, and the implications are discussed in terms of relative poverty intensity rather than absolute poverty levels.
The generalized method of moments (GMM) is adopted to address dynamic relationships and potential endogeneity between Islamic finance, economic growth, and poverty alleviation in OIC countries. OLS estimation in dynamic panels is biased due to correlation between lagged dependent variables and unobserved country effects [53]. Following Arellano and Bond [54], GMM first differences the data to remove time-invariant effects and uses lagged endogenous variables as instruments.
Two estimators are considered: First-Difference GMM (FD-GMM) and System GMM (SYS-GMM). FD-GMM is suitable for long panels, while SYS-GMM improves efficiency in short or highly persistent panels by jointly estimating equations in differences and levels [55].
The estimation strategy begins with pooled OLS and proceeds to GMM. Model validity is assessed through: (1) the Sargan/Hansen test (p > 0.05 indicates valid instruments), (2) the Arellano-Bond AR(2) test (p > 0.05 confirms no serial correlation), and (3) the theoretical bounds check for the lagged dependent variable coefficient. Only models passing all diagnostics are retained, with significance set at p < 0.01, p < 0.05, and p < 0.10.
4.1 Model specification and multicollinearity considerations
Islamic financial assets (ASSET), financial development (FINDEV), and Islamic financial inclusion (FINC) capture conceptually distinct yet related dimensions of Islamic finance. To mitigate multicollinearity and enhance interpretability, the study estimates alternative model specifications in which these variables are introduced separately rather than simultaneously. This strategy follows best practice in cross-country finance growth studies and allows the effects of each Islamic finance dimension to be isolated more clearly.
As presented in Table 2, all pairwise Pearson correlation coefficients among the main variables are below the conventional threshold of 0.80, suggesting the absence of severe multicollinearity.
Table 2. Correlation matrix
|
Variable |
ASSET |
FINDEV |
FINC |
|
ASSET |
1.000 |
0.42 |
0.36 |
|
FINDEV |
0.42 |
1.000 |
0.48 |
|
FINC |
0.36 |
0.48 |
1.000 |
Table 3. Variance inflation factor (VIF) results
|
Variable |
Variance Inflation Factor |
|
ASSET |
2.31 |
|
FINDEV |
2.74 |
|
FINC |
2.18 |
|
TO |
1.92 |
|
FDI |
1.45 |
|
GOVE |
1.88 |
|
INFL |
1.36 |
|
HDI |
2.05 |
|
LAB |
1.41 |
|
INSQUA |
2.62 |
|
CPI |
1.97 |
|
COVID |
1.12 |
As reported in Table 3, all Variance Inflation Factor (VIF) values are well below the conservative threshold of 5, confirming that multicollinearity does not materially affect the regression estimates.
4.2 Dynamic panel estimation strategy and generalized method of moments design
This research utilizes dynamic panel GMM estimators to mitigate potential endogeneity, unobserved country-specific variability, and dynamic persistence in economic development and poverty. The analysis employs the FD-GMM and SYS-GMM estimators as per Arellano and Bond and Blundell and Bond.
The categorization of variables adheres to established conventions in the Research on growth and poverty. The lagged dependent variable is considered endogenous. Essential Islamic finance variables (ASSET, FINDEV, and FINC) are treated as predetermined, indicating potential feedback effects from historical economic conditions while assuming no simultaneous association with the error term. Control variables, including trade openness, foreign direct investment, inflation, and institutional indicators, are regarded as exogenous.
To provide valid instruments, lagged levels from time t−2 and earlier serve as instruments for differenced equations, whereas lagged differences are utilized as instruments for level equations in the SYS-GMM framework. To reduce instrument proliferation and prevent overfitting, the instrument matrix is condensed, and the lag depth is meticulously constrained. Consequently, the overall number of instruments is kept below the number of cross-sectional units (N = 40), in line with established best practices.
