A model frequently applied in developing-country contexts is the neoclassical growth framework formulated by Robert Solow. The production function proposed within this framework explains the relationship between output and input utilization (Khan & Reinhart, 1990). Accordingly, the basic specification derived from Solow’s Neoclassical Growth Model, expressed through an aggregated production function, is presented as follows:

(1)

where:

y = Gross Domestic Product

L = labor

K = stock of physical capital and human capital

A = a constant reflecting the baseline level of technology

Z = other factors outside the main inputs

e^μt= the rate of technological progress

Differentiating equation (1) with respect to y yields the following growth model:

After deriving equation (1) with respect to y, equation (2) is obtained,and the estimation of output growth can be developed into the following equation:

Where:

The constant β0 represents total factor productivity, β1 captures the marginal productivity of capital, β2 expresses the elasticity linking output and labor, and β3 reflects the elasticity associated with factors operating beyond the core structure of the model.

Kremerian growth theory advances the view that welfare can be attained through population expansion. Kremer argues that population increases raise the probability of generating a larger pool of scientists, inventors, and engineers, thereby fostering innovation, particularly within production technology (Makroekonomi, 2007). Regions characterized by substantial population size tend to experience more frequent technological discoveries and advances. From this perspective, a large population functions as a foundational condition for sustained technological progress (Makroekonomi, 2007).

The OIC is the second-largest international organization after the United Nations, comprising 57 member states across four continents. The organization serves as a collective platform for Muslim countries, seeking to protect and advance the interests of the Islamic world while promoting cooperation and global peace.

The working-age population comprises individuals aged 15–64, commonly classified as the productive age group, who possess the capacity to generate goods and services when labor demand exists and show a willingness to participate in economic activity (Mulyadi, 2014). (Rahman, 2018) reports a positive relationship between the working-age population and economic growth, since expanded labor availability raises the production of goods and services, signaling stronger national productivity. Empirical evidence from South Asia further confirms a positive and statistically significant effect of the working-age population on gross domestic product, consistent with the results documented in related studies (Rahman, 2018); (Kurniawati, 2021); (Saputra & Rusdi, 2024).

The term trade openness is closely related to international trade, referring to the exchange of goods through exports and imports between countries under specific agreements. Many nations leverage international trade, trade policies, and national income to stimulate economic growth as increased trade intensity can contribute positively to development(Jordaan & Eita, 2007);(Ullah et al., 2009);(Raghutla & Chittedi, 2020). A panel-data study focusing on Indonesia, the Philippines, and Malaysia further emphasizes a significant relationship between exports and economicgrowth, with two of the three countries being members of the OIC (Pazim, 2009).

Population plays an important role in economic growth. This perspective is in line with (Malthus & Stimson, 2018) “Principle of Population,” which posits a bidirectional link between population dynamics and aggregate income. Some studies show that while population growth can positively impact economic expansion, the reverse effect does not always occur(Furuoka, 2013);(Musa, 2015);(Sebikabu et al., 2020). Empirical evidence from the Middle East shows a positive and significant association between population growth and economic performance (Bawazir et al., 2020), a result corroborated by additional study (Cili & Alkhaliq, 2022).

(Mankiw, 2018) defines the real interest rate as the difference betweenthe nominal interest rate and inflation. The nominal rate reflects growth inthe monetary value of a bank balance, while the real interest rate captures changes in its purchasing power over time. Study by(Sutarjo & S, 2021)shows a negative impact of interest rates on economic growth in Indonesia,a result supported by additional studies reporting similar results (Suciany et al., 2024). Conversely, (Ugochukwu & Chinyere, 2013) observed a negativerelationship that was not statistically significant.

Gross fixed capital (GFC), or gross fixed capital formation, refers toan investment process in which a portion of current income is saved anddirected toward generating future returns (Bakare, 2011). Capital formation contributes to economic development by enabling societies to sustain andenhance their quality of life. Evidence from South Africa shows a positiveand significant relationship between gross fixed capital and economic growth (Ncanywa & Makhenyane, 2016), a result that corresponds withresults from other studies (Ugochukwu & Chinyere, 2013); (Ali, 2015); (Akalpler & Shamadeen, 2017).

Inflation represents a sustained rise in the prices of goods across multiple commodities over time. It influences economic growth throughseveral channels. Study by (Aydın et al., 2016) and (Kusumatrisna et al., 2022) reported that inflation exceeding a certain threshold negatively affected economic growth. Additional studies also documented a negative and significant relationship between inflation and growth(Mamo, 2012);(Sutarjo & S, 2021). On the flip side, some evidence suggests that inflation could positively and significantly impact economic performance (Cili & Alkhaliq, 2022).