Temporal factors are incorporated in all estimations to account for shared shocks impacting OIC nations, including global financial cycles and the COVID-19 pandemic. The Arellano evaluates the model's validity using Bond serial correlation tests and the Hansen test for overidentifying restrictions. Although the Hansen p-values are somewhat elevated in certain specifications, the restricted and collapsed-instrument techniques ensure that the validity tests remain informative and that the results are not influenced by instrument proliferation.
The reported SYS-GMM results are based on two-step estimations with Windmeijer-corrected robust standard errors. Diagnostic tests indicate the presence of first-order serial correlation but no evidence of second-order serial correlation, while the Hansen test supports overall instrument validity. Time dummy variables are included to control for common cross-country shocks. This section examines the dual impact of Islamic finance on economic growth and poverty in OIC countries using a two-stage analytical approach.
First, static panel estimations of the common effect model (CEM), fixed effects model (FEM), and random effects model (REM) are conducted to establish baseline relationships and detect underlying data structures. Second, dynamic estimators, FD-GMM and SYS-GMM, are employed to address endogeneity, unobserved heterogeneity, and the dynamic persistence of both growth and poverty. FD-GMM removes fixed effects through differencing, while SYS-GMM incorporates both level and differenced equations, improving efficiency when variables are highly persistent. The results for the first stage are presented in Table 4.
Then, the results from applying the FD-GMM and SYS-GMM were obtained to address endogeneity and dynamic panel bias. FD-GMM controls for unobserved heterogeneity via differencing, while SYS-GMM enhances efficiency in the presence of persistent variables by jointly estimating differenced and level equations, as reported in Table 5.
The FD-GMM and SYS-GMM estimations indicate a complex interplay among the determinants of poverty. Financial inclusion and institutional quality exhibit a statistically significant poverty-reducing effect, while poverty persistence and, counterintuitively, higher levels of corporate asset ownership are positively associated with poverty. Moreover, inflation, the HDI, and the COVID-19 pandemic demonstrate significant influences, underscoring the multifaceted nature of poverty dynamics. The next analysis will focus on selecting the best-fitting model, which is crucial for making informed decisions and formulating targeted policy recommendations. The results are presented in Table 6.
Table 4. Estimation results of common effect model (CEM), fixed effects model (FEM), and random effects model (REM)
|
Variable |
CEM Coefficient |
FEM Coefficient |
REM Coefficient |
|
ECG |
0.214 (0.004)*** |
-0.067 (0.424) |
0.214 (0.004)*** |
|
ASSET (Log) |
-0.078 (0.897) |
-3.554 (0.186) |
-0.078 (0.897) |
|
FINDEV |
-0.023 (0.719) |
0.020 (0.920) |
-0.023 (0.719) |
|
FINC |
0.025 (0.526) |
0.169 (0.147) |
0.025 (0.525) |
|
TO |
-0.007 (0.504) |
0.014 (0.735) |
-0.007 (0.503) |
|
FDI |
0.000 (0.289) |
0.000 (0.625) |
0.000 (0.289) |
|
GOVE |
-0.042 (0.496) |
-0.113 (0.347) |
-0.042 (0.496) |
|
INFL |
-0.104 (0.000)*** |
-0.104 (0.000)*** |
-0.104 (0.000)*** |
|
HDI |
-0.033 (0.576) |
0.231 (0.599) |
-0.033 (0.575) |
|
LAB |
-0.006 (0.888) |
0.072 (0.812) |
0.006 (0.888) |
|
LEGORCM |
0.801 (0.526) |
— |
0.801 (0.526) |
|
INSQUA |
1.608 (0.361) |
1.956 (0.673) |
1.608 (0.360) |
|
CPI |
-0.041 (0.615) |
-0.127 (0.492) |
-0.041 (0.615) |
|
COVID |
-0.910 (0.350) |
-2.209 (0.117) |
-0.910 (0.350) |
|
Constant |
8.993 (0.128) |
-5.926 (0.866) |
8.993 (0.127) |
|
Observations |
400 |
400 |
400 |
|
Countries |
40 |
40 |
40 |
|
R² |
17.96% |
14.83% |
59.62% |
|
F-stat / Wald χ² |
F(14,385) = 7.27, p = 0.000 |
F(13,347) = 4.16, p = 0.000 |
χ²(14) = 101.76, p = 0.000 |
Table 5. Dynamic generalized method of moments (GMM) results: The effect of Islamic finance on economic growth
|
Variable |
FD-GMM Coefficient |
SYS-GMM Coefficient |
|
ECG (Lag) |
-0.121 (0.000)*** |
-0.087 (0.000)*** |
|
ASSET (Log) |
-9.120 (0.000)*** |
-5.674 (0.000)*** |
|
FINDEV |
-0.085 (0.144) |
-0.347 (0.000)*** |
|
FINC |
-0.054 (0.219) |
-0.129 (0.003)*** |
|
TO |
0.081 (0.000)*** |
0.049 (0.000)*** |
|
FDI |
0.000 (0.002)*** |
8.740 (0.648)*** |
|
GOVE |
-0.227 (0.000)*** |
-0.435 (0.000)*** |
|
INFL |
-0.102 (0.000)*** |
-0.096 (0.000)*** |
|
HDI |
-1.348 (0.000)*** |
0.990 (0.000)*** |
|
LAB |
0.424 (0.000)*** |
0.161 (0.214) |
|
LEGORCM |
- |
10.087 (0.070)* |
|
INSQUA |
0.206 (0.951) |
3.468 (0.282) |
|
CPI |
-0.146 (0.013)** |
-0.131 (0.030)** |
|
COVID |
0.688 (0.294) |
1.657 (0.000)*** |
|
Hansen Test p-value |
0.839 |
0.948 |
|
AR(2) p-value |
0.198 |
0.346 |
Note: FD-GMM: First-Difference Generalized Method of Moments; SYS-GMM: System GMM
Table 6. Optimal model selection for economic growth
|
Test |
Statistic |
P-Value |
Decision |
|
Hausman Test |
1.85 |
0.996 |
Accept REM |
|
F-test (CEM vs FEM) |
0.59 |
0.861 |
Accept CEM |
|
Breusch–Pagan LM Test |
43.22 |
0.000 |
Accept REM |
|
Hansen Test (SYS-GMM) |
— |
0.389 |
Instruments valid |
The GMM results reveal several counterintuitive patterns. Islamic financial assets (–5.67, p < 0.01) depress growth, reflecting the dominance of debt-based contracts murabahah, ijarah, bai’ bithaman ajil that transfer risk to clients, while profit-sharing modes remain rare due to monitoring costs and risk aversion [56]. Weak capital markets, fragmented regulation, and lower efficiency relative to conventional banks further limit impact [57]. Similarly, the IFDI (–0.347, p < 0.01) and Sharia financial inclusion (–0.129, p = 0.003) underperform due to resource misallocation, urban concentration, and competition from conventional banks [58].
By contrast, trade openness (0.049, p = 0.000) and FDI enhance growth via industrial exports, supply chain integration, and technology transfer, though FDI effects remain governance-dependent [59]. Government expenditure (–0.435, p = 0.000) and inflation constrain growth, while human development (0.990, p = 0.00) strongly promotes it through education and health [60]. Labor force growth is positive but insignificant, suggesting “jobless growth.” Common Law origin (10.087, p = 0.07) favors growth via stronger property rights, while corruption (–0.131, p = 0.03) generally hinders it, with occasional short-term gains [61, 62]. The COVID-19 dummy is positive, reflecting stimulus-driven rebounds rather than sustained growth.
The coefficient on lagged economic growth is negative and statistically significant across specifications, indicating a mean reversion process consistent with conditional convergence among OIC countries. This suggests that economies that grew faster in the previous period tend to grow more slowly subsequently, reflecting adjustment dynamics rather than persistent divergence. The results of the dynamic GMM with poverty are presented in Tables 7 and 8.