Overall, key economic variables such as GDP, exports (EXP), imports (IMP), and gross fixed capital formation (GFC) exhibit a wide range between their minimum and maximum values, reflecting substantial heterogeneity in economic size and trade/investment activities across the sampled countries. In particular, GDP averages 141.75 billion and ranges from 0.65 billion to 1,113.04 billion (Table 2), while exports and imports average 45.19 billion and 45.12 billion, respectively, with quartile patterns indicating that trade volumes are concentrated in a small number of countries—suggesting a potentially skewed distribution. GFC also shows wide dispersion, with a mean of 37.71 billion and values spanning from 0.07 billion to 349.05 billion, highlighting cross-country differences in physical investment capacity and development stages.

The inflation variable (INF) shows high variability, with an average of 7.70% and a range from –25.10% (deflation) to 235.52%, indicating substantial differences in price stability across countries and periods, including extreme inflation episodes. Meanwhile, the real interest rate (RIR) ranges from –34.70% to 43.30% (mean 4.90%), capturing the diversity of monetary conditions and real borrowing costs, including periods of negative real interest rates. In terms of demographic and labor market aspects, PAK and TPOP also display wide dispersion (means of 25.18 million and 41.23 million), suggesting differences in population capacity and labor market size among countries.

Figure 1 presents the evolution of the working-age population rate from 2000 to 2021. The black line indicates a declining trend, while the green line represents an increasing trend. In addition, the red dots denote negative growth rates (below 0%). Based on the observed patterns, 12 countries exhibit an Increasing Return trend, characterized by accelerating growth, whereas 22 countries display a Diminishing Return trend, indicating a decelerating growth pattern.

OIC member countries classified under Increasing Return include Burkina Faso, Mali, Mauritania, Niger, Ivory Costa, Nigeria, Pakistan, Sudan, Senegal, Sierra Leone, Uganda, and Togo. These countries demonstrate a sustained increase in working-age population, reflecting a growing proportion of the working-age population. This condition suggests the availability of abundant productive human resources, commonly associated with the potential for a demographic dividend.

Conversely, OIC member countries categorized as Diminishing Return include Albania, Algeria, Bahrain, Bangladesh, Benin, Brunei Darussalam, Gabon, Indonesia, Iran, Cameroon, Kazakhstan, Comoros, Kyrgyzstan, Malaysia, Morocco, Egypt, Oman, Saudi Arabia, Tunisia, Türkiye, the United Arab Emirates, and Uzbekistan. These countries generally experience a slowdown in working-age population growth. In particular, Algeria, Brunei Darussalam, Indonesia, Malaysia, Egypt, and Tunisia exhibit relatively steady declining trends. Meanwhile, other countries within the Diminishing Return group show more heterogeneous patterns, with some periods of relative stability. Notably, Albania, Bahrain, Oman, and Saudi Arabia record persistent declines that reach negative growth rates, indicating not only a weakening expansion of the working-age population but also a contraction in working-age population dynamics.

Classic assumption tests in this study are conducted to ensure model adequacy and data quality prior to regression estimation. In panel data analysis, not all classical assumption tests are necessarily applied because some tests are considered less relevant. Therefore, this study focuses on multicollinearity and heteroskedasticity tests, which are regarded as the most pertinent for panel data (Basuki & Yuliadi 2015).

The residual normality test is not performed because, with a large sample size, the t- and F-statistics exhibit asymptotic properties such that their distributions closely approximate their theoretical distributions. Accordingly, significance testing remains valid even when the normality assumption of the error term is not explicitly tested (Gujarati & Porter, 2009). This study uses 746 observations, which meets the criterion for a large sample (more than 100 observations).

The selection of the best model was conducted using model specification tests (Mulyasari 2016). These tests aim to assess the extent to which the independent variables, net exports, inflation, gross fixed capital, the real interest rate, working-age population, and population, affect the dependent variable, namely gross domestic product (GDP). Model evaluation was carried out by considering the results of the Goodness of Fit tests.

Based on the comparison of Goodness of Fit across the three models—Common Effects Model (CEM), Fixed Effect Model (FEM), and Random Effects Model (REM)—the appropriate model was selected through the Chow test and the Hausman test. The Chow test (likelihood ratio test) was employed to compare CEM and FEM, while the Hausman test was used to compare REM and FEM.