Table 7. Estimation results of common effect model (CEM), fixed effects model (FEM) and random effects model (REM): The presence of poverty
|
Variable |
CEM Coefficient |
FEM Coefficient |
REM Coefficient |
|
POV |
0.996 (0.000)*** |
0.714 (0.000)*** |
0.959 (0.000)*** |
|
ASSET (Log) |
0.708 (0.010)*** |
2.312 (0.042)** |
0.744 (0.028)** |
|
FINDEV |
-0.054 (0.067)* |
-0.020 (0.816) |
-0.058 (0.102)* |
|
FINC |
-0.024 (0.526) |
-0.759 (0.127) |
-0.032 (0.147) |
|
TO |
0.005 (0.274) |
0.026 (0.139) |
0.005 (0.395) |
|
FDI |
-0.000 (0.640) |
-0.000 (0.658) |
-0.000 (0.683) |
|
GOVE |
0.010 (0.726) |
-0.056 (0.287) |
0.002 (0.936) |
|
INFL |
0.019 (0.011)*** |
0.033 (0.000)*** |
0.020 (0.009)*** |
|
HDI |
0.016 (0.576) |
-0.197 (0.289) |
-0.004 (0.899) |
|
LAB |
0.005 (0.777) |
0.001 (0.990) |
0.005 (0.809) |
|
LEGORCM |
0.023 (0.967) |
— |
-0.045 (0.949) |
|
INSQUA |
0.012 (0.988) |
-4.730 (0.020)** |
0.621 (0.520) |
|
CPI |
-0.031 (0.407) |
0.916 (0.240) |
-0.010 (0.803) |
|
COVID |
0.359 (0.421) |
1.437 (0.019)** |
0.578 (0.204) |
|
Constant |
-3.323 (0.910) |
10.032 (0.502) |
1.338 (0.695) |
|
Observations |
400 |
400 |
400 |
|
Countries |
40 |
40 |
40 |
|
R² |
97.05% |
94.74% |
97.13% |
|
F-stat / Wald χ² |
F(14,385) = 939.12, p = 0.000 |
F(13,347) = 80.27, p = 0.000 |
χ²(14) = 8393.15, p = 0.000 |
Table 8. Dynamic generalized method of moments (GMM) results
|
Variable |
FD-GMM Coefficient |
SYS-GMM Coefficient |
|
POV (Lag) |
0.558 (0.000)*** |
0.670 (0.000)*** |
|
ASSET (Log) |
4.599 (0.000)*** |
3.735 (0.000)*** |
|
FINDEV |
-0.103 (0.000)*** |
0.021 (0.439) |
|
FINC |
-0.120 (0.000)*** |
-0.186 (0.000)*** |
|
TO |
0.037 (0.000) *** |
0.024 (0.008)*** |
|
FDI |
0.000 (0.598) |
8.740 (0.648) |
|
GOVE |
-0.045 (0.003) *** |
0.054 (0.000)*** |
|
INFL |
0.142 (0.000)*** |
0.018 (0.000)*** |
|
HDI |
-0.310 (0.000)*** |
0.328 (0.000)*** |
|
LAB |
0.041 (0.401) |
-0.226 (0.000)*** |
|
LEGORCM |
- |
-3.650 (0.079)* |
|
INSQUA |
-7.831 (0.000) *** |
-11.830 (0.000)*** |
|
CPI |
0.067 (0.006) *** |
-0.006 (0.806) |
|
COVID |
2.307 (0.000) *** |
0.163 (0.000)*** |
|
Hansen Test p-value |
0.959 |
0.999 |
|
AR(2) p-value |
0.329 |
0.448 |
A dynamic panel regression analysis of OIC countries confirms strong poverty persistence, with lagged poverty (L1.POV = 0.670, p = 0.000) indicating entrenched poverty traps caused by limited access to capital, education, healthcare, and social networks, as well as weak institutions and ineffective policies. Sustainable poverty reduction, therefore, requires interventions that address both immediate needs and long-term structural barriers.
The study finds a complex relationship between financial development, Islamic finance, and poverty. Conventional Islamic financial assets (ASSET = 3.735, p = 0.000) appear ineffective in alleviating poverty, reflecting the dominance of debt-based instruments like murabahah and ijarah, which favor clients with higher repayment capacity, while profit-and-loss sharing instruments (mudarabah and musharakah), which are theoretically more conducive to poverty reduction, remain underutilized [63, 64]. Financial development (FINDEV) shows a positive but insignificant association with poverty (0.021, p = 0.439), suggesting benefits often accrue to wealthier groups, whereas Sharia financial inclusion (FINC = -0.186, p = 0.000) significantly reduces poverty by enhancing access to savings, financing, and risk management [65, 66].
·Dynamic Panel Model Results
·Model Selection
Table 9. Optimal model selection for poverty
|
Test |
Statistic |
P-Value |
Decision |
|
Hausman Test |
17.28 |
0.139 |
Accept REM |
|
F-test (CEM vs FEM) |
1.44 |
0.145 |
Accept CEM |
|
Breusch–Pagan LM Test |
38.17 |
0.000 |
Accept REM |
|
Hansen Test (SYS-GMM) |
— |
0.487 |
Instruments valid |
Other macroeconomic and institutional factors also play crucial roles. Trade openness (TO = 0.024, p = 0.008), government expenditure (GOVE = 0.054, p = 0.000), and inflation (INFL = 0.018, p = 0.000) are associated with higher poverty, reflecting unequal benefit distribution, inefficiencies, and reduced purchasing power. Labour (LAB) mitigates poverty, while the impact of human development, measured by the HDI, differs across estimators. In the FD-GMM specification, the HDI coefficient is negative and statistically insignificant, whereas in the SYS-GMM model, HDI becomes positive and statistically significant. Given that SYS-GMM is more efficient in the presence of persistent regressors and is less prone to small-sample bias, greater weight is placed on the SYS-GMM results (Table 9). Accordingly, improvements in human development are associated with higher economic growth in OIC countries, likely because of their limited coverage of broader human development dimensions [67]. Strong institutions, including common law legal systems (LEGORCM = -3.650, p = 0.07) and higher institutional quality (INSQUA), are associated with lower poverty, highlighting the importance of governance, property rights, and effective public service provision. Finally, COVID-19 had a substantial positive impact on poverty, emphasizing the need for inclusive recovery and strengthened social safety nets.
The number of instruments is carefully controlled and remains lower than the number of countries, thereby reducing concerns about instrument proliferation. Consequently, the Hansen test results should be interpreted as supporting overall instrument validity rather than indicating overfitting.
This study re-examines the role of Islamic finance in economic growth and poverty reduction across OIC countries using dynamic panel estimators. The findings provide a more nuanced understanding of the development implications of Islamic finance. The SYS-GMM results indicate that the expansion of Islamic financial assets does not automatically promote economic growth and may, in fact, be associated with higher poverty intensity. This outcome likely reflects the dominance of debt-based financing structures that do not sufficiently support productive investment or inclusive economic participation. By contrast, Islamic financial inclusion emerges as a critical channel for poverty reduction. Greater access to and usage of Islamic financial services are found to significantly alleviate poverty, underscoring the importance of inclusiveness rather than balance-sheet expansion. The results carry important policy implications for OIC countries. Policymakers should avoid equating the growth of Islamic financial assets with developmental success. Instead, regulatory frameworks should encourage a shift away from excessive reliance on debt-based instruments toward more inclusive and productivity-enhancing modes of Islamic finance. Strengthening Islamic financial inclusion particularly through microfinance, SME financing, and digital Sharia-compliant services can play a more effective role in poverty alleviation. Additionally, improving institutional quality and governance is essential to ensure that Islamic finance supports sustainable and inclusive development rather than merely expanding balance sheets. Overall, the evidence suggests that Islamic finance contributes to development not solely through asset accumulation but also through inclusive and functionally effective financial intermediation. These findings call for a reorientation of Islamic finance strategies to enhance inclusion, improve institutional quality, and strengthen the productive use of Sharia-compliant financial instruments.
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