The results of the Chow test indicate a cross-section F value of 167.430342 with a probability of 0.0000, which is significant at the 5% level (α = 0.05). Accordingly, the Fixed Effect Model (FEM) was identified as the most appropriate model. This result is further supported by the Hausman test, which yields a test statistic of 101.843074 with a probability of 0.0000, also significant at the 5% level, confirming that FEM is the preferred model. The FEM estimation results are expressed by the following equation: LnGDP = 3.553786 – 0.007252(INF) + 0.228393(LnNetExp) + 0.321654(LnGFC) – 0.008265(RIR) + 0.007255(PAK) + 0.790510(LnPOP) + µi,t. The constant term of 3.553786 implies that, holding all independent variables constant, GDP increases by 3.553786%. The Fixed Effects Model (FEM) estimation shows that, ceteris paribus, macroeconomic stability and real-sector dynamics are key determinants of GDP in OIC member countries. Inflation (INF) exerts a negative influence on economic growth, where a one percentage point increase in inflation reduces GDP by 0.7252%, while the real interest rate (RIR) also has a negative effect, with a one percentage point increase lowering GDP by 0.8265%. These results imply that higher inflationary pressures and tighter real borrowing conditions may constrain investment and weaken output performance.

In contrast, trade and investment variables are positively associated with GDP. A 1% increase in net exports (LnNetExp) is associated with an increase in GDP of 0.228393%, indicating that stronger external sector performance contributes to higher output. Similarly, gross fixed capital formation (LnGFC) shows a positive elasticity, where a 1% increase in investment leads to a 0.321654% increase in GDP, ceteris paribus. This finding is consistent with the neoclassical growth framework, which emphasizes capital accumulation as a central driver of economic growth. Demographic capacity also plays a significant role in explaining GDP variations across OIC countries. The coefficient of the working-age population (PAK), measured in million people, indicates that an increase of one million individuals aged 15–64 raises GDP by 0.7255%, while a 1% increase in total population (LnPOP) increases GDP by 0.790510%, ceteris paribus.

The parameter significance tests reveal an R-squared (R²) value of 0.997722, indicating that 99.7% of the variation in GDP across OIC member countries is explained by inflation, net exports, gross fixed capital, the real interest rate, working-age population, and total population. The remaining approximately 0.3% is attributed to other factors not included in the model.

The partial t-test results show that inflation has a negative and statistically significant effect on GDP (t = –5.348926; p = 0.0000). Net exports exert a positive and significant influence (t = 13.13339; p = 0.0000), as does gross fixed capital (t = 17.74364; p = 0.0000). The real interest rate also has a negative and significant effect on GDP (t = –5.435609; p = 0.0000). working-age population positively and significantly affects GDP (t = 2.574417; p = 0.0104), and population has a positive and statistically significant impact on GDP (t = 14.64378; p = 0.0000) at the 5% significance level.

Next, this study conducts the F-test. According to Mulyasari (2016), the F-test is used to determine whether the independent variables jointly (simultaneously) affect the dependent variable. The test is performed by comparing the calculated F-statistic with the critical F-value, where the numerator degrees of freedom (df1) are 5 (6–1) and the denominator degrees of freedom (df2) are 470 (476–6), resulting in an F-table value of 2.2331 at the 5% significance level (α = 0.05). Based on Table 5, the regression results using the Fixed Effect Model (FEM) yield an F-statistic of 6735.187 with a corresponding probability value of 0.0000. Since the F-statistic exceeds the critical F-value (6735.187 > 2.2331) and the probability value is below 0.05 (Table 6), it can be concluded that inflation, net exports, gross fixed capital, the real interest rate, working-age population, and population jointly (simultaneously) have a significant effect on gross domestic product.

In addition, the adjusted R-squared value of 0.997574 or 99.7574% (Table 7), indicates that the independent variables, such as INF, LN_NETT_EXP, LN_GFC, RIR, PAK, and LN_POP, explain 99.7574% of the variation in LN_Y (economic growth) across OIC member countries, while the remaining 0.2426% is explained by other variables not included in this model.

In conclusion, the analysis conducted in this study, which comprised the gross domestic product of 34 OIC countries from 2000 to 2021 using the Fixed Effect Model (FEM), showed several significant results. Net exports, gross fixed capital, and total population were found to possess positive and statistically significant effects at the 1 percent level. Accordingly, inflation and the real interest rate reflected negative and significant impacts at the 1 percent level, while working-age population showed a positive effect at the 5 percent significance level. The contribution of the total population exceeded that of the working-age population (ages 15–64), emphasizing the importance of enhancing labor productivity through government support, such as industry-specific training, to empower the productive-age workforce. These results also suggest that unpaid workers, family workers, students outside the working-age population, and workers beyond the 15–64 age range played meaningful roles in supporting economic growth across the 34 OIC countries during the study period.

Future explorations could incorporate more detailed components within the working-age population variable, such as specific age groups (15–24 years, 25–50 years, and 50–64 years) and classifications based on education level and gender. Similarly, the population variable could be refined by distinguishing between “de facto” and “de jure” residents, providing a clearer representation of factors influencing economic growth in OIC countries. This form of detailed analysis would enable policymakers to design more targeted interventions, grounded in the relationships among macroeconomic variables, including inflation, net exports, gross fixed capital, real interest rates, working-age population, and total population, identified in the present study.

The authors declare no conflict of interest.